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1
De Boeck, Paul, and Iven Van Mechelen. "Traits and taxonomies: A hierarchical classes approach." European Journal of Personality 4, no. 2 (June 1990): 147–56. http://dx.doi.org/10.1002/per.2410040207.
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Three types of taxonomy involving traits are discerned. In the first type, traits are objects of trait categories based on trait attributes. In the second type, traits are attributes of persons, person types being categories. In the third type, traits are categories of acts grouped on the basis of act attributes. All three types are analysed and discussed from the perspective of the hierarchical classes model for object by attribute data. Categories are considered external variables that can be mapped onto the object by attribute structure by way of category rules. It is suggested that this conceptual analysis yields a framework for studying how trait terms are understood and used.
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Kiørboe, Thomas, André Visser, and Ken H. Andersen. "A trait-based approach to ocean ecology." ICES Journal of Marine Science 75, no. 6 (July 19, 2018): 1849–63. http://dx.doi.org/10.1093/icesjms/fsy090.
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Abstract Trait-based ecology merges evolutionary with classical population and community ecology and is a rapidly developing branch of ecology. It describes ecosystems as consisting of individuals rather than species, and characterizes individuals by few key traits that are interrelated through trade-offs. The fundamental rationale is that the spatio-temporal distribution of organisms and their functional role in ecosystems depend on their traits rather than on their taxonomical affiliation. The approach respects that interactions are between individuals, not between species or populations, and in trait-based models ecosystem structure emerges as a result of interactions between individuals and with the environments, rather than being prescribed. It offers an alternative to classical species-centric approaches and has the potential to describe complex ecosystems in simple ways and to assess the effects of environmental change on ecosystem structure and function. Here, we describe the components of the trait-based approach and apply it to describe and model marine ecosystems. Our description is illustrated with multiple examples of life in the ocean from unicellular plankton to fish.
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Cormier, Emily C., Danielle R. Sisson, Kathleen M. Rühland, John P. Smol, and Joseph R. Bennett. "A morphological trait-based approach to environmental assessment models using diatoms." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 1 (January 2020): 108–12. http://dx.doi.org/10.1139/cjfas-2018-0376.
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Diatom assemblages are excellent indicators for environmental monitoring. However, enumerating diatoms using fine-level taxonomy takes considerable effort, which must be undertaken by specialist taxonomists. One alternative is to enumerate assemblages using morphological traits. In this study, we compared the accuracy of models using 20 morphological traits with those using species assemblages to infer lake water pH, salinity, depth, and total phosphorus concentrations in four data sets, each comprising over 200 lakes. Assemblages aggregated by trait combinations were used to predict environmental variables via weighted averaging regressions, and richness of trait combinations was regressed against the environmental variables. Trait-based weighted averaging regressions showed slightly lower accuracy than species-level analyses and higher accuracy than analyses at the family and sometimes genus level. Richness of trait combinations showed relationships with pH, salinity, and lake depth that were marginally stronger than relationships using species richness. Although species-level analyses are the best approach when time and budgets allow, we suggest that trait combinations could provide an alternative method for water quality assessment programs, where funds do not allow the use of specialist taxonomists or where diatoms are being used as part of a multi-indicator analysis.
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de Raad, Boele. "The psycholexical approach to the structure of interpersonal traits." European Journal of Personality 9, no. 2 (June 1995): 89–102. http://dx.doi.org/10.1002/per.2410090203.
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In this study the structure of interpersonal behaviour is investigated following the principles of the so‐called psycholexical approach. The interpersonal trait descriptors are selected from a comprehensive set of 1203 trait descriptive adjectives, constructed by Brokken (1978). Self‐ratings and peer ratings (N = 400) on the subset of 454 interpersonal trait adjectives were subjected to Principal Components analysis. The main results centre around a two‐factor solution, approximately reflecting the axis of the traditional interpersonal circumplex, Dominance and Nurturance. The relationship of the two interpersonal factors with the Big Five is discussed, as well as the relationship with the set of traits judged non‐interpersonal.
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Conington, J., S. Bishop, A. Waterhouse, and G. Simm. "A bio-economic approach to estimating economic values for UK hill sheep." Proceedings of the British Society of Animal Science 2000 (2000): 39. http://dx.doi.org/10.1017/s1752756200000405.
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Profitability of sheep production systems depends on several different animal characteristics rather than a single trait. Economic selection indexes combine information from more than one trait into an overall score, to maximise genetic gain. Economic values (EVs) are required for each trait in the breeding goal so that selection emphasis is proportional to the economic importance of each trait. Defining clear breeding goals is more complex for hill breeds than for other sectors of the sheep industry because they provide breeding females in addition to lambs for slaughter. The aims of this paper are to i) describe how EVs for breeding goal traits suitable for UK hill sheep were derived for a combination of carcass, maternal and ‘sustainability’ traits using a bio-economic model, and ii) show how these EVs vary between different production systems as a result of the differences in the physical constraints of farm size, pasture availability and the biological limits of sheep in extensive rearing environments.
