About Ewe

• Address ecological questions;
• Evaluate ecosystem effects of fishing;
• Explore management policy options;
• Evaluate impact and placement of marine protected areas;
• Evaluate effect of environmental changes.

The foundation of the EwE suite is an Ecopath model (Christensen and Pauly 1992, Pauly et al. 2000), which creates a static mass-balanced snapshot of the resources in an ecosystem and their interactions, represented by trophically linked biomass ‘pools’. The biomass pools consist of a single species, or species groups representing ecological guilds. Pools may be further split into ontogenetic (juvenile/adult) groups that can then be linked together in Ecosim. Ecopath data requirements are relatively simple, and generally already available from stock assessment, ecological studies, or the literature: biomass estimates, total mortality estimates, consumption estimates, diet compositions, and fishery catches.

The parameterization of an Ecopath model is based on satisfying two ‘master’ equations. The first equation describes the how the production term for each group can be divided:

Production = catch + predation + net migration + biomass accumulation + other mortality

It is the aim with the Ecopath model to describe all mortality factors; hence the ‘other mortality’ should only include generally minor factors as mortality due to old age, diseases, etc. The second ‘master’ equation is based on the principle of conservation of matter within a group:

Consumption = production + respiration + unassimilated food

In general, an Ecopath model requires input of three of the following four parameters: biomass, production/biomass ratio (or total mortality), consumption/biomass ratio, and ecotrophic efficiency for each of the functional groups in a model. Here, the ecotrophic efficiency expresses the proportion of the production that is used in the system, (i.e. it incorporates all production terms apart from the ‘other mortality’). If all four basic parameters are available for a group the program can instead estimate either biomass accumulation or net migration. Ecopath sets up a series of linear equations to solve for unknown values establishing mass-balance in the same operation. The approach, its methods, capabilities and pitfalls are described in detail by Christensen and Walters (2000).

Ecosim provides a dynamic simulation capability at the ecosystem level, with key initial parameters inherited from the base Ecopath model. The key computational aspects are in summary form:

• Use of mass-balance results (from Ecopath) for parameter estimation;
• Variable speed splitting enables efficient modeling of the dynamics of both ‘fast’ (phytoplankton) and ‘slow’ groups (whales);
• Effects of micro-scale behaviors on macro-scale rates: top-down vs. bottom-up control incorporated explicitly.
• Includes biomass and size structure dynamics for key ecosystem groups, using a mix of differential and difference equations. As part of this EwE incorporates:
• Juvenile size/age structure by monthly cohorts, density- and risk-dependent growth;
• Adult numbers, biomass, mean size accounting via delay-difference equations;
• Stock-recruitment relationship as ‘emergent’ property of competition/predation interactions of juveniles.

Ecosim uses a system of differential equations that express biomass flux rates among pools as a function of time varying biomass and harvest rates, (for equations see Walters et al. 1997, 2000). Predator prey interactions are moderated by prey behavior to limit exposure to predation, such that biomass flux patterns can show either bottom-up or top down (trophic cascade) control (Walters 2000). By doing repeated simulations Ecosim allows for the fitting of predicted biomasses to time series data.

Ecosim can thus incorporate (and indeed benefits from) time series data on:

• relative abundance indices, (e.g., survey data, catch per unit effort [CPUE] data);
• absolute abundance estimates;
• catches;
• fleet effort;
• fishing rates; and
• total mortality estimates.

For many of the groups to be incorporated in the model the time series data will be available from single species stock assessments. EwE thus builds on the more traditional stock assessment, using much of the information available from these, while integrating to the ecosystem level.

The time series fitting use either fishing effort or fishing mortality data as driving factors for the Ecosim model runs. A statistical measure of goodness of fit to the time series data outlined above is generated each time Ecosim is run. This goodness of fit measure is a weighted sum of squared deviations (SS) of log biomasses from log predicted biomasses, scaled in the case of relative abundance data by the maximum likelihood estimate of the relative abundance scaling factor q in the equation y=q·B (y=relative abundance, B=absolute abundance). Each reference data series can be assigned a relative weight representing a prior assessment of relative data reliability.

The model allows four types of analysis with the SS measure:

1. determine sensitivity of SS to the critical Ecosim vulnerability parameters by changing each one slightly (1%) then rerunning the model to see how much SS is changed, (i.e., how sensitive the time series predictions ‘supported’ by data are to the vulnerabilities);
2. search for vulnerability estimates that give better ‘fits’ of Ecosim to the time series data (lower SS), with vulnerabilities ‘blocked’ by the user into sets that are expected to be similar;
3. search for time series values of annual relative primary productivity that may represent historical productivity ‘regime shifts’ impacting biomasses throughout the ecosystem;
4. estimate a probability distribution for the null hypothesis that all of the deviations between model and predicted abundances are due to chance alone, i.e. under the hypothesis that there are no real productivity anomalies.

In addition to the nonlinear optimization routines described above the fit to data can also be improved in a feedback-process by examining some of the crucial ecological parameters in the EwE model (notably total mortality rates and the settings for top-down/bottom-up control). It is important to note here that such fitting does not include any ‘fiddling-factors’ internal to the model, instead the type of question that is addressed after each run is "which species parameters or ecological settings are not set such that the model captures the observed trends over time adequately?"

