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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

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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).

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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

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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.

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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.

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Table 1: Direct and indirect impacts of fishing on ecosystems

Impact Direct effect Indirect effect References Removal of target species and incidental catch

• Mortality of target species and other fish and invertebrate species incidental to catch

• Changes in trophic interactions such as predator-prey dynamics, trophic cascades

Anderson & Ursin, 1977; Carpenter et al., 1985; Hall,1999b

• Altered life history parameters resulting from density dependence

• Prey decreases for top predators such as marine mammals, seabirds and elasmobranchs

Hall, 1999b; Kaiser and de Groot, 2000; Stevens et al., 2001

By-catch and discards

• Mortality of undersized target fish

• Increases in scavengers including species of benthos, demersal fish and seabirds

Frid et al., 1999; Hall, 1999b

• Mortality of non-target species including fish, marine mammals, seabirds and elasmobranchs

• Changes in trophic interactions such as predator-prey dynamics, trophic cascades

Brothers, 1991; Heessen & Daan, 1996; Hall, 1999b; Stevens et al., 2001

Seabed disturbance due to trawling and dredging

• Mortality of benthic assemblages, in particular vulnerable, fragile species

• Changes in food-web dynamics (bottom-up control?)

Kaiser & Spencer, 1995; Hall, 1999b

• Increases in scavengers including species of benthos, demersal fish

Kaiser & Spencer, 1994; Frid et al., 1999; Hall, 1999b

• Resuspension of nutrients and particulates

• Knock-on effects of resuspension of nutrients including temporary phytoplankton and nutrient cycling changes

ICES, 1998; Hall, 1999b

• Habitat disturbance and modification

Hall, 1999b

Lost gear and litter

• Incidental mortality (ghost fishing) of seabirds, marine mammals, fish and elasmobranchs

• Changes in trophic interactions such as predator-prey dynamics, trophic cascades

Hall, 1999b

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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 marketable catch

Abundance changes in commercial stocks 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 trawling and dredging

Increase in ‘nuisance’ species 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

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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

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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

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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.

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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).

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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.

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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.

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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.

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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 ✔ ✔ ✔ ✖ ✔ ✖ ✖ ✔ ✔ ✔ ✔ ✔ ✔

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Table 3 cont.

Study Number (Appendix 1) 14 15 16 17 18 19 20 21 22 23 24 Total Model type (as defined by ICES, 2000)) 4 5 6 6 6 5 2 6 2 2 2

Which ecosystem components considered?

% of models with this

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

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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 fish, 2 marine mammals, 3 seabirds, 4 elasmobranchs, 5 benthos, 6 particulates, 7 primary production, 8 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 (Direct effect)

★★★ ★★ 3, 4 ★★ 1, 4 ★★ 1, 2,

4 ★ 1 ★★ 3, 4 ★ 1 ★ 1

Increase in scavengers including species of benthos, demersal fish & seabirds (Indirect effect) ★★★ ★★★ ★★ 5, 1 ★★ 5, 1 ★★ 5, 1 ★★★ ★★ 5, 1 ★★ 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 (Direct effect)

★★★ ★★ 4 ★★ 1, 4 ★★ 2, 1,

4 ★ 1 ★★ 4 ★ 1 ★ 1

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? ✖✖✖✖ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✔✔✔✔ ✖✖✖✖ ✖✖✖✖ ✔✔✔✔

18

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.

REFERENCES Alino, P. M., McManus, L. T., McManus, J. W., Nanola, C. L., Fortes, JR., M. D.

Trono, G. C. and Jacinto, JR. & G. S. 1993. ‘Initial parameter estimations of a coral reef flat ecosystem in Bolinao, Pangasinan, Northwestern Philippines’, in Christensen, V. and Pauly, D. (Eds.) Trophic Models of Aquatic Ecosystems, ICLARM Conference Proceedings, 26, 252-258

Anderson, K.P. and Ursin, E. 1977. ‘A multispecies extension to the Beverton and Holt theory of fishing, with accounts of phosphorus circulation and primary production’, Meddelelser Fra Danmarks Fiskeri-og Havundersøgelser, N.S., 7, 319-435.

Auster, P.J., Malatesta, R.J. and Larosa, S. 1995. ‘Patterns of microhabitat utilisation by mobile megafauna on the southern New England (USA) continental shelf and slope’, Marine Ecology Progress Series, 12, 77-85.

Baretta, J.W., Ebenhoh,W., and Ruardij, P. 1995. ‘The European regional seas ecosystem model, a complex marine ecosystem model’, Netherlands Journal of Sea Research, 33, 233-246.

