Model and Data Adequacy for Marine Stewardship Council Key Low Trophic Level Species Designation and Criteria and a Proposed New Assessment Index
Marine Stewardship Council Science Series
Model and data adequacy for Marine Stewardship Council key low trophic level species designation and criteria and a proposed new assessment index
Tim Essington1 and Éva E. Pláganyi2 1 University of Washington School of Aquatic and Fishery Sciences. Box 355020 Seattle WA 98195 U.S.A. [email protected]
2 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Marine and Atmospheric Research, Wealth from Oceans Flagship, PO Box 2583, Brisbane, QLD, 4001 Australia. eva.plaganyi- [email protected]
Abstract
There is a great opportunity to use ecological models to assist with identifying possible issues and management measures appropriate to low trophic level (LTL) fisheries. Specifically, there is potential to use models to identify important LTL species and to assess the impacts of different fishing pressures on ecosystems. The ability of models to achieve these aims is a function of the quality data used and the detail of the food-web. This report demonstrates the significant effects of aggregating species into guilds or functional groups on the ability of models to identify connectance levels (a key metric for identifying vulnerable LTL ecosystems). A new index is proposed that appears to better identify situations where exploitation of LTL species can impact on ecosystems.
Citation: Essington T and Pláganyi EE (2013) Model and data adequacy for Marine Stewardship Council key low trophic level species designation and criteria and a proposed new assessment index. Marine Stewardship Council Science Series 1: 171 – 191.
Date submitted: January 2013 | Date published: November 2013
Disclaimer: The Marine Stewardship Council Science Series has been commissioned by Marine Stewardship Council (MSC) as part of its goal to extend scientific research and understanding of marine ecosystems and fisheries. The views expressed in this publication do not necessarily reflect the views of the MSC. The MSC certification program changes over time; every attempt is made to ensure all details within this paper are accurate at the time of publication. An internal review process has been established to ensure as far as possible the accuracy, content, completeness, legality and reliability of the information presented. For full detail of the review process visit: www.msc.org/business-support/science-series/science- series-review-process
Copyright: © The Author 2013. Published by Marine Stewardship Council Science Series. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.
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Model and data adequacy for MSC key LTL species designation and criteria and a proposed new assessment index
Introduction
Low trophic level (LTL) species are characteristically small, pelagic and planktivorous fish that have a shoaling habit. They account for over 30% of fisheries landings and are often of critical importance as food fish to economically important species higher up the food chain (Smith et al., 2011). With an awareness of their importance has come a realisation of the need to assess their relationship with fisheries with regard to both the impact of removal or excessive proportions of LTLs on predators and vice versa (Yemane et al. 2009).
This report reviews data and models available from fisheries currently in the Marine Stewardship Council (MSC) program or under assessment to evaluate them against the key low trophic level (LTL) requirements, Performance Indicator (PI) 1.1.2. These requirements specify: (A) a method to identify stocks as key LTL and (B) default upper bounds for limit and target biomass reference points for key LTL stocks. Biomass reference points may fall below default levels only when there is credible scientific information indicating they would not induce moderate (>40% decline) effects on more than 15% of the species in the ecosystem or have severe (>70% decline) effects on any individual species. Thus, LTL fisheries must pass through two filters during assessment: Filter 1 to identify key LTL species and Filter 2 to set biomass reference points.
The report screens fisheries for data and model adequacy relevant for these two filters, and provides guidance for Conformity Assessment Bodies (CABs) on how to assess fisheries against these filters. This report also explores the extent to which Filter 1 correctly identifies key LTL species and distinguishes them from species for which depletion has less significant impacts.
Methods
Fisheries in the MSC program and in assessment were surveyed. The survey identified 32 (twenty-five certified; seven in assessment) that would be flagged as default key LTL species based on taxonomy (for example, fisheries targeting herrings, sardines, anchovies and krill). Many of the fisheries target the same stocks so the total number of stocks in the program is much smaller.
Table 1. List of stocks and ecosystems in analysis (stocks marked * assumed to be key LTL). Stock Certification status Ecosystem/Area Anchoveta Pre-Assessment Humboldt Current Sandeel*, Herring, Sprat* Herring certified North Sea Capelin* Unknown Barents Sea Menhaden Unknown Chesapeake Bay Sardine, Anchovy* Unknown Benguela Herring, Sprat In assessment Baltic Sea Herring, Sardine, Sprat Certified/ In assessment Celtic Sea Sardine, Thread Herring Certified/ In assessment Gulf of California Krill* Certified Southern Ocean Cornwall Sardine, Sprat, Herring Certified/ In assessment/ Unknown English Channel Southwest Atlantic (Argentine and Uruguayan seas) in Food and Anchovy Certified Agriculture Organisation (FAO) statistical area 41 International Council for the Mackerel Certified Exploration of the Sea (ICES) areas II, III, IV, V & VI, VII, VIII, XII and XIV Herring Certified Atlanto-Scandian Sardine Certified Bay of Biscay Sardine Unknown California Current Sardine Unknown Canary Current Gulf Menhaden, Bay Anchovy* Unknown Gulf of Mexico Small Pelagics (Sardinops, other) Unknown Southeast Australia
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Evaluation of the existing models for several stocks was undertaken (Table 1). This list details fisheries that are currently in the MSC program (in assessment or certified), may choose to pursue certification in the future or that are informative as well-known case studies where predator dependencies have been documented. The review spans 18 ecosystem models that cover 27 LTL stocks covering a range of years between 1990 and 2007 (Table 2).
