DOI: 10.5061/dryad.4j0zpc8k0
title: Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings

description: 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 at the same
 locations. We tested the ability of the Royle-Nichols and
 time-to-detection models to estimate abundance of two species from
 detection histories generated by applying a machine-learning classifier to
 ARU-gathered data. We compared abundance estimates from these models to
 estimates from the same models fit using point-count data and to two
 additional models appropriate for point counts, the N-mixture model and
 distance models. We found that the Royle-Nichols and time-to-detection
 models can be used with ARU data to produce abundance estimates similar to
 those generated by a point-count based study but with greater precision.
 ARU-based models produced confidence or credible intervals that were on
 average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our
 findings were consistent across two species with differing relative
 abundance and habitat use patterns. The higher precision of models fit
 using ARU data is likely due to higher cumulative detection probability,
 which itself may be the result of greater survey effort using ARUs and
 machine-learning classifiers to sample significantly more time for focal
 species at any given point. Our results provide preliminary support the
 use of ARUs in abundance-based study applications, and thus may afford
 researchers a better understanding of habitat quality and population
 trends, while allowing them to make more informed conservation actions and
 recommendations.
description: # 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_dryad.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_dryad.csv and WOTH_det_hist_2020-2021_dryad.csv contain detection histories for Cerulean Warbler (CERW) and Wood Thrush (WOTH) from passive acoustic data processed with a machine-learning classifier and verified by human listener. 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 of code (each chunk pertaining to a different species and data type; 1: Wood Thrush point counts, 2: Cerulean Warbler point counts, 3: Wood Thrush ARUs, 4: Cerulean Warbler ARUs). The time-to-detection categorical abundance model written in JAGS language (JAGS software must be installed) is defined at the top of the code and called within each chunk. Within each chunk, abundance estimates are derived from the "best" (lowest AIC) model from a comparison of models with different detection covariates. Abundance estimates are the result of back-transformed beta-coefficients. C-hat (lack of fit ratio) was estimated in each chunk and used to inflate confidence/credible intervals. **Change Log** Aug 26 version: 1) includes modification to model code which previously referred to old naming conventions of data files. 2) Updated CERW_det_hist_2020-2021_dryad.csv and WOTH_det_hist_2020-2021_dryad.csv so to include covariates "wind" and "date" which were missing from the initial upload.