Data from: A meta-analysis of factors affecting local adaptation between interacting species ...
Adaptive divergence among populations can result in local adaptation, whereby genotypes in native environments exhibit greater fitness than genotypes in novel environments. A body of theory has developed that predicts how different species traits, such as rates of gene flow and generation times, inf...
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| Format: | Dataset |
| Language: | English |
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Dryad
2011
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| Online Access: | https://dx.doi.org/10.5061/dryad.8845 https://datadryad.org/stash/dataset/doi:10.5061/dryad.8845 |
| Summary: | Adaptive divergence among populations can result in local adaptation, whereby genotypes in native environments exhibit greater fitness than genotypes in novel environments. A body of theory has developed that predicts how different species traits, such as rates of gene flow and generation times, influence local adaptation in coevolutionary species interactions. We used a meta‐analysis of local‐adaptation studies across a broad range of host‐parasite interactions to evaluate predictions about the effect of species traits on local adaptation. We also evaluated how experimental design influences the outcome of local adaptation experiments. In reciprocally designed experiments, the relative gene flow rate of hosts versus parasites was the strongest predictor of local adaptation, with significant parasite local adaptation only in the studies in which parasites had greater gene flow rates than their hosts. When nonreciprocal studies were included in analyses, species traits did not explain significant variation in ... : Summary data for the studies used in the meta-analysis of local adaptation (Table 1 from the publication)This table contains the data used in this published meta-analysis. The data were originally extracted from the publications listed in the table. The file corresponds to Table 1 in the original publication.tb1.xlsSAS script used to perform meta-analysesThis file contains the essential elements of the SAS script used to perform meta-analyses published in Hoeksema & Forde 2008. Multi-factor models were fit to the data using weighted maximum likelihood estimation of parameters in a mixed model framework, using SAS PROC MIXED, in which the species traits and experimental design factors were considered fixed effects, and a random between-studies variance component was estimated. Significance (at alpha = 0.05) of individual factors in these models was determined using randomization procedures with 10,000 iterations (performed with a combination of macros in SAS), in which effect sizes and their weights were ... |
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