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Spatial dynamics in a metapopulation network: recovery of a rare grasshopper Stauvoderus scalaris from population refuges
Author(s) -
Carlsson Allan,
Kindvall Oskar
Publication year - 2001
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/j.1600-0587.2001.tb00480.x
Subject(s) - metapopulation , occupancy , ecology , extinction (optical mineralogy) , habitat fragmentation , grasshopper , population , local extinction , colonisation , geography , fragmentation (computing) , endangered species , biology , habitat , colonization , biological dispersal , demography , paleontology , sociology
A characteristic feature of the spatial distribution of many species is patchiness. This spatial patchiness may be generated by very different processes, e.g. fragmentation, succession and extinction‐colonisation dynamics. In this study, we apply a spatial realistic metapopulation model to analyse the occupancy pattern of a rare and endangered grasshopper, Stauroderus scalaris. found in an extensive network of 158 patches. When the study was initiated in 1985 the regional occupancy was 9.3% declining down to 7.1% in 1989. Then there was a spatial expansion of the population and in 1993 as many as 27.3% of the patches were occupied and 32.9% in 1995. During this expansion phase, the dynamics obeyed metapopulation principles: large patches and less isolated ones were more likely to be colonised. In the beginning, local extinction risks were negatively related to patch size and positively influenced by isolation. However, later on neither area nor isolation affected extinction probabilities. Altogether, 20 extinctions and 56 colonisations were observed. The shift in regional occupancy, with a growth of ca 20%, coincides with perturbations to the patch network and the warmest summer in 140 yr. Our results suggest that S. scalaris persists on a dynamic habitat mosaic, where refuges are crucial during adverse periods, and stochastic environmental factors (disturbances and climate), that are correlated over large areas, are generating population dynamic patterns that are hard to predict using current modelling techniques.

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