Three keys to the radiation of angiosperms into freezing environments

Abstract

Early flowering plants are thought to have been woody species restricted to warm habitats. This lineage has since radiated into almost every climate, with manifold growth forms. As angiosperms spread and climate changed, they evolved mechanisms to cope with episodic freezing. To explore the evolution of traits underpinning the ability to persist in freezing conditions, we assembled a large species-level database of growth habit (woody or herbaceous; 49,064 species), as well as leaf phenology (evergreen or deciduous), diameter of hydraulic conduits (that is, xylem vessels and tracheids) and climate occupancies (exposure to freezing). To model the evolution of species' traits and climate occupancies, we combined these data with an unparalleled dated molecular phylogeny (32,223 species) for land plants. Here we show that woody clades successfully moved into freezing-prone environments by either possessing transport networks of small safe conduits and/or shutting down hydraulic function by dropping leaves during freezing. Herbaceous species largely avoided freezing periods by senescing cheaply constructed aboveground tissue. Growth habit has long been considered labile, but we find that growth habit was less labile than climate occupancy. Additionally, freezing environments were largely filled by lineages that had already become herbs or, when remaining woody, already had small conduits (that is, the trait evolved before the climate occupancy). By contrast, most deciduous woody lineages had an evolutionary shift to seasonally shedding their leaves only after exposure to freezing (that is, the climate occupancy evolved before the trait). For angiosperms to inhabit novel cold environments they had to gain new structural and functional trait solutions; our results suggest that many of these solutions were probably acquired before their foray into the cold.

Causes of ecological gradients in leaf margin entirety: Evaluating the roles of biomechanics, hydraulics, vein geometry, and bud packing

“…Here we conduct the fi rst phylogenetically structured tests of the correlations among leaf margin entirety (mostly due to the presence/absence of marginal teeth), leaf thickness, and latitude on a global basis, superimposing data for all traits gleaned from Royer et al (2009) on a broad-scale angiosperm phylogeny provided by Zanne et al (2014) and a more inclusive phylogeny to increase the match of species using S.PhyloMaker ( Qian and Jin, 2015 ). Branch lengths are those given by the authors and based on time.…”

SUPERSMART: ecology and evolution in the era of big data

“…Construction toolkit (PHLAWD; http://phlawd.net/), which efficiently assembles sequence data 634 for a pre-specified list of target species (Zanne et al 2014). SUPERSMART has similar goals 635 but differs from PHLAWD by dealing more extensively with name resolution, homology 636 assessment, optimal taxonomic vs. genetic coverage, and time-calibration through a native 637 curated fossil table and plugged-in tools, among other differences in scope and functionality.…”

“…Nonetheless, if we want to 86 start assembling the big picture to reveal how ecological and evolutionary processes generate and 87 maintain biodiversity (Zanne et al 2014), we need approaches that integrate ecological data and 88 evolutionary history in a user-friendly framework to study biodiversity at various temporal, 89 spatial and taxonomic scales (Chave 2013). 90…”

Seed size and its rate of evolution correlate with species diversification across angiosperms

“…We used the most comprehensive phylogenetic tree for land plants [17,43] that comprises 31,389 species. Taxonomic information for our phylogenetic tree was run through Taxonstand …”

“…Introduction phylogenetic timetree available [17] with an unparalleled dataset of seed mass measurements from over 30,000 angiosperm species [18]. We estimated rates of speciation, extinction, and seed size evolution across the phylogeny using Bayesian, method-of-moments, and maximum likelihood analyses-each with different assumptions regarding rate variation through time.…”