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. Further, 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 suggests 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.

Presence/absence data from environmental monitoring

This file contains observed frequencies for the species Leucanthemum vulgare, Pimpinella saxifraga, and Scorzonera humilis. The data were obtained from three monitoring projects associated with the National Inventory of Landscapes in Sweden (NILS), where presence/absence-data of plants were recorded in nine subplots per plot. See Section 2.4.2 of the paper for detailed information about the data. The observed frequencies, n₀, ..., n₇, in the data file are from a "multinomial experiment" described in our paper (see Example 2 and the first paragraph of Section 2 for details), and were used for estimating parameters of Matérn cluster processes (Section 3.2; Table 5).