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Harpy Eagle climate change SDMs

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DataCite Commons2022-07-20 更新2024-07-28 收录
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https://figshare.com/articles/dataset/Harpy_Eagle_climate_change_SDMs/16707244/11
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Climate change is expected to have a profound impact on species distributions, contracting suitable climate space. Biodiversity areas are important to mitigate these negative effects but are static by design and thus do not account for future projections of species distributions. The Harpy Eagle <em>Harpia harpyja</em> has a broad range across lowland Neotropical forests and thus its distribution could be negatively affected by climate change when combined with current rates of habitat loss. To test this hypothesis, we use spatial point process models fitted with climatic, topographic, and landcover covariates to identify current distribution. We then project to 24 future climate scenarios, using three General Circulation Models (GCMs), and two emission scenarios between the years 2021 and 2100 averaged over four 20-year periods. Our current model identified a core range across Amazonia and the Guiana Shield, with evergreen forest (71 %), mean diurnal temperature range (13 %), and elevation (6 %) the most important predictors.<strong> </strong>Reclassifying the current model to a binary prediction estimated a range size of ~7.6 million km<sup>2</sup>, with the Important Bird and Biodiversity Area (IBA) network covering 18 % of habitat (~1.4 million km<sup>2</sup>) within this range. By 2090, range size was predicted to decrease on average by 14.4 % under a higher emissions scenario, and 7.3 % under a lower emissions scenario. The IBA network would cover 14 % less area under a higher emissions scenario, and 3.3 % less distribution area under a lower emissions scenario by 2090. Southern Amazonia is predicted to have the greatest reduction in range size and subsequently highest loss of Harpy Eagle habitat within the IBA network. Our work demonstrates that the combination of climate change and subsequent habitat loss may result in substantial losses in distribution for this raptor across the southern edge of its range.<br> <br> Data to support publication: Reduced range size and Important Bird and Biodiversity Area coverage for the Harpy Eagle (Harpia harpyja) predicted from multiple climate change scenarios <br> Species Occurrences: <br> GBIF Occurrence Download: https://doi.org/10.15468/dl.6ikhnj <br> eBird data: Download Data - eBird <br> Data from Miranda et al. 2019:<sup> </sup>Species distribution modeling reveals strongholds and potential reintroduction areas for the world’s largest eagle (plos.org) <br> Nest locations from the Darien region of Panama are available on request. <br> Environmental data available from: <sub>EarthEnv</sub> <sub>ENVIREM</sub> <sub>WorldClim</sub><br>

气候变化预计将对物种分布产生深远影响,压缩其适宜气候空间。生物多样性保护地虽有助于缓解这些负面影响,但本质上属于静态设计,无法适配物种分布的未来预测情况。角雕 <em>Harpia harpyja</em> 分布广泛,涵盖新热带低地森林,因此结合当前的栖息地丧失速率,其分布可能会受到气候变化的负面影响。为验证该假说,本研究采用结合气候、地形与土地覆盖协变量的空间点过程模型,以确定其当前分布范围。随后,本研究借助3种通用循环模式(General Circulation Models, GCMs)与2种排放情景,对2021年至2100年间四个20年时段的平均情况进行模拟,共涵盖24种未来气候情景。本研究的当前模型识别出亚马逊平原与圭亚那盾地为核心分布区,其中常绿森林(71%)、昼夜平均温度差(13%)与海拔(6%)是最重要的预测因子。将当前模型重分类为二元预测后,估算得到分布范围面积约为760万km²,重要鸟类与生物多样性保护区(Important Bird and Biodiversity Area, IBA)网络覆盖该范围内18%的栖息地(约140万km²)。至2090年,在高排放情景下,分布范围预计平均缩减14.4%;在低排放情景下则平均缩减7.3%。至2090年,高排放情景下IBA网络覆盖的栖息地面积将减少14%,低排放情景下则减少3.3%的分布区面积。亚马逊南部地区的分布范围缩减幅度预计最大,同时也是IBA网络内角雕栖息地丧失最严重的区域。本研究表明,气候变化与后续的栖息地丧失相结合,可能导致该猛禽在其分布范围南缘出现大面积的分布区缩减。 支撑本研究发表的数据:基于多种气候变化情景预测的角雕(Harpia harpyja)分布范围缩减及重要鸟类与生物多样性保护区覆盖情况 物种出现数据: 全球生物多样性信息设施(Global Biodiversity Information Facility, GBIF)出现记录下载:https://doi.org/10.15468/dl.6ikhnj eBird数据:下载数据 - eBird 数据源自Miranda等人2019年的研究:<sup> </sup>物种分布模型揭示了全球最大猛禽的分布核心区与潜在再引入区域(plos.org) 巴拿马达连地区的巢址信息可按需获取。 环境数据可从以下平台获取:<sub>EarthEnv</sub> <sub>ENVIREM</sub> <sub>WorldClim</sub>
提供机构:
figshare
创建时间:
2021-09-30
搜集汇总
数据集介绍
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背景与挑战
背景概述
该数据集专注于研究气候变化对哈佩雕(Harpia harpyja)分布的影响,通过物种分布模型(SDMs)预测当前和未来(至2100年)的分布范围。关键发现包括:当前核心分布区位于亚马逊和圭亚那地盾,而到2090年,分布范围预计在高排放情景下减少14.4%,低排放情景下减少7.3%,同时重要鸟类和生物多样性区域(IBA)的覆盖范围也会相应减少。数据集包含模型数据和环境变量,用于支持相关保护规划研究。
以上内容由遇见数据集搜集并总结生成
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