Comparing Trophic Levels Estimated from a Tropical Marine Food Web Using an Ecosystem Model and Stable Isotopes

Estuarine, Coastal and Shelf Science 233 (2020) 106518

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Estuarine, Coastal and Shelf Science

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Comparing trophic levels estimated from a tropical marine food web using an ecosystem model and stable isotopes

Jianguo Du a,*, Petrus Christianus Makatipu b, Lily S.R. Tao c, Daniel Pauly d, William W. L. Cheung d, Teguh Peristiwady b, Jianji Liao a, Bin Chen a,** a Third Institute of Oceanography, Ministry of Natural Resources, Xiamen, 361005, China b Bitung Marine Life Conservation, Research Centre for Oceanography, Indonesian Institute of Sciences, Bitung, 97255, Indonesia c The Swire Institute of Marine Science, University of Hong Kong, Hong Kong, China d Institute for the Oceans and Fisheries, The University of British Columbia, Vancouver, V6T 1Z4, Canada

ARTICLE INFO ABSTRACT

Keywords: Comparing the outputs of food web models with those from other independent approaches is necessary to build Ecopath model confidence in the use of these models to help manage fisheries. Mass-balance models such as Ecopath with Stable isotope analysis Ecosim (EwE) and stable isotope analysis are widely used to describe food webs, but the results from these Trophic level methodologies are rarely compared. In this study, an Ecopath model was developed to study the food web in the Niche width Bitung area, North Sulawesi, Indonesia and compare it with results from stable isotopes. Stable isotope data were Coral reef North Sulawesi available for 19 out of 50 functional groups defined in the model, including fishes, crustaceans, squids, sea cucumbers and other invertebrates. The trophic levels and niches of these functional groups estimated from the Ecopath model were compared with those calculated from nitrogen and carbon isotope data. The trophic levels of 19 functional groups were estimated to range from 2.00 (sea cucumber) to 3.84 (coral trout). Trophic levels 2 estimated from Ecopath were correlated with those derived from stable isotopes (r spearman ¼ 0.71, n ¼ 19, p < 0.001). On the average, Ecopath overestimated trophic levels of the functional groups in the model by about 2.4% compared to those calculated from stable isotopes, which is very encouraging. It is still suggested, however, that trophic level estimation should be cross-validated by using mass-balance models and SIA whenever possible.

1. Introduction and Christian, 2008). Amongst the many outputs that Ecopath models produce, trophic levels (TLs) are a useful metric for model validation. As ecosystem-based management is increasingly being adopted for Ecopath models calculate TLs for different functional groups in a given marine conservation and natural resource management worldwide ecosystem based on the diet composition matrices specified among (Barbier et al., 2008; Leslie, 2018), the use of ecosystem models for groups, usually based on previous analyses of stomach contents, and the management and forecasting purposes has also strongly increased. Ex relative abundance of each group in the model. Validating the estimated amples of commonly used ecosystem models include Ecopath with TLs from a model can help build confidence in the representation of its Ecosim (EwE) model (Christensen et al. 2008, 2014; Downing et al., trophic relationships, as required for an accurate representation of 2012), OSMOSE (Shin and Cury, 2001), the Atlantis model (Fulton et al., ecosystem structure and functions. 2011) and Linear Inverse Modelling (Grami et al., 2011; Legendre and Stable isotope analysis (SIA) is considered one of the most effective Niquil, 2013), Amongst these models, the most widely applied model is methods to validate trophic levels estimated from food web models EwE, with over 570 EwE models published worldwide by the early (Dame and Christian, 2008), and has become an important approach for 2000s (Colleter� et al., 2015). investigating trophic interactions of food webs in the past few decades Unfortunately, validation of EwE model outputs are only performed (Peterson and Fry, 1987; Post, 2002). Given that the difficulty and in a small subset of those published, even though this is an important limitation of stomach content analysis, carbon and nitrogen stable step towards building confidence in their practical applications (Dame isotope ratios have been shown to be very useful tool to understand

* Corresponding author. Third Institute of Oceanography, Ministry of Natural Resources, China. ** Corresponding author. E-mail addresses: [email protected] (J. Du), [email protected] (B. Chen). https://doi.org/10.1016/j.ecss.2019.106518 Received 24 September 2019; Received in revised form 26 November 2019; Accepted 2 December 2019 Available online 3 December 2019 0272-7714/© 2019 Elsevier Ltd. All rights reserved. J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

animal diets (Papiol et al., 2012), from primary producers (Vizzini and Ecopath model (predicted values) and SIA (empirical data), in order to Mazzola, 2003; Christianen et al., 2017) to top predators (Estrada et al., evaluate whether Ecopath models make reasonable predictions about 2003; Stewart et al., 2017), and even at the community level, i.e., within the trophic structure of ecosystems, considering the complexity of entire food webs (Layman et al., 2007; Phillips et al., 2014; Flynn et al., models used for ecosystem-based management and decision making. 2018). Comparing TLs and trophic niche widths estimates from Ecopath and from SIA allows validation of the model (Dame and Christian, 2008; 2. Materials and methods Deehr et al., 2014). Such validation has been undertaken in several in stances (Kline and Pauly, 1998; Nilsen et al., 2008; Milessi et al., 2010; 2.1. Study area Navarro et al., 2011; Du et al., 2015; Lassalle et al., 2014). However, the use of independent methods for evaluating whether these models pro The province of North Sulawesi is near the centre of the Coral Tri vide reasonable results is not routinely applied (Christensen and Wal angle region with a typical equatorial climate. Sea surface temperatures � ters, 2004; Fulton et al., 2011; Lassalle et al., 2014). vary between 20 and 28 C, and the water visibility is 10–25 m. The � 0 Quantitative information on the biodiversity of the Bitung marine Bitung study area covers about 215 km2, located from 125 7.5 to � 0 � 0 � ecosystem in North Sulawesi has been reported, including fishery 125 18 E and from 1 34.5 to 1 22’ N, along the northeast coast of landings (Naamin et al., 1996; Dharmadi et al., 2015), fish diversity North Sulawesi (Fig. 1), and includes coral reefs (Du et al., 2016a), (Kimura and Matsuura, 2003; Du et al. 2016a, 2018, 2016b; Peristiwady mangrove and seagrass meadows (Du et al., 2016b, 2018). et al., 2016), seagrass (Riani et al., 2012), coral reefs (Hadi et al., 2016) and benthos cover (Lin et al., 2018). However, the system as a whole has 2.2. Mass-balanced model development not been described using a mass-balance model, that could be used to support ecosystem-based management initiatives, though this area is at Ecopath was originally used to model coral reef ecosystems (Polo ’ the centre of multiple fishing activities in Indonesia s Eastern Region. vina, 1984), then it was developed into the generic Ecopath with Ecosim Here, using the marine ecosystems in Bitung as a case study, we (EwE) software package, applied to a wide range of aquatic ecosystems compared the TLs and trophic niches of key functional groups from an in past decades (Christensen and Pauly, 1992; Christensen et al., 2005;

Fig. 1. Map of study area of the Bitung, North Sulawesi, Indonesia.

