Study design
Forest Fire Management Victoria conducted a prescribed burn within our study area in mid-May 2019. The fire affected 292 ha, of which 246 ha was forested and 46 ha was heathland (DEECA 2020). The majority of the fire (66%) burned at low-medium severity (DELWP, 2020), although severity was not quantified for heathland areas. We conducted five repeated mammal surveys using camera traps with infrared flash (Reconyx HF2X) across 30 sites (Figure 1) over a 12-month period. Surveys were conducted at six- and two months pre-fire and two weeks, three months, and six months post-fire, with each survey period being approximately two months in length (Appendix S1: Table A1). Using a Geographic Information System, we determined camera trap locations by positioning a 900 m grid over the study area and placing 30 survey points at grid intersections (ESRI 2014). During deployment, some camera traps were moved up to 150 m from grid points to account for access issues or to target nearby game trails or old vehicle/walking tracks, as feral cat and fox detectability is generally higher on trails compared to off trails (Geyle et al., 2020). We did not place cameras on public roads or heavily-used walking trails to reduce the risk of theft.
Of the 30 camera trap sites, 40% (12/30) were burnt during the prescribed burn (Appendix S1: Table A1: Figure 1), and the mean area burnt within a 100 m radius (i.e., Fire extent, Table 1) for these sites was 54% (range 34–95%); highlighting the patchy burning style that is common of prescribed fires in our study region (e.g., Sitters et al. 2015; Hradsky et al. 2017a). The burnt and unburnt sites were not spaced far enough apart to be considered independent for all of our study species (i.e., they were within the feasible movement range of some species). Therefore, our study design would be more appropriately described as a quasi-BACI (before-after, control-impact) design, acknowledging this potential for spatial dependence between the sites.
At each site, cameras were attached to a tree at a height of approximately 40 cm facing a lure station two metres away. Each lure station was comprised of wadding soaked in tuna oil encased in a polyvinyl chloride (PVC) vent cowl. Lure stations were pegged securely into the ground, and vegetation in each camera’s line of sight was cleared to prevent false triggers and to ensure animals were clearly visible. Cameras were set to record three images per trigger at medium-high sensitivity, with no delay between trigger events.

Statistical analyses

Images were processed using CPW Photo Warehouses (Ivan and Newkirk 2016). Animals were identified to species level where possible, otherwise, they were categorised according to the finest taxonomic/functional group possible (e.g., ‘unknown small mammal species’). Each photo sequence was treated as a single point in time and a detection event was defined as images of the same species on the same camera that were separated by at least 60 minutes. Species detection matrices were created using the camtrapR package (Niedballa et al. 2016) in R version 4.2.2 (R Core Team 2022).
To test the influence of the fire, habitat, anthropogenic, and prey variables (Table 1) on mammal activity, we fitted generalised linear mixed models (GLMMs) to each species/group with sufficient data (Appendix S1: Table A2). There were many zeros (i.e., days in which a species was not detected) in the detection matrices due to long intervals between detection events. To account for this, we defined the response variable as the number of days a species was detected in each survey period relative to the number of days it was not detected. There were four species included in our analyses: the red fox, feral cat, swamp wallaby, and eastern grey kangaroo. To fit models and test our predictions on smaller mammals (<2,000 g), we pooled detections from small mammals (<800 g) and medium-sized mammals (800 – 2,000 g). The species comprising these two groups (see Appendix S1: Table A2) were recorded too infrequently to fit models to individual species, and many detections were not identifiable to species level. We conducted all model fitting and verification using the glmmTMB (Brooks et al. 2017), MUMIn (Barton 2022), and DHARMa (Hartig 2022) packages in program R version 4.2.2 (R Core Team 2022).
Before testing the covariates for each species/species group, we constructed models to test the effect of two possible detection covariates, namely camera placement (on or off trail) and age of lure (Table 1) on each response variable. Camera placement can influence the detectability of cats and foxes (Geyle et al., 2020), while the age of a lure might impact mammal activity either through reduced potency over time or behavioural alterations (Frey et al. 2017; McHughet al. 2019). While the five survey periods were similar in length (refer to Appendix S1: Table A1), there was inconsistency in the timing of lure replacement. Lures for surveys four and five were replaced part-way through the survey periods, unlike those in surveys one, two, and three, which were replaced at the beginning. These detection models incorporated the main effects of both camera placement and lure age, along with random effects of Survey period and Site, allowing us to account for repeat sampling over time and any camera-level variability. We assessed the output of these models and included camera placement and/or lure age as fixed effects in subsequent analyses if the 95% confidence intervals did not cross zero.
To test the effect of our remaining variables on mammal activity, we fitted binomial GLMMs containing three-way interactions between Treatment (CI), Before-After (BA) and each of the remaining non-detection covariates (Table 1). These models included a total of 14 variables: two Fire History variables, three variables relating to the 2019 Prescribed Fire, two Vegetation variables, one Topography variable, two variables representing proximity to Anthropogenic Features, and potentially one or both of the Detection variables if they influenced the activity of the species/group (Table 1). We included both the Large mammal and Small mammal Prey Activity variables (Table 1) for the fox, and the Small mammal Prey Activity variable for the cat. We used the ‘dredge’ function from the MuMIn package for model selection. This function only allows a maximum of 31 variables in the global model, including interaction terms. Due to the complexity arising from fitting three-way interaction between BA, CI, and most of the aforementioned variables, we fitted two unique ‘sub-global’ models containing different sets of variables, each with <31 terms. We then used the ‘merge.model.selection’ to combine the two model selection tables per species/group and reranked the models by AICc. The selection criteria for well-supported models were based on a delta Akaike Information Criterion (ΔAICc) of less than 2 (Burnham and Anderson 2004).
For the Fire extent variable, we fitted a simplified two-way interaction with BA. This is because all unburnt sites had a Fire extent of 0%, making it redundant to include the CI variable. We did not fit an interaction between BA or CI and Vegetation Type, Time Since Fire, or Fire Frequency, as this resulted in model convergence issues. Moderately and highly correlated variables (i.e., Pearson’s r ≥0.5) were not included in the same model, and we excluded NDVI and TPI (Pearson’sr = 0.49) from appearing in the same model because preliminary analyses showed that well-supported models containing both variables reported influential interactions that were not supported by the underlying data. We included the random effects of Site and Survey Period as per the initial detection models, and we scaled and centred each of the continuous variables prior to modelling. We limited the maximum number of variables per model to 10 to avoid issues associated with overfitting, which meant that only one three-way interaction could feature in any given model.
Table 1. Descriptions of the predictor variables included in our generalized linear mixed models of mammal activity in the eastern Otway Ranges, Victoria. The spatial data from the 2019 prescribed fire was sourced from DELWP (2020).