scholarly journals Hake catchability by the French trawler fleet in the Bay of Biscay: estimating technical and biological components

2010 ◽  
Vol 68 (1) ◽  
pp. 107-118 ◽  
Author(s):  
Stéphanie Mahévas ◽  
Verena M. Trenkel ◽  
Mathieu Doray ◽  
Arnaud Peyronnet
Keyword(s):  
Mixed Models ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Anthropogenic Factors ◽  
Trawl Survey ◽  
Bay Of Biscay ◽  
Biological Factors ◽  
Merluccius Merluccius ◽  
Relative Contribution ◽  
The Impact

Abstract Mahévas, S., Trenkel, V. M., Doray, M., and Peyronnet, A. 2011. Hake catchability by the French trawler fleet in the Bay of Biscay: estimating technical and biological components. – ICES Journal of Marine Science, 68: 107–118. Several factors affect trawl catchability: (i) gear and vessel technical characteristics, (ii) anthropogenic factors, and (iii) biological factors. The objectives of this study were to assess the relative contribution of each factor to variations in hake (Merluccius merluccius) landings by the French trawler fleet operating on the shelf of the northern Bay of Biscay (ICES Subdivision VIIIa). Using generalized linear models, the impact of technical and anthropogenic factors was evaluated using landings per unit effort (lpue) obtained from logbooks. Variations in hake lpue were explained primarily by anthropogenic factors. For studying the biological components of catchability, the results of a scientific trawl survey in July 2006 involving three similar trawlers of the French trawler fleet were used. Daytime accessibility to large hake was lower than at night, and about zero for small hake (<19 cm). Estimates of spatial variation made using generalized linear mixed models showed a patchy fine-scale spatial distribution, but a random larger-scale distribution of hake over the area surveyed.

scholarly journals Spatial interactions between saithe (Pollachius virens) and hake (Merluccius merluccius) in the North Sea

2014 ◽  
Vol 71 (6) ◽  
pp. 1342-1355 ◽  
Author(s):  
Xochitl Cormon ◽  
Christophe Loots ◽  
Sandrine Vaz ◽  
Youen Vermard ◽  
Paul Marchal
Keyword(s):  
North Sea ◽  
Linear Models ◽  
Spatial Dynamics ◽  
Distribution Patterns ◽  
Trawl Survey ◽  
Spatial Interactions ◽  
Merluccius Merluccius ◽  
The North ◽  
Sediment Types ◽  
The North Sea

Spatial interactions between saithe (Pollachius virens) and hake (Merluccius merluccius) were investigated in the North Sea. Saithe is a well-established species in the North Sea, while occurrence of the less common hake has recently increased in the area. Spatial dynamics of these two species and their potential spatial interactions were explored using binomial generalized linear models (GLM) applied to the International Bottom Trawl Survey (IBTS) data from 1991 to 2012. Models included different types of variables: (i) abiotic variables including sediment types, temperature, and bathymetry; (ii) biotic variables including potential competitors and potential preys presence; and (iii) spatial variables. The models were reduced and used to predict and map probable habitats of saithe, hake but also, for the first time in the North Sea, the distribution of the spatial overlap between these two species. Changes in distribution patterns of these two species and of their overlap were also investigated by comparing species’ presence and overlap probabilities predicted over an early (1991–1996) and a late period (2007–2012). The results show an increase in the probability over time of the overlap between saithe and hake along with an expansion towards the southwest and Scottish waters. These shifts follow trends observed in temperature data and might be indirectly induced by climate changes. Saithe, hake, and their overlap are positively influenced by potential preys and/or competitors, which confirms spatial co-occurrence of the species concerned and leads to the questions of predator–prey relationships and competition. Finally, the present study provides robust predictions concerning the spatial distribution of saithe, hake, and of their overlap in the North Sea, which may be of interest for fishery managers.

Testing the differences of LT50, LD50, or ED50

2018 ◽  
Vol 48 (6) ◽  
pp. 729-734 ◽  
Author(s):  
Juha Lappi ◽  
Jaana Luoranen
Keyword(s):  
Approximate Method ◽  
Generalized Linear Models ◽  
Mixed Models ◽  
Logistic Model ◽  
Linear Models ◽  
Generalized Linear Mixed Models ◽  
Wald Test ◽  
Delta Method ◽  
Treatment Variable ◽  
The Relationship

An approximate method is derived for testing the differences of LT50, LD50, or ED50, which indicate the temperature or dose needed to kill or damage half of the plants, respectively. It is assumed that a logistic model is used to describe the relationship between probability and a treatment variable in the framework of generalized linear mixed models or generalized linear models. The method is based on the delta method and the Wald test. In the forest sciences, this method can be used when dose, temperature, or time responses are compared in different treatments, cultivars, or origins.

