Unifying occupancy-detection and local frequency scaling (Frescalo) models

Frescalo’s “local frequency scaling” and classical occupancy-detection models both seek to recover true species-occurrence signals from imperfect data. In this paper, we show that the two approaches rest on the same underlying detection mathematics. Occupancy models treat each site’s repeat visits as independent detection trials and separately estimate occupancy probability and per-visit detectability. Frescalo, by contrast, pools data across ecologically defined neighbourhoods, standardises for uneven effort, and infers a single discovery rate per species plus a species-specific “time-factor” to capture time trends. The occupancy–detection Bernoulli formulation can be linked directly to Frescalo’s Poisson/discovery framework, where occupancy and detectability combine into one rate parameter (which, when sampling is light, closely matches the product of occupancy and per-visit detectability). This connection clarifies how Frescalo’s neighbourhood-scale and time corrections function as a coarser-scale analogue of repeat-visit models. By casting Frescalo in occupancy modelling terms, we hope to promote further investigation into the adoption of occupancy model diagnostics, extensions and other tests within Frescalo analyses, improving transparency and rigour when working with less-structured biodiversity data. • “Frequency scaling local” (Frescalo) can be linked to occupancy modelling. • Frescalo-estimated “discoverability” permits the modelling of less structured data. • Unifying these frameworks opens doors to further model developments and applications.