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Environmental Biology of Fishes ISSN 0378-1909Volume 92Number 4 Environ Biol Fish (2011) 92:525-538DOI 10.1007/s10641-011-9875-2

Tracking and mapping sun-synchronousmigrations and diel space use patterns ofHaemulon sciurus and Lutjanus apodus inthe U.S. Virgin Islands

Steven Hitt, Simon J. Pittman & KerryA. Brown

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Tracking and mapping sun-synchronous migrations and dielspace use patterns of Haemulon sciurus and Lutjanus apodusin the U.S. Virgin Islands

Steven Hitt & Simon J. Pittman & Kerry A. Brown

Received: 20 September 2010 /Accepted: 23 June 2011 /Published online: 16 July 2011# Springer Science+Business Media B.V. 2011

Abstract The spatially explicit diel movement patternsof fish using coral reef ecosystems are not wellunderstood, despite the widespread recognition thatmany common species undergo distinct migrations toutilize different resources during night and day. We usedmanual acoustic telemetry coupled with global posi-tioning technology to track the detailed spatially explicitdaily movements (24 h) of multiple individuals of twocommon Caribbean fish species, Haemulon sciurus(bluestriped grunt) and Lutjanus apodus (schoolmastersnapper). Movement pathways and day and nightactivity spaces were mapped and quantified in aGeographic Information System (GIS). Directionalsun-synchronous migrations occurred close to astro-nomical sunset and sunrise. Site fidelity within day andnight activity spaces was high. Nine of twelve

individuals exhibited overlap of day and night activityspaces and three fish (L. apodus) exhibited completespatial segregation. Night activity spaces (H. sciurus:11,309±3,548 m2; L. apodus: 9,950±3,120 m2) weresignificantly larger than day activity spaces (H. sciurus:2,778±1,979 m2; L. apodus: 1,291±636 m2). Thedistance between sequential position fixes (steplengths) was significantly greater at night than day,indicative of nocturnal foraging and day restingbehavior. Integrating acoustic telemetry, GIS techni-ques and spatial statistics to study fish movementbehavior revealed both individual variability and somebroader generality in movement paths and activityspaces suggestive of complex underlying behavioralmechanisms influencing diel movements.

Keywords Acoustic telemetry . Fish .Movement .

Space use . Diel migrations . Caribbean

Introduction

Movement behavior is a key ecological process inanimal ecology that determines the way individualsutilize their environment in time and space and isinterlinked with growth, survival, predator–prey inter-actions and population distribution and abundance(Turchin 1998; Pittman and McAlpine 2003; Nathanet al. 2008). Diel migration behavior is a widespreadstrategy for marine organisms associated with coralreef ecosystems where optimal combinations of key

Environ Biol Fish (2011) 92:525–538DOI 10.1007/s10641-011-9875-2

S. Hitt : S. J. Pittman (*)Center for Marine and Environmental Studies,University of the Virgin Islands,2 John Brewers Bay,St. Thomas, Virgin Islands 00802, USAe-mail: simon.pittman@noaa.gov

S. J. PittmanNOAA/NOS/NCCOS/CCMA,Biogeography Branch, 1305 East–west Highway,Silver Spring, MD 20910, USA

K. A. BrownSchool of Geography, Geology, and the Environment,Kingston University London,Kingston-Upon-Thames KT1 1LQ, UK

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resources are distributed over a mosaic of patch types(Krumme 2009). A diel migration involves two-waymovements between spatially separated day and nightactivity spaces often scheduled to coincide withchanges in solar light intensity (i.e., sun-synchronous)(McFarland et al. 1979; Krumme 2009). This strategyis thought to allow fish to selectively utilize spatiallysegregated resources within different portions of theirhome range and may also help maximize growth whileminimizing predation risk and ultimately enhancingsurvival (Hobson 1973; Dahlgren and Eggleston 2000;Halpin 2000). From an ecosystem dynamics perspec-tive, migrating fish connect different patch types acrossthe seascape and facilitate nutrient and energy flowthrough trophic relationships, diel shifts in food webdynamics and expulsion of waste products thatinfluence ecological processes (Meyer et al. 1983;Meyer and Schultz 1985; Clark et al. 2009).

Several common fish species associated withCaribbean coral reef ecosystems have been observedto undertake twilight migrations to move to and fromday and night portions of their daily home range(Ogden and Buckman 1973; Dubin and Baker 1982;Rooker and Dennis 1991; Nagelkerken et al. 2000).The phenomenon has most often been documented forhaemulids (e.g. Ogden and Ehrlich 1977; McFarland etal. 1979; Helfman et al. 1982; Rooker and Dennis1991; Tulevech and Recksiek 1994; Nagelkerken et al.2000), lutjanids (e.g. Nagelkerken et al. 2000; Verweijet al. 2007) and parrotfish (Ogden and Buckman 1973;Dubin and Baker 1982).

