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\begin{document}

\title{Larger workers outperform smaller workers across resource environments:
an evaluation of demographic data using functional linear models}



\author[1]{Natalie Kerr}%
\author[2]{Rosemary Malfi}%
\author[3]{Neal Williams}%
\author[4]{Elizabeth Crone}%
\affil[1]{Duke University}%
\affil[2]{University of Massachusetts Amherst}%
\affil[3]{University of California, Davis}%
\affil[4]{Tufts University}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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