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Towards the Development of a More Accurate Monitoring Procedure for Invertebrate Populations in the Presence of an Unknown Spatial Pattern of Population Distribution in the Field

机译:在田间种群分布的空间格局未知的情况下致力于开发更准确的无脊椎动物种群监测程序

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摘要

Studies addressing many ecological problems require accurate evaluation of the total population size. In this paper, we revisit a sampling procedure used for the evaluation of the abundance of an invertebrate population from assessment data collected on a spatial grid of sampling locations. We first discuss how insufficient information about the spatial population density obtained on a coarse sampling grid may affect the accuracy of an evaluation of total population size. Such information deficit in field data can arise because of inadequate spatial resolution of the population distribution (spatially variable population density) when coarse grids are used, which is especially true when a strongly heterogeneous spatial population density is sampled. We then argue that the average trap count (the quantity routinely used to quantify abundance), if obtained from a sampling grid that is too coarse, is a random variable because of the uncertainty in sampling spatial data. Finally, we show that a probabilistic approach similar to bootstrapping techniques can be an efficient tool to quantify the uncertainty in the evaluation procedure in the presence of a spatial pattern reflecting a patchy distribution of invertebrates within the sampling grid.
机译:解决许多生态问题的研究需要准确评估总人口规模。在本文中,我们将根据在采样位置空间网格上收集的评估数据,重新评估用于评估无脊椎动物种群数量的采样程序。我们首先讨论在粗糙的采样网格上获得的有关空间人口密度的信息不足如何影响总人口规模评估的准确性。当使用粗网格时,由于人口分布的空间分辨率(空间可变的人口密度)不足,可能会导致田野数据中的此类信息不足,尤其是在对高度异质的空间人口密度进行采样时。然后,我们认为,如果从太粗糙的采样网格中获取平均陷阱计数(通常用于量化丰度的数量),则是随机变量,因为采样空间数据的不确定性。最后,我们表明,类似于自举技术的概率方法可以是一种有效的工具,可在存在反映采样网格内无脊椎动物的零散分布的空间模式的情况下,量化评估过程中的不确定性。

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