History and taxonomy: their roles in the core-satellite hypothesis

Citation
L. Mehranvar et Da. Jackson, History and taxonomy: their roles in the core-satellite hypothesis, OECOLOGIA, 127(1), 2001, pp. 131-142
Citations number
124
Categorie Soggetti
Environment/Ecology
Journal title
OECOLOGIA
ISSN journal
00298549 → ACNP
Volume
127
Issue
1
Year of publication
2001
Pages
131 - 142
Database
ISI
SICI code
0029-8549(200103)127:1<131:HATTRI>2.0.ZU;2-E
Abstract
Metapopulation models are important in explaining the distribution and abun dance of species through time and space. These models combine population dy namics with stochastic variation in extinction and immigration parameters a ssociated with local populations. One of the predictions of metapopulation models is a bimodal distribution of species frequency of occurrence, a patt ern that led to the development of the core satellite species hypothesis. T he spatial scale and taxonomic classification of past core-satellite studie s has often been undefined. In our study, we have integrated metapopulation dynamics with the roles that differential dispersal ability and history pl ay in the shaping of communities. The differences in distribution patterns between landbridge islands and oceanic islands, and among Various taxa (bir ds, mammals, herptiles, arthropods, fish, and plants) are analyzed. The maj ority of landbridge islands comprised locally and regionally abundant speci es (core species), whereas the majority of oceanic islands had a uniform di stribution (or no end-peak in their distribution). The patterns of distribu tion among the taxonomic groups also showed differences. Birds (good disper sers) consistently showed bimodal- and core-distribution patterns. The bimo dal prediction of species distribution is best exemplified in the landbridg e islands and in birds, and least in oceanic islands and in organisms other than birds. These results illustrate the importance of testing models with various taxonomic groups and at different spatial scales and defining thes e scales before formally testing the predictions of the models.