Spatial models for distance sampling data: recent developments and future directions
By David L. Miller, M. L. Burt, E. Rexstad, L. Thomas. 2013
323 citations recorded by the source corpus.
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. 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 an R package developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.
Subfields: Economics and Econometrics, Environmental Chemistry, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/6ffc7e2c6b1bf8a437ebe94e77c997b7ad3645a8
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Citations in the corpus
- Seasonal habitat-based density models for a marine top predator, the harbor porpoise, in a dynamic environment (2016, result): “We also compared the model- based abundance estimates for all three seasons between two grid resolutions (5 × 5 and 10 × 10 km) and found that they were identical, consistent with what is expected from spatial models (Miller et al. 2013).”