Physiological stress levels in wild koala sub-populations facing anthropogenic induced environmental trauma and disease

Australian small mammals such as koalas must cope with immense pressure from anthropogenic induced stressors or trauma such as bushfires, vehicle collision impacts and habitat disturbance and land clearance. In addition, they must cope with diseases such as chlamydia. To date, there is no published literature on physiological stress levels in wild koala populations compared with identified environmental stressors. This study investigated physiological stress levels within sub-populations of wild koalas encountering environmental trauma and disease from New South Wales (NSW), Queensland (QLD) and South Australia (SA). Physiological stress was determined using a faecal glucocorticoid (or cortisol) metabolites (FGMs) enzyme-immunoassay (EIA) from 291 fresh faecal samples collected from wild koalas at the point of rescue. A healthy breeding sub-population from a forest reserve in QLD acted as a control group. Clearance of prime Eucalyptus habitat had the largest impact on FGMs, followed by bushfire related factors (e.g. flat demeanour, dehydration and burns injury). Koalas with other sources of physical injury (dog-attacks and vehicle collisions) and disease (chlamydia) also had higher FGMs compared to healthy wild koalas. Healthy wild koalas expressed the lowest median levels of FGMs. Overall, the results highlight that anthropogenic-induced stressors tend to increase physiological stress in wild koalas. Thus, the ultimate stressors such as habitat clearance and bush fire events could increase the incidences of proximate stressors such as dog attacks and vehicle collisions, and increase risks of foliage shortage, diseases and mortality. Therefore, there is need for ecological monitoring, conservation management actions and policy changes to curb the koala population crisis, especially within on-going and future land and road development programs.

Population genetics of the koala (Phascolarctos cinereus) in north-eastern New South Wales and south-eastern Queensland

Habitat loss and fragmentation are key threats to local koala (Phascolarctos cinereus) populations. Broad-scale management is suboptimal for koalas because distribution models are not easily generalised across regions. Therefore, it is imperative that data relevant to local management bodies are available. Genetic data provides important information on gene flow and potential habitat barriers, including anthropogenic disturbances. Little genetic data are available for nationally significant koala populations in north-eastern New South Wales, despite reported declines due to urbanisation and habitat loss. In this study, we develop 14 novel microsatellite loci to investigate koala populations in north-eastern New South Wales (Port Macquarie, Coffs Harbour, Tyagarah, Ballina) and south-eastern Queensland (Coomera). All locations were significantly differentiated (FST = 0.096–0.213; FʹST = 0.282–0.582), and this pattern was not consistent with isolation by distance (R2 = 0.228, P = 0.058). Population assignment clustered the more northern populations (Ballina, Tyagarah and Coomera), suggesting contemporary gene flow among these sites. For all locations, low molecular variation among (16%) rather than within (84%) sites suggests historical connectivity. These results suggest that koala populations in north-eastern New South Wales and south-eastern Queensland are experiencing contemporary impediments to gene flow, and highlight the importance of maintaining habitat connectivity across this region.

Interpreting patterns of population change in koalas from long-term datasets in Coffs Harbour on the north coast of New South Wales

We examined a long-term, repeat dataset for the koala population within Coffs Harbour Local Government Area. Analyses of these data have led to the conclusion that, following a perceived population decline in the 1980s, the koala population of Coffs Harbour has endured between 1990 and 2011 and showed no evidence of a precipitous decline during this period. Rather, the population change is best characterised as stable to slowly declining. This conclusion appears to contradict a common view of recent koala population declines on the north coast of New South Wales. There are four possible explanations for the population’s apparent stability: that conservation efforts and planning regulations have been effective; that surviving adults are persisting in existing home ranges in remnant habitat; that the broader Coffs Harbour population is operating as a ‘source and sink’ metapopulation; and/or that the standard survey methods employed are not sufficiently sensitive to detect small population changes. These findings do not mean there is no need for future conservation efforts aimed at koalas in Coffs Harbour; however, such efforts will need to better understand and account for a koala population that can be considered to be stable to slowly declining.

Distribution models for koalas in South Australia using citizen science-collected data

The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAIC c ∼ 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R (2)(m) (76.4) and R (2)(c) (81.0), and had a good performance according to Cohen's κ (0.46). Cross-validation error was low (∼ 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km(2)) of the Adelaide-Mount Lofty Ranges, a density-suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685-199,723; average density = 5.0-35.8 km(-2)). We demonstrate the power of citizen science data for predicting species' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.

Ranking and mapping koala habitat quality for conservation planning on the basis of indirect evidence of tree-species use: a case study of Noosa Shire, south-eastern Queensland

