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Classification of unlabeled observations in Species Distribution Modelling using Point Process Models.
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  • Emy Guilbault,
  • Ian Renner,
  • Michael Mahony,
  • Eric Beh
Emy Guilbault
The University of Newcastle Faculty of Science
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Ian Renner
The University of Newcastle Faculty of Science
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Michael Mahony
The University of Newcastle Faculty of Science
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Eric Beh
The University of Newcastle Faculty of Science
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Abstract

1. Species distribution modelling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit species distribution models. However, the species observations used in species distribution models can have varying levels of quality and can have incomplete information, such as uncertain species identity. 2. In this paper, we develop two algorithms to reclassify observations with unknown species identities which simultaneously predict different species distributions using spatial point processes. We compare the performance of the different algorithms using different initializations and parameters with models fitted using only the observations with known species identity through simulations. 3. We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that didn't use the misspecified data. 4. These models represent an helpful and promising tool for opportunistic surveys where misidentification happens or for the distribution of species newly separated in their taxonomy.

Peer review status:ACCEPTED

21 Oct 2020Submitted to Ecology and Evolution
23 Oct 2020Submission Checks Completed
23 Oct 2020Assigned to Editor
26 Oct 2020Review(s) Completed, Editorial Evaluation Pending
23 Dec 2020Editorial Decision: Revise Minor
08 Feb 20211st Revision Received
09 Feb 2021Submission Checks Completed
09 Feb 2021Assigned to Editor
09 Feb 2021Review(s) Completed, Editorial Evaluation Pending
11 Feb 2021Editorial Decision: Accept