Nature Ecology & Evolution

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High-throughput phenotypic assessment via automated image analysis tool DAPCHA
a, Phenotypic assessment of Daphnia using DAPCHA involves three major steps: conversion of a standardized, raw image to grayscale (i and ii); automated identification of key landmarks (eye, tail tip and tail base) (ii); and automated tracing of the dorsal edge of the carapace (blue line) via identification of equally spaced landmarks along the dorsal axis. In total, we used 600 dorsal positions; yellow points in the figure highlight major landmarks (iii). Defined landmarks subsequently allow for the quantification of different phenotypic traits, including animal length (iv) and dorsal height (v; here exemplified by the dorsal position where dorsal height was largest). b–d, Accuracy of phenotypic estimates by DAPCHA were validated via contrasting manual estimates with automated data: animal length estimates across three different observers (b); unit conversion of length estimates using a microstage meter with manual estimates assessed in two different runs (Methods) (c); and morphological changes in the head region (square bracket in a, iii) under control and predation conditions in second instar animals; manual scores are averaged across three independent observers giving rise to values other than 0, 30 and 50 (d). e, Using a mixture model on animal length and animal dorsal area, test animals were retroactively assigned to distinct developmental stages (first instar, I1; second instar, I2).
Effects of predation risk on morphological changes in genetically unique strains
a, Risk of predation induces marked shape changes along the dorsal axis in first and second instar D. pulex (control, black line; predation, red line). b, Strongest morphological changes are observed in the head region, with maximal dorsal height shifting towards anterior head regions under predation risk. c, Predator-induced defence morphologies, here measured as maximal dorsal height, increase in response to predation risk exposure in both instars (control (C), black points; predation (P), red points). d, The number of neckteeth increases in response to predator cue, particularly in second instar animals. e, Effect sizes from a linear model along the dorsal axis reveal distinct patterns of treatment (predation risk, red line), genotype (blue line) and G × E interaction (grey line) effects on morphological changes in second instar animals. f,g, Broad-sense (f) and narrow-sense (g) heritability estimates of dorsal height in second instar Daphnia vary along the dorsal axis in response to control conditions (black line) and predation risk (red line), with a strong reduction of both measures of heritability for dorsal height upon exposure to predator cues in the dorsal head region where defence morphologies are expressed (a, Fig. 1a,iii). Data in e, f and g are presented as mean values, with shaded areas indicating upper (0.95) and lower (0.05) confidence intervals. Vertical lines in e, f and g highlight morphological independent shape modules, separating head and posterior body areas (Extended Data Fig. 5). h, Boxplots of narrow-sense heritability estimates (see g; black, control; red, predation) and treatment effect sizes (see e) across the identified three independent shape modules (top panel) in second instar animals. Data in c and h are based on biologically independent samples: n = 192 (control, I1), n = 220 (predation, I1), n = 193 (control, I2), n = 188 (predation, I2).
Effects of natural selection on predator-induced plastic responses in second instar Daphnia
a, The log10(Vg/Vm) estimates of dorsal height vary along the dorsal axis in response to control conditions (black line) and predator cue (red line). Notably, genetic diversity is strongly reduced in the dorsal head region of second instar Daphnia (module 2; Extended Data Fig. 5 and Supplementary Table 1, section III) upon exposure to predator cue (red line). Data are presented as mean values, with shaded areas indicating upper (0.95) and lower (0.05) confidence intervals. Vertical lines highlight morphological independent shape modules, separating head and posterior body areas (Extended Data Fig. 5). b, Boxplots of log10(Vg/Vm) estimates across the identified three independent shape modules (top panel) in second instar animals. Data in b are based on n = 193 (control) and n = 188 (predation) biological samples.
  • Dörthe BeckerDörthe Becker
  • Karen Barnard-KubowKaren Barnard-Kubow
  • Robert PorterRobert Porter
  • [...]
  • Alan O. BerglandAlan O. Bergland
The adaptive nature of phenotypic plasticity is widely documented. However, little is known about the evolutionary forces that shape genetic variation of plasticity within populations. Whether genetic variation in plasticity is driven by stabilizing or diversifying selection and whether the strength of such forces remains constant through time, remain open questions. Here, we address this issue by assessing the evolutionary forces that shape genetic variation in antipredator developmental plasticity of Daphnia pulex. Antipredator plasticity in D. pulex is characterized by the growth of a pedestal and spikes in the dorsal head region upon exposure to predator cue. We characterized genetic variation in plasticity using a method that describes the entire dorsal shape amongst >100 D. pulex strains recently derived from the wild. We observed the strongest reduction in genetic variation in dorsal areas where plastic responses were greatest, consistent with stabilizing selection. We compared mutational variation (Vm) to standing variation (Vg) and found that Vg/Vm is lowest in areas of greatest plasticity, again consistent with stabilizing selection. Our results suggest that stabilizing selection operates directly on phenotypic plasticity in Daphnia and provide a rare glimpse into the evolution of fitness-related traits in natural populations.
The diversity of resistance challenges the ability of pathogens to spread and to exploit host populations. Yet, how this host diversity evolves over time remains unclear because it depends on the interplay between intraspecific competition among host genotypes and coevolution with pathogens. Here we study experimentally the effect of coevolving phage populations on the diversification of bacterial CRISPR immunity across space and time. We demonstrate that the negative-frequency-dependent selection generated by coevolution is a powerful force that maintains host resistance diversity and selects for new resistance mutations in the host. We also find that host evolution is driven by asymmetries in competitive abilities among different host genotypes. Even if the fittest host genotypes are targeted preferentially by the evolving phages, they often escape extinctions through the acquisition of new CRISPR immunity. Together, these fluctuating selective pressures maintain diversity, but not by preserving the pre-existing host composition. Instead, we repeatedly observe the introduction of new resistance genotypes stemming from the fittest hosts in each population. These results highlight the importance of competition on the transient dynamics of host–pathogen coevolution.
Organismal-grade multicellularity has been achieved only in animals, plants and fungi. All three kingdoms manifest phenotypically disparate body plans but their evolution has only been considered in detail for animals. Here we tested the general relevance of hypotheses on the evolutionary assembly of animal body plans by characterizing the evolution of fungal phenotypic variety (disparity). The distribution of living fungal form is defined by four distinct morphotypes: flagellated; zygomycetous; sac-bearing; and club-bearing. The discontinuity between morphotypes is a consequence of extinction, indicating that a complete record of fungal disparity would present a more homogeneous distribution of form. Fungal disparity expands episodically through time, punctuated by a sharp increase associated with the emergence of multicellular body plans. Simulations show these temporal trends to be non-random and at least partially shaped by hierarchical contingency. These trends are decoupled from changes in gene number, genome size and taxonomic diversity. Only differences in organismal complexity, characterized as the number of traits that constitute an organism, exhibit a meaningful relationship with fungal disparity. Both animals and fungi exhibit episodic increases in disparity through time, resulting in distributions of form made discontinuous by extinction. These congruences suggest a common mode of multicellular body plan evolution.
The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.
Mass spectrometry-based identification of short amidated peptides in basal metazoans
a, Phylogenic relationships of animals related to this study and known peptidergic systems are shown at the top. Total ion chromatograms of endogenous peptide fractions of basal metazoans N. vectensis (Cnidaria), E. fluviatilis (Porifera) and B. mikado (Ctenophora) are shown below. b, Data processing workflow of mass spectrometry-based neuropeptide identification. c, Schematic representation of the primary structure of the B. mikado VWYamide peptide. The positions of fragmentations observed in the fragment spectrum are indicated. d, Fragment spectra of endogenous and synthetic VWYamide. e, Schematic representation of neuropeptide precursors of FGLa, VWYa (B. mikado), PRPa and VRHa (N. vectensis). Grey, red, green and yellow boxes indicate signal peptide, cleavage site, mature neuropeptide and glycine as amide donor, respectively. Triangles denote the putative cleavage sites of neprilysin endopeptidase. No neuropeptide was detected from E. fluviatilis. f, Sequence logo map of N-terminal and C-terminal flanking regions and AA compositions of the cleavage sites (boxed) of neuropeptides in Metazoa. Basic (K and R) and acidic (D and E) AAs are shown in blue and red, respectively. Illustrations of fly, sea anemone and sponge are from
Morphologies and distributions of ctenophore peptide-expressing cells
The staining shown in each row focuses on the area enclosed by the red square in the adjacent schematic diagram. a–f, Side views of B. mikado larvae stained with antibodies against VYWa (a), WTGa (b), NPWa (c), FGLa (d), RWFa (e) and GVFa (f). g–i, High-magnification view of the SNN consisting of VWYa⁺ (g), WTGa⁺ (h) and NPWa⁺ (i) neurons. The dotted line in h indicates the outline of the larval body. j–l, High-magnification view of VWYa⁺ (j), WTGa⁺ (k) and NPWa⁺ (l) neurons at the nerve plexuses beneath comb rows. m, Aboral view of the apical organ stained by anti-FGLa antibody. The dotted line indicates the outline of the apical organ. n,o, VWYa⁺ (n) and WTGa⁺ (o) cells at the epithelial floor of the apical organ. The dotted line indicates the outline of the epithelial floor. p–s, Distribution of FGLa⁺ (p) NPWa⁺ (q), RWFa⁺ (r) and GVFa⁺ (s) cells in the pharynx. t–v, Expression of RWFa (t), VWYa (u) and WTGa (v) in the tentacle. w–w′′, Staining of VWYa⁺ neural network (green, w) and tyrosinated tubulin (tyr-tub; magenta, w′) and costaining of both (w′′). x,x′, Schematic diagram of the spatial distribution of amidated-peptide-expressing neurons and cells in B. mikado larva (x) and precursor-mRNA-positive neurons and cells in M. leidyi (x′). The left and right sides of the side view show a superficial view and the tentacular plane, respectively. In x, the aboral view is also shown on the left. y, Schematic diagram of the spatial distribution of amidated-peptide-expressing neurons and cells in V. multiformis adults. The left and right show the aboral side and oral side, respectively. Neurons with neurites are represented by circles with lines, and neurosecretory-like cells are represented by ovals. AO, apical organ; CR, comb row; MO, mouth; PH, pharynx; TB, tentacle bulb; TC, tentacle. Scale bar, 50 µm (a–f) or 20 µm (others). Pseudo-colours in a–w were applied using ImageJ software.
Genetic signature of peptidergic cells in the ctenophore M. leidyi
Expression of gene homologues involved in peptidergic neuronal function across the different cell clusters of M. leidyi based on scRNA-seq data¹². The dot plot shows the normalized expression values (molecules per 10,000 UMIs) scaled by gene. Dot size from smallest to largest corresponds to lowest and highest expression, respectively. The genes with highest expression in each gene family were selected (Methods). Dots are coloured according to gene functional categories. Schematic representations of the major functional modules required for peptidergic signalling are shown on the right side. The molecules abundantly expressed in the peptidergic clusters are shown in grey.
Functional characterization and receptor prediction of ctenophore neuropeptides
a, Synthetic NPWa neuropeptide (IGSDIKLVPGAGGNPWa) induced contraction of the adradial canal (orange arrowheads) and mouth opening of B. mikado larvae. A peptide with the reverse AA sequence was used as a negative control. b, Typical change in the relative length of the adradial canal following treatment with seawater, 10 µM reverse peptide and 10 µM NPWa peptide. The time points at which the changes of medium took place are indicated by blue (seawater), light blue (reverse peptide) and orange (NPWa peptide) arrows. c, Quantitative analysis of the effect of increasing peptide concentrations on adradial canal contraction (n = 5 biologically independent animals for each concentration). Error bars represent the s.e.m. (**P < 0.01). d, Synthetic VWYa neuropeptide (ARVYKGYNGGNRVWYa) induced expansion of the epithelium (orange arrowheads). e, Quantitative analysis of the effect of VWYa on body volume (n = 5 biologically independent animals for each concentration). Error bars represent the s.e.m. (*P < 0.05, **P < 0.01). f, Cell cluster distribution of putative peptide GPCR expression. The representative peptide sequences from each peptide gene (in parentheses) in M. leidyi are shown on the left. The dot plot shows the normalized expression values (molecules per 10,000 UMIs) scaled by gene. Dot size from smallest to largest corresponds to lowest and highest expression, respectively. Dots are coloured according to the cluster category. g, Cell targets of representative peptides based on the expression of their predicted GPCRs (Methods). Scale bars, 50 µm (a,d). Statistics for c: two-tailed paired t test (1 µM: t = 2.448, d.f. = 4, P = 0.07059, 95% CI = −0.01615414, 0.25699004; 10 µM: t = 11.521, d.f. = 4, P = 0.0003241, 95% CI = 0.4416221, 0.7220451; 100 µM: t = 8.6694, d.f. = 4, P = 0.0009742, 95% CI = 0.3668352, 0.7125043); **P < 0.01. Statistics for e: two-tailed paired t test (10 µM: t = −3.0913, d.f. = 4, P = 0.03653, 95% CI = −0.4612434, −0.1623270; 100 µM: t = −5.792, d.f. = 4, P = 0.004417, 95% CI = −0.129532465, −0.006951329); *P < 0.05, **P < 0.01. Rev., reverse.
The evolutionary origins of neurons remain unknown. Although recent genome data of extant early-branching animals have shown that neural genes existed in the common ancestor of animals, the physiological and genetic properties of neurons in the early evolutionary phase are still unclear. Here, we performed a mass spectrometry-based comprehensive survey of short peptides from early-branching lineages Cnidaria, Porifera and Ctenophora. We identified a number of mature ctenophore neuropeptides that are expressed in neurons associated with sensory, muscular and digestive systems. The ctenophore peptides are stored in vesicles in cell bodies and neurites, suggesting volume transmission similar to that of cnidarian and bilaterian peptidergic systems. A comparison of genetic characteristics revealed that the peptide-expressing cells of Cnidaria and Ctenophora express the vast majority of genes that have pivotal roles in maturation, secretion and degradation of neuropeptides in Bilateria. Functional analysis of neuropeptides and prediction of receptors with machine learning demonstrated peptide regulation of a wide range of target effector cells, including cells of muscular systems. The striking parallels between the peptidergic neuronal properties of Cnidaria and Bilateria and those of Ctenophora, the most basal neuron-bearing animals, suggest a common evolutionary origin of metazoan peptidergic nervous systems. Biochemical identification of neuropeptides in Cnidaria and Ctenophora, followed by analyses of their expression, suggests that peptidergic neurons were present at early stages of nervous system evolution.
Global eukaryotic 18S–28S phylogeny from environmental samples and the distribution of habitats
a, This tree corresponds to the best maximum-likelihood tree inferred using an alignment with 7,160 sites and the GTRCAT model in RAxML⁹⁹. The tree contains 16,821 OTUs generated from PacBio sequencing of 21 environmental samples (no reference sequences were included). Ring no. 1 around the tree indicates taxonomy of the environmental sequences, with all major eukaryotic lineages considered in this study labelled. Ring no. 2 depicts percentage similarity with the references in the PR² database as calculated using BLAST and was set with a minimum of 70% with the two black lines in the middle indicating 85% and 100% similarity levels. Ring no. 3 depicts the habitat origin of each OTU. b, Hierarchical clustering of the four habitats based on a phylogenetic distance matrix generated using the unweighted UniFrac method (n = 7, n = 5, n = 4 and n = 5 samples for soil, freshwater, marine euphotic and marine aphotic, respectively). All communities were found to differ significantly from each other using Monte Carlo simulations (Bonferroni-adjusted P < 0.001). c, Stacked density plot of branch lengths between taxa pairs from the same or different habitats (n = 14,977,604 taxa pairs with a maximum patristic distance of 1.5 substitutions/site). Note that this plot should be interpreted with caution as taxa pairs do not represent independent datapoints due to phylogenetic relatedness.
