Electronic supplementary material for ‘Kin selection in den sharing develops under
limited availability of tree hollows for a forest marsupial’ by Sam Banks, David
Lindenmayer, Lachlan McBurney, David Blair, Emma Knight and Michaela Blyton.
Parentage analysis of mountain brushtail possums
We attempted to identify the parents of all mountain brushtail possum (Trichosurus
cunninghami) offspring captured between 1992 and 2009 using the program
CERVUS 3.0.3 [1]. Many juveniles (65) were captured in the mother’s pouch, thus
only paternity relationships were estimated from the genetic data. For a further 39
sampled juveniles (0-2 years of age), neither parent was known and we attempted to
identify both parents with genetic data. These analyses were performed using seven
validated microsatellite markers [2]. Individuals with missing genotypes at more than
two loci were excluded from the analysis. We assigned parentage to an offspring if
the number of allelic mismatches between the offspring and the most likely candidate
parent did not exceed one and the Delta LOD score (difference in LOD score between
the most likely and second most likely candidate parent) exceeded a critical value set
by a 95% confidence interval based on 1000000 simulated offspring. The mistyping
rate was estimated to be 1% after microsatellite validation.
We defined candidate parents as all adult individuals caught within a year
either side of the breeding season in which the young was born. This improved the
chance of including parents that were present at the site but not captured during that
breeding season. In the maternity analysis of juveniles, only females that possessed
the same mitochondrial haplotype (see below for details) as the offspring were
included as candidate mothers. We estimated from offspring simulations that 85% of
candidate fathers of pouch young with a known mother were sampled and that 55% of
parents of juveniles were recorded (data not shown).
A shift in the population allele frequencies at Cambarville over the course of
the 18 year study was detected during our analysis (data not shown). Thus, we pooled
individuals captured over a five year period centred on the year of an offspring’s birth
to estimate the population allele frequencies at that time. This window provided an
adequate sample size (46-84) to estimate allele frequencies. No detectable change in
allele frequencies was found between years within this timeframe.
Mitochondrial control region sequencing
We sequenced an 815 base pair fragment of the mitochondrial control region for all
mothers, offspring, candidate mothers and juveniles, to aid in the maternity
assignment of juveniles (n=183). The fragment was amplified by Polymerase Chain
Reaction
(PCR)
using
the
primers:
L15999M:
5’-
ACCATCAACACCCAAAGCTGA-3’ (Fumagalli et al. 1997) and Tv3.R: 5’TGTATCCCATATTATCACTT-3’(Chapman 2001). Each 40 µl PCR reaction
contained 4 µl of 10 x PCR buffer (Qiagen), 100 µM of each dNTP (Astral Scientific
DNTPSET1425), 1.5 mM MgCl2 (Qiagen), 5 µg of BSA (BioLabs), 100 pM of each
primer, 1 unit of Taq polymerase (Qiagen) and approximately 5 ng of template DNA.
Samples were amplified using a step-down thermal profile protocol; initial extension
94˚C for 3 minutes, followed by 34 cycles. All cycles had a denaturing temperature of
94˚C for 30 seconds, annealing for 40 seconds and an extension temperature of 72˚C
for 1 minute 30 seconds followed by a final 10 minute 72˚C step. The annealing
temperature was initially 55ºC for the first 2 cycles; it was then lowered to 52ºC for 2
cycles, followed by 30 cycles at 49ºC. The PCR products were prepared for
sequencing following the ExoSAP-IT protocol as described by the manufacturer
(USB) and sequenced using BigDyeTM Terminator Version 3.1 (Applied Biosystems)
on an Applied Biosystems 3100 capillary DNA sequencer. Sequences were edited in
the computer program Geneious Pro 4.6.1 (Drummond et al. 2009). Mitochondrial
haplotypes were analysed in GenAlEx, version 6.3 (Peakall and Smouse 2006).
Graphical analysis of two-dimensional variation in fine scale genetic structure.
We used Double et al.’s [3] two-dimensional index of local spatial genetic structure
calculated in GenAlEx 6 [4] from seven microsatellite markers [2] to represent
variation multilocus spatial autocorrelation of each individual to its eight nearest
neighbours. These local indices of spatial autocorrelation (LISA) were calculated for
each individual sampled during the four trapping surveys conducted at Cambarville
from January 2008 to February 2009 (See Supplementary Figures 1 and 2).
References
1.
Kalinowski, S.T., Taper, M.L. & Marshall, T.C. 2007 Revising how the
computer program CERVUS accommodates genotyping error increases success in
paternity assignment. Molecular Ecology 16, 1099-1106.
2.
Banks, S.C., Dubach, J., Viggers, K.L. & Lindenmayer, D.B. 2010 Adult
survival and microsatellite diversity in possums: effects of major histocompatibility
complex-linked microsatellite diversity but not multilocus inbreeding estimators.
Oecologia 162, 359-370.
3.
Double, M.C., Peakall, R., Beck, N.R. & Cockburn, A. 2005 Dispersal,
philopatry, and infidelity: Dissecting local genetic structure in superb fairy-wrens
(Malurus cyaneus). Evolution 59, 625-635.
4.
Peakall, R. & Smouse, P.E. 2006 GENALEX 6: genetic analysis in Excel.
Population genetic software for teaching and research. Molecular Ecology Notes 6,
288-295.
Supplementary Figure 1. Maps showing spatial variation in local genetic
(microsatellite) spatial autocorrelation among individuals sampled in January 2008
(1a), March 2008 (1b), August 2008 (1c) and February 2009 (1d). Each circle
represents the local index of spatial autocorrelation (LISA) calculated for each
individual in regard to its eight nearest neighbours. Red represents high LISA and
blue represents low LISA. There is no apparent association with hollow tree
availability for any of these surveys (represented the same way as Figure 1 in the
paper (Black shading is high, white is low).
1a.
1b.
1c.
1d.
Supplementary Figure 2. A chart-based representation of the mean (± 1 standard
deviation) of the LISA values (From Supplementary Figures 1a-1d) at
different levels of hollow tree availability.
0.1
0.08
0.06
LISA (nn=8)
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.1
-0.12
0_1
1_2
2_3
3_4
4_5
Hollow tree availability (per hectare)
5_6
>6