Stem Damage Modifies the Impact of Wind on Norway Spruces

Bark stripping caused by cervids can have a long-lasting negative effect on tree vitality. Such trees of low vitality might be more susceptible to other disturbances. The amplifying effects of disturbance interactions can cause significantly more damage to forest ecosystems than the individual effects of each disturbance. Therefore, this study aimed to assess the impact of bark stripping (stem damage) on the probability of wind damage and snapping height for Norway spruces (Picea Abies (L.) H. Karst.). In this study, we used the Latvian National Forest Inventory data from the period 2004–2018. In the analysis, we used data based on 32,856 trees. To analyse the data, we implemented a Bayesian binary logistic generalised linear mixed-effects model and the linear mixed-effects model. Our results showed that stem damage significantly increased the probability of wind damage and affected the snapping height of Norway spruces. Similarly, root damage, the slenderness ratio, the stand age, the stand density, the soil type, and the dominant tree species had a significant influence on the probability of wind damage. In both periods, trees with stem damage had significantly (p < 0.05) higher probability (odd ratio 1.68) to be wind damaged than trees without stem damage. The stem damaged Norway spruce trees snapped in the first 25% of the tree height, while trees without stem damage snapped around half (50%) of the tree height. Our results show that stem damage significantly alters the effect of wind damage on Norway spruces, suggesting that such damage must be incorporated into wind-risk assessment models.

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Tree- and stand-scale factors shape the probability of wind damage to birch in hemiboreal forests

Silva Fennica ◽

10.14214/sf.10483 ◽

2021 ◽

Vol 55 (2) ◽

Author(s):

Māra Kitenberga ◽

Guntars Šņepsts ◽

Jānis Vuguls ◽

Didzis Elferts ◽

Ieva Jaunslaviete ◽

...

Keyword(s):

Slenderness Ratio ◽

Mixed Effects ◽

Mixed Effects Model ◽

Wind Damage ◽

Deciduous Tree ◽

Stand Age ◽

Strong Wind ◽

Individual Tree ◽

Linear Mixed Effects Model ◽

Linear Mixed Effects

Strong wind is the major natural disturbance in European forests, that periodically causes tremendous damages to forestry. Yet, factors that affect the probability of wind damage for birch ( Roth and Ehrh.), the most common deciduous tree species in hemiboreal forests, are studied scarcely. This study aimed to assess the effects of several tree- and stand-scale variables on the probability of wind damage to birch using data from the Latvian National Forest Inventory (2004â2018), and determine individual tree characteristics that affect the height of the stem breakage. The data analysis was done using the Bayesian binary logistic generalized linear mixed-effects model and a linear mixed-effects model. The probability of wind damage significantly increased by stand age, basal area, and slenderness ratio. Trees with prior damage had a significantly higher probability (odds ratio 4.32) for wind damage. For wind-damaged trees, the snapping height was significantly decreased by an increase in the slenderness ratio (â=â0.03) and prior damage (â=â0.003). Previously damaged trees were more frequently (73%) snapped in the lowest 40% of tree height than trees without prior damage (54%). The probability of wind damage is largely set by factors related to the selection of site, species composition, and rotation. The damage probability could be decreased by management measures that lower competition within the stand with particular regard to preserving intact remaining trees during these manipulations. Factors that reduce the probability of the damage simultaneously increase the snapping height, emphasizing their relevance for mitigation of the wind damages.Betula pendulaB. pubescenspp

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Evaluation of Rhodotorula growth on solid substrate via a linear mixed effects model

Czech Journal of Food Sciences ◽

10.17221/327/2009-cjfs ◽

2011 ◽

Vol 29 (No. 4) ◽

pp. 400-410 ◽

Cited By ~ 1

Author(s):

T. Krulikovská ◽

E. Jarošová ◽

P. Patáková

Keyword(s):

Growth Rate ◽

Confidence Intervals ◽

Solid Substrate ◽

Mixed Effects ◽

Repeated Measurements ◽

Mixed Effects Model ◽

Cultivation Conditions ◽

Linear Mixed Effects Model ◽

Linear Mixed Effects ◽

The Impact

The growth of Rhodotorula glutinis and Rhodotorula mucilaginosa was studied under optimal and stress cultivation conditions at 10°C and 20°C for 14 days. The method of image analysis was used to determine the size of colonies. The linear mixed effects model implemented in the statistical program S-PLUS was applied to analyse the repeated measurements. Two-phase kinetics was confirmed and the mean growth rates in the second linear phase under various stress conditions were estimated. The results indicated a higher growth rate of R. mucilaginosa than was that of R. glutinis under all cultivation conditions. The highest growth rate of was observed during the cultivation of R. mucilaginosa in media with 2% of NaCl at 20°C. The impact of neglecting the fact that repeated data are not independent and using the classical regression model instead of the mixed effects model was demonstrated through the comparison of the confidence intervals for the parameters based on both approaches. While the point estimates of the corresponding parameters were similar, the width of the confidence intervals differed substantially.

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Comparison of linear mixed effects model and generalized model of the tree height-diameter relationship

Journal of Forest Science ◽

10.17221/68/2015-jfs ◽

2016 ◽

Vol 61 (No. 10) ◽

pp. 439-447 ◽

Cited By ~ 5

Author(s):

Z. Adamec

Keyword(s):

Tree Height ◽

Mixed Effects ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Generalized Model ◽

Linear Mixed Effects

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The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach

10.21203/rs.3.rs-73814/v2 ◽

2021 ◽

Author(s):

Jabed Tomal ◽

Saeed Rahmati ◽

Shirin Boroushaki ◽

Lingling Jin ◽

Ehsan Ahmed

Keyword(s):

Cognitive Skills ◽

Missing Values ◽

Hierarchical Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Bayesian Hierarchical ◽

Linear Mixed Effects ◽

Face To Face Learning ◽

The Impact

Abstract Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impact of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed eﬀects model ﬁtted to longitudinal data. The Bayesian linear mixed eﬀects model is designed for this application which allows student-speciﬁc error variances to vary. The novel Bayesian missing value imputation method is ﬂexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom’s Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

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The Impact of COVID-19 on Students' Marks: A Bayesian Hierarchical Modeling Approach

10.21203/rs.3.rs-73814/v1 ◽

2020 ◽

Author(s):

Jabed Tomal ◽

Saeed Rahmati ◽

Shirin Boroushaki ◽

Lingling Jin ◽

Ehsan Ahmed

Keyword(s):

Cognitive Skills ◽

Missing Values ◽

Hierarchical Modeling ◽

Mixed Effects ◽

Mixed Effects Model ◽

Linear Mixed Effects Model ◽

Bayesian Hierarchical ◽

Linear Mixed Effects ◽

Face To Face Learning ◽

The Impact

Abstract Due to COVID-19, universities across Canada were forced to undergo a transition from classroom-based face-to-face learning and invigilated assessments to online-based learning and non-invigilated assessments. This study attempts to empirically measure the impacts of COVID-19 on students’ marks from eleven science, technology, engineering, and mathematics (STEM) courses using a Bayesian linear mixed effects model fitted to longitudinal data. The Bayesian linear mixed effects model is designed for this application which allows student-specific error variances to vary. The novel Bayesian missing value imputation method is flexible which seamlessly generates missing values given complete data. We observed an increase in overall average marks for the courses requiring lower-level cognitive skills according to Bloom's Taxonomy and a decrease in marks for the courses requiring higher-level cognitive skills, where larger changes in marks were observed for the underachieving students. About half of the disengaged students who did not participate in any course assessments after the transition to online delivery were in special support.

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