INAUGURAL ARTICLE by a Recently Elected Academy Member:When adaptive radiations collide: Different evolutionary trajectories between and within island and mainland lizard clades

Oceanic islands are known as test tubes of evolution. Isolated and colonized by relatively few species, islands are home to many of nature’s most renowned radiations from the finches of the Galápagos to the silverswords of the Hawaiian Islands. Despite the evolutionary exuberance of insular life, island occupation has long been thought to be irreversible. In particular, the presumed much tougher competitive and predatory milieu in continental settings prevents colonization, much less evolutionary diversification, from islands back to mainlands. To test these predictions, we examined the ecological and morphological diversity of neotropical Anolis lizards, which originated in South America, colonized and radiated on various islands in the Caribbean, and then returned and diversified on the mainland. We focus in particular on what happens when mainland and island evolutionary radiations collide. We show that extensive continental radiations can result from island ancestors and that the incumbent and invading mainland clades achieve their ecological and morphological disparity in very different ways. Moreover, we show that when a mainland radiation derived from island ancestors comes into contact with an incumbent mainland radiation the ensuing interactions favor the island-derived clade.

Historically, conceptions of island evolution have been contradictory. Because oceanic islands initially have few species, island resources often can be underutilized, presenting an “ecological opportunity” for evolutionary diversification. Indeed, essentially all textbook cases of adaptive radiation come from clades that evolved on islands or in island-like settings (e.g., lakes) (1–3). Yet, the reduced species richness on islands has led to the presumption that interspecific interactions are less intense there and that island species rarely reinvade, much less diversify, in continental settings because they are not adapted to strong competitive and predatory interactions (4–7). Many examples now show this premise to be incorrect: Mainland-to-island colonization is not a one-way street (8–13). Nonetheless, a disparity in evolutionary outcomes is still evident: Continental species can give rise to spectacular adaptive radiations on islands, yet the converse has scarcely been reported.Island anoles (Fig. 1 A–C) are a textbook example of adaptive radiation (1, 14). Anoles have radiated independently on each of the main islands of the Greater Antilles, resulting in highly similar suites of habitat specialist species—termed ecomorphs—on each island (15). Correlations between habitat use and morphology suggest species have evolved to capitalize on different microhabitats, and detailed studies of behavior, biomechanics, and natural selection have bolstered our understanding of the adaptive basis of these radiations (14).Open in a separate windowFig. 1.Morphological and ecological diversity in Caribbean (A–C) and Mainland (D–F) Anolis lizards. (A) Anolis guamuhaya, a twig anole, (B) Anolis occultus, a twig anole, (C) Anolis bartschi, (D) Anolis proboscis, (E) Anolis uniformis, (F) Anolis alvarezdeltoroi.Anoles, however, are distributed much more widely than the Greater Antilles, their range encompassing all of the West Indies, Central America (except parts of Mexico), the northern half of South America, and the southeastern United States. For reasons both biological (anoles have lower abundance and are more cryptic on the mainland) and historical [the pioneer in anole studies, Ernest Williams, focused his work on island species (15)], the diversity of mainland anoles (Fig. 1 D–F) has received much less attention. This geographical discrepancy in research effort has occurred even though ecological and morphological diversity of mainland anoles rivals that of the islands (1, 16–19), local mainland communities support as many as 11 to 15 sympatric anole species (14), and more described species occur on the mainland (204 species) than on islands (166 species—counts excluding 9 species that secondarily colonized islands; SI Appendix, Fig. S1) (20).A curious quirk of mainland anole diversity is that it is the result of two, partially overlapping evolutionary radiations. Anolis originated in South America ∼51 Ma (Bayesian credible interval of crown age = 42.4 to 61.7 Ma; dates from Poe et al. (see Fig. 2 legend), diversifying there while that continent was isolated from Central America (20, 21) (we henceforth refer to this clade of mainland anoles as M1; Fig. 2 A and B). A colonization event ∼43 to 51 Ma then gave rise to the anole faunas of the Greater Antilles and northern Lesser Antilles (referred to herein as GA; Fig. 2B) (20). In contrast to the diverse radiation of the GA anoles, a second clade colonized the islands of the southern Lesser Antilles (herein SLA) but did not undergo extensive diversification, no doubt a result of the small size of those islands (22). Approximately 35 Ma, an anole from the Greater Antilles colonized previously anole-free Central America, seeding an expansive species radiation (M2) in which anoles diversified and dispersed throughout the region. This second mainland radiation eventually invaded South America, where species came into contact with members of the older, incumbent mainland radiation (Fig. 2B and ref. 20).Open in a separate windowFig. 2.Phylogeny, paleogeographic colonization history, and morphological and ecological disparity across Anolis. (A) Morphological rates of evolution across Anolis using PCs 1 to 5 as inferred by BAMM and a time-calibrated phylogeny (20). Warmer colors indicate faster rates (see inset legend and note the nonlinear scale). (B) Colonization history of Anolis, with paleogeographic reconstructions drawn from Scotese et al. (81) for reference time points. From left to right: Colonization of northern Lesser and Greater Antilles producing the radiation we refer to as GA (42.4 to 61.7 Ma); the clade ancestrally occupying South America we refer to as Mainland 1 (M1); colonization of Central America either from Cuba or Jamaica giving rise to the clade we refer to as Mainland 2 (M2: 29.9 to 41 Ma); colonization of the southern Lesser Antilles by a clade referred to herein as SLA (23.9 to 40.1 Ma); repeated southward dispersal by M2 beginning ∼15 Ma, and the earliest instance of limited northward dispersal by M1 ∼14 Ma. (C) Perch height plotted against perch diameter, each in log-scale. (D and E) Contemporary disparity calculated as the average Euclidean distance among all pairs of points, using (D) morphological or (E) ecological traits among GA, M1, and M2. Observed values are large, filled circles; 95% CIs calculated from 1,000 bootstrap replicates are plotted as error bars. P values correspond to the probability that the difference in disparity among groups equals zero, calculated from the bootstrap replicates.Herein, we characterize the evolutionary outcomes of this mainland recolonization and subsequent radiation by ancestrally Caribbean Anolis lizards. Specifically, we use a nearly complete time-calibrated Anolis phylogeny (20) and measurements for 10 adaptively relevant morphological and two ecological traits to address the following questions. First, how do the dynamics of adaptive radiation differ between island and mainland groups? For instance, the pace of lineage and morphological diversification is commonly thought to be greatest early in adaptive radiation (1, 23); these patterns have been observed in anoles of the Greater Antilles (24), but does the tempo and mode of diversification differ in mainland radiations? Second, does a clade recolonizing the mainland adapt in ways more similar to the earlier diverging mainland clade or to their more immediate island ancestors? For example, perhaps environmental conditions are so distinct between island and mainland settings that recolonization of the mainland by island anoles leads to the reversion to ecomorphologies more typical of the earlier diverging mainland clade. The approaches used to address the previous two questions ultimately enable us to investigate our third and primary question: What happens when closely related but independently evolving adaptive radiations collide? Do the clades exclude each other from their ancestral ranges? Or is success in radiation and dispersal asymmetrical, the outcome favoring one clade over the other?     

From the Cover: Environmental drivers of annual population fluctuations in a trans-Saharan insect migrant

Many latitudinal insect migrants including agricultural pests, disease vectors, and beneficial species show huge fluctuations in the year-to-year abundance of spring immigrants reaching temperate zones. It is widely believed that this variation is driven by climatic conditions in the winter-breeding regions, but evidence is lacking. We identified the environmental drivers of the annual population dynamics of a cosmopolitan migrant butterfly (the painted lady Vanessa cardui) using a combination of long-term monitoring and climate and atmospheric data within the western part of its Afro-Palearctic migratory range. Our population models show that a combination of high winter NDVI (normalized difference vegetation index) in the Savanna/Sahel of sub-Saharan Africa, high spring NDVI in the Maghreb of North Africa, and frequent favorably directed tailwinds during migration periods are the three most important drivers of the size of the immigration to western Europe, while our atmospheric trajectory simulations demonstrate regular opportunities for wind-borne trans-Saharan movements. The effects of sub-Saharan vegetative productivity and wind conditions confirm that painted lady populations on either side of the Sahara are linked by regular mass migrations, making this the longest annual insect migration circuit so far known. Our results provide a quantification of the environmental drivers of large annual population fluctuations of an insect migrant and hold much promise for predicting invasions of migrant insect pests, disease vectors, and beneficial species.

Insect migration occurs on an enormous scale (1), with billions of individuals undertaking multigenerational migrations between seasonally favorable climatic zones around the globe (2–6). These long-range migration cycles profoundly influence terrestrial ecosystems via the large-scale transfer of biomass, energy, and nutrients (4–8), the provision of ecosystem services (8–10), impacts on agricultural productivity (11), and spread of disease (5, 12); thus, it is imperative that we better understand insect movement patterns. Recently, there has been a step change in our knowledge of the year-round spatial distribution and migratory routes of a few well-studied species (13, 14), particularly the monarch butterfly (Danaus plexippus) (15, 16) and (to a lesser extent) the painted lady butterfly (Vanessa cardui) (17, 18). However, interannual population dynamics of such insect migrants remain poorly known. One of the characteristic features is the interannual variation in the abundance of the first wave of immigrants to reach the temperate zone, which can vary by several orders of magnitude between successive years (3, 11, 17, 19). It is generally believed that this variation is driven by the effect of winter climate on breeding success and survival in the tropical and subtropical winter-breeding regions, particularly when these regions are arid or semiarid (1, 3, 20).Here, we study the painted lady butterfly, a cosmopolitan, continuously breeding migrant that undertakes seasonally predictable, long-range movements between tropical/subtropical winter-breeding regions and temperate zone summer-breeding regions (17–20). We focus on the western portion of its Afro-Palearctic migration system (from the Gulf of Guinea to Fennoscandia) due to the unparalleled monitoring data available on the spring and summer generations in parts of this range (Butterfly Monitoring Scheme [BMS] data from western Europe; Fig. 1 A and B) and in order to quantify the environmental drivers of interannual variation in abundance. In this western section, winter breeding was traditionally considered to occur predominantly in the Maghreb region of northwestern (NW) Africa (21, 22). However, recent studies suggest that it can occur over a much larger latitudinal range, from the Gulf of Guinea coast to the north Mediterranean coast, with two-way movement across the Sahel and Sahara Desert linking European and sub-Saharan African populations (23–26). The colonization of Western Europe consists of a northward progression of successive generations throughout spring and summer. The European component starts when butterflies that had emerged in the Maghreb (Morocco, Algeria, and Tunisia) a few days previously (17, 27) arrive in the Mediterranean region during March and April and immediately produce the next generation there. What is unclear is just how important the winter generations produced south of the Sahara are in seeding or reinforcing the early-spring generation in the Maghreb. Subsequent late-spring and summer generations reach as far north as Fennoscandia, and then the autumn generation undertakes an extremely long migration [often high above the ground, utilizing fast tailwinds (17, 28)] back to NW Africa and sub-Saharan West Africa (17, 29). Here, the annual cycle, comprising six or more generations per year, resumes (17–20, 24).Open in a separate windowFig. 1.Painted lady population data in western Europe. (A) Phenology of painted ladies in Europe showing peaks that correspond to either migrants or local generations. In the Mediterranean region (NE Spain), the light-blue period corresponds to the spring immigration and the dark-blue period to the summer emergence of a locally bred generation. In NW Europe (NL: the Netherlands; Eng & Wal: England and Wales), the light-pink period corresponds to the early-summer immigration and the dark-pink period to the late-summer emergence of a locally bred generation. (B) Log-collated annual index (across all sites in each country) for NE Spain in spring (1 March to 30 May) and summer (1 June to 31 July) and for NW Europe in early summer (15 May to 15 July) and late summer (16 July to 30 September). Abundance indices are expressed on a log scale, with zero reflecting the average for that region and season across all years. See Fig. 4 for the factors explaining years of peak abundance (e.g., 1996, 2003, 2006, 2009, and 2015).Extreme interannual variation in the abundance of the spring immigrants (and subsequent summer population) is a feature of painted lady population dynamics in both Europe (17, 19, 20, 30) and North America (29, 31, 32). Some painted ladies arrive in western Europe every spring; however, the pattern of abundance is one of irregular spectacular mass arrivals interspersed with years of much-reduced immigration (Fig. 1B). Here, we determine the key environmental conditions, and when/where they act during the migratory cycle, that drive this extreme annual variability in the European population dynamics each summer. In particular, we tackle the question of whether sub-Saharan, North African, and/or southern Iberian environmental conditions during the previous winter or spring are the primary drivers of the size of the spring and early-summer immigrations, initially to the Mediterranean and ultimately to northern Europe. To identify links between the generations monitored in Europe and the African breeding cycles both north and south of the Sahara, we use the following: 1) winter and spring environmental data (normalized difference vegetation index [NDVI], precipitation, temperature, and frequency of favorable tailwinds) covering the critical regions and periods (Fig. 2) in which large populations could potentially originate; 2) 21 y of BMS records from the Mediterranean (northeastern [NE] Spain) and NW Europe (the United Kingdom and the Netherlands); and 3) atmospheric trajectory simulations along the migratory route from sub-Saharan Africa to Europe.Open in a separate windowFig. 2.Correlations between spring painted lady counts in NE Spain with the NDVI, precipitation, and temperature. Red areas on the maps indicate regions that have positive significant correlations between the variable plotted and spring painted lady counts in NE Spain, while blue areas are negative correlations. See also Fig. 3 and SI Appendix, Fig. S2 for plots of painted lady spring numbers against the winter NDVI. These correlation plots were used to identify the ecoregions that were likely to be important (see delineation of these ecoregions in Fig. 3 and SI Appendix, Fig. S1) and to select the most important variables for the modeling (SI Appendix, Tables S1 and S2).     

