The Trophic Role of the Endangered Caribbean Manatee Trichechus manatus in an Estuary with low...

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Estuaries and CoastsJournal of the Coastal andEstuarine Research Federation ISSN 1559-2723 Estuaries and CoastsDOI 10.1007/s12237-011-9420-8

The Trophic Role of the EndangeredCaribbean Manatee Trichechus manatusin an Estuary with low Abundance ofSeagrass

Delma Nataly Castelblanco-Martínez,Everardo Barba, Juan Jacobo Schmitter-Soto, Héctor Abuid Hernández-Arana &Benjamín Morales-Vela

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The Trophic Role of the Endangered Caribbean ManateeTrichechus manatus in an Estuary with low Abundanceof Seagrass

Delma Nataly Castelblanco-Martínez & Everardo Barba &

Juan Jacobo Schmitter-Soto & Héctor Abuid Hernández-Arana &

Benjamín Morales-Vela

Received: 23 February 2011 /Revised: 8 May 2011 /Accepted: 26 May 2011# Coastal and Estuarine Research Federation 2011

Abstract Chetumal Bay is a refuge for the manatee,Trichechus manatus, a large and strictly herbivorous aquaticmammal. The ecosystem is notoriously poor in subaquaticvegetation, the main components of manatees’ diet. Due tothe constant presence of manatees in the bay and theirability to consume large volumes of plant material, it isassumed that the species has a relevant trophic impact onthe system. A mass–balance trophic model was designed todescribe the flows of energy and matter in the bay, with thegoal of assessing the role of manatees in the system. Thesystem was aggregated in eight effective trophic levels. Thebiomass was intensely concentrated in the detritus, suggestingthat the matter on the bottom sediment is the main regulator ofthe energy flow in the system. Primary producers comprisedof detritus, mangrove, benthic autotrophs, and phytoplankton.The apex predators were dolphins and large piscivorousfishes. Manatees occupied the trophic level 2.0. Manateeswere directly or indirectly impacted by autotrophs, mangrove,and detritus; but the competition between manatees and othergroups was insignificant. In comparison to other ecosystemswhere manatees occur, Chetumal Bay (BCh) has the lowestrelative biomass of seagrasses. Several ecological andbehavioral mechanisms to compensate the lack of macro-phytes biomass (or a combination of several) are suggested.

Ecopath with Ecosimmodels are useful to describe the flow ofenergy and matter in the ecosystems. However, there are stillcritical gaps in the knowledge of BCh and its manateepopulation. It is difficult to assess the uncertainty associatedwith the estimates obtained; therefore, results should beinterpreted with caution. Improving this preliminary modelwith robust local information on the Chetumal Bay ecologyand its manatee population is recommended.

Keywords Manatee . Chetumal Bay. Trophic model .

Grazing . Ecology. Ecopath with Ecosim

Introduction

Chetumal Bay (BCh) is an important refuge for severalspecies and it is recognized as a spawning and nursery areafor fishes (Schmitter-Soto et al. 2009). The system supportsa considerable population of the endangered Caribbeanmanatee Trichechus manatus manatus (Morales-Vela et al.2000). For that reason, the bay area has various protectedstatus—as the Sanctuary of the Manatee in the Mexicanside (Morales-Vela 1997) and as Corozal Bay WildlifeSanctuary in the Belizean side (Anonymous 1998).

The Caribbean manatee occurs from the southern USA(Florida Bay) along the Caribbean and Central Americancoasts to northern South America, including Marque Secain Brazil (Odell 1982), usually at low densities (Lefebvre etal. 2001). However, BCh stands out because of the constantpresence of a relatively large number of individuals,compared to similar systems elsewhere (Morales-Vela etal. 2000; Morales-Vela and Olivera-Gómez 1994). The dietof manatees in BCh is composed mainly of seagrasses likeHalodule wrightii and Thalassia testudinum, but mangroves

D. N. Castelblanco-Martínez (*) : J. J. Schmitter-Soto :H. A. Hernández-Arana :B. Morales-VelaEl Colegio de la Frontera Sur,Av. Centenario km 5.5,Chetumal, Quintana Roo, Mexicoe-mail: [email protected]

E. BarbaKm. 15.5 carretera a Reforma,Villahermosa, Tabasco, Mexico

Estuaries and CoastsDOI 10.1007/s12237-011-9420-8

Author's personal copy

could also be an important component of their diet(Castelblanco-Martínez et al. 2009a). Algal species such asBatophora spp. and Chara sp, are also common in BCh andthe channel system of Laguna Guerrero, and therefore are apotential food resource for manatees.

Sirenians graze destructively by uprooting seagrasses whenthe rhizomes are accessible or by cropping their leaves (Marshet al. 1982, 1999; Aragones et al. 2006; Marshall et al. 2003).Therefore, manatees can severely impact habitats where theyfeed, especially seagrass beds (Provancha and Hall 1991).Adults can consume up to 8–15% of their body weight daily(Best 1981), and given the size that manatees reach (370 kgon average with some up to 460 kg; Morales-Vela andPadilla-Saldívar 2009) and their constant presence in BCh, itis assumed that their trophic impact is important in thissystem. However, this has not yet been quantified. Previousstudies have shown that unlike other systems where manateesoccur, BCh has an extremely low abundance, biomass, anddiversity of seagrass (Espinoza-Ávalos et al. 2009). Thus, it isof interest to study the trophic interactions that maintain thedynamics of BCh under the grazing pressure of the manateepopulation.

The dynamics of an aquatic ecosystem involves complexinteractions of physical, biogeochemical and biological pro-cesses. Measuring the flow of matter and energy among thedifferent elements of the ecosystem, as well as the efficiency toassimilate, transfer and dissipate energy, provides informationrelevant to the understanding of structure and function of thesystem (Baird and Ulanowicz 1993). To date, there is nocomprehensive study of the trophic relationships and dynam-ics of BCh. Mass–balance models, such as Ecopath withEcosim (EwE; Christensen and Walters 2004), are useful forthis purpose. Although trophic models are multispecific andtry to provide a holistic approach to the system, they are alsouseful in identifying species that play key roles in a givensystem (Díaz-López et al. 2008).

The functional characteristics of BCh were examined bymeans of a static trophic model of the system, elaboratedusing EwE, with the objective of describing the trophicfunction of the manatee in Chetumal Bay.

