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One of the key challenges in recreational fisheries management is to provide a rational basis for decisions in the face of conflicting objectives, such as improving angling opportunities, maintaining satisfaction across a diverse fishing client base, controlling costs, and conserving wild populations. We developed a multi-criteria decision analysis approach for managing recreational trout fisheries (e.g., Rainbow Trout Oncorhynchus mykiss). The approach was implemented in a Bayesian decision network. The decision support tool, called “Stock-Optim,” provides a user-friendly interface for predicting fishery performance from alternate stocking prescriptions. The tool integrates survey information on angler typology and satisfaction with previously developed models for fish biology and fishery dynamics to more fully consider the biological and social outcomes of management decisions. Specifically, the tool evaluated alternative stocking options given three performance criteria: angler effort, angler s...

Ecosystem models are thought to offer advantages over single-species models in terms of management policy analysis. This hypothesis has proven difficult to test because of underlying system complexities, coupled with short time series and minimal contrast in environmental conditions or management policies. This paper presents a Bayesian statistical catch-at-age model to compare ecosystem models and test hypotheses about the management of a recreational fishery based on a predator–prey system using a relatively simple and data-rich ecosystem in a large lake, Kootenay Lake, British Columbia, where kokanee (Oncorhynchus nerka) are the prey and piscivorous rainbow trout (Oncorhynchus mykiss) are the predator. A model that explicitly incorporates the predator–prey interaction explained long-term data of field and fishery surveys much better than single-species models without any interactions. Minimally realistic multispecies models that treated predation identically but differed in their representation of the effects of prey abundance on predator mortality produced quite different results. Management reference points, for example, differed considerably between the models. Our study thus emphasizes that the choice of a management approach for this type of fishery will depend strongly on the model form and should take into consideration results from empirically based models that include species interactions.

Using a management strategy evaluation approach, we compare a range of new and established management procedures (MPs) for setting catch-limits in fisheries. Performance is evaluated with respect to fish life history type, level of stock depletion, data quality, and autocorrelation in recruitment strength. We quantify the robustness of each MP with respect to the various observation processes. Methods using observations of absolute biomass or stock depletion offer the best overall performance and this is consistent across life history types, data qualities, and stock depletion levels. Simple MPs can outperform conventional data-limited methods and data-rich assessments that use time-series of catch and effort data. MP performance is most sensitive to biases in catch data. Our results indicate that often tuning MPs for specific stocks is important, though this may not be viable in data-poor assessment scenarios because of insufficient data and analysis resources.

Lethal control is widely employed to suppress the numbers of target wildlife species within restricted management areas. The success of such measures is expected to vary with local circumstances affecting rates of removal and replacement. There is a need both to evaluate success in individual cases and to understand variability and its causes. In Britain, red fox (Vulpes vulpes) populations are culled within the confines of shooting estates to benefit game and wildlife prey species. We developed a Bayesian state-space model for within-year fox population dynamics within such restricted areas and fitted it to data on culling effort and success obtained from gamekeepers on 22 shooting estates of 2 to 36 km2. We used informative priors for key population processes-immigration, cub recruitment and non-culling mortality-that could not be quantified in the field. Using simulated datasets we showed that the model reliably estimated fox density and demographic parameters, and we showed that conclusions drawn from real data were robust to alternative model assumptions. All estates achieved suppression of the fox population, with pre-breeding fox density on average 47% (range 20%-90%) of estimated carrying capacity. As expected, the number of foxes killed was a poor indicator of effectiveness. Estimated rates of immigration were variable among estates, but in most cases indicated rapid replacement of culled foxes so that intensive culling efforts were required to maintain low fox densities. Due to this short-term impact, control effort focussed on the spring and summer period may be essential to achieve management goals for prey species. During the critical March-July breeding period, mean fox densities on all estates were suppressed below carrying capacity, and some maintained consistently low fox densities throughout this period. A similar model will be useful in other situations to quantify the effectiveness of lethal control on restricted areas.

We develop and test spatial population dynamics models that estimate age-dependent offshore movement of fish populations from spatial fishery data. Spatially aggregated population dynamics models produced biased estimates of maximum sustainable yield (MSY) reference points when spatial dynamics were simulated. Spatial population dynamics models provided relatively unbiased estimates of MSY reference points regardless of whether spatial dynamics were simulated. We demonstrate that by using conventional fishery data that are disaggregated spatially, it is possible to estimate movement with age and obtain more accurate estimates of management reference points. The new spatial models were fitted to data for Gulf of Mexico red grouper (Epinephelus morio) and gag grouper (Mycteroperca microlepis) to estimate spatial distribution and offshore movement with age. Offshore ontogeny was estimated to be stronger for gag grouper than red grouper and predicted a larger fraction of older gag grouper in deeper waters.