Table 2. Summary of model sources used in analysis. Stock(s) Model ecosystem Reference Atlanto-Scandian Herring Norwegian Sea and Barents Sea Dommasnes et al. (2001) Clupea harengus 1950-2000 Argentina Anchovy North and Central Patagonia Koen-Alonso & Yodzis (2005) Engraulis anchoita 1900-2000 Southeastern Australia Sardine S.E. Australia Shelf and Slope Bulman et al. (2011) Sardinops sagax 1994-2002 Baltic Sea Herring and Sprat Baltic Sea Harvey et al. (2003) C. harengus & Sprattus sprattus 1974-2000 Barents Sea Capelin Barents Sea Blanchard et al. (2002) Mallotus villosis 1973-1999 California Current Sardine Northern California Current Field et al. (2006) Sardinops sagax 1960-2004 Bay of Biscay Sardine Bay of Biscay Lassalle et al. (2011) Sardina pilchardus 1996-2003 Canary Current Sardine Arguin Bank Sidi and Diop (2004) Sardinops sagax 1988-1998 Celtic Sea Sprat and Herring Celtic Sea Guénette & Gascuel (2009) C. harengus & S. sprattus 1980-2006 Chesapeake Bay Menhaden Chesapeake Bay Christensen et al. (2009) Brevoortia tyrannus 1950-2002 Gulf of California Sardine and et al Thread Herring Northern Gulf of California Morales-Zarate . (2004); et al S. sagax & Opisthonema libertate Ecopath only, 1985-2008 Ainsworth . (2011) Gulf of Mexico Menhaden and Bay Gulf of Mexico Anchovy Walters et al. (2008) B. patronus & Anchoa mitchilli 1950-2004 et al et al Humboldt Current Anchovy North Humboldt Current Guénette, . (2008); Marzloff . et al et al E. ringens 1953-1984, 2000-2006, 1950- (2009); Smith . (2011);Tam . 2009,1995-1998, 1995-2004 (2008); Taylor et al. (2008) Northeast Atlantic Mackerel North Sea Mackinson and Daskalov (2007) Scomber scombrus 1973-2003 North Sea Sprat, Herring, Sandeel and Mackerel North Sea Mackinson & Daskalov (2007) Sprattus sprattus, Clupea harengus, 1973-2003 S. Scombrus South Africa Sardine and Anchovy Southern Benguela Shannon et al. (2003, 2004, 2008); S. sagax, Engraulis ecrisicolus 1980-1997, 1978-2002, 1978-2003, Shin et al. (2004); Smith et al. (2011) Southern Ocean Krill Euphausia superba Various Plaganyi & Butterworth (2012); et al 1970-2006, 1970-2007, 1970-2007 Watters . (2005, 2008) Western English Channel Sprat Western English Channel and Herring Araújo et al. (2005) S. sprattus, C. harengus 1973/1994 * See also Hill (2013) and Watters et al. (2013)
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Model and data adequacy for Filter 1 and Filter 2
In order to accurately identify key LTL species a set of appropriate evaluation criteria were developed (Table 3). The aim was to try to evaluate the utility of the models for assessing impacts of LTL fisheries – despite the fact that they were not originally designed with this in mind. Models are built around the data available, both temporally and spatially. This can lead to a false impression of the variability of a fished stock, as a restricted sample may not pick up on longer term ecological shifts (Samb and Pauly 2000). If models have restricted spatial coverage they may not adequately identify all major predators and their resilience to fishing (Essington and Pláganyi 2013). Models necessarily cannot comprehensively represent the real world and are always simplifications. As such inadequate models may not have all main predators or LTL species represented and may, therefore, inadequately represent the complexities around size and predator/prey relationships.
Food web models require extensive data, some of which may be unavailable. Modellers faced with this situation will often resort to generic data from similar ecosystems or standardised information for particular groups. Ecosystem models may have to be constructed using incomplete information of population dynamics and species interactions; it is essential, therefore, to understand the sensitivity of the model to these groups (Essington & Pláganyi 2013).
Before models can be used to forecast the impacts of effects, such as LTL depletion, they should demonstrate the ability to hindcast using available data. The more diverse the range of data sources used by a model the better; a good model should, therefore, respond appropriately to environmental drivers. The use of models to test for effects of LTL depletion on dependent predators was assessed – those that have done so provide information that can be used to inform Filter 2 in the MSC key LTL requirements. An assessment was made of the models that have been used for this purpose and, of these, those that used environmental drivers as part of the analysis and those that included local depletion. The final point is critical, because LTL fisheries can have localised effects due to the restricted foraging ranges of some central place foragers (Essington and Pláganyi, 2013). Thus, a good model would explicitly or implicitly incorporate spatial processes for assessing the impacts of LTL fisheries.
