The quest for a null model for macroecological patterns: geometry of species distributions at multiple spatial scales
Language English Country England, Great Britain Media print
Document type Journal Article, Research Support, Non-U.S. Gov't
PubMed
18638301
DOI
10.1111/j.1461-0248.2008.01206.x
PII: ELE1206
Knihovny.cz E-resources
- MeSH
- Models, Biological * MeSH
- Demography MeSH
- Ecosystem * MeSH
- Fractals MeSH
- Conservation of Natural Resources MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
There have been several attempts to build a unified framework for macroecological patterns. However, these have mostly been based either on questionable assumptions or have had to be parameterized to obtain realistic predictions. Here, we propose a new model explicitly considering patterns of aggregated species distributions on multiple spatial scales, the property which lies behind all spatial macroecological patterns, using the idea we term 'generalized fractals'. Species' spatial distributions were modelled by a random hierarchical process in which the original 'habitat' patches were randomly replaced by sets of smaller patches nested within them, and the statistical properties of modelled species assemblages were compared with macroecological patterns in observed bird data. Without parameterization based on observed patterns, this simple model predicts realistic patterns of species abundance, distribution and diversity, including fractal-like spatial distributions, the frequency distribution of species occupancies/abundances and the species-area relationship. Although observed macroecological patterns may differ in some quantitative properties, our concept of random hierarchical aggregation can be considered as an appropriate null model of fundamental macroecological patterns which can potentially be modified to accommodate ecologically important variables.
References provided by Crossref.org
Universal species-area and endemics-area relationships at continental scales
Species abundance distribution results from a spatial analogy of central limit theorem