Predictive distribution models and their application in wildlife conservation
Gibson, Lesley, Wilson, Barbara, Cahill, David and Hill, John 2003, Predictive distribution models and their application in wildlife conservation, in 3rd International Wildlife Management Congress : programme and abstracts, Landcare New Zealand, New Zealand, pp. 43-43.
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Title
Predictive distribution models and their application in wildlife conservation
3rd International Wildlife Management Congress : programme and abstracts
Publication date
2003
Start page
43
End page
43
Publisher
Landcare New Zealand
Place of publication
New Zealand
Summary
Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.
Language
eng
Field of Research
050299 Environmental Science and Management not elsewhere classified
Socio Economic Objective
970105 Expanding Knowledge in the Environmental Sciences
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