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Trait Covariance (trait + covariance)

Distribution by Scientific Domains

Life Sciences	50%

Selected Abstracts

Test of Nyborg's General Trait Covariance (GTC) model for hormonally guided development by means of structural equation modeling

EUROPEAN JOURNAL OF PERSONALITY, Issue 3 2003
Martin Reuter
Nyborg's General Trait Covariance (GTC) model for hormonally guided development investigates the influence of gonadal hormones and fluid intelligence on body build, achievement, and socioeconomic variables. According to the model, testosterone should be negatively related to height, fat/muscle ratio, intelligence, income, and education. It is conceived that this influence should be determined to a great extent by mutual relationships between these variables. The model was tested by means of structural equation modeling (SEM) in a sample of 4375 males who had served in the United States Armed Forces. The results largely confirm Nyborg's androtype model but in addition reflect the relationships between the variables included in a quantitative causal manner. It could be shown that testosterone has a negative influence on crystallized intelligence and that this effect is mainly mediated by the negative influence of testosterone on education. An additional multiple group analysis testing for structural invariance across age groups revealed that the mediating role of education is more pronounced in old veterans. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Behavioural syndromes differ predictably between 12 populations of three-spined stickleback

JOURNAL OF ANIMAL ECOLOGY, Issue 6 2007
NIELS J. DINGEMANSE
Summary 1Animals often differ in suites of correlated behaviours, comparable with how humans differ in personality. Constraints on the architecture of behaviour have been invoked to explain why such ,behavioural syndromes' exist. From an adaptationist viewpoint, however, behavioural syndromes should evolve only in those populations where natural selection has favoured such trait covariance, and they should therefore exist only in particular types of population. 2A comparative approach was used to examine this prediction of the adaptive hypothesis. We measured behavioural correlations in 12 different populations of three-spined stickleback (Gasterosteus aculeatus) and assessed whether they indeed varied consistently according to the selective environment, where population was unit of analysis. 3For a sample of fry from each population, we measured five different behaviours within the categories of (i) aggression (towards conspecifics); (ii) general activity; and (iii) exploration,avoidance (of novel foods, novel environments and altered environments). 4We show that behavioural syndromes are not always the same in different types of stickleback population: the often-documented syndrome between aggressiveness, activity and exploratory behaviour existed only in large ponds where piscivorous predators were present. In small ponds where predators were absent, these behaviours were not (or only weakly) associated. 5Our findings imply that population variation in behavioural syndromes does not result from stochastic evolutionary processes, but may result instead from adaptive evolution of behaviour favouring what should prove to be optimal trait combinations. [source]

On testing predictions of species relative abundance from maximum entropy optimisation

OIKOS, Issue 4 2010
Stephen H. Roxburgh
A randomisation test is described for assessing relative abundance predictions from the maximum entropy approach to biodiversity. The null model underlying the test randomly allocates observed abundances to species, but retains key aspects of the structure of the observed communities; site richness, species composition, and trait covariance. Three test statistics are used to explore different characteristics of the predictions. Two are based on pairwise comparisons between observed and predicted species abundances (RMSE, RMSESqrt). The third statistic is novel and is based on community-level abundance patterns, using an index calculated from the observed and predicted community entropies (EDiff). Validation of the test to quantify type I and type II error rates showed no evidence of bias or circularity, confirming the dependencies quantified by Roxburgh and Mokany (2007) and Shipley (2007) have been fully accounted for within the null model. Application of the test to the vineyard data of Shipley et al. (2006) and to an Australian grassland dataset indicated significant departures from the null model, suggesting the integration of species trait information within the maximum entropy framework can successfully predict species abundance patterns. The paper concludes with some general comments on the use of maximum entropy in ecology, including a discussion of the mathematics underlying the Maxent optimisation algorithm and its implementation, the role of absent species in generating biased predictions, and some comments on determining the most appropriate level of data aggregation for Maxent analysis. [source]