Continuous Traits Research Articles

Taxonomy of the Palearctic socially parasitic Temnothorax (Myrmoxenus) ants (Hymenoptera: Formicidae).

The ant genus Temnothorax is one of the most diverse in the Palearctic region, comprising several species with different life histories and uncertain taxonomic backgrounds. Socially parasitic Temnothorax ant species were typically described decades ago, primarily based on traditional morphological traits. In some aspects, these species have come back into the spotlight in recent years, necessitating a comprehensive taxonomic revision of the species of the genus. In this paper, we present a quantitative morphology-based taxonomic revision of the Temnothorax corsicus species group (formerly called Myrmoxenus genus) based on the analysis of 20 continuous morphometric traits collected from 394 worker and 19 traits from 473 gyne individuals belonging to 240 samples. Based on morphometric analyses, we propose junior synonymy for Temnothorax tamarae (Arnol'di, 1968) under T. ravouxi (André, 1896), and T. microcellatus (Soudek, 1925) is revived and is considered a senior synonym of T. menozzii (Finzi, 1924). Detailed descriptions, measurements, distribution, and host usage of all ten species are given. Dichotomous keys to workers, known gynes, and photographs of all species are presented.

Evolutionary branching in multi-level selection models.

We study a model of group-structured populations featuring individual-level birth and death events, and group-level fission and extinction events. Individuals play games within their groups, while groups play games against other groups. Payoffs from individual-level games affect birth rates of individuals, and payoffs from group-level games affect group extinction rates. We focus on the evolutionary dynamics of continuous traits with particular emphasis on the phenomenon of evolutionary diversification. Specifically, we consider two-level processes in which individuals and groups play continuous snowdrift or prisoner's dilemma games. Individual game strategies evolve due to selection pressure from both the individual and group level interactions. The resulting evolutionary dynamics turns out to be very complex, including branching and type-diversification at one level or the other. We observe that a weaker selection pressure at the individual level results in more adaptable groups and sometimes group-level branching. Stronger individual-level selection leads to more effective adaptation within each group while preventing the groups from adapting according to the group-level games.

Supergenes are not necessary to explain the maintenance of complex alternative phenotypes.

Evolutionary biology aims to explain the diversity seen in nature. Evolutionary theory provides frameworks to understand how simple polymorphisms or continuous variation are maintained, but phenotypes inherited as discrete suites of quantitative traits are difficult to fit into this framework. Supergenes have been proposed as a solution to this problem-if causal genes are co-located, they can be inherited as if a single gene, thus bridging the gap between simple polymorphisms and continuous traits. We develop models to ask: how are critical supergenes for maintaining phenotypic diversity? In our simplest model, without explicit genetic architectures, three alternative reproductive morphs are maintained in many of the parameter combinations we evaluated. For these same parameter values, models with demographic stochasticity, recombination and mutation (but without explicit genetic architecture) maintained only two of these three morphs, with stochasticity determining which morphs persisted. With explicit genetic architectures, regardless of whether causal loci were co-located in a supergene or distributed randomly, this stochasticity in which morphs are maintained was reduced. Even when phenotypic variation was lost, genetic diversity was maintained. Altogether, categorical traits with polygenic bases exhibited similar evolutionary dynamics to those determined by supergenes. Our work suggests that supergenes are not the only answer to the puzzle of how discrete polygenic phenotypic variation is maintained.

Comparison of predictive ability of single-trait and multitrait genomic selection models for body growth traits in Maiwa yaks

Yaks are grazed extensively on the Qinghai–Tibet Plateau, which has a long history of semi-domestication. The predicted weight of yaks over consecutive years helps make strategic decisions when selecting yak calves for breeding. To achieve more accurate predictions of genomic estimated breeding values, we used a dataset comprising the genotype and weight records of 396 Maiwa yaks collected from 2015 to 2020. We compared the predictive accuracy of the genome best linear unbiased prediction (GBLUP) model with that of six other models. Based on the GBLUP model, we applied two prediction strategies. In the first strategy, the year was treated as a fixed effect in the GBLUP model, and the kinship from all individuals and the markers were treated as random effects. In the second strategy, all individuals were divided into six age groups, with GBLUP performed for each group, and the phenotypes of the closest age groups were treated as fixed effects. Although the GBLUP model provided better prediction accuracy than other single-trait models, most of the predictive capacity was derived from the best linear unbiased estimation. Additionally, incorporating the phenotype of the closest age group as a factor in multitrait prediction enhanced the accuracy of the model. Our findings provide a robust and credible strategy for predicting continuous economic traits in the presence of strong correlations.

