Stefan A. Little

Paleotemperature Proxies from Leaf Fossils Reinterpreted in Light of Evolutionary History

Present-day correlations between leaf physiognomic traits (shape and size) and climate are widely used to estimate paleoclimate using fossil floras. For example, leaf-margin analysis estimates paleotemperature using the modern relation of mean annual temperature (MAT) and the site-proportion of untoothed-leaf species (NT). This uniformitarian approach should provide accurate paleoclimate reconstructions under the core assumption that leaf-trait variation principally results from adaptive environmental convergence, and because variation is thus largely independent of phylogeny it should be constant through geologic time. Although much research acknowledges and investigates possible pitfalls in paleoclimate estimation based on leaf physiognomy, the core assumption has never been explicitly tested in a phylogenetic comparative framework. Combining an extant dataset of 21 leaf traits and temperature with a phylogenetic hypothesis for 569 species-site pairs at 17 sites, we found varying amounts of non-random phylogenetic signal in all traits. Phylogenetic vs. standard regressions generally support prevailing ideas that leaf-traits are adaptively responding to temperature, but wider confidence intervals, and shifts in slope and intercept, indicate an overall reduced ability to predict climate precisely due to the non-random phylogenetic signal. Notably, the modern-day relation of proportion of untoothed taxa with mean annual temperature (NT-MAT), central in paleotemperature inference, was greatly modified and reduced, indicating that the modern correlation primarily results from biogeographic history. Importantly, some tooth traits, such as number of teeth, had similar or steeper slopes after taking phylogeny into account, suggesting that leaf teeth display a pattern of exaptive evolution in higher latitudes. This study shows that the assumption of convergence required for precise, quantitative temperature estimates using present-day leaf traits is not supported by empirical evidence, and thus we have very low confidence in previously published, numerical paleotemperature estimates. However, interpreting qualitative changes in paleotemperature remains warranted, given certain conditions such as stratigraphically closely-spaced samples with floristic continuity.

Computer vision cracks the leaf code

Significance The botanical value of angiosperm leaf shape and venation (“leaf architecture”) is well known, but the astounding complexity and variation of leaves have thwarted efforts to access this underused resource. This challenge is central for paleobotany because most angiosperm fossils are isolated, unidentified leaves. We here demonstrate that a computer vision algorithm trained on several thousand images of diverse cleared leaves successfully learns leaf-architectural features, then categorizes novel specimens into natural botanical groups above the species level. The system also produces heat maps to display the locations of numerous novel, informative leaf characters in a visually intuitive way. With assistance from computer vision, the systematic and paleobotanical value of leaves is ready to increase significantly. Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies.

Anatomy and development of fruits of Lauraceae from the Middle Eocene Princeton Chert

Investigations of the Middle Eocene Princeton Chert reveal evidence for the connection of lauraceous flowers to fruits through a developmental series. Youngest fruits are found with attached floral remnants. Later stages show receptacle enlargement, fruit wall thickening, and the development of abundant sclereid clusters. Mature fruits are borne on a shallow receptacle and have an endocarp palisade layer of radially elongate cells with stellate outlines, an inner mesocarp layer of radiately arranged sclereid clusters, and a fleshy outer mesocarp layer containing numerous idioblasts with contents. Each mature fruit bears a single seed retaining the outer integument with an innermost radially elongate transfusion cell layer. Mature seeds contain a cellular embryo bearing idioblasts. Fruits are distinguishable from previously described anatomically preserved fossil taxa. This study represents the only documented developmental reconstruction of fossil fruits of Lauraceae and that self-pruning evolved prior to the Eocene. Anatomical modifications over the developmental sequence indicate that different stages of maturity preserved together, may be erroneously identified as several taxa at a fossil locality. Fossil morphotypes typically underestimate species number, but this study suggests that the number of inferred species based on fruit types may be inflated for Lauraceae, potentially exaggerating the tropical interpretation of the paleoenvironment.

