scholarly journals Ecology and life history of Meta bourneti (Araneae: Tetragnathidae) from Monte Albo (Sardinia, Italy)

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6049 ◽  
Author(s):  
Enrico Lunghi
Keyword(s):  
Mixed Models ◽  
Detection Probability ◽  
Linear Models ◽  
Linear Mixed Models ◽  
High Humidity ◽  
Model Species ◽  
Significant Relationships ◽  
History Of ◽  
Spatial Use ◽  
Spider Abundance

The orb-weaver spider Meta bourneti Simon 1922 (Araneae: Tetragnathidae) is one of the most common cave predators occurring in the Mediterranean basin. Although the congeneric M. menardi represented the model species in several studies, our knowledge of M. bourneti is only founded on observations performed on a handful of populations. In this study M. bourneti spiders were studied in caves of Monte Albo (Sardinia, Italy) over a year. Generalized Linear Mixed Models were used to analyze spider occupancy inside cave environments, as well as spider abundance. Analyses on M. bourneti occupancy and abundance were also repeated for adults and juveniles separately. Generalized Linear Models, were used to weight species absence based on its detection probability. Linear Mixed Models were used to detect possible divergences in subterranean spatial use between adult and juvenile spiders. Although widespread on the mountain, M. bourneti generally showed low density and low detection probability. Most of the individuals observed were juveniles. The spiders generally occupied cave sectors with high ceilings that were deep enough to show particular microclimatic features. Adults tended to occupy less illuminated areas than juveniles, while the latter were more frequently found in sectors showing high humidity. The abundance of M. bourneti was strongly related to high humidity and the presence of two troglophile species, Hydromantes flavus Wake, Salvador & Alonso-Zarazaga, 2005 (Amphibia: Caudata) and Oxychilus oppressus (Shuttleworth, 1877) (Gastropoda: Panpulmonata). The abundance of juveniles was related to sector temperature and humidity, the presence of H. flavus and O. oppressus and to morphological sector features. However, when only adults were considered, no significant relationships were found. Adult and juvenile spiders did not differ in their spatial distribution inside the caves studied, but a seasonal distribution of the species along cave walls was observed. Microclimate was one of the most important features affecting both the presence and abundance of M. bourneti in subterranean environments. Individuals tended to occupy lower heights during hot seasons.

scholarly journals Evaluation of Linear Models and Linear Mixed Models to Predict the Effects of Antimicrobial Peptides on Broiler Performance

2020 ◽  
Vol 478 ◽  
pp. 012002
Author(s):  
M M Sholikin ◽  
M D Alifian ◽  
A T Wahyudi ◽  
A Jayanegara ◽  
Nahrowi
Keyword(s):  
Antimicrobial Peptides ◽  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Broiler Performance

PERFORMANCE OF MIXED EFFECTS FOR CLUSTERED COUNTING DATA MODELS

2020 ◽  
Vol 3 (2) ◽  
pp. 34-41
Author(s):  
Intesar N. El-Saeiti ◽  
Khalil Mostafa ALsawi
Keyword(s):  
Generalized Linear Models ◽  
Standard Error ◽  
Mixed Models ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Likelihood Estimation ◽  
Real Data ◽  
Estimation Technique ◽  
Counting Data

This article is concerned with hierarchical generalized linear models. It includes generalized linear models and generalized linear mixed models, which are related to linear models. In generalized linear mixed models, the dependent variable and the standard error follow any distribution from the exponential family, e.g. normal, Poisson, binomial, gamma, etc. We studied counting data, and then use the Poisson-gamma model,where the dependentvariable follows the Poisson distribution and the standard error follow the gamma distribution. Several estimation techniques can be used for generalized linear mixed model. In this paperthe hierarchical likelihood estimation technique was used to prove the performance of H-likelihood methodwhen thecounting data were balanced or unbalanced. Real data were used to test the performance of Poisson-gamma H-likelihood estimation method in case of balanced and unbalanced counting data.When real data used in the past research for another problem, it was noticed that the performance of the hierarchical likelihood estimation technique gave a close approximations in the event of balanced and unbalanced counting data, and the output of the technique was approximately equivalent in both instances.

scholarly journals Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

2011 ◽  
Vol 11 ◽  
pp. 42-76 ◽  
Author(s):  
Daniel T. L. Shek ◽  
Cecilia M. S. Ma
Keyword(s):  
Longitudinal Data ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Hierarchical Linear Models ◽  
Data Analyses ◽  
Basic Concepts ◽  
User Friendly ◽  
Over Time

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

scholarly journals New Applications of Statistical Tools in Plant Pathology

2004 ◽  
Vol 94 (9) ◽  
pp. 999-1003 ◽  
Author(s):  
K. A. Garrett ◽  
L. V. Madden ◽  
G. Hughes ◽  
W. F. Pfender
Keyword(s):  
Plant Pathology ◽  
Statistical Theory ◽  
Mixed Models ◽  
Ordinal Data ◽  
Linear Models ◽  
New Technologies ◽  
Linear Mixed Models ◽  
Population Level ◽  
Experimental Unit ◽  
Normally Distributed