Habitat transition rates and number of transition events estimated for each major eukaryotic lineage
a, Posterior probability distributions of the global rate of habitat evolution, which indicate the overall speed at which transitions between marine and non-marine habitats have occurred in each clade regardless of direction. Rates were estimated along clade-specific phylogenies (Extended Data Fig. 6) using MCMC in BayesTraits with a normalized transition matrix. b, The posterior probability distribution of transition rates from marine to non-marine habitats (top in orange) and from non-marine to marine habitats (below in blue). c,d, Number of transitions from marine to non-marine habitats (c) and in the reverse direction (d) for each clade as estimated by PASTML using maximum likelihood (Methods). The boxplots in c and d show the median as centre line, box sizes indicate the lower (Q1) and upper (Q3) quartiles, whiskers indicate extreme values within 1.5× the interquartile range and dots beyond the whiskers indicate outliers.
Ridgeline histogram plots displaying the timing of transition events
The plots were estimated from relative chronograms obtained with Pathd8 (ref. ³⁷). The x axis depicts the relative age for each clade.
Ancestral states of major eukaryotic clades as estimated by BayesTraits on a set of 100 global PacBio phylogenies
Pie charts at each node indicate the posterior probabilities of likelihoods for the character states as follows: blue, marine; orange, non-marine. Nodes with empty circles indicate wherever there was insufficient taxon sampling to infer ancestral habitats but a reasonable estimate could be made from existing literature (Supplementary Note 3). a, Ancestral habitat of the LECA as inferred using two different roots. b, Ancestral states of major eukaryotic lineages. For the two cases where the incorporation of Illumina data inferred a different likely ancestral state, the results are shown in boxes. The pie chart on the right was obtained using the global eukaryotic phylogeny, while the pie chart on the left was obtained from clade-specific phylogenies. The tree is adapted from ref. ²⁹.
The successful colonization of new habitats has played a fundamental role during the evolution of life. Salinity is one of the strongest barriers for organisms to cross, which has resulted in the evolution of distinct marine and non-marine (including both freshwater and soil) communities. Although microbes represent by far the vast majority of eukaryote diversity, the role of the salt barrier in shaping the diversity across the eukaryotic tree is poorly known. Traditional views suggest rare and ancient marine/non-marine transitions but this view is being challenged by the discovery of several recently transitioned lineages. Here, we investigate habitat evolution across the tree of eukaryotes using a unique set of taxon-rich phylogenies inferred from a combination of long-read and short-read environmental metabarcoding data spanning the ribosomal DNA operon. Our results show that, overall, marine and non-marine microbial communities are phylogenetically distinct but transitions have occurred in both directions in almost all major eukaryotic lineages, with hundreds of transition events detected. Some groups have experienced relatively high rates of transitions, most notably fungi for which crossing the salt barrier has probably been an important aspect of their successful diversification. At the deepest phylogenetic levels, ancestral habitat reconstruction analyses suggest that eukaryotes may have first evolved in non-marine habitats and that the two largest known eukaryotic assemblages (TSAR and Amorphea) arose in different habitats. Overall, our findings indicate that the salt barrier has played an important role during eukaryote evolution and provide a global perspective on habitat transitions in this domain of life.
A large dataset of aquatic biodiversity across multiple trophic levels from several wetlands in Brazil reveals that biodiversity–multifunctionality relationships break down with human pressures.
Schematic of study design
See Methods for an associated, detailed description of each step and the relationships between each step. Arrows from ‘Step 1’ to ‘Step 2’ indicate the source of the individuals of each species used in the competition experiments. Arrow colours correspond to the colours of species i and j. In ‘Step 3’, the terms in the subscripted square brackets indicate the plasticity-induction treatment—conspecific (con) or heterospecific (het)—associated with species i or j for that parameter. The methods were repeated for both species as the focal species i.
Effects of plasticity on species coexistence
a, Posterior probability densities of the difference in the invasion growth rates (IGRs) between plasticity treatments. Positive differences indicate that plasticity in response to interspecific competition causes invasion growth rates of the focal species to increase. b, The outcome of competition in each treatment based on mutual invasibility. Points show individual samples from the posterior distributions of the invasion growth rates, with colours indicating the plasticity-induction treatment of the focal invading species. In all cases, the resident heterospecific species was conditioned to its own conspecific plasticity-induction treatment, as would be the case in nature. Black dashed lines perpendicular to each axis indicate the boundary between negative and positive invasion growth rates. Species are expected to coexist when invasion growth rates of both species are positive.
Ecological explanations for species coexistence assume that species’ traits, and therefore the differences between species, are fixed on short timescales. However, species’ traits are not fixed, but can instead change rapidly as a consequence of phenotypic plasticity. Here we use a combined experimental–theoretical approach to demonstrate that plasticity in response to interspecific competition between two aquatic plants allows for species coexistence where competitive exclusion is otherwise predicted to occur. Our results show that rapid trait changes in response to a shift in the competitive environment can promote coexistence in a way that is not captured by common measures of niche differentiation. The authors use a theoretical model along with competition experiments between two aquatic plant species to show that phenotypic plasticity affects the outcome of competition.
Many studies have shown that biodiversity regulates a multitude of ecological functions that are needed to maintain the productivity and efficiency of a variety of types of ecosystems. What is not known is how human activities may change the ‘multifunctionality’ of ecosystems as they have both direct impacts on ecosystems and indirect effects on the biodiversity that serves to control ecological functions. Using a database on hundreds of lakes spanning four large neotropical wetlands, we demonstrate that species richness and the functional diversity of fish, macrophytes, microcrustaceans, rotifers, protists, and phytoplankton are positively associated with ecosystem multifunctionality, including nutrient concentrations, standing biomass, and ecosystem metabolism. However, we also found that the relationship between biodiversity and multifunctionality is weakened by human pressures and that part of this impact occurs through changes in biodiversity. Our results suggest that human activities may break down the biological controls needed to maintain the suite of ecosystem functions that sustain wetlands.
Coevolutionary warfare between bacteria and phage results in the diversification of anti-phage CRISPR arrays among the most successful bacterial competitors
Nature Positive is an aspirational term that is increasingly being used by businesses, governments and NGOs, but there is a danger that its meaning is being diluted away from measurable overall net gain in biodiversity towards merely any action that benefits nature, argues E.J. Milner-Gulland.
Cnidarians and ctenophores have morphologically simpler nervous systems than those of bilaterians. Discovery and characterization of neuropeptides in a comb jelly and a sea anemone support a common origin of animal peptidergic neurons from digestive cells that could sense their environment.
A modelling study suggests that the proposed energetic barrier between prokaryotes and eukaryotes may not be relevant to the complexity gap between the two domains. The energetic advantage of early mitochondria was probably small, and eukaryotes likely emerged without the help of an endosymbiont.
The origin of eukaryotic cell size and complexity is often thought to have required an energy excess supplied by mitochondria. Recent observations show energy demands to scale continuously with cell volume, suggesting that eukaryotes do not have higher energetic capacity. However, respiratory membrane area scales superlinearly with the cell surface area. Furthermore, the consequences of the contrasting genomic architectures between prokaryotes and eukaryotes have not been precisely quantified. Here, we investigated (1) the factors that affect the volumes at which prokaryotes become surface area-constrained, (2) the amount of energy divested to DNA due to contrasting genomic architectures and (3) the costs and benefits of respiring symbionts. Our analyses suggest that prokaryotes are not surface area-constrained at volumes of 100‒103 µm3, the genomic architecture of extant eukaryotes is only slightly advantageous at genomes sizes of 106‒107 base pairs and a larger host cell may have derived a greater advantage (lower cost) from harbouring ATP-producing symbionts. This suggests that eukaryotes first evolved without the need for mitochondria since these ranges hypothetically encompass the last eukaryotic common ancestor and its relatives. Our analyses also show that larger and faster-dividing prokaryotes would have a shortage of respiratory membrane area and divest more energy into DNA. Thus, we argue that although mitochondria may not have been required by the first eukaryotes, eukaryote diversification was ultimately dependent on mitochondria. Analysing the energetic constraints on prokaryotic cell size, the energetic implications of eukaryotic genome architecture, and the presence of endosymbionts, the authors suggest that mitochondria were not required for the initial origin of eukaryotes, but did facilitate their subsequent diversification and expansion.
Holotype specimen of Auroralumina attenboroughii
a, In context alongside rangeomorph fossils preserved in a comparable manner and distinct from the textured background substrate (GSM 105874); imaged under low-angle light. b,c, Plastotype (GSM 106119) (b) and interpretative drawing (c) showing the differentiated stalk and cup of each goblet, well-defined corner sulci (now ridges) and texturally distinct tentacles. The proximal portions of both goblets, including their mutual branching point, are concealed beneath a thin cover of sediment but are nonetheless discernible as topographically and texturally distinct tracts (dashed grey line); see Fig. 2 for more information. RTI file available⁷⁶.
Details of the proximal part of the holotype specimen of Auroralumina (GSM 106119)
a, Interpretative drawing of entire specimen, with area shown in b–d outlined. b, Base of the preserved specimen, showing progressive cover of the left-hand goblet towards the bifurcation point and the mostly concealed proximal part of the right-hand goblet. The margins of the fossil in the concealed area are impressed—albeit weakly—through the sediment and the area underlain by the skeleton is defined by a change in sediment texture. Fossil photographed under low-angle light. c, Interpretative overlay, generated by combining observations made under multiple lighting directions. d, Interpretative drawing from c, showing symmetrical bifurcation of the two goblets and probable broken proximal termination of the specimen. Key in d covers all annotations in this figure. Scale bar in b and c, 5 cm.
Details of the distal anatomy of Auroralumina attenboroughii (GSM 106119) and the mode of preservation
a, Left-hand goblet, with dense crown of overlapping tentacles and conspicuous corner sulcus (now a ridge) and band (now a trench) near the aperture rim. The margins of the fossil are well-defined and the tentacle crown texturally and topographically distinct from the smooth periderm. b, Interpretative drawing of region in a. c, Right-hand goblet, principally preserving only one face but with a second partially visible where its edge (and intervening corner sulcus) was twisted into the plane of preservation, towards the right-hand side. d, Interpretative drawing of region in c. Specimen photographed under low-angle light and interpretations based on features revealed by varying the lighting direction. Scale bar in a and c, 5 cm. e,f, Preservation of the goblet and tentacles of A. attenboroughii. e, Apical view of the two goblets showing how their different orientations at the time of burial generated different views of the tetraradial structure in the fossil in lateral aspect. Schematic goblets (labelled 1 and 2) are representative of the two goblets in Auroralumina. The interpretative drawing of Auroralumina is also shown, with goblets labelled 1 and 2 next to a conulariid cnidarian (OUMNH DU17), also inferred to have been tetraradial in life, to illustrate analogous preservation of multiple faces in lateral view. f, Hypothetical arrangement of the tentacles in oral view in vivo and probable arrangement of tentacles in lateral view at time of burial along with proposed preservational pathway of the tentacles. 1: Tentacles, mostly overlapping, buried by sediment. 2: Partial retraction and deflation postmortem. 3: Decay and casting of the resultant space by sediment from below.
The Phylogenetic position of Auroralumina attenboroughii
a, Artistic reconstruction of Auroralumina. b, Bayesian phylogenetic analysis of animals (348 characters, 108 taxa, mk + gamma model) incorporating Auroralumina attenboroughii. Numbers indicate posterior probabilities and scale bar shows expected number of substitutions per site. Fossils are indicated by dagger symbols. Raw polyp width is shown on the right, with the mean size shown for the extant groups sampled (for logged polyp size graph, see Extended Data Fig. 3). Branch length shown. Maximum polyp width data also shown in Extended Data Fig. 3. NA indicates where ancestral state values were inapplicable because they were derived from characters recovered as absent.
Tubular morphospace occupation across the Ediacaran–Cambrian transition
a–c, The sum of variances (a), sum of ranges (b) and the median of centroids (c) for tubular morphospace occupation. The sum of variances examines the evenness of morphospace occupation, the sum of ranges examines the extent of morphospace occupation in all computed dimensions and the median of centroids measures the clustering of taxa around a central point. Adding Auroralumina increases the sum of variances, ranges and (marginally) the median of centroids compared to Ediacaran morphospace excluding Auroralumina. The boxes represent the interquartile range, with black line showing the median. The whiskers indicate minimum (Q1 − 1.5 × IQR) and maximum (Q3 + 1.5 × IQR), excluding outliers. Outliers are shown in black squares. d, Morphospace occupation with convex hulls showing Ediacaran morphospace occupation with and without Auroralumina and Cambrian morphospace occupation. Black circles represent Ediacaran taxa and white circles represent Cambrian taxa.
Cnidarians are a disparate and ancient phylum, encompassing corals and jellyfish, and occupy both the pelagic and benthic realms. They have a rich fossil record from the Phanerozoic eon lending insight into the early history of the group but, although cnidarians diverged from other animals in the Precambrian period, their record from the Ediacaran period (635–542 million years ago) is controversial. Here, we describe a new fossil cnidarian—Auroralumina attenboroughii gen. et sp. nov.—from the Ediacaran of Charnwood Forest (557–562 million years ago) that shows two bifurcating polyps enclosed in a rigid, polyhedral, organic skeleton with evidence of simple, densely packed tentacles. Auroralumina displays a suite of characters allying it to early medusozoans but shows others more typical of Anthozoa. Phylogenetic analyses recover Auroralumina as a stem-group medusozoan and, therefore, the oldest crown-group cnidarian. Auroralumina demonstrates both the establishment of the crown group of an animal phylum and the fixation of its body plan tens of millions of years before the Cambrian diversification of animal life. A new fossil cnidarian, Auroralumina attenboroughi, from the Ediacaran of Charnwood Forest, UK, described as showing mosaic anthozoan and medusozoan characters, is the oldest yet-known crown-group cnidarian.
Ageing was associated with reduction in social connectedness
a–c, Age-related declines based on 4,203 annual observations of 712 individuals, where each point is an observation, with point shading denoting individual age (lighter colour indicates older). Panels a–c represent age-related declines in group size (a), degree (b), and strength (c). The text at the top of a–c displays the effect estimate (expressed in units per year), with 95% CI in brackets, and the P value. Opaque black lines are derived from linear model fits from model set 1, taking the mean of the posterior distribution. Transparent grey lines represent 100 fits drawn randomly from the posterior estimate distributions of each model, to demonstrate error in the slope and intercepts. Dotted black lines display the effect for the SPDE model, demonstrating the result’s robustness to controlling for spatial autocorrelation. d, Model effect estimates taken from model set 2 (N = 3,873; effects expressed in units of standard deviations) for age and longevity effects for each social behaviour trait with increasingly complex model formulations, demonstrating that selective disappearance was not responsible for the age-related declines in social connectedness. Dots represent the mean of the posterior effect estimate distribution; error bars denote the 95% CI of the effect. Note: each additional model includes the variables of the models above it (for example, the longevity model also includes the ID random effect). See Supplementary Table 2 for full effect estimates.
The spatial distributions of age of mature females and grazing quality within the study area
a,b, Spatial distributions of age of mature females (a) and grazing quality (b). Darker colours relate to greater age or grazing quality. Both panels were obtained by plotting the two-dimensional distribution of the SPDE random effect in INLA GLMMs (N = 4,203), with age and grazing quality as response variables, respectively. Triangle points represent the population’s centroid, obtained by taking the average location of all individuals’ annual centroids. Ten axis units = 1 km.
