Electronic Supplementary Methods:
Materials and Methods
Sampling
Hair samples from individual Burramys parvus for DNA extraction were collected from
eight populations across two regions (Table S1) between November 1993 and December
2006. Animals were captured overnight using using aluminium collapsible live traps (Type
A Elliott traps) baited with walnuts. Trapping grids consisted of 8-10 parallel transects at
10 or 12.5 m spacings with 15 or 20 trap stations located along each transect at 5 m
spacings – a trapping grid area of 0.63-0.86 ha (Mansergh 1989). Trapping was undertaken
for 4-5 consecutive nights with animals being released at point of capture as early in the
morning as possible.
A small amount of hair was removed from the rump region of each Burramys and used for
the genetic analysis. Hair samples were immediately placed in 2 ml Eppendorf tubes and
stored in liquid nitrogen following each trapping session. A minimum of 20 individuals
were obtained from each population, except where inclement weather reduced trapping
efforts and/or populations were small.
Habitat loss estimation
Boulderfields and associated mountain plum pine heath vegetation provide most of the
preferred habitat for Burramys (Heinze & Harvey 2006; Mansergh 1989; Mansergh &
Broome 1994; Mansergh et al. 1989). Such habitat usually occurs as discrete patches or
strips that may extend from close to the summit of peaks (1800-1900 m - treeless or alpine
zone) down to the sub-alpine (snow gum woodland) and montane (> 1300m – alpine ash)
zones. The distribution and area of preferred habitat was mapped using aerial photographs
incorporated into a GIS system (ArcGIS, version 9.0), coupled with habitat verification in
the field (ground-truthing) most recently in 2006 at Mt. Buller (Heinze & Harvey 2006)
and in 2003 on the Bogong High Plains (Heinze et al. 2004). A series of historical aerial
photographs dating back to 1964 were used to calculate the levels of habitat loss and
degradation due primarily to development of ski resort and road infrastructure, as well as
the extent that habitat was burnt in the 2003 bush fires (Heinze et al. 2004).
Non-preferred habitat lacks a boulderfield component; however closed heathland provides
a relatively high level of protective cover for Burramys and can provide an important
dispersal or migratory link between patches of preferred habitat. Prior to the 2003 fires the
preferred habitat at Mount McKay was closed heath without boulderfields, whilst
elsewhere preferred habitat consisted mainly of boulderfields.
We report on the loss and degradation of “preferred habitat” as well as fire damage,
although actual loss or fragmentation of habitat may be greater. Boulderfields were
targeted for early ski runs as they are often along cold air drainage lines where there were
few snow gums (Eucalyptus pauciflora). This led to the blasting of rocks at high points and
hand slashing of heath vegetation (eg. Podocarpus lawrencei). In the 1980’s and early
1990’s this continued with large-scale loss of preferred habitat (e.g. Southside access road,
Federation chairlift base station, Wombat Valley egress trails to mention some) leading to
80% habitat modification/degradation in the Federation area.
Genetic effects associated with habitat loss
To test if there were genetic effects associated with these habitat changes, animals were
initially collected in the mid 1990’s and populations sampled annually until 2006 unless
poor weather conditions prevented trapping (Table S1). Standard genetic parameters (see
below) were calculated for each population to examine their response to habitat changes.
Populations affected by the 2003 fires may have gone through little more than a single
generation, however, allele numbers are expected to decrease immediately (rare alleles). In
addition, post-fire trapping rates, animal captures and population census estimates (20042006) were examined and compared to the previous twenty years (1984-2003) of
population monitoring performed in the same manner.
