Trait Datasets Research Articles

Natural and Anthropogenic Influences on the Mating System of the Common Morning Glory.

Evolutionary biologists remain puzzled by the often dramatic variation of mating strategies within single species. Of particular interest is the extent to which environmental conditions shape patterns of variation of mating system components within mixed mating species, and how widespread anthropogenic manipulations may influence these associations. Here, we address this question in the common morning glory (Ipomoea purpurea) by combining a dataset of floral traits, estimates of the mating system, and relevant environmental factors compiled for 22 populations of this species distributed along a wide range of environments from the Southeastern and Midwestern United States. We identify a disparate set of environmental factors to influence population-level variation in selfing, inbreeding, and flower morphology. Although floral traits are primarily associated with climatic variation, the outcrossing rate and inbreeding coefficient are primarily influenced by the level of herbicide resistance. Furthermore, we find that populations with higher levels of herbicide resistance exhibit a stronger correlation between mating system-floral traits and mating system estimates (outcrossing rate and inbreeding coefficient). Altogether, these results demonstrate the dominant role that herbicide application plays in the determination of I. purpurea's mating system, and more generally uncover the complex and unforeseen evolutionary consequences of anthropogenic manipulations in natural systems.

Genome-wide assessment of genetic diversity and fiber quality traits characterization in Gossypium hirsutum races

Gossypium hirsutum races are believed to be potential reservoirs of desirable traits, which can play crucial roles to overcome the existing narrow genetic base of modern Upland cotton cultivars. However, prior to utilizing the races in cotton improvement programs, understanding their genetic constitutions is needed. Thus, this study used molecular and morphological techniques to characterize 110 G. hirsutum germplasm including 109 semi-wild accessions and one Upland cotton cultivar, CRI12. In the study, 104 SSR markers detected 795 alleles, with an average of 7.64 alleles per marker, ranging from 3 to 14, and average polymorphism information content (PIC) value of 0.71. And 96 of the markers were found to be highly informative, with PIC value≥0.50. Pairwise genetic similarity coefficient across the accessions ranged from 0.19 to 1.00, with an average value of 0.46. Morphological characterization was done using fiber length, fiber strength, micronaire, fiber uniformity index, and fiber elongation. Pairwise taxonomic distance within the accessions ranged from 0.17 to 3.41, with a mean of 1.33. The SSR and fiber quality traits data set based unweighted pair group method of arithmetic mean (UPGMA) analysis grouped the accessions into 7 and 12 distinct clusters, respectively, that corresponds well with the results of principal component analysis (PCA). Our study revealed the existence of vast molecular and morphological diversities within the accessions and provided valuable information on each semi-wild accession for quick and better informed germplasm utilization in cotton breeding programs.

Comparative studies need to rely both on sound natural history data and on excellent statistical analysis.

We certainly agree with Schradin [1] that comparative studies need to use the best information available—as well as clear definitions, up-to-date statistical techniques and common sense. We have divided our response into two sections: the first dealing with Schradin's critique of our 2017 paper and the classification of the social organization of shrews; the second dealing with three general issues that his commentary raises. The paper that Schradin is commenting on [2] asks whether cooperative breeders (which we define as species where non-breeding helpers assist breeders to raise their young) in mammals are more commonly found in arid environments than monogamous species (defined as species where breeding pairs of males and females remain together for more than one season) from which cooperative breeders appear to be derived [3]. It shows that, as in birds, cooperative breeders tend to live in relatively arid habitats compared to monogamous species. As we describe below, these conclusions are unaffected by differences between our classification of the social organization of shrews and the classification suggested by Schradin. Schradin's first criticism is that the categorizations of shrews that we have used in this analysis were based on one paper on a single species. This is incorrect, for our categorization of shrews was based on more than 70 separate sources (see electronic supplementary material, appendix S1). When we published our dataset [4], we were required to reduce the associated reference list by the editors of the journal and, to comply, we removed around 75% of our 2000+ sources, often listing a single reference per species or group of species, and frequently using generic reviews for particular groups where these were available: for example, for many rodent species, we simply listed Wolff and Sherman's Rodent Societies [5], and …

Patterns and biases of climate change threats in the IUCN Red List.