Offshore lingcod ( Ophiodon elongatus ) in British Columbia (BC) make seasonal migrations that differ by sex: in winter most adult males aggregate in shallow water and most adult females aggregate in deep water, while in summer both sexes distribute equally in deep and shallow water. We developed a semi-age structured delay-difference model that accommodates sex-, and age/depth-specific seasonal migration and examined the sensitivity of biological reference points for BC offshore lingcod to migration pattern and the allocation of fishing effort across seasons and depths. When a migration pattern with a homogeneous distribution of males and females in shallow and deep water throughout the year was assumed, the estimated maximum sustainable yield (MSY), biomass at MSY ( B MSY ), and percent spawning per recruitment at MSY (%SPR MSY ) were robust to changes in effort allocation. However, when a migration pattern that assumed a winter distribution of 95% of adult males in shallow water and 95% of adult females in deep water was used, the estimated MSY, B MSY , and %SPR MSY were markedly sensitive to changes in effort allocation. These results suggest that incorporating sex-, age/depth-, and season-specific structuring in stock assessment models could enable improved evaluations of management options for BC lingcod and other species with sex- and season-specific migrations.

a b s t r a c t The majority of global fish stocks lack adequate data to evaluate stock status using conventional stock assessment methods. This poses a challenge for the sustainable management of these stocks. Recent requirements to set scientifically based catch limits in several countries, and growing consumer demand for sustainably managed fish have spurred an emerging field of methods for estimating overfishing thresholds and setting catch limits for stocks with limited data. Using a management strategy evaluation framework we quantified the performance of a number of data-limited methods. For most life-histories, we found that methods that made use of only historical catches often performed worse than maintaining current fishing levels. Only those methods that dynamically accounted for changes in abundance and/or depletion performed well at low stock sizes. Stock assessments that make use of historical catch and effort data did not necessarily out-perform simpler data-limited methods that made use of fewer data. There is a high value of additional information regarding stock depletion, historical fishing effort and current abundance when only catch data are available. We discuss the implications of our results for other data-limited methods and identify future research priorities.

Context Relative abundance indices of wildlife can be scaled to give estimates of absolute abundance. Choice of scaling parameter depends on the data available and assumptions made about the relationship between the index and absolute abundance. Predation-mechanics theory suggests that a parameterisation involving the rate of successful search, s, will be useful where the area searched is unknown. An example arises during fox culling on shooting estates in Britain, where detection and cull data from gamekeepers using a spotlight and rifle are available, and can potentially be used to understand the population dynamics of the local population. Aims We aimed to develop an informative prior for s for use within a Bayesian framework to fit a fox population-dynamics model to detection data. Methods We developed a mechanistic model with a rate of successful search parameter for the gamekeeper–fox system. We established a mechanistic prior for s, using Monte Carlo simulation to combine relevant information on its component factors (detection probability, observer field of view and speed of travel). We obtained empirical estimates of s from a distance-sampling study of fox populations using similar survey methods, and used these as data in a Bayesian model to develop a mechanistic–empirical prior. We then applied this informative prior within a state–space model to estimate fox density from fox-detection rate on four estates. Key results The mechanistic–empirical prior for the rate of successful search was lognormally distributed with a median of 2.01 km2 h–1 (CV = 0.56). Underlying assumptions of the parameterisation were met. Local fox-density estimates obtained using informative priors closely reflected regional density. Conclusions A mechanistic understanding of the search process leading to fox detections by gamekeepers, and the use of Bayesian models, allowed the use of diverse sources of information to develop an informative prior for s that was useful in estimating fox density from detection data. Implications Careful use of prior knowledge within a Bayesian modelling framework can reduce uncertainty in population estimates derived from index data, and lead to improved management decisions. The mechanistic approach we have used will have parallel applications in many other contexts.

