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Data from: Spatial autocorrelation in fitness affects the estimation of natural selection in the wild
- 负责人：
- DOI：
- doi:10.5061/dryad.pm86c
- 摘要：
- -err)) and a 5-step procedure using the principal coordinates of neighbour matrices (PCNM) method based on the extraction of spatial descriptors
Data from: Landscape-scale variation in an anthropogenic factor shapes immune gene variation within a wild population
- 负责人：
- DOI：
- doi:10.5061/dryad.40vv8
- 摘要：
- Understanding the spatial scale at which selection acts upon adaptive genetic variation in natural populations is fundamental to our understanding of evolutionary ecology, and has important ramifications for conservation. The environmental factors to which individuals of a population are exposed can vary at fine spatial scales, potentially generating localized patterns of adaptation. Here, we compared patterns of neutral and major histocompatibility complex (MHC) variation within an island population of Berthelot's pipit (Anthus berthelotii) to assess whether landscape-level differences in pathogen-mediated selection generate fine-scale spatial structuring in these immune genes. Specifically, we tested for spatial associations between the distribution of avian malaria, and the factors previously shown to influence that distribution, and MHC variation within resident individuals. Although we found no overall genetic structure across the population for either neutral or MHC loci, we did find localized associations between environmental factors and MHC variation. One MHC class I allele (ANBE48) was directly associated with malaria infection risk, while the presence of the ANBE48 and ANBE38 alleles within individuals correlated (positively and negatively, respectively) with distance to the nearest poultry farm, an anthropogenic factor previously shown to be an important determinant of disease distribution in the study population. Our findings highlight the importance of considering small spatial scales when studying the patterns and processes involved in evolution at adaptive loci.
Data from: Everything is not everywhere: marine compartments shape phytoplankton assemblages
- 负责人：
- DOI：
- doi:10.5061/dryad.qfttdz0c8
- 摘要：
- environmental pressures. We used PCNM and AEM models to partition the effects that spatial structures, environmental conditions and their overlap had on the variation
Data from: Relating species richness to the structure of continuous landscapes: alternative methodological approaches
- 负责人：
- DOI：
- doi:10.5061/dryad.vc51n47
- 摘要：
- Numerous studies have focused on the relationship between landscape structure and plant diversity based on the patch-mosaic landscape paradigm, by deriving structural data from classified images. Since the use of discrete classes poses limitations for predicting biodiversity patterns in complex, low human-impacted ecosystems, two alternative methods have been used to analyze changes of landscape attributes in a continuum: moving-window metrics and surface metrics (image texture). Here we compare these two approaches for predicting richness of all plant species, legume species, legume trees, legume shrubs, legume forbs and legume climbers across a tropical landscape in Mexico, based on records of vascular plants in 250 10 × 10 m-plots. Multiple regression and variation partitioning methods were used to analyze the effects of the two landscape descriptors (moving-window and surface metrics), scale (400 and 200 m moving window sides) and space (based on the extraction of principal coordinates of neighbor matrices’ vectors) on species richness. The predictive power of all metrics was relatively small for total species richness, but generally higher for legume species. For legume forbs, surface metrics-based models indicated a direct association between species richness and landscape homogeneity. Moving-window metrics were highly sensitive to the biological group and to spatial scale, likely due to a leftover effect of image classification procedures. Conversely, surface metrics were more independent from scale and taxonomy. Attempts to predict species richness in highly diverse, low human-impacted tropical ecosystems more rapidly and accurately should better rely on surface metrics rather than on moving-window metrics, in line with the continuous landscape paradigm.
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