Modeling stand mortality using Poisson mixture models with mixed-effects

Abstract

Stand mortality models play an important role in simulating stand dynamic processes. Periodic stand mortality data from permanent plots tend to be dispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using the Poisson distribution and Poisson mixture models, such as the zero-inflated Poisson model (ZIP), and the Hurdle Poisson model (HP). Based on mortality data obtained from sixty Chinese pine (Pinus tabulaeformis) permanent plots near Beijing, we added the random-effects to the Poisson mixture models. Results showed that the random-effects in the ZIP model was not convergent, and HP mixed-effects model performed better in modeling stand mortality than the Poisson fixed-effects model, the Poisson mixed-effects model, the ZIP fixed-effects model and the HP fixed-effects model. Moreover, the HP model accounts for two sources of dispersion, the first accounting for extra zeros and the second accounting to some extent for the dispersion due by individual heterogeneity in the positive set. We also found that stand mortality was negatively related to stand arithmetic mean diameter and positively related to dominant height.

Generalized or general mixed-effect modelling of tree morality of Larix gmelinii subsp. principis-rupprechtii in Northern China

“…Stand factors include stand density, competition index, stand productivity, stand structure, etc. In most cases, these factors may be considered simultaneously or several of them are considered (Affleck 2006;Zhang et al 2014;Das and Nathan 2015). Based on the research data, the main factors affecting stand level mortality were calculated, including stand density, stand mean diameter, stand dominant height, basal area per hectare, relative spacing index.…”

“…The many factors and their interactions of the factors affecting mortality at the same time in the same place make it difficult to describe variability of the observed mortality data using traditional modelling approaches such as ordinary least square regression. Alternatively, mixed-effects modelling makes it possible to describe mortality more effectively than the traditional modeling approach (Zhang et al 2014(Zhang et al , 2017b. Thus, mixed-effects tree mortality models should be developed to improve predictions of forest damage at stand levels.…”

“…A generalized linear model is often used to describe the variability of tree mortality, in which a dependent variable follows an exponential distribution, which may not be appropriate for potential mortality patterns. Other probability distributions such as negative binomial distribution, binomial distribution and Poisson distribution are commonly used for mortality modeling with better results (Zhang et al 2014).…”

“…Bayesian estimation methods have also been used (Alspach and Sorenson 1972) for mortality modeling. In forestry, counting models are mainly used for counting forest fire incidents (Kwak et al 2012;Xiao et al 2015;Susaeta et al 2016) and rarely applied to tree mortality modeling (Affleck 2006;Li et al 2019;Zhang et al 2014). A two-step method has also been used to build standlevel mortality models (Woollons 1998;Eid and Oyen 2003) and involves fitting a logistic function to tree mortality data, and stand-level mortality is obtained by summing the number of dead trees in the stand.…”

Estimation of the Basic Wood Density of Native Species Using Mixed Linear Models

“…The growing application of mixed models to solve regression problems has been successful in modeling the hypsometric relationship of (Gómez-García et al, 2016;Mendonça et al, 2015;Özçelik et al, 2018) growth in diameter (Bohora & Cao, 2014;Xu et al, 2014;Ruslandi et al, 2017), mortality (Groom et al, 2012;Zhang et al, 2015) biomass and volume (Meng et al, 2007; Bueno-López & Bevilacqua, 2012; Guangyi et al, 2015), and tree taper (Cao & Wang, 2011). Hence mixed models, which enhances the accuracy of the produced estimates, proves to be a potential technique for modeling the basic density of wood.…”