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Evolutionary biology is a study of life's history on Earth. In researching this history, biologists are often interested in attempting to reconstruct phenotypes for the long extinct ancestors of living species. Various methods have been developed to do this on a phylogeny from the data for extant taxa. In the present article, I introduce a new approach for ancestral character estimation for discretely valued traits. This approach is based on the threshold model from evolutionary quantitative genetics. Under the threshold model, the value exhibited by an individual or species for a discrete character is determined by an underlying, unobserved continuous trait called \"liability.\" In this new method for ancestral state reconstruction, I use Bayesian Markov chain Monte Carlo (MCMC) to sample the liabilities of ancestral and tip species, and the relative positions of two or more thresholds, from their joint posterior probability distribution. Using data simulated under the model, I find that the method has very good performance in ancestral character estimation. Use of the threshold model for ancestral state reconstruction relies on a priori specification of the order of the discrete character states along the liability axis. I test the use of a Bayesian MCMC information theoretic criterion based approach to choose among different hypothesized orderings for the discrete character. Finally, I apply the method to the evolution of feeding mode in centrarchid fishes.

Regressions of biological variables across species are rarely perfect. Usually, there are residual deviations from the estimated model relationship, and such deviations commonly show a pattern of phylogenetic correlations indicating that they have biological causes. We discuss the origins and effects of phylogenetically correlated biological variation in regression studies. In particular, we discuss the interplay of biological deviations with deviations due to observational or measurement errors, which are also important in comparative studies based on estimated species means. We show how bias in estimated evolutionary regressions can arise from several sources, including phylogenetic inertia and either observational or biological error in the predictor variables. We show how all these biases can be estimated and corrected for in the presence of phylogenetic correlations. We present general formulas for incorporating measurement error in linear models with correlated data. We also show how alternative regression models, such as major axis and reduced major axis regression, which are often recommended when there is error in predictor variables, are strongly biased when there is biological variation in any part of the model. We argue that such methods should never be used to estimate evolutionary or allometric regression slopes.

Phylogenetic comparative methods thatincorporate intraspecific variability are relatively new and, so far, not especially widely used in empirical studies. In the present short article we will describe a new Bayesian method for fitting evolutionary models to comparative data that incorporates intraspecific variability. This method differs from an existing likelihood-based approach in that it requires no a priori inference about species means and variances; rather it takes phenotypic values from individuals and a phylogenetic tree as input, and then samples species means and variances, along with the parameters of the evolutionary model, from their joint posterior probability distribution. One of the most novel and intriguing attributes of this approach is that jointly sampling the species means with the evolutionary model parameters means that the model and tree can influence our estimates of species mean trait values, not just the reverse. In the present implementation, we first apply this method to the most widely used evolutionary model for continuously valued phenotypic trait data (Brownian motion). However, the general approach has broad applicability, which we illustrate by also fitting the λ model, another simple model for quantitative trait evolution on a phylogeny. We test our approach via simulation and by analyzing two empirical datasets obtained from the literature. Finally, we have implemented the methods described herein in a new function for the R statistical computing environment, and this function will be distributed as part of the 'phytools' R library.

The evolution of organisms is crucially dependent on the evolution of intraspecific variation. Its interactions with selective agents in the biotic and abiotic environments underlie many processes, such as intraspecific competition, resource partitioning and, eventually, species formation. Nevertheless, comparative models of trait evolution neither allow explicit testing of hypotheses related to the evolution of intraspecific variation nor do they simultaneously estimate rates of trait evolution by accounting for both trait mean and variance. Here, we present a model of phenotypic trait evolution using a hierarchical Bayesian approach that simultaneously incorporates interspecific and intraspecific variation. We assume that species-specific trait means evolve under a simple Brownian motion process, whereas species-specific trait variances are modeled with Brownian or Ornstein-Uhlenbeck processes. After evaluating the power of the method through simulations, we examine whether life-history traits impact evolution of intraspecific variation in the Eriogonoideae (buckwheat family, Polygonaceae). Our model is readily extendible to more complex scenarios of the evolution of inter-and intraspecific variation and presents a step toward more comprehensive comparative models for macroevolutionary studies.