The inclusion of time series data in EwE facilitates its use for exploring policy options for ecosystem-based management of fisheries. The time series fitting has so far been done for a few ecosystems only (French Frigate Shoals, Strait of Georgia, Gulf of Thailand, North Sea, while a dozen or some applications are known to be in progress) as the facility is very recent; however, the results from these studies have been very encouraging. An important preliminary conclusion is that the model is capable of producing a reasonable fit, (i.e. fits that can be compared to those obtained using single species models) for all available time series related to the ecological resources of an ecosystem in one go. This indicates a capability or at least a potential to replicate the known history of the ecosystems. In turn this lends some confidence to how the model can be used for policy exploration.

The application to the Strait of Georgia and the French Frigate Shoals both indicate that after the models fit to observed time series had been optimized through the feed back process involving changes to the ecological interaction parameters, there were still considerable deviations between observed and estimated parameter estimates. The third nonlinear search type above was therefore used on both systems to search for time series anomalies that may indicate regime shifts. In both cases it was found that the routine indicated changes in primary productivity patterns in line with observed changes at the decadal scale. An implication of this is that the fitting of time series in Ecosim may be used not just for identification of ecosystem effects of fishing but also to address questions of environmental impact at the ecosystem level (as well as for individual groups of course).

An FAO workshop was convened at UBC in July 2000 aimed at exploring ‘The Use of Ecosystem Models to Investigate Multispecies Management Strategies for Capture Fisheries’. At the workshop around 40 scientists from throughout the world worked with 15-20 EwE models to investigate the impact of different multispecies harvesting strategies on the community structure and fishery yields with a view to identifying preferred harvesting strategies. A central aim of fisheries management is to regulate fishing mortality rates over time so as to achieve economic, social and ecological sustainability objectives. An important dynamic modeling and assessment objective is thus to provide insight about how high these mortality rates should be, and how they should be varied over time (at least during development or recovery from past overfishing). We cannot expect models to provide very precise estimates of optimum fishing mortality rates, but we should at least be able to define reasonable and prudent ranges for the rates.

The Ecosim module of EwE was updated for the FAO workshop to provide two ways to explore impacts of alternative fishing policies:

1. Fishing rates can be ‘sketched’ over time and results (catches, economic performance indicators, biomass changes) examined for each sketch. This is using Ecosim in a ‘gaming’ mode, where the aim is to encourage rapid exploration of options.
2. Formal optimization methods can be used to search for fishing policies that would maximize a particular policy goal or ‘objective function’ for management.

The first of these approaches has been implemented in Ecosim since its first version, and has been widely applied for exploring ecosystem effects of changes in fishing effort. The second, and favored approach at the workshop was a newly developed ‘open loop’ policy exploration simulation developed explicitly for the workshop and incorporated in the EwE software system. The approach acknowledges that policy may be defined as an approach towards reaching a broadly defined goal, that fisheries policies are often implemented via TACs that are recalculated annually, and through regulation that affects fleet structure and deployment. Admittedly, most fisheries research has up to now been on policy implementation only, and the intention with the tool is to enable fisheries scientists to advise both on policy formulation and on its implementation.

The goal function for policy optimization is defined by the user in Ecosim, based on an evaluation of four weighted policy objectives:

1. Maximize fisheries rent;
2. Maximize social benefits;
3. Maximize mandated rebuilding of species;
4. Maximize ecosystem structure or ‘health’.

The first of these, maximizing profits, is based on calculating profits as the value of the catch (catch · price, by species) less the cost of fishing (fixed + variable costs). Giving a high weight to this objective often results in phasing out most fleets except the most profitable ones, and the wiping out of ecosystems groups competing with or preying on the more valuable target species.

The second objective, maximizing social benefits, is expressed through the employment supported by each fleet. The benefits are calculated as number of jobs relative to the catch value, and are fleet specific. Therefore social benefits are largely proportional to fishing effort. Optimizing efforts often leads to even more extreme (with regards to overfishing) fishing scenarios than optimizing for profit.

The maximization of mandated rebuilding of species (or guilds) is incorporated to capture that external pressure (or legal decisions) may force policy makers to concentrate on preserving or rebuilding the population of a given species in a given area. In Ecosim this corresponds to setting a threshold biomass (relative to the biomass in Ecopath) for the species or group, and optimizing towards the fleet effort structure that will most effectively ensure this objective. The implications of this are case-specific: we are finding that the optimization routine may rigorously hammer (through increased fishing) competitors and predators of the species in question; or at the other extreme that fisheries may be shut down without social or economic consideration (as is indeed often the case when legal considerations take over).

The last objective included, maximizing ecosystem structure (or 'health') is inspired by E.P. Odum’s description of ecosystem ‘maturity’, wherein mature ecosystems are dominated by large, long-lived organisms, (see Christensen 1995). The default setting we have incorporated for ecosystem structure is therefore the group-specific biomass/production ratio as this measure is indicative of the longevity of the groups. The ecosystem structure optimization often implies reduction of fishing effort for all fleets except those targeting species with low weighting factors.

