Ecosystem Modelling Using the Ecopath with Ecosim

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Marta Coll, Alida Bundy and Lynne J. Shannon

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8.1 Introduction

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Marine ecosystems are dynamic and complex, with interactions, feedback loops and environmental effects occurring concurrently. Fishing activities impact on their structure and functioning, modifying their features and affecting the interactions established between their biological components. The task of making predictions of future states of the ecosystem and understanding marine resource dynamics might seem Herculean or madness, even with reductionist modelling approaches. It is a large task which has been simplified and made tractable with the development of the ecosystem modelling software system, Ecopath with Ecosim (Polovina 1984, Walters et al. 1997, Pauly et al. 2000). In recent years, it has become an ecosystem modelling tool that is used globally for static analyses of marine ecosystems and tropho-dynamic and spatial simulations. This chapter briefly reviews the history and development of Ecopath with Ecosim (EwE) and describes the theory and assumptions on which is it based. Then it uses case studies to illustrate EwE utility and the insights it can bring to understand ecosystem structure and functioning, ecosystem changes and to examine the likely consequences/benefits of different management options at the ecosystem level.

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8.1.1 History and Development

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Fisheries management efforts have largely failed given that at least 75% of the world’s major fisheries resources are either fully exploited (‘‘mature’’) or overexploited, with clear signs of declines in catches (FAO 2005). Improvement of our fisheries management requires new approaches and techniques. These need to accommodate the net effects of alternative fishing strategies on the ecosystem as a whole, through taking into account the effects on ecosystem

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M. Coll (*) Institute of Marine Science (ICM-CSIC), Passeig Marı´ tim de la Barceloneta, 37-49, 08003 Barcelona, Spain

B.A. Megrey, E. Moksness (eds.), Computers in Fisheries Research, 2nd ed., DOI 10.1007/978-1-4020-8636-6_8, Ó Springer ScienceþBusiness Media B.V. 2009

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structure and functioning as well as the effects on the biology and dynamics of the targeted stocks. Single-species fisheries models are unable to capture interactions between species (especially trophic interactions), and do not accommodate spatial aspects of fish stocks and their prey and predators. Thus they are limited in the breadth and scope of management objectives and strategies that can be explored for future management. Careful consideration and quantification of trophic interactions is important when addressing management objectives to exploit or conserve species that may be interacting strongly with a particular species targeted by fisheries. Information on the spatial distribution and overlap between species is also needed if closed areas are to be implemented as effective management tools. In both respects, the dynamic ecosystem modelling approach of EwE (through Ecosim and Ecospace) offers a means to incorporate interactions and spatial constraints into an approach in which fisheries effects can be explored and carefully examined, providing a useful tool for ecosystembased fisheries management. The Ecopath and Ecosim modelling tool (EwE) is composed of a core mass balance model (Ecopath, which stands for Ecological Pathways Model) (Polovina 1984, Pauly et al. 2000; Christensen and Walters 2004a, Christensen et al. 2005) from which temporal and spatial dynamic simulations can be developed (Walters et al. 1997, 1999, Christensen and Walters 2004a). This tool has been widely used to quantitatively describe aquatic systems and the ecosystem impacts of fishing (Christensen and Pauly 1993, Christensen and Walters 2004a). Ecopath with Ecosim has its roots in classic ecology. Food chains are considered to be based on trophic flows between discrete trophic levels (Lindeman 1942) and thus species are allocated to distinct trophic levels and positions in a food chain or food web. Based on this theory and using the concept of mass balance and energy conservation, Polovina (1984) developed the first Ecopath model for the French Frigate Shoals in the Northwestern Hawaiian Islands. Christensen and Pauly (1992) further developed the model to include fractional trophic levels to take into account species that feed across a range of trophic levels. The latter forms the basis of network analysis and the current Ecopath modelling approach (e.g. Wulff et al. 1989, Pauly et al. 2000). Since the mid-1990s, with the coalescence of increased computing power and new ideas, the scope of Ecopath has exploded: the trophodynamic simulation model Ecosim (Walters et al. 1997, Christensen et al. 2005) has introduced the capability to conduct multispecies simulations to explore ecosystem structure and functioning, the impact of fishing, policy exploration and more; a year later, the development of Ecospace, a spatially explicit simulation model, began (Walters et al. 1999, Christensen et al. 2005). This immediately addressed questions relating to marine protected areas and spatial management, in addition to exploring aspects related to spatial distribution of organisms, and behaviour and the role of water movement. Ecopath with Ecosim is thus the first ecosystem level simulation model that is widely accessible. There are 3681 registered users in 150 different countries from

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November 2003 to 8 March, 2007 (www.ecopath.org, 14 September, 2006) and over 200 publications making Ecopath with Ecosim the default modelling approach to explore ecosystem related questions in the world of fisheries science. A total of 325 EwE models have been constructed to date, 42% to describe ecosystem structure, 30% to address fisheries management problems, 9% to address policy issues, 6% for marine protective areas and 11% to explore questions in theoretical ecology (Morissette 2007). In this chapter, we briefly describe the theory and assumptions on which EwE is based (see Section 8.2). We use a selection of case studies to illustrate the scope of EwE and the insights it can bring to understanding ecosystem structure and functioning, ecosystem changes, the impacts of fishing and policy exploration (see Section 8.3). Finally, we take a step back and discuss the limits of EwE, give a critical perspective and finish with a discussion of future directions (Section 8.4).

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8.2 Ecopath with Ecosim

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8.2.1 Fundamental Theory and Equations of Ecopath

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8.2.1.1 Mass Balance Modelling

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An Ecopath model provides a quantitative representation of the studied ecosystem, or a snapshot, in terms of trophic flows and biomasses for a defined time period. The ecosystem is represented by functional groups, which can be composed of species, groups of species with ecological similarities or ontogenetic fractions of a species. The key principle of Ecopath is mass balance: for each group represented in the model, the energy removed from that group, for example by predation or fishing, must be balanced by the energy consumed, i.e. consumption. Two linear equations represent the energy balance within a group and the energy balance among groups. The production (P) of each functional group (i) in the ecosystem is divided into predation mortality (M2ij) caused by the biomass of the other predators (Bj); exports from the system both from fishing activity (Yi) and other exports (Ei); biomass accumulation in the ecosystem (BAi); and baseline mortality or other mortality (1-EEi), where EE is the ecotrophic efficiency of the group within the system, or the proportion of the production of (i) that is exported out of the ecosystem (i.e. by fishing activity) or consumed by predators within it.

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Pi ¼

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(8:1)

Equation (8.1) can be re-expressed as:

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Bj  M2ij þ Yi þ Ei þ BAi þ Pi  ð1  EEi Þ

j

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B

  X     P Q P ¼ Bj   DCij þ Yi þ Ei þ BAj þ Bi  ð1  EEi Þ B i B B j i j

(8:2)

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where (P/B)i indicates the production of (i) per unit of biomass and is equivalent to total mortality, or Z, under steady-state conditions (Allen 1971); (Q/B)i is the consumption of (i) per unit of biomass; and DCij indicates the proportion of (i) that is in the diet of predator (j) in terms of volume or weight units. Ecopath parameterizes the model by describing a system of linear equations for all the functional groups in the model. For each functional group, three of the basic parameters: Bi, (P/B)i, (Q/B)i or EEi have to be known in addition to the fisheries yield (Yi) and the diet composition. The energy balance within each group is ensured when consumption by group (i) equals production by (i), respiration by (i) and food that is unassimilated by (i). The units of the model are expressed in terms of nutrient or energy related currency by unit of surface (frequently expressed as tkm–2 yr1).

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8.2.1.2 Definition of Functional Groups and Balancing Procedure An ecological model includes different functional groups spanning the whole ecosystem, from lower to higher trophic levels (i.e. from primary producers to top predators) and detritus groups (natural detritus and detritus generated by discarding during fishing operations). Definition of these groups is based on similarities between species in their ecological and biological features (e.g. feeding, habitat, mortality), their ecological role and on their importance as harvestable resources. In very speciose systems, multivariate statistical methods can be used to define mixed groups composed of various species by applying a systematic analysis of available ecological information. For example, Factorial Correspondence Analysis (FCA) and Hierarchical Cluster Analysis have been applied to stomach-content data in the Mediterranean Sea (Pinnegar 2000, Coll et al. 2006a, 2007). To encompass ontogenetic changes in feeding, behaviour and habitat preference, a multiple stanza representation has been incorporated into the model (Christensen and Walters 2004a), succeeding an earlier two-stanza version (Walters et al. 2000). The multiple stanza model enables the representation of all life-stages and ensures consistency between ontogenetic groups. Values of (P/B)i and diet composition have to be provided for all multiple stanza groups, while Bi and (Q/B)i need to be introduced for the leading stanza group only. A key stage in the development of a mass balance model is the process of assembling the data from different components of an ecosystem into one coherent picture, with flows that meet the mass balance criteria. It should be an exercise where information is gained about the ecosystem, since a single species view of the ecosystem (from which much of the input data is derived) will often not reflect the demands and constraints of the multispecies world these species inhabit. Preconceived ideas may have to be revisited for these models to balance. Thus, after parameterization, the model is considered balanced when the results show consistent values for the following: (1) estimates of EE > 1; i.e. Lotka-Volterra dynamics or predator control). In addition, a vulnerability value assigned to a given predator-prey interaction represents the factor by which a large increase in the predator biomass will cause predation mortality exerted by the predator on the prey to increase. For example, for an interaction assigned a high  (e.g.  =100), a doubling in the predator biomass would cause an approximately two-fold increase in the predation mortality inflicted upon the prey. Conversely, for a predator-prey interaction characterised by a low vulnerability (e.g.  close to 1), a large increase in predator biomass would have an unnoticeable effect on the predation mortality exerted by that predator on the prey in question (V. Christensen, pers. comm.).

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8.2.2.1 Tuning the Model to Real Data There are many variables that can affect Ecosim simulations, including assumptions about flow control (Bundy 1997, Walters et al. 1997, Bundy

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Fig. 8.4 Example of Ecosim gaming simulations applied to the Southern Benguela ecosystem: Using EwE to simulate the potential effects on biomass of increased fishing mortality (F; lower panel in each scenario) under three types of flow control assumptions: top–down, bottom–up and mixed flow control: (a) fourfold increase in F of small pelagic fish (anchovy, sardine, round herring) from year 10–50; (b) pulsed fourfold increase in F of small pelagics from year 10–15 and (c) pulsed fourfold increase in F of hake from year 10–15. Biomass plotted relative to original biomass. Species groups as follows: 1 round herring; 2 hake; 3 sardine; 4 anchovy; 5 cephalopods; 6 other small pelagic fish; 7 chub mackerel; 8 seals; 9 large pelagic fish; 10 seabirds; 11 cetaceans; 12 horse mackerel; 13 chondrichthyans; 14 mesopelagic fish. Reprinted from Shannon et al. 2000. Modelling effects of fishing in the Southern Benguela ecosystem. Shannon et al. (2000), with permission from Elsevier

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01

Biomass of predator (Bj)

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aijViBj

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Non-vulnerable biomass of the prey

(Bi-Vi)

v(Bi-Vi)

v’(Vi)

Vulnerable biomass of the prey (Vi)

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Fig. 8.5 Graphic representation of the foraging arena theory where the prey population is split into a vulnerable (Vi) and invulnerable (Bi–Vi) group. There is a steady flow of biomass between the two groups, and with the assumption that v = vij = vji. Prey from the vulnerable group is removed using the standard Lotka-Volterra equation, aijViBj, where Bj is the biomass of the predator and aij is the instantaneous rate of search. Adapted from Walters et al. 1997

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2001, Shannon et al. 2004b). To this end, dynamic simulations with Ecosim can be carried out to test hypotheses and to calibrate the model to time series data. Ecosim simulations can be tuned to a time series of biomass and catch data as references, along with estimates of how fishing impacts have changed over a period of time (e.g. total or fishing mortality by functional group or fishing effort by fleet, Fig. 8.6). This enables the estimation of a statistical measure of goodness-of-fit to these data each time dynamic simulations are performed, comparing predicted model results to available (observed) trajectories. This goodness-of-fit measure is a weighted sum of squared deviations (SS) of log biomasses and catches from log predicted biomasses and catches (Christensen and Walters 2004a, Walters and Martell 2004).

