Linking habitat and population dynamics to inform conservation benchmarks for data-limited salmon stocks

Abstract

AbstractManagement of data-limited populations is a key challenge to the sustainability of fisheries around the world. For example, sockeye salmon (Oncorhynchus nerka) spawn and rear in many remote coastal watersheds of British Columbia (BC), Canada, making population assessment a challenge. Estimating conservation and management targets for these populations is particularly relevant given their importance to First Nations and commercial fisheries. Most sockeye salmon have obligate lake-rearing as juveniles, and total abundance is typically limited by production in rearing lakes. Although methods have been developed to estimate population capacity based on nursery lake photosynthetic rate (PR) and lake area or volume, they have not yet been widely incorporated into stock-recruit analyses. We tested the value of combining lake-based capacity estimates with traditional stock-recruit based approaches to assess population status using a hierarchical-Bayesian stock-recruit model for 70 populations across coastal BC. This analysis revealed regional variation in sockeye population productivity (Ricker α), with coastal stocks exhibiting lower mean productivity than those in interior watersheds. Using moderately-informative PR estimates of capacity as priors reduced model uncertainty, with a more than five-fold reduction in credible interval width for estimates of conservation benchmarks (e.g. SMAX - spawner abundance at carrying capacity). We estimated that almost half of these remote sockeye stocks are below one commonly applied conservation benchmarks (SMSY), despite substantial reductions in fishing pressure in recent decades. Thus, habitat-based capacity estimates can dramatically reduce scientific uncertainty in model estimates of management targets that underpin sustainable sockeye fisheries. More generally, our analysis reveals opportunities to integrate spatial analyses of habitat characteristics with population models to inform conservation and management of exploited species where population data are limited.