Managing non‐target,data‐poor species using catch limits: lessons from the Alaskan groundfish fishery

Abstract The 2006 reauthorisation of the Magnuson‐Stevens Fishery Conservation and Management Act requires annual catch limits for all target and non‐target species within federally managed fisheries in the United States. In Alaska, both target and non‐target species in the Alaska groundfish fisheries have been managed using catch limits since the early 1990s. Non‐target species that are caught incidentally in a fishery require monitoring to ensure that the population is not negatively impacted by commercial fishing. Resource assessment scientists have been challenged with obtaining sufficient data to recommend an acceptable catch level for management of these species. This paper reviews three case studies where a catch limit is determined for non‐target species when certain data are limited: (1) varying levels of biomass and catch data for all species within a species group or complex; (2) adequate catch data but no biomass data; (3) emerging target fishery of data‐poor species, plus an example of how a complex of ecosystem component species is managed.     

Assessing and managing data‐limited ornamental fisheries in coral reefs

Coral reefs support numerous ornamental fisheries, but there are concerns about stock sustainability due to the volume of animals caught. Such impacts are difficult to quantify and manage because fishery data are often lacking. Here, we suggest a framework that integrates several data‐poor assessment and management methods in order to provide management guidance for fisheries that differ widely in the kinds and amounts of data available. First, a resource manager could assess the status of the ecosystem (using quantitative metrics where data are available and semi‐quantitative risk assessment where they are not) and determine whether overall fishing mortality should be reduced. Next, productivity susceptibility analysis can be used to estimate vulnerability to fishing using basic information on life history and the nature of the fishery. Information on the relative degree of exploitation (e.g. export data or ratios of fish density inside and outside no‐take marine reserves) is then combined with the vulnerability ranks to prioritize species for precautionary management and further analysis. For example, species that are both highly exploited and vulnerable are good candidates for precautionary reductions in allowable capture. Species that appear to be less vulnerable could be managed on a stock‐specific basis to prevent over‐exploitation of some species resulting from the use of aggregate catch limits. The framework could be applied to coral reef ornamental fisheries which typically lack landings, catch‐per‐unit‐effort and age‐size data to generate management guidance to reduce overfishing risk. We illustrate the application of this framework to an ornamental fishery in Indonesia.     

Spatio‐temporal analyses of marine predator diets from data‐rich and data‐limited systems

Accounting for variation in prey mortality and predator metabolic potential arising from spatial variation in consumption is an important task in ecology and resource management. However, there is no statistical method for processing stomach content data that accounts for fine‐scale spatio‐temporal structure while expanding individual stomach samples to population‐level estimates of predation. Therefore, we developed an approach that fits a spatio‐temporal model to both prey‐biomass‐per‐predator‐biomass data (i.e. the ratio of prey biomass in stomachs to predator weight) and predator biomass survey data, to predict “predator‐expanded‐stomach‐contents” (PESCs). PESC estimates can be used to visualize either the annual landscape of PESCs (spatio‐temporal variation), or can be aggregated across space to calculate annual variation in diet proportions (variation among prey items and among years). We demonstrated our approach in two contrasting scenarios: a data‐rich situation involving eastern Bering Sea (EBS) large‐size walleye pollock (Gadus chalcogrammus, Gadidae) for 1992–2015; and a data‐limited situation involving West Florida Shelf red grouper (Epinephelus morio, Epinephelidae) for 2011–2015. Large walleye pollock PESC was predicted to be higher in very warm years on the Middle Shelf of the EBS, where food is abundant. Red grouper PESC was variable in north‐western Florida waters, presumably due to spatio‐temporal variation in harmful algal bloom severity. Our approach can be employed to parameterize or validate diverse ecosystem models, and can serve to address many fundamental ecological questions, such as providing an improved understanding of how climate‐driven changes in spatial overlap between predator and prey distributions might influence predation pressure.     