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8.2.2.2 Ecosim Properties and Routines Based on SS measures, three types of analyses are available and depend on a nonlinear SS minimization procedure. A non parametric procedure can be used to determine the sensitivity of SS to the vulnerability of functional groups () by changing each one slightly and then re-running the model to see how much SS is changed. After that, the best vulnerability values by functional groups can be estimated to give the better ‘‘fits’’ of Ecosim to the time series data (giving a reduction of the SS). In addition, an automatic procedure can be implemented to search for time series values of forcing functions (e.g. annual relative primary productivity) that represent productivity changes impacting biomasses throughout the ecosystem. A forcing function is sketched over time and applied to user-defined interactions only, usually to primary production. If applied to a primary producer, the forcing function alters the P/B of the producer, whereas in the case of a consumer-prey interaction, the rate of consumption of a prey

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Fig. 8.6 (continued)

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group by the predator is altered. Thus, when calibrating the model with available time series data, the roles of internal ecosystem forcing factors (e.g. flow controls determining trophic interactions) and external ones (e.g. fishing activities and environmental forcing) in driving the dynamics of marine resources over time can be explored (Christensen and Walters 2004a). Searching for a forcing function that produces an EwE model better fitted to observed time series data corresponds to searching for ‘‘nuisance parameter’’ estimates of the ‘‘process errors’’ in single-species assessment (Hilborn and Walters 1992). Christensen and Walters (2004b) introduced a new routine within Ecosim to search for alternative exploitation patterns setting different sustainability objectives. The ‘‘optimum policy search’’ routine is used to evaluate what changes in fishing effort or fishing mortality over time would maximize the performance of a particular measure for management. Implementing this routine facilitates searching for fishing rates that would maximize a combination of social, economical and ecological criteria (for further details see Section 8.3.2).

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8.2.3 The Spatial Dynamic Module Ecospace Ecospace is a spatially explicit version of Ecosim that represents biomass dynamics over 2-D space (Walters et al. 1999), removing the assumption of homogenous spatial distribution and behaviour implicit in Ecopath and Ecosim. It employs the same set of differential equations as Ecosim (see Equations (8.4) and (8.5)), but additionally takes into account habitat preferences, movement due to advection and migration, the spatial behaviour of fishing fleets as well as trophic interactions and population dynamics.

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Fig. 8.6 (continued) Example of fitting of the model to data for the southern Benguela: Abundance (A. biomass) and catch (B) time-series estimated by EwE (lines) and from timeseries data (dots) for the period 1978–2002. Biomass time series data are treated as relative and are scaled to match the EwE model series. Catches are scaled similarly (t km2 yr1) for both model and data. (A). (a) Estimated total biomass of anchovy compared to EwE anchovy biomass; (b) Sardine spawner biomass (surveys) compared to EwE sardine biomass; (c) Pairs of breeding gannets off South Africa compared to EwE seabird biomass; (d) Seal pups in the southern Benguela (census data) compared to EwEl seal biomass; (e) Exploitable biomass (single species model) of M. paradoxus on the West coast (R. Rademeyer, UCT, pers. comm.) compared to EwE large M. paradoxus biomass; (f) Exploitable biomass (single species model) of M. capensis on the South coast (R. Rademeyer, UCT, pers. comm.) compared to EwE large M. capensis biomass; (g) Estimated combined biomass of small and large M. capensis on both the West and South coasts (from surveys) compared to EwE large M. capensis biomass (Ecosim); (h) Estimated combined biomass of small and large M. paradoxus on both the West and South coasts (from surveys) compared to EwE large M. paradoxus biomass (Ecosim). (B).(a) Anchovy, (b) Sardine, (c) Chokka Squid, (d) Large horse mackerel, (e) Snoek, (f) Large M. capensis, (g) Large M. paradoxus, (h) Small M. paradoxus. Adapted from Shannon et al. (2004) with permission of NISC

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The model area is defined by a grid of cells, representing up to 8 different habitat types. In each habitat type, the cells share common attributes which affect movement, feeding rate and survival. Commonly these habitats will support a particular sub-food web, although they usually include the entire water column, from the benthos to the pelagic zone. Species that transcend habitat types (e.g. marine mammals, plankton) connect the sub-food webs together. The user sketches the different habitat types (e.g. shelf, coastal, deep, reef) on a base map, and then assigns which are the preferred habitats for the various functional groups in the model. An Eulerian approach is used in Ecospace to explicitly model movement, where the movement or the flow of biomass occurs with respect to fixed reference points, the grid of cells. Minimally this approach approximates the changing centre of distribution of the biomass (Christensen and Walters 2004a). Movement occurs across each of the four faces of the grid cell, unless the cell is on the boundary, in which case it is assumed that emigration out of the boundary cell is equivalent to immigration into the boundary cell. Movement between cells is determined by several factors which account for (1) movements of each functional group due to dispersal (mi) and advection (Vi), (2) predation risk and food availability and (3) fishing effort (Walters et al. 1999). Initial estimates of dispersal rates are measured as average swimming speed (km yr1) and advection is estimated from current fields (see below). The emigration term in Equation (8.4) is the sum of the flows across each cell face, represented by (mi+Vi)Bi (Martell et al. 2005). Immigration is also represented by (mi+Vi)Bi input rates, in this case proportional to the biomass in the adjacent cells. The instantaneous movement rates, mi, vary with pool type, habitat type in the source cell and response to predation risk and feeding conditions (risk ratio). Note that mi is not a directed rate, but should be considered as dispersal. A ‘‘habitat gradient function’’ enables a more realistic representation of movement where organisms respond to gradients in the environment (e.g. depth, salinity, temperature) and intentionally move towards cells with favourable habitat types. Martell et al. (2005) have explored this further and investigated the effects of different fitness dispersal rates (see Section 8.3). Two other forms of movement can also be explicitly modelled in Ecospace: migration and advection (advances since the first published model in 1999, see Christensen and Walters 2004a, Walters et al. 2004). Migration is modelled by defining a monthly series of preferred positions for the migrating species and associated concentration parameters (the spatial spread of the migrating fish around these preferred cells). Advection is a critical oceanic process for the distribution of larvae, nutrients and productivity in general. Ecospace models consider advection by first importing user defined current patterns or other types of physical forcing to define surface movement in the model. Using a series of linearised pressure field and velocity equations, which include sea surface anomalies, bottom friction force, the Coriolis force, down-welling/ up-welling rate and acceleration due to sea surface slope, Ecospace estimates equilibrium flow fields (horizontal and upwelling/down-welling) across each

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cell face in the Ecospace grid (Walters et al. 2004). The flow fields maintain water mass balance and Coriolis force. Once defined, the user specifies which functional groups are subject to advection. A great advantage of spatial modelling is the ability to better represent the spatially aggregated patterns of fishing: fishermen generally know the best places to fish! Furthermore, open and closed areas are important tools for fisheries management and their efficacy can be explored using Ecospace (Fig. 8.7). Ecospace uses a ‘‘gravity model’’ to distribute fishing effort spatially. The distribution of effort is based on the habitat type, whether the area is open or closed, biomass of species of interest, price of fish and cost of fishing. This is essentially a spatial economic model where it is assumed that fishing fleets will operate in areas which are most cost effective providing they are accessible. Once an Ecospace model has been parameterized, with the biomass of the functional groups assigned to preferred habitats, the user-set preferences described above will determine the movement of the biomass pools. At this stage, an iterative approach is recommended where the results from Ecospace are compared with the Ecopath model for consistency (Walters et al. 1999). The predicted distribution maps of species or groups of species can be used to validate model results.

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Fishing zone

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Island

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No fishing zone

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Fig. 8.7 A simple diagrammatic representation of the potential effects of a fisheries exclusion zone on pepino (S. fuscus) biomass at the end of a 10-year Ecospace simulation at a hypothetical Gala´pagos island. Darker areas represent high biomasses and lighter areas represent low biomasses. Catchable emigration of pepinos can be seen as dark shading outside the dotted lines that demarcate the boundaries of the hypothetical fisheries exclusion zone. Pepinos still decline to a biomass lower than present, but the no-fishing zone prevents the intense fishery from extirpating them. Reprinted from Okey et al. 2004. A trophic model of a Galapagos subtidal rocky reef. Ecological Modelling, 172: 383–401, with permission from Elsevier

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8.2.4 Data Requirements, Sources and Shortcuts

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During the parameterization of an Ecopath model, three of the four basic parameters in Equation (8.2): Bi, (P/B)i, (Q/B)i or EEi, need to be provided per functional group, in addition to catch data and trophic behaviour by functional group. Biomass of functional groups can be obtained from different sources of information: e.g. swept area method, egg production method, acoustic surveys and visual census. Ideally production/biomass (P/B) and consumption/biomass ratios (Q/B) should be calculated empirically, but are usually calculated by the application of empirical equations using length, weight and growth data (Nilsson and Nilsson 1976, Pauly 1980, Innes et al. 1987, Pauly et al. 1990, Christensen et al. 2005). Diet composition can be estimated from stomach contents analysis. Data on total catches need to be included in the model by functional group and fishing fleet, considering official landings statistics, discards and estimates of illegal, unregulated or unreported (IUU) landings. The Ecosim module uses initial parameters inherited from the baseline Ecopath model, therefore essential data requirements do not increase substantially when performing temporal simulations. However, time series of data for fishing effort by fleet and total or fishing mortality and time series of biomass and catches for various functional groups are required to tune the model to data (Christensen and Walters 2004a, see Section 8.2.2). The latter step of fitting an Ecosim model to time series data (see Section 8.3.2), although a time-consuming and demanding process, is advisable before detailed exploratory simulations are performed. Ecospace also uses the initial parameters from Ecopath. Additional data requirements (see Section 8.2.2) include the identification of habitat types, sketched on to base map and assignation of the functional groups to these habitats. In its most simple implementation, the only further input required by Ecospace are the dispersal rates for each functional group, movement rates in different types of habitat (good, bad) and identification of the location of fishing activity for each fleet. Other optional input data include importing GIS maps for the basemap, setting up MPAs, importing nutrient data and current patterns or surface currents forcing patterns to set up advection patterns (e.g. Martell et al. 2005, see below).

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8.2.5 Allowing for Uncertainty

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Mass balance models are deterministic and require many input parameters, some of which may be poorly known, or adapted from other ecosystems or Ecopath models. This introduces a high level of uncertainty to the results of the model estimates. Therefore the uncertainty associated with model output should always be explored. To this end, EwE includes a data pedigree routine

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% change in model estimate

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–50% –40% –30% –20% –10% 0%

10% 20% 30% 40% 50%

% change in input parameter

–0.4 Zooplankton

Phytoplankton

Sergestid Shrimp

Fig. 8.8 Simple sensitivity analysis of model estimates of biomass to the sergestid shrimp production/biomass and ecotrophic efficiency values in a model of San Miguel Bay, the Philippines. Adapted from Bundy 1997

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(see Section 8.2.3), a simple sensitivity analysis, the Ecoranger utility, mixed trophic impact assessment and the autobalance routine. The simple sensitivity analysis in Ecopath quantifies the effects of increasing and decreasing each of the basic input parameters (B, P/B, Q/B and EE) in steps of 10%, by up to 50% of its original value (Fig. 8.8). Model output is in the form of a table of the difference between the new estimated output and its original value, as a proportion of the original value (Christensen and Walters 2004a). Mixed trophic impact assessment (MTI, see Section 8.2.1, Fig. 8.3) assumes that trophic structure is constant. This means that the technique cannot be used for predictive purposes, but should rather be considered as a simple form of sensitivity analysis. It is an indicator of which groups have negligible effects on others within the system, and for which there is likely to be little gained from an effort to collect additional data to refine estimates. On the other hand, it identifies groups having large trophic impacts on others, and for which it would be useful to refine estimates. Ecoranger is a Monte Carlo approach within Ecopath (Christensen and Pauly 1995, 1996, Pauly et al. 2000), enabling the incorporation of variability around values for the basic input parameters: B, Q/B, P/B, EE and diet composition for all groups. The mean or mode and range of these parameters can be entered and a frequency distribution (uniform, normal, log-normal or triangular) defined from which random samples are drawn to generate distributions for output variables. To put Ecoranger into a semi-Bayesian context, a ‘‘sampling/importance resampling’’ procedure based upon that of McAllister et al. (1994) is used (Christensen and Pauly 1996). Each possible model output is evaluated and of all the runs, the best-fit model is selected using a least square method. The best-fit model is that giving the smallest residuals (based on mean/mode of each selected parameter

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and the squared deviations of each), or the smallest deviation from three other user-defined criteria, namely maximum system biomass, maximum throughput or maximum ascendancy. Ecoranger can also be used in the Ecosim dynamic module using the confidence intervals from the model pedigree to run repeated temporal simulations. Each simulation begins by selecting random input combinations from normal distributions centered on the initial inputs of the baseline model, then the resulting model (if balanced) is used to develop dynamic simulations. This result can be seen as bands of uncertainty giving an idea of how sensitive Ecosim results are to input parameters (Christensen and Walters 2004a). The Autobalance routine is another means to explore uncertainty. However, only the biomass and diet parameters can be directly changed in the current Autobalance routine, thus it is not a full sensitivity analysis. Bundy (2005) used the Autobalance routine as a perturbation analysis of balanced Ecopath models to explore the effects of uncertainty on model results. For tractability reasons, only thirty Autobalance model runs were completed, but these provided estimates of 95% confidence limits for all input and output parameters. When comparing two models, a Mann-Whitney U, two independent samples test was used to test whether differences between the models were significant, or an artefact created by the uncertainty of the input parameters. Though this approach used only 30 replicates, it does provide some rigour for this type of comparative analysis. Model estimates can also be compared with alternative estimates of certain input parameters. For example, an alternative estimate of biomass of a poorly known yet important group such as gelatinous zooplankton was available and thus tested for the northern Benguela ecosystem model of the period 1980–1989 (Shannon and Jarre-Teichmann 1999a,b). Generally, these tools are basic and more rigorous sensitivity analyses are needed to formally analyze the propagation of uncertainty of input variables on the value of outputs, providing ranges of output values (Aydin and Friday 2001, Bundy 2005, Gaichas 2006).