Ageing was associated with changes in a range of socio-spatial behaviours
Each point represents an annual observation of an individual, with point shading denoting individual age (lighter colour indicates older). Each panel a–f represents the effect of age on density (a), annual centroid distance (b), population centroid distance (c), home range area (d), home range overlap (e), and grazing quality (f). The text at the top of the panels displays the effect estimate for the base models, with 95% CI in brackets, and the P value, taken from model set 4 (N = 4,203). Opaque black lines are derived from linear model fits, taking the mean of the posterior distribution. Transparent grey lines represent 100 fits drawn randomly from the posterior estimate distributions of each model, to demonstrate error in the slope and intercepts. Dotted black lines display the effect for the SPDE model, demonstrating the result’s robustness to controlling for spatial autocorrelation. The y axes are in units of standard deviations, and were centred around the mean.
Social relationships are important to many aspects of animals’ lives, and an individual’s connections may change over the course of their lifespan. Currently, it is unclear whether social connectedness declines within individuals as they age, and what the underlying mechanisms might be, so the role of age in structuring animal social systems remains unresolved, particularly in non-primates. Here we describe senescent declines in social connectedness using 46 years of data in a wild, individually monitored population of a long-lived mammal (European red deer, Cervus elaphus). Applying a series of spatial and social network analyses, we demonstrate that these declines occur because of within-individual changes in social behaviour, with correlated changes in spatial behaviour (smaller home ranges and movements to lower-density, lower-quality areas). These findings demonstrate that within-individual socio-spatial behavioural changes can lead older animals in fission–fusion societies to become less socially connected, shedding light on the ecological and evolutionary processes structuring wild animal populations.
Time-calibrated phylogeny of Acanthomorpha, continued in Fig. 2
The phylogeny is condensed to represent taxonomic families at the tips. Monotypic families are represented by the species or genus name. Shaded tabs to the right of taxon labels identify inclusive taxonomic orders. Maximum likelihood bootstrap support (BSS) values for relationships are in Supplementary Figs. 1–15, but unmarked nodes have 100% BSS and nodes with BSS values <97% are indicated by light-grey circles. Nodes with black circles indicate subtending branches with sCF values lower than one or two site discordance (sDF) values, while nodes with dark-grey circles indicate lower sCF values and BSS values <97%. Horizontal grey bars at each node portray the 95% highest posterior density (HPD) credible interval of node age estimates. The blue shaded region reflects the 95% HPD credible interval of the crown age of Acanthomorpha. A vertical red dashed line marks the K–Pg. The red shaded region corresponds to the disparity through time plot in Fig. 2 and reflects a period of heightened among-clade morphological disparity in Acanthomorpha. Fish illustrations by Julie Johnson.
Time-calibrated phylogeny and subclade disparity through time for Acanthomorpha, continued from Fig. 1
Continuation of the time-calibrated phylogeny condensed to represent taxonomic families at the tips. Shaded tabs to the right of taxon labels identify inclusive taxonomic orders. Maximum likelihood bootstrap support (BSS) values for relationships are in Supplementary Figs. 16–25, but unmarked nodes have 100% BSS and nodes with BSS values <97% are indicated by light-grey circles. Nodes with black circles indicate subtending branches with sCF values lower than one or two site discordance (sDF) values, while nodes with dark-grey circles indicate lower sCF values and BSS values <97%. Horizontal grey bars at each node portray the 95% highest posterior density (HPD) credible interval of node age estimates. The blue shaded region reflects the 95% HPD credible interval of the crown age of Acanthomorpha. A vertical red dashed line marks the K–Pg. The solid blue line shows observed average relative morphological disparity through time (DTT) for all of Acanthomorpha (represented in both Figs. 1 and 2), and the orange portion of the line, which corresponds to the vertical red shading, reflects the high among-clade disparity present in the early Eocene. The dashed black line and surrounding grey envelope represent the mean DTT and 95% confidence interval for Acanthomorpha as predicted under Brownian evolution, respectively. Fish illustrations by Julie Johnson.
Bayesian concordance factor analyses used to compare alternative phylogenetic hypotheses concerning the sister taxa of three major acanthomorph lineages
BUCKy-inferred genome-wide posterior mean concordance factor estimates, with 95% confidence intervals, representing the proportion of n = 250 post-burnin gene trees exhibiting alternative topologies among major acanthomorph lineages. Lengths of blue bars reflect the posterior mean concordance factors for the topology represented in Figs. 1 and 2 and Supplementary Figs. 1–25, while lengths of grey bars represent the mean concordance factors of alternative topologies. Fish illustrations by Julie Johnson. a, Gene tree concordance for Percopsiformes, Polymixia and all other Paracanthopterygii. b, Lampriformes, Paracanthopterygii and Acanthopterygii. c, Alternative sister lineages of Percomorpha.
Robustness of DTT analyses
Effect of individual lineages or body shape traits on the length of the estimated period of among-clade morphological expansion after the K–Pg (that is, the period in which the observed disparity falls below that expected from a Brownian motion process). There is no single major lineage driving the duration of this period of phenotypic diversification, but the more pronounced contribution of depth measurements to this pattern indicates their importance over width measurements in the expansion of body shapes along the elongation axis. a, Duration of the period of morphological expansion when a single major lineage is excluded from the analysis. Note that not all major acanthomorph lineages are represented. ‘Squamipinnes’ refers to the acanthuriform clade in Supplementary Fig. 23 defined by Chaetodon kleinii and Luvarus imperialis, and Lophioidei and Tetraodontoidei are major subclades of Acanthuriformes. b, Duration of the period of morphological expansion when data for one of the seven body shape traits in the original analysis are excluded. c, Duration of the period of morphological expansion when the disparity through time analysis is based on a single body shape trait.
Changes in body elongation and compression
Patterns of acanthomorph morphological diversity. Note that not all major acanthomorph lineages are represented. ‘Squamipinnes’ refers to the acanthuriform clade in Supplementary Fig. 23 defined by Chaetodon kleinii and Luvarus imperialis, and Lophioidei and Tetraodontoidei are major subclades of Acanthuriformes. a, The first two principal components (PCs) of morphospace (elongation and compression), with seven major acanthomorph lineages colour-coded by taxon. The proportion of variance explained by PC1 is 70.5% and by PC2 is 12.1%. b, Phenogram depicting the evolutionary history of head depth (size-corrected), which is negatively correlated to standard length and can reveal patterns of elongation, across seven major acanthomorph lineages that arose around the K–Pg. Note that all major lineages except Syngnathiformes originate near the K–Pg boundary, but the three acanthuriform lineages ‘Squamipinnes’, Lophioidei and Tetraodontoidei have markedly different ancestral trait values from Carangiformes, Scombriformes and Perciformes.
Spiny-rayed fishes (Acanthomorpha) dominate modern marine habitats and account for more than a quarter of all living vertebrate species. Previous time-calibrated phylogenies and patterns from the fossil record explain this dominance by correlating the origin of major acanthomorph lineages with the Cretaceous–Palaeogene mass extinction. Here we infer a time-calibrated phylogeny using ultraconserved elements that samples 91.4% of all acanthomorph families and investigate patterns of body shape disparity. Our results show that acanthomorph lineages steadily accumulated throughout the Cenozoic and underwent a significant expansion of among-clade morphological disparity several million years after the end-Cretaceous. These acanthomorph lineages radiated into and diversified within distinct regions of morphospace that characterize iconic lineages, including fast-swimming open-ocean predators, laterally compressed reef fishes, bottom-dwelling flatfishes, seahorses and pufferfishes. The evolutionary success of spiny-rayed fishes is the culmination of multiple species-rich and phenotypically disparate lineages independently diversifying across the globe under a wide range of ecological conditions. The authors construct a time-calibrated phylogeny spanning >90% of spiny-rayed fishes to explore patterns of body shape disparity within acanthomorphs. They find a trend of steady accumulation of lineages from the Cenozoic, with an increase in morphological disparity following the Cretaceous–Palaeogene event, facilitating the radiation of diverse morphotypes that characterize acanthomorphs’ widespread ecological success today.
Low GFP expression promotes evolution of a novel cyan fluorescence phenotype
a, Experimental design. We evolved eight replicate populations (four at high and four at low GFP expression) for six rounds (‘generations’) of directed evolution, where mutagenesis alternated with selection in each generation. Specifically, we evolved GFP towards cyan fluorescence, which requires a shift in excitation wavelength from 488 nm to 405 nm. b, Green fluorescence intensity for the three replicate ancestral populations that express GFP at a high (H) or a low (L) level (two-tailed unpaired t-test, n = 3, P = 0.0004, Cohen’s d = 31.4 ± 25.4, Student's t (henceforth 't') = 38.54, 95% Confidence Interval (henceforth '95%CI') = 609.8, 754.1, degrees of freedom (henceforth 'df') = 2.11). c, Fold change in cyan fluorescence compared with the ancestor after six generations of directed evolution in four H and four L populations (two-tailed unpaired t-test, n = 4, P = 0.0015, Cohen’s d = 5.74 ± 3.92, t = 8.12, 95%CI = 9.2, 19.1, df = 3.79). d, Absolute cyan fluorescence after six generations in four H and four L populations (two-tailed unpaired t-test, n = 4, P = 0.002, Cohen’s d = 3.74 ± 2.88, t = 38.54, 95%CI = 400.5, 1,101.4, df = 5.3). In panels b–d, red and blue represent the H and L populations, respectively. The boxes represent interquartile range while the solid line represents the median. The whiskers extend to the lowest and the highest value in the dataset. Each circle represents an ancestral (b) or evolved (c and d) population. * denotes a significant difference between H and L populations at P < 0.01 based on the unpaired t-test (Methods).
L populations harbour fewer non-synonymous and neo-functionalizing variants
a, The average number of non-synonymous variants per GFP molecule is significantly lower in L (blue) than in H (red) populations (two-tailed unpaired t-test, n = 6, P = 0.003, Cohen’s d = 2.95 ± 1.87, t = 5.12, 95%CI = 0.28, 0.86, df = 5; Methods). b, The average number of neo-functionalizing variants per GFP molecule is significantly lower in L populations (blue) than in H (red) populations for six generations of directed evolution (two-tailed unpaired t-test, n = 6, P = 0.007, Cohen’s d = 2.52 ± 1.73, t = 4.37, 95%CI = 0.096, 0.37, df = 5; Methods). Not all sequenced GFP molecules harbour the neo-functionalizing variant, which is why the average is lower than one in all experiments. In both panels, each dashed line corresponds to data from one replicate population, and the solid line represents the mean of the four replicate populations.
Modelled H populations retain more destabilizing mutations
a, We simulated the evolution of GFP from green (excitation λ =488 nm) to cyan (excitation λ = 405 nm) fluorescence using a fitness function that relates the fluorescence intensity of GFP molecules to their fluorescence output (the product of extinction coefficient and quantum yield), their stability and their abundance. b, We modelled selection by allowing a fixed percentile of fluorescent cells to survive, just as in our experiments. In this hypothetical example, the stringency of selection varies from 100% survival (light grey, no selection) to 10% survival (black). c, For GFP molecules to survive selection in the model, the product of the relative stability factor and abundance must exceed a minimum threshold that is determined by the strength of selection. In this schematic figure, the shade of grey corresponds to the percentile of cells that survive selection in panel b. We calculated relative protein stability and abundance by dividing their values by their respective maxima in the population. d, Relative stability factor versus relative abundance for GFPs before selection (in grey) and for GFPs surviving selection when only the top 10% of cells in terms of fluorescence intensity survived selection for L populations (blue). e, As in d, but for H populations (red). f, The relative stability factor is significantly higher in L populations (blue) compared with H populations (red) (P = ~10⁻¹⁶, two-sided Wilcoxon’s rank sum test, effect size calculated as Z/√N = 0.93, n = 1,000 independently selected cells). The boxes in the box plots range from the first quantile to the third quantile. The median is represented by a line across the box. The whiskers represent 1.5 times the interquartile range. The circles located above the top whisker are outliers whose values are higher than 1.5 times the interquartile range (third quartile–first quartile) above the first quartile. g, Theoretically predicted relationship between folding stability of GFP variants in evolving populations (vertical axis) and their abundance in simulations (horizontal axis). Grey colour saturation is proportional to the strength of selection, ranging from a selection strength of 0.1 (90% of GFP population survives, light grey) to 0.9 (10% of GFP population survives, black). We selected cells from the replicates of our original population of 10,000 cells until we had collected n = 10,000 survivors. h, Experimentally measured refolding yield (y axis) during 1,000 minutes (x axis) for GFP variants in L and H populations after six generations of directed evolution. Black lines (composed of black circles) denote the average over four replicate populations, while coloured error bars show one standard deviation. L populations show a significantly higher refolding yield than H populations (P = ~0.0001; two-sided unpaired t-test, effect size calculated as Z/√N = 1.23).
Evolvability of GFP becomes indistinguishable in H and L populations when the starting population is robust to destabilizing variants
a, We evolved eight replicate populations (four at high expression level and four at the low expression level) in two phases. In the first phase, we imposed weak stabilizing selection on green fluorescence, where we allowed the top 70% of green-fluorescing cells to survive. This phase lasted three generations. In the second phase, we imposed strong directional selection on cyan fluorescence, as in our main experiment (Fig. 1a). b, Refolding yield measured over 1,000 minutes for GFP in L and H populations after three generations of stabilizing selection. Black lines (composed of circles) denote the average refolding yield over four replicates, while error bars indicate one standard deviation of the mean. Stabilizing selection has rendered refolding yield statistically indistinguishable between H and L populations (P = 0.24, two-sample t-test; Methods). c, Fold change in cyan fluorescence compared with ancestral GFP after five generations of second phase of directed evolution in four H and four L populations. Stabilizing selection has rendered the fold change in cyan fluorescence statistically indistinguishable between L and H populations (two-tailed unpaired t-test, n = 4, P = 0.58, Cohen’s d = 0.41 ± 1.75, t = −0.58, 95%CI = −17.89, 10.9, df = 5.98; Methods). The box represents the interquartile range, while the solid line represents the median. The whiskers extend to the lowest and the highest value in the dataset while each circle represents an evolved population. Red and blue represent H and L populations, respectively.
Protein abundance affects the evolution of protein genotypes, but we do not know how it affects the evolution of protein phenotypes. Here we investigate the role of protein abundance in the evolvability of green fluorescent protein (GFP) towards the novel phenotype of cyan fluorescence. We evolve GFP in E. coli through multiple cycles of mutation and selection and show that low GFP expression facilitates the evolution of cyan fluorescence. A computational model whose predictions we test experimentally helps explain why: lowly expressed proteins are under stronger selection for proper folding, which facilitates their evolvability on short evolutionary time scales. The reason is that high fluorescence can be achieved by either few proteins that fold well or by many proteins that fold less well. In other words, we observe a synergy between a protein’s scarcity and its stability. Because many proteins meet the essential requirements for this scarcity–stability synergy, it may be a widespread mechanism by which low expression helps proteins evolve new phenotypes and functions. Directed evolution shows that low expression of the green fluorescent protein facilitates the evolution of cyan fluorescence in E. coli, which can be explained by synergy between the protein’s scarcity and its stability.
Experimental set-up using Plantago lanceolata as model plant
Pots (length = 12.5 cm, width = 8 cm, height = 8.5 cm) contain three compartments. The plant zone (compartment a) and the buffer zone (compartment b) were filled with the collected field soils. Compartment c contained a standardized, sterilized soil that was injected with the tracer ³³P. Compartment a was separated from b using a 40 µm mesh (narrow dashed line), restricting root penetration. The mash barrier between b and c compartments had a pore size of 500 µm (wide dashed line).
