Supplementary materials for Jones et al.
1
Supplementary Information
2
Jones et al, Journal of Animal Ecology, 2015
S1: Methods
3
S1: Methods
4
Tick exposure risk mapping methods.
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Exploring seasonal patterns in tick infestation data.
6
Results from exploratory GLM and GLMM of the number of ticks counted on lizards
indicated significant seasonal differences in the emergence of different tick life stages.
Figures S1 and S2 show the pattern in the mean number of ticks counted on lizards per
month for each of the two species infesting sleepy lizards in the study area (Amblyomma
limbatum or Bothriocroton hydrosauri). The total number of ticks of both species increased
from August to a peak in October and then decreased again later in the season. This
seasonality appears to be driven by larval and nymph stages for A. limbatum, but only
nymphs for B. hydrosauri. In both species the adult stage was fairly constant through the
season (with only a small increase from Aug – Sept).
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These seasonal patterns needed to be taken into account when calculating the long-term
average lizard infestation counts for each patch (500m sub-section) of the study area. This
is because the study area is large and patches could not be visited simultaneously; therefore
the patch-averaged tick infestation index needed to ensure that counts of ticks from
different life stages and months were standardised and comparable, in case patches were
visited at different times during the season. We used a scaling factor to adjust tick counts
based on the month that each count was made in and the life stages that were recorded.
In order to make tick counts from different tick species sp, lifestage l month m comparable,
we scaled all counts ysp,l(m) against the proportion of the smallest monthly average
𝑌̂𝑚𝑖𝑛Spec-stage and the average from the month in question m 𝑌̂Spec-stage(m) such that
𝑦̃sp,l(m) = ysp,l(m) * 𝑌̂𝑚𝑖𝑛Spec-stage/ 𝑌̂Spec-stage(m)
Here, the scaling factor (𝑌̂𝑚𝑖𝑛Spec-stage/ 𝑌̂Spec-stage(m)) ranges between zero and one such that,
l(m) < ysp,l(m) from months with relatively larger average values. Using these adjusted count
values meant that we could account for the fact that the number of ticks encountered
during a month with relatively low overall abundance resembles a larger exposure risk than
the same number encounter during a month when average abundance was increased
throughout the study area because of the seasonal emergence of larvae and nymphs.
We then summarized all standardized tick counts per patch P and time period T to a tick
exposure index TEISp,P(T) such that
(𝑃)
TEISp,P(T) = ∑𝑖=1
𝑦̃sp(i)/N(P)
1
Supplementary materials for Jones et al.
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S1: Methods
Where N(P) is the total number of lizard individuals surveyed for ticks in each patch. The
time periods T were set as ten-year intervals (1982-1991, 1992-2001, 2002-2011). We have
chosen these relatively long time periods as a trade-off to account for temporal variation in
exposure risk and large enough sample sizes N(P) per patch to calculate representative
index values.
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Figure S1: Mean Amblyomma limbatum (‘lim’) tick count by month from all lizard
captures within the study area through the 30-year study period. Coloured bars show the
different life stages (all life stages are included in the overall mean showed by the red bars).
Error bars show 95% confidence intervals.
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Supplementary materials for Jones et al.
S1: Methods
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Figure S2: Mean Bothriocroton hydrosauri (‘hyd’) tick count by month from all lizard
captures within the study area through the 30-year survey period. Coloured bars show the
different life stages (all life stages are included in the overall mean showed by the red bars).
Error bars show 95% confidence intervals.
3
Supplementary materials for Jones et al.
S1: Methods
52
Capture-mark-recapture model selection and goodness of fit testing.
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Model structure and goodness-of-fit testing
54
We initially considered a simple, time- and age- dependent, Cormack-Jolly-Seber (CJS)
55
model; however the estimate of the variance inflation factor c for this model was very high
56
(c-hat = 8.49), signalling severe overdispersion and indicating that this simple model
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structure was not appropriate for our data (see Table S1). Using a Jolly Move (JMV)
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multistate modelling approach, which accounted for the spatial variability in tick exposure
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risk, reduced c-hat to 2.77; providing strong justification for using a multistate structure in
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our models (Table S1).
