1. Presence–absence sampling is an important method for monitoring state and change of both individual plant species and communities. With this method, only the presence or absence of the target species is recorded on plots and thus the method is straightforward to apply and less prone to surveyor judgement compared to other vegetation monitoring methods. However, in the basic setting, all plots must be equally large or otherwise it is unclear how data should be analysed. In this study, we propose and evaluate five different methods for estimating plant density based on presence–absence registrations from surveys with variable plot sizes.
2. Using artificial plant population data as well as empirical data from the Swedish National Forest Inventory, we evaluated the performance of the proposed methods. The main analysis was conducted through sampling simulation in artificial populations, whereby bias and variance of density estimators for the different methods were quantified and compared.
3. Both for state and change estimation of plant density, we found that the best method to handle variable plot size was to perform generalized least squares regression, using plot size as an independent variable. Methods where plots smaller than a certain threshold were excluded or their registrations recalculated were, however, almost as good. Using all registrations as if they were obtained from plots with the nominal plot size resulted in substantial bias.
4. Our findings are important for plant population studies in a wide range of environmental monitoring programmes. In these programmes, plots are typically randomly laid out and may be located across boundaries between different land‐use or land‐cover classes, resulting in subplots of variable size. Such splitting of plots is common when large plots are used, for example, with the 100 m² plots used in the Swedish National Forest Inventory. Our methods overcome problems to estimate plant density from presence–absence data observed in plots that vary in size.

中文翻译：

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使用有无样地的调查数据进行有无抽样以估算植物密度

1. 在场/不在场采样是监视单个植物物种和群落状态和变化的重要方法。使用这种方法，仅在地块上记录目标物种的存在或不存在，因此与其他植被监测方法相比，该方法易于直接应用且不易被测量员判断。但是，在基本设置中，所有图都必须相等，否则不清楚如何分析数据。在这项研究中，我们提出并评估了五种不同的方法，这些方法基于可变地块大小的调查中存在与否的注册来估计植物密度。
2. 使用人工植物种群数据以及瑞典国家森林清单的经验数据，我们评估了所提出方法的性能。通过对人工种群的抽样模拟进行主要分析，从而量化和比较了不同方法的密度估计量的偏差和方差。
3. 对于状态和植物密度的变化估计，我们发现处理可变样地大小的最佳方法是使用样地大小作为自变量进行广义最小二乘回归。但是，排除小于特定阈值的地块或重新计算其配准的方法几乎一样好。使用所有注册，就好像它们是从具有标称地块大小的地块获得的那样，会导致很大的偏差。
4. 我们的发现对于广泛的环境监测计划中的植物种群研究非常重要。在这些程序中，地块通常是随机布置的，并且可能跨越不同土地利用或土地覆盖类别之间的边界，从而导致子图的大小可变。当使用大型地块时，例如，瑞典国家森林清单中使用的100 m ²地块，这种地块划分很常见。我们的方法克服了从大小不一的样地中观察到的有无数据估计植物密度的问题。