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1
Britt, Thomas W., and James A. Shepperd. "Trait Relevance and Trait Assessment." Personality and Social Psychology Review 3, no. 2 (May 1999): 108–22. http://dx.doi.org/10.1207/s15327957pspr0302_2.
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Growing agreement exists among psychologists that all traits are not equally relevant to allpeople. The assumption that traits differ in their relevance across individuals has implications for psychometric theory and the assessment of reliability and validity. Specifically, the less relevant a trait is to an individual, the more error that person is contributing to the measurement process. This article discusses the construct of trait relevance, explicates why differences in trait relevance need to be incorporated into models of trait assessment, and suggests new possibilities for the measurement of trait relevance.
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Xu, Liguo, Dalong Pang, Jing Ge, and Youmin Xi. "Understanding the categories of leader traits in socialization: the case of Haier group’s CEO in China." Nankai Business Review International 8, no. 3 (August 7, 2017): 344–66. http://dx.doi.org/10.1108/nbri-11-2016-0039.
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Purpose The purpose of this study is to explore the categories of leader traits, their generation and their relationships in leaders’ socialization. Design/methodology/approach The authors take the case study method, which is the most suitable method to answer research questions on why and how to fulfill the study purpose on the basis of the case of Ruimin Zhang. Findings Leader traits are classified into four categories with respect to socialization, namely, root trait, driving trait, thinking trait and affair trait. The root trait and the driving trait form from the leader’s insight with the impact of key events, mutually promote and consolidate each other, and together derive the thinking trait and the affair trait on the basis of critical events, culture, family, education, etc. The thinking trait is the premise of the affair trait to be expressed in leadership behavior. The root trait and the driving trait together determine a leader’s growth direction and efficiency and can distinguish leaders from non-leaders. The thinking trait and the affair trait together determine the pattern and effectiveness of leadership behavior and can distinguish effective leadership from ineffective leadership. Research limitations/implications This study transcends prior integral leader trait research by categorizing leader traits from the socialization perspective, makes a clear delineation on the interrelationships among categories of leader traits, analyzes their holistic functions on the leaders, reveals the formation and relationship mechanism of leader traits and identifies the types of leader traits that can work as the standards for distinguishing effective leaders from ineffective leaders or non-leaders. Originality/value This study promotes the development of the leader trait theory in the classification, formations, relationships and overall effect of leader traits.
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Dawson, Samantha K., Carlos Pérez Carmona, Manuela González‐Suárez, Mari Jönsson, Filipe Chichorro, Max Mallen‐Cooper, Yolanda Melero, Helen Moor, John P. Simaika, and Alexander Bradley Duthie. "The traits of “trait ecologists”: An analysis of the use of trait and functional trait terminology." Ecology and Evolution 11, no. 23 (November 11, 2021): 16434–45. http://dx.doi.org/10.1002/ece3.8321.
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Obertegger, Ulrike, and Robert Lee Wallace. "Trait-Based Research on Rotifera: The Holy Grail or Just Messy?" Water 15, no. 8 (April 8, 2023): 1459. http://dx.doi.org/10.3390/w15081459.
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In recent years, trait-based research on plankton has gained interest because of its potential to uncover general roles in ecology. While trait categories for phytoplankton and crustaceans have been posited, rotifer trait assessment has lagged behind. Here, we reviewed the literature to assess traits key to their life histories and provided a data matrix for the 138 valid genera of phylum Rotifera. We considered seven traits: habitat type, trophi type, presence of lorica and foot, predation defense attributes, corona type, and feeding traits. While most traits were morphological attributes and supposedly easy to assess, we were faced with several challenges regarding trait assignment. Feeding traits were especially difficult to assess for many genera because relevant information was missing. Our assembled trait matrix provides a foundation that will initiate additional research on rotifer functional diversity, diminish the misclassification of rotifer genera into trait categories, and facilitate studies across trophic levels.
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Li, Yanpeng, Yue Bin, Han Xu, Yunlong Ni, Ruyun Zhang, Wanhui Ye, and Juyu Lian. "Understanding Community Assembly Based on Functional Traits, Ontogenetic Stages, Habitat Types and Spatial Scales in a Subtropical Forest." Forests 10, no. 12 (November 21, 2019): 1055. http://dx.doi.org/10.3390/f10121055.
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Community assembly in natural communities is commonly explained by stochastic and niche-based processes such as environmental filtering and biotic interactions. Many studies have inferred the importance of these processes using a trait-based approach, however, there are still unknowns around what factors affect the importance of different assembly processes in natural communities. In this study, the trait dispersion patterns of 134 species were examined across different functional traits, habitat types, ontogenetic stages and spatial scales from a 20-ha Dinghushan Forest Dynamic Plot in China. The results showed that (1) functional traits related to productivity such as specific leaf area and leaf area mainly showed functional clustering, indicating these two functional traits were more affected by environmental filtering. However, trait dispersion patterns depended on more than the ecological significances of functional traits. For example, trait dispersions of leaf dry matter content, leaf thickness and maximum height did not show consistent patterns across habitat types and ontogenetic stages, suggesting more complex mechanisms may operate on these traits; (2) the trait dispersion varied with the habitat types and ontogenetic stages. Specifically, we found that habitat types only affected the strength of trait dispersions for all the five traits, but ontogenetic stages influenced both the strength and direction of trait dispersions, which depended on the traits selected; (3) the relative importance of soil, topography and space to trait dispersion varied with ontogenetic stages. Topography and space were more important for trait dispersion of saplings but soil was more important for trait dispersion of adults; (4) biotic interactions dominated community assembly at smaller spatial scales but environmental filtering dominated community assembly at larger spatial scales. Overall, the results highlight the importance of functional traits, habitat types, ontogenetic stages and spatial scales to community assembly in natural communities.
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Devauchelle, B. "Trait pour trait." Annales de Chirurgie Plastique Esthétique 54, no. 1 (February 2009): 5–6. http://dx.doi.org/10.1016/j.anplas.2008.11.005.
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Lease, Loren R. "A descriptive study of African American deciduous dentition." Dental Anthropology Journal 26, no. 3 (September 1, 2018): 45–55. http://dx.doi.org/10.26575/daj.v26i3.51.
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Descriptive studies of the deciduous dentition morphology have been presented as an inclusion in permanent dentition studies, the focus of archaeological populations or on specific traits within modern populations.The present study describes 25 morphological traits of deciduous dentition in two African American samples from Memphis, TN and Dallas, TX (N= 218), and a European American sample (N=100) from Cleveland, OH. These traits represent the most commonly used traits in population microevolution studies, describing various ancestral groups.Results indicate trait frequency variation between the two African American samples, as well as in comparison to European American samples. Traits varying in frequency between the two sample populations include maxillary lateral incisor shovel shape trait (69% vs. 46%), canine tuberculum dentale (40% vs. 22%), canine mesial ridge (3% vs. 7%), and maxillary posterior molar hypocone development (76% vs. 92%). Trait frequencies higher than found in previous studies include maxillary central incisor shovel shape trait (38%) and maxillary lateral incisor shovel shape trait (68%), canine tuberculum dentale (40%), maxillary molar complexity (20%), cusp six (33%) and seven (68%), and the Y-groove on the mandibular posterior molar (69%). Trait frequencies seen lower in previous studies include tuberculum dentale trait on both maxillary incisors (8% and 3%) and the hypocone development of the maxillary posterior molar (76%). The level of trait expression is informative when comparing populations, especially the molar traits. For example, Carabelli’s pit/fissure is the most common trait expression in African American samples, unlike European American samples.
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Ofria, Charles, Wei Huang, and Eric Torng. "On the Gradual Evolution of Complexity and the Sudden Emergence of Complex Features." Artificial Life 14, no. 3 (July 2008): 255–63. http://dx.doi.org/10.1162/artl.2008.14.3.14302.
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Evolutionary theory explains the origin of complex organismal features through a combination of reusing and extending information from less-complex traits, and by needing to exploit only one of many unlikely pathways to a viable solution. While the appearance of a new trait may seem sudden, we show that the underlying information associated with each trait evolves gradually. We study this process using digital organisms, self-replicating computer programs that mutate and evolve novel traits, including complex logic operations. When a new complex trait first appears, its proper function immediately requires the coordinated operation of many genomic positions. As the information associated with a trait increases, the probability of its simultaneous introduction drops exponentially, so it is nearly impossible for a significantly complex trait to appear without reusing existing information. We show that the total information stored in the genome increases only marginally when a trait first appears. Furthermore, most of the information associated with a new trait is either correlated with existing traits or co-opted from traits that were lost in conjunction with the appearance of the new trait. Thus, while total genomic information increases incrementally, traits that require much more information can still arise during the evolutionary process.