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 Association between COVID-19 Vaccine Side Effects and Body Mass Index in Spain

Vaccines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1321
Author(s):  
Isabel Iguacel ◽  
Aurelio Luna Maldonado ◽  
Aurelio Luna Ruiz-Cabello ◽  
Marta Casaus ◽  
Luis Alberto Moreno ◽  
...  
Keyword(s):  
Side Effects ◽  
Sex Education ◽  
Mixed Models ◽  
Weight Status ◽  
Online Survey ◽  
Adverse Reactions ◽  
Generalized Linear Mixed Models ◽  
Post Vaccination ◽  
Overweight Status ◽  
The Impact

COVID-19 vaccines have shown high efficacy, with most side effects being mild–moderate and more frequently reported by females and people at younger ages. Since no studies have assessed the impact that weight status could have on the reported adverse reactions, we aim to study the association between weight status and reported side effects. We included data on 2136 adults from an online survey conducted from 6 May to 9 June 2021. The questionnaire was filled in by participants over Google forms. Generalized Linear Mixed Models were used. A higher risk of presenting fever ≥38°, vomiting, diarrhea and chills was found in those with a non-overweight status compared to those overweight after adjusting for age, sex, education, medication to prevent/relieve post-vaccination effects and vaccine administered. When adjusting, most of the significant effects, in the association between side effects of the COVID-19 vaccine and weight status, did not remain significant. In conclusion, a non-overweight status was associated with a higher risk of presenting fever ≥38°, vomiting, diarrhea and chills compared to those overweight. Nevertheless, most of the reported side effects to COVID-19 vaccine were not associated with a higher risk of presenting more adverse effects, and individual differences were determined by sex and age.

scholarly journals Social-norms interventions for light and nondrinking students

2011 ◽  
Vol 14 (5) ◽  
pp. 651-669 ◽  
Author(s):  
Clayton Neighbors ◽  
Megan Jensen ◽  
Judy Tidwell ◽  
Theresa Walter ◽  
Nicole Fossos ◽  
...  
Keyword(s):  
College Students ◽  
Social Norms ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Heavy Drinking ◽  
Attention Control ◽  
Alcohol Prevention ◽  
Control Data ◽  
Social Norms Marketing ◽  
The Impact

Social-norms approaches to alcohol prevention are based on consistent findings that most students overestimate the prevalence of drinking among their peers. Most interventions have been developed for heavy-drinking students, and the applicability of social-norms approaches among abstaining or light-drinking students has yet to be evaluated. The present research aimed to evaluate the impact of two types of online social-norms interventions developed for abstaining or light-drinking students. Identification with other students was evaluated as a moderator. Participants included 423 freshmen and sophomore college students who reported never or rarely drinking at screening. Students were randomly assigned to one of three conditions: (a) personalized-norms feedback, (b) social-norms marketing ads, or (c) attention control. Data were analyzed using generalized linear mixed models. Results provided some support for both interventions but were stronger for social-norms marketing ads, particularly among participants who identified more closely with other students.

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.

scholarly journals A spatial analysis of the spread of mumps: the importance of college students and their spring-break-associated travel

2009 ◽  
Vol 138 (3) ◽  
pp. 434-441 ◽  
Author(s):  
P. M. POLGREEN ◽  
L. C. BOHNETT ◽  
M. YANG ◽  
M. A. PENTELLA ◽  
J. E. CAVANAUGH
Keyword(s):  
Risk Factors ◽  
College Students ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Age Groups ◽  
County Level ◽  
Multivariable Model ◽  
Spring Break ◽  
Final Multivariable Model ◽  
The Impact

SUMMARYTo characterize the association between county-level risk factors and the incidence of mumps in the 2006 Iowa outbreak, we used generalized linear mixed models with the number of mumps cases per county as the dependent variable. To assess the impact of spring-break travel, we tested for differences in the proportions of mumps cases in three different age groups. In the final multivariable model, the proportion of Iowa's college students per county was positively associated (P<0·0001) with mumps cases, but the number of colleges was negatively associated with cases (P=0·0002). Thus, if the college students in a county were spread among more campuses, this was associated with fewer mumps cases. Finally, we found the proportion of mumps cases in both older and younger persons increased after 1 April (P=0·0029), suggesting that spring-break college travel was associated with the spread of mumps to other age groups.

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