Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

Abstract

Species responses to climate change are widely detected as range and abundance changes. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. We built spatially-explicit, process-based models for eight Swiss breeding bird populations. They jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes - juvenile survival, adult survival and fecundity. The models were calibrated to two-decade abundance time-series in a Bayesian framework. We assessed goodness-of-fit and discriminatory power of the models with different metrics, indicating fair to excellent model fit. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Maps of overall growth rate highlighted demographically suitable areas. Further, benefits from contemporary climate change were detected for typical mountain birds, whereas lowland birds were adversely affected. Embedding generic process-based models in a solid statistical framework improves our mechanistic understanding of range dynamics and allows disentangling the underlying abiotic and biotic processes. For future research, we advocate a stronger integration of experimental and empirical measurements and more detailed predictors in order to generate precise insights into the processes by which climate affects populations.

Range and climate niche shifts in European and North American breeding birds

“…Our results can neither corroborate nor refute these findings but suggest time-lagged responses and transient dynamics as potential explanation for such misalignment. In the future, mechanistic models that explicitly consider important ecological processes such as dispersal, demography and species interactions [8,13] could help in attributing trends to different abiotic drivers and biotic processes [70,71].…”

Detecting and attributing the causes of biodiversity change: needs, gaps and solutions

“…Malchow et al . [53] analyse trends in the distribution and abundance of different Swiss breeding birds using spatially explicit, mechanistic models. Models are calibrated within a Bayesian statistical framework and jointly considered the effect of dispersal and demographic processes on observed range and population dynamics.…”

Demography–environment relationships improve mechanistic understanding of range dynamics under climate change

“…All scripts and data required to run the presented analyses can be accessed from the public GitHub repository https://github.com/UP-macroecology/Malchow_Demo gEnv_2022 or from the Zenodo archive with doi:10.5281/zenodo. 7830083 [68]. The used R packages are open-source software.…”

“…The study uses data from the Swiss breeding bird survey and the Swiss breeding bird index provided by the Swiss ornithological institute, Sempach. All scripts and data required to run the presented analyses can be accessed from the public GitHub repository or from the Zenodo archive with doi:10.5281/zenodo.7830083 10.5281/zenodo.7830083 [ 68 ]. The used R packages are open-source software.…”