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Poyatos, Rafael, Oliver Sus, Llorenç Badiella, Maurizio Mencuccini, and Jordi Martínez-Vilalta. "Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information." Biogeosciences 15, no. 9 (May 4, 2018): 2601–17. http://dx.doi.org/10.5194/bg-15-2601-2018.
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Abstract. The ubiquity of missing data in plant trait databases may hinder trait-based analyses of ecological patterns and processes. Spatially explicit datasets with information on intraspecific trait variability are rare but offer great promise in improving our understanding of functional biogeography. At the same time, they offer specific challenges in terms of data imputation. Here we compare statistical imputation approaches, using varying levels of environmental information, for five plant traits (leaf biomass to sapwood area ratio, leaf nitrogen content, maximum tree height, leaf mass per area and wood density) in a spatially explicit plant trait dataset of temperate and Mediterranean tree species (Ecological and Forest Inventory of Catalonia, IEFC, dataset for Catalonia, north-east Iberian Peninsula, 31 900 km2). We simulated gaps at different missingness levels (10–80 %) in a complete trait matrix, and we used overall trait means, species means, k nearest neighbours (kNN), ordinary and regression kriging, and multivariate imputation using chained equations (MICE) to impute missing trait values. We assessed these methods in terms of their accuracy and of their ability to preserve trait distributions, multi-trait correlation structure and bivariate trait relationships. The relatively good performance of mean and species mean imputations in terms of accuracy masked a poor representation of trait distributions and multivariate trait structure. Species identity improved MICE imputations for all traits, whereas forest structure and topography improved imputations for some traits. No method performed best consistently for the five studied traits, but, considering all traits and performance metrics, MICE informed by relevant ecological variables gave the best results. However, at higher missingness (> 30 %), species mean imputations and regression kriging tended to outperform MICE for some traits. MICE informed by relevant ecological variables allowed us to fill the gaps in the IEFC incomplete dataset (5495 plots) and quantify imputation uncertainty. Resulting spatial patterns of the studied traits in Catalan forests were broadly similar when using species means, regression kriging or the best-performing MICE application, but some important discrepancies were observed at the local level. Our results highlight the need to assess imputation quality beyond just imputation accuracy and show that including environmental information in statistical imputation approaches yields more plausible imputations in spatially explicit plant trait datasets.
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Persa, Reyna, Arthur Bernardeli, and Diego Jarquin. "Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi-Trait Approaches in Soybeans." Agriculture 10, no. 8 (July 22, 2020): 308. http://dx.doi.org/10.3390/agriculture10080308.
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The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information of the associated traits ([AT]; full: all traits observed for the same genotype; and partial: some traits observed for the same genotype) under an alternative single-trait model and the multi-trait approach. The alternative single-trait model included the information of the AT for yield prediction via the phenotypic covariances while the multi-trait model jointly analyzed all the traits. The performance of these strategies was assessed using the marker and phenotypic information from the Soybean Nested Association Mapping (SoyNAM) project observed in Nebraska in 2012. The results showed that the alternative single-trait strategy, which combines the marker and the information of the AT, outperforms the multi-trait model by around 12% and the conventional single-trait strategy (baseline) by 25%. When no information on the AT was available for those genotypes in the testing sets, the multi-trait model reduced the baseline results by around 6%. For the cases where genotypes were partially observed (i.e., some traits observed but not others for the same genotype), the multi-trait strategy showed improvements of around 6% for yield and between 2% to 9% for the other traits. Hence, when yield drives the selection of superior genotypes, the single-trait and multi-trait genomic prediction will achieve significant improvements when some genotypes have been fully or partially tested, with the alternative single-trait model delivering the best results. These results provide empirical evidence of the usefulness of the AT for improving the predictive ability of prediction models for breeding applications.
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Stolier, Ryan M., Eric Hehman, Matthias D. Keller, Mirella Walker, and Jonathan B. Freeman. "The conceptual structure of face impressions." Proceedings of the National Academy of Sciences 115, no. 37 (August 23, 2018): 9210–15. http://dx.doi.org/10.1073/pnas.1807222115.
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Humans seamlessly infer the expanse of personality traits from others’ facial appearance. These facial impressions are highly intercorrelated within a structure known as “face trait space.” Research has extensively documented the facial features that underlie face impressions, thus outlining a bottom-up fixed architecture of face impressions, which cannot account for important ways impressions vary across perceivers. Classic theory in impression formation emphasized that perceivers use their lay conceptual beliefs about how personality traits correlate to form initial trait impressions, for instance, where trustworthiness of a target may inform impressions of their intelligence to the extent one believes the two traits are related. This considered, we explore the possibility that this lay “conceptual trait space”—how perceivers believe personality traits correlate in others—plays a role in face impressions, tethering face impressions to one another, thus shaping face trait space. In study 1, we found that conceptual and face trait space explain considerable variance in each other. In study 2, we found that participants with stronger conceptual associations between two traits judged those traits more similarly in faces. Importantly, using a face image classification task, we found in study 3 that participants with stronger conceptual associations between two traits used more similar facial features to make those two face trait impressions. Together, these findings suggest lay beliefs of how personality traits correlate may underlie trait impressions, and thus face trait space. This implies face impressions are not only derived bottom up from facial features, but also shaped by our conceptual beliefs.
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Bhatia, Sudeep, Geoffrey P. Goodwin, and Lukasz Walasek. "Trait Associations for Hillary Clinton and Donald Trump in News Media." Social Psychological and Personality Science 9, no. 2 (January 16, 2018): 123–30. http://dx.doi.org/10.1177/1948550617751584.
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We study media representations of Hillary Clinton and Donald Trump in the 2016 U.S. presidential election. In particular, we train models of semantic memory on a large number of news media outlets that published online articles during the course of the election. Based on the structure of word co-occurrence in these media outlets, our models learn semantic representations for the two presidential candidates as well as for widely studied personality traits. We find that models trained on media outlets most read by Clinton voters and media outlets most read by Trump voters differ in the strength of association between the two candidates’ names and trait words pertaining to morality. We observe some differences for trait words pertaining to warmth, but none for trait words pertaining to competence.
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Yamamichi, Masato, and Stephen P. Ellner. "Antagonistic coevolution between quantitative and Mendelian traits." Proceedings of the Royal Society B: Biological Sciences 283, no. 1827 (March 30, 2016): 20152926. http://dx.doi.org/10.1098/rspb.2015.2926.
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Coevolution is relentlessly creating and maintaining biodiversity and therefore has been a central topic in evolutionary biology. Previous theoretical studies have mostly considered coevolution between genetically symmetric traits (i.e. coevolution between two continuous quantitative traits or two discrete Mendelian traits). However, recent empirical evidence indicates that coevolution can occur between genetically asymmetric traits (e.g. between quantitative and Mendelian traits). We examine consequences of antagonistic coevolution mediated by a quantitative predator trait and a Mendelian prey trait, such that predation is more intense with decreased phenotypic distance between their traits (phenotype matching). This antagonistic coevolution produces a complex pattern of bifurcations with bistability (initial state dependence) in a two-dimensional model for trait coevolution. Furthermore, with eco-evolutionary dynamics (so that the trait evolution affects predator–prey population dynamics), we find that coevolution can cause rich dynamics including anti-phase cycles, in-phase cycles, chaotic dynamics and deterministic predator extinction. Predator extinction is more likely to occur when the prey trait exhibits complete dominance rather than semidominance and when the predator trait evolves very rapidly. Our study illustrates how recognizing the genetic architectures of interacting ecological traits can be essential for understanding the population and evolutionary dynamics of coevolving species.
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Yao, Liangjin, Yi Ding, Lan Yao, Xunru Ai, and Runguo Zang. "Trait Gradient Analysis for Evergreen and Deciduous Species in a Subtropical Forest." Forests 11, no. 4 (March 25, 2020): 364. http://dx.doi.org/10.3390/f11040364.
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Leaf habits (e.g., evergreen or deciduous) can reflect strategies of species adapting to varied environmental conditions. However, how species with different leaf habits coexist within a plant community is still poorly understood. Trait gradient analysis is a new approach to partition plant functional trait variations into alpha (within-community) and beta (among-community) components to quantify the effects of environmental filtering and biotic competition on community assembly. Here, on the basis of establishing forty-eight forest dynamic plots in a subtropical evergreen and deciduous broadleaved mixed forest in central China and measuring of seven functional traits, we compared the trait variation patterns and influencing factors of evergreen and deciduous species by using the trait gradient analysis method. The results showed that there were significant differences between functional traits for evergreen and deciduous species. Alpha trait components consistently varied more widely than beta components. The correlation between species trait mean and the alpha of each trait was highly significant, but there was no significant correlation between beta and alpha trait values. There were relatively weak or nonexistent significant correlations among species mean trait values and alpha trait values of different functional traits. However, the beta trait value showed high and significant correlations in both evergreen and deciduous species. Our results indicated that evergreen and deciduous species adopt similar adaptation strategies (beta component) in the context of environmental change in the community. However, the species initially came to coexist via the ecological positioning of traits (alpha component), which helped reduce competition so individuals could obtain more resources.
