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Fisheries Research 179 (2016) 237–250

Contents lists available at ScienceDirect

Fisheries Research

journa l homepage: www.e lsev ier .com/ locate / f i shres

rogression of a Gulf of Mexico food web supporting Atlantiscosystem model development

oseph H. Tarnecki a,∗, Amy A. Wallace a, James D. Simons b, Cameron H. Ainsworth a

University of South Florida, College of Marine Science, 140 7th Avenue South, St. Petersburg, FL 33701, United StatesTexas A&M University-Corpus Christi, Center for Coastal Studies, 6300 Ocean Drive, Corpus Christi, TX 78412, United States

r t i c l e i n f o

rticle history:eceived 23 October 2015eceived in revised form 24 February 2016ccepted 25 February 2016andled by A.E. Punt.

eywords:tlantis ecosystem model

a b s t r a c t

This article develops a marine food web matrix for the Gulf of Mexico (GOM) based on local stomachsampling and online diet information. Working at the level of functional groups, we fit diet informationto a statistical model based on the Dirichlet distribution. This allows us to quantify likely contributionsof prey to predators’ diets. Error ranges on these values reflect diet variability and data quality, and helpin identifying functional groups that would benefit from additional sampling. We perform hierarchicalcluster analysis to determine functional groups that have similar prey requirements, then produce a foodweb diagram representing the interactions between predators and prey. A meta-analysis using principle

ietirichlet distributioneeding ecologyood web

coordinate analysis allows us to compare this study’s diet matrix with ten other published GOM foodwebs and determine where variation in food web structure exists. We also compare our new food web tothe diet matrix used by the Ainsworth et al. (2015) Atlantis ecosystem model, a strategic tool developed toassess ecosystem dynamics in the GOM. A hindcast from 1980 to 2010 using Atlantis shows an improvedfit to observational data and reduced error in biomass projections using the revised diet information.

© 2016 Elsevier B.V. All rights reserved.

. Introduction

As human demand for marine resources continues to increase,anagement strategies have shifted focus from analyzing indi-

idual components to entire ecosystems (Curtin and Prellezo,010). These analyses are aimed at developing ecosystem-basedsheries management (EBFM) strategies focused on sustainingnd rehabilitating marine ecosystems for the benefit of futureenerations (Demer et al., 2009; Curtin and Prellezo, 2010;ampan et al., 2011). EBFM considers ecosystem-wide interactions

Marasco et al., 2007), including factors such as habitat avail-bility, food limitation, and predator–prey interactions (Bohnsack,989; Hilborn, 2011). Models created to account for connectivityetween ecosystem components require large amounts of data andre computationally intensive (Hollowed et al., 2000; Crowder andorse, 2008; Levin and Lubchenco, 2008). Diet data derived fromsheries-independent sampling has proven effective at determin-

ng connectivity between predators and prey within an ecosystem

Pikitch et al., 2004).

Ecosystem models consider the diets of numerous species, oftenundreds to thousands. Therefore it is often necessary to group

∗ Corresponding author.E-mail address: jtarnecki83@gmail.com (J.H. Tarnecki).

ttp://dx.doi.org/10.1016/j.fishres.2016.02.023165-7836/© 2016 Elsevier B.V. All rights reserved.

species together by niche and dietary habits. This reduction ofspecies structure is needed to efficiently parameterize dietaryrelationships. Numerous methodologies have been developed todescribe the diets of fish (Hynes, 1950; Hyslop, 1980; Pierce andBoyle, 1991). Of these, indices such as frequency of occurrence,biomass or volume, or numbers of prey are the most commonlyused to describe the diets of individual predators. The evalua-tion of single species diet-based studies are generally accompaniedwith error, as diet only reflects short-term feeding strategies thatare limited by time and location (Ahlbeck et al., 2012). By com-bining methodologies and using composite data from multiplestudies, ecosystem models are able to reduce error and providebroader insight into the long-term feeding strategies of predators(Ainsworth et al., 2010; Ahlbeck et al., 2012).

When calculating the diet matrices of predators using aggregatediets, the volume or weight of individual prey are generally aver-aged either directly or in proportion to their relative consumption(Hyslop, 1980; Masi et al., 2014). However this technique is prob-lematic as it does not take into account uncertainty or provide ameasure of variability that would be useful in sensitivity analysis(Ainsworth et al., 2010). Furthermore, the occurrence of rarely con-

sumed prey can be overemphasized when using simple averages,particularly when diet datasets are small, leading to misrepresen-tation of typical feeding habits (Walters et al., 2006).

2 es Research 179 (2016) 237–250

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Table 1Functional groups and number (no.) of viable stomachs containing identifiable preyand used in diet analysis. Functional groups containing asterisk (*) are composed ofa single species.