Bianchi, G., Gislason, H., Graham, K., Hill, L., Koranteng, K., Manickchand-Heileman, S., Paya, I., Sainsbury, K., Sanchez, F., Jin, X., and Zwanenburg, K. 2000. ‘Impacts of fishing on demersal fish assemblages’, ICES Journal of Marine Science, 57, 558-571.

Booth, A. J. 2000. ‘Incorporating the spatial component of fisheries data into stock assessment models’, ICES Journal of Marine Science, 57, 858-865.

Brothers, N. 1991. ‘Albatross mortality and associated bait loss in the Japanese longline fishery in the Southern Ocean’, Biological Conservation, 55, 255-68.

Camphuysen, C.J., Calvo, B., Durinck, J., Ensor, K., Follestad, A., W., F.R., Garthe, S., Leaper, G., Skov, H., Tasker, M.L. and N., W.C.J. 1995. Consumption of discards in the North Sea. Final report EC DG XIV research contract BIOECO/93/10’, Netherlands Institute for Sea Research, Texel, NIOZ, Rapport, 5.

Carpenter, S.R., Kitchell, J.F. and Hodgson, J.R. 1985. ‘Cacading trophic interactions and lake productivity’, BioScience, 35, 634-639.

Christensen, V. 1998. ‘Fishery-induced changes in a marine ecosystem: insight from models of the Gulf of Thailand’, Journal of Fish Biology, 53 (Supplement A), 128-142.

Churchill, J.H. 1989. ‘The effect of commercial trawling on sediment resuspension and transport over the Middle Atlantic Bight Continental Shelf’, Continental Shelf Research, 9, 841-864.

19

Collie, J.S. and Spencer, P.D. 1994. ‘Modelling predator-prey dynamics in a fluctuating environment’, Canadian Journal of Fisheries and Aquatic Sciences, 51, 2665-2672.

Collie, J.S., Escanero, G.A. and Valentine, P.C. 1997. ‘Effects of bottom fishing on the benthic megafauna of Georges Bank’, Marine Ecology Progress Series, 155, 159-172.

Dayton, P.K. 1998. ‘Ecology - reversal of the burden of proof in fisheries management AU’, Science, 279, 821-822.

Dayton, P.K., Thrush, S.F., Agardy, M.T. and Hofman, R.J. 1995. ‘Environmental effects of marine fishing’, Aquatic Conservation: Marine and Freshwater Ecosystems, 5, 205-232.

Frid, C.L.J. and Clark, R.A. 2000. ‘Long term changes in North Sea benthos: discerning the role of fisheries’, in Kaiser, M.J. and de Groot, S.J. (Eds.), Effects of Fishing on Non-target Species and Habitats, Blackwells Scientific Publishers, Oxford, 198-216.

Frid, C.L.J. and Huliselan, N.V. 1996. ‘Far-field control of long-term changes in Northumberland (NW North Sea) coastal zooplankton’, ICES Journal of Marine Science 53: 972-977.

Frid, C.L.J., Clark, R.A. and Hall, J.A. 1999. Long-term changes in the benthos on a heavily fished ground off the NE coast of England’, Marine Ecology Progress Series, 188, 13-20.

Frid, C.L.J., Hansson, S., Ragnarsson, S.A., Rijnsdorp, A. and Steingrimsson, S.A. 1999. ‘Changing levels of predation on benthos as a result of exploitation of fish populations’, Ambio, 28, 578-582.

Frid, C.L.J., Harwood, K.G., Hall, S.J. and Hall, J.A. 2000. ‘Long-term changes in the benthic communities on North Sea fishing grounds’, ICES Journal of Marine Science, 57, 1303-1309.

Furness, R.W. 1978. ‘Energy requirements of seabird communities: a bioenergetics model’, Journal of Animal Ecology, 47, 39-53.

Gislason, H. 1994.’Ecosystem Effects of Fishing Activities in the North Sea’, Marine Pollution Bulletin, 29, 520-527.

Gislason, H., Sinclair, M., Sainsbury, K. and O'Boyle, R. 2000. ‘Symposium overview: incorporating ecosystem objectives within fisheries management’, ICES Journal of Marine Science, 57, 468-475.

Gislason, H. and Rice, J. C. 1998. ‘Modelling the response of size and diversity spectra of fish assemblages to changes in exploitation’, ICES Journal of Marine Science, 55, 362-370.