Ecosim internally uses a nonlinear optimization procedure known as the Davidson-Fletcher-Powell (DFP) method to iteratively improve an objective function by changing relative fishing rates. DFP runs the Ecosim model repeatedly while varying these parameters. The parameter variation scheme used by DFP is known as a ‘conjugate-gradient’ method, which involves testing alternative parameter values so as to locally approximate the objective function as a quadratic function of the parameter values, and using this approximation to make parameter update steps. It is one of the more efficient algorithms for complex and highly nonlinear optimization problems like the one of finding a best fishing pattern over time for a nonlinear dynamic model.

The objective function can be thought of as a ‘multi-criterion objective’, represented as a weighted sum of the four objectives: economic, social, legal, and ecological. Assigning alternative weights to these components is a way to see how they conflict or tradeoff with one another in terms of policy choice. Indeed, a very interesting aspect of the FAO workshop referred to above was the many discussions that arose on how to balance the policy objectives in the Ecosim routine. There is nothing new in considering these policy objectives; we have done so through time, even if in an implicit, qualitative way. What’s new, and what was stimulating at the FAO workshop and in working with other applications since then, is to be able to address the objectives through an explicit approach incorporated in a quantitative model. Even if we would not dream of incorporating the results into today’s management without very thorough considerations of inherent risks and uncertainties, it is for now very rewarding to be able to participate in a process where the questions addressed are of the sort: "How do we want this ecosystem to look in the future, and what are the implications of our choices?"

The fishing policy search routine described above estimates time series of relative fleet sizes that would maximize a multi-criterion objective function. In Ecosim, the relative fleet sizes are used to calculate relative fishing mortality rates by each fleet type, assuming the mix of fishing rates over biomass groups remains constant for each fleet type, (i.e., reducing a fleet type by some percentage results in the same percentage decrease in the fishing rates that it causes on all the groups that it catches). However, density-dependent catchability effects can be entered, and if so reductions in biomass for a group may result in fishing rate remaining high despite reductions in total effort by any/all fleets that harvest it. Despite this caveat, the basic philosophy in the fishing policy search is that future management will be based on control of relative fishing efforts by fleet type, rather than on multispecies quota systems. It is not yet clear that there is any way to implement multispecies quotas safely anyway, without either using some arbitrary conservative rule like closing the fleet when it reaches the quota for the first (weakest) species taken or else allowing wasteful discarding of species once their quotas are reached.

If future multispecies management is indeed implemented by regulation of fleet fishing efforts so as to track time-varying fishing mortality rate targets as closely as possible, then a key practical issue is how to monitor changes in gear efficiency (catchability coefficients) so as to set effort limits each year that account for such changes in efficiency. Such monitoring is particularly important for fisheries that can show strong density-dependence in catchability, such that a unit of fishing effort takes a much higher proportion of some stocks (exerts a higher fishing mortality rate per unit of effort) when their stock sizes are small.

There are at least two possible ways to monitor changes in catchability (gear efficiency), both based on monitoring fishing mortality rates, F_t, over time and using the relationship q_t=F_t/f_t, where q_t is fishing rate per unit effort and f_t is effort. The first approach is to do traditional biomass stock assessments each year, and to estimate F_t as F_t=C_t/B_t, where C_t is total catch and B_t is estimated vulnerable stock biomass. The second approach is to directly monitor the fishing mortality rate, estimating probabilities of harvest using methods such as annual tagging experiments and within-year estimates of relative decrease in fish abundance during fishing ‘seasons’.

A routine in Ecosim developed for the FAO workshop referred to above allows users to do ‘closed loop policy simulations’ to evaluate these monitoring alternatives in terms of their implications for temporal variation in biomasses and also the objective function value components used in searches for optimum long-term fishing rate plans. The idea in the closed loop simulation is to model not only the ecological dynamics over time, but also the dynamics of the stock assessment and regulatory process. That is, a closed loop simulation includes ‘submodels’ for the dynamics of assessment (data gathering, random and systematic errors in biomass and fishing rate estimates) and for the implementation of assessment results through limitation of annual fishing efforts.

The closed loop simulation module includes options to:

1. decide how many closed loop stochastic simulation trials to do;
2. set the type of annual assessment to be used (F=C/B versus F directly from tags);
3. set the accuracy of the annual assessment procedures (coefficient of variation of annual biomass or F estimates, by stock);
4. set value or importance weights for the F’s caused on various species by each fishing fleet; and
5. do the simulation trials and display time series and mean value results.

The value weights are used for each fleet/species combination to calculate a weighted average catchability q_t for each fleet type, recognizing that some species may be more important than others in terms of the effect that they might be allowed to have on effort reduction should q increase over time.

Closed loop policy simulations could obviously include a wide range of complications related to the details of annual stock assessment procedures, survey designs, and methods for direct F estimation. We suggest using other assessment modeling tools to examine these details, and so need only consider overall performance information (coefficients of variation in estimates) for the ecosystem-scale analysis performed using Ecosim.