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Table 3. Model Quality Evaluation Criteria. Stock Certification status Match to fishery?: 1 = perfect match, 2 = some overlap, 3 = nearby or Spatial coverage adjacent system, 4 = no model match 1 = recent decade; 2 = more than 10 years old but no known shift since Time period then, 3 = over 10 years old, known ecosystem shifts 1 = by species with age structure, 2 = by species no age structure, 3 = Low trophic level detail some species and others aggregated, 4 = potential key LTL aggregated, 5 = no pelagic LTL 1 = by species with age structure, 2 = by species no age structure, 3 = Predator detail some species and others predators aggregated, 4 = most key LTL predators aggregated Predator breadth (includes large pelagic 1 = most predator guilds represented, 2 = most represented but one major fish, marine mammals, group omitted, 3 = two or more major groups omitted sea birds) 1 = diet data mostly from diet studies conducted in that region, 2 = diet Quality of trophic data data mostly from diet studies in nearby and similar regions, 3 = diet data mostly from summaries of standardized diets Model publication 1 = peer-reviewed journal or agency report, 2 = unknown or no review Simulation (time Yes/No dynamic?) Fitted to data? Yes/No 1 = survey or stock assessment data for most commercially important stocks, 2 = survey or assessment outputs for most commercially important Type of stock data stocks, no environmental data, 3 = time series of a few stocks, but they include forage fish and most main predators, 4 = time series of fish stocks, but not forage fish and/or main predators Other data used in Yes/No (list) fitting? Fitting include dynamic environmental variables Yes/No as inputs? 1 = good statistical fit, 2 = reasonable fit but without statistical treatment, 3 Quality of fit = poor fit Simulations conducted to look at forage fish Yes/No dependency? Environmental drivers Yes/No (list) included? 1 = detailed treatment of parameter uncertainty, including data pedigree, alternative simulations and sensitivity analysis, 2 = parameter uncertainty Account for uncertainty? is reported but not explicitly included in model runs, 3 = no information on parameter uncertainty given Represent local Yes/No depletion? Match to fishery?: 1 = perfect match, 2 = some overlap, 3 = nearby or Spatial coverage adjacent system, 4 = no model match
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Results
In general, the majority of the models had a good spatial match to fisheries (Table 4). Eleven (61%) of the models had a very good match, and all models had some spatial overlap with the fishery of interest. The Majority of models (83%) used data that were suitably contemporary with only 3 being substantially out of date.
Most models scored well on model depth (level of detail), especially with regard to LTL species and finfish predators (Table 4). All but three models represented LTL species as individual stocks, and roughly half of these included age/stage structure for one or more LTL species. Model depth scores for predator species were poorer than those for LTL species. These scores reflect the tendency for marine mammals and sea birds in particular to be aggregated into functional groups. Model breadth was generally high, with 12 of the 18 models having marine mammals, seabirds and large pelagic fish present. Four models were missing at least one predator type and two were missing two predator types (i.e. no sea birds or marine mammals). Trophic data used to develop the models varied in quality. More than half the models received the best possible score (indicating that models were parameterised from data specific to the individual ecosystem). Five models used data from nearby or adjacent ecosystems and only one model used standardised diets as the primary basis of diet matrices (Table 4).
Very few models scored well on treatment of uncertainties (Table 4). In most cases, description of uncertainty was within the text of relevant model documents. At least half the models had at least a table showing degrees of certainty of model inputs. Of those models that were dynamic, few (n=3) included alternative model runs to capture uncertainty in model inputs.
Most models have been published in some sort of peer-reviewed outlet – only three were published in a report for which the type of review was not specified (Mackinson and Daskalov 2007; Watters, 2005, 2008; Bulman et al. 2011).
Most of the available models included a dynamic model simulation run, but in many cases these were either not intended for evaluation of ecosystem effects of LTL fisheries or insufficient to fully capture key processes governing stock dynamics. Of the models, 16 were time dynamic, 13 were fitted data and 10 included one or more environmental variables (Table 4). Most model fitting used time series of relative stock abundance of LTL species and their predators, but only three used other types of data in fitting (for example, time series of natural mortality; fishing effort and catch; and plankton production).
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Table 4. Summary of model evaluation results (for details of methodology refer to Table 3). A low score indicates a positive assessment (e.g. spatial coverage of the model matches the fishery) and numbers are matched by colours with green being good and red being poor. Blank cells indicate a lack of data.
Atlanto- Southeast Baltic Sea Barents Bay of Argentina Scandian Australia Sprat and Sea Biscay Anchovy Herring Sardine Herring Capelin Sardine Ecosim Ecopath Type of model MRM Ecosim and and Ecosim Ecosim Atlantis Ecosim Spatial coverage 2 2 1 1 1 1 Time period 3 1 1 2 3 1 Low trophic level detail 3 2 2 1 1 2 Predator detail 3 2 2 2 3 2 Predator breadth (includes large pelagic 3 1 1 2 1 1 fish, marine mammals, sea birds) Quality of trophic data 2 1 1 1 1 1 Model publication 1 1 1 1 2 1 Simulation (time Yes No Yes Yes Yes Yes dynamic?) Fitted to data? Yes Yes Yes No No Type of stock data 1 2 1 Other data used in fitting? No No No No Fitting includes dynamic environmental variables No No 1 Yes as inputs? Quality of fit 1 1 1 2 Simulations conducted to look at forage fish No No No No No No dependency? Environmental drivers
included? Account for uncertainty? 1 3 2 2 1 3 Represent local depletion? No No No No No No
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Table 4 Continued
Gulf of California Canary Celtic Sea Chesapeake Mexico Humboldt Current Current Sprat and Bay Menhaden Current Sardine Sardine Herring Menhaden and Bay Anchoveta Anchovy Ecosim EWE and Type of model and Ecopath Ecosim Ecosim Ecosim OSMOSE Atlantis Spatial coverage 1 2 1 2 1 1 Time period 2 2 1 3 1 1 Low trophic level detail 1 4 2 1 1 2 Predator detail 1 4 3 1 1 3 Predator breadth (includes large pelagic 1 1 2 2 3 2 fish, marine mammals, sea birds) Quality of trophic data 2 3 1 2 3 1 Model publication 1 2 1 2 1 1 Simulation (time Yes No Yes Yes Yes Yes dynamic?) Fitted to data? Yes Yes Yes Yes Yes Type of stock data 1 1 1 1 1 Other data used in No No No No Yes Yes fitting? Fitting includes dynamic Yes No Yes Yes No Yes environmental variables as inputs? Quality of fit 1 2 1 2 Simulations conducted to look at forage fish Yes No No Yes Yes Yes dependency? Environmental drivers No No No included? Account for uncertainty? 3 3 3 2 2 2 Represent local depletion? No No No No No No
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Table 4 Continued
Western Northeast North Sea Sprat, South Africa Southern English Atlantic Herring, Sandeel Sardine and Ocean Channel Sprat Mackerel and Mackerel Anchovy Krill and Herring Ecosim and Type of model Ecosim Ecosim MRM Ecosim OSMOSE Spatial coverage 2 1 1 1 2 Time period 1 1 1 1 2 Low trophic level 2 2 2 2 2 detail Predator detail 2 1 3 3 2 Predator breadth (includes large pelagic 1 1 1 1 1 fish, marine mammals, sea birds) Quality of trophic data 1 1 2 1 2 Model publication 1 1 1 1 1 Simulation (time Yes Yes Yes Yes Yes dynamic?) Fitted to data? Yes Yes Yes Yes Yes Type of stock data 1 1 2 1 2 Other data used in No No No No No fitting? Fitting includes dynamic Yes Yes Yes No Yes environmental variables as inputs? Quality of fit 2 3 2 2 1 Simulations conducted to look at forage fish No Yes Yes Yes No dependency? Environmental drivers No Yes included? Account for uncertainty? 3 2 3 1 2 Represent local depletion? No No No Yes No
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Summary from Filter 1: What is a key LTL species?
