The Ecology Of Marine Top Predators At The Easter Island ...

THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER

ISLAND ECOREGION: A BASELINE FOR MANAGEMENT AND

CONSERVATION

Doctorado en Biología y Ecología Aplicada

Programa cooperativo entre la Universidad Católica del Norte y la Universidad de la Serena

by

Naití Andrea Morales Serrano

Supervisors: Dr. Carlos F. Gaymer and Dr. Alan M. Friedlander

Coquimbo, 2020

ii

FACULTAD DE CIENCIAS DEL MAR UNIVERSIDAD CATÓLICA DEL NORTE

DOCTORADO EN BIOLOGÍA Y ECOLOGÍA APLICADA

“THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER ISLAND ECOREGION: A

BASELINE FOR MANAGEMENT AND CONSERVATION”

por: Naití Andrea Morales Serrano

Departamento Biología Marina

Fecha: 17 de noviembre de 2020

Aprobado Comisión de Calificación

______________________________ ______________________________

Juan Macchiavello Armengol Carlos F. Gaymer Decano Facultad Ciencias del Mar Profesor Guía

______________________________ ______________________________

Alan M. Friedlander Marcelo Rivadeneira Profesor Guía Comité tutorial

______________________________ ______________________________

Guillermo Luna-Jorquera David Véliz Comité Tutorial Profesor Externo

Tesis entregada como un requisito para obtener el título de Doctor en Biología y Ecología Aplicada

en la Facultad de Ciencias del Mar. Universidad Católica del Norte. Sede Coquimbo.

iii



Departamento de Biología Marina

“THE ECOLOGY OF MARINE TOP PREDATORS AT THE EASTER ISLAND ECOREGION: A

BASELINE FOR MANAGEMENT AND CONSERVATION”

Actividad de Titulación presentada

para optar al Título de Doctor en

Biología y Ecología Aplicada

NAITÍ ANDREA MORALES SERRANO

Coquimbo, noviembre de 2020

i



DECLARACIÓN DEL AUTOR

Se permiten citas breves sin permiso especial de la Institución o autor, siempre y cuando se

otorgue el crédito correspondiente. En cualquier otra circunstancia, se deberá solicitar permiso

de la Institución o el autor.

Naití Andrea Morales Serrano

Firma

2020

ii

ABSTRACT

Top predators are a key component of marine ecosystems and play an important role in top-down

ecosystem regulation of lower trophic levels. The continuing decline of their populations has

increased the concern about their conservation and the possible effects on the entire ecosystem.

To develop effective management and conservation strategies for these predators, it is imperative

to better understand their ecology, from community-level (e.g., species assemblage composition

and their interactions) to individual species-specific aspects (e.g., abundance, distribution, and

spatial dynamics).

Rapa Nui (Easter Island) and Salas y Gómez Island are considered the most isolated islands and

the south-eastern coral most reefs in the Pacific Ocean. The marine ecosystems in this area have

been understudied in comparison to other locations in the Pacific. Previous studies showed

contrasting top-predator assemblages between the two islands. Rapa Nui displays signs of

historical overfishing and the reef fish assemblage is dominated by smaller planktivorous species.

In contrast, at Salas y Gómez Island species such as the Galapagos shark (Carcharhinus

galapagensis) and jacks (Seriola lalandi, Caranx lugubris, and Pseudocaranx dentex) are

abundant and dominate the ecosystem. However, despite its remoteness, Salas y Gómez Island

has not escaped from anthropogenic impacts. Sharks at Salas y Gómez Island are small and

cautious. Some individuals have been observed with fishing hooks in their mouths, suggesting

that recent illegal fishing is likely occurring in the area.

Using multiple approaches, this thesis provides the basic information needed for developing

science-based management strategies across top predator species within the Easter Island

ecoregion. A general introduction, including background and specific aims is presented in

CHAPTER 1, following by four chapters which used different approaches to answer a variety of

ecological questions:

iii

CHAPTER 2.- To characterize the assemblage of top predator species around Rapa Nui, a baited

remote underwater video system (BRUVS) was used for one year. I found significant spatial and

seasonal differences in the fish assemblage around the island. The southern coast of Rapa Nui,

which is the most exposed to large swells, was different in fish assemblage structure from the

other sites and concentrated more top predator species. Winter season was distinct from the other

seasons, probably associated to the strong oceanic swells and winds coming from the south

during that time of year. Weather conditions also limits fishing pressure which could contribute to

the higher concentration of these predators. These findings are essential in the implementation

of conservation and management strategies, such as the newly created Rapa Nui multiple uses

coastal marine protected areas (MUMPA).

CHAPTER 3.- To determinate the trophic position and to define the trophic interaction of sympatric

species, δ13C and δ15N isotopic signatures of large fishes inhabiting Rapa Nui were used. The

results suggest that not all large fishes sampled should be considered as top predator species,

and that there is a high degree of overlap among the isotopic niche of four of these species,

suggesting potential interspecific competition. This chapter contributes to correctly identifying the

role of large fishes inhabiting Rapa Nui in order to create a wider understanding of how these

species interact in an isolated and species-poor ecosystem.

CHAPTER 4.- To study the movement patterns of the two more abundant two predator species

and then shed lights about the effectiveness of the borders of the Motu Motiro Hiva Marine Park

(MMHMP) around Salas y Gómez Island, I studied the movement patterns of the two most

abundant top predators: the Galapagos shark, Carcharhinus galapagensis, and the yellowtail

amberjack, Seriola lalandi. Specimens from both species were tagged around Salas y Gómez

Island using miniPAT satellite tags. The results showed that most of the satellite geolocations

come from inside the MMHMP. However, all the individuals crossed the park borders at some

point during the tracking time. These findings, thus, endorse the expansion of the current border

iv

to the west in order to protect mobile species. Additionally, a female Galapagos shark travelled a

maximum linear distance of 236 km extending the maximum distance previously reported for

juveniles of this species (< 50 km). These results raise the question whether adults are

maintaining genetic connectivity between Rapa Nui and Salas y Gómez Island through long

distance movements.

CHAPTER 5.- To determine the levels of genetic connectivity of the Galapagos shark between

Rapa Nui and Salas y Gómez Island, a genome-wide neutral Single Nucleotide Polymorphism

(13496 neutral SNP) and a section of the mtDNA (636 pb) markers were used. The results

suggested that individuals inhabiting both islands belong to the same population and could be

considered as one conservation unit. This study highlights the importance of the MMHMP, a non-

take zone, in preserving the local populations of the Galapagos shark. In addition, the

comparatively low genetic diversity found at the Easter Island Ecoregion suggests the occurrence

of few colonization events due to the isolation of the area.

CHAPTER 6.- In this section I discussed the most significant results found through the thesis in

a general context. Additionally, I discussed the limitation of the study, and the importance of

comparative studies between Rapa Nui and Salas y Gómez Island. Finally, I included

recommendations for future studies regarding top predator biodiversity and their protection at the

Easter Island Ecoregion.

v

RESUMEN

Los depredadores topes son un componente clave de los ecosistemas marinos y desempeñan

un papel fundamental en la regulación del ecosistema top-down de los niveles tróficos inferiores.

El continuo declive de sus poblaciones ha generado una creciente preocupación por su

conservación y por los posibles efectos en el ecosistema. Para desarrollar estrategias efectivas

de manejo y conservación para estas especies, es imperativo comprender mejor su ecología,

desde el nivel de comunidad (por ejemplo, la composición del conjunto de especies y sus

interacciones) hasta los aspectos específicos de cada especie (por ejemplo, abundancia,

distribución y dinámica espacial).

Rapa Nui (también conocida como Isla de Pascua) y la Isla Salas y Gómez albergan los arrecifes

de coral más al sureste del Océano Pacífico. Estudios previos dan cuenta de una gran diferencia

en la abundancia de depredadores topes entre ambas islas. Rapa Nui por su parte muestra

signos de una sobrepesca histórica y donde el ensamble de peces de arrecife está dominado por

especies planctívoras. En contraste, el ecosistema de Salas y Gómez está dominado por

especies como el tiburón de Galápagos (Carcharhinus galapagensis) y carángidos (Seriola

lalandi, Caranx lugubris y Pseudocaranx dentex). Sin embargo, y a pesar de su lejanía, la isla

Salas y Gómez también se ha visto afectada con impactos antropogénicos. Los tiburones de la

isla Salas y Gómez son pequeños y cautelosos. Se ha observado a algunos individuos con

anzuelos en la boca, lo que sugiere la ocurrencia de pesca ilegal reciente en el área.

Utilizando múltiples enfoques, esta tesis proporciona la información básica necesaria para

desarrollar estrategias de manejo basadas en conocimiento científico de las principales especies

de depredadores topes dentro de la ecorregión de Isla de Pascua. En el CAPÍTULO 1 se presenta

una introducción general, que incluye antecedentes y objetivos específicos, seguida de cuatro

vi

capítulos que utilizan diferentes enfoques para responder a una variedad de preguntas

ecológicas:

CAPÍTULO 2.- Para caracterizar el ensamblaje de los peces pelágicos de Rapa Nui, se utilizó un

sistema de video submarino remoto con carnada (BRUVS) durante un periodo de un año. Dentro

de los resultados principales se destaca la presencia de diferencias espaciales y estacionales

significativas alrededor de la isla. La costa sur de Rapa Nui fue diferente en la estructura del

ensamble de peces con respecto a los otros sitios y concentró más especies de depredadores

topes. La temporada de invierno también fue estadísticamente diferente a las otras temporadas,

probablemente debido a las fuertes marejadas oceánicas y los vientos provenientes del sur

durante esa época del año. Las condiciones climáticas también limitan la presión de pesca, lo

que podría contribuir a una mayor concentración de estos depredadores en la costa sur. Estos

hallazgos son esenciales para la implementación de estrategias de conservación y manejo, como

el área marina protegida costeras de múltiples usos de Rapa Nui (MUMPA) recientemente

creada.

CAPÍTULO 3.- Para determinar la posición trófica y definir las interacciones tróficas de los

grandes peces que habitan Rapa Nui, se analizaron sus señales isotópicas de δ13C y δ15N. Los

resultados sugieren que no todas las especies muestreadas deben considerarse depredadores

topes. Los resultados también sugieren un alto grado de superposición de nicho isotópico entre

Thunnus albacares y Katsuwonus pelamis, lo que podría significar una competencia

interespecífica. Este capítulo contribuye a identificar correctamente el papel de grandes peces

que habitan en Rapa Nui con el fin de crear una comprensión más amplia de cómo estas especies

interactúan en un ecosistema aislado y pobre en recursos.

CAPÍTULO 4.- En este capítulo busqué investigar los patrones de movimiento del tiburón de

Galápagos, Carcharhinus galapagensis, y la vidriola, Seriola lalandi para evaluar la efectividad

vii

de los límites actuales del Parque Marino Motu Motiro Hiva (MMHMP) en la protección de

especies móviles. Para esto se utilizaron marcas satelitales miniPAT y marcas convencionales.

Los resultados mostraron que todos los individuos estudiados pasan la mayor cantidad de tiempo

dentro del MMHMP. Sin embargo, todos los individuos cruzaron los límites del parque en algún

momento durante el tiempo de seguimiento. Estos hallazgos, por tanto, avalan la idea de una

expansión hacia el oeste de los limites actuales. Además, una hembra de tiburón de Galápagos

recorrió una distancia lineal máxima de 236 km aumentando la distancia máxima previamente

reportada para los juveniles de esta especie (<50 km). Estos resultados plantean la pregunta de

si los adultos son capaces entonces de mantener una conectividad genética entre Rapa Nui y la

isla Salas y Gómez a través de movimientos de larga distancia. Por último, cuantificamos la

presión de pesca industrial en el área. Los resultados sugieren que no existe pesca ilegan dentro

de la Zona Económica Exclusiva (ZEE). Sin embargo, producto del aislamiento de esta area y el

constante avistamiento de barcos industriales dentro de la ZEE por parte de los habitantes de

Rapa Nui se hace necesario un aumento en la fiscalización por parte de las autoridades.

CAPÍTULO 5.- Para determinar los niveles de conectividad genética del tiburón de Galápagos

entre Rapa Nui y la isla Salas y Gómez, utilicé marcadores de polimorfismo de nucleótido único

(SNP) neutro de todo el genoma (13496 SNP neutro) y una sección del ADN mitocondrial

(mtDNA; 636 pb). Los resultados obtenidos en este capítulo sugieren que los individuos que

habitan en ambas islas pertenecen a la misma población y podrían ser considerados como una

sola unidad de conservación. Este estudio destaca la importancia del MMHMP para preservar

las poblaciones locales del tiburón de Galápagos. Además, la diversidad genética

comparativamente baja encontrada en la Ecorregión de Isla de Pascua sugiere la ocurrencia de

pocos eventos de colonización debido al aislamiento del área.

CAPÍTULO 6.- En este apartado expuse los principales resultados encontrados durante la tesis

en un contexto general. Adicionalmente, discutí las limitaciones del estudio, y la importancia de

viii

los estudios comparativos entre Rapa Nui e Isla Salas y Gómez. Finalmente, incluí

recomendaciones para estudios futuros sobre la biodiversidad de las principales especies de

depredadores topes y su protección en la Ecorregión de Isla de Pascua.

ix

PUBLICATIONS FROM THIS THESIS

Published chapters

Chapter 2.- Morales NA, Easton EE, Friedlander AM, Harvey ES, Garcia R, Gaymer CF. 2019.

Spatial and seasonal differences in the top predators of Rapa Nui: Essential data for implementing

the new Rapa Nui multiple‐uses marine protected area. Aquatic Conserv: Mar Freshw Ecosyst.29:

118-129; 1–12. https://doi.org/10.1002/aqc.3068

Chapter 4.- Morales NA, Heidemeyer M, Bauer R, Hernandez S, Acuña E, Friedlander AM,

Gaymer CF. In press. Residential movements of top predators at Chile’s most isolated Marine

Protected Area: implications for the conservation of the Galapagos shark, Carcharhinus

galapagensis, and the yellowtail amberjack, Seriola lalandi. Aquatic Conserv: Mar Freshw

Ecosyst.

Related articles

Morales NA, Coghlan AR, Hayden G, Guajardo P. First sighting of a tropical benthic reef shark

species at Rapa Nui: chance dispersal or a sign of things to come? J Fish Biol. 2019; 95: 642–

646. https://doi.org/10.1111/ jfb.13977.

Thiel Martin, Guillermo Luna-Jorquera, Rocio Álvarez-Vargas, Camila Gallardo, Ivan A Hinojosa,

Nicolás Luna, Diego Miranda-Urbina, Naiti Morales, Nicolas Ory, Aldo Pacheco, Matias Portflitt-

Toro, Carlos Zavalaga. 2018. Impacts of marine plastic pollution from continental coasts to

subtropical gyres – Fish, seabirds and other vertebrates in the SE Pacific. Frontiers in Marine

Science. 5(238).

x

Easton EE, Sellanes J, Gaymer CF, Morales NA, Gorny M & Berkenpas E. 2017. Diversity of

deep-sea fishes of the Easter Island Ecoregion. Deep Sea Research Part II: Topical Studies in

Oceanography, 137, 78-88.

xi

Esta tesis está dedicada a todos los amantes del mar, especialmente

a mi abuelo Raúl y a mis dos amores, Chris y Lukas…

xii

AGRADECIMIENTOS

Esta tesis no podría haberse llevado a cabo sin el constante apoyo de muchas personas e

instituciones. Me gustaría partir agradeciendo a mi familia, especialmente a mis padres quienes

siempre han apoyado mis sueños. Mis amigos de la vida y los nuevos que hice durante estos

años alrededor del mundo quienes han tenido la paciencia para escucharme hablar por horas del

mar. Un agradecimiento especial a Francisco Concha quien desde pequeña me inspiró en el

amor por los tiburones.

Esta tesis de investigación forma parte del Núcleo Milenio de Ecología y Manejo Sustentable de

Islas Oceánicas (ESMOI) y tiene como objetivo contribuir a brindar la información necesaria para

apoyar las estrategias de manejo y conservación marina para la Ecorregión de Isla de Pascua.

Por lo que me gustaría agradecer a todos los integrantes de la casita ESMOI quienes me

ayudaron en la toma de muestras y el planeamiento de los diferentes capítulos de esta tesis.

Además, agradecer el compañerismo y la buena onda que sin duda hicieron de este proceso uno

mucho más fácil y entretenido. Mas que compañeros muchos de ellos se transformaron en

amigos y familia. Agradecer también a todos aquellos que me ayudaron en la Isla: Michel Garcia,

Orca Dive Center, Alex Tuki, entre muchos otros.

Agradecer por supuesto a mis profesores tutores Carlos Gaymer y Alan Friedlander quienes

siempre estuvieron disponibles para cualquier problema o consulta que tuviera y supieron guiar

esta tesis de manera ejemplar. Gracias también a la comisión por las grandes contribuciones que

hicieron en cada uno de los capítulos.

Finalmente me gustaría agradecer al Gobierno de Chile quien mediante las Becas de Doctorado

Nacional financio todos estos años de estudio. A Save Our Seas Foundation e Idea Wild por

creer en mis locuras y financiar el primer capítulo de la tesis que dio origen a todos los demás.

xiii

TABLE OF CONTENTS

DECLARACIÓN DEL AUTOR ..................................................................................................... i

ABSTRACT ................................................................................................................................. ii

RESUMEN .................................................................................................................................. v

PUBLICATIONS FROM THIS THESIS....................................................................................... ix

AGRADECIMIENTOS ............................................................................................................... xii

LIST OF FIGURES .................................................................................................................. xvi

LIST OF TABLES .................................................................................................................... xvii

LIST OF ACRONYMS ............................................................................................................. xviii

CHAPTER 1. Background .......................................................................................................... 1

1.1 Introduction ...................................................................................................................... 1

1.1.1 Research questions ................................................................................................... 2

1.2 Specific Aims ................................................................................................................... 4

1.2.1 Diversity, abundance, and distribution of top predators around Rapa Nui .................. 4

1.2.2 Species trophic position and interaction among sympatric species top predator species

at Rapa Nui ......................................................................................................................... 4

1.2.3 Movement patterns and connectivity of top predators inhabiting the Easter Island

Ecoregion ........................................................................................................................... 5

CHAPTER 2. Spatial and Seasonal Differences in the Top Predators of Rapa Nui: Essential Data

Used as a Key Tool for Implementing the New Rapa Nui Multiple-Uses MPA ............................ 8

2.1 Abstract............................................................................................................................ 8

2.2 Introduction ....................................................................................................................... 9

2.3 Methods ..........................................................................................................................12

2.3.1 Study area .................................................................................................................12

2.3.2 Sample collection ......................................................................................................13

2.3.3 Data analyses ............................................................................................................15

2.3.4 Environmental data collection and analysis ...............................................................16

2.4 Results ............................................................................................................................17

2.4.1 Spatial differences .....................................................................................................19

2.4.2 Seasonal differences .................................................................................................20

2.4.3 Environmental analysis ..............................................................................................21

2.5 Discussion ......................................................................................................................21

xiv

2.5.1 Spatial and temporal patterns ....................................................................................21

2.5.2 Conservation actions .................................................................................................26

CHAPTER 3. The Trophic Role of Large Fishes Inhabiting the Easter Island Ecoregion ...........28

3.1 Abstract...........................................................................................................................28

3.2. Introduction .....................................................................................................................28

3.3 Methods ..........................................................................................................................30

3.3.1 Sample collection ......................................................................................................30

3.3.2 Sample preparation and stable isotopes analysis ......................................................31

3.3.3 Trophic positions estimations.....................................................................................31

3.3.4 Trophic structure and isotopic niche .........................................................................32

3.4 Results ............................................................................................................................33

3.4.1 Trophic position ........................................................................................................35

3.4.2 Isotopic niche............................................................................................................36

3.5. Discussion ..................................................................................................................39

3.5.1 Stable isotope signatures .........................................................................................39

3.5.2 Trophic position ........................................................................................................40

3.5.3 Isotopic niche............................................................................................................42

3.5.4 Conservation aspects ...............................................................................................44

CHAPTER 4. Residential Movements of Top Predators at Chile’s Most Isolated Marine Protected

Area: Implications for the Conservation of the Galapagos Shark, Carcharhinus galapagensis, and

the Yellowtail Amberjack, Seriola lalandi. ..................................................................................46

4.1 Abstract...........................................................................................................................46

2.2 Introduction .....................................................................................................................47

4.3 Methods ..........................................................................................................................51

4.3.1 Study area ................................................................................................................51

4.3.2 Capture and tagging .................................................................................................52

4.3.4 Analysis of vertical behaviour ...................................................................................56

4.4 Results ............................................................................................................................57

4.4.1 Horizontal behaviour .................................................................................................57

4.4.2 Vertical behaviour .....................................................................................................60

4.5 Discussion ......................................................................................................................65

4.5.1 Horizontal and vertical migratory behaviour...............................................................65

4.5.2 Fishing activities around Rapa Nui and Salas y Gómez ............................................66

4.5.3 Future Perspectives of the MMHMP .........................................................................69

CHAPTER 5. Genetic Connectivity of the Galapagos Shark, Carcharhinus galapagensis, in the

Easter Island Ecoregion. ...........................................................................................................73

5.1 Abstract...........................................................................................................................73

5.3 Methods ..........................................................................................................................77

5.3.1 Sample collection .....................................................................................................77

5.3.2 DNA extraction and sequencing for SNP ..................................................................77

xv

5.3.3 SNPs filtering and Outlier detection ..........................................................................78

5.3.4 Genetic structure analysis.........................................................................................78

5.3.5 Mitocondrial (mtDNA) extraction, amplification, sequencing, and alignment ..............79

5.4 Results ............................................................................................................................80

5.4.1 SNPs analysis ..........................................................................................................80

5.4.2 Mitochondrial DNA analysis ......................................................................................80

5.5 Discussion ......................................................................................................................82

5.5.1 Connectivity within the Easter Island Ecoregion ........................................................82

5.5.3 Implication for the conservation of the species ..........................................................86

CHAPTER 6. Conclusions ........................................................................................................88

6.1 Main findings and conservation implications ...................................................................88

6.2 Limitation of the Study and Suggestions for Future Research Directions ........................91

REFERENCES .........................................................................................................................94

APPENDIXES ......................................................................................................................... 117

xvi

LIST OF FIGURES

Figure 1.1: Flow diagram outlining the background and general structure of the thesis. ............ 3

Figure 2.2: Map of Rapa Nui (Easter Island) and Salas y Gómez Island showing sampling locations....................................................................................................................................14

Figure 2.2. Canonical analysis of principal coordinates (CAP) ordination of the variation in fish assemblage ..............................................................................................................................20

Figure 3.1. δ13C and δ15N signatures of large fishes inhabiting Rapa Nui. ..............................34

Figure 3.2. Isotopic niche space of four species sampled during the study and density plots showing the credibility interval of Bayesian standard ellipses areas. .........................................38

Figure 4.1. Bathymetry of the Easter Island Ecoregion. ................ ¡Error! Marcador no definido.

Figure 4.2. Temporal coverage data per deployed tag ..............................................................58

Figure 4.3. Geolocations of each individual combined and kernel densities ..............................59

Figure 4.4. Distance between geolocations and their distance from Salas y Gómez per species .................................................................................................................................................60

Figure 4.5. Diel vertical movement patterns of the five fish tagged. ...........................................61

Figure 4.6. Vertical behaviour patterns for the vertical behaviour clusters and their spatial distribution ................................................................................................................................62

Figure 5.1. Location of Rapa Nui and Salas y Gómez Island .....................................................75

Figure 5.2. Principal Coordinates Analysis ................................... ¡Error! Marcador no definido.

Figure 5.3. Number of cluster (k) suggested by BIC values .......... ¡Error! Marcador no definido.

Figure 5.4. Haplotype network based on the informative mtDNA control region ........................81

xvii

LIST OF TABLES

Table 2.1: List of 15 species recorded using BRUVS at Rapa Nui.............................................18

Table 2.2. Summary of fish sightings and relative abundance. ..................................................18

Table 3.1. Summary of mean and standard deviation (SD) of stable isotopes (δ13C and δ15N) composition of large fishes included in this study. .....................................................................35

Table 3.2. Summary of outputs from trophic positions (TP) models (model-1: “oneBaseline” and model-2: “twoBaseline”). ...........................................................................................................36

Table 3.3 Isotopic niche area ....................................................................................................37

Table 4.1. Metadata of the individual tagged during this study. .... ¡Error! Marcador no definido.

Table 5.1. Genetic diversity determinate by mtDNA Control Region ..........................................81

xviii

LIST OF ACRONYMS

CAP: Analysis of Principal Coordinates

BRUVS: Baited Remote Underwater Video Systems

BIC: Bayesian Information Criterion

CR: Control Region

cMax: Corrected Maxn

Seac: Corrected Version of Standard Ellipse Area

DistLM: Distance-Based Linear Modelling

dbRDA: Distance-Based Redundancy Analysis

EEZ: Economic Exclusive Zone

EBM: Ecosystem-Based Management

FDR: False Discovery Rate

HWE: Hardy–Weinberg Equilibrium

IUU: Illegal, Unreported, And Unregulated Fishing

LD: Linkage Disequilibrium

MPAs: Marine Protected Areas

Maxn: Maximum Number of Individuals

CD: Mean Distance to the Centroid

MAF: Minor Allele Frequencies

MtDNA: Mitochondrial DNA

PERMANOVA: Permutational Multivariate Analysis of Variance

PAT: Pop-Up Archiving Tags

PCA: Principal Component Analyses

PDT: Profiles of Depth at Temperature

Rapa Nui MUMPA: Rapa Nui Multiple Uses Coastal Marine Protected Areas

SST: Sea Surface Temperature

SNP: Single Nucleotide Polymorphisms

SIA: Stable Isotope Analysis

SDNND: Standard Deviation of Nearest Neighbour Distance

xix

SEA: Standard Ellipse Area

TAD: Time-At-Depth

TAT: Time-At-Temperature

TA: Total Area

TL: Total Length

TEF: Trophic Enrichment Factor

TP: Trophic Position

UVC: Underwater Visual Census

1

CHAPTER 1. BACKGROUND

1.1 INTRODUCTION

Top predators, species that represent the highest trophic level in a community, are a key

component of marine ecosystems and play an important role in top-down ecosystem regulation

(Stevens et al., 2000; Roff et al. 2016). For example, top predators (e.g., sharks, jacks, groupers,

and tunas) have been found to be efficient in controlling demography, life history, and behaviour

(risk effect) of organisms from lower trophic levels (Steven et al., 2000; Shears et al., 2002;

Daskalov et al., 2007; Heithaus et al., 2008; Ruttenberg et al., 2011). This control can have

profound impacts on the structure of marine communities by increasing ecosystem stability and

preventing phase shifts in fragile ecosystems such as coral reefs (Hughes et al., 2010) or algal

forests (Estes & Duggins, 1995; Shears et al., 2002).

The continuing decline of top-predator populations, principally due to overfishing and habitat

degradation (Myers et al., 2003; Robbins et al., 2006; Daskalov et al., 2007; Ferretti et al., 2010;

Dulvy et al., 2014), has led to concerns about both predator conservation and possible effects on

the ecosystem as a whole (Myers et al., 2003; Daskalov et al., 2007; Myers et al., 2007; Sandin

et al., 2008; 2010; Dulvy et al., 2014). In this context, several authors have suggested that the

effective management and conservation of top predators relies in part on a more thorough

understanding of their ecology from a community level (e.g., species composition and species

interaction) to individual species-specific aspects (e.g., abundance, distribution, and spatial

dynamics) (Garla et al., 2006; Dale et al., 2010; Simpfendorfer et al., 2010; Vaudo et al., 2017).

2

1.1.1 Research questions

Little is known about the marine biodiversity and the marine ecosystem health of the Easter Island

Ecoregion (Friedlander et al., 2013). Only a few recent surveys have examined the fishes from

Rapa Nui and the associated seamounts, with most of these studies focusing on benthic, rather

than pelagic species (Fernandez et al., 2014). Moreover, virtually no information is available on

the ecology of pelagic top predator fishes at the ecosystem scale making it difficult to understand

the dynamic of reef communities and to predict the ecological consequences of common threats,

such as fishing. Nowadays, multispecies studies are needed to implement ecosystem-based

management, because it gives a more comprehensive understanding of the ecosystem, which

can lead to better management (Toonen et al., 2011).

Therefore, the general objective of this thesis was to study some important ecological aspects of

the marine top predator assemblage needed for developing science-based management and

effective conservation strategies across species within the Easter Island Ecoregion. Here, I

present the specific aims that are addressed in subsequent chapters (Fig. 1.1). Each chapter is

written as a stand-alone manuscript to facilitate publication; thus, chapters have their specific

introduction sections and consequently may include some elements of the background

information presented here.

3

Figure 1.1. Flow diagram outlining the background and general structure of the thesis.

4

1.2 SPECIFIC AIMS

1.2.1 Diversity, abundance, and distribution of top predators around Rapa Nui

Precise and accurate information on the diversity and abundance of fish populations is important

for studying most aspects of their ecology, and therefore is widely sought after for management

and conservation purposes (Pita et al., 2014). Moreover, changes in these parameters usually

indicate alteration in the community structure in response to ecological, climatic, or

anthropological changes (Schlosser, 1990; Westera et al., 2003; Jeppesen et al., 2010; Dale et

al., 2011). In Chapter 2, I characterized the top predator assemblage of Rapa Nui throughout a

year around the island of Rapa Nui using a novel and non-lethal technique (Baited Remote

Underwater Video System [BRUVS]). This technique has been widely used in the study of fish

assemblage structure in a variety of environments including both temperate and tropical reefs

(Harvey et al., 2012, Langlois et al., 2010). BRUVS increase the number of sampled species,

(Stobart et al., 2007, Willis & Babcock, 2000), since the bait attracts the fishes into the field of

view of a camera so they can be identified and counted (Dorman et al. 2012; Hardinge et al.

2013), and therefore, are especially effective in the study of cryptic and rare predators, such as

sharks and fishery-target species (Harvey et al., 2012). Determining which species inhabit Rapa

Nui and how they are distributed will contribute to the identification of priority conservation areas

within the recently created Rapa Nui multiple use marine protect area (Rapa Nui MUMPA).

1.2.2 Species trophic position and interaction among sympatric species top predator

species at Rapa Nui

The estimation of trophic levels has become increasingly useful in the analysis of marine food

webs (Stergiou & Karpouzi, 2002, Young et al., 2010) because it facilitates the understanding of

ecosystem function and the relation among sympatric species (Frisch et al., 2016). For instance,

5

trophic interactions are known to influence the larger patterns of community dynamics, such as

species composition, abundance, biomass, distribution, and others (Polovina et al., 2001;

DeMartini & Friedlander 2006; Dale et al., 2011, Speed et al., 2012). Large fishes are usually

considered as top predators because their size and behaviour; however, it is well known that is

not always the case (Frisch et al., 2016), and that the trophic role of species can change (e.g.,

among habitats; Ferreira et al., 2017). The misclassification of the trophic level and their relation

within other sympatric species can lead to erroneous conclusions about ecosystem dynamics,

and therefore, ineffective management strategies may be implemented (Heithaus et al., 2008;

Frisch et al., 2016). In Chapter 3, I used stable isotope analysis to determine the trophic position

and the isotopic niche of large fishes inhabiting Rapa Nui. The study of trophic relationships will

clarify the ecological role of large predatory fishes previously classified as top predators, while

the isotopic niche will provide insights of how resources are being partitioned among sympatric

species.

