Using historical catch data to evaluate predicted changes in fish relative abundance in response to a warming climate

Abstract

Using models to predict future changes in species distributions in response to projected climate change is a common tool to aid management and species conservation. However, the assumption underlying this approach, that ecological processes remain stationary through time, can be unreliable, and more empirical tests are needed to validate predictions of biotic outcomes of global change. The scarcity of reliable historical and long‐term datasets can make these tests difficult. Moreover, incorporating abundance and multiple sampling methods can improve model predictions and usability for management. Our study 1) provides insight into how well models can predict environmental change under a warming climate, 2) incorporates multiple sampling gears and abundance data in modeling to better capture changes in populations and 3) shows the value of historical datasets for improving predictive models of population change. We used contemporary (2003–2019) and historical (1936–1964) abundance datasets of the North American fish largemouth bass Micropterus salmoides in lakes across the state of Michigan, USA. We developed Bayesian hierarchical models that leverage the use of multiple gears in contemporary lake surveys to estimate the relative catchabilities of largemouth bass for each gear and hindcast the models to predict historical abundance. Our estimates of relative density change over time were correlated with temperature change over time; increasing surface water temperature led to increasing largemouth bass density. Hindcasting models to historical lake temperatures performed similarly in predicting historical density to models predicting contemporary density. Our results suggest that models built using spatial environmental gradients can reliably predict population changes through time. Understanding the sampling methods and the environmental context of observational datasets can help researchers test for potential sampling biases and identify confounding factors that will improve predictions of future impacts of environmental change.