Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings

The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring ..., , , # Data and code for ARU and point-count abundance estimates

[https://doi.org/10.5061/dryad.4j0zpc8k0](https://doi.org/10.5061/dryad.4j0zpc8k0)

####  Description of the data and file structure

point_count_data.csv file contains Wood Thrush (WOTH) and Cerulean Warbler (CERW) point-count data. Columns are formatted as such: \"Species Code\_ Data type Visit number. For example, WOTH_count1 contains the number of Wood Thrush counted during visit 1. Rows=sites.

CERW_det_hist_2020-2021.csv and WOTH_det_hist_2020-2021.csv contain ARU-derived detection histories for Cerulean Warbler (CERW) and Wood Thrush (WOTH). The \"det1\", \"det2\", \"det3\", etc, columns represent whether the species occurred on day 1, 2, or 3, etc. of the survey day. The \"ttd\" column indicates which day the species was first detected over the 10-day window. 

#### Code/Software

.R file contains code for four different models (time-to-detection, N-mixture, Distance, Royle-Nichols) each of which occurs in four different chunks...