Determining the distributions of plant communities in subantarctic vegetation using species distribution models
Nicholas B. Fitzgerald
A * , Jamie B. Kirkpatrick A , Catherine R. Dickson B , Laura K. Williams C , Alexander J. Fergus D and Jennie Whinam A
+ Author Affiliations
- Author Affiliations
A School of Geography and Spatial Sciences, University of Tasmania, Hobart, Tas. 7005, Australia.
B Tasmanian Land Conservancy, Hobart, Tas. 7005, Australia.
C Previously with School of Environmental and Rural Science, The University of New England, Armidale, NSW 2351, Australia.
D Manaaki Whenua–Landcare Research, Lincoln, 7640, New Zealand.
Australian Journal of Botany 70(4) 311-322 https://doi.org/10.1071/BT21124
Submitted: 11 October 2021 Accepted: 30 May 2022 Published: 8 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC)
Abstract
Context: Mapping of vegetation is important in understanding its dynamics in relation to climate change and disturbance. We investigated using species distribution models to predict plant species assemblages in a subantarctic environment where traditional image interpretation methods of vegetation mapping are limited by image availability and ability to discriminate vegetation types.
Aims: We test the efficacy for mapping of modelling the range and core range of common species. We also determine the relative importance of predictor variables for each of nine species.
Methods: We used random forest models to predict the total range and core range (>25% projected foliage cover) of nine potentially dominant plant species and determined the contributions of predictor variables to the models for each species.
Key results: Widespread species with extensively overlapping ranges were spatially more partitioned with modelling based on core range than with presence or absence modelling. The core range input produced a vegetation map that better approximated observed vegetation patterns than that from presence or absence data. The most important predictor variable varied between species, with elevation, distance from coast, latitude and an across island gradient (similar to longitude) being most influential.
Conclusions: Species distribution models using three categories (absent, <25% cover, ≥25% cover) and topographic variables derived from a digital elevation model can be used to model the distribution of vegetation assemblages in situations where presence or absence species models cannot discriminate assemblages.
Implications: Readily collected point location species data could be used to investigate change over time in the spatial extent of both species and vegetation types.
Keywords: feldmark, grassland, herbaceous plants, herbfield, megaherb, plant communities, random forest model, tundra, vegetation boundaries.
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