Hall, S.J. 1999a. ‘Managing fisheries within ecosystems: can the role of reference points be expanded?’ Aquatic Conservation: Marine and Freshwater Ecosystems, 9, 579-583.

Hall, S.J. 1999b. The Effects of Fishing on Marine Ecosystems and Communities. Blackwell Science, Oxford.

Hays, G.C., Carr, M.R. and Taylor, A.H. 1993. ‘The relationship between Gulf-Stream position and copepod abundance derived from the Continuous Plankton Recorder Survey – separating biological signal from sampling noise’, Journal of Plankton Research, 15, 1359-1373.

20

Heessen, H. J. L. and Daan, N. 1996. ‘Long term trends in non-target North Sea fish species’, ICES Journal of Marine Science, 53, 1063-1078.

Hollowed, A. B., Bax, N., Beamish, R., Collie, J., Fogarty, M., Livingston, P., Pope, J., and Rice, J. C. 2000. ‘Are multispecies models an improvement on single-species models for measuring fishing impacts on marine ecosystems?’ ICES Journal of Marine Science, 57, 707-719.

ICES 1998. Report of the Working Group on the Ecosystem Effects of Fishing Activities. ICES, Copenhagen, ICES CM 1998/ACFM/ACME:1 Ref.:E, 263 pp.

ICES 2000. Report of the Working Group on Ecosystem Effects of Fishing Activities. ICES, Copenhagen, ICES CM 2000/ACME:02 Ref.: ACFM + E, 93 pp.

Jarre-Teichmann, A. 1998. ‘The potential role of mass balance models for the management of upwelling ecosystems’, Ecological Applications, 8, Supplement, s93-s103.

Jones, R. 1982. ‘Species interactions in the North Sea’, Canadian Special Publication of Fisheries and Aquatic Sciences, 59, 48-63.

Kaiser, M.J. and Spencer, B.E. 1994. ‘Fish scavenging behaviour in recently trawled areas’, Marine Ecology Progress Series, 112, 41-49.

Kaiser, M.J. and Spencer, B.E. 1995. ‘Survival of by-catch from a beam trawl’, Marine Ecology Progress Series, 126, 31-38.

Kaiser, M.J. and de Groot, S.J., Eds. 2000. Effects of Fishing on Non-target Species and Habitats. Blackwell Scientific, Oxford

Kröncke, I., Dippner, J.W., Heyen, H. and Zeiss, B. 1998. ‘Long-term changes in macrofaunal communities off Norderney (East Frisia, Germany) in relation to climate variability’, Marine Ecology Progress Series, 167, 25-36.

Law, R. 2000. ‘Fishing, selection, and phenotypic evolution’, ICES Journal of Marine Science, 57, 659-668.

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’. ICES Journal of Marine Science, 57, 1050-1059.

Livingston, P. A. and Jurado-Molina, J. 2000. ‘A multispecies virtual population analysis of the eastern Bering Sea’, ICES Journal of Marine Science, 57, 294-299.

May, R.M., Beddington, J.R., Clark, C.W., Holt, S.J. and Laws, R.M. 1979. ‘Management of multispecies fisheries’, Science, 205, 267-276.

Messieh, S. 1991. ‘Fluctuations in Atlantic herring succession in relation to organic enrichment and pollution of the marine environment’, Oceanography and Marine Biology Annual Review, 16, 229-311.

Moll, A. 2000. ‘Assessment of three-dimensional physical-biological ECOHAM1 simulations by quantified validation for the North Sea with ICES and ERSEM data’, ICES Journal of Marine Science, 57, 1060-1069.

NRC. 1999. Sustaining Marine Fisheries. National Academy Press, Washington, D.C.

Opitz, S. 1993. ‘A quantitative model of the trophic interactions in a Caribbean coral reef ecosystem’, in Christensen, V. and Pauly, D. (Eds), Trophic

21

Models of Aquatic Ecosystems. ICLARM Conference Proceedings, 26. 259-267

Pope, J.G., MacDonald, D. S., Daan, N., Reynolds, J. D. and Jennings, S. 2000. ‘Gauging the impact of fishing mortality on non-target species’, ICES Journal of Marine Science, 57, 689-696.

Prins, T.C. and Smaal, A.C. 1990. ‘Benthic-pelagic coupling: the release of inorganic nutrients by an intertidal bed of Mytilus edulis’, in Barnes, M. and Gibson, R.N. (Eds.), Trophic Interactions in the Marine Environment, Aberdeen University Press, Aberdeen, 89-103

Rice, J. C. 1999. ‘How complex should operational ecosystem objectives be?, ICES C.M., Z:07.

