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Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model.

Authors :
van Oppen, Yulan B.
Milder-Mulderij, Gabi
Brochard, Christophe
Wiggers, Rink
de Vries, Saskia
Krijnen, Wim P.
Grzegorczyk, Marco A.
Source :
Journal of Applied Statistics; Aug2023, Vol. 50 Issue 10, p2171-2193, 23p, 1 Diagram, 6 Charts, 5 Graphs
Publication Year :
2023

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02664763
Volume :
50
Issue :
10
Database :
Complementary Index
Journal :
Journal of Applied Statistics
Publication Type :
Academic Journal
Accession number :
164872855
Full Text :
https://doi.org/10.1080/02664763.2022.2068513