Climate warming has triggered shifts in plant distributions, resulting in changes within communities, characterized by an increase in warm-demanding species and a decrease in cold-adapted species – referred to as thermophilization. Researchers conventionally rely on co-occurrence data from vegetation assemblages to examine these community dynamics. Despite the increasing availability of presence-only data in recent decades, their potential has largely remained unexplored due to concerns about their reliability. Our study aimed to determine whether climate-induced changes in community dynamics, as inferred from presence-only data from the Global Biodiversity Information Facility (GBIF), corresponded with those derived from co-occurrence plot data. To assess the differences between these datasets, we computed a community temperature index (CTI) using a transfer function, weighted-averaging partial least squares regression (WA-PLS). We calibrated the transfect function model based on the species–temperature relationship using data before recent climate warming. Then we assessed the differences in CTI and examined the temporal trend in thermophilization. In a preliminary analysis, we assessed the performance of this calibration using three datasets: 1) Norwegian co-occurrence data, 2) presence-only data from a broader European region organized into pseudo-plots (potentially capturing a larger part of the species niches), and 3) a combined dataset merging 1) and 2). The transfer function including the combined dataset performed best. Subsequently, we compared the CTI for the co-occurrence plots paired up spatially and temporally with presence-only pseudo-plots. The results demonstrated that presence-only data can effectively evaluate species assemblage responses to climate warming, with consistent CTI and thermophilization values to what was found for the co-occurrence data. Employing presence-only data for evaluating community responses opens up better spatial and temporal resolution and much more detailed analyses of such responses. Our results therefore outline how a large amount of presence-only data can be used to enhance our understanding of community dynamics in a warmer world.

中文翻译：

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用于评估植物群落对气候变暖反应的仅存在数据的可靠性


气候变暖引发了植物分布的变化，导致群落内部发生变化，其特点是需温暖物种的增加和适应寒冷物种的减少——称为嗜热化。研究人员通常依靠植被组合的共现数据来检查这些群落动态。尽管近几十年来仅存在数据的可用性不断增加，但由于对其可靠性的担忧，它们的潜力在很大程度上仍未得到开发。我们的研究旨在确定气候引起的群落动态变化（根据全球生物多样性信息设施（GBIF）的仅存在数据推断）是否与从共现图数据得出的数据相对应。为了评估这些数据集之间的差异，我们使用传递函数、加权平均偏最小二乘回归 (WA-PLS) 计算了社区温度指数 (CTI)。我们使用近期气候变暖之前的数据，根据物种-温度关系校准了转染函数模型。然后我们评估了 CTI 的差异并检查了嗜热的时间趋势。在初步分析中，我们使用三个数据集评估了这种校准的性能：1）挪威共现数据，2）来自更广泛的欧洲地区的仅存在数据，组织成伪图（可能捕获大部分物种生态位） )，以及 3) 合并 1) 和 2) 的组合数据集。包括组合数据集的传递函数表现最佳。随后，我们将空间和时间上配对的共现图的 CTI 与仅存在的伪图进行了比较。 结果表明，仅存在数据可以有效评估物种组合对气候变暖的响应，并且 CTI 和嗜热值与共现数据一致。使用仅存在的数据来评估社区反应可以提供更好的空间和时间分辨率，并对此类反应进行更详细的分析。因此，我们的结果概述了如何使用大量仅存在的数据来增强我们对变暖世界中社区动态的理解。