Prediction of bird community composition based on point-occurrence data and inferential algorithms: a valuable tool in biodiversity assessments

• Published in: Diversity & distributions Vol. 8; no. 2; pp. 49 - 56
• Main Authors: FERIA A, T. Patricia, PETERSON, A. Townsend
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Science Ltd 01.03.2002; Blackwell Science; Blackwell
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Balsa; Biodiversity; Biodiversity conservation; Biodiversity Research; Biological and medical sciences; Birds; Community; Ecological modeling; ecological niche modelling; Ecological niches; Fundamental and applied biological sciences. Psychology; Global atmospheric research program; Modeling; Natural history; predicted distributions; Species; Synecology; Terrestrial ecosystems; Central America; Mexico; America; Species diversity; Vertebrata; Geographic distribution; Animal community; Niche; Aves; Forecast model; Ecological abundance
• ISSN: 1366-9516; 1472-4642
• DOI: 10.1046/j.1472-4642.2002.00127.x

Local biological communities are made up of species, each of which has its own particular relationship with the environment. To the extent that these autecological niches limit species' distributions, and by extension community composition, models of species' ecological niches can predict species composition at particular sites, or at least provide a null hypothesis of potential species composition in the absence of effects of species interactions. We developed distributional predictions (ecological niche models) for 89 species occurring in dry tropical forest in the Balsas Basin of south-western Mexico using an interpolation technique, and predicted the species likely to occur at 8 sites across the region. Onsite field inventory data were then used to test the community predictions, all of which were statistically significant. These results suggest that inventory efforts can be made more efficient by development beforehand of hypotheses that focus onsite collecting and inventory.