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Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

机译:气候变化情景下中国翼龙潜在地理分布的变化

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摘要

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of can provide reference for the development of forest management and protection strategies.
机译:气候变化对生物多样性构成了严重威胁。预测气候变化对物种栖息地分布的影响可以帮助人类应对可能改变物种范围和分布的潜在威胁。是常见的快速生长的树种,通常用于中国中部和东部的河岸和高山森林的生态恢复。到目前为止,该物种的栖息地分布特征还鲜为人知,而影响其首选栖息地的环境因素也鲜为人知。在本研究中,通过选择236个已知发生地点和14个环境变量,使用最大熵建模(Maxent)算法和规则集生产遗传算法(GARP)来建立该物种潜在分布的模型。结果表明,两种模型均具有良好的预测能力。最冷月份的最低温度(Bio6),最暖季的平均温度(Bio10),年降水量(Bio12)和最干燥月份的降水量(Bio14)是影响Maxent模型预测的重要环境变量。根据这些模型,中国东部的温带和亚热带地区对该物种具有很高的环境适应性,而该物种已被记录下来。在每种气候变化情景下,该物种现有范围的气候适应性都会增加,其气候生态位在地理上向北部和更高海拔扩展。 GARP预测会更加保守。的预计时空格局可为森林管理和保护战略的发展提供参考。

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