Analysis

All analyses were undertaken in R v.4.1.0 (R Core Team, 2021) and where required, specialised packages used are referenced below.
Camera data – Video metadata tagging was undertaken in ‘digiKam’ v.7.0.0 (digiKam Team, 2022) and we used R packages ‘camtrapR’ v.2.2.0 (Niedballa et al. , 2016) and ‘exiftoolr’ v.0.2.0 (Harvey, 2016) to extract and prepare the camera data to create a record table and spatial detection history of sugar glider observations. Capture-history, camera operation and trap location data were prepared for spatially explicit capture-recapture analysis. Sampling occasions were defined as commencing from midday to centre detections on nights as sugar gliders are nocturnal. Capture histories were filtered to a single detection per individual per night at each detector. Varying effort (e.g., camera malfunctions) were accounted for by including camera usage in our capture history.
To evaluate how our capture and trapping results compared to published literature, we calculated log-transformed incidence rates for our data per the steps outlined in the meta-analysis (Supporting Information).
SECR models – SECR analyses were performed using the ‘secr’ package v.4.5.8 (Efford, 2023). The suggested buffer width (439m) from ‘secr’ was revised upwards to 532m (4 × σ ) based on exploratory modelling and confirmed against an effective sampling area (ESA) plot. The habitat mask was created using this buffer distance with a 40m spacing, and any non-forest (detected from satellite imagery) boundaries were excluded. We assumed the population was closed as the total sampling period (30 nights per survey; 60 nights total) was brief compared to sugar glider longevity (5-7 years) and recruitment of den young (3-4 months) (Jackson, 2000b, Smith, 1973, Suckling, 1984).
Factors influencing probability of detection - Using data from the entire survey, we contrasted the impact of: bait type (i.e., effect of bait-type was constant over time, but different between bait types) and rebaiting period (i.e., effect of bait-type was constant within each rebaiting period, but different between bait type and rebaiting period) with other environmental variables that might influence the probability of detection (g0 ) and movement parameters (σ) (i.e., min and max temperature, tree species and tree height). In total, 12 models were fit (plus the null model, Table 1). Models of a linear time trend failed to converge and were not considered further. We compared model results to the null model and selected the best model based on Akaike’s Information Criterion corrected for small sample size (AICc, Buckland et al. 1979). Models with a difference in AICc <2 were considered equivalent (Burnham and Anderson, 2002).
Estimating density - Data were subset to the period when only the fish-bait was used to explore behavioural models. We held σ (σ ) parameters constant and investigated the influence of the following factors on g0 : b , learned response; B; transient response; k , site response; bk , animal x site learned response, and; Bk , animal x site transient response. We estimated density with our preferred parameterization (based on AICc model selection).
Home range statistics were estimated using all data. We calculated the mean maximum distance moved pooled across individuals (MMDM) and core home ranges (i.e., root pooled spatial variance; RPSV) (Borchers & Efford, 2008).