1.2.3 Movement patterns and connectivity of top predators inhabiting the Easter Island

Ecoregion

Oceanic islands and seamounts often aggregate highly mobile species (Holland et al., 1999;

Worm et al., 2003; Morato et al., 2010; Garrigue et al., 2015). They facilitate the dispersion of

organisms between distant areas (Wilson & Kaufman, 1987; Friedlander et al., 2013) by serving

as navigational marks for resting and/or feeding areas (Rogers, 1994; Garrigue et al., 2015). Rapa

Nui and Salas y Gómez Island are connected, by several dozen seamounts that could play the

role of stepping-stones (Newman & Foster 1983; Friedlander et al., 2013), and thus creating a

biological corridor. Despite their potential, little is known about the connectivity between these two

neighbouring islands.

6

Marine protected areas (MPAs) have become an effective tool for the protection of biodiversity

(Botsford et al., 2003; Lubchenco & Grorud-Colvert, 2015; Sala et al., 2018); however, many of

these MPAs were implemented without sufficient empirical knowledge of the species inhabiting

the area (Botsford et al., 2003; Pasmiño et al. 2017). Therefore, understanding the association

between species and their habitat requirements is crucial to create MPAs that include appropriate

habitat types, and are large enough to provide effective, long-term protection for the species of

concern (Botsford et al., 2003; Friedlander et al., 2007; Meyer et al., 2007). Additionally, the

clarification of complex processes, such as ecosystem connectivity (e.g., source-sink dynamics),

are becoming increasingly recognized in the protection of key species and in the implementation

of effective MPAs (Wilson et al., 2006; Simpfendorfer et al., 2010; Papastamatiou et al., 2010;

Espinoza et al., 2014; Vaudo et al., 2017).

To study the connectivity of large predators between both islands, I used two different

approaches, each one corresponding to a different chapter. In Chapter 4, I studied the horizontal

and vertical movement patterns of two abundant top predator species in the Easter Island

Ecoregion, the Galapagos shark (Carcharhinus galapagensis) and the yellowtail amberjack

(Seriola lalandi) using satellite tags. Pop-up archiving tags (PAT) record horizontal and vertical

movements, through depth and time, plus positional water temperature (Brill et al., 2002; Luo et

al., 2006). PAT pop-up tags have been widely used to address questions of large-scale

movements and behaviour of species that do not spend enough time at the surface (e.g., Wilson

et al., 2006; Holmes et al., 2014). Moreover, I examined if the MMHMP´s borders were appropriate

for protecting these species from fishing pressure outside of the MPA.

Finally, in Chapter 5, I studied the population genetic of the Galapagos shark using single

nucleotide polymorphisms (SNP) and mitochondrial DNA (mtDNA). SNPs have proven to be

informative markers for Galapagos shark population structure (Pazmiño et al., 2017; 2018).

Determining the degree of population connectivity among geographic areas, with the estimated

7

location of genetic breaks, enables assessing the appropriate scale at which management

strategies for marine species should be applied to continue demographic exchange and prevent

local extinctions (Crowder & Norse, 2008; Toonen et al., 2011). In this context, the establishment

of a synergistic source-sink relationship between the marine ecosystems of Rapa Nui and Salas

y Gómez Island would increase the value of the existing MMHMP for the health and sustainability

of the entire ecosystem.

8

CHAPTER 2. SPATIAL AND SEASONAL DIFFERENCES IN THE TOP PREDATORS

OF RAPA NUI: ESSENTIAL DATA USED AS A KEY TOOL FOR IMPLEMENTING

THE NEW RAPA NUI MULTIPLE-USES MPA

2.1 ABSTRACT

Reef fishes are an important component of marine biodiversity and changes in the composition

of the assemblage structure may indicate ecological, climatic, or anthropogenic disturbances. To

examine spatial differences in the reef fish assemblage structure around Rapa Nui, we sampled

eight sites during autumn and summer 2016-2017 with Baited Remote Underwater Video systems

(BRUVs). To determine seasonal changes, we conducted quarterly seasonal sampling at five of

those eight sites. A total of 15 pelagic species of fishes were recorded during this study, some of

which have not previously been recorded in SCUBA surveys, including the Galapagos shark

(Carcharhinus galapagensis) and tunas (Scombridae). Significant spatial and seasonal

differences were found in the fish assemblage. Fish assemblages from the south coast differed

significantly from those along the west and the east coasts, mainly due the occurrence of top

predators. Winter differed from other seasons, especially along the south coast were the island is

more exposed to large oceanic swells and winds from Antarctica. Due to the variety and high

relative abundance of species recorded during this survey, BRUVs seemed to be an effective

method for studying top predators at Rapa Nui. Future studies should examine deeper zones

around the island and the surrounded seamounts. The identification of priority zones for the

protection of top predator species represent an important contribution of this study, in order to

develop management and conservation strategies to be implemented in the newly created Rapa

Nui multiple uses coastal marine protected areas (Rapa Nui MUMPA).

9

2.2 INTRODUCTION

Reef fishes play an important role in ecosystem function (Stevens et al., 2000), and are the target

of recreational, commercial, and subsistence fisheries in many coastal locations (Henry & Lyle,

2003; Kingsford et al., 1991). Precise and accurate information on the diversity and abundance

of fish populations is important for understanding their ecology and is critical for developing

effective management and conservation strategies (Pita et al., 2014). Changes in the fish

assemblage composition usually indicate alteration in the community structure in response to

ecological, climatic, or anthropogenic drivers (Jeppesen et al., 2010; Schlosser, 1990; Westera

et al., 2003).

Reef fish assemblages vary spatially and temporally in response to biotic variables, such as food

availability (Tickler et al., 2017), predation or competition (Almany, 2004), and abiotic variables,

such as habitat complexity and environmental characteristics like wave exposure and temperature

(Anderson & Millar, 2004; Coles & Tarr, 1990; Curley et al., 2003; Friedlander & Parrish, 1998).

For example, spatial variation in reef fish assemblages can occur on scales of meters to

kilometers (Connell & Jones, 1991; Curley et al., 2003; Malcolm et al., 2007), and are usually

associated with habitat complexity and the environmental conditions that structure that habitat

(Asher et al., 2017; Coles & Tarr, 1990; Friedlander & Parrish, 1998). Seasonal changes are more

evident in reef ecosystems from sub-tropical latitudes because of greater environmental variability

(Coles & Tarr, 1990; Friedlander & Parrish, 1998). However, these influences differ by location.

For example, Coles & Tarr (1990) found that the large variation in temperature between winter

and summer (about 20ºC) in the Western Arabian Gulf determines the richness and abundance

of inshore species. In Hawaii, Friedlander & Parrish (1998) observed that fish assemblages

responded to high wind and wave energy during winter by taking refuge at deeper depths and in

more complex habitats. Understanding the natural variations in the fish assemblage provides

essential baseline information for designing and evaluating the effectiveness of marine protected

10

areas (MPA) (Charton et al., 2000). Having accurate information of where to protect is especially

valuable in highly urbanized areas, where area protection is constrained owing to conflicts among

multiple users (Curley et al., 2003).

Marine Protected Areas (MPAs) have been shown to be a highly effective means of conserving

biodiversity and managing fisheries, while also restoring and preserving overall ecosystem

functions (Gaines et al., 2010; Lubchenco & Grorud-Colvert, 2015). Through the establishment of

fishing regulations such as minimum size, effort control and/or regulation of total catches (Botsford

et al., 2003; Hilborn et al., 2006), MPAs are usually associated with the increase of abundance,

biomass and size of focal species (Micheli et al., 2004) as well as catch-per-unit-effort (CPUE) in

adjacent areas (Roberts et al., 2000). In Chile, 23 MPAs have been created in the last decade,

protecting over 41% of its economic exclusive zone (EEZ) (Petit et al., 2017). The most recent

three MPAs were announced during the 2017 International Marine Protected Areas Congress

(IMPAC4 2017): Islas Diego Ramirez-Paso Drake, Juan Fernandez archipelago and Rapa Nui.

The Rapa Nui Multiple Uses Coastal Marine Protected Area (MUMPA) covers the entire Easter

Island Ecoregion and extends from the Rapa Nui coastline to the limit of the EEZ, embracing

~579,000 km2.

Easter Island, also known by its Polynesian name Rapa Nui, is the most south-eastern coral reef

ecosystem in the Pacific Ocean and harbours a unique fish assemblage with a high level of

endemism (Randall & Cea, 2011). Rapa Nui is one of the most isolated inhabited islands in the

Pacific Ocean; yet, long-term overfishing has dramatically reduced the abundance of targeted

species (Aburto et al., 2015; Friedlander et al., 2013; Randall & Cea, 2011; Zylich et al., 2014).

Modern fishing equipment and the demand for local fish from increasing tourism has compounded

the effects of overfishing (Randall & Cea, 2011; Zylich et al., 2014). There have been a limited

number of surveys of fishes around Rapa Nui (e.g., Easton et al., 2018; Fernández et al., 2014;

Friedlander et al., 2013), with most of these studies focusing on reef fishes, rather than pelagic

11

species. Using underwater visual census (UVC), Friedlander et al. (2013) found contrasting reef

fish assemblages between Rapa Nui and its nearest neighbour, Salas y Gómez, a small island

located ~390 km to the northeast. Salas y Gómez is one of the most isolated islands in the Pacific

Ocean and is fully protected from fishing as part of the Motu Motiro Hiva Marine Park. Sharks,

primarily the Galapagos shark (Carcharhinus galapagensis), and jacks account for more than

40% of the fish biomass around Salas y Gómez, whereas Rapa Nui is dominated by smaller

planktivorous species, with top predators virtually absent (Friedlander et al., 2013).

In the past, ecological studies of fishes at Rapa Nui have relied on fishery-dependent data from

commercial fisheries and UVC, performed by scuba divers (Acuña et al., 2018). The use of

fishery-dependent sampling is destructive (Skomal, 2007) and inefficient due to sampling biases

from gear selectivity and different fishing effort between species, habitats, seasons, and vessels

(Bishop, 2006; Murphy & Jenkins, 2010; Thorson & Simpfendorfer; 2009). Additionally, this

technique is less effective in locations with insufficient and inaccurate landing information, like

Rapa Nui (Aburto & Gaymer, 2018). UVC is the most-used observational technique for reef

ecosystems (Medley et al., 1993; Samoilys & Carlos, 2000). However, it also has several well-

documented limitations and problems, including intra- and inter-observer variability (Thompson &

Mapstone, 1997) and the effect of divers on the species behaviour (Chapman et al., 1974; Cole,

1994; Emslie et al., 2018; Gray et al., 2016; Kulbicki, 1998; Lindfield et al., 2014). In contrast,

remote underwater video systems, such as Baited Remote Underwater Video Systems (BRUVs),

are effective, non-destructive fishery-independent techniques used to sample fish assemblages

without these diver-associated problems.

BRUVs attract a wide range of marine species from different trophic groups into the field of view

of a camera so that they can be identified and counted (Dorman et al., 2012; Hardinge et al.,

2013). BRUVs increase the number of sampled species (Stobart et al., 2007; Willis & Babcock,

2000), and are especially effective in the detection of cryptic and rare predators, such as sharks

12

and fishery-targeted species, that are not well sampled using UVC (Brooks et al., 2011a; Harvey

et al., 2012; Malcolm et al., 2007; Watson et al., 2005). Pelagic BRUVs are even more novel than

traditional BRUVs, allowing the study of species that inhabit the water column, including highly

mobile species (Santana-Garcon et al., 2014; Santana‐Garcon et al., 2014a). Pelagic species are

ecologically important to marine ecosystems (Freon et al., 2005) and highly valuable for the

fishing industry (Pauly, 2002; Worm et al., 2006). Despite their importance and that they are

constantly threatened by multiple factors, such as pollution, climate change, and overfishing (see

Game et al., 2009), the pelagic ecosystems, at a community scale, are still data poor worldwide.

Given the lack of quantitative data on the pelagic fish assemblages of Rapa Nui, the fragility of

the marine ecosystem, and the importance of baseline information for the implementation of

conservation strategies, the general objective of this study was to characterize the assemblage

of marine top predator inhabiting Rapa Nui. The specific objectives were: (1) to assess spatial

and seasonal variability in the pelagic fishes around Rapa Nui using BRUVs; (2) to determine

which environmental factors best explain the observed differences; and (3) to provide key data

for advising management and conservation of the coastal areas, with particular emphasis on

zoning the recently created MUMPA.

2.3 METHODS

2.3.1 Study area

Rapa Nui (27°13´S and 109°37´W) has a land area of 166 km2 and ~5600 inhabitants. Located

2250 km east from Pitcairn Island and 3760 km south-west from mainland Chile, it is one of the

most isolated places on earth. The nearest island is Salas y Gomez Island (26º28`S and

105º21`W), which is an uninhabited volcanic island with a total area of 0.15 km2. Both islands

and more than several dozen seamounts are part of the Salas y Gómez Ridge, which extends

13

2232 km before reaching the Nazca Ridge in the south-eastern Pacific Ocean (Randall & Cea,

2011; Friedlander et al., 2013).

2.3.2 Sample collection

Mid-water BRUVs were constructed according to Santana-Garcon et al. (2014b). Each BRUVs

was constructed using a single GoPro Hero 4 camera (mono-camera) held in their own

underwater housing. GoPros were set to record a wide-angle of view and 1080p. A mix of fresh

local fishes (~300 gr) and one can of Chilean jack mackerel (Trachurus murphyi) were used as

bait. Deployments were carried out during daylight hours, avoiding dusk and dawn. Four

simultaneous 1-h deployments (replicates), having a minimum separation of 500 m to avoid plume

dispersion overlap (Santana-Garcon et al., 2014b), were conducted at a depth of ~25 m at each

site; a minimum of six deployments were conducted per site. Local knowledge, previous studies

and limitations related to weather conditions were used to guide the spatial coverage of sites.

Date, hour and location (latitude and longitude) were recorded during every deployment. To study

spatial differences around Rapa Nui, eight sites were sampled during autumn and summer 2017

(Fig. 2.1). To determine seasonal changes in the fish assemblage, quarterly seasonal sampling

was undertaken at five of those sites during 2016-2017.

14

Figure 2.1: Map of Rapa Nui (Easter Island) and Salas y Gómez Island showing sampling locations (a) Map of Rapa Nui (Easter Island) and Salas y Gómez Island in relation to South America. Dark lines

represent the exclusive economic zone. (b) Sampling locations around Rapa Nui for seasonal variability

(yellow dots). Purple dots represent the 3 extra sites used for assessing spatial variability during summer

and autumn.

Every BRUVs was deployed for a minimum of 70 minutes. Following the recommendations of

Acuña -Marrero et al. (2018), we discarded the first and the last 5 minutes from every video to

avoid any potential influence caused by the presence of the boat. Species assignments were

made following Randall & Cea, (2011), FishBase (ver. 02/2018, R. Froese & D. Pauly, see

www.fishbase.org, accessed 2018), and consultations with world fish specialists. Each species

15

was assigned to a functional group (herbivores, planktivores, secondary consumers, and top

predators) following Friedlander et al. (2013) and FishBase (ver. 02/2018, R. Froese & D. Pauly,

see www.fishbase.org, accessed 2018). Additionally, all the species were classified as “Target

Species” or “Not Target Species” according to Zylich et al. (2014) and discussions by the first

author with local fishermen. The maximum number of individuals of the same species appearing

in a video frame at the same time (MaxN), plus any other individual that was uniquely and clearly

distinguishable from the other individuals, was used as an estimate of relative abundance or a

corrected MaxN (cMaxN; see Acuña-Marrero et al., 2018). MaxN is a conservative measurement

of relative abundance that avoids any error associated with recounting the same fish (Cappo et

al., 2003; Priede et al., 1994; Willis et al., 2003); however, it usually underestimates the real

abundance in a single deployment (Kilfoil et al., 2017). By including any other individual that was

undoubtedly distinguishable within the deployment and that was not already included in the MaxN

calculation, cMaxN tends to solve, in part, the underestimation problem of sampled species.

cMaxN per hour was used to standardize effort across deployments of different soak times, as

suggested by Santana-Garcon et al. (2004a). Measurement of length was not considered during

this study; therefore, a biomass calculation could not be included in the analysis.

2.3.3 Data analyses

All statistical analyses were conducted in PRIMER v. 7.0.13 software package (Clarke & Gorley,

2006) with the PERMANOVA+ add-on (Anderson et al., 2008), unless otherwise specified. A

Bray–Curtis similarity matrix was created on the 4th-root transformed cMaxN data. All

permutational multivariate analysis of variance (PERMANOVA) tests were run with default

settings and 9999 permutations to obtain p-values (Anderson et al., 2008). Statistically significant

(p < 0.05) interactions were further explored with appropriate post hoc pairwise tests. To test

spatial variance around Rapa Nui, cMaxN data of each site were analyzed using “Sites” as a fixed

factor in a PERMANOVA. To test seasonal difference on fish assemblage, data were analyzed

16

using seasons (winter, spring, summer and autumn) and five sites as fixed factors. A canonical

analysis of principal coordinates (CAP) was used as a general test to evaluate structural

differences in overall fish assemblage. CAP maximizes group differences finding the axis that

best separates each group (Anderson et al., 2008). CAP analyses were run on the resemble

matrix of average values between sites and seasons.

2.3.4 Environmental data collection and analysis

To determine the role of seasonal and spatial environmental variation on the fish assemblage

structure, sea surface temperature (SST), long-term and recent wave energy, distance of each

deployment site from the shore, and shelf width were considered. For each site, SST MUR (Multi-

scale Sea Surface Temperature) satellite data at a 1 km spatial resolution

(https://mur.jpl.nasa.gov) were used after we verified the accuracy of these satellite data with in

situ SST data collected at Omohi, Motu Tautara, Ovahe and Kari Kari sites by Dr. Evie Wieters

(unpublished data) from deployed temperature sensors (Onset, tidbit) set to record SST every ten

minutes at 12-15 m depth. Long-term and recent wave energy were computed from NOAA’s Wave

Watch III (WWIII; http://polar.ncep.noaa.gov/waves), were binned into 16 discrete sectors each

spanning 22.5 degrees. The long-term wave energy ranged from Jan 2010 to Jul 2015, meanwhile

recent wave energy was calculated using mean values corresponding to the month each

deployment was made. Distance from shore and shelf width were calculated for each site using

Google Earth Pro (http://earth.google.com) (Table S2.1). For seasonal analysis, only wave

energy, long-term wave energy, and SST were considered. Environmental and biological data

were analysed using distance-based linear modelling (DistLM) and a distance-based redundancy

analysis (dbRDA). DistLM is a routine for analysing and modelling the relationship between a

multivariate data cloud, as described by a resemblance matrix, and one or more predictor

variables. The dbRDA analysis was used to visualize the given model in a multi-dimensional

17

space (Anderson et al., 2008). Environmental values used in the DistLM-dbRDA are shown in

Table S2.2.

2.4 RESULTS

Fifteen species were recorded during the study (Table 2.1). Planktivores and herbivores were the

largest components of the pelagic fish assemblage at Rapa Nui, accounting for 73.8% and 16.9%,

respectively (Table 2.2). The most abundant species around Rapa Nui were Xanthichthys mento

and Chromis randalli. Both occurred at every site-season combination, except at Vaihu during

spring. Top predators, while having the highest species richness (9 species), were not well

represented in abundance except at Vaihu. Fistularia commersonii was the most abundant

species among top predators, followed by Seriola lalandi (Table 2.2). Some species such as

Aulostomus chinensis and Caranx lugubris showed seasonal occurrence and other species such

as Carcharhinus galapagensis and Pseudocaranx dentex displayed more site-specific

occurrences. Nine target species were recorded, seven of which were top predators. The most

abundant and well distributed was Kyphosus sandwicensis, which was abundant along the east

and west coasts of Rapa Nui year-round; however, low abundances were reported at Vinapu, and

it was absent at Vaihu. The black trevally C. lugubris was rare during the entire study.

18

Table 2.1: List of 15 species recorded using BRUVs at Rapa Nui

Family Species Rapa Nui name Trophic level Target

Carcharhinidae Carcharhinus galapagensis Mango Top predator Yes

Aulostomidae Aulostomus chinensis Toto amo Top predator No

Fistulariidae Fistularia commersonii Toto amo hiku kioé Top predator No

Carangidae Pseudocaranx dentex Poópoó Top predator Yes

Carangidae Caranx lugubris Ruhi Top predator Yes

Carangidae Seriola lalandi Toremo Top predator Yes

Carangidae Decapterus muroadsi ature Planktivores Yes

Kyphosidae Kyphosus sandwicensis Nanue Herbivorous Yes

Chaetodontidae Chaetodon litus Tipi tipi uri Secondary consumer No

Pomacentridae Chromis randalli Mamata Planktivores No

Sphyraenidae Sphyraena helleri Barracuda Top predator Yes

Scombridae Thunnus albacares Kahi Top predator Yes

Scombridae Katsuwonus pelamis Bonito Top predator Yes

Balistidae Xanthichthys mento Kokiri Planktivores No

Monacanthidae Aluterus scriptus Paoa Secondary consumer No

Table 2.2. Summary of fish sightings and relative abundance recorded by Baited Remote Underwater Video systems (BRUVS) at Rapa Nui. cMaxN: corrected MaxN.

Trophic level Total no. Individuals % of total Highest cMaxN

Top predator 685 8,12

Carcharhinus galapagensis 112 1,33 21

Aulostomus chinensis 27 0,32 2

Fistularia commersonii 147 1,74 4

Caranx lugubris 12 0,14 4

Pseudocaranx dentex 78 0,92 12

Seriola lalandi 108 1,28 5

Sphyraena helleri 25 0,30 25

Katsuwonus pelamis 1 0,01 1

Thunnus albacares 175 2,07 133

Sec. Cons 97 1,15

Chaetodon litus 47 0,56 9

Aluterus scriptis 50 0,59 3

Planktivore 6227 73,80

Chromis randalli 2838 33,63 163

Xanthichthys mento 3279 38,86 140

Decapterus muroadsi 110 1,30 43

Herbivore 1429 16,94

19

Kyphosus sandwicensis 1429 16,94 241

Total 8438 100

2.4.1 Spatial differences

PERMANOVA revealed that the fish assemblages differed significantly among sites (Pseudo-F =

4.795, p < 0.001). Sites along the south-east side of Rapa Nui, Ana hukahu, Vaihu and Vinapu,

were significantly different from all the other sites around the island (Table S3). CAP illustrates

the difference in the fish assemblage found using PERMANOVA (Fig. 2.2a). The size of the first

two axes were δ1 = 0.9823 and δ2 = 0.9339, respectively, over 5 (m) principal coordinate axes.

The estimation of misclassification error indicates low allocation success (31%); however, most

of the misclassifications occurred within two groups (Fig. 2.2a): (1) Vinapu-Vaihu-Ana hukahu,

and (2) Ovahe-Omohi-Poike-Kari Kari-Motu Tautara (Table S2.2). Vaihu was the only site with

100% allocation success. Vector length and direction from CAP revealed that the abundance of

a few species such as C. galapagensis, F. commersonii and P. dentex drove the differences

between Vaihu-Vinapu-Ana hukahu, and all the other sites (Fig. 2.2a). The occurrence of Thunnus

albacares and Decapterus muroadsi distinguished Poike from other sites (Fig. 2.2a), meanwhile

the occurrence of Katsuwonus pelamis was a consequence of the differences at Omohi.

20

Figure 2.2. Canonical analysis of principal coordinates (CAP) ordination of the variation in fish assemblage among (a) sites and (c) seasons. (b) and (d) CAP loadings shown graphically.

2.4.2 Seasonal differences

Highest richness and abundances were found in autumn and summer. Fish assemblages during

winter significantly differed from the other seasons (Pseudo-F = 3.366, p < 0.001, Table S2.3).

Principal axes values from CAP were δ1 = 0.909 and δ2 = 0.546, over m = 3 principal coordinate

axes (Fig. 2.2b). The overall estimation of misclassification error showed an allocation success of

only 60%. Winter had the highest allocation success with 80%, while success for autumn (60%),

21

summer (60%), and spring (40%) were lower. In general, the occurrence and abundance of

species such as X. mento, A. chinensis and S. lalandi, were associated with winter, while Aluterus

scriptus and C. lugubris were associated with the summer season.

2.4.3 Environmental analysis

DistLM-dbRDA ordination showed that shelf width explained 26.6% of the spatial variation in the

fish assemblage around Rapa Nui (p = 0.002). Recent wave energy and distance from the coast,

when considered alone, explained 15.4 %, (p=0.028) and 14.5% (p= 0.039) of the variation,

respectively. Long-term wave energy was the only variable explaining significant seasonal

variability (~ 17.2% of the variation, p = 0.031) (Table S2.4).

2.5 DISCUSSION

2.5.1 Spatial and temporal patterns

This study is the first on spatial and temporal patterns of the pelagic fish assemblage at Rapa Nui,

highlighting the importance of specific areas of occurrence and abundance. We found the pelagic

fish assemblage at Rapa Nui to be dominated numerically by two small planktivore species, C.

randalli and X. mento, followed by the herbivorous K. sandwicensis. The numerical dominance of

planktivorous and herbivorous species observed in our study is consistent with Friedlander et al.

(2013) findings that these two trophic groups accounted for 40% and 31% of the total reef fish

biomass, respectively. Top predator species, although less abundant, constituted the richest

trophic group in our study (nine species). In contrast, Friedlander et al. (2013) only observed six

species of this trophic group, and with lower abundances. These differences in richness and

abundance of top predator species might be explained by differences in sampling methods. UVCs

is a reliable observational technique (Medley et al., 1993; Samoilys & Carlos, 2000), and it is

widely used for sampling reef-associated species at shallow, nearshore habitats. However, the

effect of divers on animal behaviour has led to the underestimation of some species abundance,

22

such is the case of cryptic and fishery-target species within fishing areas (Chapman et al., 1974;

Cole, 1994; Gray et al., 2016; Kulbicki, 1998; Lindfield et al., 2014), especially pelagic species

(De Girolamo & Mazzoldi, 2001; Stanley & Wilson, 1995). The higher occurrence of rare species

and species undersampled by UVCs, such as C. galapagensis, K. pelamis, T. albacare and C.

lugubris, during our study proved the effectiveness of BRUVs in studying the pelagic fish

assemblages at Rapa Nui, especially top predators.

Top predators play an important role in the top-down ecosystem regulation (Stevens et al., 2000),

yet these species are the most vulnerable to overfishing and their removal could lead to

environmental changes affecting ecosystem function in fragile ecosystems (Hughes et al., 2010;

Shears & Babcock, 2002). The continued decline of top-predator populations at Rapa Nui has

likely caused a phase shift from a healthy community dominated by large top predators, such as

at Salas y Gómez, to a disturbed community dominated by smaller planktivorous species

(Friedlander et al., 2013). Seven of the nine species of top predators recorded in this study are

targeted by fisheremen at Rapa Nui. Together with the herbivorous Pacific rudderfish, K.

sandwicensis, top predators like S. lalandi, S. helleri and T. albacares are the most targeted

pelagic fishes at Rapa Nui (Zylich et al., 2014). Subsistence catches are also dominated by K.

sandwicensis and other jacks such as C. lugubris and P. dentex (Zylich et al., 2014). According

to local residents, C. lugubris was abundant in the past, but now is uncommon. Similarly, the

Galapagos shark, which is currently classified as Near Threatened on the IUCN Red List, has

been reported by local residents to have declined considerably around Rapa Nui, possibly as a

result of direct and indirect fishing impacts (Zylich et al., 2014; N. Morales, pers. obs), although

the overfishing of prey may also be contributing to this decline (DiSalvo et al., 1988). Even though

fishermen on Rapa Nui do not directly target C. galapagensis, they seem to be susceptible to

bycatch in coastal and offshore fisheries. Likewise, their population has declined considerably in

23

Central America (Bennett et al., 2003), where the major threat comes from bait-fishing activities

around islands and seamounts (Bennett et al., 2003; Zylich et al., 2014).

Carcharhinus galapagensis is the most common coastal shark around Rapa Nui (Randall & Cea,

2011; Zylich et al., 2014), and it was the only species of shark observed during the current study.

A similar BRUVs study in the Galapagos Archipelago found that the C. galapagensis was also

the most abundant among 12 species of sharks in the area (Acuña-Marrero et al., 2018). In that

study, C. galapagensis showed a similar mean cMaxN (0.52) per deployment to our observations

(0.58), despite the fact that the highest cMaxN found in the Galapagos (8) was almost three times

lower than in the current study (21). Total number of individuals observed was 334 in the

Galapagos Archipelago, and 112 in the current study. These contrasting numbers could be a

result of a higher local (i.e., site) concentration of this species but a lower regional (i.e., island)

abundance at Rapa Nui than at the Galapagos Archipelago.

Spatial and seasonal differences in the composition of pelagic fish species were found during this

study. Species composition along the south coast (Ana hukahu, Vaihu and Vinapu) was

significantly different from the east and west coasts of the island. Spatial differences in

assemblage structure were driven by the occurrence and abundance of the top predators such

as C. galapagensis, F. commersonii, and P. dentex, which showed more site specificity,

suggesting the presence of specific habitat characteristics unique to certain areas. Habitat

structure and complexity have been indicated as important characteristics in the composition of

fish assemblages, e.g., more complex habitats provide greater food availability and refuge

(Anderson & Millar, 2004; Asher et al., 2017; Coles & Tarr, 1990; Curley et al., 2003; Heupel &

Hueter, 2002). Shelf width was the most influential pelagic fish assemblage driver. Along the

southern coast of the island, the shelf break (30 m) occurs further from the coastline creating an

extended shallow platform (Table S2.2). The sharks observed during this study were likely

juveniles (less than 200 cm TL; Wetherbee et al., 1996), based on size estimates of those sharks

24

that closely approached bait canisters (used for scale), suggesting juveniles have an apparent

strong association with that shallow shelf habitat. Our observations suggests that the south-east

coast of Rapa Nui could be serving as a nursery area for juvenile Galapagos sharks, which is

consistent with nursery areas for Carcharhinus species often occurring in shallow waters

(Springer, 1967) with a low-predation environment and ample prey availability (Branstetter, 1990;

Heupel & Hueter, 2002; Simpfendorfer & Milward, 1993).

Abiotic (environmental) variables also influence the abundance of fish species within an area,

leading to spatial variability within the ecosystem (Felley & Felley, 1986). Wave energy has been

noted as an important driver of reef habitats and benthic communities at Rapa Nui where the

dominance of different coral species depends on the degree of exposure (Easton, et al., 2018;

Friedlander et al., 2013). Wave energy came mainly from the south-west (202°) (Table S2.1);

however, it only explained a small amount of the spatial variability in the pelagic fish assemblage.

These results may be explained by the low resolution of the satellite data for each site, which

probably did not reflect the real effect of wave energy in the total area. Furthermore, in situ

measurement of this environmental variable may provide finer resolution and explanatory power.

Although, top predator species are often associated with high-energy environments, the

occurrence of top predators and target species at the south-easternmost part of the island (From

Vinapu to Poike) could be also explained by the effect of adverse weather conditions (e.g., wind,

currents, and wave energy) on the local fishing effort, forcing fishing into more sheltered areas.

