Detecting rare carnivores using scats: Implications for monitoring a fox incursion into Tasmania.

The ability to detect the incursion of an invasive species or destroy the last individuals during an eradication program are some of the most difficult aspects of invasive species management. The presence of foxes in Tasmania is a contentious issue with recent structured monitoring efforts, involving collection of carnivore scats and testing for fox DNA, failing to detect any evidence of foxes. Understanding the likelihood that monitoring efforts would detect fox presence, given at least one is present, is therefore critical for understanding the role of scat monitoring for informing the response to an incursion. We undertook trials to estimate the probability of fox scat detection through monitoring by scat-detector dogs and person searches and used this information to critically evaluate the power of scat monitoring efforts for detecting foxes in the Tasmanian landscape. The probability of detecting a single scat present in a 1-km2 survey unit was highest for scat-detector dogs searches (0.053) compared with person searches (x¯â‰…0.015) for each 10 km of search effort. Simulation of the power of recent scat monitoring efforts undertaken in Tasmania from 2011 to 2015 suggested that single foxes would have to be present in at least 20 different locations or fox breeding groups present in at least six different locations, in order to be detected with a high level of confidence (>0.80). We have shown that highly structured detection trials can provide managers with the quantitative tools needed to make judgments about the power of large-scale scat monitoring programs. Results suggest that a fox population, if present in Tasmania, could remain undetected by a large-scale, structured scat monitoring program. Therefore, it is likely that other forms of surveillance, in conjunction with scat monitoring, will be necessary to demonstrate that foxes are absent from Tasmania with high confidence.

Integrating survey and molecular approaches to better understand wildlife disease ecology.

Infectious wildlife diseases have enormous global impacts, leading to human pandemics, global biodiversity declines and socio-economic hardship. Understanding how infection persists and is transmitted in wildlife is critical for managing diseases, but our understanding is limited. Our study aim was to better understand how infectious disease persists in wildlife populations by integrating genetics, ecology and epidemiology approaches. Specifically, we aimed to determine whether environmental or host factors were stronger drivers of Salmonella persistence or transmission within a remote and isolated wild pig (Sus scrofa) population. We determined the Salmonella infection status of wild pigs. Salmonella isolates were genotyped and a range of data was collected on putative risk factors for Salmonella transmission. We a priori identified several plausible biological hypotheses for Salmonella prevalence (cross sectional study design) versus transmission (molecular case series study design) and fit the data to these models. There were 543 wild pig Salmonella observations, sampled at 93 unique locations. Salmonella prevalence was 41% (95% confidence interval [CI]: 37-45%). The median Salmonella DICE coefficient (or Salmonella genetic similarity) was 52% (interquartile range [IQR]: 42-62%). Using the traditional cross sectional prevalence study design, the only supported model was based on the hypothesis that abundance of available ecological resources determines Salmonella prevalence in wild pigs. In the molecular study design, spatial proximity and herd membership as well as some individual risk factors (sex, condition score and relative density) determined transmission between pigs. Traditional cross sectional surveys and molecular epidemiological approaches are complementary and together can enhance understanding of disease ecology: abundance of ecological resources critical for wildlife influences Salmonella prevalence, whereas Salmonella transmission is driven by local spatial, social, density and individual factors, rather than resources. This enhanced understanding has implications for the control of diseases in wildlife populations. Attempts to manage wildlife disease using simplistic density approaches do not acknowledge the complexity of disease ecology.