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Trugman, Anna T., Leander D. L. Anderegg, John D. Shaw, and William R. L. Anderegg. "Trait velocities reveal that mortality has driven widespread coordinated shifts in forest hydraulic trait composition." Proceedings of the National Academy of Sciences 117, no. 15 (March 30, 2020): 8532–38. http://dx.doi.org/10.1073/pnas.1917521117.
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Understanding the driving mechanisms behind existing patterns of vegetation hydraulic traits and community trait diversity is critical for advancing predictions of the terrestrial carbon cycle because hydraulic traits affect both ecosystem and Earth system responses to changing water availability. Here, we leverage an extensive trait database and a long-term continental forest plot network to map changes in community trait distributions and quantify “trait velocities” (the rate of change in community-weighted traits) for different regions and different forest types across the United States from 2000 to the present. We show that diversity in hydraulic traits and photosynthetic characteristics is more related to local water availability than overall species diversity. Finally, we find evidence for coordinated shifts toward communities with more drought-tolerant traits driven by tree mortality, but the magnitude of responses differs depending on forest type. The hydraulic trait distribution maps provide a publicly available platform to fundamentally advance understanding of community trait change in response to climate change and predictive abilities of mechanistic vegetation models.
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Alves, Rodrigo, João Rocha, Larissa Teodoro, Luiz Carvalho, Francisco Farias, Marcos Resende, Leonardo Bhering, and Paulo Teodoro. "Path analysis under multiple-trait BLUP: application in the study of interrelationships among traits related to cotton fiber length." Revista de la Facultad de Ciencias Agrarias UNCuyo 53, no. 1 (July 7, 2021): 1–10. http://dx.doi.org/10.48162/rev.39.001.
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Multi-trait best linear unbiased prediction (BLUP) is, generally, the most appropriate method to genetic evaluation because it considers the genetic and residual correlations among traits and conduct to higher selection accuracy. Thus, the present study aimed to identify traits correlated to the fiber length via path analysis under multi-trait BLUP for the cotton breeding. To this end, thirty-six elite lines were evaluated in three environments and phenotyped for many traits related to fiber quality and agronomic traits. Variance components were estimated via residual maximum likelihood (REML). The genetic correlation coefficients among traits were obtained through mixed model output, and to graphically express these results a correlation network was built. Subsequently, we performed path analysis considering fiber length as a principal dependent variable. Genetic parameters obtained by multi-trait BLUP model indicate that the phenotypic variance for most traits is mostly composed of residual effects, which reinforces the need for using more accurate statistical methods such as multi-trait BLUP. The results found for genetic correlations and path analysis under multi-trait BLUP reveal the difficulty of selection based on important fiber quality traits, especially fiber length, since most traits show very low cause-and-effect relationship, and other important traits present undesirable cause-and-effect relationship.
Highlights
Multiple-trait BLUP is the most appropriate method to predict genetic values.
This is the first study in cotton to perform path analysis under multiple-trait BLUP.
The findings of this study indicate that there is no genotype presenting all desirable traits.
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Krupová, Zuzana, Emil Krupa, Ludmila Zavadilová, Eva Kašná, and Eliska Žáková. "Current challenges for trait economic values in animal breeding." Czech Journal of Animal Science 65, No. 12 (December 21, 2020): 454–62. http://dx.doi.org/10.17221/161/2020-cjas.
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Modern selection approaches are expected to bring about the cumulative and permanent improvement of animal performance and profitability of animal production. Breeding values of traits along with trait economic values (EVs) are utilised for economic selection purposes with many species all over the world. Currently, some challenges related to trait EVs in animal breeding should be considered. First, the selection response based on the higher accuracy of genomic selection may be reduced due to improper weighting of the trait breeding values of selection candidates. A comprehensive approach applied in bioeconomic models allows suitable trait EV calculations. Further challenges comprise the new breeding objectives associated with climate change, environmental mitigation and animal adaptability. The estimation of EVs for traits influencing greenhouse gas (GHG) emissions has been mostly based on including the value of CO<sub>2</sub> emission equivalent in the trait EVs, on calculating EVs for feed efficiency traits and on methane yield as a direct trait of GHG emission. Genetic improvement of production, functional, feed efficiency and methane traits through the application of multi-trait selection indices was found to be crucial for mitigation of emissions and farm profitability. Defining the non-market values of traits connected with climate protection could be a useful solution for including these traits in an economic breeding objective. While GHG emissions mostly change the costs per unit of production, animal adaptability in its complexity influences animal performance. Clear definitions of disease, fertility, mortality and other breeding objective traits allow the proper calculation of trait EVs, and an accurate estimation of trait genetic parameters could lead to sufficient economic selection response. This complex approach could be beneficial for more effective utilisation of inputs and overall economic and environmental sustainability of animal production.
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Snowder, G. D., and N. M. Fogarty. "Composite trait selection to improve reproduction and ewe productivity: a review." Animal Production Science 49, no. 1 (2009): 9. http://dx.doi.org/10.1071/ea08184.
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Reproduction and ewe productivity are complex composite traits that are influenced by several component traits. Genetic improvement by selection for an individual component trait may not always be advantageous because adverse or neutral genetic relationships can exist among the component traits. Selection for an overall composite trait of ewe productivity, defined as litter weight weaned per ewe joined, can result in a balanced biological composite trait with favourable responses in component traits including fertility, number of lambs born, lamb survival, lactation and lamb growth. Selection for litter weight weaned may also overcome adverse genetic relationships among reproductive component traits. This review examines the genetic responses of selecting directly for litter weight weaned, compared with selection responses for one or more of its component traits. It is concluded that selection to improve reproductive efficiency and ewe productivity, under most production and environmental systems, would benefit from selection for a composite trait such as litter weight weaned, rather than for a single component trait.
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Nédellec, Claire, Clara Sauvion, Robert Bossy, Mariya Borovikova, and Louise Deléger. "TaeC: A manually annotated text dataset for trait and phenotype extraction and entity linking in wheat breeding literature." PLOS ONE 19, no. 6 (June 13, 2024): e0305475. http://dx.doi.org/10.1371/journal.pone.0305475.
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Wheat varieties show a large diversity of traits and phenotypes. Linking them to genetic variability is essential for shorter and more efficient wheat breeding programs. A growing number of plant molecular information networks provide interlinked interoperable data to support the discovery of gene-phenotype interactions. A large body of scientific literature and observational data obtained in-field and under controlled conditions document wheat breeding experiments. The cross-referencing of this complementary information is essential. Text from databases and scientific publications has been identified early on as a relevant source of information. However, the wide variety of terms used to refer to traits and phenotype values makes it difficult to find and cross-reference the textual information, e.g. simple dictionary lookup methods miss relevant terms. Corpora with manually annotated examples are thus needed to evaluate and train textual information extraction methods. While several corpora contain annotations of human and animal phenotypes, no corpus is available for plant traits. This hinders the evaluation of text mining-based crop knowledge graphs (e.g. AgroLD, KnetMiner, WheatIS-FAIDARE) and limits the ability to train machine learning methods and improve the quality of information. The Triticum aestivum trait Corpus is a new gold standard for traits and phenotypes of wheat. It consists of 528 PubMed references that are fully annotated by trait, phenotype, and species. We address the interoperability challenge of crossing sparse assay data and publications by using the Wheat Trait and Phenotype Ontology to normalize trait mentions and the species taxonomy of the National Center for Biotechnology Information to normalize species. The paper describes the construction of the corpus. A study of the performance of state-of-the-art language models for both named entity recognition and linking tasks trained on the corpus shows that it is suitable for training and evaluation. This corpus is currently the most comprehensive manually annotated corpus for natural language processing studies on crop phenotype information from the literature.
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Hambrecht, Leonard, Arko Lucieer, Zbyněk Malenovský, Bethany Melville, Ana Patricia Ruiz-Beltran, and Stuart Phinn. "Considerations for Assessing Functional Forest Diversity in High-Dimensional Trait Space Derived from Drone-Based Lidar." Remote Sensing 14, no. 17 (August 30, 2022): 4287. http://dx.doi.org/10.3390/rs14174287.
