Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

2026-06-25

AI Summary

This study addresses the reliability of deep learning models in ecological image analysis, which often rely on spurious correlations or dataset biases, thereby compromising conservation decisions. For the first time, explainable artificial intelligence (XAI) is systematically integrated into ecological vision tasks as a standard validation component to visualize and diagnose model reasoning processes, supporting the verification and refinement of image classification, object detection, and segmentation models. Applied to aerial imagery—such as harbor seal detection and cetacean anatomical segmentation—XAI reveals biologically plausible features leveraged by models, identifies misclassifications caused by background clutter, shape ambiguity, and occlusion, and informs targeted data augmentation and retraining strategies. This approach effectively aligns model decisions with ecological knowledge, substantially enhancing the trustworthiness and practical utility of AI in biodiversity conservation.

Abstract

Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems. These tools can expand the scale and speed of conservation assessments, yet many computer vision models remain difficult to inspect, making it challenging to determine whether predictions are based on ecologically meaningful signals or on spurious correlations, sampling biases, and other artifacts that may undermine conservation decisions. We argue that explainable artificial intelligence (XAI) should become a standard component of ecological model validation because conservation practitioners increasingly depend on understanding not only whether a model is accurate, but why it is accurate. We provide practical guidance for applying XAI to three common ecological computer vision tasks: image classification, object detection, and image segmentation. To illustrate how XAI can support ecological model auditing, refinement, and deployment, we present two case studies using aerial imagery: harbor seal detection and cetacean anatomical segmentation. These examples demonstrate how explanation methods can identify biologically meaningful cues, reveal false positives driven by background and shape confounds, uncover edge and occlusion effects, and guide data collection, augmentation, and retraining strategies. More broadly, they show how explainability can help assess whether model reasoning aligns with ecological understanding. We conclude by identifying key challenges and opportunities. By making model behavior more transparent and scientifically interrogable, XAI can help ensure that AI-supported ecological evidence is more reliable, understandable, and actionable for biodiversity conservation.

Problem

• Explainable AI
• Biodiversity Monitoring
• Ecological Image Analysis
• Model Interpretability
• Conservation Decision-making

Innovation

• Explainable AI
• Biodiversity Monitoring
• Ecological Image Analysis
• Model Interpretability
• Computer Vision