2 resultados para Hierarchical sampling

em Publishing Network for Geoscientific


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Seagrasses are ecosystem engineers that offer important habitat for a large number of species and provide a range of ecosystem services. Many seagrass ecosystems are dominated by a single species; with research showing that genotypic diversity at fine spatial scales plays an important role in maintaining a range of ecosystem functions. However, for most seagrass species, information on fine-scale patterns of genetic variation in natural populations is lacking. In this study we use a hierarchical sampling design to determine levels of genetic and genotypic diversity at different spatial scales (centimeters, meters, kilometers) in the Australian seagrass Zostera muelleri. Our analysis shows that at fine-spatial scales (< 1 m) levels of genotypic diversity are relatively low (R (Plots) = 0.37 ± 0.06 SE), although there is some intermingling of genotypes. At the site (10's m) and meadow location (km) scale we found higher levels of genotypic diversity (R (sites) = 0.79 ± 0.04 SE; R (Locations) = 0.78 ± 0.04 SE). We found some sharing of genotypes between sites within meadows, but no sharing of genotypes between meadow locations. We also detected a high level of genetic structuring between meadow locations (FST = 0.278). Taken together, our results indicate that both sexual and asexual reproduction are important in maintaining meadows of Z. muelleri. The dominant mechanism of asexual reproduction appears to occur via localised rhizome extension, although the sharing of a limited number of genotypes over the scale of 10's of metres could also result from the localised dispersal and recruitment of fragments. The large number of unique genotypes at the meadow scale indicates that sexual reproduction is important in maintaining these populations, while the high level of genetic structuring suggests little gene flow and connectivity between our study sites. These results imply that recovery from disturbances will occur through both sexual and asexual regeneration, but the limited connectivity at the landscape-scale implies that recovery at meadow-scale losses is likely to be limited.

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Models and data used to describe species-area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species-area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species-area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density-area relationships and occurrence probability-area relationships can alter the form of species-area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.