Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

Abstract In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence ⩾0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.

[1]  Dr Robert Bryant,et al.  Modelling landscape-scale habitat use using GIS and remote sensing : a case study with great bustards , 2001 .

[2]  Ralph Mac Nally,et al.  Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables , 2002, Biodiversity & Conservation.

[3]  A. Newsome,et al.  Habitat preferences of mammals inhabiting heathlands of warm temperate coastal, montane and alpine regions of southeastern Australia , 1979 .

[4]  Jyrki Kangas,et al.  Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling , 2001 .

[5]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[6]  N. A. Wakefield,et al.  Some Revision in Antechinus (Marsupialia)— 2 , 1967 .

[7]  D. Lindenmayer,et al.  The conservation of arboreal marsupials in the montane ash forests of the central highlands of Victoria, south-eastern Australia. VIII. Landscape analysis of the occurrence of arboreal marsupials , 1999 .

[8]  R. William Mannan,et al.  Multiscaled habitat selection by elegant trogons in southeastern Arizona , 1999 .

[9]  S. Manel,et al.  Alternative methods for predicting species distribution: an illustration with Himalayan river birds , 1999 .

[10]  R. Loyn,et al.  Modelling landscape distributions of large forest owls as applied to managing forests in north-east Victoria, Australia , 2001 .

[11]  B. Wilson,et al.  Effects of fragmented habitat and fire on the distribution and ecology of the swamp antechinus (Antechinus minimus maritimus) in the eastern Otways, Victoria , 2001 .

[12]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[13]  N. Coops,et al.  Prediction of the distribution and abundance of small mammals in the eucalypt forests of south-eastern Australia from airborne videography* , 1999 .

[14]  P. Catling,et al.  Models of the distribution and abundance of ground-dwelling mammals in the eucalypt forests of south-eastern New South Wales , 1998 .

[15]  R. Itami,et al.  GIS-based habitat modeling using logistic multiple regression : a study of the Mt. Graham red squirrel , 1991 .

[16]  J. Michael Scott,et al.  Predicting Species Occurrences: Issues of Accuracy and Scale , 2002 .

[17]  Stuart J. Marsden,et al.  Habitat associations of parrots on the Wallacean islands of Buru, Seram and Sumba , 1999 .

[18]  H. P. Possingham,et al.  Modelling the inter-relationships between habitat patchiness, dispersal capability and metapopulation persistence of the endangered species, Leadbeater's possum, in south-eastern Australia , 1996, Landscape Ecology.

[19]  W. Laidlaw.  Effects of phytophthora cinnamomi on heathland flora and fauna of the Eastern Otway Ranges , 1997 .

[20]  N. Coops,et al.  Prediction of the spatial distribution and relative abundance of ground-dwelling mammals using remote sensing imagery and simulation models , 2002, Landscape Ecology.

[21]  Ross Ihaka,et al.  Gentleman R: R: A language for data analysis and graphics , 1996 .

[22]  G. Luck.  The habitat requirements of the rufous treecreeper (Climacteris rufa). 1. Preferential habitat use demonstrated at multiple spatial scales , 2002 .

[23]  D. Moro.  The Distribution of small mammal species in relation to heath vegetation near Cape Otway, Victoria , 1991 .

[24]  M. Austin.  Spatial prediction of species distribution: an interface between ecological theory and statistical modelling , 2002 .

[25]  B. Wilson,et al.  Spatial model for predicting the presence of cinnamon fungus (Phytophthora cinnamomi) in sclerophyll vegetation communities in south‐eastern Australia , 2003 .

[26]  R. M. Nally.  Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models , 2000, Biodiversity & Conservation.

[27]  A. Guisan,et al.  Modelling the distribution of bats in relation to landscape structure in a temperate mountain environment , 2001 .

[28]  Frank E. Harrell,et al.  Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis , 2001 .

[29]  B. Wilson,et al.  Ecology of small mammals in coastal heathland at Anglesea, Victoria , 1986 .

[30]  Simon Ferrier,et al.  Evaluating the predictive performance of habitat models developed using logistic regression , 2000 .

[31]  J. Aberton.  Succession of small mammal communities after fire and reintroduction of the swamp antechinus 'Antechinus minimus' , 1996 .

[32]  Nicholas C. Coops,et al.  Predicting the complexity of habitat in forests from airborne videography for wildlife management , 1997 .

[33]  G. Luck.  The habitat requirements of the rufous treecreeper (Climacteris rufa). 2. Validating predictive habitat models , 2002 .

[34]  Antoine Guisan,et al.  Predictive habitat distribution models in ecology , 2000 .

[35]  Erich Barke,et al.  Hierarchical partitioning , 1996, Proceedings of International Conference on Computer Aided Design.

[36]  F. E. Egler.  Ecosystems of the World , 1960 .

[37]  P. Catling,et al.  Studies of the ground-dwelling mammals of eucalypt forests in South-eastern New South Wales : the effect of habitat variables on distribution and abundance , 1995 .