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Regional variation in habitat–occupancy thresholds: a warning for conservation planning
Jonathan R.Rhodes1,2,3*, John G.Callaghan4, Clive A.McAlpine1,2, Carolde Jong4, Michiala E.Bowen1,2, David L.Mitchell4, DanielLunney5 and Hugh P.Possingham2
1Centre for Remote Sensing and Spatial Information Science, School of Geography, Planning and Architecture, University of Queensland, Brisbane, QLD 4072, Australia; 2The Ecology Centre, School of Integrative Biology, University of Queensland, Brisbane, QLD 4072, Australia; 3Wealth from Oceans Flagship, CSIRO, GPO Box 1538, Hobart, TAS 7001, Australia; 4Australian Koala Foundation, GPO Box 2659, Brisbane, QLD 4001, Australia; and 5Department of Environment and Climate Change (NSW), PO Box 1967, Hurstville, NSW 2220, Australia
*Correspondence and present address: Jonathan R. Rhodes, Centre for Remote Sensing and Spatial Information Science, School of Geography, Planning and Architecture, University of Queensland, Brisbane, QLD 4072, Australia. E-mail: j.rhodes@uq.edu.au
Journal of Applied Ecology (2007) doi: 10.1111/j.1365-2664.2007.01407.x
ABSTRACT
1.
An important target for conservation planning is the minimum amount of habitat needed in a landscape to ensure the persistence of a species. Appropriate targets can be determined by identifying thresholds in the amount of habitat, below which persistence, abundance or occupancy declines rapidly. Although some studies have identified habitat thresholds, we currently have little understanding of the extent to which thresholds vary spatially. This is important for establishing whether we can apply the same planning targets across broad geographical regions.
2.
We quantified habitat–occupancy relationships for the koala Phascolarctos cinereus (Goldfuss) in three study regions that span much of its geographical range. Standard and piecewise (broken-stick/segmented) logistic regression were used to model linear and threshold habitat–occupancy relationships. We then used an information-theoretic approach to test: (1) whether habitat–occupancy relationships were described better by threshold or linear models and (2) where threshold models were better, whether, and to what extent, threshold points varied among study regions.
3.
There was substantially greater support for the threshold than the linear models across a range of habitat qualities and landscape extents. The threshold models generally predicted a rapid decline in occupancy below the threshold points.
4.
Estimated threshold points varied, sometimes substantially, among study regions. This may relate to cross-regional differences in habitat quality, demographic rates, and land-use patterns. The role of habitat fragmentation is unclear.
5.
Synthesis and applications. Variation in threshold points among study regions suggests that we should be wary of using thresholds derived in one region for setting conservation planning targets in another. Rather, we should aim to set specific targets for individual locations (and species), while acknowledging the inherent uncertainties in these targets. This has implications for our ability to make general conservation prescriptions for widely distributed species. Future research should aim to develop generic models capable of predicting threshold responses across different landscapes and life-history characteristics.
Received 16 February 2007; accepted 14 August 2007 Handling Editor: Philip Stephens
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