Developing Methodologies for Studying Elasmobranchs and Other Data-Poor Species

Fisheries have become increasingly important to manage and conserve, and this is particularly challenging for data-poor species. Elasmobranchs are commonly considered data-poor or data-limited species. Their life history characteristics make their populations susceptible to depletion from fishing pressures and habitat degradation. Thus, it is important to understand the movement patterns and habitat use of the targeted species as well as the models used in the stock assessment for the species. This thesis involves developing techniques and information for data-poor species, such as elasmobranchs. The objectives of this research were to 1) identify the wintering grounds for the cownose rays (Rhinoptera bonasus) from Chesapeake Bay, 2) determine summer and fall movement patterns for this species, and 3) understand how changes in the data input (i.e., catch and effort) affect the parameter estimates from a simple surplus production model. Cownose rays have received negative attention in Chesapeake Bay for presumably heavy predation on commercial shellfish. Although the population size is unknown, there are concerns about the increase in abundance of this species, resulting in the need for management to control its population size. However, there are many questions regarding the movement patterns and habitat use for cownose rays, particularly for males. A total of 16 cownose rays in Chesapeake Bay were tagged with pop-up satellite archival tags (PSATs) to determine their wintering grounds and summer and fall movement patterns. Six tags (3 on females and 3 on males) were released on the programmed date and contained data on temperature, pressure (for depths) and light-level (for geolocations). The end locations from the satellite tags indicated that both sexes migrated to the coastal waters of central Florida for the winter. Females were exited Chesapeake Bay at the end of September and early October and migrated south to Florida. Males left the bay at the end of July and traveled northward to a second feeding ground in the coastal waters of southern New England. At the end of summer and early fall, the males made the southerly migration down the coast to Florida. There were no diel differences detected; however, male rays occupied a wider depth and temperature range compared to females. Data-poor stocks are often regulated based on surplus production models when only catch and effort data are available. However, reported catch and effort rarely equal the true values. Reported data may not include bycatch, illegal fishing or local consumption, resulting in higher true catch and effort values than that reported. I used ASPIC (A Surplus Production Model Incorporating Covariates) software to examine the viii PREVIEW effects of underestimated catch and effort on parameter and ratio estimates (e.g., MSY, F/Fmsy and B/Bmsy) in a production model. Using three example fisheries, I determined that a fishery with constant underestimation of catch and effort over time can be managed based on the parameter estimates from the production model. The parameter estimates either yielded no errors or were underestimated by the same percentage as the underreported data; however, the ratios of parameter estimates were free of error due to cancellation of errors. Trends in underestimation of catch and effort (e.g., improved reporting rates or increased illegal fishing) caused the errors in the estimates from the production model to be highly variable and scenario-dependent. Consequently, if underreporting of catch and effort is suspected, I would recommend conducting additional simulations specific to the fishery

Identifying species complexes based on spatial and temporal clustering from joint dynamic species distribution models

Data-limited species are often grouped into a species complex to simplify management. Commonalities between species that may indicate if species can be adequately managed as a complex include the following: shared habitat utilization (e.g., overlapping fine-scale spatial distribution), synchrony in abundance trends, consistent fishing pressure or gear susceptibility, or life history parameters resulting in similar productivity. Using non-target rockfish species in the Gulf of Alaska as a case study, we estimate spatial and temporal similarities among species to develop species complexes using the vector autoregressive spatio-temporal (VAST) model, which is a joint dynamic species distribution model. Species groupings are identified using Ward\u27s hierarchical cluster analysis based on spatial and temporal species correlations. We then compare the spatial and temporal groupings with cluster analysis groupings that use exploitation and life history characteristics data. Based on the results, we conclude that there are some rockfish species that consistently group together, but the arrangement and number of clusters differ slightly depending on the data used. Developing species complexes for fisheries management requires a variety of analytical approaches including species distribution models and cluster analyses, and these can be applied across the full extent of available data sources

Oceans of plenty? Challenges, advancements, and future directions for the provision of evidence-based fisheries management advice

Marine population modeling, which underpins the scientific advice to support fisheries interventions, is an active research field with recent advancements to address modern challenges (e.g., climate change) and enduring issues (e.g., data limitations). Based on discussions during the ‘Land of Plenty’ session at the 2021 World Fisheries Congress, we synthesize current challenges, recent advances, and interdisciplinary developments in biological fisheries models (i.e., data-limited, stock assessment, spatial, ecosystem, and climate), management strategy evaluation, and the scientific advice that bridges the science-policy interface. Our review demonstrates that proliferation of interdisciplinary research teams and enhanced data collection protocols have enabled increased integration of spatiotemporal, ecosystem, and socioeconomic dimensions in many fisheries models. However, not all management systems have the resources to implement model-based advice, while protocols for sharing confidential data are lacking and impeding research advances. We recommend that management and modeling frameworks continue to adopt participatory co-management approaches that emphasize wider inclusion of local knowledge and stakeholder input to fill knowledge gaps and promote information sharing. Moreover, fisheries management, by which we mean the end-to-end process of data collection, scientific analysis, and implementation of evidence-informed management actions, must integrate improved communication, engagement, and capacity building, while incorporating feedback loops at each stage. Increasing application of management strategy evaluation is viewed as a critical unifying component, which will bridge fisheries modeling disciplines, aid management decision-making, and better incorporate the array of stakeholders, thereby leading to a more proactive, pragmatic, transparent, and inclusive management framework–ensuring better informed decisions in an uncertain world

