Leveraging Sparse Observations to Predict Species Abundance Across Space and Time
Md Zahidul Islam, Cameron S. Fletcher, Ke Sun, Amir Dezfouli, Iadine Chades
Abstract
Biodiversity is declining globally at an unprecedented rate. Managers urgently need to allocate limited resources to control pest species where interventions have the highest ecological impact. However, many species are hard to detect, and data collection is often expensive, irregular, and incomplete, thus posing significant challenges for machine learning models that traditionally require large and regular datasets. We present a novel deep learning architecture that estimates the spatiotemporal abundance of hard-to-detect species from sparse, zero-inflated, and irregular data. Our method combines Graph Convolutional Networks (GCNs) to model spatial dependencies across monitoring sites with Recurrent Neural Networks (RNNs) to capture long-range temporal dynamics explicitly addresses the challenges of data sparsity, heterogeneity, and irregular sampling. We apply our model to the Crown-of-Thorns Starfish (COTS) on Australia
BibTeX
@inproceedings{aaai2026_leveragingsparse,
title = {Leveraging Sparse Observations to Predict Species Abundance Across Space and Time},
author = {Md Zahidul Islam and Cameron S. Fletcher and Ke Sun and Amir Dezfouli and Iadine Chades},
booktitle = {AAAI 2026},
year = {2026}
}