Divergent Abiotic Stressors Drive Grassland Community Assembly of Tibet and Mongolia Plateau

Multiple ecological processes simultaneously govern community assembly, but it remains unclear how abiotic stressors regulate the relative importance of these processes among different biogeographic regions. Therefore, we conducted a comprehensive study on the responses of community assembly to varying environmental gradients, using the mean, variance, skewness, and kurtosis of plant height (height), specific leaf area (SLA) and leaf dry matter content (LDMC) distributions on the Tibetan Plateau (TP) and the Mongolian Plateau (MP). Our results showed that the prevalence of trait convergence across all grasslands in both TP and MP seem to be the result of abiotic filtering or weaker competitive exclusion etc. These trait-convergence assembly processes decrease the functional dispersion but increase the evenness of the trait frequency distribution. The mean, variance, skewness, and kurtosis responses of grassland communities to abiotic stress varied between the TP and MP. On average, plant trait distribution was mainly driven by temperature on the TP, and low-temperature stress altered the community assembly rules. In contrast, water availability shaped plant trait frequency distributions on the MP, and drought stress mediated the balance between different assembly processes. Our results provide empirical evidence that divergent abiotic stressors regulate the grassland community assembly on the TP and MP. Together, our study speculates that different aspects of future climate change, such as climate warming and changing precipitation patterns, on community assembly are dependent on regional climatic regimes.

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Abnormal Portfolio Asset Allocation Model: Review

International Journal of Business, Economics, and Social Development ◽

10.46336/ijbesd.v1i1.18 ◽

2020 ◽

Vol 1 (1) ◽

pp. 46-54

Author(s):

Nurfadhlina Bt Abdul Halima ◽

Dwi Susanti ◽

Alit Kartiwa ◽

Endang Soeryana Hasbullah

Keyword(s):

Asset Allocation ◽

Asset Returns ◽

Higher Moments ◽

Investment Portfolio ◽

Allocation Model ◽

Model Review ◽

The Mean ◽

Mean Variance ◽

Skewness And Kurtosis ◽

Normally Distributed

It has been widely studied how investors will allocate their assets to an investment when the return of assets is normally distributed. In this context usually, the problem of portfolio optimization is analyzed using mean-variance. When asset returns are not normally distributed, the mean-variance analysis may not be appropriate for selecting the optimum portfolio. This paper will examine the consequences of abnormalities in the process of allocating investment portfolio assets. Here will be shown how to adjust the mean-variance standard as a basic framework for asset allocation in cases where asset returns are not normally distributed. We will also discuss the application of the optimum strategies for this problem. Based on the results of literature studies, it can be concluded that the expected utility approximation involves averages, variances, skewness, and kurtosis, and can be extended to even higher moments.

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On estimating the shape and dynamics of phenotypic distributions in ecology and evolution

10.22541/au.162196147.76797968/v1 ◽

2021 ◽

Author(s):

Brian Maitner ◽

Aud Halbritter ◽

Richard Telford ◽

Tanya Strydom ◽

Julia Chacon-Labella ◽

...

Keyword(s):

Intraspecific Variation ◽

Evolutionary Biology ◽

R Package ◽

Sampling Unit ◽

Weighted Mean ◽

Central Moments ◽

Community Weighted Mean ◽

The Mean ◽

Mean Variance ◽

Skewness And Kurtosis

Estimating the distribution of phenotypes in populations and communities is central to many questions in ecology and evolutionary biology. These distributions can be characterized by their moments: the mean, variance, skewness, and kurtosis. Typically, these moments are calculated using a community-weighted approach (e.g. community-weighted mean) which ignores intraspecific variation. As an alternative, bootstrapping approaches can incorporate intraspecific variation to improve estimates, and also quantify uncertainty in the estimate. Here, we compare the performance of different approaches for estimating the moments of trait distributions across a variety of sampling scenarios, taxa, and datasets. We introduce the traitstrap R package to facilitate inferences of trait distributions via bootstrapping. Our results suggest that randomly sampling ~9 individuals per sampling unit and species, focusing on covering all species in the community, and analysing the data using nonparametric bootstrapping generally enables reliable inference on trait distributions, including the central moments, of communities.

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The recurrence relations of order statistics moments for power Lomax distribution

10.47302/jsr.2018520105 ◽

2018 ◽

Vol 52 (1) ◽

pp. 75-90

Author(s):

DEVENDRA KUMAR ◽

SANKU DEY ◽

MAZEN NASSAR ◽

PREETI YADAV

Keyword(s):

Bladder Cancer ◽

Order Statistics ◽

Recurrence Relations ◽

Sample Sizes ◽

Double Product ◽

Cancer Data ◽

Lomax Distribution ◽

The Mean ◽

Mean Variance ◽

Skewness And Kurtosis

The power Lomax distribution due to Rady et al. (2016) is an alternative to and provides better fits for bladder cancer data (Lee and Wang, 2003) than the Lomax, exponential Lo- max, Weibull Lomax, extended Poisson Lomax and beta Lomax distributions. Exact explicit expressions as well as recurrence relations for the single and double (product) moments have been derived from the power Lomax distribution. These recurrence relations enable computation of the mean, variance, skewness and kurtosis of all order statistics for all sample sizes in a simple and efficient manner. By using these relation, the mean, variance, skewness and kurtosis of order statistics for sample sizes up to 5 for various values of shape and scale parameters are tabulated. Finally, remission times (in months) of bladder cancer patients have been analyzed to show how the proposed relations work in practice.