Comparison of methods for building polygenic scores for diverse populations

Polygenic scores (PGSs) are a promising tool for estimating individual-level genetic risk of disease based on the results of genome-wide association studies (GWASs). However, their promise has yet to be fully realized because most currently available PGSs were built with genetic data from predominantly European-ancestry populations, and PGS performance declines when scores are applied to target populations different from the populations from which they were derived. Thus, there is a great need to improve PGS performance in currently under-studied populations. In this work we leverage data from two large and diverse cohorts the Million Veterans Program (MVP) and All of Us (AoU), providing us the unique opportunity to compare methods for building PGSs for multi-ancestry populations across multiple traits. We build PGSs for five continuous traits and five binary traits using both multi-ancestry and single-ancestry approaches with popular Bayesian PGS methods and both MVP META GWAS results and population-specific GWAS results from the respective African, European, and Hispanic MVP populations. We evaluate these scores in three AoU populations genetically similar to the respective African, Admixed American, and European 1000 Genomes Project superpopulations. Using correlation-based tests, we make formal comparisons of the PGS performance across the multiple AoU populations. We conclude that approaches that combine GWAS data from multiple populations produce PGSs that perform better than approaches that utilize smaller single-population GWAS results matched to the target population, and specifically that multi-ancestry scores built with PRS-CSx outperform the other approaches in the three AoU populations.

Accelerated Longitudinal Glycemic Changes in Relation to Urinary Toxic/Essential Metals and Metal Mixtures Among Mexican Americans Living in Starr County, Texas.

Metal and metalloid exposures (hereafter "metals") are associated with adverse health outcomes, including type 2 diabetes; however, previous studies were largely cross-sectional or underpowered. Furthermore, underserved racial and ethnic groups are underrepresented in environmental health research despite having higher rates of type 2 diabetes and a greater risk of metal exposures. Consequently, we evaluated continuous glycemic traits in relation to baseline urinary toxic metal, essential metal, and metal mixtures in a cohort of Mexican American adults. A total of 510 participants were selected based upon self-reported diabetes status and followed over 3 years. Urinary metals were assessed at baseline. Linear mixed-effects models were used to estimate per-month changes in hemoglobin A1c, fasting plasma glucose, and postload glucose in relation to urinary metal levels. Multiple statistical approaches were used to assess the associations between glycemic traits and metal mixtures. After adjustment, higher urinary levels of arsenic, selenium, copper, molybdenum, nickel, and tin were associated with faster increases in measures of glycemia. The toxic metal mixture composed of arsenic, lead, cadmium, nickel, and tin was associated with faster increases in postload glucose. Using postload glucose criteria, highest versus lowest arsenic was predicted to accelerate conversion of normoglycemia to prediabetes and diabetes by 23 and 65 months, respectively. In this underrepresented, high-risk Mexican American population, exposure to toxic metals and alterations in essential metal homeostasis were associated with faster increases in glycemia over time that may accelerate type 2 diabetes development.

PSLBII-20 Estimation of genetic parameters for methane emissions and residual feed intake in Nellore cattle

Abstract The objective of this study was to obtain (co)variance components, heritability, and genetic correlation estimates for enteric methane emissions, residual feed intake, growth, carcass and reproductive indicator traits. Data from 43 feed efficiency trials conducted over a 13-yr period on a single purebred Nellore herd in Central Brazil, comprising 3,400 intact males and 2,167 females, were analyzed. Trial data along with diet NDF content were used to estimate the methane emission (g CH4/kg carcass gain) for each individual, using an equation developed by Medeiros et al., 2014: CH4 (g/d) = -0.1011 + 0.02062*DMI (kg/d) + 0.001648*NDF (% of DMI). Other data included pedigree information, body weight (BW), carcass traits, and reproductive indicator traits. Continuous traits were analyzed using a linear animal model, and threshold traits were analyzed using a threshold animal model. Two-trait analyses were performed using the restricted maximum likelihood to estimate the variance components, heritabilities and genetic and phenotypic correlations among traits. The estimated heritability of methane emissions was 0.136 ± 0.0314. The genetic correlations of methane emissions with growth and carcass traits were low and not different from zero: BW at 450 d (0.064 ± 0.140), scrotal circumference at 365 d (0.188 ± 0.155), longissimus muscle area (-0.090 ± 0.132), 12th-13th rib fat (-0.122 ± 0.142), rump fat (0.080 ± 0.119), intramuscular fat % (-0.108 ± 0.152), BW at 210 d (-0.077 ± 0.168). By contrast, there were the low genetic correlation of methane emissions with early calving probability (-0.260; -0.504 – 0.026); moderate genetic correlations of methane emissions with age at first calving (0.519 ± 0.202) and DM intake (0.512 ± 0.116); and the high correlation of methane with residual feed intake (RFI; 0.832 ± 0.073). Thus, selection to improve weaning and yearling weights, and carcass traits would not affect methane emissions. Genetic selection to reduce methane emissions is feasible and would also reduce DMI and RFI, as well as female sexual precocity traits. Conversely, selection to improve RFI can be used to identify animals with decreased methane emissions. Medeiros, S. R, L. G. Baioni, A. Berndt, M. C. Freua, T. Z. Albertini, C. Costa Jr., e G. B. Feltrin (2014) Modeling enteric methane emission from beef cattle in Brazil: A proposed equation performed by principal component analyses and mixed modeling multiple regression. Proceedings, Livestock, Climate Change and Food Security Conference, Madrid.

