Journal article
Climate-driven context-dependent structure of population cycles
Royal Society open science, Vol.11(8), 240047
08/28/2024
DOI: 10.1098/rsos.240047
Abstract
Multiannual population cycles of small mammals are of interest within population biology. We propose an approach for multidimensional autoregressive (AR) time series and analyse monitoring data on grey-sided voles (Myodes rufocanus) in Japan to investigate one or possibly multiple multiannual cycles that drive population dynamics. Temperature, through modifying rodent communities, is found to be a key factor shaping population dynamics. Warmer areas are the main habitat for other rodent species resulting in low vole abundance/dominance, as opposed to higher vole dominance in colder areas—a pattern associated with the AR structure and population cycle. Vole populations in simple rodent communities exhibit an AR(2) cycle of 2–3 years. In areas with complex rodent communities, vole dynamics follows an AR(4) process and a combination of two cycles with different lengths. The AR structure varies in relatively small spatial scales, thus widening the scope of AR analyses needed. Historically, vole abundance increased in the late 1970s and decreased from the 1980s, with warm winters shown to be associated with the decline of vole abundance in the AR(4) populations. This significant association between the AR order, population dynamics, temperature and rodent community provides insights into the declining trends observed in rodent populations of the Northern Hemisphere.
Details
- Title: Subtitle
- Climate-driven context-dependent structure of population cycles
- Creators
- Noelle I. Samia - Northwestern UniversityOsnat Stramer - University of IowaTakashi Saitoh - Hokkaido UniversityNils Chr Stenseth - University of Oslo
- Resource Type
- Journal article
- Publication Details
- Royal Society open science, Vol.11(8), 240047
- Publisher
- The Royal Society
- DOI
- 10.1098/rsos.240047
- ISSN
- 2054-5703
- eISSN
- 2054-5703
- Grant note
- ;
- Language
- English
- Date published
- 08/28/2024
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984699518102771
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