Conversely, the most abundant target species K. sandwicensis was rare on the south coast and

virtually absent between Vaihu and Ana hukahu. The nanue (Rapanui name for the K.

sandwicensis) is an herbivore species that feeds primarily on red algae. At Rapa Nui, the

occurrence of algae is concentrated at the most protected sites (north-east) of the island (see

Easton et al., 2018). On the other hand, this species is one of the most prized species on Rapa

Nui and is considered over-exploited by local people (Gaymer et al., 2013). According to Acuña

25

et al. (2018), nanue are usually caught by traditional shoreline fishing and spearfishing, especially

from Vinapu to Hanga Nui, where shoreline access is easier and fishing pressure is higher. The

heavy fishing pressure together with the species habitat preference could explain the localized

depletion in these areas.

Seasonal variability in pelagic fish assemblage structure was evident during this study, with winter

been significantly different from the other seasons. Autumn and spring are transition seasons, as

has been described from other subtropical areas (Friedlander & Parrish, 1998). Sites located

along the coasts most exposed to winter swells and winds (Ana hukahu, Vaihu and Vinapu)

showed higher variability among seasons in comparison with more protected sites. Similar results

were found by Coles & Tarr (1990) in the western Arabian Gulf, and by Friedlander & Parrish

(1998) in the Hawaiian Archipelago. In both cases, the authors noticed that some mobile fishes

seem to migrate from exposed to more protected and deeper locations that provide refuge from

high wave energy during winter. In contrast, more protected sites seem to have more stable

assemblages throughout the year. Asher et al. (2017) also found an increase in abundance of

jacks and sharks in shallow and mesophotic reefs in the Hawaiian Archipelago with increasing

depth, due probably to the avoidance of environmental (e.g., wave energy) and anthropogenic

factors (e.g., fishing) in shallow waters. Rapa Nui has been understudied in comparison to other

islands in the Pacific Ocean, and studies at deeper depths are even more limited (Easton et al.,

2017). Seriola lalandi and P. dentex were recorded at ~280 m and ~170 m, respectively, using

ROV (remotely operated vehicle) and Drop-Cams around Rapa Nui and the surrounding

seamounts (Easton et al., 2017). The occurrence of inshore species at deeper depths could also

suggest that deeper habitats are being used as a refuge from natural and anthropogenic

influences. The presence of particular species during certain seasons and at certain sites could

be explored by expanding the survey area in order to include mesophotic zones and incorporate

surrounding seamounts in future designs.

26

2.5.2 Conservation actions

Randall & Cea (2011) proposed the establishment of marine reserves around Rapa Nui to allow

resident fishes to grow until they reached full reproductive maturity. Some of the areas suggested

for reserves were Motu Nui and Motu Iti (in front of Kari-Kari), Ovahe, Motu Tautara, Hanga Nui,

and Motu Marotiri. The last two areas correspond to the southeast side of the island, close to

where the greatest abundance of top predators was recorded and a possible nursery area for C.

galapagensis was identified. Carcharhinus galapagensis show ontogenetic segregation, where

juveniles are more likely to inhabit shallow coastal waters, meanwhile adults occur in deeper

waters away from the coast (Acuña-Marrero et al., 2018; Kohler et al., 1998; Wetherbee et al.,

1996). Areas used by early life stages are vital for population stability and recovery (Bonfil, 1997),

and therefore, their protection is necessary.

Several initiatives have proposed other strategies to protect marine coastal and offshore

ecosystems at Rapa Nui. An effort has been made in the last seven years to raise awareness and

capacity building in the Rapanui community (Aburto et al. 2017; Gaymer et al., 2013). These

efforts ultimately resulted in a participatory process that lead to the creation of a Multiple Uses

coastal Marine Protected Area, MUMPA, around the entire EEZ of Easter and Salas and Gómez

islands, completing the protection initially provided by the MMHMP in 2010. In order to implement

this large-scale MPA, a participatory management plan has to be built, which includes the zoning

of the MUMPA in both the coastal and offshore areas. Zoning will include establishing fully no-

take coastal areas that could allow recovery of some over-exploited target fishes, but also to

protect areas were top predators (such as C. galapagensis) are concentrated. Top predators play

a crucial role in ecosystem function (Friedlander & DeMartini, 2002), thus their protection is

necessary for maintaining ecological processes and ecosystem services. The current study is an

important contribution for planning the management and conservation strategies to be

implemented in the newly created Rapa Nui MUMPA. A Marine Council, with a majority of

27

Rapanui-elected members, will place the administration of this area under a co-management

strategy, in which is an unprecedented model of MPA administration in Chile (Aburto et al., 2017)

Over the last decades, there has been an increasing awareness of the added value that

ecosystem services and sustainable management can offer to small human communities that

inhabit coastal areas (Arkema et al., 2006). Biodiversity has been recently recognized as an

economic resource (Admiraal et al. 2013), enhancing ecotourism and helping local inhabitants

shift from non-sustainable practices (overfishing) to a broader array of sustainable activities with

added value such as community-based ecotourism. In this sense, the year-round occurrence of

the Galapagos shark in one specific area of the island could be considered a shark-based

ecotourism spot, where local operators benefit from long-lived animals ensuring decades of

incomes. Thus, not only the protection of the Galapagos shark, but also its potential for ecotourism

(e.g., shark-watching by SCUBA divers), should be key elements for taking into account for the

zoning of the Rapa Nui MUMPA, that will allow activities such as traditional fishing practices,

ecotourism, scientific research and others that should be defined in the management plan.

28

CHAPTER 3. THE TROPHIC ROLE OF LARGE FISHES INHABITING THE EASTER

ISLAND ECOREGION

3.1 ABSTRACT

Large fishes are culturally and economically valuable in the remote island of Rapa Nui.

Nonetheless, historical overfishing resulting in the removal of top predators could affect the

abundance and distribution of other species from lower trophic position within the ecosystem due

to top-down regulation. Large fishes are usually assumed to be top predators; however, their

trophic role needs to be evaluated on each location where they occurred. Here, we used δ13C and

δ15N isotopic signatures of large reef fishes previously listed as top predator species to: (1)

determine the trophic position of large fishes and highlight their role in the ecosystem; and to (2)

define the trophic niche of these species to evaluate the potential for dietary overlap. Our results

indicate that not all large fishes inhabiting Rapa Nui can be considered as top predator species.

It is also shown that there is a high overlap in isotopic signatures among studied species,

suggesting the potential for interspecific competition. The current study highlights the needed for

multispecies studies to elucidate the tropho-dynamics of this isolated and understudied

ecosystem.

3.2. INTRODUCTION

Top predators exert strong top-down influences on communities, controlling the demography of

other organisms (Steven et al., 2000; Heithaus et al., 2008; Ruttenberg et al., 2011). The

continued decline of these predators has led to increased concerns about how flow-on effects

could impact the abundance and distribution of other species from lower trophic position (Stevens

et al., 2000; Myers et al., 2003; Dulvy et al., 2014; Myers et al., 2007).

29

Since the emergence of stable isotope technology, stable isotope analysis (SIA) are been

increasingly used to investigate the tropho-dynamics within food webs (Cherel et al., 2008). The

stable isotope signatures of carbon (δ13C) and nitrogen (δ15N) reflects nutrients assimilated over

many feeding events (Futuyma & Moreno 1988). As the isotopic signature of carbon (δ13C)

undergoes very little enrichment as trophic position increases, it is a useful indicator of the source

of primary production (Harrigan et al 1989; Sweeting et al., 2007), whereas nitrogen (δ15N)

signatures increase by 3-4 ‰ from prey to predator providing an index of trophic position (Vander

Zanden et al., 1997; Sweeting et al., 2007; Frisch et al., 2014). Using this technique, relevant

information on trophic interaction such as trophic position, trophic niche, and intra- or inter-specific

competition can be easily achieved (Post 2002; Papastamatiou et al., 2006). Reef sharks and

larger bony fishes are typically assumed to have the highest trophic position in their food webs

(Sandin et al., 2008; Roff et al., 2016), mainly because of their relatively large body size and

predatory behaviour (Frisch et al., 2014; 2016). The misclassification of a species trophic position

has the potential to result in erroneous conclusions on the food web dynamic of coral reefs, and

therefore, inappropriate management decisions (Heithaus et al., 2008; Frisch et al., 2016). The

trophic niche in a food web provides insight into how a community is structured within an

ecosystem (Layman et al., 2007), and how the available resources are partitioned among

sympatric species (Papastamatiou et al., 2006). Niche dynamics are susceptible to environmental

and biotic changes such as intra- and inter-specific competition and prey abundance (Bearhop et

al., 2004). This information is especially relevant in ecosystems where anthropogenic stressors

impact community structure (Layman et al., 2007).

Rapa Nui is one of the most isolated islands in the Pacific Ocean. Due to its geological age, small

size, and high latitude location, this island is characterized by a low number of shore species

compared to other islands in the Pacific Ocean (Randall & Cea, 2011). Fishes at Rapa Nui are

culturally and economically valuable for the rapanui people. However, historical local overfishing

30

has led to a decline of commercially important fishes over the pass years (Aburto et al. 2015;

Friedlander et al., 2013; Zylich et al., 2014). Fisheries landings predominately consist of large

pelagic fishes like tunas, jacks, and swordfishes, among others, although the herbivorous

rudderfish Kyphosus sandwicensis is also an important and culturally valued inshore species

(Zylich et al., 2014; Easton et al., 2017; Friedlander, 2018). Species like the Galapagos shark,

Carcharhinus galapagensis, are not commercially targeted; however, its population is also

decreasing mainly due to incidental fisheries take (Zylich et al., 2014). In addition, some (legal)

catches of large pelagic fishes such as swordfishes, oceanic sharks, and marlins commonly occur

inside (Vega et al., 2009) and outside (Morales unpublished data) the EEZ of the Easter Island

Ecoregion. As a result, large species are either absent or in extremely low abundance around the

island, raising questions about the health of the entire ecosystem (Friedlander et al., 2013).

Trophic interactions can have a strong influence on certain aspects of community ecology such

as demography (Myers et al., 2007). Therefore, determining tropho-dynamics is an important step

in understanding ecosystem functioning (Cherel et al., 2008). Here, we used stable isotope

signatures of δ13C and δ15N to: (1) determine trophic position of large fishes and clarify their role

within the Rapa Nui ecosystem; and (2) define the trophic niche of sympatric species, evaluate

the potential for dietary overlap and hypothesize about potential competition for food in this

nutrient poor ecosystem.

3.3 METHODS


Muscle samples were collected from specimens caught by local fishermen around Rapa Nui from

2016-2019. Our study focused on the top predator species described by Friedlander et al., (2013),

and those observed in Morales et al. (2019a). Muscle samples were labelled and stored at -20°C

31

after collection. Body size could not be measured for each individual therefore, were not

considerate in the analyses.

3.3.2 Sample preparation and stable isotopes analysis

Approximately 10 mg of wet muscle tissue was dissected, washed with milli-Q water, and placed

in pre-combusted vials. All samples were oven-dried (60º C for 48 h) and then ground to a fine

power in an agate mortar. Lipids, which can affect isotopic composition, were dissolved with

cyclohexane according to Lorrain et al., (2011). Approximately 0.5 mg were transferred to tin

capsules (5 x 9 mm) and stored in a desiccator until analysed for stable isotopes.

Analyses of carbon (δ 13C) and nitrogen (δ15N) stable isotope ratios were conducted at the School

of Biological Sciences, Washington State University, using a Eurovector elemental analyzer,

coupled to a Micromass Isoprime isotope ratio mass spectrometer. Stable isotope ratios were

reported in the δ notation as the deviation relative to international standards (Vienna Pee Dee

Belemnite for δ 13C and atmospheric N 2 for δ 15N), so δ 13C or δ 15N = [(R sample /R standard) – 1] ×

10 3, where R is 13C/12C or 15N/14N, respectively. Typical precision of the analyses was ± 0.1 ‰

for both δ 15N and δ 13C.

3.3.3 Trophic positions estimations

Calculations of trophic positions of consumers were performed using “oneBaseline” (model 1) and

“twoBaseline” (model 2) models, based on the trophic enrichment factor (TEF) for nitrogen

detailed by Post (2002). We then incorporated uncertainty through Bayesian inference using the

tRophicPosition package in R software v0.98.977 (R Core Team 2019; Quezada-Romegialli et

al., 2018):

(Model 1-oneBaseline):

δ15Nc = δ15Nb1 + ΔN (TP – λ)

32

(Model 2-twoBaseline):

δ15Nc = ΔN (TP + λ) + α (δ15Nb1 + δ15Nb2) – δ15Nb2

α = (δ13Cc – δ13Cb2) / (δ13Cb1 – δ13Cb2)

Where δ15Nc and δ13Cc refer to the δ15N and δ13C values of consumers, respectively. ΔN

corresponds to the trophic enrichment factor (TEF) for nitrogen and λ is the trophic position of the

baseline. The δ15Nb1 and δ13Cb1 are the nitrogen and carbon signatures for baseline 1 and

δ15Nb2 and δ13Cb1 for baseline 2. α is the proportion of nitrogen derived from baseline 1. The

models were run with 2 chains, 20.000 adapting samplings and 20.000 iterations.

Assuming that the Rapa Nui marine ecosystem could be supported by both pelagic and benthic

N2-fixation pathways (e.g., N2-fixing pelagic cyanobacteria and diazotrophic symbiont in corals,

respectively), a two-baseline model based in nitrogen signatures was used. (Zapata et al.

unpublished data). The δ15Nb1 signature was estimated as the average from three herbivorous

zooplankton taxa (i.e., calanoid and cyclopoid copepods and euphausids) (baseline 1) and the

coralivorous gastropod Coraliophilla violacea (baseline 2). The ΔN and ΔC were taken from global

meta-analyses and correspond to muscle tissue signatures (ΔN=2.9‰ ±0.3 and ΔC=1.3+0.3‰)

(McCutchan et al., 2003).

3.3.4 Trophic structure and isotopic niche

To determine the trophic diversity between marine assemblages, a sample-size corrected version

of standard ellipse area (SEAc) was utilized as a measure of the mean core of the isotopic niche

occupied by different taxa in each assemblage (Jackson et al., 2011). This metric represents a

measure of the total amount of niche occupied in the isotopic space and allows for robust

statistical comparisons between data sets with different sample sizes and corrects for bias when

sample sizes are small (Jackson et al., 2011). Moreover, this metric allowed for calculating the

33

overlapping area of the standard ellipses (and their respective %) between assemblages

(employing 95% of data) and was used as a measure of trophic partitioning between different

assemblages. Additionally, other measurements of isotopic niche widths and trophic structure

proposed by Layman et al., (2007), were calculated for each assemblage: (1) the total area (TA)

occupied was calculated as the area of the convex hull that incorporated all individuals and

represents a measure of niche width and reflects the isotopic diversity of a group (Vaudo &

Heithaus, 2011); (2) mean distance to the centroid (CD) represents the average degree of trophic

diversity within the species. CD is calculated by determining the Euclidean distance of each

individual to the δ13C and δ15N centroid of all individual; and (3) standard deviation of nearest

neighbour distance (SDNND) giving a measure of evenness of spatial density and packing. These

metrics were estimated using the SIBER package in R (Jackson et al., 2011).

3.4 RESULTS

In total, 105 individuals from 10 large fish species were sampled and analyzed for isotopic

composition (Table 3.1). Mean δ15N and δ13C signatures for all fish species are shown on a

bivariate plot in Figure 3.1. Thunnus albacares had the largest range of δ13C (6 ‰), while

Acanthocybium solandri and Pseudocaranx dentex had the largest ranges of δ15N (7.5 ‰).

Species-specific mean δ13C signatures ranged from -18.40 ± 1.29 ‰ (Katsuwonus pelamis) to

16.45 ± 0.67‰ (Carcharhinus galapagensis), and mean signatures of δ15N range from 13.85 ±

1.75‰ (Pseudocaranx dentex) to 17.53 ± 0.93‰ (Coryphaena hippurus), although the single

sample of Alustomus chinensis measured 11.6‰. Statistical comparisons showed significant

differences between species (Pseudo-F = 5.148; df = 9; P< 0.001). The most significant

differences were from P. dentex and C. galapagensis to the other species inhabiting the island

(Table S3.1).

34

Figure 3.1. δ13C and δ15N signatures of large fishes inhabiting Rapa Nui. Data points are group means, and error bars are standard deviations. Open symbols represent species with less than five samples. Species abbreviations are defined in Table 3.1.

35

Table 3.1. Summary of mean and standard deviation (SD) of stable isotopes (δ13C and δ15N) composition of large fishes included in this study. Abbreviation (Abb), sampling size (n).

Taxón Abb

δ13C δ15N

n

Mean SD Range Mean SD Range

Acanthocybium solandri AS -17.77 0.97 4.2 16.03 1.58 7.5 31

Aulostomus chinensis AC - - - - - - 1

Carcharhinus galapagensis CG -16.45 0.67 1.5 15.62 1.26 2.9 6

Coryphaena hippurus CH -17.79 0.93 2.4 17.53 0.93 2.4 7

Katsuwonus pelamis KP -18.40 1.29 5.5 15.94 1.65 6.6 19

Pseudocaranx dentex PD -17.20 1.32 4.8 13.85 1.75 7.5 19

Seriola lalandi SL -18.10 0.34 0.7 14.15 2.51 5.3 4

Kajikia audax KA -17.28 0.44 0.9 15.74 1.30 3 5

Thunnus albacares ThA -18.38 1.06 6 16.01 1.67 6.3 36

Thyrsites atun TyA -18.16 1.09 3.3 16.21 0.84 2.2 7

3.4.1 Trophic position

Large fishes inhabiting Rapa Nui encompassed three trophic positions: secondary (3.0-3.9),

tertiary (4.0-4.9), and quaternary (≥5.0) consumers. Only one species was recoded as a

secondary consumer, seven as tertiary, and one as quaternary (Table 3.2). Within the tertiary

consumers, Pseudocaranx dentex and Seriola lalandi had the lowest trophic positions (4.2 and

4.4, respectively), while Katsuwonus pelamis, Thunnus albacares, Thyrsites atun, and

Acanthocybium solandri had the highest signatures, nearly approximating quaternary consumers.

Only one species, Coryphaena hippurus, encompassed the quaternary group.

36

Table 3.2. Summary of outputs from trophic position (TP) models (model-1: “oneBaseline” and model-2: “twoBaseline”). Mode signatures and 95% Bayesian confidence interval are presented. TP from Fishbase are based on food items.

Taxon TP-model 1 TP-model 2 TP from

Fishbase Mode Lower Upper Mode Lower Upper

Acanthocybium solandri 5.0 4.3 6.0 4.9 4.2 5.9 4.3 ± 0.2

Aulostomus chinensis 3.4 2.0 9.3 3.3 2.0 9.3 4.2 ± 0.7

Carcharhinus

galapagensis 4.8 4.0 6.0 4.6 3.9 5.7 4.2 ± 0.4

Coryphaena hippurus 5.4 4.6 6.8 5.4 4.5 6.5 4.4 ± 0.0

Katsuwonus pelamis 4.9 4.2 6.0 4.8 4.1 5.8 4.4 ± 0.5

Pseudocaranx dentex 4.2 3.6 5.0 4.1 3.6 5.0 3.9 ± 0.6

Seriola lalandi 4.3 2.6 6.5 4.1 2.3 6.1 4.2 ± 0.1

Kajikia audax 4.8 4.0 6.2 4.8 3.8 6.1 4.5 ± 0.7

Thunnus albacares 4.9 4.2 5.9 4.8 4.2 5.7 4.4 ± 0.4

Thyrsites atun 4.9 4.3 6.2 4.9 4.2 5.9 3.6 ± 0.3

3.4.2 Isotopic niche

The isotopic niche of large fishes inhabiting Rapa Nui differed in size but not much in position,

indicating a low to moderate degree of trophic diversity (Fig. 3.2). The ellipse areas (SEAc) was

smaller for Acanthocybium solandri (4.16) and larger for Pseudocaranx dentex (7.4). Except for

A. solandri, all groups had similar CD signatures. NND ranged from 0.46 (T. albacares) to 0.82

(K. pelamis and P. dentex), and SDNND ranged from 0.49 (T. albacares) to 1.21 (K. pelamis)

(Table 3.3).

The niche overlap was high (≥50%) between A. solandri, K. pelamis, and T. albacares.

Pseudocaranx dentex was the only species that showed less overlap with the others, suggesting

that this species feeds on different prey than the other species examined (Table 3.3; Fig.3.2a).

37

Table 3.3. Isotopic niche area described by Standard Ellipse Area (SEA), corrected SEA (SEAc), total area of the convex hull (TA) and sample size (n) for the four species with sample size over 10 as was recommended by Jackson et al. (2011). Acanthocybium solandri (AC); Katsuwonus pelamis (KP); Pseudocaranx dentex (PD); Thunnus albacares (ThA). Other abbreviations are defined in methods.

Metrics AS KP PC ThA

SEA 4.01 6.55 6.99 5.58

SEAc 4.16 6.93 7.40 5.75

TA 17.65 23.70 22.75 21.03

CD 1.27 1.55 1.84 1.74

NND 0.49 0.82 0.82 0.46

SDNND 0.55 1.21 0.72 0.49

n 31 19 19 36

Ellipse overlap (%) Species 1

Species 2 AS KP PD ThA

AS - 49.9 10.0 51.6

KP 83.2 - 15.2 98.4

PD 17.8 16.3 - 17.5

ThA 71.3 81.5 13.5 -

*Data are the percentage of SEAc of species 1 that is occupied by the SEAc of species 2.

38

Figure 3.2. Isotopic niche space of four species sampled during the study and density plots showing the credibility intervals of Bayesian standard ellipses areas. (a) Isotopic niche space of four species sampled during the study. (b) Density plots showing the credibility interval of Bayesian standard ellipses areas (SEA). Black circles and red crosses indicate mode SEAB and small sample size corrected (SEAC), respectively. Shaded boxes indicate the 50, 75, and 95% credibility intervals for each species. We only considered species with > 10 samples for these analyses. Acanthocybium solandri (AS); Katsuwonus pelamis (KP); Pseudocaranx dentex (PD); Thunnus albacares (ThA).

39

3.5. DISCUSSION

3.5.1 Stable isotope signatures

Large fishes from Rapa Nui are broadly distributed throughout δ13C - δ15N space (Fig. 3.1),

indicating a broad range of trophic diversities. In general, it is well established that inshore

(benthic) and offshore (pelagic) systems have enriched and depleted δ13C, respectively (Speed

et al., 2012). The δ13C signatures found in this study suggests that most of the studied species

are more related to a planktonic (pelagic) rather than benthic food source. Only Pseudocaranx

dentex and Carcharhinus galapagensis appeared to be more closely related with the benthic coral

reef resource.

Thunnus albacares displayed the largest range of δ13C (6‰), suggesting a broad carbon source.

The wide range and high mean signature could be explained by its migratory behaviour (Bearhop

et al., 2004), and high trophic position, due to the integration of different carbon sources along

their movement routes (Hecky & Hesslein, 1995; Estrada et al., 2003). During these migrations,

T. albacares may consume both benthic prey from islets and seamounts, in addition to pelagic

prey from the open ocean. Moreover, the latitudinal variation in food carbon signatures to which

this species encounters during their migrations can also explain the wide range of δ13C found

here (Kelly et al., 2006; Cherel & Hobson, 2007). In contrast, Acanthocybium solandri and P.

dentex showed the largest range of δ15N (7.5 ‰), which indicates that they feed over a broad

range of trophic positions (Table 3.1; Fig. 3.1). Large ranges of δ15N signatures are more common

on inshore species due to the higher availability of prey from different trophic positions (Link 2002;

Estrada et al., 2003). On Rapa Nui, P. dentex is a benthic feeder that feeds primarily on bivalves,

gastropods, and small fishes (Randal & Cea, 2011). However, personal observations by divers in

our study area indicate that P. dentex also eats the feces (coprophagia) of larger fishes, like the

Galapagos sharks. This interaction could explain the broad range of δ15N values despite its low

40

mean. Coryphaena hippurus showed the highest δ15N signatures (17.53 ± 0.93) suggesting that

this species is at the top of the food web and feeds on high trophic position prey.

3.5.2 Trophic position

Derived trophic position (TP) from δ15N signatures showed that most large fishes at Rapa Nui

have high positions in the trophic web (TP= 4.1 – 5.4). Only Aulostomus chinensis, which was

previously reported as a top predator at Rapa Nui (Friedlander et al., 2013), was not a top predator

based on trophic position (TP= 3.4). However, this should be interpreted with caution since we

only analysed one sample of this species. Even though, the TP of A. chinensis (and the size of

its mouth) seems consistent since they are much smaller than the other fishes sampled here

(maximum size = ~76 cm total length; Randall & Cea, 2011), and therefore are limited to smaller

prey, which are generally low-level consumers. Pseudocaranx dentex and Seriola lalandi had a

TP of 4.2 and 4.3, respectively. Frisch et al., (2016) suggest that species with TP between 4.0

and 4.5 should be considerate more as high-level mesopredators, an alternative trophic group

that better fits their trophic role. The highest trophic positions in our study correspond to C.

hippurus (TP = 5.4) indicating that in the South Pacific it feeds on prey with a high trophic position,

such as pelagic fishes and epipelagic cephalopods (Olson & Galván-Magaña, 2002).

Nevertheless, Teffer et al., (2015) suggested that C. hippurus and other species such as tuna

have similar diets and might be competing for available resources. The differences between C.

hippurus and the other top predators studied here may be a result of their foraging habits (e.g.,

vertical migration and maximum depth, feeding hours, etc.; Moteki et al., 2001). Menard et al.,

(2007) also suggested that sympatric species with similar diets, but slightly different TP´s might

be results of one species feeding over larger specimens of the same prey species. The two

species of tuna (Katsuwonus pelamis and T. albacares) displayed close δ15N signatures, and

therefore TP´s suggesting a similar diet. These two species are mainly piscivorous with similar

feeding behaviour, which could be explaining our results and those findings by Koladinovic et al.,

41

(2008). Even though we could not sample other large species inhabiting the area, such as

Prionace glauca (blue shark) and Isurus oxyrinchus (Mako shark), we hypothesize that these

large roving sharks might also be occupying the role of apex predators in the area as has been

suggested for other locations (Estrada et al. 2003; Bugoni et al., 2010)

The differences found between our TP estimates and those from Fishbase could be explained by

different hypothesis: (1) a close relationship between body size and d15N enrichment has been

proposed (Estrada et al., 2006; Papastamatiou et al., 2010; Speed et al., 2012), meaning that

larger individuals eat higher trophic position prey. We were unable to collect the length

measurements of our fishes which may explain these differences since some specimens were

already processed when landed; (2) different habitats could also have different prey, and thus

display different isotopic signatures within the same species. Ferreira et al., (2017) found that

Galeocerdo cuvier (tiger shark), a widely distributed species occupies different trophic position

according to the habitat (offshore vs. inshore) due to different prey availability; and (3) TPs from

Fishbase were calculated using stomach content, and therefore, they might be showing different

time-frames than our results. Stomach content analysis usually underestimates the presence of

rapidly digested prey such as pelagic species (clupeids and engraulids; Frisk et al., 2016)

resulting in a possible underestimation of TP (Hussey et al., 2012). That said, our results are

consistent with other TPs from stable isotope surveys, such as for T. albacares (TP= 4.8; Graham

2010, and TP= 4.7; Bugoni et al., 2010).

Carcharhinus galapagensis appears to have a high TP in the Rapa Nui ecosystem (TP = 4.8)

even though only juveniles were sampled. According to our results, its presence is important and

should be considered a priority for the health and stability of the entire ecosystem, especially

considering that it is the only resident shark species inhabiting the area (Morales et al., 2019a).

Several studies have noted the influence of reef shark species on the structure of different

ecosystems. For example, Barley et al., (2017), found that on shark-depleted reefs, mesopredator

42

species were more abundant and consumed a different diet (fishes and squids), than those

inhabiting shark-rich reefs. In the latter situation, mesopredators were less abundant and

consumed mainly benthic invertebrates. It is worth remembering that sharks sampled during this

study were juveniles and most likely young-of-year. Juvenile elasmobranchs keep their mothers

isotopic signal during their first year of life (and up to two years; Matchi et al., 2010), especially

when slow incorporation rate tissues (e.g., muscle) are sampled (Matich et al., 2010; Olin et al.,

2011). As a result, our findings were most likely the signature of the mother’s isotopic signal and

not the juveniles. Additionally, most of elasmobranchs maintain high levels of urea in their tissues

as a response to osmoregulation processes (Olson 1999). These nitrogenous waste products

could interfere when comparing δ15N signatures between individuals or taxa (Frisch et al., 2012)

by creating lower δ15N signatures (0.1-1.4‰; Hussey et al., 2012), and therefore, lower trophic

position (-0.2 TL; Churchill et al., 2015) than expected. This may mean that C. galapagensis could

eventually occupy an even higher TP in the Rapa Nui ecosystem.

3.5.3 Isotopic niche

Katsuwonus pelamis and P. dentex showed the widest isotopic niche (TA and SEAc) among the

study species suggesting a more generalist diet than T. albacares and A. solandri. If other

conditions remain the same, trophic generalist species seem to be less susceptible to extinction,

and therefore, more resilient to environmental changes due to their ability to change prey (Layman

et al., 2007). Nevertheless, the utilization of TA (convex hull area) to describe the isotopic niche

width should be used with caution, since wider niches are usually related with larger populations

(Bond et al., 2016) and/or larger sample sizes (Jackson et al., 2011). Our results suggest that P.

dentex and K. pelamis have the widest isotopic niches despite their low sample sizes among the

study species (Table 3.3). Pseudocaranx dentex is a species frequently inhabiting nearshore

areas and feeds mainly on benthic prey (Randal & Cea, 2011), but interacts with larger fishes as

mention above, and therefore, we expect this species to have a wider isotopic niche. Katsuwonus

43

pelamis and T. albacares are sympatric species that frequently occur in mixed pelagic schools

(Sardenne et al., 2016). Adults of T. albacares usually displays a deeper vertical distribution than

K. pelamis and preys upon a larger size range of prey (Graham & Dickson, 2004). However,

during our study, we sampled T. albacares of small size (~ 10 kg) called “pitufos” (smurfs in

english) by the rapanui and only a few large individuals (~ 40 kg) were sampled. The diving ability

of T. albacares increases with size, and therefore, we would expect that larger T. albacares to

display wider isotopic niches than K. pelamis.

The high isotopic overlap found between the studied species could be a result of feeding on

species from similar TPs and sources of production (Frisk et al., 2014), and does not necessarily

suggests interspecific competition. Usually, species partition (spatially and temporally) available

resources in order to avoid competition and enable coexistence (Frisch et al., 2014; 2016; Kinney

et al., 2011; Layman et al., 2012). Muscle tissue of pelagic bony fishes only reveals the diet from

3-4 months (Maruyama et al., 2001) up to 6 months for the stable isotope rations of a fish to reflect

consuming prey within a specific area (e.g., Rapa Nui) giving insights into past diet but not the

current one. In this sense, highly migratory species with high niche overlap are not necessarily

competing for food, but instead might have consumed prey from similar TPs or even the same

species at different location.

Nevertheless, Rapa Nui is an impoverished ecosystem in terms of habitat types and species

number compared to other oceanic island in the Pacific Ocean (Randall & Cea, 2011; Friedlander

et al., 2013), with fewer prey species. In this sense, our results might suggest a degree of

interspecific competition between taxa. A high degree of interspecific competition is especially

concerning at Rapa Nui since the depletion of important species populations such as the nanue

(Kyphosus sandwicensis; Friedlander et al., 2013) could trigger a shift in the ecosystem due a

bottom-up regulation. For example, Myers et al., (2007) suggested that after a reduction of

primary prey, the consumer could display a decrease in the breeding performance and a reduction

44

in their population size. Additionally, the occurrence of new species in the ecosystem, such as

Triaenodon obesus (Morales et al., 2019b) and Seriola rivoliana (N. Morales in prep.), could

eventually cause a change in the ecosystem structure through a niche expansion or “ecological

release”. Bolnick et al., (2010) suggested that niche expansion occurs when invading species

arrive in species–poor habitats, such as oceanic islands. Invading species access resources that

were previously monopolized by former competitors, forcing them to expand their niche and diet.