International Union for Conservation of Nature (IUCN) Red List assessments rely on published data and expert inputs, and biases can be introduced where underlying definitions and concepts are ambiguous. Consideration of climate change threat is no exception, and recently numerous approaches to assessing the threat of climate change to species have been developed. We explored IUCN Red List assessments of amphibians and birds to determine whether species listed as threatened by climate change display distinct patterns in terms of habitat occupied and additional nonclimatic threats faced. We compared IUCN Red List data with a published data set of species' biological and ecological traits believed to infer high vulnerability to climate change and determined whether distributions of climate change-threatened species on the IUCN Red List concur with those of climate change-threatened species identified with the trait-based approach and whether species possessing these traits are more likely to have climate change listed as a threat on the IUCN Red List. Species in some ecosystems (e.g., grassland, shrubland) and subject to particular threats (e.g., invasive species) were more likely to have climate change as a listed threat. Geographical patterns of climate change-threatened amphibians and birds on the IUCN Red List were incongruent with patterns of global species richness and patterns identified using trait-based approaches. Certain traits were linked to increases or decreases in the likelihood of a species being threatened by climate change. Broad temperature tolerance of a species was consistently related to an increased likelihood of climate change threat, indicating counterintuitive relationships in IUCN assessments. To improve the robustness of species assessments of the vulnerability or extinction risk associated with climate change, we suggest IUCN adopt a more cohesive approach whereby specific traits highlighted by our results are considered in Red List assessments. To achieve this and to strengthen the climate change-vulnerability assessments approach, it is necessary to identify and implement logical avenues for further research into traits that make species vulnerable to climate change (including population-level threats).

Traits as Essential Biodiversity Variables

Essential Biodiversity Variables (EBVs) are harmonized biodiversity variables and their asssociated measurements needed for developing indicators of global biodiversity change. EBVs can serve the important purpose of aligning biodiversity monitoring efforts, much as Essential Climatic Variables (ECVs) help align allied efforts in climate science. One of six initially proposed EBV classes is devoted to species’ traits, since traits form the crucial link between the evolutionary history of organisms, their assembly into communities, and the nature and dynamic functioning of ecosystems. Despite their importance, prevalence, and scientific promise, the biodiversity community is still developing the conceptual, informatics, technical, and legal frameworks required for the large scale implementation and uptake. As part of an international consortium called GLOBIS-B, and in coordination with the The Group on Earth Observations Biodiversity Observation Network (GEO BON; geobon.org), we report on recent efforts to synthesize current efforts in trait data collection and trait datasets, computational workflows, ways to standardize data and metadata, and assessments of the openness and accessibility of existing species trait datasets. Members of the GLOBIS-B (www.globis-b.eu/) consortium also produced a set of candidate EBVs within the broader trait class ('Phenology', 'Organism morphology', 'Reproduction', 'Physiology' and 'Movement'). In this presentation, we begin by introducing the concept of EBVs, the current working definition of traits in the context of the EBV process, and workflows that have been developed for other EBV classes ('Species Populations') and the importance of standardizing EBV classes and the trait class, in particular. Building on this introduction, we discuss how the EBV concept is operationalized, focusing on workflows for trait integration, and the importance of data and metadata standards,following work from Kissling et al. 2017 (http://onlinelibrary.wiley.com/doi/10.1111/brv.12359/full). On the legal front, we suggest that the Creative Commons (CC) framework provides effective tools for designating legally interoperable and open data, especially when trait data are in the public domain (CC0, CC PDM) or assigned with a CC BY license, and metadata citation and other forms of attribution are available in both human and machine-readable form. We also suggest how EBVs can inform policy at national and global scales. Moving forward, renewed efforts of repeated trait data collection as well as standardised protocols for data and metadata collection are needed to improve the empirical basis of species traits EBVs. Moreover, open data as well as computational workflows are required for comprehensively assessing progress towards conservation policy targets and sustainable development goals. We conclude with a call to action for the TDWG community to consider their role in further developing, implementing, and scaling biodiversity monitoring under the EBV framework.

The ancestral flower of angiosperms and its early diversification

Recent advances in molecular phylogenetics and a series of important palaeobotanical discoveries have revolutionized our understanding of angiosperm diversification. Yet, the origin and early evolution of their most characteristic feature, the flower, remains poorly understood. In particular, the structure of the ancestral flower of all living angiosperms is still uncertain. Here we report model-based reconstructions for ancestral flowers at the deepest nodes in the phylogeny of angiosperms, using the largest data set of floral traits ever assembled. We reconstruct the ancestral angiosperm flower as bisexual and radially symmetric, with more than two whorls of three separate perianth organs each (undifferentiated tepals), more than two whorls of three separate stamens each, and more than five spirally arranged separate carpels. Although uncertainty remains for some of the characters, our reconstruction allows us to propose a new plausible scenario for the early diversification of flowers, leading to new testable hypotheses for future research on angiosperms.

