Challenges and opportunities for using natural history collections to estimate insect population trends. Issue 2 (27th June 2022)

• Record Type: Journal Article
• Authors: Davis, Courtney L.; Guralnick, Robert P.; Zipkin, Elise F.
• Is Part Of: Journal of animal ecology. Volume 92:Issue 2(2023)
• Page Start: 237
• Page End: 249
• Publication Date: 2022-06-27
• Subjects: butterflies -- citizen science -- data integration -- museum records -- population trends; Animal ecology -- Periodicals; 591.7
• DOI: 10.1111/1365-2656.13763
• Languages: English
• ISSNs: 0021-8790

Abstract

Abstract: Natural history collections (NHC) provide a wealth of information that can be used to understand the impacts of global change on biodiversity. As such, there is growing interest in using NHC data to estimate changes in species' distributions and abundance trends over historic time horizons when contemporary survey data are limited or unavailable. However, museum specimens were not collected with the purpose of estimating population trends and thus can exhibit spatiotemporal and collector‐specific biases that can impose severe limitations to using NHC data for evaluating population trajectories. Here we review the challenges associated with using museum records to track long‐term insect population trends, including spatiotemporal biases in sampling effort and sparse temporal coverage within and across years. We highlight recent methodological advancements that aim to overcome these challenges and discuss emerging research opportunities. Specifically, we examine the potential of integrating museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) in a unified analytical framework that accounts for the sampling biases associated with each data source. The emerging field of integrated modelling provides a promising framework for leveraging the wealth of collections data to accurately estimate long‐term trends of insect populations and identify cases where that is not possible using existing data sources. Abstract : This paper examines the potential of integrating digitized museum records and other contemporary data sources (e.g. collected via structured, designed surveys and opportunistic citizen science programs) to estimate long‐term population trends in a unified analytical framework that explicitly accounts for the unique sampling biases associated with each data source.