The evolution of generation time in metapopulations of monocarpic perennial plants: some theoretical considerations and the example of the rare thistle Carlina vulgaris

Jong, Tom J. de; Klinkhamer, Peter G. L.; Heiden, J.L.H. de

doi:10.1023/a:1011063625087

Cited by 25 publications

(26 citation statements)

References 28 publications

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“…Overwinter seed predation on the ground may be high; in one study more than 60% of seeds were consumed by small mammals (Greig-Smith and Sagar 1981). Seeds germinate from April to June, and there is little evidence of a persistent seed bank (Eriksson and Eriksson 1997;de Jong et al 2000).…”

Section: The Biology Of Carlina Vulgarismentioning

confidence: 99%

“…In the constant environment model, the flowering strategy should not have an age dependent component as neither growth nor mortality are age- dependent; this is indeed what we observe in the individualbased simulation. In the random environment things are more complicated, and we believe there are at least three possible reasons why flowering might have an age-dependent component: (1) the population may persist within a metapopulation and de Jong et al (2000) have shown that in such a setting there is selection for early flowering, which allows individuals to rapidly exploit new areas after initial colonization; (2) there is substantial temporal variation in mortality, which makes delaying reproduction for long periods of time very risky; age-dependent flowering would reduce the chance that individuals are exposed to a year when mortality is high; this interpretation is supported by recent theoretical work (Benton and Grant 1999); and (3) age-dependence provides an additional way to control flowering, which might allow fine-tuning of the flowering strategy; this is related to point (2) and is supported by subjective field observations (P. Grubb, pers. obs.…”

Section: Age-dependent Floweringmentioning

confidence: 99%

“…Most were designed to show that delayed flowering is adaptive. Others (Kachi and Hirose 1985;de Jong et al 1989de Jong et al , 2000Wesselingh et al 1997;Rees et al 1999Rees et al , 2000 attempted to predict the optimal size and age at flowering, with variable levels of success: Often plants were predicted to flower at sizes considerably smaller or larger than observed in the field. Rees et al (1999) tested the ability of a range of different mathematical models, parameterized using field data, to predict the optimal size and age at flowering for the monocarpic thistle Onopordum illyricum.…”

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Evolution in the Real World: Stochastic Variation and the Determinants of Fitness in Carlina Vulgaris

2002

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Empirical studies of life histories often ignore stochastic variation, despite theoretical demonstrations of its potential impact on life-history evolution. Here we use a novel approach to explore the effects of stochastic variation on life-history evolution and estimate the selection pressures operating on the monocarpic perennial Carlina vulgaris, in which flowering may be delayed by up to eight years. The approach is novel in that we use modern theoretical techniques to estimate selection pressures and the fitness landscape from a fully parameterised individual-based model. These approaches take into account temporal variation in demographic rates and density dependence. Analysis of 16 years' data revealed significant temporal variation in growth, mortality, and recruitment in our study population. Flowering was strongly size dependent and, unusually for such a species, also age dependent. Individual-based models of the flowering strategy, parameterized using field data, consistently underestimated the size at flowering, when temporal variation in demographic rates was ignored. In contrast, models that incorporated temporal variation in growth, mortality, and recruitment predicted sizes at flowering not significantly different from those observed in the field. Temporal variation in mortality, which had the largest effect on the flowering strategy, selected for increased size at flowering. An analytical approximation is presented to explain this result, extending the "1-year look-ahead criterion" presented in Rees et al. (2000). A fitness landscape generated by following the fate of rare mutant invaders with a broad range of alternative flowering strategies demonstrated that the observed parameters were adaptive. However, the fitness landscape reveals that approximately equal fitness is achieved by a broad range of strategies, providing a mechanism for the maintenance of genetic variation. To understand how the different parameters that defined our models determine the fitness of rare mutants, we numerically estimated the elasticities and sensitivities of mutant fitness. This demonstrated strong selection on a number of the parameters. Elasticities and sensitivities estimated in constant and random environments were significantly positively correlated, and both were negatively related to the standard error of the parameter. This last result is surprising and, we argue, reflects the genetic and phenotypic responses to selection.