Functional groups, (no. of stomachs) Functional groups (continued)

Benthic Feeding Sharks, (n = 21) Other Demersal Fish (n = 2113)Bioeroding Fish, (n = 30) Other Tuna (n = 6)*Black Drum (n = 26) *Pinfish (n = 133)*Blacktip Shark (n = 32) *Pompano (n = 22)*Blue Marlin (n = 2) *Red Drum (n = 1440)*Bluefin Tuna (n = 22) *Red Grouper (n = 440)Deep Serranidae (n = 63) *Red Snapper (n = 134)Deep Water Fish (n = 11) *Scamp (n = 15)Filter Feeding Sharks (n = 1) Sciaenidae (n = 300)Flatfish (n = 846) Seatrout (n = 1270)*Gag Grouper (n = 1216) Shallow Serranidae (n = 922)*Greater Amberjack (n = 24) *Sheepshead (n = 11)Jacks (n = 299) Skates and Rays (n = 125)*King Mackerel (n = 125) Small Demersal Fish (n = 2155)*Ladyfish (n = 69) Small Pelagic Fish (n = 163)Large Pelagic Fish (n = 66) Small Reef Fish (n = 573)Large Reef Fish (n = 440) Small Sharks (n = 23)Large Sharks (n = 116) *Snook (n = 1317)*Little Tunny (n = 1) *Spanish Mackerel (n = 143)Lutjanidae (n = 2166) *Spanish Sardine (n = 55)Medium Pelagic Fish (n = 21) *Swordfish (n = 9)Menhaden (n = 17) *Vermillion Snapper (n = 671)Mullets (n = 61) *White Marlin (n = 2)Other Billfish (n = 1) *Yellowfin Tuna (n = 1)

38 J.H. Tarnecki et al. / Fisheri

This paper works to overcome multiple statistical issues ofreparing diet matrices for an Atlantis ecosystem model, or anyther subsequent analyses, using data from the Gulf of Mexico as

case study. We first combine data from literature and empiricaltudies to facilitate identifying sparse predator–prey linkages thatere missing from previous work (Masi et al., 2014). Second, we

ecategorize predators and prey into functional groups more suit-ble for end-to-end ecosystem models using principle coordinatenalysis. Third, we overcome the lack of uncertainty estimates byootstrapping the aggregate diets of fish then statistically quan-ifying the diet estimates and associated error (Ainsworth et al.,010). We compare the estimated diet matrix to diet matrices pre-iously described for the region. Finally, we assess the performancef an end-to-end Atlantis ecosystem model for the Gulf of Mexicotilizing the revised diet matrix.

. Methods and materials

.1. Functional groups and data sources

We analyze diet information of 48 predator functional groupsTable 1). The analysis of Masi et al. (2014) was augmented by (1)ecategorizing predator and prey species into functional groupsased on ecological factors, and (2) incorporating data (Table 2)rom a larger spatial area to represent the feeding ecology relevanto the whole GOM. Diet data were obtained from: (1) Florida Fishnd Wildlife Conservation Commission’s (FWC’s) Fisheries Inde-endent Monitoring (FIM) group based in St. Petersburg, Florida;2) GoMexSI database (Simons et al., 2013) developed by Texas&M University in Corpus Christi, Texas (http://gomexsi.tamucc.du/); (3) diet information acquired through Fishbase.org (Froesend Pauly, 2013); (4) dissections performed by Masi et al. (2014),

nd (5) diet studies of longline-caught fish collected by the Univer-ity of South Florida (USF; S. Murawski, Pers. Comm.).

The statistical analysis considers only fish. However, we didbtain diet information pertaining to marine mammals, turtles,

Fig 1. Catch locations of the Gulf of Mexico Species Interactions (GoMexSI) and Florida

birds, and invertebrate species from online sources includ-ing: Animaldiversity.org (Myers et al., 2015) and Sealifebase.org(Palomares and Pauly, 2015), to form a complete picture of theGOM food web. Collectively, this study’s compiled dataset repre-sents the feeding ecology of predators throughout the GOM andwill be referred to as the ‘revised’ food web matrix hereafter. In

total, 17,719 fish belonging to 474 unique species were analyzed forthis study. Capture locations were provided for FWC and GoMexSI

Fish and Wildlife Conservation Commission (FWC) samples in the Gulf of Mexico.

J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250 239

Table 2Capture location(s), unit of measure, number (no.) of samples, and number of species for each data.

Data source Location of catch Weight (g), vol., % Diet No. of samples No. of species

FWC Primarily west coast of Florida, restricted to the Florida shelf Vol. 16,220 166GoMexSI Primarily western and northern Gulf of Mexico Vol. 592 117Fishbase No locations specified % Diet 867 330Masi et al., 2014 Gulf wide sampling efforts Weight (g) 25 7USF Gulf wide sampling efforts Weight (g) 15 7Total 17,719 474 Unique spp.

Source: Florida Fish and Wildlife Conservation Commission (FWC), Gulf of Mexico SpeciesUniversity of South Florida (USF). Raw data was measured in either weight (g), Volume (V

Fig. 2. Hierarchical cluster analysis showing diet similarity by functional group.Dashed lines indicate statistically similar predators and black brackets illustratesimilar clusters of predators. Black brackets at 0% dissimilarity indicate identicalpredator–prey relationships (e.g. white, brown, and pink shrimp functional groups).Number of hierarchical clusters is 25.

Interaction Database (GoMexSI), Fishbase.org studies (), Masi et al. (2014), and theol.) or percent diet (% Diet).

samples (Fig. 1), but were not specified for Fishbase, USF studies,or non-fish groups (only indicated as GOM).