Sampling roots to capture plant and soil functions

Summary Roots vary in anatomy, morphology and physiology, both spatially (different parts of the same root system) and temporally (plastic changes, root ageing), suggesting that root trait measurements are strongly affected by root sampling categories. In this context, it is urgent to clarify the functional significance of current root sampling categories (e.g. fine roots of the first order, the first three orders, ≤1 mm or ≤2 mm), establish guidelines for choosing between sampling methods and revise root ontology to account for functional differences between traits measured on distinct root categories. Here, we used a worldwide database of fine‐root traits to test the hypothesis that distinct fine‐root trait values – with link to fine‐root functions – were generally affected by different root sampling categories. We observed indeed a clear functional break between first‐order roots and roots of all three other sampling categories, and a smaller but substantial break between roots of the three first orders and the ≤2 mm category, demonstrating globally that different sampling methodologies capture different functional parts of roots. Our synthesis suggests that all current root sampling categories present both advantages and pitfalls and that no single method can appropriately tackle the main current challenge of root functional ecology: i.e. linking fine roots to plant and ecosystem functions in a truly comparable way across all plants. We argue instead that a small set of complementary standardized sampling methods is necessary to capture the linkages between root forms and functions. To assist experimenters selecting adequate sampling we developed a decision table following three logical questions: (i) what plant or ecosystem function must be addressed; (ii) what root categories are involved in this function and (iii) what traits should be measured on these root categories. Challenging, strengthening and expending such common reference framework would be a substantial step towards wider comparability of future functional trait datasets. A lay summary is available for this article.

Lateral transfer in Stochastic Dollo models

Lateral transfer, a process whereby species exchange evolutionary traits through nonancestral relationships, is a frequent source of model misspecification in phylogenetic inference. Lateral transfer obscures the phylogenetic signal in the data as the histories of affected traits are mosaics of the overall phylogeny. We control for the effect of lateral transfer in a Stochastic Dollo model and a Bayesian setting. Our likelihood is highly intractable, as the parameters are the solution of a sequence of large systems of differential equations representing the expected evolution of traits along a tree. We illustrate our method on a data set of lexical traits in Eastern Polynesian languages, and obtain an improved fit over the corresponding model without lateral transfer.

Genetic Characterization of Dog Personality Traits.

The genetic architecture of behavioral traits in dogs is of great interest to owners, breeders, and professionals involved in animal welfare, as well as to scientists studying the genetics of animal (including human) behavior. The genetic component of dog behavior is supported by between-breed differences and some evidence of within-breed variation. However, it is a challenge to gather sufficiently large datasets to dissect the genetic basis of complex traits such as behavior, which are both time-consuming and logistically difficult to measure, and known to be influenced by nongenetic factors. In this study, we exploited the knowledge that owners have of their dogs to generate a large dataset of personality traits in Labrador Retrievers. While accounting for key environmental factors, we demonstrate that genetic variance can be detected for dog personality traits assessed using questionnaire data. We identified substantial genetic variance for several traits, including fetching tendency and fear of loud noises, while other traits revealed negligibly small heritabilities. Genetic correlations were also estimated between traits; however, due to fairly large SEs, only a handful of trait pairs yielded statistically significant estimates. Genomic analyses indicated that these traits are mainly polygenic, such that individual genomic regions have small effects, and suggested chromosomal associations for six of the traits. The polygenic nature of these traits is consistent with previous behavioral genetics studies in other species, for example in mouse, and confirms that large datasets are required to quantify the genetic variance and to identify the individual genes that influence behavioral traits.

Local neighbourhood and regional climatic contexts interact to explain tree performance.

Tree neighbourhood modelling has significantly contributed to our understanding of the mechanisms structuring communities. Investigations into the impact of neighbouring crowding on tree performance have generally been conducted at local scales, missing important regional-scale context such as the suitability of the climate for each species. Favourable climates may enhance tree performance, but this may come at the cost of increased neighbourhood crowding and competition negatively impacting survival and growth. Through the synthesis of continental-scale forest inventory and trait datasets from the northeast USA and Puerto Rico we present an analytical approach that elucidates the important interactions between local competitive and regional climatic contexts. Our results show strong asymmetries in competitive interactions and significant niche differences that are dependent on habitat suitability. The strong interaction between local neighbourhood and regional climate highlights the need for models that consider the interaction between these two processes that have been previously ignored.

