Drysdalia rhodogaster persists after wildfires 773
TABLE 1 Candidate model structures and model selection to investigate hypotheses regarding the detection p(.) and occupancy ψ(.) of the mustard-bellied snake Drysdalia rhodogaster in south-eastern Australia (Fig. 1). Models are ranked by quasi-likelihood Akaike information criterion corrected for small sample size (QAICc), which was used because of the small sample sizes and model variances being inflated by a global modal ĉ value of 2.14. Models used in model averaging are marked with an asterisk (*).
Hypothesis/model Null model*
1* 3* 4 2 6 5
Global model
Model parameters1 n/a
p(temp + day) p(temp + day) ψ(ext)
p(temp + day) ψ(sev + ext) p(temp + day) ψ(ext + year) p(temp + day) ψ(park + ext) p(temp + day) ψ(sev + year)
p(temp + day) ψ(sev + park + ext + year)
QAICc 119.04
120.66 120.98 123.16 124.81 124.94 127.08 131.03
ΔQAICc 0.00
1.62 1.94 4.12 5.77 5.90 8.04
11.99
Model weight 0.48
0.21 0.18 0.06 0.03 0.03 0.01 0.00
1day, day of year (difference between the survey date and the start of the austral spring on 1 September); ext, fire extent; park, location (national park in which the survey took place); sev, fire severity; temp, air temperature; year, survey year
model. We ran goodness-of-fit tests with 10,000 iterations. χ2 test results returned non-significant values (χ2 = 1,423.24, P=0.07), indicating that our global model fit the data; how- ever,wefound themodels
tohavemildoverdispersion
(ĉ = 2.14). To account for this overdispersion, we inflated the variances of the candidate model covariates by the value of ĉ prior to model selection.We assessed model suitability using the quasi-likelihood Akaike information criterion corrected for small sample size (QAICc) and considered model struc- turessuitableiftheyhad thelowestQAICc valueor ΔQAICc,2 compared to the leading model. To provide inferences on covariate impacts on detection and occupancy, we used model averaging with shrinkage on the supported models to generate beta estimates and confidence intervals using the modavgShrink function of AICcmodavg with an adjusted ĉ (Mazerolle, 2023). We generated site occupancy and detection estimates for the most parsimonious model using the predict function in R.
Results
Habitat suitability models Our model predicted the range of D. rhodogaster to be con- siderably larger than current presence records indicate. We predict that the range of D. rhodogaster extends north of Hunter Valley along the Great Dividing Range towards the border with Queensland (Fig. 2b). North of 34 °S the predicted distribution of D. rhodogaster is restricted to areas .250 m elevation (Fig. 3c). When intersected with fire extent mapping, c. 46% of the predicted range of D. rhodogaster was burnt during the Black Summer.
Field detections and occupancy models
Across the three parks we surveyed 61 sites, representing a total effort of 542 person-hours. Of the 61 sites, we classified
24 as burnt at high severity, 19 as burnt at lowseverity and 18 as unburnt. We recorded 41 detections of D. rhodogaster, with 20 individuals recorded under tin sheets, seven under roof tiles and 14 found during active searches. We detected D. rhodogaster at 16 sites: 11 observations in
five sites classified as unburnt, 10 snakes in three sites burnt at low severity and 20 snakes in eight sites burnt at high se- verity. In the first 12 months after the Black Summer bush- fires, 80% of the snakes detected were in burnt areas. The most parsimonious occupancy model was the null
model. However, two additional models were supported. One included detection covariates of daily maximum tem- perature and day of year and the other contained these detec- tion covariates and also fire extent (Table 1). Park and year were poorly supported (Table 1). Model averaging of the
top models found no support for temperature (β =−0.29, 95%CI: −1.14, 0.55)or day of year (β =−0.1, 95%CI:
−0.39, 0.59) influencing detectionor of fire extent influencing occupancy (β =−0.11, 95%CI:−0.66, 0.45).When only con- sidering the detection covariate model, detection decreased
with higher daily maximum temperature (β =−0.64, 95% CI: −1.21, −0.07), and there was no effect of day of year (β = 0.19, 95%CI:−0.26, 0.65). Similarly, themodel including fire extent found decreased detection with higher maximum
temperature (β =−0.66, 95%CI: −1.22, −0.10) as well as no effects of day of year (β = 0.23, 95%CI:−0.21, 0.69)or fireex- tent (β =−0.53, 95%CI: −1.07, 0.01). However, we refrain from making inferences regarding these findings based on
the low weight of this model relative to the null. Hereafter, we only present data from the most parsimonious (null) model. D. rhodogaster showed a low mean site occupancy (0.3 ± SD 0) as well as low detectability (0.2 ± SD 0).
Post-fire presence records
Excluding snakes observed by our survey team, a total of 38 records of D. rhodogaster were recorded in the Atlas of
Oryx, 2024, 58(6), 769–778 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605324000048
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