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A significant result for the U-CARE Goodness of Fit (GoF) test 3G.SR on the JMV model
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assumptions (2 = 1361.6, df = 229, p = <0.001) indicated that some individuals sampled
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were transients. Transient behaviour manifests as lower apparent survival probability for
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the inter-capture period directly after marking when compared to subsequent periods. This
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can be caused by the capture of non-resident individuals who are just passing through the
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study area and therefore have effectively zero probability of being recaptured (Pradel et al.
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1997). C-hat calculations based on U-CARE GoF tests (Table 1) show that accounting for
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transient individuals in the JMV model improved the estimate of c-hat, reducing it from 2.77
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to 2.05 (calculated according to the methods outlined in Pradel et al (2003), Choquet et al
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(2009) and Lebreton et al (2009)).
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To address the issue of transience, we attempted to include a ‘time since mark’ (tsm)
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parameter within our multistate model for S; thus allowing apparent survival in the year of
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marking to differ from that in subsequent years (as described in Cooch and White 2014).
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However this additional model complexity caused model instability and parameter
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estimates did not converge. Therefore, the tsm parameter was dropped from the models
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and we reverted back to the standard multistate structure, without accounting for
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transience. The U-CARE goodness of fit tests on the raw capture data indicated that
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inclusion of a transience parameter (tsm) would only minimally reduce model
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overdispersion (Table S1); therefore removal of this parameter would not have considerably
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impacted model fit. In all multistate models that we present in the main paper, the U-CARE
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Supplementary materials for Jones et al.
S1: Methods
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estimate of c-hat for the saturated multistate JMV model (2.77) was used to correct for
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overdispersion.
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Table S1: Variation correction factor (c-hat) for goodness of fit to different model
structures. C-hat estimates were calculated from the results of Chi-squared tests for
goodness of fit to different model assumptions using the software packages U-CARE and
RELEASE. The model structures considered were Cormack-Jolly-Seber (CJS) with and without
accounting for transience and the Jolly-Move (a general multistate model) with and without
accounting for transience. The lowest c-hat (variance correction factor) was achieved by the
JMV multistate model that accounted for transience (c-hat = 2.05). This model was not fit
due to issues with convergence and parameter estimation, therefore the next best model
structure was used, which was the multistate model that did not account for transience (chat = 2.77). Note that, currently, no methods are available in program RELEASE to assess
the goodness of fit of multistate models.
CJS with transience
Juvenile c-hat
RELEASE c-hat
1.31
U-CARE c-hat
1.6
Adult c-hat
8.24
7.54
Full dataset c-hat
7.29
6.61
Standard CJS
RELEASE c-hat
U-CARE c-hat
Juvenile c-hat
1.79
1.91
Adult c-hat
11.34
10.04
Full dataset c-hat
9.57
8.49
Standard JMV
RELEASE c-hat
U-CARE c-hat
Juvenile c-hat
1.059
Adult c-hat
3.353
Full dataset c-hat
2.77
JMV with transience
RELEASE c-hat
U-CARE c-hat
Juvenile c-hat
2.11
Adult c-hat
0.473
Full dataset c-hat
2.05
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Supplementary materials for Jones et al.
S1: Methods
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Preliminary investigation of state transition.
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We hypothesised a priori that there would be a low probability of state transition because
of the small size of the adult stable home ranges, the low average movement distances
between captures and because location is not used explicitly to define a state (therefore the
transition between states doesn’t necessarily reflect distance moved).
We also
hypothesised that tick risk may influence transition as lizards moved to avoid high levels of
tick exposure. We found clear evidence that transition between states varied as a function
of the state being moved out of (‘from state’) and the state being moved into (to-state), see
Psi model selection table S2. Plots of the model-averaged estimates of state transition
probability are shown in the main text, Fig 5.
