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Generalizing systematic adaptive cluster sampling for forest ecosystem inventory
Forest Ecology and Management ( IF 3.7 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.foreco.2021.119051
Qing Xu , Göran Ståhl , Ronald E. McRoberts , Bo Li , Timo Tokola , Zhengyang Hou

Reliable statistical inference is central to forest ecology and management, much of which seeks to estimate population parameters for forest attributes and ecological indicators for biodiversity, functions and services in forest ecosystems. Many populations in nature such as plants or animals are characterized by aggregation of tendencies, introducing a big challenge to sampling. Regardless, a biased or imprecise inference would mislead analysis, hence the conclusion and policymaking. Systematic adaptive cluster sampling (SACS) is design-unbiased and particularly efficient for inventorying spatially clustered populations. However, (1) oversampling is common for nonrare variables, making SACS a difficult choice for inventorying common forest attributes or ecological indicators; (2) a SACS sample is not completely specified until the field campaign is completed, making advance budgeting and logistics difficult; (3) even for rare variables, uncertainty regarding the final sample still persists; and (4) a SACS sample may be variable-specific as its formation can be adapted to a particular attribute or indicator, thus risking imbalance or non-representativeness for other jointly observed variables. Consequently, to solve these challenges, we aim to develop a generalized SACS (GSACS) with respect to the design and estimators, and to illustrate its connections with systematic sampling (SS) as has been widely employed by national forest inventories and ecological observation networks around the world. In addition to theoretical derivations, empirical sampling distributions were validated and compared for GSACS and SS using sampling simulations that incorporated a comprehensive set of forest populations exhibiting different spatial patterns. Five conclusions are relevant: (1) in contrast to SACS, GSACS explicitly supports inventorying forest attributes and ecological indicators that are nonrare, and solved SACS problems of oversampling, uncertain sample form, and sample imbalance for alternative attributes or indicators; (2) we demonstrated that SS is a special case of GSACS; (3) even with fewer sample plots, GSACS gives estimates identical to SS; (4) GSACS outperforms SS with respect to inventorying clustered populations and for making domain-specific estimates; and (5) the precision in design-based inference is negatively correlated with the prevalence of a spatial pattern, the range of spatial autocorrelation, and the sample plot size, in a descending order.



中文翻译:

对森林生态系统清单进行系统的系统自适应聚类抽样

可靠的统计推断对于森林生态和管理至关重要,其中许多工作旨在估算森林属性的种群参数以及森林生态系统中生物多样性,功能和服务的生态指标。自然界中的许多种群(例如动植物)的特征在于趋势的聚集,这给采样带来了巨大挑战。无论如何,有偏见或不精确的推论都会误导分析,从而得出结论和制定政策。系统的自适应聚类抽样(SACS)不受设计偏见,对于盘点空间聚类的种群特别有效。但是,(1)非稀有变量普遍存在过采样现象,这使得SACS成为盘点常见森林属性或生态指标的困难选择;(2)在野外活动完成之前,没有完全指定SACS样本,这使得提前预算和后勤工作变得困难;(3)即使对于稀有变量,最终样本的不确定性仍然存在;(4)SACS样本可能是变量特定的,因为其形成可以适应特定的属性或指标,因此冒着其他共同观察到的变量失衡或不具有代表性的风险。因此,为解决这些挑战,我们旨在针对设计和估算器开发通用的SACS(GSACS),并说明其与系统抽样(SS)的联系,该方法已被国家森林资源清查和周围的生态观测网络广泛采用世界。除了理论推论之外,GSACS和SS的经验抽样分布得到了验证,并通过抽样模拟进行了比较,抽样模拟纳入了一组展示不同空间格局的综合森林种群。有五个结论是有意义的:(1)与SACS相比,GSACS明确支持盘点非稀有的森林属性和生态指标,并解决了SACS的过采样,样本形式不确定以及替代属性或指标样本不平衡的问题;(2)我们证明了SS是GSACS的特例;(3)即使样本量较少,GSACS也会给出与SS相同的估计值;(4)在盘点聚类人口和进行特定领域估算方面,GSACS优于SS。(5)基于设计的推理的精度与空间模式的普遍性负相关,

更新日期:2021-03-01
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