DNA extraction and microsatellite analysis
Extraction of DNA from hair samples was performed with Chelex (Bio-Rad™) as in
Mitrovski et al. (2005). Briefly, ten hairs with follicles were transferred to a 0.5 ml
microcentrifuge tube that was placed in liquid nitrogen (1 minute) and then centrifuged at
20,800 g for 1 min to move the hairs to the bottom of the tube. 200 µl of a 5% Chelex
solution was added, samples were mixed, incubated at 90 °C for 10 min and stored at -20
°C. Prior to PCR, samples were centrifuged at 20800 g for 2 min. We genotyped all
samples for eight microsatellite loci described in Mitrovski et al. (2005)
Microsatellite loci were amplified using PCR in 10-μL volumes with cycling conditions
composed of initial denaturation at 93 °C (2 min), followed by 35 cycles of 93 °C (20 s),
primer annealing (Ta) (30 s), and extension at 72 °C (30 s) with an extension increase of 1
s/cycle for the last 20 cycles. Amplified fragments were separated through a 5% denaturing
polyacrylamide gel at 65 W for 2-3.5 h and exposed for 12-36 hours to autoradiography
film (BioMax, Kodak). Allele sizes were determined by comparison with λgt11 ladders
(fmol® DNA Cycle Sequencing System, Promega). To minimise errors associated with
PCR and scoring of loci, 100 individuals were chosen at random, re-amplified for all eight
microsatellite loci and scored as above. Re-amplified samples for all loci matched the
original genotypes obtained.
FSTAT version 2.9.3 (Goudet 1995) was used to calculate the average number of alleles
per locus, allelic richness averaged over loci and Weir and Cockerham’s (Weir &
Cockerham 1984) measure of FIS. Observed and expected heterozygosity were estimated
and deviations from Hardy-Weinberg equilibrium were determined by exact tests and
permutation in GDA version 1.1 (Lewis & Zaykin 2001). Genetic diversity between
samples was compared using paired t tests after angular transformation where individual
loci form the sampling points (Hood 2002). To investigate population differentiation,
pairwise measures of FST between populations were calculated and significance determined
(82,000 permutations) using FSTAT.
The effective population size (Ne) was estimated using some short- and long-term
approaches for all populations sampled in this study. A short-term Ne estimate was
calculated in NEESTIMATOR version 1.3 (Peel et al. 2004) using the temporal-based
method of Waples (1989). The temporal based method assesses fluctuations in allele
frequencies between two temporal samples separated by a known number of generations
(Waples 1989). Generation length was estimated using the method of Miller &
Kapuscinski (1997) in which the generation length is equal to the mean age of parents
when offspring are born in populations with overlapping generations. Direct estimates of
Ne were estimated using the formula 4NmalesNfemales/(Nmales+Nfemales) (Caballero 1994). Adult
population counts and sex ratios for each population were calculated from trapping data
collected from 1993 to 2005 using the intensive trapping scheme described in Mansergh
(1989). Two indirect methods were used to calculate long-term Ne estimates. One method
calculates the effective population size as Ne = He/4ν(1-He) and follows the assumptions of
the infinite alleles model (IAM) of microsatellite evolution (Kimura & Crow 1963). The
second method is based on the stepwise mutation model (SMM) (Ohta & Kimura 1973)
and calculates the effective population size as Ne = [(1/(1-He)2)-1]/8ν. For each method, He
is the average expected heterozygosity per population and ν is the mutation rate of
microsatellite loci (estimates of 10-3 were used here (Dallas 1992; Weber & Wong 1993)).
Changes in population size were expressed proportionally because exact estimates of
population size will vary depending on mutation rates and other parameters that cannot be
verified for the microsatellites used in this study. All tests involving multiple comparisons
were corrected at the table-wide α = 0.05 level with the Dunn-Šidák method (Sokal &
Rohlf 1995). To test for evidence of recent reductions in the effective population size for
each population we ran the program BOTTLENECK (Cornuet & Luikart 1996). Due to the
relatively small number of loci, we used the Wilcoxon’s signed-rank test to determine
significance (Maudet et al. 2002; Spencer et al. 2000) under a two-phase model (TPM) of
mutation (90% SMM and 10% multistep changes).
References
Caballero, A. 1994 Developments in the prediction of effective population size. Heredity
73, 657-679.
Cornuet, J. M. & Luikart, G. 1996 Description and power analysis of two tests for
detecting recent population bottlenecks from allele frequency data. Genetics 144,
2001-2014.
