Evaluation of uncertainty in Fraser sockeye (Oncorhynchus nerka) Wild Salmon Policy status using abundance and trends in abundance metrics

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Abstract

The Department of Fisheries and Oceans (DFO) Wild Salmon Policy (WSP) goal is “to restore and maintain healthy salmon populations and their habitats for the benefit and enjoyment of the people of Canada in perpetuity” (Fisheries and Oceans Canada 2005). In order to achieve this goal, the WSP outlines a number of strategies, including ‘Strategy 1: standardized monitoring of wild salmon statuses’, which is the subject of this paper. In the current paper, Fraser Sockeye (Oncorhynchus nerka) conservation units (CUs) from ‘WSP Action Step 1.1: the identification of conservation units’ are used to update ‘Action Step 1.2: the development of criteria to assess CUs and identify benchmarks to represent biological statuses’, and to address ‘Action Step 1.3: CU status assessment’, for the 22 current CUs and two de novo ‘CUs’. Using a previously developed toolkit for CU status assessment (Holt et al. 2009; Holt 2009), abundance benchmarks (unique to each CU) were estimated for each CU with stock-recruitment data, and trends in abundance upper and lower benchmarks (common across all CUs) were modified to apply to Fraser Sockeye. These benchmarks were used to delineate the three WSP biological status zones (Red, Amber, and Green). Abundance benchmarks were estimated across a range of stock-recruitment models, including the standard Ricker model that assumes constant productivity and other Ricker model forms that assume time varying productivity. Consideration of time varying productivity in the estimation of abundance benchmarks was important since most Fraser Sockeye CUs have exhibited systematic declines in productivity over recent decades (Grant et al. 2011) and extirpation risk can increase when a CUs productivity is linearly decreasing or low (Holt 2009; Holt and Bradford 2011). Abundance benchmarks were also estimated across a range of probability levels to reflect uncertainty in the estimation process. Estimates of a CU’s spawner abundances at maximum juvenile production (Smax) were also updated and used as carrying capacity priors in Ricker models, where available and appropriate. In the evaluation of status using the abundance metric, both the geometric and arithmetic means of the recent CU abundance were compared against benchmarks. Since multiple metrics (one abundance and three trends in abundance metrics, depending on the CU) and uncertainty in abundance benchmarks are presented in the current paper, statuses for a single CU can comprise all three WSP status zones. Status integration will be explored in future processes and publications.