2 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

Colleter el al. 2015; Villasante et al., 2016). This study used EwE version consumption, DC is the diet composition, BA is the biomass accumula 6.5, available at http://www.ecopath.org/. tion and E is the net immigration (Christensen et al., 2008). All fluxesare To reduce the complexity of the food web, species with similar assumed to remain self-similar during the period covered, here 2012 to ecological roles were aggregated into similar ‘functional groups’ or 2017. The model is balanced by solving Equation (1) simultaneously for guilds. The model assumes that the total amount produced or consumed all groups in the model; therefore, one of the input parameters (such as by a functional group is equal to the amount that goes out of the func B, P/B, Q/B or EE) for each group can be left to be estimated by the tional group through fishing mortality, predation, migration and model. biomass accumulation, i.e.: For the Bitung ecosystem, 50 functional groups were aggregated Xn from 297 species exploited by local fisheries,and other floraand fauna, Bi⋅ðP=BÞi⋅EEi ¼ Yi þ Bj⋅ðQ=BÞj ⋅ DCij þ Bi⋅BAi þ Ei (1) including 19 functional groups for which stable isotope data were j¼1 available. Fisheries catches originated from the Fishery Bureau of Bitung for the years 2012–2017; the catch of each species was allocated to 17 where i and j are prey and predator groups, B is biomass, P is the pro functional groups, using information from different fishinggears such as duction, EE is the ecotrophic efficiency, Y is the fishery catch, Q is trawls, purse seines, gillnets, poles and lines, and traps. The values of P/

Table 1 Basic input and estimated outputs (bold) parameters for the functional groups in the Bitung model. (P/B: production-biomass ratio; Q/B: consumption-biomass ratio). À Group name Landing (t km 2 Biomass (t Trophic P/Q P/B Q/B Ecotrophic Fishing mortality À À À À À À year 1) km 2) level (year 1) (year 1) (year 1) efficiency (year 1)

1 Sharks\whale\dolphin 0.00265[1] 0.42[2] 3.85 0.76[2] 4.86[2] 0.26 0.0062 2 Birds 0.37[2] 3.47 0.38[2] 63.95[2] 0.00 3 Turtles 0.02[2] 2.75 0.07[2] 3.50[2] 0.62 4 Adult groupers 0.0506[1] 0.32[3] 3.45 0.03[2] 9.12[2] 0.92 0.158 5 Subadult groupers 0.0313[1] 0.11[3] 3.53 0.40[2] 13.10[2] 0.97 0.275 6 Juvenile groupers 0.0169[1] 0.03[3] 3.62 1.20[2] 26.68[2] 0.85 0.516 7 Snappers 0.00734[1] 0.20[3] 3.54 0.80[2] 11.00[2] 0.92 0.0367 8 Napoleon wrasse 0.15[3] 3.58 0.56[2] 13.43[2] 0.99 9 Tuna\Billfish 2.714[1] 2.06[3] 3.37 1.74[2] 5.79[2] 0.99 1.318 10 Coral trout 0.01[3] 3.84 0.39[2] 3.97[2] 0.86 11 Rays 0.21[3] 3.27 0.69[2] 2.94[2] 0.33 0.000603 12 Adult butterflyfish 0.20[3] 2.97 1.00[2] 6.72[2] 0.50 13 Juvenile butterflyfish 0.08[3] 2.71 2.00[2] 11.16[2] 0.92 14 Cleaner wrasse 0.01[3] 3.35 3.78[2] 13.10[2] 0.91 15 Medium pelagic 0.150[1] 0.44[3] 3.28 2.09[2] 12.36[2] 0.96 0.338 16 Adult large reef 0.0242[1] 3.18[3] 2.92 0.25[2] 4.00[2] 0.53 0.00953 associated 17 Juvenile large reef 0.0242[1] 2.54[3] 3.01 0.60[2] 5.89[2] 0.93 0.00953 associated 18 Adult medium reef 0.00284[1] 1.50[3] 2.93 0.80[2] 5.00[2] 0.95 0.00189 associated 19 Juvenile medium reef 0.00295[1] 1.56[3] 2.35 1.40[2] 8.15[2] 0.80 0.00189 associated 20 Adult small reef 0.00137[1] 0.19[3] 2.68 3.00[2] 15.00[2] 0.71 0.00722 associated 21 Juvenile small reef 0.000711[1] 0.10[3] 2.65 4.00[2] 30.34[2] 0.94 0.00722 associated 22 Demersal 0.0103[1] 0.44[3] 3.31 1.58[2] 8.25[2] 0.88 0.0235 23 Large planktivore 0.00330[1] 1.27[3] 3.31 1.50[2] 5.92[2] 0.92 0.00261 24 Small planktivore 0.00293[1] 1.12[3] 2.70 2.00[2] 9.87[2] 0.61 0.00261 25 Macro-algal browsing 0.54[3] 2.25 1.38[2] 17.18[2] 0.90 26 Adult scraping grazers 0.22[3] 2.24 2.34[2] 12.74[2] 0.78 27 Juvenile scraping grazers 1.03[3] 2.32 3.00[2] 22.73[2] 0.96 28 Detritivore fish 0.01[3] 2.23 2.34[2] 8.33[2] 0.92 29 Anchovy 0.00793[1] 2.39[3] 2.57 2.98[2] 21.65[2] 0.93 0.00332 30 Deeperwater fish 0.000945[1] 1.16[3] 3.39 1.04[2] 4.61[2] 0.70 0.000814 31 Hard corals 2.54 2.51 1.73[2] 3.23[2] 0.95 32 Soft corals 0.52 2.51 0.92[2] 1.91[2] 0.95 33 Anemonies 0.33 3.03 0.05[2] 0.07[2] 0.95 34 Sponges 2.43 2.35 1.48[2] 5.27[2] 0.95 35 Shrimps 0.00127[1] 3.20[3] 2.28 3.03[2] 28.95[2] 0.85 0.000396 36 Squid and Octopus 0.0104[1] 1.04[3] 3.30 2.71[2,4] 13.54[2,4] 0.93 0.0101 37 Sea cucumbers 0.77[3] 2.00 0.74[2] 8.25[2] 0.87 38 Lobsters 0.148[1] 0.28[3] 3.18 0.80[2] 15.21[2] 0.93 0.530 39 Crabs 0.00291[1] 0.38[3] 2.76 1.78[2,4] 17.38[2,4] 0.96 0.00762 40 Giant triton 0.03 3.17 1.22[2] 4.08[2] 0.95 41 Herbivorous echiniods 0.52[3] 2.00 0.54[2] 9.42[2] 0.89 42 Bivalves 8.50[3] 2.26 2.51[2] 5.62[2] 0.85 43 Other invertebrates 29.85 2.16 3.66[2] 17.75[2] 0.95[5] 44 Jellyfishand hydroids 0.09[3] 3.07 10.23[2] 26.46[2] 0.93 45 Zooplankton 4.95 2.31 75.93[2] 242.48[2] 0.95 46 Phytoplankton 39.89 1.00 26.10[2] 0.00[2] 0.95 47 Macro/Calcareous algae 39.49[2] 1.00 10.20[2] 0.00[2] 0.31 48 Seagrass 20.16[2] 1.00 13.76[2] 0.00[2] 0.64 49 Mangroves 19.14[2] 1.00 0.07[2] 0.00[2] 0.03 50 Detritus 100.00[2] 1.00 0.34

