The Challenge & Limits of SDMs

  • Critical Challenge: Predicting species’ responses to global environmental change is vital for conservation and management.

  • Usual Tool: Species Distribution Models (SDMs) have been the workhorse, correlating occurrences with environmental variables.

  • Limitations of SDMs:

    • Struggle to predict responses under novel future conditions (Pagel and Schurr 2012).
    • Lack Mechanism: They do not explicitly model the underlying biological processes.
    • Equilibrium Assumption: Often violated (Guisan and Thuiller 2005).

Improving the Forecast

What Traditional SDMs Do

DRMs: A Mechanistic Approach

The drmr package

  • What is it?: An open-source R package for fitting (and forecasting) Dynamic Range Models in a Bayesian framework.

  • Key Features:

    • Leverages Stan via cmdstanr for efficient fitting (Gabry et al. 2024).
    • User-friendly interface.
    • Easily relate environmental drivers to recruitment and survival rates.

A diagram

A diagram

Case Studies

  • We compared a DRM fit with the drmr package to a traditional SDM using data for: Summer flounder (Paralichthys dentatus) and Red-bellied woodpecker (Melanerpes carolinus).

  • Out-of-sample predictions:

    • Summer flounder: The RMSE was 43% lower and the IS was 37% lower when compared to a traditional SDM.
    • Red-bellied woordpecker: The improvement on the predictions was modest.

Environment-Dependent Demographic Rates

Concluding remarks

  • Summary: The drmr substantially lowers the barrier for ecologists to use the DRM in their applications.

  • Impact: A user-friendly tool which enables:

    • Testing which mechanistic hypotheses are more likely to drive speces distributions changes;
    • Generate more robust ecological forecasts

  • Availability: drmr is open-source and will be available on GitHub once the review process is over.

Acknowledgments

References

Fordham, D. A., Haythorne, S., Brown, S. C., Buettel, J. C., and Brook, B. W. (2021), “poems: R package for simulating species’ range dynamics using pattern-oriented validation,” Methods in Ecology and Evolution, 12, 2364–2371. https://doi.org/https://doi.org/10.1111/2041-210X.13720.
Gabry, J., Češnovar, R., Johnson, A., and Bronder, S. (2024), cmdstanr: R interface to CmdStan.
Guisan, A., and Thuiller, W. (2005), “Predicting species distribution: Offering more than simple habitat models,” Ecology letters, Wiley Online Library, 8, 993–1009. https://doi.org/https://doi.org/10.1111/j.1461-0248.2005.00792.x.
Malchow, A.-K., Bocedi, G., Palmer, S. C. F., Travis, J. M. J., and Zurell, D. (2021), “RangeShiftR: An r package for individual-based simulation of spatial eco-evolutionary dynamics and species’ responses to environmental changes,” Ecography, 44, 1443–1452. https://doi.org/10.1111/ecog.05689.
Markowska, K., Malinowska, K., and Kuczyński, L. (2025), “Rangr: An r package for mechanistic, spatially explicit simulation of species range dynamics,” Methods in Ecology and Evolution, 16, 468–476. https://doi.org/10.1111/2041-210X.14475.
Pagel, J., and Schurr, F. M. (2012), “Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics,” Global Ecology and Biogeography, 21, 293–304. https://doi.org/https://doi.org/10.1111/j.1466-8238.2011.00663.x.
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