Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys

Note: for occupancy model implementations and other R and Python scripts,
please see the "Software" DOI hosted by Zenodo:
https://doi.org/10.5281/zenodo.6353948 Ecologists often study biodiversity
by evaluating species occupancy and the relationship between occupancy and
other covariates. Occupancy models are now widely used to account for
false absences in field surveys and to reduce bias in estimates of
covariate relationships. Existing occupancy models take as inputs binary
detection/non-detection observations of species at each visit to each
site. However, autonomous sensing devices and machine learning models are
increasingly used to survey biodiversity, generating a new type of
observation record (i.e., continuous-score data) that reflects the model’s
confidence a species is present in each autonomously sensed file, instead
of binary detection/non-detection data. These data are not directly
compatible with traditional binary occupancy modeling methods. Here, we
develop a new occupancy model that models continuous scores on a visit
level as a Gaussian mixture, combining a distribution of scores for files
that do contain the species of interest and a distribution of scores for
files that do not. The model takes as input continuous scores for each
autonomously sensed and classified file, along with an optional small
number of binary, manually verified detection and non-detection
annotations. We present a simulation study that shows that over a range of
empirically realistic parameters, our model outperforms traditional
occupancy models that are based on binary annotation alone. We also apply
this new model to an empirical case study using data generated from five
machine learning classifiers applied to autonomous acoustic recordings
gathered in the eastern United States. Because our occupancy model
generalizes allowable input data beyond binary observations, it is
particularly well-suited to the increasing volume of machine learning
classified data in ecology and conservation.