Methods

Study Area

The BCh is situated in the southeastern corner of the Stateof Quintana Roo, Mexico and the northeastern part ofBelize (17º52′–18º50′ N, 87º50′–88º25′ W; Fig. 1). It is alarge estuary covering 2,600 km2 including the area inBelize (the Bay of Corozal). BCh is part of the limestoneplatform of the Yucatan Peninsula and a component of the

North American Plate. The extensive karstic coastal systemhas low productivity (Herrera-Silveira et al. 2009) andvariable marine influence. Freshwater comes in from theHondo and New Rivers and associated karstic lagoonsystems as well as from underground runoff through manysprings (Carrillo et al. 2009b). Although salinity variesbetween 2 and 35, most of the bay remains oligohaline tomesohaline (10–15) year round (Schmitter-Soto et al.2001). BCh is shallow, 1–7 m deep, 3.2 m in average, withdeeper sinkholes (to 40 m deep and 100 m in diameter;Carrillo et al. 2009a). The wet season lasts from July toOctober.

The region has three climatic seasons typified by drought(February to May), rains (June to October), and north winds(November to January). The rainy season is dominated byfrequent thunderstorms, tropical storms, and hurricanes(Carrillo et al. 2009a). Mean ambient temperature is 28°C,with a minimum of 22°C in January and February, and amaximum of 31°C in August. Habitat diversity in BChincludes lagoons, wetland channels, and sandy cays coveredbymangrove, as well as sparse seagrass, algae, andmangroves.

Elaboration of the Trophic Model

A mass–balance trophic model was designed using theprogram EwE vers. 6 (Christensen and Pauly 1992;Christensen and Walters 2004; Kavanagh et al. 2004;Walters et al. 1997). EwE allows for an exploratory analysisof a system by means of the organization of the biologicalcharacteristics of the organisms in order to create a trophicweb that can be analyzed numerically (Christensen andPauly 1992). Its construction is based on defining a studyarea and time, placing species (and detritus) in functionalgroups, and estimating the biological and energetic charac-teristics of each group. Once these aspects are defined, theelements are balanced in terms of mass or energy so that aholistic picture of the biological community is obtained(Christensen and Walters 2004).

The parameterization of an Ecopath model is based onsatisfying two equations. The first equation assumes thatthe production of a given ecological group is equal to thebiomass lost to fishing or export, predation, and naturalmortality other than predation (other mortality; Christensenet al. 2005; Pauly et al. 2000).

Production ¼ catchþ predationþ net migration

þ biomass accumulationþ other mortality

The second equation is based on the principle ofconservation of matter within a group:

Consumption ¼ productionþ respirationþ unassimilated food

Estuaries and Coasts


The master equations can be expressed as (Pauly et al.2000):

BiP

B

� �i

EEi ¼ Yi þX

jBjQ

B

� �j

DCij ð1Þ

where:

Bi and Bj BiomassesP/B Production/biomass ratioEEi Ecotrophic efficiency, defined as the

proportion of the production that is used in thesystem (i.e., it incorporates all production termsapart from the “other mortality”).

Yi Fisheries catch(Q/B)j Food consumption per unit biomass of j, andDCij The contribution of i to the diet of j.

In general, an EwE model requires input of three of thefollowing four parameters: biomass, production/biomassratio (or total mortality), consumption/biomass ratio, andecotrophic efficiency for each of the functional groups in amodel. EwE sets up a series of linear equations to solve forunknown values establishing mass–balance in the sameoperation (Christensen and Walters 2004). The equationsare equal to zero because a balanced state is assumed(Pauly et al. 2000).

Definition of Functional Groups

To build the model, 128 animal and plant species known tooccur in BCh were selected, in addition to the zooplanktonand phytoplankton groups. For every animal group, dietaryitems and their proportions were investigated to establish

Fig. 1 Location of ChetumalBay and surrounding areas



feeding habits (Espinoza-Ávalos et al. 2009; Áxis-Arroyoet al. 1998; Morales-Vela et al. 2000; Llanes-Baeza andGonzález 2002; Olivera-Gómez and Olivera-Gómez 2002;González-Escalante and Salazar-Vallejo 2003; Quan-Younget al. 2006; Ortiz-León et al. 2007; Schmitter-Soto et al.2009). From this information, 26 functional groups—including detritus—were defined.

Parameterization

The parameters biomass (B), production/biomass ratio (P/B),and consumption/biomass ratio (Q/B) were obtained fromseveral sources, depending on the availability of informationfor each group, as explained below. In the case of the fractionof unassimilated food, 0.2 was set as a default value. Itmeans that 80% of the consumption is assumed to bephysiologically useful while the non-assimilated food(consisting of feces) is directed to the detritus. While thisestimate may be appropriate for carnivorous fish, it is typicallytoo low for some herbivorous species. This is especially truefor species relying on low-energy food, notably zooplankton,where a value of 0.4 results in more appropriate respiration/biomass ratios (Christensen and Walters 2004).

Aquatic Mammals (Functional Groups A and B)

Three species of aquatic mammals have been recorded inBCh: dolphin (Tursiops truncatus; Olivera-Gómez andOlivera-Gómez 2002; Zacarías-Araujo 1992), manatee(T. m. manatus; Morales-Vela et al. 2000), and otter (Lontralongicaudis; Calmé and Sanvicente 2009; Orozco-Meyer1998). Otters were not included in the model because ofinsufficient information.

The presence of dolphins in BCh seems to be permanent(Olivera-Gómez and Olivera-Gómez 2002); however, thereare no local estimates of abundance, so the density value of0.087 ind·km−2 found in the southern Mexican Caribbean byZacarías-Araujo (1992) was used. According to a standardizedmarine mammal diet (Pauly et al. 1998), dolphins are mainlypiscivorous, although their diet can include up to 25%cephalopods. Biomass per unit area was calculated as densitymultiplied by the average individual biomass for the species(Trites and Pauly 1998). P/B (0.01 year−1) and Q/B(41.07 year−1) for dolphins obtained for the trophic modelof the continental shelf of the Gulf of Mexico was used(Browder 1993; Okey and Mahmoudi 2002).

Studies on manatee presence in BCh define the region asone of the most important areas for the species in theCaribbean region (Morales-Vela et al. 2000). In 1994, themanatee population size in the bay was estimated by aerialsurveys to be from 51 to 130 animals with a centralestimation of 90 (Morales-Vela and Olivera-Gómez 1994).Manatee density assessed in BCh was 0.24 ind·km−2

(Olivera-Gómez and Mellink 2002). The mean individualweight of 46 manatees caught in BCh and Ascensión Baywas 277.76 kg (Morales-Vela and Padilla-Saldívar 2009).The annual death and birth rates in Florida are approxi-mately equal at 10% (B. Ackerman, personal communi-cation in Okey and Mahmoudi 2002), so a P/B=0.1 year−1

was assumed. In BCh, manatee feed mainly on benthicmacrophytes and mangrove (Castelblanco-Martínez et al.2009a). Daily food consumption is 10% of the bodyweight (Best 1981), that is, Q/B=36.5 year−1. One or twomanatees per year could migrate out of BCh so it wasestimated that 98%, not 100%, of the animals contribute tolocal detritus.

