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Hyperoverlap: detecting biological overlap in n-dimensional space

Citation

Brown, MJM and Holland, B and Jordan, GJ, Hyperoverlap: detecting biological overlap in n-dimensional space, Methods in Ecology and Evolution ISSN 2041-210X (2020) [Refereed Article]


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Copyright Statement

© 2020 British Ecological Society. This is the peer reviewed version of the following article, Brown MJM, Holland BR, Jordan GJ. Hyperoverlap: Detecting biological overlap in n-dimensional space. Methods Ecol Evol. 2020;00:1–11. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article has been published in final form at:

DOI: doi:10.1111/2041-210X.13363

Abstract

  1. Comparative biological studies often investigate the morphological, physiological or ecological divergence (or overlap) between entities such as species or populations. Here, we discuss the weaknesses of using existing methods to analyse patterns of phenotypic overlap and present a novel method to analyse co-occurrence in multidimensional space.
  2. We propose a ‘hyperoverlap’ framework to detect qualitative overlap (or divergence) between point data sets and present the HYPEROVERLAP R package which implements this framework, including functions for visualisation. HYPEROVERLAP uses support vector machines (SVMs) to train a classifier based on point data (such as morphological or ecological data) for two entities. This classifier finds the optimal boundary between the two sets of data and compares the predictions to the original labels. Misclassification is evidence of overlap between the two entities. We demonstrate the theoretical and practical advantages of this method compared to existing approaches (e.g. single-entity hypervolume models) using the bioclimatic data extracted from global occurrence records of conifers.
  3. We find that there are instances where single-entity hypervolume models predict overlap, but there are no observations of either entity in the shared hypervolume. In these instances, hyperoverlap reports nonoverlap. We show that our method is stable and less likely to be affected by sampling biases than current approaches. We also find that hyperoverlap is particularly effective for situations involving entities with a small number of data points (e.g. narrowly endemic species) for which single-entity models cannot be reliably constructed.
  4. We argue that overlap can be reliably detected using HYPEROVERLAP, particularly for descriptive studies. The method proposed here is a valuable tool for studying patterns of overlap in multidimensional space.

Item Details

Item Type:Refereed Article
Keywords:ecospace, hyperoverlap, hypervolume, machine learning, morphospace, overlap, support vector machines
Research Division:Mathematical Sciences
Research Group:Applied mathematics
Research Field:Biological mathematics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Brown, MJM (Ms Matilda Brown)
UTAS Author:Holland, B (Professor Barbara Holland)
UTAS Author:Jordan, GJ (Professor Greg Jordan)
ID Code:137708
Year Published:2020
Funding Support:Australian Research Council (DP160100809)
Web of Science® Times Cited:4
Deposited By:Plant Science
Deposited On:2020-02-28
Last Modified:2020-04-02
Downloads:2 View Download Statistics

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