Summary Competitor, stress‐tolerator, ruderal ( CSR ) theory is a prominent plant functional strategy scheme previously applied to local floras. Globally, the wide geographic and phylogenetic coverage of available values of leaf area ( LA ), leaf dry matter content ( LDMC ) and specific leaf area ( SLA ) (representing, respectively, interspecific variation in plant size and conservative vs . acquisitive resource economics) promises the general application of CSR strategies across biomes, including the tropical forests hosting a large proportion of Earth's diversity. We used trait variation for 3068 tracheophytes (representing 198 families, six continents and 14 biomes) to create a globally calibrated CSR strategy calculator tool and investigate strategy–environment relationships across biomes world‐wide. Due to disparity in trait availability globally, co‐inertia analysis was used to check correspondence between a ‘wide geographic coverage, few traits’ data set and a ‘restricted coverage, many traits’ subset of 371 species for which 14 whole‐plant, flowering, seed and leaf traits (including leaf nitrogen content) were available. CSR strategy/environment relationships within biomes were investigated using fourth‐corner and RLQ analyses to determine strategy/climate specializations. Strong, significant concordance ( RV = 0·597; P < 0·0001) was evident between the 14 trait multivariate space and when only LA , LDMC and SLA were used. Biomes such as tropical moist broadleaf forests exhibited strategy convergence (i.e. clustered around a CS / CSR median; C:S:R = 43:42:15%), with CS ‐selection associated with warm, stable situations (lesser temperature seasonality), with greater annual precipitation and potential evapotranspiration. Other biomes were characterized by strategy divergence: for example, deserts varied between xeromorphic perennials such as Larrea divaricata, classified as S‐selected (C:S:R = 1:99:0%) and broadly R‐selected annual herbs (e.g. Claytonia perfoliata ; R/ CR ‐selected; C:S:R = 21:0:79%). Strategy convergence was evident for several growth habits (e.g. trees) but not others (forbs). The CSR strategies of vascular plants can now be compared quantitatively within and between biomes at the global scale. Through known linkages between underlying leaf traits and growth rates, herbivory and decomposition rates, this method and the strategy–environment relationships it elucidates will help to predict which kinds of species may assemble in response to changes in biogeochemical cycles, climate and land use.

We compiled a global database for leaf, stem and root biomass representing c. 11 000 records for c. 1200 herbaceous and woody species grown under either controlled or field conditions. We used this data set to analyse allometric relationships and fractional biomass distribution to leaves, stems and roots. We tested whether allometric scaling exponents are generally constant across plant sizes as predicted by metabolic scaling theory, or whether instead they change dynamically with plant size. We also quantified interspecific variation in biomass distribution among plant families and functional groups. Across all species combined, leaf vs stem and leaf vs root scaling exponents decreased from c. 1.00 for small plants to c. 0.60 for the largest trees considered. Evergreens had substantially higher leaf mass fractions (LMFs) than deciduous species, whereas graminoids maintained higher root mass fractions (RMFs) than eudicotyledonous herbs. These patterns do not support the hypothesis of fixed allometric exponents. Rather, continuous shifts in allometric exponents with plant size during ontogeny and evolution are the norm. Across seed plants, variation in biomass distribution among species is related more to function than phylogeny. We propose that the higher LMF of evergreens at least partly compensates for their relatively low leaf area: leaf mass ratio.

1. The temporal dynamics of plant phenology and pollinator abundance across seasons should influence the structure of plant-pollinator interaction networks. Nevertheless, such dynamics are seldom considered, especially for diverse tropical networks. 2. Here, we evaluated the temporal variation of four plant-pollinator networks in two seasonal ecosystems in Central Brazil (Cerrado and Pantanal). Data were gathered on a monthly basis over 1 year for each network. We characterized seasonal and temporal shifts in plant-pollinator interactions, using temporally discrete networks. We predicted that the greater floral availability in the rainy season would allow for finer partitioning of the floral niche by the pollinators, i.e. higher specialization patterns as previously described across large spatial gradients. Finally, we also evaluated how sampling restricted to peak flowering period may affect the characterization of the networks. 3. Contrary to our expectations, we found that dry season networks, although characterized by lower floral resource richness and abundance, showed higher levels of network-wide interaction partitioning (complementary specialization and modularity). For nestedness, though, this between-seasons difference was not consistent. Reduced resource availability in the dry season may promote higher interspecific competition among pollinators leading to reduced niche overlap, thus explaining the increase in specialization. 4. There were no consistent differences between seasons in species-level indices, indicating that higher network level specialization is an emergent property only seen when considering the entire network. However, bees presented higher values of specialization and species strength in relation to other groups such as flies and wasps, suggesting that some plant species frequently associated with bees are used only by this group. 5. Our study also indicates that targeted data collection during peak flowering generates higher estimates of network specialization, possibly because species activity spans longer periods than the targeted time frame. Hence, depending on the period of data collection, different structural values for the networks of interactions may be found. 6. Synthesis. Plant-pollinator networks from tropical environments have structural properties that vary according to seasons, which should be taken into account in the description of the complex systems of interactions between plants and their pollinators in these areas.