Probing large conformational rearrangements in wild-type and mutant spectrin using structural mass spectrometry

Conformational changes of macromolecular complexes play key mechanistic roles in many biological processes, but large, highly flexible proteins and protein complexes usually cannot be analyzed by crystallography or NMR. Here, structures and conformational changes of the highly flexible, dynamic red cell spectrin and effects of a common mutation that disrupts red cell membranes were elucidated using chemical cross-linking coupled with mass spectrometry. Interconversion of spectrin between closed dimers, open dimers, and tetramers plays a key role in maintaining red cell shape and membrane integrity, and spectrins in other cell types serve these as well as more diverse functions. Using a minispectrin construct, experimentally verified structures of closed dimers and tetramers were determined by combining distance constraints from zero-length cross-links with molecular models and biophysical data. Subsequent biophysical and structural mass spectrometry characterization of a common hereditary elliptocytosis-related mutation of α-spectrin, L207P, showed that cell membranes were destabilized by a shift of the dimer–tetramer equilibrium toward closed dimers. The structure of αL207P mutant closed dimers provided previously unidentified mechanistic insight into how this mutation, which is located a large distance from the tetramerization site, destabilizes spectrin tetramers and cell membrane integrity.Solving static structures of protein complexes and probing dynamic conformational rearrangements have frequently provided mechanistic insights into macromolecular functions as well as effects of disease-related mutations. However, high-resolution structural techniques such as X-ray crystallography and NMR usually cannot be applied to large proteins that are highly flexible, intrinsically disordered, or undergo large conformational changes. Chemical cross-linking coupled with mass spectrometry (CX-MS) is a powerful tool that identifies proximal amino acid residues of proteins in solution. These spatial constraints can greatly enhance and experimentally validate molecular modeling to result in reliable medium-resolution structures. This approach has been effectively used in numerous studies (1–5), although homobifunctional lysine-specific cross-linkers with relatively long spacer arms were mostly used. Such reagents have been preferred because they enable introduction of isotope labels and other functional sites that facilitate cross-linked peptide identifications (6–9). In contrast, zero-length cross-linkers such as 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide form a covalent bond between reactive amines (N-terminal amine or lysine side chain) and carboxyls (C-terminal carboxyl or aspartic or glutamic acid side chains) without inserting extra atoms (Fig. 1). Hence, reactive groups have to be within salt bridge distances to react. This results in tighter distance constraints that outperform those from longer cross-links when these data are used to refine structural models, and a much lower density of cross-links is needed to achieve high-quality structures (7). In this study we used a recently developed strategy for in-depth identification of zero-length cross-links, as summarized in Fig. 1, to probe structures and conformational changes in spectrin, a highly flexible, dynamic protein with multiple functions that is typically associated with cell membranes. In red cells, spectrin is the central component of a highly specialized, 2D, net-like submembraneous complex that confers both structural integrity and elasticity to the cell membrane. The 1,052-kDa spectrin tetramer is a long, highly flexible, worm-like protein composed primarily of many tandem, homologous, “spectrin-type” domains (Fig. 2A). In all reported crystal structures, these domains are approximately 50-Å-long three-helix bundles with helical connectors (Fig. 2 B and C). To date, crystallization of more than four spectrin-type domains has not been feasible, presumably owing to spectrin’s highly flexible nature. In the membrane skeleton, spectrin tetramers bridge short actin oligomers, and membrane stability is highly dependent upon the dimer–tetramer equilibrium because conversion to dimers breaks the spectrin bridges between actin oligomers (10, 11).Open in a separate windowFig. 1.Schematic for the zero-length CX-MS data acquisition and data analysis pipeline.Open in a separate windowFig. 2.Spectrin topography and minispectrin tetramer structure. (A) Schematic showing spectrin domains, the dimer–tetramer equilibria, and minispectrin. The spectrin-type domains that constitute most of the molecule are represented as rounded rectangles. Red asterisks in the minispectrin cartoon indicate the approximate location of the αL207P mutation. (B) Superimposition of the four crystal structures of spectrin-type domains [Protein Data Bank (PBD) ID: 1CUN, 1U5P, 3FB2, and 1S35] used as template building blocks for homology modeling. (C) Crystal structure for the spectrin tetramerization interface (PDB ID: 3LBX). (D) Locations of interdomain cross-links used to model minispectrin tetramer; blue lines, cross-links identified previously (21); red lines, previously unidentified cross-links; dashed lines, the same cross-links repeated in the second half of the tetramer. (E) Superimposition of present and previous tetramer structures. (F) Space-filling representations of tetramer models. β-Spectrin domains are colored in bright or pale cyan, and α-spectrin domains are colored in bright or pale orange to distinguish the two strands.The spectrin dimer–tetramer equilibrium actually involves three states, including closed dimers, open dimers, and bivalent head-to-head tetramers (Fig. 2A). Conversion from open to closed dimers involves a large conformational rearrangement of the longer α-subunit, where it folds back upon itself and forms a head-to-head association analogous to the head-to-head associations in tetramers (Fig. 2A). Closed dimers play a critical but poorly understood role in this equilibrium and are responsible for a high-energy threshold that regulates kinetics of the dimer–tetramer equilibrium (12). Furthermore, hereditary elliptocytosis (HE) and hereditary pyropoikilocytosis (HPP) are common human clinical disorders characterized by reduced spectrin tetramerization resulting in abnormal red cell shape, increased membrane fragility, and in some cases, severe anemia that is transfusion dependent (13, 14). Many HE and HPP mutations are located within the tetramerization site (13), although a number of interesting mutations are located large distances from this binding domain and destabilize tetramer formation through unknown mechanisms (15–19). For example, the very common αL207P mutation is located in the middle of the α2 domain, which is ∼75 Å from the tetramerization site. However, it seemed likely that this region could undergo important conformational changes during the closed–open dimer transition (Fig. 2A). We previously developed a 90 kDa fused minispectrin dimer (Fig. 2A) to further study the dimer–tetramer equilibrium (20) and used it here for the CX-MS experiments because it is substantially simpler than the 526-kDa full-length spectrin dimer and retains physiological tetramer binding properties.     

Conjugated polymers based on metalla-aromatic building blocks

Conjugated polymers usually require strategies to expand the range of wavelengths absorbed and increase solubility. Developing effective strategies to enhance both properties remains challenging. Herein, we report syntheses of conjugated polymers based on a family of metalla-aromatic building blocks via a polymerization method involving consecutive carbyne shuttling processes. The involvement of metal d orbitals in aromatic systems efficiently reduces band gaps and enriches the electron transition pathways of the chromogenic repeat unit. These enable metalla-aromatic conjugated polymers to exhibit broad and strong ultraviolet–visible (UV–Vis) absorption bands. Bulky ligands on the metal suppress π–π stacking of polymer chains and thus increase solubility. These conjugated polymers show robust stability toward light, heat, water, and air. Kinetic studies using NMR experiments and UV–Vis spectroscopy, coupled with the isolation of well-defined model oligomers, revealed the polymerization mechanism.

Conjugated polymers are macromolecules usually featuring a backbone chain with alternating double and single bonds (1–3). These characteristics allow the overlapping p-orbitals to form a system with highly delocalized π-electrons, thereby giving rise to intriguing chemical and physical properties (4–6). They have exhibited many applications in organic light-emitting diodes, organic thin film transistors, organic photovoltaic cells, chemical sensors, bioimaging and therapies, photocatalysis, and other technologies (7–10). To facilitate the use of solar energy, tremendous efforts have been devoted in recent decades to developing previously unidentified conjugated polymers exhibiting broad and strong absorption bands (11–13). The common strategies for increasing absorption involve extending π-conjugation by incorporating conjugated cyclic moieties, especially fused rings; modulating the strength of intramolecular charge transfer between donor and acceptor units (D–A effect); increasing the coplanarity of π conjugation through weak intramolecular interactions (e.g., hydrogen bonds); and introducing heteroatoms or heavy atoms into the repeat units of conjugated polymers (11–16). Additionally, appropriate solubility is a prerequisite for processing and using polymers and is usually achieved with the aid of long alkyl or alkoxy side chains (12, 17).Aromatic rings are among the most important building blocks for conjugated polymers. In addition to aromatic hydrocarbons, a variety of aromatic heterocycles composed of main-group elements have been used as fundamental components. These heteroatom-containing conjugated polymers show unique optical and electronic properties (4–10). However, while metalla-aromatic systems bearing a transition metal have been known since 1979 due to the pioneering work by Thorn and Hoffmann (18), none of them have been used as building blocks for conjugated polymers. The HOMO–LUMO gaps (E_g) of metalla-aromatics are generally narrower (Fig. 1) than those of their organic counterparts (19–22). We reasoned that this feature should broaden the absorption window if polymers stemming from metalla-aromatics are achievable.Open in a separate windowFig. 1.Comparison of traditional organic skeletons with metalla-aromatic building blocks (the computed energies are in eV). (A) HOMO–LUMO gaps of classic aromatic skeletons. (B) Carbolong frameworks as potential building blocks for novel conjugated polymers with broad absorption bands and improved solubility.In recent years, we have reported a series of readily accessible metal-bridged bicyclic/polycyclic aromatics, namely carbolong complexes, which are stable in air and moisture (23–25). The addition of osmium carbynes (in carbolong complexes) and alkynes gave rise to an intriguing family of d_π–p_π conjugated systems, which function as excellent electron transport layer materials in organic solar cells (26, 27). These observations raised the following question: Can this efficient addition reaction be used to access metalla-aromatic conjugated polymers? It is noteworthy that incorporation of metalla-aromatic units into conjugated polymers is hitherto unknown. In this contribution, we disclose a polymerization reaction involving M≡C analogs of C≡C bonds, which involves a unique carbyne shuttling strategy (Fig. 2A). This led to examples of metalla-aromatic conjugated polymers (polycarbolongs) featuring metal carbyne units in the main chain. On the other hand, the development of polymerization reactions plays a crucial role in involving certain building blocks in conjugated polymers (28–32). These efficient, specific, and feasible polymerizations could open an avenue for the synthesis of conjugated polymers.Open in a separate windowFig. 2.Design of polymers and synthesis of monomers. (A) Schematic illustration of the polymerization strategy. (B) Preparation of carbolong monomers. Insert: X-ray molecular structure for the cations of complex 3. Ellipsoids are shown at the 50% probability level; phenyl groups in PPh₃ are omitted for clarity.     

From the Cover: Structural adaptations to diverse fighting styles in sexually selected weapons

The shapes of sexually selected weapons differ widely among species, but the drivers of this diversity remain poorly understood. Existing explanations suggest weapon shapes reflect structural adaptations to different fighting styles, yet explicit tests of this hypothesis are lacking. We constructed finite element models of the horns of different rhinoceros beetle species to test whether functional specializations for increased performance under species-specific fighting styles could have contributed to the diversification of weapon form. We find that horns are both stronger and stiffer in response to species-typical fighting loads and that they perform more poorly under atypical fighting loads, which suggests weapons are structurally adapted to meet the functional demands of fighting. Our research establishes a critical link between weapon form and function, revealing one way male–male competition can drive the diversification of animal weapons.Sexually selected traits are renowned for their extreme size and diversity (1, 2). Some sexual traits, such as elaborate feathers in birds of paradise and widowbirds, are used as ornaments to attract choosy females, whereas others, such as giant elk antlers and stag beetle mandibles, are used as weapons in male–male battles over access to females. Numerous empirical (3–9) and theoretical (10–15) studies have shown how female choice can drive the diversification of male ornaments. Surprisingly few studies, however, have examined whether male–male competition drives the diversification of weapons, and the mechanisms responsible for weapon divergence remain largely unexplored (16). As a consequence, although sexually selected weapons are just as diverse as ornaments, it is not clear why this should be so.The most intuitive explanation for weapon diversity is that weapons are adapted to species-specific fighting styles. Specifically, differences either in the way males fight or in where they fight may favor corresponding changes in weapon shape (16). This hypothesis has been explored most thoroughly for the horns and antlers of ungulates (17–20). For example, males in species with short, smooth horns tend to be stabbers; males with robust, curved horns typically ram opponents; and males with long, reaching horns wrestle or fence (18, 19). Although these broad comparative patterns provide evidence that different fighting styles have contributed to the divergence of weapon forms, all the studies are correlative. Explicit tests of the functional performance of weapons in response to forces incurred during fights are still lacking, and no studies have tested whether animal weapons perform better at their own style of fighting than they do at others. Thus, although functional specialization of weapons for diverse styles of fighting remains the most intuitive and widely cited driver of weapon diversity, it has yet to be directly tested for any type of animal weapon.Rhinoceros beetles (Coleoptera: Dynastinae) are ideal for studying weapon diversity for three reasons. First, species vary in the number, size, and shape of their horns, with species wielding long pitchforks, robust pincers, or thin spears, to name just a few of the diverse horn types (16, 21) (Figs. 1 and ​and2).2). Second, horns are used as weapons during combat with rival males over access to females. There is no evidence that females choose males on the basis of the shape or size of their horns (22–25), so horn morphology is expected to reflect differences in how horns are used during fights without conflicting selective pressures from female choice. Third, species fight on a variety of substrates (e.g., on broad tree trunks, on narrow shoots, or inside tunnels) and use their horns in different ways, which may select for qualitatively different fighting structures (16, 23, 24).Open in a separate windowFig. 1.Variation in horn morphology and fighting styles in rhinoceros beetles. (A) Trypoxylus dichotomus males have a long, forked head horn that is used like a pitchfork to lift and twist opponents off tree trunks during fights. (B) Dynastes hercules males have a long head horn and long thoracic horn that are used together similar to pliers to lift, squeeze, and then toss opponents to the ground. (C) Golofa porteri males have a long, slender head horn that is used similar to a fencing sword to both lift opponents off narrow shoots and push them sideways off balance. Vectors represent the typical forces experienced by horns during fights: vertical bending (red), lateral bending (blue), twisting (green). Illustrations by David J. Tuss.Open in a separate windowFig. 2.Horns are stronger under species-specific fighting loads. Von Mises stress distributions and maximum stress values from finite element models of Trypoxylus (A–C), Dynastes (D–F), and Golofa (G–I) horns under vertical bending (A, D, G), lateral bending (B, E, H), and twisting (C, F, I) loads. Typical fighting loads for each species are outlined in gray; atypical fighting loads are not outlined. In all three species, maximum Von Mises stresses in the horn are higher (warmer colors) under atypical loading conditions, indicating a higher likelihood of breaking. Contour plots are scaled to 80 MPa maximum stress. The high stresses at the base of the horn are artifacts from constraining the models and are not included in calculating the maximum stress values in the horn.Here, we perform a functional analysis of rhinoceros beetle horns to test whether horns are structurally suited for diverse fighting styles. Specifically, we compare the mechanical performance of various beetle horn morphologies using finite element analysis, a standard and powerful engineering analysis technique used to predict how complex structures deform, and ultimately fail, in response to applied loads (26). We test whether beetle horns are adapted to meet the functional demands of fighting by constructing finite element models of the head horns of three rhinoceros beetle species and loading the model horns in ways that mimic the forces incurred during both species-typical and species-atypical fights.     

