The recent past and promising future for data integration methods to estimate species’ distributions

David Andrew Miller, Krishna Pacifici, Jamie S. Sanderlin, Brian J. Reich

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point-process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.

Original languageEnglish (US)
Pages (from-to)22-37
Number of pages16
JournalMethods in Ecology and Evolution
Volume10
Issue number1
DOIs
StatePublished - Jan 1 2019

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biogeography
harness
autocorrelation
methodology
sampling
distribution
method

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecological Modeling

Cite this

Miller, David Andrew ; Pacifici, Krishna ; Sanderlin, Jamie S. ; Reich, Brian J. / The recent past and promising future for data integration methods to estimate species’ distributions. In: Methods in Ecology and Evolution. 2019 ; Vol. 10, No. 1. pp. 22-37.
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The recent past and promising future for data integration methods to estimate species’ distributions. / Miller, David Andrew; Pacifici, Krishna; Sanderlin, Jamie S.; Reich, Brian J.

In: Methods in Ecology and Evolution, Vol. 10, No. 1, 01.01.2019, p. 22-37.

Research output: Contribution to journalArticle

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