Rapid Characterisation of Vegetation Structure to Predict Refugia and Climate Change Impacts across a Global Biodiversity Hotspot

PLoS One

Steven E. Franklin¹  Kimberley P. Van Niel²  Colin J. Yates³  Grant W. Wardell-Johnson³  Antonius G. T. Schut⁴  Ladislav Mucina⁵  Ireneusz Baran⁶  Margaret Byrne⁷  Gunnar Keppel⁸  Stephen D. Hopper⁹ 

[1] AAM Pty Limited, Perth, Western Australia, Australia;Centre of Excellence in Natural Resource Management, The University of Western Australia, Albany, Western Australia, Australia;Curtin Institute for Biodiversity and Climate, Curtin University, Bentley, Western Australia, Australia;Department of Spatial Sciences, Curtin University, Bentley, Western Australia, Australia;School of Earth and Environment, The University of Western Australia, Crawley, Western Australia, Australia;School of Natural and Built Environments and Barbara Hardy Institute, University of South Australia, Adelaide, South Australia, Australia;School of Plant Biology, The University of Western Australia, Crawley, Western Australia, Australia;Science Division, Department of Parks and Wildlife, Bentley, Western Australia, Australia;Trent University, Peterborough, Ontario, Canada

关键词: Climate change;    Lidar;    Biodiversity;    Rain;    Habitats;    Shrubs;    Conservation science;    Forests;   

DOI  :  10.1371/journal.pone.0082778

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region. Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia.

【 授权许可】

CC BY