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Table S2: Model selection table for Psi (transition) parameter in the multistate capture
mark recapture models of sleepy lizards. S and p models were held as highly parameterised
(S ~ age * state + time, p ~ state + time) during preliminary selection of the most
parsimonious model for Psi.
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model
Global: Psi(~from-state + to-state)
Psi(~to-state)
Psi(~from-state)
Null: Psi(~1)
np
85
80
80
75
ΔQAICc
0
29.28
395.07
412.6
wQAICc
1.00
< 0.00
< 0.00
< 0.00
-2LnL
84277.1
84386.13
85399.36
85475.85
Dev R-sq
1.00
0.91
0.06
0.00
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np = number of parameters, QAICc = quasi AIC corrected for small sample size, ΔQAICc = change in
Quasi AICc compared to best model (with lowest QAICc value), wQAICc = QAICc weight, -2LnL = -2 log
negative likelihood, Dev R-sq = deviance explained relative to the null and global models. Model
notation: to-state = tick exposure risk state being moved into, from-state = tick exposure risk state
being moved out of, ~1 = constant/intercept only model.
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Climate data and prediction of future climate conditions.
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Future climate data were generated by applying modelled climate anomalies to the
contemporary data time series from local Australian Bureau of Meteorology (BOM) weather
stations. Future climate anomalies were projected under both reference (no greenhouse
gas abatement) and policy global emissions scenarios (for more details see methods in main
paper).
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Supplementary materials for Jones et al.
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S1: Methods
Demographic model parameterisation.
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Here we describe the demographic model of the sleepy lizard (Tiliqua rugosa) implemented
in RAMAS Metapop (v5), a generic, stage-based population viability analysis simulation
software package (Akcakaya and Root 2005). Model parameters are based on the capturemark-recapture (CMR) analysis presented in this paper and published literature (Bull 1995).
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Demographic Structure
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We used a female-only, age-structured, pre-breeding census model, parameterised
according to CMR data from 1982-2011. There are two stages in the model: sub-adults
(non-breeding 1-2 year olds) and breeding adults (≥ 2 years old, Bull C.M, unpublished data).
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Survival rates
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Using CMR model estimates of average annual survival during years that were not classified
as ‘hot and dry’ or ‘cool and wet’ (N = 22 years), we calculated time-averaged survival rates
for sub-adults/adults (i.e. any animals ≥ 12 months) and juveniles (animals <12 months) to
be 0.915 and 0.342 respectively.
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Fecundity
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Because the model is parameterised according to a pre-breeding census, the first age class is
12-month-old individuals (‘sub-adults’), and fecundity is calculated as the product of
average clutch size (Bull et al. 1993), proportion of females at birth, proportion of breeding
females in a given year and survival rate from birth to 12 months (0.342 from CMR model
estimates). Only individuals in the second age class (‘adult’) could reproduce and therefore
had a fecundity value.
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

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


Average clutch size for animals = 1.77 (Bull et al. 1993)
S0 (survival from 0 to 12 months) = 0.342 (from the CMR models - although note that
the juveniles captured in the survey are not necessarily neonates, they are just
captured at some time during their first year; therefore this value is likely to be an
overestimate of survival rate from birth).
Proportion of females at birth = 0.5 (Bull 1988)
Proportion of females that breed = 0.8 (pers. obs. Bull C.M)
F2+ (fecundity) = 1.7 * 0.8 * 0.342 * 0.5 = 0.2326
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Environmental Stochasticity
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We estimated temporal variability in fecundity rates between 1982 – 1985 (a period without
extreme events) from a capture-mark-recapture study (Bull 1995). After removing expected
variation due to demographic stochasticity (as described by Akcakaya 2002), we estimated
process variance in fecundity to have standard deviation (SD) value of 0.055 and a
coefficient of variation (CV) of 0.396. We used CMR data shown in this paper to estimate
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Supplementary materials for Jones et al.
