多种复原力指标揭示生态系统持续性的互补驱动因素:海藻森林系统的应用。

Ecology Pub Date : 2024-10-27 DOI:10.1002/ecy.4453
Jorge Arroyo-Esquivel, Riley Adams, Sarah Gravem, Ross Whippo, Zachary Randell, Jason Hodin, Aaron W E Galloway, Brian Gaylord, Marissa L Baskett
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引用次数: 0

摘要

人类造成的全球变化所产生的生物和非生物条件增加了恢复工作的不确定性和失败风险。注重恢复能力的管理,即系统应对干扰的能力,有可能减少这种不确定性和风险。然而,如何确定恢复能力的驱动因素可能取决于如何衡量它。加利福尼亚州北部海岸就是一个例子,在那里,由于强烈的海洋热浪和啃食海藻的紫色海胆的主要捕食者向日葵海星的功能性灭绝,海藻的覆盖率下降了 95% 以上。尽管在该系统中以清除海胆和重新引入海藻为重点的恢复工作正在进行中,但如何提高该系统对未来海洋热浪的适应能力仍是一个未决问题。在本文中,我们介绍了一个动态模型,该模型描述了由海带、紫海胆和紫海胆捕食者(如向日葵海星)组成的三营养食物链。我们对海藻森林三种不同的恢复力指标(恢复可能性、恢复率和抗干扰能力)进行了全局敏感性分析,以确定其生态驱动因素。我们发现,每个指标都最依赖于一组独特的驱动因素:恢复可能性最大的因素是活海带和漂流海带的产量,恢复率最大的因素是海胆产量和决定海胆吃活海带的反馈,而抗扰性最大的因素是决定捕食者消耗海胆的反馈。因此,要了解捕食者重新引入或恢复在海带系统中的潜在作用,必须采用综合方法来衡量恢复力。
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Multiple resiliency metrics reveal complementary drivers of ecosystem persistence: An application to kelp forest systems.

Human-caused global change produces biotic and abiotic conditions that increase the uncertainty and risk of failure of restoration efforts. A focus of managing for resiliency, that is, the ability of the system to respond to disturbance, has the potential to reduce this uncertainty and risk. However, identifying what drives resiliency might depend on how one measures it. An example of a system where identifying how the drivers of different aspects of resiliency can inform restoration under climate change is the northern coast of California, where kelp experienced a decline in coverage of over 95% due to the combination of an intense marine heat wave and the functional extinction of the primary predator of the kelp-grazing purple sea urchin, the sunflower sea star. Although restoration efforts focused on urchin removal and kelp reintroduction in this system are ongoing, the question of how to increase the resiliency of this system to future marine heat waves remains open. In this paper, we introduce a dynamical model that describes a tritrophic food chain of kelp, purple urchins, and a purple urchin predator such as the sunflower sea star. We run a global sensitivity analysis of three different resiliency metrics (recovery likelihood, recovery rate, and resistance to disturbance) of the kelp forest to identify their ecological drivers. We find that each metric depends the most on a unique set of drivers: Recovery likelihood depends the most on live and drift kelp production, recovery rate depends the most on urchin production and feedbacks that determine urchin grazing on live kelp, and resistance depends the most on feedbacks that determine predator consumption of urchins. Therefore, an understanding of the potential role of predator reintroduction or recovery in kelp systems relies on a comprehensive approach to measuring resiliency.

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