Fishes (Functional Groups C-M)

Of the fish present on the checklist for BCh (Schmitter-Sotoet al. 2009), we removed species known only fromichthyoplankton, species with density below 0.001 g·m−2

and species restricted to freshwater environments associatedto, but not within, the bay. Estimates of abundance for mostspecies were taken from Pimentel-Cadena (2001). Whenabundance data for BCh or similar systems were unavailable,qualitative abundance estimates were assigned based on thecomments of García-Téllez (2002) and Schmitter-Soto et al.(2001) for BCh. This qualitative estimate of abundance wastranslated to a numerical scale (ind·m−2) in accordance withOpitz (1996).

The sharksucker, Echeneis neucratoides, is a commensalfish, whose main potential hosts are large pelagic fishes andsea mammals (Williams et al. 2003). Given the high relativeabundance of T. m. manatus in the system, the populationdensity of this fish was derived from its association tomanatee density in BCh, using a conservative estimate ofone sharksucker per manatee.

Production (P/B) and consumption (Q/B) values weretaken from the following sources: Abarca-Arenas and Valero-Pacheco 1993; Amezcua-Linares and Yáñez-Arancibia 1980;Angelini et al. 2006; Barrientos 2005; Chávez et al. 1993; doCouto 2006; García and Duarte 2002; Opitz 1996; Pimentel-Cadena 2001; Rivera-Arriaga et al. 2003; Vega-Cendejas etal. 1993; Yáñez-Arancibia and Lara-Domínguez 1983.

Larger fish, mostly carnivores, migrate out of BCh. In theabsence of quantitative data, the energy export was estimatedat 10%, based on personal observations and communicationwith local fishermen. Diets were largely taken from Claro(1994) or FishBase (Froese and Pauly 2007), and local dataon cichlids (Valtierra-Vega and Schmitter-Soto 2000).

Invertebrates (Functional Groups N-U)

The model incorporated 42 invertebrate species (González-Escalante and Salazar-Vallejo 2003; González et al. 2009;



Llanes-Baeza and González 2002; Ortiz-León et al. 2007;Hernández-Arana, unpublished data). The abundance ofCallinectes sapidus was estimated from data published byOrtiz-León et al. (2007) for BCh. For other benthicorganisms, the abundance was obtained from core sam-plings, with an area of ca. 200 cm2 to a depth of 20 cm ofsediment (Hernández-Arana et al., unpublished data).Biomass estimates for oligochaetes and polychaetes arisefrom data obtained in the bay itself (Hernández-Arana,unpublished data). For the taxa Aoridae, Alpheidae,Lucinidae, and Gastropoda, biomass estimates for the Gulfof Mexico (Hernández-Arana et al. 2005) were used and theremainder was taken from the literature (Llanes-Baeza andGonzález 2002; Ramírez-Félix et al. 2003; Rodrigues-Capitulo et al. 1995).

Invertebrate production was extrapolated from biomass(B) and abundance (A) data by means of the followingallometric equation (Hernández-Arana et al. 2005; Warwickand Clark 1993):

P ¼ B

A

� �0:73

� A ð2Þ

For all invertebrates, consumption per unit biomass Q/Bwas estimated on the basis of a gross alimentary conversionefficiency (P/Q) of 0.2 (Chávez et al. 1993).

Zooplankton (Functional Group V)

Biomass data for BCh were taken from Gasca andCastellanos (1993). The proportion of dietary items,production, and consumption came from research in theGulf of Mexico (Chávez et al. 1993). As explained above,unassimilated consumption was set at 0.4, higher than thedefault 0.2 for other groups.

Phytoplankton (Functional Group W)

Estimates for biomass, productivity, and consumption werethose determined by Chávez et al. (1993) for southern Gulfof Mexico.

Benthic Autotrophs (Functional Group X)

Information for benthic autotrophs was collected in February,May, and September 2008 in BCh. The sampling design wasnested and randomized with 600 sampling points and threereplicates for each point. At every replicate, bottom vegetationcover was estimated using PVC quadrats, with an area of0.25 m2. The species recorded included the seagrasses T.testudinum, H. wrightii, Najas sp., and Ruppia maritima, andthe macroalgae Batophora spp. and Chara sp. Density wasdetermined for each species in terms of number of

individuals or percentage cover. In addition, during the rainyseason, samples of H. wrightii, Batophora spp., Najas sp.,Chara sp., and R. maritima were weighted. Thirty-tworandomly distributed quadrats were cropped of all plantmaterial. The samples were sieved, washed, dried to constantweight in the oven, and weighed using an analytic balance.Total biomass for these species was estimated through linearregressions of biomass on density for each species. Biomass ofT. testudinum was estimated through linear regressions fromprevious samplings in BCh (Hoil-Baeza 2009). For Ruppiasp., a gross estimate of biomass was made by averaging datafrom Áxis-Arroyo et al. (1998). Production (P/B) of benthicautotrophs was fixed at 4.5 (Chávez et al. 1993).

Mangrove (Functional Group Y)

Four mangrove species occur in BCh: Rhizophora mangle,Laguncularia racemosa, Avicennia germinans, and Cono-carpus erectus. Information on mangrove litter biomass andleaf production was obtained from de Jesús-Navarrete andOliva-Rivera (2002).

Detritus (Functional Group Z)

Surface sediment was collected with cores (5 cm diameter,5 cm depth), dried in cold, and carbon and nitrogen weredetermined accordingly (Molina-Ramírez 2007; Hernández-Arana, unpublished data).

Model Balance, Uncertainty and Sensitivity Analyses

A trophic matrix was created using diet proportions forevery functional group (Table 1). The strategy employed inbalancing the model was to first examine the basic inputsand assess their credibility. A confidence level was assignedfor each input data depending on the way it was obtained(sample based with low or high precision, approximate orindirect method, guesstimated, taken from other model orestimated by EwE). On the basis of these assignments, a“pedigree index” P was calculated to quantify the uncertaintyrelated to the input values in the model, which can beexpressed by the equation:

P¼Xni¼I

Xj¼I

Iijn

ð3Þ

where, Iij is the pedigree index for model group i andparameter j, and n is the total number of modeled groups(Christensen and Walters 2004).