3 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

B, Q/B (Table 1) and diet composition (Table 2) for each functional 2.5. Trophic levels and trophic niche width from SIA group were adapted from the Raja Ampat model, which also pertains to a coral reef ecosystem. This model of an area 500 km to the southeast, TLs of each fauna were calculated according to Post (2002): À �� consists of 98 functional groups (Pitcher et al., 2007; Bailey and Pitcher, 15 15 TLconsumer ¼ TLbasis þ δ Nconsumer À δ Nbasis TEF (5) 2008; Piroddi et al., 2010; Varkey et al., 2012; Hoover et al., 2013). The biomass values of 35 out of 50 functional groups in the Bitung ecosystem where TL is the TL of a primary consumer, used to calculate the TLs of model, such as fishes, crabs, shrimps, squids and sea cucumbers, were basis other consumers in the ecosystem (Vander Zanden and Rasmussen, estimated by the survey data from Underwater Visual Census (UVC) 1999; Post, 2002), and is usually assumed to be 2. δ15N is the conducted from 2012 to 2017. For the biomass of multi-stanza groups, consumer value measured for other consumers. The δ15N is δ15N of fauna that such as groupers, butterfly fish, large reef associated fish, planktivore basis are herbivores. In this study, Cypraea tigris, Purpura lapillus and Trochus fish and scraping grazers, the biomass of each stanza group was esti sp. were identifiedas the common species at the base of the Bitung food mated by the proportion published in the Bird’s Head area (Bailey and web. TEF is the δ15N trophic enrichment factor for a difference between a Pitcher, 2008). The biomass values of 7 out of 50 functional groups consumer and its source, and the average δ15N enrichment per trophic which were not estimated in the survey, such as birds, turtle and sea level used in this study is 3.4 parts per thousand (Vander Zanden and grass values from the Raja Ampat model were used (Table 1). For 8 other Rasmussen, 2001). functional groups, including difficult and soft corals, whose biomasses were hard to estimate, the EE values were set to 0.95 based on the Raja Ampat model, and the biomasses were left for the model to estimate. 2.6. Trophic niche width calculation

2.3. Trophic levels and omnivory index from the Ecopath model In the stable isotope analysis, the niche width of each group or species was represented in terms of the area the population occupies on ¼ 13 15 Trophic levels start with TLs 1 in primary producers and detritus, the δ C-δ N biplot based on all specimens within a group/species þ ’ and a TL of 1 [the weighted average of the preys TL] for consumers. (Table 3). The area was determined by the standard ellipse area (SEAc) ¼ Following this method, a consumer eating 20% plants (with TL 1) and (Jackson et al., 2011). All analyses were performed using the SIAR ¼ þ � þ � ¼ 80% herbivores (with TL 2) will have a TL of 1 [0.2 1 0.8 2] package (Stable Isotope Analysis in R 3.3.2, version 4.2) and SIBER 2.8. metrics (Stable Isotope Bayesian Ellipses, version 2.1.3). Original TLs can be formulated as follows: methods of the community level metrics and tools for analyzing food Xn web using stable isotopic data can be seen in Layman et al. (2007, 2012). TLi ¼ 1 þ DCij � TLj (2) j¼1 2.7. Comparison between Ecopath and stable isotope results where i is the predator of prey j, DCij is the fraction of prey j in the diet of predator i and TLj is the TL of prey j (Christensen et al., 2008). The TLs of each functional group calculated by the Ecopath model The omnivory index (OI) is estimated as the variance of the TL of a were plotted against the corresponding TLs calculated by SIA, and their consumer’s prey groups (Pauly et al., 1993). When the OI is zero, the relationship was tested by the Spearman-rank correlation coefficienttest consumer is specialized to feed on a single TL, while a large value il (Zar, 1999). For multi-species functional groups, TLSIA was determined lustrates that the consumer is a generalist which feeds on many TLs as the mean TLs of the species included in the model compartment for (Pauly et al., 1993; Christensen et al., 2008). which SIA data existed, weighted by their relative biomass. Similarly, Xn comparisons were done between the square root of OI from Ecopath and À �2 OIi ¼ TLj À ðTLi À 1Þ � DCij (3) the SD of TLSIA, and also between SEAc and OI values. j¼1

3. Results where, TLj is the TL of prey j, TLi is the TL of the predator i, and, DCij is the proportion prey j constitutes to the diet of predator i (Christensen 3.1. Ecopath model outputs et al., 2008). The square root of the OI is the standard error of the TLs measuring The input data such as landings, biomass, P/B, Q/B, diet composition the uncertainty of its exact value as a result of both sampling and and basic estimated like TLs, ecotrophic efficiency and mortality rates omnivory variability. from the Bitung model are summarized in Tables 1 and 2. In the Ecopath model, sharks, coral trout, groupers and Napoleon wrasse are considered 2.4. Stable isotopes process the major top predators, with the TLs >3.5. Groupers and tuna/billfish were the main target of the fisheries. The mean TLs of the major The SIA was performed on 19 functional groups, including 14 fish exploited groups in the ecosystem is 3.35. Phytoplankton, macroalgae, groups, shrimps, squid, sea cucumber, crab and other invertebrates, seagrass, mangroves and detritus were the main food sources for lower consisting of 315 individuals from 115 species. All samples were trophic level organisms, with the TLs ¼ 1.0 (Fig. 2). Groups with TL � collected in July and August of 2016; the white muscle of the specimens � 3.0 represent about 14.89% of the total biomass in Bitung ecosystem. were collected, and then were freeze-dried at minus 40 C, ground into Moreover, besides the higher TL groups like sharks and birds, and the powder, then sieved with 120 meshes (aperture diameter: 125 μm). The lower TL groups like mangrove, the EE of most groups (77%) is over 0.85 stable isotope signals were measured using a mass spectrometer con (Table 3), suggested that most productions from these groups was nected to an elemental analyzer (Flash EA1112 HT). The δ15N was transferred to their grazers and/or predators. calculated by the following equation: For those functional groups for which stable isotope signatures are 15 Rsample À Rstandard available, the model estimated TLs ranging between 2.00 (sea cucum δ Nð‰Þ ¼ � 1000 (4) Rstandard ber) and 3.84 (coral trout). The OI calculated by the Ecopath model suggested that the Bitung ecosystem consisted of functional groups The limits of detection were 0.2‰ for δ15N. R represents the 15N/14N. ranging from highly specialized consumer (OI ¼ 0.13 for groupers and coral trout) to generalist predator (OI ¼ 0.67 for rays, OI ¼ 0.44 for small planktivores) (Table 3).

