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 observational 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. This architecture explicitly addresses the challenges of ecological data sparsity, heterogeneity, and irregular sampling. We apply our model to the Crown-of-Thorns Starfish (COTS) on Australia's Great Barrier Reef, a species with devastating impact on coral reefs and a major target of pest control programs. Our method significantly outperforms baseline approaches and the current resource-intensive approach, manta-tow surveillance, in both accuracy and detectability. Simulations indicate a 20\% increase in starfish removal efficiency over a year, enabling more effective coral protection. This work demonstrates how tailored deep learning methods can overcome ecological data limitations and substantially improve conservation outcomes. The code is available at \url{https://github.com/XXX}.