Analysis of brood sex ratios: implications of offspring clustering
By S. Krackow, E. Tkadlec. Behavioral Ecology and Sociobiology, 2001
156 citations recorded by the source corpus.
Subfields: Ecology, Nature and Landscape Conservation, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/759e0385a19f572ef2c1839f77d96f73eb63c715
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In Ecology
In Nature and Landscape Conservation
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Citations in the corpus
- Primary sex ratio adjustment to experimentally reduced male UV attractiveness in blue tits (2006, methodology): “We used multilevel mixed models with a binomial error distribution with a logit link function following Krackow and Tkadlec (2001) and Rasbash et al. (2004) to analyze sex ratio, with nestlings nested within broods (i.e., brood identity was fitted as a random effect).”
- Do extra‐pair paternities provide genetic benefits for female blue tits Parus caeruleus? (2004, methodology): “We used a generalized linear mixed model (PROC MIXED, SAS Institute 1992) with morphological or physiological traits as the response variable, brood identity as a random effect ( Krackow and Tkadlec 2001 ), and tested for effects of EPP.”
- Maternal testosterone affects the primary sex ratio and offspring survival in zebra finches (2006, methodology): “We analysed the probability that an egg contained a male embryo and the probabilities of hatching and survival by fitting a generalized linear mixed model using the GLIMMIX macro in SAS version 8 (SAS 2000) with logit link function and binomial error variance (Krackow & Tkadlec 2001).”
- Generalized linear mixed models for phylogenetic analyses of community structure (2011, unknown): “…a regression for the presence/absence of species among communities that takes the form of a generalized linear mixed model (GLMM; Milner et al. 1999, Krackow and Tkadlec 2001, Kizilkaya and Tempelman 2005, Faes et al. 2006, Gelman and Hill 2007, McCulloch et al. 2008, Bolker et al. 2009).”
- Litter size and fetal sex ratio adjustment in a highly polytocous species: the wild boar (2007, methodology): “Once the best model was selected, we checked whether its residual mean deviance was close to 1 and when it exceeded 1.5, we corrected for overdispersion (e.g., Krackow and Tkadlec 2001; Wilson and Hardy 2002).”