Thus, future studies should include different turn-over tissues and stomach contents studies in

order to elucidate if migratory and resident species are effectively competing for resources at

Rapa Nui. Tagging programs also would help to determinate important ecological aspects that

could explain the coexistence of sympatric species such as feeding areas, feeding time, and

depth, among others.

3.5.4 Conservation aspects

Coral reef ecosystems are at risk due to the effects of both local stressors such as overfishing

and habitat lost (Wilson et al. 2010), and global stressors such as global warming and ocean

acidification (Hoegh-Guldberg et al., 2007). The biodiversity at Rapa Nui is especially susceptible

to global changes. Its subtropical location makes tropical species vulnerable to extended periods

of cool sea temperature, while subtropical species are vulnerable to long periods of warm sea

temperature (Randall & Cea, 2011). Moreover, historical high fishing pressure on large fishes is

putting pelagic stocks at risk, causing a drastic decline of their population (Myers & Worm 2003).

Thus, the decrease of both high and low trophic position species could cause both a bottom-up

and/or top-down shift with unexpected consequences for the entire ecosystem.

Understanding the role of co-occurring species and how those species use inshore areas is

crucial to implementing effective strategies to manage and conserve both the habitats and the

species inhabiting them (Gallagher et al., 2016). This current study contributes to the

45

understanding of the role of large fishes at Rapa Nui, how they interact, and the relationships

occurring within this isolated and understudied area. Moreover, this information could be used to

improve conservation actions such as the implementation of no-take areas where key species

occur, or the implementation of management strategies for economically important species.

Finally, future studies should concentrate on comparing both Rapa Nui, a shark-depleted

ecosystem, and Salas y Gómez island, a nearly pristine ecosystem full of reef sharks (Friedlander

et al., 2013; Morales pers. obs.), in order to determine if Rapa Nui corresponds to a “new” state

of organization of food web caused by anthropogenic pressure.

46

CHAPTER 4. RESIDENTIAL MOVEMENTS OF TOP PREDATORS AT CHILE’S

MOST ISOLATED MARINE PROTECTED AREA: IMPLICATIONS FOR THE

CONSERVATION OF THE GALAPAGOS SHARK, CARCHARHINUS

GALAPAGENSIS, AND THE YELLOWTAIL AMBERJACK, SERIOLA LALANDI.

4.1 ABSTRACT

Marine Protected Areas (MPAs) are becoming a widely used tool for the conservation of

biodiversity and fishery management. However, most of these areas are designed without

knowledge of the basic ecological aspects of the species they are trying to protect. This study

investigated the movement of two top predators: Galapagos shark, Carcharhinus galapagensis,

and the yellowtail amberjack, Seriola lalandi in and around the Motu Motiro Hiva Marine Park

(MMHMP) using miniPAT satellite tags to determine the effectiveness of this MPA for the

protection of these species. The Galapagos sharks (n = 4) spent most of their tag deployment

periods inside the MMHMP. However, high intraspecific variability was observed in their

movement dynamics. Daily individual maximum movements ranged from 17 to 58 km and the

maximum distance from Salas y Gómez Island, the only emergent island within the MMHMP, was

31 to 139 km. The maximum linear distance travelled for a female juvenile Galapagos shark (152

cm TL) was 236 km, which is greater than the maximum distance previously documented for

juveniles of this species (< 50 km). For yellowtail amberjack (n = 1), 91% of the satellite

geolocations were within the MMHMP, with a maximum daily distanced travelled of 6 km.

Maximum distance travelled between points was 111 km and the maximum distance from Salas

y Gómez Island was 62 km. All archival-tagged fish spent most of their time at depths <50 m and

never left the epipelagic zone. Day vs night-time differences were pronounced in all individuals

but showed high inter-individual variability. This study provides a baseline on the movement of

47

these two top predators in the MMHMP and provides valuable insights for the creation of MPAs

in the region and elsewhere.

2.2 INTRODUCTION

The establishment of Marine Protected Areas (MPAs) has become a widely applied tool for the

conservation of biodiversity and fishery management (Botsford, Micheli, & Hastings, 2003;

Pendleton et al., 2018). Numerous studies have demonstrated their capacity to protect a variety

of marine fauna, including mobile species (e.g., White et al. 2017), while restoring and preserving

overall ecosystem functions (Gaines et al., 2010; Lubchenco & Grorud-Colvet, 2015). Features

such as isolation, size (> 100 km2), age, enforcement, and the establishment of fully protected

no-take zones have been shown to be key features for the success of MPAs (Edgar et al., 2014).

However, at least 94% of the world’s MPAs allow some form of fishing activity (Costello &

Ballantine, 2015), which negatively impacts biodiversity within these MPAs and the fisheries

benefits due the reduced reproductive output and adult spillover (Boonzaier & Pauly, 2015;

Hilborn, 2016; Klein et al., 2016).

Fishing activities outside an MPA may also negatively affect populations and biodiversity inside

the MPA (Moffitt, Botsford, Kaplan, & O'Farrell, 2009). This particularly applies to mobile species

such as tunas and sharks that are wide-ranging and can move between national and international

boundaries. As a result, many mobile species within MPAs are considered to be overfished

because of poorly managed fisheries operating in international waters (Sala et al., 2018), and

within national boundaries (Agnew et al., 2010). Evaluating the efficacy of MPAs thus becomes

imperative for the long-term conservation of marine ecosystems. For this to occur, it is imperative

to have an in-depth knowledge of species habitat utilization patterns, habitat requirements

(Roberts, 2000; Field et al., 2011), and the degree of overlap between the habitat and the area

under protection (Knip, Heupel, & Simpfendorfer, 2012).

48

With the rapid technological advancement of remote tracking applications, it is now possible to

gain insights into the movement patterns of marine organisms and its relevance for marine

conservation. Satellite tagging studies on whales, sea turtles, and sharks have significantly

improved our understanding of their migratory routes and habitat use, revealing specific areas

and corridors as biodiversity hotspots (Block et al., 2011; Robinson et al., 2016). Furthermore,

satellite telemetry has been employed to develop management strategies that minimize

anthropogenic impacts, such as incidental mortalities in high-seas fisheries (Shillinger et al., 2011;

Poisson et al., 2016), vulnerability of species to certain fishing gears (Cortés et al., 2010), and the

effectiveness of modified gears by tracking the survival of released fisheries bycatch (Moyes et

al., 2006; Swimmer et al., 2006). With the increase in available information, satellite data have

allowed us to evaluate the effectiveness of established MPAs for the protection of top predator

species. For example, White et al. (2017) overlaid the habitat utilization of grey reef sharks

(Carcharhinus amblyrhynchos) at Palmyra Atoll MPA with the boundaries of the no-take U.S

Pacific Remote Island Marine National Monument and found that while the MPA was somewhat

effective, it did not cover the species’ entire home range. This highlights the need for much larger

MPAs to guarantee the conservation of highly migratory species. In a similar study by Queiroz et

al., (2016), tracking data of coastal and pelagic shark species, including critically endangered

(Sphyrna mokarran and S. lewini), endangered (Isurus oxyrinchus), and near-threatened species

(Galeocerdo cuvier, Prionace glauca) overlapped so extensively with the fishing efforts of longline

vessels fishing in international waters, that the creation of effective MPAs in the high seas was

considered infeasible due to socio-economic reasons.

Chile is among the top ten fish producers globally and has among the most productive waters

worldwide (Daneri et al., 2000). However, the overall conservation of Chile’s marine biodiversity

is at risk, especially along its coastline, where anthropogenic impacts are greatest and often

conflict with established or planned MPAs (Tognelli, Fernández, & Marquet, 2009; Cárcamo et

49

al., 2011). To confront the fishing pressure imposed on Chile’s marine biodiversity and to protect

critical ecosystems, in 2010 the Chilean government created the Motu Motiro Hiva Marine Park

(MMHMP), a large-scale (150,000 km2) no-take marine protected area surrounding Salas y

Gómez Island (Figure 4.1). It is considered one of the most isolated islands in the world, with the

south-easternmost distribution of coral reef systems (Randall & Cea, 2011; Friedlander et al.,

2013; Arana, 2014). This ecosystem is dominated by top predator species such as the Galapagos

shark (Carcharhinus galapagensis) and three species of jacks (Seriola lalandi, Caranx lugubris,

and Pseudocaranx cheilio). The marine ecosystem surrounding Salas y Gómez Island is in sharp

contrast to Rapa Nui (Easter Island) where historical overfishing has depleted these top predator

species (Friedlander et al., 2013).

Figure 4.1. Bathymetry of the Easter Island Ecoregion. Rapa Nui, Salas y Gómez Island, Motu Motiro Hiva Marine Park (violet), Exclusive Economic Zone (white).

50

Top predators, such as sharks, jacks, groupers, and tunas are key elements for the maintenance

of ecosystem health, as they regulate the demography and behaviour of organisms from lower

trophic levels (Myers et al., 2007; Heithaus et al., 2008; Ferretti et al., 2010; Ruttenberg et al.,

2011). However, these large predators are becoming increasingly scarce in coral reef and pelagic

ecosystems, largely due to overfishing and habitat degradation (Steven et al., 2000; Myers &

Worm, 2003; Dulvy et al., 2014).

Carcharhinus galapagensis, is one of the most abundant reef sharks inhabiting the Tropical Indo-

Pacific Ocean (Duffy, 2016). It has a circumglobal, albeit patchy, distribution and is associated

with warm and temperate waters and oceanic islands (Wetherbee Crow, & Lowe, 1996; Kohler,

Casey, & Turner, 1998; Meyer, Papastamatiou, & Holland, 2010; Duffy, 2016). Their slow growth,

fragmented geographical distributions, and unknown levels of local connectivity have likely

contributed to their population declines in areas of high fishing pressure in the Pacific and Atlantic

oceans (Kyne et al. 2019). Carcharhinus galapagensis was recently reassigned as “Least

concern” by the IUCN Red List; however, the habitat specificity of this species make it susceptible

to localized depletion (Kyne et al. 2019). Major threats to this species come from fishing activities

around islands and seamounts (Zylich et al., 2014; Kyne et al. 2019). Carcharhinus galapagensis

accounts for 26% of the biomass at Salas y Gómez Island (Friedlander et al., 2013). At Rapa Nui,

their population has declined as a result of direct and indirect fisheries impacts (Zylich et al.,

2014), although a resident population still inhabits the area (Morales et al., 2019a). Another

important predator in the Easter Island Ecoregion is the yellowtail amberjack Seriola lalandi, a

coastal pelagic species restricted to subtropical waters (Randall & Cea, 2011; Martinez-Takeshita

et al., 2015). It is currently classified as ‘Least Concern’ (IUCN, 2018), with no apparent population

declines (Smith-Vaniz & Williams, 2015). In total, Seriola lalandi together with Caranx lugubris

and Pseudocaranx cheilio account for 19% of the total biomass at Salas y Gómez Island, and as

51

with C. galapagensis, their populations around Rapa Nui are virtually absent (Friedlander et al.,

2013).

Despite being the most common top predators within MMHMP, little information is currently

available for C. galapagensis and S. lalandi within the Easter Island Ecoregion. Ecological

information, such as spatial dynamics, habitat use, and connectivity, are essential in generating

realistic and effective conservation plans for the protection of these species. Therefore, this

current study aims to investigate the movement patterns and habitat use of both species within

MMHMP in order to give some insights into the effectiveness of the current MMHMP borders for

the protection of these key species. Additionally, industrial fishing pressure in the area was

examined and compared with the current MPA borders and predator movement patterns to

explore the potential impact of fishing on these species.

4.3 METHODS

4.3.1 Study area

Salas y Gómez Island (26°27'S, 105°28'W) is a 0.15 km2 small, emergent rocky outcrop located

400 km east of Rapa Nui (Easter Island), and ~ 3,300 km from the Chilean coast (Figure 1).

Together, both islands are the only two exposed landmasses of the numerous submerged

volcanic seamounts that make up the 2,232 km long Salas y Gómez Ridge (Ray et al. 2012;

Friedlander et al., 2013). Even though both islands are part of the Chilean territory, Rapa Nui and

Salas y Gómez Island are very different from continental Chile in terms of marine ecosystems

and species composition (Aburto, Gaymer, & Cundill, 2017). Located at the eastern limit of the

South Pacific Gyre (Figure 1), this area belongs to the Easter Island Province and Ecoregion,

which is characterized by oligotrophic waters (Pizarro et al., 2006; Andrade, Hormazábal, &

Correa-Ramírez, 2014).

52

4.3.2 Capture and tagging

Fishes were caught and tagged in November 2015 and 2017, during two research trips to Salas

y Gómez Island (during the CIMAR 21 cruise organized by the Chilean Navy and a research trip

of ESMOI,). During each trip, approximately six handlines were equipped with #14/0 circle hooks,

baited with chub mackerel (Scomber japonicus), and set between 15 to 30 m deep for ~ 10 min.

Sharks were secured head-forward alongside an inflatable boat and immobilised for less than five

minutes, following common tagging practice of pelagic sharks (Brooks et al., 2011). Each

individual shark was sexed and measured from snout to the tip of the tail in horizontal position to

determine the animal’s total length (TL). Seriola were measured (TL) and tagged inside the

inflatable boat.

Two types of tags were used during this study. To facilitate the identification of the fish after

potential recapture, each individual was tagged with conventional dart tags with a stainless-steel

metal blade anchor (model FH-69 SS, Floy Tag & Mfg., Inc.) inserted into the musculature at the

base of the dorsal fin between the pterygiophores. To study the movements and habitat use of

these top predators in the area, six large fishes (five C. galapagensis > 140 cm TL and one S.

lalandi, measuring 124 cm TL) were equipped with antifouling coated pop-up satellite archival

tags (model MiniPAT, Wildlife Computers, Redmond, Washington, USA), which were tethered to

the fish next to the conventional tags, using either a plastic Domeier Dart or a stainless-steel metal

blade anchor (Bradford et al., 2009; Table 4.1).

53

Table 4.1. Metadata of each fish tagged during this study. Female (F), male (M), total length (TL), deployment and pop-up latitude (Lat.), longitude (Long.), total number of position estimated per individual (TNPE), resolution (Resol.), and percentage of position estimates inside the MMHMP (EGP) as well as the average overlap of the 50%, 95% and 99% likelihood areas with the MMHMP. We assumed ID 154066 died shortly after tagging and therefore it was not included in the analysis.

Species Tag ID Anchor type Sex TL (cm)

Deployment TNPE per

ind.

Pop-up Model % inside MMHMP Max linear distance (km)

Date Lat. Long. Duration

(days)

Date Lat. Long. Resol. Speed

(km/h) Score

EGP 50% 95% 99% From Salas

y Gómez

per

day

between all

geolocations

Seriola lalandi 154061 Plastic Dart - 124 04 Nov 15 -26,43 -105,38 148 296 31 Mar 16 -27,27 -105,53 600 3 87,48 91,3 84.1 73.4 70.2 62,2 6,2 111,3

Carcharhinus

galapagensis 154062 Plastic Dart F 160 02 Nov 15 -26,45 -105,35 35

69 07 Dec 15 -26,35 -105,22 600 3,5 79,26

72,2 68.6 63.6 62.6 31,3 29,6 47,6

Carcharhinus

galapagensis 154064 Plastic Dart M 160 03 Nov 15 -26,38 -105,39 28

56 01 Dec 15 -26,46 -105,12 600 3 82,05

69 61.8 62.3 62.1 98,9 58 172,4

Carcharhinus

galapagensis 154065 Plastic Dart F 170 05 Nov 15 -26,45 -105,39 18

37 23 Nov 15 -26,4 -105,4 600 3,5 79,07

42,1 54.6 52.9 53.2 35,4 17,5 32,5

Carcharhinus

galapagensis 154066 Plastic Dart M 145 03 Nov 15 -26,62 -105,39 1

7 04 Nov 15 -26,62 -105,42 300 - -

- - - - - - -

Carcharhinus

galapagensis 173480 Steel blade F 152 21 Nov 17 -26,33 -105,39 95

190 24 Feb 18 -26,17 -105,43 600 3,5 73,49

79,2 79.0 79.9 78.6 138,6 45 235,8

54

By default, MiniPATs record depth (1 – 1700 m, 0.5 m ± 1.0%), water temperature (−40 to +60°C;

0.05 ± 0.1°C), and light level (10-2- 10-10W/cm2 at 440 nm) time series data throughout their

deployment period. The sampling interval thereby depends on the deployment duration, which in

this case was every 15 sec for 365 days. If selected for transmission, only a subset of this time

series data, as well as other user-specified (summary) data products, were transferred to the

ARGOS satellite system. Depth and temperature time series data at a temporal resolution of 10

min were transmitted to facilitate a fine-scale analysis of vertical behaviour (Bauer et al., 2017),

and thus their potential vulnerability to commercial fisheries (e.g., longliners; Tolotti et al., 2017).

Long deployment durations can result in transmission gaps, especially in the transmitted depth

time series data, due to the limited transmission capacity of the tag (Bauer, Forget, & Fromentin,

2015). Time-at-depth (TAD) and time-at-temperature (TAT) profiles (histogram data) represent a

summary data product that can complement depth time series data analyses and can be

transmitted in fewer messages. For this purpose, archived depth and temperature time series

data from user-specified time intervals were aggregated by the tag into user specified bins. Here,

a 24h TAD and TAT data with the following depth and temperature bins were selected: Depth

bins: 0, 1, 5, 15, 30, 50, 70, 100, 200, 300, 500, >1000 m; Temperature bins: 0, 6, 9, 12, 15, 21,

24, 27, 30, >33°C. Other selected data products included daily Profiles of Depth at Temperature

(PDT), daily light curves, as well as minimum and maximum depth records. Given the long

deployment duration programmed, it was decided that the summary and depth time series data

should be transmitted on duty cycle (2 days of data to be transmitted, followed by 3 days of data

gaps), in order to increase battery life during data transmission. In the event of premature release,

transmission was programmed to initiate upon the tag being at the surface or remaining at depths

of > 1800m (indicating death of the animal) for more than three days.

55

4.3.3 Analysis of horizontal behaviour

Geolocation analysis was performed using the manufacturer’s proprietary Hidden Markov Model

(HMM, WC-GPE3, Wildlife Computers) to estimate positions. This approach uses a gridded HMM

that computes posterior probability distributions to estimate the most likely state (positions,

hereafter referred as geolocations) at each time point using light levels, sea-surface temperature

(SST), and bathymetric data. The model allows the user to define a typical travelling speed,

deployment and pop-up locations of the tags. Travelling speeds are thereby used by the model

to define the allowable distance moved per day, which restricts the daily diffusion kernels.

Different travelling speeds (1.9, 2.5, 3, and 3.5 km/h) were tested based on available literature

values (Holland, Meyer, & Dagorn, 2009; Meyer et al., 2010; Palstra et al., 2015). First transmitted

geolocations of each tag were thereby used as pop-up locations. The selection of the optimal

speed and model was done based on the model score produced by the GPE3 software, with

higher scores indicating better fits to the transmitted data. Transmitted data from all MiniPAT tags

were de-coded with the manufacturer’s cloud-based portal software and analysed using the

Rchival Package (Bauer, 2020a) in the R Statistical Environment (v. 3.5; R Core Team, 2018).

Likelihood areas were generated from the netcdf files in the GPE3 model runs using the

get_geopos function in the R-package RchivalTag (Bauer, 2020a). This function is based on a

transformation suggested by Wildlife Computers (2015). In order to identify species-specific high-

use areas within the study regions, kernel densities were calculated per species based on the

daily geolocations of all related individuals, using the “kde2d”-function from the R-package

“MASS” with a search radius of 1 degree to account for the uncertainty in the geolocations

(Venables & Ripley, 2002; Teo et al., 2004).

56

4.3.4 Analysis of vertical behaviour

Time-at-Depth and depth time series data, transmitted on duty cycle (two days of data to be

transmitted, followed by three days of data gaps), facilitated a vertical behaviour analysis of the

tagged fish. A preliminary analysis of the transmitted data sets revealed additional gaps in the

Time-at-Depth (and Time-at-Temperature) data of the amberjack (ID 154061) and one shark (ID

154065). By contrast, the depth time series data showed good data coverage with only some

minor additional transmission gaps (Figure 2). Given this, as well as its higher informational value,

further analysis focused on the depth time series data. To analyse the diel vertical behaviour of

the tagged fish with respect to the time of sunrise and sunset, these moments were first estimated

based on the “get_DayTimeLimits” and “classify_DayTime” functions in the R-package

RchivalTag (Bauer, 2020a), which uses daily geolocation estimates from the tags as input data.

To determine the general patterns of diel vertical behaviour per fish, their hourly changes in depth

via boxplots were analysed. To assess regional differences in the dive patterns during daytime

and night-time, daily geolocation estimates were mapped in relation to the maximum dive depths

per day and night, the bathymetry of the study area, and the borders of the EEZ and MPA

(MMHMP), using the v-function in the R-Package “oceanmap” (Bauer, 2020b).

In order to identify species-specific dive behaviour patterns from the different Galapagos sharks,

the average depth per hour of each deployment day and fish was first estimated, and then

clustered the combined data matrix from all individuals using the k-means clustering method.

Cluster selection was done based on 30 different clustering indices that were provided by the

NbClust function of the same-named R-package (Charrad et al., 2014).

Finally, the distance of the clusters’ geolocations was estimated to the shore by applying the

distHaversine function in the R-package “geosphere” (Hijmans, 2017).

57

4.3.5. Commercial fishing pressure

Commercial fishing activities were calculated around the Easter Island Ecoregion (117°5´-

97°5´E, 32°5´-17°5´S) during the period 2012- 2016 using Automatic Identification System (AIS).

Daily data of vessel flagged nationality, type of fishing, and fishing hours were obtained from

Global Fishing Watch (www.globalfishingwatch.org). Finally, fishing hours were summed by 5°

resolution according to flagged nationality and gear type.

4.4 RESULTS

Over both campaigns, 48 C. galapagensis and 18 S. lalandi (Table S4.1; Figure S4.1) were

captured. Six MiniPATs were deployed on five C. galapagensis and one S. lalandi (Table 4.1;

Table S4.1). One of the five C. galapagensis (ID: 154066) died shortly after tagging and was

therefore not included in the analysis. The five remaining MiniPAT had premature releases, due

to unknown reasons, resulting in relatively short deployment periods of 18-148 days (Table 4.1).

None of the conventionally or archival-tagged fish have been recaptured to date; however, some

sharks tagged in 2015 were observed swimming with the tags during a dive survey in 2017.

4.4.1 Horizontal behaviour

The GPE3 models yielded best fits for the 3 km/h (IDs 154061 and 154064) and 3.5 km/h (IDs

154062, 154065 and 173480) travelling speeds (Table 4.1). Thus, derived daily geolocations

indicated that all fish left MMHMP at least once throughout the tag deployment period but

remained within the EEZ (Figures 4.2, 4.3, S4.2-4.6), even taking in account different likelihood

areas (Table 4.1, Figure S4.7-4.9) . For C. galapagensis (IDs 154062, 154064, 154065 and

173480), 42-79% of the estimated geolocation points were inside the MMHMP (Table 4.1), whilst

91% of the single S. lalandi track remained within the MMHMP. The range of all 99% likelihood

area within the MMHMP, the most conservative estimate (large) of occurrence, was 53.2-78.6%,

58

whereas the 95% likelihood was 52.9-79.9% and the 50% was 54.6-79% (Table 4.1). The

maximum linear distance per geolocations for S. lalandi (ID 154061) was 6.2 km, and 17.5-58 km

for C. galapagensis (Table 4.1; Figure 4.4).

Figure 4.2. Temporal coverage of available (green) Depth, TS data per deployed tag. Data gaps due to transmission failure or duty cycle are shown in red. Blue bars indicate the periods spent inside the Motu Motiro Hiva Marine Park.

The maximum linear distance from Salas y Gómez Island was 62 km for S. lalandi and 31-139

km for the Galapagos sharks. The maximum linear distance between distant points was 111 km

for the S. lalandi and 235.8 km for C. galapagensis (Table 4.1; Figure 4.4). The kernel density

analysis indicated a common high-use area for both species, which surrounded Salas y Gómez

Island but exceeded the western limits of the MMHMP (Figure 4.3).

59

Figure 4.3. Geolocations of each individual combined (upper panel) and kernel densities (lower panel) indicating areas of high use for the four Galapagos sharks (left) and one amberjack (right). The borders of the EEZ and MMHMP are indicated in white and violet, respectively.

60

Figure 4.4. Distance between subsequent geolocations (left) as well as their distance from Salas y Gómez (right) per species (amberjack indicated in grey).

4.4.2 Vertical behaviour

All archival-tagged fishes spent most of their time at depths <50 m (average depth <60 m) and

never left the epipelagic zone (0-200 m; Table S4.2; Figure S4.10). Even though all fishes reached

depths of ~100 m, only one C. galapagensis (ID 154064) descended to a depth of 195.5 m. The

remaining fishes stayed within 0-131.5 m throughout the deployment period of their tags (18-148

days). The one Seriola lalandi (ID 154061) maximum depth was 100.5 m during night-time (Table

S4.2).

Day vs. night-time differences were pronounced in all individuals but showed high inter-individual

variability (Figures 4.5 and S4.10). Clear diel vertical behaviour patterns were evident for S.

lalandi (ID 154061) and two C. galapagensis (IDs 154062 and 173480), with the amberjack and

one shark (ID 154062) staying in deeper waters during the night compared with the daytime. The

vertical data from shark 173480 showed an opposite pattern. Strong fluctuations were observed

in the diel vertical behaviour of sharks 154064 and 154065, leading to the assumption of changes

in the diel vertical behaviour. The maximum dive depths per day and night, as a proxy for such

61

changes and the species vulnerability to longline gear, showed no relation to local bathymetry or

diel vertical patterns (Figures S4.2-4.6).

Cluster analysis of the hourly depth averages of the different C. galapagensis revealed two distinct

vertical behaviour patterns (Figure 4.6; Table S4.3). Cluster 1 indicated a continuous descent of

sharks from twilight until the end of the subsequent day. This cluster was infrequent, accounting

for 16.5% of the data, but was limited to the vicinity of Salas y Gómez Island (Figures 4.6 and

S4.11). Cluster 2 showed periodical descents to deeper depths both during the day and night-

time, with an occupation of shallower water during the twilight periods. This behaviour pattern

occurred at higher frequencies (83.5%) both in coastal and offshore areas.

Figure 4.5. Diel vertical movement patterns of the five fish tagged with MiniPat tags. Average night and twilight periods are indicated in dark and light grey, respectively.

62

Figure 4.6. Behaviour patterns (upper panel) for the vertical behaviour clusters and their spatial distribution (lower panel) in relation to the bathymetry of the study area. The EEZ and MMHMP borders are shown by white and violet lines, respectively. Clusters are based on the hourly depth averages of all available deployment days from the Galapagos sharks.

4.4.3 Fishing activities within the Easter Island Ecoregion

Analysis of fishing effort from AIS data showed a total of 194,541 detections in the area, of which

118,086 (60.7%) corresponded to active fishing activities. Virtually no fishing was detected within

the EEZ around Rapa Nui and Salas y Gómez Island (only 17 detection; 0.014%) (Figure 4.7).

63

However, high fishing pressure occurred in the surrounding waters outside of the EEZ, which

consisted of longliners from China, Vanuatu, and Spain (60.3%, 28.3%, and 11.2%, respectively).

China and Spain fishing effort were mainly recorded in the northern and southern limits of the

EEZ, meanwhile Vanuatu fishing effort was concentrated towards the south-western limit of the

MPA.

64

Figure 4.7. Commercial fishing effort by all as well as by each of the three major fishing nations (representing together 99.9% of all fishing detections in the area) between 2012-01-03 and 2016-12-31 in the study region as well as the borders of the EEZ (orange) and the MMHMP (violet). Fishing locations are based on daily AIS derived vessel positions with fishing hours > 0 at a 10th degree resolution that were gridded at a 5th degree resolution to illustrate effort densities (https://globalfishingwatch.org).

65

4.5 DISCUSSION

The current study is the first archival tagging study on C. galapagensis and S. lalandi in the Easter

Island Ecoregion. The insights gained on the habitat use and migratory behaviour of these top

predators will help to address the potential vulnerability of these species in the MMHMP.

4.5.1 Horizontal and vertical migratory behaviour

The geolocation estimates from the archival tagging data indicated that C. galapagensis and S.

lalandi remained largely in the waters around Salas y Gómez Island, even considering the

uncertainty in the estimated positions. This is consistent with earlier findings from the Hawaiian

Archipelago where top predators, such as jacks and sharks, were site-attached to islands or atolls

(Dale, Meyer, & Clark, 2011). However, all fish left the MMHMP at least once over the tracking

periods, travelling into unprotected waters. Although such excursions were not frequent and many

fish returned later to the island, we cannot exclude the possibility that this “homing behaviour” is

weaker during other seasons due to the limited time period of the current study (November-

February). In fact, one C. galapagensis (ID: 173480) travelled up to 139 km away from Salas y

Gómez Island, and 235.8 km between distant geolocations points. While sharks in this study were

only juveniles (< 200 cm TL; Wetherbee et al., 1996), such a behaviour has previously been

observed to be common in adults that are known to migrate into deeper oceanic waters

(Compagno, 1984; Wetherbee et al., 1996; Kohler et al., 1998; Lizardi et al. 2020). In this context,

the seamount chain along the Salas y Gómez Ridge may serve as stepping-stones for these

mobile predators as well as for other species (Friedlander et al., 2013). Significant genetic

differences have been detected for Galapagos sharks at both small and large scales (Pazmiño et

al., 2017; Pazmiño et al., 2018). Rapa Nui and Salas y Gómez Island are the southernmost remote

coral reef in the southern hemisphere, so further studies using genetic tools should be employed

66

to address the potential connectivity between Rapa Nui, Salas y Gómez Island and other islands

in the Pacific Ocean where these species are distributed.

Vertical behaviour data revealed that C. galapagensis and S. lalandi remained within the

epipelagic layer (0-200 m) but showed distinct diel vertical migration patterns during day and

night-time. These patterns were particularly consistent in the case of the yellowtail amberjack.

The one S. lalandi tagged during this study remained close to the surface during the daytime but

descended to deeper waters during night. Carcharhinus galapagensis showed a similar behaviour

as S. lalandi during night-time, but then either returned to the surface waters and then descended

during daytime or descended immediately at dawn to even deeper waters until twilight.

It is interesting to note that similar behaviour patterns have been found for other epipelagic

species such as oceanic whitetip sharks (Carcharhinus longimanus; Tolotti et al., 2017). This

similarity might be related to comparable feeding habits of pelagic species that are foraging on

epi- and mesopelagic prey. Although the type of habitat use remains unclear due the low sample

size and short duration of the study, these findings contribute to the development of management

strategies to minimize the fishing mortality of these species (e.g., by altering the timing and depth

of longline sets). For instance, hook depth of commercial tuna fishing fleets can be adjusted to

reduce the bycatch of sharks and other predators critical to local ecosystems (Beverly et al., 2008;

Zhu et al., 2012). Based on the vertical movements of C. galapagensis, longlines should be set

at depth deeper than 200 m to reduce incidental mortality outside the MMHMP and other oceanic

areas.

