Dynamic Ecosystem Models and the Evaluation of Ecosystem Effects of Fishing: Can We Make Meaningful Predictions?

ICES CM 2001/ T:13 Use and Information Content of Ecosystem Metrics and Reference Points

Dynamic ecosystem models and the evaluation of ecosystem effects of fishing: can we make meaningful predictions?

L. A. ROBINSON and C.L.J. FRID Dove Marine Laboratory, Department of Marine Sciences & Coastal Management, University of Newcastle upon Tyne, Cullercoats, North Shields. NE30 4PZ. U.K.

ABSTRACT

The effects of fishing on marine ecosystems are probably the most widespread anthropogenic influence. The introduction of ecosystem considerations into fisheries management requires a knowledge of the relative scale of the various fisheries effects, development of suitable metrics of these and consideration of suitable limit values (reference points) for them. This will involve the application of suitable models. The direct and indirect effects of fishing on marine ecosystems are catalogued. We then identify 31 applications of models of marine ecosystems that might provide useful insights into the ecological effects of fisheries. Analysis was possible for only 22 of the models due to poor documentation of the other 9. These however included representatives of 7 generic model types. No model formulation provided coverage in all the areas necessary to cover the identified effects of fisheries.

Eight models provided good coverage – nutrient dynamics and benthos were the least well represented aspects of the ecosystem. The ECOPATH with Ecosim family of models, the European Regional Seas Ecosystem Model (ERSEM) and the Anderson & Ursin multispecies extension to the Beverton & Holt model all seem likely to yield good insights. In developing these models consideration must be given to explicitly incorporate spatial factors and extrinsic forcing functions, such as climate.

KEY WORDS: fishing impacts; ecosystem, models, predictions

INTRODUCTION

In recent times it has become necessary to find measurable metrics of the impacts of fisheries on the whole ecosystem (ICES, 1998; Frid et al., 1999; Hall, 1999a & b; ICES, 2000). This ecosystem approach has in part been driven by the need to uphold the key provisions of the convention agreed at

the UN Rio summit, which include conservation of biological diversity and sustainable use of the biosphere (Tasker et al., 2000). It is perhaps this urgency that has prompted the inclusion of an entire session on ‘Ecosystem and Environmental Management’ at this years’ ICES ASC. The scientific community has accepted that a holistic approach to understanding ecosystem effects is appropriate, but in many cases, particularly when considering fisheries effects, the focus is still restricted to an incomplete section of the whole system.

Marine ecosystems are influenced by fishing activities at all levels and in a variety of ways (for reviews see Gislason, 1994; Dayton et al., 1995; Hall, 1999b; Gislason et al., 2000). These include: I. direct removal of target species II. direct changes in size structure of target populations III. alteration in non-target populations of fish and benthos (Rumohr and Krost, 1991; Camphuysen et al., 1995; Tuck et al., 1998) IV. alteration of the physical environment (Churchill, 1989; Messieh, 1991; Riemann and Hoffmann, 1991; Auster et al., 1995; Schwinghamer et al., 1996; Collie et al., 1997). V. alterations in the chemical environment, including nutrient availability (ICES, 1998). VI. trophic cascades (Carpenter et al., 1985) and altered predation pressure (Stevens et al., 2000; Frid et al., 1999).

To offer an insight at the whole system level any tool should provide a good representation of all ecosystem components that can be impacted by fishing, whether the impacts are direct or indirect. It is evident that changes occur across the full spectrum of trophic levels, ranging from the phytoplankton through altered nutrient fluxes, to the top-predators by provisioning or direct mortality and altered food resources (Kaiser and de Groot, 2000; Stevens et al., 2000; Tasker et al., 2000). These ecological effects also extend over multi-decadal time scales (Frid and Clark, 2000) and operate at spatial scales ranging from processes within the trawl tracks (Kaiser and Spencer, 1994) to changes at the scale of the coastal sea (Hall, 1999b; Frid et al., 2000).