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Lozada, Dennis N., and Arron H. Carter. "Genomic Selection in Winter Wheat Breeding Using a Recommender Approach." Genes 11, no. 7 (July 11, 2020): 779. http://dx.doi.org/10.3390/genes11070779.
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Achieving optimal predictive ability is key to increasing the relevance of implementing genomic selection (GS) approaches in plant breeding programs. The potential of an item-based collaborative filtering (IBCF) recommender system in the context of multi-trait, multi-environment GS has been explored. Different GS scenarios for IBCF were evaluated for a diverse population of winter wheat lines adapted to the Pacific Northwest region of the US. Predictions across years through cross-validations resulted in improved predictive ability when there is a high correlation between environments. Using multiple spectral traits collected from high-throughput phenotyping resulted in better GS accuracies for grain yield (GY) compared to using only single traits for predictions. Trait adjustments through various Bayesian regression models using genomic information from SNP markers was the most effective in achieving improved accuracies for GY, heading date, and plant height among the GS scenarios evaluated. Bayesian LASSO had the highest predictive ability compared to other models for phenotypic trait adjustments. IBCF gave competitive accuracies compared to a genomic best linear unbiased predictor (GBLUP) model for predicting different traits. Overall, an IBCF approach could be used as an alternative to traditional prediction models for important target traits in wheat breeding programs.
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Skelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, and Elissa J. Chesler. "Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples." Genetics 212, no. 3 (May 21, 2019): 919–29. http://dx.doi.org/10.1534/genetics.118.301865.
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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.
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Castillo-Figueroa, Dennis, and Jairo Pérez-Torres. "On the development of a trait-based approach for studying Neotropical bats." Papéis Avulsos de Zoologia 61 (February 18, 2021): e20216124. http://dx.doi.org/10.11606/1807-0205/2021.61.24.
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New World bats are involved in key ecological processes and are good indicators of environmental changes. Recently, trait-based approaches have been used in several taxa to better understand mechanisms underlying species assemblages, biotic interactions, environmental relationships and ecosystem functions. However, despite the relevance of bats on ecosystem dynamics, so far, there is no conceptual framework that relies on the measurement of bat traits to address functional studies. Here, we present a set of 50 bat biological traits, which are suitable to assess environmental stressors and can potentially affect ecological processes. Several examples were provided to show the applicability of this framework in the study of Neotropical bat ecology. We suggest some considerations regarding trait-based approach including the importance of intraspecific variation, correlations between traits, response-effect framework, global dataset, and future directions to assess the reliability of functional relations across species and Neotropical regions by using traits. This could be helpful in tackling ecological questions associated with community assembly and habitat filtering, species diversity patterns along environmental gradients, and ecological processes. We envision this paper as a first step toward an integrative bat functional trait protocol held up with solid evidence.
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Faheem, Muhammad. "Selection of Wheat Ideotype Based on Multiple Traits using Genotype by Yield-Trait Approach." International Journal of Agriculture and Biology 25, no. 06 (June 1, 2021): 1367–74. http://dx.doi.org/10.17957/ijab/15.1799.
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In plant breeding, a novel genotype-by-yield trait (GYT) biplot approach was introduced to select superior genotypes based on multiple traits. The present study demonstrated the application of the GYT biplot model to evaluate the superior wheat advanced lines from a panel of 24 genotypes to select the ideotype for end users. Results show that the genotype-by-trait (GT) biplot covered 57% of the total variation of the data to reveal that grain yield was strongly associated with 1000-grain weight and grain width. In contrast, the GYT biplot explained 90.2% of the total variation which was significantly much higher than GT biplot. According to tester vector view of GYT biplot almost all the yield trait combinations were associated with each other at different degree of association; whereas the genotypes present within the acute angles of tester vectors (yield trait combinations) had the trait profile contributed positively towards grain yield. The polygon biplot of GYT had eight sectors, out of which only three had the yield trait combinations. The eight genotypes were the polygon vertex among which the advanced line DF1906 of first sector was designated as the best genotype for spike length, number of spikelets per spike, grain weight per spike and number of grains per spike. Additionally, the DF1912 of second sector was early maturing coupled with high 1000-grain weight while DF1917 of third sector had short stature and gave the highest harvest index. The average tester coordination (ATC) biplot grouped 13 genotypes as superior and nine as inferior genotypes and recommended two advanced lines DF1912 and DF1917 as ideotype based on balanced traits profile. These findings strengthened the argument that the GYT biplot analysis is better than other selection indices and guaranteed the selection of superior genotypes and rejection of inferior ones based on multiple traits yield combinations. © 2021 Friends Science Publishers
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De Raad, Boele. "An expedition in search of a fifth universal factor: Key issues in the lexical approach." European Journal of Personality 8, no. 4 (November 1994): 229–50. http://dx.doi.org/10.1002/per.2410080403.