Taxonomic and functional ecology of montane ants

Why is biological diversity distributed in the way that it is? This question has been central to ecology and biogeography for centuries and is of great importance for pure and applied reasons. I use a functional trait view of ecology to complement standard sampling protocols to better understand the distribution and structure of ant (Hymenoptera: Formicidae) diversity across mountains. I use a long-term dataset of ant diversity and abundance, combined with a recently collected morphological trait dataset to examine how the alpha and beta diversity of ants responds to changes in temperature along an extensive elevational gradient in southern Africa. In addition, I link morphological thermoregulatory traits to each other and to the environment with a new database of ant elevational abundances from across the globe. Finally, I analyse how physiological thermal tolerances vary and constrain foraging patterns in montane ants. I find that temperature is a strong driver of both alpha and beta diversity patterns. In addition, morphological traits such as colour and body size are found to have a significant relationship to ambient temperatures. This relationship also implies that the relative abundances of different ant species change depending on their thermoregulatory traits (colour and body size) and the surrounding thermal environment. Furthermore, the critical thermal minimum (CTmin) of the ant species investigated and the lowest environmental temperatures are found to be key in constraining foraging activity patterns. The data presented here strengthen and link existing ideas about how thermoregulation can influence ecological communities and also suggests important ways in which diversity patterns may change in the future.

Climate, soil and plant functional types as drivers of global fine‐root trait variation

Summary Ecosystem functioning relies heavily on below‐ground processes, which are largely regulated by plant fine‐roots and their functional traits. However, our knowledge of fine‐root trait distribution relies to date on local‐ and regional‐scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world‐wide fine‐root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine‐root trait variation. Most particularly, we tested the competing hypotheses that fine‐root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine‐root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine‐root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine‐root morphology, by favouring thicker, denser fine‐roots; (iii) Fine‐roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N2‐fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine‐root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine‐root trait variation. High trait variation occurred at local scales, suggesting that wide‐ranging below‐ground resource economics strategies are viable within most climatic areas and soil conditions.

Unsupervised Discretization of Continuous Variables in a Chicken Egg Quality Traits Dataset

Discretization is a data pre-processing task transforming continuous variables into discrete ones in order to apply some data mining algorithms such as association rules extraction and classification trees. In this study we empirically compared the performances of equal width intervals (EWI), equal frequency intervals (EFI) and K-means clustering (KMC) methods to discretize 14 continuous variables in a chicken egg quality traits dataset. We revealed that these unsupervised discretization methods can decrease the training error rates and increase the test accuracies of the classification tree models. By comparing the training errors and test accuracies of the model applied with C5.0 classification tree algorithm we also found that EWI, EFI and KMC methods produced the more or less similar results. Among the rules used for estimating the number of intervals, the Rice rule gave the best result with EWI but not with EFI. It was also found that Freedman-Diaconis rule with EFI and Doane rule with EFI and EWI slightly performed better than the other rules.

Structure and Applications of BRYOTRAIT-AZO, a Trait Database for Azorean Bryophytes

Trait databases are invaluable sources of information in ecological studies exploring the links between species traits and their surrounding environment. While digital vascular plant trait databases are already numerous, sets of bryophyte trait data are not equally available online. To help fill in this gap, we present the BRYOTRAIT-AZO database, a trait dataset for the Azorean bryoflora which aims to gather and facilitate access to all the published morphological information for the archipelago's bryophytes. As an example of its applications we examined the variation of moss leaf size, orientation and nerve extension along Terceira Island's elevational gradient, testing hypothesis related with trait presence and function. We identified a shift from mosses with twisted and shorter but longly costate leaves at lower elevation to mosses with longer untwisted leaves, with short or absent nerves at higher ground. These changes reflect the transition from sunnier, warmer and drier conditions at low elevation to shadier, cooler and damper settings at the island summit, in accordance with the hypothesis that smaller, twisted and longly costate leaves are better adapted to more xeric environments. As exemplified, this database can be a valuable tool for future studies at a regional or even a global scale, coupling functional data with bryophyte distribution information to identify trait roles on ecosystem functioning, but also general diversity and species co-occurrence patterns and community assembly rules.