4 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518 al., et page ) 45 0.005 0.018 0.026 0.019 0.598 0.015 0.105 0.021 0.001 0.002 0.008 0.048 0.006 0.008 0.016 0.004 0.005 0.001 0.003 0.042 0.001 0.003 0.002 0.008 0.001 0.018 0.001 0.004 0.001 0.002 0.007 23 next Varkey on 0.041 0.054 0.108 0.221 0.008 0.014 0.028 0.001 0.012 0.017 0.188 0.012 0.025 0.001 0.094 0.004 0.005 0.007 0.021 0.026 0.026 0.008 0.010 0.026 0.024 0.009 0.003 0.002 0.003 22 44 2010 ; 0.150 0.143 0.217 0.220 0.192 0.001 0.001 0.001 0.007 0.030 0.001 0.005 0.026 0.001 0.003 21 al., ( continued 43 et 0.028 0.183 0.188 0.080 0.162 0.001 0.232 0.006 0.001 0.004 0.001 0.026 0.037 0.001 0.003 0.016 0.003 0.003 0.003 0.006 0.007 0.003 0.001 0.001 0.002 20 42 Piroddi 0.050 0.358 0.336 0.113 0.004 0.072 0.001 0.005 0.001 0.001 0.005 0.001 0.003 0.003 0.006 0.006 0.001 0.002 0.003 0.001 0.001 0.001 0.012 0.012 0.002 19 2008 ; 41 0.067 0.093 0.125 0.020 0.202 0.005 0.246 0.001 0.040 0.002 0.006 0.008 0.020 0.011 0.007 0.010 0.001 0.030 0.010 0.007 0.002 0.005 0.005 0.002 0.001 0.010 0.043 0.013 0.004 0.001 0.002 0.001 18 40 Pitcher, and 0.093 0.102 0.011 0.329 0.001 0.367 0.023 0.001 0.001 0.005 0.001 0.008 0.001 0.011 0.004 0.004 0.003 0.010 0.002 0.001 0.020 0.002 17 39 Bailey 0.116 0.090 0.115 0.006 0.170 0.001 0.225 0.001 0.024 0.005 0.003 0.017 0.030 0.010 0.002 0.021 0.079 0.002 0.005 0.003 0.014 0.004 0.002 0.002 0.010 0.030 0.005 0.002 0.001 0.001 0.001 0.001 16 38 2007 ; al., 0.175 0.134 0.325 0.002 0.008 0.001 0.003 0.021 0.016 0.163 0.002 0.001 0.028 0.002 0.001 0.006 0.004 0.003 0.040 0.002 0.033 0.007 0.001 0.019 15 et 37 0.550 0.308 0.008 0.001 0.012 0.001 0.021 0.003 0.004 0.050 0.032 0.004 0.004 0.002 14 Pitcher 36 0.068 0.142 0.240 0.060 0.002 0.356 0.006 0.006 0.001 0.006 0.003 0.008 0.031 0.030 0.002 0.037 13 model, 35 0.037 0.030 0.101 0.096 0.215 0.005 0.269 0.001 0.008 0.001 0.004 0.011 0.010 0.060 0.005 0.005 0.124 0.001 0.001 0.001 0.001 0.010 0.001 0.001 0.001 0.001 12 Ampat 34 Raja 0.800 0.008 0.081 0.001 0.054 0.001 0.002 0.001 0.002 0.003 0.042 0.002 0.001 0.001 0.001 0.001 11 the 33 from 0.013 0.002 0.002 0.001 0.021 0.018 0.051 0.060 0.056 0.048 0.012 0.028 0.034 0.012 0.090 0.023 0.013 0.015 0.216 0.170 0.004 0.041 0.005 0.032 0.031 0.001 0.002 0.002 10 32 0.764 0.035 0.005 0.001 0.002 0.013 0.011 0.092 0.015 0.022 0.006 0.036 9 (adapted one 31 to 0.006 0.080 0.204 0.039 0.069 0.006 0.045 0.006 0.050 0.083 0.073 0.014 0.002 0.016 0.024 0.003 0.012 0.049 0.012 0.037 0.001 0.028 0.017 0.006 0.040 0.002 0.008 0.001 0.008 0.019 0.001 0.003 0.001 0.038 8 equal 0.001 30 0.152 0.005 0.141 0.008 0.013 0.002 0.002 0.032 0.029 0.111 0.075 0.018 0.001 0.081 0.002 0.028 0.027 0.032 0.042 0.009 0.008 0.026 0.026 0.018 0.035 0.042 0.009 0.001 0.002 0.001 0.007 0.009 0.001 0.001 0.001 0.002 7 being 29 0.068 0.194 0.001 0.001 0.024 0.047 0.024 0.036 0.001 0.126 0.020 0.045 0.011 0.009 0.042 0.018 0.060 0.011 0.087 0.021 0.101 0.034 0.004 0.001 0.008 0.002 0.001 0.001 6 column 28 0.103 0.185 0.007 0.001 0.001 0.005 0.003 0.090 0.025 0.001 0.067 0.050 0.007 0.012 0.061 0.014 0.030 0.006 0.181 0.073 0.030 0.035 0.002 0.001 0.004 0.002 0.001 0.001 0.002 5 each of 27 sum 0.112 0.240 0.002 0.001 0.003 0.019 0.002 0.167 0.016 0.070 0.050 0.020 0.047 0.010 0.025 0.007 0.014 0.050 0.097 0.010 0.015 0.001 0.006 0.002 0.009 0.003 0.001 4 the 26 0.205 0.255 0.008 0.113 0.188 0.007 0.075 0.006 0.018 0.126 3 model, 25 0.798 0.005 0.061 0.020 0.100 0.007 0.009 2 Bitung the 0.002 24 0.455 0.065 0.097 0.110 0.001 0.032 0.014 0.002 0.002 0.001 0.027 0.048 0.020 0.001 0.002 0.011 0.014 0.008 0.052 0.002 0.001 0.018 0.001 0.016 1 in groups associated associated associated associated algae grazers reef associated associated 2013 ). grazers reef reef reef functional hydroids fish echiniods browsing reef reef of fish al., Octopus wrasse groupers scraping small medium large butterflyfish groupers and Pelagic et wrasse predator invertebrates and planktivore scraping planktivore small medium large butterflyfish groupers triton trout corals \ corals cucumbers Sharks\whale\dolphin Prey Import Mangroves Detritus Seagrass Macro/Calcareous Phytoplankton Zooplankton Jellyfish Other Herbivorous Bivalves Giant Sea Lobsters Crabs Squid Shrimps Hard Soft Anemonies Sponges Deeperwater Detritivore Anchovy Juvenile Adult Macro-algal Small Large Demersal Juvenile Adult Juvenile Adult Juvenile Adult Medium Cleaner Juvenile Rays Adult Coral Sharks/whales/dolphins Snappers Birds Turtles Adult Subadult Juvenile Napoleon Tuna/billfish Prey/predator 2 Hoover composition 1 51 49 50 48 47 46 45 44 43 41 42 40 37 38 39 36 35 31 32 33 34 30 28 29 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 11 12 10 1 7 2 3 4 5 6 8 9 2012 ; Diet Table