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Remotely sensed morphological traits have been used to assess functional diversity of forests. This approach is potentially spatial-scale-independent. Lidar data collected from the ground or by drone at a high point density provide an opportunity to consider multiple ecologically meaningful traits at fine-scale ecological units such as individual trees. However, high-spatial-resolution and multi-trait datasets used to calculate functional diversity can produce large volumes of data that can be computationally resource demanding. Functional diversity can be derived through a trait probability density (TPD) approach. Computing TPD in a high-dimensional trait space is computationally intensive. Reductions of the number of dimensions through trait selection and principal component analysis (PCA) may reduce the computational load. Trait selection can facilitate identification of ecologically meaningful traits and reduce inter-trait correlation. This study investigates whether kernel density estimator (KDE) or one-class support vector machine (SVM) may be computationally more efficient in calculating TPD. Four traits were selected for input into the TPD: canopy height, effective number of layers, plant to ground ratio, and box dimensions. When simulating a high-dimensional trait space, we found that TPD derived from KDE was more efficient than using SVM when the number of input traits was high. For five or more traits, applying dimension reduction techniques (e.g., PCA) are recommended. Furthermore, the kernel size for TPD needs to be appropriate for the ecological target unit and should be appropriate for the number of traits. The kernel size determines the required number of data points within the trait space. Therefore, 3–5 traits require a kernel size of at least 7×7pixels. This study contributes to improving the quality of TPD calculations based on traits derived from remote sensing data. We provide a set of recommendations based on our findings. This has the potential to improve reliability in identifying biodiversity hotspots.
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Taraszka, Kodi, Noah Zaitlen, and Eleazar Eskin. "Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations." PLOS Genetics 18, no. 11 (November 7, 2022): e1010447. http://dx.doi.org/10.1371/journal.pgen.1010447.
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We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.
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Krah, Franz-Sebastian, and Claus Bässler. "What can intraspecific trait variability tell us about fungal communities and adaptations?" Mycological Progress 20, no. 7 (July 2021): 905–10. http://dx.doi.org/10.1007/s11557-021-01708-6.
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AbstractAnalyses of species functional traits are suitable to better understand the coexistence of species in a given environment. Trait information can be applied to investigate diversity patterns along environmental gradients and subsequently to predict and mitigate threats associated with climate change and land use. Species traits are used to calculate community trait means, which can be related to environmental gradients. However, while species traits can provide insights into the mechanisms underlying community assembly, they can lead to erroneous inferences if mean trait values are used. An alternative is to incorporate intraspecific trait variability (ITV) into calculating the community trait means. This approach gains increasing acceptance in plant studies. For macrofungi, functional traits have recently been applied to examine their community ecology but, to our knowledge, ITV has yet to be incorporated within the framework of community trait means. Here, we present a conceptual summary of the use of ITV to investigate the community ecology of macrofungi, including the underlying ecological theory. Inferences regarding community trait means with or without the inclusion of ITV along environmental gradients are compared. Finally, an existing study is reconsidered to highlight the variety of possible outcomes when ITV is considered. We hope this Opinion will increase awareness of the potential for within-species trait variability and its importance for statistical inferences, interpretations, and predictions of the mechanisms structuring communities of macro- and other fungi.
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Esvelt Klos, Kathy, Belayneh A. Yimer, Catherine J. Howarth, Michael S. McMullen, Mark E. Sorrells, Nicholas A. Tinker, Weikai Yan, and Aaron D. Beattie. "The Genetic Architecture of Milling Quality in Spring Oat Lines of the Collaborative Oat Research Enterprise." Foods 10, no. 10 (October 16, 2021): 2479. http://dx.doi.org/10.3390/foods10102479.
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Most oat grains destined for human consumption must possess the ability to pass through an industrial de-hulling process with minimal breakage and waste. Uniform grain size and a high groat to hull ratio are desirable traits related to milling performance. The purpose of this study was to characterize the genetic architecture of traits related to milling quality by identifying quantitative trait loci (QTL) contributing to variation among a diverse collection of elite and foundational spring oat lines important to North American oat breeding programs. A total of 501 lines from the Collaborative Oat Research Enterprise (CORE) panel were evaluated for genome-wide association with 6 key milling traits. Traits were evaluated in 13 location years. Associations for 36,315 markers were evaluated for trait means across and within location years, as well as trait variance across location years, which was used to assess trait stability. Fifty-seven QTL influencing one or more of the milling quality related traits were identified, with fourteen QTL mapped influencing mean and variance across location years. The most prominent QTL was Qkernel.CORE.4D on chromosome 4D at approximately 212 cM, which influenced the mean levels of all traits. QTL were identified that influenced trait variance but not mean, trait mean only and both.
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Nakao, Takashi, and Makoto Miyatani. "Affective Integration Speeds Trait Processing." Psychological Reports 97, no. 3 (December 2005): 810–18. http://dx.doi.org/10.2466/pr0.97.3.810-818.
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We investigated whether affective integration increases the speed of processing of personality trait knowledge. The fan effect was compared between cases where trait knowledge is stored with the affective value and cases where it is not stored with the affective value. 18 college students first memorized a set of traits about fictitious individuals and then made recognition judgments. In the 2 × 2 factorial repeated-measures design, the number of traits learned about a fictitious individual and whether those traits were integrated by a shared affective value were manipulated. The significant interaction showed that knowledge of personality trait with affective integration was processed quickly even if the particular person's memory had rich connections with traits.
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Li, Yuling, Yongbin Dong, Suzhun Niu, and Dangqun Cui. "The genetic relationship among plant-height traits found using multiple-trait QTL mapping of a dent corn and popcorn cross." Genome 50, no. 4 (April 2007): 357–64. http://dx.doi.org/10.1139/g07-018.
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Plant height (PH) is one of the most important traits in maize breeding programs. In popcorn, inferior plant traits can be improved with the dent/flint corn germplasm. In the current study, a total of 259 F2:3 families, developed from a cross between a dent corn inbred and a popcorn inbred, were evaluated for 4 PH traits. Quantitative trait loci (QTLs) for each trait were detected using composite interval mapping methods. In addition, genetic interrelationships were investigated using multiple-trait joint analysis for PH with ear height (EH), and for PH with top height (TH). In total, 6, 5, 2, and 6 QTLs were identified for PH, EH, TH, and TH/PH in single-trait analysis, respectively. Joint-analysis data suggest a strong and complex genetic relationship between PH and EH, and between PH and EH, with no QTLs controlling any single trait independently. In addition, 4 kinds of QTLs detected were classified as closely linked QTLs, pleiotropic QTLs, QTLs with opposite effects, and additional QTLs. It was, consequently, difficult to improve lodge resistance through selection on any individual PH trait. The current study demonstrates that multiple-trait joint analysis not only identified additional QTLs, but also revealed the genetic relationship among different highly correlated traits at the molecular level.
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Zhou, Yan, Aaron Kusmec, Seyed Vahid Mirnezami, Lakshmi Attigala, Srikant Srinivasan, Talukder Z. Jubery, James C. Schnable, Maria G. Salas-Fernandez, Baskar Ganapathysubramanian, and Patrick S. Schnable. "Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping." Plant Cell 33, no. 8 (May 20, 2021): 2562–82. http://dx.doi.org/10.1093/plcell/koab134.
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Abstract The accuracy of trait measurements greatly affects the quality of genetic analyses. During automated phenotyping, trait measurement errors, i.e. differences between automatically extracted trait values and ground truth, are often treated as random effects that can be controlled by increasing population sizes and/or replication number. In contrast, there is some evidence that trait measurement errors may be partially under genetic control. Consistent with this hypothesis, we observed substantial nonrandom, genetic contributions to trait measurement errors for five maize (Zea mays) tassel traits collected using an image-based phenotyping platform. The phenotyping accuracy varied according to whether a tassel exhibited “open” versus. “closed” branching architecture, which is itself under genetic control. Trait-associated SNPs (TASs) identified via genome-wide association studies (GWASs) conducted on five tassel traits that had been phenotyped both manually (i.e. ground truth) and via feature extraction from images exhibit little overlap. Furthermore, identification of TASs from GWASs conducted on the differences between the two values indicated that a fraction of measurement error is under genetic control. Similar results were obtained in a sorghum (Sorghum bicolor) plant height dataset, demonstrating that trait measurement error is genetically determined in multiple species and traits. Trait measurement bias cannot be controlled by increasing population size and/or replication number.
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Muscarella, Robert, and María Uriarte. "Do community-weighted mean functional traits reflect optimal strategies?" Proceedings of the Royal Society B: Biological Sciences 283, no. 1827 (March 30, 2016): 20152434. http://dx.doi.org/10.1098/rspb.2015.2434.
