Land abandonment is a global phenomenon whose environmental consequences are difficult to assess. The Murcia region is one of the most arid regions in southern Europe and also one of the most prone to land abandonment. This study researches which environmental features are more relevant to explain abandonment at the agricultural plot scale. Geomorphometric features were measured at different scales to investigate which scales could be more relevant. Two different models have been used: logistic regression, a statistical model that allows the interpretation of the involved features, and Random Forest, a machine learning model with a higher predictive power but lower interpretability. The combined use of both such models allows a set of predictors to be selected, which, when used with Random Forest, produce a map that is highly accurate for predicting abandonment and, when used with logistic regression, produce an interpretable model. The main conclusion is that climate is the most relevant factor to explain land abandonment. Copyright © 2015 John Wiley & Sons, Ltd.

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Abstract:The aim of this study was to evaluate three different strategies to improve classification accuracy in a highly fragmented semiarid area using, (i) different classification algorithms with parameter optimization in some cases; (ii) different feature sets including spectral, textural and terrain features; and (iii) different seasonal combinations of images. A three-way ANOVA was used to discern which of these approaches and their interactions significantly increases accuracy. Tukey-Kramer contrast using a heteroscedasticity-consistent estimation of the kappa covariances matrix was used to check for significant differences in accuracy. The experiment was carried out with Landsat TM, ETM and OLI images corresponding to the period 2000-2015. A combination of four images using random forest and the three feature sets was the best way to improve accuracy. Maximum likelihood, random forest and support vector machines do not significantly increase accuracy when textural information was added, but do so when terrain features were taken into account. On the other hand, sequential maximum a posteriori increased accuracy when textural features were used, but reduced accuracy substantially when terrain features were included. Random forest using the three feature subsets and sequential maximum a posteriori with spectral and textural features had the largest kappa values, around 0.9.

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Populations of the Mediterranean fan mussel Pinna nobilis have progressively decreased over the last decades as a result of anthropogenic activities. The rate of decline has strongly increased since 2016, when a mass mortality event triggered by the parasite Haplosporidium pinnae occurred, and evidence exists that Mycobacterium species may also have played a major role in the event. Indeed, the epidemic has spread throughout the Mediterranean, although coastal lagoons seem to offer a degree of ‘resistance’ against the parasite. In the early 1980s, P. nobilis appeared in the Mar Menor lagoon and rapidly became an important component of the benthos. However, colonization of the lagoon by the fan mussel was cut short in 2016 when a massive mortality event occurred, possibly as a consequence of the environmental collapse that occurred in the lagoon, parallel to the mortality that the species suffered in the Mediterranean that same year. In this study, we estimated the spatial distribution of P. nobilis in the Mar Menor for 3 periods: 2003-2004, 2013 and 2016. The first 2 periods use published data, and the last period uses data collected in a new campaign. The probability of occurrence for the 3 periods was estimated using random forest and random forest regression-kriging models. The main environmental variables that determined the dispersion and colonization of the bivalve in the lagoon before 2016 are also identified.