BackgroundOnly limited information is available about the occurrence of ticks and tick-borne pathogens in public parks, which are areas strongly influenced by human beings. For this reason, Ixodes ricinus were collected in public parks of different Bavarian cities in a 2-year survey (2009 and 2010) and screened for DNA of Babesia spp., Rickettsia spp. and Bartonella spp. by PCR. Species identification was performed by sequence analysis and alignment with existing sequences in GenBank. Additionally, coinfections with Anaplasma phagocytophilum were investigated.ResultsThe following prevalences were detected: Babesia spp.: 0.4% (n = 17, including one pool of two larvae) in 2009 and 0.5 to 0.7% (n = 11, including one pool of five larvae) in 2010; Rickettsia spp.: 6.4 to 7.7% (n = 285, including 16 pools of 76 larvae) in 2009. DNA of Bartonella spp. in I. ricinus in Bavarian public parks could not be identified. Sequence analysis revealed the following species: Babesia sp. EU1 (n = 25), B. divergens (n = 1), B. divergens/capreoli (n = 1), B. gibsoni-like (n = 1), R. helvetica (n = 272), R. monacensis IrR/Munich (n = 12) and unspecified R. monacensis (n = 1). The majority of coinfections were R. helvetica with A. phagocytophilum (n = 27), but coinfections between Babesia spp. and A. phagocytophilum, or Babesia spp. and R. helvetica were also detected.ConclusionsI. ricinus ticks in urban areas of Germany harbor several tick-borne pathogens and coinfections were also observed. Public parks are of particularly great interest regarding the epidemiology of tick-borne pathogens, because of differences in both the prevalence of pathogens in ticks as well as a varying species arrangement when compared to woodland areas. The record of DNA of a Babesia gibsoni-like pathogen detected in I. ricinus suggests that I. ricinus may harbor and transmit more Babesia spp. than previously known. Because of their high recreational value for human beings, urban green areas are likely to remain in the research focus on public health issues.

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The return of the Eurasian Lynx to Central Europe has led to a number of conflicts. A primary subject of discussion involves its predation on other wildlife species. Here, we investigated the influence of lynx on its main prey, Roe Deer, in the Bavarian Forest National Park in south-eastern Germany. We compared the survival rates of deer before and after reintroduction of lynx. The analysis is based on data from 1984 to 1988 and 2005 to 2008 of 88 and 99 radio-collared Roe Deer, respectively. During the first period, 35 deer deaths were documented; during the second period, 41 deaths were documented. The causes of death in the second period were lynx 44%, road kill 15%, hunting 12%, and other causes 29%. We used the Cox model to determine the influence of covariables on the hazard rate, which made it possible to consider interactions between the variables. The resulting model includes the four main effects sex, age, presence of lynx, and severity of first winter, and the three interactions-presence of lynx:sex, age:severity of first winter, and sex:severity of first winter, which had a statistically significant influence on Roe Deer survival.

Activity patterns of predators are influenced by several factors including season and temperature as well as the availability of prey species. We investigated the activity of six free‐living Eurasian lynx Lynx lynx (four males and two females without kittens) in the Bohemian Forest along the border between Germany and the Czech Republic. The lynx were tagged with GPS‐collars with acceleration sensors in 2005, 2010 and 2011. Activity was measured every 5 minutes on 1,360 days (403,467 measurements) to detect circadian activity patterns. All lynx were predominantly crepuscular, with an average activity of 8.9 hours/day and with the lowest activity at midday. The activity patterns of male and female lynx did not differ significantly. With each 10°C increase in the mean air temperature per day, the lynx decreased their daily activity by 30 minutes. In winter, activity was concentrated at dusk. We also investigated whether lynx activity was influenced by the availability of freshly killed roe deer Capreolus capreolus, red deer Cervus elaphus or European hare Lepus europaeus. We compared the activity data of 357 days with a kill (109 recorded kills) and 316 days without a kill and calculated generalised additive mixed models. On days with a kill, the lynx were 3.3 hours/ day less active than on days without a kill. The activity on consecutive days with a killed prey did not differ. The pattern of activity on days with a kill differed little from the pattern of activity on days without a kill.

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.