Evolutionary demography of iteroparous plants: incorporating non-lethal costs of reproduction into integral projection models

Miller, Tom E. X.; Williams, Jennifer L.; Jongejans, Eelke; Brys, Rein; Jacquemyn, Hans

doi:10.1098/rspb.2012.0326

Cited by 43 publications

(53 citation statements)

References 48 publications

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“…Opuntia imbricata is a cactus found throughout the deserts and arid grasslands of Southwestern North America (Benson 1982). Our data came from a population in eastern Belgium where 914 unique individuals were censused between 2003 and 2015 (see , Miller et al 2012. Finally, Orchis purpurea is an orchid that occurs in forest understories throughout the Mediterranean region of southern Europe (Rose 1948).…”

Section: Focal Species and Demographic Data Setsmentioning

confidence: 99%

The effect of demographic correlations on the stochastic population dynamics of perennial plants

Compagnoni

Bibian

Ochocki

et al. 2016

Ecological Monographs

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Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-toyear variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λ s )? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from −1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λ s (mean effects ranging from −0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that, in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data intensive and technically challenging.

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Section: Focal Species and Demographic Data Setsmentioning

confidence: 99%

The effect of demographic correlations on the stochastic population dynamics of perennial plants

Compagnoni

Bibian

Ochocki

et al. 2016

Ecological Monographs

Self Cite

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“…; Miller et al . ). Seed dormancy (Rees et al . ; Gremer & Venable ). When to produce twins in Soay sheep (Childs et al .…”

Section: Introductionmentioning

confidence: 97%

Evolving integral projection models: evolutionary demography meets eco‐evolutionary dynamics

Rees

Ellner

2016

Methods Ecol Evol

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1. Patterns of survival and reproduction determine fitness, and there is a rich body of theory linking demography with evolution. For example, selection analysis can be used to predict changes in trait values, and by approximating selection, equations describing trait dynamics derived and evolutionary endpoints predicted. Here, we provide an overview of how these methods can be used to understand evolutionary dynamics and selection in a structured population modelled by an integral projection model (IPM). 2. General expressions for selection are given, and we show how these can be approximated using eigenvalue sensitivities. Approximations for the dynamics of both the trait mean and variance are presented and compared to simulation results (IPMs and individual-based models) for monocarpic perennials. 3. We describe how selection can be decomposed into components related to survival and reproduction and into components resulting from changes in demography versus changes in population structure. We show that, in an empirically based IPM, effects of changes in population structure can be a major component of the overall selection pressure. Most studies of selection in natural populations have focused entirely on selection due to changes in demography, so our results may help explain why directional selection is often predicted but no evolutionary response is observed. 4. The endpoints of evolution, what we expect to see in nature, are explored using ideas from adaptive dynamics theory. The key methods are based on evolutionarily stable strategies (ESS) and convergence stability. Efficient methods for finding an ESS are described. We then extend these ideas to function-valued traits, where it is assumed that an entire function can evolve, rather than a few parameters defining it. 5. By combining theory for IPMs with ideas from several different fields, we show how the dynamics of ecologically complex, evolving systems can be understood.

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“…elasticity and LTRE values are not sums of multiple perturbations). The broad reach of this approach can be particularly useful for examining questions regarding the parameter space for evolutionary stable strategies (Childs et al , Miller et al ), as well as for examining potentially more realistic perturbations where multiple transitions values are influenced simultaneously (Wallace et al ). One can also perturb only intercepts in order to consider changes in the overall magnitude of different vital rates (which is conceptually similar to approach 4 above; Salguero‐Gómez et al ).…”

Section: Simulated Dataset and Perturbation Fundamentalsmentioning

confidence: 99%

Perturbation approaches for integral projection models

Griffith

2017

Oikos

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Perturbation analysis of population models is fundamental to elucidating mechanisms of population dynamics and examining scenarios of change. The use of integral projection models (IPMs) has increased in the last decade, and while many of the tools and approaches developed for matrix models remain relevant, the nature of IPMs expands the framework of perturbation analysis, with different approaches often requiring important considerations. This article provides a review of – and practical guide to – different perturbation approaches for IPMs, formalizes methodologies for perturbing IPM size transition probabilities, and highlights areas where researchers should be particularly careful and critical when conducting and interpreting perturbation analysis. I use a simulated dataset to compare five hierarchical perturbation approaches for IPMs found within 63 published studies, and apply a combination of approaches to the example of an invasive perennial plant. Other perturbation approaches for IPMs are also highlighted. Most perturbation analyses for IPMs to date have focused on the response of the asymptotic population growth rate (λ) to changes in elements of the discretized projection kernel and/or the growth– survival and reproduction– recruitment sub‐kernels. Perturbations to vital rate functions and regression predictions underlying these kernels provide mechanistic insight, but are less common and can require important considerations regarding the perturbation of size transitions separate from survival and the nature of the state variable (used to represent size). The second most common approach is more specific to IPMs and examines the influence of vital rate regression parameters, each of which can have broad influence on the population growth rate. Researchers using IPMs have many perturbation options available and should carefully consider which approach or combination of approaches is most applicable and interpretable for their specific questions.

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Evolutionary demography of iteroparous plants: incorporating non-lethal costs of reproduction into integral projection models

Cited by 43 publications

References 48 publications

The effect of demographic correlations on the stochastic population dynamics of perennial plants

The effect of demographic correlations on the stochastic population dynamics of perennial plants

Evolving integral projection models: evolutionary demography meets eco‐evolutionary dynamics

Perturbation approaches for integral projection models

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