There is a growing need for accurate distribution data for both common and rare plant
species for conservation planning and ecological research purposes. A database of more than
500 observations for nine tree species with different ecological and geographical
distributions and a range of frequencies of occurrence in south-eastern New South Wales
(Australia) was used to compare the predictive performance of logistic regression models,
generalised additive models (GAMs) and classification tree models (CTMs) using different
data restriction regimes and several model-building strategies. Environmental variables
(mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature,
mean maximum summer temperature, mean minimum winter temperature, mean daily
radiation, mean daily summer radiation, mean daily June radiation, lithology and
topography) were used to model the distribution of each of the plant species in the study
area.
Model predictive performance was measured as the area under the curve of a receiver
operating characteristic (ROC) plot. The initial predictive performance of logistic regression
models and generalised additive models (GAMs) using unrestricted, temperature restricted,
major gradient restricted and climatic domain restricted data gave results that were contrary
to current practice in species distribution modelling. Although climatic domain restriction
has been used in other studies, it was found to produce models that had the lowest predictive
performance. The performance of domain restricted models was significantly (p = 0.007)
inferior to the performance of major gradient restricted models when the predictions of the
models were confined to the climatic domain of the species. Furthermore, the effect of data
restriction on model predictive performance was found to depend on the species as shown by
a significant interaction between species and data restriction treatment (p = 0.013). As found
in other studies however, the predictive performance of GAM was significantly (p = 0.003)
better than that of logistic regression. The superiority of GAM over logistic regression was
unaffected by different data restriction regimes and was not significantly different within
species.
The logistic regression models used in the initial performance comparisons were based on
models developed using the forward selection procedure in a rigorous-fitting model-building
framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding
framework involved testing for the significant reduction in model deviance (p =
0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter
estimates and their standard errors were inspected because large estimates and/or standard
errors are an indication of model degradation from overfilling or effecls such as mullicollinearily.
For additional variables to be included in a model, they had to contribule
significantly (p = 0.025) to the model prediclive performance. An attempt to improve the
performance of species distribution models using logistic regression models in a rigorousfitting
model-building framework, the backward elimination procedure was employed for
model selection, bul it yielded models with reduced performance.
A liberal-filling model-building framework that used significant model deviance reduction at
p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the
major criterion for variable selection was employed for the development of logistic
regression models using the forward selection and backward elimination procedures. Liberal
filling yielded models that had a significantly greater predictive performance than the
rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the
former models was comparable to that of GAM and classification tree models (CTMs). The
low significance liberal-filling models had a much larger number of variables than the high
significance liberal-fitting models, but with no significant increase in predictive
performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was
used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size
of a full CTM for a given species. The 10-fold cross-validated model deviance for the
subtrees was plotted against the size of the subtree as a means of selecting an appropriate
tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance.
Species geographical range and species prevalence within the study area were used to
categorise the tree species into different distributional forms. These were then used, to
compare the effect of plant species rarity on the predictive performance of logistic regression
models, GAMs and CTMs. The distributional forms included restricted and rare (RR)
species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC)
species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides),
widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC)
species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were
significant differences (p = 0.076) in predictive performance among the distributional forms
for the logistic regression and GAM. The predictive performance for the WR distributional
form was significantly lower than the performance for the other plant species distributional
forms. The predictive performance for the RC and RR distributional forms was significantly
greater than the performance for the WC distributional form. The trend in model predictive
performance among plant species distributional forms was similar for CTMs except that the
CTMs had poor predictive performance for the RR distributional form.
This study shows the importance of data restriction to model predictive performance with
major gradient data restriction being recommended for consistently high performance. Given
the appropriate model selection strategy, logistic regression, GAM and CTM have similar
predictive performance. Logistic regression requires a high significance liberal-fitting
strategy to both maximise its predictive performance and to select a relatively small model
that could be useful for framing future ecological hypotheses about the distribution of
individual plant species. The results for the modelling of plant species for conservation
purposes were encouraging since logistic regression and GAM performed well for the
restricted and rare species, which are usually of greater conservation concern.
| Identifer | oai:union.ndltd.org:ADTP/218617 |
| Date | January 2002 |
| Creators | Mugodo, James, n/a |
| Publisher | University of Canberra. Resource, Environmental & Heritage Sciences |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | ), Copyright James Mugodo |
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