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8.2.6 User Beware – A Guide to Common Pitfalls and Advice for Avoidance

34 35 36 37 38 39 40 41 42 43 44 45

EwE carries several of the same risks as single-species models, such as uncertainty associated with estimates of biomass, misinterpretation of trends in data series, and problems pertaining to disentangling the often confounding effects of environmental changes and the effects of fishing (Christensen and Walters 2004a). Users should make careful choices when selecting periods to be modelled. The period over which simulations can be considered reliable is often limited by data quality (Walters et al. 1997). On the other hand, focusing on such short time periods runs the real risk that important long-term effects may be missed (Mackinson et al. 1997, Walters et al. 1997). Use of erroneous or poor input data and parameters will obviously severely reduce the value of an EwE model and can lead to model outputs that are unrealistic and incorrect.

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However, these kinds of erroneous model outputs normally arise from errors in a few key input parameters rather than from general uncertainties in the model as a whole (Christensen and Walters 2000). Christensen and Walters (2000) list five main pitfalls of EwE:

05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

(i) prey that is rarely found in the diet of a predator may be omitted from diet composition estimates, leading to inaccuracies in the modelled effects of the predator on these prey, and visa-versa; (ii) trophic mediation effects, though they can be included in EwE, they may be overlooked (these are the indirect effects that the behaviour or presence of a third group may have on a predator-prey interaction); (iii) predation vulnerabilities are often underestimated, lessening the modelled impacts of predation; (iv) predators share foraging arenas; if abundance of one predator decreases, another may fill its place so that prey species do not benefit; (v) temporal variation in factors affecting species-specific habitats is not incorporated. In addition, non-trophic mediation effects (such as the effect of habitat type, the presence of refuges or other behavioural effects) can also be modelled in EwE, although they have not been frequently included in the models. These pitfalls can be overcome with good quality data and a notable knowledge of the system to model. Plaga´nyi and Butterworth (2004) provide several suggestions for EwE users to avoid common pitfalls. Users are warned against adopting default parameter settings without questioning their meaning or examining their effects on the results they obtain. A further warning has been sounded that care should be taken when focussing EwE applications on marine mammals and seabirds, given their very different life history traits to the traditional fish groups for which the EwE modelling approach was specifically developed. The authors advise that data quality should guide decisions on which functional groups to include in an EwE model and that effort be made to ensure that time-specific and spatially-specific diet compositions are used wherever possible. Regarding analysis and presentation of EwE model results, they emphasize the need for recognition of model complexity and uncertainty through presentation of model outputs as a range of likely scenarios. Sensitivity of EwE model results to the choice of vulnerability settings in Ecosim has been shown to be a major factor in interpretation of model outputs for fisheries management advice. In response to this, Plaga´nyi and Butterworth (2004) have proposed some guiding steps for EwE users, including searching for group-specific values for vulnerabilities rather than adopting default settings across all groups, and in particular, using available time series to fit Ecosim models by searching for ‘‘best’’ fit vulnerabilities (Christensen et al. 2000). It is advisable to follow the lead set by Arreguı´ n-Sa´nchez (2000) and Bundy (2001), for example, who presented fisheries management scenarios for a range of flow control (vulnerability) assumptions.

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8.3 Case Studies

02 03 04 05 06 07 08 09 10

Ecopath models have been used to examine the trophic structure and functioning of a host of aquatic ecosystems, including lakes, aquaculture systems, estuaries, small bays, coastal systems and coral reefs, shelf systems, upwelling systems, and open seas (see examples in Table 8.1). This section will discuss a selection of the numerous available Ecopath models. In many cases, these models have served as the basis for Ecosim and Ecospace simulations to explore the trophic functioning and the ecosystem effects of fishing in these systems.

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Table 8.1 Examples of Ecopath, Ecosim and Ecospace case studies System modelled Application (use and/or model output) References A. Ecopath models 1. Energy budgets, trophic structures and network analyses Tiahura reefs, Moorea Examined ecosystem structure and Island (French functioning of the fringing and barrier Polynesia) reefs North and Central Highlighted key role of benthic-pelagic Adriatic Sea coupling, small pelagics and jellyfish (Mediterranean) and the importance of the microbial food web. The ecosystem was described as highly impacted by fishing South Catalan Sea Showed the ecosystem was pelagically (NW Mediterranean) dominated, important pelagic-benthic coupling and importance of detritus and detritivors. The ecosystem showed high fishing intensity with large ecosystem impacts Cantabrian Sea Represented the Cantabrian Shelf (Bay (Bay of Biscay) of Biscay, Spain) in 1994 Strong relationships between pelagic, demersal and benthic compartments were identified Southern Plateau Described a low productive ecosystem (New Zealand) with importance in terms of feeding seabirds, seals and fish and of commercial fishing Kuosheng Bay, Taiwan Investigated effect of power plant on bay ecosystem Explored the definition of the Pribilof archipelago boundaries of open marine (Southeast Bering ecosystems by comparing the area of Sea) maximum energy balance by means of a mass balanced model from the 1990s with estimates of the foraging rage of the northern fur seals

Arias-Gonza´lez et al. 1997 Coll et al. 2007

Coll et al. 2006a

Sa´nchez and Olaso 2004

Bradford-Grieve et al. 2003

Lin et al. 2004 Ciannelli et al. 2005

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Table 8.1 (continued) System modelled Central Pacific Ocean and northern Gulf of Mexico

06 07 08 09

Tongoy Bay (Northern Chile)

245

Application (use and/or model output)

References

The maximum biomass of top predators possible to accommodate within the models excluding fishing and non changed primary production to investigate the carrying capacity of ecosystems Examined ecosystem structure of a suspended scallop culture system.

Christensen and Pauly 1998

Wolff 1994

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

2. Placing fisheries within their ecosystem context San Miguel Bay (Pacific To examine the ecosystem effects of Coast of Southeast large-scale and small-scale fishing Luzon, Philippines) gears Gulf of California To describe the trophic web of the (Mexico) California Gulf during the late 1970s and study the shrimp trawling exploitation to understand the role of by-catch Black Sea (Eastern Models applied to the Black Sea Mediterranean) ecosystem to study the outburst of the gelatinous Mnemiopsis leidyi and the decline of small pelagic fish South Humboldt A mass balance model was used to Upwelling describe the system in 1992 and assess (Central Chile) the impacts of fishing activities Investigated the consequences of fishing Northern and Southern on small pelagic fish: market effects Benguela, Southern both on the higher and lower trophic Humboldt and the levels of the food web, causing Mediterranean Sea decrease of predators, the proliferation of other species and the disruption of energy flows.

Bundy and Pauly 2001 Arreguı´ n-Sa´nchez et al. 2002

Gucu 2002

Neira and Arancibia 2004 Shannon et al. in press

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

3. Comparing ecosystems through time Southern Benguela Similar trophic functioning in 1980s and (South Africa) 1990s despite changes in abundance of species groups; fishing-induced changes from pristine ecosystem state Northern Benguela Large changes in ecosystem structure (Namibia) and trophic functioning since 1970s; shift from pelagic to demersaldominated ecosystem South Humboldt Compared models of 1992 and 1998. (Central Chile) Showed importance of predation mortality on fish production, quantified strong fishing effects, higher biomass supported in 1998 than in 1992 but smaller catches

Shannon et al. 2003; Watermeyer 2007, and Watermeyer et al. 2008 Heymans et al. 2004, Roux and Shannon 2004 Neira et al. 2004

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Table 8.1 (continued) System modelled Eastern Scotian shelf (North Atlantic)

05 06 07 08 09

Eastern Scotian shelf (North Atlantic)

10 11 12 13 14

Venice lagoon (NE Italy)

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Orbetello lagoon (Central western Italy)

M. Coll et al.

Application (use and/or model output)

References

Models of before and after collapse of Atlantic cod explored changes in ecosystem structure and demonstrated a shift from benthic-feeder dominance to pelagic-feeder dominance and an increase in piscivory Models of before and after collapse of Atlantic cod explored reasons for the non-recovery of Atlantic cod and concluded that competition for food from the large biomass of pelagic fish contributes to the non-recovery Two models representing the lagoon from 1988–1991 and 1998 were compared to analyze the fishing impacts of Manila clam dredging in the area, which was developed from the middle 1980s Two models described the lagoon in 1995 and 1996 to analyze the effects of management activities developed in the area to control eutrophication.

Bundy 2005

4. Comparing studies across ecosystems Upwelling ecosystems Four models representing the Northern and Southern Humboldt and the Northern and Southern Benguela models were standardized and compared Upwelling ecosystems A model from representing the South and NW Catalan Sea (NW Mediterranean in Mediterranean Sea 1994, Coll et al. 2006a) was compared with the four models reported in Moloney et al. (2005) to assess ecosystem effects of fishing taking into account differences on ecosystem features Different habitats of the Comparison of two models representing two different habitats of the Venice Venice lagoon lagoon: the seagrass meadows and (NE Italy) Manila clam (Tapes philippinarum) fishing grounds.

Bundy and Fanning 2005

Pranovi et al. 2003

Brando et al. 2004

Moloney et al. 2005

Coll et al. 2006b

Libralato et al. 2002

38 39 40 41 42 43 44 45

B. Ecosim models 1. Exploring impacts of fishing and management simulations Gulf of Thailand Two mass-balance models were used to reproduce changes on the ecosystem from 1963 to the 1980s by developing dynamic simulations of increasing or decreasing fishing activity

Christensen 1998

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Table 8.1 (continued) System modelled

03

Upwelling ecosystems

01

04 05 06 07

Different habitats of Tongoy Bay (Chile)

08 09 10 11

Gulf of Mexico

12 13 14 15 16 17

South Catalan Sea (NW Mediterranean)

18 19 20

Northern Colombian Caribbean Sea

21 22 23 24

South Catalan Sea (NW Mediterranean)

25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

Central North Pacific ecosystem

247

Application (use and/or model output)

References

Exploration of ecosystem effects of fishing on small pelagic fish from three upwelling ecosystems: Peru, Venezuela and Monterey Bay Exploration of different fishing options in four models representing different benthic habitats of Tongoy Bay, Chile: seagrass, sand-gravel, sand and total ecosystem Dynamic simulations were performed using a mass-balance model of two ecosystems of the Gulf of Mexico to evaluate the ecological role of snappers and the impact of their exploitation Showed low resistance of the ecosystem to fisheries, an increase in fishing effort resulted in decreased catches under all scenarios analysed Explored the ecosystem impacts of reducing fishing mortality due to by-catch reduction devices applied to a tropical industrial shrimp fishery Available field data from bottomtrawling selectivity by applying sorting grids and square meshes have been put into ecosystem context simulating the consequent reduction of fishing mortality on target species in the area Assessed the ecological and economic impacts of alternative fishing methods to reduce the by-catch of marlin.