Integration of high-content screening and untargeted metabolomics for comprehensive functional annotation of natural product libraries

Traditional natural products discovery using a combination of live/dead screening followed by iterative bioassay-guided fractionation affords no information about compound structure or mode of action until late in the discovery process. This leads to high rates of rediscovery and low probabilities of finding compounds with unique biological and/or chemical properties. By integrating image-based phenotypic screening in HeLa cells with high-resolution untargeted metabolomics analysis, we have developed a new platform, termed Compound Activity Mapping, that is capable of directly predicting the identities and modes of action of bioactive constituents for any complex natural product extract library. This new tool can be used to rapidly identify novel bioactive constituents and provide predictions of compound modes of action directly from primary screening data. This approach inverts the natural products discovery process from the existing ‟grind and find” model to a targeted, hypothesis-driven discovery model where the chemical features and biological function of bioactive metabolites are known early in the screening workflow, and lead compounds can be rationally selected based on biological and/or chemical novelty. We demonstrate the utility of the Compound Activity Mapping platform by combining 10,977 mass spectral features and 58,032 biological measurements from a library of 234 natural products extracts and integrating these two datasets to identify 13 clusters of fractions containing 11 known compound families and four new compounds. Using Compound Activity Mapping we discovered the quinocinnolinomycins, a new family of natural products with a unique carbon skeleton that cause endoplasmic reticulum stress.Notwithstanding the historical importance of natural products in drug discovery (1) the field continues to face a number of challenges that affect the relevance of natural products research in modern biomedical science (2). Among these are the increasing rates of rediscovery of known classes of natural products (3–6) and the high rates of attrition of bioactive natural products in secondary assays due to limited information about compound modes of action in primary whole-cell assays (7). Although pharmaceutical companies recognize that natural products are an important component of drug discovery programs because of the different pharmacologies of natural products and synthetic compounds (8), there is a reluctance to return to “grind and find” discovery methods (9). Therefore, there is a strong need for technologies that address these issues and provide new strategies for the prioritization of lead compounds with unique structural and/or biological properties (10).Natural product drug discovery is challenging in any assay system because extract libraries are typically complex mixtures of small molecules in varying titers, making it difficult to distinguish biological outcomes (11). This is compounded by issues of additive effects of multiple bioactive compounds and the presence of nuisance compounds that cause false positives in assay systems (12). To address these issues, our laboratory has recently developed several image-based screening platforms that are optimized for natural product discovery (13–16). The cytological profiling platform optimized by Schulze and coworkers characterizes the biological activities of extracts using untargeted phenotypic profiling. These phenotypic profiles are compared with natural products extracts and a training set of compounds with known modes of action to characterize the bioactivity landscape of the screening library (17, 18). This cytological profiling tool forms the basis of the biological characterization component of the Compound Activity Mapping platform, as described below.In the area of chemical characterization of natural product libraries, untargeted metabolomics is gaining attention as a method for evaluating chemical constitution (3, 19–22). Modern “genes-to-molecules” and untargeted metabolomics approaches taking advantage of principal component analysis and MS² spectral comparisons have also been developed to quickly dereplicate complex extracts and distinguish noise and nuisance compounds from new molecules (23–27). Unfortunately, although these techniques are well suited to the discovery of new chemical scaffolds, they are unable to describe the function or biological activities of the compounds they identify. Therefore, there is still a need for new approaches to systematically identify novel bioactive scaffolds from complex mixtures.To overcome some of these outstanding challenges we have developed the Compound Activity Mapping platform to integrate phenotypic screening information from the cytological profiling assay with untargeted metabolomics data from the extract library (Fig. 1). By correlating individual mass signals with specific phenotypes from the high-content cell-based screen (Fig. 2), Compound Activity Mapping allows the prediction of the identities and modes of action of biologically active molecules directly from complex mixtures, providing a mechanism for rational lead selection based on desirable biological and/or chemical properties. To evaluate this platform for natural products discovery we examined a 234-member extract library, from which we derived 58,032 biological measurements (Fig. 1C) and 10,977 mass spectral features (Fig. 1A). By integrating and visualizing these data we created a Compound Activity Map for this library composed of 13 clusters containing 16 compounds from 11 compound classes (Fig. 3). This integrated data network enabled the discovery of four new compounds, quinocinnolinomycins A–D (1–4, Fig. 4), which are the first examples to our knowledge of microbial natural products containing the unusual cinnoline core (Fig. 5). Clustering the cytological profiles of the quinocinnolinomycins with those of the Enzo library training set suggests that these compounds induce endoplasmic reticulum (ER) stress and the protein unfolding response.Open in a separate windowFig. 1.Overview of Compound Activity Mapping. (A) Representation of the chemical space in the tested extract library. The network displays extracts (light blue) connected by edges to all m/z features (red) observed from the metabolomics analysis, illustrating the chemical complexity of even small natural product libraries. (B) Histograms of activity and cluster scores for all m/z features with cutoffs indicated as red lines (for full-size histograms see SI Appendix, Fig. S5). (C) Compound Activity Map, with the network displaying only the m/z features predicted to be associated with consistent bioactivity, and their connectivity to extracts within the library. (D) Expansion of the staurosporine cluster (dotted box in C) with extract numbers and relevant m/z features labeled.Open in a separate windowFig. 2.Determination of synthetic fingerprints and cluster and activity scores. (A) Table of Pearson correlations for the cytological profiles between all extracts containing a specific m/z feature (m/z of 489.1896, rt of 1.59). In each cytological profile, yellow stripes correspond to positive perturbations in the observed cytological attribute and blue stripes correspond to negatively perturbed attributes. The cluster score is determined by calculating the average of the Pearson correlation scores for all relevant extracts. (B) Calculated synthetic fingerprint and activity score for feature (m/z of 489.1896, rt of 1.59). Synthetic fingerprints are calculated as the averages of the values for each cytological attribute to give a predicted cytological profile for each bioactive m/z feature in the screening set.Open in a separate windowFig. 3.Annotated Compound Activity Map. An expanded view of the Compound Activity Map from Fig. 1C, with the extracts and m/z features separated into subclusters and colored coded using the Gephi modularity function. Each bioactive subcluster is composed of extracts containing a family of compounds with a defined biological activity. The Compound Activity Map is annotated with a representative molecule from each of the families of compounds that have been independently confirmed by purification and chemical analysis.Open in a separate windowFig. 4.The prioritization, isolation, and confirmation of the quinocinnolinomycins A–D (1–4). (A) Bioactive m/z features plotted on a graph of activity score vs. cluster score. The color of the dot corresponds to the retention time of the m/z feature with the color bar and scale below in minutes. (B) Isolated cluster from Fig. 1C and Fig. 3 containing both the relevant extracts (blue) and bioactive m/z features (red). (C) HPLC trace of extract RLPA-2003E and the isolation of quinocinnolinomycins A–D (highlighted with blue boxes on HPLC trace). (D) Cell images of pure compounds screened as a twofold dilution series for quinocinnolinomycins A and B in both stain sets compared with images of vehicle (DMSO) wells. (E) Comparison of the synthetic and actual cytological fingerprints of the pure compounds is presented below the relevant images, demonstrating the relationship between experimental and calculated cytological profiles for these two metabolites.Open in a separate windowFig. 5.Structure elucidation of quinocinnolinomycins A–D (1–4). (A) Structures of quinocinnolinomycins A–D. (B) Key NMR correlations used in the structure elucidation of quinocinnolinomycin A. COSY correlations are indicated by bold lines. Heteronuclear multiple-bond correlations are indicated by curved arrows. (C) ∆δ^SR values for the Mosher’s α-methoxy-α-trifluoromethylphenylacetic acid (MTPA) ester analysis of the secondary alcohol in quinocinnolinomycin A (1) to assign the absolute configuration at position C11.     

Insect nicotinic receptor interactions in vivo with neonicotinoid,organophosphorus, and methylcarbamate insecticides and a synergist

The nicotinic acetylcholine (ACh) receptor (nAChR) is the principal insecticide target. Nearly half of the insecticides by number and world market value are neonicotinoids acting as nAChR agonists or organophosphorus (OP) and methylcarbamate (MC) acetylcholinesterase (AChE) inhibitors. There was no previous evidence for in vivo interactions of the nAChR agonists and AChE inhibitors. The nitromethyleneimidazole (NMI) analog of imidacloprid, a highly potent neonicotinoid, was used here as a radioligand, uniquely allowing for direct measurements of house fly (Musca domestica) head nAChR in vivo interactions with various nicotinic agents. Nine neonicotinoids inhibited house fly brain nAChR [³H]NMI binding in vivo, corresponding to their in vitro potency and the poisoning signs or toxicity they produced in intrathoracically treated house flies. Interestingly, nine topically applied OP or MC insecticides or analogs also gave similar results relative to in vivo nAChR binding inhibition and toxicity, but now also correlating with in vivo brain AChE inhibition, indicating that ACh is the ultimate OP- or MC-induced nAChR active agent. These findings on [³H]NMI binding in house fly brain membranes validate the nAChR in vivo target for the neonicotinoids, OPs and MCs. As an exception, the remarkably potent OP neonicotinoid synergist, O-propyl O-(2-propynyl) phenylphosphonate, inhibited nAChR in vivo without the corresponding AChE inhibition, possibly via a reactive ketene metabolite reacting with a critical nucleophile in the cytochrome P450 active site and the nAChR NMI binding site.The nicotinic nervous system has two principal sites of insecticide action, the nicotinic receptor (nAChR) activated by acetylcholine (ACh) and neonicotinoid agonists (1–6), and acetylcholinesterase (AChE) inhibited by organophosphorus (OP) and methylcarbamate (MC) compounds to generate and maintain localized toxic ACh levels (Fig. 1) (7). The nAChR and AChE targets have been identified in insects by multiple techniques but not by direct assays of the ACh binding site in the brain of poisoned insects. Here we use the outstanding insecticidal potency of the nitromethyleneimidazole (NMI) analog of imidacloprid (IMI) (8) as a radioligand (9), designated [³H]NMI, to directly measure the house fly (Musca domestica) nAChR not only in vitro but also in vivo, allowing us to validate by a previously undescribed method the neonicotinoid direct and OP/MC indirect nAChR targets (Fig. 2). This approach also helped solve the intriguing mechanism by which an O-(2-propynyl) phosphorus compound strongly synergizes neonicotinoid insecticidal activity (10) by dual inhibition of cytochrome P450 (CYP) (11–13) and the nAChR agonist site (described herein). Insecticide disruption at the insect nAChR can now be readily studied in vitro and in vivo with a single radioligand allowing better understanding of the action of several principal insecticide chemotypes (Fig. 3).Open in a separate windowFig. 1.The insect nicotinic receptor is the direct or indirect target for neonicotinoids, organophosphorus compounds and methylcarbamates, which make up about 45% of the insecticides by number and world market value (2, 7).Open in a separate windowFig. 2.In this study, Musca nicotinic receptor in vivo interactions with major insecticide chemotypes are revealed by a [³H]NMI radioligand reporter assay. *Position of tritium label.Open in a separate windowFig. 3.Two neonicotinoid nicotinic agonists and two anticholinesterase insecticides.     

Abrupt increases in Amazonian tree mortality due to drought–fire interactions

Interactions between climate and land-use change may drive widespread degradation of Amazonian forests. High-intensity fires associated with extreme weather events could accelerate this degradation by abruptly increasing tree mortality, but this process remains poorly understood. Here we present, to our knowledge, the first field-based evidence of a tipping point in Amazon forests due to altered fire regimes. Based on results of a large-scale, long-term experiment with annual and triennial burn regimes (B1yr and B3yr, respectively) in the Amazon, we found abrupt increases in fire-induced tree mortality (226 and 462%) during a severe drought event, when fuel loads and air temperatures were substantially higher and relative humidity was lower than long-term averages. This threshold mortality response had a cascading effect, causing sharp declines in canopy cover (23 and 31%) and aboveground live biomass (12 and 30%) and favoring widespread invasion by flammable grasses across the forest edge area (80 and 63%), where fires were most intense (e.g., 220 and 820 kW⋅m⁻¹). During the droughts of 2007 and 2010, regional forest fires burned 12 and 5% of southeastern Amazon forests, respectively, compared with <1% in nondrought years. These results show that a few extreme drought events, coupled with forest fragmentation and anthropogenic ignition sources, are already causing widespread fire-induced tree mortality and forest degradation across southeastern Amazon forests. Future projections of vegetation responses to climate change across drier portions of the Amazon require more than simulation of global climate forcing alone and must also include interactions of extreme weather events, fire, and land-use change.Large areas of moist tropical forests are being altered by land-use practices and severe weather. People are clearing, thinning, and changing the composition of tropical forests (1, 2). Severe drought events superimposed upon these land-use activities increase forest susceptibility to fires (1–5). In the 2000s, for example, 15,000–26,000 km² of Amazonian forests burned during years of severe drought (6). Widespread forest fires may become even more common in the Amazon Basin if the frequency of extreme weather events increases, particularly in the southeastern Amazon (1, 7). However, most model simulations of future trajectories of Amazonian forests have relied on global and regional climate forcing that do not consider the effects of fire on vegetation dynamics and structure (8–10).Our ability to predict future fire regimes in moist tropical forests is constrained by a lack of understanding of what triggers and controls high-intensity fires (7, 11). In nondrought years, primary forests typically do not catch fire during the dry season because the fine fuel layer is too humid to carry a fire (12). This characteristic of primary forests helps explain why forest fires were less frequent in pre-Colombian times than today (13), although indigenous peoples of the Amazon have used fire as a management tool for hundreds or thousands of years (14). Current anthropogenic disturbances in moist tropical forests (e.g., logging, forest conversion for crops and livestock, and the resulting fragmentation of forests) tend to thin forest canopies (5, 11) and expose forest interiors to warm air flowing horizontally from neighboring clearings, allowing the forest floor to dry more rapidly during rainless periods. When forest fires do occur under average weather conditions, they typically move through the understories slowly (15–25 m⋅hour⁻¹), release little energy (50 kW⋅m⁻¹), and are of short duration (4, 5, 15), extinguishing at night when relative humidity increases. Despite their low intensity, understory fires still exert strong influences on forest dynamics and structure because many tropical tree species are thin-barked and vulnerable to fire damage (12, 16, 17).During years of severe drought, Amazon forest fires are atypically intense, killing up to 64% of the trees (18, 19). This happens because fuel (e.g., twigs, leaves, branches, etc.) not only becomes drier, but also tends to become more abundant due to drought-related leaf and branch fall (20). Thus, compared with low-intensity fires that occur in nondrought years, severe droughts can trigger high-intensity fires that kill more trees. Unfortunately, the role of extreme weather events in the fire dynamics of moist tropical forests is difficult to study because they are hard to predict. As a result, the relationships between fire-induced tree mortality and extreme weather remain poorly understood, restricted mostly to postfire observations of tree mortality.To fill this gap, in 2004 we established a large-scale, long-term prescribed forest fire experiment in a transitional forest (between Amazon forests and savannas) in the southeastern Amazon (Figs. 1 and ​and2)—a2)—a region that is highly vulnerable to changes in fire regime, climate change, and their interactions (2). The experimental area consists of three adjacent 50-ha (1.0 × 0.5 km; Fig. 1) plots burned annually (B1yr), every 3 y (B3yr), or not at all (control) to represent a range of possible future forest fire frequencies (details in ref. 21). We used within-plot variability between the forest edge (0–100 m into the forest from the adjacent agricultural area) (Fig. 1) and forest interior (100–1,000 m) and the temporal variability in weather between 2004 and 2010 to address two questions: (i) Are there weather- and fuel-related thresholds in fire behavior that are associated with high levels of fire-induced tree mortality across two different fire regimes? (ii) What are the effects of an intense fire event on forest structure, flammability, and aboveground live carbon stocks? We also conducted a regional analysis of weather and fire scars to assess the spatial-temporal dynamics of forest fires in the 87,000 km² of remaining forests in the Upper Xingu River Basin (Fig. 1).Open in a separate windowFig. 1.High-resolution image (i.e., 1.85 m) of the experimental area in 2011 captured with the sensor Worldview-2. The dashed line represents the border between the North–South forest edge (0–100 m) and the forest interior (100–1,000 m). The North–South edge of the plots is bordered by a road and open agricultural fields, and the other plot boundaries are in contiguous forest. The control represents an unburned area, and B1yr and B3yr areas that were burned annually and every 3 y, respectively, from 2004 to 2010 (with the exception of 2008).Open in a separate windowFig. 2.(Left) Annual MCWD between 2000 and 2010 for the Upper Xingu Basin (solid circles) and the experimental field site (Fazenda Tanguro, solid triangles). The shaded area represents the SD of the mean and accounts for the spatial variability in MCWD across the Upper Xingu Basin. (Right) Average dry-season length (i.e., number of months with precipitation ≤100 mm) and the locations of both the Upper Xingu Basin (in gray) and the fire experiment (triangle). MCWD and monthly precipitation were derived from the TRMM.     