5 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518 0.764 0.236 45 0.221 0.001 0.006 0.001 0.001 0.004 0.006 0.001 0.001 0.002 0.037 0.011 0.018 0.690 44 0.151 0.273 0.442 0.002 0.007 0.015 0.109 43 0.291 0.509 0.200 42 0.555 0.445 41 0.097 0.903 40 0.097 0.185 0.097 0.001 0.198 0.138 0.285 39 0.002 0.030 0.189 0.769 38 0.400 0.600 37 0.055 0.002 0.012 0.002 0.002 0.002 0.002 0.006 0.010 0.002 0.052 0.001 0.248 0.001 0.090 0.159 0.212 0.142 36 0.158 0.127 0.479 0.006 0.001 0.034 0.014 0.177 0.005 35 0.139 0.592 0.269 34 0.128 0.128 0.064 0.006 0.027 0.031 0.006 0.096 0.514 33 0.122 0.488 0.390 32 0.122 0.488 0.390 31 0.020 0.013 0.001 0.036 0.004 0.001 0.007 0.009 0.010 0.002 0.004 0.003 0.005 0.001 0.002 0.002 0.009 0.011 0.016 0.016 0.023 0.016 0.030 0.012 0.271 0.042 0.430 30 0.567 0.010 0.004 0.419 29 0.823 0.043 0.013 0.007 0.014 0.010 0.026 0.013 0.051 28 0.301 0.245 0.148 0.072 0.003 0.001 0.002 0.003 0.003 0.001 0.006 0.015 0.003 0.002 0.005 0.002 0.003 0.009 0.177 27 0.351 0.338 0.103 0.048 0.002 0.001 0.001 0.002 0.011 0.020 0.080 0.001 0.001 0.001 0.008 0.013 0.003 0.017 26 0.537 0.004 0.150 0.132 0.001 0.002 0.002 0.002 0.002 0.003 0.001 0.002 0.016 0.006 0.015 0.127 25 0.212 0.160 0.091 0.001 0.002 0.001 0.002 0.002 0.001 0.001 0.004 0.008 0.001 0.002 0.002 0.001 0.001 0.099 0.403 24 associated associated associated associated algae grazers reef associated associated grazers reef reef reef hydroids fish ) echiniods browsing reef reef fish Octopus wrasse groupers groupers butterflyfish large medium small scraping and pelagic wrasse predator invertebrates and planktivore groupers butterflyfish large medium small planktivore scraping triton trout corals \ corals cucumbers ( continued Macro/Calcareous Seagrass Mangroves Detritus Import Phytoplankton Subadult Juvenile Snappers Juvenile Birds Turtles Adult Napoleon Coral Rays Adult Cleaner Adult Juvenile Tuna\Billfish Medium Adult Juvenile Adult Juvenile Demersal Large Small Macro-algal Adult Juvenile Giant Herbivorous Sponges Bivalves Anemonies Crabs Detritivore Anchovy Deeperwater Hard Soft Lobsters Shrimps Sea Squid Other Jellyfish Zooplankton Prey 2 47 48 49 50 51 46 7 5 6 13 2 3 4 8 10 11 12 14 16 17 9 15 18 19 20 21 22 23 24 25 26 27 40 41 34 42 33 39 28 29 30 31 32 38 35 37 36 43 44 45 Table

6 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

3.2. SIA outputs the prey and predator (Fry, 2006), and that the ‘base’ herbivorous or ganisms are representative for the system as a whole. TLs estimated from δ15N values varied between 2.03 (sea cucumber) Regarding the first of these assumptions, we used 3.4 parts per and 3.75 (coral trout). SD and SEAc, the two indicators of trophic niche thousand as our TFE, but it has been reported to vary from 0.5 to 5.5 width, suggested two distinct clusters of functional groups according to parts per thousand (Vander Zanden and Rasmussen, 2001; Post, 2002). the breath of their diet and their omnivory. For the fish, the functional As for the second assumption, we used here Trochus sp. as ‘base’ her group of small planktivores (SEAc ¼ 12.26) and large reef associated fish bivore, as it was the most common primary consumers in the study area. (SEAc ¼ 5.9) possessed the widest range of prey consumption, while However, we did not estimate the isotopic signature of bivalves, another small demersal fish (SEAc ¼ 0.03) had the smallest range (Fig. 3). important component of the benthos. Also, some input parameters in the Ecopath model were relatively 3.3. Comparing results between Ecopath and SIA uncertain, as they were inferred from estimates for other ecosystems. For instance, while the fish biomass was from a local survey, the diet in The 115 species analyzed with stable isotopes, distributed in 19 formation was mostly modifiedfrom the model of Bird’s Head Seascape functional groups, corresponded to 39% (115 of 297 species) of 50 nearby (Pitcher et al., 2007; Bailey and Pitcher, 2008; Varkey et al., groups in the Ecopath model of Bitung food web (Table 3). Regarding 2012). In addition, there were seasonal variation of fish biomass in the species analyzed using SIA, their biomass were between 53% and Bitung (Du et al., 2018), which will have influenced trophic dynamics. 100% of the total biomass in each functional group (Table 3), confirming that the major groups were analyzed. 4.2. Comparison of trophic niche width derived from Ecopath and SIA The TLs calculated from Ecopath were positively and highly corre 15 2 lated with those derived from δ N values (r spearman ¼ 0.71, n ¼ 19, p < There was no correlation between the trophic niche width indices, as 0.001) (Fig. 4), indicating a consistency between the two sets of TLs also reported in several previous studies (Navarro et al., 2011; Lassalle calculated from both methods. The slope of the linear regression is less et al., 2014), which may be due to the differences in the definitionof OI than 1, implying that the Ecopath analysis tended to slightly over in the Ecopath model and the SIA. The omnivory index refers to the estimate trophic levels. A perfect agreement was found in two functional variances of TLs in a consumer’s diets. A population with a wide large groups, shrimps, consisting of two species (group 35), and large reef- dietary niche may consist of (i) the individuals in a population associated fish, consisting of 84 species (group 16 þ 17). consuming a broad range of diet items and therefore all individuals Regarding the indices of trophic niche width OIEcopath and SEAcSIA, having the same OI (‘individual generalism’) or (ii) consist of individuals OIEcopath and SDTLSIA, there is no significant relationship between these in the population having different diets, each focused on a narrow range metrics. Only few functional groups showed a good correspondence of diet items (‘population generalism’) (Van Valen, 1965; Grant et al., (Figs. 5 and 6). The large trophic niche width was recorded in small 1976). It is not possible to distinguish between those two types of gen planktivores by using both methods, suggesting some degrees of dietary eralisms using the diet matrix and OI in Ecopath model. On the other plasticity in this group (Fig. 3, Table 3). We also noted a positive rela hand, the stable isotope signature of muscle tissue integrates TL-var tionship between the niche widths quantified by SEAcSIA and their iations over a relatively long time. Thus, the high values of SD around biomass estimated from Ecopath (Fig. 7), suggesting that niche width the mean trophic level based on stable isotope data collected at the in increases with assemblage biomass. dividual level would primarily identify item population generalism (Bearhop et al., 2004). 4. Discussion 4.3. Usefulness of decreased uncertainty of TL estimates from the two 4.1. Comparison of trophic levels derived from Ecopath and SIA complementary methods

