地球三极地衣关键属性的地理空间数据集。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zhula Alatan, Wenjin Wu, Xinwu Li, Liqing Zhao, Huadong Guo, Jinfeng Li, Chengzhi Hao
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引用次数: 0

摘要

南极、北极和青藏高原被认为是地球的三极,环境极其恶劣,地衣在生态系统中占主导地位,作为先驱物种发挥着至关重要的作用。尽管地衣非常重要,但由于缺乏适当的数据集,调查地衣属性空间分布模式的研究却很少。为了弥补这一不足,加深我们对地衣在这些地区生长偏好的了解,我们在此提出了一个地理空间数据集,涵盖了 2800 多种地衣物种和 170,000 条原地地衣记录的关键属性,如颜色类型和生长形式。该数据集有助于绘制第一张空间分布图,说明地衣属性在不同经纬度的变化情况。这可以作为研究地衣类型与其生长环境之间关系的基础资源,而这正是生态学领域的一个重要科学问题。此外,它还有助于开发专门用于地衣监测的遥感技术,这是目前所缺乏的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A geospatial dataset of lichen key attributes in the Earth's three poles.

In the Antarctic, Arctic, and Tibetan Plateau-recognized as the Earth's three poles characterized by extremely harsh environments-lichens prevail in the ecosystem and play crucial roles as pioneer species. Despite their importance, studies investigating the spatial distribution patterns of lichen attributes are scarce due to a lack of appropriate datasets. To bridge this gap and enhance our understanding of the growth preferences of lichens in these areas, here we present a geospatial dataset encompassing key attributes of lichens, such as color type and growth form, for over 2800 lichen species and 170,000 in-situ lichen records. The dataset facilitates the creation of the first spatial distribution map illustrating the variation of lichen attributes across different latitudes and longitudes. This can serve as a foundational resource for studies on the relationship between lichen types and their growing environment, which is a vital scientific question in the ecology domain. Additionally, it can contribute to the development of specialized remote sensing technique tailored for lichen monitoring, which is currently lacking.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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