An Ecosim Model for Exploring Gulf of Mexico Ecosystem Management Options: Implications of including Multistanza Life- History Models for Policy Predictions

An Ecopath-Ecosim ecosystem model under development for coastal areas of the Gulf of Mexico simulates responses of 63 biomass pools to changes in fisheries and primary productivity. Ten key species are represented by detailed, multistanza population-dynamics models (31 of the biomass pools) that attempt to account explicitly for possible changes in recruitment rates due to changes in by-catch rates and trophic interactions. Over a 1950–2004 historical reference period, the model shows good simulated agreement with time-series patterns estimated from stock assessment and relative abundance index data for many of the species, and in particular it offers an explanation for apparent nonstationarity in natural mortality rates of menhaden (declining apparent M over time). It makes one highly counterintuitive policy prediction about impacts of management efforts aimed at reducing by-catch in the shrimp trawl fishery, namely that by-catch reduction may cause negative impacts on productivity of several valued species [menhaden, Brevoortia patronus Goode, 1878; red drum, Sciaenops ocellatus (Linnaeus, 1766); red snapper, Lutjanus campechanus (Poey, 1860)] by allowing recovery of some benthic predators such as catfishes [Arius felis (Linnaeus, 1766), Bagre marinus (Mitchill, 1815)] that have been reduced by trawling but are also potentially important predators on juveniles of the valued species. Recognition of this policy implication would have been impossible without explicit, multistanza representation of juvenile life histories and trophic interactions, because the predicted changes in predation regimes represent only very small overall biomass fluxes. Fisheries management councils have been under considerable pressure to take account of “ecosystem” effects in setting harvest policies, because of concerns ranging from impacts of by-catch and habitat damage effects by some fishing activities to impacts of fishing on capabilities of stocks to support other valued species. When harvest controls have been based only on reference points from single-species assessments, even including by-catch mortality effects, assessments do not actually ignore ecological interactions entirely but typical single-species models make very particular assumptions about how natural mortality and recruitment rates somehow remain stable despite changes in ecological circumstances (e.g., changes in predation risk and food availability). Discomfort about these very restrictive assumptions has led to investment in development of models that account explicitly for at least some major trophic interaction effects. As part of an evaluation of ecosystem modeling tools for comparing fisheries management options in the Gulf of Mexico, the Ecosystem Scientific Committee of the Gulf of Mexico Fisheries Management Council requested development of a demonstration Ecosim model using the widely available Ecopath with Ecosim software (http://www.ecopath.org). "is software facilitates management of basic BULLETIN OF MARINE SCIENCE, VOL. 83, NO. 1, 2008 252 biomass and trophic interaction (food-habits) data for whole ecosystems and checks for consistency of the data through the Ecopath “mass balance” process, in which estimated total mortality rates for biomass components are checked against estimated total predation and fishing-loss rates calculated from predator abundances, diet compositions, and historical fisheries data. Once a plausible (or at least physically possible without the need for spontaneous creation of mass to satisfy input predator food demands and fishery removal rates) mass-balanced biomass and trophic flow pattern has been found, the resulting ecosystem state/flow estimate can be passed to Ecosim, a dynamic model that simulates temporal responses to policy changes such as fleet reductions, along with impacts of changes in factors such as nutrient loading and primary productivity. "e Florida Marine Research Institute (FMRI) had already contracted for development of an Ecopath/Ecosim model of the West Florida Shelf (Mackinson et al., 2001; Okey and Mahmoudi, 2002; Okey et al., 2004), and that model had in turn been modified extensively to simulate effects of changing nutrient loading and fishing effort on the fishes of Tampa Bay and other Florida Fisheries Independent Monitoring (FIM) study areas. "e Tampa Bay analysis demonstrated surprisingly good ability of Ecosim to fit historical time series of abundances of a wide variety of fish species, and it encouraged us to use that model as a starting point for wider analysis of the Gulf of Mexico coastal ecosystem as a whole. Starting with the Tampa Bay biomasses, feeding ecology (consumption