A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models

TL;DR

This paper introduces a novel concept-based XAI method for species distribution models using high-resolution landscape data, enhancing ecological insights and model validation.

Contribution

It presents the first implementation of concept-based XAI for SDMs, with a new open-access landscape concept dataset and demonstrates its utility in ecological case studies.

Findings

Concept-based XAI validates SDMs against expert knowledge.

Uncovers new ecological hypotheses through landscape concept influence.

Provides landscape-level insights useful for policy and land management.

Abstract

Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are the primary tools for this task, serving two purposes: achieving robust predictive performance while providing ecological insights into the driving factors of distribution. However, the increasing complexity of deep learning SDMs has made extracting these insights more challenging. To reconcile these objectives, we propose the first implementation of concept-based Explainable AI (XAI) for SDMs. We leverage the Robust TCAV (Testing with Concept Activation Vectors) methodology to quantify the influence of landscape concepts on model predictions. To enable this, we provide a new open-access landscape concept dataset derived from high-resolution multispectral and LiDAR drone imagery. It includes 653 patches across 15 distinct landscape concepts and 1,450 random reference patches, designed to suit a wide range of species. We demonstrate this approach through a case study of two aquatic insects, Plecoptera and Trichoptera, using two Convolutional Neural Networks and one Vision Transformer. Results show that concept-based XAI helps validate SDMs against expert knowledge while uncovering novel associations that generate new ecological hypotheses. Robust TCAV also provides landscape-level information, useful for policy-making and land management. Code and datasets are publicly available.