Comparison of grassland and cropland soils
a, Recovery of ³³P in the shoot material of Plantago lanceolata plants grown in grassland (green), cropland (orange) and sterilized control soils (blue). The low ³³P recovery in sterilized control soils confirm that ³³P recovery corresponds to hyphal activity in the field soils. b,c, Shoot biomass (b) and total P uptake per pot (c) in the grassland versus cropland soils. Boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and ‘x’ indicates the mean value. Bonferroni-corrected P values <0.05 (based on two-sided Wilcoxon rank test) indicate significant differences between land-use systems; ns, no significant difference.
Relative importance of predictors
a,b, Relative importance of predictors for hyphal ³³P transfer, measured as ³³P recovery in plant shoots based on a multi-model inference analysis, in grassland (n = 58) (a) versus cropland soils (n = 146) (b). Note that the crop-management predictors were used only in the cropland model. Predictors were ordered according to their total importance (that is, sum of both land-use types). Negative and positive correlations are shown in red and blue, respectively. Asterisks indicate a significant correlation at P < 0.001 (***), P < 0.01 (**) and P < 0.05 (*) based on the averaged model coefficients. Model coefficients, standard errors, z-values and exact P values are reported in Supplementary Tables 6a and 7a.
Recovery of ³³P in grassland sites
a,b, Correlation of ³³P recovery with soil pH (a) and available soil P (b) in the grassland sites (n = 60). The green error bands mark the 95% confidence interval of the two-sided ordinary least squares regression models. F1,58 = 8.375 (a) and F1,58 = 16.21 (b) (F-statistic with degrees of freedom in subscript). R² corresponds to the adjusted R² value.
Recovery of ³³P in cropland sites
a, Effects of an increasing number of fungicide application events in cropland soils on ³³P recovery. Significant differences between the number of applications are indicated with different letters examined using a Kruskal–Wallis rank test where X² represents the model fit with the degrees of freedom in brackets (n = 150). Boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and ‘x’ indicates the mean value. b, The relationship of ³³P recovery and AMF richness in P. lanceolata roots in the cropland soils (n = 146), estimated using two-sided ordinary least squares regression (F1,144 = 13.02). The orange error band marks the 95% confidence interval. R² corresponds to the adjusted R² value.
Phosphorus (P) acquisition is key for plant growth. Arbuscular mycorrhizal fungi (AMF) help plants acquire P from soil. Understanding which factors drive AMF-supported nutrient uptake is essential to develop more sustainable agroecosystems. Here we collected soils from 150 cereal fields and 60 non-cropped grassland sites across a 3,000 km trans-European gradient. In a greenhouse experiment, we tested the ability of AMF in these soils to forage for the radioisotope 33P from a hyphal compartment. AMF communities in grassland soils were much more efficient in acquiring 33P and transferred 64% more 33P to plants compared with AMF in cropland soils. Fungicide application best explained hyphal 33P transfer in cropland soils. The use of fungicides and subsequent decline in AMF richness in croplands reduced 33P uptake by 43%. Our results suggest that land-use intensity and fungicide use are major deterrents to the functioning and natural nutrient uptake capacity of AMF in agroecosystems. Combining field data and greenhouse experiments, the authors show how agricultural management practices like fungicide applications can affect the degree to which arbuscular mycorrhizal fungi in the soil provision phosphorus to plants.
Nutrient balance and vegetation demand of N, P, Ca, K and Mg along a tropical forest succession from 5-year-old forest (5 yr) to 60-year-old forest (60 yr)
a–c, Net input (net deposition minus leaching at 80 cm) (a); nutrient demand to sustain woody biomass increment (b); and the surplus (positive) or deficit (negative) resulting from a minus b, as an estimation for the net vegetation nutrient demand (c). Numbers above show the average ± standard deviation of the three field replicates per successional stage. The numbers indicate the results from a linear regression, with age as a numeric independent variable. Only the significant trends (*P < 0.1; NS, not significant) with stand age are shown in the percentage change per year below. Black dots indicate the plot-level values per successional stage.
Soil nutrient availability in the 0–10 cm layer along tropical forest succession from agriculture (Ag) over 60-year-old forest (60 yr) to old-growth forest (OG)
a–f, Including: resin phosphorus (Presin), TBP and inorganic over organic P ratio (Pi:Po) (a); potential extracellular acidic phosphodiesterase and phosphomonoesterase activity per gram microbial carbon (b); microbial C:N, C:P and N:P ratios (c); soil pH (d); ammonium acetate exchangeable aluminium and iron (e); and ammonium acetate exchangeable calcium, potassium and magnesium (f). The numbers indicate the results from a linear regression, with age as a numeric independent variable. Only significant trends (*P < 0.1; NS, not significant) with stand age are shown in the percentage change per year below. Coloured bars/dots and whiskers show the mean and standard deviation of the triplicate setup. Black dots indicate the plot-level values per successional stage.
Plant tissue mass-based stoichiometric ratios along tropical forest succession, from 5-year-old forest (5 yr) to old-growth forest (OG)
a–c, Including: foliar stoichiometry (a); litterfall stoichiometry (b); and fine root stoichiometry (c). The numbers indicate the results from a linear regression, with age as a numeric independent variable. Only significant trends (*P < 0.1; NS, not significant) with stand age are shown in percentage change per year below. Coloured dots and whiskers show the mean and standard deviation of the triplicate setup. Black dots indicate tree individuals (canopy), the individual litter samples (litter) and the individual fine root samples (fine roots).
Total nutrient stocks for N, P, Ca, K and Mg in wood, in the upper 10 cm of soil and in the wood and soil together along forest succession, from agriculture (Ag) over 60-year-old forest (60 yrs) to old-growth forest (OG)
Bars and whiskers show the mean and standard deviation of the triplicate setup. For the wood stocks, the larger graphs show the same scale as the soil stocks, with the inset graphs showing wood stocks at higher resolution. Black dots indicate the plot-level values per successional stage.
Secondary forests constitute an increasingly important component of tropical forests worldwide. Although cycling of essential nutrients affects recovery trajectories of secondary forests, the effect of nutrient limitation on forest regrowth is poorly constrained. Here we use three lines of evidence from secondary forest succession sequences in central Africa to identify potential nutrient limitation in regrowing forests. First, we show that atmospheric phosphorus supply exceeds demand along forest succession, whereas forests rely on soil stocks to meet their base cation demands. Second, soil nutrient metrics indicate that available phosphorus increases along the succession, whereas available cations decrease. Finally, fine root, foliar and litter stoichiometry show that tissue calcium concentrations decline relative to those of nitrogen and phosphorus during succession. Taken together, these observations suggest that calcium becomes an increasingly scarce resource in central African forests during secondary succession. Furthermore, ecosystem calcium storage shifts from soil to woody biomass over succession, making it a vulnerable nutrient in the wake of land-use change scenarios that involve woody biomass export. Our results thus call for a broadened focus on elements other than nitrogen and phosphorus regarding tropical forest biogeochemical cycles and identify calcium as a scarce and potentially limiting nutrient in an increasingly disturbed and dynamic tropical forest landscape.
Sex differentiation and hormones are essential for the development of sexual signals in animals, and the regulation of sexual signals involves complex gene networks. However, it is unknown whether a core gene is able to connect the upstream regulators for controlling sexual signal outputs and behavioural consequences. Here, we identify a single gene that integrates both sex differentiation and hormone signalling with sexual attractiveness in an insect model. CYP4PC1 in the German cockroach, Blattella germanica, controls the rate-limiting step in producing female-specific contact sex pheromone (CSP) that stimulates male courtship. As revealed by behavioural, biochemical, molecular, genetic and bioinformatic approaches, in sexually mature females, CYP4PC1 expression and CSP production are coordinately induced by sex differentiation genes and juvenile hormone (JH) signalling. In adult males, direct inhibition of CYP4PC1 expression by doublesexM binding in gene promoter and lack of the gonadotropic hormone JH prevent CSP production, thus avoiding male–male attraction. By manipulating the upstream regulators, we show that wild-type males prefer to court cockroaches with higher CYP4PC1 expression and CSP production in a dose-dependent manner, regardless of their sex. These findings shed light on how sex-specific and high sexual attractiveness is conferred in insects. A multidisciplinary approach, including genetics and behavioural assays, identifies a single gene, CYP4PC1, which integrates both sex differentiation and hormone signalling with sexual attractiveness in the German cockroach.
Phylogenetic tree from maximum likelihood analysis of the concatenated alignment of 3,921,975 bp from 2,395 single-copy target nuclear gene orthologues
The number associated with branches to the left of the slash is ultrafast bootstrap support. The number to the right of the slash is local posterior probability from ASTRAL coalescent gene tree/species tree analysis. Dashes indicate nodes not present in the coalescent tree. Pie charts on nodes represent the relative likelihoods from maximum likelihood reconstruction of bird (red) or mammal (blue) host under the all-rates-different model (root age: 92 Ma). Major louse groups are indicated with coloured shading on the right (Rh., Rhynchophthirina) and louse images on the left. Bird and mammal images are representative hosts for louse parasites in the tree. Grey box and dashed lines indicate key louse genera from Afrotheria hosts. Credit: louse images on the left, ©Jacqueline Mahannah; bird and mammal images on the right, ©Lynx Edicions. Scale bar indicates number of substitutions per site.
Cophylogenetic comparison of dated mammal (left) and mammal louse (right) phylogenies
Mammal tree and dates from a comprehensive tree of mammals⁵. Louse tree is dated using a combination of fossil and codivergence calibrations and the residual least squares method (Methods). Blue lines link lice with their respective mammal hosts. Coloured circles link louse and mammal nodes that are shared cospeciation events between mammal and louse trees. Geological timescale is indicated by coloured shading (Quartenary 0–2.6 Ma, Neogene 2.6–23 Ma, Palaeogene 23–66 Ma and Cretaceous 66–145 Ma) to the same scale for both mammal and louse trees. Dashed lines with arrows indicate key host-switching events mentioned in the main text. Asterisk indicates the mammal lineage (Afrotheria) on which mammal lice were inferred to have originated. Elephant silhouette extracted from Phylopic (
Mammals host a wide diversity of parasites. Lice, comprising more than 5,000 species, are one group of ectoparasites whose major lineages have a somewhat patchwork distribution across the major groups of mammals. Here we explored patterns in the diversification of mammalian lice by reconstructing a higher-level phylogeny of these lice, leveraging whole genome sequence reads to assemble single-copy orthologue genes across the genome. The evolutionary tree of lice indicated that three of the major lineages of placental mammal lice had a single common ancestor. Comparisons of this parasite phylogeny with that for their mammalian hosts indicated that the common ancestor of elephants, elephant shrews and hyraxes (that is, Afrotheria) was the ancestral host of this group of lice. Other groups of placental mammals obtained their lice via host-switching out of these Afrotherian ancestors. In addition, reconstructions of the ancestral host group (bird versus mammal) for all parasitic lice supported an avian ancestral host, indicating that the ancestor of Afrotheria acquired these parasites via host-switching from an ancient avian host. These results shed new light on the long-standing question of why the major groups of parasitic lice are not uniformly distributed across mammals and reveal the origins of mammalian lice. Mammals host a diversity of parasites including lice. Using cophylogenetics and phylogenetic comparative methods, the authors show that the main lineages of placental mammal lice had a single common ancestor and find that all parasitic lice had an avian ancestral host.
The effect of different calibration approaches in divergence-time estimates in TED analyses of the hominin phylogeny
The dots indicate the mean and the lines correspond to the associated 95% HPD of the divergence-time estimations for each node. Different colours indicate different calibration approaches. ‘Original’ indicates the analysis in Püschel et al.² using the Dembo et al.⁶ topological hypothesis. ‘Corrected H. sapiens age’ is the same treatment as Original but changing the age to 39.475 ± 0.645 ka, which is the correct age for the Tianyuan 1 specimen used in the analyses for H. sapiens¹⁰. ‘Redundant characters removed + Corrected H. sapiens age’, is the same treatment as the latter but with 25% of the redundant characters (according to Mongle et al.) removed. Mongle et al.¹ corresponds to the Mongle et al. analysis. It is important to note that Mongle et al. did not include the 95% HPDs for their estimated node mean ages but it is likely that, if present, these intervals would considerably overlap with the three other calibration approaches.
In their recent communication, Mongle et al. argue that there are several problems with our recent analysis: a problematic character matrix, a problematic geochronology and questionable body mass estimates. For the sake of brevity, we only focus on these main criticisms and refer to Supplementary Table 1 for further details but note that even if one considers their analysis to be correct and ours wrong, the discrepancies in divergence-time estimates for the nodes between the two analyses are a minimal 2.9% mean percentage difference and a 1.1% median percentage difference (Supplementary Table 2). In our view, dismissing our results and conclusions on the basis of such negligible differences is unmerited, especially when considering that almost all their mean divergence-time estimates are within our 95% highest posterior density intervals (HPD) (Fig. 1). Additionally, using point estimates (for example, mean values) is inappropriate in Bayesian analyses comparing divergence-time estimates, as the uncertainty around these values is not considered. Instead, posterior distributions should have been compared using the 95% HPD.
Zebra finches dynamically change their spatial positions within the flying flock
a, Schematic representation of the experimental setup in the flight section (not to scale). Blue-shaded areas: cameras’ fields of view. b,c, Tracked positions (sample rate: 24 Hz) of each bird are indicated for a time period of 586 ms in colour-reversed cutouts of freeze frames of the footage taken with Camera 1 (b) and Camera 2 (c). Coloured circles indicate the birds’ positions at the end of the sequence. Note the alignment of movement trajectories of the birds Pink, Lilac and Light blue, and of the birds Orange and Green. d, Pseudo 3D representation of all birds’ spatial positions (sample rate: 24 Hz) during one example of flight sessions (session 8, duration: 85.4 s), indicating the preferred area in the flight section occupied by each bird. e–g, Reconstructed flight paths (sample rate: 24 Hz) in the horizontal (e) and vertical (f) dimension, and in wind direction (g), for each bird during the flight session shown in d. f, frequency of position change (Hz = cycles per second); n.s., not significant. Note the rhythmic fluctuations of flight paths in the horizontal dimension. Axis scaling in b to g: negative values = bottom, left and downwind positions in the flight section. Marker colours in b to g correspond to the birds’ IDs. h, Mean (red dots) and s.d. (grey lines) of spatial distances normalized to the maximum distance detected for each bird pairing are shown for n = 15 bird pairings in the horizontal and vertical dimension, and in wind direction for the flight session shown in d.
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Horizontal position changes are accompanied by head turns
a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. Cutouts of freeze frames of the footage taken with Camera 2 show the bird’s head and body posture for 11 time points during the position change. b, In all birds, the median angle of head turn during horizontal position change in flocking flight is positively correlated (linear mixed effects model (LMM), estimates ± s.e.m.: 2.05 ± 0.1, P < 0.001, t = 21.0) with the direction of position change relative to zero degrees in wind direction. Coloured dots: individual data points; coloured lines: fitted linear regression models (R² values are indicated for each bird); colours: bird IDs. Negative values: left-hand positions in the flight section. n = 10 horizontal position changes per bird. c,d, Schematic representation of a bird head’s orientation (dorsal view) during straight flight (c) and during a horizontal position change to the right (d). The overall visual field of a zebra finch²² and spatial areas with high visual acuity²³ are indicated in blue and yellow, respectively. Black arrow: direction of position change; grey arrow: wind direction. e, Head turn angles (red circles) and horizontal positions (blue circles) of bird Orange during the horizontal position change shown in a. Light red area: time period of significant head turn; light blue area: time period of significant position change; purple area: product of the overlap of the light red and blue areas; black line: time period shown in a; black dots: most lateral positions.
