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Demography–environment relationships improve mechanistic understanding of range dynamics under climate change
Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography–climate relationships from distribution and abu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225853/ https://www.ncbi.nlm.nih.gov/pubmed/37246385 http://dx.doi.org/10.1098/rstb.2022.0194 |
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author | Malchow, A.-K. Hartig, F. Reeg, J. Kéry, M. Zurell, D. |
author_facet | Malchow, A.-K. Hartig, F. Reeg, J. Kéry, M. Zurell, D. |
author_sort | Malchow, A.-K. |
collection | PubMed |
description | Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography–climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes—juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’. |
format | Online Article Text |
id | pubmed-10225853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-102258532023-05-30 Demography–environment relationships improve mechanistic understanding of range dynamics under climate change Malchow, A.-K. Hartig, F. Reeg, J. Kéry, M. Zurell, D. Philos Trans R Soc Lond B Biol Sci Articles Species respond to climate change with range and abundance dynamics. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. Here, we aim to infer demography–climate relationships from distribution and abundance data. For this, we developed spatially explicit, process-based models for eight Swiss breeding bird populations. These jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes—juvenile survival, adult survival and fecundity. The models were calibrated to 267 nationwide abundance time series in a Bayesian framework. The fitted models showed moderate to excellent goodness-of-fit and discriminatory power. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Contemporary climate change benefitted the population trends of typical mountain birds leading to lower population losses or even slight increases, whereas lowland birds were adversely affected. Our results emphasize that generic process-based models embedded in a robust statistical framework can improve our predictions of range dynamics and may allow disentangling of the underlying processes. For future research, we advocate a stronger integration of experimental and empirical studies in order to gain more precise insights into the mechanisms by which climate affects populations. This article is part of the theme issue ‘Detecting and attributing the causes of biodiversity change: needs, gaps and solutions’. The Royal Society 2023-07-17 2023-05-29 /pmc/articles/PMC10225853/ /pubmed/37246385 http://dx.doi.org/10.1098/rstb.2022.0194 Text en © 2023 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Malchow, A.-K. Hartig, F. Reeg, J. Kéry, M. Zurell, D. Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title | Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title_full | Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title_fullStr | Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title_full_unstemmed | Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title_short | Demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
title_sort | demography–environment relationships improve mechanistic understanding of range dynamics under climate change |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10225853/ https://www.ncbi.nlm.nih.gov/pubmed/37246385 http://dx.doi.org/10.1098/rstb.2022.0194 |
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