It is the large spatial and temporal scales of fishing activities in addition to the great number of potential indirect effects that makes the application of classical experimental approaches untenable (Dayton et al., 1995; Underwood, 1996; Dayton, 1998). Instead research has been directed towards community metrics that give a measurable and easily communicable understanding of the ‘health’ or ‘state’ of the ecosystem following fishery disturbance. It is desirable that these metrics have ‘target’ and ‘limit’ reference points, as have been used in single species management, as the outcomes can then be easily translated directly to frame management measures.

ICES has long been involved in the management of single components of the ecosystem, such as the target fish stocks. Spawning stock biomass (SSB) is one example of such a measure, and it is set at a minimum acceptable level as a limit reference point. In theory, it should be possible to apply reference points to any, or all, taxa in the ecosystem. ICES (2000) have however contended that even if this were practical for a significant number of taxa, it may not ensure adequate protection of all the ecosystem components at risk. There is a need therefore to develop reference points for system level emergent properties as a measure of ecosystem health (Hall, 1999b; Gislason et al., 2000).

The utilization of sound ecological models as a tool in the exploration and evaluation of ecosystem ‘health’ and ‘state’, has been encouraged and endorsed by the leading bodies in ecosystem-based fisheries research and management (NRC, 1999; ICES, 1999). Dynamic ecosystem models provide an opportunity to make advances in this area, as they can both evaluate the state of the system and also make predictions about the ecosystem under various fishing scenarios. Further they allow an examination of the behaviour of possible metrics such as a change in energy flow or average trophic level, both of which can be easily translated into understandable reference points.

Through systems modelling it should be possible to gain an understanding of the indirect (higher order) effects and to develop metrics of the ecosystem

which could form the basis for precautionary management. The potential of the available dynamic ecosystem models, to make measurable and meaningful predictions about the effects of fishing on ecosystems, has not however been fully assessed. Although over the last two decades there has been considerable growth in the use of multispecies models to answer fisheries questions, there are problems associated with their use in the development of theory and predictions concerning ecosystem effects. Too often they are still restricted to a subset of the complete ecosystem and data is often aggregated over functionally different species or age groups (Hollowed et al., 2000).

Yodzis (2001) has highlighted the problems associated with the over- simplification of marine ecosystems in modelling. When considering competition between a fishery and a top predator for example, a simple model would predict that with the removal (cull) of the top predator, prey release would lead to an increase in the target fish stocks, therefore benefiting the fishery. If however one were to add a fish predator that competed with the top predator for the target species, but was also eaten by the top predator, a cull would lead to a lag increase in the fish predator and therefore ultimately a further decline in the target fish stock. This example illustrates the complexity of the issues when trying to make meaningful predictions about complex ecosystems.

In order to resolve the problems surrounding the potential use of dynamic ecosystem models for the evaluation of ecosystem effects of fishing, the inherent mismatch between the wide spectrum of impacts and the narrow outlook of the majority of ecosystem models must be addressed.

In this paper we set out to; I. Provide a succinct summary of the proposed ecosystem level effects arising from fishing activities – this includes the direct and indirect effects but for now we restrict our analyses to effects at the species and ecosystem levels.

II. Review the applications of dynamic ecosystem models currently available (and documented in the scientific literature) for coastal marine ecosystems. In particular we consider the extent to which various components of the ecosystem are represented and their ability to

respond to extrinsic drivers i.e. climatic variation.

III. Based on (I) and (II) we examine the available models for their ability to generate quantitative, testable, predictions about the response of the ecosystem to fishing effects.

Ultimately these models will be the ones that may provide a basis for the consideration of ecosystem properties that might form useful metrics of ecosystem health and so become the basis for ecosystem reference points.