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Vocal activity during flight depends on social context
a, Calling activity differs between flocking and solo flight sessions. Histogram bin size: 2 ms. Red lines: duration of flight sessions. n = 308 calls emitted by 5 birds in 13 flocking flight sessions and n = 22 calls emitted by 1 bird in 19 solo flight sessions. b, Spectrogram (fast Fourier transform length: 512, 99.6% overlap, Hamming window) of a stack call emitted by bird Green during flocking flight. Light colours represent high energy. c,d, When emitting a stack call during flight, the calling bird (n = 93; light green dots) was positioned at a significantly (LMM, estimates ± s.e.m.: 252.3 ± 49.9, P < 0.001, t = 5.06) lower, significantly (LMM, estimates ± s.e.m.: 154.6 ± 74.3, P = 0.038, t = 2.08) more right (c) and significantly (LMM, estimates ± s.e.m.: 564.8 ± 97.2, P < 0.001, t = 5.81) further upwind (d) position than its flock mates (n = 465; light blue dots). Grey dots: all positions of all birds during four flight sessions; dark blue, dark green and dark grey lines: IQRs of horizontal and vertical positions, and of horizontal and wind direction positions of calling birds at call onset, their flock mates at call onset and all birds during four flight sessions, respectively; the lines’ intersections are at the medians of the distributions; thin black lines: flight section’s outline.
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Movement directions of calling birds and reactions of flock mates
a,b, Movement directions of a calling bird within 209 ms following the onset of stack calls showed a significant (Rayleigh test, P = 0.018, z = 3.99, n = 93) directionality in the horizontal/vertical plane (a), but not (Rayleigh test, P = 0.277, z = 1.29, n = 73) in the horizontal/wind direction plane (b). Light green circles and fans: individual movement directions and counts, respectively; dark green line: median movement direction. c–e, Box plots of flock mates’ spatial positions relative to a focal bird’s spatial position at the onset of 46 stack calls (blue) and at the time of initiation of 579 call-unaccompanied upwards movements (cyan). Dots: individual data points; boxes: 25th and 75th percentiles of distributions; horizontal lines: medians. LMM, estimates ± s.e.m.: 80.5 ± 19.9, P < 0.001, t = 4.04 (c); −127.2 ± 12.5, P < 0.001, t = 10.17 (d); 105.5 ± 37.9, P = 0.005, t = 2.78 (e). Sample sizes in c and d as in f, sample sizes in e as in g. f,g, Call-accompanied upwards movements caused flock mates to significantly reduce movement activity in the horizontal/vertical (LMM, estimates ± SE: 20.6 ± 4.9, p < 0.001, t = 4.2, n = 230; f) and the horizontal/wind direction plane (LMM, estimates ± s.e.m.: 52.3 ± 10.2, P < 0.001, t = 5.2, n = 202; g). Box plots show distances travelled by flock mates within 209 ms following the initiation of 46 call-accompanied (blue) and 535 call-unaccompanied upwards movements (cyan). Dots: individual data points; boxes: 25th and 75th percentiles of distributions; horizontal lines: medians; P values of LMMs are indicated. h–k, Movement directions of upwards-moving birds’ flock mates within 209 ms after call onset (h and i; Rayleigh test, P = 0.001, z = 7.13, n = 230, and P = 0.036, z = 3.31, n = 202, respectively) differ from movement directions of upwards-moving birds’ flock mates within 209 ms after initiation of call-unaccompanied upwards movements (j and k; Rayleigh test, P < 0.001, z = 27.43, n = 2,637, and P < 0.001, z = 15.05, n = 2,284, respectively). Light blue and light cyan circles and fans: individual movement directions and counts, respectively; dark blue and dark cyan lines: median movement direction.
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Vision and vocal communication play a role in collision avoidance during flocking flight
a,b, In contrast to the take-off phase (LMM, estimates ± s.e.m.: 0.03 ± 0.05, P = 0.517, t = 0.65; a), call emission rates during sustained flocking flight are affected by the ambient illuminance level (LMM, estimates ± s.e.m.: −0.02 ± 0.01, P = 0.02, t = −2.33; b). Coloured circles and lines indicate individual data points and means, respectively. Colours represent bird ID. Grey diamonds and thick lines mark population means ± s.d., respectively. n = 60 for each light condition. c, The rate of collisions between birds during flocking flight is not affected by the ambient illuminance level (LMM, estimates ± s.e.m.: −0.01 ± 0.01, P = 0.5, t = −0.68). Black asterisks mark individual data points. Meaning of remaining markers as in a. n = 10 per illuminance level. d,e, In contrast to the take-off phase (LMM, estimates ± s.e.m.: 0.05 ± 0.04, P = 0.172, t = 1.37; d), call emission rates during sustained flocking flight are affected by the presence of masking noise (LMM, estimates ± s.e.m.: 0.02 ± 0.004, P < 0.001, t = 5.22; e). Meaning of colours and markers as in a. n = 60 for each noise condition. f, The rate of collisions between birds during flocking flight is significantly affected by the presence of masking noise (LMM, estimates ± s.e.m.: −0.014 ± 0.006, P = 0.036, t = −2.26). Meaning of markers as in c. n = 10 per noise condition. At the top of each panel, the P value of a linear mixed effects model is provided.
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Animal collective motion is a natural phenomenon readily observable in various taxa. Although theoretical models can predict the macroscopic pattern of group movements based on the relative spatial position of group members, it is poorly understood how group members exchange directional information, which enables the spatial coordination between individuals during collective motion. To test if vocalizations emitted during flocking flight are used by birds to transmit directional information between group members, we recorded vocal behaviour, head orientation and spatial position of each individual in a small flock of zebra finches (Taeniopygia guttata) flying in a wind tunnel. We found that the finches can use both visual and acoustic cues for three-dimensional flock coordination. When visual information is insufficient, birds can increasingly exploit active vocal communication to avoid collisions with flock mates. Our study furthers the mechanistic understanding of collective motion in birds and highlights the impact interindividual vocal interactions can have on group performances in these animals. Zebra finches flying in a wind tunnel use both vocal and visual communication to orientate themselves within the flock, and are able to enhance their use of one form of communication over another depending on circumstance.
The coral reef microbiome is central to reef health and resilience. Competitive interactions between opportunistic coral pathogens and other commensal microbes affect the health of coral. Despite great advances over the years in sequencing-based microbial profiling of healthy and diseased coral, the molecular mechanism underlying colonization competition has been much less explored. In this study, by examining the culturable bacteria inhabiting the gastric cavity of healthy Galaxea fascicularis, a scleractinian coral, we found that temperate phages played a major role in mediating colonization competition in the coral microbiota. Specifically, the non-toxigenic Vibrio sp. inhabiting the healthy coral had a much higher colonization capacity than the coral pathogen Vibrio coralliilyticus, yet this advantage was diminished by the latter killing the former. Pathogen-encoded LodAB, which produces hydrogen peroxide, triggers the lytic cycle of prophage in the non-toxicogenic Vibrio sp. Importantly, V. coralliilyticus could outcompete other coral symbiotic bacteria (for example, Endozoicomonas sp.) through LodAB-dependent prophage induction. Overall, we reveal that LodAB can be used by pathogens as an important weapon to gain a competitive advantage over lysogenic competitors when colonizing corals. Competition among species in the coral microbiome has important outcomes in terms of coral health, but little is known about the mechanisms allowing pathogens to gain a competitive advantage. Here the authors show that the pathogen Vibrio coralliilyticus outcompetes commensal bacteria by inducing prophages.
Chaotic dynamics in relation to variability, predictability, nonlinearity and non-stationarity
a–d, Histograms show the number of chaotic and non-chaotic time series in relation to: variability, as measured by the coefficient of variation (a); predictability, as measured by the leave-one-out prediction R² for abundance (b); nonlinearity, as measured by the local weighting parameter (θ)⁴³, where 0 indicates linear dynamics (c); and monotonic trend, as measured by the squared Spearman rank correlation coefficient (d). Horizontal axis labels give the midpoint of each bin with the exception of c which displays the discrete values that were used. Key in a applies to all panels.
Chaotic dynamics by taxonomic group and model dimensionality
Bars show the number of chaotic and non-chaotic time series by taxonomic group with unconstrained embedding dimension (free E) and with embedding dimension fixed to 1 (E = 1) using the Jacobian method.
Chaotic dynamics in relation to generation time
a, Proportion of time series classified as chaotic using the Jacobian method. Chaotic series are coded as 1 and non-chaotic series as 0. Points are vertically jittered to reduce overlap. The line is a logistic (Bernoulli) regression and associated band is the 95% confidence interval. b, Values of the LE plotted against generation time. In a and b, colour indicates the embedding dimension, E.
Positive LEs in relation to body mass
Colour distinguishes broad taxonomic groups. Includes data from this study (GPDD and supplemental results from three lake systems) and positive LEs compiled by ref. ⁴⁷ (AG2020). The log–log scale is in keeping with previous studies⁴⁷. Note that the lakes data (squares) were not used to fit the regression line. Supplementary Fig. 9 shows the same points with lower confidence intervals.
Chaotic dynamics are thought to be rare in natural populations but this may be due to methodological and data limitations, rather than the inherent stability of ecosystems. Following extensive simulation testing, we applied multiple chaos detection methods to a global database of 172 population time series and found evidence for chaos in >30%. In contrast, fitting traditional one-dimensional models identified <10% as chaotic. Chaos was most prevalent among plankton and insects and least among birds and mammals. Lyapunov exponents declined with generation time and scaled as the −1/6 power of body mass among chaotic populations. These results demonstrate that chaos is not rare in natural populations, indicating that there may be intrinsic limits to ecological forecasting and cautioning against the use of steady-state approaches to conservation and management.
A conceptual diagram illustrating how plant diversity influences soil P cycling in terrestrial ecosystems
Rectangles represent the main biogenic forms of soil P; hexagons are biogeochemical processes. Symbols ‘+’, ‘−’ and ‘±’ indicate expected positive, negative and unclear responses to increased plant diversity, respectively. P-ase, soil phosphatase.
Comparison of soil total P, phosphatase activity and available P in species mixtures versus monocultures among bulk and rhizosphere soils
The effect size is defined as the percentage changes in observed to the expected values in plant mixtures. Each point and error bar represent mean and 95% CIs, respectively. For each selected soil P variable, the number of observations is shown beside each attribute without parentheses and the number of studies is given in parentheses.
Comparison of soil total P, phosphatase activity and available P in species mixtures versus monocultures in relation to SR and FR
a–f, Soil total P (a,d); soil phosphatase activity (b,e); and soil-available P (c,f). The effect size is defined as the percentage changes in observed values to the expected values in plant mixtures. Black lines represent the average responses with their 95% CIs shaded in grey. The size of each dot represents the relative weights of corresponding observations. We used the one-sided F-test to calculate P values.
The interactive effects of the plant SR (or FR) in mixtures and SC on soil phosphatase activity and available P
a–d, Soil phosphatase activity (a,c) and soil-available P (b,d). The effect size is defined as the percentage changes in observed values to the expected values in plant mixtures. Blue and red lines represent the bulk soil-specific and rhizosphere soil-specific responses, respectively, with their 95% CIs shaded. Solid and dashed lines represent the responses of bulk soil and rhizosphere soil, respectively. We use both one-sided F-test and two-sided t-test to calculate P values.
The influence of soil phosphatase activity on plant productivity and soil-available P
a, Comparison of plant productivity in species mixtures versus monocultures in relation to SR in mixtures. b, Bivariate relationships between the effects of plant mixture on plant production with soil phosphatase activity. c, Bivariate relationships between the effects of plant mixture on soil-available P with soil phosphatase activity. d, Structural equation model depicting the influence of the lnRR of SR in mixtures and soil phosphatase activity on the lnRR of plant productivity and soil-available P (n = 137, Fisher.C  = 10.51, P = 0.105). In a, red square and error bar represent the overall mean and its 95% CIs. In a, b and c, the effect size is defined as the percentage changes in observed to expected values in plant mixtures. The black and coloured lines represent the average and SC-specific responses, respectively, with their bootstrapped 95% confidence. The sizes of the dots represent the relative weights of corresponding observations. The number of observations is shown beside each attribute without parentheses and the number of studies is shown in parentheses. In d, the arrows represent the directional influence of one variable on another. The number beside the arrow is the corresponding standardized coefficient (r) with significance levels indicated (*P < 0.05, **P < 0.01 and ***P < 0.001). All fitted coefficients are significant at α = 0.05. R²marginal and R²conditional represent the level of deviance of the variable explained by all paths from the fixed effects and both the fixed and random effects (‘study’), respectively. We use both one-sided F-test and two-sided t-test to calculate P values.
Soil phosphorus (P) availability is critical to plant productivity in many terrestrial ecosystems. How soil P availability responds to changes in plant diversity remains uncertain, despite the global crisis of rapid biodiversity loss. Our meta-analysis based on 180 studies across various ecosystems (croplands, grasslands, forests and pot experiments) shows that, on average, soil total P, phosphatase activity and available P are 6.8%, 8.5% and 4.6%, respectively, higher in species mixtures than in monocultures. The mixture effect on phosphatase activity becomes more positive with increasing species and functional group richness, with more pronounced increases in the rhizosphere than in the bulk soil. The mixture effects on soil-available P in the bulk soil do not change, but with increasing species or functional group richness these effects in the rhizosphere soil shift from positive to negative. Nonetheless, enhanced soil phosphatase activity stimulated available P in diverse species mixtures, offsetting increased plant uptake effects that decrease soil-available P. Moreover, the enhancement effects of species richness on soil phosphatase activity are positively associated with increased plant productivity. Our findings highlight that preserving plant diversity could increase soil phosphatase activity and P availability, which sustain the current and future productivity of terrestrial ecosystems.
Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection. Comparing the regulatory behaviour of naturally segregating promoter variants to randomly mutated promoters, the authors demonstrate both stabilizing and directional selection that reduce variation in phenotype.
Ant colonies with permanent division of labour between castes and highly distinct roles of the sexes have been conceptualized to be superorganisms, but the cellular and molecular mechanisms that mediate caste/sex-specific behavioural specialization have remained obscure. Here we characterized the brain cell repertoire of queens, gynes (virgin queens), workers and males of Monomorium pharaonis by obtaining 206,367 single-nucleus transcriptomes. In contrast to Drosophila, the mushroom body Kenyon cells are abundant in ants and display a high diversity with most subtypes being enriched in worker brains, the evolutionarily derived caste. Male brains are as specialized as worker brains but with opposite trends in cell composition with higher abundances of all optic lobe neuronal subtypes, while the composition of gyne and queen brains remained generalized, reminiscent of solitary ancestors. Role differentiation from virgin gynes to inseminated queens induces abundance changes in roughly 35% of cell types, indicating active neurogenesis and/or programmed cell death during this transition. We also identified insemination-induced cell changes probably associated with the longevity and fecundity of the reproductive caste, including increases of ensheathing glia and a population of dopamine-regulated Dh31-expressing neurons. We conclude that permanent caste differentiation and extreme sex-differentiation induced major changes in the neural circuitry of ants. Using single-cell transcriptomics, the authors generate a brain cell atlas for the pharaoh ant including individuals of different sexes and castes and show changes in cell composition underlying division of labour and reproductive specialization.