With the goal of introducing a statistically basedapproach to fitting the model and to selecting the modelthat best fits a given set of constraints, the Ecorangermodule of EwE 5.0 was used. This tool incorporates a



Table

1Matrixof

thediet

compo

sitio

nof

thepredator

(row

s)in

percentage

ofvo

lumeof

prey

grou

ps(colum

ns),assembled

from

research

data

toconstructtheartificialecosystem

mod

elof

Chetumal

Bay

Prey/predator

AB

CD

EF

GH

IJ

KL

MN

OP

QR

ST

UV

A.Dolph

ins

B.Manatees

C.Fish—

bigpisciv

0.09

0.08

0.01

0.01

D.Fish—

mid

pisciv

0.09

0.08

0.01

0.01

E.Fish—

bigzoob

enthiv

0.09

0.08

0.01

0.01

F.Fish—

mid

zoob

enthiv

0.09

0.08

0.10

0.01

0.01

0.02

G.Fish—

smallzoob

enthiv

0.09

0.08

0.10

0.01

0.01

0.02

H.Fish—

zoop

lank

tiv0.09

0.08

0.10

0.01

0.01

0.02

I.Fish—

mid

herbiv

0.09

0.08

0.10

0.01

0.02

0.01

0.01

0.02

0.02

J.Fish—

smallherbiv

0.09

0.08

0.10

0.01

0.02

0.01

0.01

0.02

0.02

K.Fish—

bigom

niv

0.09

0.08

0.10

0.01

0.02

0.01

0.01

0.02

0.02

L.Fish—

smallom

niv

0.09

0.08

0.10

0.01

0.02

0.01

0.01

0.02

0.02

M.Fish—

detritiv

0.09

0.08

0.10

0.01

0.02

0.01

0.01

0.02

0.02

N.Wormsdetritiv.

0.03

0.02

0.05

0.02

0.03

0.01

0.03

0.01

0.13

O.Wormsom

niv

0.03

0.03

0.05

0.02

0.03

0.01

0.03

0.01

0.13

P.Crust.om

niv

0.04

0.24

0.30

0.21

0.02

0.02

0.09

0.07

0.04

0.01

0.04

0.13

Q.Crust.detritiv

0.03

0.03

0.16

0.21

0.20

0.02

0.09

0.07

0.04

0.06

0.04

0.13

R.Crust.ph

ytop

lank

tiv0.04

0.16

0.13

0.13

0.02

0.09

0.07

0.04

0.01

0.04

0.13

S.Moll.zoob

enthiv

0.01

0.06

0.05

0.03

0.02

0.10

0.01

0.01

0.01

0.07

0.13

T.Moll.zoop

lank

tiv0.01

0.03

0.04

0.02

0.01

0.03

0.01

0.01

0.01

0.02

0.13

U.Moll.ph

ytop

lank

tiv0.01

0.03

0.04

0.01

0.01

0.03

0.01

0.01

0.01

0.03

0.13

V.Zoo

plankton

0.17

0.05

0.14

0.91

0.02

0.16

0.13

0.16

0.20

1.00

0.20

W.Phy

toplankton

0.02

0.50

0.40

0.01

0.12

0.06

0.13

0.17

0.05

1.00

1.00

0.20

X.Benthic

autotrop

hs0.75

0.01

0.04

0.06

0.21

0.26

0.09

0.13

0.06

0.03

0.22

0.14

0.20

Y.Mangrov

e0.25

0.21

0.26

0.09

0.13

0.06

0.03

0.01

0.01

0.20

Z.Detritus

0.09

0.01

0.05

0.13

0.07

0.52

0.93

0.30

0.19

0.95

0.20

The

sum

oftherowsis1



Bayesian approach for explicitly considering uncertainty inEcopath inputs (Pauly et al. 2000) by using range andmean/mode values for all the basic parameters. Randominput variables were then drawn using uniform frequencydistributions for each parameter type and the resultingmodel was evaluated (based on user-defined criteria andphysiological and mass–balance constraints). The processwas repeated in a Monte Carlo fashion; and of the modelruns that passed the selection criteria, the best-fitting onewas chosen.

Using the best-fit model, a simple sensitivity routinewas performed. The routine varied all basic inputparameters in steps from −50% to +50% and checkedwhat effect each of these steps had for each of the inputparameters on all of the “missing” basic parameters foreach group in the system. The effects of these changeson all the missing basic parameters for all groups in thesystem were examined. The impact (IC) was expressedas (Xu et al. 2010):

IC ¼ estimated parameter � original parameter

original parameterð4Þ

The model was used to define the trophic level for everygroup, mixed trophic impacts, transfer efficiency amonglevels, connectivity index, and omnivory index (Christensenand Pauly 1992; Christensen and Walters 2004). It wasassumed that the bulk of flows of matter within theecosystem are greater than the exchange with adjacentecosystems.

The weighted average for each trophic level wasestimated by means of a flow diagram (Medina et al.2007). In addition, EwE performed a routine that allowsthe elements of the system to be separated into discretetrophic levels. The main result of this trophic aggregationis an estimate of the energy transfer efficiency betweenlevels (Christensen and Pauly 1992). To analyze thepartition of the energy flow by functional group, themethod suggested by Ulanowicz and implemented byChristensen and Pauly (1992) was used. In this analysis,flow through each group is distributed into trophic levelsaccording to its origin.

The interaction between the different components of thesystem using the mixed trophic impact routine in EwE bymeans of a Leontief matrix was analyzed (Christensen andPauly 1992). This procedure qualitatively estimates the impactof direct and indirect interactions, including competition,within the system.

In order to describe the trophic characteristics of thepotential manatee habitat, this model was compared to 11other trophic models built with EwE in areas where T.manatus occurs or historically occurred (Abarca-Arenasand Valero-Pacheco 1993; Barba-Macías and Castelblanco-

Martínez 2008; Chávez et al. 1993; Freire et al. 2008;Manickchand-Heileman et al. 1998a, b, 2004; Okey andPugliese 2001; Vidal and Basurto 2003; Wolff et al. 2000).