Little evidence for enhanced phenotypic evolution in early teleosts relative to their living fossil sister group

Since Darwin, biologists have been struck by the extraordinary diversity of teleost fishes, particularly in contrast to their closest “living fossil” holostean relatives. Hypothesized drivers of teleost success include innovations in jaw mechanics, reproductive biology and, particularly at present, genomic architecture, yet all scenarios presuppose enhanced phenotypic diversification in teleosts. We test this key assumption by quantifying evolutionary rate and capacity for innovation in size and shape for the first 160 million y (Permian–Early Cretaceous) of evolution in neopterygian fishes (the more extensive clade containing teleosts and holosteans). We find that early teleosts do not show enhanced phenotypic evolution relative to holosteans. Instead, holostean rates and innovation often match or can even exceed those of stem-, crown-, and total-group teleosts, belying the living fossil reputation of their extant representatives. In addition, we find some evidence for heterogeneity within the teleost lineage. Although stem teleosts excel at discovering new body shapes, early crown-group taxa commonly display higher rates of shape evolution. However, the latter reflects low rates of shape evolution in stem teleosts relative to all other neopterygian taxa, rather than an exceptional feature of early crown teleosts. These results complement those emerging from studies of both extant teleosts as a whole and their sublineages, which generally fail to detect an association between genome duplication and significant shifts in rates of lineage diversification.Numbering ∼29,000 species, teleost fishes account for half of modern vertebrate richness. In contrast, their holostean sister group, consisting of gars and the bowfin, represents a mere eight species restricted to the freshwaters of eastern North America (1). This stark contrast between teleosts and Darwin''s original “living fossils” (2) provides the basis for assertions of teleost evolutionary superiority that are central to textbook scenarios (3, 4). Classic explanations for teleost success include key innovations in feeding (3, 5) (e.g., protrusible jaws and pharyngeal jaws) and reproduction (6, 7). More recent work implicates the duplicate genomes of teleosts (8–10) as the driver of their prolific phenotypic diversification (8, 11–13), concordant with the more general hypothesis that increased morphological complexity and innovation is an expected consequence of genome duplication (14, 15).Most arguments for enhanced phenotypic evolution in teleosts have been asserted rather than demonstrated (8, 11, 12, 15, 16; but see ref. 17), and draw heavily on the snapshot of taxonomic and phenotypic imbalance apparent between living holosteans and teleosts. The fossil record challenges this neontological narrative by revealing the remarkable taxonomic richness and morphological diversity of extinct holosteans (Fig. 1) (18, 19) and highlights geological intervals when holostean taxonomic richness exceeded that of teleosts (20). This paleontological view has an extensive pedigree. Darwin (2) invoked a long interval of cryptic teleost evolution preceding the late Mesozoic diversification of the modern radiation, a view subsequently supported by the implicit (18) or explicit (19) association of Triassic–Jurassic species previously recognized as “holostean ganoids” with the base of teleost phylogeny. This perspective became enshrined in mid-20th century treatments of actinopterygian evolution, which recognized an early-mid Mesozoic phase dominated by holosteans sensu lato and a later interval, extending to the modern day, dominated by teleosts (4, 20, 21). Contemporary paleontological accounts echo the classic interpretation of modest teleost origins (22–24), despite a systematic framework that substantially revises the classifications upon which older scenarios were based (22–25). Identification of explosive lineage diversification in nested teleost subclades like otophysans and percomorphs, rather than across the group as a whole, provides some circumstantial neontological support for this narrative (26).Open in a separate windowFig. 1.Phenotypic variation in early crown neopterygians. (A) Total-group holosteans. (B) Stem-group teleosts. (C) Crown-group teleosts. Taxa illustrated to scale.In contrast to quantified taxonomic patterns (20, 23, 24, 27), phenotypic evolution in early neopterygians has only been discussed in qualitative terms. The implicit paleontological model of morphological conservatism among early teleosts contrasts with the observation that clades aligned with the teleost stem lineage include some of the most divergent early neopterygians in terms of both size and shape (Fig. 1) (see, for example, refs. 28 and 29). These discrepancies point to considerable ambiguity in initial patterns of phenotypic diversification that lead to a striking contrast in the vertebrate tree of life, and underpins one of the most successful radiations of backboned animals.Here we tackle this uncertainty by quantifying rates of phenotypic evolution and capacity for evolutionary innovation for the first 160 million y of the crown neopterygian radiation. This late Permian (Wuchiapingian, ca. 260 Ma) to Cretaceous (Albian, ca. 100 Ma) sampling interval permits incorporation of diverse fossil holosteans and stem teleosts alongside early diverging crown teleost taxa (Figs. 1 and ​and2A2A and Figs. S1 and ​andS2),S2), resulting in a dataset of 483 nominal species-level lineages roughly divided between the holostean and teleost total groups (Fig. 2B and Fig. S2). Although genera are widely used as the currency in paleobiological studies of fossil fishes (30; but see ref. 31), we sampled at the species level to circumvent problems associated with representing geological age and morphology for multiple congeneric lineages. We gathered size [both log-transformed standard length (SL) and centroid size (CS); results from both are highly comparable (Figs. S3 and ​andS4);S4); SL results are reported in the main text] and shape data (the first three morphospace axes arising from a geometric morphometric analysis) (Fig. 2A and Figs. S1) from species where possible. To place these data within a phylogenetic context, we assembled a supertree based on published hypotheses of relationships. We assigned branch durations to a collection of trees under two scenarios for the timescale of neopterygian diversification based on molecular clock and paleontological estimates. Together, these scenarios bracket a range of plausible evolutionary timelines for this radiation (Fig. 2B). We used the samples of trees in conjunction with our morphological datasets to test for contrasts in rates of, and capacity for, phenotypic change between different partitions of the neopterygian Tree of Life (crown-, total-, and stem-group teleosts, total-group holosteans, and neopterygians minus crown-group teleosts), and the sensitivity of these conclusions to uncertainty in both relationships and evolutionary timescale. Critically, these include comparisons of phenotypic evolution in early crown-group teleosts—those species that are known with certainty to possess duplicate genomes—with rates in taxa characterized largely (neopterygians minus crown teleosts) or exclusively (holosteans) by unduplicated genomes. By restricting our scope to early diverging crown teleost lineages, we avoid potentially confounding signals from highly nested radiations that substantially postdate both genome duplication and the origin of crown teleosts (26, 32). This approach provides a test of widely held assumptions about the nature of morphological evolution in teleosts and their holostean sister lineage.Open in a separate windowFig. 2.(A) Morphospace of Permian–Early Cretaceous crown Neopterygii. (B) One supertree subjected to our paleontological (Upper) and molecular (Lower) timescaling procedures to illustrate contrasts in the range of evolutionary timescales considered. Colors of points (A) and branches (B) indicate membership in major partitions of neopterygian phylogeny. Topologies are given in Datasets S4 and S5. See Dataset S6 for source trees.Open in a separate windowFig. S1.Morphospace of 398 Permian–Early Cretaceous Neopterygii. Three major axes of shape variation are presented. Silhouettes and accompanying arrows illustrate the main anatomical correlates of these principal axes, as described in Open in a separate windowFig. S2.Morphospace of 398 Permian–Early Cretaceous Neopterygii, illustrating the major clades of (A) teleosts and (B) holosteans.Open in a separate windowFig. S3.Comparisons of size rates between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Identical taxon sampling leads the CS and pruned SL datasets to yield near identical results. Although the larger SL dataset results often differ slightly, the overall conclusion from each pairwise comparison (i.e., which outcome is the most likely in an overall majority of trees) is identical in all but one comparison (E, under molecular timescales).Open in a separate windowFig. S4.Comparisons of size innovation between (A) holosteans and teleosts, (B) crown teleosts and all other neopterygians, (C) crown teleosts and stem teleosts, (D) crown teleosts and holosteans, and (E) stem teleosts and holosteans. Comparisons were made using the full-size SL dataset, a CS dataset, and a smaller SL dataset pruned to exactly match the taxon sampling of the CS dataset. Comparisons of size innovation are presented for K value distributions of the three datasets resemble each other closely.     

Temperate origins of long-distance seasonal migration in New World songbirds

Migratory species exhibit seasonal variation in their geographic ranges, often inhabiting geographically and ecologically distinct breeding and nonbreeding areas. The complicated geography of seasonal migration has long posed a challenge for inferring the geographic origins of migratory species as well as evolutionary sequences of change in migratory behavior. To address this challenge, we developed a phylogenetic model of the joint evolution of breeding and nonbreeding (winter) ranges and applied it to the inference of biogeographic history in the emberizoid passerine birds. We found that seasonal migration between breeding ranges in North America and winter ranges in the Neotropics evolved primarily via shifts of winter ranges toward the tropics from ancestral ranges in North America. This result contrasts with a dominant paradigm that hypothesized migration evolving out of the tropics via shifts of the breeding ranges. We also show that major lineages of tropical, sedentary emberizoids are derived from northern, migratory ancestors. In these lineages, the winter ranges served as a biogeographic conduit for temperate-to-tropical colonization: winter-range shifts toward the tropics during the evolution of long-distance migration often preceded southward shifts of breeding ranges, the loss of migration, and in situ tropical diversification. Meanwhile, the evolution of long-distance migration enabled the persistence of old lineages in North America. These results illuminate how the evolution of seasonal migration has contributed to greater niche conservatism among tropical members of this diverse avian radiation.The evolution of seasonal migratory behavior among animals involves a suite of behavioral, physiological, morphological, and neurological adaptations that enable migrants’ extraordinary feats of endurance and navigation (1–3). However, the evolution of migration also is an inherently geographic process during which a species’ breeding range and nonbreeding range (henceforth, winter range) become physically and ecologically separated (4). Understanding the evolution of migration therefore requires reconciling the fascinating adaptations of migratory individuals with the biogeographic factors that control the shifting boundaries of a species’ range (5). The field of historical biogeography has shed considerable light on the geographic histories of organisms (6, 7) but has largely ignored migratory species due to the difficulty of simultaneously reconstructing the evolution of the breeding and winter ranges, which in migratory species are often ecologically disparate and separated by long distances (5). Consequently, progress in our understanding of the evolution of migration has been impeded by a biogeographic conundrum: testing hypotheses on the evolution of migration requires knowledge of the geographic histories of migratory species (4, 8, 9), but the existence of migratory behavior in a lineage confounds our ability to infer these histories (10).This difficulty in resolving the geographic provenance of migratory species not only has left incomplete our understanding of the geographic histories of many lineages that contain migrants but also has impaired our ability to discriminate among hypotheses on the selective forces that drive the evolution of migratory behavior. For over a century, the principal dichotomy among hypotheses on the evolution of bird migration has hinged on a question of geographic ancestry: does seasonal migration evolve through a geographic shift of the breeding grounds away from an ancestral year-round range, or via a shift of the wintering grounds (11–13)? The most visible bird migrations occur between breeding regions at temperate latitudes to wintering areas at lower, more tropical latitudes (2). The dominant paradigm in the literature on the evolution of migration has imagined these long-distance migrations as evolving via shifts of the breeding range out of the tropics, driven by increased reproductive success and reduced competition in temperate regions (14–18). An opposing camp has hypothesized that migration evolves when species resident year-round in temperate latitudes shift their winter ranges to lower latitudes to increase survival during the harsh and resource-depleted temperate winters (11, 13, 19). Much debate has occurred over the selective forces that would make a tropical versus temperate ancestry of migratory birds more likely (12, 13). However, due to the absence of historical biogeographic models capable of handling the complex geographic ranges of migratory species, previous studies have had difficulty determining which geographic shifts produced the distributions of migratory species observed today, as well as where migratory lineages originated (4, 8–10, 20, 21).To address this challenge, we designed a phylogenetic model specifically for inferring the biogeographic history of migratory lineages. Our model is inspired by the dispersal–extinction–cladogenesis (DEC) model (22, 23), in which geographic range evolves via discrete events of dispersal and local extinction along phylogenetic branches and via inheritance and subdivision at speciation events. Our model follows similar logic but is novel in jointly considering the evolution of both the breeding and winter range.The discrete states of our model are presence–absence grids whose cells signify breeding and wintering in three latitudinal regions (Materials and Methods). These grids, which we refer to as “dominos,” summarize the ranges of New World bird species in each season (Fig. 1 and Table S1). Ancestor-to-descendant transitions between dominos represent expansion or contraction events of the breeding and/or winter range and describe broad patterns of change in the geography of seasonal migration (Fig. 2 and Fig. S1). We focus on the evolution of long-distance bird migration between summer breeding grounds in temperate North America and winter grounds at subtropical or tropical latitudes. This migratory system, known as Nearctic-Neotropical migration (henceforth, Neotropical migration), involves the largest number of species of any avian migratory system in the New World (24, 25).Open in a separate windowFig. 1.In the domino model, 3 × 2 grids (“dominos”) describe species’ breeding and winter ranges. Grid rows correspond to latitudinal zones (North America, Middle America and the Caribbean, and South America). Orange dots (left column) and blue dots (right column) indicate whether a species breeds and winters, respectively, in a latitudinal zone. The 15 dominos in this figure describe the distributions of all New World emberizoids and are the tip states in the phylogenetic analysis. We classify dominos according to four distributional patterns: (A) endemic in breeding and winter range to North America, (B) endemic in breeding and winter range to the tropics (Middle and/or South America), (C) migratory between North America and the tropics (Neotropical migration), and (D) widespread in breeding and winter range. Range maps illustrate species distributions that exemplify each of four dominos in bold outline: Artemisiospiza belli (A), Tangara chilensis (B), Setophaga striata (C), Sturnella magna (D). Purple in range maps indicates areas of overlap of breeding and winter range.Open in a separate windowFig. 2.Transitions between dominos represent instantaneous expansions or contractions of the range along branches (between i and ii) or dichotomous subdivision or inheritance at speciation (between ii and iii). Here, the breeding range has expanded from Middle America to North America (i to ii) during the evolution of Neotropical migration. A speciation event occurs at ii; illustrated here is one possible scenario of range subdivision/inheritance following speciation, resulting in one tropical endemic domino (iiia) and one Neotropical migratory domino (iiib).Applied to a phylogenetic tree, the domino model enables estimation of ancestral breeding and winter ranges (that is, ancestral dominos), as well as rates of ancestor–descendant transitions between dominos (Materials and Methods). Our estimation of transition rates results in an adjacency matrix that describes every possible geographic change throughout phylogenetic history (Fig. 3A). The complexity of this matrix illustrates the principal challenge of inferring the biogeographic histories of migratory species: many different sequences of geographic change could explain the evolution of a given migratory species’ range (5, 26). To overcome this challenge, we used graph theory and network analysis to extract from the rate matrix the dominant pathways of geographic evolutionary change that led to the evolution of Neotropical migration (Fig. 3 B and C and Materials and Methods).Open in a separate windowFig. 3.Network analysis. The instantaneous transition rates in the model can be summarized as a 23 × 23 adjacency matrix (A). Axis ticks represent the 23 dominos as ancestor (x) and descendant (y) states; three example dominos representing tropical, Neotropical migratory (NM), and North American (NA) states are shown on axes. Outlined squares represent permitted direct pairwise transitions; white space indicates pairwise transitions that are not permitted instantaneously. Permitted transitions are shaded on a gradient corresponding to estimated mean rate, from 0 (white) to 1.0 (black). For hypothesis testing, we treated the adjacency matrix of transition rates in A as a weighted, directed graph (B) wherein each node (circles) represents one of 23 dominos, each edge (arrows) represents a transition (corresponding to outlined squares in A), and edge weights (thickness) correspond to mean transition rate. A reduced set of nodes is shown here for illustrative purposes. To test how Neotropical migration evolved, we calculated weighted path lengths between the NA domino (Fig. 1A) and each of the NM dominos (Fig. 1C), and between each of the tropical dominos (Fig. 1B) and the NM dominos (Fig. 1C). One possible path from NA to a NM domino is depicted in B with blue arrows, indicating shifts of the winter range, and from a tropical domino to the same NM domino with orange arrows, indicating shifts of the breeding range. (C) Boxplots with median and quartile values show that the shortest paths from NA to the NM dominos (blue, n = 7) were shorter than shortest paths from tropical to NM dominos (orange, n = 21). Shorter weighted paths indicate higher rate sequences of range evolution (Materials and Methods and Fig. S2); therefore, winter-range shifts from North America had a more dominant influence on the evolution of Neotropical migration than breeding-range shifts from the tropics.We applied the model in a study of the largest New World radiation of migratory birds, the emberizoid passerines (superfamily Emberizoidea), for which we had a recent and comprehensive species-level molecular phylogeny (27). This lineage of ∼823 songbird species contains all New World warblers, sparrows, blackbirds, orioles, cardinals, buntings, tanagers, and allies. Most emberizoid diversity is comprised of nonmigratory, tropical species, mirroring the more general global trend of higher species diversity in the tropics than in temperate regions (28). However, all major lineages of Emberizoidea except the Thraupidae (tanagers) also contain Neotropical migrants (species that breed in North America and spend the northern winter in the Neotropics); in total, the group contains 120 species of Neotropical migrants, which together represent 25% of all Neotropical migratory bird species (24).The ancestral emberizoid is thought to have colonized the Americas via Beringia and thus have a northern origin in the New World (27, 29). However, insight into the group’s biogeographic history in the New World has been complicated by the dilemma of Neotropical migration, particularly because multiple gains and losses of migration are evident throughout emberizoid history (10, 20, 30). Did major emberizoid lineages originate in the tropics, implying that Neotropical migration evolved in these lineages via shifts of the breeding ranges to North America? Or did migratory emberizoid lineages originate in North America and evolve Neotropical migration via shifts of the winter ranges toward the equator? What does the geographic history of Neotropical migration imply for the origins, geographic spread and diversification of this diverse, widespread radiation?     

