I prefer the other less scientific conservation management model based on an
assumption that if you hear or see it, its there.
Regards
Ian
knightl wrote:
The following paper is available from Lesley Gibson -
Spatial prediction of rufous bristlebird habitat in a coastal heathland:
a GIS-based approach
Gibson L.A.; Wilson B.A.; Cahill D.M.; Hill J.
Journal of Applied Ecology April 2004, vol. 41, no. 2, pp. 213-223
• To develop a conservation management plan for a species, knowledge of
its distribution and spatial arrangement of preferred habitat is
essential. This is a difficult task, especially when the species of
concern is in low abundance. In south-western Victoria, Australia,
populations of the rare rufous bristlebird Dasyornis broadbenti are
threatened by fragmentation of suitable habitat. In order to improve the
conservation status of this species, critical habitat requirements must
be identified and a system of corridors must be established to link
known populations. A predictive spatial model of rufous bristlebird
habitat was developed in order to identify critical areas requiring
preservation, such as corridors for dispersal.
• Habitat models generated using generalized linear modelling techniques
can assist in delineating the specific habitat requirements of a
species. Coupled with geographic information system (GIS) technology,
these models can be extrapolated to produce maps displaying the spatial
configuration of suitable habitat.
• Models were generated using logistic regression, with bristlebird
presence or absence as the dependent variable and landscape variables,
extracted from both GIS data layers and multispectral digital imagery,
as the predictors. A multimodel inference approach based on Akaike's
information criterion was used and the resulting model was applied in a
GIS to extrapolate predicted likelihood of occurrence across the entire
area of concern. The predictive performance of the selected model was
evaluated using the receiver operating characteristic (ROC) technique. A
hierarchical partitioning protocol was used to identify the predictor
variables most likely to influence variation in the dependent variable.
Probability of species presence was used as an index of habitat
suitability.
• Negative associations between rufous bristlebird presence and
increasing elevation, ‘distance to creek’, ‘distance to coast’ and sun
index were evident, suggesting a preference for areas relatively low in
altitude, in close proximity to the coastal fringe and drainage lines,
and receiving less direct sunlight. A positive association with
increasing habitat complexity also suggested that this species prefers
areas containing high vertical density of vegetation.
• The predictive performance of the selected model was shown to be high
(area under the curve 0·97), indicating a good fit of the model to the
data. Hierarchical partitioning analysis showed that all the variables
considered had significant independent contributions towards explaining
the variation in the dependent variable. The proportion of the total
study area that was predicted as suitable habitat for the rufous
bristlebird (using probability of occurrence at a >= 0·5 level) was 16%.
• Synthesis and applications. The spatial model clearly delineated areas
predicted as highly suitable rufous bristlebird habitat, with evidence
of potential corridors linking coastal and inland populations via
gullies. Conservation of this species will depend on management actions
that protect the critical habitats identified in the model. A multiscale
approach to the modelling process is recommended whereby a spatially
explicit model is first generated using landscape variables extracted
from a GIS, and a second model at site level is developed using
fine-scale habitat variables measured on the ground. Where there are
constraints on the time and cost involved in measuring finer scale
variables, the first step alone can be used for conservation planning.
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