RMIT University
Browse

Classifying animals into ecologically meaningful groups: A case study on woodland birds

journal contribution
posted on 2024-11-02, 04:30 authored by Hannah Fraser, Cindy Hauser, Libby Rumpff, Georgia Garrard, Michael McCarthy
Ecologists often classify species into binary groupings such as woodland or non-woodland birds. However, each ecologist may apply a different classification, which might impede progress in ecology and conservation by precluding direct comparison between studies. This study describes and tests a method for deriving empirically-based, ecologically-relevant species groups, using Australian woodland birds as a case study. A Bayesian hierarchical model investigates how vegetation and species traits drive birds' preference for woodland vegetation, characterised by low density trees with an open canopy structure. Birds are then classified according to their affinity to areas with high tree cover and woodland vegetation. Interestingly, no traits are strongly associated with species occurrence in woodland habitats, but occurrence in open country and forests differ depending on dispersal ability and foraging habits. Our results suggest that Australian woodland birds may be united by their avoidance of both sparsely-treed and densely-treed habitat, rather than by shared traits. Classifying species according to our groupings provides results consistent with literature on how woodland birds respond to clearing, grazing and urbanisation. Thus, our model is consistent with current ecological understanding regarding woodland birds; it also provides more nuanced inference across 'closed-woodland', 'open-woodland', 'forest' and 'open country' groups. We propose that our modelling approach could be used to classify species for other locations and taxa, providing transparent, ecologically-relevant animal groupings.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1016/j.biocon.2017.07.006
  2. 2.
    ISSN - Is published in 00063207

Journal

Biological Conservation

Volume

214

Start page

184

End page

194

Total pages

11

Publisher

Elsevier

Place published

Netherlands

Language

English

Copyright

© 2017 Elsevier Ltd

Former Identifier

2006078912

Esploro creation date

2020-06-22

Fedora creation date

2017-10-20

Usage metrics

    Scholarly Works

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC