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

TL;DR

This paper introduces a transfer learning approach that leverages capture-recapture data to improve temporal abundance estimates from CPUE data by accounting for variable detection probabilities.

Contribution

It presents a novel method to transfer detection functions learned from CR data to CPUE models, enhancing abundance inference across large spatial and temporal scales.

Findings

Simulation study shows improved abundance estimates.

Method enhances detection of temporal trends.

Application to smallmouth bass fisheries demonstrates practical utility.

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.