Results and Discussion

Trophic Structure of Chetumal Bay

In order to balance the model, some parameters wereobtained by EwE, mainly biomasses, which was especiallyimportant for mollusks. Dolphins and most of the fishesconsume mollusks, thus the model was forced to increaseinvertebrate biomasses to support superior groups. Sensi-tivity analysis showed that in general the model isinsensitive to a ±50% change in input parameters and achange in a group’s input parameter is mainly reflected inthe output of the same group. However, the impact ofchanges of one group on another is influenced by thetrophic dependency of the impacting group on the impactedgroup. Groups impacting several groups (for example, largeomnivorous fishes) have the most important impact on thesystem dynamics when their input parameters change.

Detritus, plankton, and invertebrate groups wereprominent in terms of biomass and energy flow in thesystem. The highest trophic levels were obtained fordolphins (3.95) and large piscivorous fishes (3.87),which therefore are the apex predators in the system(Table 2). EwE aggregated the groups with eight effectivetrophic levels (Fig. 2). Primary producers (level I) werecomprised of detritus, mangrove, benthic autotrophs, andphytoplankton. Transfer efficiencies calculated as geomet-ric mean for trophic levels II–IV were 28.0% fromprimary producers and 27.2% from detritus. The propor-tion of total flow originating from detritus was 46%.Almost all groups are interrelated as consumers (ofdetritus and benthic producers), consuming them directlyor indirectly throughout worms and crustaceans. Thesegroups play a role as the link to higher trophic levels assecondary consumers (Rosado-Solórzano and Guzmán delProo 1998).

As biomass is intensely concentrated in the detritus, thesystem can be said to be detritus controlled. These resultssuggest that sediments and organic matter on the bottom arethe main regulators of energy flow for the system, as anutritional agent in the litter recycling (Yáñez-Arancibia1986), or as a real food supply for detritivores (Fenchel1970). This is consistent with what has been described forother coastal lagoons and shallow estuaries (Abarca-Arenasand Valero-Pacheco 1993; Chávez et al. 1993; Manickchand-Heileman et al. 1998a, 2004; Vega-Cendejas 2003; Vidal andBasurto 2003). In food webs based on primary production,biomass of organisms within trophic levels can be simulta-



neously controlled by resources (bottom-up) and consumers(top-down). In contrast, very little is known about top-downand bottom-up control in detritus-based food webs (Rosemondet al. 2001) and the static model does not give enoughinformation to clarify further.

Ecotrophic efficiency, i.e., the fraction of production that isconsumed within, or caught from the system, was low fordetritus, which suggests that this resource is not heavilyexploited, and it remains deposited in the bottom of BCh.Surface currents in the bay are not strong (Carrillo et al.2009b), which makes this water body a low-energy coastallagoon, prone to sediment deposition. This also coincideswith the findings by de Jesús-Navarrete et al. (2000), whofound soft sediment as deep as 30 cm or more in certain areasof BCh, implying a long accumulation period. Vector analysisof the textural parameters of sediments in BCh has shownthat the trajectory of the surface sedimentary material goes

from the innermost part of the bay towards the mouth, i.e.,SW and SE, with a convergence towards the central anddeepest part of the bay (Sánchez et al. 2008), where mostsediments are deposited.

Mixed trophic impacts among functional groups of themodel are shown in Fig. 3. Lower-level groups (detritus,benthic autotrophs, mangrove, and plankton) have asignificant and positive impact on most other groups. Largepiscivorous fishes have a negative impact on all the fishgroups.

General statistics for BCh are summarized in Table 3. InBCh, Omnivory Index (SOI) was 0.25, similar to the SOIobtained for Florida (Okey and Pugliese 2001) and YucatanPeninsula (Arreguín-Sánchez et al. 1993). Such a low valueindicates that the functional groups present tend to bespecialists, consuming dietary items from a limited range oftrophic levels.

Table 2 Trophic level (TL), biomass, production/biomass (P/B), consumption/biomass (Q/B), ecotrophic efficiency (EE), production/consumption (P/Q), and unassimilated consumption (UC)

Group name TL Biomass P/B Q/B EE P/Q UC

A. Dolphins 3.95 0.01 0.01 42.33 0.00 0.00 0.2

B. Manatees 2.00 0.07 0.11 39.71 0.00 0.00 0.2

C. Fish-big pisciv 3.87 37.92 0.62 5.02 0.76 0.12 0.2

D. Fish—mid pisciv 3.60 8.13 2.48 7.29 0.86 0.34 0.2

E. Fish—big zoobenthiv 3.27 13.67 1.73 4.16 0.98 0.42 0.2

F. Fish—mid zoobenthiv 3.40 50.82 0.84 8.84 0.88 0.10 0.2

G. Fish—small zoobenthiv 3.18 6.81 5.79 14.28 0.84 0.41 0.2

H. Fish—zooplanktiv 3.02 6.75 4.74 20.18 0.97 0.24 0.2

I. Fish—mid herbiv 2.13 133.20 0.39 11.41 0.91 0.03 0.2

J. Fish—small herbiv 2.10 16.32 2.69 16.44 0.99 0.16 0.2

K. Fish—big omniv 3.13 24.97 2.12 14.01 0.82 0.15 0.2

L. Fish—small omniv 2.66 4.02 10.20 32.99 0.99 0.31 0.2

M. Fish—detritiv 2.36 3.30 13.44 33.63 0.99 0.40 0.2

N. Worms detritiv. 2.00 56.96 4.04 24.37 0.99 0.17 0.2

O. Worms omniv 2.40 25.10 8.59 18.94 0.99 0.45 0.2

P. Crust. omniv 2.66 26.44 18.95 52.35 0.99 0.36 0.2

Q. Crust. detritiv 2.00 49.84 8.76 44.14 0.99 0.20 0.2

R. Crust. phytoplanktiv 2.00 33.45 11.26 55.15 0.97 0.20 0.2

S. Moll. zoobenthiv 3.43 118.95 3.06 10.31 0.93 0.30 0.2

T. Moll. zooplanktiv 3.05 126.75 2.05 10.75 0.89 0.19 0.2

U. Moll. phytoplanktiv 2.00 132.44 2.27 11.57 0.81 0.20 0.2

V. Zooplankton 2.05 107.12 23.96 89.28 0.95 0.27 0.4

W. Phytoplankton 1.00 88.75 79.47 – 0.99 – 0.2

X. Benthic autotrophs 1.00 7.63 393.66 – 0.99 – 0.2

Y. Mangrove 1.00 0.31 305854.00 – 0.03 – 0.2

Z. Detritus 1.00 219.21 – – 0.06 – 0.2

Biomass in g·m−2 , flow in g·m−2 ·year−1 . Numbers in bold were obtained by EwE



Fig. 2 Flux diagram of Chetumal Bay as estimated using EwE fromthe input data in Tables 1 and 2, which biomasses are expressed ing·m−2 and proportion of the diet in g·m−2 year−1. Each group is

aligned as a function of the estimated trophic level. Line widthrepresents the relative flux between boxes. The area of each box isproportional to the logarithm of the biomass of each group

