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S.T. Buckland, K.P. Burnham, N.H. Augustin (1997)
Model selection: an integral part of inferenceBiometrics, 53
B.F.J. Manly (1997)
Randomization, Bootstrap and Monte Carlo Methods in Biology
R.H. Loyn (1987)
Nature Conservation: the Role of Remnants of Native Vegetation
R. Mac Nally (1996)
Hierarchical partitioning as an interpretative tool in multivariate inferenceAustralian Journal of Ecology, 21
R. Christensen (1992)
Comment on Chevan and SutherlandThe American Statistician, 46
D.T. Bolger, A.C. Alberts, M.E. Soulé (1991)
Occurrence patterns of bird species in habitat fragments: sampling, extinction, and nested species subsetsAmerican Naturalist, 137
R. Mac Nally (2000)
Regression and model-building in conservation biology, biogeography and ecology: the distinction between — and reconciliation of — ‘predictive’ and ‘explanatory’ modelsBiodiversity and Conservation, 9
R. Mac Nally, A.F. Bennett, G. Horrocks (2000)
Forecasting the impacts of habitat fragmentationEvaluation of species-specific predictions of the impact of habitat fragmentation on birds in the box-ironbark forests of central Victoria, Australia. Biological Conservation, 95
M.J. Anderson (2001)
A new method for non-parametric multivariate analysis of varianceAustralian Ecology, 26
K.R. Clarke (1993)
Non-parametric multivariate analyses of changes in community structureAustralian Journal of Ecology, 18
K.P. Burnham, D.R. Anderson (1998)
Model Selection and Inference: A Practical Information-Theoretic Approach
A. Chevan, M. Sutherland (1991)
Hierarchical partitioningThe American Statistician, 45
Ecologists and conservation biologists frequently use multipleregression (MR) to try to identify factors influencing response variables suchas species richness or occurrence. Many frequently used regression methods maygenerate spurious results due to multicollinearity. argued that there are actually two kinds of MR modelling: (1)seeking the best predictive model; and (2) isolating amounts of varianceattributable to each predictor variable. The former has attracted most attentionwith a plethora of criteria (measures of model fit penalized for modelcomplexity – number of parameters) and Bayes-factor-based methods havingbeen proposed, while the latter has been little considered, althoughhierarchical methods seem promising (e.g. hierarchical partitioning). If the twoapproaches agree on which predictor variables to retain, then it is more likelythat meaningful predictor variables (of those considered) have been found. Therehas been a problem in that, while hierarchical partitioning allowed the rankingof predictor variables by amounts of independent explanatory power, there was no(statistical) way to decide which variables to retain. A solution usingrandomization of the data matrix coupled with hierarchical partitioning ispresented, as is an ecological example.
Biodiversity and Conservation – Springer Journals
Published: Oct 12, 2004
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