2.2. Statistical model

A statistical analysis was applied to the composition data whichbootstraps the diet of each functional group and fits the normalizeddata to a Dirichlet distribution (Ainsworth et al., 2010). As a firststep, we arranged the normalized predator diets into an 89 × 89matrix, using predator versus prey Atlantis functional groups todescribe interaction. The most-frequently observed diet valueswere most often zero as it is unlikely for a predator to feed acrossa large proportion of the 89 prey categories. To correct for zeroinflated data we adopted the methodology of Masi et al. (2014).We randomly selected 15% of the total number of stomachs fora given functional group and averaged the diet values together.This creates a pseudo predator stomach that should represent atime-integrated diet composition. We then bootstrapped 10,000such samples with replacement and fit the bootstrapped values tothe Dirichlet density function using a maximum likelihood fittingprocedure (Ainsworth et al., 2010; Masi et al., 2014). The Dirich-let function is the multivariate generalization of the beta function.Thus, the marginal beta distributions provide us with a mode, rep-resenting the most frequently observed diet proportion for thatpredator–prey combination in percentage wet weight, as well asconfidence intervals.

There are 89 potential prey functional groups (corresponding tofunctional groups of the GOM Atlantis model). If we let p denotea random vector of i = 89 prey item proportions that sum to one,∑

I

pi = 1, and whose elements are greater than or equal to zero,

pi ≥ 0, then we can express the probability density at pi with aparameter vector using the Dirichlet function (Eq. (1)):

p(a)∼f (p1, p2, . . ., pI)|˛1, ˛2, . . ., ˛I) = � (�IaI)�I� (˛I)

�ip˛i−1 (1)

vector is estimated from a set of training data (bootstrapped datato which the statistical model is fit) representing diet proportionsD = {p1, . . ., pN} using a maximum likelihood fitting procedure thatmaximizes p (D|˛) = ∏

Ip (pi|�I). We employed VGLM in the VGAMpackage (Yee and Wild, 1996) in the R statistical environment (RCore Team, 2015).

2.3. Hierarchical clusters analysis

A dendrogram of predator diets was created using hierarchicalclustering analysis (Clarke et al., 2008; Masi et al., 2014) and Bray-Curtis measures of distance (Bray and Curtis, 1957) between diets.First, the Bray-Curtis dissimilarity measure was computed between

each pair of samples (j and k):

Djk =∑n

i=1|yij − yik|∑ni=1

(yij + yik

) (2)

240 J.H. Tarnecki et al. / Fisheries Rese

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arch 179 (2016) 237–250

where yij is the count of the ith species in the jth sample, yik isthe count of the ith species in the kth sample, and n is the num-ber of species. Cluster analysis was performed on the dissimilaritymeasures by computing the cluster mode group averages alongwith similarity profile analysis (SIMPROF; Clarke et al., 2008) with999 permutations to produce significant (P < 0.05) aggregations ofpredator groups as hierarchical clusters.

2.4. Comparison of the revised food web to other models

This study’s revised diet matrix was compared to the diet matri-ces used by ten other Gulf-wide multispecies models (Passarellaand Hopkins, 1991; Luczkovich et al., 2002; Okey and Mahmoudi,2002; Walters et al., 2006; Carey et al., 2013; Chagaris, 2013;Dynamic Solutions, 2013; Geers et al., 2014; Grüss et al., 2014; T.Minello, National Oceanic and Atmospheric Administration (NOAA)Pers. Comm.). For the comparisons, larval stages were omitted andonly juvenile and adult predator diets were considered. To facilitatecomparisons, functional groups and species within each study wereaggregated into 13 ‘supergroups’: Benthic Fauna, Cephalopods,Deepwater Fish, Elasmobranchs, Inshore Fish, Marine Mammals,Pelagic Fish, Mollusks and Echinoderms, Planktonic Fauna, ReefAssociated Fish, Seabirds, Reef Dependent Fish, and Turtles. Super-groups were chosen based on no particular basis, but rather tosimply aggregate the diverse datasets into one broad matrix forcomparisons. Additionally, the diet compositions for each super-group were normalized.

To facilitate comparisons between studies, we performed a one-way Analysis of Similarities (ANOSIM) with 999 permutations tocompare the aggregated normalized diets for each supergroupbetween studies. These results were compiled using a respectivebinary connectivity matrix to reveal congruence. The ‘congruencymatrix’ enumerates percent similarity of one study to another.Within the congruency matrix, 100% congruence implies allpredator–prey relationships are identical between studies whereas0% implies complete dissimilarity among predator–prey feedingecology. A 2D Multidimensional Scaling (MDS) scatterplot cre-ated from the percent similarity illustrates the findings. Tightlyclustered points indicate similar food webs and broad clusteringindicates dissimilar food webs.

To compare individual supergroup diets from this study with theten other studies, predator similarity was tested in the Primer sta-tistical package (ver. 6) using Principal Coordinates (PCO) graphs(Anderson and Willis, 2003). The Bray-Curtis similarity measurewas calculated between each pair of samples then plotted todescribe variation on two axes.