Nocturnal migrations that follow consistent migra-tory pathways have been linked to effective partition-ing of foraging grounds in these species (Ogden andZieman 1977). H. sciurus and L. apodus are nocturnalcarnivores that are thought to move at night fromstructurally complex diurnal shelters (Verweij andNagelkerken 2007; Verweij et al. 2007) to feed onmacro-benthic invertebrates that occur in elevatedabundances in seagrass and macroalgal bed sediments(Rooker and Dennis 1991; Burke 1995; Nagelkerken etal. 2000; Beets et al. 2003; Cocheret de la Morinière etal. 2003a, b; Nagelkerken and van der Velde 2004a, b;Verweij et al. 2007). However, L. apodus transitionsprimarily to a piscine diet following ontogeneticshifts to coral reefs (Cocheret de la Morinière et al.2003a, b). The necessity forH. sciurus and L. apodus topartition soft-sediment foraging habitats, such as sand,seagrass and macroalgal beds may, therefore, be

augmented by the high degree of dietary overlap thatexists between these and other grunt and snapperspecies (Schoener’s Index=33–70%; Cocheret de laMorinière et al. 2003b). Moreover, nocturnal migrationbehaviors allow fish to forage away from high predationrisk ecotones that exist between adjacent habitats suchas mangroves and seagrass beds (Hammerschlag et al.2010a, b). Therefore, nocturnal migrations and thepreferential use of seagrass beds at night appear tobe functionally and ecologically advantageous for-aging strategies that simultaneously capitalize onhigher prey densities, mitigate predation risk andintra- and inter-specific competition, and maximizeforaging efficiency (Dahlgren and Eggleston 2000;Hammerschlag et al. 2010a).

Much of the knowledge of fish movement behaviorhas been generated from underwater observationalstudies using visual census techniques (i.e., belt trans-ects, roving diver counts, tag re-sightings, etc.), whichhave provided great insight into many key behavioralpatterns. These studies, however, are usually carried outin daylight or twilight hours and thus offer limitedinformation on the movements of fish over the fullspatial extent of their daily home range. Furthermore,observational studies rarely provide data suitable forreconstruction and quantitative, spatially explicit analy-ses of detailed movement pathways and space usepatterns throughout the home range. The lack of data onspace use, especially, areal estimates of activity spacesand their physical arrangement in space and timeseverely limits our capacity to conduct comparativestudies among species.

Manual acoustic telemetry coupled to a globalpositioning system (GPS) provides real-time spatialmovement data in a cost-effective way (i.e., comparedto autonomous acoustic receiver arrays) with minimalbias on the behavior of the target organism. At night,acoustic tracking allows the researcher to locate individ-ual animals that would be very difficult to visuallyobserve. Spatially referenced tracking data can then beused to reconstruct detailed movement pathways andquantify both the physical complexity of pathways(Papastamatiou et al. 2009), the spatial characteristicsof activity spaces (Lowe et al. 2003; Childs et al. 2008)and to link movement pathways to seascape structureusing remote sensing and a suite of GeographicalInformation System (GIS) tools (Hitt et al. 2011).Recent advances in marine acoustic telemetry andspatial technologies (i.e., GPS and GIS) to reliably

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record geographical positions and to analyze space usepatterns at a range of spatial and temporal scales haveadvanced the study of movement ecology for marinespecies. Movement paths are typically analyzed as aseries of consecutive positional fixes connected by finitesegments (usually straight lines) (Getz and Saltz 2008).These segments are referred to as ‘step lengths’ and canbe analyzed to quantify variables such as total distancetraveled, movement speed, turning angles, path com-plexity or tortuosity, and site fidelity measures whichprovide characterizations of organismal movementcapabilities, search patterns, and habitat preferences(Turchin 1998). Linearity, which serves as a measure ofsite fidelity (e.g. Morrissey and Gruber 1993) providesinformation about the preferential utilization of certainhabitat types (or individual patches within habitats) byan organism through time, since it is an indication ofhow an organism correlates the progression of move-ment (Bell and Kramer 1979). In other words, long,straight movement paths will have start and endpointsfarther away from one another than highly convolutedor circular paths (Fig. 1); the former indicates departure/repulsion from a habitat whereas the latter indicatesrelative site attachment. For marine management,mapping space use patterns provides information onecologically meaningful spatial scales for key speciesand this can be used to delineate essential fish habitat; toexamine the suitability of size, shape and seascapeinclusion within marine protected areas (MPAs); and theoptimal spacing within networks of MPAs (Pittman and

McAlpine 2003; Sale et al. 2005; Monaco et al. 2007).The importance of fish movement for MPA efficacy hasbeen noted by several authors. Kramer and Chapman(1999) hypothesized that species with low mobility willbenefit most from marine reserve protection, with morehighly mobile species benefiting less.

