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