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It is argued that the fifth factor of the Big Five Model of personality traits cannot yet claim universal status. In order to identify a fifth factor within the lexical approach it is necessary to make full use of the potentialities of the psycholexical principles. Several flaws in the lexical enterprise are discussed, both regarding the theoretical delineation of traits and the operational—dictionary‐related—identification of trait descriptors. Hitherto largely implicit definitions of traits should be made explicit, and agreement should be reached about the theoretical width of the trait domain. Also, in order to obtain cross‐culturally comparable results, the procedural steps in the lexical search for trait terms should follow an agreed‐upon standard. None of the nominated fifth factors, for instance, Culture, Intellect, or Openness to Experience, has both proceeded from the lexical method and received unquestionable cross‐cultural affirmation.
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Rumeu, Beatriz, Danny J. Sheath, Joseph E. Hawes, and Thomas C. Ings. "Zooming into plant-flower visitor networks: an individual trait-based approach." PeerJ 6 (September 17, 2018): e5618. http://dx.doi.org/10.7717/peerj.5618.
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Understanding how ecological communities are structured is a major goal in ecology. Ecological networks representing interaction patterns among species have become a powerful tool to capture the mechanisms underlying plant-animal assemblages. However, these networks largely do not account for inter-individual variability and thus may be limiting our development of a clear mechanistic understanding of community structure. In this study, we develop a new individual-trait based approach to examine the importance of individual plant and pollinator functional size traits (pollinator thorax width and plant nectar holder depth) in mutualistic networks. We performed hierarchical cluster analyses to group interacting individuals into classes, according to their similarity in functional size. We then compared the structure of bee-flower networks where nodes represented either species identity or trait sets. The individual trait-based network was almost twice as nested as its species-based equivalent and it had a more symmetric linkage pattern resulting from of a high degree of size-matching. In conclusion, we show that by constructing individual trait-based networks we can reveal important patterns otherwise difficult to observe in species-based networks and thus improve our understanding of community structure. We therefore recommend using both trait-based and species-based approaches together to develop a clearer understanding of the properties of ecological networks.
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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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Guo, Bin, and Baolin Wu. "Integrate multiple traits to detect novel trait–gene association using GWAS summary data with an adaptive test approach." Bioinformatics 35, no. 13 (November 23, 2018): 2251–57. http://dx.doi.org/10.1093/bioinformatics/bty961.
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Abstract Motivation Genetics hold great promise to precision medicine by tailoring treatment to the individual patient based on their genetic profiles. Toward this goal, many large-scale genome-wide association studies (GWAS) have been performed in the last decade to identify genetic variants associated with various traits and diseases. They have successfully identified tens of thousands of disease-related variants. However they have explained only a small proportion of the overall trait heritability for most traits and are of very limited clinical use. This is partly owing to the small effect sizes of most genetic variants, and the common practice of testing association between one trait and one genetic variant at a time in most GWAS, even when multiple related traits are often measured for each individual. Increasing evidence suggests that many genetic variants can influence multiple traits simultaneously, and we can gain more power by testing association of multiple traits simultaneously. It is appealing to develop novel multi-trait association test methods that need only GWAS summary data, since it is generally very hard to access the individual-level GWAS phenotype and genotype data. Results Many existing GWAS summary data-based association test methods have relied on ad hoc approach or crude Monte Carlo approximation. In this article, we develop rigorous statistical methods for efficient and powerful multi-trait association test. We develop robust and efficient methods to accurately estimate the marginal trait correlation matrix using only GWAS summary data. We construct the principal component (PC)-based association test from the summary statistics. PC-based test has optimal power when the underlying multi-trait signal can be captured by the first PC, and otherwise it will have suboptimal performance. We develop an adaptive test by optimally weighting the PC-based test and the omnibus chi-square test to achieve robust performance under various scenarios. We develop efficient numerical algorithms to compute the analytical P-values for all the proposed tests without the need of Monte Carlo sampling. We illustrate the utility of proposed methods through application to the GWAS meta-analysis summary data for multiple lipids and glycemic traits. We identify multiple novel loci that were missed by individual trait-based association test. Availability and implementation All the proposed methods are implemented in an R package available at http://www.github.com/baolinwu/MTAR. The developed R programs are extremely efficient: it takes less than 2 min to compute the list of genome-wide significant single nucleotide polymorphisms (SNPs) for all proposed multi-trait tests for the lipids GWAS summary data with 2.5 million SNPs on a single Linux desktop. Supplementary information Supplementary data are available at Bioinformatics online.
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Church, A. Timothy, Marcia S. Katigbak, Lilia G. Miramontes, Alicia M. del Prado, and Helena F. Cabrera. "Culture and the behavioural manifestations of traits: an application of the Act Frequency Approach." European Journal of Personality 21, no. 4 (June 2007): 389–417. http://dx.doi.org/10.1002/per.631.