Methods for Identifying Species Complexes Using a Novel Suite of Multivariate Approaches and Multiple Data Sources: A Case Study With Gulf of Alaska Rockfish

International and national laws governing the management of living marine resources generally require specification of harvest limits. To assist with the management of data-limited species, stocks are often grouped into complexes and assessed and managed as a single unit. The species that comprise a complex should have similar life history, susceptibility to the fishing gear, and spatial distribution, such that common management measures will likely lead to sustainable harvest of all species in the complex. However, forming complexes to meet these standards is difficult due to the lack of basic biological or fisheries data to inform estimates of biological vulnerability and fishery susceptibility. A variety of cluster and ordination techniques are applied to bycatch rockfish species in the Gulf of Alaska (GOA) as a case study to demonstrate how groupings may differ based on the multivariate techniques used and the availability and reliability of life history, fishery independent survey, and fishery catch data. For GOA rockfish, our results demonstrate that fishing gear primarily defined differences in species composition, and we suggest that these species be grouped by susceptibility to the main fishing gears while monitoring those species with high vulnerabilities to overfishing. Current GOA rockfish complex delineations (i.e., Other Rockfish and Demersal Shelf Rockfish) are consistent with the results of this study, but should be expanded across the entire GOA. Differences observed across species groupings for the variety of data types and multivariate approaches utilized demonstrate the importance of exploring a diversity of methods. As best practice in identifying species complexes, we suggest using a productivity-susceptibility analysis or expert judgement to begin groupings. Then a variety of multivariate techniques and data sources should be used to identify complexes, while balancing an appropriate number of manageable groups. Thus, optimal species complex groupings should be determined by commonality and consistency among a variety of multivariate methods and datasets

Appendix A. Size distributions of coho salmon sampled throughout the summer in four streams.

Size distributions of coho salmon sampled throughout the summer in four streams

The stock assessment theory of relativity: deconstructing the term \u27data-limited\u27 fisheries into components and guiding principles to support the science of fisheries management

The term \u27data-limited fisheries\u27 is a catch-all to generally describe situations lacking data to support a fully integrated stock assessment model. Data conditions range from data-void fisheries to those that reliably produce quantitative assessments. However, successful fishery assessment can also be limited by resources (e.g., time, money, capacity). The term \u27data-limited fisheries\u27 is therefore too vague and incomplete to describe such wide-ranging conditions, and subsequent needs for management vary greatly according to each fishery’s context. Here, we acknowledge this relativity and identify a range of factors that can constrain the ability of analyses to inform management, by instead defining the state of being \u27data-limited\u27 as a continuum along axes of data (e.g., type, quality, and quantity) and resources (e.g., time, funding, capacity). We introduce a tool (the DLMapper) to apply this approach and define where a fishery lies on this relativity spectrum of limitations (i.e. from no data and no resources to no constraints on data and resources). We also provide a ranking of guiding principles, as a function of the limiting conditions. This high-level guidance is meant to identify current actions to consider for overcoming issues associated with data and resource constraints given a specific \u27data-limited\u27 condition. We apply this method to 20 different fisheries to demonstrate the approach. By more explicitly outlining the various conditions that create \u27data-limited situations\u27 and linking these to broad guidance, we aim to contextualize and improve the communication of conditions, and identify effective opportunities to continue to develop and progress the science of \u27limited\u27 stock assessment in support of fisheries management

Oceans of plenty? Challenges, advancements, and future directions for the provision of evidence-based fisheries management advice

Marine population modeling, which underpins the scientific advice to support fisheries interventions, is an active research field with recent advancements to address modern challenges (e.g., climate change) and enduring issues (e.g., data limitations). Based on discussions during the ‘Land of Plenty’ session at the 2021 World Fisheries Congress, we synthesize current challenges, recent advances, and interdisciplinary developments in biological fisheries models (i.e., data-limited, stock assessment, spatial, ecosystem, and climate), management strategy evaluation, and the scientific advice that bridges the science-policy interface. Our review demonstrates that proliferation of interdisciplinary research teams and enhanced data collection protocols have enabled increased integration of spatiotemporal, ecosystem, and socioeconomic dimensions in many fisheries models. However, not all management systems have the resources to implement model-based advice, while protocols for sharing confidential data are lacking and impeding research advances. We recommend that management and modeling frameworks continue to adopt participatory co-management approaches that emphasize wider inclusion of local knowledge and stakeholder input to fill knowledge gaps and promote information sharing. Moreover, fisheries management, by which we mean the end-to-end process of data collection, scientific analysis, and implementation of evidence-informed management actions, must integrate improved communication, engagement, and capacity building, while incorporating feedback loops at each stage. Increasing application of management strategy evaluation is viewed as a critical unifying component, which will bridge fisheries modeling disciplines, aid management decision-making, and better incorporate the array of stakeholders, thereby leading to a more proactive, pragmatic, transparent, and inclusive management framework–ensuring better informed decisions in an uncertain world