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Evaluating model-agnostic post-hoc methods in explainable artificial intelligence: augmenting species distribution

Don Enrico Buebos-Esteve1,2,3, Nikki Heherson A Dagamac4,5,6

  • 1Initiatives for Conservation, Landscape Ecology, Bioprospecting, and Biomodeling (ICOLABB), Research Center for the Natural and Applied Sciences, University of Santo Tomas, España, 1008, Manila, Philippines.

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Summary

This study enhances species distribution models (SDMs) for the endangered Mindoro warty pig by applying explainable AI. Findings reveal local bioclimatic factors decrease predicted presence, urging targeted conservation efforts.

Keywords:
Interpretable machine learningRegularizationSuidSurrogate modelTropical ecology

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Area of Science:

  • Ecology
  • Conservation Biology
  • Artificial Intelligence

Background:

  • Species distribution models (SDMs) are crucial for conservation planning, but often lack local-scale explainability.
  • Existing SDM research primarily focuses on global predictions, neglecting site-specific factors vital for conservation actions.
  • The Mindoro warty pig (Sus oliveri), an endangered endemic species, requires precise habitat assessments for effective conservation.

Purpose of the Study:

  • To bridge the spatial gap in SDM explainability by applying model-agnostic post-hoc methods in explainable AI.
  • To analyze the importance, effects, and interactions of bioclimatic features on SDMs for the Mindoro warty pig at both global and local scopes.
  • To provide actionable insights for the conservation of Mindoro warty pigs by understanding local environmental influences.

Main Methods:

  • Application of model-agnostic explainable AI techniques, including Permutation Feature Importance, SHAP, Accumulated Local Effect, Local Interpretable Model-agnostic Explanations, and Break Down.
  • Development and analysis of SDMs for the Mindoro warty pig across Mindoro Island, Philippines.
  • Comparative analysis of global and local explainability for SDM predictions on potential conservation sites.

Main Results:

  • Global SDM predictions indicate higher elevation and annual precipitation correlate with increased probability of Mindoro warty pig presence.
  • Local explainability methods reveal that the cumulative effect of bioclimatic features within specific 1 km² conservation sites leads to a decrease in predicted presence probability.
  • Annual precipitation was identified as a key driver in the island-wide distribution trend.

Conclusions:

  • There is a critical need for enhanced local monitoring of Mindoro warty pig populations due to decreased predicted presence in key conservation areas.
  • The study successfully extends the SDM pipeline by integrating explainable AI, offering a novel approach to interpreting model predictions.
  • Findings underscore the importance of considering both global and local environmental factors for effective species conservation strategies.