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Community assembly along a soil depth gradient: contrasting patterns of plant trait convergence and divergence in a Mediterranean rangeland

Journal of Ecology ◽

10.1111/1365-2745.12003 ◽

2012 ◽

Vol 100 (6) ◽

pp. 1422-1433 ◽

Cited By ~ 190

Author(s):

Maud Bernard-Verdier ◽

Marie-Laure Navas ◽

Mark Vellend ◽

Cyrille Violle ◽

Adeline Fayolle ◽

...

Keyword(s):

Community Assembly ◽

Soil Depth ◽

Depth Gradient ◽

Plant Trait ◽

Trait Convergence ◽

Convergence And Divergence

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Patterns of Understory Community Assembly and Plant Trait-Environment Relationships in Temperate SE European Forests

Diversity ◽

10.3390/d12030091 ◽

2020 ◽

Vol 12 (3) ◽

pp. 91 ◽

Cited By ~ 2

Author(s):

Janez Kermavnar ◽

Lado Kutnar

Keyword(s):

Community Assembly ◽

Environmental Conditions ◽

Spatial Scales ◽

Forest Vegetation ◽

Forest Community ◽

Soil Productivity ◽

Dry Matter Content ◽

Forest Sites ◽

Trait Convergence ◽

The Impact

We analyzed variation in the functional composition and diversity of understory plant communities across different forest vegetation types in Slovenia. The study area comprises 10 representative forest sites covering broad gradients of environmental conditions (altitude, geology, light availability, soil type and reaction, nutrient availability, soil moisture), stand structural features and community attributes. The mean and variation of the trait values were quantified by community-weighted means and functional dispersion for four key plant functional traits: plant height, seed mass, specific leaf area and leaf dry matter content. At each study site, forest vegetation was surveyed at two different spatial scales (4 and 100 m2) in order to infer scale-dependent assembly rules. Patterns of community assembly were tested with a null model approach. We found that both trait means and diversity values responded to conspicuous gradients in environmental conditions and species composition across the studied forests. Our results mainly support the idea of abiotic filtering: more stressful environmental conditions (e.g., high altitude, low soil pH and low nutrient content) were occupied by communities of low functional diversity (trait convergence), which suggests a selective effect for species with traits adapted to such harsh conditions. However, trait convergence was also detected in some more resource-rich forest sites (e.g., low altitude, high soil productivity), most likely due to the presence of competitive understory species with high abundance domination. This could, at least to some extent, indicate the filtering effect of competitive interactions. Overall, we observed weak and inconsistent patterns regarding the impact of spatial scale, suggesting that similar assembly mechanisms are operating at both investigated spatial scales. Our findings contribute to the baseline understanding of the role of both abiotic and biotic constraints in forest community assembly, as evidenced by the non-random patterns in the functional structure of distinct temperate forest understories.

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Are Fund Investors Smart Enough to Know About the Mean-Variance Relation?

SSRN Electronic Journal ◽

10.2139/ssrn.2070221 ◽

2012 ◽

Author(s):

Fabian Irek ◽

Thorsten Lehnert

Keyword(s):

The Mean ◽

Mean Variance

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The Sampling Distribution of the Mean-Variance Efficient Frontier and the Optimal Fisher Information Portfolio

SSRN Electronic Journal ◽

10.2139/ssrn.2495621 ◽

2014 ◽

Author(s):

Majeed Simaan ◽

Yusif Simaan ◽

Yi Tang

Keyword(s):

Fisher Information ◽

Efficient Frontier ◽

Sampling Distribution ◽

The Mean ◽

Mean Variance

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Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience

Cancers ◽

10.3390/cancers13102421 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2421

Author(s):

Roberta Fusco ◽

Vincenza Granata ◽

Mauro Mattace Raso ◽

Paolo Vallone ◽

Alessandro Pasquale De Rosa ◽

...

Keyword(s):

Standard Deviation ◽

Univariate Analysis ◽

Mean Value ◽

Blood Oxygenation ◽

Breast Lesions ◽

Dce Mri ◽

Malignant Lesions ◽

The Mean ◽

And Diffusion ◽

Skewness And Kurtosis

Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including Ktrans, kep, ve, and vp were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R2*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp), and tissue diffusivity (Dt)). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered. Results. R2* and D had a significant negative correlation (−0.57). The mean value, standard deviation, Skewness and Kurtosis values of R2* did not show a statistical significance between benign and malignant lesions (p > 0.05) confirmed by the ‘poor’ diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of D, Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (p-value = 0.02). Significant results for the mean value of Ktrans, mean value, standard deviation value and Skewness of kep, mean value, Skewness and Kurtosis of ve were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of kep and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered. Conclusions. Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R2* and D.

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