Modelling soil prokaryotic traits across environments with the trait sequence database ampliconTraits and the R package MicEnvMod

We present a comprehensive, customizable workflow for inferring prokaryotic phenotypic traits from marker gene sequences and modelling the relationships between these traits and environmental factors, thus overcoming the limited ecological interpretability of marker gene sequencing data. We created the trait sequence database ampliconTraits, constructed by cross-mapping species from a phenotypic trait database to the SILVA sequence database and formatted to enable seamless classification of environmental sequences using the SINAPS algorithm. The R package MicEnvMod enables modelling of trait – environment relationships, combining the strengths of different model types and integrating an approach to evaluate the models' predictive performance in a single framework. Traits could be accurately predicted even for sequences with low sequence identity (80 %) with the reference sequences, indicating that our approach is suitable to classify a wide range of environmental sequences. Validating our approach in a large trans-continental soil dataset, we showed that trait distributions were robust to classification settings such as the bootstrap cutoff for classification and the number of discrete intervals for continuous traits. Using functions from MicEnvMod, we revealed precipitation seasonality and land cover as the most important predictors of genome size. We found Pearson correlation coefficients between observed and predicted values up to 0.70 using repeated split sampling cross validation, corroborating the predictive ability of our models beyond the training data. Predicting genome size across the Iberian Peninsula, we found the largest genomes in the northern part. Potential limitations of our trait inference approach include dependence on the phylogenetic conservation of traits and limited database coverage of environmental prokaryotes. Overall, our approach enables robust inference of ecologically interpretable traits combined with environmental modelling allowing to harness traits as bioindicators of soil ecosystem functioning.

An Extended Application of the Fast Multi-Locus Ridge Regression Algorithm in Genome-Wide Association Studies of Categorical Phenotypes.

Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits.

Propagation phenomena for a nonlocal reaction-diffusion model with bounded phenotypic traits

In this paper we study the stability of cylinder front waves and the propagation of solutions of a nonlocal Fisher-type model describing the propagation of a population with nonlocal competition among bounded and continuous phenotypic traits. By applying spectral analysis and separation of variables we prove the spectral and local exponential stability of the cylinder waves with the noncritical speeds in some exponentially weighted spaces. By combining the detailed analysis with the spectral expansion and the special construction of sub-supersolutions, we further prove the uniform boundedness of the solutions and the global asymptotic stability of the cylinder waves for more general nonnegative bounded initial data, and prove that the spreading speeds and the asymptotic behavior of the solutions are determined by the decay rates of the initial data. Our results also extend some classical results on the stability of planar waves for Fisher-KPP equation to the nonlocal Fisher model in multi-dimensional cylinder case.