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The behavioural manifestations of Big Five traits were compared across cultures using the Act Frequency Approach. American (n = 176) and Filipino (n = 195) students completed a Big Five measure and act frequency ratings for behaviours performed during the past month. Acts for specific traits cohered to an equivalent degree across cultures. In both cultures, the structure of act composites resembled the Big Five and the strength of trait‐behaviour relationships was very similar. Many acts were multidimensional and analyses revealed cultural commonalities and differences in the relevance and prevalence of acts for the Big Five traits. The results were more consistent with trait than cultural psychology perspectives, because traits predicted behaviour equally well, on average, in the two cultures. Copyright © 2007 John Wiley & Sons, Ltd.
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Wetzel, Eunike, and Claus H. Carstensen. "Multidimensional Modeling of Traits and Response Styles." European Journal of Psychological Assessment 33, no. 5 (September 2017): 352–64. http://dx.doi.org/10.1027/1015-5759/a000291.
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Abstract. Response styles can influence item responses in addition to a respondent’s latent trait level. A common concern is that comparisons between individuals based on sum scores may be rendered invalid by response style effects. This paper investigates a multidimensional approach to modeling traits and response styles simultaneously. Models incorporating different response styles as well as personality traits (Big Five facets) were compared regarding model fit. Relationships between traits and response styles were investigated and different approaches to modeling extreme response style (ERS) were compared regarding their effects on trait estimates. All multidimensional models showed a better fit than the unidimensional models, indicating that response styles influenced item responses with ERS showing the largest incremental variance explanation. ERS and midpoint response style were mainly trait-independent whereas acquiescence and disacquiescence were strongly related to several personality traits. Expected a posteriori estimates of participants’ trait levels did not differ substantially between two-dimensional and unidimensional models when a set of heterogeneous items was used to model ERS. A minor adjustment of trait estimates occurred when the same items were used to model ERS and the trait, though the ERS dimension in this approach only reflected scale-specific ERS, rather than a general ERS tendency.
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Manyak, Patrick. "Character Trait Vocabulary: A Schoolwide Approach." Reading Teacher 60, no. 6 (March 2007): 574–77. http://dx.doi.org/10.1598/rt.60.6.8.
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Eysenck, Michael W. "A cognitive approach to trait anxiety." European Journal of Personality 14, no. 5 (2000): 463–76. http://dx.doi.org/10.1002/1099-0984(200009/10)14:5<463::aid-per393>3.0.co;2-3.
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Eysenck, Michael W. "A cognitive approach to trait anxiety." European Journal of Personality 14, no. 5 (September 2000): 463–76. http://dx.doi.org/10.1002/1099-0984(200009/10)14:5<463::aid-per393>3.3.co;2-v.
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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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Kruglyak, L., and E. S. Lander. "A nonparametric approach for mapping quantitative trait loci." Genetics 139, no. 3 (March 1, 1995): 1421–28. http://dx.doi.org/10.1093/genetics/139.3.1421.
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Abstract Genetic mapping of quantitative trait loci (QTLs) is performed typically by using a parametric approach, based on the assumption that the phenotype follows a normal distribution. Many traits of interest, however, are not normally distributed. In this paper, we present a nonparametric approach to QTL mapping applicable to any phenotypic distribution. The method is based on a statistic ZW, which generalizes the nonparametric Wilcoxon rank-sum test to the situation of whole-genome search by interval mapping. We determine the appropriate significance level for the statistic ZW, by showing that its asymptotic null distribution follows an Ornstein-Uhlenbeck process. These results provide a robust, distribution-free method for mapping QTLs.
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Álvarez-Yépiz, Juan C., Alberto Búrquez, Angelina Martínez-Yrízar, and Martin Dovciak. "A trait-based approach to the conservation of threatened plant species." Oryx 53, no. 3 (February 12, 2019): 429–35. http://dx.doi.org/10.1017/s003060531800087x.
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AbstractTraditionally the vulnerability of threatened species to extinction has been assessed by studying their environment, genetics and population dynamics. A more comprehensive understanding of the factors promoting or limiting the long-term persistence of threatened species could be achieved by conducting an analysis of their functional responses to changing environments, their ecological interactions, and their role in ecosystem functioning. These less traditional research areas can be unified in a trait-based approach, a recent methodological advance in ecology that is being used to link individual-level functions to species, community and ecosystem processes to provide mechanistic explanations of observed patterns, particularly in changing environments. We illustrate how trait-based information can be translated into well-defined conservation strategies, using the example of Dioon sonorense, an Endangered cycad endemic to north-western Mexico. Scientific information yielded by trait-based research, coupled with existing knowledge derived from well-established traditional approaches, could facilitate the development of more integrative conservation strategies to promote the long-term persistence of individual threatened species. A comprehensive database of functional traits of threatened species would be of value in assisting the implementation of the trait-based approach.
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Kamal, Alnor Gorafi, Abdelrahman, Abdellatef, and Tsujimoto. "Stay-Green Trait: A Prospective Approach for Yield Potential, and Drought and Heat Stress Adaptation in Globally Important Cereals." International Journal of Molecular Sciences 20, no. 23 (November 20, 2019): 5837. http://dx.doi.org/10.3390/ijms20235837.
