Forecasting rodent population dynamics and community transitions with dynamic nonlinear models

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Nicholas Joshua Clark, SKM Ernest, Henry Senyondo, Juniper Simonis, Ethan P White, Glenda M Yenni, KANK Karunarathna


Ecological communities are dynamic. These dynamics are influenced by many sources of variation, making it difficult to understand or predict future change. Biotic interactions, and other sources of multi-species dependence, are major contributors. But ecological prediction overwhelmingly focuses on models that treat individual species in isolation. Here, we model the relative importance of nonlinear environmental responses and multi-species temporal dependencies for a community of semi-arid rodents. We use a hierarchical, Dynamic Generalized Additive Model (DGAM) to analyze monthly capture time series for nine rodents across a 25-year period. A vector autoregression to model unobserved trends allowed us to ask targeted questions about population dynamics. We find that multi-species dependencies are important for capturing unmeasured drivers of community change. Variation in captures for some species are expected to have delayed, often nonlinear effects on captures for others. These complexities are useful for inference but also for prediction. Models that captured multi-species dependence gave better near-term forecasts of community change than models that ignored it. We also quantify nonlinear effects of temperature change and positive effects of vegetation greenness on captures for nearly all species. Models that describe biological complexity, both through nonlinear covariate functions and multi-species dependence, are useful to ask targeted questions about population dynamics and drivers of change.



Desert Ecology, Ecology and Evolutionary Biology, Life Sciences, Multivariate Analysis, Population Biology, Statistical Methodology, Statistical Models, Statistics and Probability


ecological forecasting, Generalized additive model, population dynamics, regime shift, time series, vector autoregression


Published: 2023-03-13 06:26

Last Updated: 2023-03-13 10:26


CC BY Attribution 4.0 International

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Data and Code Availability Statement:
R code to replicate data extraction / analyses is included in supplementary materials and will be uploaded to Zenodo on acceptance

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