Conceptual figure of the hypotheses for the response of plant diversity to herbivore exclusion
a,b, The conceptual figure outlines our hypotheses for the response of plant diversity to herbivore exclusion expressed as a log response ratio (LRR: ln(exclusion/grazed)) in long-history sites (a) and short-history sites (b), defined as, respectively, greater than or less than 500 years of evolutionary history with ungulate grazers (Supplementary Note 1). These hypotheses are based upon theory predicting responses contingent on evolutionary grazing history, current grazing intensity and ecosystem productivity10,11. Here we have adapted the model predictions to focus on resource availability generalized across grazing intensities. We test our hypotheses using a natural precipitation gradient and a soil nitrogen gradient as measures of resource availability. We also decomposed plant diversity into richness (number of species) and evenness (inverse of species dominance). In a single equilibrium ecosystem (as expected for long-history sites), we hypothesize that, with the exclusion of herbivores, plant diversity will increase at low resource availability (positive LRR) and decrease at high resource availability (negative LRR) (a). At low resource availability, we expect plant diversity to increase both through gains in native, grazing-intolerant species (increase in richness) and decreases in dominance of native, grazing-tolerant species (increase in evenness). At high resource availability, we expect tall, native species not adapted to grazing to dominate when herbivores are excluded, suppressing other plant species. In the long-history sites, these changes are driven by native species and exotic species are less common with lower abundance than in short-history sites. We expect short-history sites to diverge from the single equilibrium model, with some native species unable to recover due to lack of seed supply or altered ecosystem conditions (for example, altered nutrient cycling). Hence, b indicates potential for zero or more restricted recovery of native species at low–mid resource availability and greater increases at high resource availability if changes are reversible. We expect the response of exotic species (which mostly originate from long-history regions) to be closer to the single equilibrium ecosystem in the long-history sites (grey line).
Geographic and climatic distribution of experimental sites
Location of the 57 NutNet sites at which the full factorial experiment of herbivore exclusion and nutrient addition was replicated. a, Sites were classified as subject to a long evolutionary history of grazing (large herds of ungulates present >500–10,000 years ago; 24 sites) or a short one (<500 years; 33 sites). b, The 57 sites represent a wide range of mean annual temperature (MAT) and mean annual precipitation (MAP) conditions. Additional site details are provided in Supplementary Note 1 and Supplementary Table 2.
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Herbivore exclusion effects on plant diversity, richness and evenness
a–c, Effect of herbivore exclusion on inverse Simpson’s diversity (a), richness (b) and Simpson’s evenness (c) calculated as LRR = ln(fenced/unfenced) for unfertilized (green) and fertilized (NPKµ) plots (purple) in sites with a long- or short-history of grazing. Points represent the mean effect across all 57 sites with the LRRs calculated per block (n = 76 per fertilization treatment for the long-history sites and n = 103 for the short-history sites) and error bars the range of 95% confidence intervals. Effects are considered significant when error bars do not overlap with zero.
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Herbivore exclusion effects on native and exotic species richness
a,b, Effect of herbivore exclusion on native species richness (a) and exotic species richness (b) calculated as LRR = ln(fenced/unfenced) for unfertilized (green) and fertilized (NPKµ) plots (purple) in sites with a long- or short-history of grazing. Points represent the mean effect across all 57 sites with the LRRs calculated per block (n = 76 per fertilization treatment for the long-history sites and n = 103 for the short-history sites) and error bars the range of 95% confidence intervals. Effects are considered significant when error bars do not overlap with zero.
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Herbivore exclusion effects on plant richness related to rainfall
Relationship between mean annual precipitation and the LRR of total plant richness plotted by a, treatment, unfertilized (green points) and fertilized (purple points), and b, plant origin, native (orange points) and exotic (grey points) plant richness to herbivore exclusion in sites with a long (n = 152 plots from 24 sites) or short (n = 206 plots from 33 sites) evolutionary history of grazing. Sites included unfertilized control plots and plots fertilized with NPKµ, which are green and purple in a but not defined with a colour in b. For the long-history sites, there were significant relationships across the precipitation gradient, so trendlines were produced using predicted values of the linear mixed effects models. In a, this line was predicted to all points regardless of fertilization as this experimental treatment did not have a significant effect (see Table 1), while in b the line was predicted for native richness. The coloured bands represent the 95% confidence intervals.
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Ecological models predict that the effects of mammalian herbivore exclusion on plant diversity depend on resource availability and plant exposure to ungulate grazing over evolutionary time. Using an experiment replicated in 57 grasslands on six continents, with contrasting evolutionary history of grazing, we tested how resources (mean annual precipitation and soil nutrients) determine herbivore exclusion effects on plant diversity, richness and evenness. Here we show that at sites with a long history of ungulate grazing, herbivore exclusion reduced plant diversity by reducing both richness and evenness and the responses of richness and diversity to herbivore exclusion decreased with mean annual precipitation. At sites with a short history of grazing, the effects of herbivore exclusion were not related to precipitation but differed for native and exotic plant richness. Thus, plant species’ evolutionary history of grazing continues to shape the response of the world’s grasslands to changing mammalian herbivory. A NutNet experiment in 57 grasslands across six continents shows that when herbivores are excluded from grasslands with a long coevolutionary history of grazing plant diversity is reduced, while in grasslands without a long grazing history the evolutionary history of the plant species regulates the response of plant diversity.
Molecular evolution of the Or67a subfamily in Drosophila
a, Drosophila species tree (branches not to scale) illustrating Or67a copy number changes in a subset of available genome assemblies that is3,10,25,28. These numbers exclude pseudogenes that are recognizable Or67a family members. b, Illustration of the evolutionary scenarios that are being investigated for the Or67a family. To what extent are the receptors tuned to different ligands within (and between) species? For D. simulans and D. mauritiana, are the three Or67a paralogues expressed in distinct neuron populations that project to different regions (glomeruli) of the antennal lobe, or are they co-expressed in the same neuron population(s)? An intermediate scenario (not shown) is the co-expression of two receptors in one OSN population and singular expression of the third receptor in a second population. c, Bayesian protein tree inferred for the intact Or67a subfamily members. The black numbers near the nodes indicate posterior support. The branch with a dashed line was inferred to have a significant elevation in protein evolution (dN/dS = 1.5–1.9, red text); the remaining branches were inferred to have been under functional constraint (dN/dS < 0.5). d, Overview of the parallel loss of the Or67a.D and Or67a.3R genes in D. melanogaster and the simulans group, using D. yakuba and D. santomea as outgroup species. On the left is a species tree for the samples included in the analyses (branches not to scale). The numbers at the tree nodes indicate divergence dates in millions of years for D. melanogaster and the D. simulans group30,31. To the right of the tree are schematics of the alignments of the Or67a-containing chromosomal regions. Shown are the conserved genes (grey rectangles) that flank the Or67a-containing regions (coloured rectangles) and the independent deletions of the Or67a.D and 3R genes (dashed lines). The deletions are mapped onto the species tree with red dots. Many remnants of transposable elements were identified within these intervals, illustrated with grey triangles (the schematic is not to scale, but see Extended Data Figs. 1 and 2).
Evolutionary changes in Or67a orthologue and paralogue odour responses
a, Schematic for single-sensillum recordings from a sensillum housing two OSNs (A and B), differentiable by spike amplitudes. b, Illustration of the D. melanogaster ‘decoder’ system⁴¹ used to screen the response profiles of Or67a orthologues and paralogues. c, Quantification of Or67a.P/D/3R responses to nine odours at 10⁻² (v/v) concentration delivered in 1 s pulses (the tree is not to scale). The circles are individual data points, the squares indicate the means and the error bars display the standard deviations. The number of independent sensilla recorded per odour–receptor combination is: for D. melanogaster, Or67a.P = 11; for D. sechellia, Or67a.P = 9; for D. mauritiana, Or67a.P = 9, Or67a.D = 8 and Or67a.3R = 8; and for D. mauritiana, Or67a.P = 11, Or67a.D = 4 and Or67a.3R = 12. d, Relative effects of the odours on Or67a.P/D/3R responses at 10⁻² (v/v) concentration. These values provide the probability that a given odour-receptor response will be the largest given the full dataset. e, Principal component analyses based on the data from Fig. 2b. The percentages along the axes indicate the amount of variation explained by the principal components. Species names have been abbreviated to the first three letters. f, The two odour–receptor combinations that resulted in the largest dose–response differences among the Or67a.P/D/3R orthologues (see Extended Data Fig. 4 for the other odours). For simplicity, the level of significance indicated above each concentration’s comparison is only for the single species comparison with the largest difference (see Supplementary Table 4 for the full set of tests; *P < 0.05; **P < 0.01; ***P < 0.001). The P values were calculated with a two-sided Dunn test; correction for multiple comparisons was done using the Holm method. The number of independent sensilla recorded for Or67a.D responses to ethyl hexanoate and 6-methyl-5-hepten-2-one is (low to high concentrations): for D. simulans, 11, 8 and 4; and for D. mauritiana, 9, 9 and 8. The sample sizes for Or67a.P responses to ethyl hexanoate and 6-methyl-5-hepten-2-one are: for D. simulans, 11, 8 and 11; for D. mauritiana, 10, 9 and 9; for D. melanogaster, 10, 9 and 11; and for D. sechellia, 8, 8 and 9.
Evolutionary analyses of Or67a expression and circuit neuroanatomy
a, Whole-mount antennal RNA expression of Or67a paralogues and orthologues in D. simulans (Drosophila Species Stock Center (DSSC) 14021-0251.004), D. mauritiana (DSSC 14021-0241.151), D. sechellia (DSSC 14021-0248.07) and D. melanogaster (CantonS) (top to bottom). Scale bar, 25 μm. The number of Or67a-expressing OSNs (±standard deviation) is indicated at the bottom left corner (simOr67a.P, n = 12 antennae; simOr67a.D, n = 12; mauOr67a.P, n = 10; secOr67a.P, n = 11; melOr67a.P, n = 11). Weak staining prevented the quantification of OSN numbers expressing Or67a.3R in D. simulans and Or67a.3R and Or67a.P in D. mauritiana. The arrowheads indicate weakly labelled cells. b, Antennal co-expression of knock-in Gal4 transcriptional reporters (simOr67a.PGal4 and simOr67a.3RGal4, visualized by UAS–GCaMP6s; GCaMP6s detected by anti-GFP) and Or67a.P (top) and Or67a.D (bottom) RNA in D. simulans. Scale bar, 25 μm. c, Top, antennal lobe innervation of neurons labelled by a previously described²⁴ promoter transcriptional reporter for Or67a in D. melanogaster (Bloomington Drosophila Stock Center (BDSC) no. 52633); neuropil is visualized with nc82 (magenta). Bottom, schematic illustrating the innervation of DM6 by Or67a-expressing neurons. Right, Gal4 and promoter transcriptional reporters for Or67a paralogues in D. simulans. All reporter lines label neurons innervating the glomerulus that is homologous to D. melanogaster’s DM6 (arrowheads). Scale bar, 25 μm. d, Antennal lobe innervation of promoter transcriptional reporters for all three D. simulans paralogues in D. melanogaster (the arrowheads point to DM6); neuropil is visualized with nc82 (magenta). Scale bar, 25 μm. In a–d, the experiments were repeated at least three times for each staining on independent days, and the pictures show representative examples for each condition. e, Putative regulatory motifs identified in the 5′ DNA sequences of the Or67a paralogues in D. simulans and melOr67a.P (1.5–2 kb; Methods). The boxes indicate the placement of candidate motifs, with colours illustrating the same motif sequence. Positive-strand motifs are above the horizontal line, and negative-strand motifs are below. The sequences have been arranged to approximate a DNA alignment without gaps. The plot to the right summarizes the number of motifs per sequence and the overlap of motifs between the four sequences.
Unique but overlapping contributions of Or67a paralogues to endogenous neuronal responses
a, Quantification of wild-type ab10 sensilla recordings for D. melanogaster (left) and D. simulans (right) to a panel of nine odours (as in Fig. 2c). For D. melanogaster, n = 4 for all odours; for D. simulans, n = 8 (6-methyl-5-hepten-2-one, methyl benzoate, ethyl hexanoate and methyl acetate), 9 (methyl hexanoate and geraniol) and 10 (2-heptanone, phenethyl alcohol and pentanoic acid). The circles represent individual data points, the squares indicate the means and the error bars display the standard deviations. b, Comparison between the standardized odour responses from the ‘decoder’ neuron and the wild-type D. simulans ab10 sensilla. On the left are the standardized (to the strongest odour-evoked response) mean responses for the Or67a paralogue that produced the maximum response to a given odour; on the right are the standardized mean responses from the wild-type recordings (as plotted in a). The colour code matches that in a. c, Right, quantification of simOr67a.3R loss-of-function responses to the panel of nine odours; n = 6 for all odours. Left, quantification of simOr67a.P loss-of-function responses to the panel of nine odours; n = 6 (6-methyl-5-hepten-2-one and phenethyl alcohol), 7 (2-heptanone, methyl hexanoate, geraniol and methyl acetate), 8 (methyl benzoate and pentanoic acid) and 9 (ethyl hexanoate). The circles represent individual data points, the squares indicate the means and the error bars display the standard deviations.
Despite numerous examples of chemoreceptor gene family expansions and contractions, how these relate to modifications in the sensory neuron populations in which they are expressed remains unclear. Drosophila melanogaster’s odorant receptor (Or) family is ideal for addressing this question because most Ors are expressed in distinct olfactory sensory neuron (OSN) types. Between-species changes in Or copy number may therefore indicate increases or reductions in the number of OSN populations. Here we investigated the Or67a subfamily, which exhibits copy number variation in D. melanogaster and its closest relatives: D. simulans, D. sechellia and D. mauritiana. These species’ common ancestor had three Or67a paralogues that had already diverged adaptively. Following speciation, two Or67a paralogues were lost independently in D. melanogaster and D. sechellia, with ongoing positive selection shaping the intact genes. Unexpectedly, the functionally diverged Or67a paralogues in D. simulans are co-expressed in a single neuron population, which projects to a glomerulus homologous to that innervated by Or67a neurons in D. melanogaster. Thus, while sensory pathway neuroanatomy is conserved, independent selection on co-expressed receptors has contributed to species-specific peripheral coding. This work reveals a type of adaptive change largely overlooked for olfactory evolution, raising the possibility that similar processes influence other cases of insect Or co-expression.
Stages of the classical research life cycle
We consider that any suboptimal study planning leads to waste in data collection and data analysis. This is because data collection and analysis should conceptually happen at the study planning stage even though physically conducted later. Further, the study planning stage influences the publication stage because badly planned studies are less likely to be published. The components of the research life cycle translate into components of research waste (right) where core waste represents all unpublished work (due to either low-quality study planning or publication bias) and exploitative waste represents the component of published research with a limited ability to inform future work (that is, to be exploited by the users) either because the study conducted (and later published) was of low quality (for example, issues with study design) or because the results of the study were reported in a way that prevents their use (for example, effect size or sample size not reported).
Overall estimate of research waste of ecological research based on a meta-analysis of waste at each stage (with examples of causes)
In the best-case scenario, 82% of research is wasted and thus is unused because all under reporting is assumed to happen in poorly planned studies. In the worst-case scenario, 89% of the research is unused because all of the under reporting is assumed to happen in the otherwise well-planned research. Consequently, only 11–18% of conducted ecological research can inform users (other researchers, public, policymakers) fully.
