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

Predicting koala (Phascolarctos cinereus) distribution from incidental sighting data in South-East Queensland, Australia

Species distribution maps are important tools for wildlife conservation planning and management. To model koala distributions, usually, a spatially representative sample of koala populations is collected through systematic field surveys. Details of koala sightings collected by members of the public could potentially be used to develop species distribution models if appropriate analytical approaches are applied to address the inherent biases in such datasets. We developed a stepwise approach for applying bias correction techniques to estimate and map koala distributions. Using a Boosted regression tree approach, we modelled indirectly the search effort made by observers to identify or sight koalas. Land lot density (58%) and human population density (19%) had the strongest positive impact on the indirect search effort, while the distances to roads were negatively associated with the indirect search effort. To estimate the koala distribution across South-East Queensland, we then developed models describing koala habitat (environmental model), access to koala habitat (accessibility model) and the search effort (search effort model), with the latter including the indirect search effort covariate. Finally, we corrected the estimates derived from these models (bias-corrected search effort and accessibility model). Three independent statistical modelling approaches (Lasso penalty Poisson regression, Down-weighted Poisson regression, and Maximum entropy) were used to compare the five koala distribution models. Based on assessments of areas under curves, the predictive accuracy of models improved when area accessibility and search effort were included. Overall, the spatial extent of koala distributions increased in the prediction maps when models were corrected for accessibility and indirect search effort (except for Down-weighted Poisson regression)

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

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