In order to identify and quantitatively characterize dielmigrations and day and night space use patterns in twocommon Caribbean fish, Haemulon sciurus and Lutja-nus apodus, we tagged and tracked twelve fish over24 h using manual acoustic telemetry. We then mappedand quantified space use patterns and movement path-ways using a suite of spatial tools within a GIS. Severaltracking studies have calculated home range areas as aminimum convex polygon (MCP) (McKibben andNelson 1986; Morrissey and Gruber 1993; Meyer etal. 2000; Ortega et al. 2009). Instead, we examinetemporal components of movement activity within thedaily home range. As such, we have adopted the term‘activity space’ to represent day and night (and twilight)portions of the 24 h daily home range. Our overall aimwas to identify and characterize diel migrations and tomap and quantify inter- and intra- species space usepatterns for multiple individuals of H. sciurus and L.apodus in two embayments on two different islandswithin the U.S. Virgin Islands. We employed aquantitative approach to test three hypotheses:

H1: Step length in the movement pathway willincrease significantly after sunset and preceding

Fig. 1 Conceptualrepresentations of themeasures used to describesite fidelity, spatialsegregation of activityspaces and migration,respectively: a) LinearityIndex, b) Overlap Index(OI) and c) centroid tocentroid distance

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sunrise indicating a highly directional sun-synchronous diel migration.

H2: Day and night activity spaces are spatiallydiscrete areas (i.e. zero overlap) and;H3(a): Site fidelity is significantly higher duringthe day than at night (i.e., high site attachment),but (H3b) step length is lower during the day-indicative of diurnal resting versus nocturnalforaging behavior.

Materials and methods

Study sites

The study was conducted within two bays in theUnited States Virgin Islands (Lameshur Bay, St.John and Brewer’s Bay, St. Thomas) between July2008 and March 2010 (Fig. 2). The bays are bothlocated on the southern coasts and each have similar

seascape features, including, large granite promon-tories and rocky headlands, extensive Syringodiumfilliforme and Thalassia testudinum seagrass beds,Montastraea annularis dominated fringing coralreefs, and colonized bedrock and boulder areas.Lameshur Bay is located within the Virgin IslandsNational Park (VINP). VINP is not a no-take areaand small-scale artisanal fishing occurs in bothbays.

Tagging and tracking

Between July 2008 and April 2010 baited fish trapswere used to acquire adult (> 24 cm total length, TL)Haemulon sciurus (n=6) and Lutjanus apodus (n=6)individuals. Baited traps were set on soft bottomhabitats (i.e. sand, seagrass, etc.) within 5 m of coralreef and were allowed to soak for 48 h (Fig. 2). Bothspecies were obtained during daylight hours (10:00–17:00), often at the same trap site or even within thesame trap and were transported in aerated containers

Fig. 2 Brewer’s Bay (left) and Lameshur Bay (right) contin-uous fish tracking locations in the U.S. Virgin Islands and theirdominant benthic habitat types. Stars indicate areas where fish

were trapped. On St. John, Lameshur Bay is located within theVirgin Islands National Park (VINP) and adjacent to the VirginIslands Coral Reef National Monument (VICRNM)

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between capture sites and the field station. Each fish wasmeasured (total length, TL) and a V9-2 L acoustictransmitter with a one second ping interval (dimensions9×29 mm, carrier frequencies 75–84 kHz; Vemco,Halifax, Nova Scotia, Canada) was surgically implantedinto their abdominal cavity ~1 cm anterior to the anus.To aid recovery, post-operative fish were kept forapproximately 1 h under observation in a flow-throughseawater holding tank. Fish were then released at thecapture site by a snorkeler to ward off predators duringthe descent to the substratum and observed for a fewminutes in-situ. Tracking of each fish commenced atleast 24 h after tag implantation to allow the fish torecover sufficiently and resume normal home rangemovement behavior.

Diel movements were quantified using establishedcontinuous tracking techniques (Lowe et al. 2003;Topping et al. 2005). A minimum of two researcherswere required; one manually operated a directionalhydrophone (Vemco model VH110) and acousticreceiver (Vemco model VR100) at the bow of thevessel, while the other operated the vessel (5.2 mmotorized catamaran) and recorded waypoints at 15 minintervals using a handheld global positioning system(GPS). GPS waypoints were only taken when thetransmitter signal strength was greater than 85% directlybelow the vessel. During tracking events a single fishwas tracked for a total of 24 h by operator pairs workingin three 8 h shifts to gather movement pathwaylocations totaling approximately 288 h of boat time forthe study. Fish were tracked in water ≤18 m depth withno observable behavioral effects due to boat presence.