2.5. Comparison of the revised matrix with Atlantis

Atlantis is a biogeochemical marine ecosystem model repre-senting ocean physics, nutrient cycling, high trophic level dynamicsand fisheries, in three spatial dimensions. Fulton et al. (2007) pro-vide a thorough fisheries management application in Australia, andFulton et al. (2011) present a meta-analysis of global applications.The best resources for the theory and code are Fulton (2001, 2004)and Link et al. (2011).

Rather than using diet proportions, Atlantis’ trophic model isbased on ‘availability’ parameters, a scaling value in a Type IIpredator–prey functional response (see Eq. 69 in Link et al., 2011).Availability reflects total consumption potential in addition to dietpreference. Therefore, it is sensitive to functional group aggre-

gation, predator and prey abundance, spatial co-occurrence andother factors, and the availabilities matrix must be tuned as part ofthe calibration process. There are conventions on how initial esti-mates can be derived from diet proportion data. Gamble (Northeast

J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250 241

Fig. 3. Food web diagram illustrating predator–prey connectivity in the Gulf of Mexico. Each box represents a cluster from the hierarchical cluster analysis and is named afterthe predator functional group containing the highest biomass estimates, provided by Drexler and Ainsworth (2013). Boxes are proportional to area log biomass estimatesand arrows indicate the flow of energy from prey to predator. Dotted lines indicate groups with ≥10% − 20% prey contributions, thin solid lines represent prey contributionsranging from >20% − 40%, and thick solid lines indicate >40% prey contributions. Diets <10% were omitted from the diagrams. Estimated trophic level of each grouping isindicated on the Y-axis.

F ix. Syi es amo

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Gop

ig. 4. 2D Multidimensional Scaling (2D-MDS) plot of area cluster congruency matrn predator–prey relationships. The degree of correspondence between the distanc

isheries Science Center-NOAA, Pers. Comm.) developed method-logy to estimate availabilities numerically (for applications, see:insworth et al., 2011; Link et al., 2011). Brand et al. (2007) andinsworth et al. (2015) started with low, medium and high sets ofalues to approximate total flows. Later revisions of the Brand et al.2007) model scaled proportional diet data to a desired mean (I.aplan, Northwest Fisheries Science Center-NOAA, Pers. Comm.).he connectivity pattern is generally informed by diet data.

Ainsworth et al. (2015) developed an Atlantis model of theOM using a food web provided by Masi et al. (2014) as the basisf representing the availabilities matrix. In this study, we com-are our updated and revised diet matrix against the (calibrated)

mbols (representing GOM models) use spatial distribution to represent similaritiesng point values or ‘stress’ is 0.14.

Ainsworth et al. (2015) diet matrix rather than the uncalibratedMasi et al. (2014) matrix, and conduct Atlantis simulations (below)that maintain the absolute flows of the Ainsworth et al. (2015)model changing only the pattern of connectivity to represent thenew data. There is a theoretical justification for this. If we were toreplicate the entire tuning procedure, improvements in model fitwould reflect not only the improved diet information but also the(rather subjective) tuning process. Thus, the value of the new data

would not be clearly discernable. In contrast, a comparison can bemade if we maintain in Atlantis the absolute magnitude of flowsthat were tuned by Ainsworth et al. (2015) and have been shownto yield realistic model behavior.

242 J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250

F revisep

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ig. 5. Principle coordinates (PCO) panel plots illustrating similarities between theredator–prey relationships.

To address the comparisons between the Ainsworth et al.2015) availabilities matrix with the current diet information, weompared presence/absence of prey and percent contribution toetermine differences in connectivity. To streamline this analysis,rey items were broadly classified into four supergroups: Elasmo-ranchs, Large Pelagic Fishes, Reef Fishes and Nearshore/Inshoreishes. Comparisons in volume and prey composition were madeoth visually and by using similarity percentages (SIMPER), whichrovides a measure of resemblance between studies (Clarke et al.,008). High percentages indicate prey contribution and volumere similar between studies, while low percentages indicate highariability between studies.

Canonical analysis of principal coordinates (CAP) was performed

sing Primer to illustrate supergroup similarity in the Atlantisodel and to illustrate how the incorporation of this study’s revised

iet data could improve supergroup correlation (see Andersonnd Willis (2003) for a full description of CAP methodology). We

d food web matrix (black symbols) and other multispecies models’ (gray symbols)

applied the Bray-Curtis dissimilarity measure to the diet matri-ces for Atlantis and Atlantis + revised food web. CAP was then usedto constrain the ordination of data points. Within each CAP plot,clustering indicates similarity in diet composition. These similar-ities in feeding ecology are represented within supergroups andamong predator functional groups. Groups clustered together indi-cate greater similarity in feeding behavior than compared to groupsthat do not cluster.

Finally, we ran a hindcast (following Ainsworth et al., 2015)from 1980 to 2010 and compared functional group biomass pro-jections against observed biomass time series data (see Ainsworthet al. (2015) for data sources). The fit against this scaled observedbiomass data is measured by the sum of squared residuals (SS)∑ ( )

where [SS = n

i=1 Yi − Yi2] and where Yi is predicted biomass

in year i, Yi is observed biomass and n is total simulation years(n = 30). We also employed discrepancy as a metric of model skill

es Rese

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J.H. Tarnecki et al. / Fisheri

discrepancy =∑2010

i=1990

(Yi − Yi

)]. Low values of SS and discrep-

ncy near zero indicate good model performance. We consider theears 1990 to 2010 when calculating these metrics to discount earlyransient dynamics.

