Author
Listed:
- Yulan B. van Oppen
- Gabi Milder-Mulderij
- Christophe Brochard
- Rink Wiggers
- Saskia de Vries
- Wim P. Krijnen
- Marco A. Grzegorczyk
Abstract
We develop a generalized linear mixed model (GLMM) for bivariate count responses for statistically analyzing dragonfly population data from the Northern Netherlands. The populations of the threatened dragonfly species Aeshna viridis were counted in the years 2015–2018 at 17 different locations (ponds and ditches). Two different widely applied population size measures were used to quantify the population sizes, namely the number of found exoskeletons (‘exuviae’) and the number of spotted egg-laying females were counted. Since both measures (responses) led to many zero counts but also feature very large counts, our GLMM model builds on a zero-inflated bivariate geometric (ZIBGe) distribution, for which we show that it can be easily parameterized in terms of a correlation parameter and its two marginal medians. We model the medians with linear combinations of fixed (environmental covariates) and random (location-specific intercepts) effects. Modeling the medians yields a decreased sensitivity to overly large counts; in particular, in light of growing marginal zero inflation rates. Because of the relatively small sample size (n = 114) we follow a Bayesian modeling approach and use Metropolis-Hastings Markov Chain Monte Carlo (MCMC) simulations for generating posterior samples.
Suggested Citation
Yulan B. van Oppen & Gabi Milder-Mulderij & Christophe Brochard & Rink Wiggers & Saskia de Vries & Wim P. Krijnen & Marco A. Grzegorczyk, 2023.
"Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model,"
Journal of Applied Statistics, Taylor & Francis Journals, vol. 50(10), pages 2171-2193, July.
Handle:
RePEc:taf:japsta:v:50:y:2023:i:10:p:2171-2193
DOI: 10.1080/02664763.2022.2068513
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:japsta:v:50:y:2023:i:10:p:2171-2193. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.