4.5.2 Fishing activities around Rapa Nui and Salas y Gómez

These results support previous findings that seamounts and oceanic islands attract and aggregate

highly migratory pelagic species (Holland et al., 1999; Worm, Lotze, & Myers, 2003; Garrigue et

al., 2015; Morato et al., 2010). Therefore, these areas are frequently exploited by commercial

67

fishing fleets (Pitcher et al., 2008; Morato et al., 2010). Analysis of fishing effort from AIS data

showed that fishing activities (longliners) concentrates in the surrounding waters outside of

Chile´s EEZ. However, it is important to note that AIS does not include data from the “dark fleet”,

small artisanal vessels, and other vessels that are not equipped with AIS technology (FAO, 2016;

Kroodsma et al., 2018). In addition, vessels may intentionally turn off their transponders, falsifying

position data or transmitting inadequate identification data in order to escape detection (McCauley

et al., 2016). While there are no means to verify the correct functioning of the vessel transponders

that fish around Chile’s EEZ at Rapa Nui and Salas y Gómez Island, from our AIS analysis it is

evident that they precisely identify the EEZ borders and with only a few exceptions do not enter

it. However, fishing effort is concentrated along the EEZ border, and among the highest fishing

hours registered are located south-east and south-west of MMHMP, with some Chinese vessels

apparently entering the EEZ to a limited extent (Figure 4.7). China and Spain are the most

prominent fishing nations in this area. These two countries have been recently characterized as

poor performing countries based on traceable fishing scores (Macfadyen et al., 2019). These poor

performances together with the limited surveillance in the area, and anecdotal observations by

locals regarding industrial vessels nearby the islands, make it questionable to what extent the AIS

data reflects the true fishing positions of these industrial fleets. The Easter Island Ecoregion also

has been described as an area where trans-shipment activities may be occurring (Boerder, Miller,

& Worm, 2018; Miller et al., 2018). Trans-shipment vessels are known to be associated with

longliners fishing on highly migratory species, such as, tunas, billfishes, and sharks in the high

seas (Miller et al., 2018). These practices increase the efficiency of fishing, especially on the high

seas; however, they can also contribute to illegal fishing and other criminal activities (Miller et al.,

2018; Rezac, 2018).

Illegal, unreported, and unregulated (IUU) fishing (which includes catches taken within an EEZ

and unreported catches) have a strong effect on stocks and their dependent ecosystem (Agnew

68

et al., 2010). Illegal fishing, vessels in violation of national laws or international obligations (FAO,

2001), usually occur along the borders of established MPAs (known as the “border effect”; Gribble

& Robertson, 1998) or at locations with weak fisheries management and control, where a small

number of patrol vessels are available (Agnew et al., 2010; Petrossian, 2015). In this regard, the

rapanui people regularly mention seeing the lights of industrial vessels at night close to Rapa Nui

and report longline fishing gear frequently washing up on the shores of Rapa Nui (Yáñez et al.,

2007; Hernández Mares, 2016; Thiel et al., 2018). A few incidents of illegal fishing within the

MMHMP have been documented (Muñoz, 2011; Friedlander et al., 2013). While there is a limited

capacity for naval patrol vessels and planes to enforce the MMHMP, it is clear that the protection

of this important refuge for oceanic species requires an effective ecosystem-based management

plan that implements strict controls on IUU fishing (Agnew et al., 2010). Countries with less

effective fisheries management measures and those with poor patrol capacity are more likely to

suffer from IUU fishing (Petrossian, 2015). Even though IUU fishing is a complex matter because

it involves international waters, several international measures have been adopted, such as The

International Plan of Action on Illegal, Unreported and Unregulated Fishing (Edeson, 2001), the

Agreement on Port State Measures (Flothmann et al., 2010), and The Project Catch (Detsis et

al., 2012), among others (see Lindley & Techera, 2017). Nevertheless, the distance of Salas y

Gómez Island from mainland Chile (~3600 km), and the virtual absence of Chilean industrial

fishing vessels inside the EEZ of Rapa Nui, limits the amount of IUU fishing from smaller

continental fishing boats. For example, the Juan Fernandez MPA is located only ~700 km from

mainland Chile and has significant legal and illegal fishing pressure inside the EEZ and around

the MPAs borders, much of it coming from Chilean vessels (Friedlander et al., 2017). Therefore,

while isolated areas seem more protected from IUU fishing because of their remoteness, they

also create the opportunity for larger fishing fleets to go undetected when entering MPAs, because

of the absence or limited surveillance and enforcement at these distant areas. This paradox has

recently been debated, pointing out the political advantages of closing isolated marine areas,

69

where little participation of local communities is anticipated and whose commercial interest is

outnumbered by foreign fishing vessels (Devillers et al., 2015; Jones & De Santo, 2016). Despite

being less susceptible to IUU fisheries than nearshore MPAs, isolated areas also account for

much higher biomass of sharks and jacks (Edgar et al., 2014), highlighting the need for accurate

ecological evaluations of the effectiveness of these remote MPAs.

4.5.3 Future Perspectives of the MMHMP

The implementation of management actions, such as MPAs, that do not reflect ecological patterns

and processes are destined to fail (Crowder & Norse, 2008). To achieve a realistic approach,

scientists and resource managers need to have a broad understanding of the movement

dynamics and habitat requirements when planning the spatial protection of a given species (Dale

et al., 2010, Carlisle et al. 2018). This study provides the first insights into the habitat use and

migratory behaviour of C. galapagensis and S. lalandi in one of the most isolated and

understudied areas of the Southeast Pacific Ocean. However, the small sample size, the inclusion

of only juvenile sharks, and the short duration of this study limits the ability to make broader

inferences about the application of this work. These limitations are common in electronic tagging

studies due to premature release and transmission failures (Williams, Nicol & Leroy, 2010;

Domingo et al., 2018; Hagihara et al. 2018). The high cost of satellite archival tags, which is

reflected in small sample sizes, and the logistical challenges associated with expeditions to

remote locations further constrain the number of animals that can be tagged and tracked (Carlisle

et al. 2018). Hence, future studies should attempt to obtain larger sample sizes, which include

individuals of different sizes and sexes to define the complete extent of species home ranges.

Additionally, sampling through the year should also be performed to avoid seasonal bias. For

instance, due to the premature release of the tags it is not possible to assure that the patterns

found here (horizontal and vertical movements) are repeated throughout the year or if they only

occur during the austral summer, as observed here. This is an important issue in determining their

70

entire home ranges since sharks shift their movement patterns seasonally in response to current

exposure, storms, and temperatures throughout the year (Lizardi et al. 2020), or for biological

reasons such as feeding and reproduction (e.g., Bessudo et al. 2011; Acuna et al. 2017). In this

sense, the intraspecific variability found in habitat use also requires larger sample sizes (Carlisle

et al., 2018).

Despite the limitations described above, the information generated by this study is essential for

the management of these species. Movement dynamics should be considered not only for MPA

implementation, but also for planning and designation of other MPAs, in order to define proper

limits and buffer zones to ensure effective protection of large mobile predators. The results

presented here show that top predators at Salas y Gómez have a strong fidelity to the island and

the surrounding seamounts. However, the home range of the juveniles C. galapagensis tagged

during this study, was larger than previously reported (up to 235.8 km vs. < 50 km, Wetherbee et

al., 1996), and extended beyond the boundaries of the MMHMP. The only S. lalandi tagged here

also extended beyond these borders over the tracking period. Based on these findings, the mayor

deficiency of the existing MMHMP is not the size of the protected area (150.000 km2), but the

location of the western border due to its close proximity to Salas y Gómez Island. This island has

been described as the biological hotspot in the area and the core of the MMHMP (Friedlander et

al. 2013). MPAs boundaries should be designed to reduce exposure of conservation targeted

species (edge porosity; Roberts, 2000). By knowing the movement patterns of these species, the

limits can easily be adapted (Kramer & Chapman, 1999).

Originally, the MMHMP borders were established to avoid conflict with the Rapa Nui EEZ

boundaries (SUBPESCA, 2010), and therefore with the local artisanal fishing activities coming

from Rapa Nui. In 2012, an initiative to expand the MMHMP from 150,000 km2 to 411,000 km2

to the west was intended to provide better protection for Salas y Gómez Island and the

surrounding seamounts. The enlarged no-take zone would have encompassed most of the EEZ

71

around Salas y Gómez Island and part of the EEZ of Rapa Nui. However, this initiative never

happened due to negative reactions from the local fishing community (Gaymer et al., 2013).

Finally, in 2017 an unprecedented participatory initiative conducted by the Chilean Government

and the Rapa Nui Development Commission, promoted the creation of a Multiple Uses Coastal

Marine Protected Area (MUMPA) around the entire EEZ of the Easter Island Ecoregion,

expanding the protection initially provided by the MMHMP. However, this new MPA did not

change the existing boundaries of the MMHMP; and while the MUMPA regulations permit

artisanal fishing, it strictly prohibits industrial fishing activities from both foreign and Chilean

vessels within its boundaries. It is hoped that the zoning process for the MUMPA that is currently

underway will adopt a precautionary approach, which accounts for the movement patterns of the

top predators identified here. A no-take zone west of the MMHMP is a possible way forward in

this regard.

The Salas y Gómez Ridge connects the fully protected MMHMP with unprotected Rapa Nui. At

Rapa Nui, individuals of C. galapagensis are caught periodically by fishermen who still use coastal

nets during night-time. Sharks are caught as bycatch and usually used as baits for economically

important species such as the endemic lobster Panulirus pascuensis. However, the last large

shark catch event that we know of occurred in early 2020, where more than 30 juvenile sharks

were caught and killed because, according to the fishermen, they represented a risk to tourists.

Even though the sharks tagged during this study did not leave the Salas y Gómez area, we believe

adults are capable of reaching Rapa Nui and near seamounts, probably using the ridge as

stepping-stones (Friedlander et al. 2013). Therefore, it is imperative that fishing regulations are

implemented at Rapa Nui to ensure the survival of this key species within the entire area.

Moreover, Lizardi et al. (2020) suggested the need for cooperation between countries due to the

occurrence of movement corridors (swimways) that connect the different populations of C.

galapagensis. Future studies should therefore focus on the current swimways that connect this

72

southeast hotspot with other islands (countries) in the Pacific Ocean, especially in light of the

recent arrival of new pelagic species to the area such as Triaenodon obesus (Morales et al.

2019b), Seriola rivoliana and Rhincodon typus (N. Morales unpublished data).

Despite their isolation, remote islands such as Salas y Gómez are becoming increasingly exposed

to a variety of anthropogenic stressors, such as overfishing and IUU fishing. This remoteness also

poses governance and enforcement challenges, especially for countries with limited resources

(Game et al., 2009). Despite these challenges, the MMHMP remains a healthy ecosystem with

an abundance of top predators that deserves maximum protection so as to preserve what is one

of the last relatively pristine areas in the world.

73

CHAPTER 5. GENETIC CONNECTIVITY OF THE GALAPAGOS SHARK,

CARCHARHINUS GALAPAGENSIS, IN THE EASTER ISLAND ECOREGION.

5.1 ABSTRACT

The Galapagos shark Carcharhinus galapagensis is the only resident species of shark inhabiting

Rapa Nui and Salas y Gómez Island, and therefore should be a priority for conservation. Recent

studies have suggested a genetic relationship between both islands. Determining the degree of

genetic connectivity between locations is important for developing effective conservation and

management strategies that aims to ensure gene flow, and thus prevent local extinctions. We

determined the degree of population connectivity of this species between Rapa Nui and Salas y

Gómez islands using variability of both genome-wide neutral Single Nucleotide Polymorphism

(13496 neutral SNP), and a section of the mitochondrial DNA (636 pb). The results showed no

evidence for genetic structure, thus suggesting only one genetic population occurring within the

Easter Island Ecoregion. Our findings were consistent with previous tagging data in the area

where individuals seemed capable of migrating between both islands. The results of the mtDNA

showed also a low genetic diversity in the Easter Island Ecoregion population when compared to

others in the Indo-Pacific Ocean, which may be a result of only a few colonization events due to

the isolation of this area. Ongoing studies in a global context will identify structure and global

patterns of colonization in the central-south Pacific. Our results also highlight the importance of

the Motu Motiro Hiva Marine Park (MMHMP) within the ecosystem, thus, future studies should

also be conducted on more species to determine the genetic relationship between these two very

isolated islands.

74

5.2 Introduction

Oceanic islands and seamounts are usually used as resting and/or feeding areas for mobile

species (Rogers 1994; Holland et al., 1999; Worm et al., 2003; Morato et al., 2010; Garrigue et

al., 2015), and thus facilitating the dispersion of organisms and connecting communities between

distant areas (Wilson & Kaufman 1987; Friedlander et al., 2013).

The Salas y Gómez Ridge extends 2232 km eastwards to the Nazca Seamount, where it merges

with the Nazca Ridge (Gálvez-Larach, 2009). Rapa Nui (Easter Island) and Salas y Gómez Island

are the only two places where this seamount chain rises above sea level (Ray et al., 2012). These

two islands are connected by several dozen seamounts, which can act as stepping-stones

(Newman & Foster 1983; Friedlander et al., 2013). Rapa Nui is one of the most isolated islands

in the Pacific Ocean (Fig. 5.1). Its historical overfishing, together with geographic characteristics

such as limited habitats, small size, and sub-tropical location has resulted in a low number of

shore species compared to other islands in the Pacific Ocean (Randall & Cea, 2011). In contrast,

Salas y Gómez Island is a small rocky island (0.15 km2) located 400 km east of Rapa Nui. The

waters of Salas y Gómez Island together with dozens of seamounts are currently part of the Motu

Motiro Hiva Marine Park (MMHMP), a 150,000 km2 no-take area that is characterized by a healthy

ecosystem dominated by top predator species (Friedlander et al., 2013).

75

Figure 5.1. Location of Rapa Nui and Salas y Gómez Island in relation to other islands in the Pacific Ocean. The pink polygon represents the Motu Motiro Hiva Marine Park (MMHMP) borders. Black circles represent the EEZ.

The Galapagos shark, Carcharhinus galapagensis, is one of the most common top predatory

species and the only resident shark species in both Rapa Nui and Salas y Gómez Island (Morales

et al., 2019a). It has a circumglobal but patchy distribution associated with warm and temperate

water and oceanic islands (Duffy, 2016; Kohler et al., 1998; Meyer et al., 2010; Wetherbee et al.,

1996). This species was recently assessed as a Least Concern species on the IUCN Red List,

since its population is suspected to be stable in large part of its distribution in the Pacific Ocean.

However, the habitat specificity of this species and their limited biological productivity make it

susceptible to local population reduction, especially at places where it has been under fishing

pressure (Kyne et al., 2020)

76

Studies on the genetic structure along its distribution suggest that this widely distributed species

shows at least two genetically discrete geographic groups (subpopulations): the east central

pacific (Mexico, and the east and west Galapagos Islands), and the west central pacific (Lord

Howe Island, Middleton Reef, Norfolk Island, Elizabeth Reef, Kermadec, Hawaii, and Southern

Africa) (Pazmiño et al., 2018). The study did not include samples from what is thought to be a

third subpopulation from the Atlantic Ocean (Kyne et al. 2020), and neither from the South-eastern

Pacific islands (Pazmiño et al., 2018). Using Baited Remote Underwater Videos System

(BRUVS), Morales et al. (2019a) identified a resident population inhabiting the Easter Island

Ecoregion, which is likely to be at risk due to direct and indirect fishing pressure (Friedlander et

al., 2013; Zylich et al., 2014). According to the rapanui, sharks used to be abundant in the past,

while they are hardly seen today. In comparison, at Salas y Gómez Island, C. galapagensis

accounts for 26% of the total biomass (Friedlander et al., 2013). Recent studies at Salas y Gómez

Island showed that even though this species is usually classified as reef associated (Compagno

1984; Kohler et al., 1988), juveniles are capable of traveling longer distances (up to 236 km,

Morales et al., in press; Chapter 4). This new record together with the recent finding of Lizardi et

al. (2020) that described a 3,000 km migration through the eastern tropical Pacific raises the

question about the genetic connectivity between Rapa Nui and Salas y Gómez Island.

Connectivity between these two islands has been poorly studied. Using genetic analysis and

oceanographic modelling, Meerhoff et al. (2018) and Valencia et al. (in press) found a lack of

genetic population structure but low connectivity in two benthic species, the endemic rapanui

lobster Panulirus pascuensis and the rudderfish Kyphosus sandwicensis, respectively. The low

levels of genetic connectivity were attributed to the low dispersal capacity of both adults and

larvae. However, further studies using more species are needed in order to reveal the connection

between these two isolated islands.

77

Therefore, the aim of this study was to determine the degree of population connectivity of C.

galapagensis between Rapa Nui and Salas y Gómez Island using two different genetic markers,

mitochondrial DNA (mtDNA) and Single Nucleotide Polymorphism (SNPs). Mitochondrial

genomes are based on maternity inheritance, which tells us about the historic population structure

of the species (Heuter et al., 2005). SNPs, on the other hand, provides both historic and

contemporary evidence including bi-parental inheritance (Allendorf et al., 2010). Determining the

degree of population connectivity among geographic areas, with the estimated location of genetic

breaks, allows determining an appropriate scale at which conservation and management

strategies should be applied to continue demographic exchange and prevent local extinctions

(Crowder & Norse 2008; Toonen et al., 2011). Additionally, estimating connectivity at these spatial

scales will allow us to determine the relevance of current marine protected areas like MMHMP,

and to recommend conservation strategies to manage important components of the marine

ecosystem in a sustainable way.

5.3 METHODS


Samples from Galapagos sharks were collected between 2015 and 2017 at Rapa Nui (muscle)

and Salas y Gómez (fin clip). Samples from Salas y Gómez were collected during two research

cruises in November 2015 (CIMAR 21) and 2017 (ESMOI-Save Our Seas Foundation). Samples

were labelled and stored in 95% ethanol.

5.3.2 DNA extraction and sequencing for SNP

Samples from Rapa Nui (n=24) and Salas y Gómez (n=29) were sent to the Diversity Arrays

Technology Pty Ltd (DArT, Canberra-Australia) (https://www.diversityarrays.com/) for DNA

extraction and sequencing using protocols available in Ren et al., (2015) and Marie et al., (2019).

https://www.diversityarrays.com/

78

5.3.3 SNPs filtering and Outlier detection

The genotype dataset was filtered to retain only the highly informative SNPs. We filtered data

according to the following criteria: Minor Allele Frequencies (MAF) > 1%, Hardy–Weinberg

Equilibrium (HWE), Linkage disequilibrium (LD), discarding monomorphic markers, and a call rate

threshold for both loci and individual of 90% using the dartR package in R software v0.98.977 (R

Core Team 2019; Jombart & Collins 2015).

To identify putative loci under selection and removal for demographic connectivity analyses, two

genome scan analysis based on the distribution of FST were run: OUTFLANK (Whitlock &

Lotterhos, 2015) and BayeScan v.2.1 (Foll & Gaggiotti, 2008) software using a false discovery

rate (FDR) of 0.05.

5.3.4 Genetic structure analysis

Pairwise FST was calculated using the package adegenet implemented in R software v0.98.977

(Jombart & Collins 2015) on neutral SNP loci to determine the level of gene flow and subdivision

between populations (Wright, 1965; Schneider et al., 2000). A Discriminant Analysis of Principal

Component (DAPC) was used to estimate the number of cluaters k with high probability in the

data pool. The function find.cluster on the adegent library was used to estimate the probable

number of clusters (k) within the samples. The calculation of k optimizes the variability between

groups and minimizes the variability within groups, thus exacerbating the occurrence of different

groups. The calculated different clustering solutions were then compared using Bayesian

Information Criterion (BIC) and the best solutions correspond to the lowest BICs. All analyses

were run using the adegenet package.

79

5.3.5 Mitocondrial (mtDNA) extraction, amplification, sequencing, and alignment

Samples from Rapa Nui (n=28) and Salas y Gómez (n=46) were analysed. DNA was extracted

with the Wizard Genomics DNA Purification Kit (PROMEGA®, Promega Inc., Madison, WI). The

control region (CR) was amplified through polymerase chain reaction (PCR), using species-

specific primers designed for Carcharhinidae using Primer3 software (Rozen & Skalersky, 2000)

based on the reference sequences of Carcharhinus leucas (NC_023522; Chen et al.,

2015) and Galeocerdo cuvier (KX858828; Bustamante et al, unpublished data). The CR

for Carcharhinus galapagensis was amplified with the primers: 41F 5’-ATT CTG CCT AAA CTG

CCC CC-3’ and 1190R 5’-AGC ATC TTC AGT GCC ATG CT-3’. An initial denaturing step was

carried out at 94°C for 2 minutes, followed by 30 cycles with an optimized profile of 30 seconds

at 94°C, 30 seconds at 59°C, and 1 minute at 72°C, followed by a final extension step of 2 minutes

at 72°C. The PCR products were sent to the Molecular Cloning Laboratory (MCLAB) in the USA

for Sanger 3730 XL sequencing with both the forward and reverse primers. The sequences were

edited and aligned using the GENEIOUS v10.0.8 software. The BLASTn tool was used in

GenBank to confirm the correct genetic identification of each species.

5.3.6 Haplotype & nucleotide diversities

Haplotype (h) and nucleotide (π) diversities were estimated as an indicator of genetic diversity of

the mitochondrial DNA. Both indices (h and π) and pairwise FST were calculated in Arlequin v3.5

(Excoffier et al., 2010). h and π where calculated following Nei (1989) and Nei & Li (1979)

equations, respectively.

80

5.4 RESULTS

5.4.1 SNPs analysis

A total of 32,660 SNPs were genotyped from the 53 individuals. After filtering, a total of 13,496

SNPs and 52 individuals were retained for analyses. Candidate loci under selection were not

detected with the software used and none of the loci showed significant departures from HWE.

Population genetic structure assessed by the FST index showed statistical significances (FST =

0.006; p < 0.0001). The DAPC analysis showed k= 1 as with the lowest BIC value, suggesting

the occurrence of only one population (Figure S5).

5.4.2 Mitochondrial DNA analysis

Mitochondrial control region sequences (636 pb) were analysed and four mtDNA haplotypes were

found in total. Haplotype 1 (H1) was the dominant in both locations and occurred in 64 individuals.

H3 was also shared between locations but it only occurred in 7 individuals. H2 and H4 were

unique to Salas y Gómez Island and Rapa Nui, respectively (Table 5.1; Fig. 5.2). Pairwise FST

values showed no evidence of population differences (FST = 0.00909; p-value = 0.29) suggesting

the presence of one population inhabiting both islands. Haplotype (h) and nucleotide (π)

diversities values are showed in Table 5.1.

81

Figure 5.2. Haplotype network based on the informative mtDNA control region. Each circle represents a different haplotype. Haplotype frequencies are relative to the size of the circles. The number of branches reflects the mutations between haplotypes. Red correspond to Rapa Nui and blue to Salas y Gómez.

Table 5.1. Genetic diversity parameters determined by mtDNA Control Region for Rapa Nui and Salas y Gómez Island.

Rapa Nui Salas y Gómez

Sample size (n) 24 29

Number of Haplotypes (H) 3 3

Polymorphic sites 4 3

Nucleotide diversity (π) 0.468 0.295

Haplotypes diversity (h) 0.315 0.204

82

5.5 DISCUSSION

The current study is the first use of genome-wide technology to investigate the genetic

connectivity of C. galapagensis within the Easter Island Ecoregion. The results suggest a high

genetic flow between islands, resulting in low genetic differentiation. These findings are also

consistent with the migratory capacity of C. galapagensis in the area (Morales et al., in press;

Chapter 4). The occurrence of only one population within the ecoregion has conservation and

management significance. Additionally, the absence of outlier loci in our samples could be the

result of the homogeneous conditions within the ecoregion, since outliers usually reflect local

adaptation (Candy et al., 2015).

5.5.1 Connectivity within the Easter Island Ecoregion

The main results obtained from SNPs suggest that individuals sampled at both islands belong to

the same population. This result is surprising and unexpected considering previous studies on

this species at smaller scales. Using SNPs, Pazmiño et al. (2017) found clear population structure

for Galapagos sharks within the Galapagos Archipelago. They suggested that the genetic

structure found there was the result of oceanic currents that created five bioregions within the

archipelago. Glynn et al. (2007) suggested that lower levels of differentiation between distant

locations was attributed to large dispersal capacities together with a lack of physical barriers.

Unlike the Galapagos Archipelago, Rapa Nui and Salas y Gómez Island share similar

oceanographic conditions, and therefore are part of the same ecoregion (Friedlander et al., 2013;

Andrade et al., 2014). With no mayor oceanic currents or any other oceanographic condition that

could divide the Easter Island Ecoregion, the only evident biogeographical barrier between both

islands is distance (~400 km). However, geographical distance by its own is a poor predictor of

83

genetic breaks (Tonnen et al. 2011). Moreover, the seamount chain between Rapa Nui and Salas

y Gómez Island, as part of the Salas y Gómez Ridge, might be efficiently acting as a corridor for

mobile species as previously suggested by Newman & Foster (1983) and Friedlander et al.,

(2013). Stepping-stone dispersal on this species has been suggested by Green et al. (2014).

Based on mtDNA, the authors analysed four locations within the south-west Pacific and found an

increase in the genetic differentiation over geographic distance.

The lack of genetic structure using mtDNA was also found for C. galapagensis in the southwest

Pacific (Green et al. 2014) and the Galapagos Archipelago (Pazmiño et al. 2017). These results

were attributed mainly to a low mutation rate in elasmobranch mtDNA (Martin et al. 1992),

together with a short distance between sampling sites (250 km max.) relative to the dispersion

capacity of this species (Kohler 1998). Usually, female Galapagos sharks display higher levels of

site-fidelity to shallower water, while males are less resident to reef systems (Kohler et al 1998;

Meyer et al 2010). However, in a recent study (Chapter 4) a juvenile female C. galapagensis was

documented travelling a lineal distance of 236 km, which greatly exceeded the previous estimate

of the movement for juveniles of this species (~50 km; Kohler et al 1998). In this sense, the low

genetic differentiation found here using both SNPs and mtDNA analysis might suggest a sufficient

rate of gene flow coming from both sexes, supporting the idea of a female exchange between

sites. Also, the low FST found in the mtDNA analyses, a valid indicator for female philopatry when

high (Heuter et al., 2005), supports the lack of genetic structure.

Genetic and spatial approaches are complementary in the study of population connectivity of

mobile species (Boulet et al., 2007). Genetic connectivity is defined as the degree to which gene

flow affect evolutionary processes within population (Lowe & Allendorf, 2010), and where only a

few migrants per generation are required to maintain apparent panmixia (Planes & Fauvelot,

84

2002). Demographic connectivity, on the other hand, is when intrinsic parameter of a single

population, such as population growth rates, are affected by migration (Lowe & Allendorf, 2010).

However, directionally biased movement can produce asymmetrical rates of interpopulation

dispersal (source vs. sink). According to Kawecki & Holt (2002), “sources” are those locations that

generate migrants, and “sinks” are the recipients of those migrants. The demographic contribution

of immigrates will depend among others on local density and competition for recruitment. When

these components are high in a population, we could expect a low contribution from immigrants,

and vice versa (Lowe & Allendorf, 2010). Salas y Gómez Island is a healthy ecosystem were the

Galapagos sharks and other top predator species are abundant. On the contrary, Rapa Nui has

experienced historical overfishing that has depleted top predator populations, including the

Galapagos shark (Friedlander et al., 2013). Due to the higher population density of Galapagos

sharks at Salas y Gómez Island, we could expect a higher dispersal rate going from there to Rapa

Nui. Meerhoff et al. (2018) and Valencia et al. (in review) identified a higher genetic flow rate for

the endemic rapanui lobster Panulirus pascuensis and the rudderfish Kyphosus sandwicensis,

from Salas y Gómez Island to Rapa Nui, caused mainly by currents direction. These findings

corresponded to the first real evidence of the MMHMP seeding Rapa Nui’s unprotected waters.

A similar patterns of gene flow from east to west was also described in the Hawaiian Archipelago

(Rivera et al. 2011). However, a broader and longer study is needed to address connectivity rates

properly. For instance, using satellite and telemetry tags placed at Rapa Nui and Salas y Gómez

Island we could determine if immigrants are moving between islands and define the direction of

its movement. Using a similar approach Lizardi et al. (2020) studied the dispersal of the

Galapagos shark between the oceanic islands in the central east Pacific. Additionally, studies that

include other species, should be conducted to determinate the genetic relationship of species

85

inhabiting the Easter Island Ecoregion and the importance of the MMHMP in the health of the

entire ecosystem.

5.5.2. Genetic diversity within the Easter Island Ecoregion

In terms of genetic diversity parameters, Galapagos sharks display high overall genetic diversity

(Pazmiño et al., 2018). Rapa Nui and Salas y Gómez Island have a genetic diversity similar to

west Galapagos and Mexico in terms of number of haplotypes. However, the haplotype (h) and

nucleotide (π) diversity are much lower than the ones recorded along its distribution in the Indo-

Pacific Ocean (see Pazmiño et al., 2018). The low genetic diversity found in this study might be

the result of only a few colonization events due to the isolation of this region (Fig. 5.1). The

opposite scenario occurred in the Hawaiian Archipelago where several probable events have

occurred from neighbouring locations, providing the Hawaiian population a higher genetic

diversity (Pazmiño et al., 2018). Therefore, future studies should examine the relationship of the

single population inhabiting the Easter Island Ecoregion with other locations in the Pacific Ocean

where Galapagos sharks occur. Such studies could also shed lights into the more probable route

from where Galapagos sharks and other mobile species colonized this area. For example, using

neutral and outlier SNPs, Pazmiño et al. (2018) suggested that the central-west Pacific population

of the Galapagos shark was colonized westward via Mexico. This route (from the Eastern Pacific)

is feasible due to the relatively shallow seamounts along the Nazca and Salas y Gómez Ridges

(Glynn et al., 2007). Oceanic islands and seamounts are important in connecting distant locations

for the Galapagos shark (Pazmiño et al., 2018). Additionally, the low genetic diversity found here,

when compared to other locations (see Pazmiño et al., 2018), implies that the Rapa Nui population

is younger than the others. Grant & Bowen (1998) suggest that populations with low h (< 0.5) and

low π (<0.5) might correspond to recent founder events by a single or a few mtDNA lineages.

86

Finally, genetic diversity can also be considered an indicator of population resilience. Low genetic

diversity in small populations is expected to increase extinction risk since populations drastically

reduce their abilities to cope with new conditions (Frankham, 2005). The low values observed in

this study are disturbing due to the extreme isolation of the region (and the low probability of new

colonization events), and the historical (un)regulated fishing pressure in the area.