Estimates of the main components of research waste
a, Estimates of the main components of research waste, from each meta-study, and a boxplot of their distribution. b, Breakdown of research waste generated during the study planning stage, partitioned between different temporal stages of the research life cycle. Left panels: estimates of research waste (circles) as reported by each meta-study (whisker plot denotes their distribution). The circle size is proportional to the sample size used in each meta-study. The circles are coloured by the degree of generality, with 1 representing meta-studies covering narrow ecological subfields and 3 representing meta-studies not limited to a certain ecological subfield (that is, are broad). The boxplot central line represents the median of the estimates, the lower and upper edge of the boxplot represent the 25th and 75th percentiles of the distribution and the whiskers are the smallest and largest value within the 1.5 times interquartile range below and above the 25th and 75th percentiles. Right panels: meta-analytical mean of all effect sizes, i.e., proportion of research wasted (black circles), effect sizes coming from meta-studies with a narrow scope (generality 1, blue circles) and broad scope (generality of 2 and 3, grey circles), with a 95% CI.
Source data
Research inefficiencies can generate huge waste: evidence from biomedical research has shown that most research is avoidably wasted and steps have been taken to tackle this costly problem. Although other scientific fields could also benefit from identifying and quantifying waste and acting to reduce it, no other estimates of research waste are available. Given that ecological issues interweave most of the United Nations Sustainable Development Goals, we argue that tackling research waste in ecology should be prioritized. Our study leads the way. We estimate components of waste in ecological research based on a literature review and a meta-analysis. Shockingly, our results suggest only 11–18% of conducted ecological research reaches its full informative value. All actors within the research system—including academic institutions, policymakers, funders and publishers—have a duty towards science, the environment, study organisms and the public, to urgently act and reduce this considerable yet preventable loss. We discuss potential ways forward and call for two major actions: (1) further research into waste in ecology (and beyond); (2) focused development and implementation of solutions to reduce unused potential of ecological research.
In many regions of the world, forest management has reduced old forest and simplified forest structure and composition. We hypothesized that such forest degradation has resulted in long-term habitat loss for forest-associated bird species of eastern Canada (130,017 km2) which, in turn, has caused bird-population declines. Despite little change in overall forest cover, we found substantial reductions in old forest as a result of frequent clear-cutting and a broad-scale transformation to intensified forestry. Back-cast species distribution models revealed that breeding habitat loss occurred for 66% of the 54 most common species from 1985 to 2020 and was strongly associated with reduction in old age classes. Using a long-term, independent dataset, we found that habitat amount predicted population size for 94% of species, and habitat loss was associated with population declines for old-forest species. Forest degradation may therefore be a primary cause of biodiversity decline in managed forest landscapes.
The origin of snake venom involved duplication and recruitment of non-venom genes into venom systems. Several studies have predicted that directional positive selection has governed this process. Venom composition varies substantially across snake species and venom phenotypes are locally adapted to prey, leading to coevolutionary interactions between predator and prey. Venom origins and contemporary snake venom evolution may therefore be driven by fundamentally different selection regimes, yet investigations of population-level patterns of selection have been limited. Here, we use whole-genome data from 68 rattlesnakes to test hypotheses about the factors that drive genomic diversity and differentiation in major venom gene regions. We show that selection has resulted in long-term maintenance of genetic diversity within and between species in multiple venom gene families. Our findings are inconsistent with a dominant role of directional positive selection and instead support a role of long-term balancing selection in shaping venom evolution. We also detect rapid decay of linkage disequilibrium due to high recombination rates in venom regions, suggesting that venom genes have reduced selective interference with nearby loci, including other venom paralogues. Our results provide an example of long-term balancing selection that drives trans-species polymorphism and help to explain how snake venom keeps pace with prey resistance. Analysing whole-genome sequences from 68 rattlesnakes, the authors show a role of long-term balancing selection in maintaining diversity of multiple venom gene families and find reduced selective interference of venom genes with neighbouring loci.
Phylogeny and biogeography of Oreinotinus
a, Historical biogeographic reconstruction for Oreinotinus inferred using a time-calibrated species-level phylogeny and a DEC model (Methods). Species-level tips are coloured by areas of endemism, and branch/node colours display the most probable (P > 0.5) ancestral ranges. Widespread lineages are assigned multiple colours, with one colour per inhabited area in the range. Dashed grey lines indicate that no one range was supported at P > 0.5. Phylogenetic relationships are well supported (bootstrap values of >95%) except as noted, and our inferences are robust to phylogenetic uncertainty marked at several nodes (Methods and Supplementary Fig. 3). b, The distribution of Oreinotinus in 11 areas of endemism in montane forests of the neotropics; coloured points mark herbarium specimen localities; pins mark locations of accessions in a (Methods and Extended Data Fig. 3). Photographs show clouds shrouding Tucumano forest in the vicinity of Tarija, Bolivia (top) and moss-covered understory of a cloud forest in the Santiago Comaltepec highlands, Oaxaca, Mexico (bottom). c, Dimensionality reduction methods applied to RAD-seq SNPs (Methods and Extended Data Fig. 4) show genetic similarities within and among areas of endemism. A UMAP projection distinguishes local and global structure matching geography; PCAs show differences among samples spanning the entire Oreinotinus range: Mexico, Central America and the Caribbean and South America. E=East, S=South, W=West, CA=Central America, Oax=Oaxaca.
Leaf form evolution in Oreinotinus
a, Upper left: NMDS morphospace showing the position of 39 Oreinotinus species scored for 3 quantitative and 2 categorical leaf variables (Methods, Extended Data Fig. 5 and Supplementary Fig. 4). Colours correspond to the four leaf ecomorphs: DEN, pink; LPT, green; SGE, blue; IGE, orange (see main text). The species present in each of the 11 areas of endemism are shown on the same NMDS plot, coloured by leaf ecomorph. E=East, N=North, S=South, W=West, Bol=Bolivia CA=Central America, Col=Colombia, CR=Costa Rica, Ecu=Ecuador, Jam=Jamaica, Mex=Mexico, Ven=Venezuela. b, Representative leaf silhouettes (Methods) for each species show the distribution of leaf forms on the RAxML species tree showing branch lengths and bootstrap values based on 100 replicates (black nodes >85%). Nine areas of endemism contain species with two or more ecomorphs, four areas contain three ecomorphs and one (Oaxaca) contains all four. c, Representative leaves: SGE, V. acutifolium (Oaxaca); DEN, V. dentatum (outgroup, eastern North America); IGE, V. fuscum (Oaxaca); LPT, V. microcarpum (eastern Mexico).
Admixture and network analyses
a, Selected ABBA-BABA tests using V. dentatum of eastern North America as an outgroup, and an ingroup with two species from the same area of endemism with contrasting leaf forms and one species from a different area with either form (Methods). Background colours correspond to areas in Fig. 1 and leaf silhouette colours to ecomorphs in Fig. 2; names in boxes correspond to species names in Figs. 1 and 2 (also see Supplementary Tables 1 and 2) b, Admixture is rare among species from different regions. Each test shows D-statistics for 500 bootstrap replicates, coloured dark if the test deviates from zero with a Z-score > 5. c, Phylogenetic network analysis shows correspondence with the species tree and areas in Figs. 1 and 2 (Supplementary Fig. 10). Only V. sulcatum (Oaxaca; asterisk) shows connections with species from another region (Extended Data Fig. 7). d, ABBA-BABA tests on a hypothetical alternative phylogeny in which species with similar leaf forms share a most recent common ancestor, showing significant discordance in all tests. e, Admixture among species within the same regions is uncommon.
Ecological differentiation of species with different leaf forms in Chiapas, Mexico
a, Representative leaves of V. jucundum (LPT), V. lautum (SGE) and V. hartwegii (IGE). Leaf margin images of V. jucundum (I: dense pubescence of stalked stellate trichomes on the blade and margin, showing one tooth) and V. lautum (II: glabrous, with simple trichomes on margins only, lacking teeth). b, Collection localities for niche analyses and the location of climate stations on the Central Plateau of Chiapas, Mexico. c, Environmental data from georeferenced collection localities (Methods) show V. lautum (N = 77) in drier sites and V. hartwegii (N = 73) at lower elevations than V. jucundum (N = 82). All CHELSA climate variables for these three Chiapas species are displayed in Extended Data Fig. 8. Box plot elements: centre line, median; box limits, upper and lower quartiles; whiskers span 1.5× the interquartile range. Density ridgeline plots drawn using the geom_density_ridges function in the ggridges R package⁹⁴. Vertical line within each density ridge represents the mean value for that region. N = 181 biologically independent samples (59 V. hartwegii specimens, 65 V. jucundum specimens, 57 V. lautum specimens). d, Canopy openness estimated using fisheye lens photography (Methods) shows that V. jucundum (N = 20) occupies more closed forests than V. lautum (N = 20) and V. hartwegii (N = 10); *** p<0.001, box plot elements are as in c. e, Climate station data show V. jucundum exposed to more consistent fog, especially during the dry season (February–May); coloured bars for each month: one standard deviation above and below the mean (dot). f, Climate station data show V. lautum experiencing higher daily and seasonal temperature fluctuations; temperatures at V. jucundum sites are more stable throughout the day and year; coloured bars as in e. Credit: leaf margin images in a, J. Negvesky, Keyence Corp.
Replicated radiations, in which sets of similar forms evolve repeatedly within different regions, can provide powerful insights into parallel evolution and the assembly of functional diversity within communities. Several cases have been described in animals, but in plants we lack well-documented cases of replicated radiation that combine comprehensive phylogenetic and biogeographic analyses, the delimitation of geographic areas within which a set of ‘ecomorphs’ evolved independently and the identification of potential underlying mechanisms. Here we document the repeated evolution of a set of leaf ecomorphs in a group of neotropical plants. The Oreinotinus lineage within the angiosperm clade Viburnum spread from Mexico to Argentina through disjunct cloud forest environments. In 9 of 11 areas of endemism, species with similar sets of leaf forms evolved in parallel. We reject gene-flow-mediated evolution of similar leaves and show, instead, that species with disparate leaf forms differ in their climatic niches, supporting ecological adaptation as the driver of parallelism. Our identification of a case of replicated radiation in plants sets the stage for comparative analyses of such phenomena across the tree of life. Several cases of replicated radiations (in which sets of similar forms evolve repeatedly within different regions) have been described in animals. Here the authors provide a well-documented example in plants, specifically the Oreinotinus lineage within the angiosperm clade Viburnum in its spread from Mexico to Argentina through disjunct cloud forest environments.
The evolution of costly traits such as deer antlers and peacock trains, which drove the formation of Darwinian sexual selection theory, has been suggested to both reflect and affect patterns of genetic variance across the genome, but direct tests are missing. Here, we used an evolve and resequence approach to reveal patterns of genome-wide diversity associated with the expression of a sexually selected weapon that is dimorphic among males of the bulb mite, Rhizoglyphus robini. Populations selected for the weapon showed reduced genome-wide diversity compared to populations selected against the weapon, particularly in terms of the number of segregating non-synonymous positions, indicating enhanced purifying selection. This increased purifying selection reduced inbreeding depression, but outbred female fitness did not improve, possibly because any benefits were offset by increased sexual antagonism. Most single nucleotide polymorphisms (SNPs) that consistently diverged in response to selection were initially rare and overrepresented in exons, and enriched in regions under balancing or relaxed selection, suggesting they are probably moderately deleterious variants. These diverged SNPs were scattered across the genome, further demonstrating that selection for or against the weapon and the associated changes to the mating system can both capture and influence genome-wide variation. Populations of a bulb mite that were experimentally selected for a male weapon showed reduced diversity across the genome, indicating that strong sexual selection increases the strength of purifying selection.
Overview of the data and methods used in our study
a, Map of continental USA showing one configuration of the sites used for fitting the species-climate models (blue dots) and the sites used for performing the spatially blocked cross-validation (red dots inside the spatial blocks identified as grey circles). The sites were the same across the 30-year period. b, We used GAM and BRT to model species abundance (Yobs, corresponding to the blue sites in a) as a function of eight climatic predictors (Methods). c, Cross-validation was performed using 50 different spatial configurations of training and test data, by varying the number and position of the spatial blocks and calculating the squared Spearman rank correlation (r²) between model predictions (Ypred) and test observations (Ytest). Climate matching was the mean r² across the 50-fold tests. d, Lastly, we estimated the temporal trends in climate matching using Bayesian multilevel models with a varying intercept and slope for species.
Overall temporal trend in climate matching
a, Visualization of the temporal trends using the annual mean values of climate matching across species. The lines represent a linear model, with the respective shaded 95% CI band, fitted to the annual means calculated with the different species-climate models. b, The posterior distributions of slope estimates of the Bayesian multilevel models are shown for each species-climate model used. The bars below the distribution show the 50% (thick bar) and 90% (thin bar) quantiles of the distributions. All methods give consistent negative trends; thus, we combined the models’ posterior distributions in the following analyses to pool all the uncertainty derived from model choice. The species-climate models were BRT with a learning rate of 0.01 (BRT_lr.01) or 0.001 (BRT_lr.001) and GAM with k = 3 (GAM_k3) or k = 9 (GAM_kest) basis functions.
Species traits underlying the temporal trends of climate matching
a,b, Mean temporal trends (that is, slopes) of climate matching as a function of the species’ body mass (a) and habitat specialization (b). The black lines represent the linear models and respective shaded 95% CIs.
Association between temporal trends in climate matching and demography or threat status
a,b, Association of trends in climate matching with trends in abundance (a) or occupancy (b), respectively. The dots represent the mean trends of species with the respective 90% CI. The red and blue dots identify significant (under a 90% CI, represented as grey error lines) change in climate matching and demography, respectively; the purple dots identify significant change in both trends. c,d, Boxplots showing how the trends in climate matching varied across groups of bird species classified according to their population size trends (c) (significant large increase (1); significant small increase, possible increase, stable (2); uncertain population change, possible small decrease, significant small decrease (3); moderate decrease, possible large decrease (4); significant large decrease (5)) and threat status (d)—future conditions for breeding expected to significantly improve (1), remain stable (2), slightly or moderately decline (3), deteriorate severely (4) and deteriorate extremely (5). The numbers on the right represent the number of species in each category. The box widths represent the interquartile range (IQR), the median is shown as a vertical thick line within each box and the whiskers extend to the largest (upper) and smallest (lower) value no further than 1.5× IQR. Data beyond the end of the whiskers are represented by dots.
Species abundances and distributions are changing in response to changing climate and other anthropogenic drivers but how this translates into how well species can match their optimal climate conditions as they change is not well understood. Using a continental-scale 30-year time series, we quantified temporal trends in climate matching of North American bird species and tested whether geographical variation in rates of climate and land use change and/or species traits could underlie variation in trends among species. Overall, we found that species abundances and distributions are becoming more decoupled from climate as it changes through time. Species differences in climate matching trends were related to their ecological traits, particularly habitat specialization, but not to average rates of climate and land use change within the species’ ranges. Climatic decoupling through time was particularly prominent for birds that were declining in abundance and occupancy, including threatened species. While we could not discern whether climate decoupling causes or is caused by the negative population trends, higher climatic decoupling in declining species could lead to a feedback as birds experience increasing exposure to suboptimal climatic conditions.
Framework linking cognition, neuron numbers and brain size
a, Enhanced cognition is assumed to require more neurons in the pallial telencephalon and perhaps also in the cerebellum. Thus, an increase in pallial neurons relative to the ancestor is expected in species that have been selected for higher intelligence. b, Because the pallium comprises a large fraction of the mass of the brain, a disproportionate accumulation of neurons in this area should enlarge the brain relative to body size. c, If the net benefits of enhanced cognition increase with body size, selection for cognition should further increase brain size in larger species. As a result, species that excel at cognitive performance should have brains that are large in both absolute and relative terms. d, A mechanism that may allow accumulation of more neurons in the pallium is to extend the period of development, particularly in the later stages. According to some evo-devo theories, extending the later stages of development increases neurogenesis in the areas of the brain where progenitor cell multiplication stops later, that is, the pallial areas of the telencephalon. Thus, if a longer development period facilitates neurogenesis in pallial regions, it may be targeted by selection for increased intelligence. e, Phylogenetic relationships among the species analysed for neuron numbers to address possibilities a–d (for a tree with species names see Supplementary Fig. 1). Silhouette illustrations are from PhyloPic (, contributed by F. Sayol and J. Louys under public domain licence.