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S1: Methods
temporal variability in survival rates between 1993 and 2001: the longest consecutive
period without climatic extremes (8 years). We estimated process variability in adult
survival (without demographic stochasticity) to have a SD of 0.006 and a CV of 0.007. This
low measure of variability is similar to what was reported elsewhere (Fordham et al. 2012)
and is a typical feature of long-lived reptiles in stable environments.
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Thus environmental variation in the model heavily affects fecundity (which includes the
survival of zero to 12 month old animals), but only has a small effect on sub-adult and adult
survival. This is supported by previous studies showing that lizard survival over the first 12
months tends to be highly variable and strongly affected by climatic variation (Bull 1995;
Fordham et al. 2012).
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Density Dependence
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Density dependence was modelled using a ceiling function that allows exponential growth
of the population until carrying capacity (K) is reached, at which point abundance plateaus.
This is a simple way of representing contest competition (e.g. packing associated with
defense of home ranges). Density dependence only affected fecundity (i.e. juvenile survival)
because adults are known to have stable home ranges (Bull and Freake 1999) and therefore
the primary factor limiting abundance is thought to be the establishment of juveniles within
available suitable habitat patches. Suitable habitat must contain thermal refuges and
adequate food plants (Kerr and Bull 2004; Kerr and Bull 2006; Kerr et al. 2003) and will be in
short supply when the population is at carrying capacity.
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To calculate K, we first generated an abundance time series from the CMR data using the
Horvitz-Thompson type estimator (Seber 1982), by dividing the number of individuals
captured from each age category in each year of the survey by the recapture probability for
that year (N capturest/recapture probabilityt). We then summed the resulting age-specific
abundance estimates to get an estimate of total population abundance through time for the
period 1983 – 2011 (Fig. S5). We fitted a Ricker-Logistic density dependence model to the
abundance time series data, which estimated a population level carrying capacity of 2360,
which was halved to 1180 to represent only the females in the population (there is a 1:1 sex
ratio) and used as the ceiling in the density dependence function for the female only model.
This model also gave an estimate of RMax = 1.53 (upper and lower 95% confidence intervals
= 1.25 and 1.87); however Rmax is not a required parameter for the ceiling model of density
dependence.
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Catastrophes
190
Extreme climate combinations (‘hot and dry’ and ‘cool and wet’) during the winter and
spring periods were identified as the most important climatic drivers of lizard survival in our
CMR models. We explicitly modelled the effect of changes in the frequency of ‘hot and dry’
and ‘cool and wet’ winter and spring conditions on the vital rates of sleepy lizards by using
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Supplementary materials for Jones et al.
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S1: Methods
the catastrophes functionality in RAMAS Metapop. This function applies user-specified
multipliers to the vital rates in the stage matrix, which allowed us to explicitly model the
impact of extreme climate on sleepy lizard survival and fecundity.
In the main text of the paper we present the predictions from a demographic model
parameterised with the maximum estimated effects of an increase in hot and dry conditions
on the sleepy lizard survival (worst case scenario). These are based on the difference
between mean survival in ‘normal years’ and the lower and upper confidence limits of the
mean survival estimates in ‘hot & dry’ and ‘cool and wet’ years respectively. Under this
assumption of a worse-case scenario, our CMR analyses suggested that adult survival might
decrease by as much as 6.46 % and juvenile survival by as much as 67 % in years with hostile
‘hot and dry’ conditions during the winter and spring period. These percentages are based
on the difference between average age-specific survival in ‘normal’ (not extreme) years and
the lower confidence limit of the mean, age-specific, survival for lizards in hot and dry years
between 1982 and 2010. Conversely, in more favourable ‘cool and wet’ winter and spring
conditions, adult and juvenile survival might increase by as much as 6.23 % and 78.78 %
respectively (based on the upper confidence limits of the mean survival in cool and wet
years between 1982 and 2010). The frequency of ‘hot and dry’ and ‘cool and wet’ winterspring conditions over the 30-year period from 1982 – 2011, along with how these
frequencies are forecast to change in the future are detailed in Table S2.
