Negative biotic interactions drive predictions of distributions for species from a grassland community
| Autor: | Phillip P. A. Staniczenko, Richard G. Pearson, K. Blake Suttle |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
model selection Range (biology) Information Theory Biology 010603 evolutionary biology 01 natural sciences Models Biological Grassland California biotic interactions normalized maximum likelihood minimum description length principle species distribution models geography geography.geographical_feature_category Ecology 010604 marine biology & hydrobiology Model selection 15. Life on land Normalized maximum likelihood Agricultural and Biological Sciences (miscellaneous) Biota Community Ecology species geographical ranges General Agricultural and Biological Sciences Research Article |
| Zdroj: | Biology Letters |
| ISSN: | 1744-957X 1744-9561 |
| Popis: | Understanding the factors that determine species' geographical distributions is important for addressing a wide range of biological questions, including where species will be able to maintain populations following environmental change. New methods for modelling species distributions include the effects of biotic interactions alongside more commonly used abiotic variables such as temperature and precipitation; however, it is not clear which types of interspecific relationship contribute to shaping species distributions and should therefore be prioritized in models. Even if some interactions are known to be influential at local spatial scales, there is no guarantee they will have similar impacts at macroecological scales. Here we apply a novel method based on information theory to determine which types of interspecific relationship drive species distributions. Our results show that negative biotic interactions such as competition have the greatest effect on model predictions for species from a California grassland community. This knowledge will help focus data collection and improve model predictions for identifying at-risk species. Furthermore, our methodological approach is applicable to any kind of species distribution model that can be specified with and without interspecific relationships. |
| Databáze: | OpenAIRE |
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