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Seasonal breeding drives the incidence of a chronic bacterial infection in a free-living herbivore population

Published online by Cambridge University Press:  14 October 2010

A. K. PATHAK
Affiliation:
Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, USA
B. BOAG
Affiliation:
Birch Brae, Knapp Perth and Kinross, UK
M. POSS
Affiliation:
Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, USA
E. T. HARVILL
Affiliation:
Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, USA
I. M. CATTADORI*
Affiliation:
Center for Infectious Disease Dynamics, Department of Biology, The Pennsylvania State University, University Park, PA, USA
*
*Author for correspondence: Dr I. M. Cattadori, Center for Infectious Disease Dynamics, Department of Biology, 508 Mueller Laboratory, The Pennsylvania State University, University Park 16802 PA, USA. (Email: imc3@psu.edu)
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Summary

Understanding seasonal changes in age-related incidence of infections can be revealing for disentangling how host heterogeneities affect transmission and how to control the spread of infections between social groups. Seasonal forcing has been well documented in human childhood diseases but the mechanisms responsible for age-related transmission in free-living and socially structured animal populations are still poorly known. Here we studied the seasonal dynamics of Bordetella bronchiseptica in a free-living rabbit population over 5 years and discuss the possible mechanisms of infection. This bacterium has been isolated in livestock and wildlife where it causes respiratory infections that rapidly spread between individuals and persist as subclinical infections. Sera were collected from rabbits sampled monthly and examined using an ELISA. Findings revealed that B. bronchiseptica circulates in the rabbit population with annual prevalence ranging between 88% and 97%. Both seroprevalence and antibody optical density index exhibited 1-year cycles, indicating that disease outbreaks were seasonal and suggesting that long-lasting antibody protection was transient. Intra-annual dynamics showed a strong seasonal signature associated with the recruitment of naive offspring during the breeding period. Infection appeared to be mainly driven by mother-to-litter contacts rather than by interactions with other members of the community. By age 2 months, 65% of the kittens were seropositive.

Information

Type
Original Papers
Copyright
Copyright © Cambridge University Press 2010
Figure 0

Fig. 1. B. bronchiseptica infection in a wild rabbit population based on serological analysis from 2005 to 2009. Monthly (mean±s.e.) prevalence (•) and optical density (OD) index (○) (only positive cases used) in: (a) total sampled population, (b) kittens, (c) juveniles, (d) adults.

Figure 1

Fig. 2. Changes in B. bronchiseptica infection (monthly mean±s.e.) by host age in a wild rabbit population sampled from 2005 to 2009. •, Seroprevalence; ○, antibody optical density (OD) index.

Figure 2

Fig. 3. Spectrogram of spectral density vs. cycle period (1/frequency) of B. bronchiseptica time-series for (a) seroprevalence and (b) optical density index data.

Figure 3

Fig. 4. Relationship between infection of B. bronchiseptica in seropositive 2- or 3-month-old kittens and seropositive breeding females. (a) Monthly number of positive cases with breeding females sampled the previous month (time lag 1). Monthly mean optical density (OD) index from positive cases with breeding females sampled: (b) the previous month (time lag 1) or (c) 2 months earlier (time lag 2).

Figure 4

Table 1. Summary of generalized linear models between 2- or 3-month-old kittens, as a response variable, and other age-related demographic groups as independent variables. Analysis based on number of positive cases, or optical density (OD) index from positive cases. Models with a direct (data collected the same month, i.e. time lag 0) or delayed (data shifted by 1 or 2 months, i.e. time lag 1 or lag 2) relationship between the response and the independent variable were examined (Gaussian errors). The sign of the relationship (+ or −) and the significant relationships [P(χ2) value] are reported

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