Use of community-composition data to predict the fecundity and abundance of species.
Species distribution models are critical tools for the prediction of invasive species spread and conservation of biodiversity. The majority of species distribution models have been built with environmental data. Community ecology theory suggests that species co-occurrence data could also be used to predict current and potential distributions of species. Species assemblages are the products of biotic and environmental constraints on the distribution of individual species and as a result may contain valuable information for niche modeling. We compared the predictive ability of distribution models of annual grassland plants derived from either environmental or community-composition data. Composition-based models were built with the presence or absence of species at a site as predictors of site quality, whereas environment-based models were built with soil chemistry, moisture content, above-ground biomass, and solar radiation as predictors. The reproductive output of experimentally seeded individuals of 4 species and the abundance of 100 species were used to evaluate the resulting models. Community-composition data were the best predictors of both the site-specific reproductive output of sown individuals and the site-specific abundance of existing populations. Successful community-based models were robust to omission of data on the occurrence of rare species, which suggests that even very basic survey data on the occurrence of common species may be adequate for generating such models. Our results highlight the need for increased public availability of ecological survey data to facilitate community-based modeling at scales relevant to conservation.
Elmendorf
Sarah C
SC
Graduate Group in Ecology, Department of Environmental Science and Policy, University of California, One Shields Avenue, Davis, CA 95616, USA. scelmend@interchange.ubc.ca
Moore
Kara A
KA
D044822:Biodiversity; D002140:California; D003247:Conservation of Natural Resources / Q000379:methods*; D004777:Environment; D005298:Fertility / Q000502:physiology; D008954:Models, Biological; D016571:Neural Networks, Computer; D006109:Poaceae / Q000502:physiology; D013045:Species Specificity