Overcoming the limitations of wildlife disease monitoring

Abstract

Abstract Integrated wildlife monitoring (IWM) combines infection dynamics and the ecology of wildlife populations, including aspects defining the host community network. Developing and implementing IWM is a worldwide priority that faces major constraints and biases that should be considered and addressed when implementing these systems. We identify eleven main limitations in the establishment of IWM, which could be summarized into funding constraints and lack of harmonization and information exchange. The solutions proposed to overcome these limitations and biases comprise: (i) selecting indicator host species through network analysis, (ii) identifying key pathogens to investigate and monitor, potentially including nonspecific health markers, (iii) improve and standardize harmonized methodologies that can be applied worldwide as well as communication among stakeholders across and within countries, and (iv) the integration of new noninvasive technologies (e.g., camera trapping (CT) and environmental nucleic acid detection) and new tools that are under ongoing research (e.g., artificial intelligence to speed-up CT analyses, microfluidic polymerase chain reaction to overcome sample volume constraints, or filter paper samples to facilitate sample transport). Achieving and optimizing IWM is a must that allows identifying the drivers of epidemics and predicting trends and changes in disease and population dynamics before a pathogen crosses the interspecific barriers.