For comparison with the results presented in the main text, and in order to give the reader
some idea of the uncertainty of the estimates of population change, we present the
minimum estimated effect of climate extremes on lizard survival and population dynamics
(best-case scenario) into the future under climate change (see results in S2). In this case the
demographic models were parameterised using the percentage difference between the
mean estimate of age-specific survival estimates from normal years and the mean estimate
of age-specific survival rate from extreme event years (‘hot & dry’ and ‘cold & wet’).
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Additional model parameterization notes.
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The initial female population size was set at 80 % of carrying capacity (N = 960).
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A 10-year burn-in period (1000 simulations) in the baseline population projection (under
contemporary conditions) ensured that stability was reached prior to the generation of
trajectory predictions.
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Supplementary materials for Jones et al.
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S1: Methods
Table S3: Contemporary (1982 – 2011) climate data from BOM weather stations and future climate projections for
2080-2110. ‘Hot and dry’ conditions (*) = average winter and spring temperature ≥75th percentile (12.46 °C) and
cumulative winter and spring rainfall ≤25th percentile (204 mm); ‘cool and wet’ conditions (**) = average winter and
spring temperature ≤25th percentile (11.78 °C) and cumulative winter and spring rainfall ≥75th percentile (260.5 mm).
Bold text in the columns of future climate data indicate conditions that are indicative of ‘hot and dry’ years based on
the application of the criteria described above (temperature at or exceeding the 75th percentile, and rainfall at or
below the 25th percentile of contemporary observation records from local BOM stations, 1982 - 2011). The bottom
row shows the predicted probability of any single year in a 30-year period commencing in 2080 having conditions
that qualify as ‘hot and dry’ or ‘cool and wet’.
Contemporary time series (observed data from BOM
stations)
Year
1982 *
1983
1984
1985
1986 **
1987
1988
1989
1990
1991
1992 **
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002 *
2003
2004
2005
2006 *
2007 *
2008
2009
2010 **
2011 *
Probability of hot & dry conditions
Probability of cold & wet conditions
TempWS
(°C)
RainWS
(mm)
12.46
74
11.76
256
11.37
231
11.68
206
11.1
268
11.99
172
12.46
238
11.15
231
12.34
244
12.49
287
10.97
369
12.37
206
12.06
216
12.21
251
11.82
290
11.54
236
11.9
230
12.15
199
12.36
218
11.85
271
12.64
124
11.83
222
12.56
219
12.56
335
12.83
86
12.98
140
12.19
164
13.24
308
11.36
262
12.86
204
5/30 = 0.166
3/30 = 0.1
30 years from 2080: GCM-averaged rain and
temperature anomalies applied to contemporary
data
Ref.
Ref.
TempWS
RainWS
(°C)
(mm)
14.19
55.54
13.49
192.13
13.10
173.37
13.41
154.60
12.83
201.13
13.72
129.09
14.19
178.62
12.88
173.37
14.07
183.12
14.22
215.39
12.70
276.93
14.10
154.60
13.79
162.11
13.94
188.38
13.55
217.65
13.27
177.12
13.63
172.62
13.88
149.35
14.09
163.61
13.58
203.39
14.37
93.06
13.56
166.61
14.29
164.36
14.29
251.42
14.56
64.54
14.71
105.07
13.92
123.08
14.97
231.15
13.09
196.63
14.59
153.10
25/30 = 0.833
0/30 = 0
Policy
Policy
TempWS
RainWS
(°C)
(mm)
13.51
66.27
12.81
229.25
12.42
206.86
12.73
184.47
12.15
239.99
13.04
154.03
13.51
213.13
12.20
206.86
13.39
218.50
13.54
257.01
12.02
330.44
13.42
184.47
13.11
193.43
13.26
224.77
12.87
259.70
12.59
211.34
12.95
205.97
13.20
178.20
13.41
195.22
12.90
242.68
13.69
111.04
12.88
198.80
13.61
196.11
13.61
299.99
13.88
77.01
14.03
125.37
13.24
146.86
14.29
275.81
12.41
234.62
13.91
182.68
14/30 = 0.466
0/30 = 0
10
Supplementary materials for Jones et al.