Predicting recruitment density dependence and intrinsic growth rate for all fishes worldwide using a data‐integrated life‐history model

Fisheries scientists use biological models to determine sustainable fishing rates and forecast future dynamics. These models require both life‐history parameters (mortality, maturity, growth) and stock‐recruit parameters (juvenile production). However, there has been little research to simultaneously predict life‐history and stock‐recruit parameters. I develop the first data‐integrated life‐history model, which extends a simple model of evolutionary dynamics to field measurements of life‐history parameters as well as historical records of spawning output and subsequent recruitment. This evolutionary model predicts recruitment productivity (steepness) and variability (variance and autocorrelation in recruitment deviations) as well as mortality, maturity, growth, and size, and uses these to predict intrinsic growth rate (r) for all described fishes. The model confirms previous analysis showing little correlation between steepness and either natural mortality or asymptotic maximum size (). However, it does reveal taxonomic patterns, where family Sebastidae has lower steepness () and Salmonidae has elevated steepness () relative to the prediction for bony fishes (class Actinopterygii, ). Similarly, genus Sebastes has growth rate (0.09) approaching that of several shark families (Lamniformes: 0.02; Carcharhiniformes: 0.02). A cross‐validation experiment confirms that the model is accurate, explains a substantial portion of variance (32%–67%), but generates standard errors that are somewhat too small. Predictive intervals are tighter for species than for higher‐level organizations (e.g. families), and predictions (including intervals) are available for all fishes worldwide in R package FishLife. I conclude by outlining how multivariate predictions of life‐history and stock‐recruit parameters could be useful for stock assessment, decision theory, ensemble modelling and strategic management.     

Natural mortality estimators for information‐limited fisheries

The 29 estimators of natural mortality (M) that have been proposed for ‘information‐limited’ fisheries are reviewed, together with a new alternative presented here. Each is applied to 13 example populations for which well‐founded estimates are available of both M and the estimators' parameters. None of the 30 can provide accurate estimates for every species, and none appears sufficiently precise for use in analytical stock assessments, while several perform so poorly as to have no practical utility. If the growth coefficient K has been reliably estimated, either M = 1.5 K or Pauly's long‐established estimator can provide useful estimates of M, but they fail with species that have long adult lives after swift juvenile growth, with those that never reach their asymptotic lengths and with species that otherwise deviate from archetypal teleost life histories. If a pre‐exploitation maximum observed age (T_max) can be established, M can be estimated for both teleosts and sharks using M = 4.3/T_max but that seriously underestimates when the effective sample size (n_e) is large and overestimates with species showing pronounced senescence. The new estimator presented here addresses n_e but is upset by even mild senescence. Some estimators of M‐at‐size, particularly ones recently advanced by Gislason et al. and Charnov et al., also show promise but require further examination. It is recommended that fisheries scientists measure M by more advanced methods whenever possible. If ‘information‐limited’ estimators must be used, their uncertainties should be acknowledged and their errors propagated into management advice.     

Reconstructing 290 years of a data‐poor fishery through ethnographic and archival research: The East Pacific green turtle (Chelonia mydas) in Baja California,Mexico

Evaluating historical changes in the exploitation of marine organisms is a key challenge in fisheries ecology and marine conservation. In the Eastern Pacific, marine turtles were exploited for millennia before systematic monitoring began <50 years ago. Using ethnographic and historical data, we generated a detailed reconstruction of the East Pacific green sea turtle (Chelonia mydas) fishery in Mexico's Baja California peninsula from 1700 to 1990. Sea turtles from the region's important feeding areas were a staple food source from the earliest phases of human occupation, dating back at least 12,000 years. In contrast with regions such as the Caribbean, small human populations and limited market access resulted in apparently sustainable turtle harvests until the second half of the 20th century. We found that the estimated annual catches between 1960 and 1980 exceeded the estimated annual catches of the previous 250 years by an order of magnitude, leading to the collapse of the fishery and the depletion of the green turtle population. A total ban on sea turtle captures in 1990, comprehensive nesting beach protection, and significant conservation efforts resulted in increases in breeding females on nesting beaches and catch rates in scientific monitoring on main feeding grounds since the early 2000s. This provides a positive outlook for this once‐depleted population segment. Although further research is needed to evaluate current conservation status, we have identified a date, between 1950 and 1960, which can serve as a reliable temporal reference for future evaluations of historical baseline abundance in this region.     