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The yield losses in cereal crops because of abiotic stress and the expected huge losses from climate change indicate our urgent need for useful traits to achieve food security. The stay-green (SG) is a secondary trait that enables crop plants to maintain their green leaves and photosynthesis capacity for a longer time after anthesis, especially under drought and heat stress conditions. Thus, SG plants have longer grain-filling period and subsequently higher yield than non-SG. SG trait was recognized as a superior characteristic for commercially bred cereal selection to overcome the current yield stagnation in alliance with yield adaptability and stability. Breeding for functional SG has contributed in improving crop yields, particularly when it is combined with other useful traits. Thus, elucidating the molecular and physiological mechanisms associated with SG trait is maybe the key to defeating the stagnation in productivity associated with adaptation to environmental stress. This review discusses the recent advances in SG as a crucial trait for genetic improvement of the five major cereal crops, sorghum, wheat, rice, maize, and barley with particular emphasis on the physiological consequences of SG trait. Finally, we provided perspectives on future directions for SG research that addresses present and future global challenges.
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Guo, Xingche, Yumou Qiu, Dan Nettleton, Cheng-Ting Yeh, Zihao Zheng, Stefan Hey, and Patrick S. Schnable. "KAT4IA: K-Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes." Plant Phenomics 2021 (August 3, 2021): 1–12. http://dx.doi.org/10.34133/2021/9805489.
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High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.
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Moeinizade, Saba, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Multi-trait Genomic Selection Methods for Crop Improvement." Genetics 215, no. 4 (June 1, 2020): 931–45. http://dx.doi.org/10.1534/genetics.120.303305.
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Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier of selection decisions, which represents different trade-offs across multiple traits. We propose a new approach, which maximizes certain traits while keeping others within desirable ranges. Optimal selection decisions are made using a new version of the look-ahead selection (LAS) algorithm, which was recently proposed for single-trait genomic selection, and achieved superior performance with respect to other state-of-the-art selection methods. To demonstrate the effectiveness of the new method, a case study is developed using a realistic data set where our method is compared with conventional index selection. Results suggest that the multi-trait LAS is more effective at balancing multiple traits compared with index selection.
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Jamil, Tahira, Wout Opdekamp, Rudy van Diggelen, and Cajo J. F. ter Braak. "Trait-Environment Relationships and Tiered Forward Model Selection in Linear Mixed Models." International Journal of Ecology 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/947103.
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To understand patterns of variation in species biomass in terms of species traits and environmental variables a one-to-one approach might not be sufficient, and a multitrait multienvironment approach will be necessary. A multitrait multienvironment approach is proposed, based on a mixed model for species biomass. In the model, environmental variables are species-dependent random terms, whereas traits are fixed terms, and trait-environment relationships are fixed interaction terms. In this approach, identifying the important trait-environment relationship becomes a model selection problem. Because of the mix of fixed and random terms, we propose a novel tiered forward selection approach for this. In the first tier, the random factors are selected; in the second, the fixed effects; in the final tier, nonsignificant terms are removed using a modified Akaike information criterion. We complement this tiered selection with an alternative selection method, namely, type II maximum likelihood. A mesocosm experiment on early community assembly in wetlands with three two-level environmental factors is analyzed by the new approach. The results are compared with the fourth corner problem and the linear trait-environment method. Traits related to germination and seedling establishment are selected as being most important in the community assembly in these wetland mesocosms.
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Ellis, Christopher J., Johan Asplund, Renato Benesperi, Cristina Branquinho, Luca Di Nuzzo, Pilar Hurtado, Isabel Martínez, et al. "Functional Traits in Lichen Ecology: A Review of Challenge and Opportunity." Microorganisms 9, no. 4 (April 6, 2021): 766. http://dx.doi.org/10.3390/microorganisms9040766.
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Community ecology has experienced a major transition, from a focus on patterns in taxonomic composition, to revealing the processes underlying community assembly through the analysis of species functional traits. The power of the functional trait approach is its generality, predictive capacity such as with respect to environmental change, and, through linkage of response and effect traits, the synthesis of community assembly with ecosystem function and services. Lichens are a potentially rich source of information about how traits govern community structure and function, thereby creating opportunity to better integrate lichens into ‘mainstream’ ecological studies, while lichen ecology and conservation can also benefit from using the trait approach as an investigative tool. This paper brings together a range of author perspectives to review the use of traits in lichenology, particularly with respect to European ecosystems from the Mediterranean to the Arctic-Alpine. It emphasizes the types of traits that lichenologists have used in their studies, both response and effect, the bundling of traits towards the evolution of life-history strategies, and the critical importance of scale (both spatial and temporal) in functional trait ecology.
