Bright spots as climate-smart marine spatial planning tools for conservation and blue growth.
Bright spots as climate-smart marine spatial planning tools for conservation and blue growth.
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DOI：
10.1111/gcb.15827
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发表时间：
2021-11
影响因子：
11.6
通讯作者：
中科院分区：
文献类型：
--
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应对海洋气候驱动变化的海洋空间规划（“气候智能型MSP”）是支持经济增长，粮食安全和生态系统可持续性的全球愿望。海洋气候变化（CC）建模可能成为MSP的关键决策支持工具，但传统的建模分析和沟通挑战阻碍了它们的广泛应用。我们采用MSP特定的海洋气候建模分析，为真实的MSP过程提供信息;解决自然保护和渔业如何适应CC。我们发现，由于气候变化，目前计划的这些活动的分布在政策实施期间可能变得不可持续，导致其可持续性和蓝色增长目标的不足。对支持指定地点和捕捞活动的海洋组成部分在气候驱动下的生态系统层面的重大变化进行了估计，反映了底栖与中上层以及近岸与近海生境的不同程度的变化。支持适应，我们然后确定：CC避难所（生态系统保持在其目前状态的边界内的区域）; CC热点（气候驱动生态系统走向新的状态，与每个部门目前的使用分布不一致）;以及首次确定的亮点（海洋过程驱动范围扩展机会的区域，可能支持中期可持续增长）。因此，我们创造了一种方法：确定与部门相关的生态系统变化可归因于CC;将保护和可持续生态系统管理目标的弹性交付纳入MSP;并利用存在的蓝色增长机会。在保护区内捕获CC亮点和避难所可能为实现可持续性目标提供重要机会，同时帮助支持气候变化中的渔业部门。通过利用海洋生态系统内气候弹性的自然分布，这种气候适应性空间管理战略可以被视为基于自然的解决方案，以限制CC对海洋生态系统和依赖的蓝色经济部门的影响，为气候智能型MSP铺平道路。海洋气候变化（CC）建模可能成为海洋空间规划（MSP）的关键决策支持工具，但传统的建模分析和沟通挑战阻碍了它们的广泛应用。在这里，海洋CC建模被用作统计方法的输入，测试在实施真实的生命计划的时间范围内，支撑每个MSP部门的生态系统条件，资源和自然资本中是否出现气候信号。确定支撑每个部门的生态系统中气候变化热点，亮点和气候避难所的分布，可以制定气候适应性空间管理战略，支持可持续发展目标以及蓝色增长。
Marine spatial planning that addresses ocean climate‐driven change (‘climate‐smart MSP’) is a global aspiration to support economic growth, food security and ecosystem sustainability. Ocean climate change (‘CC’) modelling may become a key decision‐support tool for MSP, but traditional modelling analysis and communication challenges prevent their broad uptake. We employed MSP‐specific ocean climate modelling analyses to inform a real‐life MSP process; addressing how nature conservation and fisheries could be adapted to CC. We found that the currently planned distribution of these activities may become unsustainable during the policy's implementation due to CC, leading to a shortfall in its sustainability and blue growth targets. Significant, climate‐driven ecosystem‐level shifts in ocean components underpinning designated sites and fishing activity were estimated, reflecting different magnitudes of shifts in benthic versus pelagic, and inshore versus offshore habitats. Supporting adaptation, we then identified: CC refugia (areas where the ecosystem remains within the boundaries of its present state); CC hotspots (where climate drives the ecosystem towards a new state, inconsistent with each sectors’ present use distribution); and for the first time, identified bright spots (areas where oceanographic processes drive range expansion opportunities that may support sustainable growth in the medium term). We thus create the means to: identify where sector‐relevant ecosystem change is attributable to CC; incorporate resilient delivery of conservation and sustainable ecosystem management aims into MSP; and to harness opportunities for blue growth where they exist. Capturing CC bright spots alongside refugia within protected areas may present important opportunities to meet sustainability targets while helping support the fishing sector in a changing climate. By capitalizing on the natural distribution of climate resilience within ocean ecosystems, such climate‐adaptive spatial management strategies could be seen as nature‐based solutions to limit the impact of CC on ocean ecosystems and dependent blue economy sectors, paving the way for climate‐smart MSP. Ocean climate change (‘CC’) modelling may become a key decision‐support tool for marine spatial planning (‘MSP’), but traditional modelling analysis and communication challenges prevent their broad uptake. Here, ocean CC modelling is used as input to a statistical method testing whether a climate signal emerges in the ecosystem conditions, resources and natural capital underpinning each MSP sector of interest, within the time‐frame of implementation of a real‐life plan. Identifying the distribution of climate change hotspots, bright spots and climate refugia in the ecosystem underpinning each sector allows for climate‐adaptive spatial management strategies to be developed, supporting sustainability aims alongside blue growth.
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影响因子：
64.8
作者：
Halpern, Benjamin S.;Longo, Catherine;Zeller, Dirk
通讯作者：
Zeller, Dirk
影响因子：
30.7
作者：
Gallo, Natalya D.;Victor, David G.;Levin, Lisa A.
通讯作者：
Levin, Lisa A.
影响因子：
11.6
作者：
Hiddink, Jan G.;Burrows, Michael T.;Molinos, Jorge Garcia
通讯作者：
Molinos, Jorge Garcia
DOI：
10.1016/0197-2456(86)90046-2
发表时间：
1986-09-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
--
作者：
DERSIMONIAN, R;LAIRD, N
通讯作者：
LAIRD, N
影响因子：
4
作者：
Hale, Rachel;Godbold, Jasmin A.;Solan, Martin
通讯作者：
Solan, Martin