Skip to main content

Advertisement

Table 4 Full output of Model 3: best model of factors affecting bill surface area

From: Spatial variation in avian bill size is associated with humidity in summer among Australian passerines

  Wald statistic Df p-value parameter SE
(Intercept) 500.722 1 <0.001 5.650 0.56786
Wing length 636.932 1 <0.001 0.011 0.000568
SeasonCapture.autumn 51.499 3 <0.001 . .
 .winter   −0.010 0.005387
 .summer   0.031 0.007547
 .spring   0.013 0.005351
Sex (male relative to female) 60.443 1 <0.001 0.031 0.004217
YearCapture (continuous) 11.150 1 0.001 −0.00091 0.00027
Humidity 26.335 1 <0.001 0.0160 0.00544
Tmax 0.338 1 0.561 0.00587 0.00274
Humidity.Tmax 4.277 1 0.039 −0.0003467 0.000168
Variance/covariance of random effects Component SE  
Region 0.000446 0.000142  
YearCapture 0.000166 0.000082  
Species:Phylo 0.108706 0.070089  
Species:NonPhylo 0.053945 0.017836  
cov(SpeciesNonPhylo,SpNonPhylo:Humidity) 0.001227 0.001389  
SpNonPhylo:Humidity 0.000480 0.000211  
Residual 0.008343 0.000227  
  1. Results are from a full mixed model of bill size (log-transformed) with fixed effects of body size (wing length), season of capture, sex, humidity, Tmax and the interaction between humidity and Tmax. The table shows Wald statistics, df, p-values, parameter estimates and standard errors (SE). Significant weather terms (p < 0.05) are shown in bold. The random effects were region, year of capture (multi-level factor), phylogenetic species effect (SpeciesPhylo), a non-phylogenetic species effect (SpeciesNonPhylo), and an effect of humidity for each species (not linked to the phylogeny; SpNonPhylo:Humidity); see further details of Model 3 in Methods. The table shows the variance components for each random effect (and SE), and also the covariance between the intercepts and slope for the non-phylogenetic species effect, which was also fitted. Parameter estimates for Season of Capture are relative to autumn, and for Sex are for males relative to females. N = 2864 individuals; 36 species; 41 years; 77 IBRA Regions. Log-likelihood of model = 5187.482