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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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Di Croce, F. A., A. M. Saxton, D. Casanova, and F. N. Schrick. "21 GENETIC APPROACH TO IMPROVE FERTILITY IN CATTLE." Reproduction, Fertility and Development 22, no. 1 (2010): 168. http://dx.doi.org/10.1071/rdv22n1ab21.
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In spite of the economic importance of fertility and increased knowledge of reproductive biology in the cow, efficiency of reproductive performance has continued to decline in dairy cattle. Improvements in fertility through genetic selection may be a possible approach to increase reproductive efficiency in dairy cattle. Although progress may be slow because of low heritabilities, ignoring fertility in genetic improvement programs has contributed to the current fertility problems. The objectives of this study were to (1) estimate genetic parameters and breeding values for fertility traits based on data from milk recording programs on a Holstein population and (2) develop a multi-trait selection index (dollar fertility value; $F) based on estimated breeding values. Data representing 3,282,843 lactations and 1,622,088 animals (cows and bulls from 1936 to 2007) were used for the analysis and obtained from official records maintained by the Argentinean Holstein Association (ACHA). Data were collected from official milk records, and lactations were standardized to 305 DIM for milk, protein, and fat. Gestation period was considered to be 282 days and restrictions were applied to ensure the quality of data (days open <40 and >350 days; calving interval <300 and >600 days; and contemporary groups with <25 lactations were eliminated). Days open (DO), calving interval (CI), age to first calving (AFC), and daughter pregnancy rate (DPR) were traits chosen for genetic parameter estimation. Daughter pregnancy rate was calculated from DO as PR = 0.25 × (233 DO); Kuhn et al. 2004 J. Dairy Sci. 87, 2277-2284. Animal models by lactation for each fertility trait included contemporary group (dairy herd and calving year), calving month, animal effect, milk production as a covariate and error term. Multiple-trait derivative-free restricted maximum likelihood (MTDFREML) was used to estimate covariance components (Boldman et al. 1995 ARS, USDA). Solutions for fixed effects, breeding values, and sampling variances (accuracies) were obtained for each trait. The proposed selection index ($F) included AFC and CI, with economic weights from -0.28 and -4.9 $/year per cow for AFC and CI, respectively (Gonzalez-Recio et al. 2004 J. Dairy Sci. 87, 3053-3061). Heritabilities for DO and DPR ranged from 2 to 7%; CI ranged from 3 to 8%; and AFC was 16%. Predicted transmitting ability values across different lactations ranged from -18.5 to 15.8 days, -23.15 to 16.0 days, -4.0 to 4.6%, and -120.3 to 76.2 days for DO, CI, DPR, and AFC, respectively. Values for $F ranged from -$89.16 to $147.12 in this Holstein population. Results indicate substantial variation in fertility traits, suggesting that genetic selection may be effective in improving declines in fertility. Asociacion Criadores Holando Argentino (ACHA).
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Walworth, Nathan G., Jana Hinners, Phoebe A. Argyle, Suzana G. Leles, Martina A. Doblin, Sinéad Collins, and Naomi M. Levine. "The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga." Proceedings of the Royal Society B: Biological Sciences 288, no. 1953 (June 16, 2021): 20210940. http://dx.doi.org/10.1098/rspb.2021.0940.
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Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.
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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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Brun, Philipp, Mark R. Payne, and Thomas Kiørboe. "A trait database for marine copepods." Earth System Science Data 9, no. 1 (February 14, 2017): 99–113. http://dx.doi.org/10.5194/essd-9-99-2017.
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Abstract. The trait-based approach is gaining increasing popularity in marine plankton ecology but the field urgently needs more and easier accessible trait data to advance. We compiled trait information on marine pelagic copepods, a major group of zooplankton, from the published literature and from experts and organized the data into a structured database. We collected 9306 records for 14 functional traits. Particular attention was given to body size, feeding mode, egg size, spawning strategy, respiration rate, and myelination (presence of nerve sheathing). Most records were reported at the species level, but some phylogenetically conserved traits, such as myelination, were reported at higher taxonomic levels, allowing the entire diversity of around 10 800 recognized marine copepod species to be covered with a few records. Aside from myelination, data coverage was highest for spawning strategy and body size, while information was more limited for quantitative traits related to reproduction and physiology. The database may be used to investigate relationships between traits, to produce trait biogeographies, or to inform and validate trait-based marine ecosystem models. The data can be downloaded from PANGAEA, doi:10.1594/PANGAEA.862968.
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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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Gagic, Vesna, Ignasi Bartomeus, Tomas Jonsson, Astrid Taylor, Camilla Winqvist, Christina Fischer, Eleanor M. Slade, et al. "Functional identity and diversity of animals predict ecosystem functioning better than species-based indices." Proceedings of the Royal Society B: Biological Sciences 282, no. 1801 (February 22, 2015): 20142620. http://dx.doi.org/10.1098/rspb.2014.2620.