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The notion that relationships between community-weighted mean (CWM) traits (i.e. plot-level trait values weighted by species abundances) and environmental conditions reflect selection towards locally optimal phenotypes is challenged by the large amount of interspecific trait variation typically found within ecological communities. Reconciling these contrasting patterns is a key to advancing predictive theories of functional community ecology. We combined data on geographical distributions and three traits (wood density, leaf mass per area and maximum height) of 173 tree species in Puerto Rico. We tested the hypothesis that species are more likely to occur where their trait values are more similar to the local CWM trait values (the ‘ CWM-optimality’ hypothesis) by comparing species occurrence patterns (as a proxy for fitness) with the functional composition of forest plots across a precipitation gradient. While 70% of the species supported CWM-optimality for at least one trait, nearly 25% significantly opposed it for at least one trait, thereby contributing to local functional diversity. The majority (85%) of species that opposed CWM-optimality did so only for one trait and few species opposed CWM-optimality in multivariate trait space. Our study suggests that constraints to local functional variation act more strongly on multivariate phenotypes than on univariate traits.
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Standing, Lionel, and Gregory Keays. "COMPUTER ASSESSMENT OF PERSONALITY: A DEMONSTRATION OF GULLIBILITY." Social Behavior and Personality: an international journal 14, no. 2 (January 1, 1986): 197–202. http://dx.doi.org/10.2224/sbp.1986.14.2.197.
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Subjects (N= 64) were shown a list of 66 personality traits, and indicated whether or not they possessed each one. Three weeks later, they were shown their trait list, another subject's trait list, a computerized personality profile derived from their traits, or a profile derived from another subject's traits. In each case they rated how well their personality was described by the trait list or profile, on three seven point scales. It was found that the spurious personality descriptions (both traits and profiles) were rated as highly for accuracy as were the genuine descriptions. Interpretive personality profiles were rated no higher than simple lists of trait names. It is argued that these results should be attributed to general gullibility rather than, e.g., the impressiveness of psychological tests.
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Degen, Renate, and Sarah Faulwetter. "The Arctic Traits Database – a repository of Arctic benthic invertebrate traits." Earth System Science Data 11, no. 1 (February 25, 2019): 301–22. http://dx.doi.org/10.5194/essd-11-301-2019.
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Abstract. The recently increased interest in marine trait-based studies highlights one general demand – the access to standardized, reference-based trait information. This demand holds especially true for polar regions, where the gathering of ecological information is still challenging. The Arctic Traits Database is a freely accessible online repository (https://doi.org/10.25365/phaidra.49; https://www.univie.ac.at/arctictraits, last access: 20 February 2019) that fulfils these requests for one important component of polar marine life, the Arctic benthic macroinvertebrates. It accounts for (1) obligate traceability of information (every entry is linked to at least one source), (2) exchangeability among trait platforms (use of most common download formats), (3) standardization (use of most common terminology and coding scheme) and (4) user-friendliness (granted by an intuitive web interface and rapid and easy download options, for the first time including the option to download a fuzzy coded trait matrix). The combination of these aspects makes the Arctic Traits Database the currently most sophisticated online accessible trait platform in (not only) marine ecology and a role model for prospective databases of other marine compartments or other (also non-marine) ecosystems. At present the database covers 19 traits (80 trait categories) and holds altogether 14 242 trait entries for 1911 macro- and megabenthic taxa. Thus, the Arctic Traits Database will foster and facilitate trait-based approaches in polar regions in the future and increase our ecological understanding of this rapidly changing system.
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Khan, M. S., K. Z. Gondal, S. H. Raza, and A. A. Asghar. "First Lactation Genetic Parameters of Buffaloes Under Multiple Trait Animal Model." Journal of Agricultural and Marine Sciences [JAMS] 2 (January 1, 1997): 27. http://dx.doi.org/10.24200/jams.vol2iss0pp27-30.
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There are many computational algorithms available for estimating (co) variance components under multiple trait models. Paternal half-sib correlation is the most commonly used method for estimating genetic parameters of economic traits of buffaloes. The models used are Single trait, ignoring covariances with other traits. The relationship matrices are also ignored. This study was undertaken to estimate genetic parameters of first lactation traits of Nili Ravi buffaloes under a multiple trait animal model. First lactation milk yield was l7% heritable when traits such as age of calving interval were considered. Age at first calving was 18% heritable with very low genetic correlations with other traits. Lactation length, dry period and calving interval were lowly heritable traits. Estimates differed by inclusion or exclusion of traits due to the covariances present among all these traits. A high genetic correlation between mills yield and lactation length (0.74) does not warrant the selection of milk yield as the only trait.
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Chen, Shu-Yueh, and Hui-Chen Hsu. "Nurses’ reflections on good nurse traits." Nursing Ethics 22, no. 7 (September 22, 2014): 790–802. http://dx.doi.org/10.1177/0969733014547973.
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Background: Good nurses show concern for patients by caring for them effectively and attentively to foster their well-being. However, nurses cannot be taught didactically to be “good” or any trait that characterizes a good nurse. Nurses’ self-awareness of their role traits warrants further study. Objectives: This study aimed (a) to develop a strategy to elicit nurses’ self-exploration of the importance of good nurse traits and (b) to explore any discrepancies between such role traits perceived by nurses as ideally and actually important. Research design: For this mixed-method study, we used good nurse trait card play to trigger nurses’ reflections based on clinical practice. Nurse participants appraised the ideal and actual importance of each trait using a Q-sort grid. The gap between the perceived ideal and actual importance of each trait was examined quantitatively, while trait-related clinical experiences were analyzed qualitatively. Participants and research context: Participants were 35 in-service nurses (mean age = 31.6 years (range = 23–49 years); 10.1 years of nursing experience (range = 1.5–20 years)) recruited from a teaching hospital in Taiwan. Ethical considerations: The study was approved by the Institutional Review Board of the study site. Findings: Good nurse trait card play with a Q-sort grid served as an icebreaker to help nurse participants talk about their experiences as embodied in good quality nursing care. Nurses’ perceived role–trait discrepancies were divided into three categories: over-performed, least discrepant, and under-performed. The top over-performed trait was “obedience.” Discussion: Patients’ most valued traits (“patient,” “responsible,” “cautious,” and “considerate”) were perceived by participants as ideally important but were under-performed, perhaps due to experienced nurses’ loss of idealism. Conclusion: Good nurse trait card play with Q-sort grid elicited nurses’ self-dialogue and revealed evidence of the incongruity between nurses’ perceived ideal and actual importance of traits. The top over-performed trait, “obedience,” deserves more study.
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Kazemi, Baharan, Gabriella Gamberale-Stille, and Olof Leimar. "Multi-trait mimicry and the relative salience of individual traits." Proceedings of the Royal Society B: Biological Sciences 282, no. 1818 (November 7, 2015): 20152127. http://dx.doi.org/10.1098/rspb.2015.2127.
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Mimicry occurs when one species gains protection from predators by resembling an unprofitable model species. The degree of mimic–model similarity is variable in nature and is closely related to the number of traits that the mimic shares with its model. Here, we experimentally test the hypothesis that the relative salience of traits, as perceived by a predator, is an important determinant of the degree of mimic–model similarity required for successful mimicry. We manipulated the relative salience of the traits of a two-trait artificial model prey, and subsequently tested the survival of mimics of the different traits. The unrewarded model prey had two colour traits, black and blue, and the rewarded prey had two combinations of green, brown and grey shades. Blue tits were used as predators. We found that the birds perceived the black and blue traits to be similarly salient in one treatment, and mimic–model similarity in both traits was then required for high mimic success. In a second treatment, the blue trait was the most salient trait, and mimic–model similarity in this trait alone achieved high success. Our results thus support the idea that similar salience of model traits can explain the occurrence of multi-trait mimicry.
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Jauk, Emanuel, H. Harald Freudenthaler, and Aljoscha C. Neubauer. "The Dark Triad and Trait Versus Ability Emotional Intelligence." Journal of Individual Differences 37, no. 2 (April 2016): 112–18. http://dx.doi.org/10.1027/1614-0001/a000195.
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Abstract. A growing body of studies investigates emotional intelligence (EI) in relation to the Dark Triad (DT) personality traits. DT traits seem to be generally associated with lower EI, but findings are still inconsistent, especially with respect to narcissism. The vast majority of studies investigating EI correlates of DT traits relied on self-report measures in terms of trait EI. Currently, there are only sparse reports of relationships between DT traits, trait EI, and performance measures of ability EI. Thus, we investigated the relationships between these constructs in a sample of N = 540 individuals. We performed analyses separately for both sexes as recent research indicates that correlations might differ between women and men. Results showed that in women, reduced trait and ability EI are linked to psychopathy, but not the other DT traits. In men, the pattern of results was more complex: Narcissism was primarily related to lower ability EI, whereas psychopathy was primarily associated with lower trait EI. Machiavellianism was related to higher levels of trait EI in men. These findings suggest that among the DT traits, psychopathy goes along with reduced EI in women, whereas in men narcissism is associated with lower ability EI.
