Biodiversity and Conservation

, Volume 21, Issue 1, pp 115–130

Predicting spatially explicit coral reef fish abundance, richness and Shannon–Weaver index from habitat characteristics

  • Jesús Ernesto Arias-González
  • Gilberto Acosta-González
  • Néstor Membrillo
  • Joaquín Rodrigo Garza-Pérez
  • José Manuel Castro-Pérez
Original Paper

DOI: 10.1007/s10531-011-0169-y

Cite this article as:
Arias-González, J.E., Acosta-González, G., Membrillo, N. et al. Biodivers Conserv (2012) 21: 115. doi:10.1007/s10531-011-0169-y


The assessment of biodiversity in coral reefs requires the application of geographic information systems (GIS), remote sensing and analytical tools in order to make cost-effective spatially explicit predictions of biodiversity over large geographic areas. Here we present a spatially explicit prediction for coral reef fish diversity index, as well as habitat classification according to reef fish diversity index values in Chinchorro Bank Biosphere Reserve, one of the most important plain/atoll type reef systems in the Caribbean. We have used extensive ecological data on depth, fish and habitat characteristics to perform such prediction. Fish species assemblages and different biotic variables of benthic organisms were characterized using visual censuses and video-transects, respectively at 119 sampling stations. The information was integrated in a GIS, along with satellite imagery (LANSDAT 7 ETM+) and a digital bathymetric model. From the recorded data and a hierarchical classification procedure, we obtained nine different classes of habitats. We used a generalized regression analysis and spatial prediction methodology to create predictive maps (GIS layers) of the different reef benthic components, and a second modeling run produced predictive maps of coral reef fish diversity index. Predictive accuracy of the diversity index map presented a good correlation coefficient (r = 0.87), with maximum diversity index values en reefscapes composed of aggregation of coral colonies with seagrass beds. The implementation of our application was successful for the prediction of fish diversity hot spots and surrogate habitats.


DiversityCaribbeanCoral reefsChinchorro BankFishMexicoGRASPProxiesReefscapeSpatial prediction



Biodiversity spatially explicit predictions


Broad-functional groups


Classification and regression trees


Chinchorro Bank Biosphere Reserve


Digital bathymetric model


Generalized additive models


Geographic information systems


Generalized linear models


Generalized additive models and spatial prediction


Morpho-structural groups


Response variables


Predictive variables

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jesús Ernesto Arias-González
    • 1
  • Gilberto Acosta-González
    • 1
  • Néstor Membrillo
    • 1
  • Joaquín Rodrigo Garza-Pérez
    • 2
  • José Manuel Castro-Pérez
    • 3
  1. 1.Laboratorio de Ecología de Ecosistemas de Arrecifes CoralinosCentro de Investigación y Estudios Avanzados del I.P.N-Unidad MéridaMéridaMexico
  2. 2.Unidad Multidisciplinaria de Docencia e Investigación Sisal, Facultad de CienciasUniversidad Nacional Autónoma de MéxicoSisalMexico
  3. 3.Laboratorio de EcologíaInstituto Tecnológico de ChetumalChetumalMexico