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Between-individual variation in nematode burden among juveniles in a wild host

Published online by Cambridge University Press:  22 November 2016

H. M. V. GRANROTH-WILDING*
Affiliation:
Ashworth Laboratories, Wellcome Centre for Infection, Immunity and Evolution/Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
F. DAUNT
Affiliation:
NERC Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, UK
E. J. A. CUNNINGHAM
Affiliation:
Ashworth Laboratories, Wellcome Centre for Infection, Immunity and Evolution/Institute of Evolutionary Biology, University of Edinburgh, Charlotte Auerbach Road, Edinburgh EH9 3FL, UK
S. J. BURTHE
Affiliation:
NERC Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, UK
*
*Corresponding author. P.O. Box 65, 00014 Helsinki. E-mail: hanna@granroth-wilding.co.uk

Summary

Parasite infection in young animals can affect host traits related to demographic processes such as survival and reproduction, and is therefore crucial to population viability. However, variation in infection among juvenile hosts is poorly understood. Experimental studies have indicated that effects of parasitism can vary with host sex, hatching order and hatch date, yet it remains unclear whether this is linked to differences in parasite burdens. We quantified gastrointestinal nematode burdens of wild juvenile European shags (Phalacrocorax aristotelis) using two in situ measures (endoscopy of live birds and necropsy of birds that died naturally) and one non-invasive proxy measure (fecal egg counts (FECs)). In situ methods revealed that almost all chicks were infected (98%), that infections established at an early age and that older chicks hosted more worms, but FECs underestimated prevalence. We found no strong evidence that burdens differed with host sex, rank or hatch date. Heavier chicks had higher burdens, demonstrating that the relationship between burdens and their costs is not straightforward. In situ measures of infection are therefore a valuable tool in building our understanding of the role that parasites play in the dynamics of structured natural populations.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2016 
Figure 0

Fig. 1. Histograms showing the spread of worm counts from necropsy (left panel) and endoscopy (right panel). The dissection data does not show two high-leverage individuals excluded from the analysis, a hatchling with no worms and a near-fledgling with 243 worms.

Figure 1

Fig. 2. Total worm burden in relation to chick age for necropsied chicks (left panel) and endosocped chicks (right panel). Among endoscoped chicks (which covered an older age range than necropsied chicks) there was some evidence that rank affected worm count, and to illustrate this, in the endoscopy panel AB chicks are shown with solid symbols and C chicks with open symbols. The regression line shown is for the best-fit model, which did not include a rank term. Excluding the oldest chicks, which did not include any C chicks were found, did not alter the ordering of best-fit models. Note the difference in scale for worm counts and age ranges between the two measures. The mean lines show a fitted model without random effects using poisson errors and a log link, with 95% confidence intervals shown by the fine-dotted lines.

Figure 2

Fig. 3. The proportion of worms that were large in relation to chick age for necropsied (left panel) and endoscoped (right panel) chicks. In contrast to the worm count, excluding the oldest chicks here slightly changed the order of the best-fit models to: Age + Hatch date; Age; Age + Hatch date + Rank, Age + Rank. The mean lines show a fitted model without random effects and the fine-dotted lines show its 95% confidence intervals.

Figure 3

Table 1. The top five best-fitting models of worm burden (left columns) and the proportion of worms that were large (right columns) as measured by necropsy (top model set) or endoscopy (bottom model set) in relation to host phenotypic traits. Models in each set are shown in order of decreasing fit with their AICc and ΔAICc relative to the best-fit model. The candidate model set for each variable included all combinations of the following predictor variables: age, hatch date, rank, sex and for endoscopy also visibility. In the necropsy models, age and hatch date and could not be included in the same models as they were closely correlated. Accordingly, models containing hatch date gave almost identical fits to those instead containing age; to illustrate a broader range of model fits, we show only the age models here

Figure 4

Table 2. A summary of patterns of variation in nematode burdens between shag chicks, as quantified using necropsy or endoscopy, in relation to phenotypic host traits. Patterns were investigated in both the total worm burden (top set of variables) and the proportion of worms that were large, indicative of how long the chick had been infected (bottom set of variables). Traits that robustly affected worm measures (occurred in all equivalent-fit models) are indicated with a tick, traits that had some support (occurred in more than one equivalent-fit model) are shown with a tick in brackets, and traits with no robust effects are shown with a cross. Hatch date for dissected chicks is indicated with a dash to show that it could not be tested simultaneously with age, as they were tightly correlated

Figure 5

Fig. 4. The relationship among endoscoped chicks between mass at endoscopy and worm count. The solid line shows the fitted relationship and the dotted line the 95% confidence intervals. To account for other factors affecting mass it is shown as the residual from a LMM containing age as the only predictor, following the best-fit model for chick mass.

Figure 6

Table 3. The top five best fit models of mass of endoscoped chicks. The set of candidate models included all combinations of the following variables: worm count (measured by endoscopy), log(age), sex and rank

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