Fig. 3 Mixed trophic impacts inChetumal Bay for the 1970ssensu Leontief (1951), calculatedas how much a prey i contributesto the diet of a predator j minusthe proportion of the predationon j due to predator i. The figureshows the combined direct andindirect trophic impacts that aninfinitesimal increase of any ofthe groups on the right ispredicted to have on the groupsin the columns. The magnitudeof the impact is represented bythe circle size



The Trophic Role of Manatees in Chetumal Bay

In Ecopath models where T. manatus occurs, manatees wereonly included in the models for Florida (Okey andMahmoudi 2002; Okey and Pugliese 2001), northeastBrazil (Freire et al. 2008), and Chetumal Bay (presentstudy; Fig. 4). The studied systems consist of coastallagoons, estuarine areas, and marine platforms (Appendix 1).The size of the systems modeled, as per the total energy flow,was variable. It is difficult to compare the models due todifferences in quality and quantity of input data. However, allof them have the majority of the biomass and trophic flowsconcentrated in the lower levels of the system, that is, detritusand primary producers (seagrasses or mangrove), as in thecase of Celestún, Mexico (Chávez et al. 1993; Vega-Cendejas2003) and the Gulf of Paria, Venezuela (Manickchand-Heileman et al. 2004).

As expected, the manatees had a trophic level of 2.00. Thiswas the same as in Florida (Okey and Pugliese 2001) andnortheastern Brazil (Freire et al. 2008), and close to the otherprimary consumers (=herbivores) of the system. In themodel for northeastern Brazil, manatee biomass could havebeen overestimated, because the authors used individualweights determined for Florida manatee (400 kg·ind−1),whereas in the present study the manatee weight and densitywere directly estimated for the study area, thus ensuringconfidence in the data.

The trophic regulation of manatees through directherbivory is limited to the lowest levels of the system. Inthe mixed trophic impacts (Fig. 3), it is clear that manateesare directly and/or indirectly impacted by benthic autotrophs>mangroves>detritus. Freire et al. (2008) found similar resultsbut they did not consider the mangrove as a part of themanatees’ diet. Competition for resources between manateesand other groups is insignificant owing to the fact that othergroups are exploiting a variety of energy sources, while thestrictly herbivorous nature of manatees force them toexclusively exploit the compartments of primary producers,making their omnivory level zero.

This study suggests that in BCh foraging on sub-merged aquatic vegetation fulfills an important role innutrient recycling, being the fraction of the trophic chainthat is exploited with the greatest efficiency. This issupported by the presence of a large manatee population,which, compared to other functional groups in the system,contributes a large relative biomass and a high consumption/biomass ratio (41.07). Caribbean manatee numbers have beenreduced in most of their distribution area due to heavyexploitation and reduction in available foraging areas.However, in BCh, manatees are relatively well-conservedand grazing by these and other macroherbivores (for examplethe fishes Cyprinodon artifrons, Archosargus rhomboidalis,Paraneetroplus melanurus, and Lophogobius cyprinoides)can have a profound effect on seagrass communities.Herbivores have been shown to alter plant productivity,distribution, community structure, nutrient relations, andtissue nutrient content (Thayer et al. 1984). Grazing bysirenians has the capacity to alter the subsequent nutritionalquality of seagrass regrowth (Aragones et al. 2006). Thefragmentation of living seagrass and production of particulatedetritus coincident with feeding could be quite important.Further, defecation serves to provide a rapid recycling path fornutrients (Thayer et al. 1984). However, this recycling maybe functionally more important in transferring nutrients toadjacent ecosystems than in supplying nutrients to theseagrass ecosystem.

Since manatees are large exclusively herbivorous mam-mals, their habitat requires high macrophyte biomass. In

Table 3 Summary statistics for Chetumal Bay model

Parameter Value

Sum of all consumption 24,264.81 g·m2·year−1

Sum of all exports 88,788.13 g·m2·year−1

Sum of all respiratory flows 14,728.53 g·m2·year−1

Sum of all flows into detritus 94,522.77 g·m2·year−1

Total system throughput 222,304.20 g·m2·year−1

Sum of all production 111,127.30 g·m2·year−1

Total net primary production 103,974.90 g·m2·year−1

Total primary production/total respiration 7.05

Net system production 89,246.34 g·m2.year−1

Total primary production/total biomass 96.52

Total biomass/total throughput 0.01

Total biomass (excluding detritus) 1,077.15 g·year−1

Connectance Index 0.37

Omnivory Index 0.25

The total system throughput is the sum of all flows in a system, expressedin g·m2 ·year−1 . It is estimated as the sum of four flow components: totalconsumption+total export+total respiration+total flows to detritus. Thetotal net primary production is calculated as the summed primaryproduction from all producers. Primary production is a flow expressed ing·m2 ·year−1 . The total primary production/total respiration is the ratiobetween total primary production and total respiration in a system. Thetotal biomass/total throughput is the total system biomass that issupported by the available energy flow in a system, and can be expectedto increase to a maximum for the most mature stages of a system. Theconnectance index is understood as the real number of connectionsamong groups relative to the potential number of connections in thetrophic web, and it is roughly proportional to the number of groups in thesystem. The omnivory index measures the degree of partition of trophicinteractions among diverse trophic levels



comparison to the rest of the models, BCh showed the lowestrelative biomass of primary producers (only 0.07% of the totalbiomass), the main food source for manatees. BCh is thereforeconsidered a poor system, with low biodiversity and abun-dance of aquatic macrophytes. The permanent presence of ahundred manatees suggests that some sort of ecotrophicregulation is occurring to successfully maintain the equilibriumin this ecosystem, as explained below:

Manatees are opportunistic, generalist herbivores, and itis probable that the low density biomass of seagrasses in thebay is compensated by a higher consumption of mangrovematerial. Recent studies of manatee diet in BCh showed

that 88.33% of manatee fecal samples in BCh presentedtraces of R. mangle (Castelblanco-Martínez et al. 2009a).These structures consisted mainly in leaf tissues and H-shaped, brilliant idioblasts which are especially abundant inaerial roots and seedling hypocotyls of red mangrove(Castelblanco-Martínez et al. 2009a). It is important to notethat part of the mangrove cells observed in manatee fecaland digestive samples could have been obtained mainlyfrom foraging activities on the detritus. Since the proportion ofmangrove structures consumed from detritus is unknown, aconservative value of 25% for mangrove was used in order tobuild the model.

