Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials

Recent attempts to explain the susceptibility of vertebrates to declines worldwide have largely focused on intrinsic factors such as body size, reproductive potential, ecological specialization, geographical range and phylogenetic longevity. Here, we use a database of 145 Australian marsupial species to test the effects of both intrinsic and extrinsic factors in a multivariate comparative approach. We model five intrinsic (body size, habitat specialization, diet, reproductive rate and range size) and four extrinsic (climate and range overlap with introduced foxes, sheep and rabbits) factors. We use quantitative measures of geographical range contraction as indices of decline. We also develop a new modelling approach of phylogenetically independent contrasts combined with imputation of missing values to deal simultaneously with phylogenetic structuring and missing data. One extrinsic variable – geographical range overlap with sheep––was the only consistent predictor of declines. Habitat specialization was independently but less consistently associated with declines. This suggests that extrinsic factors largely determine interspecific variation in extinction risk among Australian marsupials, and that the intrinsic factors that are consistently associated with extinction risk in other vertebrates are less important in this group. We conclude that recent anthropogenic changes have been profound enough to affect species on a continent–wide scale, regardless of their intrinsic biology.

[1]  Mark V. Lomolino,et al.  Dynamic biogeography and conservation of endangered species , 2000, Nature.

[2]  A. Sinclair,et al.  Predicting Effects of Predation on Conservation of Endangered Prey , 1998 .

[3]  I. Owens,et al.  Evolutionary Ecology of Birds: Life Histories, Mating Systems and Extinction , 2002 .

[4]  I. Owens,et al.  Ecological basis of extinction risk in birds: habitat loss versus human persecution and introduced predators. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[5]  A. Balmford,et al.  Phylogeny and the selectivity of extinction in Australian marsupials , 2002 .

[6]  K. Gaston,et al.  Evolutionary age and risk of extinction in the global avifauna , 1997, Evolutionary Ecology.

[7]  P. Harvey,et al.  Variation in Geographical Range Size Among Mammals of the Palearctic , 1994, The American Naturalist.

[8]  T. Garland,et al.  Effects of branch length errors on the performance of phylogenetically independent contrasts. , 1998, Systematic biology.

[9]  Graeme Caughley,et al.  Directions in conservation biology , 1994 .

[10]  T. Garland,et al.  TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE , 2003, Evolution; international journal of organic evolution.

[11]  Andrew P. Smith,et al.  MAMMAL DECLINE AND RECOVERY IN AUSTRALIA , 1994 .

[12]  R. Little Missing-Data Adjustments in Large Surveys , 1988 .

[13]  J L Schafer,et al.  Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective. , 1998, Multivariate behavioral research.

[14]  I. Owens,et al.  Variation in extinction risk among birds: chance or evolutionary predisposition? , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[15]  K. Gaston,et al.  Extrinsic factors and the population sizes of threatened birds , 2002 .

[16]  J. Felsenstein Phylogenies and the Comparative Method , 1985, The American Naturalist.

[17]  A. Harcourt,et al.  Rarity, specialization and extinction in primates , 2002 .

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

[19]  D. Rubin Formalizing Subjective Notions about the Effect of Nonrespondents in Sample Surveys , 1977 .

[20]  L. Bromham,et al.  Body Size and Risk of Extinction in Australian Mammals , 2001 .

[21]  D. Rubin,et al.  Small-sample degrees of freedom with multiple imputation , 1999 .

[22]  J. Felsenstein Phylogenies and quantitative characters , 1988 .

[23]  D. Rubin,et al.  Music, emotion, and autobiographical memory: They’re playing your song , 1999, Memory & cognition.

[24]  A. P. Smith,et al.  Patterns and causes of extinction and decline in Australian conilurine rodents , 1996 .

[25]  J. Lockwood,et al.  Extinction in a field of bullets: a search for causes in the decline of the world's freshwater fishes , 2001 .

[26]  Tim M. Blackburn,et al.  Conservation Biology in Theory and Practice , 1996 .

[27]  J. L. Gittleman,et al.  Predicting extinction risk in declining species , 2000, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[28]  Korbinian Strimmer,et al.  APE: Analyses of Phylogenetics and Evolution in R language , 2004, Bioinform..

[29]  F. J. Anscombe,et al.  THE TRANSFORMATION OF POISSON, BINOMIAL AND NEGATIVE-BINOMIAL DATA , 1948 .

[30]  P. Allison Multiple Imputation for Missing Data , 2000 .

[31]  N. L. McKenzie,et al.  Patterns in the modern decline of western Australia's vertebrate fauna: Causes and conservation implications , 1989 .

[32]  D. Ackerly TAXON SAMPLING, CORRELATED EVOLUTION, AND INDEPENDENT CONTRASTS , 2000, Evolution; international journal of organic evolution.

[33]  Jeff Short,et al.  The extinction of rat-kangaroos (Marsupialia:Potoroidae) in New South Wales, Australia , 1998 .

[34]  S. van Buuren,et al.  Multivariate Imputation by Chained Equations : Mice V1.0 User's manual , 2000 .

[35]  R. Pressey,et al.  Mammals of particular conservation concern in the Western Division of New South Wales , 1993 .

[36]  Christopher N. Johnson,et al.  THE ECOLOGICAL BASIS OF LIFE HISTORY VARIATION IN MARSUPIALS , 2001 .

[37]  Roger A. Sugden,et al.  Multiple Imputation for Nonresponse in Surveys , 1988 .

[38]  M. Cardillo Biological determinants of extinction risk: why are smaller species less vulnerable? , 2003 .

[39]  A. Burbridge,et al.  The 1996 action plan for Australian marsupials and monotremes , 1996 .