A climate sensitive mixed-effects diameter class mortality model for Prince Rupprecht larch (Larix gmelinii var. principis-rupprechtii) in northern China

2021 ◽  
Vol 491 ◽  
pp. 119091
Author(s):  
Xiao Zhou ◽  
Qiao Chen ◽  
Ram P. Sharma ◽  
Yihao Wang ◽  
Peng He ◽  
...  
Keyword(s):  
Northern China ◽  
Mixed Effects ◽  
Larix Gmelinii ◽  
Diameter Class ◽  
Mortality Model

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

2021 ◽  
Author(s):  
Xiao Zhou ◽  
Liyong Fu ◽  
Ram P. Sharma ◽  
Peng He ◽  
Yuancai Lei ◽  
...  
Keyword(s):  
Tree Mortality ◽  
Basal Area ◽  
Northern China ◽  
Mixed Effects ◽  
Larix Gmelinii ◽  
Important Species ◽  
Dummy Variable ◽  
Dominant Height ◽  
Mortality Models ◽  
Mean Diameter

AbstractTree 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.

scholarly journals Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1144
Author(s):  
Yangang Han ◽  
Zeyong Lei ◽  
Albert Ciceu ◽  
Yanping Zhou ◽  
Fengyan Zhou ◽  
...  
Keyword(s):  
Management Practice ◽  
Predictor Variable ◽  
Tree Height ◽  
Cost Effective ◽  
Northern China ◽  
Mixed Effects ◽  
Pine Plantations ◽  
Sampling Effort ◽  
Individual Tree ◽  
D Model

Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.

Release of Neurosecretion in the Crayfish Sinus Gland

1968 ◽  
Vol 26 ◽  
pp. 150-151
Author(s):  
Richard R. Shivers
Keyword(s):  
Chemical Nature ◽  
Storage Site ◽  
Neurosecretory Granules ◽  
Sinus Gland ◽  
Diameter Class ◽  
Dense Membrane ◽  
Neurohemal Organ ◽  
Dense Vesicles

The sinus gland is a neurohemal organ located in the crayfish eyestalk and represents a storage site for neurohormones prior to their release into the circulation. The sinus gland contains 3 classes of dense, membrane-limited granules: 1) granules measuring less than 1000 Å in diameter, 2) granules measuring 1100-1400 Å in diameter, and 3) granules measuring 1500-2000 Å in diameter. Class 3 granules are the most electron-dense of the granules found in the sinus gland, while class 2 granules are the most abundant. Generally, all granules appear to undergo similar changes during release.Release of neurosecretory granules may be initiated by a preliminary fragmentation of the “parent granule” into smaller, less dense vesicles which measure about 350 Å in diameter (V, Figs. 1-3). A decrease in density of the granules prior to their fragmentation has been observed and may reflect a change in the chemical nature of the granule contents.

Modeling Intraindividual Variability in Three-Level Multilevel Models

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 95-108 ◽  
Author(s):  
Steffen Nestler ◽  
Katharina Geukes ◽  
Mitja D. Back
Keyword(s):  
Multilevel Model ◽  
Multilevel Models ◽  
Mixed Effects ◽  
Repeated Measurements ◽  
Scale Model ◽  
Suggested Approach ◽  
Psychological Study ◽  
Level Data ◽  
Ecological Momentary ◽  
Momentary Assessment

Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

Extremism Leads to Ostracism

2020 ◽  
Vol 51 (3) ◽  
pp. 149-156
Author(s):  
Andrew H. Hales ◽  
Kipling D. Williams
Keyword(s):  
Mixed Effects ◽  
Social Connection ◽  
Mixed Effects Modeling ◽  
Activist Groups

Abstract. Ostracism has been shown to increase openness to extreme ideologies and groups. We investigated the consequences of this openness-to-extremity from the perspective of potential ostracizers. Does openness-to-extremity increase one’s prospects of being ostracized by others who are not affiliated with the extreme group? Participants rated willingness to ostracize 40 targets who belong to activist groups that vary in the type of goals/cause they support (prosocial vs. antisocial), and the extremity of their actions (moderate vs. extreme). Mixed-effects modeling showed that people are more willing to ostracize targets whose group engages in extreme actions. This effect was unexpectedly stronger for groups pursuing prosocial causes. It appears openness-to-extremity entails interpersonal cost, and could increase reliance on the extreme group for social connection.

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