1

2

3

4

6 7

8

9

10

11

12

Manatee distributionTrophic models including manatee

5

Fig. 4 Trophic models built using EwE for some areas wheremanatees (Trichechus manatus) have been reported. References are:1 USA (Okey and Pugliese 2001), 2 Madre Lagoon (Barba-Macíasand Castelblanco-Martínez 2008), 3 Tamiahua Lagoon (Abarca-Arenas and Valero-Pacheco 1993), 4 Campeche Shelf (Manickchand-Heileman et al. 1998b), 5 Terminos Lagoon (Manickchand-Heileman

et al. 1998a), 6 Celestun Lagoon (Chávez et al. 1993), 7 YucatanPeninsula (Arreguín-Sánchez et al. 1993), 8 Bay of Ascensión (Vidaland Basurto 2003), 9 Bay of Chetumal (this study), 10 Paria Gulf(Manickchand-Heileman et al. 2004), 11 Caeté Estuary (Wolff et al.2000), 12 Northeast Brazil (Freire et al. 2008)



Manatees are capable of fasting for long periods of time.Evidence from Amazonian manatees (Trichechus inunguis)suggests that during the dry season, when the foodresources are limited or absent, manatees are able to eatautochthonous and allochthonous organic matter relying onhindgut fermentation to derive nutritive value from suchmaterial (Best 1983). Research on habitat use and feedingecology of manatees in the Orinoco Basin agrees with thisassessment (Castelblanco-Martínez et al. 2009b). Therefore,it could be possible that manatees in BCh experience a typeof fasting for a period of time.

In order to balance the model, it was necessary tocalculate the benthic autotrophs production/biomass ratio.The production/biomass value obtained for aquatic plantswas 393.66, remarkably higher than those obtained forCelestún Lagoon (4.5) (Chávez et al. 1993). Thus, it isprobable that the low biodiversity and abundance ofbenthic vegetation in BCh may be compensated by a highleaf productivity, as suggested by Espinoza-Ávalos et al.(2009).

Lastly, a final explanation is that the manatee’s foragingbehavior in BCh is different from that in areas/seasons withhigher plant biodiversity; as has been suggested for othermanatee populations (Landero-Figueroa 2010). When noenvironmental cues are obvious, spatial patterns in foragingmay result because the animals respond to patterns offorage availability. If animals cross nutrient-rich patches orfeeding sites more often, it would result in proportionallymore time spent in the nutrient-rich area (Bailey et al.1996). In a nutrient-poor area, such as BCh, the foragingpath would be less tortuous; and animals would soon transitthe area.

Despite the plasticity of manatees to adapt to differentenvironments, changes in the general structure of thetrophic web, especially on the detritus path, could haveimplications on the maintenance of the manatee populations.Mangroves, seagrass, and algal communities are the onlysource of food for the local manatee population; thus, theprotection of these habitats is a priority for the species’conservation. On the other hand, intensive feeding by largeherbivores might tend to increase subsequent nutritionalquality of forage (Aragones et al. 2006). Consequently,substantial reductions in the densities of manatee have thepotential to lead to the degradation of BCh in nutritional andfunctional terms.

Limits of the Model

Among the spectrum of representative models, steady-state models provide for the most explicit representationof trophic interactions between species. EwE provides an“instantaneous” estimate of biomasses, trophic flows, andinstantaneous mortality rates, for a chosen reference year

or multiyear averaging window (Christensen and Walters2004). It is necessary to recognize that biomasses could infact be changing over the period for which EwE referencedata (B, P/B, Q/B, diet composition) were provided. Thesnapshots cannot be used to directly assess effects ofpolicy changes that would result in changes in rates (e.g.,changes in manatee abundance) since the cumulativeeffects of such changes cannot be anticipated from thesystem state at one point in time. The usefulness of themodel can be notably increased by a combination of thesimulation and the real data of the system (Pauly et al.2000).

When the bulk of the trophic flow takes place betweenparts of the ecosystem, and this flow exceeds the flowbetween the ecosystem and its neighbors, the problem dueto the open system nature of the ecosystems is minimized(Christensen and Pauly 1998). In the present model, theexchange of matter and energy between neighboringsystems has not been quantified, so the openness of thesystem is still an issue to take into account when analyzingthe results.

Ecopath and Ecosim should be understood as ananalytical tool, which when properly used can provideinteresting approaches to the study of the role of aspecific animal group. However, EwE does not replacesingle-species assessment methods (Christensen andWalters 2004). For example, in order to assess the impacton the ecosystem trophic dynamics by removing mana-tees, it is recommended to combine single-speciesanalysis with EwE iterations. Therefore, in order to getmore conclusive results, further models with timeseries data for the single-species biomass changes arerequired.

In spite of the amount of scientific research in BCh,the knowledge of the system—and of the local manateepopulation—is still far to be complete. The presentmodel is the first attempt to describe the ecologicalfeatures of BCh, and some of the input data were takenfrom similar ecosystems or even guessed according toour experience researching in the study area. EwE doesnot include complete formal sensitivity analyses to testthe effect of all input parameters and this reduces theability to determine the confidence level of the results.Therefore, the results of this work must be taken withcaution and should be improved with complementarylocal research. This preliminary analysis pinpointedcritical gaps in the present knowledge of BCh. Somerelevant local issues that still remain unknown are: thedetritus dynamics, the exchange of matter between thebay and adjacent systems, and the seagrass and mangroveproduction. Also, updated information on the ecology,physiology, and behavior of the manatee population in BChis needed. As more data become available and included into



the model, estimates can be improved and the uncertaintyreduced.

Summary

A trophic model of Chetumal Bay has been developed todescribe the flows of energy and matter in the system inorder to describe the trophic role of the endangeredmanatee. The system was aggregated in eight effectivetrophic levels. Primary producers comprised of detritus,mangrove, benthic autotrophs, and phytoplankton. Theapex predators were dolphins and large piscivorous fishes.Manatees occupied the trophic level 2.0. The biomass wasintensely concentrated in the detritus suggesting that thematter on the bottom sediment is the main regulator of theenergy flow in the system. Manatees were directly orindirectly impacted by autotrophs, mangrove, and detritus;but the competition between manatees and other groupswas insignificant. In comparison to other ecosystems wheremanatees occur, BCh has the lowest relative biomass ofseagrasses. Several mechanisms to compensate the lack ofmacrophyte biomass (or a combination of several) aresuggested.

