Figure 1.
Study area and spatial distribution of data.
Close sampling points are summarised by black circles. Countries (sampled bird individuals): Mauritania (85), Morocco (105), Spain (70,321), Denmark (2,394), Romania (1,827), Ukraine (1,175) and Kazakhstan (37).
Figure 2.
Prevalence of feather mites of birds.
Feather mite prevalence (proportion of birds with feather mites) in bird species with data for more than 1,000 individual birds with corresponding 95% confidence interval estimates.
Figure 3.
Species repeatability and adjusted repeatability of feather mite prevalence.
Species repeatability (R; white circles) and adjusted repeatability (Radj; black circles) for feather mite prevalence with corresponding 95% confidence interval estimates for different intensity data subsets (either the entire dataset, “All observers”, or for data from seven different researchers separately). The following confounding effects were retained in the models: “breeding” was retained for observers 1–7; "habitat" was also retained for observers 2, 5 and 6; the “PC1” was also added for observers 1 and 4; finally, six variables of the spatial autocorrelation term were retained for observer 4. For “All Observers” the final model included the fixed effects shown in Table 1. All R and Radj estimates were statistically significant at α = 0.001. 95% CI could not be calculated for Radj (see Methods).
Table 1.
Parameter estimates for GLMM of the dataset of prevalence of feather mites on birds.
Figure 4.
Feather mite prevalence (proportion of birds with feather mites) for ten species of well-sampled resident passerines in three habitats.
Species are ordered from left to right within each habitat according to their prevalences in wetlands as follows: Phylloscopus collybita, Acrocephalus scirpaceus, Carduelis chloris, Cettia cetti, Cyanistes caeruleus, Luscinia megarhynchos, Erithacus rubecula, Serinus serinus, Sylvia atricapilla and Fringilla coelebs. “Breeding” and “habitat” variables were retained as fixed factors in the GLMM, while “observer” and “species” were included as random factors.
Figure 5.
Feather mite intensity of birds.
Box-plot of feather mite intensity (on log10 axis) for species with more than 300 records. Species are ordered according to their median intensity.
Figure 6.
Species repeatability and adjusted repeatability of feather mite intensity.
Species repeatability (R; white circles), and adjusted repeatability (Radj; black circles) for feather mite intensity with corresponding 95% confidence interval estimates for different intensity data subsets (either the entire dataset, “All observers”, or for data from five different researchers separately). Results are shown separately for five different observers (see Methods), and also for the entire dataset (“All observers”). Adjusted repeatability estimates for observers 1, 3, 4 and 5 were obtained using “breeding” as a fixed effect. For observer 2 “habitat” was retained as a fixed effect. For observers 1 and 2, two and one variables of the autocorrelation term were also added as fixed effects, respectively (see Methods).
Table 2.
Parameter estimates for LME of the entire dataset of log10-intensity of feather mites in birds.
Figure 7.
Boxplot of feather mite intensity for ten different species of well-sampled resident passerines in three habitats.
Species are ordered from left to right within each habitat according to the intensities of feather mites in individuals captured in wetlands, as follows: Phylloscopus collybita, Acrocephalus scirpaceus, Luscinia megarhynchos, Erithacus rubecula, Cettia cetti, Fringilla coelebs, Cyanistes caeruleus, Carduelis chloris, Serinus serinus and Sylvia atricapilla. “Breeding”, “habitat” and four variables from the spatial autocorrelation term were retained as fixed factors in LME, while “observer” and “species” were included as random factors.