Density-dependent mortality of juvenile fishes is a key population regulation mechanism that is usually assumed to occur before fish recruit to fisheries. When density-dependent mortality occurs simultaneously with bycatch of juvenile fish but is not accounted for in stock assessments, estimates of population size and fishery reference points may be biased as a result. This paper develops an instantaneous, age-structured model accounting for simultaneous density-dependent mortality and bycatch in age-0 red snapper ( Lutjanus campechanus ), using equations derived from the Beverton–Holt stock-recruit function. Model equations are first presented and deterministic equilibrium properties of the model are explored. Two simplified Bayesian models (with and without post-recruitment density-dependent mortality) are then fitted to data from the 2009 Gulf of Mexico red snapper stock assessment to illustrate the effects of post-recruitment density-dependent mortality on estimates of stock size, stock status and recovery potential. Finally, the Bayesian models are re-run using simulated indices of abundance and age composition data to explore the estimability of model parameters under different amounts of process and observation error; and also to illustrate policy implications of model mis-specification in terms of timing of density-dependent processes. All analyses show that failure to account for post-recruitment density-dependent mortality in stock assessments can lead to overestimation of true abundance and recovery potential. Equilibrium analyses show that fishery reference points MSY and B MSY may be overestimated if true post-recruitment density-dependent mortality is not accounted for. These analyses also illustrate the problematic nature of defining and calculating reference points in the presence of numerous sources of mortality that affect different demographic components of the population. We note that when population dynamics were simulated without density-dependent age-0 mortality simultaneous with bycatch, estimates of stock size and productivity obtained from an assessment model that assumed density-dependent age-0 mortality had relatively little bias. This was because the density-dependence parameter was correctly estimated to be close to zero. This suggests that it may be advisable to use the density-dependence model if there is a possibility that post-recruitment density-dependent mortality is a factor governing population dynamics.

Proposals for marine conservation measures have proliferated in the last 2 decades due to increased reports of fishery declines and interest in conservation. Fishers and fisheries managers have often disagreed strongly when discussing controls on fisheries. In such situations, ecosystem-based models and fisheries-stock assessment models can help resolve disagreements by highlighting the trade-offs that would be made under alternative management scenarios. We extended the analytical framework for modeling such trade-offs by including additional stakeholders whose livelihoods and the value they place on conservation depend on the condition of the marine ecosystem. To do so, we used Bayesian decision-network models (BDNs) in a case study of an Indonesian coral reef fishery. Our model included interests of the fishers and fishery managers; individuals in the tourism industry; conservation interests of the state, nongovernmental organizations, and the local public; and uncertainties in ecosystem status, projections of fisheries revenues, tourism growth, and levels of interest in conservation. We calculated the total utility (i.e., value) of a range of restoration scenarios. Restricting net fisheries and live-fish fisheries appeared to be the best compromise solutions under several combinations of settings of modeled variables. Results of our case study highlight the implications of alternate formulations for coral reef stakeholder utility functions and discount rates for the calculation of the net benefits of alternative fisheries management options. This case study may also serve as a useful example for other decision analyses with multiple stakeholders.

a b s t r a c t Fisheries management often requires efficient methods for assessing populations across a variety of sampling units. We develop a model to assess the status of rainbow trout (Oncorhynchus mykiss) in lakes using a fixed length of multimesh gillnets. We use mark-recapture data to examine how gillnet capture efficiency varies with lake-basin characteristics. Once we accounted for variation in lake-size (net-effort density), we found that capture efficiency was best explained by surface water temperature at the time of gillnetting and lake-basin characteristics (proportion of littoral area). We use Bayesian techniques to fit our model to the data and explore how uncertainty in the estimated parameters leads to uncertainty in population estimates. Our analysis suggests that lake size, temperature and the proportion of littoral area can be used to predict population size using this index netting method. In addition to providing density estimates, our results help to better understand how fish ecology and behavior influences gillnet catches.

We applied a spatially and seasonally structured stock reduction analysis (SRA) model to white sturgeon ( Acipenser transmontanus ) in the lower Fraser River, British Columbia, to estimate trends in abundance since the 1800s and evaluate the current status of the population. We used a sequential Bayesian state–space estimation approach to incorporate prior information from other analyses and evaluate the updating of prior knowledge within the SRA model. The estimated ratio of the abundance of spawning fish in 2004 to relative to unfished conditions was slightly higher than estimates from other studies; on average, 27% of the posterior probability was associated with a 2004 spawning stock abundance of 50% or less of the unfished abundance. Estimates of the current abundance of fish vulnerable to the lower Fraser River recreational fishery were higher than those obtained in other recent SRAs that ignored spatial structure. We also performed the analysis using a spatially aggregated version of the SRA model and obtained lower estimates of unfished biomass and depletion and higher estimates of fishing mortality rates compared with the spatially structured model. We evaluated two structural hypotheses about age-specific vulnerabilities in the historical commercial fishery; assumed vulnerabilities had a marked impact on estimated fishing mortality rates.

