Estuarine, Coastal and Shelf Science 233 (2020) 106518 Contents lists available at ScienceDirect Estuarine, Coastal and Shelf Science journal homepage: http://www.elsevier.com/locate/ecss 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