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Seeley, Megan M., and Gregory P. Asner. "Large-Scale Controls on the Leaf Economic Spectrum of the Overstory Tree Species Metrosideros polymorpha." Remote Sensing 15, no. 19 (September 26, 2023): 4707. http://dx.doi.org/10.3390/rs15194707.
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The role of intraspecific trait variation in functional ecology has gained traction in recent years as many papers have observed its importance in driving community diversity and ecology. Yet much of the work in this field relies on field-based trait surveys. Here, we used continuous canopy trait information derived from remote sensing data of a highly polymorphic tree species, Metrosideros polymorpha, to quantify environmental controls on intraspecific trait variation. M. polymorpha, an endemic, keystone tree species in Hawai’i, varies morphologically, chemically, and genetically across broad elevation and soil substrate age gradients, making it an ideal model organism to explore large-scale environmental drivers of intraspecific trait variation. M. polymorpha canopy reflectance (visible to shortwave infrared; 380–2510 nm) and light detection and ranging (LiDAR) data collected by the Global Airborne Observatory were modeled to canopy trait estimates of leaf mass per area, chlorophyll a and b, carotenoids, total carbon, nitrogen, phosphorus, phenols, cellulose, and top of canopy height using previously developed leaf chemometric equations. We explored how these derived traits varied across environmental gradients by extracting elevation, slope, aspect, precipitation, and soil substrate age data at canopy locations. We then obtained the feature importance values of the environmental factors in predicting each leaf trait by training random forest models to predict leaf traits individually. Of these environmental factors, elevation was the most important predictor for all canopy traits. Elevation not only affected canopy traits directly but also indirectly by influencing the relationships between soil substrate age and canopy traits as well as between nitrogen and other traits, as indicated by the change in slope between the variables at different elevation ranges. In conclusion, intraspecific variation in M. polymorpha traits derived from remote sensing adheres to known leaf economic spectrum (LES) patterns as well as interspecific LES traits previously mapped using imaging spectroscopy.
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Petrides, K. V., Philip A. Vernon, Julie Aitken Schermer, and Livia Veselka. "Trait Emotional Intelligence and the Dark Triad Traits of Personality." Twin Research and Human Genetics 14, no. 1 (February 1, 2011): 35–41. http://dx.doi.org/10.1375/twin.14.1.35.
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This study presents the first behavioral genetic investigation of the relationships between trait emotional intelligence (trait EI or trait emotional self-efficacy) and the Dark Triad traits of narcissism, Machiavellianism, and psychopathy. In line with trait EI theory, the construct correlated positively with narcissism, but negatively with the other two traits. Generally, the correlations were consistent across the 4 factors and 15 facets of the construct. Cholesky decomposition analysis revealed that the phenotypic associations were primarily due to correlated genetic factors and secondarily due to correlated nonshared environmental factors, with shared environmental factors being nonsignificant in all cases. Results are discussed from the perspective of trait EI theory with particular reference to the issue of adaptive value.
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Ellis, Allan G., Samuel F. Brockington, Marinus L. de Jager, Gregory Mellers, Rachel H. Walker, and Beverley J. Glover. "Floral trait variation and integration as a function of sexual deception in Gorteria diffusa." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1649 (August 19, 2014): 20130563. http://dx.doi.org/10.1098/rstb.2013.0563.
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Phenotypic integration, the coordinated covariance of suites of morphological traits, is critical for proper functioning of organisms. Angiosperm flowers are complex structures comprising suites of traits that function together to achieve effective pollen transfer. Floral integration could reflect shared genetic and developmental control of these traits, or could arise through pollinator-imposed stabilizing correlational selection on traits. We sought to expose mechanisms underlying floral trait integration in the sexually deceptive daisy, Gorteria diffusa , by testing the hypothesis that stabilizing selection imposed by male pollinators on floral traits involved in mimicry has resulted in tighter integration. To do this, we quantified patterns of floral trait variance and covariance in morphologically divergent G. diffusa floral forms representing a continuum in the levels of sexual deception. We show that integration of traits functioning in visual attraction of male pollinators increases with pollinator deception, and is stronger than integration of non-mimicry trait modules. Consistent patterns of within-population trait variance and covariance across floral forms suggest that integration has not been built by stabilizing correlational selection on genetically independent traits. Instead pollinator specialization has selected for tightened integration within modules of linked traits. Despite potentially strong constraint on morphological evolution imposed by developmental genetic linkages between traits, we demonstrate substantial divergence in traits across G. diffusa floral forms and show that divergence has often occurred without altering within-population patterns of trait correlations.
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Firn, Jennifer, Huong Nguyen, Martin Schütz, and Anita C. Risch. "Leaf trait variability between and within subalpine grassland species differs depending on site conditions and herbivory." Proceedings of the Royal Society B: Biological Sciences 286, no. 1907 (July 24, 2019): 20190429. http://dx.doi.org/10.1098/rspb.2019.0429.
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Plant traits are commonly used to predict ecosystem-level processes, but the validity of such predictions is dependent on the assumption that trait variability between species is greater than trait variability within a species—the robustness assumption. Here, we compare leaf trait intraspecific and interspecific variability depending on geographical differences between sites and 5 years of experimental herbivore exclusion in two vegetation types of subalpine grasslands in Switzerland. Four leaf traits were measured from eight herbaceous species common to all 18 sites. Intraspecific trait variability differed significantly depending on site and herbivory. However, the amount and structure of variability depended on the trait measured and whether considering leaf traits separately or multiple leaf traits simultaneously. Leaf phosphorus concentration showed the highest intraspecific variability, while specific leaf area showed the highest interspecific variability and displayed intraspecific variability only in response to herbivore exclusion. Species identity based on multiple traits was not predictable. We find intraspecific variability is an essential consideration when using plant functional traits as a common currency not just species mean traits. This is particularly true for leaf nutrient concentrations, which showed high intraspecific variability in response to site differences and herbivore exclusion, a finding which suggests that the robustness assumption does not always hold.
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Korol, A. B., Y. I. Ronin, and V. M. Kirzhner. "Interval mapping of quantitative trait loci employing correlated trait complexes." Genetics 140, no. 3 (July 1, 1995): 1137–47. http://dx.doi.org/10.1093/genetics/140.3.1137.
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Abstract An approach to increase the resolution power of interval mapping of quantitative trait (QT) loci is proposed, based on analysis of correlated trait complexes. For a given set of QTs, the broad sense heritability attributed to a QT locus (QTL) (say, A/a) is an increasing function of the number of traits. Thus, for some traits x and y, H(xy)2(A/a) > or = H(x)2(A/a). The last inequality holds even if y does not depend on A/a at all, but x and y are correlated within the groups AA, Aa and aa due to nongenetic factors and segregation of genes from other chromosomes. A simple relationship connects H2 (both in single trait and two-trait analysis) with the expected LOD value, ELOD = -1/2N log(1-H2). Thus, situations could exist that from the inequality H(xy)2(A/a) > or = H(x)2(A/a) a higher resolution is provided by the two-trait analysis as compared to the single-trait analysis, in spite of the increased number of parameters. Employing LOD-score procedure to simulated backcross data, we showed that the resolution power of the QTL mapping model can be elevated if correlation between QTs is taken into account. The method allows us to test numerous biologically important hypotheses concerning manifold effects of genomic segments on the defined trait complex (means, variances and correlations).
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McLean, Matthew, Rick D. Stuart-Smith, Sébastien Villéger, Arnaud Auber, Graham J. Edgar, M. Aaron MacNeil, Nicolas Loiseau, Fabien Leprieur, and David Mouillot. "Trait similarity in reef fish faunas across the world’s oceans." Proceedings of the National Academy of Sciences 118, no. 12 (March 15, 2021): e2012318118. http://dx.doi.org/10.1073/pnas.2012318118.
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Species’ traits, rather than taxonomic identities, determine community assembly and ecosystem functioning, yet biogeographic patterns have been far less studied for traits. While both environmental conditions and evolutionary history shape trait biogeography, their relative contributions are largely unknown for most organisms. Here, we explore the global biogeography of reef fish traits for 2,786 species from 89 ecoregions spanning eight marine realms with contrasting environmental conditions and evolutionary histories. Across realms, we found a common structure in the distribution of species traits despite a 10-fold gradient in species richness, with a defined “backbone” of 21 trait combinations shared by all realms globally, both temperate and tropical. Across ecoregions, assemblages under similar environmental conditions had similar trait compositions despite hosting drastically different species pools from separate evolutionary lineages. Thus, despite being separated by thousands of kilometers and millions of years of evolution, similar environments host similar trait compositions in reef fish assemblages worldwide. Our findings suggest that similar trait-based management strategies can be applied among regions with distinct species pools, potentially improving conservation outcomes across diverse jurisdictions.