1. The low density and biomass of seagrasses is compensatedfor by a higher consumption of mangrove.

2. As reported for other tropical ecosystems whereresources are limited or absent, manatees in BCh couldhave some fasting capabilities.

3. The low abundance of seagrasses can be compensatedby high leaf productivity.

4. Manatees in BCh may perform some adaptive foragingbehavior like moving constantly through the bay insearching for food, instead of feeding on the samenutrient-rich seagrass patch.

EwE models are useful to describe the flow of energyand matter in the ecosystems. However, it is difficult toassess the uncertainty associated with the estimatesobtained; therefore, results should be interpreted withcaution.

Acknowledgments Part of the field work was possible thanks to thelogistic support of El Colegio de la Frontera Sur (ECOSUR),Chetumal. The authors thank field assistants who helped in thevegetation data collection. CONACYT is thanked for giving a Ph.D.studies grant to DNC-M. Thanks are due to Dr. Villy Christensen, whoimproved the work with his comments and advice. We also thankStuart Fulton, Alexander M. Costidis, Ivan Valiela, Iris C. Anderson,and two anonymous reviewers for the helpful suggestions on themanuscript.

Appendix 1

Table

4Com

parisonof

trop

hicmod

elsbu

iltusingEcopa

thforareaswith

inthedistribu

tionof

manatee

(Trichechu

sman

atus)

1.USA

2.Madre

3.Tamiahu

a4.

Cam

peche

5.Términos

6.Celestún

7.Yu

catan

8.Bay

ofAscensión

9.Bay

ofChetumal

10.Gulf

ofParia

11.Caeté

12.NE

Brazil

System

characteristics

Con

tinental

shelf

Coastal

lago

onCoastal

lago

onCon

tinental

shelf

Coastal

lago

onCoastal

lago

onCon

tinental

shelf

Estuary

Estuary

Estuary

Estuary

Con

tinental

shelf

Area(km

2)

174,30

02,45

080

065

,000

2,50

025

010

0,00

026

02,56

07,60

022

01,07

5,00

0

Mam

mal

diversity

NP

00

NP

NP

0NP

NP

2NP

NP

NP

Fishdiversity

NP

6580

NP

NP

71NP

NP

70NP

NP

NP

Invertebrate

diversity

NP

118

99NP

NP

40NP

NP

42NP

NP

NP

Plant

diversity

NP

11

NP

NP

3NP

NP

11NP

NP

NP

Totalspecies

diversity

>60

018

418

0NP

NP

114

NP

NP

128

NP

NP

NP

Fun

ctionalgrou

ps42

1511

1920

1621

1926

2419

41

Maxim

umtrop

hiclevel

4.3

3.2

3.4

4.3

3.31

3.4

43.86

3.95

4.1

44.65

Appendix

1



Table

4(con

tinued)

1.USA

2.Madre

3.Tamiahu

a4.

Cam

peche

5.Términos

6.Celestún

7.Yu

catan

8.Bay

ofAscensión

9.Bay

ofChetumal

10.Gulf

ofParia

11.Caeté

12.NE

Brazil

Sum

ofall

consum

ption

(g·m

−2year

−1)

6,08

9.38

138

0.4

1,86

5.8

NP

2,80

11,05

0.1

1,68

6.82

624

,261

576.6

3,74

6.7

NP

Sum

ofallexpo

rts

(g·m

−2·year−1)

1,80

7.01

811.4

0.3

NP

1,48

31.9

984.12

28,87

8NP

2,18

5.8

NP

Sum

ofall

respirationflow

(g·m

−2·year−1)

2,52

9.10

724

4.9

1,13

2.3

NP

1,68

760

2.3

1,07

5.62

314

,728

368.8

948.2

NP

Sum

ofallflow

todetritu

s(g·m

−2year

−1)

4,09

2.10

266

518

5.6

4,71

4.3

NP

2,99

839

4.6

1,06

8.18

994

,522

1,33

8.9

3,67

8NP

System

throug

hput

(g·m

−2year

−1)

14,518

1,98

982

2.4

7,71

2.7

3,70

9.5

8,96

92,04

94,81

522

2,30

42,28

510

,558

.623

,042

Sum

ofall

prod

uctio

n(g·m

−2year

−1)

5,42

031

5.8

5,02

8.6

NP

2,44

369

23,68

611,127

NP

3,55

5.1

10,364

Totalcalculated

prim

ary

prod

uctio

n(g·m

−2year

−1)

4,33

5.95

5NP

4,66

8.3

NP

1,89

045

43,41

6.67

110

3,97

41,38

8.8

3,13

48,37

5

Totalprim

ary

prod

uctio

n/total

respiration

0.9

NP

NP

NP

NP

0.8

3.17

67.05

93.8

3.30

56.6

Net

prod

uctio

nof

thesystem

(g·m

−2year

−1)

1,80

6.84

8NP

3,53

5.9

2,611.6

NP

−148

.067

2,34

1.67

189

,246

1,01

9.9

3,13

4NP

Totalprim

ary

prod

uctio

n/total

biom

ass(year−1)

9.2

NP

44.46

NP

NP

736

.124

96.52

340.23

937

.6

Totalbiom

ass/

totalflow

0.03

0.12

770.01

36NP

NP

0.03

0.02

0.00

50.06

4547

046

1.24

40.01

Totalbiom

ass

(excluding

detritu

s)

469.73

721

.08

101

NP

NP

6594

.49

1,07

7.15

41.16

13,132

.17

222.5

Totalbiom

ass

987.73

731

.08

105

NP

NP

65.152

107.49

1,20

4.41

147.49

13,132

.17

424.44

%Detritus

0.52

440.30

000.32

180.03

670.04

590.84

790.1119

0.12

090.16

900.72

320.00

000.47

57

%Primary

prod

ucers

0.12

280.20

000.08

040.41

720.86

920.12

490.18

150.49

650.07

450.13

450.99

040.44

37

%TL

1=(2−2

.5)

0.26

570.22

000.43

760.42

720.06

970.02

460.62

170.30

620.41

510.07

300.00

850.04

99

%TL

2=(2.5–3

)0.04

370.15

000.15

990.03

940.01

410.00

030.04

750.05

760.23

170.02

050.00

050.01

29

%TL3(≥3)

0.04

350.13

000.00

030.07

950.00

110.00

230.03

730.01

880.10

940.04

890.00

050.01

79



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