Dallas, J. F. 1992 Estimation of Microsatellite Mutation-Rates in Recombinant Inbred
Strains of Mouse. Mammal. Gen. 3, 452-456.
Goudet, J. 1995 FSTAT (Version 1.2): A computer program to calculate F-statistics. J.
Heredity 86, 485-486.
Heinze, D. A., Broome, L. & Mansergh, I. 2004 A review of the ecology and
conservation of the mountain pygmy-possum Burramys parvus (Broom 1896). In
The biology of the Australian possums and gliding possums (ed. R. Goldingay &
S. Jackson), pp. 254-67. Chipping Norton: Surrey Beatty & Sons.
Heinze, D. A. & Harvey, A. 2006 Habitat mapping for the mountain pygmy-possum,
Burramys parvus, at Mount buller, Victoria. Melbourne: Biosis Research Pty. Ltd.
Hood, G. 2002 POPTOOLS. Canberra: CSIRO. http://www.cse.csiro.au/poptools/.
Kimura, M. & Crow, J. F. 1963 The measurement of effective population number.
Evolution 17, 279-288.
Lewis, O. P. & Zaykin, D. 2001 Genetic Data Analysis: a computer program for the
analysis of allelic data.
http://hydrodictyon.eeb.uconn.edu/people/plewis/software.php
Mansergh, I. 1989 The ecology and conservation of the mountain pygmy-possum
(Burramys parvus) (Marsupialia: Burramyidae) in Victoria; with comparisons to
populations in New South Wales, Australia. PhD thesis, Melbourne: La Trobe
University.
Mansergh, I. & Broome, L. 1994 The mountain pygmy-possum of the Australian Alps.
Australian History Series: New South Wales University Press.
Mansergh, I. M., Kelly, P. & Scotts, D. J. 1989 Management strategy and guidelines for
the conservation of the mountain pygmy-possum (Burramys parvus) in Victoria.
Melbourne: Arthur Rylah Institute of Environmental Research, Victoria,
Australia.
Maudet, C., Miller, C., Bassano, B., Breitenmoser-Wursten, C., Gauthier, D., ObexerRuff, G., Michallet, J., Taberlet, P. & Luikart, G. 2002 Microsatellite DNA and
recent statistical methods in wildlife conservation management: applications in
Alpine ibex Capra ibex (ibex). Mol. Ecol. 11, 421-436.
Miller, L. M. & Kapuscinski, A. R. 1997 Historical analysis of genetic variation reveals
low effective population size in a northern pike (Esox lucius) population. Genetics
147, 1249-1258.
Mitrovski, P., Heinze, D. A., Guthridge, K. & Weeks, A. R. 2005 Isolation and
characterization of microsatellite loci from the Australian endemic mountain
pygmy-possum, Burramys parvus Broom. Mol. Ecol. Notes 5, 395-397.
Ohta, T. & Kimura, M. 1973 Model of mutation appropriate to estimate number of
electrophoretically detectable alleles in a finite population. Genet. Res. 22, 201204.
Peel, D., Ovenden, J. R. & Peel, S. L. 2004 NeEstimator: software for estimating
effective population size, Version 1.3.: Queensland Government, Department of
Primary Industries and Fisheries, Australia.
Sokal, R. R. & Rohlf, F. J. 1995 Biometry: the principles and practices of statistics in
biological research. New York: W. H. Freeman and Company.
Spencer, C. C., Neigel, J. E. & Leberg, P. L. 2000 Experimental evaluation of the
usefulness of microsatellite DNA for detecting demographic bottlenecks. Mol.
Ecol. 9, 1517-1528.
Waples, R. S. 1989 A generalized-approach for estimating effective population-size from
temporal changes in allele frequency. Genetics 121, 379-391.
Weber, J. L. & Wong, C. 1993 Mutation of Human Short Tandem Repeats. Hum. Mol.
Genet. 2, 1123-1128.
Weir, B. S. & Cockerham, C. C. 1984 Estimating F-statistics for the analysis of
population-structure. Evolution 38, 1358-1370.