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Drastic biodiversity declines have raised concerns about the deterioration of ecosystem functions and have motivated much recent research on the relationship between species diversity and ecosystem functioning. A functional trait framework has been proposed to improve the mechanistic understanding of this relationship, but this has rarely been tested for organisms other than plants. We analysed eight datasets, including five animal groups, to examine how well a trait-based approach, compared with a more traditional taxonomic approach, predicts seven ecosystem functions below- and above-ground. Trait-based indices consistently provided greater explanatory power than species richness or abundance. The frequency distributions of single or multiple traits in the community were the best predictors of ecosystem functioning. This implies that the ecosystem functions we investigated were underpinned by the combination of trait identities (i.e. single-trait indices) and trait complementarity (i.e. multi-trait indices) in the communities. Our study provides new insights into the general mechanisms that link biodiversity to ecosystem functioning in natural animal communities and suggests that the observed responses were due to the identity and dominance patterns of the trait composition rather than the number or abundance of species per se .
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Harmon-Jones, Cindy, Brandon J. Schmeichel, Michael Inzlicht, and Eddie Harmon-Jones. "Trait Approach Motivation Relates to Dissonance Reduction." Social Psychological and Personality Science 2, no. 1 (August 11, 2010): 21–28. http://dx.doi.org/10.1177/1948550610379425.
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Scandell, Donald J. "The Gate-Trait/Contextual Approach to Assessment." Journal of Human Behavior in the Social Environment 2, no. 4 (December 1999): 51–65. http://dx.doi.org/10.1300/j137v02n04_04.
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Krapohl, E., H. Patel, S. Newhouse, C. J. Curtis, S. von Stumm, P. S. Dale, D. Zabaneh, G. Breen, P. F. O'Reilly, and R. Plomin. "Multi-polygenic score approach to trait prediction." Molecular Psychiatry 23, no. 5 (August 8, 2017): 1368–74. http://dx.doi.org/10.1038/mp.2017.163.
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Muscarella, Mario E., Xia Meng Howey, and Jay T. Lennon. "Trait‐based approach to bacterial growth efficiency." Environmental Microbiology 22, no. 8 (June 28, 2020): 3494–504. http://dx.doi.org/10.1111/1462-2920.15120.
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Baadel, Said, Fadi Thabtah, and Joan Lu. "A clustering approach for autistic trait classification." Informatics for Health and Social Care 45, no. 3 (February 3, 2020): 309–26. http://dx.doi.org/10.1080/17538157.2019.1687482.
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Jones, Robert E. "The Personality-Trait Approach to Leader Effectiveness." Psychological Reports 65, no. 2 (October 1989): 601–2. http://dx.doi.org/10.2466/pr0.1989.65.2.601.
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Noting that the 1989 article by Jaffe, Nebenzahl, and Gotesdyner yields data partially confirming prior work, it is recommended that research should focus on identifying the circumstances and personality traits to understand their interaction.
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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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Huang, Ping, Rebecca T. Kimball, and Colette M. St. Mary. "Does the use of a multi-trait, multi-test approach to measure animal personality yield different behavioural syndrome results?" Behaviour 155, no. 2-3 (2018): 115–50. http://dx.doi.org/10.1163/1568539x-00003480.
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Abstract A multi-trait, multi-test approach to investigate the convergence or discrimination between behavioural tests putatively targeting the same or different animal personality traits has been recommended, yet whether and how the approaches affect the identification of behavioural syndrome(s), the suite of correlated personality traits, requires investigation. Here, we used behavioural measures collected from five commonly used behavioural tests targeting three personality traits, evaluated their convergence/discrimination through exploratory factor analysis (EFA), and then explored whether the identification of syndrome changed based on the approach we used to quantify personality traits. Our results indicated that tests presumably targeting the same personality trait actually measured distinct behavioural aspects. Syndrome defined using correlation changed due to how we identified personality traits, but not when using structural equation models (SEMs). Overall, this study emphasizes that it is critical to clarify the approach and terms we use for ‘personality traits’ in the field of animal personality.
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Basu, S., M. Stephens, J. S. Pankow, and E. A. Thompson. "A Likelihood-Based Trait-Model-Free Approach for Linkage Detection of Binary Trait." Biometrics 66, no. 1 (May 18, 2009): 205–13. http://dx.doi.org/10.1111/j.1541-0420.2009.01270.x.
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Hampson, Sarah E. "Reconciling inconsistent information: Impressions of personality from combinations of traits." European Journal of Personality 4, no. 2 (June 1990): 157–72. http://dx.doi.org/10.1002/per.2410040208.
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Research is described that examines the kinds of impressions formed from congruent and incongruent combinations of trait‐descriptive adjectives. Impressions formed from congruent traits are easier to imagine than impressions formed from incongruent traits. Descriptions of targets specified by congruent traits are more likely to be integrated (i.e. to relate one trait to the other), whereas descriptions of targets specified by incongruent traits are more likely to be aggregated (i.e. to retain both traits but fail to relate them). Using an approach from cognitive psychology for the study of conceptual combinations, the inheritance of attributes by trait combinations was examined. More congruent combinations than incongruent ones exhibited a pattern of complete attribute inheritance, in which behaviors rated as likely for at least one of the constituents were also rated as likely for the combination. These findings illuminate the dificulties in reconciling incongruent trait combinations. Incongruent combinations are less likely to generate impressions in which the two traits can be related, and consequently may result in representations that fail to incorporate all the attributes of each constituent.