Strong genotype-by-environment interaction across contrasted sites for summer mortality syndrome in the Pacific oyster Crassostrea gigas

Genotype-by-environment (GxE) interaction in aquaculture is usually estimated for continuous traits and based on data from a limited number of 2–3 rearing sites. Here we report the results of a GxE study for resistance to Pacific oyster mortality syndrome (POMS), a multi-factorial disease that severely impacts Pacific oyster production worldwide. The syndrome is largely associated with Ostreid Herpes Virus 1 (OsHV-1). Resistance to OsHV-1 in Crassostrea gigas has been shown to be heritable, meaning that selective breeding is a suitable strategy for reducing mortalities. However, limited information was available about GxE interaction or the possible need to consider it in selective breeding. Survival of two cohorts (C1 and C2), consisting of a total of 104 full-sib families, was evaluated during the summer of 2013 in 7 sites along the French Atlantic, Channel and Mediterranean coasts. Mean survivals in autumn 2013 were 12.6% ± 10.9 and 4.6% ± 6.4 for C1 and C2, respectively. Genetic parameters were computed by MCMC, which is suitable for binary data like survival. Heritability estimates ranged from 0.16 to 0.42 depending on site and cohort, with a mean of 0.24 [0.20; 0.27] when including all data. GxE interactions were estimated by the genetic correlations between pairs of sites. Genetic correlations were high (ρ > 0.80) for C1 between most tidal Atlantic and Channel sites, and intermediate between tidal sites and a Mediterranean lagoon site, while they were lower and more variable for C2 (0.21–0.77). Expected genetic gains were maximal when production site was the same as selection site. They were closed to this expected maximum when the selection site was different from the production site along the Atlantic or Channel coast. Limited GxE interaction along the French Atlantic coast is favorable to wide dissemination of genetically improved oysters along this coast. Limited potential improvement was shown in the Mediterranean site if selection was carried out elsewhere, confirming the specificity of this environment. Consequently, a specific strategy such as dedicated breeding should be used to achieve genetic progress for this site.

Phyddle: software for phylogenetic model exploration with deep learning.

Many realistic phylogenetic models lack tractable likelihood functions, prohibiting their use with standard inference methods. We present phyddle, a pipeline-based toolkit for performing phylogenetic modeling tasks using likelihood-free deep learning approaches. phyddle coordinates modeling tasks through five analysis steps (Simulate, Format, Train, Estimate, and Plot) that transform raw phylogenetic datasets as input into numerical and visualized model-based output. Benchmarks show that phyddle accurately performs a range of inference tasks, such as estimating macroevolutionary parameters, selecting among continuous trait evolution models, and passing coverage tests for epidemiological models, even for models that lack tractable likelihoods. phyddle has a flexible command-line interface, making it easy to integrate deep learning approaches for phylogenetics into research workflows. Learn more about phyddle at https://phyddle.org.

The genetics of phenotypic plasticity. XVIII. Developmental limits restrict adaptive plasticity.

After environmental change, the trait evolution needed to rescue a population depends on the functional form of the plastic change (reaction norm) of that trait. Nearly all previous models of plasticity evolution for continuous traits have assumed that the functional form is linear, i.e., no limits on the range of plasticity. This paper examines the effect of developmental limits, modeled as a sigmoidal reaction norm, on evolutionary rescue after an abrupt environmental change and the subsequent evolution of plasticity, including genetic assimilation. We examined four different scenarios: (1) developmental limits only, (2) developmental limits plus a cost of plasticity, (3) developmental limits with developmental noise, and (4) developmental limits plus environmental variation. The probability of evolutionary rescue increased with an increase in phenotypic variation allowed by plastic development. With a smaller limit to the range of the plastic phenotype, the evolution of adaptive plasticity was limited, meaning the evolution of non-plastic genes was necessary. The addition of developmental constraints to the model did not speed up genetic assimilation, suggesting new theory is needed to understand empirical observations. The modeling framework presented here could be extended to different ecological and evolutionary conditions, alternative reaction norm shapes, the evolution of additional reaction norm parameters such as the range or the location of the inflection point on the environmental axis, or other function-valued traits.

Optimizing Genomic Parental Selection for Categorical and Continuous-Categorical Multi-Trait Mixtures.

This study presents a novel approach for the optimization of genomic parental selection in breeding programs involving categorical and continuous-categorical multi-trait mixtures (CMs and CCMMs). Utilizing the Bayesian decision theory (BDT) and latent trait models within a multivariate normal distribution framework, we address the complexities of selecting new parental lines across ordinal and continuous traits for breeding. Our methodology enhances precision and flexibility in genetic selection, validated through extensive simulations. This unified approach presents significant potential for the advancement of genetic improvements in diverse breeding contexts, underscoring the importance of integrating both categorical and continuous traits in genomic selection frameworks.

Leaf functional trait evolution and its putative climatic drivers in African Coffea species.

