Predicting Avian Observation Patterns in Mediterranean Wetlands: Spatiotemporal Deep Learning Fusion of Multi-Source Surveys, Citizen Science, and Autonomous Acoustic Sensing

Protected wetlands in the Mediterranean are vital biodiversity hotspots that face growing pressure from climate change and human activity. Traditional bird monitoring relies on professional field surveys, which, although highly standardized, are resource-constrained and limited in temporal frequency. Here, we present ValWet-Birds, a multi-source spatiotemporal dataset and fusion framework that harmonises and integrates professional counts, eBird citizen science registries, and passive acoustic monitoring (via a BirdNET classifier deployed on a Raspberry Pi 5 node) for three protected wetlands in Alicante, Spain (El Hondo, Santa Pola, and La Mata–Torrevieja), from 2010 to 2025. We normalize incompatible observation protocols into a unified monthly relative observation proportion target across 12 sub-regions and 394 taxa (349 resolved to species level after a taxonomic audit). Spatiotemporal prediction models are implemented using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks. A preliminary evaluation using non-matched test sets suggested a 42.7% reduction in Test Mean Squared Error for the LSTM under multi-source augmentation. Re-evaluating every source condition on an identical held-out set of professional-census observations shows that this benefit does not hold in aggregate: both baselines outperform naive temporal reference predictors by a wide margin, but adding eBird and BirdNET data does not reduce error relative to census-only training overall. The exception is conservation-relevant taxa (a 50-species subset cross-referenced against Annex I of the EU Birds Directive), for which multi-source augmentation reduces LSTM error by 29% and MLP error by 9%, plausibly because these less-common species have sparser census history to draw on. We report this reversal explicitly as a methodological finding in its own right. Finally, we describe the design and interactive user flows of the deployed web visualization platform Avistory, which presents observation summaries and model outputs; it should not be interpreted as a validated population-monitoring or conservation-decision system.