Space oddity: The mission for spatial integration

Abstract

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.

Introduction to “Space Oddity: Recent Advances Incorporating Spatial Processes in the Fishery Stock Assessment and Management Interface”

“…Improved communication among scientists, managers, and stakeholders about spatial model structure, limitations, available data, and future needs should prove beneficial for bridging the gap from exploratory to operational spatial models (see figure 1 and table 1 in Berger et al 2017).…”

“…Improved abundance indices, which better account for spatial attributes and autocorrelation across the spatial domain, will directly translate to more accurate (and precise) assessment outputs and resulting spatial management advice. Postassessment, geostatistical methods can then be utilized to also improve real-time adaptive spatial management (e.g., bycatch avoidance; see figure 3 in Berger et al 2017). By combining spatial methods, the entire assessment-management framework can be continually refined and improved to account for spatial patterns at each step of the management process.…”

“…Several common difficulties for implementing spatial analyses as the basis of management advice were identified by Berger et al (2017). The main barriers include a lack of information on spatial structure or limited spatially explicit data sets; an inability to develop operational spatial models in the limited time provided during typical stock assessment cycles; a history of institutional inertia such that there is hesitancy to alter the status quo stock Fig.…”

“…The importance of incorporating spatial structure into population dynamics models and resultant management decisions for many aquatic species has been widely recognized for more than half a century (e.g., see the spatial models proposed by Beverton and Holt 1957); however, there has historically been a lack of empirical data to adequately support these types of analyses (Goethel et al 2011). Particularly over the last two decades, the expanding field of spatial ecology has demonstrated the importance of spatial population structure for its role in regulating population productivity, which has led to a growing awareness of the need to incorporate spatial processes into the stock assessment-management interface (i.e., the interaction between scientific advice and resulting management actions; see figure 1 in Berger et al 2017). Acknowledgement of the need to match scientific outputs to the spatiotemporal scale of fisheries management decisions has led to a rapid proliferation of spatial modeling techniques, advances in stock assessment methodology, and analyses that suggest improved policy performance .…”

“…Here, we provide a brief treatment of the main research themes arising in the special issue. A comprehensive review of recent advances in spatial modeling, including further discussion of symposium themes and research presented in the special issue, can be found in Berger et al (2017).…”