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Estimating density from presence/absence data in clustered populations
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden.ORCID iD: 0000-0002-7886-0911
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
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2020 (English)In: Methods in Ecology and Evolution, ISSN 2041-210X, E-ISSN 2041-210XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

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. New theory was derived for estimating plant density under Neyman-Scott-type cluster models such as the Matern 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 Matern cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application. 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. 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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2020.
Keywords [en]
independent cluster process, intensity, Matern cluster process, plant monitoring, sample plots, spatial models, Thomas cluster process, vegetation survey
National Category
Ecology Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-168328DOI: 10.1111/2041-210X.13347ISI: 000511348700001OAI: oai:DiVA.org:umu-168328DiVA, id: diva2:1395960
Available from: 2020-02-25 Created: 2020-02-25 Last updated: 2020-02-25

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Ekström, MagnusEsseen, Per-Anders

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