From the Cover: Eleventh-century shift in timber procurement areas for the great houses of Chaco Canyon

An enduring mystery from the great houses of Chaco Canyon is the origin of more than 240,000 construction timbers. We evaluate probable timber procurement areas for seven great houses by applying tree-ring width-based sourcing to a set of 170 timbers. To our knowledge, this is the first use of tree rings to assess timber origins in the southwestern United States. We found that the Chuska and Zuni Mountains (>75 km distant) were the most likely sources, accounting for 70% of timbers. Most notably, procurement areas changed through time. Before 1020 Common Era (CE) nearly all timbers originated from the Zunis (a previously unrecognized source), but by 1060 CE the Chuskas eclipsed the Zuni area in total wood imports. This shift occurred at the onset of Chaco florescence in the 11th century, a time with substantial expansion of existing great houses and the addition of seven new great houses in the Chaco Core area. It also coincides with the proliferation of Chuskan stone tools and pottery in the archaeological record of Chaco Canyon, further underscoring the link between land use and occupation in the Chuska area and the peak of great house construction. Our findings, based on the most temporally specific and replicated evidence of Chacoan resource procurement obtained to date, corroborate the long-standing but recently challenged interpretation that large numbers of timbers were harvested and transported from distant mountain ranges to build the great houses at Chaco Canyon.The high desert landscape of Chaco Canyon, New Mexico was the locale of a remarkable cultural development of Ancestral Puebloan peoples, including the construction of some of the largest pre-Columbian buildings in North America (1) (Fig. 1). The monumental “great houses” of Chaco Canyon reflect an elaborate socioecological system that spanned much of the 12,000-km² San Juan Basin from 850 to 1140 Common Era (CE) (2). These massive stone masonry structures required a wealth of resources to erect, including an estimated 240,000 trees incorporated as roof beams, door and window lintels, and other building elements (3). The incongruity of the great houses located in a nearly treeless landscape has led archaeologists and paleoecologists to investigate the origins of timbers used in construction (4–9). Beyond the simple curiosity driving this question, the answer has important implications for understanding the complexities of human–environmental interactions, the sociopolitical organization, and the economic structure of Chacoan society (10–12).Fig. 1.Aerial view of Pueblo Bonito, the largest of the Chaco Canyon great houses. Image courtesy of Adriel Heisey.The first excavators of the great houses in the early 20th century speculated that construction timbers were harvested locally, perhaps resulting in deforestation of the surrounding landscape (13). Paleoecological studies conducted during the late 1970s and early 1980s, however, showed that ponderosa pine (Pinus ponderosa), the primary tree species used in construction, was not abundant enough at the relatively low elevations (1,800–2,000 m above sea level) of Chaco Canyon and nearby mesas to support timber demand (14–16). Spruce (Picea spp.) and fir (Abies spp.), which account for tens of thousands of construction beams, have been absent from Chaco Canyon for at least 12,000 y and could have only been logged from distant, higher-elevation sites (2,500–3,450 m above sea level) (4). An inadequate supply of timbers in Chaco Canyon and its immediate surroundings during Puebloan occupation strongly suggests long-distance procurement from surrounding mountain ranges, where all three conifers now grow in abundance. This inference was corroborated by strontium isotope (⁸⁷Sr/⁸⁶Sr)-based sourcing. Through a comparison of ⁸⁷Sr/⁸⁶Sr values from great-house timbers to ₈₇Sr/₈₆Sr values from conifer stands growing today in mountains surrounding the San Juan Basin, two studies concluded that the Chuska Mountains (75 km west) and Mount Taylor (85 km southeast) were the most likely sources for spruce, fir, and ponderosa pine trees (6, 7). Recently, the explanation of long-distance timber transport and the related interpretations of ⁸⁷Sr/⁸⁶Sr evidence have been challenged and an alternative has been proposed that most great-house timbers (particularly ponderosa pine) were just as likely to have originated from nearby and low-elevation sites within, east, and south of Chaco Canyon (8, 9).We assessed probable timber origins independently from previous efforts by applying tree-ring width-based sourcing techniques to a set of 170 beams from our archives at the University of Arizona. These beams comprise six tree species from seven great-house structures (17) (Fig. S1). Each site chronology, as the average of 40–100 trees, represents tree-ring growth patterns peculiar to an individual landscape. This method of identifying the probable origin of timbers has been applied widely in Europe in the study of archaeological and nautical timbers and artifacts, musical instruments, and paintings on oak panels (17–20). These techniques are underused in North America, but recent efforts in the northeastern United States have revealed distant, inland sources for 18th- and 19th-century nautical timbers (21, 22).Table S1.Number of sourced beams by species and great-house structureFig. S1.An example of sourcing a great-house beam via tree-ring-width methods. (A) An individual beam (black line), the ponderosa pine JPB-88 from Pueblo del Arroyo, and the Chuskas chronology (red line). (B) Bivariate plot comparing the ring-width indices of ...Tree-ring sourcing can only be applied where tree growth patterns are distinguishable between the potential locations of origin. In the southwestern United States tree growth primarily responds to regionally coherent winter precipitation (23, 24), and as a consequence trees across the region tend to share roughly half of their interannual variability (25). Differences between site chronologies are predominantly attributed to variations in topography and subregional-scale climate conditions (26).We compared great-house beams to the site chronologies of eight potential harvesting areas surrounding the San Juan Basin. Chaco Canyon was not included as one of our sites because it lacked enough remnant wood from the Chaco era to build a local site chronology. To assess the efficacy and accuracy of the tree-ring sourcing method within the San Juan Basin, we tested whether tree-ring growth patterns could be distinguished between the various mountain ranges surrounding Chaco Canyon by applying sourcing methods to living trees of known origin (SI Text and Fig. S2).Table S2.Tree-ring sites used to evaluate tree-ring-based sourcing in the San Juan BasinFig. S2.Evaluation of dendroprovenance in the San Juan Basin. (A–F) Each tile provides a different test for a set of modern trees (green triangles). Circle sizes are proportional to the number of trees (labeled in the circle) sourcing to a given location. ...     

Bacterial lipids activate,synergize, and inhibit a developmental switch in choanoflagellates

In choanoflagellates, the closest living relatives of animals, multicellular rosette development is regulated by environmental bacteria. The simplicity of this evolutionarily relevant interaction provides an opportunity to identify the molecules and regulatory logic underpinning bacterial regulation of development. We find that the rosette-inducing bacterium Algoriphagus machipongonensis produces three structurally divergent classes of bioactive lipids that, together, activate, enhance, and inhibit rosette development in the choanoflagellate Salpingoeca rosetta. One class of molecules, the lysophosphatidylethanolamines (LPEs), elicits no response on its own but synergizes with activating sulfonolipid rosette-inducing factors (RIFs) to recapitulate the full bioactivity of live Algoriphagus. LPEs, although ubiquitous in bacteria and eukaryotes, have not previously been implicated in the regulation of a host–microbe interaction. This study reveals that multiple bacterially produced lipids converge to activate, enhance, and inhibit multicellular development in a choanoflagellate.The foundational event in animal origins—the transition to multicellularity (1–3)—occurred in oceans filled with diverse bacteria (4–7). There is a growing appreciation that specific bacteria direct diverse animal developmental processes, including light organ development in the Hawaiian bobtail squid and immune system development and maturation in organisms as diverse as cnidaria and mammals (8–20). However, the multicellularity of animals and the complex communities of bacteria with which they often interact hinder the complete characterization of many host–microbe dialogues.Choanoflagellates, a group of microbial eukaryotes that are the closest living relatives of animals (21–24), promise to help illuminate the mechanisms by which bacteria influence animal development. As did cells in the first animals, choanoflagellates use a distinctive collar of actin-filled microvilli surrounding a flow-generating apical flagellum to capture bacteria as prey (25–27). Indeed, choanoflagellate-like cells likely formed the basis for the evolution of animal epithelial cells that today provide a selective barrier for mediating interactions with bacteria (27–29).In many choanoflagellates, including Salpingoeca rosetta, a developmental program can be initiated such that single cells develop into multicellular rosettes. Importantly, rosette development does not occur through cell aggregation. Instead, as in the development of an animal from a zygote, rosettes develop from a single founding cell that undergoes serial rounds of oriented cell division, with the sister cells remaining stably adherent (Fig. 1). The orientation of the nascently divided cells around a central focus, the production of extracellular matrix, and the activity of a C-type lectin called Rosetteless, ultimately result in the formation of spherical, multicellular rosettes (30–32). Rosettes resemble morula-stage embryos, and the transition to multicellularity in S. rosetta evokes ancestral events that spawned the first animals (26, 27, 33).Open in a separate windowFig. 1.Stages of rosette development in S. rosetta. During rosette development, a single founding cell undergoes serial rounds of cell division, resulting in a structurally integrated rosette. Importantly, rosette development does not involve cell aggregation. Shown are a single cell (A), a pair of cells (B), a 4-cell rosette (C), an 8-cell rosette (D) and a 16-cell rosette (E).The initiation of rosette development was recently found to be induced by a coisolated environmental bacterium, Algoriphagus machipongonensis (phylum Bacteroidetes) (34, 35). The ecological relevance of the interaction between A. machipongonensis (hereafter, Algoriphagus) and S. rosetta is evidenced by the coexistence of these organisms in nature (35) and the predator–prey relationship between choanoflagellates and bacteria (25, 36). Indeed, rosettes likely have a fitness advantage over single cells in some environments, as multicellular choanoflagellates are predicted to produce increased flux of water past each cell (37), and prey capture studies reveal that rosettes collect more bacterial prey/cell/unit time than do single cells (38). However, in other environments, rosette development would likely reduce fitness as rosettes have reduced motility relative to single cells. Therefore, we hypothesize that choanoflagellates use bacterially produced molecules to identify environments in which rosette development might provide a fitness advantage.The simplicity of the interaction between S. rosetta and Algoriphagus, in which both members can be cultured together or independently, offers a biochemically tractable model for investigating the molecular bases of bacteria–eukaryote interactions. Using rosette development as a bioassay, the first rosette-inducing molecule, Rosette Inducing Factor-1 (RIF-1), was isolated from Algoriphagus. The observation that RIF-1 fails to fully recapitulate the bioactivity of the live bacterium (Fig. 2 A and C) raised the possibility that additional molecules might be required (35). To gain a more complete understanding of the molecules and regulatory logic by which bacteria regulate rosette development, we set out to identify the minimal suite of molecules produced by Algoriphagus that are necessary and sufficient to regulate rosette development in S. rosetta.Open in a separate windowFig. 2.Maximal rosette development requires lipid cofactor interactions. (A) When treated with media that lack necessary bacterial signals (Media Control), S. rosetta does not produce rosettes. In contrast, when treated with live Algoriphagus, Algoriphagus-conditioned media, OMVs from Algoriphagus, or bulk lipids extracted from Algoriphagus, rosettes develop at maximal (∼90% cells in rosettes) or near-maximal levels. (B) A heat map depicts the rosette-inducing activity of Algoriphagus lipid fractions used to treat SrEpac, either in isolation or in combination, at a final lipid concentration of 2 μg/mL Sulfonolipid-enriched fraction 11 was the only fraction sufficient to induce rosette development when tested alone (30% of cells in rosettes). Tests of each of the lipid fractions in combination revealed previously unidentified inhibitory and enhancing activity. Fractions 4 and 5 decreased rosette development (to 12% and 8%, respectively) in fraction 11-treated cells, whereas fraction 7 increased rosette development to 65%. (C) The RIF mix (solid square) and purified RIF-2 (solid circle) induced rosette development at micromolar concentrations. (Inset) RIF-1 (open circle) is active at femtomolar to nanomolar concentrations, but induces 10-fold lower levels of rosette development than RIF-2. The long gray box in the main graph indicates the range of concentrations at which RIF-1 is active and the range of its rosette-inducing activity. Rosette development was quantified 24 h after induction. Minor ticks on x axis are log-spaced.     

