Plasticity and the costs of incorrect responses

Hoffmann, Ary A.; Bridle, Jon R.

doi:10.1016/j.tree.2022.11.012

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Identifying the mechanisms that underpin the ability of natural populations to buffer However, not all environmental components affect populations the same way (Hoffmann & Bridle 2022;Vinton et al 2022Vinton et al , 2023. The shape of demographic rates across a life history varies widely across the tree of life (Jones et al 2014;Salguero-Gómez et al 2017;Paniw et al 2018;Healy et al 2019;Varas-Enriquez et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Gascoigne

Kajin

Tuljapurkar

et al. 2023

Preprint

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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.

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Section: Discussionmentioning

confidence: 99%

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Gascoigne

Kajin

Tuljapurkar

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Gascoigne,

Kajin,

Tuljapurkar

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

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“…4a-d). In addition, we recommend modelling approaches of stochastic environments built on underlying spectra (e.g., 1/f noise: Halley, 1996;Halley & Inchausti, 2004) to test the degree to which life histories evolve plastic or canalized strategies based on how environments contribute to the evolution of phenotypic plasticity (Dupont et al, 2023;Hoffmann & Bridle, 2022;Vinton et al, 2022Vinton et al, , 2023. (Morris et al, 2008;Paniw et al, 2018)) and disturbance regimes (c, (Capdevila et al, 2022;Compagnoni et al, 2021)) across slow and fast life histories.…”

Section: The Issue Of Time In Studying Life Historiesmentioning

confidence: 99%

“…The differences only arise due to differences in generation time. The implications of this mismatch in generation time is that species A may evolve plasticity mechanisms that allow it to track its environment, more so than species B and C, due to generation time (also see Dupont et al, 2023;Hoffmann & Bridle, 2022;Vinton et al, 2023).…”

Section: The Issue Of Time In Studying Life Historiesmentioning

confidence: 99%

Current perspectives and future directions in animal life history evolution

Gascoigne,

Rosen,

Sanghvi

et al. 2023

Preprint

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1. A life history strategy, the collection of actions, timings and characteristics individuals employ to optimize fitness, represents the evolutionary answer to a species' ecological problems. From the fatally reproductive salmon to the seemingly immortal jellyfish Turritopsis dohrnii, different species have found vastly different answers to their ecological problems, generating the vast suite of life histories observed across the animal kingdom. To explain this variation, life history theorists have generated and tested specific hypotheses to describe this variance and define what drives it. 2. Since Stearns (1992) and Roff (2002), animal life history evolution has pushed new frontiers. Specifically, insights from theoretical modelling, experiments, fieldwork and comparative studies have elucidated: how to describe life histories, what drives variance in life histories and what are the mechanisms that underlie life history traits. However, despite this progress, gaps in knowledge still remain.3. In turn, here we review current perspectives, developed over the past 20 years, that support much of life history research today. These perspectives include: (1) the two-axes framework to describe life histories across taxa, (2) three different types of variance that impact life history evolution (i.e., variance within time-steps, across time-steps and variance in life history outcomes) and (3) the utility of integrating ultimate and proximate modes of research to understand life history evolution. Subsequently, we outline future directions that represent new frontiers in animal life history evolution. These future directions are targeted at specific gaps in knowledge that offer timely insights for the broader ecology and evolutionary biology community: (1) where does selection act in a 3 life history, (2) a new representation of life histories in variable environments and (3) dealing with time in life history evolution. 4. In summary, this review provides a holistic perspective (from molecules to selection gradients) on how life histories are studied and why life history research requires interdisciplinarity. The further discussion of current perspectives and future directions provides a cross-section of animal life history research today: where we are, how we got here and where we are likely heading.

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Plasticity and the costs of incorrect responses

Cited by 4 publications

References 12 publications

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Structured demographic buffering: A framework to explore the environment drivers and demographic mechanisms underlying demographic buffering

Current perspectives and future directions in animal life history evolution

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