Relation Between Individual Tree Mortality and Tree Characteristics in a Polluted and Non-Polluted Environmentx

Juknys, Romualdas; Venclovienė, Jonė; Jurkonis, Nerijus; Bartkevičius, Edmundas; Šepetienė, Janina

doi:10.1007/s10661-005-9152-y

Cited by 13 publications

(8 citation statements)

References 30 publications

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“…Its main features are altered forest structure and release of additional resources for the remaining trees and other organisms (Kö ster et al, 2005). The process of tree mortality is critical for understanding forest stand dynamics (Juknys et al, 2006;Ozolincius et al, 2005;Monserud and Sterba, 1999) and a likely indicator of naturalness (Debeljak, 2006;Rouvinen et al, 2002). Assessment of individual tree mortality permits evaluation of stand development stage and the level of human influence.…”

Section: Introductionmentioning

confidence: 99%

Analysis of forest naturalness and tree mortality patterns in Estonia

Laarmann

Korjus

Sims

et al. 2009

Forest Ecology and Management

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Section: Introductionmentioning

confidence: 99%

Analysis of forest naturalness and tree mortality patterns in Estonia

Laarmann

Korjus

Sims

et al. 2009

Forest Ecology and Management

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“…Conversely, the effect of D on tree mortality was negative; that is, as the stand mean diameter became smaller, the tree mortality increased. This result indicates that tree mortality was more likely in forests with many small trees compared to forests with larger trees (Juknys et al 2006;Larson and Franklin 2010).…”

Section: Discussionmentioning

confidence: 90%

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

Zhou

Sharma

et al. 2021

J. For. Res.

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Tree mortality models play an important role in predicting tree growth and yield, but existing mortality models for Larix gmelinii subsp. principis-rupprechtii, an important species used for regeneration and afforestation in northern China, have overlooked potential regional influences on tree mortality. This study used data acquired from 102 temporary sample plots (TSPs) in natural stands of Prince Rupprecht larch in the state-owned Guandi Mountain Forest (n = 67) and state-owned Boqiang Forest (n = 35) in northern China. To model stand-level tree mortality, we compared seven model forms of county data. Three continuous (dominant height, plot mean diameter, and basal area per hectare) and one dummy variable with two levels (region) were used as fixed effects variables. Tree morality variations caused by forest blocks were accounted for using forest blocks as a random effect in selected models. Results showed that tree mortality significantly positively correlated with stand basal area and dominant height, but negatively correlated with stand mean diameter. Incorporating both the dummy variables and random effects into the tree mortality models significantly increased the fitting improvements, and Hurdle Poisson mixed-effects model showed the most attractive fit statistics (largest R2 and smallest RMSE) when employing leave-one-out cross-validation. These mixed-effects dummy variable models will be useful for accurately predicting Larix tree mortality in different regions.

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“…According to previous studies (Diéguez-Aranda et al 2005), stand mortality decreases when the relative spacing is larger. Similarly, the effect of stand arithmetic mean diameter (size index) was negative (i.e., the greater Dm, the smaller stand mortality), indicating that stand mortality is more likely in forests with many small trees compared to forests with larger trees (Juknys et al 2006). Due to the random effects on plots, altitude was not significant in the mixed models (Poisson mixed-effects model, HP mixed-effects model).…”

Section: Resultsmentioning

confidence: 96%

Modeling stand mortality using Poisson mixture models with mixed-effects

Zhang¹,

Lei²,

Liu³

2015

iForest

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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.

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Relation Between Individual Tree Mortality and Tree Characteristics in a Polluted and Non-Polluted Environmentx

Abstract: a higher mortality of damaged (defoliated) trees should be considered while assessing losses in forest productivity in a polluted environment.

Cited by 13 publications

References 30 publications

Analysis of forest naturalness and tree mortality patterns in Estonia

Analysis of forest naturalness and tree mortality patterns in Estonia

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

Modeling stand mortality using Poisson mixture models with mixed-effects

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