从欧洲筑巢猛禽的GPS跟踪数据中提取繁殖参数

IF 1.5 3区 生物学 Q1 ORNITHOLOGY
Steffen Oppel, Ursin M. Beeli, Martin U. Grüebler, Valentijn S. van Bergen, Martin Kolbe, Thomas Pfeiffer, Patrick Scherler
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引用次数: 0

摘要

了解种群动态需要估算死亡率和生产力等人口参数。由于在野外获取这些参数所需的数据需要耗费大量人力物力,因此从现有数据中估算人口参数的替代方法非常有用。大型鸟类通常都有高分辨率的生物记录数据,可用于估算存活率和生产力。我们对现有方法进行了扩展,并提出了一种可免费使用的工具("NestTool"),该工具使用每小时分辨率的 GPS 跟踪数据来估算重要的生产力参数,如家域建立、繁殖启动和繁殖成功率。NestTool 首先从每个繁殖季节的原始跟踪数据中提取 42 个运动指标,如在用户指定半径内停留的时间、重访次数、家园范围大小以及最常用的白天和夜间地点之间的距离。然后将这些变量用于三个独立的随机森林模型,以预测个体是否表现出家域行为、是否开始筑巢尝试以及是否成功育雏。我们利用对瑞士 258 只红鸢(Milvus milvus)个体长达 7 年的追踪数据来训练模型,并将这些模型应用于德国不同红鸢种群的追踪数据中,以验证这些数据对筑巢及其结果的详细观察。在验证数据中,这些模型对红鸢家域和筑巢行为的分类准确率达到了 90%,但对筑巢尝试结果的分类准确率略低(80-90%),因为有些个体尽管失败了,但仍经常返回巢中。NestTool提供了一个图形用户界面,允许用户手动注释模型预测超出用户定义的不确定性阈值的个别季节。我们鼓励鸟类学家对不同物种的 NestTool 进行测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe

Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe

Understanding population dynamics requires estimation of demographic parameters like mortality and productivity. Because obtaining the necessary data for such parameters can be labour-intensive in the field, alternative approaches that estimate demographic parameters from existing data can be useful. High-resolution biologging data are frequently available for large-bodied bird species and can be used to estimate survival and productivity. We extend existing approaches and present a freely available tool (‘NestTool') that uses GPS tracking data at hourly resolution to estimate important productivity parameters such as home range establishment, breeding initiation, and breeding success. NestTool first extracts 42 movement metrics such as time spent within a user-specified radius, number of revisits, home range size, and distances between most frequently used day and night locations from the raw tracking data for each individual breeding season. These variables are then used in three independent random forest models to predict whether individuals exhibited home range behaviour, initiated a nesting attempt, and successfully raised fledglings. We demonstrate the use of NestTool by training models with data from 258 individual red kites Milvus milvus from Switzerland tracked for up to 7 years, and then applied those models to tracking data from different red kite populations in Germany where detailed observations of nests and their outcomes existed for validation. The models achieved > 90% accurate classification of home range and nesting behaviour in validation data, but slightly lower (80–90%) accuracy in classifying the outcome of nesting attempts, because some individuals frequently returned to nests despite having failed. NestTool provides a graphical user interface that allows users to manually annotate individual seasons for which model predictions exceed a user-defined threshold of uncertainty. NestTool will facilitate the estimation of demographic parameters from tracking data to inform population assessments, and we encourage ornithologists to test NestTool for different species.

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来源期刊
Journal of Avian Biology
Journal of Avian Biology 生物-鸟类学
CiteScore
3.70
自引率
0.00%
发文量
56
审稿时长
3 months
期刊介绍: Journal of Avian Biology publishes empirical and theoretical research in all areas of ornithology, with an emphasis on behavioural ecology, evolution and conservation.
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