Mackinson et al. 1997

2. Examining energy flow controls Southern Benguela Exploring the effects of fishing on small (South Africa) pelagic fish and hake were explore under different scenarios of top–down and bottom–up flow control Southern Benguela EwE was used to simulate changes in the (South Africa) Southern Benguela ecosystem from an anchovy-dominated system to a sardine-dominated one NewfoundlandExploration of effects of fishing and Labrador, (Canada) predation on the ecosystem from 1980s to see if they could reproduce ecosystem changes observed from the early 1990s

Ortiz and Wolff 2002a

Arreguı´ n-Sa´nchez and ManickchandHeileman 1998

Coll et al. 2006a

Criales-Hernandez et al. 2006

Coll et al. 2008a

Kitchell et al. 2002

Shannon et al. 2000

Shannon et al. 2004a

Bundy 2001

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Table 8.1 (continued) System modelled South Brazil Bight coastal ecosystem

05 06

Barents Sea

07 08 09 10 11

San Miguel Bay (Philippines)

12

M. Coll et al.

Application (use and/or model output)

References

Exploration of the effects of changing fishing strategies in terms of increasing squid’s catches and of livebaitfish for sardines A mass-balance model was used to explored different functional response hypotheses of minke whales, their prey and theirfisheries Application of an adaptive management approach to explore management options under different flow assumptions.

Gasalla and RossiWongtschowski 2004 Mackinson et al. 2003

Bundy 2004a

13 14 15 16 17 18 19 20 21 22 23 24

3. Analyzing environmental forcing of ecosystem dynamics Black Sea Exploration of fishing and (Mediterranean) eutrophication on the Black Sea ecosystem and contrast of results with biological time series Pelagic system (eastern Study of how the effects of el Nin˜o – tropical Pacific Southern Oscillation (ENSO) might Ocean) affect different organisms at middle and high trophic levels. 4. Fitting models to data Southern Benguela (South Africa)

25 26 27 28 29 30

Northern Benguela (Namibia)

31 32 33 34 35

South Catalan Sea (Mediterranean)

36 37 38 39 40 41 42 43 44 45

Southern Humboldt upwelling (Chile)

A mass-balance model was fitted to available time series data for a 25-year period from 1978 to 2002, exploring how fish stock dynamics may be determined by feeding interaction patterns (flow controls), fishing strategies and environmental changes Temporal dynamics affecting the ecosystem were explored by fitting the model to time series of data over 30 years from 1970s and assuming wasp–waist flow control by small pelagic fish A model was fitted to available time series of data from 1978 to 2003 explaining 78% of data variability taking into account trophic control (67%), fishing (7%), and environmental factors (4%) The model fitted from 1970 to 2004 showed that fishing mortality explained 28% of the variability in the times series, vulnerability parameters explained 21%, and a forcing function affecting primary production explained a further 11–16% of the observed variability

Daskalov 2002

Watters et al. (2003)

Shannon et al. 2004b.

Heymans 2004

Coll et al. 2008b

Neira et al. in prep.

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Table 8.1 (continued) System modelled

Exploration of results of fitting eleven mass-balance models to time series of data and of ecosystem effects of harvesting species to their single species maximum sustainable catch. 5. Policy optimization and management scenarios Southern Benguela The optimization routine was applied to (South Africa) this upwelling system. Results showed that extreme optimal fishing scenarios forced the model parameters beyond their likely ranges producing highly unrealistic outcomes Prince Willinam Sound The optimization routine was used (Alaska) to analyze management options for within the context of rebuilding pinnipeds populations Gulf of Thailand Searching for alternative exploitation patterns setting different sustainability objectives optimizing for profit, value and conservation Application of the optimization routine La Paz Bay, Baja to the artisanal fisheries based on California Sur hook-and-line and on gillnets coexist, (Mexico) in conjunction with a shrimp fishery. 6. Back to the future simulations Reconstructing past systems using Strait of Georgia in modelling and traditional or local British Columbia, knowledge, historical documentation, Newfoundland, and archaeological data to explore (Northern British Columbia) future policy goals. 7. Pollution studies Prince William Sound, (Alaska)

32 33 34 35 36 37 38 39 40 41 42 43 44 45

Application (use and/or model output)

Eleven ecosystems from different areas

Faroe Islands ecosystem

Exploration of impacts produced by Exxon Valdez oil spill by performing simulations changing mortalities of different functional groups Methyl mercury concentration on food web and marine mammals was modelled to explore the implications of human diet on cod and pilot whales.

C. Ecospace models 1. Modelling spatial dynamics Tongoy Bay, Chile Explored policies for sustainable exploitation of four benthic species in different benthic habitats, including exploitation exclusively in one habitat to exploitation across all habitats.

249

References Walters et al. 2005

Shannon 2002

Okey and Wright 2004

Christensen and Walters 2004b

Arreguı´ n-Sa´nchez et al. 2004

Dalsgaard et al. 1998 Pitcher et al. 2002 Ainsworth et al. 2002

Okey and Pauly 1999, Okey 2004

Booth and Zeller 2005

Ortiz and Wolff 2002b

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Table 8.1 (continued) System modelled

M. Coll et al.

Application (use and/or model output)

2. Establishment and assessment of MPAs Brunei Darussalam, Introduction of Ecospace as an Southeast Asia important exploratory tool for MPA definition and function and policy exploration. Authors discuss the affects of harvesting on MPA boundaries, trophic cascades and density dependent effects and concluded that few large MPAs are more effective than more small MPAs Gwaii Haana National Explored the consequences of alternative Marine Conservation MPA zoning policies. Authors concluded that, coupled with a Area, British Columbia, Canada reduction in harvest pressure, a large MPA with a buffer area around it leads to the greatest increase in biomass Galapagos subtidal Authors demonstrate how the rocky reef functional extinction of sea cucumbers could be avoided by protecting some of the reef from fishing Central North Pacific Exploration of the relative importance of different assumptions about dispersal and advection under different fishing policy scenarios with respect to marine protected areas and concluded that MPAs for large pelagics need to be large Hong Kong Spatial policy exploration such as tradeoffs between compliance with fishery regulations and conservation in the Hong Kong artificial reef system, for example where fishing was permitted in one artificial reef, assuming that this would lead to greater support for the artificial reef scheme and self-enforcement.

References Walters et al. 1999

Solomon et al. 2002

Okey et al. 2004

Martell et al. 2005

Pitcher et al. 200b

35 36 37

8.3.1 ECOPATH

38 39

8.3.1.1 Energy Budget, Trophic Structure and Network Analysis

40 41 42 43 44 45

A fundamental use of Ecopath models is the estimation of energy budgets, trophic flow and structure. Arias-Gonzalez et al. (1997) modelled two Tiahura reefs (Moorea Island, French Polynesia) (Arias-Gonzalez et al. 1997) highlighting the high proportion of primary productivity processed and recycled within both systems and the importance of detritus and microbially mediated food web.

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A model of the North and Central Adriatic Sea, representing the widest continental shelf of the Mediterranean Sea, highlighted the key role of benthic-pelagic coupling, small pelagics and jellyfish in this exploited Mediterranean shelf ecosystem (Coll et al. 2007). In addition, results indirectly underlined the importance of the microbial food web in the Adriatic Sea. Another model representing the exploited ecosystem of the South Catalan Sea (NW Mediterranean) in 1994 also showed that the ecosystem was pelagically dominated but that pelagic-benthic coupling, by means of flows from the pelagic food webs to detritus, and the abundance of detritivores, were important in the system (Coll et al. 2006a) (Fig. 8.1). Similarly, Sa´nchez and Olaso (2004) modelled the Cantabrian Shelf (Bay of Biscay, Spain) demonstrating a strong relationship between the pelagic, demersal and benthic compartments in the ecosystem. Table 8.2 presents several results from mass balance models of the South Catalan Sea, North and Central Adriatic Sea and Cantabrian Sea with respect to global statistics, network flow indices and information indices. Ecopath has also been applied to food webs with very limited primary production. For example, the Southern Plateau of New Zealand (BradfordGrieve et al. 2003) is characterized by low levels of phytoplankton biomass and was described by means of an ecosystem model that highlighted the importance of the microbial loop. The system was dominated in terms of trophic flows by the pelagic compartment, mainly retaining 69% of the biomass and 99% of the production. The mean transfer efficiency of the ecosystem between trophic levels II and IV was very high (23%), underlining the energy limitation in that ecosystem. The site of a coastal nuclear power plant in Kuosheng Bay, Taiwan, is one of the more unique applications of the Ecopath modelling approach (Lin et al. 2004). The Ecopath model was used to explore whether the impingement and entrainment of organisms during the intake of vast quantities of water for cooling of the power plant, and the expelling of warm water into the bay, impacted the coastal ecosystem. The total ecosystem attributes (total biomass, total system throughput, etc.) suggested that the bay system behaved like a normal coastal ecosystem in terms of structure and functioning. At the bay scale, the power plant was not having large impacts; the effects were likely to be localized to the warm water radius of the plume of expelled water. However, the Kuosheng Bay ecosystem was found to be more detritus-dependent than other coastal ecosystems to which it was compared, related to the rapid turnover rates of phytoplankton feeding into the detritus box. The boundaries of open marine ecosystems were explored by Ciannelli et al. (2005) using an Ecopath model of the Pribilof archipelago (Southeast Bering Sea) and comparing the area of maximum energy balance with estimates of the foraging range of the northern fur seals. Considering foraging theory, an ecosystem boundary should include the foraging range of the species that live within it for a part of their life cycle; considering ecosystem energetics an ecosystem should be the area within which the predatory demand is in balance

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Table 8.2 Global statistics, network flow indices and information indices from mass balance models of the South Catalan Sea (Coll et al. 2006a), North and Central Adriatic Sea (Coll et al. 2007) and Cantabrian Sea (Sa´nchez and Olaso 2004) Adriatic Cantabric Units Catalan sea(2) sea(3) sea(1) Statistics and flows Sum of all consumptions Sum of all exports Sum of all respiratory flows Sum of all flows into detritus Total system throughput Total primary production/total respiration Net system production Total primary production/total biomass Total biomass/total throughput Total biomass (excluding detritus) Total respiration/total biomass Ecopath Pedigree index Gross efficiency of the fishery Mean trophic level of the catch Mean trophic level of the community Network flow indices Predatory cycling index (% of throughput w/o detritus Finn’s cycling index (% of total throughput) Finn’s mean path length System Omnivory Index

851.73 1251.89 326.86 1607.52 4038.0 4.83

1305.04 730.75 421.09 1388.07 3845.0 2.73

2528.35 1075.86 950.88 1513.15 6068.0 2.13

t/km2/yr t/km2/yr t/km2/yr t/km2/yr t/km2/yr

1250.14 26.74

729.37 8.83

1074.12 10.60

t/km2/yr

0.02 58.97 5.54 0.670 0.003 3.12 1.50

0.03 130.30 3.23 0.665 0.002 3.07 1.39

0.03 191.00 4.98 0.669 0.006 3.76 2.31

3.33

3.97

3.55

%

6.77

14.69

4.89

%

2.56 0.22

3.34 0.19

2.99 0.27

t/km2

28 29 30 31 32 33

Information indices Ascendency (%) Capacity (Total) (1) Coll et al. 2006a; (2) Coll et al. 2007; (3) Sanchez and Olaso 2004.

35.08 12738.9

27.0 15409.6

25.9 29577.2

% Flowbits

34 35 36 37 38 39 40 41 42 43 44 45

with the prey production. This work examined the limitations of the current definition of an open ocean ecosystem in a spatial context. Christensen and Pauly (1998) used Ecopath to estimate the maximum biomass of top predators that it was possible to accommodate in two different ecosystems, under two scenarios, one excluding fishing and one with no change in primary production. They observed that in both models, the Central Pacific Ocean and the northern Gulf of Mexico, top predators biomass were able to be increased by an order of magnitude and changes in food web structure were in agreement with the theory of ecosystem development sensu Odum (1969). Based on these results they proposed a functional definition of the carrying

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capacity: ‘‘the upper limit of biomass that can be supported by a set primary production and within a variable food web structure is reached when the total system respiration equals the sum of primary production and detritus import’’. Aquaculture systems have also been analyzed using ecosystem models. In Northern Chile, for example, the suspended scallop culture located in Tongoy Bay has been studied using a 17 compartment model (Wolff 1994). Benthic invertebrates dominated the system in terms of total food intake and total biomass in the water column. The system was described to be of low maturity and to have high capacity to withstand ecological perturbations.

