Accounting for variable detection functions in temporal abundance modeling via transfer learning

2026-05-08

AI Summary

Traditional CPUE models assume constant detection probability and overlook individual and environmental heterogeneity, leading to biased abundance estimates. This study pioneers the integration of transfer learning into ecological abundance modeling by leveraging small-scale capture–recapture (CR) data to construct a transferable detection function that incorporates environmental covariates. This function is then embedded within a large-scale CPUE time-series model, enabling effective fusion of absolute and relative abundance data. Both simulation and empirical analyses demonstrate that the proposed approach substantially improves the accuracy of largemouth bass population abundance estimates and enhances the detection of temporal trends, thereby overcoming a key limitation of conventional models that ignore variability in detection probability.

Abstract

Relative abundance, measured as the number of animals caught per unit of sampling effort (CPUE), is commonly used to monitor fish and wildlife populations, largely because sampling methods are cost-effective to implement. Modeling relative abundance, however, requires the assumption that the detection probability is constant across sampling events. This assumption is likely not valid, as the probability of detection often varies as a function of several factors, including the characteristics of individual animals and environmental conditions at the time of sampling. In contrast, methods to estimate absolute abundance, such as capture-recapture (CR), account for variable detection, but are often infeasible to implement across large spatiotemporal scales. Despite this, CR data are sometimes available for species of interest, albeit at smaller spatiotemporal extents. Leveraging information on detection probabilities from CR data to help inform estimates of widely available CPUE data could strengthen inferences about the status of fish and wildlife populations. We propose an approach to (i) learn the effect of environmental covariates on detection probabilities from CR data and (ii) transfer these detection functions to CPUE models for improved inference. Shown empirically through a simulation study, this approach improves estimates of abundance and the ability to detect temporal trends. We apply our transfer learning method using CR and CPUE data to recreationally important smallmouth bass (\textit{Micropterus dolomieu}) fisheries in Pennsylvania, USA rivers.

Problem

• relative abundance
• detection probability
• capture-recapture
• CPUE
• transfer learning

Innovation

• transfer learning
• detection probability
• relative abundance
• capture-recapture
• CPUE