Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

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 rich...

Estimating animal abundance by distance sampling techniques

Reliable and repeatable methods for estimating animal abundance are fundamental to the effective study and management of New Zealand species. New Zealand scientists and conservation managers currently rely on measures of conspicuousness rather than density for indexing the sizes of many New Zealand animal populations. Distance sampling offers a group of methods which estimate the absolute densi...

Estimating Species Abundance

Attenuation of species abundance distributions by sampling

Quantifying biodiversity aspects such as species presence/ absence, richness and abundance is an important challenge to answer scientific and resource management questions. In practice, biodiversity can only be assessed from biological material taken by surveys, a difficult task given limited time and resources. A type of random sampling, or often called sub-sampling, is a commonly used techniq...

Extending species-area relationships (SAR) to diversity-area relationships (DAR)

Aim: I extend the traditional SAR, which has achieved status of ecological law and plays a critical role in global biodiversity assessment, to the general (alphaor beta-diversity in Hill numbers) diversity area relationship (DAR). The extension was motivated to remedy the limitation of traditional SAR that only address one aspect of biodiversity scaling—species richness scaling over space. Inno...