The environment to the rescue: can physics help predict predator–prey interactions?

IF 11 1区 生物学 Q1 BIOLOGY Biological Reviews Pub Date : 2024-06-10 DOI:10.1111/brv.13105
Mehdi Cherif, Ulrich Brose, Myriam R. Hirt, Remo Ryser, Violette Silve, Georg Albert, Russell Arnott, Emilio Berti, Alyssa Cirtwill, Alexander Dyer, Benoit Gauzens, Anhubav Gupta, Hsi-Cheng Ho, Sébastien M. J. Portalier, Danielle Wain, Kate Wootton
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Abstract

Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food-web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator–prey interactions, and a generic, modular model is needed to describe all the possible variation in predator–prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food-web impacts of well-studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world.

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环境救星:物理学能否帮助预测捕食者与猎物之间的相互作用?
了解决定特定非生物和生物条件下生态相互作用的发生和强度的因素至关重要,因为生态群落稳定性和生态系统功能的许多方面都取决于物种之间的相互作用模式。目前绘制食物网的方法大多基于特征、专家知识、实验和/或统计推断。然而,这些方法并没有提供明确的机制来解释营养相互作用如何受到生物特征与物理环境(如温度、光照强度或粘度)之间相互作用的影响。因此,它们还不能准确预测当地食物网如何应对人为压力,特别是气候变化和物种入侵。在此,我们提出了一个综合食物网理论最新发展的框架,将体型和新陈代谢与生态系统的物理特性结合起来。我们主张将运动范式与捕食序列的模块化定义相结合,因为运动是捕食者与被捕食者相互作用的核心,需要一个通用的模块化模型来描述捕食者与被捕食者相互作用中所有可能的变化。在获得足够的经验和理论知识之前,我们的框架将有助于预测已被充分研究的物理因素对食物网的影响,如温度和氧气供应,以及较少被考虑的变量,如风、浊度或电导率。预测能力的提高将有助于更好地了解生态系统对不断变化的世界的反应。
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来源期刊
Biological Reviews
Biological Reviews 生物-生物学
CiteScore
21.30
自引率
2.00%
发文量
99
审稿时长
6-12 weeks
期刊介绍: Biological Reviews is a scientific journal that covers a wide range of topics in the biological sciences. It publishes several review articles per issue, which are aimed at both non-specialist biologists and researchers in the field. The articles are scholarly and include extensive bibliographies. Authors are instructed to be aware of the diverse readership and write their articles accordingly. The reviews in Biological Reviews serve as comprehensive introductions to specific fields, presenting the current state of the art and highlighting gaps in knowledge. Each article can be up to 20,000 words long and includes an abstract, a thorough introduction, and a statement of conclusions. The journal focuses on publishing synthetic reviews, which are based on existing literature and address important biological questions. These reviews are interesting to a broad readership and are timely, often related to fast-moving fields or new discoveries. A key aspect of a synthetic review is that it goes beyond simply compiling information and instead analyzes the collected data to create a new theoretical or conceptual framework that can significantly impact the field. Biological Reviews is abstracted and indexed in various databases, including Abstracts on Hygiene & Communicable Diseases, Academic Search, AgBiotech News & Information, AgBiotechNet, AGRICOLA Database, GeoRef, Global Health, SCOPUS, Weed Abstracts, and Reaction Citation Index, among others.
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