Threshold Size for Flowering in Different Habitats: Effects of Size-Dependent Growth and Survival

Abstract

For organisms with indeterminate growth, life history theory predicts that in environments where organisms experience high survival rates or gain fecundity with age or size, natural selection favors delayed maturity. In semelparous perennial plants the onset of reproduction is regulated by a threshold size for flowering. We tested this prediction by comparing sand dune populations of the facultative biennial herb Cynoglossum officinale. We collected data on flowering probability, survival, and growth rate in relation to plant size in two habitat types, open areas and poplar thickets, in Meijendel, The Netherlands, and in Holkham, England. Survival of established rosettes was highest in Holkham and lowest in open areas in Meijendel. Relative growth rates in Holkham were about three times as high as those in Meijendel. These findings agreed with the differences in threshold sizes found among the sites: the Holkham field population harbored higher threshold sizes than the Meijendel sites. We used the field data to compare optimal threshold sizes for flowering predicted by three existing models. All three models gave the same rank order for threshold sizes as found in the field: Meijendel open < Meijendel poplar thicket < Holkham. One model, which maximized population growth rate λ, predicted optimal threshold sizes that agreed very well with threshold sizes found in the field. The predictions of the two other models, both maximizing R0, were consistently lower than the threshold sizes observed in Meijendel, while their predictions for Holkham were too high.

Age and size at maturity: sex, environmental variability and developmental thresholds

“…However, male and female reaction norms may also vary in shape if males and females have different developmental thresholds. Although we know that developmental thresholds exist in numerous organisms, including insects (Moed et al 1999), crustaceans (Ebert 1994), amphibians (Morey & Reznick 2000) and flowers (Wesselingh et al 1997), and may vary between species (Morey & Reznick 2000) or between different genetic lines of the same species (Wesselingh et al 1997), sex differences in the position of a developmental threshold have, to our knowledge, never previously been tested for.…”

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

“…The development of demographic tools, especially continuously size-structured integral projection models (IPMs) [6,7], has allowed for the estimation of this optimum and quantitative comparisons of observed and optimal reproductive strategies in semelparous plants. We have now accumulated a wealth of such studies [8][9][10][11][12][13][14][15][16][17][18][19]. The popularity of semelparous plants for the study of life-history evolution and reproductive delay is due in part to the ease and elegance with which lethal costs of reproduction can be incorporated into demographic models: the probability of survival is simply conditioned on the probability of not flowering.…”

“…Our goals were to (i) quantify costs of reproduction using long-term demographic data, (ii) incorporate reproductive costs into a demographic model, and (iii) use the parametrized model to predict optimal (evolutionarily stable, ES) reproductive strategies, including size at reproduction and size-dependent reproductive effort. Consequences of reproductive costs depend greatly on demographic context (background rates of growth and survival) and may differ between environments, if demographic rates also differ between environments [10,17,18,35]. We therefore contrasted selection on life histories in two habitat types that are known to be associated with different demographic rates: open (light) and forest understorey (shade) habitats.…”

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

“…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.…”