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Ecological landscape elements: long-term monitoring in Great Britain, the Countryside Survey 1978–2007 and beyond

机译:生态景观要素:英国的长期监测,1978-2007年及以后的农村调查

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The Countryside Survey (CS) of Great Britain (GB) provides a?unique and statistically robust series of datasets, consisting of an extensive set of repeated ecological measurements at a?national scale, covering a?time span of 29?years. CS was first undertaken in 1978 to provide a?baseline for ecological and land use change monitoring in the rural environment of GB, following a?stratified random design, based on 1?km squares. Originally, eight random 1?km squares were drawn from each of 32 environmental classes, thus comprising 256 sample squares in the 1978 survey. The number of these sites increased to 382 in 1984, 506 in 1990, 569 in 1998 and 591 in 2007. Detailed information regarding vegetation types and land use was mapped in all five surveys, allowing reporting by defined standard habitat classifications. Additionally, point and linear landscape features (such as trees and hedgerows) are available from all surveys after 1978. From these stratified, randomly located sample squares, information can be converted into national estimates, with associated error terms. Other data, relating to soils, freshwater and vegetation, were also sampled on analogous dates. However, the present paper describes only the surveys of landscape features and habitats. The resulting datasets provide a?unique, comprehensive, quantitative ecological coverage of extent and change in these features in GB. Basic results are presented and their implications discussed. However, much opportunity for further analyses remains. Data from each of the survey years are available via the following DOIs: Landscape area data 1978: https://doi.org/10.5285/86c017ba-dc62-46f0-ad13-c862bf31740e, 1984: https://doi.org/10.5285/b656bb43-448d-4b2c-aade-7993aa243ea3, 1990: https://doi.org/10.5285/94f664e5-10f2-4655-bfe6-44d745f5dca7, 1998: https://doi.org/10.5285/1e050028-5c55-42f4-a0ea-c895d827b824, and 2007: https://doi.org/10.5285/bf189c57-61eb-4339-a7b3-d2e81fdde28d; Landscape linear feature data 1984: https://doi.org/10.5285/a3f5665c-94b2-4c46-909e-a98be97857e5, 1990: https://doi.org/10.5285/311daad4-bc8c-485a-bc8a-e0d054889219, 1998: https://doi.org/10.5285/8aaf6f8c-c245-46bb-8a2a-f0db012b2643 and 2007: https://doi.org/10.5285/e1d31245-4c0a-4dee-b36c-b23f1a697f88, Landscape point feature data 1984: https://doi.org/10.5285/124b872e-036e-4dd3-8316-476b5f42c16e, 1990: https://doi.org/10.5285/1481bc63-80d7-4d18-bcba-8804aa0a9e1b, 1998: https://doi.org/10.5285/ed10944f-40c8-4913-b3f5-13c8e844e153 and 2007: https://doi.org/10.5285/55dc5fd7-d3f7-4440-b8a7-7187f8b0550b.
机译:英国(GB)的乡村调查(CS)提供了一系列独特且统计稳定的数据集,其中包括在全国范围内进行的一系列广泛的重复生态测量,涵盖了29年的时间跨度。 CS于1978年首次开展,它是根据1 km平方的分层随机设计,为GB的农村环境提供生态和土地利用变化监测的基线。最初,从32个环境类别的每一个中抽取了8个随机的1 km正方形,因此在1978年的调查中包括256个样本正方形。这些地点的数量在1984年增加到382个,在1990年增加到506个,在1998年增加到569个,在2007年增加到591个。在所有五次调查中都绘制了有关植被类型和土地利用的详细信息,从而可以通过已定义的标准栖息地分类进行报告。此外,1978年以后的所有调查都提供了点和线性景观特征(例如树木和树篱)。从这些分层的,随机分布的样本正方形中,信息可以转换成国家估计,并带有相关的误差项。其他与土壤,淡水和植被有关的数据也在类似的日期进行了采样。但是,本文仅描述景观特征和栖息地的调查。所得的数据集提供了GB中这些特征的程度和变化的唯一,全面,定量的生态覆盖。介绍了基本结果并讨论了其含义。但是,仍然存在很多进一步分析的机会。可以通过以下DOI获得每个调查年度的数据:风景区数据1978:https://doi.org/10.5285/86c017ba-dc62-46f0-ad13-c862bf31740e,1984:https://doi.org/10.5285 /b656bb43-448d-4b2c-aade-7993aa243ea3,1990:https://doi.org/10.5285/94f664e5-10f2-4655-bfe6-44d745f5dca7,1998:https://doi.org/10.5285/1e050028-5c55-42f4 -a0ea-c895d827b824和2007:https://doi.org/10.5285/bf189c57-61eb-4339-a7b3-d2e81fdde28d;景观线性特征数据1984:https://doi.org/10.5285/a3f5665c-94b2-4c46-909e-a98be97857e5,1990:https://doi.org/10.5285/311daad4-bc8c-485a-bc8a-e0d054889219,1998: https://doi.org/10.5285/8aaf6f8c-c245-46bb-8a2a-f0db012b2643和2007年:https://doi.org/10.5285/e1d31245-4c0a-4dee-b36c-b23f1a697f88,风景点要素数据1984:https: //doi.org/10.5285/124b872e-036e-4dd3-8316-476b5f42c16e,1990:https://doi.org/10.5285/1481bc63-80d7-4d18-bcba-8804aa0a9e1b,1998:https://doi.org/ 10.5285 / ed10944f-40c8-4913-b3f5-13c8e844e153和2007:https://doi.org/10.5285/55dc5fd7-d3f7-4440-b8a7-7187f8b0550b。

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