Neurons and innovation propensity
Relationship between neuron numbers and innovation propensity for the entire brain and the pallium, cerebellum and brainstem, as predicted by models. a, Absolute neuron numbers. b, Neuron numbers adjusted by body size by including body mass (previously subtracting brain mass) as covariate in the model. c, Density of neurons (cells per mg). All models account for the effect of phylogeny, biogeographic realm and confounding variables (Supplementary Tables 1 and 2). Lines show the values predicted by Bayesian phylogenetic mixed models and the lower and upper bounds are the credibility intervals representing the uncertainty interval of the prediction. Sample size is 99 species, as nocturnal specialists (owls) are excluded from the innovation database.
Neuron numbers and brain mass as a function of body size
a,b, Distribution of neuron numbers among pallium, cerebellum and brainstem for clades belonging to low-slope (a) and highest slope (b) grades. The assignation of species to each slope-grade group is based on ref. ⁵¹. c, Variation in neuron numbers in the entire brain as a function of body size. d,e, Variation in brain mass as a function of body size for the sample of species used in analyses of neurons (d) and for the entire brain–body dataset (e). In c–e, clades with low-slope grades are shown in yellow while clades with the highest slope grades are shown in purple. In all plots, owls have been excluded. For plots based on the entire sample of species, see Supplementary Fig. 3.
Neuron numbers as a function of absolute and relative brain size
a, Bivariate dependence plots representing neuron numbers in the entire brain and main brain regions as a function of absolute and relative brain size, based on the predictions from random forests. Colours describe neuron numbers, with low numbers represented by dark-blue colours and higher numbers by yellow-green colours. Relative brain size was estimated by means of the normalized scaled brain index, with the allometric exponent estimated excluding clades that have been found to exhibit substantial grade shifts in brain:body allometries (NSBIgrades; Methods). b, Univariate representations (partial dependence plots) for relative brain size to further interpret the bivariate dependence plots. The plots show the dependence between neuron numbers and relative brain size, marginalizing over the values of absolute brain size. c, Univariate representation of the bivariate dependence plot for absolute brain size. In b and c, lines show the values predicted by random forests and the lower and upper bounds are the credibility intervals representing the uncertainty of the prediction. In all analyses, owls have been excluded. For analyses with the entire sample of species, see Supplementary Figs. 6 and 7. M, million.
Neurons and development in species belonging to low-slope and highest slope grades
a,b, Neuron numbers as a function of the duration of development (embryonic stage plus postnatal growth) (a) and the fraction of total development time represented by the postnatal growth (b), for low-slope grades (yellow bar) and the highest slope grades (purple bar). Lines show the values predicted by Bayesian phylogenetic mixed models and the lower and upper bounds are the credibility intervals representing the uncertainty of the prediction. In all analyses, owls have been excluded (for analyses with the entire sample size, see Supplementary Fig. 8).
A longstanding issue in biology is whether the intelligence of animals can be predicted by absolute or relative brain size. However, progress has been hampered by an insufficient understanding of how neuron numbers shape internal brain organization and cognitive performance. On the basis of estimations of neuron numbers for 111 bird species, we show here that the number of neurons in the pallial telencephalon is positively associated with a major expression of intelligence: innovation propensity. The number of pallial neurons, in turn, is greater in brains that are larger in both absolute and relative terms and positively covaries with longer post-hatching development periods. Thus, our analyses show that neuron numbers link cognitive performance to both absolute and relative brain size through developmental adjustments. These findings help unify neuro-anatomical measures at multiple levels, reconciling contradictory views over the biological significance of brain expansion. The results also highlight the value of a life history perspective to advance our understanding of the evolutionary bases of the connections between brain and cognition. Using estimation data on neuron numbers in 111 bird species across 24 families, the authors show that number of neurons is positively associated with innovation propensity and encephalization.
Triploids are rare in nature because of difficulties in meiotic and gametogenic processes, especially in vertebrates. The Carassius complex of cyprinid teleosts contains sexual tetraploid crucian carp/goldfish (C. auratus) and unisexual hexaploid gibel carp/Prussian carp (C. gibelio) lineages, providing a valuable model for studying the evolution and maintenance mechanism of unisexual polyploids in vertebrates. Here we sequence the genomes of the two species and assemble their haplotypes, which contain two subgenomes (A and B), to the chromosome level. Sequencing coverage analysis reveals that C. gibelio is an amphitriploid (AAABBB) with two triploid sets of chromosomes; each set is derived from a different ancestor. Resequencing data from different strains of C. gibelio show that unisexual reproduction has been maintained for over 0.82 million years. Comparative genomics show intensive expansion and alterations of meiotic cell cycle-related genes and an oocyte-specific histone variant. Cytological assays indicate that C. gibelio produces unreduced oocytes by an alternative ameiotic pathway; however, sporadic homologous recombination and a high rate of gene conversion also exist in C. gibelio. These genomic changes might have facilitated purging deleterious mutations and maintaining genome stability in this unisexual amphitriploid fish. Overall, the current results provide novel insights into the evolutionary mechanisms of the reproductive success in unisexual polyploid vertebrates. Genome sequencing and haplotype assembly of two cyprinid teleosts, a sexual tetraploid and an unisexual hexaploid, reveal insights into the evolutionary mechanisms underpinning the reproductive success of unisexual polyploid vertebrates.
Relationships of seasonal stem increment to previous season’s litterfall and its induced N recycling at two alpine treelines during 2007–2017
a–c, Monthly stem increments were positively correlated with previous monthly litterfall (a), N-res (b) and N-ret (c) in A. georgei var. smithii forest. d–f, Bimonthly stem increments were positively correlated with previous bimonthly litterfall (d), N-res (e) and N-ret (f) in J. saltuaria forest. The symbols are for seasonal stem increments. The relationships were tested using a simple linear model. The predicted mean (solid lines) is bounded by the 95% confidence intervals (shaded areas). The significance of correlation coefficient is estimated by two-tailed t-test with no adjustment for multiple comparisons.
Relationships of annual stem increment to previous year’s litterfall and its induced N recycling at two alpine treelines during 2007–2017
a–c, Annual stem increments were positively correlated with previous year’s (from previous mid-June to current mid-June) litterfall (a), N-res (b) and N-ret (c) in A. georgei var. smithii forest. d–f, Annual stem increments were positively correlated with previous year’s (from previous mid-June to current mid-June) litterfall (d), N-res (e) and N-ret (f) in J. saltuaria forest. Grey solid circles indicate annual stem increment by dendrometers and blue solid circles are for tree-ring width index by increment borers. The relationships were tested using a simple linear model. The predicted mean (solid lines) is bounded by the 95% confidence intervals (shaded areas). The significance of correlation coefficient is estimated by two-tailed t-test with no adjustment for multiple comparisons.
Relative contributions of climatic factors and atmospheric CO2 to annual variations of litterfall, N recycling and tree-ring growth at two alpine treelines during 2007–2017
a, A simple linear model was used for testing variation trends in annual litterfall (g m⁻² yr⁻¹), nitrogen resorption (N-res, g m⁻² yr⁻¹), nitrogen return (N-ret, g m⁻² yr⁻¹), nitrogen use efficiency (NUE, g DM g⁻¹ N) and tree-ring width index (TRWI) for A. georgei var. smithii (AGES, green solid circles) and J. saltuaria (JSA, red solid circles), and the trends in climatic factors of growing season (May–August) mean minimum temperature (T, °C) and precipitation (P, mm) and solar radiation (Ra, MJ m⁻² d⁻¹) observed at the two treelines, and in atmospheric CO2 concentration (CO2, ppm) from Mauna Loa Observatory, Hawaii ( b–e, A simple linear model was used for testing relationships of litterfall (b), N-res (c), N-ret (d) and TRWI (e) to atmospheric CO2 at both treelines. f–i, Partial correlation coefficients of multiple linear regressions for relationships of litterfall (f), N-res (g), N-ret (h) and TRWI (i) with climatic factors (T, P, Ra) and atmospheric CO2 at both treelines; the partial correlation coefficients in different seasons and their exact P values are found in Supplementary Table 3. The significance of the correlation coefficient is estimated by two-tailed t-test with no adjustment for multiple comparisons. Significance level: #P <0.10, *P <0.05, **P <0.01.
Structural equation models quantifying direct effects of climatic factors and atmospheric CO2 and their indirect effects through interactions with litterfall and N-ret/N-res on tree-ring width index at two alpine treelines during 2007–2017
a,c, Standardized path coefficients (a) and the total, direct and indirect effects (c) of climatic factors and atmospheric CO2 on TRWI in A. georgei var. smithii forest (χ² = 0.310, P = 0.578, CFI = 1.00, RMSEA = 0.000, AIC = 40.31). b,d, Standardized path coefficients (b) and the total, direct and indirect effects (d) of climatic factors and atmospheric CO2 on TRWI in J. saltuaria forest (χ² = 0.507, P = 0.477, CFI = 1.00, RMSEA = 0.000, AIC = 40.51). Abbreviations of the measured variables are the same as in Fig. 3. Black solid arrows denote significant paths and arrow width indicates the strength of the relationship. Grey dashed arrows represent non-significant paths. The figures adjacent to arrows are for standardized path coefficients. R² value represents the proportion of variance explained for each dependent variable in the models. Significance level: *P <0.05, **P <0.01, ***P <0.001. The exact P values are found in Supplementary Table 4.
Relative contributions of climatic factors and atmospheric CO2 to annual variations of tree-ring width index (since 1986) across 8 tree species and 13 treeline sites on the Tibetan Plateau
a, Spatial distribution of 13 tree-ring width chronologies. The background is the distribution map of vegetation types, which was drawn using ArcGIS 10.3 for Desktop with the vector data for Vegetation Atlas of China (1:1,000,000)⁵⁰, available free online at Species abbreviations: AGES, Abies georgei var. smithii; JSA, Juniperus saltuaria; JTI, Juniperus tibetica; JPR, Juniperus przewalskii; PLIB, Picea likiangensis var. balfouriana; AFA, Abies faxoniana; AFO, Abies forrestii; PLI, Picea likiangensis. b,c, Partial correlation coefficients of multiple linear regression for relationships of tree-ring width indices (b, TRWI) and their ten-year moving averages (c) to previous and current years' early season (May–June) mean minimum temperatures (PT, T) and precipitation (PP, P), and atmospheric CO2 concentration (CO2); the partial correlation coefficients between TRWI and climate factors in different seasons and their exact P values are found in Supplementary Tables 5 and 6. Detailed site information is found in Supplementary Table 1. The statistical significance is estimated by two-tailed t-test with no adjustment for multiple comparisons. Significance level: *P <0.05, **P <0.01, ***P <0.001.
Whether increased photosynthates under elevated atmospheric CO2 could translate into sustained biomass accumulation in forest trees remains uncertain. Here we demonstrate how tree radial growth is closely linked to litterfall dynamics, which enhances nitrogen recycling to support a sustained effect of CO2 fertilization on tree-ring growth. Our ten-year observations in two alpine treeline forests indicated that annual (or seasonal) stem radial increments generally had a positive relationship with the previous year’s (or season’s) litterfall and its associated nitrogen return and resorption. Annual tree-ring width, annual litterfall and annual nitrogen return and resorption all showed an increasing trend during 2007–2017, and most of the variations were explained by elevated atmospheric CO2 rather than climate change. Similar patterns were found in the longer time series of tree-ring width index from 1986–2017. The regional representativeness of our observed patterns was confirmed by the literature data of six other tree species at 11 treeline sites over the Tibetan Plateau. Enhanced nitrogen recycling through increased litterfall under elevated atmospheric CO2 supports a general increasing trend of tree-ring growth in recent decades, especially in cold and nitrogen-poor environments. A ten-year dataset from the Tibetan Plateau shows a general increase in tree-ring growth that is largely explained by enhanced nitrogen recycling through increased litterfall under elevated atmospheric CO2.
The 15 horizon issues presented in thematic groups: ecosystem impacts, resource exploitation and new technologies
Numbers refer to the order presented in this article, rather than final ranking. Image of brine pool courtesy of the NOAA Office of Ocean Exploration and Research, Gulf of Mexico 2014. Image of biodegradable bag courtesy of Katie Dunkley.
Stepwise process used to identify, score and present the 15 horizon issues likely to impact marine and coastal biodiversity conservation in the next 5–10 years
Left and right columns show the process for the first and second rounds of scoring, respectively.
Median rank of each issue versus proportion of issues participants had previously heard of
a, Round 1. Each point represents an individual issue. For all issue titles, see Supplementary Table 1. Issues in dark blue were retained for the second round. Issues that were ranked higher were generally those that participants had not heard of (Spearman rank correlation = 0.38, P < 0.001). b, Round 2. Scores as in round 1. For titles of the second round of 32 issues, see Supplementary Table 2. The 15 final issues (marked in red) achieved the top ranks (horizontal dashed line) and had only been heard of by 50% of participants (vertical dashed line). Red circles, squares and triangles denote issues relating to ecosystem impacts, resource exploitation and new technologies, respectively. The two grey issues marked with crosses were discounted during final discussions because participants could not identify the horizon component of these issues.
Source data
The biodiversity of marine and coastal habitats is experiencing unprecedented change. While there are well-known drivers of these changes, such as overexploitation, climate change and pollution, there are also relatively unknown emerging issues that are poorly understood or recognized that have potentially positive or negative impacts on marine and coastal ecosystems. In this inaugural Marine and Coastal Horizon Scan, we brought together 30 scientists, policymakers and practitioners with transdisciplinary expertise in marine and coastal systems to identify new issues that are likely to have a significant impact on the functioning and conservation of marine and coastal biodiversity over the next 5–10 years. Based on a modified Delphi voting process, the final 15 issues presented were distilled from a list of 75 submitted by participants at the start of the process. These issues are grouped into three categories: ecosystem impacts, for example the impact of wildfires and the effect of poleward migration on equatorial biodiversity; resource exploitation, including an increase in the trade of fish swim bladders and increased exploitation of marine collagens; and new technologies, such as soft robotics and new biodegradable products. Our early identification of these issues and their potential impacts on marine and coastal biodiversity will support scientists, conservationists, resource managers and policymakers to address the challenges facing marine ecosystems. A panel of scientists, policymakers and practitioners have used an iterative voting process to collate a list of 15 priority emerging issues likely to affect marine and coastal biodiversity over the next 5–10 years.
Populations of cancer cells are subject to the same core evolutionary processes as asexually reproducing, unicellular organisms. Transmissible cancers are particularly striking examples of these processes. These unusual cancers are clonal lineages that can spread through populations via physical transfer of living cancer cells from one host individual to another, and they have achieved long-term success in the colonization of at least eight different host species. Population genetic theory provides a useful framework for understanding the shift from a multicellular sexual animal into a unicellular asexual clone and its long-term effects on the genomes of these cancers. In this Review, we consider recent findings from transmissible cancer research with the goals of developing an evolutionarily informed perspective on transmissible cancers, examining possible implications for their long-term fate and identifying areas for future research on these exceptional lineages. Transmissible cancers are governed by the same evolutionary processes as asexually reproducing, unicellular organisms. This Review discusses population genetics processes that determine the evolution of clonally transmissible cancers.
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Josep Penuelas
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