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45

8.3.1.2 Placing Fisheries Within an Ecosystem Context Fisheries can be considered as top predators in ecosystems, exerting strong top– down control and often causing cascading effects down the food web. Fisheries may compete with natural top predators and with other fisheries within an ecosystem; the ways that one fishery impacts various ecosystem components will have impacts on other fisheries operating in the same ecosystem. The Ecopath models of the South Catalan Sea and the North and Central Adriatic Sea, Mediterranean (Coll et al. 2006a, 2007) showed that these ecosystems were heavily fished during the 1990s. Similarly, in the Cantabrian Sea model (Sa´nchez and Olaso 2004), the ecosystem effects of different fleets operating in the area were analysed. Results suggested that fishing activity in the area was comparable to the most intensively exploited temperate shelves of the world. In both the Mediterranean and the Cantabrian Sea, the trawling fleet was identified to be the gear having the strongest impacts in the ecosystem. Bundy and Pauly (2001) modelled the San Miguel Bay fisheries (Pacific Coast of Southeast Luzon, Philippines) to examine the ecosystem effects of large-scale and small-scale fishing gears. Results highlighted that both fishing sectors had high impacts on the ecosystem, but that the cumulative impact of the range of small-scale fishing gears was greater and more diverse than that of the large-scale fishery sector. While the large scale sector fished across most trophic levels, the small-scale sector as a whole caught a greater number of species across an even wider trophic range. These results demonstrated the complexity of the interactions that occur amongst the effects of fishing mortality, predation mortality and competition. Arreguı´ n-Sa´nchez et al. (2002) described the trophic web of the California Gulf (Mexico) during the late 1970s to examine the ecosystem effects of shrimp trawling and to understand the impacts of by-catch (Fig. 8.3). They concluded that maintaining by-catch would be beneficial to maximize shrimp yields due to the fact that some important predators of shrimps were captured by trawling in notable proportions. Therefore, reducing by-catch would lead to an increase in the biomass of species preying on shrimps. However, the analysis highlighted that if the fisheries management objective was to maximize overall production

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of the ecosystem or reduce the impact of fishing on other species, a reduction of by-catch would be compulsory. Gucu (2002) studied the outburst of the gelatinous Mnemiopsis leidyi in the 1990s and the decline of small pelagic fish in the Black Sea ecosystem using Ecopath. Results suggested that gelatinous species only played a small role in the decline of small pelagic fish, while overfishing was singled out as the primary cause. Overfishing, however, did play a crucial role in the successful development of gelatinous species by vacating the ecological niche occupied by small pelagics, and enabling gelatinous organisms to proliferate, taking advantage of the increase in plankton productivity associated with eutrophication during the 1980s. Similarly, modelling studies of the upwelling system of Central Chile in 1992 suggested that fishing activities removed about 15% of total calculated system primary production (Neira and Arancibia 2004), exploiting organisms with an intermediate to low trophic level. Although natural mortality by predation was found to be high off Chile, for example predation mortalities of small pelagic fish were estimated to have between 0.7 and 1.4 yr1 (Shannon et al. in press), the fishery also removed a large proportion of commercial species like anchovy, horse mackerel and Chilean hake production. Shannon et al. in press compared the consequences of fishing on small pelagic fish in the ecosystems of Northern and Southern Benguela, Southern Humboldt and the Mediterranean Sea. They underlined that fishing mortality rates for small pelagics were high in the Mediterranean Sea, Northern Benguela and Humboldt. In addition, models showed that a decrease in small pelagic fish abundance would have market effects both on the higher and lower trophic levels of the food web, causing decrease of predators, the proliferation of other species that are prey or competitors (e.g. gelatinous zooplankton and benthopelagic fish) and, generally, the disruption of energy flows. Together, these could result in an increase in flows to detritus, increasing importance of demersal processes in the system and often reduced summed impacts of the demersal compartment on the degraded pelagic food web.

32 33 34 35 36 37 38 39 40 41 42 43 44 45

8.3.1.3 Comparative Studies: Examining Changes in an Ecosystem Over Time Much can be learned from comparative modelling studies where Ecopath models of the same system are developed for different time periods; changes in ecosystem structure and trophic relationships due to fishing, environmental perturbations or a combination of both can be quantitatively assessed. Given the uncertainty associated with Ecopath estimates, and recent advances in estimating the effects of uncertainty, it is critical to include confidence limits when comparing the results of models (see above) when possible. Several models have been constructed for upwelling regions (e.g. JarreTeichmann et al. 1998, Neira et al. 2004, Heymans et al. 2004, Roux and Shannon 2004, Shannon 2001, Shannon et al. 2003, 2004b, Moloney et al.

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2005). Upwelling ecosystems are characterized by large fluctuations in abundance of dominant species (anchovy and sardine regime shifts), respond fairly vigorously to environmental events or changes, and have been shown to undergo major changes in their food web structure and sometimes also their trophic functioning (e.g. Lluch-Belda et al. 1989, 1992a, 1992b, Schwartzlose et al. 1999). Thus it is not surprising that most of the examples of Ecopath models developed for the same system in more than one period are from upwelling ecosystems, some of which have been selected for discussion in this section. For example, Ecopath models of the southern Benguela ecosystem (Fig. 8.9) have been constructed for the period 1980–1989, when anchovy was the dominant small pelagic fish species and for 1990–1997, when sardine began to increase (Shannon et al. 2003), for the period 2000–2004, when anchovy and sardine were both present at high levels of abundance (Shannon et al. in press, Shannon in prep.), and for three earlier periods selected to mark the undisturbed/pristine, pre-industrial (1652–1910), industrial (1960s) periods (Watermeyer et al. 2008). Comparing steady-state model outputs of the 1980s and 1990–1997 models, biomass per trophic level, transfer efficiencies, mixed trophic impacts and whole system properties suggested that the system functioned in a similar way trophically in these two contrasted periods (Shannon et al. 2003). However, there is some suggestion that the ecosystem was more mature in the 1990s than in the 1980s according to ecosystem-level attributes extending E.P. Odum’s ecosystem development theory (Odum 1969), such as the smaller total primary production/total respiration, net system production, total primary production/total biomass, residence time and relative ascendancy in the 1990s model, and the larger flows to/from detritus, connectance and total respiration/total biomass. In the 1990s, smaller catches were made whereas model zooplankton and small pelagic fish biomasses were larger, leading to the conclusion that the southern Benguela ecosystem was less tightly constrained by predators, fishing and food availability in the 1990s than the 1980s (Shannon 2001, Shannon et al. 2003). Pending completion of the three earlier models, the effects of man’s intervention on the Benguela ecosystem will be quantified (Watermeyer et al. 2008). Industrial fisheries sequentially exploited and depleted sardine (1960s–1970s), Cape hake (1970s–1980s) and horse mackerel (1980s–1990s) in the northern Benguela ecosystem off Namibia. Heymans et al. (2004) presented Ecopath models of these three periods, highlighting major changes in fishing, the food web structure and possibly also the trophic functioning of the northern Benguela ecosystem (Fig. 8.9). In particular, there has been a large increase in jellyfish off Namibia since the 1970s (Venter 1988, Fearon et al. 1992), and top predators switched from preying predominantly on sardine and anchovy to pelagic goby (Sufflogobius bibarbatus), following the likely dramatic increase in goby biomass since the late 1960s (Crawford et al. 1985) and the reduction in abundance of anchovy and sardine. By the 1990s, energy was flowing through few pathways and trophic efficiency was lower than in the 1980s. The trophic level of the catch increased in the 1980s, when hake catches were large, and

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Fig. 8.9 Diagramatic summary of the changes in ecosystem structure and relative abundance of dominant species (small pelagic fish, hake, goby, jellyfish, Cape gannet, Cape fur seal; number of individuals represents approximate abundance) in the northern and southern Benguela between the 1970s (left) and the early 2000s (right). Reprinted from van der Lingen et al. 2006, Benguela: Predicting a Large Marine Ecosystem, with permission from Elsevier

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decreased again in the early 1990s, before environmental forcing in the form of the Benguela Nin˜o caused huge declines in the pelagic stocks. Two Ecopath models were also constructed for the Humboldt ecosystem off central Chile for 1992 and 1998 (Neira et al. 2004). These models showed the importance of predation mortality on fish production in the system, especially

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of juvenile fish, and quantified the strong impact of fishing mortality exerted on Chilean hake, common sardine, horse mackerel and anchovy. Although biomass was 1.5 times larger in 1998 than in 1992, catches were 20% smaller (Neira et al. 2004), suggesting that the Chilean ecosystem was less tightly constrained later in the decade. However, the distribution of biomass and throughput across trophic levels was similar in the 2 years, suggesting that the ecosystem structure had not changed dramatically (Neira et al. 2004). Comparative studies have been conducted for other shelf and coastal ecosystems which undergo temporal changes in ecosystem structure. In the North West Atlantic, several Atlantic cod populations collapsed in the early 1990s. Bundy (2005) constructed two models of the eastern Scotian shelf, one before and one after the collapse of the Atlantic cod stock to explore changes in the structure and functioning of the ecosystem and to explore theories for the nonrecovery of the Atlantic cod stock. Despite similar total biomass and productivity before and after the cod collapse, there were marked differences in the trophic structure and energy flows through the system. Piscivory increased as a result of the increase in small pelagic fish abundance, and the system switched from a demersally-dominated system to a pelagically-dominated system (the pelagic:demersal ratio increased from 0.3 to 3.0.) indicating a shift in trophic flow from the demersal to the pelagic part of the food web. The pelagic:demersal ratio is an indicator of the negative effects of fishing (Zwanenburg 2000, Rochet and Trenkel 2003). The rationale is that as longer-lived large demersal predators are removed by fishing, the abundance of small, short-lived pelagics increase, due to a release from predation pressure. An analysis of the trophic interactions of Atlantic cod within the eastern Scotian Shelf ecosystem suggested that the lack of recovery of cod after their collapse in the early 1990s could be explained by trophic factors (Fig. 8.10), at least for small cod (Bundy and Fanning 2005). Their low biomass makes them vulnerable to both predation, and to increased competition for prey. Small cod compete for their prey with highly abundant forage fish competitors, and this likely leads to food limitation. Pranovi et al. (2003) constructed two models comparing the Venice lagoon (Adriatic Sea, Mediterranean) in 1998 and during 1988–1991, a period prior to the spread of the Manila clam and its intensive exploitation in the area by mechanical dredges that pose high impacts to the system. Manila clam (Tapes philippinarum) was introduced in the Venice lagoon in 1983 and their exploitation became the first exploitation activity due to high economic value of manila clam. However, until the end of 1980s, biological resources in the area were exploited only by the artisanal fishery. These models enabled the detailed study of the complex effects of clam harvesting in the area and the ‘‘Tapes paradox’’ (Libralato et al. 2002): the Manila clam population was apparently enhanced by dredging due to the nutritional advantages that this species was gaining from the re-suspended organic matter. The model also demonstrated the indirect negative impacts, mediated through the food web, of the clam fishery on the artisanal fishery.

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Fig. 8.10 Comparison of predation mortality on (a) small cod and (b) large cod on the eastern Scotian Shelf, Canada in 1980–1985 and 1995–2000. Horizontal lines represent the median, the boxes represent 50% of the values, and the whiskers extend to the highest and lowest levels (excluding outliers). L.cod=large cod (>40 cm), Hadd=haddock, L.Hal=large halibut, SDFs=small demersal feeders, SDPisc=small demersal piscivores, LDPisc=large demersal piscivores, Cetea=ceteacea, S.cod=small cod (40 cm), SLDF=small large demersal feeders, L.Shak=large silver hake (>30 cm), LDF=large demersal feeders. Adapted from Bundy (2005)

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On the west coast of Italy, two models were constructed to describe the shallow water coastal system of Orbetello lagoon (Central Western Italy) for 1995 and 1996 (Brando et al. 2004) and to analyze management activities developed in the area to control eutrophication. This lagoon is characterized by limited exchange with the sea and a high availability of nutrients, showing increasing eutrophication from 1975 to 1993. From 1993 a series of management activities were carried out to reduce eutrophication by reducing nutrient loading, increasing water circulation and selective harvesting of macroalgae. Network analysis differentiated between the models for 1995 and 1996, revealed the first effects of algal harvesting and indicated that the ecosystem was more mature and stable in 1996.