Roles of multiple-proton transfer pathways and proton-coupled electron transfer in the reactivity of the bis-FeIV state of MauG

The high-valent state of the diheme enzyme MauG exhibits charge–resonance (CR) stabilization in which the major species is a bis-Fe^IV state with one heme present as Fe^IV=O and the other as Fe^IV with axial heme ligands provided by His and Tyr side chains. In the absence of its substrate, the high-valent state is relatively stable and returns to the diferric state over several minutes. It is shown that this process occurs in two phases. The first phase is redistribution of the resonance species that support the CR. The second phase is the loss of CR and reduction to the diferric state. Thermodynamic analysis revealed that the rates of the two phases exhibited different temperature dependencies and activation energies of 8.9 and 19.6 kcal/mol. The two phases exhibited kinetic solvent isotope effects of 2.5 and 2.3. Proton inventory plots of each reaction phase exhibited extreme curvature that could not be fit to models for one- or multiple-proton transfers in the transition state. Each did fit well to a model for two alternative pathways for proton transfer, each involving multiple protons. In each case the experimentally determined fractionation factors were consistent with one of the pathways involving tunneling. The percent of the reaction that involved the tunneling pathway differed for the two reaction phases. Using the crystal structure of MauG it was possible to propose proton–transfer pathways consistent with the experimental data using water molecules and amino acid side chains in the distal pocket of the high-spin heme.MauG (1) is a diheme enzyme that catalyzes a six-electron oxidation required for posttranslational modification of a precursor of methylamine dehydrogenase (preMADH) (2) to complete the biosynthesis of its protein-derived cofactor (3) tryptophan tryptophylquinone (TTQ) (4). The hemes of MauG are unusual in several respects. One is a high-spin five-coordinate heme that is ligated by His35. The other is a low-spin six-coordinate heme with two ligands provided by His205 and Tyr294 (1, 5). The latter is, to our knowledge, the first example of natural His–Tyr ligation of a protein-bound heme cofactor, and the first example of Tyr ligation of a c-type heme. An intervening residue, Trp93, “connects” the two hemes (Fig. 1) via rapid electron transfer (ET) (6–9). A unique feature of MauG is that the oxidation of diferric MauG by H₂O₂, or of diferrous MauG by O₂, generates a high-valent bis-Fe^IV state (8) in which the high-spin heme is present as Fe^IV=O with the His35 ligand, and the other heme is present as Fe^IV with the His–Tyr axial ligation retained (5, 10, 11). Formation of the bis-Fe^IV state is accompanied by changes in the visible absorbance spectrum. One observes a decrease in intensity and shift of the Soret peak from 406 to 408 nm and appearance of minor peaks at 526 and 559 nm (Fig. 2) (9, 12).Open in a separate windowFig. 1.Diheme site of MauG. A portion of the crystal structure of the MauG-preMADH complex [Protein Data Bank (PDB) ID code 3L4M] is shown with MauG in pink, the MADH β-subunit in green, and the α subunit in blue. Shown in sticks are the hemes of MauG, the intervening Trp93, the three Met residues that are susceptible to autooxidation, the residues on preMADH that are modified by MauG, and Trp-199 which mediates ET from preMADH to bis-Fe^IV MauG. This figure was produced using PyMOL (www.pymol.org).Open in a separate windowFig. 2.Changes in the absorption spectrum of MauG caused by addition of H₂O₂ to diferric MauG. Spectra of MauG were recorded before (solid line) and after (dashed line) the addition of a stoichiometric amount of H₂O₂.The entire absorbance spectrum (A) is presented and the changes in the Soret region (B) and NIR region (C) are magnified.Despite being a highly potent oxidant, the bis-Fe^IV species displays extraordinary stability with a half-life of several minutes in the absence of its substrate (8). A basis for this stability was inferred from the observation of a near-infrared (NIR) electronic absorption feature centered at 950 nm that was observed in bis-Fe^IV MauG (Fig. 2C). This spectral feature is characteristic of a charge–resonance (CR) transition phenomenon (6, 9). A model was presented in which the CR occurs in the absence of direct heme–heme contact by ultrafast and reversible ET between the two high-valent hemes, via hopping through the intervening Trp93 residue (9). In this model the high-valent form of MauG comprises an ensemble of resonance structures including compound ES-like and compound I-like forms of the hemes, with the bis-Fe^IV as the dominant species.The catalytic mechanism of MauG is unusual in that the preMADH substrate does not make direct contact with either heme but instead binds to the surface of MauG several angstroms away (5). Catalysis requires long-range ET to bis-Fe^IV MauG from the residues on preMADH that are modified via a hole-hopping mechanism through Trp199 (13, 14), which resides at the MauG–preMADH interface (Fig. 1). Concomitant with this ET is the formation of free-radical intermediates on preMADH that go on to form the TTQ product (15). In the absence of preMADH, the autoreduction of the bis-Fe^IV redox state to the diferric state leads to inactivation of MauG (16). Analysis of the damaged MauG revealed that this process involves the oxidation of three Met residues (108, 114, and 116) which are located 7.5–15.2 Å from the high-spin heme iron (Fig. 1) (17).To further investigate the dynamic nature of the ensemble of resonance forms of MauG that comprise the high-valent state and the basis for its stability, temperature-dependence and kinetic solvent isotope effect (KSIE) studies were performed. These studies provide evidence for a redistribution within the ensemble of resonance structures before loss of CR stabilization of the high-valent redox state which is linked to the reduction to the diferric state. Thermodynamic analysis of the rates of reaction of these processes reveals that the rates of the initial redistribution of the ensemble of resonance structures and the subsequent loss of CR stabilization exhibit different dependencies on temperature. This accounts for the fact that the early phase is only observable at lower temperatures. Proton inventories of the KSIE indicate that the rates of both the initial redistribution of the ensemble of high-valent species and the loss of CR stabilization are rate-limited by multiple proton-transfer (PT) steps involving two alternative pathways. The likely pathways are identified from the crystal structure of MauG.     