5.5.3 Implication for the conservation of the species

The results presented here suggest a level of historical migration, where males and females can

travel between Rapa Nui and Salas y Gómez Island. For conservation purposes, this mean that

the Easter Island Ecoregion should be considered as a single conservation unit. A conservation

unit is defined as a population unit considered different for purposes of conservation (Funk et al.,

2012). They are usually used by managers and policy makers to identify the boundaries of

population requiring management and conservation actions (Funk et al., 2012), since each unit

might need different strategies (Ward, 2000). The identification of one large genetic stock (or

conservation unit) in the entire ecoregion provides important ecological information that should

be used by the Chilean authorities to enhance protection of the unique population inhabiting the

area. For example, this information could be used to support expanding the current MMHMP

borders, where the Galapagos shark is most abundant, to the west. It can also be used to protect

the seamount ridge that connects the MMHMP and Rapa Nui since it is highly likely that it is acting

as a swimway or biological corridor between both areas. Seamounts often aggregate large

amounts of biomass and therefore concentrate fishery attention (Pitcher et al., 2008; Morato et

al., 2010). In this sense, if sharks are caught at these seamounts it could decrease the dispersal

rates of these species, which would directly affect population viability in the entire ecosystem

(Lowe & Allendorf, 2010). Additionally, this information should be used to zone the new Rapa Nui

multiple uses coastal marine protected areas (MUMPA) and protect important areas where this

87

species occurs (e.g., south coast; Morales et al. 2019a). Due to the unlikeliness of establishing

large no-take areas at Rapa Nui, we believe that the best approach for the conservation of the

Galapagos shark should be a mix of small no-take areas (e.g., nursery areas; Bonfil, 1997)

combined with conventional management strategies (e.g., fishing regulation) where the catch of

juveniles, and ideally adult sharks (Kinney & Simpfendorfer, 2008), is strictly prohibited. Similar

actions were taken in the Galapagos Archipelagos after a severe population reduction of sharks

(Wolff et al., 2012), which has recently led to one of the largest recorded shark biomasses

estimates anywhere (Salinas-de-León et al., 2016).

Sharks usually exert a top-down regulation in the ecosystem where they occur by controlling the

demography and behaviour of lower trophic levels (Roff et al., 2016). Even though there are

several shark species found around the Easter Island Ecoregion (Randal & Cea, 2011), the

Galapagos shark is the only resident species recorded in the area (Morales et al. 2019a).

Considering the occurrence of only one population within the Easter Island Ecoregion, and the

limited reproductive capacity of this species (Kyne et al. 2020), its rapid decline at Rapa Nui is

extremely disturbing and could have catastrophic consequences for the health of the entire

ecosystem if conservation actions are not taken in the near future.

88

CHAPTER 6. CONCLUSIONS

6.1 MAIN FINDINGS AND CONSERVATION IMPLICATIONS

The development of effective strategies for the management and conservation of key species

requires a broad knowledge of their ecology. This thesis represents the first investigation focused

on top predator species inhabiting the Easter Island Ecoregion. The use of BRUVS in Chapter 2

allowed me to determine which species inhabit Rapa Nui, what is their relative abundance, and

their general distribution. In this chapter I found that the south coast of the island is the main

habitat for top predators, where species such as jacks, tunas, and the Galapagos shark are

abundant year-round, and a possible nursery area for the Galapagos shark. I hypothesized that

the main reason for these differences is the exposure to high swells and winds coming from

Antarctica, and as a result, lower fishing pressure in this area. BRUVS also confirmed the

occurrence of a resident population of Galapagos sharks around Rapa Nui, which was thought to

be missing due to historical overfishing. This information is crucial for the establishment of the

new Rapa Nui MUMPA and the identification of priority areas for conservation (e.g., no-take

areas).

In Chapter 3, I determined the trophic role and defined the isotopic niche of predator species

observed in Chapter 2 and other species described in the area (Acanthocybium solandri,

Aulostomus chinensis, Carcharhinus galapagensis, Coryphaena hippurus, Katsuwonus pelamis,

Pseudocaranx dentex, Seriola lalandi, Kajikia audax, Thunnus albacares, Thyrsites atun). Using

δ13C and δ15N isotopic signatures, I discovered that not all large fishes should be classified as top

predators. The correct identification of top predator species contributes to the understanding of

Rapa Nui ecosystem dynamics and the identification of important conservation areas (e.g., within

the Rapa Nui MUMPA). My results also showed a high isotopic overlap between four species

89

(Acanthocybium solandri, Katsuwonus pelamis, Pseudocaranx dentex, and Thunnus albacares),

suggesting potential for interspecific competition of trophic resources. This chapter highlights the

need for multispecies studies to elucidate the tropho-dynamics of this isolated and largely

understudied ecosystem.

Identifying movement patterns of fish populations is essential for interpreting results of catch data

and underwater visual surveys, and to develop effective managements plans. These may be used

to infer changes in populations of reef fishes. In Chapter 4 I studied the movement patterns of the

most abundant top predators within the MMHMP, the Galapagos shark and the yellowtail

amberjack. Results showed that even though all individuals spent most of the time within the limits

of the marine park, all of them crossed the borders during portions of the tracking period. My

results also indicated that the travel distance of juvenile Galapagos sharks in open waters is much

higher than noted in previous studies. This means that the Galapagos shark may not be a strictly

reef-associated species as previously thought, which could also explain its wide distribution.

Additionally, I used data from the Automatic Identification System (https://globalfishingwatch.org)

to study fishing activities within Chile’s Exclusive Economic Zone (EEZ) and to put into

perspective the potential threat that mobile species are exposed to from these fishing activities.

No fishing activities were recorded inside the Chilean EEZ from 2012 to 2016. However, it is

evident that fishing vessels accurately identified the EEZ border, but it is unclear to what extent

they adhered to these boundaries. Summarizing, these results supports the idea of expanding

the current borders of the MMHMP to the west in order to improve protection of highly mobile

species. I also suggest the implementation of a better fisheries monitoring program within the

MMHMP, since the remoteness of this area increases the opportunity for illegal fishing without

punishment. Currently, only one local patrol boat “Tokerau” is available in the area. Unfortunately,

this patrol boat does not have the capacity to intercept vessels engaged in suspicious activities

https://globalfishingwatch.org/

90

or to reach, and therefore protect, the MMHMP. Further, even though the Chilean navy runs two

oceanic fishing enforcement operations twice a year, including an offshore patrol vessel and a

radar equipped plane, this is not enough to have a dissuasive presence in the region.

It is well known that connectivity studies should include both tracking and genetic studies. While

tracking studies provide information on where species move, genetic studies determine if those

species are reproducing (gene flow) within those areas. In Chapter 5 I studied the genetic

connectivity of the Galapagos shark between Rapa Nui and Salas y Gómez islands. Genetic

markers (SNPs and mtDNA) suggest that individuals at both islands are part of the same

population. These results are unexpected since genetic structure has been demonstrated in

smaller scales (e.g., within the Galapagos Archipelago; Pazmiño et al. 2017) for this species.

However, the lack of geographic barriers and the occurrence of a seamount chain between

islands, which may serve as stepping-stones, could help to explain my results. Additionally,

results from Chapter 5 were consistent with the findings of Chapter 4, suggesting that both female

and male Galapagos sharks can travel between islands. My results highlight the important role of

the MMHMP in keeping the entire ecosystem healthy.

Three additional papers have been published (Easton et al., 2016; Thiel et al., 2018; and Morales

et al., 2019b), and two more are being prepared regarding large fishes of Rapa Nui. In Easton et

al. (2016), we described the biodiversity of deeper areas, including the top predator species

around Rapa Nui. In Thiel et al. (2018), we showed how marine species interact with marine

plastic pollution and demonstrated how plastic pollution threatens top predator species. Finally,

in Morales et al. (2019b), we recorded the first observation of the whitetip reef shark Triaenodon

obesus at Rapa Nui. Additionally, two other publication are being prepared on the first records of

Seriola rivoliana, and Rhincodon typus on Rapa Nui. The increasing new records in the area

91

highlights the scarce knowledge of the local biodiversity and the need for longer monitoring

programs in the entire region.

6.2 LIMITATION OF THE STUDY AND SUGGESTIONS FOR FUTURE RESEARCH

DIRECTIONS

The most important limitation of working at the Easter Island Ecoregion is the isolation of the area.

Its remoteness from the mainland increases the general costs of research, which at the same

time is reflected in a limited sampling effort. For example, in Chapter 3 a more constant sampling

effort would have increased the low n of some species and allowed me to examine more specific

patterns such as seasonal differences in trophic dynamics. However, the chapter most affected

by economic constrains was Chapter 4. The extreme remoteness of Salas y Gómez Island (~ 400

km from Rapa Nui) limited the field investigation to only two short campaigns, which allowed for

the sampling of only a few individuals (see discussion of Chapter 4 for more details). Another

reason for the limited sampling effort is the exposure of the study area to unfavorable weather

conditions. This resulted in a constant uncertainty in field work and in the ability of meeting my

objectives. For example, in Chapter 2, the sampling effort was affected by the weather, as certain

areas could not be sampled during winter months due to strong wind and sea conditions.

Despite the limitations described above, this thesis represents a baseline for the development of

more effective management and conservation strategies for the protection of top predator species

in the Easter Island Ecoregion. However, it is worth emphasizing that this baseline does not

represent the pristine state of the Rapa Nui ecosystem. On the contrary, what we see now

corresponds to a highly impacted state or a “shifted baseline” as defined by Pauly (1995) and

Pinnegar & Engelh (2008). In this sense, if the Rapa Nui ecosystem has shifted due to historical

overfishing, then perhaps we need to focus our attention on a less impacted “control” system such

92

as the Salas y Gómez ecosystem as a reference for a past ecological state. “There (Salas y

Gómez Island) you will see how Rapa Nui used to be before the first settlement arrived here

(Rapa Nui) … You will see how the world was 2,000 years ago” (Edmundo Edwards from The

Lost Sharks of Rapa Nui National Geographic documentary). That said and based on the results

and the conclusion described above, I propose:

• A long-term monitoring programs should occur at Rapa Nui and Salas y Gómez Island. In

Chapter 2 BRUVS are shown as a reliable technique to study the marine biodiversity in

the area. In this regard, longer-term temporal and spatial surveys should include

mesophotic zones and seamounts to understand the patchy occurrence and distribution

of some of the species described in Chapter 2.

• A tropho-dynamic comparison between Rapa Nui and Salas y Gómez should be

conducted. Due to the large differences in the fish assemblages (e.g., top predator

abundance), considerable variations in how these ecosystems are structured might be

found.

• An extensive tagging (satellite and telemetry tags) program that includes adults of the

Galapagos shark and other mobile species at both islands should be implemented. The

use of a mix of tagging studies together with a monitoring program would provide important

short- and long-term information on the habitat use of key species. That information could

be then used to design more effective conservation measures on the MPAs.

• The expansion westward of the current borders of the MMHMP to better protect the mobile

species of Salas y Gómez Island. In this regard, a more constant and efficient surveillance

of these distant areas is imperative to prevent illegal fishing within the EEZ and the

MMHMP. Chapter 4 shows that even though no fishing activity inside the MMHMP or the

93

EEZ was recorded, fishing vessels could go undetected since the legal borders are an

obstacle easy to circumvent and vessel monitoring systems can be turned off.

A broad genetic study to compare present results with other locations where Galapagos

sharks occurs is needed. This will allow for a better understanding of how colonization

processes occurred in this isolated area. Easter Island ecoregion can be considered as an

oasis of life in the middle of the Pacific Ocean. Most probably due to its isolated location, this

area is widely understudied, when compared to other Polynesian islands. Overall, this thesis

lays the foundation for top predator species research, development of science-based

management approaches and effective conservation strategies in the Easter Island

ecoregion. Knowing the species composition of this ecologically important trophic group,

together with the understanding of how these species use the few available habitats in the

ecoregion, is essential for identifying important areas for conservation. This research also

provides scientific evidence for the existence of a single widespread population of the

Galapagos shark within the area, which would result in a single conservation unit for

management purposes. I hope that these findings encourage other researchers to explore

some of the several related topics that remain unsolved in the area.

94

REFERENCES

Aburto, J. A., & Gaymer, C. F. (2018). Struggling with social-ecological mismatches in marine management

and conservation at Easter Island. Marine Policy, 92: 21-29.

Aburto, J. A., Gaymer, C. F., & Cundill, G. (2017). Towards local governance of marine resources and

ecosystems on Easter Island. Aquatic Conservation: Marine and Freshwater Ecosystems, 27: 353-371.

Aburto, J. A., Gaymer, C. F., Haoa, S., & González, L. (2015). Management of marine resources through a

local governance perspective: Re-implementation of traditions for marine resource recovery on Easter

Island. Ocean & Coastal Management, 116: 108-115.

Acuña, E., Gaymer, C., Hinojosa, I., Aburto, J., Cortes, A., Sfeir, R., … Canales, C. (2018). Informe Pre-

final. Proyecto FIPA 2016-35. Estudio biológico pesquero y evaluación del estado de situación de las

pesquerías costeras de importancia para Isla de Pascua. 360 pp.

Acuna-Marrero, D., Smith, A. N., Hammerschlag, N., Hearn, A., Anderson, M. J., Calich, H., … Salinas-de-

León, P. (2017). Residency and movement patterns of an apex predatory shark (Galeocerdo cuvier) at the

Galapagos Marine Reserve. PLOS ONE, 12: e0183669.

Acuña-Marrero, D., Smith, A. N., Salinas-de-León, P., Harvey, E. S., Pawley, M. D., & Anderson, M. J.

(2018). Spatial patterns of distribution and relative abundance of coastal shark species in the Galapagos

Marine Reserve. Marine Ecology Progress Series, 593: 73-95.

Admiraal, J. F., Wossink, A., de Groot, W. T., & de Snoo, G. R. (2013). More than total economic value:

How to combine economic valuation of biodiversity with ecological resilience. Ecological Economics, 89:

115-122.

Agnew, D. J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J. R., & Pitcher, T. J. (2010).

Estimating the worldwide extent of illegal fishing. PLOS ONE, 4(2), e4570.

Allendorf, F. W., Hohenlohe, P. A., & Luikart, G. (2010). Genomics and the future of conservation genetics.

Nature Reviews Genetics, 11: 697–709. doi:10.1038/nrg2844

Almany, G. R. (2004). Does increased habitat complexity reduce predation and competition in coral reef

fish assemblages? Oikos, 106: 275-284.

Anderson, M. J., Gorley, R. N., & Clarke, K. R. (2008). PERMANOVA+ for Primer. Plymouth, UK: Primer-

E.

Anderson, M.J., & Millar, R.B. (2004). Spatial variation and effects of habitat on temperate reef fish

assemblages in northeastern New Zealand. Journal of Experimental Marine Biology and Ecology, 305:

191–221

95

Andrade, I., Hormazábal, S., & Correa-Ramírez, M. (2014). Time-space variability of satellite chlorophyll-a

in the Easter Island Province, southeastern Pacific Ocean. Latin American Journal of Aquatic Research,

42: 871-887.

Andrew, N. L., & Mapstone, B. D. (1987). Sampling and the description of spatial pattern in marine ecology.

Oceanography and Marine Biology, 25: 39–90.

Arana, P. M. (2014). Actividades pesqueras realizadas en la antigüedad en la Isla de Pascua. Latin

American Journal of Aquatic Research, 42: 673-689.

Arkema, K. K., Abramson, S. C., & Dewsbury, B. M. (2006). Marine ecosystem‐based management: from

characterization to implementation. Frontiers in Ecology and the Environment, 4: 525-532.

Asher, J., Williams, I. D., & Harvey, E. S. (2017). An Assessment of Mobile Predator Populations along

Shallow and Mesophotic Depth Gradients in the Hawaiian Archipelago. Scientific Reports, 7.

Barley, S. C., Meekan, M. G., & Meeuwig, J. J. (2017). Diet and condition of mesopredators on coral reefs

in relation to shark abundance. PLOS ONE, 12: e0165113.

Bauer R. (2018a) RchivalTag: Analyzing Archival Tagging Data. R package version 0.0.8. https://cran.r-

project.org/package=RchivalTag

Bauer, R. K, Forget, F., & Fromentin, J. M. (2015) Optimizing PAT data transmission: Assessing the

accuracy of temperature summary data to estimate environmental conditions. Fisheries Oceanography, 24:

533–539.

Bauer, R. K., Fromentin, J. M., Demarcq, H., & Bonhommeau, S. (2017). Habitat use, vertical and horizontal

behaviour of Atlantic bluefin tuna (Thunnus thynnus) in the Northwestern Mediterranean Sea in relation to

oceanographic conditions. Deep Sea Research Part II: Topical Studies in Oceanography, 141: 248-261.

Bauer, R.K. (2020a). RchivalTag: Analyzing Archival Tagging Data. R package version 0.1.2. https://cran.r-

project.org/package=RchivalTag.

Bauer, RK. (2018b) oceanmap: Data. A Plotting Toolbox for 2D Oceanographic Data. R package version

0.1.0. https://cran.r-project.org/package=oceanmap

Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A., & MacLeod, H. (2004). Determining trophic niche

width: a novel approach using stable isotope analysis. Journal of Animal Ecology, 73: 1007-1012.

Bennett, M.B., Gordon, I. & Kyne, P.M. (SSG Australia & Oceania Regional Workshop, March 2003) 2003.

Carcharhinus galapagensis. The IUCN Red List of Threatened Species 2003: e.T41736A10550977.

http://dx.doi.org/10.2305/IUCN.UK.2003.RLTS.T41736A10550977.en. Downloaded on 07 July 2019.

Bessudo, S., Soler, G. A., Klimley, P. A., Ketchum, J., Arauz, R., Hearn, A. et al. (2011). Vertical and

horizontal movements of the scalloped hammerhead shark (Sphyrna lewini) around Malpelo and Cocos

https://cran.r-project.org/package=RchivalTag

https://cran.r-project.org/package=RchivalTag

96

Islands (Tropical Eastern Pacific) using satellite telemetry. Boletín de Investigaciones Marinas y Costeras-

INVEMAR, 40: 91-106.

Beverly, S., Curran, D., Musyl, M., & Molony, B. (2009). Effects of eliminating shallow hooks from tuna

longline sets on target and non-target species in the Hawaii-based pelagic tuna fishery. Fisheries Research,

96: 281-288.

Bishop, J. (2006). Standardizing fishery-dependent catch and effort data in complex fisheries with

technology change. Reviews in Fish Biology and Fisheries, 16: 21-38.

Block, B. A., Jonsen, I. D., Jorgensen, S. J., Winship, A. J., Shaffer, S. A., Bograd, S. J., … Costa, D. P.

(2011). Tracking apex marine predator movements in a dynamic ocean. Nature, 475: 86.

Boerder, K., Miller, N. A., & Worm, B. (2018). Global hot spots of transshipment of fish catch at sea. Science

Advances, 4: eaat7159.

Bolnick, D. I., Ingram, T., Stutz, W. E., Snowberg, L. K., Lau, O. L., & Paull, J. S. (2010). Ecological release

from interspecific competition leads to decoupled changes in population and individual niche width.

Proceedings of the Royal Society B: Biological Sciences, 277: 1789-1797.

Bond, A. L., Jardine, T. D., & Hobson, K. A. (2016). Multi‐tissue stable‐isotope analyses can identify dietary

specialization. Methods in Ecology and Evolution, 7: 1428-1437.

Bonfil, R. (1997). Status of shark resources in the southern Gulf of Mexico and Caribbean: implications for

management. Fishery Research, 29: 101–117

Boonzaier, L., & Pauly, D. (2016). Marine protection targets: An updated assessment of global progress.

Oryx, 50: 27–35. doi:10.1017/S0030605315000848

Botsford, L. W., Micheli, F., & Hastings, A. (2003). Principles for the design of marine reserves. Ecological

Applications, 13: 25-31.

Boulet, M., Couturier, S., Cote, S. D., Otto, R. D., & Bernatchez, L. (2007). Integrative use of spatial, genetic,

and demographic analyses for investigating genetic connectivity between migratory, montane, and

sedentary caribou herds. Molecular Ecology, 16: 4223-4240.

Bradford, R., Hobday, A. J., Evans, K., & Lansdell, M. (2009). CMAR code of practice for tagging marine

animals. CSIRO Marine and Atmospheric Research.

Branstetter, S. (1990). Early life-history implications of selected carcharhinoid and lamnoid sharks of the

northwest Atlantic. NOAA Tech. Rep. NMFS 90: 17-28.

Brill, R., Lutcavage, M., Metzger, G., Bushnell, P., Arendt, M. D., Lucy, J., ... Foley, D. (2002). Horizontal

and vertical movements of juvenile bluefin tuna (Thunnus thynnus), in relation to oceanographic conditions

of the western North Atlantic, determined with ultrasonic telemetry. Fishery Bulletin, 100: 155.

97

Brooks, E. J., Sloman, K. A., Liss, S., Hassan-Hassanein, L., Danylchuk, A. J., Cooke, S. J., … Suski, C.

D. (2011b). The stress physiology of extended duration tonic immobility in the juvenile lemon shark,

Negaprion brevirostris (Poey 1868). Journal of Experimental Marine Biology and Ecology,409: 351–360.

doi:10.1016/j.jembe.2011.09.017

Brooks, E. J., Sloman, K. A., Sims, D. W., & Danylchuk, A. J. (2011a). Validating the use of baited remote

underwater video surveys for assessing the diversity, distribution and abundance of sharks in the Bahamas.

Endangered Species Research, 13: 231-243.

Bugoni, L., McGill, R. A., & Furness, R. W. (2010). The importance of pelagic longline fishery discards for

a seabird community determined through stable isotope analysis. Journal of Experimental Marine Biology

and Ecology, 391: 190-200.

Candy, J. R., Campbell, N. R., Grinnell, M. H., Beacham, T.D., Larson, W.A., & Narum, S. R. (2015).

Population differentiation determined from putative neutral and divergent adaptive genetic markers in

Eulachon (Thaleichthys pacificus, Osmeridae), an anadromous Pacific smelt. Molecular Ecology

Resources, 15:1421–1434.

Cappo, M., Harvey, E., Malcom, H., & Speare, P. (2003). Potential of video techniques to monitor diversity,

abundance and size of fish in studies of Marine Protected Areas. In: Beumer, J., Grant, A., Smith, D. (Eds.),

Aquatic Protected Areas — What Works Best and How do We Know? World Congress on Aquatic Protected

Areas. Australian Society for Fish Biology, Cairns, Australia. 455–464.

Cárcamo, P. F., Cortéz, M., Ortega, L., Squeo, F. A., & Gaymer, C. F. (2011). Crónica de un conflicto

anunciado: Tres centrales termoeléctricas a carbón en un hotspot de biodiversidad de importancia mundial.

Revista Chilena de Historia Natural. 84: 171-180.

Carlisle, A. B., Tickler, D., Dale, J. J., Ferretti, F., Curnick, D. J., Chapple, T. K. et al. (2019). Estimating

space use of mobile fishes in a large marine protected area with methodological considerations in acoustic

array design. Frontiers in Marine Science, 6, 256.

Carr, L. A., Stier, A. C., Fietz, K., Montero, I., Gallagher, A. J., & Bruno, J. F. (2013). Illegal shark fishing in

the Galapagos Marine Reserve. Marine Policy, 39: 317-321.

Chapman, C. J., Johnston, A. D. F., Dunn, J. R., & Creasey, D. J. (1974). Reactions of fish to sound

generated by diver’s open-circuit underwater breathing apparatus. Marine Biology, 27: 357–366.

Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R Package for Determining the

Relevant Number of Clusters in a Data Set. Journal of Statistical Software, 61: 1–36. Retrieved from

http://www.jstatsoft.org/v61/i06/

Charton, J. G., Williams, I. D., Ruzafa, A. P., Milazzo, M., Chemello, R., Marcos, C ... & Riggio, S. (2000).

Evaluating the ecological effects of Mediterranean marine protected areas: habitat, scale and the natural

variability of ecosystems. Environnemental Conservation, 27: 159-178.

98

Chen, X., Liu, M., Peng, Z., & Shi, X. (2015). Mitochondrial genome of the bull shark Carcharhinus leucas

(Carcharhiniformes: Carcharhinidae). Mitochondrial DNA, 26: 813-814.

Cherel Y, Hobson KA (2007) Geographical variation in carbon stable isotope signatures of marine

predators: a tool to investigate their foraging areas in the Southern Ocean. Molecular Ecology Resource,

329: 281–287.

Cherel, Y., Ducatez, S., Fontaine, C., Richard, P., & Guinet, C. (2008). Stable isotopes reveal the trophic

position and mesopelagic fish diet of female southern elephant seals breeding on the Kerguelen Islands.

Marine Ecology Progress Series, 370: 239-247.

Churchill, D. A., Heithaus, M. R., & Grubbs, R. D. (2015). Effects of lipid and urea extraction on δ15N

signatures of deep-sea sharks and hagfish: Can mathematical correction factors be generated?. Deep Sea

Research Part II: Topical Studies in Oceanography, 115: 103-108.

Clarke, K. R., & Gorley, R. N. (2006). PRIMER V6: user manual-tutorial. Plymouth Marine Laboratory.

Cole, R. G. (1994). Abundance, size structure, and diver-oriented behaviour of three large benthic

carnivorous fishes in a marine reserve in northeastern New Zealand. Biology Conservation, 70: 93–99.

Coles, S. L., & Tarr, B. A. (1990). Reef fish assemblages in the western Arabian Gulf: a geographically

isolated population in an extreme environment. Bulletin of Marine Science, 47: 696-720.

Compagno, L. J. (1984). Sharks of the world: an annotated and illustrated catalogue of shark species known

to date (No. QL 638.9. C65).

Connell, S.D., & Jones, G.P. (1991). The influence of habitat complexity on postrecruitment processes in a

temperate reef fish population. Journal of Experimental Marine Biology and Ecology, 151: 271–294

Cortés, E., Arocha, F., Beerkircher, L., Calvalho, F., Domingo, A., Heupel, M., … Simpfendorfer, C. (2010).

Ecological risk assessment of pelagic sharks caught in Atlantic pelagic longline fisheries. Aquatic Living

Resources, 23: 25–34. doi: 10.1051/alr/2009044

Costello, M. J., & Ballantine, B. (2015). Biodiversity conservation should focus on no-take Marine Reserves:

94% of Marine Protected Areas allow fishing. Trends in Ecology and Evolution, 30: 507–509.

doi:10.1016/j.tree.2015.06.011

Crowder, L., & Norse, E. (2008). Essential ecological insights for marine ecosystem-based management

and marine spatial planning. Marine policy, 32: 772-778.

Curley, B. G., Kingsford, M. J., & Gillanders, B. M. (2003). Spatial and habitat-related patterns of temperate

reef fish assemblages: implications for the design of Marine Protected Areas. Marine and Freshwater

Research, 53: 1197-1210.

99

Dale, J. J., Meyer, C. G., & Clark, C. E. (2011). The ecology of coral reef top predators in the

Papahānaumokuākea Marine National Monument. Journal of Marine Biology. 14 pp.

doi:10.1155/2011/725602.

Daneri, G., Dellarossa, V., Quiñones, R., Jacob, B., Montero, P., & Ulloa, O. (2000) Primary production and

community respiration in the Humboldt Current System off Chile and associated oceanic areas. Marine

Ecology Progress Series. 197: 41–49.

Daskalov, G. M., Grishin, A. N., Rodionov, S., & Mihneva, V. (2007). Trophic cascades triggered by

overfishing reveal possible mechanisms of ecosystem regime shifts. Proceedings of the National Academy

of Sciences, 104: 10518-10523.

De Girolamo, M., & Mazzoldi, C. (2001). The application of visual census on Mediterranean rocky habitats.

Marine Environmental Research, 51: 1-16.

DeMartini, E. E., & Friedlander, A.M. (2006). Predation, endemism, and related processes structuring

shallow-water reef fish assemblages of the NWHI. Atoll Research Bulletin, 543: 237–256.

Detsis, E., Brodsky, Y., Knudtson, P., Cuba, M., Fuqua, H., & Szalai, B. (2012). Project catch: a space

based solution to combat illegal, unreported and unregulated fishing: Part I: vessel monitoring system. Acta

Astronautica, 80: 114-123.

Devillers, R., Pressey, R. L., Grech, A., Kittinger, J.N., Edgar, G.J., Ward, T., & Watson, R. (2015).

Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection?

Aquatic Conservation: Marine and Freshwater Ecosystems,25: 480–504.

http://dx.doi.org/10.1002/aqc.2445.

DiSalvo, L.H., Randall, J.E., & Cea, A., (1988). Ecological reconnaissance of the Easter Island sublittoral

marine environment. National Geographic Research, 4: 451-473.

Domingo, A., Casaca Santos, C., Carlson, J., Natanson, L., Cortés, E., Mas, F., … Coelho, R. (2018). Post-

release mortality of shortfin mako in the Atlantic using satellite telemetry: preliminary results.

Dorman, S. R., Harvey, E. S., & Newman, S. J. (2012). Bait effects in sampling coral reef fish assemblages

with stereo-BRUVS. PLOS ONE, 7: e41538.

Duffy, C. A. (2016). Misidentification of Carcharhinus galapagensis (Snodgrass & Heller, 1905) in the

Southwest Pacific Ocean. Zootaxa, 4132: 97-106.

Dulvy, N. K., Fowler, S. L., Musick, J. A., Cavanagh, R. D., Kyne, P. M., Harrison, L. R., ... Pollock, C. M.

(2014). Extinction risk and conservation of the world’s sharks and rays. Elife,3: e00590.

Easton, E. E., Gaymer, C. F., Friedlander, A. M., & Herlan, J. J. (2018). Effects of herbivores, wave

exposure and depth on benthic coral communities of the Easter Island ecoregion. Marine and Freshwater

Research, 69: 997-1006. doi.org/10.1071/MF17064

http://dx.doi.org/10.1002/aqc.2445

100

Easton, E. E., Sellanes, J., Gaymer, C. F., Morales, N., Gorny, M., & Berkenpas, E. (2017). Diversity of

deep-sea fishes of the Easter Island Ecoregion. Deep Sea Research Part II: Topical Studies in

Oceanography, 137: 78-88.

Edeson, W. (2001). The international plan of action on illegal unreported and unregulated fishing: The legal

context of a non-legally binding instrument. The international journal of marine and coastal law, 16: 603-

623.

Edgar, G. J., Stuart-Smith, R. D., Willis, T. J., Kininmonth, S., Baker, S. C., Banks, S., … Thomson, R. J.

(2014). Global conservation outcomes depend on marine protected areas with five key features. Nature,

506, 216–220. doi:10.1038/nature13022

Emslie, M. J., Cheal, A. J., MacNeil, M. A., Miller, I. R., & Sweatman, H. P. (2018). Reef fish communities

are spooked by SCUBA surveys and may take hours to recover. PeerJ, 6: e4886.

Espinoza, M., Cappo, M., Heupel, M. R., Tobin, A. J., & Simpfendorfer, C. A. (2014). Quantifying shark

distribution patterns and species-habitat associations: implications of marine park zoning. PLOS ONE, 9:

e106885.

Estrada, J. A., Rice, A. N., Lutcavage, M. E., & Skomal, G. B. (2003). Predicting trophic position in sharks

of the north-west Atlantic Ocean using stable isotope analysis. Journal of the Marine Biological Association

of the United Kingdom, 83: 1347-1350.

Estrada, J. A., Rice, A. N., Natanson, L. J., & Skomal, G. B. (2006). Use of isotopic analysis of vertebrae in

reconstructing ontogenetic feeding ecology in white sharks. Ecology, 87: 829-834.

FAO, 2001. International Plan of Action to Prevent, Deter and Eliminate Illegal, Unreported and Unregulated

Fishing. FAO, Rome, pp. 1–24

FAO. (2016). The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition

for all. Rome.200 pp.

FAO. (2018). The State of World Fisheries and Aquaculture. (SOFIA): Meeting the sustainable development

goals, Rome: Food and Agriculture Organization, 2018.

Felley, J. D., & Felley, S. M. (1986). Habitat partitioning of fishes in an urban, estuarine bayou. Estuaries,

9: 208–18.