Context Mapping the habitat and distribution of a species is critical for developing effective conservation plans. Koala (Phascolarctos cinereus, Phascolarctidae) distribution is constrained by the nutritional and shelter requirements provided by a relatively small number of key tree species in any given area. Identifying these key species provides a practical foundation for mapping koala habitat and prioritising areas for conservation. Aims To determine key tree species for koalas in Noosa Shire (south-eastern Queensland, Australia) as a basis for mapping koala habitat quality. Methods We applied a faecal-pellet survey methodology in 1996/97 to assess evidence of use by koalas of 4031 trees from 96 randomly stratified survey sites across different eucalypt-forest and woodland communities. Results were compared with those from a later survey undertaken in 2001/02 involving 5535 trees from 195 sites that were distributed across broadly similar areas with the aim to investigate aspects of koala landscape ecology. Key results A total of 66.7% of the 1996/97 survey sites contained koala faecal pellets, recorded under 953 eucalypt trees (14 species) and 1670 non-eucalypt trees (27 species). The proportion of trees at a given survey site that had koala faecal pellets at the base ranged from 2.2% to 94.7% (mean = 31.13 ± 2.59% s.e.). For the 2001/02 dataset, koala pellets were found at 55.4% of sites, from 794 eucalypt and 2240 non-eucalypt trees. The proportion of trees with pellets ranged from 3% to 80% (mean = 21.07 ± 1.77% s.e.). Both the 1996/97 and 2001/02 surveys identified the same three tree species (forest red gum, Eucalyptus tereticornis, swamp mahogany, E. robusta, and tallowwood, E. microcorys) as the highest-ranked for koala use in the study area. Three additional species (red mahogany, E. resinifera, small-fruited grey gum, E. propinqua, and grey ironbark, E. siderophloia) were identified in the 1996/97 surveys as key eucalypt species. Of the non-eucalypts in the 1996/97 dataset, coast cypress pine (Callitris columellaris) and broad-leaved paperbark (Melaleuca quinquenervia) ranked highest for use by koalas, followed by pink bloodwood (Corymbia intermedia) and brush box (Lophostemon confertus). White bottlebrush (Callistemon salignus), hard corkwood (Endiandra sieberi), M. quinquenervia and C. intermedia ranked highest in the 2001/02 dataset. The findings showed significantly greater use of larger eucalypts (i.e. 300-mm to >600-mm diameter at breast height). Conclusions The identified key eucalypt species, being the critical limiting resource for koalas, were used to assign koala habitat-quality classes to mapped regional ecosystem types to create a Koala Habitat Atlas (KHA) for Noosa Shire. The combined two highest quality classes based on abundance of the key eucalypt species comprised only 15.7% of the total land area of the Shire. Implications The KHA approach provides a practical and repeatable method for developing koala habitat-suitability mapping for national-, regional- and local-scale conservation and recovery planning purposes.

Drought-driven change in wildlife distribution and numbers: a case study of koalas in south west Queensland

Context Global climate change will lead to increased climate variability, including more frequent drought and heatwaves, in many areas of the world. This will affect the distribution and numbers of wildlife populations. In south-west Queensland, anecdotal reports indicated that a low density but significant koala population had been impacted by drought from 2001–2009, in accord with the predicted effects of climate change. Aims The study aimed to compare koala distribution and numbers in south-west Queensland in 2009 with pre-drought estimates from 1995–1997. Methods Community surveys and faecal pellet surveys were used to assess koala distribution. Population densities were estimated using the Faecal Standing Crop Method. From these densities, koala abundance in 10 habitat units was interpolated across the study region. Bootstrapping was used to estimate standard error. Climate data and land clearing were examined as possible explanations for changes in koala distribution and numbers between the two time periods. Key results Although there was only a minor change in distribution, there was an 80% decline in koala numbers across the study region, from a mean population of 59 000 in 1995 to 11 600 in 2009. Most summers between 2002 and 2007 were hotter and drier than average. Vegetation clearance was greatest in the eastern third of the study region, with the majority of clearing being in mixed eucalypt/acacia ecosystems and vegetation on elevated residuals. Conclusions Changes in the area of occupancy and numbers of koalas allowed us to conclude that drought significantly reduced koala populations and that they contracted to critical riparian habitats. Land clearing in the eastern part of the region may reduce the ability of koalas to move between habitats. Implications The increase in hotter and drier conditions expected with climate change will adversely affect koala populations in south-west Queensland and may be similar in other wildlife species in arid and semiarid regions. The effect of climate change on trailing edge populations may interact with habitat loss and fragmentation to increase extinction risks. Monitoring wildlife population dynamics at the margins of their geographic ranges will help to manage the impacts of climate change.

Modeling species' distributions to improve conservation in semiurban landscapes: koala case study

Models of species' distributions are commonly used to inform landscape and conservation planning. In urban and semiurban landscapes, the distributions of species are determined by a combination of natural habitat and anthropogenic impacts. Understanding the spatial influence of these two processes is crucial for making spatially explicit decisions about conservation actions. We present a logistic regression model for the distribution of koalas (Phascolarctos cinereus) in a semiurban landscape in eastern Australia that explicitly separates the effect of natural habitat quality and anthropogenic impacts on koala distributions. We achieved this by comparing the predicted distributions from the model with what the predicted distributions would have been if anthropogenic variables were at their mean values. Similar approaches have relied on making predictions assuming anthropogenic variables are zero, which will be unreliable if the training data set does not include anthropogenic variables close to zero. Our approach is novel because it can be applied to landscapes where anthropogenic variables are never close to zero. Our model showed that, averaged across the study area, natural habitat was the main determinant of koala presence. At a local scale, however anthropogenic impacts could be more important, with consequent implications for conservation planning. We demonstrated that this modeling approach, combined with the visual presentation of predictions as a map, provides important information for making decisions on how different conservation actions should be spatially allocated. This method is particularly useful for areas where wildlife and human populations exist in close proximity.