The role of seasonal migration in spatial population synchrony

IF 4.4 2区 环境科学与生态学 Q1 ECOLOGY Ecology Pub Date : 2023-08-26 DOI:10.1002/ecy.4158
Ellen C. Martin, Brage Bremset Hansen, Ivar Herfindal, Aline Magdalena Lee
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Abstract

Spatially synchronized population dynamics are common in nature, and understanding their causes is key for predicting species persistence. A main driver of synchrony between populations of the same species is shared environmental conditions, which cause populations closer together in space to be more synchronized than populations further from one another. Most theoretical and empirical understanding of this driver considers resident species. For migratory species, however, the degree of spatial autocorrelation in the environment may change across seasons and vary by their geographic location along the migratory route or on a nonbreeding ground, complicating the synchronizing effect of the environment. Migratory species show a variety of different strategies in how they disperse to and aggregate on nonbreeding grounds, ranging from completely shared nonbreeding grounds to multiple different ones. Depending on the sensitivity to environmental conditions off the breeding grounds, we can expect that migration and overwintering strategies will impact the extent and spatial pattern of population synchrony on the breeding grounds. Here, we use spatial population-dynamic modeling and simulations to investigate the relationship between seasonal environmental autocorrelation and migration characteristics. Our model shows that the effects of environmental autocorrelation experienced off the breeding ground on population synchrony depend on the number and size of nonbreeding grounds, and how populations migrate in relation to neighboring populations. When populations migrated to multiple nonbreeding grounds, spatial population synchrony increased with increasing environmental autocorrelation between nonbreeding grounds. Populations that migrated to the same place as near neighbors had higher synchrony at short distances than populations that migrated randomly. However, synchrony declined less across increasing distances for the random migration strategy. The differences in synchrony between migration strategies were most pronounced when the environmental autocorrelation between nonbreeding grounds was low. These results show the importance of considering migration when studying spatial population synchrony and predicting patterns of synchrony and population viability under global environmental change. Climate change and habitat loss and fragmentation may cause range shifts and changes in migratory strategies, as well as changes in the mean and spatial autocorrelation of the environment, which can alter the scale and patterns observed in spatial population synchrony.

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季节性迁徙在人口空间同步性中的作用。
空间同步的种群动态在自然界中很常见,了解其原因是预测物种持久性的关键。同一物种种群之间同步的一个主要驱动因素是共同的环境条件,这导致空间中距离更近的种群比距离更远的种群更同步。对这一驱动因素的大多数理论和经验理解都考虑了常驻物种。然而,对于迁徙物种来说,环境中的空间自相关程度可能会随季节而变化,并因其在迁徙路线上或非繁殖地上的地理位置而变化,从而使环境的同步效应复杂化。迁徙物种在如何分散到非繁殖地和在非繁殖地聚集方面表现出各种不同的策略,从完全共享的非繁殖地到多个不同的非繁殖场。根据对繁殖地外环境条件的敏感性,我们可以预期迁徙和越冬策略将影响繁殖地种群同步的程度和空间模式。在这里,我们使用空间种群动态建模和模拟来研究季节性环境自相关和迁移特征之间的关系。我们的模型表明,繁殖地外经历的环境自相关对种群同步性的影响取决于非繁殖地的数量和大小,以及种群相对于相邻种群的迁移方式。当种群迁移到多个非繁殖地时,空间种群同步性随着非繁殖地之间环境自相关的增加而增加。与随机迁移的种群相比,迁移到与近邻相同地方的种群在短距离内具有更高的同步性。然而,对于随机迁移策略,随着距离的增加,同步性下降的幅度较小。当非繁殖地之间的环境自相关较低时,迁徙策略之间的同步性差异最为明显。这些结果表明,在研究全球环境变化下空间种群同步性和预测同步性模式和种群生存能力时,考虑迁移的重要性。气候变化、栖息地的丧失和破碎化可能导致迁徙策略的范围变化和变化,以及环境的平均值和空间自相关的变化,这可能会改变在空间种群同步性中观察到的规模和模式。
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来源期刊
Ecology
Ecology 环境科学-生态学
CiteScore
8.30
自引率
2.10%
发文量
332
审稿时长
3 months
期刊介绍: Ecology publishes articles that report on the basic elements of ecological research. Emphasis is placed on concise, clear articles documenting important ecological phenomena. The journal publishes a broad array of research that includes a rapidly expanding envelope of subject matter, techniques, approaches, and concepts: paleoecology through present-day phenomena; evolutionary, population, physiological, community, and ecosystem ecology, as well as biogeochemistry; inclusive of descriptive, comparative, experimental, mathematical, statistical, and interdisciplinary approaches.
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