Leaf traits are known to be strong predictors of plant performance and can be expected to (co)vary along environmental gradients. We investigated the variation, integration, environmental relationships, and evolutionary history of leaf functional traits in the genus Coffea L., typically a rainforest understory shrub, across Africa. A better understanding of the adaptive processes involved in leaf trait evolution can inform the use and conservation of coffee genetic resources in a changing climate. We used phylogenetic comparative methods to investigate the evolution of six leaf traits measured from herbarium specimens of 58 African Coffea species. We added environmental data and data on maximum plant height for each species to test trait-environment correlations in various (sub)clades, and we compared continuous trait evolution models to identify variables driving trait diversification. A substantial leaf trait variation was detected across the genus Coffea in Africa, which was mostly interspecific. Of these traits, stomatal size and stomatal density exhibited a clear trade-off. We observed low densities of large stomata in early branching lineages and higher densities of smaller stomata in more recent taxa, which we hypothesise to be related to declining CO2 levels since the mid-Miocene. Brownian Motion evolution was rejected in favour of White Noise or Ornstein-Uhlenbeck models for all traits, implying these traits are adaptively significant rather than driven by pure drift. The evolution of leaf area was likely driven by precipitation, with smaller leaves in dryer climates across the genus. Generally, Coffea leaf traits appear to be evolutionarily labile and governed by stabilising selection, though evolutionary patterns and correlations differ depending on the traits and clades considered. Our study highlights the importance of a phylogenetic perspective when studying trait relationships across related taxa, as well as the consideration of various taxonomic ranges.

Benchmarking imputation methods for categorical biological data

Abstract Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, quantify species extinction risks, or evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance, due to incomplete sampling, limited data and resource availability. Several imputation methods exist to predict missing values and recent studies have explored their performance for continuous traits in biological datasets. However, less is known about the accuracy of these methods for categorical traits. Here we explore the performance of different imputation methods on categorical biological traits combining phylogenetic comparative methods, machine learning and deep learning models. To this end, we develop an open‐source R package, to impute trait data while integrating a simulation framework to evaluate their performance on synthetic datasets. We run a range of simulations under different missing rates, mechanisms, biases and evolutionary models. We propose an integration between phylogenetic comparative methods and machine learning imputation, and an ensemble approach, in which selected imputation methods are combined. Our simulations show that this approach provides the most robust and accurate predictions. We applied our imputation pipeline to an incomplete trait dataset of 1015 elasmobranch species (i.e. sharks, rays and skates) and found a high imputation accuracy of the predictions based on an expert‐based assessment of the missing traits. Overall, our R package facilitates the comparison of multiple imputation methods and allows robust predictions of missing trait values. Our study highlights the benefits of coupling phylogenetic evolutionary models with machine learning inference to augment incomplete biological datasets.

Blouch: Bayesian Linear Ornstein-Uhlenbeck Models for Comparative Hypotheses.

Relationships among species in the tree of life can complicate comparative methods and testing adaptive hypotheses. Models based on the Ornstein-Uhlenbeck process permit hypotheses about adaptation to be tested by allowing traits to either evolve towards fixed adaptive optima (e.g., regimes or niches) or track continuously changing optima that can be influenced by other traits. These models allow estimation of the effects of both adaptation and phylogenetic inertia - resistance to adaptation due to any source - on trait evolution, an approach known as the "adaptation-inertia" framework. However, previous applications of this framework, and most approaches suggested to deal with the issue of species non-independence, are based on a maximum likelihood approach and thus it is difficult to include information based on prior biological knowledge in the analysis, which can affect resulting inferences. Here I present Blouch, (Bayesian Linear Ornstein-Uhlenbeck Models for Comparative Hypotheses), which fits allometric and adaptive models of continuous trait evolution in a Bayesian framework based on fixed or continuous predictors and incorporates measurement error. I first briefly discuss the models implemented in Blouch, and then the new applications for these models provided by a Bayesian framework. This includes the advantages of assigning biologically meaningful priors when compared to non-Bayesian approaches, allowing for varying effects (intercepts and slopes), and multilevel modeling. Validations on simulated data show good performance in recovering the true evolutionary parameters for all models. To demonstrate the workflow of Blouch on an empirical dataset, I test the hypothesis that the relatively larger antlers of larger bodied deer are the result of more intense sexual selection that comes along with their tendency to live in larger breeding groups. While results show that larger bodied deer that live in larger breeding groups have relatively larger antlers, deer living in the smallest groups appear to have a different and steeper scaling pattern of antler size to body size than other groups. These results are contrary to previous findings and may argue that a different type of sexual selection or other selective pressures govern optimum antler size in the smallest breeding groups.