Global fishery dynamics are poorly predicted by classical models

Fisheries dynamics can be thought of as the reciprocal relationship between an exploited population and the fishers and/or managers determining the exploitation patterns. Sustainable production of protein of these coupled human‐natural systems requires an understanding of their dynamics. Here, we characterized the fishery dynamics for 173 fisheries from around the globe by applying general additive models to estimated fishing mortality and spawning biomass from the RAM Legacy Database. GAMs specified to mimic production models and more flexible GAMs were applied. We show observed dynamics do not always match assumptions made in management using “classical” fisheries models, and the suitability of these assumptions varies significantly according to large marine ecosystem, habitat, variability in recruitment, maximum weight of a species and minimum observed stock biomass. These results identify circumstances in which simple models may be useful for management. However, adding flexibility to classical models often did not substantially improve performance, which suggests in many cases considering only biomass and removals will not be sufficient to model fishery dynamics. Knowledge of the suitability of common assumptions in management should be used in selecting modelling frameworks, setting management targets, testing management strategies and developing tools to manage data‐limited fisheries. Effectively balancing expectations of future protein production from capture fisheries and risk of undesirable outcomes (e.g., “fisheries collapse”) depends on understanding how well we can expect to predict future dynamics of a fishery using current management paradigms.     

Spatial distribution and abundance of the by‐catch coastal elasmobranch Raja undulata: Managing a fishery after moratorium

The undulate ray Raja undulata Lacepède is a coastal species common along the north‐eastern Atlantic Ocean and Mediterranean Sea and is highly accessible to coastal fisheries. Between 2009 and 2015, the species was under a European Union (EU) fisheries moratorium that hampered the collection of data to assess its stock status in Portuguese waters. After that period, a small experimental EU fishing quota was set for Portugal enabling collection of fishery data under a fishermen self‐sampling scenario. Based on the data collected, R. undulata abundance was estimated along the Portuguese continental coast through the application of a N‐mixture model and incorporating environmental factors. The results support the species coastal and patchy nature across the study area with higher abundances estimated in areas associated with shallow sandy bottoms as the Southwest region. The present work constitutes an important step for the management of this fishery resource, in particular concerns about its abundance trends over time and its spatial distribution and habitat requirements.     

Ridge to reef modelling for use within land–sea planning under data‐limited conditions

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Life history demographic parameter synthesis for exploited Florida and Caribbean coral reef fishes

Age‐ or length‐structured stock assessments require reliable life history demographic parameters (growth, mortality, reproduction) to model population dynamics, potential yields and stock sustainability. This study synthesized life history information for 84 commercially exploited tropical reef fish species from Florida and the U.S. Caribbean (Puerto Rico and the U.S. Virgin Islands). We attempted to identify a useable set of life history parameters for each species that included lifespan, length at age, weight at length and maturity at length. Key aspects of the life history synthesis were development of: (a) a database that characterized study details including sampling region, biological and statistical methods, length range of sampled individuals, sample size, capture gears and sampling time frame; (b) reproducible procedural criteria for parameter identification for a given species; and (c) a reliability metric for each parameter type. Complete life history parameter sets were available for 46 species analysed. Of these, only 16 species had parameter sets meeting the highest standards for reliability, highlighting future research needs.     

