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首页> 外文期刊>Ecosystems >Lessons Learned While Integrating Habitat, Dispersal, Disturbance, and Life-History Traits into Species Habitat Models Under Climate Change
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Lessons Learned While Integrating Habitat, Dispersal, Disturbance, and Life-History Traits into Species Habitat Models Under Climate Change

机译:在气候变化下将栖息地,分散,干扰和生活史特征整合到物种栖息地模型中的经验教训

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We present an approach to modeling potential climate-driven changes in habitat for tree and bird species in the eastern United States. First, we took an empirical-statistical modeling approach, using randomForest, with species abundance data from national inventories combined with soil, climate, and landscape variables, to build abundance-based habitat models for 134 tree and 147 bird species. We produced lists of species for which suitable habitat tends to increase, decrease, or stay the same for any region. Independent assessments of trends of large trees versus seedlings across the eastern U.S. show that 37 of 40 species in common under both studies are currently trending as modeled. We developed a framework, ModFacs, in which we used the literature to assign default modification factor scores for species characteristics that cannot be readily assessed in such models, including 12 disturbance factors (for example, drought, fire, insect pests), nine biological factors (for example, dispersal, shade tolerance), and assessment scores of novel climates, long-distance extrapolations, and output variability by climate model and emission scenario. We also used a spatially explicit cellular model, SHIFT, to calculate colonization potentials for some species, based on their abundance, historic dispersal distances, and the fragmented nature of the landscape. By combining results from the three efforts, we can create projections of potential climate change impacts over the next 100 years or so. Here we emphasize some of the lessons we have learned over 16 years in hopes that they may help guide future experiments, modeling efforts, and management.
机译:我们提出了一种对美国东部树木和鸟类物种栖息地的潜在气候驱动变化进行建模的方法。首先,我们采用了经验统计模型方法,使用randomForest,结合来自国家清单的物种丰度数据以及土壤,气候和景观变量,为134种树木和147种鸟类建立了基于丰度的栖息地模型。我们列出了适合任何地区栖息地增加,减少或保持不变的物种清单。对美国东部大树和幼苗趋势的独立评估显示,两项研究中40种共有的树种中的37种目前正以模型化趋势发展。我们开发了一个框架ModFacs,在该框架中,我们使用文献为无法在此类模型中轻易评估的物种特征分配默认修饰因子评分,包括12种干扰因子(例如,干旱,火灾,虫害),9种生物学因子(例如,散布,阴影容忍度)以及新气候的评估得分,远距离外推以及气候模型和排放情景下的输出变异性。我们还使用空间上明确的细胞模型SHIFT,根据物种的丰度,历史分散距离和景观的零散性质来计算某些物种的定殖潜力。通过结合这三项工作的结果,我们可以对未来100年左右的气候变化潜在影响做出预测。在这里,我们着重强调我们在16年中学到的一些经验教训,希望它们可以帮助指导未来的实验,建模工作和管理。

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