Degradation of White Birch Shelterbelts by the Attack of White-Spotted Longicorn Beetles in Central Hokkaido, Northern Japan

A widespread decline of white birch (Betula platyphylla var. japonica) shelterbelts was observed in central Hokkaido, Japan. Many exit holes bored by white-spotted longicorn beetles (Anoplophora malasiaca) were found at the base of the trunks of trees in these stands. The present study aims to evaluate the effects of infestation on the degradation, and demonstrates whether the number of exit holes (Nholes) can be used as an index of the decline of trees. We selected 35 healthy appearing stands and 16 degraded stands in the study area. A generalized linear mixed model with zero inflation revealed that Nholes of standing dead trees tended to be greater than that of living trees, and the tree vigor decreased with increasing Nholes. These results implied that the degradation of the shelterbelts was caused by the beetle. We also found size-dependent mortality, i.e., only a few larvae can cause the death of smaller trees, but not larger trees. Furthermore, evaluation of the degradation at the stand level (Nholes) using a logistic regression analysis revealed that the degradation at the stand level could be predicted by Nholes. Our findings can be used as a useful index marker for diagnosing white birch shelterbelts.

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Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

Psychological Methods ◽

10.1037/met0000347 ◽

2020 ◽

Author(s):

James L. Peugh ◽

Sarah J. Beal ◽

Meghan E. McGrady ◽

Michael D. Toland ◽

Constance Mara

Keyword(s):

Count Data ◽

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Longitudinal Count Data

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Diagnostic Assessment of Schools Using the Generalized Linear Mixed Model Framework

Korean Society for Educational Evaluation ◽

10.31158/jeev.2018.31.1.81 ◽

2018 ◽

Vol 31 (1) ◽

pp. 81-100

Author(s):

Chanho Park ◽

Keyword(s):

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Diagnostic Assessment ◽

Model Framework

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Classification of Tree Species as Well as Standing Dead Trees Using Triple Wavelength ALS in a Temperate Forest

Remote Sensing ◽

10.3390/rs11222614 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2614 ◽

Cited By ~ 3

Author(s):

Nina Amiri ◽

Peter Krzystek ◽

Marco Heurich ◽

Andrew Skidmore

Keyword(s):

Tree Species ◽

Feature Selection Method ◽

Silver Fir ◽

Maximum Point ◽

Individual Tree ◽

Dead Trees ◽

Multi Wavelength ◽

Standing Dead ◽

Standing Dead Trees ◽

532 Nm

Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8–17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees.

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Reducing Aggression by Developing Emotional and Inhibitory Control

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18105263 ◽

2021 ◽

Vol 18 (10) ◽

pp. 5263

Author(s):

Miriam Romero-López ◽

María Carmen Pichardo ◽

Ana Justicia-Arráez ◽

Judit Bembibre-Serrano

Keyword(s):

Psychosocial Development ◽

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Emotional Control ◽

Control Group ◽

Post Intervention ◽

Mixed Model Analysis ◽

Intervention Measures ◽

Experimental Group

The objective of this study is to measure the effectiveness of a program on improving inhibitory and emotional control among children. In addition, it is assessed whether the improvement of these skills has an effect on the reduction of aggressive behavior in pre-school children. The participants were 100 children, 50 belonging to the control group and 50 to the experimental group, aged between 5 and 6 years. Pre-intervention and post-intervention measures of inhibitory and emotional control (BRIEF-P) and aggression (BASC) were taken. A Generalized Linear Mixed Model analysis (GLMM) was performed and found that children in the experimental group scored higher on inhibitory and emotional control compared to their peers in the control group. In addition, these improvements have an effect on the decrease in aggressiveness. In conclusion, preventive research should have among its priorities the design of such program given their implications for psychosocial development.

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Changes in Dairy Cow Behavior with and without Assistance at Calving

Agriculture ◽

10.3390/agriculture11080722 ◽

2021 ◽

Vol 11 (8) ◽

pp. 722

Author(s):

Bethan Cavendish ◽

John McDonagh ◽

Georgios Tzimiropoulos ◽

Kimberley R. Slinger ◽

Zoë J. Huggett ◽

...

Keyword(s):

Dairy Cows ◽

Video Surveillance ◽

Dairy Cow ◽

Mixed Model ◽

Linear Mixed Model ◽

Generalized Linear Mixed Model ◽

Behavioral Data ◽

Behavioral Patterns ◽

Holstein Friesian ◽

Time Periods

The aim of this study was to characterize calving behavior of dairy cows and to compare the duration and frequency of behaviors for assisted and unassisted dairy cows at calving. Behavioral data from nine hours prior to calving were collected for 35 Holstein-Friesian dairy cows. Cows were continuously monitored under 24 h video surveillance. The behaviors of standing, lying, walking, shuffle, eating, drinking and contractions were recorded for each cow until birth. A generalized linear mixed model was used to assess differences in the duration and frequency of behaviors prior to calving for assisted and unassisted cows. The nine hours prior to calving was assessed in three-hour time periods. The study found that the cows spent a large proportion of their time either lying (0.49) or standing (0.35), with a higher frequency of standing (0.36) and shuffle (0.26) bouts than other behaviors during the study. There were no differences in behavior between assisted and unassisted cows. During the three-hours prior to calving, the duration and bouts of lying, including contractions, were higher than during other time periods. While changes in behavior failed to identify an association with calving assistance, the monitoring of behavioral patterns could be used as an alert to the progress of parturition.

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