Supplement 1. Data and R code needed to conduct the analysis described in the main text, as well as detailed description of the functional trait data used for calculating functional diversity indices.
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File List Ziter_2013_Ecosphere_Analysis.R (MD5: a70d20be760cc94702c81defab37bb1d) LiveTrees.csv (MD5: 1eb43347a5e2e49b3e2ab5e006f6ad19) SnagsIdentified.csv (MD5: 7e51486d1eadfbf7e3a4ab030db36fa4) SnagsUnidentified.csv (MD5: 41cddd111f48d0c8a2e14cfaf70814dd) DWD.csv (MD5: 9c85df5ebea05a0d59c6c88cb14fa828) OrderedSpecies.csv (MD5: 189a0b4fcf6ca5e85a7ba0487bd254a8) Traits.csv (MD5: 596aa39feef4e755f598c8d3fb9510a8) Traits_FDis7.csv (MD5: 31a764843081b78fe4295534bb5ca3cf) Traits_Description.csv (MD5: 6597b6e7b8260ae8c193d0341c123fd2) Description Ziter_2013_Ecosphere_Analysis.R – Annotated R script containing the code necessary to conduct the analysis described in the text LiveTrees.csv – data file containing study site information (including SiteID (the individual plot identifier), transect direction, plot number (from edge (1) to interior (5)), management status, fragment connectivity, and fragment size), tree species ID, diameter at breast height (DBH, for saplings the mean of the DBH class, recorded under “DBH(Biomass)”, was used to calculate biomass), allometric coefficients, and biomass for each individual live tree measured in the field SnagsIdentified.csv – data file containing study site information (including SiteID (the individual plot identifier), transect direction, plot number (from edge (1) to interior (5)), management status, fragment connectivity, and fragment size), tree species ID, diameter at breast height (DBH, for saplings the mean of the DBH class, recorded under “DBH(Biomass)”, was used to calculate biomass), allometric coefficients, and biomass for each snag (dead tree) measured that was identifiable to species SnagsUnidentified.csv – data file containing study site information (including SiteID (the individual plot identifier), transect direction, plot number (from edge (1) to interior (5)), management status, fragment connectivity, and fragment size), diameter at breast height (DBH, for saplings the mean of the DBH class, recorded under “DBH(Biomass)”, was used to calculate biomass), allometric coefficients, and biomass for each snag (dead tree) measured that was not identifiable to species DWD.csv – data file containing site information (including SiteID (the individual plot identifier), transect direction, plot number (from edge (1) to interior (5)), fragment connectivity, and fragment size), and the diameter, decay class, and volume for each piece of downed woody debris measured in the field OrderedSpecies.csv – data file containing an alphabetically ordered list of all tree species found in the study region Traits.csv – data file containing the full set of functional traits for each tree species Traits_FDis7.csv – reduced traits data file containing only the traits used in the final analysis Traits_Description.csv – text file describing the information in the “Traits.csv” and “Traits_FDis7.csv” files, including explanatory codes for the functional traits used to compute functional diversity indices, trait type, transformations and trait weights, and literature sources.Keywords:
Trait
Code (set theory)
For a long time, ecologists have worked on gaining insight into the processes that govern the assembly of natural plant communities. Plant trait-based approaches have great potential to improve our understanding of community assembly and assessment of ecosystem services. In many trait-based studies, trait-for-species substitutions are used by assigning mean trait values to each species in a community, which means that only between-species trait variability (i.e. interspecific trait variability) is considered. As there is growing evidence of the importance of within-species trait variability (i.e. intraspecific trait variability), this work is dedicated to studying how the applicability of these trait-based approaches is constrained by intraspecific variability and scale dependence. It could be shown that both interspecific trait variability and intraspecific trait variability indeed determine the trait patterns among habitats, communities and species. As a consequence, neglecting either type of trait variability by relying on species potential trait values derived from a much larger scale than the processes studied can lead to misleading conclusions, also on the community level. The benefits of using species mean trait values derived from large databases for a trait-based study will strongly depend on the level and scale of the question. In summary, there is a need to clearly differentiate between realized and potential traits on all levels in trait-based studies.
