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Updated: Nov 28, 2025

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How decisions about fitting species distribution models affect conservation outcomes.

Angela Muscatello1, Jane Elith1, Heini Kujala1,2

  • 1School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.

Conservation Biology : the Journal of the Society for Conservation Biology
|November 25, 2020
PubMed
Summary

Species distribution models (SDMs) used in conservation planning create uncertainty. Decisions in model building, like bias treatment and thresholds, significantly alter conservation priority maps for species.

Keywords:
conservation planningestablecimiento de umbralesincertidumbreland-use planningmodelos de distribución de especiesobservation biasplaneación de la conservaciónplaneación del uso de suelopriorización espacialsesgo de observaciónspatial prioritizationspecies distribution modelsthresholdinguncertainty

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Area of Science:

  • Ecology
  • Conservation Biology
  • Biodiversity Informatics

Background:

  • Species distribution models (SDMs) are crucial tools for conservation and land-use planning, informing biodiversity patterns.
  • The construction of SDMs involves numerous choices (data prep, variable selection, fitting, evaluation) that impact predictive outcomes.
  • A significant challenge is the lack of true species distribution data, leading to uncertainty in model selection and application.

Purpose of the Study:

  • To analyze the impact of 11 routine decisions in SDM construction on conservation priority patterns.
  • To quantify the relative influence of different modeling choices on conservation planning outcomes across multiple species.

Main Methods:

  • Employed MaxEnt for building SDMs and Zonation for deriving conservation priority ranks for 25 species.
  • Systematically varied parameters including model complexity, predictor variables, bias treatment, and threshold settings.
  • Evaluated changes in conservation priority rankings based on different SDM configurations.

Main Results:

  • All tested SDM variants demonstrated good performance (AUC > 0.7) but yielded spatially divergent predictions and conservation priorities.
  • Decisions regarding bias treatment and binary threshold application had the most substantial impact, altering rankings in 40% and 35% of cells, respectively.
  • Model complexity, data quantity, and bias correction methods also influenced conservation solutions, with varying degrees of impact on priority area overlap.

Conclusions:

  • Model-based uncertainty is inherent in SDM applications for conservation planning.
  • Understanding and addressing uncertainty, particularly concerning decisions with high impact, is critical for robust conservation strategies.
  • Planners should consider alternative modeling approaches and uncertainty quantification when making critical land-use and conservation decisions.