Skip to main content
Dryad

Data from: Co-infections and environmental conditions drive the distributions of blood parasites in wild birds

Data files

Jul 27, 2017 version files 5.96 MB

Click names to download individual files

Abstract

Experimental work increasingly suggests that non-random pathogen associations can affect the spread or severity of disease. Yet due to difficulties distinguishing and interpreting co-infections, evidence for the presence and directionality of pathogen co-occurrences in wildlife is rudimentary. We provide empirical evidence for pathogen co-occurrences by analysing infection matrices for avian malaria (Haemoproteus and Plasmodium spp.) and parasitic filarial nematodes (microfilariae) in wild birds (New Caledonian Zosterops spp.). Using visual and genus-specific molecular parasite screening, we identified high levels of co-infections that would have been missed using PCR alone. Avian malaria lineages were assigned to species level using morphological descriptions. We estimated parasite co-occurrence probabilities, while accounting for environmental predictors, in a hierarchical multivariate logistic regression. Co-infections occurred in 36% of infected birds. We identified both positively and negatively correlated parasite co-occurrence probabilities when accounting for host, habitat and island effects. Two of three pairwise avian malaria co-occurrences were strongly negative, despite each malaria parasite occurring across all islands and habitats. Birds with microfilariae had elevated heterophil to lymphocyte ratios and were all co-infected with avian malaria, consistent with evidence that host immune modulation by parasitic nematodes facilitates malaria co-infections. Importantly, co-occurrence patterns with microfilariae varied in direction among avian malaria species; two malaria parasites correlated positively but a third correlated negatively with microfilariae. We show that wildlife co-infections are frequent, possibly affecting infection rates through competition or facilitation. We argue that combining multiple diagnostic screening methods with multivariate logistic regression offers a platform to disentangle impacts of environmental factors and parasite co-occurrences on wildlife disease.