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Jiang, Daohan, and Jianzhi Zhang. "Detecting natural selection in trait-trait coevolution." BMC Ecology and Evolution 23, no. 1 (September 12, 2023). http://dx.doi.org/10.1186/s12862-023-02164-4.
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AbstractNo phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general.
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Nolting, Kristen M., Rachel Prunier, Guy F. Midgley, and Kent E. Holsinger. "Intraspecific trait variation influences physiological performance and fitness in the South Africa shrub genus Protea (Proteaceae)." Annals of Botany, April 6, 2020. http://dx.doi.org/10.1093/aob/mcaa060.
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Abstract Background and Aims Global plant trait datasets commonly identify trait relationships that are interpreted to reflect fundamental trade-offs associated with plant strategies, but often these trait relationships are not identified when evaluating them at smaller taxonomic and spatial scales. In this study we evaluate trait relationships measured on individual plants for five widespread Protea species in South Africa to determine whether broad-scale patterns of structural trait (e.g. leaf area) and physiological trait (e.g. photosynthetic rates) relationships can be detected within natural populations, and if these traits are themselves related to plant fitness. Methods We evaluated the variance structure (i.e. the proportional intraspecific trait variation relative to among-species variation) for nine structural traits and six physiological traits measured in wild populations. We used a multivariate path model to evaluate the relationships between structural traits and physiological traits, and the relationship between these traits and plant size and reproductive effort. Key Results While intraspecific trait variation is relatively low for structural traits, it accounts for between 50 and 100 % of the variation in physiological traits. Furthermore, we identified few trait associations between any one structural trait and physiological trait, but multivariate regressions revealed clear associations between combinations of structural traits and physiological performance (R2 = 0.37–0.64), and almost all traits had detectable associations with plant fitness. Conclusions Intraspecific variation in structural traits leads to predictable differences in individual-level physiological performance in a multivariate framework, even though the relationship of any particular structural trait to physiological performance may be weak or undetectable. Furthermore, intraspecific variation in both structural and physiological traits leads to differences in plant size and fitness. These results demonstrate the importance of considering measurements of multivariate phenotypes on individual plants when evaluating trait relationships and how trait variation influences predictions of ecological and evolutionary outcomes.
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Pan, Yingji, Ellen Cieraad, Jean Armstrong, William Armstrong, Beverley R. Clarkson, Ole Pedersen, Eric J. W. Visser, Laurentius A. C. J. Voesenek, and Peter M. van Bodegom. "Leading trait dimensions in flood-tolerant plants." Annals of Botany, March 8, 2022. http://dx.doi.org/10.1093/aob/mcac031.
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Abstract Background and Aims While trait-based approaches have provided critical insights into general plant functioning, we lack a comprehensive quantitative view on plant strategies in flooded conditions. Plants adapted to flooded conditions have specific traits (e.g. root porosity, low root/shoot ratio and shoot elongation) to cope with the environmental stressors including anoxic sediments, and the subsequent presence of phytotoxic compounds. In flooded habitats, plants also respond to potential nutrient and light limitations, e.g. through the expression of leaf economics traits and size-related traits, respectively. However, we do not know whether and how these trait dimensions are connected. Methods Based on a trait dataset compiled on 131 plant species from 141 studies in flooded habitats, we quantitatively analysed how flooding-induced traits are positioned in relation to the other two dominant trait dimensions: leaf economics traits and size-related traits. We evaluated how these key trait components are expressed along wetness gradients, across habitat types and among plant life forms. Key Results We found that flooding-induced traits constitute a trait dimension independent from leaf economics traits and size-related traits, indicating that there is no generic trade-off associated with flooding adaptations. Moreover, individual flooding-induced traits themselves are to a large extent decoupled from each other. These results suggest that adaptation to stressful environments, such as flooding, can be stressor specific without generic adverse effects on plant functioning (e.g. causing trade-offs on leaf economics traits). Conclusions The trait expression across multiple dimensions promotes plant adaptations and coexistence across multifaceted flooded environments. The decoupled trait dimensions, as related to different environmental drivers, also explain why ecosystem functioning (including, for example, methane emissions) are species and habitat specific. Thus, our results provide a backbone for applying trait-based approaches in wetland ecology by considering flooding-induced traits as an independent trait dimension.
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Gutiérrez‐Cánovas, Cayetano, Rachel Stubbington, Daniel von Schiller, Rossano Bolpagni, Miriam Colls, Thibault Datry, Rafael Marcé, and Daniel Bruno. "Use of trait concepts and terminology in freshwater ecology: Historic, current, and future perspectives." Freshwater Biology, February 12, 2024. http://dx.doi.org/10.1111/fwb.14230.
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Abstract Trait‐based approaches have received increasing interest among freshwater scientists given their capacity to predict community structure and biodiversity effects on ecosystem functioning. However, the inconsistent development and use of trait concepts and terms across freshwater scientific disciplines may have limited realisation of the potential of traits. Here, we reviewed trait definitions and terms use to provide recommendations for their consistent application in freshwater science. To do so, we first reviewed literature to identify established trait definitions, historical and current use of trait terms and challenges restricting the application of trait‐based approaches in freshwater science. Next, we surveyed 414 freshwater researchers from 54 countries to assess variability in the current use of trait terminology in relation to respondent characteristics (i.e., professional experience, geographical region, research discipline, and focal freshwater ecosystem, biotic group, and ecosystem function). Our literature review identified two well‐established trait definitions, which emphasise individual phenotypic characteristics that influence either eco‐evolutionary aspects (i.e., organism performance and fitness) or ecosystem dynamics and processes (i.e., responses to the environment and/or effects on ecosystem functioning). Publications used a range of trait‐related terms and their frequency of use varied among scientific fields. The term functional trait dominated fields such as biodiversity conservation, environmental sciences and ecology, plant sciences and microbiology. In contrast, the terms biological trait, functional trait, and species trait were used with similar frequencies in fields such as entomology, fisheries, marine and freshwater biology, and zoology. We also found that well‐established trait definitions are difficult to apply to freshwater unicellular organisms, colonial multicellular organisms, genomic information, and cultural traits. Our survey revealed highly inconsistent use of trait terms among freshwater researchers. Terms including biological trait, functional trait, structural measure, and ecosystem function were commonly used to describe the same traits or functions. Variability in the use of terms was generally explained by research discipline, geographical region, and focal biotic group and ecosystem functions. We propose making the trait concept flexible enough to be applicable to all freshwater biota and their characteristics, while keeping and integrating links to eco‐evolutionary and ecosystem aspects. Specifically, our new definition expands the established functional trait definition by considering also supra‐individual scales of trait measurement (colonial‐ or community‐mean traits), genotypic traits (e.g., functional gene markers of enzymes) and cultural traits (e.g., feeding behaviours, communication skills). To reduce terminological ambiguity, we also recommend that researchers define trait terms, prioritising the use of functional trait as an overarching term over alternative terms (e.g., biological trait), and restricting specific terms (e.g., morphological trait) to situations in which such precision is desirable. The findings of our integrative study could help to improve terminological consistency across freshwater disciplines and to better recognise the potential of traits to elucidate the mechanisms behind ecological patterns.
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Nosková, A., A. Mehrotra, N. K. Kadri, A. Lloret-Villas, S. Neuenschwander, A. Hofer, and H. Pausch. "Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs." BMC Genomics 24, no. 1 (April 10, 2023). http://dx.doi.org/10.1186/s12864-023-09295-4.
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Abstract Background Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). Results We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. Conclusions Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
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Beier, Sara, Johannes Werner, Thierry Bouvier, Nicolas Mouquet, and Cyrille Violle. "Trait-trait relationships and tradeoffs vary with genome size in prokaryotes." Frontiers in Microbiology 13 (October 21, 2022). http://dx.doi.org/10.3389/fmicb.2022.985216.
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We report genomic traits that have been associated with the life history of prokaryotes and highlight conflicting findings concerning earlier observed trait correlations and tradeoffs. In order to address possible explanations for these contradictions we examined trait–trait variations of 11 genomic traits from ~18,000 sequenced genomes. The studied trait–trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes and (ii) at least in free living species with large effective population sizes whose evolution is little affected by genetic drift an overlap between a resilience axis and an oligotrophic-copiotrophic axis. These findings imply that resistance associated traits of prokaryotes are globally decoupled from resilience related traits and in the case of free-living communities also from traits associated with resource availability. However, further inspection of pairwise scatterplots showed that resistance and resilience traits tended to be positively related for genomes up to roughly five million base pairs and negatively for larger genomes. Genome size distributions differ across habitats and our findings therefore point to habitat dependent tradeoffs between resistance and resilience. This in turn may preclude a globally consistent assignment of prokaryote genomic traits to the competitor - stress-tolerator - ruderal (CSR) schema that sorts species depending on their location along disturbance and productivity gradients into three ecological strategies and may serve as an explanation for conflicting findings from earlier studies. All reviewed genomic traits featured significant phylogenetic signals and we propose that our trait table can be applied to extrapolate genomic traits from taxonomic marker genes. This will enable to empirically evaluate the assembly of these genomic traits in prokaryotic communities from different habitats and under different productivity and disturbance scenarios as predicted via the resistance-resilience framework formulated here.
