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Estimation of abundance from presence–absence maps using cluster models

Authors
  • Huggins, Richard1
  • Hwang, Wen-Han2
  • Stoklosa, Jakub3
  • 1 The University of Melbourne, Department of Mathematics and Statistics, Melbourne, Australia , Melbourne (Australia)
  • 2 National Chung Hsing University, Institute of Statistics, Taichung, Taiwan , Taichung (Taiwan)
  • 3 The University of New South Wales, School of Mathematics and Statistics and Evolution and Ecology Research Centre, Sydney, Australia , Sydney (Australia)
Type
Published Article
Journal
Environmental and Ecological Statistics
Publisher
Springer US
Publication Date
Nov 09, 2018
Volume
25
Issue
4
Pages
495–522
Identifiers
DOI: 10.1007/s10651-018-0415-5
Source
Springer Nature
Keywords
License
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Abstract

A presence–absence map consists of indicators of the occurrence or nonoccurrence of a given species in each cell over a grid, without counting the number of individuals in a cell once it is known it is occupied. They are commonly used to estimate the distribution of a species, but our interest is in using these data to estimate the abundance of the species. In practice, certain types of species (in particular flora types) may be spatially clustered. For example, some plant communities will naturally group together according to similar environmental characteristics within a given area. To estimate abundance, we develop an approach based on clustered negative binomial models with unknown cluster sizes. Our approach uses working clusters of cells to construct an estimator which we show is consistent. We also introduce a new concept called super-clustering used to estimate components of the standard errors and interval estimators. A simulation study is conducted to examine the performance of the estimators and they are applied to real data.

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