Recent advances in the application of stock identification methods have revealed inconsistencies between the spatial structure of biological populations and the definition of stock units used in assessment and management. From a fisheries management perspective, stocks are typically assumed to be discrete units with homogeneous vital rates that can be exploited independently of each other. However, the unit stock assumption is often violated leading to spatial mismatches that can bias stock assessment and impede sustainable fisheries management. The primary ecological concern is the potential for overexploitation of unique spawning components, which can lead to loss of productivity and reduced biodiversity along with destabilization of local and regional stock dynamics. Furthermore, ignoring complex population structure and stock connectivity can lead to misperception of the magnitude of fish productivity, which can translate to suboptimal utilization of the resource. We describe approaches that are currently being applied to improve the assessment and management process for marine fish in situations where complex spatial structure has led to an observed mismatch between the scale of biological populations and spatially-defined stock units. The approaches include: (i) status quo management, (ii) “weakest link” management, (iii) spatial and temporal closures, (iv) stock composition analysis, and (v) alteration of stock boundaries. We highlight case studies in the North Atlantic that illustrate each approach and synthesize the lessons learned from these real-world applications. Alignment of biological and management units requires continual monitoring through the application of stock identification methods in conjunction with responsive management to preserve biocomplexity and the natural stability and resilience of fish species.

Investigations into population structure have been at the forefront of fisheries research for decades, yet it is generally ignored in stock assessment models. As the complex nature of marine population structure has been uncovered, models have attempted to accurately portray it through the development of spatially explicit assessments that allow for movement between subpopulations. Although current tag-integrated movement models are highly complex, many arose from the relatively simple models of Beverton and Holt (1957). This article traces the historical development of these models and compares their features. Originally estimation of movement utilized only tag-recapture models, but now tag-integrated assessment models incorporate several sources of fishery, survey, and tag-recapture information to inform movement estimates. As spatial management measures become more widely used, it is increasingly important that assessment models include the spatial complexities of population structure and patterns of fishery removals, in order for more reliable monitoring of population rebuilding to take place. A generalized metapopulation model is proposed for use in fisheries stock assessment, which allows for adult movement among spatially discrete sub-populations. The input requirements for the model include region-specific catch-at-age, a tag-recapture dataset, and auxiliary information, such as a fishery-independent abundance index.

Fishery management decisions are commonly guided by stock assessment models that aggregate outputs across the spatial domain of the species. With refined understanding of spatial population structures, scientists have begun to address how spatiotemporal mismatches among the scale of ecological processes, data collection programs, and stock assessment methods (or assumptions) influence the reliability and, ultimately, appropriateness of regional fishery management (e.g., assigning regional quotas). Development and evaluation of spatial modeling techniques to improve fisheries assessment and management have increased rapidly in recent years. We overview the historical context of spatial models in fisheries science, highlight recent advances in spatial modeling, and discuss how spatial models have been incorporated into the management process. Despite limited examples where spatial assessment models are used as the basis for management advice, continued investment in fine-scale data collection and associated spatial analyses will improve integration of spatial dynamics and ecosystem-level interactions in stock assessment. In the near future, spatiotemporal fisheries management advice will increasingly rely on fine-scale outputs from spatial analyses.