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SPATIOTEMPORAL PREDICTIVE MODELS OF MEDITERRANEAN VEGETATION DYNAMICS
Author(s) -
Carmel Yohay,
Kadmon Ronen,
Nirel Ronit
Publication year - 2001
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(2001)011[0268:spmomv]2.0.co;2
Subject(s) - vegetation (pathology) , mediterranean climate , logistic regression , scale (ratio) , environmental science , logistic function , predictive modelling , ecology , enhanced vegetation index , physical geography , geography , statistics , mathematics , normalized difference vegetation index , climate change , cartography , medicine , vegetation index , pathology , biology
Empirical modeling of vegetation dynamics can be used for predictive purposes. The goal of the present study is to construct and evaluate possible approaches for empirical modeling of vegetation dynamics, and to investigate their potential use in planning and management. An empirical model of mediterranean vegetation dynamics was constructed using a case study of vegetation change in an area in the Galilee mountains, northern Israel, between 1964 and 1992. Present vegetation in any location was modeled as a function of past vegetation and environmental factors (e.g., topography and various disturbances); future vegetation was then modeled as a function of current vegetation and effects of environmental factors. In order to assess model performance, we compared the actual vegetation map with maps representing model realizations for the study area and for an external validation area. Three types of measures were used to compare the predicted and actual vegetation maps: overall vegetation composition, pattern indices, and cell‐by‐cell match. We compared the performance of logistic vs. linear models and of stochastic vs. deterministic realizations of a logistic model. Our results indicate that landscape‐scale vegetation dynamics can be fairly well modeled using a few biologically important variables. The logistic and linear models had similar performance, in spite of the reduced information on which the logistic models were based. The use of only a 4% sample of the database resulted in a negligible reduction in model performance. Model performance was reduced, but was still fair, when applied to an external area. The merits and limitations of this modeling approach are discussed in comparison with other approaches for modeling vegetation dynamics.