Gathering data from published sources
We collected published population genetic studies of terrestrial plant and animal species whose distributions were at least partially found within the Mediterranean basin, from endemic to widespread (Fig. 1). For this, we searched the Web of Knowledge for references published between January 1980 and October 2009. We used the following search expressions: “population genetics,” “phylogeo*,” “genetic diversit*,” “Mediterr*,” and individual Mediterranean country names. We also examined the references included in all retrieved publications for additional references.
Defining the geographic zone of interest and population sample size
From these papers, we selected all populations with estimates of genetic diversity within population (GDpop estimates) that fall within the Mediterranean basin. We used the delineation of the Mediterranean basin defined by Olson et al. (2001), which is the standard currently used by the World Wildlife Fund (WWF) to define the world eco‐regions. We used a geographic information system (GIS) for selecting among published studies, which population to allocate to that geographic envelope and to further qualify populations as continental versus insular and northern versus southern Mediterranean.
Constructing a database of GDpop estimates
We created a database where each row corresponded to an individual population from the Mediterranean, characterized by its geographical coordinates and GDpop estimates. Studies sometimes contained more than one species and a single species was sometimes studied by several published papers. Also, there was often more than one marker and/or GDpop measure used for each species and population within study. We recorded all measures for each population independently and generated a code for this redundancy. When a primary study reported genotypic data from single individuals in sampled localities but with an extensive sampling effort across an area or across the whole Mediterranean Basin (as, for example, in Ronfort et al. 2006), we grouped neighboring localities into small regions for which we computed genetic diversity indices. The new georeferenced code for each of these artificial populations was the geometric center of all coordinates within the small region. For quality, we only kept in our database those studies for which sample size was more than 3 populations per study.
Raw data analysis and calculation of effect‐sizes
No published paper we retrieved had testing for a correlation between longitude and GDpop as its primary goal. We used the raw data from these studies to correlate the longitudinal position of each population with its GDpop. The statistics we used was the Pearson product–moment correlation coefficient. The populations tested were not located on a straight longitudinal line, but rather spanned a small latitudinal gradient (which reaches its maximum in each of the Mediterranean peninsulas). Thus, in order to account for the effect of latitude, we calculated a partial correlation coefficient, which measured the degree of association of GDpop with longitude while latitude was held constant. Partial correlation coefficients in meta‐analysis represent the relationship between the independent and the dependent variable while controlling for other factors. In our case, the controlling factor was always the same, latitude.
Publications used in this study
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