The role of functional uniqueness and spatial aggregation in explaining rarity in trees

AbstractAimDetermining the drivers of species rarity is fundamental for understanding and conserving biodiversity. Rarity of a given species within its community may arise due to exclusion by other ecologically similar species. Conversely, rare species may occupy habitats that are rare in the landscape or they may be ill‐suited to all available habitats. The first mechanism would lead to common and rare species occupying similar ecological space defined by functional traits. The second mechanism would result in common and rare species occupying dissimilar ecological space and spatial aggregation of rare species, either because they are specialists in rare habitats or because rare species tend to be dispersal limited. Here, we quantified the contribution of locally rare species to community functional richness and the spatial aggregation of species across tree communities world‐wide to address these hypotheses.LocationAsia and the Americas.Time period2002 to 2012 (period that considers the censuses for the plots used).Major taxa studiedAngiosperm and Gymnosperm trees.MethodsWe compiled a dataset of functional traits from all the species present in eight tree plots around the world to evaluate the contribution of locally rare species to tree community functional richness using multi‐ and univariate approaches. We also quantified the spatial aggregation of individuals within species at several spatial scales as it relates to abundance.ResultsLocally rare tree species in temperate and tropical forests tended to be functionally unique and are consistently spatially clustered. Furthermore, there is no evidence that this pattern is driven by pioneer species being locally rare.Main conclusionsThis evidence shows that locally rare tree species disproportionately contribute to community functional richness, and we can therefore reject the hypothesis that locally rare species are suppressed by ecologically similar, but numerically dominant, species. Rather, locally rare species are likely to be specialists on spatially rare habitats or they may be ill‐suited to the locally available environments.

SNP-SNP interactions between WNT4 and WNT5A were associated with obesity related traits in Han Chinese Population

Considering the biological roles of WNT4 and WNT5A involved in adipogenesis, we aimed to investigate whether SNPs in WNT4 and WNT5A contribute to obesity related traits in Han Chinese population. Targeted genomic sequence for WNT4 and WNT5A was determined in 100 Han Chinese subjects and tag SNPs were selected. Both single SNP and SNP × SNP interaction association analyses with body mass index (BMI) were evaluated in the 100 subjects and another independent sample of 1,627 Han Chinese subjects. Meta-analyses were performed and multiple testing corrections were carried out using the Bonferroni method. Consistent with the Genetic Investigation of ANthropometric Traits (GIANT) dataset results, we didn’t detect significant association signals in single SNP association analyses. However, the interaction between rs2072920 and rs11918967, was associated with BMI after multiple testing corrections (combined P = 2.20 × 10−4). The signal was also significant in each contributing data set. SNP rs2072920 is located in the 3′-UTR of WNT4 and SNP rs11918967 is located in the intron of WNT5A. Functional annotation results revealed that both SNPs might be involved in transcriptional regulation of gene expression. Our results suggest that a combined effect of SNPs via WNT4-WNT5A interaction may affect the variation of BMI in Han Chinese population.

How many more? Sample size determination in studies of morphological integration and evolvability

1. The variational properties of living organisms are an important component of current evolutionary theory. As a consequence, researchers working on the field of multivariate evolution have increasingly used integration and evolvability statistics as a way of capturing the potentially complex patterns of trait association and their effects over evolutionary trajectories. Little attention has been paid, however, to the cascading effects that inaccurate estimates of trait covariance have on these widely used evolutionary statistics. 2. Here, we analyze the relationship between sampling effort and inaccuracy in evolvability and integration statistics calculated from 10-trait matrices with varying patterns of covariation and magnitudes of integration. We then extrapolate our initial approach to different numbers of traits and different magnitudes of integration and estimate general equations relating the inaccuracy of the statistics of interest to sampling effort. We validate our equations using a dataset of cranial traits, and use them to make sample size recommendations. 3. Our results suggest that highly inaccurate estimates of evolvability and integration statistics resulting from small sample sizes are likely common in the literature, given the sampling effort necessary to properly estimate them. We also show that patterns of covariation have no effect on the sampling properties of these statistics, but overall magnitudes of integration interact with sample size and lead to varying degrees of bias, imprecision, and inaccuracy. 4. Finally, we provide R functions that can be used to calculate recommended sample sizes or to simply estimate the level of inaccuracy that should be expected in these statistics, given a sampling design.