Table 1: Direct and indirect impacts of fishing on ecosystems

Impact Direct effect Indirect effect References Removal of • Mortality of target • Changes in trophic Anderson & target species species and other fish interactions such as Ursin, 1977; and incidental and invertebrate predator-prey dynamics, Carpenter et al., catch species incidental to trophic cascades 1985; Hall,1999b catch • Altered life history • Prey decreases for top parameters resulting predators such as Hall, 1999b; from density marine mammals, Kaiser and de dependence seabirds and Groot, 2000; elasmobranchs Stevens et al., 2001

By-catch and • Mortality of • Increases in scavengers Frid et al., 1999; discards undersized target fish including species of Hall, 1999b benthos, demersal fish and seabirds • Mortality of non-target • Changes in trophic Brothers, 1991; species including fish, interactions such as Heessen & Daan, marine mammals, predator-prey dynamics, 1996; Hall, seabirds and trophic cascades 1999b; Stevens et al., 2001 elasmobranchs

Seabed • Mortality of benthic • Changes in food-web Kaiser & disturbance due assemblages, in dynamics (bottom-up Spencer, 1995; to trawling and particular vulnerable, control?) Hall, 1999b dredging fragile species • Increases in scavengers Kaiser & including species of Spencer, 1994; benthos, demersal fish Frid et al., 1999; Hall, 1999b • Resuspension of • Knock-on effects of ICES, 1998; Hall, nutrients and resuspension of nutrients 1999b particulates including temporary phytoplankton and nutrient cycling changes • Habitat disturbance Hall, 1999b and modification

Lost gear and • Incidental mortality • Changes in trophic Hall, 1999b litter (ghost fishing) of interactions such as seabirds, marine predator-prey dynamics, mammals, fish and trophic cascades elasmobranchs

Direct and indirect impacts of fishing on ecosystems

In order to determine which ecological models can be reliably used to examine the ecosystem impacts of fishing, it is first necessary to establish what the properties altered might be. We consider both the direct and indirect ecosystem impacts that have been associated with fishing (Table 1). These effects are predominantly changes in rates of ecosystem dynamics or components. It is also important to consider how the direct and indirect impacts of fishing cause changes in the state of an ecosystem component or property (Table 2). These state effects are usually the ones that are the most likely to trigger management responses and/or public concern.

Table 2: Direct and indirect effects of fishing on the state of ecosystem properties and components

Impact Change in state of ecosystem Removal of target species and Abundance changes in commercial stocks marketable catch Temporal variability in commercial stocks Geographic range of commercial stocks Size/age distribution of commercial stocks Loss of charismatic species Increase in ‘nuisance’ species Altered biodiversity Increased vulnerability to perturbations

By-catch and discards Loss of charismatic species Increase in ‘nuisance’ species Altered biodiversity Increased vulnerability to perturbations

Seabed disturbance due to Increase in ‘nuisance’ species trawling and dredging Altered biodiversity Loss of habitat features Altered productivity Increased vulnerability to perturbations

Lost gear and litter Loss of charismatic species Altered biodiversity Loss of habitat features

Fishing effects clearly have the potential to occur across all trophic levels. Any model that will offer insights into the direct and indirect effects of fishing at the ecosystem level must as a minimum incorporate each of these levels and all the properties potentially altered. It is therefore essential when reviewing the models available, to consider how thoroughly they cover all functional groups and properties of the ecosystem, potentially impacted either directly or indirectly by a change in either the ‘rate’ or ‘state’ of an ecosystem process. It is also necessary to aggregate into functional groups to increase the practicality of the modelling exercise, but this must be done appropriately so as to allow for differentiation between the range of impacting factors.

It was therefore decided that the available ecosystem models would be examined for their inclusion of nine functional groups. These were detritus, nutrients, primary producers, benthos, target fish, non-target fish, elasmobranchs, seabirds and marine mammals. These groupings are considered sufficient to distinguish the different types of impact and the system responses. For example, it is not possible to group elasmobranchs and non-target fish together, as their underlying biology (slow growth, low fecundity) mean they respond differently to impacts (Stevens et al., 2001). They also include top predators such as the sharks, which would be effected differently by changes in food web dynamics than would non-target fish lower down the food chain. In some situations the species involved may also be regarded as species of high public concern – charismatic species.

