772 M. J. Hodgson et al.


standardize for the variable number of surveyors between sites. We deployed artificial refugia consisting of four roof tiles (42 × 33 cm) and two sheets of tin (c. 1 m2) at each site and checked these at the conclusion of the searches. Little is known about the activity periods of D. rhodogaster, with anecdotal evidence suggesting that the species is pri- marily diurnal; however, the species has also been found to be active at night (MJH, pers. obs., 2022). Because of the dearth of knowledge about D. rhodogaster activity pat- terns, we conducted the surveys under conditions assumed to maximize snake detection, avoiding excessively warm conditions or heavy rain. Searching included looking for surface active animals, turning over suitable refuges and raking litter. Where possible we minimized disturbance to the microhabitat and in all instances we replaced turned objects back to their original positions. There was a minimum of 7 days between surveys at each


site, with most sites having at least 14 days between surveys. The number of repeat surveys conducted within and across years varied between sites (1–8 surveys per site).We sampled only a subset of sites (BlueMountains National Park, n = 14; Morton National Park, n = 21) 3 years post-fire.We sampled one BlueMountains National Park site only 2 years post-fire.


Post-fire occurrences of D. rhodogaster


To complement our field surveys and further assess re- sponses of D. rhodogaster across the species range, we ana- lysed post-fire presence records from the Atlas of Living Australia (downloaded on 25 March 2022). We extracted re- cords of D. rhodogaster that were observed in the 24 months following the end of the bushfires in the greater Sydney re- gion (10 February 2020–1 March 2022). We excluded re- cords of D. rhodogaster in the Atlas of Living Australia that were generated from the field surveys conducted for this study. We determined whether each record had been detected in a burnt or unburnt region by extracting burn se- verity values from a remote sensing dataset that was created after the Black Summer fires to quantify the extent and se- verity of fires in New South Wales (Fire Extent and Severity Mapping dataset; Department of Planning, Industry and Environment, 2020).


Covariates and statistical analysis


To determine site occupancy,we ran a single-species, single- season occupancy model in R. We built detection histories for each site by assigning a score of ‘0’ if we did not detect a snake, ‘1’ ifwe did detect a snake and ‘NA’ for no survey.We ran the occupancy models in the unmarked package in R, using the occu function (Fiske&Chandler, 2011). As surveys occurred across multiple years, we included ‘year’ as a site- level covariate. We also included ‘park’ as an occupancy


covariate because the initial occupancies probably varied between the parks. To assess the impacts of bushfire on D. rhodogaster,we


calculated the extent of habitat burnt within a 1,000-m radius around each survey site using data extracted from the Fire Extent and Severity Mapping dataset in ArcGIS (Esri, USA) and estimated the fire severity at each site. We assessed fire severity based on evidence of scorching and canopy condition in the first round of surveys conducted in spring 2020 (Letnic et al., 2023). We classified sites as being burnt at low severity if their understorey showed evidence of recent burning (scorch marks on trees, burnt stumps and shrubs) but the canopy of Eucalyptus trees remained intact. We classified sites as being burnt at high severity if evidence of recent burning was observed in the understorey and in the canopy (i.e. leaves in the canopy were either absent or evident as epicormic buds). To account for seasonality in the detectability of reptiles,


we included an observation-level covariate of ‘day of year’, which we defined as the difference between the survey date and the start of the austral spring (1 September). Finally, because air temperature affects the detection of cryptic reptiles (Scroggie et al., 2019), we included ‘daily maximum temperature’ as an observational covariate. We extracted daily maximum temperature data from the SILO climate database (Jeffrey et al., 2001; Queensland Government, 2023), which provides interpolated daily tem- perature data at a 5 × 5 km grid resolution. Given that this method treats sites across years as different, if a site had no surveys in a given year, then we omitted that year from the final analysis (n = 27). We standardized fire extent, day of year and daily maximum temperature (mean = 0 ± SD 1) using the scale function in R. If daily temperature data could not be accurately assigned to a site visit, we removed it from the final occupancy analysis. We constructed a global model, a null model and several


candidatemodels (Table 1) to investigate our a priori hypoth- eses regarding D. rhodogaster occupancy. For all models excluding the null model we included daily maximum temperature and day of year as detection covariates. Our model hypotheses were: (1) site occupancy is constant, (2) occupancy will decrease with greater burn extent and decrease in the years following fire, (3) occupancy will decrease with greater burn extent, (4) occupancy will decrease with greater burn extent and greater burn severity, (5) occupancy will decrease with greater burn severity and in the years following fire and (6) occupancywill decreasewith great- er burn extent and differ across parks.We constructed the final globalmodel with burn extent and severity as well as year and park predicting occupancy probability and daily maximum temperature and day of year predicting detection probability. We confirmed model fit by performing a Mackenzie–


Bailey goodness-of-fit test using the function mb.gof.test in the R package AICcmodavg (Mazerolle, 2023) on the global


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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