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How much effort is required for proper monitoring? Assessing the effects of different survey scenarios in a dry acidic grassland

机译:进行适当的监视需要多少努力?在干旱的酸性草原上评估不同调查方案的影响

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QuestionsThe quality of any inferences derived from field studies or monitoring programmes depends on expenditure of time and effort to make the underlying observations. Here, we used a long-term data set from a succession-monitoring scheme to assess the effect of different survey scenarios. We asked: (1) how well does a survey reflect successional processes if sampling effort varies (a) in space (b) in length of total observation period, (c) in observation frequency and (d) with a combination of these factors? (2) What are the practical implications for devising monitoring programmes?LocationLignite mining region of Central Germany, post-mining landscape of Goitzsche (Saxony-Anhalt).MethodsBased on our full data set, we constructed subsamples. For the full data set and all subsets, we constructed Markov models and compared them based on the predictions made. We assessed effects of survey intensity on model performance using generalized linear models and multiple logistic regressions.ResultsExploring the effects of different survey scenarios revealed significant effects of all three main features of survey intensity (sample size, length, frequency). The most important sampling feature was study length. However, we found interactive effects of sample size with study length and observation interval on model predictions. This indicates that for long-term observations with multiple recording intervals a lower sample size in space is required to reveal the same amount of information as required in a shorter study or one with fewer intervals. Conversely, a high sample size may, to some degree, compensate for relatively short study periods.ConclusionsMonitoring activities should not be restricted to intensive sampling over only a few years. With clearly limited resources, a decrease of sampling intensity in space, and stretching these resources over a longer period would probably pay off much better than totally abandoning monitoring activities after an intensive, but short, campaign.
机译:问题从实地研究或监测计划得出的任何推论的质量取决于花费时间和精力进行基础观察。在这里,我们使用了来自继任监测方案的长期数据集来评估不同调查方案的影响。我们问:(1)如果抽样工作的变化(a)空间(b)整个观察期的长度,(c)观察频率和(d)结合这些因素,则调查对连续过程的反映如何? (2)制定监测计划的实际意义是什么?位置德国中部褐煤开采区,Goitzsche(萨克森-安哈尔特州)的采后景观。方法基于我们的完整数据集,我们构建了子样本。对于完整的数据集和所有子集,我们构建了马尔可夫模型,并根据所做的预测对它们进行了比较。我们使用广义线性模型和多元Logistic回归评估调查强度对模型性能的影响。结果探索不同调查场景的影响揭示了调查强度的所有三个主要特征(样本大小,长度,频率)的显着影响。最重要的抽样特征是研究长度。但是,我们发现样本量与研究长度和观察间隔对模型预测的交互作用。这表明对于具有多个记录间隔的长期观测,需要较小的空间样本量才能显示与较短研究或间隔较小的研究中所需的信息量相同的信息。相反,高样本量可能在一定程度上弥补了相对较短的研究时间。结论监测活动不应只限于几年内的密集样本。在资源明显有限的情况下,减少空间采样强度并延长这些资源的使用期限可能比在一次密集但短暂的运动后完全放弃监视活动要好得多。

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