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Variation in prevalence and intensity of two avian ectoparasites in a polluted area

Published online by Cambridge University Press:  07 August 2013

TAPIO EEVA*
Affiliation:
Section of Ecology, Department of Biology, FI-20014 University of Turku, Finland
TERO KLEMOLA
Affiliation:
Section of Ecology, Department of Biology, FI-20014 University of Turku, Finland
*
*Corresponding author: Section of Ecology, Department of Biology, FI-20014 University of Turku, Finland. E-mail: tapio.eeva@utu.fi
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Summary

We counted the numbers of pupae of two ectoparasitic flies (Protocalliphora sp. and Ornithomyia sp.) in the nests of a passerine bird, the pied flycatcher (Ficedula hypoleuca) to find out if their prevalence or intensity are affected by long-term environmental pollution by a copper smelter and whether such an interaction would have impacts on birds' breeding success. Fecal metal concentrations of F. hypoleuca nestlings were used to explore direct association between metal levels and parasite prevalence, but we also included other explanatory factors in our analysis, such as timing of breeding, brood size, ambient temperature, habitat quality and host population density. We found that environmental pollution decreased the prevalence of Protocalliphora via changed habitat quality but did not affect the prevalence of Ornithomyia. The prevalence of neither ectoparasite was, however, directly related to ambient metal levels. Both ectoparasites showed higher prevalence when ambient temperature during the nestling period was high, emphasizing the potential of climate change to modify host–parasite relationships. The prevalence of Ornithomyia was further highest in dense F. hypoleuca populations and late broods. Nestling survival decreased with increasing infestation intensity of Ornithomyia while no association was found for Protocalliphora. Despite relatively low numbers and overall weak effect of parasites on survival, the possible delayed and/or sublethal effects of these ectoparasites call for further studies. Our results suggest that pollution-related effects on avian ectoparasite numbers are species-specific and reflect habitat changes rather than direct toxic effect of heavy metals.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2013 
Figure 0

Fig. 1. Examples of pupae of Protocalliphora sp. (a) and Ornithomyia sp. (b) collected from the nests of Ficedula hypoleuca.

Figure 1

Fig. 2. Annual prevalences (proportion of nests parasitized±95% CL) and intensities (numbers/infected nests; means±95% CL) of pupae of ectoparasitic flies, Protocalliphora and Ornithomyia in the nests of a passerine bird, Ficedula hypoleuca, breeding in metal-polluted (near copper smelter) and unpolluted sites. Confidence limits come from the full factorial GLM models (including year and zone). The numbers above the bars indicate sample sizes.

Figure 2

Table 1. Generalized linear models (GLM) for the prevalence (% nests infested) and intensity (numbers/infected nests) of pupae of two parasitic fly species in the nests of F. hypoleuca during 2006–2012. Terms left in the final model are shown in bold

Figure 3

Fig. 3. The prevalence (probability±95% CL) of parasitic fly pupae in the nests of Ficedula hypoleuca (n=634) as a function of (a) host density (occupation rate of nest boxes) and (b) nestling time mean temperatures. Circles: Protocalliphora; triangles: Ornithomyia. Data points are predicted values from logistic regression. For the sake of clarity the other explanatory factors (see Table 2) were omitted from the logistic regression model to plot the predicted prevalence values against density or temperature.

Figure 4

Table 2. Generalized linear mixed models (GLMM)a for explaining the prevalence of pupae of two parasitic flies, Protocalliphora sp. and Ornithomyia sp. in the nests of Ficedula hypoleuca (n=634) during 2006–2012. Terms left in the final model are shown in bold

Figure 5

Fig. 4. The association between fledging probability (±95% CL) and number of Ornithomyia pupae in the nests of Ficedula hypoleuca (n=619). Open circles: unpolluted zone; filled circles: polluted zone. Probabilities are based on generalized linear model with zone, number of pupae and zone×n of pupae as explanatory factors (year is included in the model as a random factor, but common predicted values and confidence limits are presented for all years).

Figure 6

Table 3. Generalized linear mixed model (GLMM)a for explaining the breeding success (fledglings/hatchlings) of Ficedula hypoleuca (n=619) during 2006–2012. Terms left in the final model are shown in bold