A review of published ecosystem models

The accounts of multispecies models of marine ecosystems were identified in the literature (Web of Science, ISI database) to extend the list of models reviewed in ICES (2000). A total of 33 model applications were identified. Each model application was assigned to one of the seven families of ecosystem model

identified by the ICES Working Group on the Ecosystem Effects of Fishing (ICES, 2000). This was a classification developed from the original categorisation of multispecies models of Hollowed et al., (2000) who grouped together models that were based on similar constructs, required similar input variable data and produced similar output predictions. ICES (2000) extended this to include consideration of the insights they provide into how fishing may affect the ecosystem (see ICES, 2000 for details).

Having classified the model applications we then scored them for the presence of those functional groups and properties deemed essential for the assessment of the ecological impacts of fishing. These were the nine functional groups described above (i.e. non-target fish, benthos). The models were also assessed for their inclusion of several additional factors, which are either fundamental in the regulation of marine ecosystems (e.g. environmental forcing, physical forcing), or important in the classification of their role as a predictor of ecological processes (e.g. simulation, spatial properties, fishery yield/mortality) (Table 3).

For nine of the model applications the available accounts, including some of the original source papers, did not provide sufficient information to allow this assessment. Of the 24 remaining (Appendix 1) there were representatives of all the 7 classes identified by ICES (2000). One was a habitat based model, 4 were models based on community metrics, 2 were ‘predator-prey single-species’ models, 5 were multispecies production models, 3 were dynamic multispecies models, 6 were aggregate system models and 3 were ecosystem models with age/size-structure.

Amongst the 24 models that could be reviewed in detail, there were marked differences in the degree to which various components of the ecosystem were considered (Table 3). For example, target fish have been afforded much greater attention (18 out of 24 studies) than have benthic communities (11/24) or nutrient

dynamics (4/24). Over one third of the models include only one or two of the desirable ecological components while only 8 models include 5 or more.

For the 8 models with the greatest depth of ecological coverage, we then scored their ability to address each of the properties potentially altered by fishing as outlined in Tables 1 and 2. Where overlap of a ‘rate’ (Table 1) or ‘state’ (Table 2) property was found, only one category was used for this further analysis. For example, ‘Habitat disturbance & modification’ also encompassed ‘Loss of habitat features’, as a model purporting to consider habitat features could address either point. For this analysis we used a four-point scale - ranging from 0 for not considered by the model to 3 for complete representation of the ecosystem component(s) effected by that particular impact. For these 8 model formulations the rating ranged between 35 and 47 (out of 53) (Table 4). The 8 'best' models included 6 aggregate system models and 2 ecosystem models with age/size structure as classified by ICES (2000).

The highest ranked model was Opitz’s (1993) quantitative model of the trophic interactions in a Caribbean coral reef ecosystem. Six of the eight models considered, including Opitz (1993), were of the ECOPATH with Ecosim family. Their scores however varied between 35 and 47, emphasizing the differences that can arise through individual applications of the same basic model architecture. The Anderson & Ursin (1977) extension to the Beverton & Holt model (number 1 in Tables 3 & 4) was the highest ranked non-ECOPATH with Ecosim model with an aggregate score of 43, whilst the ERSEM model (number 14 in Tables 3 and 4) also ranked highly.

DISCUSSION

Any model contending to be a useful tool in predicting the direct and indirect effects of fishing on ecosystems must consider all properties of the ecosystem likely to be altered. Without wishing to repeat the comprehensive and detailed reviews of the ecosystem effects of fishing (i.e. Gislason, 1994; Dayton et al., 1995; Hall, 1999b; Gislason et al., 2000) we have summarized these properties (Table 1) and considered to what degree available models address them. We have not addressed the effects of fishing at the genetic level but note that these effects are unlikely to be trivial (Law, 2000) and must be the subject of future investigations.

