Suppr超能文献

弗勒罗夫实现数字化:来自《弗拉基米尔政府植物志》(弗勒罗夫,1902年)的地理参考记录。

Fleroff goes digital: georeferenced records from "Flora des Gouvernements Wladimir" (Fleroff, 1902).

作者信息

Seregin Alexey P, Basov Yurii M

机构信息

M.V. Lomonosov Moscow State University, Moscow, Russia M.V. Lomonosov Moscow State University Moscow Russia.

Unaffiliated, Tyumen, Russia Unaffiliated Tyumen Russia.

出版信息

Biodivers Data J. 2021 Oct 20;9:e75299. doi: 10.3897/BDJ.9.e75299. eCollection 2021.

Abstract

BACKGROUND

Global Biodiversity Information Facility (GBIF) has uneven data coverage across taxonomic, spatial and temporal dimensions. Temporal imbalances in the data coverage are particularly dramatic. Thus, 188.3M GBIF records were made in 2020, more than the whole lot of the currently available pre-1986 electronic data. This underscores the importance of reliable and precise biodiversity spatial data collected in early times. Biological collections certainly play a key role in our knowledge of biodiversity in the past. However, digitisation of historical literature is underway, being a modern trend in biodiversity data mining. The grid dataset for the flora of Vladimir Oblast, Russia, includes many historical records borrowed from the "Flora des Gouvernements Wladimir" by Alexander F. Fleroff (also known as Flerov or Flerow). Intensive study of Fleroff's collections and field surveys exactly in the same localities where he worked, showed that the quality of his data is superb. Species lists collected across hundreds of localities form a unique source of reliable information on the floristic diversity of Vladimir Oblast and adjacent areas for the period from 1894 to 1901. Since the grid dataset holds generalised data, we made precise georeferencing of Fleroff's literature records and published them in the form of a GBIF-mediated dataset.

NEW INFORMATION

A dataset, based on "Flora des Gouvernements Wladimir. I. Pflanzengeographische Beschreibung des Gouvernements Wladimir" by Fleroff (1902), includes 8,889 records of 654 taxa (mainly species) from 366 localities. The majority of records originate from Vladimir Oblast (4,611 records of 534 taxa from 195 localities) and Yaroslavl Oblast (2,013 records of 409 taxa from 66 localities), but also from Nizhny Novgorod Oblast (942 records), Ivanovo Oblast (667 records) and Moscow Oblast (656 records). The leading second-level administrative units by the number of records are Pereslavsky District (2,013 records), Aleksandrovsky District (1,318 records) and Sergievo-Posadsky District (599 records). Georeferencing was carried out, based on the expert knowledge of the area, analysis of modern satellite images and old topographic maps. For 2,460 records, the georeferencing accuracy is 1,000 m or less (28%), whereas for 6,070 records it is 2,000 m or less (68%). The mean accuracy of records of the entire dataset is 2,447 m. That accuracy is unattainable for most herbarium collections of the late 19 century. Some localities of rare plants discovered by Fleroff and included into the dataset were completely lost in the 20 century due to either peat mining or development of urban areas.

摘要

背景

全球生物多样性信息机构(GBIF)在分类学、空间和时间维度上的数据覆盖不均衡。数据覆盖的时间失衡尤为显著。因此,2020年产生了1.883亿条GBIF记录,超过了目前所有1986年以前的电子数据总量。这凸显了早期收集可靠且精确的生物多样性空间数据的重要性。生物标本馆在我们了解过去的生物多样性方面无疑发挥着关键作用。然而,历史文献的数字化正在进行中,这是生物多样性数据挖掘的一个现代趋势。俄罗斯弗拉基米尔州植物群的网格数据集包含许多从亚历山大·F·弗廖罗夫(也被称为弗廖罗夫或弗列罗夫)所著的《弗拉基米尔州植物志》中借用的历史记录。对弗廖罗夫的标本进行深入研究,并在他工作过的相同地点进行实地调查,结果表明他的数据质量非常高。在1894年至1901年期间,跨越数百个地点收集的物种清单构成了弗拉基米尔州及邻近地区植物多样性可靠信息的独特来源。由于网格数据集包含的是概括性数据,我们对弗廖罗夫的文献记录进行了精确的地理参照,并以GBIF介导的数据集形式发布。

新信息

一个基于弗廖罗夫(1902年)所著的《弗拉基米尔州植物志。I. 弗拉基米尔州的植物地理学描述》的数据集,包含来自366个地点的654个分类单元(主要是物种)的8889条记录。大多数记录来自弗拉基米尔州(来自195个地点的534个分类单元的4611条记录)和雅罗斯拉夫尔州(来自66个地点的409个分类单元的2013条记录),但也来自下诺夫哥罗德州(942条记录)、伊万诺沃州(667条记录)和莫斯科州(656条记录)。按记录数量排名靠前的二级行政区是佩列斯拉夫斯基区(2013条记录)、亚历山德罗夫斯基区(1318条记录)和谢尔吉耶夫 - 波萨德斯基区(599条记录)。地理参照是基于对该地区的专业知识、对现代卫星图像和旧地形图的分析进行的。对于2460条记录,地理参照精度为1000米或更低(28%),而对于6070条记录,精度为2000米或更低(68%)。整个数据集记录的平均精度为2447米。这种精度是19世纪后期大多数植物标本馆收藏所无法达到的。弗廖罗夫发现并纳入数据集中的一些珍稀植物的地点,在20世纪由于泥炭开采或城市发展而完全消失了。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c85/8551138/d1602dbd8aa7/bdj-09-e75299-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验