Our study revealed a clear relationship between the TLs calculated by Trophic levels, as introduced by Lindeman (1942), and further Ecopath and δ15N signature, suggesting that these methods could be developed by Odum and Heald (1975), are important ecological con used for cross validation. The results of the this study were in line with cepts (Rombouts et al., 2013). For example, the mean TL of fisheries the findings from previous reports that TLs derived from Ecopath and landings, firstused by Pauly et al. (1998) to demonstrate ‘fishingdown SIA are complementary (Table 4). Therefore, the stable isotope signature marine food webs’, was useful in summarizing fisheries impacts on obtained from this study should also be useful for modelling of other marine ecosystems. Indeed, the marine tropic index (MTI), which tropical marine ecosystems. Moreover, the significant relationship be measures the change in mean TLs of catch combined fishery landings tween TLs derived from Ecopath and SIA suggested that the diet and diet composition data, was adopted by the Convention on Biological composition used in the Bitung model was generally accurate (Coll et al., Diversity (CBD) as one of the eight indicators to measure the 2010 target 2006). (Pauly and Watson, 2005), and has also been selected as a “proxy in Several studies suggested that Ecopath overestimates TLs. For dicator” for Target 14.2 of Sustainable Development Goal 14 (‘Life example, the TLs from Ecopath were overestimated by 8% compared below Water’). Finally, biomass trophic spectra, i.e., the continuous with those estimated by SIA in the Mediterranean Sea (Polunin and distribution of biomass by TLs from herbivores to top carnivores can also Pinnegar, 2000), far more than the 2.4% overestimation based on this be used to assess the effects of, e.g., fishing pressure onto aquatic eco study. However, lower TLs calculated from Ecopath were also reported systems (Gascuel and Pauly, 2009; Lassalle et al., 2014). in several marine ecosystems, such as in Laguna Rocha lagoon in Thus, given the wide use of TLs as an ecosystem indicator, the Uruguay (Milessi et al., 2010), in the Bay of Biscay (Lassalle et al., 2014) comparison between TLs from ecosystem models and those derived from and in Xiamen Bay in China (Du et al., 2015), which were 12–14% lower SIA should be a necessary step, in order to to reduce the uncertainty than those derived from SIA. Thus, while TLs estimates from Ecopath associated with trophic level estimates (Nilsen et al., 2008; Milessi et al., and SIA are strongly correlated along a 1: 1 ratio, (Lassalle et al., 2014), 2010; Navarro et al., 2011; Lassalle et al., 2014; Du et al., 2015). it is still appropriate to cross-validate them whenever possible. Meanwhile, stable isotope data can not only be used to compare the There are several reasons for the differences between both methods. outputs estimated from Ecopath model, can also be used to refine diet One is that SIA represents time-integrated feeding information, while composition matrices (Baeta et al., 2011), and multiple stable isotope Ecopath the model reflectsgastric contents at the time of sampling. Also, data can be integrated into the modelling process using Linear Inverse the accuracy of TLs calculated from SIA depends on two assumptions Modelling, which has a demonstrated capability to decrease the uncer being met, i.e., that the δ15N trophic enrichment factor (TEF) between tainty of ecosystem models (Van Oevelen et al., 2006; Pacella et al.,

7 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

Table 3 Compartments of the Ecopath model of the Bitung ecosystem in North Sulawesi used for comparison with the isotope analysis. Species column is the composition at the species level of the functional groups in Ecopath. Except macro-algal browsing and squid, all functional groups are multi-species compartments. 115 species with the stable isotope signature were analyzed and consequently retained in the present comparative study period, Contribution is the biomass tribute of each species to their respective functional group. TLEcopath is the trophic level estimated from the Ecopath model. TLSIA is the trophic level derived from stable isotope analysis for each species, the TLSIA of each functional group is weighted by the biomass proportions, and SD is the standard deviation of each functional group. SEAc is the standard ellipse area corrected for sample size and calculated for each species. n is the number of individuals used for stable isotope analysis. OI is the omnivory index estimated from the Ecopath model. SD, SEAc, OI and the square root of OI are the four indices of trophic niche width. All indicators used in this study are unitless. 13 15 Ecopath functional group Group No. Species Contribution TLEcopath TLSIA SD SEAc n OI δ C δ N

Groupers 4 þ 5þ6 3.48 3.12 0.13 1.16 0.13 Cephalopholis argus 29.83 3.12 1 À 17.0 � 0.0 11.0 � 0.0 Cephalopholis cyanostigma 4.27 3.12 1 À 15.7 � 0.0 11.0 � 0.0 Epinephelus caeruleopunctatus 0.66 2.82 4 À 16.7 � 2.0 10.0 � 0.8 Epinephelus fasciatus 20.94 3.15 1 À 16.4 � 0.0 11.1 � 0.0 Epinephelus merra 5.03 3.06 4 À 15.2 � 0.2 10.8 � 0.4 Lutjanus decussatus 1.79 3.21 13 À 16.2 � 0.8 11.3 � 0.6 other 10 species 37.48 Coral trout 10 3.84 3.75 0.93 5.49 0.13 Plectropomus areolatus 60.75 3.24 3 À 16.1 � 0.3 11.4 � 0.3 Cephalopholis miniata 39.25 4.55 1 À 11.02 15.87 Rays 11 3.27 3.22 0.48 2.24 0.67 Dasyatis kuhlii 33.00 3.56 1 À 10.57 12.51 Dasyatis zugei 33.00 2.88 2 À 17.5 � 0.17 10.20 Taeniura lymna 34.00 Butterflyfish 12 þ 13 2.90 3.24 0.33 3.91 0.39 Chaetodontoplus mesoleocus 4.11 2.62 1 À 9.95 9.32 Heniochus diphreutes 14.07 3.36 1 À 15.01 11.81 Heniochus varius 9.98 3.52 1 À 14.44 12.36 Heniochus chrysostomus 9.46 3.37 1 À 14.45 11.85 Chaetodon kleinii 8.76 3.12 1 À 14.50 11.01 Centropyge tibicen 6.70 2.93 1 À 11.03 10.37 other 35 species 46.94 Medium pelagic 15 3.28 3.53 0.00 0.48 Sphyraena pinguis 59.43 3.53 2 À 16.2 � 0.6 12.4 � 0.0 Sphyraena flavicauda 40.57 Large reef associated 16 þ 17 2.96 2.95 0.35 5.90 0.40 Tylosurus crocodilus 2.21 3.00 1 À 17.5 � 0.0 10.6 � 0.0 Caesio caerulaurea 0.81 2.94 1 À 17.5 � 0.0 10.4 � 0.0 Caranx melampygus 3.40 3.21 1 À 11.6 � 0.0 11.3 � 0.0 Platax orbicularis 3.30 2.68 4 À 17.7 � 0.1 9.5 � 0.6 Diagramma pictum 0.83 3.15 5 À 15.4 � 0.1 11.1 � 0.7 Plectorhinchus chaetodonoides 4.58 3.24 3 À 16.5 � 0.5 11.4 � 0.4 Plectorhinchus lineatus 1.49 2.91 1 À 14.9 � 0.0 10.3 � 0.0 Plectorhinchus vittatus 0.66 3.09 3 À 15.9 � 0.8 10.9 � 0.3 Sargocentron caudimaculatum 0.74 3.26 2 À 16.4 � 0.3 11.5 � 0.5 Sargocentron rubrum 0.18 2.88 1 À 15.3 � 0.0 10.2 � 0.0 Cheilinus fasciatus 1.69 3.06 2 À 14.9 � 0.0 10.8 � 0.0 Cheilinus trilobatus 1.76 3.03 4 À 15.0 � 1.9 10.7 � 0.1 Choerodon anchorago 2.97 2.79 1 À 11.4 � 0.0 9.9 � 0.0 Coris gaimard 0.37 2.97 3 À 15.7 � 0.2 10.5 � 0.3 Epibulus insidiator 4.79 3.09 5 À 14.0 � 1.6 10.9 � 0.8 Halichoeres hortulanus 0.37 2.82 4 À 15.1 � 0.6 10.0 � 0.3 Hemigymnus melapterus 2.72 2.88 4 À 13.4 � 0.6 10.2 � 0.7 Oxycheilinus celebicus 0.12 3.15 1 À 15.7 � 0.0 11.1 � 0.0 Oxycheilinus diagrammus 1.96 3.03 2 À 15.8 � 0.7 10.7 � 0.3 Lethrinus erythropterus 1.48 3.15 2 À 14.7 � 0.1 11.1 � 1.0 Lethrinus harak 2.68 3.09 1 À 14.3 � 0.0 10.9 � 0.0 Lethrinus obsoletus 0.35 3.09 6 À 15.5 � 0.9 10.9 � 0.4 Monotaxis grandoculis 2.56 2.88 3 À 13.6 � 1.1 10.2 � 0.9 Parupeneus barberinoides 0.00 2.88 3 À 14.5 � 2.1 10.2 � 0.2 Parupeneus barberinus 1.60 3.12 2 À 14.0 � 1.9 11.0 � 0.4 Parupeneus bifasciatus 0.58 3.03 1 À 14.4 � 0.0 10.7 � 0.0 Parupeneus cyclostomus 0.82 3.03 1 À 16.7 � 0.0 10.7 � 0.0 Parupeneus multifasciatus 1.57 2.97 5 À 15.9 � 0.9 10.5 � 0.4 Scolopsis affinis 0.30 3.09 9 À 15.5 � 0.4 10.9 � 0.4 Scolopsis auratus 0.53 3.06 1 À 14.9 � 0.0 10.8 � 0.0 Scolopsis bilineatus 1.13 3.44 4 À 15.6 � 0.5 12.1 � 0.2 Scolopsis margaritifer 0.57 3.41 2 À 14.7 � 0.0 12.0 � 0.3 Siganus canaliculatus 0.11 3.00 1 À 14.4 � 0.0 10.6 � 0.0 Siganus coralinus 0.23 2.21 1 À 16.2 � 1.3 7.9 � 0.9 Siganus doliatus 0.68 2.35 7 À 16.8 � 1.3 8.4 � 0.9 Siganus gutatus 1.14 1.85 2 À 16.2 � 2.1 6.7 � 0.0 Siganus puellus 0.22 2.35 1 À 18.8 � 0.0 8.4 � 0.0 Siganus stellatus 0.24 2.15 3 À 17.1 � 3.1 7.7 � 0.5 Siganus vulpinus 1.12 2.41 3 À 17.9 � 0.9 8.6 � 0.2 Zanclus cornutus 0.20 3.09 1 À 17.4 � 0.0 10.9 � 0.0 Plotosus lineatus 2.69 2.37 4 À 12.49 � 0.04 8.46 � 0.11 (continued on next page)

