Climate Change May Increase Environmental Suitability of the Babassu Complex ( Attalea spp., Arecaceae)

Abstract

Aim This study aimed to identify the key bioclimatic factors driving the distribution of the Babassu Complex across the Neotropics in the current scenario (2011–2040), and to project their range shifts under future climate scenarios (2041–2070 and 2070–2100). Taxon Babassu Complex (Arecaceae): Attalea barreirensis, A. eichleri, A. funifera, A. maripa, A. phalerata, A. speciosa, and A. vitrivir. Location Neotropical region. Methods We employed Species Distribution Modelling using four algorithms: Maximum Entropy (MaxEnt), Random Forest (RF), Boosted Regression Trees (BRT), and Generalised Linear Models (GLM) for the seven babassu species and for the combined Babassu Complex dataset. GBIF presence-only data was combined with CHELSA 2.1 bioclimatic variables from current and future scenarios to fit the models. Projections for 2041–2070 and 2071–2100 were derived for two high-emission climate scenarios (SSP3 7.0 and SSP5 8.5), using an ensemble of five global climate models. Results The RF and BRT algorithms provided more conservative predictions for the current scenario, while MaxEnt and GLM projected broader distributions. Temperature seasonality was the most important suitability predictor. Attalea maripa, A. phalerata, and A. speciosa showed the broadest suitability ranges, while A. funifera and A. vitrivir were most constrained. Future scenarios projected major suitability increases (up to 871.80% for the Complex under SSP5-8.5 by 2071–2100), particularly in Amazonian and Cerrado regions. Only A. funifera and A. vitrivir showed declines (−8.55% and −20.97% respectively under SSP3-7.0). Main Conclusions We anticipate that climate change may favour babassu species that tolerate warmer and more variable conditions, promoting their expansion. While this may support restoration and livelihoods, unmanaged spread could disrupt local ecosystems. It is recommended that future research focus on incorporating anthropogenic variables, validating predictions with field data, and exploring species-specific ecological responses to climate change.