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8.3.1.4 Comparative Studies: Examining Different Ecosystems Comparative modelling across ecosystems using standardized models provides a context for the interpretation of a single ecosystem’s attributes and a relative measure of the intensity and impacts that fishing may be having in an ecosystem. Furthermore, it enables the comparison of emergent properties and the evaluation of potential generic ecosystem properties. EwE is a particularly useful tool for comparative studies as it enables a standardized approach (e.g. by using the same number of functional groups in each model with similar species in ecological terms), allowing the separation of biological features from modelling artefacts, and alleviating problems and biases that may result from the way in which groups have been aggregated in a model, or ecosystem attributes that relate to discrepancies in life history parameter estimates. For example, Moloney et al. (2005) compared four different upwelling ecosystems representing different areas and periods: models represented the Southern Humboldt (Chilean) upwelling ecosystem in 1992, the northerncentral Humboldt (Peruvian) upwelling ecosystem in 1973–1981, the southern Benguela (South African) ecosystem in 1980–1989 and the northern Benguela (Namibian) ecosystem in 1995–2000. After the standardization process, the four models shared similar structures based on 27 groups but differed in terms of representation of some species. A comparison of indicators revealed differences between the Humboldt and Benguela systems, while indicators based on integrated biomass, total production and total consumption were able to differentiate the Namibian model (where some exploited resources have been severely depleted) from the others. Coll et al. (2006b) standardized an ecological model representing a NW Mediterranean exploited ecosystem and compared it with the four standardized models from coastal upwelling ecosystems describe above (Moloney et al. 2005) (Fig. 8.11). A comparison of biomasses, flows and trophic levels indicated important expected differences between the ecosystems, mainly caused by differences in primary production, which was lowest in the Mediterranean model. In addition, fishing pressure was high relative to the low primary production in the Mediterranean ecosystem. Comparisons of %PPR (the proportion of primary

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production required to support the fishery, see Section 8.4.3), the trophic level of the community (TLco), the biomass of consumers and exploitation ratios (F/Z) captured the ecosystem effects of fishing; stronger in the NW Mediterranean, Namibian and Peruvian models, weaker in the Southern Humboldt. The importance of pelagic-demersal coupling and of gelatinous zooplankton in the consumption of production in the Namibian and Mediterranean case studies was in remarkable contrast to the other ecosystems. These identified similarities were related to ecosystem effects of fishing. Libralato et al. (2002) also compared standardized models from two different habitats of the Venice lagoon: the seagrass meadows (Fig. 8.2) and Manila clam fishing grounds. The seagrass meadows showed higher primary production, species diversity and complexity and appeared to represent an ecosystem at a higher stage of ecological succession. The Manila clam exploited ecosystem was dominated by consumption and respiration flows and most of the energy were stored within the detritus compartment.

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8.3.2 ECOSIM

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The dynamic simulation modelling tool Ecosim has broadened immensely the capabilities of Ecopath for exploring the temporal impacts of fishing and environmental factors. Ecosim allows users to change fishing mortality or fishing effort over time, enabling the exploration of fishing options and changes in

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ecosystem functioning. In this section, some examples of Ecosim implementation are reviewed (Table 8.1).

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8.3.2.1 Exploring the Impacts of Fishing and Management Simulations In the early 1960s, fishing in the Gulf of Thailand was mainly artisanal and restricted to coastal areas. Subsequently an intense trawl fishery was developed in the area and changes in the community were well documented from the early 1960s to the 1980s. Christensen (1998) used two mass-balance models of the area representing the ecosystem in 1963 (early trawling) and the 1980s (highly exploited) to reproduce changes over time, developing temporal simulations and increasing fishing activity in accordance with field data. Observed depletion of several demersal species was well reproduced from 1963 to the 1980s. The biomass of cephalopods, however, increased with increased fishing, as did shrimps and scads. The study also explored the possible effects of reducing fishing pressure from the 1980s to analyse whether ecosystem changes would be similar to those characterizing the 1963 scenario. Simulations predicted an increase in biomass to past levels within a few years if fishing effort were to have been reduced. However, this result should be interpreted with caution since other studies have shown that reducing fishing effort can lead to an increase in modelled biomass that is not observed in the field (e.g. Bundy 2001), and the reality of the non-reversibility of fishing impacts should be recognized (e.g. Scheffer et al. 2001, Bundy and Fanning 2005). Mackinson et al. (1997) used a dynamic mass-balance model to compare the impacts of different exploitation options on small pelagic fish within the three different upwelling ecosystems of Peru, Venezuela and Monterey Bay. Exploratory simulations showed that by intensively fishing small pelagic fish, positive effects were seen in both prey and competitors in all ecosystems, while predators located at higher trophic levels had the longest recovery times. Moreover, fishing mortalities corresponding to the maximum sustainable catches predicted by the models were higher than those obtained from single species models. Ortiz and Wolff (2002a) used Ecosim to analyze different management scenarios from five Ecopath models representing different benthic communities of Tongoy Bay (Chile) in order to explore strategies of sustainable use. Different simulations were explored by modifying fishing mortality and energy control (bottom–up, mixed control and top–down). Management options explored the exploitation of scallops Argopecten purpuratus, their principal predator (the sea star Meyenaster gelatinosus) and the snail Xantochorus cassidiformis. The results indicated that the estimated maximum sustainable catch was lower than when estimated from single species approaches. Furthermore, simulating the exploitation of the clam Mulinia sp. showed the strong impact of this group on other functional groups, indicating that this species was a keystone element of the ecosystem. The internal stability of the system was measured in each simulation by means of the System Recovery Time.

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The ecological role of snappers and the impact of their exploitation in two ecosystems in the Gulf of Mexico (Western Gulf of Mexico and the Continental Shelf of Yucatan) was evaluated using Ecosim (Arreguı´ n-Sa´nchez and Manickchand-Heileman 1998). The impacts of fishing on snappers were analyzed in terms of individual functional groups and in terms of the stability of the ecosystem using measures of persistence, recovery time, magnitude of change and resilience of groups. Snappers occupied a top predator role in both systems and when exploited had low values of persistence in the ecosystem. However, they exhibited different dynamics under different fishing scenarios and the authors suggested that the snapper stocks in the two areas should be managed independently due to these differences. Coll et al. (2006a) used Ecosim simulations to support the theory that the Mediterranean system has a low resistance and is thus highly susceptible to fishing and environmental impacts. Their results demonstrated that since 1994, an increase in fishing effort always resulted in decreased catches. Combining different scenarios of moderate increase of fishing effort, flow control and an environmental forcing affecting the availability of prey groups to small and medium-sized pelagic fish under wasp–waist control, the decline in observed catch and biomass was consistently reproduced by the model. Ecosim has also been used to put selectivity measures applied to trawl fisheries into the ecosystem context. Criales-Hernandez et al. (2006) used an Ecosim model of northern Colombian system (Caribbean Sea) and data from the Gulf of Mexico to explore the reduction of fishing mortality due to bycatch reduction devices applied to a tropical industrial shrimp fishery. Coll et al. 2008a used field data from bottom-trawling selectivity by applying sorting grids and square meshes and simulated the consequent reduction of fishing mortality on target species in a North-Western Mediterranean system. Kitchell et al. (2002) assessed the ecological and economic impacts of alternative fishing methods to reduce the by-catch of marlin in the central north pacific ecosystem. In the three cases, the potential benefits of the implementation of selectivity management options within an ecosystem are highlighted.

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8.3.2.2 Examining Energy Flow Controls Early use of Ecosim underscored the importance of assumptions about energy flow control on the dynamics of the ecosystem (Bundy 1997, Walters et al. 1997). Shannon et al. (2000) explored the ecosystem dynamics of the Southern Benguela system in the 1980s using EwE under different flow control scenarios: bottom–up control of predators by their zooplankton prey; wasp–waist flow control of small pelagic fish (both top–down control of zooplankton and bottom–up control of predators by small pelagic fish) and mixed control (neither bottom–up nor top–down control), Fig. 8.4. Results obtained from simulations were very different under different flow controls and highlighted the importance of considering trophic flow control while assessing the effects of fishing. Under bottom–up flow control, effects of fishing were smaller than

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under a wasp–waist scenario, where they vigorously propagated thought the ecosystem. Mixed control scenario showed intermediate results. EwE was then used to simulate changes in the Southern Benguela ecosystem from an anchovy-dominated system to a sardine-dominated one (Shannon et al. 2004a). Two hypotheses of mechanisms that may have caused the observed ecosystem changes were tested: fishing and environmentally-induced changes in the structure of the zooplankton community. Scenarios of altered fishing mortality on sardine, anchovy and horse mackerel were examined using Ecosim, and ‘‘forcing functions’’ (see Section 8.2.2) were applied to mesozooplankton were used to test the effects of altered prey availability to anchovy and sardine, which by virtue of their different feeding behaviours are better suited to different sized zooplankton prey (Van der Lingen 1999). Simulations suggest that it is unlikely that observed changes in pelagic fish catches between the two decades examined played a large role in driving the changes in abundance of anchovy and sardine. Rather, shifts between anchovy- and sardine-dominated periods may have been caused by environmentally-mediated changes in the availability of mesozooplankton prey to anchovy and sardine. Ecosim was used to explore whether ecosystem changes in the NewfoundlandLabrador ecosystem could be explained by considering changes in fishing and predation mortality under different trophic controls (Bundy 2001). Atlantic cod and other ground fish species collapsed or seriously decreased in the early 1990s causing enormous economic, social and ecological impacts. This study replicated the collapse (and posterior non recovery) of cod under top–down flow control situations and increasing fishing mortality. Predicted results from the model also suggested an increase in seal populations and shrimps. Simulations showed that an increase in the seal population would have negative effects on the recovery of cod under top–down and mixed flow control. The study supported the hypothesis that the Newfoundland cod collapse was due to overfishing and that, under the depleted cod stock situation, seal population increases could retard its recovery. Gasalla and Rossi-Wongtschowski (2004) explored the effects of changing fishing strategies in the South Brazil Bight coastal area in terms of increasing catches of squid and live-baitfish for sardines. They found that ecosystem effects of altered squid fishing were more pronounced under the assumption of top–down control, which led them to propose that a precautionary fisheries management measure should refer to simulations assuming top–down control. Fishing on live-baitfish for sardines had little impact in the area with the exception of impacts on sharks and rays. Mackinson et al. (2003) explored different functional response hypotheses using an Ecosim model of minke whales, their prey and fisheries in the Barents Sea. Results showed clear patterns in the response of the ecosystems irrespective of the functional response used. For example, impacts of intense fishing on whale prey had longer lasting impacts on whale biomass than direct exploitation of whales. However results also showed that simulations with different vulnerability settings led to different feeding and biomass dynamics of minke whales.

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Uncertainty concerning the nature of flow control in ecosystems, and the relative ecosystem impacts of fishing need not result in management inaction. Bundy (2004a) used an adaptive management approach to explore management options under different flow assumptions for San Miguel Bay, the Philippines and concluded that, with respect to the management options under consideration, there was no value in learning more about the uncertainty or distinguishing between the different resource models. She reached this conclusion by exploring the effect of five management options on four models of the resource, using six performance criteria to evaluate their impact. For each of the performance criterion, there was a robust policy for all models. Furthermore, the results demonstrated that top–down control assumptions led to more precautionary management.

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The effects of fishing and eutrophication on the Black Sea ecosystem were investigated using Ecosim and a biological time series of data to explore trends and correlations (Daskalov 2002). Thirty year simulations were run, where fishing mortality was changed over time and a forcing function was included to consider the effects of increased primary production. Results showed the occurrence of a trophic cascade where a decrease in predators led to an increase in forage fish, a decrease in zooplankton and an increase in phytoplankton. The trophic cascade was related to overfishing together with eutrophication. Watters et al. (2003) studied how the El Nin˜o – Southern Oscillation (ENSO) might affect different organisms at mid- and high trophic levels. A mass-balance model of the pelagic system of the eastern tropical Pacific Ocean was dynamically explored by forcing it with two environmental functions affecting phytoplankton biomass and predator recruitment. Results showed that environmental effects applied to recruitment of predators may be the dominant source of interannual variability in the ecosystem and that top–down flow control posed by fishing may be dampened by these effects (example in Fig. 8.12).