Reading the Leaves: A Comparison of Leaf Rank and Automated Areole Measurement for Quantifying Aspects of Leaf Venation

The reticulate venation that is characteristic of a dicot leaf has excited interest from systematists for more than a century, and from physiological and developmental botanists for decades. The tools of digital image acquisition and computer image analysis, however, are only now approaching the sophistication needed to quantify aspects of the venation network found in real leaves quickly, easily, accurately, and reliably enough to produce biologically meaningful data. In this paper, we examine 120 leaves distributed across vascular plants (representing 118 genera and 80 families) using two approaches: a semiquantitative scoring system called “leaf ranking,” devised by the late Leo Hickey, and an automated image-analysis protocol. In the process of comparing these approaches, we review some methodological issues that arise in trying to quantify a vein network, and discuss the strengths and weaknesses of automatic data collection and human pattern recognition. We conclude that subjective leaf rank provides a relatively consistent, semiquantitative measure of areole size among other variables; that modal areole size is generally consistent across large sections of a leaf lamina; and that both approaches—semiquantitative, subjective scoring; and fully quantitative, automated measurement—have appropriate places in the study of leaf venation.

Reinvestigation of Leaf Rank, an Underappreciated Component of Leo Hickey's Legacy

Abstract The widespread recognition of the scientific importance of leaf architecture, the description and interpretation of leaf shape and venation, is a cornerstone contribution of Leo Hickeys career. One leaf architectural trait that Hickey developed is leaf rank, which describes the level of organization of leaf venation in a single, discrete, ordinal variable. He used this scoring system to provide a rapid summary of overall venation complexity and organization in modern and fossil leaves. Leaves with the most reduced and disorganized venation are scored as low rank, whereas leaves with complex and well-organized venation are given high values. Leaf rank data facilitated comparisons of early angiosperms in fossil floras and were invoked in hypotheses regarding angiosperm evolution and ecology. This study presents a large data set of leaf ranks that Leo Hickey scored while he was a researcher at the Smithsonian Institution from 1969 to 1982. The data set represents at least 2,435 observations of US National Herbarium specimens. These formative observations of leaf venation have never been published. Here, we examine Hickey’s data in light of current angiosperm phylogeny as a way to reinvestigate several of his hypotheses regarding leaf rank and angiosperm evolution: (1) leaf ranks tend to be consistent within a family; (2) leaf rank was low in early angiosperms, and high-rank venation occurred later; and (3) leaf rank is correlated with environmental conditions, often with reduced leaf rank values appearing in xeric taxa compared with close relatives under mesic conditions. These hypotheses have not been tested in the light of DNA-based, angiosperm-wide phylogeny, which was not available at the time when leaf rank was developed. We show that even with a DNA-based phylogeny, family-level comparisons show significant differences in average leaf rank; inferred leaf rank states along the early backbone of the angiosperm phylogeny are low, with high-rank taxa occurring across derived lineages; and there is a definite trend toward reduced leaf rank in xeric taxa, all consistent with Hickey’s hypotheses. This taxonomically rich set of observations can serve as a foundation for further investigations of the evolution of leaf vein organization.

Degradome and Secretome of Pollination Drops of Ephedra

Although secreted proteins (a secretome) are known to occur in gymnosperm pollination drops, this study shows evidence for the presence of a protein degradome for the first time. A protein degradome is composed of protein and peptide fragments, a product of protein breakdown, whereas a secretome is composed of whole, secreted, and often biologically active extracellular proteins. Harvested Ephedra pollination drops from seven species were pooled either by collection date or, in the case of less abundant sample volumes, by species. Samples were processed by one of two methods: 1. gel electophoresis or by 2. liquid-liquid extraction, followed by chromatographic separation. Processed samples were trypsin-digested and analyzed with a Thermo Scientific LTQ Orbitrap Velos. On average, two-thirds of the detected and characterized proteins found in Ephedra spp. pollination drops were intracellular proteins, such as ubiquitin. The remaining third represent proteins known to be secreted, often involved in apoplastic processes such as defense and carbohydrate-modification, typical of known conifer pollination drop proteins. Characterized proteins detected in our comparative study of Ephedra spp drops ranged from 6 in E. monosperma to 20 in E. foeminea. We propose that the intracellular proteins detected are present as the result of nucellar tissue degeneration during pollination drop formation; previous proteomic investigations of pollination drops were in taxa that lack nucellar degeneration during drop formation Discovery of a degradome in pollination drops is novel and significant in that its presence has biological implications for pollination biology. We predict that degradomes in pollination drops are not restricted to Ephedra, but should also occur in species with nucellar tissue breakdown that coincides with pollination drop formation, such as in cycads and Ginkgo and some Pinaceae. Analysis of several collection dates of E. monosperma shows a large number of proteins that change over the course of the pollination drop secretion period, which suggests that variation in pollination drop contents over time may be important in the pollination biology of Ephdera.

