Conservation and sustainable use of species diversity require a description of the environment where they develop. The objectives were to determine ecological descriptors and climatic diversity of areas along the distribution range of 12 species of wild tomatoes (Solanum sect. Lycopersicon) and four wild species of phylogenetically related groups (Solanum sect. Juglandifolia and sect. Lycopersicoides), as well as their ecological similarity in Latin America. With 4228 selected tomato accessions and an environmental information system (EIS) composed of 21 climatic variables, diversity patterns of the distribution areas were identified for each species, as well as ecological descriptors through the use of geographic information systems (GIS). The contribution of climatic variables to the species geographical distribution was identified by principal component analysis (PCA), and similarity in species distribution as a function of the variables identified with cluster analysis (CA). Climatic characteristics and the environmental amplitude of wild tomatoes and related species along their distributional range were satisfactorily determined by ecological descriptors. Eleven climate types were identified, predominantly BSk (arid, steppe, cold), BWh (arid, desert, hot), and Cfb (temperate, no dry season, warm summer). PCA determined 10 most important variables were the most important for the geographical distribution. Six groups of species were identified according to CA and climatic distribution similarity. This approach has shown promissory applications for biodiversity conservation of valuable genetic resources for tomato crop breeding.

Tomato (Solanum lycopersicum L.) is a vegetable with worldwide importance. Its wild or close related species are reservoirs of genes with potential use for the generation of varieties tolerant or resistant to specific biotic and abiotic factors. The objective was to determine the geographic distribution, ecological descriptors, and patterns of diversity and adaptation of 1296 accessions of native tomato from Mexico. An environmental information system was created with 21 climatic variables with a 1 km² spatial resolution. Using multivariate techniques (Principal Component Analysis, PCA; Cluster Analysis, CA) and Geographic Information Systems (GIS), the most relevant variables for accession distribution were identified, as well as the groups formed according to the environmental similarity among these. PCA determined that with the first three PCs (Principal Components), it is possible to explain 84.1% of the total variation. The most relevant information corresponded to seasonal variables of temperature and precipitation. CA revealed five statistically significant clusters. Ecological descriptors were determined and described by classifying accessions in Physiographic Provinces. Temperate climates were the most frequent among tomato accessions. Finally, the potential distribution was determined with the Maxent model with 10 replicates by cross-validation, identifying areas with a high probability of tomato presence. These results constitute a reliable source of useful information for planning accession sites collection and identifying accessions that are vulnerable or susceptible to conservation programs.

Arid and semi-arid areas are highly productive ecosystems that have a great diversity of species, among which grasses stand out. These species have ecological, environmental, and economic importance. The objective was to identify the ecological descriptors of 15 native species of grasses and diversity patterns through multivariate analysis techniques (principal component analysis, PCA, and cluster analysis, CA), in addition to identifying potential distribution areas (current and future) and regions of high and low diversity from 3841 accessions and 21 climatic variables. Among the main results, the extreme values, coefficient of variation, and median for each species could be determined. PCA determined that, with 10 variables, it was possible to explain 54.36% of the variation between the analyzed species. CA resulted in the formation of four statistically significant groups, with specific climatic characteristics. Regarding potential distribution areas and climate change, a reduction in distribution is expected (2050) on most of the current surface, with a possible opportunity zone in the north of the country. Finally, two hotspots (high diversity areas) and four coldspots (low diversity areas) were identified among the analyzed species. These results can be used to create strategies for sustainable use and the conservation of these resources.