Fernández, M., Pappalardo, P., Rodríguez-Ruiz, M.C. & Castilla, J.C., (2014). Síntesis del estado del

conocimiento sobre la riqueza de especies de macroalgas, macroinvertebrados y peces en aguas costeras

y oceánicas de Isla de Pascua e Isla Salas y Gómez. Latin American Journal of Aquatic Research, 42: 760-

802.

Ferreira, L. C., Thums, M., Heithaus, M. R., Barnett, A., Abrantes, K. G., Holmes, B. J., ... Vaudo, J. (2017).

The trophic role of a large marine predator, the tiger shark Galeocerdo cuvier. Scientific Reports, 7: 1-14.

101

Ferretti, F., Worm, B., Britten, G. L., Heithaus, M. R., & Lotze, H. K. (2010). Patterns and ecosystem

consequences of shark declines in the ocean. Ecology Letters, 13: 1055-1071.

Field, I. C., Meekan, M. G., Speed, C. W., White, W., & Bradshaw, C. J. A. (2011). Quantifying movement

patterns for shark conservation at remote coral atolls in the Indian Ocean. Coral Reefs, 30: 61-71.

Flothmann, S., von Kistowski, K., Dolan, E., Lee, E., Meere, F., & Album, G. (2010). Closing loopholes:

getting illegal fishing under control. Science, 328: 1235-1236.

Foll, M., & Gaggiotti, O. (2008). A genome-scan method to identify selected loci appropriate for both

dominant and codominant markers: a bayesian perspective. Genetics, 180: 977–993. doi:

10.1534/genetics.108.092221.

Frankham, R. (2005). Genetics and extinction. Biological Conservation, 126: 131–140.

doi:10.1016/j.biocon.2005.05.002

Fréon, P., Cury, P., Shannon, L., & Roy, C. (2005). Sustainable exploitation of small pelagic fish stocks

challenged by environmental and ecosystem changes: a review. Bulletin of Marine Science, 76(2): 385-

462.Friedlander, A. M., & Parrish, J. D. (1998). Temporal dynamics of fish communities on an exposed

shoreline in Hawaii. Environmental Biology of Fishes, 53: 1-18.

Friedlander, A.M., 2018. Marine conservation in Oceania: Past, present, and future. Marine Pollution

Bulletin, 135: 139-149.

Friedlander, A. M., & DeMartini, E. E. (2002). Contrasts in density, size, and biomass of reef fishes between

the northwestern and the main Hawaiian Islands: the effects of fishing down apex predators. Marine

Ecology Progress Series, 230: 253-264.

Friedlander, A. M., & Parrish, J. D. (1998). Temporal dynamics of fish communities on an exposed shoreline

in Hawaii. Environmental Biology of Fishes, 53: 1-18.

Friedlander, A. M., Ballesteros, E., Beets, J., Berkenpas, E., Gaymer, C. F., Gorny, M., & Sala, E. (2013).

Effects of isolation and fishing on the marine ecosystems of Easter Island and Salas y Gómez, Chile.

Aquatic Conservation: Marine and Freshwater Ecosystems, 23: 515-531

Friedlander, A. M., Ballesteros, E., Berkenpas, E., Betz, J., Giddens, J., Gorny, M., … Sala, E. (2017).

Informe Científico Archipiélago de Juan Fernandez: Biodiversidad marina y necesidad de conservación.

NATIONAL GEOGRAPHIC. 83 pp.

Frisch A. J, Cole A. J, Hobbs J. A, Rizzari J. R, & Munkres K. P. (2012). Effects of spearfishing on reef fish

populations in a multi-use conservation area. PLOS ONE, 7: e51938

Frisch, A. J., Ireland, M., & Baker, R. (2014). Trophic ecology of large predatory reef fishes: energy

pathways, trophic level, and implications for fisheries in a changing climate. Marine Biology, 161: 61-73.

102

Frisch, A. J., Ireland, M., Rizzari, J. R., Lönnstedt, O. M., Magnenat, K. A., Mirbach, C. E., & Hobbs, J. P.

A. (2016). Reassessing the trophic role of reef sharks as apex predators on coral reefs. Coral Reefs, 35:

459-472.

Funk WC, McKay JK, Hohenlohe PA, Allendorf FW. (2012). Harnessing genomics for delineating

conservation units. Trends in Ecology & Evolution, 27:489–496.

Futuyma, D.J. & Moreno, G. (1988). The evolution of ecological specialization. Annual Review of Ecology

and Systematics, 19: 207-233.

Gaines, S. D., White, C., Carr, M. H., & Palumbi, S. R. (2010). Designing marine reserve networks for both

conservation and fisheries management. Proceedings of the National Academy of Sciences of the United

States of America, 107: 18286–18293. https://doi.org/10.1073/

Gallagher, A. J., Shiffman, D. S., Byrnes, E. E., Hammerschlag-Peyer, C. M., & Hammerschlag, N. (2017).

Patterns of resource use and isotopic niche overlap among three species of sharks occurring within a

protected subtropical estuary. Aquatic Ecology, 51: 435-448.

Game, E. T., Grantham, H. S., Hobday, A. J., Pressey, R. L., Lombard, A. T., Beckley, L. E., ... Richardson,

A. J. (2009). Pelagic protected areas: the missing dimension in ocean conservation. Trends in Ecology &

Evolution, 24: 360-369.

Garla, R. C., Chapman, D. D., Wetherbee, B. M., & Shivji, M. (2006). Movement patterns of young

Caribbean reef sharks, Carcharhinus perezi, at Fernando de Noronha Archipelago, Brazil: the potential of

marine protected areas for conservation of a nursery ground. Marine Biology, 149: 189-199.

Garrigue, C., Clapham, P. J., Geyer, Y., Kennedy, A. S., & Zerbini, A. N. (2015). Satellite tracking reveals

novel migratory patterns and the importance of seamounts for endangered South Pacific humpback whales.

Royal Society Open Science, 2: 150489.

Gaymer, C., Aburto, J., Acuña, E., Bodini, A., Cárcamo, F., Stotz, W., & Tapia, C. (2013). Base de

conocimiento y construcción de capacidades para el uso sustentable de los ecosistemas y recursos

marinos de la ecorregión de Isla de Pascua. Licitación 4728-33-le12. Subsecretaría Pesca.

Glynn, P. W., Wellington, G. M., Riegl, B., Olson, D. B., Borneman, E., & Wieters, E. A. (2007). Diversity

and Biogeography of the Scleractinian Coral Fauna of Easter Island (Rapa Nui). Pacific Science, 61: 67–

90. doi:10.1353/psc.2007.0005

Graham, B. S., Koch, P. L., Newsome, S. D., McMahon, K. W., & Aurioles, D. (2010). Using isoscapes to

trace the movements and foraging behaviour of top predators in oceanic ecosystems. In Isoscapes (pp.

299-318). Springer, Dordrecht.

Graham, N. A., Spalding, M. D., & Sheppard, C. R. (2010). Reef shark declines in remote atolls highlight

the need for multi‐faceted conservation action. Aquatic Conservation: Marine and Freshwater Ecosystems,

20: 543-548.

103

Grant, W. A. S., & Bowen, B. W. (1998). Shallow population histories in deep evolutionary lineages of

marine fishes: insights from sardines and anchovies and lessons for conservation. Journal of Heredity, 89:

415-426.

Gray, A. E., Williams, I. D., Stamoulis, K. A., Boland, R. C., Lino, K. C., Hauk, B. B., ... & Kosaki, R. K.

(2016). Comparison of reef fish survey data gathered by open and closed circuit SCUBA divers reveals

differences in areas with higher fishing pressure. PLOS ONE, 11: e0167724.

Green, M. E. (2014). A mitochondrial and genome wide genetic investigation of Galapagos shark

(Carcharhinus galapagensis) populations in the southwest Pacific Ocean (Honours dissertation). Retrieved

from James Cook University Library Database.

Hagihara, R., Jones, R. E., Sobtzick, S., Cleguer, C., Garrigue, C., & Marsh, H. (2018). Compensating for

geographic variation in detection probability with water depth improves abundance estimates of coastal

marine megafauna. PLOS ONE, 13: e0191476.

Hardinge, J., Harvey, E. S., Saunders, B. J., & Newman, S. J. (2013). A little bait goes a long way: the

influence of bait quantity on a temperate fish assemblage sampled using stereo-BRUVS. Journal of

Experimental Marine Biology and Ecology, 449: 250-260.

Harrigan, P., Zieman, J. C., & Macko, S. A. (1989). The base of nutritional support for the gray snapper

(Lutjanus griseus): an evaluation based on a combined stomach content and stable isotope analysis.

Bulletin of Marine Science, 44: 65-77.

Harvey, E. S., Newman, S. J., McLean, D. L., Cappo, M., Meeuwig, J. J., & Skepper, C. L. (2012).

Comparison of the relative efficiencies of stereo-BRUVs and traps for sampling tropical continental shelf

demersal fishes. Fisheries Research, 125: 108-120.

Hecky, R. E., & Hesslein, R. H. (1995). Contributions of benthic algae to lake food webs as revealed by

stable isotope analysis. Journal of the North American Benthological Society, 14: 631-653.

Heithaus, M. R., Frid, A., Wirsing, A. J., & Worm, B. (2008). Predicting ecological consequences of marine

top predator declines. Trends in Ecology & Evolution, 23: 202-210.

Henry, G. W. & Lyle, J. M. (2003). The national recreational and indigenous fishing survey. FDRC Project

No 99/158, Tasmanian Aquaculture Fisheries Institute, Hobart.

Hernández Mares, P. (2016). Illegal fishing threatens Easter Island’s natural resources. Mongbay.com, 2

November 2016. https://news.mongabay.com/2016/11/illegal-fishing-threatens-easter-islands-natural-

resources/

Heupel, M. R., & Hueter, R. E. (2002). The importance of prey density in relation to the movement patterns

of juvenile sharks within a coastal nursery area. Marine and Freshwater Research, 53: 543–550.

Heupel, M. R., Simpfendorfer, C. A., & Hueter, R. E. (2004). Estimation of shark home ranges using passive

monitoring techniques. Environmental Biology of Fishes, 71: 135-142.

104

Hijmans, R. J. (2017) geosphere: Spherical Trigonometry. R package version 1.5-7. https://cran.r-

project.org/package=geosphere.

Hilborn, R. (2016). Policy: Marine biodiversity needs more than protection. Nature, 535 : 224–226.

doi:10.1038/535224a.

Hilborn, R., Micheli, F., & De Leo, G. A. (2006). Integrating marine protected areas with catch regulation.

Canadian Journal of Fisheries and Aquatic Sciences, 63: 642-649.

Hoegh-Guldberg O., Mumby P.J., Hooten A.J., Steneck R.S., Greenfield P., Gomez E., … Hatziolos M.E.

(2007). Coral reefs under rapid climate change and ocean acidification. Science, 318: 1737–1742.

Holland, K. N., Meyer, C. G., & Dagorn, L. C. (2009). Inter-animal telemetry: results from first deployment

of acoustic ‘business card’tags. Endangered Species Research, 10: 287-293.

Holland, K. N., Wetherbee, B. M., Lowe, C. G., & Meyer, C. G. (1999). Movements of tiger sharks

(Galeocerdo cuvier) in coastal Hawaiian waters. Marine Biology, 134: 665-673.

Holmes, B. J., Pepperell, J. G., Griffiths, S. P., Jaine, F. R., Tibbetts, I. R., & Bennett, M. B. (2014). Tiger

shark (Galeocerdo cuvier) movement patterns and habitat use determined by satellite tagging in eastern

Australian waters. Marine Biology, 161: 2645-2658.

Hueter, R. E., Heupel, M. R., Heist, E. J., & Keeney, D. B. (2005). Evidence of philopatry in sharks and

implications for the management of shark fisheries. Journal of northwest Atlantic fishery Science, 35: 239-

247.

Hughes, T. P., Graham, N. A., Jackson, J. B., Mumby, P. J., & Steneck, R. S. (2010). Rising to the challenge

of sustaining coral reef resilience. Trends in Ecology & Evolution, 25: 633-642.

Hussey, N. E., MacNeil, M. A., Olin, J. A., McMeans, B. C., Kinney, M. J., Chapman, D. D., & Fisk, A. T.

(2012). Stable isotopes and elasmobranchs: tissue types, methods, applications and assumptions. Journal

of Fish Biology, 80: 1449-1484.

Jackson, A. L., Inger, R., Parnell, A. C., & Bearhop, S. (2011). Comparing isotopic niche widths among and

within communities: SIBER–Stable Isotope Bayesian Ellipses in R. Journal of Animal Ecology, 80: 595-602.

Jeppesen, E., Meerhoff, M., Holmgren, K., González-Bergonzoni, I., Teixeira-de Mello, F., Declerck, S. A.,

… Lazzaro, X. (2010). Impacts of climate warming on lake fish community structure and potential effects

on ecosystem function. Hydrobiologia, 646: 73-90.

Jombart T. & Ahmed I. (2011). Adegenet 1.3-1: new tools for the analysis of genome-wide SNP data.

Bioinformatics.

Jones, P. J. S., & De Santos, E. M. (2016). Viewpoint – Is the race for remote, very large marine protected

areas (VLMPAs) taking us down the wrong track? Marine Policy, 73: 231-234.

105

Kawecki, T. J., & Holt, R. D. (2002). Evolutionary consequences of asymmetric dispersal rates. American

Naturalist, 160, 333– 347.

Kelly, B., Dempson, J. B., & Power, M. (2006). The effects of preservation on fish tissue stable isotope

signatures. Journal of Fish Biology, 69: 1595-1611.

Ketchum, J. T., Hearn, A., Klimley, A. P., Peñaherrera, C., Espinoza, E., Bessudo, S., ... Arauz, R. (2014).

Inter-island movements of scalloped hammerhead sharks (Sphyrna lewini) and seasonal connectivity in a

marine protected area of the eastern tropical Pacific. Marine Biology, 161: 939-951.

Kilfoil, J.P., Wirsing, A.J., Campbell, M.D., Kiszka, J.J., Gastrich, K.R., Heithaus, M.R., & Zhang, Y., Bond,

M.E., (2017). Baited remote underwater video surveys undercount sharks at high densities: insights from

full-spherical camera technologies. Marine Ecology Progress Series, 585: 113-121.

Kingsford, M. J., Underwood, A. J., & Kennelly, S. J. (1991). Humans as predators on rocky reefs in New

South Wales, Australia. Marine Ecology Progress Series. Oldendorf, 72: 1-14.

Kinney, M. J., Hussey, N. E., Fisk, A. T., Tobin, A. J., & Simpfendorfer, C. A. (2011). Communal or

competitive? Stable isotope analysis provides evidence of resource partitioning within a communal shark

nursery. Marine Ecology Progress Series, 439: 263–276.

Kinney, M. J., & Simpfendorfer, C. A. (2009). Reassessing the value of nursery areas to shark conservation

and management. Conservation Letters, 2: 53-60.

Klein, C. J., Brown, C. J., Halpern, B. S., Segan, D. B., McGowan, J., Beger, M., & Watson, J. E. M. (2015).

Shortfalls in the global protected area network at representing marine biodiversity. Scientific Reports,5: 1–

7. doi:10.1038/srep17539.

Knip, D. M., Heupel, M. R., & Simpfendorfer, C. A. (2012). Evaluating marine protected areas for the

conservation of tropical coastal sharks. Biological Conservation, 148: 200-209.

Kohler, N. E., Casey, J. G., & Turner, P. A. (1998). NMFS cooperative shark tagging program, 1962-93: an

atlas of shark tag and recapture data. Marine Fisheries Review, 60: 1-1.

Koladinovic, J., Ménard, F., Bustamante, P., Cosson, R. P., & Le Corre, M. (2008). Trophic ecology of

marine birds and pelagic fishes from Reunion Island as determined by stable isotope analysis. Marine


Kramer, D. L. & Chapman, M. R. (1999). Implications of fish home range size and relocation for marine

reserve function. Environmental Biology of Fishes, 55: 65–79.

Kritzer, J. P. (2004). Effects of noncompliance on the success of alternative designs of marine protected‐

area networks for conservation and fisheries management. Conservation Biology, 18: 1021-1031.

Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., ... Woods, P. (2018).

Tracking the global footprint of fisheries. Science, 359: 904-908.

106

Kulbicki, M. (1998). How the acquired behaviour of commercial reef fishes may influence the results

obtained from visual censuses. Journal of Experimental Marine Biology and Ecology, 222: 11-30.

Kyne, P.M., Barreto, R., Carlson, J., Fernando, D., Fordham, S., Francis, M.P. et al. 2019. Carcharhinus galapagensis. The IUCN Red List of Threatened Species 2019: e.T41736A2954286.

Langlois, T. J., Harvey, E. S., Fitzpatrick, B., Meeuwig, J. J., Shedrawi, G., & Watson, D. L. (2010). Cost-

efficient sampling of fish assemblages: comparison of baited video stations and diver video transects.

Aquatic Biology, 9: 155-168.

Layman, C. A., Quattrochi, J. P., Peyer, C. M., & Allgeier, J. E. (2007). Niche width collapse in a resilient

top predator following ecosystem fragmentation. Ecology Letters, 10: 937-944.

Lindfield, S. J., Harvey, E. S., McIlwain, J. L., & Halford, A. R. (2014). Silent fish surveys: bubble‐free diving

highlights inaccuracies associated with SCUBA‐based surveys in heavily fished areas. Methods in Ecology

and Evolution, 5: 1061-1069.

Lindley, J., & Techera, E. J. (2017). Overcoming complexity in illegal, unregulated and unreported fishing

to achieve effective regulatory pluralism. Marine Policy, 81: 71-79.

Link, J., (2002). Does food web theory work for marine ecosystems? Marine Ecology Progress Series, 230:

1-9.

Lizardi, F. L., Hoyos-Padilla, M., Hearn, A., Klimley, A. P., Galván-Magaña, F., Arauz, R. et al. (2020). Shark

movements in the Revillagigedo Archipelago and connectivity with the Eastern Tropical Pacific. bioRxiv.

doi: https://doi.org/10.1101/2020.03.02.972844.

López-Garro, A., Zanella, I., Martínez, F., Golfín-Duarte, G., & Pérez-Montero, M. (2016). La pesca ilegal

en el Parque Nacional Isla del Coco, Costa Rica. Revista de Biología Tropical, 64: 249-261.

Lorrain, A., Argüelles, J., Alegre, A., Bertrand, A., Munaron, J. M., Richard, P., & Cherel, Y. (2011).

Sequential isotopic signature along gladius highlights contrasted individual foraging strategies of jumbo

squid (Dosidicus gigas). PLOS ONE, 6: e22194.

Lowe, W. H., & Allendorf, F. W. (2010). What can genetics tell us about population connectivity?. Molecular

Ecology, 19: 3038-3051.

Lubchenco, J., & Grorud‐Colvert, K. (2015). Making waves: The science and politics of ocean protection.

Science, 350: 382–383. https://doi.org/10.1126/science.aad5443

Luo, J., Prince, E. D., Goodyear, C. P., Luckhurst, B. E., & Serafy, J. E. (2006). Vertical habitat utilization

by large pelagic animals: a quantitative framework and numerical method for use with pop‐up satellite tag

data. Fisheries Oceanography, 15: 208-229.

Macfadyen, G., Hosch, G., Kaysser, N. & Tagziria, L. (2019). The IUU Fishing Index, 2019. Poseidon

Aquatic Resource Management Limited and the Global Initiative Against Transnational Organized Crime.

107

MacKeracher, T., Diedrich, A., & Simpfendorfer, C.A. (2019). Sharks, rays and marine protected areas: A

critical evaluation of current perspectives. Fish and Fisheries, 20: 255– 267.

https://doi.org/10.1111/faf.12337.

Malcolm, H. A., Gladstone, W., Lindfield, S., Wraith, J., & Lynch, T. P. (2007). Spatial and temporal variation

in reef fish assemblages of marine parks in New South Wales, Australia—baited video observations. Marine


Marie, A. D., Herbinger, C., Fullsack, P. M., & Rico, C. (2019). First reconstruction of kinship in a scalloped

hammerhead shark aggregation reveals the mating patterns and breeding sex ratio. Frontiers in Marine

Science. 6: 676. doi: 10.3389/fmars.2019.00676.

Martin, A. P., Naylor, G. J. P., & Palumbi, S. R. (1992). Rates of mitochondrial DNA evolution in sharks are

slow compared with mammals. Nature, 357: 153–155.

Martinez-Takeshita, N., Purcell, C. M., Chabot, C. L., Craig, M. T., Paterson, C. N., Hyde, J. R., & Allen, L.

G. (2015). A tale of three tails: cryptic speciation in a globally distributed marine fish of the genus Seriola.

Copeia, 103: 357-368.

Matich, P., Heithaus, M. R., & Layman, C. A. (2010). Size-based variation in intertissue comparisons of

stable carbon and nitrogen isotopic signatures of bull sharks (Carcharhinus leucas) and tiger sharks

(Galeocerdo cuvier). Canadian Journal of Fisheries and Aquatic Sciences, 67: 877-885.

McCauley, D. J., Woods, P., Sullivan, B., Bergman, B., Jablonicky, C., Roan, A., ... Worm, B. (2016). Ending

hide and seek at sea. Science, 351: 1148-1150.

McCutchan, J. H., Lewis, W. M., Kendall, C., & McGrath, C. C. (2003). Variation in trophic shift for stable

isotope ratios of carbon, nitrogen, and sulfur. Oikos, 102: 378–390.

Medley, P. A., Gaudian, G., & Wells, S. (1993). Coral reef fisheries stock assessment. Reviews in Fish

Biology and Fisheries, 3: 242–285.

Meerhoff, E., Yannicelli, B., Dewitte, B., Díaz-Cabrera, E., Vega-Retter, C., Ramos, M., ... Véliz, D. (2018).

Asymmetric connectivity of the lobster Panulirus pascuensis in remote islands of the southern Pacific:

importance for its management and conservation. Bulletin of Marine Science, 94: 753-774.

Menard, F., Lorrain, A., Potier, M., & Marsac, F. (2007). Isotopic evidence of distinct feeding ecologies and

movement patterns in two migratory predators (yellowfin tuna and swordfish) of the western Indian Ocean.

Marine Biology, 153: 141–152. doi:10.1007/s00227-007-0789-7.

Meyer, C. G., Holland, K. N., & Papastamatiou, Y. P. (2007). Seasonal and diel movements of giant trevally

Caranx ignobilis at remote Hawaiian atolls: implications for the design of marine protected areas. Marine


108

Meyer, C. G., Papastamatiou, Y. P., & Holland, K. N. (2010). A multiple instrument approach to quantifying

the movement patterns and habitat use of tiger (Galeocerdo cuvier) and Galapagos sharks (Carcharhinus

galapagensis) at French Frigate Shoals, Hawaii. Marine Biology, 157: 1857-1868.

Micheli, F., Halpern, B. S., Botsford, L. W., & Warner, R. R. (2004). Trajectories and correlates of community

change in no-take marine reserves. Ecology Applied, 14: 1709–1723.

Miller, N. A., Roan, A., Hochberg, T., Amos, J., & Kroodsma, D. A. (2018). Identifying global patterns of

trans-shipment behaviour. Frontiers in Marine Science, 5: 240.

Moffitt, E. A., Botsford, L. W., Kaplan, D. M., & O'Farrell, M. R. (2009). Marine reserve networks for species

that move within a home range. Ecological applications,19: 1835-1847.

Morales, N. A., Easton, E. E., Friedlander, A. M., Harvey, E. S., Garcia, R., & Gaymer, C. F. (2019a). Spatial

and seasonal differences in the top predators of Easter Island: Essential data for implementing the new

Rapa Nui multiple‐uses marine protected area. Aquatic Conservation: Marine and Freshwater

Ecosystems, 29, 118-129.

Morales, N., Coghlan, A. R., Hayden, G., & Guajardo, P. (2019b). First sighting of a tropical benthic reef

shark species at Rapa Nui: chance dispersal or a sign of things to come?. Journal of Fish Biology, 95: 642-

646.

Morales NA, Heidemeyer M, Bauer R, Hernandez S, Acuña E, Friedlander AM, Gaymer CF. (In press).

Residential movements of top predators at Chile’s most isolated Marine Protected Area: implications for

the conservation of the Galapagos shark, Carcharhinus galapagensis, and the yellowtail amberjack, Seriola

lalandi. Aquatic Conserv: Mar Freshw Ecosyst.

Morato, T., Hoyle, S. D., Allain, V., & Nicol, S. J. (2010). Seamounts are hotspots of pelagic biodiversity in

the open ocean. Proceedings of the National Academy of Sciences, 107: 9707-9711.

Moteki M, Arai M, Tsuchiya K, Okamoto H (2001) Composition of piscine prey in the diet of large pelagic

fish in the eastern tropical Pacific Ocean. Fisheries Science, 67: 1063–1074.

Moyes, C.D., Fragoso, N., Musyl, M.K. & Brill, R.W. (2006). Predicting postrelease survival in large pelagic

fish. Transactions of the American Fisheries Society, 135:1389–1397. doi: 10.1577/T05-224.1

Muñoz, A. (2011). Confronting illegal fishing in Salas y Gomez. [https://usa.oceana.org/blog/confronting-

illegal-fishing-sala-y-gomez]. Reviewed: 20 June 2019

Murphy, H. M., & Jenkins, G. P. (2010). Observational methods used in marine spatial monitoring of fishes

and associated habitats: a review. Marine and Freshwater Research, 61: 236-252.

Myers, R. A., & Worm, B. (2003). Rapid worldwide depletion of predatory fish communities. Nature, 423:

280-283.

109

Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P., & Peterson, C. H. (2007). Cascading effects of

the loss of apex predatory sharks from a coastal ocean. Science, 315: 1846-1850.

Newman, W. A., & Foster, B. A. (1983). The Rapanuian fauna district (Easter and Sala y Gomez): in search

of ancient archipelagos. Bulletin of Marine Science, 33: 633–644.

Olin, J. A., Hussey, N. E., Fritts, M., Heupel, M. R., Simpfendorfer, C. A., Poulakis, G. R., & Fisk, A. T.

(2011). Maternal meddling in neonatal sharks: implications for interpreting stable isotopes in young

animals. Rapid Communications in Mass Spectrometry, 25: 1008-1016.

Olson, K. R., (1999). Rectal gland and volume homeostasis. In: Hamlett, W.C.(Ed.), Sharks, Skates, and

Rays: The Biology of Elasmobranch Fishes. The John Hopkins University Press, Baltimore, Maryland, USA,

pp. 329–352.

Olson, R. J., & Galván-Magaña, F. (2002). Food habits and consumption rates of common dolphinfish

(Coryphaena hippurus) in the eastern Pacific Ocean. Fishery Bulletin, 100: 279-298.

Palstra, A. P., Mes, D., Kusters, K., Roques, J. A., Flik, G., Kloet, K., & Blonk, R. J. (2015). Forced sustained

swimming exercise at optimal speed enhances growth of juvenile yellowtail kingfish (Seriola lalandi).

Frontiers in Physiology, 5: 11.

Papastamatiou, Y. P., Friedlander, A. M., Caselle, J. E., & Lowe, C. G. (2010). Long-term movement

patterns and trophic ecology of blacktip reef sharks (Carcharhinus melanopterus) at Palmyra Atoll. Journal

of Experimental Marine Biology and Ecology, 386: 94-102.

Papastamatiou, Y. P., Lowe, C. G., Caselle, J. E., & Friedlander, A. M. (2009). Scale‐dependent effects of

habitat on movements and path structure of reef sharks at a predator‐dominated atoll. Ecology, 90: 996-

1008.

Papastamatiou, Y. P., Wetherbee, B. M., Lowe, C. G., & Crow, G. L. (2006). Distribution and diet of four

species of carcharhinid shark in the Hawaiian Islands: evidence for resource partitioning and competitive

exclusion. Marine Ecology Progress Series, 320: 239-251.

Pauly, D. (1995). Anecdotes and the shifting baseline syndrome of fisheries. Trends in Ecology & Evolution,

10: 430.

Pauly, D., Christensen, V., Guénette, S., Pitcher, T. J., Sumaila, U. R., Walters, C. J., ... Zeller, D. (2002).

Towards sustainability in world fisheries. Nature, 418: 689.

Pazmiño, D. A., Maes, G. E., Green, M. E., Simpfendorfer, C. A., Hoyos-Padilla, E. M., Duffy, C. J., ... van

Herwerden, L. (2018). Strong trans-Pacific break and local conservation units in the Galapagos shark

(Carcharhinus galapagensis) revealed by genome-wide cytonuclear markers. Heredity, 120: 407-421.

Pazmiño, D. A., Maes, G. E., Simpfendorfer, C. A., Salinas-de-León, P., & van Herwerden, L. (2017).

Genome-wide SNPs reveal low effective population size within confined management units of the highly

vagile Galapagos shark (Carcharhinus galapagensis). Conservation Genetics, 18: 1151-1163.

110

Pendleton, L. H., Ahmadia, G. N., Browman, H. I., Thurstan, R. H., Kaplan, D. M., & Bartolino, V. (2017).

Debating the effectiveness of marine protected areas. ICES Journal of Marine Science, 75: 1156-1159.

Petit, I. J., Campoy, A. N., Hevia, M. J., Gaymer, C. F., & Squeo, F. A. (2018). Protected areas in Chile: are

we managing them?. Revista Chilena de Historia Natural, 91: 1.

Petrossian, G. A. (2015). Preventing illegal, unreported and unregulated (IUU) fishing: A situational

approach. Biological Conservation, 189: 39-48.

Pinnegar, J. K., & Engelhard, G. H. (2008). The ‘shifting baseline’phenomenon: a global perspective.

Reviews in Fish Biology and Fisheries, 18: 1-16.

Pita, P., Fernández-Márquez, D., & Freire, J. (2014). Short-term performance of three underwater sampling

techniques for assessing differences in the absolute abundances and in the inventories of the coastal fish

communities of the Northeast Atlantic Ocean. Marine and Freshwater Research, 65: 105-113.

Pitcher, T. J., Morato, T., Hart, P. J. B., Clark, M., Haggan, N., & Santos, R. S. (2008). Seamounts: ecology,

fisheries and conservation. Oxford, UK: Blackwell Publishing Ltd.

Pizarro, G., Montecino, V., Astoreca, R., Alarcón, G., Yuras, G., & Guzmán, L. (2006). Variabilidad espacial

de condiciones bio-ópticas de la columna de agua entre las costas de Chile insular y continental, primavera

1999 y 2000. Ciencia y Tecnologia del Mar, 29: 45-58.

Planes S. & Fauvelot C (2002) Isolation by distance and vicariance drive genetic structure of a coral reef

fish in the Pacific Ocean. Evolution: An International Journal of Organic Evolution, 56: 378–399.

Poisson, F., Crespo, F. A., Ellis, J. R., Chavance, P., Pascal, B., Santos, M. N., ... Murua, H. (2016).

Technical mitigation measures for sharks and rays in fisheries for tuna and tuna-like species: turning

possibility into reality. Aquatic Living Resources, 29: 402.

Polovina, J. J., Howell, E., Kobayashi, D. R., & Seki, M. P. (2001). The transition zone chlorophyll front, a

dynamic global feature defining migration and forage habitat for marine resources. Progress in

Oceanography, 49: 469-483.

Post, D.M. (2002) Using stable isotopes to estimate trophic position: models, methods and assumptions.

Ecology, 83:703–718.

Priede, I. G., Bagley, P. M., Smith, A., Creasey, S., & Merrett, N. R. (1994). Scavenging deep demersal

fishes of the Porcupine Seabight, Northeast Atlantic — observations by baited camera, trap and trawl.

