If livestock at risk of poor welfare could be identified using a risk assessment tool, more targeted response strategies could be developed by enforcement agencies to facilitate early intervention, prompt welfare improvement and a decrease in reoffending. This study aimed to test the ability of an Animal Welfare Risk Assessment Tool (AWRAT) to identify livestock at risk of poor welfare in extensive farming systems in Australia. Following farm visits for welfare- and non-welfare-related reasons, participants completed a single welfare rating (WR) and an assessment using the AWRAT for the farm just visited. A novel algorithm was developed to generate an AWRAT-Risk Rating (AWRAT-RR) based on the AWRAT assessment. Using linear regression, the relationship between the AWRAT-RR and the WR was tested. The AWRAT was good at identifying farms with poor livestock welfare based on this preliminary testing. As the AWRAT relies upon observation, the intra- and inter-observer agreement were compared in an observation study. This included rating a set of photographs of farm features, on two occasions. Intra-observer reliability was good, with 83% of Intra-class Correlation Coefficients (ICCs) for observers ≥ 0.8. Inter-observer reliability was moderate with an ICC of 0.67. The AWRAT provides a structured framework to improve consistency in livestock welfare assessments. Further research is necessary to determine the AWRAT’s ability to identify livestock at risk of poor welfare by studying animal welfare incidents and reoffending over time.

Identifying threats to declining species and prescribing ways of preventing their extinction are basic challenges for biodiversity conservation. We analysed the causes underlying the loss of territories of the Endangered Egyptian vulture Neophron percnopterus in a key population at the north-western edge of its distribution in Europe by developing multi-scale models that combined factors from nest site to landscape. We used generalized linear models and an information-theoretic approach to identify the optimal combination of scales and resolutions that could explain territorial abandonment. Those models combining nest-site and landscape scales considerably improved prediction ability compared to those considering only one scale. The best combined model had a high predictive ability (96.9% of correctly classified cases). Small cliffs at high altitudes in rugged areas with declining livestock (especially of sheep and goats) increased the likelihood of territory abandonment. Our findings highlight the importance of developing region-specific multi-scale models to determine reliably the factors driving territory loss and of designing effective conservation strategies accordingly. Conservation measures for the studied population should be developed at two spatial scales. At the smaller scale it is necessary to closely control nest sites to avoid direct disturbances. At a larger scale it is essential to implement policies that can support traditional pastoralism.