Phenological shifts in hoverflies (Diptera: Syrphidae): linking measurement and mechanism

An understanding of ecological and evolutionary responses to global environmental change requires both a robust measurement of the change that is occurring and a mechanistic framework for understanding the drivers of that change. Such a requirement provides a challenge because biological monitoring is often ad hoc, and mechanistic experiments are often performed under highly simplified conditions. This study integrates multiple datasets to evaluate our current knowledge of the measurement and mechanism of phenological shifts in a key pollinator taxon: the hoverflies (Diptera: Syrphidae). First, two large, complementary and independent monitoring datasets are used to test for trends in phenology: an ad hoc national recording scheme containing > 620 000 records, and standardised monitoring with consistent methods over 30 yr. Results show that ad hoc and standardised recording data give quantitatively the same value for phenological advance in hoverflies (ca 12 d°C–1 on average at the beginning of the flight period), supporting the value of biological recording for the measurement of global ecological change. While the end of the flight period appears static in ad hoc recording, the standardised dataset suggests a similar advance as in the beginning of the flight period. Second, an extensive traits dataset and a novel database of laboratory‐derived developmental data on Syrphidae (153 published studies) are used to test for mechanistic patterns in phenological shifts. The only species trait that influenced phenology was voltinism, where species with more generations per year exhibit stronger phenological advances. We demonstrate considerable variation in the laboratory‐derived sensitivity to temperature but this does not match field‐derived measures of phenology. The results demonstrate that, as for many taxa, we have a strong understanding of the patterns of global ecological change but that we currently lack a detailed mechanistic understanding of those processes despite extensive research into the fundamental biology of some taxonomic groups.

Interpreting the Climatic Effects on Xylem Functional Traits in Two Mediterranean Oak Species: The Role of Extreme Climatic Events.

In the Mediterranean region, the widely predicted rise in temperature, change in the precipitation pattern, and increase in the frequency of extreme climatic events are expected to alter the shape of ecological communities and to affect plant physiological processes that regulate ecosystem functioning. Although change in the mean values are important, there is increasing evidence that plant distribution, survival, and productivity respond to extremes rather than to the average climatic condition. The present study aims to assess the effects of both mean and extreme climatic conditions on radial growth and functional anatomical traits using long-term tree-ring time series of two co-existing Quercus spp. from a drought-prone site in Southern Italy. In particular, this is the first attempt to apply the Generalized Additive Model for Location, Scale, and Shape (GAMLSS) technique and Bayesian modeling procedures to xylem traits data set, with the aim of (i) detecting non-linear long-term responses to climate and (ii) exploring relationships between climate extreme and xylem traits variability in terms of probability of occurrence. This study demonstrates the usefulness of long-term xylem trait chronologies as records of environmental conditions at annual resolution. Statistical analyses revealed that most of the variability in tree-ring width and specific hydraulic conductivity might be explained by cambial age. Additionally, results highlighted appreciable relationships between xylem traits and climate variability more than tree-ring width, supporting also the evidence that the plant hydraulic traits are closely linked to local climate extremes rather than average climatic conditions. We reported that the probability of extreme departure in specific hydraulic conductivity (Ks) rises at extreme values of Standardized Precipitation Index (SPI). Therefore, changing frequency or intensity of extreme events might overcome the adaptive limits of vascular transport, resulting in substantial reduction of hydraulic functionality and, hence increased incidence of xylem dysfunctions.

SHEsisPlus, a toolset for genetic studies on polyploid species.

Currently, algorithms and softwares for genetic analysis of diploid organisms with bi-allelic markers are well-established, while those for polyploids are limited. Here, we present SHEsisPlus, the online algorithm toolset for both dichotomous and quantitative trait genetic analysis on polyploid species (compatible with haploids and diploids, too). SHEsisPlus is also optimized for handling multiple-allele datasets. It’s free, open source and also designed to perform a range of analyses, including haplotype inference, linkage disequilibrium analysis, epistasis detection, Hardy-Weinberg equilibrium and single locus association tests. Meanwhile, we developed an accurate and efficient haplotype inference algorithm for polyploids and proposed an entropy-based algorithm to detect epistasis in the context of quantitative traits. A study of both simulated and real datasets showed that our haplotype inference algorithm was much faster and more accurate than existing ones. Our epistasis detection algorithm was the first try to apply information theory to characterizing the gene interactions in quantitative trait datasets. Results showed that its statistical power was significantly higher than conventional approaches. SHEsisPlus is freely available on the web at http://shesisplus.bio-x.cn/. Source code is freely available for download at https://github.com/celaoforever/SHEsisPlus.