We ran three simulations using the GOM Atlantis model: (1)ecreating the simulations of Ainsworth et al. (2015) using theirriginal diet matrix, (2) employing a revised diet matrix that

ncluded trophic linkages identified in this study but absent ininsworth et al. (2015), and (3) employing a revised diet matrix

hat included this study’s new trophic linkages but also eliminatedinkages used by Ainsworth et al. (2015) that were not confirmedy the current study. These scenarios are referred to as ‘Original,Revised A” and ‘Revised B’ hereafter. Where new trophic linkages

ere required, we assumed a strength of interaction similar to theean interaction strength experienced by a given prey across all

ts predators.

ig. 6. Diet comparisons between the Ainsworth et al. (2015) Atlantis model diet data aunctional groups are listed horizontally. Gray boxes refer to prey linkages that were predators are presented in order of number of missing linkages.

arch 179 (2016) 237–250 243

3. Results

3.1. Diet matrix

Diet proportions of predators in the GOM are highly variableand range from including a single functional group to 15 differentfunctional groups (see Supplementary Appendix I for diet esti-mates and 95% confidence intervals). The greatest diversity in preywas observed for the functional group Pinfish (Lagodon rhomboids,Sparidae), which included various fishes, shrimps, bivalves, plank-ton, and detritus. Filter Feeding Sharks exhibited the least diversefeeding strategy and fed exclusively on phytoplankton.

3.2. Hierarchical clusters and food web diagram

Hierarchical cluster analysis conducted on revised matrixyielded (n = 25) unique hierarchical clusters (Fig. 2), ranging

nd the revised diet data. Predator functional groups are listed vertically and preyresent in Ainsworth et al. (2015), while black boxes indicate new prey linkages.

244 J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250

(Cont

fcegpPtarLt(dCS(Fet

Fig. 6.

rom one to ten predator functional groups per cluster. Clustersontaining one functional group displayed statistically differ-nt (P < 0.05) feeding behaviors relative to all other functionalroups. These include: Spanish Sardines (Sardinella aurita, Clu-eidae), Filter Feeding Sharks, Sponges, Small Pelagic Fish, Largeelagic Fish, and Bioeroding Fish. The largest aggregation of func-ional groups (n = 10) present within a single cluster was observedmong the omnivorous fishes: Benthic Feeding Sharks, Deep Ser-anidae, Vermilion Snapper (Rhomboplites aurorubens, Lutjanidae),arge Reef Fish, Flatfish, Sciaenidae, Red Drum (Sciaenops ocella-us, Sciaenidae), Shallow Serranidae, Lutjanidae, and Red GrouperEpinephelus morio, Serranidae). Feeding behavior displaying 0%issimilarity was observed between (1) Crabs & Lobsters and Stonerabs, (2) White Shrimp (Litopenaeus setiferus, Penaeidae), Brownhrimp (Farfantepenaeus aztecus, Penaeidae), and Pink Shrimp

Penaeus duorarum, Penaeidae), and (3) Oysters and Sessile Filtereeders, indicating very similar diets with no discernable differ-nces within the context of this analysis. The cluster exhibitinghe greatest dissimilarity between functional groups was observed

inued)

among the large pelagic fishes. Within this cluster, the diet of OtherBillfish was 86% dissimilar to the other functional groups. This wasin part due to low sample size (n = 1) and high consumption (46%)of Spanish Mackerel (Scomberomorus maculatus, Scombridae). Allother groups within large pelagic fish cluster foraged primarily(46–73%) on Deepwater Fish.

Each hierarchical cluster represents a box used in the GOMfood web diagram (Fig. 3). Base-level prey (Phytoplankton,Plants/Macroalgae, Octocorals, and Detritus) that were not presentin hierarchical clusters were added to the food web diagramto complete essential linkages. These base-level prey functionalgroups represented the lowest trophic levels while Other Tunaand King Mackerel represented the highest aggregated trophic lev-els. Species included within ‘Other Tuna’ (Blackfin Tuna, Thunnusatlanticus; and Bluefin Tuna, Thunnus thynnus) were primarily feed-

ing on Large Pelagic Fish, Small Pelagic Fish, and Squid. Speciesincluded within ‘King Mackerel’ (King Mackerel, Scromberomoruscavalla, Scombridae; Little Tunny, Euthynnus alletteratus, Scombri-dae; and Spanish Mackerel) fed primarily on Small Pelagic Fish.

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J.H. Tarnecki et al. / Fisheri

.3. Comparison of the revised matrix to other models

Congruency ranged from 1.7% to 96.4% among the modelsTable 3). The lowest congruency was between the Geers et al.2014) and Grüss et al. (2014) models, which compared the dietsf Inshore Fish, Pelagic Fish, and Reef Associated Fish. The compar-

son between Luczkovich et al. (2002) with Minello (Pers. Comm.)isplayed the highest congruency (96.4%), but only evaluated theiets of Inshore Fish.