Estimating stock depletion level from patterns of catch history

The degree to which a stock is depleted is one of the most important quantities in fisheries management because it is used to quantify the success of management and to inform management responses. However, stock depletion is extremely difficult to estimate, particularly with limited data. Using the RAM Legacy database, we developed a boosted regression tree (BRT) model to correlate depletion with a range of predictors calculated from catch data, making the model usable for many fisheries worldwide. The most important predictors were found to be catch trends obtained from linear regressions of scaled catch on time, including regression coefficients for the whole catch time series, the subseries before and after the maximum catch, and in recent years. Eight predictors explain about 80% of variation in depletion. There is a correlation of .5 between measured levels of depletion and the predictions of the BRT model. Predictions are less biased when the stock is fished down below half of the carrying capacity. The BRT model outperforms comparable existing catch‐based depletion estimators and could be used to provide priors for depletion for data‐poor stock assessment methods, or used more directly to provide estimates of the probability that depletion is below a given threshold value.     

Extending electronic length frequency analysis in R

Electronic length frequency analysis (ELEFAN) is a system of stock assessment methods using length‐frequency (LFQ) data. One step is the estimation of growth from the progression of LFQ modes through time using the von Bertalanffy growth function (VBGF). The option to fit a seasonally oscillating VBGF (soVBGF) requires a more intensive search due to two additional parameters. This work describes the implementation of two optimisation approaches (“simulated annealing” and “genetic algorithm”) for growth function fitting using the open‐source software “R.” Using a generated LFQ data set with known values, the accuracy of the soVBGF parameter estimation was evaluated. The results indicate that both optimisation approaches are capable of finding high scoring solutions, yet settings regarding the initial restructuring process for LFQ bin scoring (i.e. “moving average,”) and the fixing of the asymptotic length parameter (L_∞) are found to have significant effects on parameter estimation error. An outlook provides context as to the significance of the R‐based implementation for further testing and development, as well as the general relevance of the method for data‐limited stock assessment.     

An argo‐based experiment providing near‐real‐time subsurface oceanic environmental information for fishery data

A better understanding of the relationships between oceanic environments and fishing conditions could make the utilization of fish more efficient, profitable, and sustainable. The current lack of high‐precision subsurface seawater information has long been a constraint on fishery research. Using near‐real‐time Argo observations, this paper presents a new approach called gradient‐dependent optimal interpolation. This approach provides daily subsurface oceanic environmental information according to fishery dates and locations. An experiment was conducted in the western and central Pacific Ocean using yellowfin tuna (YFT) catch data in August 2017. The results of seawater temperature and salinity represented differences of less than ±0.5°C and ±0.05, respectively, according to verification of error analysis and truth‐finding comparisons. After applying the constructed temperature and salinity profiles, we described the relationship between subsurface information and yellowfin tuna catch distribution. Statistical analysis revealed that yellowfin tuna were more adapted to warmer and saltier seawater. At the near‐surface (<5 m), the most suitable temperature was 28–29°C, although yellowfin tuna can endure a temperature range from 11 to 12°C at a depth of 300 m. The corresponding upper boundary of the thermocline was approximately 75 m, with a mean strength of 0.074°C/m, and the most suitable salinity for yellowfin tuna was 34.5–36.0 at depths shallower than 300 m. These results indicated that the constructed subsurface information was very close to the true values and they had high spatial and temporal accuracy.     

Describing ecosystem contexts with single‐species models: a theoretical synthesis for fisheries

Fished populations exist within complex ecosystems but are typically assessed using single‐species models. It is often lamented that stock assessments rarely account for other ecosystem components explicitly, but in most fisheries there are clear difficulties in implementing data‐intensive ecosystem‐based assessment approaches. Addressing these competing challenges requires prioritizing investments in expanded assessment frameworks. To provide high‐level conceptual guidance to such prioritization, here we use general analytical theory to identify (i) characteristics of fish stocks that tend to facilitate or inhibit the precision and accuracy of reference points from single‐species assessments, (ii) characteristics of ecosystem components that introduce the greatest bias/imprecision into single‐species reference points and (iii) warning signs within single‐species frameworks that important ecosystem components may not be adequately accounted for. We synthesize and expand on theories from various branches of applied mathematics addressing analogous questions. Our theory suggests that (i) slow population dynamics (relative to the dynamics of other ecosystem components) and a wide range of abundance observations promote precision and accuracy of single‐species reference points; (ii) ecosystem components that strongly influence the focal stock's growth, and change on similar timescales as the focal stock's abundance, introduce the greatest bias/imprecision to single‐species reference points; and (iii) signs of potential challenges for single‐species assessment include fast population dynamics, ‘hydra effects’ (i.e. abundance and fishing pressure simultaneously increase), and recently detected extinctions, invasions or regime shifts in closely connected ecosystem components. Our results generalize to other levels of abstraction and provide strategic insights complementing tactical simulation approaches such as management strategy evaluation.     

