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Modelling tree diversity in a highly fragmented tropical montane landscape
Author(s) -
Cayuela Luis,
Benayas José María Rey,
Justel Ana,
SalasRey Javier
Publication year - 2006
Publication title -
global ecology and biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.164
H-Index - 152
eISSN - 1466-8238
pISSN - 1466-822X
DOI - 10.1111/j.1466-8238.2006.00255.x
Subject(s) - normalized difference vegetation index , geography , biodiversity , ecology , deforestation (computer science) , beta diversity , vegetation (pathology) , disturbance (geology) , physical geography , environmental science , climate change , biology , medicine , paleontology , pathology , computer science , programming language
Aim There is an urgent need for conservation in threatened tropical forest regions. We explain and predict the spatial variation of α (i.e. within plot) and β (i.e. between plot) tree diversity in a tropical montane landscape subjected to a high deforestation rate. A major aim is to demonstrate the potential of a method that combines data from multiple sources (field data, remote sensing imagery and GIS) to evaluate and monitor forest diversity on a broad scale over large unexplored areas. Location The study covered an area of c. 3500 km 2 in the Highlands of Chiapas, southern Mexico. Methods We identified all of the tree species within 204 field plots (1000 m 2 each) and measured different environmental, human disturbance‐related, and spatial variables using remote sensing and GIS data. To obtain a predictive model of α tree diversity (Fisher's alpha) based on selected explanatory variables, we used a generalized linear model with a gamma error distribution. Mantel tests of matrix correspondence were used to determine whether similarities in floristic composition were correlated with similarities in the explanatory variables. Finally, we used a method that combines α and β tree diversity to define priority areas for conservation. Results The model for α tree diversity explained 44% of the overall variability, of which most was mainly related to precipitation, temperature, NDVI, and canopy (all relationships were positive, and quadratic for temperature and NDVI). There were no spatially structured regional factors that were ignored. Similarity in tree composition was correlated positively with climate and NDVI. Main conclusions The results were used to: (1) identify and assign conservation priority of unexplored areas that have high tree diversity, and (2) demonstrate the importance of several vegetation formations in the region's biodiversity. The method we present can be particularly useful in assessing regional needs and in developing local conservation strategies in poorly surveyed (and often at risk) tropical areas worldwide, where accessibility is usually limited.