Percent congruency comparing the revised matrix to the otherOM models ranged from 2.6% to 77.9%, with Geers et al. (2014)isplaying the lowest congruency and Grüss et al. (2014) display-

ng the highest. These models contained a number of comparableupergroups. However, Geers et al. (2014) contained more inshoreata. Overall, the revised matrix used the largest sample sizes andreatest diversity of predators compared to the other GOM foodebs. Similarities between food webs are further illustrated using

2D MDS (Fig. 4). The diet matrices of Grüss et al. (2014) andassarella and Hopkins (1991) were most similar to the revisedatrix while Geers et al. (2014) was least similar.

The best fit PCO plot (Fig. 5) comparing supergroups between

tudies had 50.8% variation on the X-axis, which is likely most influ-nced by trophic level, and 32.4% variation on the Y-axis which isikely most-influenced by feeding mode, as benthic feeding behav-ors tend to be positioned low and pelagic feeding is positioned

ig. 7. Percent diet and similarity percentages (SIMPER) of predator categories compariraphs were constructed for fish predator groups using prey grouped into 12 supergroup

arch 179 (2016) 237–250 245

high. Overall, the analysis displayed relatively tight cluster arrange-ments for most supergroups. Pelagic Fish, Inshore Fish, and ReefFish all exhibited distinct cluster arrangements, while Elasmo-branchs and Cephalopods exhibited broader clusters.

3.4. Comparison of the revised matrix with Atlantis

Diet comparisons were made to evaluate the presence versusabsence of prey across each predator functional group for theAtlantis and revised datasets (Fig. 6). Considering only the dietsof fish predator functional groups, a total of 399 prey linkageswere identified as ‘missing’ when comparing Atlantis to the reviseddataset. Comparisons of the revised matrix with the Atlantis datasetrevealed 31% of all functional groups contained >10 missing link-ages, 27% contained 6–10 missing linkages, and 42% contained ≤5missing prey linkages. Of these, King Mackerel and the Spanish Sar-dine (sample sizes n = 27 and n = 23) functional groups exhibitedthe greatest number of missing prey, while Swordfish (sample sizen = 9) exhibited zero dissimilarity between datasets.

Stacked bar graphs and similarity percentages were used tocompare differences in fish diet composition between the revised

and Atlantis datasets (Fig. 7). Similarity ranged from 5.5% to 75.5%.Of these, Menhaden (Brevoortia spp., Clupeidae) was the leastvariable functional group overall. Menhaden diet was composedprimarily of pelagic fauna and detritus. However, Menhaden diet

ng the Ainsworth et al. (2015) Atlantis model diet data and the revised diet data.s.

246 J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250

(cont

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Fig. 7.

aried in percent composition between datasets. Benthic feed-ng sharks displayed the lowest similarity between datasets. Thetlantis dataset shows this group primarily consumed Elasmo-ranchs and Pelagic fauna, while the revised dataset indicatesenthic Feeding Sharks consume mostly benthic prey (e.g. Crabsnd Lobsters, Shrimps, and Small Benthic Fauna). Overall, stackedar graphs show similar prey supergroups were consumed for mostomparable fish predators between datasets. Stacked bar graphsoupled with similarity percentages provide insight on which func-ional groups differ the greatest and therefore which functionalroups would presumably benefit from targeted sampling efforts.

CAP plots (Fig. 8) illustrate the potential improvements ofcosystem connectivity to the Atlantis ecosystem model with thentegration of this study’s updated and revised dataset. Within eachraph, data points represent individual functional groups whileymbols represent the designation of a functional group into a par-icular supergroup. CAP plots display a measure of similarity on

oth the X and Y-axis. This similarity ranged between −0.2 and0.3 on the X-axis, and −0.2 and +0.2 on the Y-axis for the Atlantisodel data. Similarity for the Atlantis + revised food web ranged

rom −0.4 and +0.4 on both X and Y axes. For Atlantis (Fig. 8A) broad

inued)

clustering was observed among all supergroup fish categories indi-cating prey resources were highly variable between individualfunctional groups occupying the same supergroup. After integrat-ing the revised data (Fig. 8B) tighter clustering was observed amongfunctional groups and greater distinction between supergroups.

The inclusion of the new diet information resulted in a modestimprovement in the Atlantis model performance. Under scenarioRevised A, 69% of the functional groups benefited from reducedresiduals relative to scenario Original. Under scenario Revised B,59% of groups benefited. The median reduction in the SS was14% for Revised A and 23% for Revised B indicating that we moreaccurately captured interannual variability in population size. Aone-tailed Wilcoxin signed rank test for paired data indicates sig-nificant improvement over the original model fit for both scenarioRevised A (p = 0.008) and scenario Revised B (p = 0.03). Under bothscenarios we observed a reduction in overall discrepancy; resultsfrom revised B are shown in (Fig. 9). A 14% reduction in discrepancy

in Revised A and a 28% reduction in Revised B, relative to scenarioOriginal, suggests fewer systematic errors exist in model predic-tions.

J.H. Tarnecki et al. / Fisheries Research 179 (2016) 237–250 247

F istribud repres

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ig. 8. Canonical analysis of principal coordinates (CAP) plots illustrating (A) the distribution of Atlantis + revised predator groups with respect to diet. Each symbolupergroups to identify similarities in feeding ecology.

