Reliable Identification of Declining Populations in an Uncertain World

Abstract

Assessments of extinction risk based on population declines are widely used, yet scientists have little quantitative understanding of their reliability. Incorrectly classifying whether a population is declining or not can lead to inappropriate conservation actions or management measures, with potentially profound societal costs. Here we evaluate key causes of misclassification of decline status and assess the reliability of 20 decline metrics using a stochastic model to simulate time series of population abundance of sockeye salmon (Oncorhynchus nerka). We show that between-year variability in population productivity (process variation) and, to a lesser extent, variability in abundance estimates (observation error) are important causes of unreliable identification of population status. We found that using all available data, rather than just the most recent three generations, consistently improved the reliability of risk assessments. The approach outlined here can improve understanding of the reliability of risk assessments, thereby reducing concerns that may impede their use for exploited taxa such as marine fishes.