Taxonomic revision reveals potential impacts of Black Summer megafires on a cryptic species
Cite this dataset
Jolly, Chris et al. (2022). Taxonomic revision reveals potential impacts of Black Summer megafires on a cryptic species [Dataset]. Dryad. https://doi.org/10.5061/dryad.wm37pvmnb
Context: Sound taxonomy is the cornerstone of biodiversity conservation. Without a fundamental understanding of species delimitations, as well as their distributions and ecological requirements, our ability to conserve them is drastically impeded. Cryptic species – two or more distinct species currently classified as a single species – present a significant challenge to biodiversity conservation. How do we assess the conservation status and address potential drivers of extinction if we are unaware of a species’ existence? Here, we present a case where the reclassification of a species formerly considered widespread and secure – the sugar glider (Petaurus breviceps) – has dramatically increased our understanding of the potential impacts of the catastrophic 2019–20 Australian megafires to this species.
Methods: We modelled and mapped the distribution of the former and reclassified sugar glider (Petaurus breviceps). We then compared the proportional overlap of fire severity classes between the former and reclassified distribution, and intersect habitat suitability and fire severity to help identify areas of important habitat following the 2019–20 fires.
Key Results: Taxonomic revision means that the distribution of this iconic species appears to have been reduced to 8% of its formerly accepted range. Whereas the 2019–20 Australian megafires overlapped with 8% of the formerly accepted range, they overlapped with 33% of the proposed range of the redefined Petaurus breviceps.
Conclusions: Our study serves as a sombre example of the substantial risk of underestimating impacts of mega-disturbance on cryptic species, and hence the urgent need for cataloguing Earth’s biodiversity in the age of megafire.
Occurrence records of P. breviceps were collected from the Atlas of Living Australia (https://www.ala.org.au) and were subject to a filtering process. Because sugar gliders were introduced to Tasmania (Campbell et al. 2018), we excluded all Tasmanian records. We then removed dubious records by clipping all records to either the former P. breviceps range (based on IUCN maps; IUCN 2020) or the proposed reclassified P. breviceps range (Cremona et al. 2021) to create two sets of occurrence data (i.e., one each for the former and reclassified P. breviceps). It is worth noting, however, that the reclassified distribution of P. breviceps proposed by Cremona et al. (2021) is an estimate based on genetic and morphological data. Although evidence currently suggests that the Great Dividing Range acts as the western edge of the distribution of P. breviceps (Cremona et al. 2021), we cannot be certain of this. However, for the purposes of this study we have assumed it to be so. In both datasets, records were removed if: (i) they were missing date information or were collected before the year 2000; or (ii) they had high locational uncertainty (e.g., vague or inaccurate locations). Records within any 1 × 1 km grid cell were collapsed into a single record. The final filtered data base consisted of 7777 presence records within the formerly considered geographic range, and 5089 within the reclassified range (see Figure S1).
Geographic range estimation
We mapped the extent of occurrence (EOO) of the former and reclassified P. breviceps using the occurrence datasets. Extent of occurrence is defined as the area enclosed by the shortest possible boundary containing all sites in which a species is known to be present (IUCN 2021). We calculated EOO as α‐hulls (a generalisation of convex polygons that allow for breaks in species ranges), using the ‘alphahull’ package in R version 3.6.2 (R Core Team 2021), specifying a α value of two (IUCN 2021). We regarded EOO as preferable to area of occupancy (AOO) because maps of the latter showed clear spatial bias indicated by high densities of records surrounding major capital cities.
Species distribution modelling
Using the maxent algorithm, we developed species distribution models (SDMs) based on the two occurrence datasets outlined above (Phillips et al. 2006). We selected SDM environmental layers based on their likely importance to P. breviceps habitat suitability. All environmental layers were resampled to 1 × 1 km resolution prior to being included in models. A set of 10,000 background points were included within the SDM to compare densities in environmental values occupied by P. breviceps with those of the surrounding unoccupied environment. We addressed sample bias within the study area with a ‘target group’ background sampling approach (Phillips et al. 2009) (see Figure S1). We defined the target group as arboreal mammal species occurring within the study area, including P. breviceps. Sampling intensity for target group species was mapped by converting species presence records of the target group to a kernel density map using the kde2d function of the ‘MASS’ package (Venables and Ripley 2002) set with the default kernel bandwidth. Model performance was measured as area under the curve (AUC) of the receiver operating characteristic (ROC) plot, and the contribution of environmental variables to the response variable was measured as permutation importance (Phillips 2005).
We overlapped the former and reclassified P. breviceps EOO with 2019–20 bushfire severity maps from the Google Earth Engine Burnt Area Mapping (GEEBAM; DIPE 2020). GEEBAM classifies the cells within the fire boundary as one of five fire severity classes: no data (cleared land, water etc.); unburnt (unburnt and lightly burnt); low and moderately burnt (some or moderate change post-fire); high severity (vegetation mostly scorched); and very high severity (vegetation clearly consumed). When calculating fire overlap, we considered only fires occurring within the Department of Agriculture, Water and Environment’s (2020) ‘preliminary area for environmental analysis’ (following Legge et al. 2020). This area encompasses bioregions that were deemed to have experienced anomalously substantial fire activity during the 2019–20 bushfire season. Overlap measures were calculated using QGIS version 3.14.1 (QGIS Development Team 2021).
We created a fire severity × habitat quality matrix to help identify the spatial intersection between fire severity and habitat quality for the reclassified P. breviceps. First, we classified the continuous output of relative habitat quality derived from the SDM into four discrete classes: low quality (relative likelihood of occurrence 0–0.25); low–medium quality (relative likelihood of occurrence 0.25–0.50); medium–high quality (relative likelihood of occurrence 0.5–0.75); and high quality (relative likelihood of occurrence 0.75–1). We then combined the reclassified SDM with the GEEBAM fire severity layer to derive a layer with 16 unique combinations of all combinations of habitat quality and fire severity and mapped this across the range of P. breviceps.