An extensive search identified 33 model applications documented in the literature but it is disappointing to note that 9 of these actually failed to give sufficient information about the models formulation to allow it to be reviewed. While all 24 remaining models were clearly dynamic ecosystem models only 8 covered most of the levels/properties of the food web necessary for a full analysis of the ecological consequences of fishing (Table 3). No model scored more than 47 out of 53 for coverage of state and rate changes in the ecosystem as a result of fisheries impacts.

The area least well described was nutrient cycling only being considered in 4 of the models. The impacts of fisheries on nutrient cycling have been highlighted (Rowe et al., 1975; Prins and Smaal, 1990) and may be significant in oligotrophic or enclosed areas. It is however evident that on inclusion of a detailed model of nutrient cycling the higher trophic level groups are often either missing or given a lower level of representation (Le Gall et al., 2000; SØiland & Skogen, 2000). Similarly complex fish-based models may have high-resolution age-structured data on predator-prey relations between particular fish species, but lack any inclusion of the rest of the ecosystem (Table 3).

Of the model applications identified in this study all 7 classes of ecosystem model recognized by ICES (2000) were represented, but only two of the classes featured amongst the 8 most comprehensive models. These two types were aggregate system models and ecosystem age/size structure models. There remains considerable potential for community metric and habitat based models to provide useful insights (Hall, 1999), but they are unlikely to provide the same level of predictive ability as the system type models. We also acknowledge that with the increasing interest in ecosystem modelling, there are likely to be model architectures currently under development that have not been reviewed in this paper, but which may score highly when applied to the same procedure.

From the analyses provided here, six of the eight models that included a comprehensive coverage of ecosystem properties were derivatives of the generic ECOPATH with Ecosim modelling approach. The ECOPATH with Ecosim group of models, ERSEM and the Anderson & Ursin (1977) extension to the Beverton & Holt model warrant further detailed investigation. These models are the ones most likely to prove useful in generating quantitative, testable predictions about the response of the ecosystem to fishing effects. The Anderson & Ursin model is however unlikely to be developed further, as others of the age/size structured class of ecosystem model, such as ERSEM, have superceded it.

ERSEM has undergone several phases in its’ development. Initially it was developed to be a spatially explicit model of carbon pathways through the North Sea (Baretta et al., 1995). It is a model based on biogeochemical fluxes and the bulk biomass of functional groups. As such it is suitable for simulating rapid turnover taxa but fails to capture the richness of the dynamics of longer-lived groups such as fish (Mike Heath, FRS, Aberdeen, pers. comm.). It is certainly more applicable to the basal groups of the ecosystem and does not include many of the top predators such as marine mammals, elasmobranchs and seabirds. It therefore cannot, in its’ present formulation, address loss of ‘charismatic species’, one of the state changes in ecosystem properties seen as important to the public.

The ERSEM model has developed from modules of primary production and therefore includes explicit consideration of extrinsic drivers such as the meteorological conditions and physico-chemical environment. It is also spatially resolved (Baretta et al., 1995). The lack of detailed representation of the higher components of the food-web, including fish and man (i.e. fisheries), limits its immediate applicability to consideration of ecosystem effects of fishing. However, it is clear that the ongoing development of this model will see advances in these areas and this combined with its inherent suitability (modular, extrinsic drivers, good basal group representation, spatial resolution) make it a potential powerful tool (Radford & Blackford, 1996; Moll, 2000; Triantafyllou et al., 2000).

The ability of the ECOPATH with Ecosim models to represent a large number of ecosystem components, including fisheries, marine mammals, target-fish and non-target fish, distinguish them from the other available models. The ECOPATH environment provides a very accessible interface such that the changes occurring in the system are readily observed (Opitz, 1993; Shannon et al., 2000). Ecosim also allows for simulation, for example of different fishery scenarios, therefore increasing the value of this model as an aid to management.

