Question: Which is the best model to predict the habitat distribution of Buxus balearica Lam. in southern Spain? Location: Málaga and Granada, Spain, across an area of 38 180 km2. Methods: Prediction models based on 17 environmental variables were tested. Six methods were compared: multivariate adaptive regression spline (MARS), maximum entropy approach to modelling species' distributions (Maxent), two generic algorithms based on environmental metrics dissimilarity (BIOCLIM and DOMAIN), Genetic Algorithm for Rule‐set Prediction (GARP), and supervised learning methods based on generalized linear classifiers (support vector machines, SVMs). To test the predictive power of the models we used the Kappa index. Results: Maxent most accurately predicted the habitat distribution of B. balearica, followed by MARS models. The other models tested yielded lower accuracy values. A comparison of the predictive power of the models revealed that climate variables made the highest contributions among the environmental variables studied. The variables that made the lowest contributions were the insolation models. To examine the sensitivity of the models to a reduction in the number of variables, a test showed that accuracy of over 0.90 was maintained by applying just three climatic variables (spring rainfall, mean temperature of the warmest month, and mean temperature of the coldest month). Maps derived from the algorithms of all models tested coincided well with the known distribution of the species. Conclusions: Model habitat prediction is a preliminary step towards highlighting areas of high habitat suitability of B. balearica. These data support the results of previous research, which show that MaxEnt is the best technique for modelling species distributions with small sample sizes.

The present study offers an analysis of regeneration patterns and diversity dynamics after a wildfire, which occurred in 1993 and affected about 7000 ha in southern Spain. The aim of the work was to analyze the rule in the succession of shrub species after fire, relating it to the changes registered in the Normalized Difference Vegetation Index (NDVI). Fractional vegetation cover was recorded from permanent plots in 2000 and 2005. NDVI data related to each time were obtained from Landsat images. Both data sets, from fieldwork and remote sensing, were analyzed through statistical and quantitative analyses and then correlated. Results have permitted the description of the change in plant cover and species composition on a global and plot scale. It can be affirmed that, from the seventh to the twelfth year after the fire, the floristic composition within the burned area remained unchanged at a global level. However, on a smaller scale (plot level), the major shrub species, Ulex parviflorus, Rosmarinus officinalis, and Cistus clusii, underwent significant changes. The regeneration dynamics established by these species conditioned plant species composition and, consequently, diversity indexes such as Shannon (H) and Simpson (D). The changes recorded in the NDVI values corresponding to the surveyed plots were highly correlated with those found in the regrowth of the main species. Areas dominated by U. parviflorus in a senile phase were related to a decrease in NDVI values and an increase in the number of species. This result describes the successional dynamics; the dryness of the main colonizer shrub species is allowing the regrowth and re-establishment of other species. Within the study area, NDVI shows sensitivity to postfire plant cover changes and indirectly expresses the diversity dynamics.

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Argania spinosa L. Skeels is an Algerian-Moroccan endemic tree. This species is part of various plant communities consisting of Mediterranean, Macaronesian and Saharan floristic elements. It has been introduced and perhaps sometimes naturalized in various regions of the Mediterranean basin. Due to its role in combating desertification, high socio-economic value, and traditional use as fodder and food, the southwestern Moroccan argan grove (Arganeraie) was declared Biosphere Reserve. It had already been subject to conservation and reforestation programs a century earlier. Its cultivation for oil production could be, besides an economic objective, an effective method to conserve its genetic diversity. Therefore, this study aims to estimate its potential distribution and establish efficient breeding programs by determining its ecological requirements, identifying its different habitats, and predicting habitat suitability models for Morocco, Algeria, Tunisia, and Spain. Using 53 occurrence points, wind speed and direction data, and 29 bioclimatic variables, multivariate methods were applied to describe the ecological profiles and characterize the heterogeneity of its habitat to subsequently, train a Maxent model that establishes, besides Morocco and Algeria, suitable cultivation areas in Tunisia and Spain. The North African potential area is limited to the western Mediterranean coast of Algeria and flat and coastal areas of eastern Tunisia. The increased likelihood of suitability remains in the southeast Iberian Peninsula. A high probability of argan cultivation is also evident in the Canary Islands. These results provide possibilities for future expansion of argan crop and a window of opportunity to improve its genetic diversity and conservation.

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