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

Authors

    • Laboratorio de Ecología de Ecosistemas de Arrecifes CoralinosCentro de Investigación y Estudios Avanzados del I.P.N-Unidad Mérida
  • Gilberto Acosta-González
    • Laboratorio de Ecología de Ecosistemas de Arrecifes CoralinosCentro de Investigación y Estudios Avanzados del I.P.N-Unidad Mérida
  • Néstor Membrillo
    • Laboratorio de Ecología de Ecosistemas de Arrecifes CoralinosCentro de Investigación y Estudios Avanzados del I.P.N-Unidad Mérida
  • Joaquín Rodrigo Garza-Pérez
    • Unidad Multidisciplinaria de Docencia e Investigación Sisal, Facultad de CienciasUniversidad Nacional Autónoma de México
  • José Manuel Castro-Pérez
    • Laboratorio de EcologíaInstituto Tecnológico de Chetumal
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

Abstract

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.

Keywords

DiversityCaribbeanCoral reefsChinchorro BankFishMexicoGRASPProxiesReefscapeSpatial prediction

Abbreviations

BSEP

Biodiversity spatially explicit predictions

BFGs

Broad-functional groups

CART

Classification and regression trees

CHBBR

Chinchorro Bank Biosphere Reserve

DBM

Digital bathymetric model

GAM

Generalized additive models

GIS

Geographic information systems

GLM

Generalized linear models

GRASP

Generalized additive models and spatial prediction

MSGs

Morpho-structural groups

RVs

Response variables

PVs

Predictive variables

Copyright information

© Springer Science+Business Media B.V. 2011