While this interface scores well on coverage of the ecosystem components it tends to fair very poorly in its’ ability to deal with the desirable 'additional factors' (Table 3), essential in its’ applicability to a holistic approach. Of considerable importance to the development of predictive capabilities in this model scheme is the lack of a mechanism to include extrinsic drivers such as climatic variation. The significance of this exclusion must not be overlooked. The models are also constrained by the fixed architecture which limits the ability to model detailed size/age structured populations (including ontogenic diet changes) and requires a ‘steady-state’. Further they are restricted by the inability to provide spatially resolved models, although the development of a spatial package (Ecospace) may improve this aspect for future applications.

The scientific community recognizes that “ecosystem management” of the coastal seas is both desirable and necessary if resource exploitation and habitat/biodiversity conservation are to be achieved. Development of suitable measures of ecosystem statue, management schemes and predictive power all require reliable ecosystem models. It would appear that no single model architecture is currently available which fulfills all the desirable qualities. However, two approaches, the Ecosim and Ecospace modules of the ECOPATH model and the development of the ERSEM offer considerable potential. These ongoing developments need to be informed by the requirements of the user/management community if this potential is to be realised.

Table 3: A comparison of the ecosystems and populations considered by 22 models (see Appendix 1 for references) and an examination of additional factors included in the construction of these models. Models are classified according to the scheme used by ICES (2000) into 7 types: 1. Habitat suitability model, 2. Model based on community metrics, 3. ‘Predator- prey single-species’ model, 4. Multispecies production model, 5. Dynamic multispecies model (age-structured), 6. Aggregate system models (time &/or spatial dynamics), 7. Ecosystem models with age/size structure .

Study Number (Appendix 1) 1 2 3 4 5 6 7 8 9 10 11 12 13 Model type (as defined by ICES, 2000) 7 3 4 7 3 6 6 4 4 1 5 7 4 Which ecosystem components considered? Target fish ✔ ✔ ✖ ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✔ ✔ ✖ Non-target fish ✔ ✔ ✖ ✔ ✔ ✔ ✔ ✖ ✖ ✖ ✖ ✔ ✖ –age/size structure of fish included? ✔ ✖ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✔ ✔ ✖ –realistic inclusion of complete fish community? ✖ ✖ ✖ ✖ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✔ ✖ Elasmobranchs ✖ ✖ ✖ ✖ ✖ ✖ ✔ ✖ ✔ ✖ ✖ ✖ ✖ Benthos –whole community? ✖ ✖ ✖ ✖ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✔ ✖ –reduced/simplified community? ✔ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ Seabirds ✖ ✔ ✖ ✖ ✖ ✖ ✔ ✖ ✖ ✔ ✖ ✖ ✖ Marine mammals ✖ ✖ ✔ ✖ ✖ ✖ ✔ ✖ ✖ ✖ ✖ ✖ ✖ Primary production ✔ ✔ ✖ ✔ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✔ ✔ Nutrient cycling – several species ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✔ ✔ – single nutrient species ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ Detritus ✔ ✖ ✖ ✖ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✔ ✔ Are the following additional factors included? Fishery yield/mortality ✔ ✖ ✔ ✔ ✔ ✔ ✖ ✔ ✔ ✖ ✔ ✖ ✖ Genetic health/diversity ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ Natural disturbance ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ Environmental forcing ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✔ ✔ ✖ ✖ ✔ ✔ Physical forcing ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✔ ✔ Food web dynamics ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✔ ✔ ✔ Spatial properties ✔ ✔ ✖ ✖ ✔ ✖ ✔ ✖ ✖ ✔ ✖ ✔ ✔ Simulation ✔ ✔ ✔ ✖ ✔ ✖ ✖ ✔ ✔ ✔ ✔ ✔ ✔

Table 3 cont.