Models for these stocks were generally appropriate for the evaluation of the MSC criteria specified for identifying key LTL species. LTL species were commonly represented as individual species in these models, and the diet matrices were generally based on data collected on each ecosystem. The major limitation was the frequent limited treatment of sea bird and marine mammal species, which were often absent from the models and, when present, were often highly aggregated.
Smith et al. (2011) identified consumer biomass and connectance (the proportion of total trophic connections in the food web for each LTL species) as key predictors of fisheries impacts. A summary of the information available for MSC certified and in-assessment stocks was undertaken with respect to each filter (Table 5). When models were available, the researchers calculated the Filter 1 scores (connectance and percentage consumer biomass). When calculating connectance, aggregated age/size-structured state variables were aggregated so that each state variable comprised a single species (for example, split adult/juvenile stages were aggregated into a single-state variable). This maintained some consistency across models and across species. The survey and evaluation of models indicate that seven stocks have adequate models for Filter 1 evaluation, spanning 12 fisheries. For those with readily available information that could be extracted from models, four fisheries were flagged as key LTL, based mostly on the connectance scores. Thus, this research anticipates the re-certification of many stocks will require some novel analysis to apply Filter 1. A large number of fisheries (11) involve two stocks – northeast Atlantic mackerel and Norway spring-spawning herring – for which there is no single model with a spatial domain that closely matches those of the stocks. However, these stocks are represented in several other models so that a synthesis of relevant models might provide a reasonable basis for Filter 1 assessment.
The Smith et al. (2011) model analysis was used to determine whether the connectance and consumer biomass criteria could distinguish Low Trophic Level species, i.e. those species for which depletion by 60% (a common single-species target biomass reference point) would have severe indirect ecological effects (>40% depletion in 15% of species or >70% depletion decline in a single species). These analyses were applied to pelagic and mesopelagic planktivores and large zooplankton (krill) in the North Sea, Southeastern Australia Shelf, California Current, Northern Humboldt and Southern Benguela models, so therefore cover many of the LTL stocks considered (Table 1). The connectance threshold alone generated two false negatives, one that would be flagged as key LTL based on consumer biomass proportion. Thus, the current requirements correctly identified seven of the eight stocks that had a Rank 3 effect. However, these criteria had a higher false positive rate: of the 15 stocks that had a lower rank effect (<15% of species with >40% depletion, no species with >70% depletion), five were classified as key LTL. This is discussed in more detail later in this paper; see ‘Evaluation of connectivity and model structure’.
Summary for Filter 2: What levels of LTL depletion have adverse ecological effects?
Few of these models have been used specifically to test for the ecosystem effects of LTL species. The Smith et al. (2011) study is responsible for most published evaluations, although Walters et al. (2008) carried out a similar analysis for forage species in the Gulf of Mexico. Moreover, many of the models are not well suited for evaluation of LTL fishery effects – only 60% of the models that were fitted to data used relevant environmental time series. Finally, only one of the models was constructed to permit exploration of local depletion. Therefore, there is little information for many stocks on the level of depletion (both target and limit biomass levels) that is permissible to avoid adverse ecological effects. Consequently, most stocks will have to use the default limit and target biomass levels in the MSC assessment process, or invest in model development and analysis to evaluate limit and reference points that do not have adverse ecological effects on predators.
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Table 5. Summary of data and model availability for LTL stocks in the MSC program. Stock Fisheries Adequate Data / Model Connectance Consumer Filter 1 Filter 2 biomass proportion Antarctic Krill Yes Yes 0.085 0.0294 Argentine No No Anchovy Astrid Fiske, DPPO, North Sea Herring Norway, PFA, Yes Yes 0.035 0.0047 SPSG, SPPO Bay of Biscay Sardine (Western English Channel Yes No 0.022 0.0087 model) Norway spring- DPPO, FPO, Norway, PFA, Yes No 0.0005 spawning Herring SPSG, SPPO Northeast Atlantic DPPO, PFA, SPSG, No No Mackerel SPPO, IPSA Gulf of California No No Sardine Portugal Sardine No No Skagerrak Herring No No Bay of Biscay Sardine (Bay of Yes No 0.057 0.0048 Biscay model) Western Mackerel IPSA, SPSG No No In assessment Celtic Sea Herring, Sprat, Yes1 No Sardine Gulf of California No No thread Herring NAFO Division 4R ? ? Herring Western Baltic Baltic Sea Herring spring-spawning Yes No2 0.192 0.123 herring, SPPO Baltic Sea Sprat SPPO Yes No2 0.192 0.084 Note: DPPO = Danish Pelagic Producers Organization, FPO = Faroese Pelagic Organization, IPSA = Irish Pelagic Sustainability Association, NAFO = Northwest Atlantic Fisheries Organization, PFA = Pelagic Freezer-trawler Association, SPPO = Sveriges Pelagiska Producent Organisation, SPSG = Scottish Pelagic Sustainability Group. 1 An ecopath model exists, but herring, sprat and sardine are aggregated into a single functional group. Species-specific diet data will have to be obtained for these species and their predators to estimate connectance. 2 Fitted food web model is available for simulation/testing.
