What does 'occupancy' mean in passive acoustic surveys?

Occupancy modelling is an increasingly common framework for analysing acoustic data to study the variation in populations through space and time, but different survey designs endow survey locations with varying degrees of biological meaning. Recording units may be preferentially deployed in areas known to be important to a species, which implies an 'area of occupancy' conception of a species range, or recording units may be randomly deployed without knowledge of focal species space-use (e.g. a survey grid), which implies an 'extent of occurrence' conception of a species range. Preferential sampling requires substantial pre-survey information (e.g. the location of focal species territories) but leads to intuitive parameter interpretation and greater precision; vastly greater survey coverage is attainable with random sampling, but the resulting data merit careful post-processing to achieve precise, ecologically meaningful results. Importantly, occupancy, detection, extinction and colonization estimates are incomparable between study designs. However, an understanding of focal species movement and vocal behaviour can inform the development of creative definitions of acoustic detections - and thus site occupancy - which may help bring the ecological realism of preferential sampling to the data yielded by the more scalable random sampling approach. Understanding the implications of study design choices on parameter interpretation is a fundamental aspect of basic ecological inference and endangered species monitoring.

Automated summary

Occupancy modelling is a common framework for studying population variation, but survey designs have different implications for survey locations and data interpretation. Preferential sampling provides intuitive interpretation and precision, while random sampling allows for greater survey coverage but requires careful post-processing. Variations in study design choices affect parameter interpretation and are important for ecological inference and species monitoring.