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Ecol Appl. 2006 Aug;16(4):1449-60.

Rediscovering the species in community-wide predictive modeling.

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  • 1Center for Limnology, University of Wisconsin-Madison, 53706, USA. olden@wisc.edu

Abstract

Broadening the scope of conservation efforts to protect entire communities provides several advantages over the current species-specific focus, yet ecologists have been hampered by the fact that predictive modeling of multiple species is not directly amenable to traditional statistical approaches. Perhaps the greatest hurdle in community-wide modeling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients. Therefore, commonly used "short-cut" methods such as the modeling of so-called "assemblage types" are problematic. Our study demonstrates the utility of a multiresponse artificial neural network (MANN) to model entire community membership in an integrative yet species-specific manner. We compare MANN to two traditional approaches used to predict community composition: (1) a species-by-species approach using logistic regression analysis (LOG) and (2) a "classification-then-modeling" approach in which sites are classified into assemblage "types" (here we used two-way indicator species analysis and multiple discriminant analysis [MDA]). For freshwater fish assemblages of the North Island, New Zealand, we found that the MANN outperformed all other methods for predicting community composition based on multiscaled descriptors of the environment. The simple-matching coefficient comparing predicted and actual species composition was, on average, greatest for the MANN (91%), followed by MDA (85%), and LOG (83%). Mean Jaccard's similarity (emphasizing model performance for predicting species' presence) for the MANN (66%) exceeded both LOG (47%) and MDA (46%). The MANN also correctly predicted community composition (i.e., a significant proportion of the species membership based on a randomization procedure) for 82% of the study sites compared to 54% (MDA) and 49% (LOG), resulting in the MANN correctly predicting community composition in a total of 311 sites and an additional 117 sites (n = 379), on average, compared to LOG and MDA. The MANN also provided valuable explanatory power by simultaneously quantifying the nature of the relationships between the environment and both individual species and the entire community (composition and richness), which is not readily available from traditional approaches. We discuss how the MANN approach provides a powerful quantitative tool for conservation planning and highlight its potential for biomonitoring programs that currently depend on modeling discrete assemblage types to assess aquatic ecosystem health.

PMID:
16937810
[PubMed - indexed for MEDLINE]
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