rates, diet compositions), and historical fishing patterns, we extended the model to whole-Gulf scale by correcting biomasses to averages over the larger area, adding a variety of species (such as red snapper and menhaden) that are not abundant in Tampa Bay, and developing a model-testing set of data that includes historical fishing-effort patterns for major fleets, estimates of abundance over time from stock assessments, historical catches, and some historical information on changes in total mortality rates (Z). In adding additional species and fisheries, we aimed to account for at least 90% of the total coastal fish (excluding tuna) and invertebrate harvest for the Gulf and to account as well for by-catch patterns and impacts for particular fisheries (especially shrimp trawling) that have been suggested to have large impact on other fish stocks. We view the development of a complex model like the current version of the Gulf of Mexico Ecosim model as an ongoing process. "at process will be facilitated by inclusion of new data and information from a variety of researchers using the Ecopath with Ecosim data-management software, but the really critical need is for continual harsh challenges of the model in the form of comparisons of its predictions to historical and spatial data and of demands for new policy predictions from it that expose (through unrealistic predictions) weaknesses in the data and model structure. Below we review just the June 2007 version of the model and compare its predictions to historical relative abundance and stock-assessment data for a variety of species for the reference period 1950–2004. We show that for one very particular fishery policy, regulation of by-catch by the shrimp fishery, the ecosystem model does indeed make predictions very different from those obtained from single-species assessment models and that these disturbing predictions arise from the particular capability of Ecosim to represent changes in predation mortality rates of prerecruitment juvenile fishes. WALTERS ET AL.: INCLUDING MULTISTANZA LIFE-HISTORY MODELS FOR POLICY PREDICTIONS 253 M S  A "e current model simulates biomass dynamics of 63 biomass “pools” (Table 1), ranging from detritus and phytoplankton at the bottom of the food web to large piscivores like red snapper, mackerels (Scomberomorus spp.), and coastal sharks at the top of the web (no marine mammals are included in the current model version). Overviews of how Ecosim represents dynamic change in these pools over time can be found in Walters and Martell (2004) and Christensen and Walters (2004). Dynamics are simulated on a monthly time step, in two alternative model structures as described below. Here, we do not attempt to present all equations and parameter values used in the ecosystem model; all are stored in an Access database that is freely available for download at http://www.ecopath.org/index.php?name=Models&sub=model&model ID=128. "e easiest way to examine the data, test effects of alternative parameter estimates, and reproduce the results described below is to use the Ecopath/Ecosim version 5 software, again freely available at http://www.ecopath.org. B D  A B P.—First, some pools are represented only by total biomass per unit area and are simulated by solution of differential equations for biomass rate of change of the form dB dt eQ t Z t B i i i i = ] ] g g (1) where Bi is biomass of pool i, e is a food conversion efficiency, Qi(t) is total rate of food consumption by the pool, and Zi(t) is instantaneous total mortality rate for the pool as a result of unexplained causes plus predation plus fishing. Qi(t) is calculated as a sum of consumption rates of various prey types, according to preferences defined initially by Ecopath diet-composition inputs. "e components of Qi(t) also form components of the total mortality rates Zj(t) of the prey types eaten by each pool. Total mortality rate at any time is represented by the sum Z t q E t Mo Q t B t i ki k k i j ij i / / = + + ] ] ] ] g g g g (2) where Moi is an unexplained natural mortality rate, predation rates Qij(t) represent total consumption rates of pool i by pool j predators, and fishing mortality rates qkiEk(t) imposed by fishing fleets k (including landed catches, by-catch, and dead discards) are represented as varying with time-dependent fishing efforts Ek(t) (k = 1 . . . 