Mapping and quantifying movement behaviorand activity spaces

Sun-synchronous migrations

United States Naval Observatory sunrise/sunset timecharts1 for Charlotte Amalie (St. Thomas) and CoralBay (St. John), USVI, were used to define day, night,dawn and dusk periods. Sunset was defined as the hourfollowing the calculated sunset time and sunrise wasdefined as the hour preceding the calculated sunrisetime. We defined day as the period of time betweendawn and dusk and night as the period between duskand dawn. To identify and quantitatively characterize

Haemulon sciurus and Lutjanus apodus diel migrationbehavior, we calculated the distance between succes-sive GPS fixes (i.e. step length) for the four periods ofthe day using Hawth’s Analysis Tools for ArcGISv3.27 (Beyer 2004). We expected the distance betweenconsecutive position fixes to increase if a directionalmigration took place during that period as the fishtraveled directly from the day activity space to thenight activity space.

Day and night activity spaces

The ABODE extension (Laver 2005) for ArcMap GISv. 9.3 (ESRI 2008) was used to generate 95% kernelutilization distributions (KUDs) for the 24 h GPS fixdata in order to create home range estimates. Addi-tionally, 95% KUDs were generated for diurnal andnocturnal GPS fixes separately to map estimates of dayand night activity spaces. The 95% KUD is aprobability distribution representing the area in whicha fish can be found 95% of the time during a trackingevent (Tolimieri et al. 2009). An Overlap Index (OI;modified from McKibben and Nelson 1986; Morrisseyand Gruber 1993) was calculated in order to ascertainthe degree of spatial segregation between Haemulonsciurus and Lutjanus apodus diurnal and nocturnalactivity spaces. The formula for OI was calculated as:

OI ¼ OðAdþAnÞ=ðAd þ AnÞ ð1Þ

where OðAdþAnÞis the area of overlap between the diurnaland nocturnal activity spaces and ðAd þ AnÞ is thecombined area of the diurnal and nocturnal activityspaces. The values range from 0.0 (no overlap, completesegregation) to 1.0 (100% overlap, no segregation).Thus, if day and night activity spaces are spatiallydistinct then we expected a value of 0% overlap.

Spatial segregation was also characterized by mea-suring the Euclidean distance between day and nightactivity space centroids (i.e. geometric centers) withXTools Pro 6.2.1 extension (DeLaune 2000) for ArcGIS9.3. Centroids were used because they generated singlepoints of reference for each day and night activityspace that could easily be used for comparisons.

Site fidelity and diel movement pathways

To compare differences in site fidelity for individualsbetween day and night a Linearity Index (LI;1 http://aa.usno.navy.mil/data/docs/RS_OneYear.php

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Morrissey and Gruber 1993) was calculated from themovement paths. The LI compares site fidelity withineach activity space and was calculated as:

LI ¼ Fn � F1ð Þ=D ð2Þwhere Fn is the location of the last GPS fix, F1 is thelocation of the first GPS fix and D is the total distancetraveled by the fish. The index ranges between 0.0(site attachment) and 1.0 (roaming). We expected theLI to be lower during the day than at night if day wasa resting period and night was a period of activesearching and foraging. In addition, differences in thestep length between day and night activity spaceswere calculated and compared with higher step lengthat night indicative of more extensive movementsassociated with foraging.

Statistical analysis

H. sciurus and L. apodus step lengths, recordedduring four time periods (i.e. day, sunset, night,sunrise), were compared using generalized linearmixed models (GLMM) (lme4 library (R 2.11.1software, R Development Core Team 2010). Steplength was included as the dependent variable (withPoisson error distribution), species and time period asfixed factors and species’ tag identification wasincluded in the model as a random factor. The effectof fixed factors was evaluated by model selection andlikelihood ratio test (LRT), while the random factor(species’ tag identification) was included as a variancecomponent, rather than as a factor of interest.

Interspecific comparisons of activity space centroiddistances and fish total lengths were made usingStudent’s t-tests or their analogous non-parametrictests (Mann–Whitney U-test). Intraspecific compar-isons were also made between day and nightactivity space areas and site fidelity measures (i.e.LI) using paired t-tests or Wilcoxon Signed Rankstests (non-parametric analog). The allometric rela-tionship between fish total length and day and nightactivity space size was evaluated using linearregression analysis.

H. sciurus and L. apodus step length and activityspace area were log10 transformed and LI data werearcsine square-root transformed prior to analysis. Non-parametric Mann–Whitney U or Wilcoxon signed rankanalyses were used for day and night LI comparisons ifdata failed to meet parametric assumptions of normality

and/or homogeneity of variance. All statistical analyseswere performed with JMP v. 8.0.1 (SAS institute, Inc.)and GraphPad Prism (GraphPad Software, Inc.).

Results

Sun-synchronous migrations

There was a significant random effect of species tagID on the model results (χ2=30.7, P<0.001; LRT).There was no effect of species on step length andconsequently there were no clear differences in thestep lengths between H. sciurus and L. apodus (χ2=0.009, P=0.976; LRT; Fig. 3a, b). There was asignificant effect of time period on step lengths (χ2=96.9, P<0.001; LRT), with higher mean step lengthvalues occurring at sunset and the lowest occurringduring the day (Fig. 3a, b). Thus, sun-synchronousmigrations were detected and hypothesis H1 wasaccepted.

