Monitoring wildlife health for diseases with visible signs by integrating camera traps with marked individuals

Monitoring wildlife health is essential for conservation and management, wildlife and livestock welfare, and public health in a One Health framework. Yet, wildlife health monitoring often requires long-term fieldwork and intensive sampling, which can be costly or logistically challenging, especially for remote, rare, or elusive populations. To address these challenges, we evaluated the feasibility and utility of integrating camera traps with a marked subset of Nubian ibex (Capra nubiana) to monitor five diseases that produce visible signs. Over six consecutive years, we deployed 51 camera traps and monitored 43 uniquely identifiable individuals (25 males and 18 females). We estimated apparent prevalence annually and modeled temporal trends with generalized additive models, which allowed flexible nonlinear relationships between disease prevalence and time. To assess recurrence dynamics and mortality risk, we fitted joint frailty models that simultaneously estimated rates of recurrent disease events and survival while accounting for individual heterogeneity. Apparent prevalence ranged from 2% to 38% depending on disease and year. For the most prevalent disease, caseous lymphadenitis (CLA), prevalence was higher in males (β = 2. 08, p = 0. 041) and did not vary across years. Despite small sample size (n = 9), joint frailty models yielded epidemiologically meaningful inference. Recurrence of CLA was positively associated with mortality (association parameter α {} = 2. 32, p = 0. 012), and male sex was a significant prognostic factor for terminal events (hazard ratio = 0. 07, p = 0. 019). Our proof of concept demonstrates that combining camera traps with marked individuals can provide a practical auxiliary or stand-alone tool for wildlife health monitoring. This approach is particularly suited to rare, elusive, or logistically inaccessible species and will gain further strength once its diagnostic specificity and sensitivity are validated.