Methods that use only fisheries catch records to determine the status of exploited fish populations have been used to draw important conclusions regarding the world's fisheries. The reliability of two such approaches is evaluated by simulating a range of fisheries development and overfishing scenarios. The success rate and bias of stock status classification by two catch-based methods is compared with those of two stock assessment methods that explicitly model population dynamics and use additional fishing effort data. On average the catch-based methods correctly classified the status of stocks in 31% and 34% of the cases considered. Two simple stock assessments successfully classified stock status in 57% and 59% of the cases. The catch-based methods and the surplus production stock assessment were negatively biased and on average provided overly pessimistic conclusions regarding stock status. Catch-based methods were more negatively biased on average than the stock assessment approaches.

In 2007, an Individual Fishing Quota (IFQ) program was introduced to the valuable red snapper (Lutjanus campechanus) fishery in the US Gulf of Mexico. This study assessed the current perceived scale and causes of red snapper discarding in the Gulf in recent years within the commercial reef fish fishery, according to commercial fishermen. Data were collected through interviews, which took place with fishermen in April and May 2010, and which were unexpectedly halted due to the Deepwater Horizon oil spill that occurred in late April 2010. Results suggest that for those fishermen fully participating in the IFQ program, snapper discarding has decreased since program implementation. For those not fully involved in the program (e.g., due to participation costs), discarding has likely since increased.

We formulate and simulation-test a spatial surplus production model that provides a basis with which to undertake multispecies, multi-area, stock assessment. Movement between areas is parameterized using a simple gravity model that includes a "residency" parameter that determines the degree of stock mixing among areas. The model is deliberately simple in order to (1) accommodate nontarget species that typically have fewer available data and (2) minimize computational demand to enable simulation evaluation of spatial management strategies. Using this model, we demonstrate that careful consideration of spatial catch and effort data can provide the basis for simple yet reliable spatial stock assessments. If simple spatial dynamics can be assumed, tagging data are not required to reliably estimate spatial distribution and movement. When applied to eight stocks of Atlantic tuna and billfish, the model tracks regional catch data relatively well by approximating local depletions and exchange among high-abundance areas. We use these results to investigate and discuss the implications of using spatially aggregated stock assessment for fisheries in which the distribution of both the population and fishing vary over time.

When deriving relative abundance indices on the basis of fishery catch-per-unit-effort (CPUE) data, biases may be introduced by systematic changes in the spatial distribution of fishing. We present and simulation test an integrated approach that combines the conventional generalized linear modelling (GLM) approach to CPUE standardization with data imputation (‘filling in’ missing data). We demonstrate that the approach can be expected to provide more accurate and more precise relative abundance information than contemporary GLM methods. When applied to Japanese longline catch and effort data from 15 stocks of tuna and billfish, the behaviour of the different standardization methods is consistent with that from simulations and often provides inferences about population trends very different from those obtained from conventional GLM approaches.

Hierarchical Bayesian meta-analysis can be a useful method for improving estimation of key parameters for harvested fish populations. In hierarchical models, data from multiple populations are used simultaneously to obtain estimates of parameters for individual populations and characterize the variability among populations. Many populations of Pacific rockfishes ( Sebastes spp.) have declined off the US West Coast since the 1980s, and there is also concern for their conservation in Canada. We develop a hierarchical Bayesian meta-analysis to improve estimates of stock–recruit parameters, characterize management-related parameters (e.g., optimal harvest rate), and address uncertainties in the structural form of the stock–recruit function for Pacific rockfishes. We estimate steepness and optimal harvest rates for 14 populations of Pacific rockfishes under alternative assumptions about the underlying stock–recruit function (Beverton–Holt and Ricker). We provide a posterior predictive distribution of steepness for rockfishes that can be used as a prior in future assessments for similar populations. We also evaluate whether F40% is an appropriate proxy for FMSY for Pacific rockfishes and show that uncertainty in the natural mortality rate can have a significant effect on management advice derived from meta-analyses of stock–recruit data.