Simulating and Analysing Seabird Flyways: An Approach Combining Least-Cost Path Modelling and Machine Learning

IF 6.3 1区 环境科学与生态学 Q1 ECOLOGY
Nomikos Skyllas, Mo A. Verhoeven, Maarten J. J. E. Loonen, Richard Bintanja
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引用次数: 0

Abstract

Aim

We develop and test a cost framework to simulate the flyways of migratory seabirds, considering various environmental factors such as wind support, crosswind, travel distance, and food availability. Using this framework, we simulate potential migratory flyways for arctic terns and compare these simulations with tracking data. Our aim is to identify which combination of factors best explains the observed flyways. Ultimately, we seek to demonstrate how different environmental factors shape flyways.

Innovation

We simulated 195 possible seabird flyways using a newly developed cost function that takes into account a number of environmental variables. We focused on the Arctic Tern, a transhemispheric migrating seabird species. Our model accurately simulated most spring and autumn flyways across the Atlantic Ocean (median RMSE ± standard deviation for all five flyways: 529 ± 201 km). The most accurate simulations for Arctic Terns breeding on Svalbard were those for which wind support made up ~70% of the total cost, while the best simulations for the Dutch population were those for which distance minimisation was ~50% and food maximisation was ~30% of total costs. Finally, by analysing tracking data using a machine-learning algorithm factoring in both wind support and crosswind, we were able to determine airspeed and subsequently infer whether the observed flyways optimised time and/or effort.

Main Conclusions

This analysis showed that Arctic Terns breeding on Svalbard followed effort-optimising flyways, whereas those that breed in the Netherlands followed time-optimising flyways. Our simulation-to-observation approach demonstrates that Earth's environmental and physical properties likely underlie the global distribution of migratory birds and therefore need to be considered in studies that evaluate the long-range movement patterns and distribution of birds.

Abstract Image

海鸟飞行路线的模拟与分析:一种结合最小成本路径建模和机器学习的方法
我们开发并测试了一个成本框架来模拟候鸟的飞行路线,考虑了各种环境因素,如风的支持、侧风、飞行距离和食物的可用性。利用这个框架,我们模拟了北极燕鸥的潜在迁徙路线,并将这些模拟与跟踪数据进行了比较。我们的目的是确定哪种因素组合最能解释观测到的飞行路线。最终,我们试图证明不同的环境因素是如何塑造飞行路线的。我们使用新开发的成本函数模拟了195种可能的海鸟飞行路线,该函数考虑了许多环境变量。我们关注的是北极燕鸥,一种跨半球迁徙的海鸟。我们的模型准确地模拟了横跨大西洋的大多数春季和秋季飞行路线(所有五个飞行路线的中位数RMSE±标准差:529±201公里)。对北极燕鸥在斯瓦尔巴群岛上繁殖的最准确模拟是那些风力支持占总成本约70%的模拟,而对荷兰人口的最佳模拟是那些距离最小化占总成本约50%和食物最大化占总成本约30%的模拟。最后,通过使用机器学习算法分析跟踪数据,将风支持和侧风考虑在内,我们能够确定空速,并随后推断观察到的飞行路线是否优化了时间和/或精力。本分析表明,北极燕鸥在斯瓦尔巴群岛的繁殖遵循“努力优化”的飞行路线,而在荷兰的繁殖遵循“时间优化”的飞行路线。我们的模拟到观测方法表明,地球的环境和物理特性可能是候鸟全球分布的基础,因此需要在评估鸟类的长期运动模式和分布的研究中加以考虑。
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来源期刊
Global Ecology and Biogeography
Global Ecology and Biogeography 环境科学-生态学
CiteScore
12.10
自引率
3.10%
发文量
170
审稿时长
3 months
期刊介绍: Global Ecology and Biogeography (GEB) welcomes papers that investigate broad-scale (in space, time and/or taxonomy), general patterns in the organization of ecological systems and assemblages, and the processes that underlie them. In particular, GEB welcomes studies that use macroecological methods, comparative analyses, meta-analyses, reviews, spatial analyses and modelling to arrive at general, conceptual conclusions. Studies in GEB need not be global in spatial extent, but the conclusions and implications of the study must be relevant to ecologists and biogeographers globally, rather than being limited to local areas, or specific taxa. Similarly, GEB is not limited to spatial studies; we are equally interested in the general patterns of nature through time, among taxa (e.g., body sizes, dispersal abilities), through the course of evolution, etc. Further, GEB welcomes papers that investigate general impacts of human activities on ecological systems in accordance with the above criteria.
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