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8.3.2.4 Fitting Models to Data Using Ecosim, a mass-balance model of the Southern Benguela ecosystem was fitted to available time series data for a 25-year period from 1978–2002, exploring how fish stock dynamics may be determined by feeding interaction patterns (flow controls), fishing strategies and environmental changes (Shannon et al. 2004b). In this model, it was estimated that fishing patterns explained about 5% of the variability in the times series, an estimated productivity forcing pattern applied to phytoplankton explained 11% of the variability and assumptions about the vulnerability of prey to predators (flow control patterns) explained around 33% of the variability. When flow control was assumed to be wasp– waist around small pelagic fish (exerting top–down control on their prey and

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Fig. 8.12 Simulated indirect effects of global warming (under modeled bottom–up forcing of large phytoplankton biomass) on middle and upper trophic levels: components in the axis of (a) large marlins, (b) spotted dolphins, (c) large sharks, (d) small dorado, (e) large yellowfin, (f) Auxis, (g) flying fishes, and (h) large phytoplankton). The simulations were forced with winter mean sea-surface temperature anomalies predicted by the Max Planck global climate model. Solid lines are from simulations with F = average F during 1993–1997, and broken lines are from simulations with F = 0. A horizontal broken line is drawn for reference at Bt/B0 = 1.0. Reprinted from Watters et al. 2003. Physical forcing and the dynamics of the pelagic ecosystem in the eastern tropical Pacific: simulations with ENSO-scale and global-warming climate drivers. Watters et al. (2003), with permission to reproduce

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bottom–up control on their predators), the model was best fit to the data series examined. The aim of studies such as these is to prepare models with improved parameterization and enhanced credibility, which may be useful in testing alternative fishing scenarios with a view to providing fisheries management advice in an ecosystem context. Figure 8.6 is an example of a Southern Benguela ecosystem model calibrated with time series data (Shannon et al. 2004b), and currently in the process of being updated (Shannon et al. in prep.). Heymans (2004), studying the Northern Benguela upwelling system, fitted a mass-balance model of the ecosystem to time series data from the 1970s to the present under different scenarios of trophic flow control. Sixty-five percentage of total variability of data was explain by considering internal (e.g. flow controls) and external (fishing and environmental factors) factors within the simulations. Environmental factors were included by using the model to calculate an

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environmental anomaly to increase the goodness of fit of the model. This environmental anomaly was then correlated to environmental variables and significant correlations were found with sea surface temperature and wind under wasp–waist flow control by small pelagic fish. This model is currently being updated with expanded data series (Heymans in prep.). Coll et al. 2008b presented results on fitting a model to available data from the temperate shelf ecosystem of the South Catalan Sea (North-Western Mediterranean) from 1978 to 2003. This work highlighted that flow control patterns explained up to 37–53% of the total variability of time series, while fishing factors and the environmental variability explained 14% and 6–16% of time series dynamics, respectively. This also showed that small pelagic fish in the area were involved in wasp–waist and bottom–up flow control situations and is also currently in the process of being updated. Neira et al. (in prep.) fitted a model of the Southern Humboldt upwelling system in 1970 to available time series of relative biomass, catch and fishing mortality for the period 1970–2004, finding that fishing mortality explained 28% of the variability in the times series, vulnerability (v) parameters explained an additional 21%, and a forcing function affecting primary production explained a further 11–16% of the observed variability. The model fitted primary productivity anomaly compared favourably to an independent time series of sea surface temperature and an upwelling index available from 1970–2000. Walters et al. (2005) reported the results of fitting eleven mass-balance models to time series of data and demonstrated the suitability of this methodology to reproduce past dynamics of exploited ecosystems. Models were fitted by using available data and were analyzed and discussed in terms of methodological procedures (e.g. trophic interactions, mediation effects). Moreover, ecosystem effects of harvesting species to their single species maximum sustainable catch was evaluated and shown to cause notable deterioration in ecosystem structure (e.g. loss of biomass from top predators). Mackinson et al. (2008) compared a series of fitted models to explore the contribution of fishing and environmental forcing as drivers of marine resources.

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8.3.2.5 Policy Optimization and Management Scenarios At an exploratory workshop held in Vancouver (2001), a set of studies explored the impacts of different fishing strategies to meet broadly defined ecological, social (employment) and economic objectives in several ecosystems (Pitcher and Cochrane 2002). In most cases, fishing strategies that optimized economic or employment objectives were relatively easily understood, whereas the ‘‘optimal’’ fishing strategies were usually ecologically unrealistic, forcing the model ecosystems to unrealistic extremes. For example, Shannon (2002) found that extreme ‘‘optimal’’ fishing scenarios forced the model parameters (biomass, diet composition, consumption and production rates) beyond their likely ranges, producing highly unrealistic outcomes in the Southern Benguela ecosystem. Nonetheless, simulations such as these are still useful measures of the economic

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and social value of careful fisheries management, and highlight the importance of interactions between species (i.e. extending beyond the traditional singlespecies approaches) in determining the ecological, social and economic impacts of various fishing policies under consideration. The workshop and subsequent studies using the EwE fishing policy optimization routine have certainly served to emphasize, in the realm of Ecosystem Approaches to Fisheries, the necessity of carefully defining policy objectives within an ecosystem context. Okey and Wright (2004) applied the optimization routine to analyse management options for Prince William Sound (Alaska) within the context of rebuilding pinniped populations. Again, maximizing economic and social criteria resulted in scenarios where predators were reduced to maximize prey production profitable for fisheries. When ecological criteria were emphasized, predators and their prey increased. Competition between fisheries and predators was evident since predators increased with decreases in fishing. This study also suggested that a 20% increase in pinniped biomass could be achieved with a modest reduction of fishing activity (Fig. 8.13). The policy optimization routine was also applied to the Gulf of Thailand and results highlighted that optimizing for profit led to an ecosystem were the emphasis was focused on maintaining productive stocks of profitable species while decreasing competitors and predators in the ecosystem (Christensen and Walters 2004b). Optimizing exploitation for economic profit produced an intense increase in effort of specialized fleets and a catch dominated by trash fish and shrimps, impacting larger fish and diversity of fishing activity.

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Fig. 8.13 Catch levels by three fishing categories at the beginning and end of the 20-yr simulations relative to the actual 1994–1996 levels in Prince Williams Sound ecosystem, Alaska. The beginning and ending levels were selected by the nonlinear search procedure as optimized solutions to the objectives specified for each run. In the ‘‘Straight compromise’’ case, the economic, employment, and ecological objectives were weighted equally. Reprinted from Okey and Wright (2004). Toward ecosystem-based extraction policies for Prince William Sound, Alaska: integrating conflicting objectives and rebuilding pinnipeds. Okey and Wright (2004), with permission to reproduce

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Optimizing for ecological considerations implied a notable reduction in effort and resulted in an increase of biomass of several functional groups. In global terms, the optimization for profit was compatible with conservation measures, while optimizing for value was in conflict with both profit and ecology. In a case study of La Paz Bay, South Baja California (Mexico) by Arreguı´ nSa´nchez et al. (2004), economic, social and ecological criteria were examined for two artisanal fisheries and a shrimp fishery. Results showed how the optimization of economic and social criteria, and aiming for the maximum sustainable yield (MSY), resulted in depletion of some stocks and in unrealistic increases in fishing effort. However, by combining economic and social criteria with ecological ones, model simulations were developed in which stock depletion was avoided.

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8.3.2.6 Back to the Future Simulations

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The back to the future approach (Pitcher 2001, 2005) reconstructs past systems using modelling, together with other forms of knowledge such as traditional or local knowledge, historical documentation and archaeological data, to explore future policy goals (Fig. 8.14). The ultimate goal is to progress towards ecosystem rebuilding of a known state that can be measured in terms of economic, social and ecological utility. When past models have been constructed, dynamic modelling is used to predict future situations of the ecosystem implementing different rebuilding measures and results are compared with past ones. This methodology has been applied to different ecosystems such as the Strait of Georgia in British Columbia (Dalsgaard et al. 1998), Newfoundland (Pitcher et al. 2002a) and Northern British Columbia (Ainsworth et al. 2002).

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8.3.2.7 Studies Considering Pollution After the Exxon Valdez oil Spill in Prince William Sound and adjacent areas in 1989, research into the biological components of the ecosystem increased and an Ecopath model was constructed (Okey and Pauly 1999). Model simulations were used to explore the consequences of fishing and other anthropogenic disturbances (e.g. oil spill), restoration and resource planning of the ecosystem. Increased mortalities due to the oil spill were integrated in the model and temporal simulations were performed to evaluate the effects. Results indicated that the oil spill severely disturbed the ecosystem and had important impacts on various functional groups, considerably reducing their biomass. In global terms, simulations also demonstrated that the impacts produced by the oil spill could produce a shift on the marine community to an alternate state (Okey 2004). Methyl mercury concentration in food webs and marine mammals was modelled for the Faroe Islands ecosystem by Booth and Zeller (2005) to explore the implications of cod and pilot whales for human consumption.

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Fig. 8.14 Diagram illustrating the ‘‘Back-to-the-Future’’ concept for the adoption of past ecosystems as future policy goals. Triangles at the left represent a time series of historical ecosystem models, constructed at appropriate past times before the present (thick grey vertical line), where the vertex angle is inversely related and the height directly related to biodiversity and internal connectance. Time lines of some representative species in the ecosystems are indicated; size of the boxes represents relative abundance and solid circles represent local extinctions (= extirpations). Sources of information for constructing and tuning the ecosystem models are illustrated by symbols for historical documents (paper sheet symbol), data archives (tall data table symbol), archaeological data (trowel), the traditional environmental knowledge of Indigenous Peoples (open balloons) and local environmental knowledge of coastal communities (solid balloons). At right are alternative future ecosystems, representing further depletion, the status quo, or restoration to ‘‘Lost Valleys’’ that may be used as alternative policy goals. Restored ‘‘Lost Valleys’’ may be fished with sustainable, responsible fisheries designed according to specified criteria, and aiming at Optimal Restorable Biomasses determined using objective quantitative policy searches. Final choice of BTF policy goals are made by comparing trade-offs, cost and benefits among possible futures using socio-economic and ecological objectives agreed among industrial and small-scale fishers, government, conservation, coastal communities and other stakeholders in order to maximize compliance. Diagram does not show evaluation of risks from climate fluctuations and model parameter uncertainty. Modified from Pitcher 2007 and Pitcher and Ainsworth 2007

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During this study, Ecotracer, an Ecosim routine that predicts the movement and accumulation of tracers within a food web (Christensen and Walters 2004a) was used. Mercury was predicted to increase in the ecosystem under present conditions and also during climate change scenarios. Results showed that the highest levels of mercury in human diet originated from whale meat consumption and that climate change would exacerbate this situation. The study also predicted that inflow rates of mercury to the ecosystem should be reduced by 50% to ensure secure levels of intake under current levels of consumption of marine resources.

