Planning for resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia.
Planning for resilience: Incorporating scenario and model uncertainty and trade-offs when prioritizing management of climate refugia.
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规划复原力：在优先考虑气候避难所管理时消除情景和模型的不确定性和权衡。
DOI：
10.1111/gcb.16167
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发表时间：
2022-07
影响因子：
11.6
通讯作者：
中科院分区：
文献类型：
--
作者：
气候变化已成为世界生态系统面临的最大威胁。确定和管理有助于关键物种在气候变化下生存的地区，对于生态系统今后的持久性至关重要。在这里，我们将“气候优先”地点确定为暴露于相对较低水平的气候压力的珊瑚礁，这些压力在未来更有可能持续存在。我们在海洋空间规划工作中提出了对气候变化情景和模型中不确定性的第一次分析，沿着多个目标，并提供了一种全面的方法来将不确定性和权衡纳入任何生态系统。我们首先使用与更高的持久性机会相关的环境特征（幼虫连接，飓风影响，以及过去和未来的急性和慢性温度条件）描述每个站点。在四种不同的气候情景下（SSP 1 2.6，SSP 2 4.5，SSP 3 7.0和SSP 5 8.5）和57次模式运行下，使用缩小尺度的数据评估了未来的温度上升。然后，我们使用稳健的决策方法对干预地点（保护，改善管理或恢复）进行优先排序，这些方法选择了在大多数气候情景和模型下具有良性气候的地点。建模工作是新颖的，因为它解决了两个重要问题。(1)它通过帕累托分析明确地考虑了多个规划目标之间的权衡;（2）它利用了未来气候变化的所有不确定性。通过当地利益攸关方的参与，包括对当地威胁、生态条件和政府优先事项的评估，对该模型确定的优先干预地点进行了核实和完善。介绍了加勒比岛屿和佛罗里达以及古巴、牙买加、多米尼加共和国和海地国家一级的工作流程。我们的方法使管理者能够考虑珊瑚礁或地球仪任何生态系统的气候智能型空间管理的不确定性和多个目标。我们确定优先珊瑚礁，将更有可能在未来的气候变化影响中生存下来。我们的方法是新颖的，因为它解决了两个重要的问题时，选择保护地点。它允许考虑一个理想的网站应该具有的多个理想属性，并选择一个对所有人都最好的网站，而不仅仅是对某些人最好的网站。此外，它利用了未来气候变化的所有不确定性，并选择了在所有气候情景和模型下都具有良好气候的地点。
Climate change has become the greatest threat to the world's ecosystems. Locating and managing areas that contribute to the survival of key species under climate change is critical for the persistence of ecosystems in the future. Here, we identify ‘Climate Priority’ sites as coral reefs exposed to relatively low levels of climate stress that will be more likely to persist in the future. We present the first analysis of uncertainty in climate change scenarios and models, along with multiple objectives, in a marine spatial planning exercise and offer a comprehensive approach to incorporating uncertainty and trade‐offs in any ecosystem. We first described each site using environmental characteristics that are associated with a higher chance of persistence (larval connectivity, hurricane influence, and acute and chronic temperature conditions in the past and the future). Future temperature increases were assessed using downscaled data under four different climate scenarios (SSP1 2.6, SSP2 4.5, SSP3 7.0 and SSP5 8.5) and 57 model runs. We then prioritized sites for intervention (conservation, improved management or restoration) using robust decision‐making approaches that select sites that will have a benign climate under most climate scenarios and models. The modelling work is novel because it solves two important issues. (1) It considers trade‐offs between multiple planning objectives explicitly through Pareto analyses and (2) It makes use of all the uncertainty around future climate change. Priority intervention sites identified by the model were verified and refined through local stakeholder engagement including assessments of local threats, ecological conditions and government priorities. The workflow is presented for the Insular Caribbean and Florida, and at the national level for Cuba, Jamaica, Dominican Republic and Haiti. Our approach allows managers to consider uncertainty and multiple objectives for climate‐smart spatial management in coral reefs or any ecosystem across the globe. We identify priority reefs that will be more likely to survive climate change impacts in the future. Our approach is novel because it solves two important issues when selecting sites for conservation. It allows considering multiple desirable properties that an ideal site should have and choosing a site that is best for all, not only best for some. Also, it makes use of all the uncertainty around future climate change and chooses sites that will have a benign climate under all climate scenarios and models.
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影响因子：
3.7
作者：
Eakin CM;Morgan JA;Heron SF;Smith TB;Liu G;Alvarez-Filip L;Baca B;Bartels E;Bastidas C;Bouchon C;Brandt M;Bruckner AW;Bunkley-Williams L;Cameron A;Causey BD;Chiappone M;Christensen TR;Crabbe MJ;Day O;de la Guardia E;Díaz-Pulido G;DiResta D;Gil-Agudelo DL;Gilliam DS;Ginsburg RN;Gore S;Guzmán HM;Hendee JC;Hernández-Delgado EA;Husain E;Jeffrey CF;Jones RJ;Jordán-Dahlgren E;Kaufman LS;Kline DI;Kramer PA;Lang JC;Lirman D;Mallela J;Manfrino C;Maréchal JP;Marks K;Mihaly J;Miller WJ;Mueller EM;Muller EM;Orozco Toro CA;Oxenford HA;Ponce-Taylor D;Quinn N;Ritchie KB;Rodríguez S;Ramírez AR;Romano S;Samhouri JF;Sánchez JA;Schmahl GP;Shank BV;Skirving WJ;Steiner SC;Villamizar E;Walsh SM;Walter C;Weil E;Williams EH;Roberson KW;Yusuf Y
通讯作者：
Yusuf Y
DOI：
10.1109/3468.650319
发表时间：
1998-01-01
影响因子：
--
作者：
Fonseca, CM;Fleming, PJ
通讯作者：
Fleming, PJ
影响因子：
3.8
作者：
Gilliland, Paul M.;Laffoley, Dan
通讯作者：
Laffoley, Dan
影响因子：
30.7
作者：
Brito-Morales, Isaac;Schoeman, David S.;Richardson, Anthony J.
通讯作者：
Richardson, Anthony J.
影响因子：
8.5
作者：
Beyer, Hawthorne L.;Kennedy, Emma, V;Hoegh-Guldberg, Ove
通讯作者：
Hoegh-Guldberg, Ove