Phylogenetic and functional signals in gymnosperm ovular secretions

Background and Aims Gymnosperms are either wind-pollinated (anemophilous) or both wind- and insect-pollinated (ambophilous). Regardless of pollination mode, ovular secretions play a key role in pollen capture, germination and growth; they are likely also involved in pollinator reward. Little is known about the broad-scale diversity of ovular secretions across gymnosperms, and how these may relate to various reproductive functions. This study analyses the sugar and amino acid profiles of ovular secretions across a range of ambophilous (cycads and Gnetales) and anemophilous gymnosperms (conifers) to place them in an evolutionary context of their possible functions during reproduction. Methods Ovular secretions from 13 species representing all five main lineages of extant gymnosperms were sampled. High-performance liquid chromatography techniques were used to measure sugar and amino acid content. Multivariate statistics were applied to assess whether there are significant differences in the chemical profiles of anemophilous and ambophilous species. Data were compared with published chemical profiles of angiosperm nectar. Chemical profiles were placed in the context of phylogenetic relationships. Key results Total sugar concentrations were significantly higher in ovular secretions of ambophilous species than wind-pollinated taxa such as Pinaceae and Cupressophyta. Ambophilous species had lower amounts of total amino acids, and a higher proportion of non-protein amino acids compared with anemophilous lineages, and were also comparable to angiosperm nectar. Results suggest that early gymnosperms likely had ovular secretion profiles that were a mosaic of those associated with modern anemophilous and ambophilous species. Ginkgo, thought to be anemophilous, had a profile typical of ambophilous taxa, suggesting that insect pollination either exists in Gingko, but is undocumented, or that its ancestral populations were insect-pollinated. Conclusions Chemical profiles of ovular secretions of ambophilous gymnosperms show a clear signal of pollinator-driven selection, including higher levels of carbohydrates than anemophilous taxa, lower levels of amino acids, and the presence of specific amino acids, such as β-alanine, that are known to influence insect feeding behaviour and physiology.

A transcriptomic resource for Douglas-fir seed development and analysis of transcription during late megagametophyte development

Key messageDouglas-fir transcriptomics.AbstractDouglas-fir (Pseudotsuga menziesii (Mirb.) Franco) is economically important with extensive breeding programs and seed trade. However, the molecular genetics of its seed development are largely unknown. We developed a transcriptome resource covering key developmental stages of megagametophytes over time: prefertilization, fertilization, embryogenesis, and early, unfertilized abortion. RNA sequencing reads were assembled de novo into 105,505 predicted high-confidence transcripts derived from 34,521 predicted genes. Expression levels were estimated based on alignment of the original reads to the reference. Megagametophytes express a distinct set of genes compared to those of vegetative tissues. Transcripts related to signaling, protein turnover, and RNA biogenesis have lower expression values in vegetative tissues, whereas cell wall remodeling, solute transport, and seed storage protein transcripts have higher expression values in megagametophytes. Seed storage protein transcripts become very abundant in both pollinated and unpollinated megagametophytes over time, even in aborting ovules. However, the absence of protein storage bodies in unfertilized megagametophytes suggests extensive posttranscriptional mechanisms that either inhibit storage protein translation or their aggregation into protein bodies. This novel transcriptome resource provides a foundation for further important insights into conifer seed development.