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Evaluation of connectivity and model structure
Smith et al. (2011) screened several metrics to identify those that might reliably indicate LTL species that have important bottom-up effects on predators and therefore need to be maintained at higher biomass levels than those typically used as fisheries management targets. Two emerged – the proportion of total consumer biomass (hereafter referred to consumer biomass proportion) and the relative connectance of species in a food web. Connectance is calculated as the number of links involving a species, and is scaled to the complexity of the food web by expressing as a percent of all food web links.
There is ample literature on the difficulties in the use and interpretation of connectivity. The report reviews the issues briefly here. Firstly, it treats all trophic links the same, regardless of whether they comprise a major or minor energy pathway for predators or prey. Thus, a predator whose diet consists of 1% LTL species, and whose consumption comprises 1% of total mortality of a prey, is given equal weight to a linkage in which predator diets consist of 100% LTL species and is responsible for all mortality. A solution to this is to define minimum threshold diet fractions needed for links to be considered in the calculation. However, preliminary analyses of this type does not appear to have any consistent effect on connectance scores – regardless of the minimum diet fraction level applied, connectance scores were equally likely to increase as decrease when applying a minimum threshold. Moreover, an analysis that used 5% minimum diet fraction threshold led to poor ability to discriminate LTL fisheries that have large adverse ecological effects on predators (based on data used in Figure 3, Smith et al., 2011).
The second main problem is that connectance is highly dependent on model structure. That is, connectance is a measure of both ecosystem properties and the decisions and assumptions that modellers make in constructing food web models. For example, many models do not include upper trophic level predators such as piscivorous sea birds and marine mammals, although these are known to be significant predators of LTL species. Moreover, when they are present in models, they are usually represented as highly aggregated groups. To illustrate the effects of this aggregation, a highly detailed model (Northern California Current EwE model, Figure 1) was used, and groups were sequentially aggregated to represent the types of decisions that are common among models. First, species were aggregated into groups: sea birds, cetaceans and pinnipeds (Figure 2). Second, several aggregations were created from 14 demersal species, including piscivorous demersal, rockfish and other demersal (Figure 3). Third fish were further aggregated, into demersal, small pelagics and large pelagics (Figure 4). Fourth, fish aggregation structure was retained but additionally all marine mammals were aggregated into a single group (Figure 5). For each aggregation scheme, we calculated the connectance and consumer biomass proportion. As models aggregate apex predators (mammals and sea birds), connectance scores of LTL species drop slightly. However, with successive aggregation of fish communities, the connectance scores of all species (including LTL species) increase. At the most extreme range of aggregation, nearly every species in the ecosystem has a connectance score greater than 4%.
A New Method for Detecting Key LTL species (SURF)
A refinement of the method for identifying key LTL that is both robust to aggregation and accounts for differences in strength of linkages is suggested. This is developed by defining pij as the diet fraction of predator j on prey i. One metric of trophic connectance of prey i is simply to sum over all j predators of this species. This presumes a linear relationship between the importance of a connection and diet proportion. However, the researchers wish to discriminate further between species that have many weak links and 2 those that have fewer stronger links. Thus, our index of prey importance is derived by summing the pij over all j predators for prey i. This is referred to as the SURF index (Supportive Role to Fishery ecosystems). Many modifications can be made to this index to account for (1) differences in the number of predators in aggregated groups (2) depletion of LTL species as reflected in pij scores. One simple correction that we use here is to scale the SURF index for each species by dividing it by the total number of linkages in a food web. In this way, models with relatively high complexity (many linkages and predators) will not, by default, have higher SURF scores. An alternative approach, not tested here, is to divide the sums of the squared diet proportions by the number of species with a trophic level greater than 3.0.
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When this method is applied to the sequential aggregation exercise described above, it is found that the SURF index is much more stable to reasonable levels of aggregation (Figures 1–3). Not surprisingly, the SURF index increases dramatically when many LTL species are aggregated. For example, it is sensitive to the degree of aggregation of LTL species but not to predators (Figure 4). The researchers therefore conclude that the SURF index is useful to avoid problems of aggregation that plague connectance scores, provided that LTL species themselves are not aggregated.
Figure 1. Biplots of consumer biomass proportion and connectance (left) and Supportive Role to Fishery Ecosystem (SURF) (right) for the base Northern California Current model. Background shading is a 2-dimensional kernel density smoother, and grey points are species not considered to be LTL stocks.
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Figure 2. Biplots of consumer biomass proportion and connectance (left) and Supportive Role to Fishery Ecosystem (SURF) (right) for the base Northern California Current model, with seabirds, cetaceans and pinnipeds aggregated into single groups.
