Modelling tree diversity in a highly fragmented tropical montane landscape

Authors

  • Luis Cayuela,

    Corresponding author
    1. Departamento de Ecología, Universidad de Alcalá, CP 28871, Alcalá de Henares, Madrid, Spain,
      *Correspondence: Luis Cayuela, Departamento de Ecología, Universidad de Alcalá, CP 28871, Alcalá de Henares, Madrid, Spain. E-mail: luis.cayuela@uah.es
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  • José María Rey Benayas,

    1. Departamento de Ecología, Universidad de Alcalá, CP 28871, Alcalá de Henares, Madrid, Spain,
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  • Ana Justel,

    1. Departamento de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, Campus de Cantoblanco, CP 28049 Madrid, Spain, and
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  • Javier Salas-Rey

    1. Departamento de Geografía, Universidad de Alcalá, CP 28801, Alcalá de Henares, Madrid, Spain
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*Correspondence: Luis Cayuela, Departamento de Ecología, Universidad de Alcalá, CP 28871, Alcalá de Henares, Madrid, Spain. E-mail: luis.cayuela@uah.es

ABSTRACT

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 km2 in the Highlands of Chiapas, southern Mexico.

Methods  We identified all of the tree species within 204 field plots (1000 m2 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.

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