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Responses to drought stress by water withholding have been studied in 1 year old Holm oak (Quercus ilex subsp. ballota [Desf.] Samp.) seedlings from seven provenances from Andalusia (southern Spain). Several physiological parameters, including predawn xylem water potentials and relative water content in soil, roots, and leaves as well as maximum quantum efficiency and yield of PSII were evaluated for 28 days in both irrigated and nonirrigated seedlings. The leaf proteome map of the two provenances that show the extreme responses (Seville, GSE, is the most susceptible, while Almerı́a, SSA, is the least susceptible) was obtained. Statistically significant variable spots among provenances and treatments were subjected to MALDI-TOF/TOF-MS/MS analysis for protein identification. In response to drought stress, ~12.4% of the reproducible spots varied significantly depending on the treatment and the population. These variable proteins were mainly chloroplastic and belonged to the metabolism and defense/stress functional categories. The 2-DE protein profile of nonirrigated seedlings was similar in both provenances. Physiological and proteomics data were generally in good agreement. The general trend was a decrease in protein abundance upon water withholding in both provenances, mainly in those involved in ATP synthesis and photosynthesis. This decrease, moreover, was most marked in the most susceptible population compared with the less susceptible one.

Highlights • Mediterranean oaks are endangered by infection with an invasive alien oomycete. • Forecasts based on SDM showed an expansion of the plant pathogen within Andalusia. • Our SDMs verified the known environmental suitability and provided new insights. • Phytosanitary management zones may be set from the current and future distribution.

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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.