Figure 3. Biplots of consumer biomass proportion and connectance (left) and Supportive Role to Fishery Ecosystem (SURF) (right) for the base northern CA Current model, with seabirds, cetaceans and pinnipeds aggregated as above, and groundfish stocks also aggregated into three groups.
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Figure 4. Biplots of consumer biomass proportion and connectance (left) and Supportive Role to Fishery Ecosystem (SURF) (right) for the base Northern California Current model, with seabirds, cetaceans and pinnipeds aggregated as before, and fish stocks highly aggregated.
Figure 5. Biplots of consumer biomass proportion and connectance (left) and Supportive Role to Fishery Ecosystem (SURF) (right) for the base Northern California Current model, with seabirds and fish aggregated as before, and all marine mammals aggregated into a single group.
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A second test and comparison of the SURF metric is to evaluate its ability to distinguish exceptionally connected species. To test this the consumer:biomass proportion, SURF index and connectance was calculated for 18 foodwebs allowing assessment of species/species groups and potential LTL species. When using connectance, all species in food webs tended to be similar to each other and only occasionally were LTL species distinguished by their combination of connectance and consumer biomass threshold. When using SURF, it is recognised that a small handful of LTL species stand out as being particularly important in the food web network, while many others have low scores and are similar to other species in the food web. Thus, the SURF index may be useful in discriminating between key and non-key LTL species (Figures 1–5).
A final test of the SURF index is to determine whether it can better distinguish LTL stocks and fisheries that have minimal effects on predators from those that have substantial effects. We used the model simulation results from Smith et al. (2011), restricting output to those based on EwE models (because our analysis of the SURF index derived entirely from Ecopath models). Connectance is a moderately effective discriminator when using 4% as the threshold level to distinguish key from non-key. Of the four stocks that had low bottom-up effects, three were correctly flagged as non-key LTL based and one was incorrectly flagged as key (Figure 6). However, this same threshold classifies as non-key LTL in three of the six stocks that had Rank 2 effects and two of the 13 stocks that have Rank 3 effects. In contrast, the SURF metric was better at separating these groups (Figure 6). If a threshold SURF score of 0.001 is applied, all the Rank 1 stocks are correctly assigned as non-key, three of the Rank 2 stocks are assigned as key, and all the 13 Rank 3 stocks are correctly identified as key. If we judge against the ability to discriminate Rank 3 from Rank 1 and 2 effects, SURF has a false positive rate of 3/15 and a false negative rate of 0/9. This metric needs to be evaluated more fully, but we believe that these initial analyses are encouraging and may be adopted to differentiate key from non-key LTL stocks.
a) Rank of impact Rankof 1 2 3
0.00 0.05 0.10 0.15
Connectance
b) Rank of impact Rankof 1 2 3
0.00001 0.00100 0.10000
SURF
Figure 6. Ecosystem rank response to fishing LTL stocks to 60% depletion vs connectance (top) and SURF (bottom). Ranks are defined as; 1) No species declined by more than 40%, 2) At least one species declined by 40-70%, 3) Ecosystem effects are evident that should likely prompt default biomass reference points.
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When this method is applied to stocks in the MSC program to the models that are available, the SURF and connectance scores yield the same Filter 1 outcome (Table 6).
Table 6. Summary of Filter 1 analysis for selected LTL stocks in the MSC programme. Stock Connectance Proportion Biomass SURF Antarctic krill 0.085 0.0294 0.0061 North Sea herring 0.035 0.0047 0.0030 Bay of Biscay sardine (Western English 0.022 0.0087 0.0006 Channel model) Bay of Biscay sardine 0.057 0.0024 (Bay of Biscay model) 0.048 Baltic Sea herring 0.192 0.123 0.0139 Baltic Sea sprat 0.1924 0.084 0.0082 Bold indicates that the metric would lead to key LTL determination. Note: different results are obtained for the Bay of Biscay sardine stock (exploited by the Cornish and Southern Brittany MSC fisheries) depending on whether the Western English Channel model or the Bay of Biscay model is used).
Discussion
In general, aggregation of predator species that are widely known to consume LTL species leads to a reduction in LTL connectivity scores. This effect is a consequence of representing several links to LTL species as a single link. In contrast, aggregation of species that do not consume LTL stocks will lead to overall higher connectivity scores of LTL stocks. This change is a consequence of reducing the total number of food web links, and thereby increasing the proportion of that total that involve LTL species. As demonstrate below, connectivity can be sensitive to a number of decisions about model structure.
In some instances, there may not be detailed diet data for some taxa. In these cases, a precautionary approach is recommended that assumes LTL-dependent taxa derive all their prey from LTL species; for example, presume a tight linkage between predators and all LTL species in the ecosystem. This will generate a liberal estimate of connectivity and would be most appropriate for predators that are widely known to rely on LTL species such as piscivorous sea birds and marine mammals.
If diet data for predators aggregates several potential key LTL species, i.e. a single functional group ‘forage fish’, a precautionary approach is to use the connectance and biomass score for this aggregated group as a measure of the connectivity of all individual species that comprise the aggregated group. This method is precautionary because aggregating species generally tends to increase the connectivity score, and it uses biomass of the entire complex instead of the biomass of individual species against the criteria. Although this approach will probably lead to most aggregated species being listed as key, in the absence of species-level data there is no robust way to disaggregate information to reveal the species-level connectance and the consumer biomass proportion.
