In order to enhance in terms of accuracy and predict the modeling of the potential distribution of species, the integration of using principal components of environmental variables as input of maximum entropy (MaxEnt) has been proposed in this study. Principal components selected previously from the principal component analysis results performed in ArcGIS in the environmental variables was used as an input data of MaxEnt instead of raw data to model the potential distribution of red spiny lobster from the year 1997 to 2015 and for three different future scenarios 2020, 2050, and 2070. One set of six original environmental variables pertaining to the years 1997–2015 and one set of four variables for future scenarios were transformed independently into a single multiband raster in ArcGIS in order to select the variables whose eigenvalues explains more than 5% of the total variance with the purpose to use in the modeling prediction in MaxEnt. The years 1997 and 1998 were chosen to compare the accuracy of the model, showing better results using principal components instead of raw data in terms of area under the curve and partial receiver operating characteristic as well as better predictions of suitable areas. Using principal components as input of MaxEnt enhances the prediction of good habitat suitability for red spiny lobster; however, future scenarios suggest an adequate management by researches to elaborate appropriate guidelines for the conservation of the habitat for this valuable specie with face to the climate change.

The uncontrolled spread of fire can have huge effects on ecosystems. In Ecuador, in 2022, wildfires caused a loss of 6566.66 hectares of vegetation cover. Ibarra is an Andean canton that has also been exposed to wildfires and their effects. The aim of this study was to map wildfire susceptibility in the Ibarra canton. Seven factors that directly affect these fires were examined: precipitation, temperature, water deficit, potential evapotranspiration, slope, proximity to roads, and land cover and land use. The variables were reclassified using Geographic Information Systems and a multicriteria analysis. The results showed that Ibarra has four susceptibility categories: very low, moderate, high, and very high. The more susceptible areas are those considered to have high and very high exposure, occupying 82% of the surface. Consequently, the most susceptible land covers are crops, pastures, shrub vegetation, and forests.