Mexico is the centre of origin of the chayote (Sechium edule (Jacq.) Sw), an important plant in human consumption and in pharmaceuticals. The objective of this study was to determine the potential distribution of domesticated S. edule in Mexico using seven species distribution algorithms, to efficiently manage S. edule resources and help its conservation by identifying patterns of geographic distribution. Otherwise, areas of high suitability can be used to produce improved seed at a lower cost. 162 GBIF occurrence points and nine layers in raster format were used to evaluate seven algorithms of species distribution models. To evaluate the reliability and performance of the models, the statistics Area Under the Curve (AUC) and true skill statistic was used. Predominant climate types were Cwb (33.3 %) and Aw (17.9 %); predominant soil types were leptosol (33.3 %) and phaozem (16.7 %). The seven models showed areas of high suitability (> 0.75) in Chiapas, Guerrero, Oaxaca, Veracruz, Tabasco, Puebla and Hidalgo states. AUC values for the seven models were > 0.8 and their performance was adequate (0.4 > TSS < 0.7). Classification tree analysis was found to be the best algorithm measured by AUC (0.90); however, the seven models were adequate to explain S. edule distribution in Mexico. S. edule climatic adaptability also allows to be distributed towards the Yucatan Peninsula and western Mexico. The distribution of S. edule in Mexico, according to the studied algorithms, is limited to total annual precipitation and temperature seasonality. México es el centro de origen del chayote (Sechium edule (Jacq.) Sw), una planta importante en la alimentación humana y en la farmacéutica. El objetivo de este estudio fue determinar la distribución potencial de S. edule domesticado en México utilizando siete algoritmos de distribución de especies, con el fin de gestionar eficientemente los recursos de S. edule y ayudar a su conservación mediante la identificación de patrones de distribución geográfica, Por otra parte, las zonas de alta idoneidad pueden ser utilizadas para la producción de semilla mejorada a un menor costo. Se utilizaron 162 puntos de ocurrencia de la GBIF y nueve capas en formato ráster para evaluar siete algoritmos de modelos de distribución de especies. Para evaluar la confiabilidad y el rendimiento de los modelos se utilizaron los estadísticos Área bajo la curva y el verdadero estadístico de habilidad. Los tipos de clima predominantes fueron Cwb (33,3 %) y Aw (17,9 %); los tipos de suelo predominantes fueron leptosol (33,3 %) y phaozem (16, 7 %). Los siete modelos mostraron áreas de alta idoneidad (> 0.75) para los estados de Chiapas, Guerrero, Oaxaca, Veracruz, Tabasco, Puebla e Hidalgo. Los valores de AUC para los siete modelos fueron > 0,8 y su rendimiento fue adecuado (0,4> TSS <0,7). Se encontró que el análisis de árboles de clasificación fue el mejor algoritmo medido por AUC (0,90), sin embargo, los siete modelos fueron adecuados para explicar la distribución de S. edule en México. La adaptabilidad climática de S. edule también le permite distribuirse hacia la Península de Yucatán y occidente de México. La distribución de S. edule en México, según los algoritmos estudiados, se limita a la precipitación total anual y la estacionalidad de la temperatura.

Objective: Determine current and potential distribution of S. tacaco in Costa Rica with seven Species Distribution Models (SDM), in order to optimize the management of S. tacaco genetic resources, aimed at identifying patterns of geographic distribution and possible climatic adaptations allowing to have perspectives on their conservation and genetic breeding. Design/Methodology/Approach: 21 points of occurrence together with 19 bioclimatic variables and altitude were used to evaluate seven machine learning models and an assembly of these. Open-source libraries running in Rstudio were used. Results: Distribution models were inferred by the variables bio1, bio2, bio3, bio4, bio12, bio13, bio14, bio18 y bio19. The generalized additive model obtained the highest values ??of area under the curve (0.96) and True skill statistic (0.90), however, the seven models tested and the assembly showed adequate performance (AUC> 0.5 and TSS> 0.4). Bioclimatic variables related to temperature were the ones with the greatest contribution to the models and the main limitations in the distribution of S. tacaco. Study limitations/implications: Possibly a greater number of occurrence points are required to evaluate distribution models. Findings/Conclusions. Areas with high potential distribution suitability for S. tacaco are found in central valleys of Costa Rica, covering regions of the provinces of Alajuela, Cartago, San José and Puntarenas. These areas can be sources of germplasm for future conservation and breeding studies.

The environmental conditions of Mexico allow the presence of several species of bamboo, where commercial uses have diversified due to their rapid growth and characteristics. The Mexican Guaduas are specimens with exemplary structural characteristics, however, there is no cartographic information in Mexico that allows locating and sizing the areas where bamboo species are located or can be located, which restricts decision making for commercial forest plantations. Therefore, it is intended to determine the probability of occurrence of the 7 species of this genus of bamboo, based on the maximum entropy approach and three arrangements of 21 environmental variables, for which 478 presence records were used. All generated models defined a good fit with the training data, with an AUC value greater than 0.90. It was found that the niche distribution of Guadua mexicana species is mainly influenced by altitude (ELEV), so they are found in areas close to coastal regions. Likewise, in defining the distribution of this genus, annual precipitation (BIO12), evapotranspiration (ETP) and average annual temperature (BIO1) stood out. This was independent of the three arrangements of environmental variables that were tested.