Day and night activity spaces

Activity space areas ranged from 175 m2 to 25 267 m2

for H. sciurus and from 167 m2 to 19 460 m2 for L.apodus (Table 1). Mean night activity space areas weresignificantly larger than mean day activity spaceareas for both species (H. sciurus: paired t-test,t=−2.904, p=0.034; L. apodus: paired t-test, t=−6.177,p=0.002). In fact, eleven out of the 12 estimatednight activity spaces were at least two times larger thantheir corresponding day activity spaces (Table 1).When day and night activity space areas werecombined for each individual, there were no significantdifferences between H. sciurus and L. apodus meandaily activity space areas (Student’s t-test: t=0.0654,p=0.948).

H. sciurus and L. apodus total body lengths (TL,Table 1) were statistically similar (Student’s t-test:t=−1.215, p=0.252) and were pooled for allometricanalysis. No significant relationship was found betweenTL and the size of day, night or total daily activity spaces(R2=0.04, 0.03 and 0.06, p>0.1 respectively).

Spatial segregation of activity spaces

The hypothesis that day and night activity spaces arespatially discrete was rejected for 9 of 12 individuals.

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All of the H. sciurus individuals exhibited someoverlap between day and night activity spaces(Table 1). For three of the individual L. apodus,however, day and night activity spaces were non-overlapping and entirely spatially segregated (Table 1).The amount of overlap between day and nightactivity spaces varied greatly between the twospecies and amongst the nine individuals withoverlap (Table 1). The largest areal overlap between

diurnal and nocturnal activity spaces was 3816 m2

for H. sciurus, whereas the largest overlap for L.apodus was 627 m2 (Table 1). Yet, these values eachrepresented <20% of the combined day and nightactivity space areas for those H. sciurus and L.apodus individuals (Table 1).

Comparison of the Overlap Index (OI) datarevealed relatively low values for all individuals inthe study (Table 1). The highest OI value was alsodisplayed by an H. sciurus individual (H2; Table 1).Overall, the mean OI for H. sciurus was approxi-mately double that of L. apodus and the mean overlaparea for H. sciurus was approximately 456% greaterthan that of L. apodus. When examining the distancesbetween the centroids of day and night activity spaceswe report a wide range between the smallest andlargest centroid distance values for both H. sciurusand L. apodus (Table 1). H. sciurus and L. apodus hadstatistically similar mean centroid distances (H. sciu-rus: 182.4±54.6; L. apodus: 199.5±83.1; Student’st-test: t=−0.172, p=0.867).

Site fidelity and diel movement behavior

LI was not significantly different for day versus nightactivity spaces for either species (H. sciurus: pairedt-test: t=1.755, p=0.140; L. apodus: Wilcoxon SignedRank test: Z=−0.105, p=1.000), therefore werejected H3 (a). However, all of the mean LI valueswere ≤0.06 indicating a very high degree of revisitationto certain areas within both day and night activityspaces. The LI values observed for H. sciurus and L.apodus exhibited a broad range during diurnal andnocturnal periods (Table 2). When H. sciurus diurnaland nocturnal LI was compared to L. apodus diurnal andnocturnal LI there was neither a significant differencebetween diurnal LI (Student’s t-test: t=−0.0333,p=0.974) nor between nocturnal LI (Mann–WhitneyU-test: U=8.000, p=0.132), respectively. There wasalso no significant difference between H. sciurus and L.apodus LI when diurnal and nocturnal data were pooled(Mann–Whitney U-test: U=44.000, p=0.112).

Step length analysis of day versus night movementbehavior revealed that eleven of the twelve fishtracked exhibited movement paths with greater steplength at night than during the day indicative offoraging behavior (Fig. 4a, b). In fact, the mean totaldistances traveled (D) within night movement path-ways were more than double those recorded during

Fig. 3 Step lengths of two reef fishes a) H. sciurus and b) L.apodus recorded during 24 h continuous acoustic trackingevents. Step lengths were grouped by time blocks (i.e. day,sunset, night and sunrise) highlighting the increase in move-ments during twilight periods (i.e. sunset and sunrise). Dotsrepresent the mean step lengths recorded for the six individualsof each species with error intervals of ±1 SE. Dashed lines joindots of the same individual. There was a significant time periodeffect (Generalized Linear Mixed Model, GLMM, p=< 0.001)

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the day for both species (Table 2). Furthermore, nightstep length was significantly greater than day step

length for both H. sciurus and L. apodus (Dunn’s Test,p<0.05), therefore, we accepted hypothesis H3(b).