Evaluation of observer‐ and industry‐based catch data in a recreational charter fishery

There is international recognition for greater inclusion of recreational fisheries catch data in species, fisheries and ecosystem assessments. Recreational charter fisheries provide important social services and contribute to total species catches. This study compares and validates industry logbook catch and effort data (1,357 trips) against observer data (154 trips) across six ports in a recreational charter fishery in eastern Australia. The mean numbers of clients and fishing effort (hours) per trip varied inconsistently between data sources and among ports. Logbooks did not adequately report released catches, and the mean number of species retained per trip was consistently underestimated in logbooks compared to observer data. For both data sources, catch rates of total individuals and key species displayed similar trends across different units of effort; catch per hour, client, client/hour and trip. The mean catch rates of total individuals and most key species, except those retained for bait, were similar across data sources, as were estimates of total fleet harvests. The length compositions of retained catches of some key species displayed truncation of larger organisms in the observer data whereas other species did not. Despite the shortcomings of the logbook data, future fishery and species monitoring strategies could include industry and observer data sources.     

A nonlinear,low data requirement model for producing spatially explicit fishery forecasts

Spatial variability can confound accurate estimates of catch per unit effort (CPUE), especially in highly migratory species. The incorporation of spatial structure into fishery stock assessment models should ultimately improve forecasts of stock biomass. Here, we describe a nonlinear time series model for producing spatially explicit forecasts of CPUE that does not require ancillary environmental or demographic data, or specification of a model functional form. We demonstrate this method using spatially resolved (1° × 1° cells) CPUE time series of North Pacific albacore in the California Current System. The spatial model is highly significant (P < 0.00001) and outperforms two spatial null models. We then create a spatial forecast map for years beyond the range of data. Such approaches can guide spatial management of resources and provide a complement to more data‐intensive, highly parameterized population dynamics and ecosystem models currently in use.     

A simple method for estimating MSY from catch and resilience

The Law of the Sea requires that fish stocks are maintained at levels that can produce the maximum sustainable yield (MSY). However, for most fish stocks, no estimates of MSY are currently available. Here, we present a new method for estimating MSY from catch data, resilience of the respective species, and simple assumptions about relative stock sizes at the first and final year of the catch data time series. We compare our results with 146 MSY estimates derived from full stock assessments and find excellent agreement. We present principles for fisheries management of data‐poor stocks, based only on information about catches and MSY.     

Examining common assumptions about recruitment: a meta‐analysis of recruitment dynamics for worldwide marine fisheries

Assumptions about the future productivity of a stock are necessary to calculate sustainable catches in fisheries management. Fisheries scientists often assume the number of young fish entering a population (recruitment) is related to the biomass of spawning adults and that recruitment dynamics do not change over time. Thus, managers often use a target biomass based on spawning biomass as the basis for calculating sustainable catches. However, we show recruitment and spawning biomass are not positively related over the observed range of stock sizes for 61% of 224 stocks in the RAM Legacy Stock Assessment Database. Furthermore, 85% of stocks for which spawning biomass may not drive recruitment dynamics over the observed ranges exhibit shifts in average recruitment, which is often used in proxies for target biomasses. Our results suggest that the environment more strongly influences recruitment than spawning biomass over the observed stock sizes for many stocks. Management often endeavours to maintain stock sizes within the observed ranges, so methods for setting management targets that include changes within an ecosystem may better define the status of some stocks, particularly as climate changes.