Data from: 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 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.

随着低成本自主录制单元（autonomous recording units, ARUs）与自动化物种识别技术的普及，大规模开展经济高效的野生动物监测的能力正快速提升，相较于传统人工调查具备诸多优势。然而，依托此类数据采集技术开展种群丰度估算仍面临挑战：多数丰度模型所需的数据，例如个体计数、个体与监测点的距离等，对于低成本单声道ARUs而言难以获取，尤其是在使用自动化物种识别输出结果的场景下。无需计数或测量目标个体距离、且可配合低成本ARUs使用的统计模型，为大规模野生动物监测项目的丰度估算提供了极具潜力的方案，但相关应用尚未得到验证。本研究开展了一项案例研究，采集了宾夕法尼亚州森林内2020年与2021年春季的鸟类野外数据，在同一监测点位同步采用传统点计数法与被动声学监测方案。我们基于将机器学习分类器应用于ARU采集数据所生成的检测历史记录，测试了Royle-Nichols模型与时间到检测模型（time-to-detection models）对两种鸟类的丰度估算能力。我们将上述模型的丰度估算结果，与使用点计数数据拟合的同款模型的估算结果，以及另外两种适用于点计数法的模型——N-mixture模型与距离模型——的估算结果进行了对比。研究结果表明，Royle-Nichols模型与时间到检测模型可配合ARU数据使用，其生成的丰度估算结果与基于点计数法的研究结果相近，但具备更高的精度。基于ARU的模型所生成的置信区间或可信区间，平均比对应点计数法模型的区间小31.9%（±11.9标准误，standard error, SE）。上述研究结果在两种种群相对丰度与栖息地利用模式存在差异的鸟类物种间保持一致。基于ARU数据拟合的模型精度更高，大概率源于其累计检测概率更高；而该概率提升的背后，或许是依托ARU与机器学习分类器可在任意监测点位为目标物种采集显著更长时长的样本，从而提升了整体调查投入。本研究结果为ARU在基于丰度的监测研究中的应用提供了初步支撑，有望帮助研究人员更深入地理解栖息地质量与种群动态趋势，同时助力其制定更具科学性的保护行动与建议。