Latitudinal and elevational gradients provide valuable experimental settings for studies of the potential impact of global warming on forest tree species. The availability of long-term phenological surveys in common garden experiments for traits associated with climate, such as bud flushing for sessile oaks (Quercus petraea), provide an ideal opportunity to investigate this impact. We sequenced 18 sessile oak populations and used available sequencing data for three other closely related European white oak species (Quercus pyrenaica, Quercus pubescens, and Quercus robur) to explore the evolutionary processes responsible for shaping the genetic variation across latitudinal and elevational gradients in extant sessile oaks. We used phenotypic surveys in common garden experiments and climatic data for the population of origin to perform genome-wide scans for population differentiation and genotype-environment and genotype-phenotype associations. The inferred historical relationships between Q. petraea populations suggest that interspecific gene flow occurred between Q. robur and Q. petraea populations from cooler or wetter areas. A genome-wide scan of differentiation between Q. petraea populations identified single nucleotide polymorphisms (SNPs) displaying strong interspecific relative divergence between these two species. These SNPs followed genetic clines along climatic or phenotypic gradients, providing further support for the likely contribution of introgression to the adaptive divergence of Q. petraea populations. Overall, the results indicate that outliers and associated SNPs are Q. robur ancestry-informative. We discuss the results of this study in the framework of the postglacial colonization scenario, in which introgression and diversifying selection have been proposed as essential drivers of Q. petraea microevolution.

1. Understanding the importance of biotic interactions in driving the distribution and abundance of species is a central goal of plant ecology. Early vascular plants likely colonized land occupied by biocrusts — photoautotrophic, surface-dwelling soil communities comprised of cyanobacteria, bryophytes, lichens and fungi — suggesting biotic interactions between biocrusts and plants have been at play for some 2,000 million years. Today, biocrusts coexist with plants in dryland ecosystems worldwide, and have been shown to both facilitate or inhibit plant species performance depending on ecological context. Yet, the factors that drive the direction and magnitude of these effects remain largely unknown. 2. We conducted a meta-analysis of plant responses to biocrusts using a global data-set encompassing 1,004 studies from six continents. 3. Meta-analysis revealed there is no simple positive or negative effect of biocrusts on plants. Rather, plant responses differ by biocrust composition and plant species traits and vary across plant ontogeny. Moss-dominated biocrusts facilitated, while lichen-dominated biocrusts inhibited overall plant performance. Plant responses also varied among plant functional groups: C₄ grasses received greater benefits from biocrusts compared to C₃ grasses, and plants without N-fixing symbionts responded more positively to biocrusts than plants with N-fixing symbionts. Biocrusts decreased germination but facilitated growth of non-native plant species. 4. Synthesis. Results suggest that interspecific variation in plant responses to biocrusts, contingent on biocrust type, plant traits, and ontogeny can have strong impacts on plant species performance. These findings have important implications for understanding biocrust contributions to plant productivity and community assembly processes in ecosystems worldwide.

1. Body size is a fundamental ecological trait and is correlated with population dynamics, community structure and function, and ecosystem fluxes. Laboratory data from broad taxonomic groups suggest that a widespread response to a warming world may be an overall decrease in organism body size. However, given the myriad of biotic and abiotic factors that can also influence organism body size in the wild, it is unclear whether results from these laboratory assays hold in nature. 2. Here we use datasets spanning 30 to 100 years to examine whether the body size of wild-caught beetles has changed over time, whether body size changes are correlated with increased temperatures, and we frame these results using predictions derived from a quantitative review of laboratory responses of 22 beetle species to temperature. 3. We found that 95% of laboratory-reared beetles decreased in size with increased rearing temperature, with larger-bodied species shrinking disproportionately more than smaller-bodied beetles. In addition, the museum datasets revealed that largerbodied beetle species have decreased in size over time, that mean beetle body size explains much of the interspecific variation in beetle responses to temperature, and that long-term beetle size changes are explained by increases in autumn temperature and decreases in spring temperature in this region. 4. Our data demonstrate that the relationship between body size and temperature of wild-caught beetles matches relatively well with results from laboratory studies, and that variation in this relationship is largely explained by interspecific variation in mean beetle body size. 5. This long-term beetle dataset is one of the most comprehensive arthropod body size datasets compiled to date, it improves predictions regarding the shrinking of organisms with global climate change, and together with the meta-analysis data, call for new hypotheses to explain why larger-bodied organisms may be more sensitive to temperature.