Table 1 Comparison of area, distance and overlap betweenHaemulon sciurus and Lutjanus apodus day and night activityspaces estimated using 95% fixed kernel utilization distribu-tions. The Overlap Index represents the ratio of the area of

overlap between day and night activity spaces over the sum ofday and night activity space areas. The centroid distance is theEuclidean distance between the day activity space centroid andthe night activity space centroid

Fish ID Bay Tag date Track date Totallength (cm)

Day activityspace (m2)

Night activityspace (m2)

Day+night (m2)

Overlaparea (m2)

Overlapindex (OI)

Centroiddistance (m)

H. sciurus

1 Lameshur 7/13/08 7/14/08 24.5 12486 8836 21322 3816 0.179 70.2

2 Lameshur 10/9/08 10/23/08 29.7 281 608 889 278 0.312 2.4

3 Lameshur 6/2/09 6/4/09 29.5 175 12619 12794 12 0.001 187.6

4 Brewer’s 8/1/09 8/26/09 30.6 284 15664 15947 284 0.018 332.7

5 Brewer’s 9/16/09 9/30/09 29.3 764 4861 5626 663 0.118 171.8

6 Brewer’s 3/25/10 4/6/10 29.0 2677 25267 27944 1226 0.044 329.6

Mean(SE)

28.8(0.9)

2778(1979)

11309(3548)

14087(4063)

1047(580)

0.112(0.048)

182.4(54.6)

L. apodus

1 Lameshur 1/8/09 1/13/09 38.3 332 2652 2983 332 0.111 9.5

2 Lameshur 4/9/09 4/22/09 29.5 627 2859 3486 627 0.180 11.0

3 Lameshur 6/2/09 6/11/09 30.1 1700 19460 21159 0 0.000 485.7

4 Brewer’s 8/1/09 9/2/09 33.1 4277 19185 23462 0 0.000 360.2

5 Brewer’s 12/9/09 12/14/09 25.1 167 9063 9231 0 0.000 277.9

6 Brewer’s 1/2/10 1/9/10 31.0 644 6482 7127 419 0.059 52.7

Mean(SE)

31.2(1.8)

1291(636)

9950(3120)

11241(3638)

230(110)

0.058(0.030)

199.5(83.1)

Day Night

Fish ID (Fn-F1) D LI (Fn-F1) D LI

H. sciurus

1 127.7 889.9 0.14 32.4 378.1 0.09

2 13.9 325.5 0.04 5.8 531.3 0.01

3 1.5 273.9 0.01 19.9 2610.4 0.01

4 4.6 338.5 0.01 17.8 1714.8 0.01

5 4.6 517.7 0.01 11.5 1298.4 0.01

6 26.8 945.8 0.03 43.7 1855.6 0.02

Mean (SE) 548.6 (121.9) 0.04 (0.02) 1398.1 (345.5) 0.02 (0.01)

L. apodus

1 6.2 259.9 0.02 18.7 1643.1 0.01

2 8.7 407.7 0.02 15.3 893.8 0.02

3 20.9 326.3 0.06 119.5 2351.6 0.05

4 20.2 933.8 0.02 89.8 2021.4 0.04

5 4.4 188.9 0.02 337.3 1517.4 0.22

6 19.5 483.6 0.04 22.4 1305.5 0.02

Mean (SE) 433.4 (108.8) 0.03 (0.01) 1622.1 (210.8) 0.06 (0.03)

Table 2 Linearity Indices(LI) calculated for sixHaemulon sciurus and sixLutjanus apodus individualsfrom GPS fixes takenduring day and night.LI = (Fn-F1)/D, Fn = lastGPS fix, F1 = first GPS fix,D = total displacement(total distance traveledin meters)

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Fig. 4 Plots of twelve individual a) Haemulon sciurus (H1-6)and b) Lutjanus apodus (L1-6) step lengths recorded during24 h continuous acoustic tracking events. Step length is theEuclidean distance between two consecutive detections. Dashed

lines indicate sunset and sunrise times. Gaps in plot are periodswhere GPS position fixes could not be obtained based on 85%signal strength requirement

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Discussion

Coupling acoustic telemetry techniques with GIStools and spatial statistics to track, map and quantifythe movement behaviors of two common Caribbeancoral reef species has provided high temporal resolu-tion, quantitative and spatially explicit information ontwo common Caribbean fish species. In addition, weprovide direct confirmation of the existence of highlydirectional sun-synchronous diel migrations. Ourstudy also revealed that although distinctive dielmigrations and diel space use patterns exist, wehighlighted important intra- and inter-species vari-ability in movement behavior highlighting thecomplexity of fish movement ecology across coralreef seascapes.