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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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Romero-Olivares, A. L., E. W. Morrison, A. Pringle, and S. D. Frey. "Linking Genes to Traits in Fungi." Microbial Ecology 82, no. 1 (January 22, 2021): 145–55. http://dx.doi.org/10.1007/s00248-021-01687-x.
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AbstractFungi are mediators of the nitrogen and carbon cycles in terrestrial ecosystems. Examining how nitrogen uptake and organic matter decomposition potential differs in fungi can provide insight into the underlying mechanisms driving fungal ecological processes and ecosystem functioning. In this study, we assessed the frequency of genes encoding for specific enzymes that facilitate nitrogen uptake and organic matter decomposition in 879 fungal genomes with fungal taxa grouped into trait-based categories. Our linked gene-trait data approach revealed that gene frequencies vary across and within trait-based groups and that trait-based categories differ in trait space. We present two examples of how this linked gene-trait approach can be used to address ecological questions. First, we show that this type of approach can help us better understand, and potentially predict, how fungi will respond to environmental stress. Specifically, we found that trait-based categories with high nitrogen uptake gene frequency increased in relative abundance when exposed to high soil nitrogen enrichment. Second, by comparing frequencies of nitrogen uptake and organic matter decomposition genes, we found that most ectomycorrhizal fungi in our dataset have similar gene frequencies to brown rot fungi. This demonstrates that gene-trait data approaches can shed light on potential evolutionary trajectories of life history traits in fungi. We present a framework for exploring nitrogen uptake and organic matter decomposition gene frequencies in fungal trait-based groups and provide two concise examples on how to use our framework to address ecological questions from a mechanistic perspective.
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Lozada, Dennis, and Arron Carter. "Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat." Agronomy 10, no. 3 (March 7, 2020): 368. http://dx.doi.org/10.3390/agronomy10030368.
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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.
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Torres, W. Jackeline, and Margaret E. Beier. "It's Time To Examine the Nomological Net of Job Knowledge." Industrial and Organizational Psychology 9, no. 1 (March 2016): 51–55. http://dx.doi.org/10.1017/iop.2015.116.
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Lievens and Motowidlo (2016) argue compellingly that situational judgment tests (SJTs) measure job-relevant general domain knowledge, conceptualized as implicit trait policies (ITPs). ITPs are defined as a person's knowledge about the utility of expressing certain traits. They develop through the feedback a person receives when acting in accordance with their trait profiles in different environments (work, life, leisure). Positive feedback reinforces the knowledge that behavior in accordance with one's own traits is appropriate, and negative feedback reinforces the knowledge that an approach that differs from one's trait tendencies may be more effective. As such, ITPs represent a person's knowledge about the effectiveness of behaviors across a variety of contexts.
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Lange, Christoph, and John C. Whittaker. "Mapping Quantitative Trait Loci Using Generalized Estimating Equations." Genetics 159, no. 3 (November 1, 2001): 1325–37. http://dx.doi.org/10.1093/genetics/159.3.1325.
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AbstractA number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.
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Korol, Abraham B., Yefim I. Ronin, Alexander M. Itskovich, Junhua Peng, and Eviatar Nevo. "Enhanced Efficiency of Quantitative Trait Loci Mapping Analysis Based on Multivariate Complexes of Quantitative Traits." Genetics 157, no. 4 (April 1, 2001): 1789–803. http://dx.doi.org/10.1093/genetics/157.4.1789.
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AbstractAn approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F2/F3 mapping population of tetraploid wheat (Triticum durum × T. dicoccoides) is provided.
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Lehmann, Laurent, Kenichi Aoki, and Marcus W. Feldman. "On the number of independent cultural traits carried by individuals and populations." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1563 (February 12, 2011): 424–35. http://dx.doi.org/10.1098/rstb.2010.0313.
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In species subject to individual and social learning, each individual is likely to express a certain number of different cultural traits acquired during its lifetime. If the process of trait innovation and transmission reaches a steady state in the population, the number of different cultural traits carried by an individual converges to some stationary distribution. We call this the trait-number distribution. In this paper, we derive the trait-number distributions for both individuals and populations when cultural traits are independent of each other. Our results suggest that as the number of cultural traits becomes large, the trait-number distributions approach Poisson distributions so that their means characterize cultural diversity in the population. We then analyse how the mean trait number varies at both the individual and population levels as a function of various demographic features, such as population size and subdivision, and social learning rules, such as conformism and anti-conformism. Diversity at the individual and population levels, as well as at the level of cultural homogeneity within groups, depends critically on the details of population demography and the individual and social learning rules.