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\begin{document}

\title{Structured demographic buffering: A framework to explore the environment
drivers and demographic mechanisms underlying demographic buffering}



\author[1]{Samuel Gascoigne}%
\author[1]{Maja Kajin}%
\author[2]{Shripad Tuljapurkar}%
\author[3]{Gabriel Santos}%
\author[4]{Aldo Compagnoni}%
\author[5]{Ulrich Steiner}%
\author[1]{Anna Vinton}%
\author[2]{Harman Jaggi}%
\author[1]{Irem Sepil}%
\author[1]{Roberto Salguero-Gomez}%
\affil[1]{University of Oxford}%
\affil[2]{Stanford University}%
\affil[3]{National Institute of the Atlantic Forest (INMA)}%
\affil[4]{Martin-Luther-Universitat Halle-Wittenberg}%
\affil[5]{Freie Universität Berlin}%


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  \date{June 14, 2024}


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\begin{abstract}
Environmental stochasticity is a key determinant of population
viability. Decades of work exploring how environmental stochasticity
influences population dynamics have highlighted the ability of some
natural populations to limit the negative effects of environmental
stochasticity, one of these strategies being demographic buffering.
Whilst various methods exist to quantify demographic buffering, we still
do not know which environment factors and demographic characteristics
are most responsible for the demographic buffering observed in natural
populations. Here, we introduce a framework to quantify the relative
effects of three key drivers of demographic buffering: environment
components (e.g., temporal autocorrelation and variance), population
structure, and demographic rates (e.g., progression and fertility).
Using Integral Projection Models, we explore how these drivers impact
the demographic buffering abilities of three plant species with
different life histories and demonstrate how our approach successfully
characterises a population's capacity to demographically buffer against
environmental stochasticity in a changing world.%
\end{abstract}%



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