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To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity.

Research Highlights: This study assesses the genetic diversity and structure of the ice-cream-bean (Inga edulis Mart., Fabaceae) in wild and cultivated populations from the Peruvian Amazon. This research also highlights the importance of protecting the biodiversity of the forest in the Peruvian Amazon, to preserve the genetic resources of species and allow further genetic improvement. Background and Objectives: Ice-cream-bean is one of the most commonly used species in the Amazon region for its fruits and for shading protection of other species (e.g., cocoa and coffee plantations). Comprehensive studies about the impact of domestication on this species&rsquo, genetic diversity are needed, to find the best conservation and improvement strategies. Materials and Methods: In the current study, the genetic structure and diversity were assessed by genotyping 259 trees, sampled in five wild and 22 cultivated I. edulis populations in the Peruvian Amazon, with microsatellite markers. Pod length was measured in wild and cultivated trees. Results: The average pod length in cultivated trees was significantly higher than that in wild trees. The expected genetic diversity and the average number of alleles was higher in the wild compared to the cultivated populations, thus, a loss of genetic diversity was confirmed in the cultivated populations. The cultivated trees in the Loreto region had the highest pod length and lowest allelic richness, nevertheless, the wild populations&rsquo, genetic structure was not clearly differentiated (significantly different) from that of the cultivated populations. Conclusions: A loss of genetic diversity was confirmed in the cultivated populations. The species could have been simultaneously domesticated in multiple locations, usually from local origin. The original I. edulis Amazonian germplasm should be maintained. Cultivated populations&rsquo, new germplasm influx from wild populations should be undertaken to increase genetic diversity.