Variations in coat morphology are well-documented among felids and are theorised to aid in camouflage during stalk and ambush hunting. A diverse array of coat types has arisen in Felis silvestris catus (feral cats) through domestication and subsequent selective breeding. This species has successfully spread across Australia over the past 200 years, raising the question of whether any specific coat types offer an adaptive advantage. We used 24 657 camera-trap images of feral cats in Tasmania, Australia, and assigned each cat observation a coat colour and pattern. We analysed these data to examine how different spatial features affect the presence or absence of coat types. We also tested if cats with differing coat types were active on different days in response to temporal features, including moon luminosity (full or new). Elevation was positively associated with the presence of orange (odds ratio= 1.74, 97.5% confidence interval= 1.25, 2.4) and tortoiseshell (odds ratio= 1.93, CI= 1.32, 2.83) cats, while blotched brown cats were negatively associated with elevation (odds ratio= 0.74, CI= 0.59, 0.93), relative to black cats. All coat types were 1.2 to 2 times more likely to be active on nights with a new moon, except for orange cats who were equally active regardless of moon luminosity (odds ratio= 0.94, CI= 0.62, 1.42). Our results indicate that coat types are equally successful across Tasmania, perhaps owing to naïve prey or limited predator competition. The high activity of orange cats irrespective of moon phase may be reflective of the male cat’s tendency to patrol territory, as opposed to favouring dark nights for hunting. Future studies should consider comparing the coat types found in feral cats to adjacent domestic populations, and against a wider array of habitat types to further investigate the potential for selective pressure on feral cat coat types in Australia.

Unoccupied aircraft systems (UAS), also known as drones, are an emerging tool for surveying animals (wildlife and domestic) and are rapidly supplanting other survey techniques. However, the behavioural response of animals to drone activities can vary according to the focal species, therefore evaluating comparisons between animals of different species and domesticity are needed. Here we investigated the impacts of drone presence on two large terrestrial mammals from Tasmania, Australia —  Bennett's wallaby  (Notamacropus rufogriseus), and Forester kangaroo (Macropus giganteus tasmaniensis), and a ground bird, the domestic chicken (Gallus  gallus domesticus) — using a commercial quadcopter model: DJI Phantom 4 Pro. We found that Forester kangaroos and Bennett's wallabies started to exhibit noticeable changes in behaviour, including evasion, when the drone motor sound exceeded ~50 decibels (dB) as heard from the ground (at flight altitudes of 30 – 50 m AGL). At lower sound levels (48 dB and below, above 50 m AGL), the macropod response was minimal. The response of domestic chickens to the drone was remarkably similar to that of the Macropus species, despite the species generally being more susceptible to, and instinctively vigilant against drone-sized aerial predators such as raptors. This study establishes essential baseline information for understanding the limitations of drone operations across species, compares the behavioural responses of wildlife and domestic animals to drone surveys, and demonstrates the viability of these surveys for both groups when disturbance is minimised.  

Dimensions of body size are an important measurement in animal ecology, though they can be difficult to obtain due to the effort and cost associated with the invasive nature of these measurements. We avoid these limitations by using camera-trap images to derive dimensions of animal size. To obtain measurements of object dimensions using this method, the size of the object in pixels, the focal length of the camera, and the distance to that object must be known. We describe a novel approach of obtaining the distance to the object through the creation of a portable distance marker, which, when photographed, creates a “reference image” to determine the position of the animal within an image. This method allows for the retrospective analysis of existing datasets and eliminates the need for permanent in-field distance markers. We tested the accuracy of this methodology under controlled conditions with objects of known size resembling Felis catus, our study species, validating the legitimacy of our method of size estimation. We then apply our method to measure feral cat body size using images collected in Tasmania, Australia. The precision of our methodology was evaluated by comparing size estimates across individual cats, revealing consistent and reliable results. The average height (front paw to shoulder) of the feral cats sampled was 25.25 cm (CI = 24.4, 26.1) and the average length (base of tail to nose) was 47.48 cm (CI = 46.0, 48.9), suggesting wild feral cats in our study area are no larger than their domestic counterparts. Given the success of its application within our study, we call for further trails with this method across a variety of species.

As a source of information on species’ geographic distributions, macroecologists and biogeographers have had to rely on expert-derived range maps to study biodiversity patterns at large scales. In addition to being biased towards well-studied taxa and subjective by nature, such maps suffer from a lack of consistency in how species’ absences are treated within the wider distribution. Using the finer resolution of the Interim Biogeographic Regionalization for Australia (subregions) and example sets of Australian species as study system, we developed a reproducible, data-driven approach to map the extent of occurrence (EOO) of hundreds—or even thousands—of species by combining presence-only data and subregions (i.e., non-equal-sized operational units that represent homogenous areas of unique environmental features) within a unifying quantitative framework. From data-driven and expert-derived range maps for 533 birds, species richness’ estimates differ at three biogeographical scales—whit bias (mean error) at coarser resolution (ecoregion) being half that at subregional scale—and the spatial association between pairs of these birds’ presence-absence maps vary from nearly zero to almost one (representing such pattern almost either differently or identically, respectively). Holes within the wider distribution of the EOO maps for pairs of amphibians, mammals, reptiles, and plants seem to respond to the demarcation of different subpopulations over Australia rather than causing an underestimation of a species’ empirical distribution. These results demonstrate that this approach can reliably map EOO of species whose distributions aligns with three broad types of geographic patterns (wide-range, habitat-specialists, and range-restricted species). This alternative to expert-derived range maps can serve as a basis for more robust, data-driven studies of biogeographic biodiversity patterns, thus improving our understanding and conservation efforts of global biodiversity.