The series of papers introduced by this one address a range of statistical applications in plant pathology, including survival analysis, nonparametric analysis of disease associations, multivariate analyses, neural networks, meta-analysis, and Bayesian statistics. Here we present an overview of additional applications of statistics in plant pathology. An analysis of variance based on the assumption of normally distributed responses with equal variances has been a standard approach in biology for decades. Advances in statistical theory and computation now make it convenient to appropriately deal with discrete responses using generalized linear models, with adjustments for overdispersion as needed. New nonparametric approaches are available for analysis of ordinal data such as disease ratings. Many experiments require the use of models with fixed and random effects for data analysis. New or expanded computing packages, such as SAS PROC MIXED, coupled with extensive advances in statistical theory, allow for appropriate analyses of normally distributed data using linear mixed models, and discrete data with generalized linear mixed models. Decision theory offers a framework in plant pathology for contexts such as the decision about whether to apply or withhold a treatment. Model selection can be performed using Akaike's information criterion. Plant pathologists studying pathogens at the population level have traditionally been the main consumers of statistical approaches in plant pathology, but new technologies such as microarrays supply estimates of gene expression for thousands of genes simultaneously and present challenges for statistical analysis. Applications to the study of the landscape of the field and of the genome share the risk of pseudoreplication, the problem of determining the appropriate scale of the experimental unit and of obtaining sufficient replication at that scale.

scholarly journals Fitting phenological curves with Generalized Linear Mixed Models (GLMMs)

2020 ◽  
Author(s):  
Collin Edwards ◽  
Elizabeth E. Crone
Keyword(s):  
Mixed Models ◽  
Ad Hoc ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Data Sets ◽  
Phenological Data ◽  
Standard Linear ◽  
Phenological Shifts ◽  
Phenological Metrics

AbstractUnderstanding organismal phenology has been an emerging interest in ecology, in part because phenological shifts are one of the most conspicuous signs of climate change. While we are seeing increased collection of phenological data and creative use of historical data sets, existing statistical tools to measure phenology are generally either limited (e.g., first day of observation, which has problematic biases) or are challenging to implement (often requiring custom coding, or enough data to fit many parameters). We present a method to fit phenological data with Gaussian curves using linear models, and show how robust phenological metrics can be obtained using standard linear regression tools. We then apply this method to eight years of Baltimore checkerspot data using generalized linear mixed models (GLMMs). This case study illustrates the ability of years with extensive data to inform years with less data and shows that butterfly flight activity is somewhat earlier in warmer years. We believe our new method fills a convenient midpoint between ad hoc measures and custom-coded models.

Reversible jump methods for generalised linear models and generalised linear mixed models

2010 ◽  
Vol 22 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Jonathan J. Forster ◽  
Roger C. Gill ◽  
Antony M. Overstall
Keyword(s):  
Mixed Models ◽  
Linear Models ◽  
Linear Mixed Models ◽  
Reversible Jump ◽  
Generalised Linear Models ◽  
Generalised Linear Mixed Models

scholarly journals Heat, Hills and the High Season: A Model-Based Comparative Analysis of Spatio-Temporal Factors Affecting Shared Bicycle Use in Three Southern European Islands

2021 ◽  
Vol 13 (6) ◽  
pp. 3274
Author(s):  
Suzanne Maas ◽  
Paraskevas Nikolaou ◽  
Maria Attard ◽  
Loukas Dimitriou
Keyword(s):  
Correlation Analysis ◽  
Mixed Models ◽  
Linear Mixed Models ◽  
Positive Association ◽  
Sustainable Mobility ◽  
Weather Factors ◽  
Gran Canaria ◽  
Bivariate Correlation ◽  
Temporal Factors

Bicycle sharing systems (BSSs) have been implemented in cities worldwide in an attempt to promote cycling. Despite exhibiting characteristics considered to be barriers to cycling, such as hot summers, hilliness and car-oriented infrastructure, Southern European island cities and tourist destinations Limassol (Cyprus), Las Palmas de Gran Canaria (Canary Islands, Spain) and the Valletta conurbation (Malta) are all experiencing the implementation of BSSs and policies to promote cycling. In this study, a year of trip data and secondary datasets are used to analyze dock-based BSS usage in the three case-study cities. How land use, socio-economic, network and temporal factors influence BSS use at station locations, both as an origin and as a destination, was examined using bivariate correlation analysis and through the development of linear mixed models for each case study. Bivariate correlations showed significant positive associations with the number of cafes and restaurants, vicinity to the beach or promenade and the percentage of foreign population at the BSS station locations in all cities. A positive relation with cycling infrastructure was evident in Limassol and Las Palmas de Gran Canaria, but not in Malta, as no cycling infrastructure is present in the island’s conurbation, where the BSS is primarily operational. Elevation had a negative association with BSS use in all three cities. In Limassol and Malta, where seasonality in weather patterns is strongest, a negative effect of rainfall and a positive effect of higher temperature were observed. Although there was a positive association between BSS use and the number of visiting tourists in Limassol and Malta, this is predominantly explained through the multi-collinearity with weather factors rather than by intensive use of the BSS by tourists. The linear mixed models showed more fine-grained results and explained differences in BSS use at stations, including differences for station use as an origin and as a destination. The insights from the correlation analysis and linear mixed models can be used to inform policies promoting cycling and BSS use and support sustainable mobility policies in the case-study cities and cities with similar characteristics.

scholarly journals Confidence, prediction, and tolerance in linear mixed models

2019 ◽  
Vol 38 (30) ◽  
pp. 5603-5622 ◽  
Author(s):  
Bernard G. Francq ◽  
Dan Lin ◽  
Walter Hoyer
Keyword(s):  
Mixed Models ◽  
Linear Mixed Models
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