Secondary succession after farmland abandonment has become a common process\ud in north Mediterranean countries, especially in mountain areas. In this paper a\ud methodology is tested which combines Markov chains and logistic multivariate\ud regression to model secondary succession after farmland abandonment in environments\ud where abiotic constraints play a major role, like mountain areas. In such landscapes a\ud decay in the succession rate with time is usually found, as the best locations are\ud progressively occupied. This is frequently addressed using non-stationary Markov\ud chains. Here, we test if the combination of logistic multivariate regression with Markov\ud chains, however, allows for spatially distributed transitions probabilities based on\ud abiotic factors and therefore it is able to reproduce the preferential colonization of the\ud most favourable locations. The model is tested in the Ijuez valley in the Spanish\ud Pyrenees, which underwent generalised land abandoned during the 50s. Results confirm\ud a substantial improve in the prediction success of the Markov-logistic model when\ud compared to the standard Markov chain approach. As a result, the decay in the\ud succession rate can be successfully modelled. The specific results for our study area are\ud discussed further in an ecological context. The methodology proposed is applicable to\ud any landscape where vegetation dynamics are constrained by environmental factors.\ud However, the inclusion of land use as an explanatory factor would be necessary in\ud human-managed landscapes
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