Evaluating the Importance of Region-Specific Bioclimatic Datasets in Projecting the Future Distribution of Lissachatina fulica in Complex Landscapes

The invasive alien, African giant snail, Lissachatina fulica, considered as a pest, poses a significant threat to ecosystems, human health and agriculture across tropical and subtropical regions. Therefore, in order to address the challenge posed by the presence of this animal outside its original habitat it is essential to to understand its current and potential future distribution. Thus, this study, which highlights the critical role of regional bioclimatic datasets in improving the predictive accuracy of species distribution models (SDMs) particularly for invasive species in ecosystems with complex orography or climate, takes advantage of global and regional bioclimatic datasets to model the future distribution of L. fulica in the Canary Islands, emphasizing the influence of the archipelago’s complex orography and unique microclimates.

Our approach integrates two distinct datasets as input of the SDM Maxent. First, we used the global distribution of L. fulica from GBif and a list of bioindicators taken from the WorldClim and Chelsa datasets to train the model, which allowed us to capture the environmental niche of the species under various climatic conditions. We then applied the BICI-ULL dataset, a high-resolution bioclimatic dataset specifically developed for the Canary Islands that accounts for the intricate topography and varied microclimates of the archipelago, providing an unprecedented resolution for regional analyses. This dataset allows us to perform our projections in two different future periods, mid- (2041-2060) and end-of-century (2081-2100) and under two scenarios for greenhouse gas concentration, namely the CMIP5 representative concentration pathway 4.5 and 8.5 (RCP4.5 and RCP8.5).

The results indicate that while the current distribution of L. fulica in the Canary Islands is limited to the wetter areas of the archipelago, namely western islands such as La Palma and El Hierro and the north of Tenerife, future projections under the CMIP5 RCP4.5 and RCP8.5 scenarios reveal notable changes. For both temporal periods and driven by warming temperatures and changing precipitation patterns, habitat suitability shows a greater shrinkage remaining only a small favorable area in the northern part of La Palma. However, future projections performed with the global datasets show opposite results, that is, a large number of high-suitability zones throughout the entire archipelago in which the probability of the presence of L. fulica is very high.

The use of BICI-ULL allowed us to identify future patterns in the high-suitability zones that would have been overestimated using global datasets. This underscores the need of incorporating region-specific data when modeling species distributions in topographically complex areas such as oceanic islands. The findings highlight the importance of developing regional datasets, like BICI-ULL, that can capture microclimatic variability. Besides, this approach serves as a model for addressing similar challenges in other biodiversity-rich but vulnerable regions, contributing to the broader understanding of invasive species dynamics.