Sun-synchronous migrations

Statistical detection of increased step length duringcrepuscular periods, through directional movements toand from night activity spaces confirmed the existenceof sun-synchronous diel migrations. Detailed records ofthe precise timing of sun-synchronous migrationshave been documented for other species from thehaemulid family. French (H. flavolineatum) andwhite grunts (H. plumierii) performed consistentlyscheduled and highly ritualized pre-migration behav-iors that eventually led to migrations into adjacentseagrass beds approximately 20–30 min after sunsetand before sunrise (Ogden and Ehrlich 1977; McFarlandet al. 1979). McFarland et al. (1979) proposed thatabsolute light irradiance levels, the rate of lightintensity changes and endogenous rhythms were allpotential cues for the precision of the migrations.The ecological motivation behind the timing of theseand other types of migrations has been attributed topredator avoidance (i.e., sharks and other large-bodied piscivorous fish), or simply a reduction inpredation risk (McFarland et al. 1979; Claydon2004). In the Virgin Islands, Gladfelter (1979)observed peak predator activity between sunset andthe end of twilight (25 to 30 min after sunset). Ouracoustic tracking in the U.S. Virgin Islands detectedH. sciurus and L. apodus migrations that commencedduring the hour following sunset and the hour priorto sunrise, which is also consistent with schedulingdocumented for a range of fish species in neighbor-ing Puerto Rico (Rooker and Dennis 1991). Never-

theless, fish in our study did not strictly adhere to thesame migration schedules witnessed in other studiesof grunts. In fact, some of the migrations took placewithin the quiet period (Fig. 4a, b) and may indicategreater plasticity in the sun-synchronous schedulingthan was evident in earlier studies in the 1970s and1980s. It is not certain whether this difference ismethodological (i.e. remote tracking versus in-situhuman observations) or reflects a reduction in therisk from predation due to removal of large piscivorousfish populations from the Caribbean region (Paddack etal. 2009; Stallings 2009).

Overall, H. sciurus and L. apodus exhibited similartrends in their sun-synchronous migration behaviors.However, the timing of H. sciurus migrations appearedto deviate less from the calculated sunrise and sunsettimes than L. apodus migrations, except for the three L.apodus individuals (discussed below) that had com-pletely segregated day and night activity spaces. L.apodus often feed opportunistically throughout the daywithin their resting habitats (Rooker 1995; Verweij etal. 2007), implying that nocturnal foraging migrationsinto seagrass beds may be facultative rather thanobligate. This difference may also be indicative of amore generalist seascape use strategy for L. apodus.Further research is required to compare diel space usepatterns in areas with a very different abundance ofknown predators.

Day and night activity spaces

The use of significantly larger nocturnal activity spacesby H. sciurus and L. apodus is indicative of moreextensive searching behavior associated with foraging.Haemulids and lutjanids are known to forage noctur-nally (Stark and Davis 1966; Ogden and Zieman 1977)and typically reside in shelters diurnally (Robblee andZieman 1984; Rooker and Dennis 1991; Beets et al.2003). Maximum linear movements of H. sciurus andL. apodus from release sites or locations of re-sightingshave documented them traveling distances up to 767 mand 325 m, respectively (Beets et al. 2003; Verweij etal. 2007). Our study has demonstrated that within 24 hthese two species are capable of moving much greaterdistances traversing substantial areas of the seascape.Contrary to other fish species associated with coral reefspecies (e.g. Morrissey and Gruber 1993; Kramer andChapman 1999), we found no allometric relationshipbetween H. sciurus and L. apodus total lengths and the

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size of activity spaces. While we acknowledge thattracking a broader range of size classes may reveal adifferent result, we argue that underlying mechanisms,such as, resource partitioning (e.g. Ogden and Ehrlich1977; Ogden and Zieman 1977), prey availability, andseascape composition and configuration (e.g. Pittman etal. 2007; Hitt et al. 2011) could be stronger determi-nants of activity space area for these species. Else-where, Lowe et al. (2003) found no relationshipbetween size of kelp bass and home range area offeringthat seasonality, differential space use between fishsexes, access to habitat, and spawning activity, likelycaused uneven home range areas. Further investigationsinto allometric relationships with space use patterns areneeded in order to form appropriate and robustconclusions.

Spatial segregation of activity spaces

Spatial segregation of activity spaces appeared to beheavily influenced by the distribution of habitat typesat the tracking sites whereby the proximity ofnocturnal feeding grounds to diurnal shelter habitatsdiffers between Brewer’s and Lameshur Bays (Hitt etal. 2011). The difference in individual space usepatterns was highlighted by fewer instances ofoverlap between diurnal and nocturnal activity spaces(i.e. lower OI values) within Brewer’s Bay thanLameshur Bay. In Lameshur Bay seagrasses existin close proximity to nearly all coral reef andboulder (i.e. shelter) habitats within the bay, aspatial arrangement of habitats that likely requiresshorter foraging migrations and, thus, resulted ingreater overlap of day/night activity spaces. Theimportance of seagrass beds as foraging habitat forhaemulids and lutjanids has been well documentedelsewhere (e.g. Meyer and Schultz 1985; Burke1995; Verweij et al. 2007)

Site fidelity and diel movement pathways

Our findings suggest very high site fidelity andextensive revisitation to preferred habitats within dayand night activity spaces, the latter of which may beexplained by confined diurnal resting/sheltering behav-ior coupled with broad nocturnal foraging behavior. Thisbehavior has been observed in aggregations of French(H. flavolineatum) and white grunts (H. plumierii) thatdemonstrated consistent, long-term (> 2 years) site

fidelity toward specific coral heads in St. Croix, whileresting during the day (Meyer et al. 1983; Meyer andSchultz 1985). Ogden and Ehrlich (1977) observedgroups of French and white grunts systematicallyleaving their daytime shelters at twilight to forage innearby seagrass beds where they separated to adoptconvoluted individual foraging movements at night.