Finally, the researchers reviewed connectance and consumer biomass proportions for several model food webs to determine whether connectivity alone (derived from diet matrix) is generally sufficient to classify stocks as key LTL. The researchers asked whether there were cases of potential LTL species having low connectance scores but high consumer biomass proportions. Of the 70 species / species groups screened across 17 model food webs, we found only one case where a potential key LTL species had a connectance of less than 0.04 but had a consumer biomass proportion that exceeded 0.05 (Gulf of Mexico menhaden). However, in this case, the connectance score and the consumer biomass proportion were both near the thresholds for key LTL designation. Thus, when a diet matrix can be generated, but a food web model cannot be developed, connectance alone may be sufficient for key LTL designation.
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There are few cases in which the MSC Conformity Assessment Bodies (CABs) will be able to cite an existing body of work to support a non-default limit or target reference point. It will be necessary to conduct novel model analyses (in some cases, using these existing models) to determine the impact of specified levels of predator depletion. We note that this should not be conducted using models that have not demonstrated the functionality to reliably hindcast past stock dynamics. CABs should interpret model outputs with appropriate caution, as few have the capacity to consider localised depletion effects. Thus, alternative model runs that represent key uncertainties need to be conducted to evaluate the worst-case scenarios. We suggest the recent summary document detailing best practices in marine ecosystem modelling as a guideline for conducting these analyses (FAO 2009).
Conclusions and Recommendations
Conformity Assessment Bodies (CABs) should apply appropriate caution when using existing models for testing stocks against the key LTL filters. Models should be reviewed for: 1. Representation of predators, i.e. are major predator groups represented? 2. Aggregation of predators, i.e. are important predators specified individually or combined into broader functional groups? When predator groups are not represented in a model, additional diet data should be gathered to more fully capture likely predators in an ecosystem. This may include developing new data matrices that incorporate predator species that are missing from the models.
The paper presents the SURF index as an indicator that might be developed for application in the MSC program. At least two major considerations still need to be explored:
a) How to treat pij scores when there are aggregated predator groups. That is, if a predator group 2 consists of many species, should the pij be given greater weight? One solution would be to multiply 2 the pij scores for aggregated groups by the number of predators. This will probably still generate 2 an underestimate of what the pij scores would be if they were summed over multiple predators, because of the non-linearity in the metric. For instance, an aggregation between a predator that receives no energy from LTL species and one that receives all energy from LTL species would 2 2 have a pij = 0.5 and a pij = 0.25. However, if we calculated pij separately, the sum of these would equal 0 + 1 = 1. b) Basing the diet proportions on an appropriate baseline condition. If diet samples are taken after LTL species have been depleted by fishing, their importance might be underestimated. It is not clear how to adjust pij scores to account for LTL depletion, but some further simulation testing via food web models might reveal good proxies.
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References
Ainsworth CH, Kaplan IC, Levin PS, Cudney-Bueno R, Fulton EA, Mangel M, Turk-Boyer P, Torre J, Pares- Sierra A and Morzaria Luna HN (2011) Atlantis model development for the Northern Gulf of California. U.S. Department of Commerce, NOAA Technical Memorandum NMFS-NWFSC-110.
Araújo JN, Mackinson S, Ellis JR and Hart PJB (2005) An ecopath model of the Western English Channel ecosystem with an exploration of its dynamic properties. Centre for Environment, Fisheries and Aquaculture Science, Lowestoft. Sci. Ser. Tech Rep., CEFAS Lowestoft, 125:45pp.
Blanchard JL, Pinnegar JK and Mackinson S (2002) Exploring marine mammal- fishery interactions using ‘Ecopath with Ecosim’: modelling the Barents Sea ecosystem. Centre for Environment, Fisheries and Aquaculture Science, Sci. Ser. Tech Rep., CEFAS Lowestoft, 117: 52pp.
Bulman CM, Condie SA, Neira FJ, Goldsworthy SD and Fulton EA (2011) The trophodynamics of small pelagic fishes in the southern Australian ecosystem and the implications for ecosystem modelling of southern temperate fisheries. CSIRO Marine and Atmospheric Research.
Christensen V, Beattie A, Buchanan C, Ma H, Martell SJD, Latour RJ, Preikshot D, Sigrist MB, Uphoff JH and Walters CJ (2009) Fisheries ecosystem model of the Chesapeake Bay: Methodology, parametrization, and model exploration. NOAA Technical Memorandum NMFS-F/SPO-106.
Dommasnes A, Christensen V, Ellertsen B, Kvamme C, Melle W, Nøttestad L, Pedersen T, Tjelmeland S and Zeller D (2001) An ecopath model for the Norwegian Sea and Barents Sea. Fisheries Impacts in North Atlantic Ecosystems: Models and Analyses. Fisheries Center Research Reports 9(4):213-239
Essington TE, Pláganyi ÉE (2013) Pitfalls and guidelines for “recycling” models for ecosystem-based fisheries management: evaluating model suitability for forage fish fisheries. ICES Journal of Marine Science On line doi: 10.1093/icesjms/fst047.
FAO (2009). Fisheries management. 2. The ecosystem approach to fisheries. 2.1 Best practices in ecosystem modelling for informing an ecosystem approach to fisheries, FAO Fisheries Technical Guidelines for Responsible Fisheries [online]. No. 4, Suppl. 2, Add. 1. Rome, FAO. 2009. 78pp. Available at www.fao.org/docrep/012/i1146e/i1146e00.htm [accessed 05.01.2013].
Field J, Francis R and Aydin K (2006) Top-down modeling and bottom-up dynamics: Linking a fisheries- based ecosystem model with climate hypotheses in the Northern California Current. Progress in Oceanography 68: 238–270.
Guénette S, Christensen V and Pauly D (2008) Trophic modelling of the Peruvian upwelling ecosystem: Towards reconciliation of multiple datasets. Progress in Oceanography 79:326–335.
