Machine Learning for Conservation Planning in a Changing Climate
Machine Learning for Conservation Planning in a Changing Climate
复制标题
气候变化下的保护规划的机器学习
DOI：
10.3390/su12187657
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Jamal Jokar Arsanjani
中科院分区：
文献类型：
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作者：
Ana Cristina Mosebo Fernandes;Rebeca Quintero Gonzalez;Marie Ann Lenihan;Ezra Francis Leslie Trotter;Jamal Jokar Arsanjani
整个北美的野生动物栖息地都受到气候变化的直接和间接影响。在考虑气候变化问题时，西部山间地区脆弱性评价将野生动植物和植被及其干扰作为生态系统的两个关键资源区。尽管某些野生动物具有适应能力，但据估计，到2050年气温将上升1.67-2°C，这将增加犹他州干旱、洪水、热浪和野火的可能性和严重程度。因此，具有复原力的动植物可能会流离失所。这项研究的目的是根据目前的气候条件确定一个典型物种，即鼠尾草的栖息地，并根据气候预测确定未来栖息地的区域。野生动物的分布位置收集自志愿者地理信息（VGI）观测数据，以及正常温度和降水、植被覆盖和其他生态系统相关数据。然后使用四种机器学习算法来定位野生动物栖息地的当前位置，并根据气候变化的影响和基于科学支持的温度升高估计的时间框架，预测野生动物可能迁移到的合适的未来地点。结果表明，随机森林模型的精度为0.897，灵敏度和特异度分别为0.917和0.885，在物种分布建模（SDM）中具有很大的应用潜力，可以为生境预测提供有用的信息。基于这个模型，我们的预测表明，由于气候变化，犹他州的鼠尾草栖息地将在未来几年继续减少，产生一个高度碎片化的栖息地，并导致其目前栖息地的近70%的损失。优先保护区（PACs）和保护区可能被认为不足以阻止这种栖息地的丧失，应该投入更多的努力来维持斑块之间的连通性，以确保鼠尾草种群的运动和遗传多样性。这项研究的潜在数据驱动的方法可能对环保主义者、研究人员、决策者和政策制定者等人有用。
Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.
DOI：
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发表时间：
1999-09
期刊：
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影响因子：
--
作者：
最首 太郎
通讯作者：
最首 太郎
希尔伯特空间上有限生成群的等距作用和性质 (T)
DOI：
--
发表时间：
2010
期刊：
影响因子：
--
作者：
Atsuhiro Nakamoto;Shoichi Tsuchiya;O. Imanuvilov and M. Yamamoto;近藤剛史
通讯作者：
近藤剛史
论可持续发展
DOI：
--
发表时间：
2011
期刊：
影响因子：
--
作者：
根市侑太郎;江島丈雄;柳原美廣;石野雅彦;加道雅孝;Kazuhiro Ueta
通讯作者：
Kazuhiro Ueta