Modeling the effects of climate change scenarios on the potential distribution of Vespa crabro Linnaeus, 1758 (Hymenoptera: Vespidae) in a Mediterranean biodiversity hotspot - Erika Bazzato; Arturo Cocco; Emanuele Salaris; Ignazio Floris; Alberto Satta; Michelina Pusceddu (Ecological Informatics)
Climate change poses unprecedented challenges to ecosystems and species, particularly in biodiversity hotspots like the European-Mediterranean regions. The ecological consequences are not easily discernible. Although the influence of climate on plants and vertebrates has been extensively studied, its impact on alien insects, especially social wasps, remains underexplored. To address this gap, this study identifies climatically suitable habitats for Vespa crabro under current conditions, projects its potential future distribution, and assesses potential range shifts driven by climate change to guide monitoring programs and management measures. We focused on Sardinia, a Mediterranean island with a heterogeneous morphological, geological, and climatic pattern, where V. crabro was accidentally introduced.We used 316 verified citizen science occurrences, high-resolution bioclimatic variables (40 × 40 m) specifically developed for the island, and two future climate and socio-economic scenarios for two temporal horizons (2040 and 2060) to model climatic suitability using an ensemble framework with three machine learning algorithms: Artificial Neural Networks (ANN), Generalized Boosting Model (GBM), and Random Forest (RF). To ensure reliable predictions, we addressed several technical challenges, including correcting for sampling biases and spatial autocorrelation. The individual models were weighted based on spatial cross-validation performance and combined to obtain the ensemble model.Performance varied among 150 individual models (3 algorithms × 10 replicates × 5 folds), depending on the algorithms, replicates, and subsets selected for training and testing. RF demonstrated the highest predictive performance, outperforming ANN and GBM. The ensemble model achieved even higher predictive accuracy with Receiver Operating Characteristics (ROC) = 0.95 ± 0.02 and True Skill Statistic (TSS) = 0.78 ± 0.06.Key factors influencing V. crabro distribution included Annual Mean Temperature, Maximum Temperature of Warmest Month, Temperature Annual Range, Precipitation of Driest Month, and Precipitation Seasonality. Currently, climatically suitable habitats are predominantly in the northern part of the island, in most coastal areas, and in specific inland regions, especially those near or inside mountainous areas. Future projections indicate a distribution range contraction by the 2040s and 2060s, primarily driven by extreme variability in precipitation and rising temperatures approaching the species' thermal tolerance limits.Our study demonstrates the value of integrating citizen science data, high-resolution climate data, and advanced modeling techniques to understand and manage alien species in the context of climate change. It highlights the importance of fine-scale studies to complement broader analyses, providing deeper insight into the impacts of climate change on species distribution, especially in heterogeneous areas like those in the Mediterranean.