Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large scale automated acoustic array

The emergence of continental to global scale biodiversity data has led to growing understanding of patterns in species distributions, and the determinants of these distributions, at large spatial scales. However, identifying the specific mechanisms, including demographic processes, and determining species distributions remains difficult, as large-scale data are typically restricted to observations of only species presence. New remote automated approaches for collecting data, such as automated recording units (ARUs), provide a promising avenue towards direct measurement of demographic processes, such as reproduction, that cannot feasibly be measured at scale by traditional survey methods. In this study, we analyze data collected by ARUs from 452 survey points across an approximately 1500 km study region to compare patterns in adult and juvenile distributions in the Great Horned Owl (Bubo virginianus). We specifically examine whether habitat associated with successful reproduction is the ..., Owl surveys:
Nighttime autonomous acoustic recordings were collected from 452 survey locations across 1500 km of the eastern United States. Two Convolutional Neural Networks were developed to classify the adult song and juvenile begging call of the Great Horned Owl (Bubo virginianus). These classifiers were run on the recordings and the highest scoring ten five-second clips occurring on ten separate days at each survey location were extracted. These clips were manually reviewed by a human listener to ensure they contained the relevant owl sounds. Presence/absence was translated into 1/0 detection histories to be used in occupancy models.
Covariates:
GPS coordinates were collected at each survey location (these are not provided to protect landowner identity). National Land Cover Database information was extracted for the amount of forest and agricultural land cover within a 1750 m radius of each survey location for use as occupancy covariates. Tree basal area and < 10 cm DBH stem dens..., , # Evaluating the predictors of habitat use and successful reproduction in a model bird species using a large scale automated acoustic array

[https://doi.org/10.5061/dryad.5hqbzkhcz](https://doi.org/10.5061/dryad.5hqbzkhcz)

## Description of the data and file structure

Data are provided as a single CSV file **owl_data.csv** with columns

* **site_number** (1-452 denoting unique survey locations),
* **survey_number** (1-10 denoting the survey number in a sequence of 10),
* **song_detections** (1 or 0 indicating presence or absence of Great Horned Owl song),
* **beg_detections** (1 or 0 indicating the presence or absence of Great Horned Owl begging calls),
* **f_cover_1750m** (the amount of forest within a 1750 m radius of the survey location, centered and scaled),
* **f_cover_250m** (the amount of forest within a 250 m radius of the survey location, centered and scaled),
* **ag_cover_1750m** (the amount of agricultural land cover within a 1750 m radius of the survey locat...

大陆乃至全球尺度生物多样性数据的涌现，推动了学界对大空间尺度下物种分布格局及其驱动因子的认知不断深化。然而，由于大尺度生物多样性数据通常仅能获取物种存在的观测记录，因此厘清包括种群动态过程在内的物种分布具体形成机制仍存在较大挑战。新兴的远程自动化数据采集手段，例如自动录音装置（Automated Recording Units, ARUs），为直接测量传统调查方法难以规模化开展的种群动态过程（如繁殖活动）提供了极具前景的路径。
本研究针对横跨约1500公里研究区域内的452个调查点位的自动录音装置采集数据展开分析，旨在对比大雕鸮（Great Horned Owl, *Bubo virginianus*）成体与幼体的分布格局。本研究重点探讨成功繁殖相关的栖息地是否为…… 鸮类调查方案：
本次研究在美国东部1500公里范围内的452个调查点位采集了夜间自主声学录音数据。研究构建了两套卷积神经网络（Convolutional Neural Networks, CNN），分别用于识别大雕鸮的成体鸣唱与幼体乞食叫声。将分类器应用于录音数据后，我们从每个调查点位的10个独立单日数据中提取出得分最高的10段5秒音频片段。随后由人工审听人员对这些片段进行复核，以确认其包含目标鸮类叫声。将物种存在/缺失的记录转换为1/0的检测历史矩阵，用于占据率模型分析。
协变量：本研究采集了每个调查点位的GPS坐标（出于保护土地所有者隐私的考虑，未公开该数据）。提取每个调查点位1750米半径范围内的森林与农业用地占比数据，源自美国国家土地覆盖数据库（National Land Cover Database），将其作为占据率模型的协变量。此外还包含树木胸高断面积与直径小于10厘米的DBH茎密度……
# 基于大规模自动化声学阵列评估模式鸟类的栖息地利用与成功繁殖的预测因子
[https://doi.org/10.5061/dryad.5hqbzkhcz](https://doi.org/10.5061/dryad.5hqbzkhcz)
## 数据与文件结构说明
本数据集以单个CSV文件**owl_data.csv**的形式提供，各字段说明如下：
* **site_number**（取值1-452，代表唯一调查点位编号）
* **survey_number**（取值1-10，代表10次连续调查中的单次调查序号）
* **song_detections**（取值1或0，分别代表大雕鸮鸣唱声的存在与缺失）
* **beg_detections**（取值1或0，分别代表大雕鸮幼体乞食叫声的存在与缺失）
* **f_cover_1750m**（调查点位1750米半径范围内的森林占比，已进行中心化与标准化处理）
* **f_cover_250m**（调查点位250米半径范围内的森林占比，已进行中心化与标准化处理）
* **ag_cover_1750m**（调查点位1750米半径范围内的农业用地占比……