Demography–environment relationships improve mechanistic understanding of range dynamics under climate change
Abstract
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’.
What the paper shows and why it matters
(AI-generated)
Shifts in species abundance and range under climate change arise from local population dynamics and dispersal. Process-based models are expected to predict such shifts more reliably than purely correlative models. Hybrid models add such dynamics to a habitat model, but assume that demography scales with a habitat suitability index, and the link of such indices to abundance or growth has been questioned. Measuring demographic rates along climate gradients would give the climate–demography relationship directly but is rarely done for animals. As an alternative, Pagel and Schurr (2012) proposed estimating it inversely, by fitting a process-based model to abundance data, but assumed logistic growth, a single growth rate combining all demographic rates, for local dynamics. The problem the paper takes on is whether separate demographic rates and their climate responses can be inferred from distribution and abundance data.
The paper shows that the climate responses of juvenile survival, adult survival and fecundity can be estimated together with dispersal from abundance time series, using individual-based models (which simulate each female separately) fitted in a Bayesian framework with priors from published trait data, here for eight Swiss breeding bird species. The models were calibrated to 267 survey sites (1999–2019); their predicted ranking of abundances agreed with the counts at 72–88% (c-index). Compared with Briscoe et al. (2021) on Swiss birds, they predicted changes over time more accurately than static species distribution models, but spatial patterns less accurately than these and than dynamic occupancy models. Comparing runs with the observed climate and with no climate change suggested that four species, among them the mountain species ring ouzel and alpine accentor, were more abundant in 2017–2019 than they would otherwise have been, and four less so. Some fitted response curves had two peaks, which the paper calls physiologically implausible and links to limited identifiability, model structural error or missing predictors.
The paper concludes that calibrating detailed process-based models to survey data is feasible, can improve predictions of range and population dynamics, and is promising for attributing observed trends to ecological and environmental processes, including climate change, a possible basis for targeted conservation. Because the fitted relationships remain partly phenomenological, it advises checking them for plausibility before projecting into the future or drawing management conclusions. It advocates adding measured relationships, for example as informative priors, and testing further predictors and model structures.
References
- Pagel & Schurr (2012). Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography 21, 293–304. doi:10.1111/j.1466-8238.2011.00663.x
- Briscoe et al. (2021). Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds. Global Change Biology 27, 4269–4282. doi:10.1111/gcb.15723