. Discussion

We combined the diets of predators from multiple sources andormulated a new food web, improving on the earlier attempts by

asi et al. (2014) and Ainsworth et al. (2015). Using the maxi-um likelihood fits of the Dirichlet distribution, we were able to

ddress issues noted by other authors concerning the overestima-ion of rarely consumed prey (Walters et al., 2006; Ainsworth et al.,010). Within our revised dataset, sample sizes were small for mostunctional groups. The statistical method employed here resulted in

ide confidence intervals in such cases, accurately characterizinghe uncertainty. Moreover, the diet proportion values used in thenal diet matrix represent the modes of the marginal beta distribu-

ions rather than the means, which down-weights the importancef rarely consumed prey. This is advantageous when dealing withmall diet data sets of opportunistic predators.

The error range generated by this technique may be usefulor sensitivity analysis of diet parameters used in the ecosystem

odeling context. In some cases, the realized diet, which may beffected by spatial and temporal overlap of predator and prey, han-ling time, satiation and other non-linear effects, may be extracted

rom the model during simulations (e.g., in Atlantis: Fulton et al.,007). A goodness of fit measure for the trophic model may beerived by comparing the realized diet proportions to the maxi-

um likelihood marginal beta distributions.

Hierarchical cluster analysis identified feeding guilds withinur dataset. While only the diets were considered for cluster

tion of the Ainsworth et al. (2015) Atlantis predator functional groups and (B) thesents an Atlantis model functional group. Functional groups were categorized into

analysis, we observed a potential interaction between diet andsampling location. Clusters were observed for predators occu-pying similar habitats including: blue-water pelagic fishes (e.g.tunas, billfishes), coastal pelagics (Pompano, Trachinotus carolinus,Carangidae; Ladyfish, Elops saurus, Elopidae), reef associated fish(snappers, groupers), as well as several other inshore and offshoreassemblages. These clusters were formed because of the consump-tion of location-specific prey (e.g. tunas and billfishes consumingdeepwater fish). However, we also observed similar species thatoccupy different clusters despite sharing commonalities in habitatand feeding ecology. For example, Seatrout and Red Drum sharesimilarities in habitat preference. As juveniles and adults, bothspecies can be found in nursery habitat (e.g. mangroves, marshes,etc.), and opportunistically feed on similar prey (Llanso et al., 1998).In this study, cluster analysis grouped Red Drum with reef associ-ated fishes (groupers, snappers and sea basses), and Seatrout withbay and coastal predatory fish (Ladyfish, Pinfish, jacks).

Disparities such as these are likely attributed to habitat differ-ences. Dennis and Bright (1988) report species assemblages differdramatically throughout the GOM, and therefore differences infeeding ecology likely differ as well. In the northern panhandle ofFlorida to the east coast of Texas, Red Drum occupy primarily sea-grass habitat (Matlock, 1987). Within this study, fish were largelycollected in areas dominated by mangrove habitat and nearshore

natural hard bottom reefs. The northern and western GOM are con-tain greater abundances of marsh and seagrass habitat (Stunz andMinello, 2001; Rooker et al., 1998), as well as deeper natural hard

248 J.H. Tarnecki et al. / Fisheries Rese

Fig. 9. Sum of squared residuals (SS) comparing the scenario Original (Ainsworthemm

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t al., 2015) diet matrix to Revised B (this study’s revised matrix). Black bars indicateodel performance using original diet matrix and gray bars show the revised dietatrix. Asterisk indicates improved fit. Median change: 23% reduction in SS.

ottom reefs and artificial habitats (Cowan et al., 2011). It is likelyhat future sampling efforts focused in these areas would be neededo uncover regional differences in predator–prey relationships.

Collectively, the revised diet matrix was most similar to otheroffshore’ datasets such as those of Grüss et al. (2014) and Passarelland Hopkins (1991). Specifically, these study’s compared the dietsf Inshore/Nearshore Fishes, Pelagic Fishes, and Reef Associatedishes. Dissimilarity was greatest within the Elasmobranchs super-roup in which diet composition varied depending on samplingocation and habitat. Within the Elasmobranchs supergroup weeported the diets for nearshore skates and rays, along with aiversity of shallow, pelagic, and deepwater sharks. Some modelsampled only coastal communities, therefore only shallow waterharks and rays were reported, if at all. Among all models congru-ncies were highest among offshore food webs (96.4%; Luczkovicht al., 2002 with Minello, Pers. Comm.), and differed the most whenomparing nearshore to offshore food webs (1.7%; Geers et al., 2014ith Grüss et al., 2014). This study’s revised food web contained

reater biodiversity in species composition relative to other foodebs and congruency consequently was lower when comparing

tudies with few representatives within a supergroup.Expanding the stomach data used in our diet matrix revealed

large number of linkages missing from the Atlantis GOM food

eb. Within our comparisons between Atlantis and the revised

ood web matrix, we ranked these predators in order of numberf missing linkages such that predators exhibiting higher numbersf missing linkages would be ideal candidates for future sam-

arch 179 (2016) 237–250

pling. Furthermore, we computed similarity between models toprovide additional insight to where additional sampling is needed.For most predator functional groups the dominant prey items arelargely consistent between studies, but for others (especially Ben-thic Feeding Sharks, Bioeroding Fish and Spanish Mackerel) thedominant prey items varied. Several authors (Ferry and Cailliet,1996; McCawley and Cowan, 2007; Llopiz and Cowen, 2009) indi-cate large sample sizes taken over many years and seasons areneeded to account for variability in diet. Differences here arelikely attributed to low sample sizes or spatial differences betweendatasets. Increases in sampling effort would be required to identifythe dominant prey as well as identify any seasonal, ontogenetic, orhabitat shifts that may influence prey consumption during the lifehistory of a predator (Cortés, 1997).

