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
[Research article]
![]() |
PDF
591kB |
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: | Research 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: |
| ||||||
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 |
Repository Staff Only: item control page