S2: Results
235
S2: Results.
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Table S4: Multi-model inference: weighted model-averaged coefficients for environmental covariates in models of year-to-year Tiliqua rugosa
survival. Generated by model-averaging the 11 highest ranked models in the candidate model set (those with QAIC values within 2 units of each
other, see Table 2 in main text). Coefficients are on logit link scale, lcl and ucl are lower and upper 95 % confidence bounds respectively. Shaded
rows highlight coefficient estimates whose confidence bounds do not include 0. ∑wAICc is the sum of Quasi AICc weights for all models in the top 11
that contained each predictor as a main effect term. This represents the relative variable importance of the environmental predictors (Zuur et al.
2009).
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Survival model parameters
Intercept (Juvenile age, B. hydrosauri low state)
Adult age group
B. hydrosauri moderate state
B. hydrosauri high state
A. limbatum low state
A. limbatum moderate state
A. limbatum high state
Cold & wet winter-spring
Hot & dry winter-spring
Winter-spring temperature
Winter-spring rainfall
Summer temperature
2-year cumulative rainfall
Age group * cold and wet winter-spring (binary)
Age group * winter-spring temperature (continuous)
Southern Oscillation Index
Coefficient
0.262
2.169
-1.086
-1.274
-1.247
-1.001
-0.119
0.294
-0.157
-0.016
0.092
-0.071
0.070
0.580
-0.248
-0.129
se
0.149
0.116
0.130
0.120
0.118
0.118
0.119
0.149
0.079
0.075
0.042
0.035
0.035
0.419
0.130
0.043
lcl
-0.030
1.942
-1.341
-1.508
-1.477
-1.234
-0.351
0.002
-0.312
-0.130
0.011
-0.140
0.001
-0.241
-0.502
-0.212
ucl
0.554
2.396
-0.832
-1.039
-1.016
-0.769
0.113
0.585
-0.002
0.163
0.174
-0.002
0.139
1.402
0.007
-0.045
n models
11
11
11
11
11
11
11
4
3
2
1
1
1
1
1
1
∑wQAICc
0.35
0.24
0.20
0.17
0.08
0.07
0.10
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Supplementary materials for Jones et al
S2: Results
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Model selection for recapture (p)
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A candidate model set for p was assessed on the basis of lowest QAICc (c-hat = 2.77). An
initial candidate model set for recapture (p) was created to select the most appropriate
grouping structure for the model of recapture (Table S5). During model selection for p, the
model for S was held as ~ age * state + time. The results indicated that a p model structure
including time and state (but not age) was the most well supported by the data. This model
grouping structure was taken forward into a second stage of recapture model selection,
including covariates that could affect recapture probability, such as rainfall and
temperature, which can affect lizard activity levels and therefore detection and capture
(Table S5). The most parsimonious model for recapture was p ~ state + time. This model
had an evidence ratio of 2.02 (wQAICc highest ranked model / wQAICc model ranked
second), indicating that it was twice as likely to be the best model than the model ranked
second in the table. The top-ranked model was used in all further modelling steps (i.e.
when selecting the model for S).