Riemann, B. and Hoffmann, E. 1991. ‘Ecological consequences of dredging and bottom trawling in the Limfjord, Denmark’, Marine Ecological Progress Series, 69, 171-178.

Rowe, G.T., Clifford, C.H., Smith jr., K.L. and Hamilton, P.L. 1975. ‘Benthic nutrient regeneration and its coupling to primary productivity in coastal waters’, Nature, 225, 215-217.

Rumohr, H. and Krost, P. 1991. ‘Experimental evidence of damage to benthos by bottom trawling, with special reference to Arctica islandica’, Helgoländer Meeresuntersuchungen, 33, 340-345.

Sánchez, F. and Olaso, I. 1999. Fisheries impacts in then Cantabrian Sea using a mass-balance model’, WGECO 1999/10.

Schwinghamer, P., Guigne, J.Y. and Siu, W.C. 1996. ‘Quantifying the impact of trawling on benthic habitat structure using high resolution acoustics and chaos theory’, Canadian Journal of Fisheries and Aquatic Science, 53, 288-296.

Shannon, L. J., Cury, P. M. and Jarre, A. 2000. ‘Modelling effects of fishing in the Southern Benguela ecosystem’, ICES Journal of Marine Science, 57, 720-722.

Søiland, H. and Skogen, M. D. 2000. ‘Validation of a three-dimensional biophysical model using nutrient observations in the North Sea’. ICES Journal of Marine Science, 57, 816-823.

Sparholt, H. 1995. ‘Using the MSVPA/MSFOR model to estimate the right-hand side of the Ricker curve for Baltic cod’, ICES Journal of Marine Science, 52, 819-826.

Spencer, P.D. and Collie, J.S. 1996. ‘A simple predator-prey model of exploited marine fish populations incorporating alternative prey’, ICES Journal of Marine Science, 53, 615-628.

Stevens, J.D., Bonfil, R., Dulvy, N.K. and Walker, P.A. 2000. The effect of fishing on sharks, rays, and chimeras (chondrichthyans), and the implications for marine ecosystems. ICES Journal of Marine Science. 57;3, 476-494.

Tasker, M.L., Knapman, P.A. and Laffoley, D. 2000. ‘Effects of fishing on non-target species and habitats: identifying key nature conservation issues, in Kaiser, M.J. and de Groot, S.J. (Eds.), Effects of Fishing on Non-target Species and Habitats, Blackwells Scientific Publishers, Oxford. 281-289.

22

Taylor, A.H., Colebrook, J.M., Stephens, J.A. and Baker, N.G. 1992. ‘Latitudinal displacements of the Gulf-Stream and the abundance of plankton in the North-East Atlantic’, Journal of the Marine Biological Association of the United Kingdom, 72, 919-921.

Tjelmeland, S. and Bogstad, B. 1998. ‘Multispec - a review of multispecies modelling project for the Barents Sea’, Fisheries Research, 37, 127-142.

Triantafyllou, G., Petihakis, G., Dounas, C., Koutsoubas, D., Arvanitidis, C., and Eleftheriou, A. 2000. ‘Temporal variations in benthic communities and their response to physiochemical forcing: a numerical approach’, ICES Journal of Marine Science, 57, 1507-1516.

Tuck, I.D., Hall, S.J., Robertson, M.R., Armstrong, E. and Basford, D.J. 1998. ‘Effects of physical trawling disturbance in a previously unfished sheltered Scottish sea loch’, Marine Ecology Progress Series, 162, 227-242.

Underwood, A.J. 1996. ‘Detection, interpretation, prediction and management of environmental disturbances - some roles for experimental marine ecology’, Journal of Experimental Marine Biology and Ecology, 200, 1-27.

Van der Meer, J. and Leopold, M. F. 1995. ‘Assessing the population of the European storm petrel (Hydrobates pelagicus) using spatial autocorrelation between counts from segments of criss-cross ship transects’, ICES Journal of Marine Science, 52, 809-818.

Walters, C. J., Stocker, M., Tyler, A. V. and Westreim, S. J. 1986. ‘Interaction between Pacific cod (Gadus macrocephalus) and herring (Clupea harengus pallasi) in the Hectate Strait, British Columbia’, Canadian Journal of Fisheries and Aquatic Sciences, 43, 830-837.

Yodzis, P. 2001. ‘Must top predators be culled for the sake of fisheries?, Trends in Ecology and Evolution, 16, 78-84.

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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.

24

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.