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Section: The Biology Of Carlina Vulgarismentioning

confidence: 99%

Section: Age-dependent Floweringmentioning

confidence: 99%

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confidence: 99%

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Evolution in the Real World: Stochastic Variation and the Determinants of Fitness in Carlina Vulgaris

2002

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“…Several studies have attempted to predict the optimal size of flowering in semelparous plants, using a variety of approaches. These approaches include analytical approximations, dynamic state variable models or computationally expensive individual-based models (Kachi & Hirose 1985;de Jong et al 1989de Jong et al , 2000Wesselingh et al 1997;Rees et al 1999Rees et al , 2000Rose et al 2002). Rees et al (1999) compared these different approaches by developing a series of parameterized demographic models of the monocarpic thistle Onopordum illyricum and found that an accurate prediction of flowering size was possible only when using an individual-based model.…”

Section: Introductionmentioning

confidence: 99%

Evolution of size–dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model

Childs

Rees

Rose

et al. 2004

Proc. R. Soc. Lond. B

146

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Understanding why individuals delay reproduction is a classic problem in evolutionary biology. In plants, the study of reproductive delays is complicated because growth and survival can be size and age dependent, individuals of the same size can grow by different amounts and there is temporal variation in the environment. We extend the recently developed integral projection approach to include size-and age-dependent demography and temporal variation. The technique is then applied to a long-term individually structured dataset for Carlina vulgaris, a monocarpic thistle. The parameterized model has excellent descriptive properties in terms of both the population size and the distributions of sizes within each age class. In Carlina, the probability of flowering depends on both plant size and age. We use the parameterized model to predict this relationship, using the evolutionarily stable strategy approach. Considering each year separately, we show that both the direction and the magnitude of selection on the flowering strategy vary from year to year. Provided the flowering strategy is constrained, so it cannot be a step function, the model accurately predicts the average size at flowering. Elasticity analysis is used to partition the size-and agespecific contributions to the stochastic growth rate, s . We use s to construct fitness landscapes and show how different forms of stochasticity influence its topography. We prove the existence of a unique stochastic growth rate, s , which is independent of the initial population vector, and show that Tuljapurkar's perturbation analysis for log( s ) can be used to calculate elasticities.

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“…Most were designed to show that delayed owering is adaptive. Others attempted to predict the optimal size and age at owering, with variable levels of success (Kachi & Hirose 1985;de Jong et al 1989de Jong et al , 2000Wesselingh et al 1997;Rees et al 1999Rees et al , 2000. The calculation of the evolutionarily stable strategy (ESS) or optimal strategy is complicated because there is substantial variation between individual growth, which means that simple optimization approaches, such as the 1 year look-ahead approach introduced by Rees et al (2000), only yield approximate solutions.…”

Section: Introductionmentioning

confidence: 99%

Evolution of flowering strategies inOenothera glazioviana: an integral projection model approach

Rees

Rose

2002

Proc. R. Soc. Lond. B

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The timing of reproduction is a key determinant of tness. Here, we develop parameterized integral projection models of size-related owering for the monocarpic perennial Oenothera glazioviana and use these to predict the evolutionarily stable strategy (ESS) for owering. For the most part there is excellent agreement between the model predictions and the results of quantitative eld studies. However, the model predicts a much steeper relationship between plant size and the probability of owering than observed in the eld, indicating selection for a 'threshold size' owering function. Elasticity and sensitivity analysis of population growth rate l and net reproductive rate R 0 are used to identify the critical traits that determine tness and control the ESS for owering. Using the tted model we calculate the tness landscape for invading genotypes and show that this is characterized by a ridge of approximately equal tness. The implications of these results for the maintenance of genetic variation are discussed.

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The evolution of generation time in metapopulations of monocarpic perennial plants: some theoretical considerations and the example of the rare thistle Carlina vulgaris

Cited by 25 publications

References 28 publications

Evolution in the Real World: Stochastic Variation and the Determinants of Fitness in Carlina Vulgaris

Evolution in the Real World: Stochastic Variation and the Determinants of Fitness in Carlina Vulgaris

Evolution of size–dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model

Evolution of flowering strategies inOenothera glazioviana: an integral projection model approach

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