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255
256
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259
Table S5: The multistate capture-mark-recapture candidate model set of group structure
and covariates for recapture (p) model. Models for survival and transitions were
respectively: S~ age * state + time and Psi ~ to-state + from-state. Models were ranked using
QAICc.
model
p(~time + state)
p(~age + time + state)
p(~time + state + RainWS)
p(~time + state + TempS)
p(~time + state + WetDays)
p(~time + state + TempWS)
p(~time + state + TempWSt + RainWS)
Global: p(~age * state + time)
p(~state)
p(~age + state)
p(~age * state)
p(~time)
p(~age + time)
p(~age)
Null: p(~1)
260
261
262
263
264
np
75
76
76
76
76
76
77
81
47
48
53
70
71
43
42
ΔQAICc
0.000
1.410
2.015
2.015
2.015
2.015
4.030
10.259
622.698
623.410
631.825
798.311
798.970
1174.201
1176.864
wQAICc
0.323
0.160
0.118
0.118
0.118
0.118
0.043
0.002
0.000
0.000
0.000
0.000
0.000
0.000
0.000
-2LnL
Dev-Rsq
85475.850
0.999
85474.170
0.999
85475.850
0.999
85475.850
0.999
85475.850
0.999
85475.850
0.999
85475.850
0.999
85470.770
1.000
87356.770
0.453
87353.180
0.454
87348.650
0.456
87715.070
0.349
87711.320
0.350
88906.690
0.004
88919.630
0.000
np = number of parameters, QAICc = quasi AIC corrected for small sample size, ΔQAICc = change in
Quasi AICc compared to best model (with lowest QAICc value), wQAICc = QAICc weight, -2LnL = -2 log
negative likelihood, Dev R-sq = deviance explained relative to the null and global models. Model
notation: * = interaction term, age = age class (juvenile or adult), state = tick exposure risk state of
occupancy, ~1 = constant/intercept only model.
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Supplementary materials for Jones et al
S2: Results
265
266
267
Figure S3: Tiliqua rugosa capture-mark-recapture model-averaged estimates of recapture probability for 1983 to 2011. Note that the model for
recapture was age-invariant. Error bars show 95 % confidence intervals.
268
13
Supplementary materials for Jones et al
S2: Results
269
270
271
272
273
274
Figure S4: Model-averaged estimates of Tiliqua rugosa recapture probability (p) by tick
exposure risk state (model for recapture was age-invariant). State codes: Hyd = B.
hydrosauri areas, LIM = A. limbatum areas, L = low risk, M = moderate risk, H = high risk.
Risk levels are based on the percentiles of all patch-averaged tick infestation counts – see
main paper methods section for more detail. Error bars show 95 % confidence intervals.
275
276
277
Figure S5: Estimated Tiliqua rugosa abundance time series (1983 – 2011) with 95%
confidence intervals shaded.
14
Supplementary materials for Jones et al
S2: Results
278
Demographic model results using best case scenario.
279
Using the minimum estimated effect of extreme climate events on the survival of lizards
from the CMR models, there would be an increase in survival under ‘cool and wet’
conditions of 1.7 % for adults and 2.3 % for juveniles, and a decrease in survival under
unfavourable ‘hot and dry’ conditions of 1 % for adults and 1.2 % for juveniles. Figure S6
shows the predictions from a stochastic demographic model of female sleepy lizards under
future climate change, parameterised with these minimum effect size values. The model
projection shows small decreases in the female population size over the period 2080 – 2110.
The model predicted a 3.5% decrease in estimated minimum abundance (EMA) under policy
and an 8.1% decrease in EMA under reference greenhouse gas emission scenarios). The
magnitudes of the predicted decreases are negligible compared with the predictions from
the model parameterised using worst case scenario effect sizes for extreme events
(presented in the main paper, Fig.6).
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
Figure S6: Female only Tiliqua rugosa population abundance projections for reference and
policy climate change scenarios, relative to contemporary climate baseline (1982-2011,
dashed black line), with shaded confidence intervals (± 1 SD). Model is parameterised using
minimum estimated effect of extreme climate events (‘hot and dry’ and ‘cool and wet’
winter and spring) on survival.
15
Supplementary materials for Jones et al
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