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Estimating density from presence/absence data in clustered populations

Ekström, Magnus and Sandring, Saskia and Grafström, Anton and Esseen, Per-Anders and Jonsson, Bengt Gunnar and Ståhl, Göran (2020). Estimating density from presence/absence data in clustered populations. Methods in Ecology and Evolution. 11 , 390-402
[Journal article]

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Abstract

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.

Authors/Creators:Ekström, Magnus and Sandring, Saskia and Grafström, Anton and Esseen, Per-Anders and Jonsson, Bengt Gunnar and Ståhl, Göran
Title:Estimating density from presence/absence data in clustered populations
Series Name/Journal:Methods in Ecology and Evolution
Year of publishing :2020
Volume:11
Page range:390-402
Number of Pages:13
Publisher:WILEY
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Accepted version
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
(A) Swedish standard research categories 2011 > 1 Natural sciences > 106 Biological Sciences (Medical to be 3 and Agricultural to be 4) > Ecology
Keywords:independent cluster process, intensity, Matern cluster process, plant monitoring, sample plots, spatial models, Thomas cluster process, vegetation survey
URN:NBN:urn:nbn:se:slu:epsilon-p-104481
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-104481
Additional ID:
Type of IDID
DOI10.1111/2041-210X.13347
Web of Science (WoS)000511348700001
ID Code:22848
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:29 Mar 2021 07:03
Metadata Last Modified:29 Mar 2021 07:11

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