Trait
Community
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Some commonly reported trait-trait relationships between species, including the leaf economic spectrum (LES), are regarded as important plant strategies but whether these relationships represent plant strategies in reality remains unclear. We propose a novel approach to distinguish trait-trait relationships between species that may represent plant strategies vs those relationships that are the result of common drivers, by comparing the direction and strength of intraspecific trait variation (ITV) vs interspecific trait variation. We applied this framework using a unique global ITV database that we compiled, which included 11 traits related to LES, size and roots, and observations from 2064 species occurring in 1068 communities across 19 countries. Generally, compared to between species, trait-trait relationships within species were much weaker or totally disappeared. Almost only within the LES traits, the between-species trait-trait relationships were translated into positive relationships within species, which suggests that they may represent plant strategies. Moreover, the frequent coincidental trait-trait relationships between species, driven by co-varying common drivers, imply that in future research, decoupling of trait-trait relationships should be considered seriously in model projections of ecosystem functioning. Our study emphasizes the importance of describing the mechanisms behind trait-trait relationships, both between and within species, for deepening our understanding of general plant strategies.
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Loss of traits can dramatically alter the fate of species. Evidence is rapidly accumulating that the prevalence of trait loss is grossly underestimated. New findings demonstrate that traits can be lost without affecting the external phenotype, provided the lost function is compensated for by species interactions. This is important because trait loss can tighten the ecological relationship between partners, affecting the mainte- nance of species interactions. Here, we develop a new perspective on so-called 'compensated trait loss' and how this type of trait loss may affect the evolutionary dynamics between interacting organisms. We argue that: (1) the frequency of compensated trait loss is currently underestimated because it can go unnoticed as long as ecological interactions are maintained; (2) by analysing known cases of trait loss, specific factors promoting compensated trait loss can be identified and (3) genomic sequencing is a key way forwards in detecting compensated trait loss. We present a comprehensive literature survey showing that compensated trait loss is taxonomically widespread, can involve essential traits, and often occurs as replicated evolution- ary events. Despite its hidden nature, compensated trait loss is important in directing evolutionary dynamics of ecological relationships and has the potential to change facultative ecological interactions into obligatory ones.
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Evolutionary Dynamics
Facultative
Trait theory
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Abstract Questions Research efforts have sought to understand trait–trait relationships among species and trait–environment relationships. However, connections between these two approaches are rare, despite the fact that species‐level trait–trait correlations constrain the possible trait–environment correlations. We ask how functional traits of grasses are related to each other and to environmental variation. Location Global, with particular focus on the continental United States. Methods We compiled distribution data for grasses with three spatial grains – TDWG Level 3 ‘botanical countries’, US counties and vegetation plots within the US. We combined these data with trait data compiled from published sources for 14 traits describing physical and chemical features of the leaves, seeds, roots and entire plant. Trait–trait relationships were explored using correlations and PCA, and trait–environment relationships using regression. Finally, we implemented a null model to predict trait–trait correlations at the assemblage level from those at the species level. Results The functional trait composition of grass species varied strongly along environmental gradients. At the species level, there were two main clusters of related traits – one describing general plant size (including height, seed mass, leaf size and rooting depth), and one describing the leaf economics spectrum (including specific leaf area, N mass and P mass ). Most trait–trait correlations at the assemblage level did not differ significantly from that predicted from the species level, suggesting that the former are strongly constrained by the latter. Trait–trait and trait–environment relationships in grasses were broadly similar to those observed for other groups, with some exceptions related to the particular growth form, physiology and ecology of grass species. Conclusions The unique evolutionary history and ecological role of grasses has led to some unusual trait–climate relationships in the group. Co‐variation among traits at the species level is an important template upon which environmental filters act to determine assemblage trait composition.
Trait
Specific leaf area
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An expanding trend for genetically engineered (GE) crops is to cultivate varieties in which two or more single trait products have been combined using conventional breeding to produce a stacked trait product that provides a useful grouping of traits. Here, we report results from compositional analysis of several GE stacked trait products from maize and soybean. The results demonstrate that these products are each compositionally equivalent to a relevant non-GE comparator variety, except for predictable shifts in the fatty acid profile in the case of stacked trait products that contain a trait, MON 87705, that confers a high-oleic-acid phenotype in soybean. In each case, the conclusion on compositional equivalence for the stacked trait product reflects the conclusions obtained for the single trait products. These results provide strong support for conducting a reassessment of those regulatory guidelines that mandate explicit characterization of stacked trait products produced through conventional breeding.
