Larger workers outperform smaller workers across resource environments: An evaluation of demographic data using functional linear models

1. Behavior and organization of social groups is thought to be vital to the functioning of societies, yet the contributions of various roles within social groups towards population growth and dynamics have been difficult to quantify. A common approach to quantifying these role-based contributions is evaluating the number of individuals conducting certain roles, which ignores how behavior might scale up to effects at the population-level. Manipulative experiments are another common approach to determine population-level effects, but they often ignore potential feedbacks associated with these various roles. 2. Here, we evaluate the effects of worker size distribution in bumblebee colonies on worker production in 24 observational colonies across three environments, using functional linear models. Functional linear models are an underused correlative technique that has been used to assess lag effects of environmental drivers on plant performance. We demonstrate potential applications of this technique for exploring high-dimensional ecological systems, such as the contributions of individuals with different traits to colony dynamics. 3. We found that more larger workers had mostly positive effects and more smaller workers had negative effects on worker production. Most of these effects were only detected under low or fluctuating resource environments suggesting that the advantage of colonies with larger-bodied workers becomes more apparent under stressful conditions. 4. We also demonstrate the wider ecological application of functional linear models. We highlight the advantages and limitations when considering these models, and how they are a valuable complement to many of these performance-based and manipulative experiments.

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Estimating Variance Components in Functional Linear Models With Applications to Genetic Heritability

Journal of the American Statistical Association ◽

10.1080/01621459.2015.1016224 ◽

2016 ◽

Vol 111 (513) ◽

pp. 407-422 ◽

Cited By ~ 10

Author(s):

Matthew Reimherr ◽

Dan Nicolae

Keyword(s):

Variance Components ◽

Linear Models ◽

Functional Linear Models ◽

Genetic Heritability

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The Application of Log-Linear Model to Selected Poison Patients

ASM Science Journal ◽

10.32802/asmscj.2020.sm26(1.21) ◽

2020 ◽

pp. 1-7

Author(s):

Fatin N.S.A. ◽

Norlida M.N. ◽

Siti Z.M.J.

Keyword(s):

Linear Model ◽

Linear Models ◽

Demographic Data ◽

Contingency Tables ◽

Categorical Variables ◽

State Variables ◽

Exposure Route ◽

Higher Dimensional ◽

Route Of Exposure ◽

Log Linear

Log-linear model is a technique used to analyze the cross-classification categorical data or the contingency table. It is used to obtain the parsimony models that describe the interaction between the categorical variables in contingency tables. Log-linear models are commonly used in evaluating higher dimensional contingency tables that involves more than two categorical variables. This study focuses on analyzing data of poisoned patients from 2012 to 2014 using log-linear model. There are two model analyzed; model for demographic data of patients and model of poisoning information. For the first model, the variables involved are gender, age, race and state. Variables for the second model are circumstance of exposure, type of exposure, location of exposure, route of exposure and types of poison. Both log-linear models are developed to investigate the association between variables in the model. As a result of this study, the best model for demographic data and poisoning information are the model with three-ways interaction. For the best model of demographic data, there is an association between gender, age and race, race, gender and state as well as age, race and state. Meanwhile, the best model for poisoning information reveals that there is relationship between circumstance of exposure, route of exposure and type of poison, location of exposure, route of exposure and type of poison, circumstance of exposure, type of exposure and route of exposure, circumstance of exposure, location of exposure and route of exposure, circumstance of exposure, type of exposure and type of poison and also type of exposure, location of exposure and type of poison. Keywords: log-linear; demographic; gender; age; race; state; circumstance of exposure; type of exposure; location of exposure; route of exposure; types of poison

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