House dust metagenome and pulmonary function in a US farming population

BackgroundChronic exposure to microorganisms inside homes can impact respiratory health. Few studies have used advanced sequencing methods to examine adult respiratory outcomes, especially continuous measures. We aimed to identify metagenomic profiles in house dust related to the quantitative traits of pulmonary function and airway inflammation in adults. Microbial communities, 1264 species (389 genera), in vacuumed bedroom dust from 779 homes in a US cohort were characterized by whole metagenome shotgun sequencing. We examined two overall microbial diversity measures: richness (the number of individual microbial species) and Shannon index (reflecting both richness and relative abundance). To identify specific differentially abundant genera, we applied the Lasso estimator with high-dimensional inference methods, a novel framework for analyzing microbiome data in relation to continuous traits after accounting for all taxa examined together.ResultsPulmonary function measures (forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio) were not associated with overall dust microbial diversity. However, many individual microbial genera were differentially abundant (p-value < 0.05 controlling for all other microbial taxa examined) in relation to FEV1, FVC, or FEV1/FVC. Similarly, fractional exhaled nitric oxide (FeNO), a marker of airway inflammation, was unrelated to overall microbial diversity but associated with differential abundance for many individual genera. Several genera, including Limosilactobacillus, were associated with a pulmonary function measure and FeNO, while others, including Moraxella to FEV1/FVC and Stenotrophomonas to FeNO, were associated with a single trait.ConclusionsUsing state-of-the-art metagenomic sequencing, we identified specific microorganisms in indoor dust related to pulmonary function and airway inflammation. Some were previously associated with respiratory conditions; others were novel, suggesting specific environmental microbial components contribute to various respiratory outcomes. The methods used are applicable to studying microbiome in relation to other continuous outcomes.3hzq1LfdnyTZVYvBm7QT8RVideo

CoRRE Trait Data: A dataset of 17 categorical and continuous traits for 4079 grassland species worldwide

In our changing world, understanding plant community responses to global change drivers is critical for predicting future ecosystem composition and function. Plant functional traits promise to be a key predictive tool for many ecosystems, including grasslands; however, their use requires both complete plant community and functional trait data. Yet, representation of these data in global databases is sparse, particularly beyond a handful of most used traits and common species. Here we present the CoRRE Trait Data, spanning 17 traits (9 categorical, 8 continuous) anticipated to predict species’ responses to global change for 4,079 vascular plant species across 173 plant families present in 390 grassland experiments from around the world. The dataset contains complete categorical trait records for all 4,079 plant species obtained from a comprehensive literature search, as well as nearly complete coverage (99.97%) of imputed continuous trait values for a subset of 2,927 plant species. These data will shed light on mechanisms underlying population, community, and ecosystem responses to global change in grasslands worldwide.

Common ant species dominate morphospace: unraveling the morphological diversity in the Brazilian Amazon Basin

Rare plant and vertebrate species have been documented to contribute disproportionately to the total morphological structure of species assemblages. These species often possess morphologically extreme traits and occupy the boundaries of morphological space. As rare species are at greater risk of extinction than more widely distributed species, human‐induced disturbances can strongly affect ecosystem functions related to assemblage morphology. Here, we assess to what extent the distributions of ant morphological traits are supported by morphologically extreme species and how they are distributed among habitats in a global biodiversity hotspot, the Brazilian Amazon. We used a morphological database comprising 15 continuous morphological traits and 977 expert‐validated ant species distributed across the Brazilian Amazon. We produced species range estimates using species distribution models or alpha hulls (when few records were available). Next, we conducted a principal components analysis to combine traits into a space with reduced dimensionality (morphospace). Then, we identified morphologically extreme species in this space and quantified their contributions to morphological diversity across different habitat types in the Brazilian Amazon Basin. We identified 114 morphologically extreme ant species across the Amazon ant morphospace. These species also accounted for a large percentage of morphospace filling, exceeding 99% representation in the most disturbed habitats in the Amazon. Our results suggest that a few morphologically extreme species capture most of the variation in ant morphology and, therefore, the spectrum of ecosystem functions performed by ants in the Brazilian Amazon Basin. Further, unlike in many other groups, these extreme morphologies were represented by the set of most common species. These results suggest greater functional redundancy and resilience in Brazilian Amazon ants, but more broadly, they contribute to our understanding of ecological processes that sustain ecosystem functions.