18 in the current Gulf of Mexico model, Table 2). Efforts are scaled so that Ek(0) = 1, i.e., are scaled to 1.0 at Ecopath base conditions, allowing estimation of “catchabilities” qki as qki = Cki(0)/Bi(0) where Cki(0) is an Ecopath base catch of species i entered for each fishing effort k. "e qki can also be made density dependent [on Bi(t) through a function of the form qki(t) = qmax/(1 + kBi(t)] where k is chosen so as to make qki equal to the Ecopath base value when Bi(t) = Bi(0); this option is particularly important for correct prediction of historical catches of menhaden, for which catchability is obviously density dependent (Vaughan et al., 2000). Each component consumption/predation rate Qij(t) of prey type i by pool j is predicted by the “foraging arena” consumption rate equation BULLETIN OF MARINE SCIENCE, VOL. 83, NO. 1, 2008 254 Table 1. Biomass pools included in Gulf of Mexico Ecosim model and Ecopath base input estimates of biomass, production per unit biomass (or total mortality rate Z for multistanza population components), total food consumption per unit biomass, ecotrophic efficiency defined as proportion of Z explained by modeled predation and total catches (landings plus by-catch and discards). Pools indicated by italicized names are represented by differential equation model, others as stanzas within single-species age-structured models. Note that low ecotrophic efficiencies for some groups imply not a lack of predation mortality but only that mortality is not explicitly explained by modeled predator groups or fisheries. Numbers before organism names represent months of age. Group name Biomass (mt km–2) Prod./biom. (Z, yr–1) Cons./biom. (yr–1) Ecotrophic efficiency Trend data sources 0–12 Snook 0.000217 5 25.5123 0.5008 3–12 Snook 0.0185 2 6.268 0.008 12–48 Snook 0.2272 0.9 2.3628 0.0978 48–90 Snook 0.0984 0.62 1.4982 0.5751 90+ Snook 0.02 0.6 1.3 0 Muller and Taylor (2006) 0–3 Red drum 0.000181 2 18.7423 0.5024 3–8 Red drum 0.00493 3.5 6.699 0.0335 8–18 Red drum 0.0323 1.1 2.9886 0 18–36 Red drum 0.1284 0.6 1.7166 0.1713 36+ Red drum 2 0.15 0.95 0.0002 Porch (2000), Murphy (2005) 0–3 Sea trout 0.000091 6 23.1667 0.7594 3–18 Sea trout 0.026 1.4 4.0109 0.1337 18+ Sea trout 0.22 0.7 1.6 0.3279 FIM sampling 0–3 Sand trout 1.97E-05 5 37.8657 0.225 3–12 Sand trout 0.00252 1.2 8.7796 0.4279 12+ Sand trout 0.1 0.7 2.7 0.2394 0–6 Mullet 0.0343 3 50.018 0.5123 6–18 Mullet 0.5224 1.8 18.2253 0.3774 18+ Mullet 2.8 0.8 8 0.5428 Mahmoudi (2005) 0–3 Mackerel 3.68E-05 4 73.1333 0.0393 3+ Mackerel 0.25 0.7 6 0.572 SEDAR 5 (2004) 0–10 Ladyfish 0.00979 2.8 17.8409 0.6864 10+ Ladyfish 0.089 1.6 6 0.1537 0 Grouper 0.0045 2 33.1643 0 1–3 Grouper 0.0246 0.6 14.9354 0.1357 3+ Grouper 0.52 0.45 6 0.4275 Stochastic SRA Jacks 0.2891 0.8 2 0.9 Bay anchovy 1.3653 2.53 14 0.6 FIM Pinfish 0.75 1.019 8 0.9507 FIM Spot 0.8 1.1 12 0.8328 FIM Silver perch 1.7134 1.4 9 0.9 FIM Scaled sardine 11 1.8 12.106 0.5487 SEAMAP Mojarra 0.631 1.9 15 0.8 FIM Threadfin herring 0.08 1.31 12.5 0.3655 FIM 0–12 Menhaden 1.5336 2.3 14.5312 0.5797 12+ Menhaden 6 1.9 6 0.6973 Vaughan et al. (2000) Menidia (silverside) 0.16 2.3 16 0.8815 FIM Catfish 0.15 0.8 7.6 0.9377 FIM Bumper 0.15 1.2 12 0.8545 FIM WALTERS ET AL.: INCLUDING MULTISTANZA LIFE-HISTORY MODELS FOR POLICY PREDICTIONS 255 Q t v a T t B t B t v a T t B t 2 ij ij ij i i j ij ij j j = + ] ] ] ] ] ] ^ g g g g g gh (3) "is rate equation can optionally also be modified to represent prey-switching (change in aij) effects, prey-handling time, and reduction in foraging time in direct response to increases in predation risk; we have not used any of those advanced options in the Gulf of Mexico model to date. In the basic foraging arena consumption equation, the rate constant vij represents “flow” of prey from behaviorally (or locationally) invulnerable to vulnerable states (which can limit predation rates to levels far lower than would be predicted from simpler mass-action models and create strong “apparent competition” among predators through the denominator term of the equation), aij represents rate of effective search or search efficiency for predator i on prey type j, and Ti(t) represents the relative amount of time that individuals of type i (and j) spend foraging and hence at risk of predation. Temporal adjustments in Ti(t) occur in the simulations when per-capita feeding rates (Q/B) drop below Ecopath input values; such adjustments lead to (1) density dependence in predation risk, because animals spend less time at risk of predation when competitors are fewer, and (2) type II functional response forms (animals try to keep total food intake constant, by changing Ti, when any or all prey types become less or more abundant). "e aij parameters can be calculated from Ecopath diet input data, whereas the vij parameters must be estimated for each pool by statistical fitting of the dynamics to Table 1. Continued. Group name Biomass (mt km–2) Prod./biom. (Z, yr–1) Cons./biom. (yr–1) Ecotrophic efficiency Trend data sources Caridian shrimp 4.2561 2.4 18 0.6 Shrimp 1 2.4 19.2 0.9233 NMFS catch, effort stats. Stone crab 0.1675 2 7 0.4 Blue crab 0.2 2.4 8.5 0.584 FIM Cyprinodontids 0.9 2.5 10 0.0897 FIM Poecilids 0.05 2.5 10 0.4511 Pigfish 0.2072 0.8 4 0.75 FIM Gobies 0.179 1.5 7.5 0.75 FIM