Study Number (Appendix 1) 14 15 16 17 18 19 20 21 22 23 24 Total % of models Model type (as defined by ICES, 2000)) 4 5 6 6 6 5 2 6 2 2 2 with this Which ecosystem components considered? component Target fish ✖ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✖ ✔ 18 75.0 Non-target fish ✖ ✔ ✔ ✔ ✔ ✔ ✖ ✔ ✔ ✔ ✔ 16 66.6 –age/size structure of fish included? ✖ ✔ ✖ ✔ ✖ ✔ ✔ ✖ ✔ ✔ ✔ 13 54.2 –realistic inclusion of complete fish community? ✖ ✖ ✔ ✔ ✔ ✖ ✖ ✔ ✖ ✖ ✖ 7 29.2 Elasmobranchs ✖ ✖ ✖ ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✔ 6 25.0 Benthos –whole community? ✖ ✖ ✔ ✔ ✔ ✖ ✖ ✔ ✖ ✖ ✖ 7 29.2 –reduced/simplified community? ✖ ✖ ✖ ✖ ✖ ✔ ✖ ✖ ✖ ✖ ✖ 4 16.7 Seabirds ✖ ✖ ✔ ✖ ✖ ✖ ✖ ✔ ✖ ✖ ✖ 5 20.8 Marine mammals ✖ ✔ ✔ ✔ ✖ ✔ ✖ ✔ ✖ ✖ ✖ 8 33.3 Primary production ✔ ✔ ✔ ✔ ✔ ✖ ✖ ✔ ✖ ✖ ✖ 12 50.0 Nutrient cycling – several species ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 3 12.5 – single nutrient species ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 1 4.2 Detritus ✔ ✖ ✔ ✔ ✔ ✖ ✖ ✔ ✖ ✖ ✖ 10 41.7 Are the following additional factors included? Fishery yield/mortality ✖ ✔ ✔ ✔ ✔ ✖ ✔ ✔ ✔ ✔ ✔ 18 75.0 Genetic health/diversity ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0 0 Natural disturbance ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 0 0 Environmental forcing ✔ ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 8 33.3 Physical forcing ✔ ✖ ✖ ✔ ✖ ✖ ✖ ✖ ✖ ✖ ✖ 4 16.7 Food web dynamics ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✔ ✖ ✖ ✖ 19 79.2 Spatial properties ✔ ✔ ✖ ✖ ✔ ✖ ✔ ✖ ✖ ✖ ✔ 11 45.9 Simulation ✔ ✔ ✖ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ 20 83.3

Table 4: Summary of components of those models from Table 3, which best incorporate the ecosystem effects of fishing in their design. ( = complete representation; = missing 1 or 2 groups; = only represents 1 group; 0 = no representation). Symbols are 1 2 3 4 , 5 6 7 8 fish, marine mammals, seabirds, elasmobranchs benthos, particulates, primary production, single nutrient species

7 21 18 17 1 16 12 6 Rate changes in ecosystem properties Removal of target species and incidental catch Mortality of target species and incidental fish & invertebrate species (Direct effect) Changes in trophic interactions (Indirect effect) Prey decreases for top predators e.g. marine mammals, seabirds & sharks (Indirect effect) 3, 4 4 2, 4 0 3, 4 0 0 By-catch and discards Mortality of undersized target fish (Direct effect) Mortality of non-target species including fish, marine mammals, seabirds and elasmobranchs 3, 4 1, 4 1, 2, 1 3, 4 1 1 (Direct effect) 4 5, 1 5, 1 5, 1 5, 1 Increase in scavengers including species of benthos, demersal fish & seabirds (Indirect effect) 5, 1 Changes in trophic interactions (Indirect effect) 5, 1 5, 1 5, 1 5, 1 5, 1 Seabed disturbance due to trawling & dredging Mortality of benthos, in particular vulnerable, fragile species (Direct effect) Changes in trophic interactions (Indirect effect) Increases in scavengers including species of benthos & demersal fish (Indirect effect) Resuspension of nutrients and particulates (Direct effect) 6 6 6 6 8, 7 6 6 Knock-on effects of resuspension on primary production & nutrient cycling (Indirect effect) 7 7 7 7 8, 7 7 7 Habitat disturbance and modification (Direct effect) 0 0 0 0 0 0 0 0 Lost gear and litter Incidental mortality (ghost fishing) of seabirds, marine mammals, fish & elasmobranchs 4 1, 4 2, 1, 1 4 1 1 (Direct effect) 4 State changes in ecosystem properties Temporal variability in commercial stocks 0 0 0 0 Geographic range of commercial stocks 0 0 0 0 0 Loss of ‘charismatic’ species 3, 4 1, 4 2, 4 0 3, 4 0 0 Increase in ‘nuisance’ species Altered biodiversity Altered productivity Total star rating for ecosystem properties 47 46 46 44 43 43 42 35 Total number of additional factors as found in Table 3 2/8 3/8 4/8 4/8 6/8 2/8 5/8 2/8 Inclusion of the fishery in the model? ✖ ✔ ✔ ✔ ✔ ✖ ✖ ✔