Guénette S and D Gascuel D (2009) Considering both fishing and climate in a model of the Celtic Sea and the Bay of Biscay: what do we learn? International Council for the Exploration of the Sea. ICES CM 2009/F:07.
Harvey CJ, Cox SP, Essington TE, Hansson S and Kitchell JF (2003) An ecosystem model of food web and fisheries interactions in the Baltic Sea. ICES Journal of Marine Science 60:939–950.
Hill SL (2013) From strategic ambiguity to technical reference points in the Antarctic krill fishery: the worst journey in the world? Environmental Conservation. On line doi:10.1017/S0376892913000088.
Koen-Alonso M and Yodzis P (2005) Multispecies modelling of some components of the marine community of northern and central Patagonia, Argentina. Canadian Journal of Fisheries and Aquatic Sciences 62:1490–1512.
189
Model and data adequacy for MSC key LTL species designation and criteria and a proposed new assessment index
Lassalle G, Lobry J, Le Loc’h F, Bustamante P, Certain G, Delmas D, Dupuy C, Hily C, Labry C and Le Pape O (2011) Lower trophic levels and detrital biomass control the Bay of Biscay continental shelf food web: Implications for ecosystem management. Progress in Oceanography 91:561–575.
Mackinson S and Daskalov G (2007) An ecosystem model of the North Sea to support an ecosystem approach to fisheries management: description and parameterisation. Sci. Ser. Tech Rep., Cefas Lowestoft, 142: 196pp.
Marzloff M, Shin YJ, Tam J, Travers M and Bertrand A (2009) Trophic structure of the Peruvian marine ecosystem in 2000–2006: Insights on the effects of management scenarios for the hake fishery using the IBM trophic model Osmose. Journal of Marine Systems 75:290–304.
Morales-Zarate MV, Arreguin-Sanchez F, Lopez-Martinez J and Lluch-Cota SE (2004). ‘Ecosystem trophic structure and energy flux in the Northern Gulf of California, Mexico’. Ecological Modelling 174:331–345.
Plaganyi EE and Butterworth DS (2012) The Scotia Sea krill fishery and its possible impacts on dependent predators – modelling localized depletion of prey. Ecological Applications 22:748–761.
Samb B and Pauly D (2000) On “ variability ” as a sampling artefact : the case of Sardinella in north- western Africa. Fish and Fisheries 1:206–210.
Shannon LJ, Christensen V and Walters C J (2004) Modelling stock dynamics in the southern Benguela ecosystem for the period 1978–2002. South African Journal of Marine Science 26:179–196.
Shannon LJ, Moloney CL, Jarre A and Field JG (2003) Trophic flows in the southern Benguela during the 1980s and 1990s. Journal of Marine Systems 39:83–116.
Shannon LJ, Neira S and Taylor M (2008) Comparing internal and external drivers in the southern Benguela and the southern and northern Humboldt upwelling ecosystems. African Journal of Marine Science 30:63–84.
Shin YJ, Shannon LJ and Cury PM (2004) Simulations of fishing effects on the southern Benguela fish community using an individual-based model: learning from a comparison with ECOSIM. African Journal of Marine Science 26:95–114.
Sidi MT and Diop MS (2004) Modèle écotrophique du Bank d’Arguin (Mauritanie) dans la période 1988 à 1998. Fisheries Centre Research Reports, Vancouver, B.C.
Smith AD, Brown CJ, Bulman CM, Fulton EA, Johnson P, Kaplan I C, Lozano-Montes H, Mackinson S, Marzloff M and Shannon L J (2011) Impacts of fishing low-trophic level species on marine ecosystems. Science 333:1147–1150.
Tam J, Taylor MH, Blaskovic V, Espinoza P, Michael Ballón R, Díaz E, Wosnitza-Mendo C, Argüelles J, Purca S and Ayón P (2008) Trophic modeling of the Northern Humboldt Current Ecosystem, Part I: Comparing trophic linkages under La Niña and El Niño conditions. Progress in Oceanography 79:352–365.
Taylor MH, Tam J, Blaskovic V, Espinoza P, Michael Ballón R, Wosnitza-Mendo C, Argüelles J, Díaz E, Purca S and Ochoa N (2008) Trophic modeling of the Northern Humboldt Current Ecosystem, Part II: Elucidating ecosystem dynamics from 1995 to 2004 with a focus on the impact of ENSO. Progress in Oceanography 79:366–378.
Walters C, Martell SJD, Christensen V and Mahmoudi B (2008) An ecosim model for exploring Gulf of Mexico ecosystem management options: implications of including multistanze life-history models for policy predictions. Bulletin of Marine Science 83:251–271.
190
Model and data adequacy for MSC key LTL species designation and criteria and a proposed new assessment index
Watters GM, Hinke JT and Hill S (2008) A risk assessment to advise on strategies for subdividing a precautionary catch limit among small-scale management units during stage 1 of the staged development of the krill fishery in subareas 48.1, 48.2, and 48.3. CCAMLR. Working paper WG-EMM-08/30.
Watters GM, Hinke JT, Reid K and Hill S (2005) A krill-predator-fishery model for evaluationg candidate mangement procedures. CCAMLR.Working paper WG=EMM-05/13.
Watters GM, Hill SL, Hinke JT, Matthews J and Reid K (2013) Decision-making for ecosystem-based management: evaluating options for a krill fishery with an ecosystem dynamics model. Ecological Applications 23(4):710-725.
Yemane D, Yunne-Jai S and Field JG (2009) Exploring the effect of marine protected areas on the dynamics of fish communities in the southern Benguela: an individual-based modelling approach. ICES Journal of Marine Science 66(2):378-387.
191