Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide

Abstract

The identification of patterns in life-history strategies across the tree of life is essential to our prediction of population persistence, extinction, and diversification. Plants exhibit a wide range of patterns of longevity, growth, and reproduction, but the general determinants of this enormous variation in life history are poorly understood. We use demographic data from 418 plant species in the wild, from annual herbs to supercentennial trees, to examine how growth form, habitat, and phylogenetic relationships structure plant life histories and to develop a framework to predict population performance. We show that 55% of the variation in plant life-history strategies is adequately characterized using two independent axes: the fast–slow continuum, including fast-growing, short-lived plant species at one end and slow-growing, long-lived species at the other, and a reproductive strategy axis, with highly reproductive, iteroparous species at one extreme and poorly reproductive, semelparous plants with frequent shrinkage at the other. Our findings remain consistent across major habitats and are minimally affected by plant growth form and phylogenetic ancestry, suggesting that the relative independence of the fast–slow and reproduction strategy axes is general in the plant kingdom. Our findings have similarities with how life-history strategies are structured in mammals, birds, and reptiles. The position of plant species populations in the 2D space produced by both axes predicts their rate of recovery from disturbances and population growth rate. This life-history framework may complement trait-based frameworks on leaf and wood economics; together these frameworks may allow prediction of responses of plants to anthropogenic disturbances and changing environments.

Functional traits explain the Hutchinsonian niches of plant species

“…In contrast, responses to disturbance, such as fire, depend on architectural traits and resprouting ability (Clarke et al, 2013), as well as seed traits (Lamont & Groom, 2013). Traits can thus be used to position species along TA B L E 1 Functional traits measured for the 26 Proteaceae study species, with inter-and intraspecific trait variation [expressed as proportion of variance (%); see Figure 2b], major expected species-mean trait-environment associations and direction of the effect (+/−) from major literature sources and whether the pattern was found in our study (Figure 5a) Díaz et al, 2016;Reich, 2014;Salguero-Gómez et al, 2016). Traits can thus be used to position species along TA B L E 1 Functional traits measured for the 26 Proteaceae study species, with inter-and intraspecific trait variation [expressed as proportion of variance (%); see Figure 2b], major expected species-mean trait-environment associations and direction of the effect (+/−) from major literature sources and whether the pattern was found in our study (Figure 5a) Díaz et al, 2016;Reich, 2014;Salguero-Gómez et al, 2016).…”

Pace-of-life syndromes: a framework for the adaptive integration of behaviour, physiology and life history

“…Under limiting resources, the trade-off between resource allocation to current versus future reproduction or survival can generate patterns of covariation between life-history traits resulting in a slow-fast life-history continuum (Stearns 1983), paralleling r-versus K-selection ideas (MacArthur and Wilson 1967;Pianka 1970). As supported by comparative analyses in mammals (Promislow and Harvey 1990;Oli 2004;Bielby et al 2007), birds (Saether 1988), reptiles (Clobert et al 1988;Bauwens and DiazUriarte 1997), fish (Winemiller and Rose 1992), insects (Johansson 2000) and plants (Franco and Silvertown 1996;Salguero-Gómez et al 2016), species at the fast end of this continuum show early reproductive maturity and short lives, whereas those at the slow end take more time to reach reproductive maturity and live longer.…”

Implications of clonality for ageing research

“…After careful data checking and species selection (below), I calculate for each species a set of key life history traits that relate to investments on population turn over, longevity, reproduction and changes in size for each examined species (Salguero-Gómez et al 2016). The underlying data that I use here are in the form of stage-or age-based matrix population models (MPMs), obtained from the COMPADRE Plant Matrix Database (Salguero-Gómez et al 2015), which describe the discrete-time dynamics of the population (Caswell 2001;Evol Ecol (2018) 32:9-28 11 Lefkovitch 1965;Leslie 1945), and for which obtaining age-based demographic properties (e.g.…”

“…The position of species on the aforementioned principal component axes is informative of the extent to which they are constrained by trade-offs. For instance, species with high PCA scores on axes of variation of longevity and reproduction are less constrained than those with positive scores on one axis but not the other (Salguero-Gómez et al 2016;Salguero-Gómez 2017). This approach, thus, allows to directly link trade-offs to the likelihood that a given species more likely to experience-or escape from-senescence, in the context of whether it is able to reproduce only clonally, only sexually, or using both modes of reproduction.…”

“…Consequently, the ''start of life'' in these species was considered as the time when individuals becomes actively established in the population. This is a common approach in comparative analysis using MPMs (Burns et al 2010;Salguero-Gómez et al 2016). The second potential issue arises because MPMs are typically parameterised with a stasis loop in the oldest/largest/most-developed stage (e.g.…”