Anthropogenic and environmental factors associated with koala deaths due to dog attacks and vehicle collisions in South-East Queensland, Australia, 2009-2013.

Populations of the iconic Australian koala are under constant decline. Their deaths are associated with rapid and extensive urbanization and the fragmentation of habitat areas. Using citizen science data on reported koala mortalities, we quantified the association between anthropogenic and environmental factors and the two leading causes of koala deaths, dog attacks and vehicle collisions. We achieved this objective using a case-control study design to compare the odds of exposure to a given risk factor for cases (a given cause of death) with the odds of exposure to a given risk factor for controls (all other causes of death). Koala deaths due to dog attacks were positively associated with registered dog population density and negatively associated with lot density whereas koala deaths due to vehicle collisions were positively associated with road density (road length per square kilometer) and negatively associated with human population density and distance to primary and secondary roads. The results of this research can be used to develop strategies to mitigate the risk of deaths due to dog attacks, for example by conducting educational awareness programs, promoting registration of dogs and discouraging free roaming of dogs. In a similar manner, in high-risk areas for vehicle collisions, over- or underpasses can be built to facilitate safe movement of koalas for road crossings or speed limits could be introduced to reduce the likelihood of premature koala deaths due to vehicle collisions.

Development of core competencies for field veterinary epidemiology training programs.

A workforce with the adequate field epidemiology knowledge, skills and abilities is the foundation of a strong and effective animal health system. Field epidemiology training is conducted in several countries to meet the increased global demand for such a workforce. However, core competencies for field veterinary epidemiology have not been identified and agreed upon globally, leading to the development of different training curricula. Having a set of agreed core competencies can harmonize field veterinary epidemiology training. The Food and Agriculture Organization of the United Nations (FAO) initiated a collective, iterative, and participative process to achieve this and organized two expert consultative workshops in 2018 to develop core competencies for field veterinary epidemiology at the frontline and intermediate levels. Based on these expert discussions, 13 competencies were identified for the frontline and intermediate levels. These competencies were organized into three domains: epidemiological surveillance and studies; field investigation, preparedness and response; and One Health, communication, ethics and professionalism. These competencies can be used to facilitate the development of field epidemiology training curricula for veterinarians, adapted to country training needs, or customized for training other close disciplines. The competencies can also be useful for mentors and employers to monitor and evaluate the progress of their mentees, or to guide the selection process during the recruitment of new staff.

Estimating koala density from incidental koala sightings in South-East Queensland, Australia (1997-2013), using a self-exciting spatio-temporal point process model.

The koala, Phascolarctos cinereus, is an iconic Australian wildlife species facing a rapid decline in South-East Queensland (SEQLD). For conservation planning, the ability to estimate the size of koala populations is crucial. Systematic surveys are the most common approach to estimate koala populations but because of their cost they are often restricted to small geographic areas and are conducted infrequently. Public interest and participation in the collection of koala sighting data is increasing in popularity, but such data are generally not used for population estimation. We modeled monthly sightings of koalas reported by members of the public from 1997 to 2013 in SEQLD by developing a self-exciting spatio-temporal point process model. This allowed us to account for characteristics that are associated with koala presence (which vary over both space and time) while accounting for detection bias in the koala sighting process and addressing spatial clustering of observations. The density of koalas varied spatially due to the heterogeneous nature of koala habitat in SEQLD, with a mean density of 0.0019 koalas per km2 over the study period. The percentage of land areas with very low densities (0-0.0005 koalas per km2) remained similar throughout the study period representing, on average, 66% of the total study area. The approach described in this paper provides a useful starting point to allow greater use to be made of incidental koala sighting data. We propose that the model presented here could be used to combine systematic koala survey data (which is spatially restricted, but more precise) with koala sighting data (which is incidental and often biased by nature, but often collected over large geographical areas). Our approach could also be adopted for modeling the density of other wildlife species where data is collected in the same manner.

The value of long-term citizen science data for monitoring koala populations.

The active collection of wildlife sighting data by trained observers is expensive, restricted to small geographical areas and conducted infrequently. Reporting of wildlife sightings by members of the public provides an opportunity to collect wildlife data continuously over wider geographical areas, at lower cost. We used individual koala sightings reported by members of the public between 1997 and 2013 in South-East Queensland, Australia (n = 14,076 koala sightings) to describe spatial and temporal trends in koala presence, to estimate koala sighting density and to identify biases associated with sightings. Temporal trends in sightings mirrored the breeding season of koalas. Sightings were high in residential areas (63%), followed by agricultural (15%), and parkland (12%). The study area was divided into 57,780 one-square kilometer grid cells and grid cells with no sightings of koalas decreased over time (from 35% to 21%) indicative of a greater level of spatial overlap of koala home ranges and human activity areas over time. The density of reported koala sightings decreased as distance from primary and secondary roads increased, indicative of a higher search effort near roads. Our results show that koala sighting data can be used to refine koala distribution and population estimates derived from active surveying, on the condition that appropriate bias correction techniques are applied. Collecting koala absence and search effort information and conducting repeated searches for koalas in the same areas are useful approaches to improve the quality of sighting data in citizen science programs.