Trait
Genetically engineered
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People differ from each other in their typical patterns of behavior, thought, and emotion and these patterns are considered to constitute their personalities (Funder, 2001). For various reasons, for example because certain trait levels may help to attain certain goals or fulfill certain social roles, people may experience that their actual trait levels are different from their ideal trait levels. In this study, we investigated (1) the impact of age on discrepancies between actual and ideal Big Five personality trait levels and (2) the impact of these discrepancies on personality trait changes across a period of two years. We use data of a large, nationally representative, and age-diverse sample (N = 4,057, 17-94 years, M = 53 years). Results largely confirmed previously reported age effects on actual personality trait levels but were sometimes more complex. Ideal trait levels exceeded actual trait levels more strongly for younger compared to older adults. Unexpectedly, neither ideal trait levels nor their interaction with beliefs about the extent to which personality is malleable vs. fixed predicted trait change over two years (controlling for actual trait levels). We conclude that ideal-actual trait level discrepancies may provide an impetus for change but that they appear to neither alone nor in combination with the belief that personality trait change is possible suffice to produce such change. We discuss commitment, self-efficacy, and strategy knowledge as potential additional predictors of trait change.
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Personality psychology
Trait theory
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This study examined young children's beliefs about trait stability based on trait type and valence. Participants included 120 children (40 three-year-olds, 40 four-year-olds, and 40 five-year olds) recruited from 3 day-care-centers and 1 kindergarten in Seoul and Kyung-Ki province. Results revealed that young children's understanding of traits differed based on trait type and valence. Children demonstrated a strong belief that social-intention traits are more stable and harder to change when compared to internal-state traits. Young children's beliefs on trait stability were also strongly influenced by trait valence. They believed that negative traits are more likely to change for the better while positive traits have greater stability and are less likely to change over time.
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The purpose of this introductory chapter is to introduce the Trait Emotional Intelligence (trait EI) construct selected as the foundation of this book, to examine the role that trait EI has in leadership and education, and to provide a history of the trait EI construct. In addition, this chapter discusses the principal arguments in the scholarly literature that reinforce the importance of the trait EI construct by examining the reliability and validity of trait EI, discussing the benefits of trait EI, and presenting the paradigms of those scholars who believe that that individuals can enhance their current trait EI skills and techniques through training. Finally, this chapter will also discuss the principal contentions in the scholarly literature that oppose the trait EI construct by examining the unreliability and invalidity of trait EI, discussing the disadvantages of trait EI, and presenting arguments that individuals cannot enhance their current trait EI skills and techniques through training.
Trait
Trait theory
Foundation (evidence)
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Abstract Trait-based ecology is greatly informed by large datasets for the analyses of inter- and intraspecific trait variation (ITV) in plants. This is especially true in trait-based agricultural research where crop ITV is high, yet crop trait data remains limited. Based on farmer-led collections, we developed and evaluated the first citizen science plant trait initiative. Here we generated a dataset of eight leaf traits for a commercially important crop species ( Daucus carota ), sampled from two distinct regions in Canada, which is 25-fold larger than datasets available in existing trait databases. Citizen-collected trait data supported analyses addressing theoretical and applied questions related to (i) intraspecific trait dimensionality, (ii) the extent and drivers of ITV, and (iii) the sampling intensity needed to derive accurate trait values. Citizen science is a viable means to enhance functional trait data coverage across terrestrial ecosystems, and in doing so, can directly support theoretical and applied trait-based analyses of plants.
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Citizen Science
Trait theory
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Recent evidence suggests that people's personality disorder (PD) trait levels relate positively to attitudes toward that PD trait, but amid this evidence has arisen an incongruity. In separate studies, people's PD trait levels relate positively to rating that PD trait as beneficial and impairing, so explanations for the positive relation between PD trait levels and PD trait attitudes are needed. We tested 2 explanations using a sample including adults (N = 457) who self-reported PD trait levels as well as PD trait benefit, impairment, and attitudes. The maximization hypothesis, which argues that higher PD trait levels correspond more strongly to trait-corresponding benefit than impairment, received some support. The weighting hypothesis, which argues that people disproportionately weigh PD trait benefits over impairments upon generating attitudes of a PD trait, received general support. Mediation analyses indicated that for each PD trait domain, the indirect effect of PD trait levels on trait-corresponding attitudes was stronger via trait-corresponding benefit compared with impairment. We also obtained evidence that relations between PD trait levels and trait-corresponding attitudes or benefit ratings, but not impairment ratings, were enhanced as perceived control over that trait's expressions increased. Findings help illuminate some of the mystery surrounding PD trait evaluation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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PsycINFO
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