Plasmonic photonic crystals realized through DNA-programmable assembly

Three-dimensional dielectric photonic crystals have well-established enhanced light–matter interactions via high Q factors. Their plasmonic counterparts based on arrays of nanoparticles, however, have not been experimentally well explored owing to a lack of available synthetic routes for preparing them. However, such structures should facilitate these interactions based on the small mode volumes associated with plasmonic polarization. Herein we report strong light-plasmon interactions within 3D plasmonic photonic crystals that have lattice constants and nanoparticle diameters that can be independently controlled in the deep subwavelength size regime by using a DNA-programmable assembly technique. The strong coupling within such crystals is probed with backscattering spectra, and the mode splitting (0.10 and 0.24 eV) is defined based on dispersion diagrams. Numerical simulations predict that the crystal photonic modes (Fabry–Perot modes) can be enhanced by coating the crystals with a silver layer, achieving moderate Q factors (∼10²) over the visible and near-infrared spectrum.Enhancing light–matter interactions is essential in photonics, including areas such as nonlinear optics (1), quantum optics (2, 3), and high-Q lasing (4). In general, there are two ways of achieving this in optical cavities: (i) with long cavity lifetimes (high Q factors) and (ii) with strong photonic confinement (small mode volume, V) (2, 3). In particular, 3D dielectric photonic crystals, with symmetry-induced photonic band gaps (Bragg gaps), enhance light–matter interactions via high Q factors (4–6). However, the coupling strength between photons and electronic transitions within such systems is intrinsically weak owing to diffraction-limited photonic confinement (3, 7). Recently, it was suggested that a plasmonic counterpart of photonic crystals can prohibit light propagation and open a photonic band gap by strong coupling between surface plasmons and photonic modes (a polariton gap) if the crystal is in deep subwavelength size regime (8); these crystals have been referred to as polaritonic photonic crystals (PPCs) (9–12). This opens up the exciting possibility of combining plasmonics with 3D photonics in the strong coupling regime and optimizing the photonic crystals as small-mode-volume devices owing to the strong plasmonic mode confinement (13). However, such systems require control over the positioning of the plasmonic elements in the crystal on the nano- or deep subwavelength scale (8), and owing to this synthetic challenge such 3D PPCs have largely remained unexplored in the visible wavelength range.The recent discovery that DNA can be used to program the assembly of high-quality single crystals with well-defined crystal habits consisting of nanoparticles occupying sites in a preconceived lattice (14) opens up possibilities for fine tuning the interaction between light and highly organized collections of particles as a function of lattice constant and particle size. Here, we report that 3D plasmonic photonic crystals made by DNA-programmable assembly can be used to establish strong light–plasmon coupling with tunability based upon the DNA interconnects and the corresponding volume fraction of the plasmonic elements. The strong coupling is manifested in crystal backscattering spectra and mode splitting (0.10 and 0.24 eV) in dispersion diagrams. Simulation results that we also include show that, by coating the crystals with a silver layer, Fabry–Perot photonic modes of crystals can be enhanced, with moderate cavity Q factors (∼10²) over the visible and near-infrared (NIR) spectrum. In addition to being the first devices made by DNA-programmable colloidal crystallization, they illustrate the potential of the technique for making novel 3D crystals for photonic studies and applications.The plasmonic PPCs are synthesized from two batches of gold nanoparticles, each functionalized with oligonucleotide sequences that are hybridized to complementary linker sequences that induce the assembly of the particles into rhombic dodecahedra single crystals with a body-centered-cubic (BCC) arrangement of the particles (14) (Supporting Information, sections S1 and S2, Fig. S1, and Tables S1 and S2). The lattice constants and gold nanoparticle diameters of the three PPCs that we present (denoted PPC1, PPC2, and PPC3) are 27.2 and 5.6 nm, 32.2 and 9.0 nm, and 44.0 and 20.0 nm, respectively, resulting in substantially different gold volume fractions (PPC1 ∼0.91, PPC2 ∼2.3, and PPC3 ∼9.8%).PPCs can exhibit Fabry–Perot cavity modes (FPMs) owing to light interference induced by two parallel facets (15) in the microcavity geometry (Fig. 1 A and B) as long as the size of the PPCs is much larger than the wavelength of light (Supporting Information, section S3 and Fig. S2). FPMs can be detected via backscattering spectra (16) (Fig. 1 A and B) and allow one to probe the optical response of the PPCs. Importantly, within the PPCs the propagating photonic modes are expected to strongly couple to the gold nanoparticle surface plasmons (Fig. 1 B and C), forming a polariton band gap (8, 17). This is probed by optical experiments and theoretical calculations (Fig. 2, Supporting Information, sections S4–S6, and Figs. S3–S6). The backscattering spectra from the PPC center spots (Fig. 2 A, C, and E, Bottom) show Fabry–Perot interference patterns in the visible region (Fig. 2 B, D, and F, red lines). The agreement between a finite-difference time-domain (FDTD) simulation with a rhombic dodecahedron shape and an infinite slab model (Supporting Information, section S5 and Fig. S5) reveals the Fabry–Perot nature of these backscattering spectra, because FPMs are the only existing modes in the infinite slab geometry. Significantly, the Fabry–Perot oscillations are suppressed only around the surface plasmon resonance energy (∼530 nm; ∼2.3 eV) for PPC1 and PPC2, indicating the suppression of light propagation owing to coupling to surface plasmons. This behavior provides direct evidence for polariton band gap formation that is consistent with the theoretical predictions (8, 9, 18). These experimental results are in remarkably good agreement with two different infinite slab models, one with BCC crystal geometry and the other an effective medium theory (EMT) approximation that is based simply on the gold volume fraction without the effect of interparticle coupling (Fig. 2 B, D, and F; blue solid and dashed lines). For PPC3, FPMs are not observed below 500 nm (Fig. 2F) because of the strong absorption caused by the gold interband transition at relatively higher gold volume fraction. The discrepancy between the two models in FPM cutoff location (Fig. 2F, denoted by the two vertical lines) indicates that a considerable amount of interparticle coupling exists close to the surface plasmon resonance because EMT does not include interparticle coupling.Open in a separate windowFig. 1.A polaritonic photonic crystal made by DNA-programmable assembly. (A) Three-dimensional illustration of a plasmonic PPC, in the shape of a rhombic dodecahedron, assembled from DNA-modified gold nanoparticles. Red arrows indicate light rays normal to the underlying substrate, impinging on and backscattering through a top facet of the crystal (FPMs). The blue ones represent light rays entering through the slanted side facets and leaving the PPC through the opposite side, not contributing to the FPMs (Fig. S2). The top right inset shows the top view of the crystal with two sets of arrows defining two polarization bases at the top and side facets. The bottom right inset shows an SEM image of a representative single crystal corresponding to the orientation of the top right inset. (Scale bar, 1 µm.) (B) A 2D scheme showing the geometric optics approximation of backscattering consistent with the explanation in A. The hexagon outline is a vertical cross-section through the gray area in the top right inset of A parallel to its long edge. The box enclosed by a dashed line depicts the interaction between localized surface plasmons and photonic modes (red arrows; FPMs) with a typical near-field profile around gold nanoparticles. The contribution of backscattering through the side facets (blue arrows) to FPMs is negligible. (C) Scheme of plasmon polariton formation. The localized surface plasmons (yellow bar) strongly couple to the photonic modes (red bars; FPMs).Open in a separate windowFig. 2.Experimental and theoretical backscattering spectra of PPC1–3. (A) SEM image (Top) and optical bright field reflection mode image (Bottom) of PPC1 on a silicon substrate. (Scale bar, 1 µm.) (B) Measured backscattering spectrum (red solid line) of PPC1 from the center red spot in A, Bottom. Calculated backscattering spectra based on two infinite slab models with BCC crystal geometry (blue solid line) and EMT approximation (blue dashed line). FPMs are indicated by markers. (C–F) The same datasets for PPC2 and PPC3 as in A and B. PPC2 and PPC3 are on indium tin oxide (ITO)-coated glass slides. The optical images show bright spots at the center owing to backscattering from the top and bottom facets. Two vertical lines in F indicate spectral positions where FPMs are suppressed. (Scale bars, 1 µm.)Based on the spectral results, we examine the strong coupling behavior between the surface plasmons and FPMs in the PPCs with dispersion diagrams generated by FDTD photonic crystal analyses, including changes in the light–matter interactions by tailoring the lattice constant and gold nanoparticle size (Fig. 3 and Supporting Information, section S5). When the mode energies of PPC1 and PPC2 grow close to that of the localized surface plasmon resonance (LSPR), ?ω₀ (∼2.3 eV), the dispersion curves of the propagating modes form band gaps (Fig. 3 A and B). This is clearer in the absence of interband transition (insets of Fig. 3 A–C and Fig. S7 A–F). The origin of these band gaps is not the BCC translational symmetry of the crystals (Bragg gap) as in conventional dielectric photonic crystals (6). For Bragg gap formation in the visible, photonic crystals require a lattice constant an order of magnitude larger than those in this work (∼λ/2). Instead, the origin of the gaps is strong coupling between the surface plasmons and photonic modes owing to deep subwavelength lattice constants that define the separation of the polarizable particle components (high-density localized surface plasmons) (8, 9). In each crystal type (Fig. 3 A and B), coupling of this kind creates plasmon polaritons with anticrossing upper and lower branches in the dispersion diagrams forming a polariton band gap between the two branches, where propagating photonic modes are prohibited (8, 9, 17). The strength of the coupling is quantified by the mode splitting, ?Ω_R, (Supporting Information, section S7 and Fig. S7), which is the energy gap between the two branches at the resonant coupling point (17) (?Ω_R∼0.10 and 0.24 eV for PPC1 and PPC2, which are about ∼5 and ∼10% of ?ω₀; Fig. 3D). These mode splittings are comparable to a recently reported value based on 1D nanowire arrays on waveguide substrates (17). The EMT-generated curve without the effect of the interband transition (9) predicts a monotonically increasing mode splitting with the increase in gold volume fraction (1–10%; Fig. 3D), which agrees well with the FDTD photonic crystal analyses (Supporting Information, sections S6 and S7 and Fig. S7). This suggests the possibility of using metal volume fraction as a parameter to control coupling strength based on fine geometric tuning afforded by the DNA-programmable assembly technique (19). For PPC3, owing to the strong gold interband transition the upper branch in the dispersion diagram (Fig. 3C; <500 nm in Fig. 2F) is not clearly observable in the experiment, and therefore the mode splitting is not measurable. Based on a photonic crystal analysis without the presence of interband transitions, the upper branch of PPC3 is observed (Fig. 3C, Inset), and the mode splitting is ∼30% of ?ω₀ (Supporting Information, section S7 and Fig. S7). This large value arises due to the capability of the PPCs to coherently couple a large number of oscillators within a single microcavity.Open in a separate windowFig. 3.Calculated photonic mode dispersion, mode splitting, and effective mode index of PPC1–3. (A) The spectral density in the ΓN direction is presented for PPC1 (red is high, blue is low). Log10 scale is used. Red triangular markers are the FPMs in Fig. 2B (red markers). They are assigned to peak positions of the spectral densities and the mode number (N) is assigned on one FPM. (Inset) The same spectral density calculated based on the Drude model for gold (where there is no interband transition). (B and C) The same information as in A for PPC2 and PPC3. (D) The mode splitting to plasmonic mode energy ratio, ?Ω_R/?ω₀, is shown in terms of gold volume fraction. Blue dots are calculated based on EMT with the Drude model for gold. Squares are generated by a FDTD photonic crystal analysis with the Drude model for gold (red, green, and black: nanoparticle diameters 5.6, 9.0, and 20 nm; volume fraction of PPC3 indicated for 20 nm), and circles with experimentally measured gold permittivity (red and green: nanoparticle diameters 5.6 and 9.0 nm; PPC1 and PPC2). (E) EMT-based effective indices, Re[n_eff], for PPC1 (dotted line), PPC2 (dash-dot line), and PPC3 (dashed line). The index of the silica host medium (black solid line) is added as a reference. Red markers are Re[n_eff] based on the FPMs in A–C.Significantly, the strong coupling that we observe is further evidenced by quantifying the effective mode indices, Re[n_eff] (Fig. 3E). As the gold volume fraction increases to that of PPC3, the effective mode index drastically increases (Re[n_eff] ∼2) close to the LSPR frequency, indicating strong light coupling to surface plasmons and a large mode momentum gain (18, 20, 21). This is also apparent in the spectral profile, which shows an abrupt suppression of FPMs (two vertical lines in Fig. 2F) and a sharp increase in reflectance from 650 to 550 nm. This transition from Fabry–Perot to mirror-like behavior is due to an increase in both Re[n_eff] and Im[n_eff] close to the LSPR frequency (Fig. S6) that causes strong facet reflection and damping of the FPMs (18).The PPCs with lattice constants in the deep subwavelength regime can also behave as plasmonic cavity devices for studies such as cavity quantum electrodynamics (QED) (3, 22, 23). The plasmonic PPCs have, within a single structure, optical elements working on two different length scales: the plasmonic nanoparticles and the Fabry–Perot microcavity. Owing to localized surface plasmons, the gold nanoparticles exhibit extremely tight light confinement [a small mode volume, V <10⁻⁴(λ/n) (3), in the visible and NIR] around their metallic surfaces that augments light–matter interactions such as exciton–photon coupling (13, 24). These highly confined modes can be further enhanced (23, 25) if Q factors of Fabry–Perot modes are increased by coating the crystals with a silver layer (10–30 nm) (see Fig. S8 for the calculation approach and Figs. S9 and S10 for the experimental process). By simplifying the 3D shape of PPCs to a slab we can use the infinite slab model with a BCC crystal geometry to predict moderate Q factors (∼10²) of FPMs with varying silver layer thickness in the visible and NIR for PPC1–3 (Fig. 4). At ∼30-nm silver layer thickness, the Q factor saturates, and PPC1 exhibits the highest Q values owing to the lowest gold volume fraction. These numbers (∼10²; Fig. 4) are comparable to those of other plasmonic cavities in the literature (20, 23). This shows the possibility of tuning not just plasmonic modes by controlling BCC crystals but also enhancing the properties of photonic modes (FPMs) for further applications with excitonic materials such as dyes and quantum dots (26).Open in a separate windowFig. 4.Prediction of backscattering spectra and Q factor of silver-coated PPC1–3. (A) Backscattering spectra of PPC1–3 (from bottom to top: PPC1, PPC2, and PPC3) based on the infinite slab model with BCC crystal geometry. The thickness of the slabs is ∼1.3 µm, and that of silver coating layer is varied from 10 to 30 nm. As the coating thickness increases the line shape becomes sharper. The spectra of PPC1 and 2 are translated for comparison. (B) Q factors of each silver-coated slab are shown at FPMs (PPC1, red; PPC2, green; and PPC3, blue). The coating thickness is 30 nm.This work has shown how bioprogrammable colloidal crystallization can be used to access a new class of PPCs and related optical devices. Although the ability to create well-formed crystals via this technique is essential, it is the ability to tune light–plasmon coupling and plasmonic particle volume fraction that makes this approach so powerful from both fundamental science and potential device application standpoints. We anticipate that the studies herein and the single crystals realizable through the methodology will open the door to studying exciton–photon coupling in novel PPC plasmonic cavities and lead to new directions in cavity QED (22, 27), quantum optics (28–30), and quantum many-body dynamics (31, 32).     

Dietary options and behavior suggested by plant biomarker evidence in an early human habitat