ACKNOWLEDGEMENTS

The ideas presented here have benefited from discussion with; the members of the ICES Planning Group for a Workshop on Ecosystem Models (PGEM), Steve Hall, Stuart Rogers, John Pinnegar and Simon Greenstreet. We would also like to thank Mike Heath for his comments on ERSEM.

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Appendix 1 List of models included in Table 3 :

1. Anderson & Ursin. 1977. A multispecies extension to the Beverton and Holt theory of fishing, with accounts of phosphorus circulation and primary production.

2. Furness. 1978. Energy requirements of seabird communities: a bioenergetics model.

3. May et al., 1979. Management of multispecies fisheries. (3 models in one; but all using the same components when assigning to Table 2).

4. Jones. 1982. Species interactions in the North Sea.

5. Walters et al., 1986. Interaction between Pacific Cod (Gadus macrocephalus) and Herring (Clupea harengus pallasi) in the Hecate Strait, British Columbia.

6. Aliño et al., 1993. Initial parameter estimations of a coral reef flat ecosystem in Bolinao, Pangasinan, Northwestern Philippines.

7. Opitz. 1993. A quantitative model of the trophic interactions in a Caribbean coral reef ecosystem.

8. Collie & Spencer. 1994. Modelling predator-prey dynamics in a fluctuating environment.

9. Spencer & Collie. 1995. A simple predator-prey model of exploited marine fish populations incorporating alternative prey. Application to the spiny dogfish – Georges Bank haddock interaction.

10. Van der Meer & Leopold. 1995. Assessing the population size of the European storm petrel (Hydrobates pelagicus) using spatial autocorrelation between counts from segments of criss-cross ship transects.

11. Sparholt. 1995. Using the MSVPA/MSFOR model to estimate the right-hand side of the Ricker curve for Baltic cod.

12. Baretta et al., 1995. The European regional seas ecosystem model, a complex marine ecosystem model (ERSEM).

13. Le Gall, A. C., Hydes, D. J., Kelly-Gerreyn, B. A. and Slinn, D. J. 2000. Development of a 2D horizontal biogeochemical model for the Irish Sea DYMONIS.

14. Søiland, H. and Skogen, M. D. 2000. Validation of a three-dimensional biophysical model using nutrient observations in the North Sea.

15. Tjelmeland & Bogstad. 1998. MULTISPEC – a review of a multispecies modelling project for the Barents Sea.

16. Jarre-Teichmann. 1998. The potential role of mass balance models for the management of upwelling ecosystems.

17. Christensen. 1998. Fishery-induced changes in a marine ecosystem: insight from models of the Gulf of Thailand.

18. Sanchez & Olaso. 1999. Fisheries impacts in the Cantabrian Sea. Using a mass-balance model.

19. Livingston & Jurado-Molina. 2000. A multispecies virtual population analysis of the eastern Bering Sea.

20. Booth. 2000. Incorporating the spatial component of fisheries data into stock assessment models.

21. Shannon et al., 2000. Modelling effects of fishing in the southern Benguela ecosystem.

22. & 23 Pope et al., 2000. Gauging the impact of fishing mortality on non- target species. Extended length-cohort analysis (20), Weighted swept area analysis (21).

24. Bianchi et al., 2000. Impact of fishing on size composition and diversity of demersal fish communities.