Integrating the new diet matrix into the Ainsworth et al. (2015)Atlantis model led to supergroups becoming more distinct in whatthey ate. Using the new diet information, both reconstructionsimulations, Revised A and Revised B, saw reduced residuals rela-tive to time series observational biomass and reduced discrepancyfor a majority of functional groups. It should be noted that thisrepresents only a rough first application of this new diet data. Sub-sequent model tuning has the potential to capitalize on this newdiet information further. Behavior of an Atlantis model dependson several influential parameter sets besides the diet matrix (e.g.,recruitment, consumption, growth rates). In the process of modelcalibration, adjustment of all parameters is done simultaneously.When Ainsworth et al. (2015) calibrated the model they did sowith the original (less accurate) diet matrix in place. Errors else-where may have been made in compensating for inaccuracies inthe diet matrix. With the improved diet in place those errors canbe resolved.

5. Conclusions

Statistical description of diet data provides modelers with atool to consider emergent diet proportions as testable predictionsfrom trophodynamic models. Few publications have made use ofpredicted diets in this regard (but see Fulton et al., 2007); moretypically, models are validated using biomass and catch obser-vations. Increasingly, stable isotope studies offer the possibilityto validate performance at the meta-level (Dame and Christian,2008; Navarro et al., 2011) and could benefit from a similar sta-tistical framework for developing goodness-of-fit criteria. Further,data limitation issues are made more manageable since error isdescribed explicitly, and error derived from these methods can pro-vide a solid basis for sensitivity analysis or probabilistic treatmentof diet data in trophic models.

The diets of individual functional groups examined in this studyprovide assessments of feeding ecology derived from Gulf-widesampling efforts. These assessments are vital to our advancement ofecosystem models, such as Atlantis, which are used to assist man-agers in developing restoration strategies and predict changes tomarine resources (Levin and Lubchenco, 2008; Ainsworth et al.,2010). Using the Dirichlet distribution, we were able to identify themost likely prey and percent contributions for each predator func-tional group. The designation of functional groups into clusters, or‘guilds’, allowed for the identification of similar species that canpotentially be managed together due to similarities in habitat andfeeding ecology.

The comparison of the revised matrix to other GOM modeldatasets showed more fidelity with previously published food webs

of deep water areas, and less fidelity with published food websof nearshore areas. This disparity may reflect greater variabilityin predator composition and prey resources in nearshore areas.This study’s data was certainly influenced by the prolific sampling

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ccurring along the west coast of Florida where estuarine depen-ent fish, such as Red Drum, may reflect a diet more similar to reefssociated fish than to other estuarine fishes. Integrating additionaliet information from other nearshore areas of the GOM would

ikely improve representation of estuarine dependent interactionsnd area specific species assemblages.

The revised diet matrix used in this study improved the Atlantisindcasts for the whole GOM. We also identified missing prey

inkages which advises where targeted sampling efforts shoulde applied. Of these, King Mackerel and Spanish Sardines arehe most variable. However as indicated within PCO plots, dis-inction between Inshore/Nearshore and Reef Fishes were alsoacking. This study’s integration of new prey linkages with thetlantis diet matrix had created more distinction between the

nshore/Nearshore and Reef Fish supergroups. Furthermore, weemonstrated the data used in our revised model allows Atlantis toredict population trends more accurately and with less discrep-ncy than the food web matrix of Ainsworth et al. (2015). Onceroperly calibrated, incorporation of this study’s data should pro-ide still better model performance.

cknowledgements

Funding for this project was provided by the U.S. Departmentf Commerce’s National Oceanic and Atmospheric AdministrationNOAA) Fisheries Southeast Regional Office Marine Fisheries Initia-ive (MARFIN) Grant number: NA13NMF4330171 and the Marineesource Assessment Program at the University of South Florida95-NA10OAR4320143). Development of Atlantis and associatedata sets was made possible by a grant from The Gulf of Mexicoesearch Initiative to the Center for Integrated Modeling and Anal-sis of Gulf Ecosystems (C-IMAGE) (GRI2011-I-072) and by NOAA’sational Sea Grant College Program Grant No. NA10-OAR4170079.ata are publicly available through the Gulf of Mexico Research

nitiative Information & Data Cooperative (GRIIDC) at https://data.ulfresearchinitiative.org (DOI: R4.x267.182:0003). The Florida

ildlife Commission and Gulf of Mexico Species Interactionatabase provided data. We also thank Joel Ortega-Ortiz for hisssistance with GIS and map making.

ppendix A. Supplementary data

Supplementary data associated with this article can be found,n the online version, at http://dx.doi.org/10.1016/j.fishres.2016.02.23.

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