The availability of plants and freshwater shapes the diets and social behavior of chimpanzees, our closest living relative. However, limited evidence about the spatial relationships shared between ancestral human (hominin) remains, edible resources, refuge, and freshwater leaves the influence of local resources on our species’ evolution open to debate. Exceptionally well-preserved organic geochemical fossils—biomarkers—preserved in a soil horizon resolve different plant communities at meter scales across a contiguous 25,000 m² archaeological land surface at Olduvai Gorge from about 2 Ma. Biomarkers reveal hominins had access to aquatic plants and protective woods in a patchwork landscape, which included a spring-fed wetland near a woodland that both were surrounded by open grassland. Numerous cut-marked animal bones are located within the wooded area, and within meters of wetland vegetation delineated by biomarkers for ferns and sedges. Taken together, plant biomarkers, clustered bone debris, and hominin remains define a clear spatial pattern that places animal butchery amid the refuge of an isolated forest patch and near freshwater with diverse edible resources.Spatial patterns in archaeological remains provide a glimpse into the lives of our ancestors (1–5). Although many early hominin environments are interpreted as grassy or open woodlands (6–8), fossil bones and plant remains are rarely preserved together in the same settings. As a result, associated landscape reconstructions commonly lack coexisting fossil evidence for hominins and local-scale habitat (microhabitat) that defined the distribution of plant foods, refuge, and water (7). This problem is exacerbated by the discontinuous nature and low time resolution often available across ancient soil (paleosol) horizons, including hominin archaeological localities. One notable exception is well-time-correlated 1.8-million-y-old paleosol horizons exposed at Olduvai Gorge. Associated horizons contain exceptionally preserved plant biomarkers along with many artifacts and fossilized bones. Plant biomarkers, which previously revealed temporal patterns in vegetation and water (8), are well preserved in the paleosol horizon and document plant-type spatial distributions that provide an ecosystem context (9, 10) for resources that likely affected the diets and behavior of hominin inhabitants.Plant biomarkers are delivered by litter to soils and can distinguish plant functional type differences in standing biomass over scales of 1–1,000 m² (11). Trees, grasses, and other terrestrial plants produce leaf waxes that include long-chain n-alkanes such as hentriacontane (nC₃₁), whereas aquatic plants and phytoplankton produce midchain homologs (e.g., nC₂₃) (12, 13). The ratio of shorter- versus long-chain n-alkane abundances distinguish relative organic matter inputs from aquatic versus terrestrial plants to sediments (13):P_aq = (nC₂₃ + nC₂₅)/(nC₂₃ + nC₂₅ + nC₂₉ + nC₃₁).Sedges and ferns are prolific in many tropical ecosystems (14). These plants both have variable and therefore nondiagnostic n-alkane profiles. However, sedges produce distinctive phenolic compounds [e.g., 5-n-tricosylresorcinol (ⁿR₂₃)] and ferns produce distinctive midchain diols [e.g., 1,13-dotriacontanediol (C₃₂-diol)] (SI Discussion).Lignin monomers provide evidence for woody and nonwoody plants. This refractory biopolymer occurs in both leaves and wood, serves as a structural tissue, and accounts for up to half of the total organic carbon in modern vegetation (11). Lignin is composed of three phenolic monomer types that show distinctive distributions in woody and herbaceous plant tissues. Woody tissues from dicotyledonous trees and shrubs contain syringyl (S) and vanillyl (V) phenols (12), whereas cinnamyl (C) phenols are exclusively found in herbaceous tissues (12). The relative abundance of C versus V phenols (C/V) is widely used to distinguish between woody and herbaceous inputs to sedimentary and soil organic matter (15).Plant biomarker ¹³C/¹²C ratios (expressed as δ¹³C values) are sensitive indicators of community composition, ecosystem structure, and climate conditions (8). Most woody plants and forbs in eastern Africa use C₃ photosynthesis (6), whereas arid-adapted grasses use C₄ photosynthesis (8, 14). These two pathways discriminate differently against ¹³C during photosynthesis, resulting in characteristic δ¹³C values for leaf waxes derived from C₃ (about –36.0‰) and C₄ (–21.0‰) plants (16). Carbon isotopic abundances of phenolic monomers of lignin amplify the C₃–C₄ difference and range between ca. –34.0‰ (C₃) and –14.0‰ (C₄) in tropical ecosystems (15). Terrestrial C₃ plant δ¹³C values decrease with increased exposure to water, respired CO₂, and shade (8), with lowest values observed in moist regions with dense canopy (17). Although concentration and δ¹³C values of atmospheric CO₂ can affect C₃ plant δ¹³C values (17), this influence is not relevant to our work here, which focuses on a single time window (SI Discussion). The large differences in leaf-wax δ¹³C values between closed C₃ forest to open C₄ grassland are consistent with soil organic carbon isotope gradients across canopy-shaded ground surfaces (6) and serve as a quantitative proxy for woody cover (f_woody) in savannas (8).As is observed for nonhuman primates, hominin dietary choices were likely shaped by ecosystem characteristics over habitat scales of 1–1,000 m² (3–5). To evaluate plant distributions at this small spatial scale (9), we excavated 71 paleosol samples from close-correlated trenches across a ∼25,000-m² area that included FLK Zinjanthropus Level 22 (FLK Zinj) at Olduvai Gorge (Fig. 1). Recent excavations (18–21) at multiple trenches at four sites (FLK^NN, FLK^N, FLK, and FLK^S, Fig. 1D) exposed a traceable thin (5–50 cm), waxy green to olive-brown clay horizon developed by pedogenic alterations of playa lake margin alluvium (22). Weak stratification and irregular redox stains suggest initial soil development occurred during playa lake regression (18, 22), around 1.848 Ma (ref. 23 and SI Discussion). To date, craniodental remains from at least three hominin individuals (18–20), including preadolescent early Homo and Paranthropus boisei, were recovered from FLK Zinj. Fossils and artifacts embedded in the paleosol horizon often protrude into an overlying airfall tuff (18, 19), which suggests fossil remains were catastrophically buried in situ under volcanic ash. Rapid burial likely fostered the exceptional preservation of both macrofossils (10) and plant biomarkers across the FLK Zinj land surface.Open in a separate windowFig. 1.Location and map of FLK Zinj paleosol excavations. (A and B) Location of FLK Zinj as referenced to reconstructed depositional environments at Olduvai Gorge during the early Pleistocene (18, 22) and the modern gorge walls. The perennial lake contained shallow saline–alkaline waters that frequently flooded the surrounding playa margin (i.e., floodplain) flats. (C) Outline of FLK Zinj paleosol excavation sites used for our spatial biomarker reconstructions. (D) Concentric (5 m) gridded distribution map of FLK Zinj paleosol excavations relative to previous archaeological trenches (18–21). Major aggregate complexes (FLK^NN, FLK^N, FLK, and FLK^S) are color-coded to show excavation-site associations.Plant biomarker signatures reveal distinct types of vegetation juxtaposed across the FLK Zinj land surface (Figs. 2–4 and Fig. S1). In the northwest, FLK^NN trenches show high nC₂₃ δ¹³C values (Fig. 2B) as well as high C/V and P_aq values (Figs. 3 and ​and4A).4A). They indicate floating or submerged aquatic plants (macrophytes) in standing freshwater (13), a finding that is consistent with nearby low-temperature freshwater carbonates (tufa), interpreted to be deposited from spring waters (22). Adjacent FLK^N trenches have lower P_aq values (Fig. 4A) with occurrences of fern-derived C₃₂-diol and sedge-derived ⁿR₂₃ (Fig. 2 C and D). These biomarker distributions indicate an abrupt (around 10 m) transition from aquatic to wetland vegetation. Less than 100 m away (Fig. 1C), low nC₃₁ δ¹³C values (Fig. 2A) and low C/V and very low P_aq values (Figs. 3 and ​and4A)4A) collectively indicate dense woody cover (Fig. 4B). In the farthest southeastern (FLK^S) trenches, high C/V values and high δ¹³C values for C lignin phenols (Fig. 3) indicate open C₄ grassland.Open in a separate windowFig. 2.Spatial distributions and δ¹³C values for plant biomarkers across FLK Zinj. Measured and modeled δ¹³C values (large and smaller circles, respectively) are shown for (A) nC₃₁ from terrestrial plants, (B) nC₂₃ from (semi)aquatic plants, (C) C₃₂-diol from ferns, and (D) ⁿR₂₃ from sedges (see refs. 12 and 13 and SI Discussion). Modeled values [inverse distance-weighted (9)] account for spatial autocorrelation (15-m radius) in standing biomass (35) over scales of soil organic matter accumulation (11). Black dots represent paleosols with insufficient plant biomarker concentrations for isotopic analysis.Open in a separate windowFig. 3.Molecular and isotopic signatures for lignin phenols across FLK Zinj. Bivariate plots are shown for diagnostic lignin compositional parameters (see refs. 12 and 15 and Fig. 1C). Symbols are colored according to respective δ¹³C values for the C lignin phenol, p-coumaric acid. FLK symbols are uncolored due to insufficient p-coumaric acid concentrations for isotopic analysis. Representative lignin compositional parameters (12, 15) are shown for monocotyledonous herbaceous tissues (G), dicotyledonous herbaceous tissues (H), cryptogams (N), and dicotyledonous woody tissues (W).Open in a separate windowFig. 4.Spatial relationships shared between local plant resources and hominin remains. Measured and modeled values (large and smaller circles, respectively) are shown for (A) P_aq (13) and (B) f_woody (8). Modeled values [inverse distance-weighted (9)] account for spatial autocorrelation (15-m radius) in standing biomass (35) over scales of soil organic matter accumulation (11). (C) Kernel density map of cut-marked bones (18–21) across the FLK Zinj land surface (Fig. S4). High estimator values indicate hotspots of hominin butchery (Fig. S5). A shaded rectangle captures the area (ca. 0.68 probability mass) with highest cut-marked bone densities and is shown in A and B for reference.Open in a separate windowFig. S1.Total ion chromatograms for saturated hydrocarbons in representative paleosols at (A) FLK^NN, (B) FLK^N, (C) FLK, and (D) FLK^S. C₂₃, tricosane; C₂₅, pentacosane; C₂₉ nonacosane; C₃₁, hentriacontane.Biomarkers define a heterogeneous landscape at Olduvai and suggest an influence of local resources on hominin diets and behavior. It is recognized (2, 24–26) that early Homo species and P. boisei had similar physiological characteristics. These similarities in physical attributes suggest behavioral differences were what allowed for overlapping ranges and local coexistence (sympatry) of both hominins. For instance, differences in seasonal subsistence strategies or different behavior during periods of drought and limited food could have reduced local hominin competition and fostered diversification via niche specialization (27–29).Physical and isotopic properties of fossil teeth indicate P. boisei was more water-dependent [low enamel δ¹⁸O values (24)] and consumed larger quantities of abrasive, ¹³C-enriched foodstuffs [flat-worn surfaces (25) and high enamel δ¹³C values (26)] than coexisting early Homo species. Although ¹³C-enriched enamel is commonly attributed to consumption of C₄ grasses or meat from grazers (14), this was not likely, because P. boisei craniodental features are inconsistent with contemporary gramnivores (24, 25) or extensive uncooked flesh mastication (26). Numerous scholars have proposed the nutritious underground storage organs (USOs) of C₄ sedges were a staple of hominin diets (14, 24, 26, 27). Consistent with this suggestion, occurrences of ⁿR₂₃ attest to the presence of sedges at FLK^NN and FLK^N (Fig. 2D). However, the low δ¹³C values measured for ⁿR₂₃ at these same sites (Fig. 2D and Fig. S2) indicate C₃ photosynthesis (12, 16), a trait common in modern sedges that grow in alkaline wetlands and lakes (30) (Fig. S3). Thus, biomarker signatures support the presence of C₃ sedges in the wetland area of FLK Zinj.Open in a separate windowFig. S2.Total ion chromatogram [TIC (A)] and selected ion chromatograms for derivatized 5-n-alkylresorcinols [m/z 268 (●)] and midchain diols [m/z 369 (○)] from a representative paleosol at FLK^N. Also shown are δ¹³C values for homologous (B) 5-n-alkylresorcinols and (C) midchain diols. C₃₂-diol, dotriacontanediol; ⁿR₂₃, tricosylresorcinol.Open in a separate windowFig. S3.Summary phyogenetic consensus tree of Cyperaceae (sedges) based on nucleotide (rcbL and ETS1f) sequence data (50–54, 95, 96). Important taxonomic distinctions discussed in SI Discussion, Fern Alkyldiols are shown explicitly. Triangle-enclosed digits represent the number of additional branches at different levels of taxonomic classification. CEFA, Cypereae Eleocharideae Fuireneae Abildgaardieae; CSD, Cariceae Scirpeae Dulichieae.Alternative foodstuffs with abrasive, ¹³C-enriched biomass include seedless vascular plants (cryptogams), such as ferns and lycophytes [e.g., quillworts (27, 30)]. Ferns are widely distributed throughout eastern Africa in moist and shaded microhabitats (31) and are often found near dependable sources of drinking water (32). Today, ferns serve as a dietary resource for humans and nonhuman primates alike (27), and fiddlehead consumption is consistent with the inferred digestive physiology [salivary proteins (33)] and the microwear on molars (34) of P. boisei in eastern Africa (25, 26). Ferns were present at FLK^N, based on measurements of C₃₂-diol (Fig. 2D). Further, the high δ¹³C values measured for these compounds are consistent with significant fern consumption by P. boisei at Olduvai Gorge.Ferns and grasses were not the only plant foods present during the time window documented by FLK Zinj. Further, the exclusive reliance on a couple of dietary resources was improbable for P. boisei, because its fossils occur in diverse localities (24–26). Aquatic plants are an additional candidate substrate, as evidenced by high P_aq values at FLK^NN and FLK^N (Fig. 4A). Floating and submerged plants proliferate in wetlands throughout eastern Africa today (13, 14), and many produce nutritious leaves and rootstock all year long (27, 28). Although C₄ photosynthesis is rare among modern macrophytes (30), they can assimilate bicarbonate under alkaline conditions, which results in C₄-like isotope signatures in their biomass (30). Their leaf waxes, such as nC₂₃ (13), are both present and carry ¹³C-enriched signatures at FLK^NN and FLK^N (Fig. 2B). It is also likely that aquatic macrophytes sustained invertebrates and fish with comparably ¹³C-enriched biomass, as they do in modern systems (14), and we suggest aquatic animal foods could have been important in P. boisei diets (27, 28).Biomarkers across the FLK Zinj soil horizon resolve clear patterns in the distribution of plants and water and suggest critical resources that shaped hominin existence at Olduvai Gorge. The behavioral implications of local conditions require understanding of regional climate and biogeography (3–5, 7), because hominin species likely had home ranges much larger than the extent of excavated sites at FLK Zinj. Lake sediments at Olduvai Gorge include numerous stacked tuffs with precise radiometric age constraints (23). These tephrostratigraphic correlations (21) tie the FLK Zinj landscape horizon to published records of plant biomarkers in lake sediments that record climate cycles and catchment-scale variations in ecology. Correlative lake sediment data indicate the wet and wooded microhabitats of FLK Zinj sat within a catchment dominated by arid C₄ grassland (8). Under similarly arid conditions today, only a small fraction of landscape area (ca. 0.05) occurs within 5 km of either forest or standing freshwater (35). Given a paucity of shaded refuge and potable water in the catchment, the concentration of hominin butchery debris (18–21) exclusively within the forest microhabitat and adjacent to a freshwater wetland (Fig. 4) is notable. We suggest the spatial patterns defined by both macro- and molecular fossils reflect hominins engaged in social transport of resources (1–5), such as bringing animal carcasses and freshwater-sourced foods from surrounding grassy or wetland habitats to a wooded patch that provided both physical protection and access to water.     

A 17-My-old whale constrains onset of uplift and climate change in east Africa

Timing and magnitude of surface uplift are key to understanding the impact of crustal deformation and topographic growth on atmospheric circulation, environmental conditions, and surface processes. Uplift of the East African Plateau is linked to mantle processes, but paleoaltimetry data are too scarce to constrain plateau evolution and subsequent vertical motions associated with rifting. Here, we assess the paleotopographic implications of a beaked whale fossil (Ziphiidae) from the Turkana region of Kenya found 740 km inland from the present-day coastline of the Indian Ocean at an elevation of 620 m. The specimen is ∼17 My old and represents the oldest derived beaked whale known, consistent with molecular estimates of the emergence of modern strap-toothed whales (Mesoplodon). The whale traveled from the Indian Ocean inland along an eastward-directed drainage system controlled by the Cretaceous Anza Graben and was stranded slightly above sea level. Surface uplift from near sea level coincides with paleoclimatic change from a humid environment to highly variable and much drier conditions, which altered biotic communities and drove evolution in east Africa, including that of primates.The Turkana ziphiid was found at Loperot in West Turkana, Kenya and described in 1975 by James G. Mead (1), who listed associated fauna, including mollusks, fish, crocodiles, turtles, and mammals, notably primates. Mead (1) detailed the anatomy of the whale fossil, estimated its length in life at some 7 m, and speculated that it was an open-ocean whale that became stranded after swimming up an eastward-flowing river and was then preserved near where it died. After the original publication of this fossil find (1), the specimen went missing until late 2011, when it was rediscovered at Harvard University and returned to the National Museums of Kenya (KNM), where it is curated under the number KNM-LP 52956.Because the whale was found during the pre-Global Positioning System era, we studied the original 1964 Harvard expedition field notes and catalog to locate the exact site of the ziphiid (2°23′30″ N, 35°52′30″ E by triangulation) (Fig. 1C) in coarse fluvial sandstones and conglomerates of the Lower to Middle Miocene Auwerwer Formation (2–4) ∼7 m below a basalt dated at 17.1 ± 1.0 Ma (4). Although located in an area repeatedly affected by extensional processes since the Cretaceous, the fossil location corresponds to the northern periphery of the Late Cenozoic East African Plateau (EAP) (Fig. 1B). The specimen consists of the rostrum and the ascending processes of the maxillae and premaxillae (length of 82 cm and width of 55 cm) broken from the rest of the skull (1). Five phylogenetically informative characters of KNM-LP 52956 (Fig. 2A and SI Appendix) were scored and entered into a data matrix of 46 characters and 29 fossil and recent taxa (5). A traditional Wagner tree search (one random seed and 10 replications) applied to unweighted and unordered characters yielded 17 most parsimonious trees of 124 steps (Fig. 2B). In all most parsimonious trees, the Turkana ziphiid falls in a derived but unresolved clade with modern Indopacetus, Hyperoodon, and Mesoplodon plus four extinct genera.Open in a separate windowFig. 1.(A) Topography of Africa and bathymetry of the Atlantic and Indian Oceans. The white box indicates the location of the principal map shown in B. (B) Geological setting of the Cenozoic East African Rift System and topography of the present EAP based on a digital elevation model derived from Satellite Radar Topography Mission data. Elevations >1,000 m are enclosed by a white line. Rock ages for the basalt overlaying the beaked whale fossil and the Yatta lava flow phonolites are based on the radioactive decay of potassium (⁴⁰K) into argon (⁴⁰Ar). The white box indicates the location of the geological map in C. (C) Geological map of the Oligo-Miocene Lokichar Basin. The red diamond indicates the beaked whale fossil locality (Williams’ Flat) within the Lower to Middle Miocene Auwerwer Formation.Open in a separate windowFig. 2.(A) Computed tomography rendering of the Turkana ziphiid (KNM-LP 52956) showing 5 of 46 characters from the work by Lambert et al. (5) that could be scored for phylogenetic analysis. 2, Mesorostral groove filled with mesorostral ossification of the vomer with convergent lateral walls of the vomer in the rostrum base area (state 2, derived); 3, mesorostral groove filled with vomer and no mediodorsal contact of the premaxilla (state 0, primitive); 4, prenarial basin absent (state 0, primitive); 27, lacks maxillary alveoli (state 1, derived); 30, rostral prominence lacking (state 0, primitive) (SI Appendix). (B) Beaked whales through time. Ziphiid cladogram calculated using the traditional Wagner tree search phylogenetic analysis program TNT (tree analysis using new technology) (unweighted and unordered; blue dots at nodes indicate clades within Ziphiidae, and numbers indicate Bremer Support values). KNM-LP 52956 falls in a derived clade with modern Indopacetus, Hyperoodon, and Mesoplodon. *Taxa with their earliest records derived from specimens dredged from the sea floor. (Inset) The analysis by Lambert et al. (5) was run with weighted and ordered characters, accounting for topological differences in the resulting trees. Temporal ranges were obtained from the Paleobiology Database (www.paleobiodb.org).Beaked whales are predicted by molecular clocks to have originated 26.52–35.82 Ma (6). The early record of fossil ziphiids is poor, but at 17.1 ± 1.0 Ma, the Kenyan specimen is currently the most precisely dated ziphiid fossil. Phylogenetic analysis nests the Turkana ziphiid with three modern genera, most notably Mesoplodon, which has species that are estimated to have diverged at 16.6 Ma (6, 7). Thus, the geochronologic constraint provided by the Turkana ziphiid is consistent with molecular predictions.