Descriptive and statistical analyses of rodent density,
tick infestation and TBEV seroprevalence in rodents
Rodent density was calculated per session and per species according to
the standardised closed population Schnabel method that takes into
account multiple marking occasions (Schnabel, 1938). Since the captures
were carried out on three consecutive days per session, we considered
that the population was closed for each session. Rodent density per
season was calculated using the mean of rodent density estimated for the
corresponding months.
The tick infestation prevalence for small mammals was defined as the
number of small mammals carrying at least one tick divided by the number
of small mammals inspected. We calculated tick infestation prevalence
per year, season, species and age class (juveniles vs. adults). Exact
95% CIs were calculated on the basis of binomial distribution. We
described the infestation of small mammals by larvae and nymphs in
season 2 (the period of highest nymph density) of 2012 and 2013 by
calculating (1) the mean number of larvae and nymphs per individual
infested by ticks, (2) the proportion of small mammals infested by ticks
carrying nymphs only or nymphs and larvae, and (3) the proportion of
larvae feeding on small mammals that were also carrying nymphs. The
results for 2012 and 2013 were compared using a Mann-Whitney Utest.
The tick infestation status of small mammals was modelled using a
logistic GLM (generalised linear model) with a binomial distribution and
logit link, and a binary response variable (absence of ticks = 0;
presence of ticks = 1). The variables considered were the small mammal
species, sex, season, year, and the interactions between year and season
(to allow seasonal patterns to vary between years) and between season
and species (to mimic seasonal variation of small mammal community
structure and density). We included the number of captures within the
season as an offset term. Some animals were captured in several
different seasons and constituted temporal pseudoreplication of
individual hosts. We therefore ran logistic GLMs for those individuals
with the tick infestation status in season 2 (respectively in season 3)
as the response variable, the infestation status in season 1
(respectively season 2), species, sex and year as the explanatory
variables and the number of captures in season 2 (respectively season 3)
as an offset term. As the infestation status of the previous season had
no effect on the infestation status of the following season, we
considered that these pseudo-replications would not bias the results of
the model.
We calculated the TBEV seroprevalence of small mammals per year, season
and species, and its exact 95% CI based on binomial distribution. The
seropositive status of captured animals was modelled using a logistic
GLM as a function of the small mammal species, season and year. As few
individuals were seropositive (see Results), we limited the number of
variables included in the model.
Collinearity was checked in the models ensuring a variance inflation
factor (VIF) < 10 (James, Written, Hastie, & Tibshirani,
2017). The backward elimination of explanatory variables was used to
identify the most parsimonious model with the smallest Akaike
information criteria (AIC).
Statistical computations were performed in R 3.5.0 (R Development Core
team, 2018). For all statistical tests, a p-value<0.05 was
considered statistically significant.
Ethical statement . The experimental protocol with small mammals
complied with EU Directive 2010/63/EU and was submitted to and approved
by the French Ministry of Research (APAFIS no. 2015120215112678). All
efforts were made to minimize animal suffering. The species studied are
not protected in France or included in the IUCN Red List of threatened
species in France. The animal trapping took place with permission from
the landowners.