Abstract
SummaryOur understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/plant distribution.Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two‐stage model and smoothing in areas with complex boundaries.The methods discussed are available in anRpackage developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.
Highlights
Summary1. Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates
When surveying biological populations, it is increasingly common to record spatially referenced data, for example coordinates of observations, habitat type, elevation or bathymetry
Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods
Summary
1. Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/ plant distribution. 2. Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods. Key-words: abundance estimation, Distance software, generalized additive models, line transect sampling, point transect sampling, population density, spatial modelling, wildlife surveys
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