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Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean

Author

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  • Pittman, S.J.
  • Christensen, J.D.
  • Caldow, C.
  • Menza, C.
  • Monaco, M.E.

Abstract

Effective management of coral reef ecosystems requires accurate, quantitative and spatially explicit information on patterns of species richness at spatial scales relevant to the management process. We combined empirical modelling techniques, remotely sensed data, field observations and GIS to develop a novel multi-scale approach for predicting fish species richness across a compositionally and topographically complex mosaic of marine habitat types in the U.S. Caribbean. First, the performance of three different modelling techniques (multiple linear regression, neural networks and regression trees) was compared using data from southwestern Puerto Rico and evaluated using multiple measures of predictive accuracy. Second, the best performing model was selected. Third, the generality of the best performing model was assessed through application to two geographically distinct coral reef ecosystems in the neighbouring U.S. Virgin Islands. Overall, regression trees outperformed multiple linear regression and neural networks. The best performing regression tree model of fish species richness (high, medium, low classes) in southwestern Puerto Rico exhibited an overall map accuracy of 75%; 83.4% when only high and low species richness areas were evaluated. In agreement with well recognised ecological relationships, areas of high fish species richness were predicted for the most bathymetrically complex areas with high mean rugosity and high bathymetric variance quantified at two different spatial extents (≤0.01km2). Water depth and the amount of seagrasses and hard-bottom habitat in the seascape were of secondary importance. This model also provided good predictions in two geographically distinct regions indicating a high level of generality in the habitat variables selected. Results indicated that accurate predictions of fish species richness could be achieved in future studies using remotely sensed measures of topographic complexity alone. This integration of empirical modelling techniques with spatial technologies provides an important new tool in support of ecosystem-based management for coral reef ecosystems.

Suggested Citation

  • Pittman, S.J. & Christensen, J.D. & Caldow, C. & Menza, C. & Monaco, M.E., 2007. "Predictive mapping of fish species richness across shallow-water seascapes in the Caribbean," Ecological Modelling, Elsevier, vol. 204(1), pages 9-21.
    • Handle: RePEc:eee:ecomod:v:204:y:2007:i:1:p:9-21
    DOI: 10.1016/j.ecolmodel.2006.12.017
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    References listed on IDEAS

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    1. Norman Myers & Russell A. Mittermeier & Cristina G. Mittermeier & Gustavo A. B. da Fonseca & Jennifer Kent, 2000. "Biodiversity hotspots for conservation priorities," Nature, Nature, vol. 403(6772), pages 853-858, February.
    2. D. R. Bellwood & T. P. Hughes & C. Folke & M. Nyström, 2004. "Confronting the coral reef crisis," Nature, Nature, vol. 429(6994), pages 827-833, June.
    3. Peter J. Mumby & Alasdair J. Edwards & J. Ernesto Arias-González & Kenyon C. Lindeman & Paul G. Blackwell & Angela Gall & Malgosia I. Gorczynska & Alastair R. Harborne & Claire L. Pescod & Henk Renken, 2004. "Mangroves enhance the biomass of coral reef fish communities in the Caribbean," Nature, Nature, vol. 427(6974), pages 533-536, February.
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    Cited by:

    1. Simon J Pittman & Kerry A Brown, 2011. "Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-12, May.
    2. Knudby, Anders & Brenning, Alexander & LeDrew, Ellsworth, 2010. "New approaches to modelling fish–habitat relationships," Ecological Modelling, Elsevier, vol. 221(3), pages 503-511.
    3. Galparsoro, Ibon & Borja, Ángel & Bald, Juan & Liria, Pedro & Chust, Guillem, 2009. "Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis," Ecological Modelling, Elsevier, vol. 220(4), pages 556-567.
    4. Li, Libo, 2018. "Predicting online invitation responses with a competing risk model using privacy-friendly social event data," European Journal of Operational Research, Elsevier, vol. 270(2), pages 698-708.
    5. Yee, Susan H. & Dittmar, John A. & Oliver, Leah M., 2014. "Comparison of methods for quantifying reef ecosystem services: A case study mapping services for St. Croix, USVI," Ecosystem Services, Elsevier, vol. 8(C), pages 1-15.
    6. Antoine Collin & Phillippe Archambault & Bernard Long, 2011. "Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-16, June.
    7. Bryan Costa & J Christopher Taylor & Laura Kracker & Tim Battista & Simon Pittman, 2014. "Mapping Reef Fish and the Seascape: Using Acoustics and Spatial Modeling to Guide Coastal Management," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-17, January.
    8. Melo-Merino, Sara M. & Reyes-Bonilla, Héctor & Lira-Noriega, Andrés, 2020. "Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence," Ecological Modelling, Elsevier, vol. 415(C).

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