Integrating learning into animal range dynamics under rapid human-induced environmental change

Job Aben* (Corresponding Author), Justin Travis, Hans Van Dyck, Sophie Vanwambeke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Human-induced rapid environmental change (HIREC) is creating environments deviating considerably from natural habitats in which species evolved. Concurrently, climate warming is pushing species’ climatic envelopes to geographic regions that offer novel ecological conditions. The persistence of species is likely affected by the interplay between the degree of ecological novelty and phenotypic plasticity, which in turn may shape an organism's range-shifting ability. Current modelling approaches that forecast animal ranges are characterized by a static representation of the relationship between habitat use and fitness, which may bias predictions under conditions imposed by HIREC. We argue that accounting for dynamic species-resource relationships can increase the ecological realism of range shift predictions. Our rationale builds on the concepts of ecological fitting, the process whereby individuals form successful novel biotic associations based on the suite of traits they carry at the time of encountering the novel condition, and behavioural plasticity, in particular learning. These concepts have revolutionized our view on fitness in novel ecological settings, and the way these processes may influence species ranges under HIREC. We have integrated them into a model of range expansion as a conceptual proof of principle highlighting the potentially substantial role of learning ability in range shifts under HIREC.
Original languageEnglish
Article numbere14367
Number of pages11
JournalEcology Letters
Volume27
Issue number2
Early online date5 Feb 2024
DOIs
Publication statusPublished - Feb 2024

Bibliographical note

Thanks are due to S. Braem for discussions at the early stage of the project and to Stephen C. F. Palmer for implementing the concept of learning in the RangeShifter model. This research was supported by ARC-Research Grant 17/22-086 of Fédération Wallonie-Bruxelles and UCLouvain to H. Van Dyck and S. Vanwambeke. This is publication BRC 414 of the Biodiversity Research Centre (Earth & Life Institute, UCLouvain).

Data Availability Statement

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ele.14367.

DATA AVAILABILITY STATEMENT
No new data were used for this study.

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