Suppr超能文献

分析运动递归以检测繁殖事件并估计其在中心地觅食者中的结局。

Analysis of movement recursions to detect reproductive events and estimate their fate in central place foragers.

作者信息

Picardi Simona, Smith Brian J, Boone Matthew E, Frederick Peter C, Cecere Jacopo G, Rubolini Diego, Serra Lorenzo, Pirrello Simone, Borkhataria Rena R, Basille Mathieu

机构信息

Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of Florida, 3205 College Ave, Davie, FL 33314 USA.

Deparmtent of Wildland Resources, Ecology Center, Utah State University, Logan, UT 84322 USA.

出版信息

Mov Ecol. 2020 Jun 3;8:24. doi: 10.1186/s40462-020-00201-1. eCollection 2020.

Abstract

BACKGROUND

Recursive movement patterns have been used to detect behavioral structure within individual movement trajectories in the context of foraging ecology, home-ranging behavior, and predator avoidance. Some animals exhibit movement recursions to locations that are tied to reproductive functions, including nests and dens; while existing literature recognizes that, no method is currently available to explicitly target different types of revisited locations. Moreover, the temporal persistence of recursive movements to a breeding location can carry information regarding the fate of breeding attempts, but it has never been used as a metric to quantify recursive movement patterns. Here, we introduce a method to locate breeding attempts and estimate their fate from GPS-tracking data of central place foragers. We tested the performance of our method in three bird species differing in breeding ecology (wood stork ( lesser kestrel () Mediterranean gull ()) and implemented it in the R package 'nestR'.

METHODS

We identified breeding sites based on the analysis of recursive movements within individual tracks. Using trajectories with known breeding attempts, we estimated a set of species-specific criteria for the identification of nest sites, which we further validated using non-reproductive individuals as controls. We then estimated individual nest survival as a binary measure of reproductive fate (success, corresponding to fledging of at least one chick, or failure) from nest-site revisitation histories during breeding attempts, using a Bayesian hierarchical modeling approach that accounted for temporally variable revisitation patterns, probability of visit detection, and missing data.

RESULTS

Across the three species, positive predictive value of the nest-site detection algorithm varied between 87 and 100% and sensitivity between 88 and 92%, and we correctly estimated the fate of 86-100% breeding attempts.

CONCLUSIONS

By providing a method to formally distinguish among revisited locations that serve different ecological functions and introducing a probabilistic framework to quantify temporal persistence of movement recursions, we demonstrated how the analysis of recursive movement patterns can be applied to estimate reproduction in central place foragers. Beyond avian species, the principles of our method can be applied to other central place foraging breeders such as denning mammals. Our method estimates a component of individual fitness from movement data and will help bridge the gap between movement behavior, environmental factors, and their fitness consequences.

摘要

背景

在觅食生态学、栖息地行为和躲避捕食者的背景下,递归运动模式已被用于检测个体运动轨迹中的行为结构。一些动物会表现出向与繁殖功能相关的地点的递归运动,包括巢穴和兽穴;虽然现有文献认识到这一点,但目前尚无方法可明确针对不同类型的回访地点。此外,向繁殖地点的递归运动的时间持续性可能携带有关繁殖尝试命运的信息,但它从未被用作量化递归运动模式的指标。在这里,我们介绍一种从中心地觅食者的GPS跟踪数据中定位繁殖尝试并估计其命运的方法。我们在三种繁殖生态不同的鸟类(林鹳、红隼、地中海鸥)中测试了我们方法的性能,并将其实现于R包“nestR”中。

方法

我们通过分析个体轨迹内的递归运动来识别繁殖地点。利用已知繁殖尝试的轨迹,我们估计了一组用于识别巢穴地点的物种特异性标准,并使用非繁殖个体作为对照进行了进一步验证。然后,我们使用贝叶斯层次建模方法,根据繁殖尝试期间巢穴地点的回访历史,将个体巢穴存活率估计为繁殖命运的二元度量(成功,对应于至少一只雏鸟出飞,或失败),该方法考虑了随时间变化的回访模式、访问检测概率和缺失数据。

结果

在这三个物种中,巢穴地点检测算法的阳性预测值在87%至100%之间,灵敏度在88%至92%之间,我们正确估计了86%至100%的繁殖尝试的命运。

结论

通过提供一种正式区分具有不同生态功能的回访地点的方法,并引入一个概率框架来量化运动递归的时间持续性,我们展示了如何将递归运动模式分析应用于估计中心地觅食者的繁殖情况。除鸟类外,我们方法的原理可应用于其他中心地觅食繁殖者,如穴居哺乳动物。我们的方法从运动数据中估计个体适应性的一个组成部分,并将有助于弥合运动行为、环境因素及其适应性后果之间的差距。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1188/7268620/6b0322f30f6e/40462_2020_201_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验