Journal of the Marine Biological Association of the UK, 74: 481–498.

Queiroz, N., Humphries, N. E., Mucientes, G., Hammerschlag, N., Lima, F. P., Scales, K. L., … Sims, D.

W. (2016). Ocean-wide tracking of pelagic sharks reveals extent of overlap with longline fishing hotspots.

Proceedings of the National Academy of Sciences, 113: 1582–1587. doi:10.1073/pnas.1510090113.

111

Quezada-Romegialli, C., Jackson, A.L., Hayden, B., Kahilainen, K.K., Lopes, C., & Harrod, C. (2018).

TROPHICPOSITION, an R package for the Bayesian estimation of trophic position from consumer stable

isotope ratios. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.13009.

Randall, J. E., & Cea, A. (2011). Shore fishes of Easter Island. University of Hawai'i Press. 176 pp.

Ray, J.S., Mahoney, J.J., Duncan, R.A., Ray, J., Wessel, P. & Naar, D.F., (2012). Chronology and

geochemistry of lavas from the Nazca Ridge and Easter Seamount Chain: an∼ 30 Myr hotspot record.

Journal of Petrology, 53: 1417-1448.

Ren, R., Ray, R., Li, P., Xu, J., Zhang, M., Liu, G., et al. (2015). Construction of a high-density DArTseq

SNP-based genetic map and identification of genomic regions with segregation distortion in a genetic

population derived from a cross between feral and cultivated-type watermelon. Molecular Genetics and

Genomics 290: 1457–1470. doi: 10.1007/s00438-015-0997-7.

Rezac, J. (2018). Transshipment Reform Needed to Ensure Legal, Verifiable Transfer of Catch. The Pew

Charitable Trusts. 12 pp.

Robbins, W. D., Hisano, M., Connolly, S. R., & Choat, J. H. (2006). Ongoing collapse of coral-reef shark

populations. Current Biology, 16: 2314-2319.

Roberts, C. M. (2000). Selecting marine reserve locations: optimality versus opportunism. Bulletin of Marine

Science, 66: 581-592.

Roberts, C. M., Bohnsack, J. A., Gell, F., Hawkins, J. P., & Goodridge, R. (2001). Effects of marine reserves

on adjacent fisheries. Science, 294: 1920–1923.

Robinson, N. J., Morreale, S. J., Nel, R., & Paladino, F. V. (2016). Coastal leatherback turtles reveal

conservation hotspot. Scientific reports, 6: 1-9. doi:10.1038/srep37851.

Roff, G., Doropoulos, C., Rogers, A., Bozec, Y. M., Krueck, N. C., Aurellado, E., ... & Mumby, P. J. (2016).

The ecological role of sharks on coral reefs. Trends in Ecology & Evolution, 31: 395-407.

Rogers, A. D. (1994). The biology of seamounts. In Advances in marine biology (Vol. 30, pp. 305-350).

Academic Press.

Rozen, S., Skalersky, H.J., (2000). Primer3 on the WWW for general users and for biologist programmers.

In:Krawetz, S., Misener, S.(Eds.), Bioinformatics Methods and Protocols: Methods in Molecular Biology.

Humana Press, Totowa, pp. 365–386.

Ruttenberg, B. I., Hamilton, S. L., Walsh, S. M., Donovan, M. K., Friedlander, A., DeMartini, E., Sala, E., &

Sandin, S. A. (2011). Predator-induced demographic shifts in coral reef fish assemblages. PLOS ONE, 6:

e21062. doi:10.1371/journal.pone.0021062.

Sala, E., Mayorga, J., Costello, C., Kroodsma, D., Palomares, M. L., Pauly, … Zeller, D. (2018). The

economics of fishing the high seas. Science Advances, 4: eaat2504.

112

Salinas-de-León P, Acuña-Marrero D, Rastoin E et al (2016) Largest global shark biomass found in the

northern Galápagos Islands of Darwin and Wolf. PeerJ, 4: e1911. doi:10.7717/peerj.1911.

Samoilys, M. A., & Carlos, G. (2000). Determining methods of underwater visual census for estimating the

abundance of coral reef fishes. Environmental Biology of Fishes, 57: 289–304.

Sandin, S. A., Smith, J. E., DeMartini, E. E., Dinsdale, E. A., Donner, S. D., Friedlander, A. M., ... Pantos,

O. (2008). Baselines and degradation of coral reefs in the Northern Line Islands. PLOS ONE, 3: e1548.

Sandin, S. A., Walsh, S. M., & Jackson, J. B. (2010). Prey release, trophic cascades, and phase shifts in

tropical nearshore ecosystems. Trophic Cascades: Predators, Prey, and the Changing Dynamics of Nature,

71-90 pp.

Santana‐Garcon, J., Braccini, M., Langlois, T. J., Newman, S. J., McAuley, R. B., & Harvey, E. S. (2014a).

Calibration of pelagic stereo‐BRUVs and scientific longline surveys for sampling sharks. Methods in

Ecology and Evolution, 5: 824-833.

Santana-Garcon, J., Newman, S. J., Langlois, T. J., & Harvey, E. S. (2014b). Effects of a spatial closure on

highly mobile fish species: an assessment using pelagic stereo-BRUVS. Journal of Experimental Marine

Biology and Ecology, 460: 153-161.

Sardenne, F., Bodin, N., Chassot, E., Amiel, A., Fouché, E., Degroote, M., ... Ménard, F. (2016). Trophic

niches of sympatric tropical tuna in the Western Indian Ocean inferred by stable isotopes and neutral fatty

acids. Progress in oceanography, 146: 75-88.

Savolainen, O., Lascoux, M., & Merilä, J. (2013). Ecological genomics of local adaptation. Nature Reviews

Genetics, 14: 807–20.

Schlosser, I. J. (1990). Environmental variation, life history attributes, and community structure in stream

fishes: implications for environmental management and assessment. Environmental Management, 14: 621-

628.

Schneider, S., Roessli, D., and Excoffier, L. (2000). Arlequin Ver. 2.0: A Software for Population Genetics

Data Analysis. Switzerland: Genetics and Biometry Laboratory.

Shears, N.T. & Babcock, R.C. (2002). Marine reserves demonstrate top-down control of community

structure on temperate reefs. Oecologia, 132: 131-142.

Shillinger, G. L., Swithenbank, A. M., Bailey, H., Bograd, S. J., Castelton, M. R., Wallace, B. P., … Block,

B. A. (2011). Vertical and horizontal habitat preferences of post-nesting leatherback turtles in the South

Pacific Ocean. Marine Ecology Progress Series, 422: 275-289.

Simpfendorfer, C. A., & Milward, N. E. (1993). Utilization of a tropical bay as a nursery area by sharks of

the families Carcharhinidae and Sphyrnidae. Environmental Biology of Fishes, 37: 337–45.

113

Simpfendorfer, C. A., Wiley, T. R., & Yeiser, B. G. (2010). Improving conservation planning for an

endangered sawfish using data from acoustic telemetry. Biological Conservation, 143: 1460-1469.

Skomal, G. (2007). Evaluating the physiological and physical consequences of capture on post release

survivorship in large pelagic fishes. Fishing and Managing Ecology, 14: 81–89

Smith-Vaniz, W.F. & Williams, I. (2015). Seriola lalandi. The IUCN Red List of Threatened Species 2015:

e.T195097A43155921. http://dx.doi.org/10.2305/IUCN.UK.2015-4.RLTS.T195097A43155921.en.

Downloaded on 07 July 2019.

Speed, C. W., Meekan, M. G., Field, I. C., McMahon, C. R., Abrantes, K., & Bradshaw, C. J. A. (2012).

Trophic ecology of reef sharks determined using stable isotopes and telemetry. Coral Reefs, 31: 357-367.

Springer, S. (1967). Social organisation of shark populations. pp. 149-174. In: Gilbert, RF. Mathewson &

D.P Rall (ed.) Sharks, Skates and Rays, John Hopkins University Press, Baltimore.

Stanley, D. R., & Wilson, C. A. (1995). Effect of SCUBA divers on fish density and target strength estimates

from stationary dual-beam hydroacoustics. Transactions of the American Fisheries Society, 124: 946-949.

Stergiou, K. I., & Karpouzi, V. S. (2002). Feeding habits and trophic levels of Mediterranean fish. Reviews

in Fish Biology and Fisheries, 11: 217-254.

Stevens, J. D., Bonfil, R., Dulvy, N. K., & Walker, P. A. (2000). The effects of fishing on sharks, rays, and

chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES Journal of Marine Science:

Journal du Conseil, 57: 476-494.

Stobart, B., García-Charton, J. A., Espejo, C., Rochel, E., Goñi, R., Reñones, O., ... Planes, S. (2007). A

baited underwater video technique to assess shallow-water Mediterranean fish assemblages:

Methodological evaluation. Journal of Experimental Marine Biology and Ecology, 345: 158-174.

SUBPESCA (Subsecretaria de Pesca). (2010). Fundamentos para establecer Parque Marino Salas y

Gómez. Informe tecnico (R.PESQ.) N° 81/2010. 21 pp.

Sweeting CJ, Barry JT, Barnes C, Polunin NVC, Jennings S (2007) Effects of body size and environment

on diet-tissue δ15N fractionation in fishes. Journal of Experimental Marine Biology and Ecology, 340:1–10.

Swimmer, Y., Arauz, R., McCracken, M., McNaughton, L., Ballestero, J., Musyl, M., … Brill, R. (2006).

Diving behaviour and delayed mortality of olive ridley sea turtles Lepidochelys olivacea after their release

from longline fishing gear. Marine Ecology Progress Series, 323: 253–261. doi:10.3354/meps323253

Teffer, A. K., Staudinger, M. D., & Juanes, F. (2015). Trophic niche overlap among dolphinfish and co-

occurring tunas near the northern edge of their range in the western North Atlantic. Marine Biology, 162:

1823–1840. doi:10.1007/s00227-015-2715-8

114

Teo, S. L. H., Boustany, A., Blackwell, S., Walli, A., Weng, K. C., & Block, B. A. (2004). Validation of

geolocation estimates based on light level and sea surface temperature from electronic tags. Marine

Ecology Progress Series, 283: 81−98

Thiel, M., Luna-Jorquera, G., Álvarez-Varas, R., Gallardo, C., Hinojosa, I. A., Luna, N., … Zavalaga, C.

(2018). Impacts of Marine Plastic Pollution from Continental Coasts to Subtropical Gyres—Fish, Seabirds,

and Other Vertebrates in the SE Pacific. Frontiers in Marine Science, 5: 238. doi:

10.3389/fmars.2018.00238.

Thompson, A. A., & Mapstone, B. D. (1997). Observer effects and training in underwater visual surveys of

reef fishes. Marine Ecology Progress Series, 154: 53-63.

Thorson, J.T., & Simpfendorfer, C.A. (2009). Gear selectivity and sample size effects on growth curve

selection in shark age and growth studies. Fisheries Research, 98: 75-84.

Tickler, D.M., Letessier, T.B., Koldewey, H.J., & Meeuwig, J.J. (2017). Drivers of abundance and spatial

distribution of reef-associated sharks in an isolated atoll reef system. PLOS ONE, 12: e0177374.

Tognelli, M. F., Fernández, M., & Marquet, P. A. (2009). Assessing the performance of the existing and

proposed network of marine protected areas to conserve marine biodiversity in Chile. Biological

Conservation, 142: 3147-3153.

Tolotti, M., Bauer, R., Forget, F., Bach, P., Dagorn, L., & Travassos, P. (2017). Fine-scale vertical

movements of oceanic whitetip sharks (Carcharhinus longimanus). Fishery Bulletin, 115: 380-402.

Toonen, R. J., Andrews, K. R., Baums, I. B., Bird, C. E., Concepcion, G. T., Daly-Engel, T. S., ... Puritz, J.

B. (2011). Defining boundaries for ecosystem-based management: a multispecies case study of marine

connectivity across the Hawaiian Archipelago. Journal of Marine Biology, 2011: 460173.

Vander Zanden, M. J., Cabana, G. & Rasmussen, J. B. (1997) Comparing trophic position of freshwater

fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Canadian Journal of

Fisheries and Aquatic Sciences, 54: 1142– 1158.

Vaudo, J. J., & Heithaus, M. R. (2011). Dietary niche overlap in a nearshore elasmobranch mesopredator

community. Marine Ecology Progress Series, 425: 247-260.

Vaudo, J. J., Byrne, M. E., Wetherbee, B. M., Harvey, G. M., & Shivji, M. S. (2017). Long‐term satellite

tracking reveals region‐specific movements of a large pelagic predator, the shortfin mako shark, in the

western North Atlantic Ocean. Journal of Applied Ecology, 54: 1765-1775.

Vega, R., Licandeo, R., Rosson, G., & Yáñez, E. (2009). Species catch composition, length structure and

reproductive indices of swordfish (Xiphias gladius) at Easter Island zone. Latin American Journal of Aquatic

Research, 37: 83-95.

115

Valencia E., Meerhoff E., Díaz-Cabrera E., Vega-Retter C., & Veliz D. Weak population connectivity in a

fish inhabiting remote Oceanic Islands”. Aquatic conservation Marine and Freshwater Ecosystems. In

review.

Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S, 4th Edition. Statistics and

Computing. Springer, New York, NY.

Ward, R. D. (2000). Genetics in fisheries management. Hydrobiologia, 420: 191-201.

Watson, D. L., Harvey, E. S., Anderson, M. J., & Kendrick, G. A. (2005). A comparison of temperate reef

fish assemblages recorded by three underwater stereo-video techniques. Marine Biology, 148: 415–425.

Westera, M., Lavery, P., & Hyndes, G. (2003). Differences in recreationally targeted fishes between

protected and fished areas of a coral reef marine park. Journal of Experimental Marine Biology and Ecology,

294: 145-168.

Wetherbee, B. M., Crow, G. L., & Lowe, C. G. (1996). Biology of the Galapagos shark, Carcharhinus

galapagensis, in Hawai'i. Environmental Biology of Fishes, 45: 299-310.

White, T. D., Carlisle, A. B., Kroodsma, D. A., Block, B. A., Casagrandi, R., De Leo, G. A., ... McCauley, D.

J. (2017). Assessing the effectiveness of a large marine protected area for reef shark conservation.

Biological Conservation, 207: 64-71.

Wildlife Computers (2015) Data Portal’s Location Processing (GPE3 & FastLoc-GPS) User Guide, 25 pp.

Williams, A., Nicol, S., & Leroy, B. (2010). South Pacific albacore tagging project: 2010 summary report.

Report for the Western and Central Fisheries Commission, Scientific Committee Sixth Regular Session,

Nuku’alofa, Tonga.

Willis, T. J., & Babcock, R. C. (2000). A baited underwater video system for the determination of relative

density of carnivorous reef fish. Marine and Freshwater Research, 51: 755-763.

Willis, T. J., Millar, R. B., & Babcock, R. C. (2003). Protection of exploited fish in temperate regions: high

density and biomass of snapper Pagrus auratus (Sparidae) in northern New Zealand marine reserves.

Journal of Applied Ecology, 40: 214-227.

Wilson Jr, R. R., & Kaufmann, R. S. (1987). Seamount biota and biogeography. Seamounts, islands, and

atolls, 43, 355-377.

Wilson, S. G., Polovina, J. J., Stewart, B. S., & Meekan, M. G. (2006). Movements of whale sharks

(Rhincodon typus) tagged at Ningaloo Reef, Western Australia. Marine Biology, 148: 1157-1166.

Wolff, M., Ruiz, D.J., & Taylor, M. (2012). El Niño induced changes to the bolivar channel ecosystem

(Galapagos): comparing model simulations with historical biomass time series. Marine Ecology Progress

Series, 448: 7–22. doi:10.3354/meps09542

116

Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., ... & Sala, E. (2006). Impacts

of biodiversity loss on ocean ecosystem services. Science, 314: 787-790.

Worm, B., Lotze, H. K., & Myers, R. A. (2003). Predator diversity hotspots in the blue ocean. Proceedings

of the National Academy of Sciences, 100: 9884-9888.

Wright, S. (1965). The interpretation of population structure by F-statistics with special regard to systems

of mating. Evolution: 395-420.

Yáñez, E., Silva, C., Trujillo, H., González, E., Álvarez, L., Manutomatoma, L., & Romero, P. (2007).

Diagnóstico del sector pesquero de la Isla de Pascua. Pontificia Universidad Católica de Valparaíso,

Valparaíso, 154 pp.

Young, J. W., Lansdell, M. J., Campbell, R. A., Cooper, S. P., Juanes, F., & Guest, M. A. (2010). Feeding

ecology and niche segregation in oceanic top predators off eastern Australia. Marine Biology, 157: 2347-

2368.

Zhu, J., Xu, L., Dai, X., Chen, X., & Chen, Y. (2012). Comparative analysis of depth distribution for

seventeen large pelagic fish species captured in a longline fishery in the central-eastern Pacific Ocean.

Scientia Marina, 76: 149-157.

Zylich, K., Harper, S., Licandeo, R., Vega, R., Zeller, D., & Pauly, D. (2017). Fishing in Easter Island, a

recent history (1950-2010). Latin American Journal of Aquatic Research, 42: 845-856.

117

APPENDIXES

Figure S4.1. Length frequency distribution per species of all fish caught during the tagging cruises CIMAR 21 in 2015 and the SOSF 380 campaign of 2017.

118

Figure S4.2. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154061 (amberjack) in relation to bathymetry (Deployment duration: 148 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.

119

Figure S4.3. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 154062 (Galapagos shark) in relation to bathymetry (Deployment duration: 35 days). Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.

120


121


122

Figure S4.6. Daily geolocations and maximum diving depths during daytime (left) and night periods (right) of tag 173480 (Galapagos shark) in relation to bathymetry (Deployment duration: 95 days. Empty symbols indicate geolocations for which depth time series data is missing due to the duty cycle configuration. Violet lines indicate the border of the MMHMP area.

123

Figure S4.7. 50% likelihood areas of each tag. Different colour represents different days.

124


125


126

Figure S4.10. Time at different depths during night and daytime periods per individual. Female (F) and male (M).

127

Figure S4.11. Distance of daily geolocations to Salas y Gómez islet per vertical behaviour cluster of all archivally-tagged Galapagos sharks.

128

Figure S5.1. Number of cluster (k) suggested by BIC values.

129

Table S2.1. Mean wave energy values (kW/m) and percentage of occurrence from every (360° degree) direction. Autumn (March- June); winter (June-September); spring (September-December); summer (December -March).

Direction

(degree)

Mean Power

Percentage occurrence

Mean Power


Mean Power


Mean Power


Long-term Wave Energy (2005-2015) Autumn Winter Spring Summer 0 45.996 0.1 49.817 0.08 29.23 0.08 15.169 0.03 22.5 0 0.01 47.896 0.18 51.586 0.16 0 0 45 0 0 30.633 0.87 32.205 0.87 20.171 0.15 67.5 22.668 0.3 38.359 2.08 29.567 2.34 18.31 1.4 90 41.308 1.34 64.37 1.8 29.915 1.68 19.312 0.61 112.5 41.924 0.78 59.407 2.02 51.097 0.6 26.563 0.55 135 80.406 1 48.923 2.2 38.107 0.34 26.459 0.44 157.5 68.59 1.15 60.981 5.15 39.376 1.24 60.248 0.19 180 68.195 18.65 68.696 14.95 53.093 9.01 37.128 4.34 202.5 61.698 53.63 77.077 44.38 54.84 52.7 36.6 41.38 225 59.686 15.53 70.942 23.24 47.086 22.07 32.513 20.93 247.5 38.733 3.16 56.676 1.55 31.134 2.9 30.431 6.21 270 36.067 1.55 44.103 0.69 32.888 1.21 32.583 5 292.5 43.165 2.07 52.508 0.54 26.892 2.34 34.588 12.62 315 42.979 0.73 54.927 0.28 35.519 2.46 38.798 6.14 337.5 0 0 0 0 0 0 0 0 Recent Wave Energy (2016-2017)

Autumn Winter Spring Summer 0 0 0 0 0.42 0 0 0 0 22.5 0 0 15.471 2.51 0 0 0 0 45 0 0 14.976 1.26 0 0 0 0 67.5 18.433 15.83 0 0 0 0 0 0 90 0 0 0 0 0 0 0 0 112.5 0 0.42 0 0 0 0 0 0 135 67.318 9.17 14.243 3.35 0 0 0 0 157.5 70.302 7.92 0 0.42 0 0 0 0 180 58.7 12.92 28.605 10.04 15.983 6.05 26.789 7.66 202.5 40.651 45.42 48.94 76.99 28.868 51.21 32.747 72.18 225 32.686 8.33 50.62 3.77 29.566 16.13 31.654 16.53 247.5 0 0 0 0 24.626 7.66 26.55 1.21 270 0 0 0 0 24.761 6.45 22.706 0.81 292.5 0 0 20.776 0.84 19.917 7.66 25.284 1.61 315 0 0 0 0.42 31.161 4.84 0 0 337.5 0 0 0 0 0 0 0 0

130

Table S2.2. Environmental variables used in the DistLM analysis for every site and season. Sample sites are showed in Figure 2.1.

Season/ Site Temperature (°C)

Historical wave energy

(kW/m)

Specific wave energy (kW/m)

Distance from shore (m)

Shelf width (m)

Winter Ana hukahu - - - - - Ovahe 20.669 30.633 14.976 392.875 250 Omohi - - - - - Kari Kari 20.69 56.676 0 324.25 250 Motu tautara 19.285 44.103 20.776 202.5 0 Poike 22.668 18.433 395.5 250 Vaihu 20 60.981 28.605 463.75 1000 Vinapu 20 77.077 48.94 311.75 750 Spring Ana hukahu - - - - - Ovahe 23.746 32.205 0 392.875 250 Omohi - - - - - Kari Kari 23.463 31.134 24.626 324.25 250 Motu tautara 23.149 32.888 19.917 202.5 0 Poike - - - - - Vaihu 22 39.376 15.963 463.75 1000 Vinapu 22 54.84 28.868 311.75 750 Summer Ana hukahu 26 26.563 0 386.5 1000 Ovahe 26.758 20.171 0 392.875 250 Omohi 26.247 38.798 0 255.25 0 Kari Kari 26.59 30.431 26.55 324.25 250 Motu tautara 26.38 32.583 25.284 202.5 0 Poike 26.43 18.31 0 395.5 250 Vaihu 26 60.248 26.789 463.75 1000 Vinapu 26 36.6 32.747 311.75 750 Autumn Ana hukahu 22.683 0 0 392.875 250 Ovahe 22.708 42.979 0 255.25 0 Omohi 22.84 38.733 0 324.25 250 Kari Kari 22.773 36.067 0 202.5 0 Motu tautara 22 22.668 18.433 395.5 250 Poike 22 68.59 58.7 463.75 1000 Vaihu 22 61.698 40.651 311.75 750 Vinapu 22 41.924 67.318 386.5 1000

131

Table S2.3. PERMANOVA test for all the pelagic fish species. Figures in bold indicate significant results.

Level Type Pseudo-F P(perm) Unique perms

MAIN TEST Site 5 Fixed 4.9648 0.0001 9943 Season 4 Fixed 8.274 0.0001 9924 Season x Site 1.3362 0.0881 9887 PAIR-WISE TEST Sites Ovahe. Kari Kari 0.1441 9964 Ovahe. Motu Tautara 0.0978 9977 Ovahe. Vaihu 0.0001 9951 Ovahe. Vinapu 0.0158 9956 Kari Kari. Motu Tautara 0.2019 9947 Kari Kari. Vaihu 0.0001 9948 Kari Kari. Vinapu 0.0047 9956 Motu Tautara. Vaihu 0.0001 9956 Motu Tautara. Vinapu 0.0005 9954 Vaihu. Vinapu 0.001 9943 Season Autumn. Spring 0.4036 9960 Autumn. Summer 0.1654 9954 Autumn. Winter 0.0001 9956 Spring. Summer 0.1402 9952 Spring. Winter 0.0001 9945 Summer. Winter 0.0001 9965

Table S2.4. DistLM test for all the pelagic fish species. Figures in bold indicate significant results.

Variable SS(trace) Pseudo-F P Prop.

Site Temperature (ºC) 913.69 1.9302 0.085 0.12117 Historical WE (kW/m) 1008.9 2.1624 0.052 0.13379 Specific WE (kW/m) 1162.3 2.5512 0.032 0.15414 Distance from shore (m) 1093.5 2.3746 0.043 0.14502 Shelf width (m) 2004.5 5.0691 0.001 0.26583 Season Temperature (ºC) 639.58 1.1143 0.3476 0.058295 Historical WE (kW/m) 1887 3.7986 0.0308 0.17199 Specific WE (kW/m) 462.36 0.92675 0.437 0.042142

132

Table S3.1. PERMANOVA test for all the large fish species. Numbers in bold indicate significant results. AC, Aulostomus chinensis; AS, Acanthocybium solandri; CG, Carcharhinus galapagensis; CH, Coryphaena hippurus; KP, Katsuwonus pelamis; PD, Pseudocaranx dentex; SL, Seriola lalandi; TeA, Kajikia audax; KA, Thunnus albacares; TyA, Thyrsites atun.

AC AS CG CH KP PD SL TeA KA

AS 0.124

CG 0.238 0.08

CH 0.197 0.08 0.039

KP 0.175 0.297 0.039 0.08

PC 0.383 0.011 0.083 0.011 0.011

SL 0.857 0.08 0.174 0.049 0.23 0.615

TeA 0.242 0.565 0.449 0.08 0.369 0.126 0.329

ThA 0.08 0.238 0.045 0.08 0.982 0.011 0.127 0.304

TyA 0.236 0.795 0.037 0.08 0.886 0.036 0.174 0.314 0.886

133

Table S4.1. Complete data of the entire catch during both expeditions 2015 and 2017. Time of deployment; No data (ND); Conv. (Conventional) Tag ID (identification number); MiniPAT (satellite tag) ID (identification number).

Species Date Time Expedition Gear type

Conv. Tag ID

Total length (cm)

Sex MiniPAT

ID

Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 01V 75 ND

Carcharhinus

galapagensis 2015-11-02 16:50 CIMAR 21 Longline 02V 76 F


Carcharhinus

galapagensis 2015-11-02 16:50 CIMAR 21 Longline 04V 160 F 154062

Seriola lalandi 2015-11-02 16:50 CIMAR 21 Longline 1 ND ND


Carcharhinus

galapagensis 2015-11-02 16:50 CIMAR 21 Longline 05VB 145 M 154066

Carcharhinus

galapagensis 2015-11-02 16:50 CIMAR 21 Longline 07V 160 M 154064

Carcharhinus



Carcharhinus


Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 65A 86 ND

Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 62A 81.5 ND

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 68A 81.5 M

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 66A 105 M

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 63A 108 F

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 64A 90.1 M

Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 61A 102 ND

Carcharhinus


Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 70A 86 F

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 2 ND ND

Carcharhinus


Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 3 ND ND

Seriola lalandi 2015-11-03 17.35 CIMAR 21 Longline 39N 124 ND 154061

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 32N 104 M

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 34N 86 M

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 4 84 M

134

Carcharhinus

galapagensis 2015-11-03 17.35 CIMAR 21 Longline 31N 120 F

Seriola lalandi 2015-11-04 9.05 CIMAR 21 Handline 37N 76.5 ND

Seriola lalandi 2015-11-04 9.20 CIMAR 21 Handline 33N 94.5 ND

Seriola lalandi 2015-11-04 9.50 CIMAR 21 Handline 36N 93 ND

Carcharhinus

galapagensis 2015-11-04 10.25 CIMAR 21 Handline 35N 91.5 F

Carcharhinus

galapagensis 2015-11-04 10.30 CIMAR 21 Handline 40N 108 F

Carcharhinus

galapagensis 2015-11-04 10.40 CIMAR 21 Handline 30B 110 M

Seriola lalandi 2015-11-04 17.40 CIMAR 21 Handline 23B 78 ND

Seriola lalandi 2015-11-04 17.46 CIMAR 21 Handline 21B 71 ND

Seriola lalandi 2015-11-04 17.51 CIMAR 21 Handline 25B 76.5 ND

Carcharhinus


Carcharhinus


Seriola lalandi 2015-11-04 18.05 CIMAR 21 Handline 22B 75.5 ND

Carcharhinus

galapagensis 2015-11-04 18.10 CIMAR 21 Handline 24B 100 F

Carcharhinus

galapagensis 2015-11-04 18.14 CIMAR 21 Handline 27B 96.5 F

Carcharhinus

galapagensis 2015-11-04 18.20 CIMAR 21 Handline 28B 93.5 M

Carcharhinus

galapagensis 2015-11-04 18.25 CIMAR 21 Handline 46AZ 101 F

Seriola lalandi 2015-11-04 18.33 CIMAR 21 Handline 47AZ 82 ND

Carcharhinus


Carcharhinus

galapagensis 2015-11-04 18.45 CIMAR 21 Handline 44AZ 82.5 F


Carcharhinus


Carcharhinus

galapagensis 2015-11-04 17.00 CIMAR 21 Handline s/n 170 F 154065

Carcharhinus



Carcharhinus

galapagensis 2017-11-20 18.30 SOSF 380 Handline ND 130 F

Carcharhinus

galapagensis 2017-11-20 18.35 SOSF 380 Handline ND 110 ND

Carcharhinus

galapagensis 2017-11-20 18.40 SOSF 380 Handline 40R 100 F

Seriola lalandi 2017-11-20 18.45 SOSF 380 Handline 12 83 ND

Carcharhinus

galapagensis 2017-11-20 18.50 SOSF 380 Handline 13 128 ND

Carcharhinus

galapagensis 2017-11-21 17.43 SOSF 380 Handline 8 119 F 173480

135

Carcharhinus

galapagensis 2017-11-21 17.59 SOSF 380 Handline 11 110 M

Carcharhinus

galapagensis 2017-11-21 18.07 SOSF 380 Handline 15 128 F

Carcharhinus

galapagensis 2017-11-21 18.20 SOSF 380 Handline 3 90 F

Carcharhinus


Carcharhinus


Carcharhinus

galapagensis 2017-11-21 18.40 SOSF 380 Handline 20 103 ND

Carcharhinus


Carcharhinus


Carcharhinus


Carcharhinus

galapagensis 2017-11-21 19.20 SOSF 380 Handline ND 85 M

136

Table S4.2. Minimum, maximum, and average depth of night and daytime periods from all tags as well as its standard deviation (SD).

Tag ID daytime n (days of data)

Depth (m)

Minimum Maximum Average SD

154061 Day 59 9.5 75 19 7.8

154061 Night 58 5.5 100.5 26.4 12.8

154062 Day 14 2 114 24.7 30.8

154062 Night 13 0.5 131.5 25.6 22.4

154064 Day 11 0.5 195.5 58.1 46.1

154064 Night 11 0.5 165.5 37.5 34.7

154065 Day 18 1.5 109 40.4 30.9

154065 Night 18 1 100.5 40.3 22.8

173480 Day 38 0.5 96 26.6 16.2

173480 Night 38 0.5 63 11.4 8.3

Table S4.3. Number of data points per vertical behaviour cluster and Galapagos shark tagged.

Tag ID

Data points (n)

Cluster 1 Cluster 2

154062 2 11

154064 5 6

154065 6 12

173480 0 37

Sum 13 66

Percentage 16.5 83.5

The Ecology Of Marine Top Predators At The Easter Island ...

Documents

Transcript of The Ecology Of Marine Top Predators At The Easter Island ...