2010
DOI: 10.1086/648509
|View full text |Cite
|
Sign up to set email alerts
|

Phylogeny, Ecology, and Heart Position in Snakes

Abstract: JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Online enhancements: data file, appendixes. ABSTRACTThe cardiovascular system of all animals is affected by gravitational pressure gradients, the intensity of which varies acc… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
63
0
1

Year Published

2011
2011
2020
2020

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 60 publications
(64 citation statements)
references
References 55 publications
0
63
0
1
Order By: Relevance
“…Alpha was 0.05. Akaike's information criterion corrected for sample size (AIC c ) was used initially to assess explanatory merits of the models [34].…”
Section: (B) Statistical Analysesmentioning
confidence: 99%
“…Alpha was 0.05. Akaike's information criterion corrected for sample size (AIC c ) was used initially to assess explanatory merits of the models [34].…”
Section: (B) Statistical Analysesmentioning
confidence: 99%
“…Similar tests can be used to compare the fit of models within the OLS, PGLS or RegOU classes when they contain nested subsets of independent variables (e.g. Lavin et al, 2008;Gartner et al, 2010).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…We utilized a model selection approach to objectively choose among models corresponding to different combinations of adaptive hypotheses for each of the regression methods (OLS, PGLS and RegOU) (e.g. see Gartner et al, 2010). For each model, we report the ln maximum likelihood, Akaike information criterion [AIC=(−2×ln maximum likelihood)+(2×number of parameters)], and AIC corrected for small sample size [AICc=(−2×ln maximum likelihood)+(2×p×n/(n-p-1)], where p is the number of parameters and n is the sample size (in these formulations, smaller numbers indicate better-fitting models) (see Burnham and Anderson, 2002).…”
Section: Statistical Analysesmentioning
confidence: 99%
“…We tested for any effect of these mutations on wheel running and open-field behavior using RegressionV2.m (Lavin et al 2008). We used a phylogenetic generalized least squares model in which the regression coefficients and the strength of phylogenetic signal in the residuals were estimated (Restricted Maximum Likelihood) simultaneously assuming an Ornstein-Uhlenbeck (OU) evolutionary process along the phylogenetic tree (sensu Swanson and Garland 2009;Gartner et al 2010). The OU model is often used to model stabilizing selection (Garland et al 1993;Hansen 1997;Butler and King 2004).…”
Section: Statistical Analysesmentioning
confidence: 99%