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8.3.3 ECOSPACE

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Models which are spatially explicit are a high priority for advancement of fisheries science and management (e.g. Walters et al. 1999, Salomon et al. 2002, Martell et al. 2005), particularly in the light of the move from single species fisheries management to the ecosystem approach to fisheries. However, most EwE applications, including those fitting models to time series data, have not simultaneously accounted for spatial considerations such as the spatial behaviour of organisms, the spatial overlaps or co-existence of species (predator-prey; competing species, etc.), the spatial behaviour of fisheries, fish migration and spatial management approaches. Ecospace provides the methodology to pursue these questions, although currently there are relatively few primary publications using Ecospace. In a complete EwE study of Tongo Bay, Ortiz and Wolff (2002b) continued their analysis using Ecospace to explore spatial management options, distinguishing 4 habitat types, seagrass meadows (0–4 m), sand and gravel (4–10 m), sand flats (10–14 m) and mudflats (>14 m). Five different scenarios were explored where fishing on the main species, a red alga, a scallop, a gastropod and a crab, occurred in either seagrass, sand-gravel or sand habitats exclusively, on both seagrass and sand-gravel habitats, or on all habitats. On the basis of biomass changes, the authors concluded that the sand-gravel habitat was the most resistant to fishing, and that fishing 2–3 habitats simultaneously had the greatest negative effect on the ecosystem. They further concluded that a rotational harvest policy should be recommended for this Bay. Most Ecospace applications have focused on Marine Protected areas, and indeed, Walters et al. (1999) identified Ecospace as an important exploratory tool for MPA definition and function and policy exploration. Using a simple model of Brunei Darussalam, they concluded that biomass gradients would exist along the edges of MPAs, resulting from fishing activity along the MPA boundary on the increased density of predators within the MPA, causing a decrease in the density of predators within the MPA (fishing would also lower immigration to and emigration from the MPA). At the same time, trophic cascades are likely to occur within the reserve due to the increase in density of large predators, resulting in a decrease of small fish. Density dependent effects would lead to the emigration of large predators, resulting in reduced predator biomass within the MPA. They further conclude that a few large MPAs are more effective than more small MPAs. This is largely due to the fact that a few large MPAs have a smaller boundary (where fishing takes place) than many small MPAs (for the same total area). Thus the more boundaries there are the more dispersion can take place, and the greater the effects of fishing on the boundaries. These results have been confirmed by other Ecospace studies (e.g. Pitcher et al. 2002b, Salomon et al. 2002, Martell et al. 2005) (Fig. 8.15). Indeed Salomon et al. (2002), who explored various options for marine protected area zoning policies in the Gwaii Haana National Marine Conservation Area, Canada, concluded

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Fig. 8.15 The Single Large or Several Small (SLOSS) debate. The percent change in ecosystem component biomass from a baseline simulation of no protection as a consequence of splitting one large Marine Protected Area (MPA) into three small MPAs. Simulations were compared once they had run for 10 years. Reprinted from Salomon et al. 2004. Modelling the trophic effects of marine protected area zoning policies: a case study, Salomon et al. (2002), with permission from Springer Science and Business Media

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that buffer zones were effective tools against edge effects and biomass density gradients but that there is no optimum reserve design since the design depends on the goal. Okey et al. (2004) used an Ecospace model to demonstrate how the functional extinction of sea cucumbers in a Galapagos subtidal rocky reef could be avoided by protecting some of the reef from fishing (Fig. 8.7). Martell et al. (2005) explored the relative importance of different assumptions about dispersal and advection for Ecospace predictions under different fishing policy scenarios for the central North Pacific with respect to marine protected areas and concluded that MPAs for large pelagics need to be large. They split the central North Pacific into two habitats, warm water and cool water and developed 3 movement models: the default advection-diffusion movement model where dispersal rates were random and non-directional and two models where movement responded to a fitness measure, where fitness was the difference between productivity and predation. Movement increased in areas of low fitness and decreased in areas of high fitness. In the variable emigration model, any movement in response to fitness was random; in the directed movement model, movement was directed towards cells with higher fitness. The authors imported monthly current information from sea surface topography to calculate advection fields. Primary production in each grid cell was both dynamic and assumed to be proportional to the rate of upwelling (dynamic models) or static with long term average primary productivities. The three scenarios represented different degrees of closed areas and protected species policy, including the status quo. The authors concluded the static versus dynamic models results were sufficiently different, regardless of the movement model, to indicate that the temporal differences in surface currents should be explicitly considered when exploring and developing closed area policies

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Fig. 8.16 A representation of the main Ecospace findings for closed area policies, with alternative movement models representing different hypotheses about animal behaviour and static forced distributions of production (first column) versus dynamic forced distributions of production, where monthly surface current information us used to predict spatial variability in primary production (second and third columns). The second row represents the spatial distribution of forage, were surface currents advect forage in the second and third columns. Each diagram represents a cross section or transect of the spatial distribution of abundance (and fishing effort) across an Ecospace map. Fishing effort distribution for top predators is represented by as the dotted line. Shaded polygons represent distributions of biomass along the transect at equilibrium; the area of each polygon is proportional to biomass. Vertical dashed lines represent marine protected area boundaries; arrows represent current directions; U, upwelling; D, downwelling or convergence zones. Reprinted from Martell et al. 2005. Interactions of productivity, predation risk, and fishing effort in the efficacy of marine protected areas for the central Pacific. Canadian Journal of Fisheries and Aquatic Sciences 60: 1320–1336, with permission from NRC Research Press

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(Fig. 8.16). In particular, they were concerned that interannual variability in oceanic processes can affect the efficacy of MPAs since the position of convergence zones, where species aggregate, including tuna, can change from year to year, potentially lying beyond the MPA boundary. In this case, protected species would be subject to high fishing mortality. In general, the results were robust to the three movement models, suggesting that further research into the movement of large pelagics is not required.

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Like Ecosim, Ecospace can also be used for policy exploration, such as tradeoffs between compliance with fishery regulations and conservation in the Hong Kong artificial reef system (Pitcher et al. 2002b). In the latter, a scenario was explored in where fishing was permitted in one artificial reef, assuming that this would lead to greater support for the artificial reef scheme and selfenforcement.

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8.4 What Ecopath with Ecosim Can Do for You (and What It Can’t) 8.4.1 Ecopath with Ecosim as a Diagnostic Tool Ecopath models have proved useful in identifying data gaps and sensitive interactions, thus guiding research (e.g. Halfon and Schito 1993, Bundy 2004b). In addition, they are useful in refining parameter estimates for poorly known groups according to the constraints of the interactions defined within the Ecopath models (Okey and Pauly 1999). By construction of an Ecopath model, the biological and ecological data available from an ecosystem is identified, analyzed, contextualized and evaluated. Ecopath models can also be used to test low quality data, for example the biomass of benthopelagic species or suprabenthos, which are difficult to estimate but represent an important proportion of the diet of many species within marine ecosystems. These models can calculate the minimum biomass necessary in the ecosystem to sustain total mortality of these groups if predation and fishing mortality are well characterized (e.g. Lam and Pauly 2005). Another example can be found in stomach contents data, where soft preys can be underestimated with respect to species with hard-body parts (like fish and crustaceans). Thus, Ecopath can be useful in the correction of trophic data given biomass of predators and prey, and estimates of consumption.

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8.4.2 Dynamic Simulations as Management Tools for an Ecosystem Approach to Fisheries

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It is readily accepted that multispecies approaches to fisheries management should not and cannot fulfil the role of single-species fisheries management approaches, but that they should rather be considered as complementary approaches in a model ‘‘toolbox’’ from which management advice can be drawn (Starfield et al. 1988, Whipple et al. 2000). In the long-term, multispecies approaches can produce totally different management advice to the more traditional single species modelling approaches (e.g. Magnu´sson 1995, Stokes 1992), yet in the short term, advice may be similar (Christensen 1996). Cox et al.

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(2002) showed that EwE was better able to represent and explain species recoveries after severe fishing of apex predators than single species models. The lack of any formal process to reconcile (or check whether it is feasible to reconcile) the management strategies of single-species management approaches to attain different goals, has led to conflicting management advice being provided at the single-species level, and thus highlights the need for a multispecies or ecosystem approach (Murawski 1991). Dynamic Ecosim simulations can shed light on the possible ecosystem effects of different fishing strategies, although the model assumptions, such as flow control parameter settings, need to be carefully acknowledged and sensitivities of simulation results explored. The more recent development of the ability to fit EwE models to time series data, which includes refinement of vulnerability settings describing flow controls, has increased confidence in model predictions. This creates a more robust basis for testing hypothetical fishing scenarios, and leads to greater confidence in the information that these may provide for fisheries management. The fishing policy search and optimization routine provides an additional means of exploring the dynamic responses of the ecosystem to hypothetical fishing strategies which may optimize one or a combination of policy objectives, and provides managers with guidelines as to the likely trade-offs that are involved in prioritising one objective over another, or in trying to optimize several simultaneously.

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8.4.3 Examining Emergent Properties Through Ecosystem Indicators

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The construction of EwE models facilitates the estimation of trophodynamic indicators and ecological analyses that can be useful tools for an ecosystem approach to fisheries. Trophodynamic indicators measure the strength of interactions between species or species groups within an ecosystem, and the structural and functional changes that occur in an ecosystem as a result of fishing (Cury et al. 2005). Of the 46 trophic indicators identified in the literature, Cury et al. (2005) selected six for closer examination using data and EwE models of the northern and southern Benguela ecosystems (Fig. 8.17), namely catch or biomass ratios, production or consumption ratios and predation mortality, primary production required to produce catch (PPR), trophic level of the catch (TLc), fishing in balance (FIB) index and mixed trophic impact (MTI), see above. The PPR expresses the catch in terms of equivalent flows of primary producers and detritus and can be normalized per unit of catch relative to the primary production and detritus of the ecosystem (%PPR). This measure is used as an indicator of the footprint of the fishery and can be employed as an indicator of fishing intensity (Pauly and Christensen 1995).

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The Fishing in Balance (FIB) index can be easily calculated from catch data and TLc over time (Christensen 2000, Pauly et al. 2000) and measures whether a change in the trophic level of the catch in a given ecosystem is matched by concurrent changes in productivity (i.e. lower trophic level of the catch, higher productivity and FIB = 0). Overfishing is evident when the trophic level of the fishery decreases, but is not matched by increased productivity (FIB < 0).

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Alternatively, FIB can indicate whether there is an expansion of the fishery (FIB > 0), whether bottom–up effects are occurring (FIB > 0) or whether discarding is not considered in the analysis of impacts of fisheries on the ecosystem and is so high that its functioning is impaired (FIB < 0). The above trophodynamic indicators have been widely applied to different ecosystems. However, Cury et al. (2005) noted that these indicators were relatively conservative as they are slow to respond to large structural ecosystem changes. For example, the mean trophic level (TL) of the Namibian catch failed to reveal the sequential depletion of Namibian commercial fish stocks because the ecosystem had shifted productivity to non-exploited species that were not reflected in the catch data. On the other hand, the FIB index, viewed in conjunction with plots of TL of catches against catches, was shown to reflect the historical development and status of fisheries in South Africa and Namibia more successfully than other indicators that can be derived from catch statistics (Fig. 8.17). Ecopath mass-balance models can also be used to calculate standardized size spectra of the ecosystem, i.e. the distribution of biomass according to size of individuals (Pauly and Christensen 2004), which can be then compared between ecosystems. Size spectra is used to characterize the structure of a system and the fishing intensity over time due to the fact that the slope of size spectra plots reflects the exploitation level, being steeper when exploitation is high (Bianchi et al. 2000). Results from Ecopath and Ecosim can also be used to track functional changes of the ecosystems. Examples of emergent properties are the transfer efficiency, the flow to detritus and production ratios presented in Section 8.2.1.3. From MTI analysis, Libralato et al. (2006) developed and applied a method for identifying keystone species (or groups of species) in different ecosystems. Keystone species are those that are present at relatively low biomass levels but have a structuring role in the ecosystem (Power et al. 1996). Therefore they can be identified when the relative overall effect and the ‘‘keystoneness’’ are plotted against one another. The index is high when species or groups of species have both low biomass proportions within the ecosystem and high overall effects. Changes in keystone species can be analysed when different trophic models of an ecosystem are available, e.g. the importance of cetaceans as keystone groups was seen to decrease over time in various ecosystems (Libralato et al. 2006) (Fig. 8.18). Comparisons of ecosystem indicators from the same ecosystem in different periods of time or from different ecosystems when using standardized models can be very useful tools as discussed in the previous case studies section. However, trophodynamic indicators are mostly still descriptive at this stage and it remains for reference points to be clearly identified. Cury et al. (2005) advise that a suite of indicators be used to monitor and quantify ecosystem changes as a result of fishing. To define quantitative reference levels to analyze fishing impacts on ecosystems, a new composite index (integrating PPR, TLc and transfer efficiency TE was defined by Libralato et al. 2008): L index. This index represents the theoretical depletion in secondary production due to fishing and is formulated as a proxy for quantifying ecosystem effects of fishing. The

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16 Echinoderms 17 Herring 18 Shrimp 19 Juv dem pisc 20 S plank dem feed 21 Small pelagics 22 Squid 23 Sandlance 24 Redfish 25 O benthic inver 26 Arctic cod 27 L plankt dem feed 28 Skates 29 Cod + 40cm 30 Aplaice