Diel movement paths and site fidelity behaviors havebeen described for other grunt species (e.g. Ogden andEhrlich 1977; Ogden and Zieman 1977; McFarland etal. 1979; Helfman et al. 1982), in which body sizeappeared to play a role in determining site fidelity andmovement pathways of juveniles. Medium sized (15–30 mm, TL) juveniles exhibited stronger site fidelity torock outcrops or Diadema spp. than other size classesand moved along borders and channels within theseagrass beds, whereas small individuals (10–15 mm,TL) exhibited transient movement patterns with noclear migration behavior (Helfman et al. 1982). Weobserved comparable patterns in this study; high sitefidelity to shelter (i.e. boulders) and migration pathsthat followed coral reef edges and channels betweenhabitat types (see also Hitt et al. 2011); although theedge use behavior was not distinguished within themore homogeneous seagrass habitats. The complexmovement behavior of H. sciurus and L. apodus mayinvolve the use of route specific landmarks and/orcognitive maps as observed for individual butterflyfishnavigating over highly structured coral reefs (Reese1989). However, finer temporal resolution tracking andhigh resolution seafloor mapping is required toelucidate further on the use of bathymetric features infish navigation.

There are few studies that have recorded datapertaining to site fidelity or between-habitat movementfor H. sciurus or L. apodus (e.g. Beets et al. 2003;Verweij and Nagelkerken 2007; Verweij et al. 2007).For H. sciurus, Beets et al. (2003) reported high siteattachment to diurnal reef habitats and high site fidelitywithin seagrass foraging areas. Burke (1995) also re-sighted H. sciurus at specific feeding locations withinseagrass beds on several occasions and at daytimeshelters each morning following a migration. Sitefidelity of tagged H. sciurus was documented byVerweij and Nagelkerken (2007) who calculated a‘core area of presence’ (CAP) (i.e., segment of belttransect with highest number of resightings) and foundthat 62% of the individuals re-sighted were foundwithin 10 m of the CAP. Similarly, Verweij et al. (2007)

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used a CAP to define site fidelity for L. apodus andfound that 80% of all resightings were within 10 m ofthe CAP. While some form of measurement wasrecorded for movements between habitats in all of theabove studies, detailed movement pathways were notprovided.

Stark and Davis (1966) emphasized caution whenmaking generalizations relative to feeding timesacross species as many exceptions and overlaps existamong trophic categories. We propose that the lack ofsignificant differences between day and night sitefidelity may be explained by more subtle differencesin individual H. sciurus and L. apodus diel movementand foraging behavior.

Limitations and future directions

Determining an appropriate temporal resolution forthe sampling protocol in any acoustic telemetry studypresents many challenges, typically because of logis-tical constraints, yet should be based on a time frameappropriate for the research questions (Otis and White1999: Cushman 2009) or based on the ecology of theorganism under investigation. Here, each fish wastracked for one 24 h period so that day, night andcrepuscular movements could be detected; however,future studies may expand this duration to includeseveral days to examine variability in daily migrationbehaviors and space use patterns. In addition to thesampling interval, location error specific to the type oftracking technology used (Bradshaw et al. 2007) andbattery life of the acoustic transmitters may reduce theresearcher’s ability to detect behavioral movementpatterns. The use of GPS technology to capturelocation fixes and continuous acoustic transmitters withrapid ping intervals, therefore, limited the spatialresolution of our data to a few meters and narrowedthe window for tag detection following implantation to~30 days. Modern applications of acoustic telemetry viapassive/autonomous acoustic arrays now allow forthree-dimensional triangulation of transmitter locationswith a high degree of spatial accuracy (< 1 m) (e.g.Rillahan et al. 2009). Coupled with the continuedminiaturization of acoustic transmitters, passive arraytechnology has begun to address many of the limi-tations and concerns experienced in traditional move-ment behavior studies, meanwhile, providing anavenue for detailed analyses of fish movement ecologyin the future.

Acknowledgements This research was supported by aNOAA cooperative agreement with the University of theVirgin Islands. We thank the staff of NOAA’s BiogeographyBranch for providing logistical support and acoustic trackingequipment and the National Park Service on St. John fortheir cooperation while operating within the Virgin IslandsNational Park. We would also like to thank the numerousUVI volunteers who helped to collect the 288 h of fishtracking data in all weather conditions. Finally, we wouldlike to extend our gratitude to the anonymous reviewers ofthis manuscript for their constructive comments.

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