8 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

Table 3 (continued )

13 15 Ecopath functional group Group No. Species Contribution TLEcopath TLSIA SD SEAc n OI δ C δ N

other 43 species 44.26 Medium reef associated 18 þ 19 2.63 2.58 0.27 4.35 0.39 Anampses meleagrides 0.04 2.85 1 À 15.7 � 0.0 10.1 � 0.0 Scolopsis ciliatus 0.93 3.29 2 À 15.2 � 0.0 11.6 � 0.1 Amblglyphidodon curacao 0.98 3.06 1 À 17.3 � 0.0 10.8 � 0.0 Amblglyphidodon leucogaster 0.84 2.97 1 À 18.2 � 0.0 10.5 � 0.0 Selaroides leptolepis 87.09 2.56 7 À 18.3 � 0.1 9.1 � 0.2 other 20 species 10.14 Small reef associated 20 þ 21 2.67 2.50 0.35 4.95 0.46 Chromis ternatensis 60.12 2.47 4 À 17.7 � 0.1 8.8 � 0.63 Archamia fucata 9.40 2.88 3 À 17.3 � 0.17 10.2 � 0.46 Apogon chrysopomus 6.92 2.19 1 À 13.29 7.85 other 17 species 23.58 Demersal 22 3.31 3.35 0.20 0.03 0.31 Pempheris oualensis 70.29 3.35 4 À 17.3 � 0.2 11.8 � 0.2 Pterapogon kauderni 29.71 Large planktivore 23 3.31 3.15 0.33 2.29 0.23 Caesio cuning 6.24 3.09 4 À 18.5 � 0.2 10.9 � 0.2 Pterocaesio pisang 3.66 2.44 1 À 17.7 � 0.0 8.7 � 0.0 Pterocaesio tille 9.43 2.41 2 À 17.6 � 0.2 8.6 � 0.7 Myripristis kuntee 1.02 3.15 4 À 17.5 � 0.1 11.1 � 0.5 Myripristis murdjan 0.31 3.06 2 À 17.2 � 0.0 10.8 � 0.0 Myripristis vitata 0.10 2.94 6 À 16.9 � 0.4 10.4 � 0.4 Abudefduf vaigiensis 0.86 3.09 4 À 18.4 � 0.3 10.9 � 0.1 Odonus niger 56.77 3.32 1 À 17.40 11.70 other 5 species 21.60 Small planktivore 24 2.70 3.14 0.66 12.26 0.44 Abudefduf sexfasciatus 11.35 3.18 4 À 18.1 � 0.1 11.2 � 0.4 Pomacentrus moluccensis 12.90 4.12 1 À 13.01 14.42 Chromis viridis 8.02 2.47 1 À 17.70 8.80 Pomacentrus brachialis 9.38 2.62 5 À 17.0 � 0.27 9.3 � 0.22 Cirrhilabrus cyanopleura 10.98 2.88 1 À 18.70 10.20 other 16 species 47.36 Macro-algal browsing 25 2.25 2.57 0.74 0.23 0.29 Mugil cephalus 100.00 2.57 3 À 14.15 � 0.56 9.13 � 0.74 Scraping grazers 26 þ 27 2.31 2.28 0.23 5.88 0.34 Acanthurus lineatus 0.22 1.85 2 À 12.9 � 0.0 6.7 � 0.0 Acanthurus mata 1.49 2.41 3 À 18.7 � 0.1 8.6 � 0.1 Acanthurus nigricans 0.45 2.56 1 À 14.9 � 0.0 9.1 � 0.0 Acanthurus nigrofuscus 0.13 2.35 1 À 18.0 � 0.0 8.4 � 0.0 Acanthurus nigroris 1.63 2.06 5 À 16.2 � 1.4 7.4 � 0.6 Acanthurus olivaceus 0.17 2.35 4 À 15.1 � 2.3 8.4 � 1.2 Acanthurus pyroferus 0.17 2.38 2 À 16.7 � 0.1 8.5 � 0.2 Acanthurus leucocheilus 0.17 2.35 1 À 18.0 � 0.0 8.4 � 0.0 Acanthurus nigricaudus 0.17 2.56 1 À 14.9 � 0.0 9.1 � 0.0 Cetoscarus bicolor 1.10 2.09 1 À 15.5 � 0.0 7.5 � 0.0 Chlorurus bleekeri 3.62 2.29 4 À 13.8 � 3.0 8.2 � 1.1 Leptoscarus vaigiensis 0.20 1.94 2 À 8.3 � 0.1 7.0 � 0.1 Scarus dimidiatus 1.54 1.74 6 À 8.3 � 1.1 6.3 � 0.8 Scarus forsteni 0.25 1.97 4 À 14.3 � 1.4 7.1 � 0.6 Scarus ghobban 1.71 2.32 2 À 17.0 � 0.8 8.3 � 0.8 Scarus niger 5.14 2.29 3 À 14.6 � 0.3 8.2 � 0.6 Scarus oviceps 0.67 2.00 1 À 1.4 � 0.0 7.2 � 0.0 Scarus psittacus 1.60 2.03 5 À 14.1 � 1.5 7.3 � 0.5 Scarus rubroviolaceus 0.70 1.97 2 À 13.6 � 0.0 7.1 � 0.1 Scarus schlegeli 0.92 2.29 4 À 13.9 � 0.7 8.2 � 0.6 Scarus spinus 0.03 2.35 7 À 11.3 � 1.8 8.4 � 0.7 Naso vlamingi 47.55 2.32 1 À 11.50 8.30 other 33 species 30.73 Detritivore fish 28 2.23 2.74 0.08 0.31 0.27 Dischistodus perspicilatus 3.89 2.85 3 À 14.7 � 0.7 10.1 � 0.2 Melichthys vidua 96.11 2.74 2 À 16.9 � 0.3 9.7 � 0.5 Shrimps 35 2.28 2.29 0.18 6.25 0.26 Penaeus monodon 50.00 2.16 4 À 18.59 � 2.45 7.75 � 0.61 Harpiosquilla harpax 50.00 2.41 4 À 15.5 � 0.06 8.6 � 0.14 Squid 36 3.30 2.91 0.30 1.47 0.20 Loligo duvaucelii 100.00 2.91 5 À 18.19 � 0.92 10.3 � 0.3 Sea cucumber 37 2.00 2.03 0.25 2.93 0.00 Bohadschia argus 50.00 1.85 2 À 14.1 � 0.71 6.7 � 1.03 Holothuria edulis 50.00 2.21 4 À 12.8 � 0.4 7.9 � 0.63 Crab 39 2.76 2.80 0.17 3.39 0.35 Portunus pelagicus 50.00 2.92 4 À 17.1 � 0.74 10.32 � 1.25 Portunus sanguinolentus 50.00 2.68 2 À 15.7 � 0.29 9.5 � 0.23 other invertebrates 21 2.16 2.07 0.18 6.20 0.17 Cypraea tigris 30.00 2.26 1 À 16.90 8.10 Purpura lapillus 30.00 2.11 2 À 17.12 � 0.03 7.57 � 0.05 Trochus sp. 40.00 1.91 7 À 15.1 � 1.55 6.9 � 1.46

