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 and infers a single combined detection rate and a temporal “time-factor” to capture trends. We demonstrate that the Bernoulli‐trial formulation of occupancy-detection converges to Frescalo’s Poisson-process framework, with occupancy and detectability collapsing into a single rate parameter. This equivalence clarifies how Frescalo’s neighbourhood 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 covariate tests within Frescalo analyses, improving transparency and rigour when working with opportunistic biodiversity data.