There is increasing interestin broad-scale analysis, modeling, and prediction of the distribution and composition of plant species assemblages under climatic, environmental, and biotic change, particularly for conservation purposes. We devised a method to reliably predict the impact of climate change on large assemblages of plant communities, while also considering competing biotic and environmental factors. To this purpose, we first used multilabel algorithms in order to convert the task of explaining a large assemblage of plant communities into a classification framework able to capture with high cross-validated accuracy the pattern of species distributions under a composite set of biotic and abiotic factors. We applied our model to a large set of plant communities in the Swiss Alps. Our model explained presences and absences of 175 plant species in 608 plots with >87% cross-validated accuracy, predicted decreases in α, β, and γ diversity by 2040 under both moderate and extreme climate scenarios, and identified likely advantaged and disadvantaged plant species under climate change. Multilabel variable selection revealed the overriding importance of topography, soils, and temperature extremes (rather than averages) in determining the distribution of plant species in the study area and their response to climate change. Our method addressed a number of challenging research problems, such as scaling to large numbers of species, considering species relationships and rarity, and addressing an overwhelming proportion of absences in presence–absence matrices. By handling hundreds to thousands of plants and plots simultaneously over large areas, our method can inform broad-scale conservation of plant species under climate change because it allows species that require urgent conservation action (assisted migration, seed conservation, and ex situ conservation) to be detected and prioritized. Our method also increases the practicality of assisted colonization of plant species by helping to prevent ill-advised introduction of plant species with limited future survival probability.

中文翻译：

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气候、生物和环境协同过滤下植物组合大尺度建模和投影的新方法

人们对气候、环境和生物变化下植物物种组合的分布和组成的广泛分析、建模和预测越来越感兴趣，特别是出于保护目的。我们设计了一种方法来可靠地预测气候变化对大型植物群落组合的影响，同时还考虑了竞争的生物和环境因素。为此，我们首先使用了多标签算法，以便将解释大量植物群落的任务转换为一个分类框架，该框架能够以高交叉验证的准确度捕捉生物和非生物因素的复合集合下的物种分布模式. 我们将我们的模型应用于瑞士阿尔卑斯山的大量植物群落。α、β和γ到 2040 年在温和和极端气候情景下的多样性，并确定了气候变化下可能具有优势和劣势的植物物种。多标签变量选择揭示了地形、土壤和极端温度（而不是平均温度）在确定研究区域内植物物种的分布及其对气候变化的响应方面的重要性。我们的方法解决了许多具有挑战性的研究问题，例如扩展到大量物种，考虑物种关系和稀有性，以及解决存在-缺失矩阵中绝大多数缺失的问题。通过在大范围内同时处理成百上千的植物和地块，我们的方法可以为气候变化下植物物种的广泛保护提供信息，因为它允许检测和优先考虑需要紧急保护行动（辅助迁移、种子保护和异地保护）的物种。我们的方法还通过帮助防止不明智地引入未来生存概率有限的植物物种，提高了植物物种辅助定殖的实用性。