9 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

Fig. 2. Trophic flowdiagram of the balanced trophic model of Bitung, North Sulawesi. The components of the system are structured along the vertical axis according to their trophic level. The area of each circle is proportional to the biomass of each functional group.

Fig. 4. Linear regression between trophic levels estimated from Ecopath model and stable isotope analysis for Bitung ecosystem (solid line). The broken line represents a 1:1 relationship.

Fig. 3. Stable isotope bi-plots illustrating the isotopic niche of each functional group. (a) Large reef associated fishes, including 41 species; (b) Small plank step in quantifying the trophic interactions in an ecosystem (Lassalle tivore, including 5 species; (c) Groupers, including 6 species. For each func et al., 2014). tional group, species are represented by identical symbols, and a line encloses its corrected standard ellipse area (SEAc). Funding

2013). Furthermore, a new isotope mixing model (‘IsoWeb’), which uses The present study was supported by grants from the National Natural stable isotope data from all components in a food web, can estimate the Science Foundation of China (no. 41676096), the China-Indonesia diet composition of its consumers (Kadoya et al., 2012). This is a critical Maritime Cooperation Fund project “China-Indonesia Bitung Ecological Station Establishment” (research permits no./FRP/E5/Dit.KI/VI/2016),

10 J. Du et al. Estuarine, Coastal and Shelf Science 233 (2020) 106518

Fig. 5. Standard ellipse areas (SEAc) estimated from SIA plotted against the Fig. 7. Standard ellipse areas (SEAc) estimated from SIA plotted against the corresponding OI value. Code corresponding to the names of functional groups corresponding square root of biomass value from the Ecopath model. Code see Table 1. corresponding to the names of functional groups see Table 1.

Table 4 Correlation analysis between TLs estimates from Ecopath model and Stable isotope in previous studies.

Study area r2 n References

Bitung in North Sulawesi 0.74 19 Present study Subtropical Bay in China 0.696 23 Du et al. (2015) Continental shelf of the Bay of Biscay 0.72 16 Lassalle et al. (2014) South Catalan Sea 0.69 24 Navarro et al. (2011) Subtropical lagoon in Uruguay 0.82 14 Milessi et al. (2010) Fjord in northern Norway 0.72 19 Nilsen et al. (2008) Prince William Sound 0.99 7 Kline and Pauly (1998)

Note: At Salt marsh ponds in Virginia, TLs from Ecopath matched those from δ15N data for three of the four networks, but no coefficient and numerical data provided (Dame and Christian, 2008).

Bailey and Pitcher (2008); [3]-this study; [4]- Piroddi et al., (2010); [5]- Hoover et al., (2013).

Acknowledgements Fig. 6. Standard deviations (SD) of TLs estimated from SIA plotted against the corresponding square root of OI derived from the Ecopath model. Code corre We would like to express our gratitude to Dr. Xijie Yin (Third Insti sponding to the names of functional groups see Table 1. tute of Oceanography, Ministry of Natural Resources) for his help in the analysis of stable isotopes. National Key R & D Program of China (no. 2017YFC1405101), and the China-Canada Marine Ecosystem Research project. DP acknowledges References support from the Sea Around Us, itself supported by a number of phil anthropic foundations. Baeta, A., Niquil, N., Marques, J.C., Patrício, J., 2011. Modelling the effects of eutrophication, mitigation measures and an extreme flood event on estuarine benthic food webs. Ecol. Model. 222, 1209–1221. Author contribution statement Ecological and economic analyses of marine ecosystems in the Bird’s Head Seascape, Papua, Indonesia: II. In: Bailey, M., Pitcher, T.J. (Eds.), Fish Cent. Res. Rep. 16 (1), 186. Jianguo Du: Conceptualization, Methodology, Writing - Original Barbier, E.B., Koch, E.W., Silliman, B.R., Hacker, S.D., Wolanski, E., Primavera, J., Draft. Petrus Christianus Makatipu: Investigation, Resources. Lily S.R. Stoms, D.M., 2008. Coastal ecosystem-based management with nonlinear ecological Tao: Formal analysis. Daniel Pauly: Writing - Review & Editing. William functions and values. Science 319 (5861), 321–323. W.L. Cheung: Writing - Review & Editing. Teguh Peristiwady: Investi Bearhop, S., Adams, C.E., Waldron, S., Fuller, R.A., MacLeod, H., 2004. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73 gation, Resources. Jianji Liao: Investigation, Resources. Bin Chen: (5), 1007–1012. Writing - Review & Editing, Supervision. Christensen, V., Pauly, D., 1992. Ecopath II - a software for balancing steady-state ecosystem models and calculating network characteristics. Ecol. Model. 61, 169–185. Declaration of competing interest Christensen, V., Walters, C.J., 2004. Ecopath with Ecosim: methods, capabilities and limitations. Ecol. Model. 172, 109–139. Christensen, V., Walters, C.J., Pauly, D., 2005. Ecopath with Ecosim: a User’s Guide. The authors declare that they have no known competing financial Fisheries Centre, University of British Columbia, Vancouver. interests or personal relationships that could have appeared to influence Christensen, V., Walters, C.J., Pauly, D., Forrest, R., 2008. Ecopath with Ecosim Version 6 the work reported in this paper. User Guide. Lenfest Ocean Futures Project. University of British Columbia, Vancouver. Notes: [1]- Fishery Statistics of Fishery Bureau of Bitung, 2017); [2]-

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