Monitoring active Osprey nests with drones is more time-efficient and less disturbing than conventional methods

Drones are used to monitor bird nesting sites at less accessible locations, such as on cliffs, human infrastructure, or within the tree canopy. While there are a growing number of studies documenting avian behavioral responses to various drones, there is a continued need to monitor taxa-specific responses to different drone models. We explored both the time efficiency and impact of different nest survey methods (drones, nest climbing, and observations from a bucket truck) and different drone model sizes (small, medium, large) on the nest defense behavior of breeding Ospreys. We conducted 166 surveys (126 drone, 25 climbing, 15 bucket truck) at 85 active nests across three nesting stages. We found variation in 4 of 6 pre-defined behavioral categories, namely for calling, flying, at nest, and perching behaviors with survey method, sex, and nest stage. Females were more responsive to all survey methods compared to males and engaged in nest-protection behaviors most frequently during incuba..., , , # Monitoring active Osprey nests with drones is more time-efficient and less disturbing than conventional methods. [https://doi.org/10.5061/dryad.mkkwh719f](https://doi.org/10.5061/dryad.mkkwh719f) ## Description of the data and file structure **Monitoring active Osprey nests with drones is more time-efficient and less disturbing than conventional methods.** Document produced by Natasha Murphy, 14/11/2024. This repository contains R code and data to run analyses as described in the paper titled *Monitoring active Osprey nests with drones is more time-efficient and less disturbing than conventional methods by Murphy et al*. This includes scripts for GLMMs, Dirichlet regression, sensitivity analysis, and Kruskal-Wallis rank sum tests. Data are provided with observer and pilot/climber names redacted. Our final dataset comprised of both counts and duration for each of the behaviors. For behavioral counts, we built a generalized linear mixed model (GLMM) to examine the effects of sur...无人机可用于监测难以抵达区域的鸟类巢址，例如悬崖、人类构筑物或树冠内部。尽管目前已有越来越多的研究记录了鸟类对各类无人机的行为响应，但仍需持续监测不同类群对不同型号无人机的特异性反应。本研究同时考察了三种巢址调查方法（无人机、攀巢作业、高空作业车观测）以及三种不同尺寸型号的无人机（小型、中型、大型）对繁殖期鹗（Osprey）巢防御行为的影响，并评估了各方法的时间效率。研究团队在三个繁殖阶段的85个活跃鹗巢中开展了166次调查，其中无人机调查126次、攀巢调查25次、高空作业车观测15次。研究发现，在预设的6类行为类别中，鸣叫、飞行、留巢和停栖这4类行为的表现随调查方法、个体性别以及繁殖阶段存在显著差异。相较于雄性鹗，雌性对所有调查方法的响应更为强烈，且在孵化期最频繁地开展巢防御行为。 # 利用无人机监测活跃鹗巢：相较于传统方法更省时且干扰更低 [https://doi.org/10.5061/dryad.mkkwh719f](https://doi.org/10.5061/dryad.mkkwh719f) ## 数据与文件结构说明 **利用无人机监测活跃鹗巢：相较于传统方法更省时且干扰更低** 文档由娜塔莎·墨菲（Natasha Murphy）编制，2024年11月14日。 本数据集仓库包含Murphy等人发表的论文《利用无人机监测活跃鹗巢：相较于传统方法更省时且干扰更低》中所述分析所需的R代码与数据，其中涵盖广义线性混合模型（Generalized Linear Mixed Models, GLMMs）、狄利克雷回归（Dirichlet regression）、敏感性分析以及克鲁斯卡尔-沃利斯秩和检验（Kruskal-Wallis rank sum tests）的相关脚本。数据已隐去观测者与无人机驾驶员/攀巢人员的姓名。 本研究的最终数据集包含各类行为的发生次数与持续时长。针对行为发生次数，我们构建了广义线性混合模型（GLMM）以考察调查相关因素的影响[原文内容截断]。