1. Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns.
2. New theory was derived for estimating plant density under Neyman–Scott‐type cluster models such as the Matérn and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness‐of‐fit test were evaluated in a Monte Carlo simulation study assuming a Matérn cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application.
3. The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough’ is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis.
4. By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction.

中文翻译：

---

根据聚集人口中的存在/不存在数据估算密度

1. 植物种群清单是生态研究和监测的基础，但此类调查通常容易出现田间评估错误。在场/不在场（P / A）采样可能比工厂覆盖评估更有利于减少此类误差。但是，P / A数据与植物密度之间的联系取决于植物空间分布的模型假设。例如，以前的研究表明，如何根据工厂位置的Poisson模型假设来估算工厂密度。在这项研究中，假设植物以群集的空间模式出现，则开发并评估了将P / A数据与植物密度联系起来的新方法。
2. 在Neyman–Scott型簇模型（例如Matérn和Thomas簇过程）下，推导出了用于估计植物密度的新理论。在假定Matérn聚类过程的蒙特卡罗模拟研究中，对建议的估计量，相应的置信区间和拟议的拟合优度检验进行了评估。此外，将估算器应用于瑞典环境监测中的工厂数据，以证明其经验应用。
3. 仿真研究表明，对于足够大的样本量，我们的方法效果很好。“足够大”的判断通常很困难，但是仿真表明，当参数估计量的采样分布对称或略有偏斜时，样本大小足够大。引导程序可用于检查是否为真。实证结果表明，所推导的方法可能对估算植物的密度有用，如菊苣和茄子。
4. 通过在有关植物空间分布的逼真的模型假设下，根据P / A数据开发植物密度估算器，P / A采样将成为植物种群清单更有用的工具。我们的新理论是朝这个方向迈出的重要一步。