Douglas H. Kerlin’s research while affiliated with Griffith University and other places

Aging is a ubiquitous component of the life history and biological function of all species. In wildlife studies, estimates of age are critical in order to understand how a species’ ecology, biology and behaviour vary in parallel with its life-history events. Longitudinal studies that track individuals as they age are limited in fruit bats, as recapture is difficult for vagile species with nomadic lifestyles. Most studies estimate age by the broad categorisation of individuals with similar biological characteristics or morphometrics into age classes (e.g. sub-adult and adult). In this review, we systematically compile and compare the age classifications used across a range of studies on Australian flying-foxes (Pteropus). We discuss the associated challenges of those classifications and identify current knowledge gaps. The terminology, methodology and explanations behind age classifications were inconsistent across reviewed studies, demonstrating that age classifications are highly subjective – particularly when identifying reproductively immature individuals. Downstream analyses and cross-disciplinary data use are likely to be compromised as a result. Further known-aged studies of flying-foxes would assist in clarifying variations of key parameters among non-adult individuals. We also encourage greater consistency in age classification and reporting, ensuring that classifications are well defined and biologically sound.

After the loss and fragmentation of habitat, vehicle collisions are one of the main threats to the long-term survival of wild koalas. Koala road strike data were analysed for a section of the Peak Downs Highway between Nebo and Spencer’s Gap, west of Mackay, Queensland, Australia. The analysis was carried out on 345 records (October 2014 to November 2023), and results suggested the spatial distribution of koala road strike followed a random pattern along this section of the highway, assuming a Poisson point pattern on a linear network. An analysis of the candidate predictors of koala vehicle collisions, including habitat and road variables, found that the amount of high-quality koala habitat (as defined by the local koalas’ tree species preference) present and the driver visibility were the only significant predictors. The relative homogeneity of landuse and vegetation across this landscape may mean that koalas do not concentrate at specific crossing points. More research, including detailed habitat mapping, is needed into this population, which currently lacks government and conservation attention, to inform mitigation efforts and reduce mortality rates for this potentially nationally significant population.

Objective To identify the size and distribution of the horse population in the Northern Rivers Region of NSW, including changes from 2007 to 2021, to better understand populations at risk of Hendra virus transmission. Methods Census data from the 2007 Equine Influenza (EI) outbreak were compared with data collected annually by New South Wales Local Land Services (LLS) (2011–2021), and with field observations via road line transects (2021). Results The horse populations reported to LLS in 2011 (3000 horses; 0.77 horses/km ² ) was 145% larger than that reported during the EI outbreak in 2007 (1225 horses; 0.32 horses/km ² ). This was inconsistent with the 6% increase in horses recorded from 2011 to 2020 within the longitudinal LLS dataset. Linear modelling suggested the true horse population of this region in 2007 was at least double that reported at the time. Distance sampling in 2021 estimated the region's population at 10,185 horses (3.89 per km ² ; 95% CI = 4854–21,372). Field sampling and modelling identified higher horse densities in rural cropland, with the percentage of conservation land, modified grazing, and rural residential land identified as the best predictors of horse densities. Conclusions Data from the 2007 EI outbreak no longer correlates to the current horse population in size or distribution and was likely not a true representation at the time. Current LLS data also likely underestimates horse populations. Ongoing efforts to further quantify and map horse populations in Australia are important for estimating and managing the risk of equine zoonoses.

Coevolution is common and frequently governs host–pathogen interaction outcomes. Phenotypes underlying these interactions often manifest as the combined products of the genomes of interacting species, yet traditional quantitative trait mapping approaches ignore these intergenomic interactions. Devil facial tumor disease (DFTD), an infectious cancer afflicting Tasmanian devils ( Sarcophilus harrisii ), has decimated devil populations due to universal host susceptibility and a fatality rate approaching 100%. Here, we used a recently developed joint genome-wide association study (i.e., co-GWAS) approach, 15 y of mark-recapture data, and 960 genomes to identify intergenomic signatures of coevolution between devils and DFTD. Using a traditional GWA approach, we found that both devil and DFTD genomes explained a substantial proportion of variance in how quickly susceptible devils became infected, although genomic architectures differed across devils and DFTD; the devil genome had fewer loci of large effect whereas the DFTD genome had a more polygenic architecture. Using a co-GWA approach, devil–DFTD intergenomic interactions explained ~3× more variation in how quickly susceptible devils became infected than either genome alone, and the top genotype-by-genotype interactions were significantly enriched for cancer genes and signatures of selection. A devil regulatory mutation was associated with differential expression of a candidate cancer gene and showed putative allele matching effects with two DFTD coding sequence variants. Our results highlight the need to account for intergenomic interactions when investigating host–pathogen (co)evolution and emphasize the importance of such interactions when considering devil management strategies.

The localized clustering of wildlife vehicle‐strike in space and time, often called ‘hot spots’, are commonly used to guide placement of road mitigation installations. We interrogated koala vehicle‐strike data across a 20‐year period between 1997 and 2016, examining trends in identified hot spots. We found that hot spots were not static and decline or emerge in line with the resultant pressures (e.g., from development and associated traffic increases) being exerted on koala populations within the surrounding landscape. We suggest that there is likely to be a relationship between hot spots waning over time and localized extinction of koalas. We feel strategies that solely focus on hot spot intervention will do little to halt and reverse the significant decline in the koala population across South East Queensland because they are almost always retrospective. It is imperative that regional wildlife movement solutions, around and across roads, are appropriately planned and implemented ahead of time (i.e., during initial construction/developmental expansion), if they are to serve as effective mitigation for remaining local koala population across South East Queensland. We caution against recommending mitigation based solely on historical vehicle‐strike data without considering contemporary population, movement and behavioural data.

We provide a comparative analysis of two koala management plans for populations in two Australian municipalities, based on principles of landscape management: Ballarat (Victoria) and Bellingen (New South Wales). A landscape-based approach is required to protect the species, but evaluation of landscape management is limited. We present an assessment framework for evaluating local koala management plans. The plans are evaluated against a common set of principles and criteria, despite very different approaches stemming from context-specific factors. Interestingly, despite a variation in the number of indicators in the plans, the overall results of the evaluation demonstrate a similar level of performance against the criteria, and common strengths and weaknesses. In the absence of consistent standards for the protection of the koala across Australia, the species will continue to decline, and management practices will fail to protect the koala from extinction, as is currently predicted.