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Hecking, Matthew J., Jenna M. Zukswert, John E. Drake, Martin Dovciak, and Julia I. Burton. "Montane Temperate-Boreal Forests Retain the Leaf Economic Spectrum Despite Intraspecific Variability." Frontiers in Forests and Global Change 4 (January 14, 2022). http://dx.doi.org/10.3389/ffgc.2021.754063.
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Trait-based analyses provide powerful tools for developing a generalizable, physiologically grounded understanding of how forest communities are responding to ongoing environmental changes. Key challenges lie in (1) selecting traits that best characterize the ecological performance of species in the community and (2) determining the degree and importance of intraspecific variability in those traits. Recent studies suggest that globally evident trait correlations (trait dimensions), such as the leaf economic spectrum, may be weak or absent at local scales. Moreover, trait-based analyses that utilize a mean value to represent a species may be misleading. Mean trait values are particularly problematic if species trait value rankings change along environmental gradients, resulting in species trait crossover. To assess how plant traits (1) covary at local spatial scales, (2) vary across the dominant environmental gradients, and (3) can be partitioned within and across taxa, we collected data on 9 traits for 13 tree species spanning the montane temperate—boreal forest ecotones of New York and northern New England. The primary dimension of the trait ordination was the leaf economic spectrum, with trait variability among species largely driven by differences between deciduous angiosperms and evergreen gymnosperms. A second dimension was related to variability in nitrogen to phosphorous levels and stem specific density. Levels of intraspecific trait variability differed considerably among traits, and was related to variation in light, climate, and tree developmental stage. However, trait rankings across species were generally conserved across these gradients and there was little evidence of species crossover. The persistence of the leaf economics spectrum in both temperate and high-elevation conifer forests suggests that ecological strategies of tree species are associated with trade-offs between resource acquisition and tolerance, and may be quantified with relatively few traits. Furthermore, the assumption that species may be represented with a single trait value may be warranted for some trait-based analyses provided traits were measured under similar light levels and climate conditions.
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Lam-Gordillo, Orlando, Andrew M. Lohrer, Judi Hewitt, and Sabine Dittmann. "NZTD - The New Zealand Trait Database for shallow-water marine benthic invertebrates." Scientific Data 10, no. 1 (July 29, 2023). http://dx.doi.org/10.1038/s41597-023-02414-6.
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AbstractMacrobenthic traits, for example feeding mode, life history, morphology, are increasingly used for determining responses of macrobenthic fauna to environmental change and influences on ecosystem functioning. Yet, trait information is scarce or non-existent in several parts of the world, such as New Zealand. This deficit makes collecting trait data a difficult and time-consuming task, limiting its potential use in trait-based assessments. Here, we present the New Zealand Trait Database (NZTD) for marine benthic invertebrates, the first comprehensive assessment of macrobenthic traits in New Zealand. The NZTD provides trait information for more than 700 macrobenthic taxa, categorised by 18 traits and 77 trait modalities. The NZTD includes five freely downloadable datasets, (1) the macrobenthic trait dataset, with outcomes from a fuzzy coding procedure, (2) the trait source information, (3) the references by taxa, (4) the full references list, and (5) the full taxa list used in the NZTD. Establishing the NZTD closes the trait knowledge gap in New Zealand and facilitates future research applying trait-based approaches to New Zealand’s coastal macrofauna.
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Famiglietti, Caroline A., Matthew Worden, Leander D. L. Anderegg, and Alexandra G. Konings. "Impacts of climate timescale on the stability of trait–environment relationships." New Phytologist, November 30, 2023. http://dx.doi.org/10.1111/nph.19416.
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Summary Predictive relationships between plant traits and environmental factors can be derived at global and regional scales, informing efforts to reorient ecological models around functional traits. However, in a changing climate, the environmental variables used as predictors in such relationships are far from stationary. This could yield errors in trait–environment model predictions if timescale is not accounted for. Here, the timescale dependence of trait–environment relationships is investigated by regressing in situ trait measurements of specific leaf area, leaf nitrogen content, and wood density on local climate characteristics summarized across several increasingly long timescales. We identify contrasting responses of leaf and wood traits to climate timescale. Leaf traits are best predicted by recent climate timescales, while wood density is a longer term memory trait. The use of sub‐optimal climate timescales reduces the accuracy of the resulting trait–environment relationships. This study concludes that plant traits respond to climate conditions on the timescale of tissue lifespans rather than long‐term climate normals, even at large spatial scales where multiple ecological and physiological mechanisms drive trait change. Thus, determining trait–environment relationships with temporally relevant climate variables may be critical for predicting trait change in a nonstationary climate system.
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Hu, Zhi-Liang, Carissa A. Park, and James M. Reecy. "A combinatorial approach implementing new database structures to facilitate practical data curation management of QTL, association, correlation and heritability data on trait variants." Database 2023 (January 1, 2023). http://dx.doi.org/10.1093/database/baad024.
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Abstract A precise description of traits is essential in genetics and genomics studies to facilitate comparative genetics and meta-analyses. It is an ongoing challenge in research and production environments to unambiguously and consistently compare traits of interest from data collected under various conditions. Despite previous efforts to standardize trait nomenclature, it remains a challenge to fully and accurately capture trait nomenclature granularity in a way that ensures long-term data sustainability in terms of the data curation processes, data management logistics and the ability to make meaningful comparisons across studies. In the Animal Quantitative Trait Loci Database and the Animal Trait Correlation Database, we have recently introduced a new method to extend livestock trait ontologies by using trait modifiers and qualifiers to define traits that differ slightly in how they are measured, examined or combined with other traits or factors. Here, we describe the implementation of a system in which the extended trait data, with modifiers, are managed at the experiment level as ‘trait variants’. This has helped us to streamline the management and curation of such trait information in our database environment. Database URL https://www.animalgenome.org/PGNET/
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Zamralita, Zamralita. "PENGARUH KEPRIBADIAN TERHADAP WORK ENGAGEMENT (The Influence Of Personality To Work Engagement)." Indonesian Journal of Applied Sciences 3, no. 3 (December 31, 2013). http://dx.doi.org/10.24198/ijas.v3i3.15054.
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AbstrakPenelitian ini membahas mengenai profil kepribadian dan work engagement pada dosen tetap. Tujuan penelitian ini untuk mengetahui pengaruh kepribadian terhadap work engagement. Selanjutnya juga diuji pengaruh setiap trait kepribadian terhadap work engagement. Partisipan penelitian adalah dosen tetap dari 5 (lima) universitas swasta terbaik di Indonesia yang berjumlah 602 (enam ratus dua) orang. Data penelitian diperoleh melalui kuesioner kepribadian dan kuesioner work engagement. Analisis data menggunakan Metode Structural Equation Modeling. Hasil penelitian membuktikan dosen memiliki profil kepribadian trait openness to experience tinggi, trait conscientiousness tinggi, trait extraversion tinggi, trait agreeableness tinggi, dan trait neuroticism yang rendah. Selain itu trait openness to experience, trait conscientiousness, trait extraversion, trait agreeableness, trait neuroticism berpengaruh terhadap work engagement. Kesimpulan dari penelitian ini profesi dosen memiliki profil kepribadian yang khas dengan trait agreeableness yang lebih menonjol dibandingkan trait lain dan membuktikan seluruh trait kepribadian berpengaruh terhadap work engagement pada profesi dosen. Kata kunci: Kepribadian, work engagement, dosen tetap, universitas swastaAbstractThis study discusses about personality profile and work engagement on full time lecturers. Purpose of this study was to examine the influence of personality traits on work engagement. Furthermore also tested the influence of each personality trait to work engagement. The participants were 602 full time lecturers from the best five private universities in Indonesia. Data were obtained through personality and work engagement questionnaires. Methods of data analysis using structural equation modelling. The results are lecturer profession has high score in openness to experience trait, conscientiousness trait, extraversion trait, agreeableness trait, and low score in neuroticism trait. Furthermore all personality traits influenced work engagement. The conclusion of this research are lecturer profession has a distinctive personality profile with the agreeableness trait that is more prominent than others and prove all personality traits as predictor work engagement.Keywords: Personality, Work Engagement, Lecturer, Private university