A general approach to the analysis of habitat selection

The investigation of animal habitat selection aims at the detection of selective usage of habitat types and the identification of covariates influencing their selection. The results not only allow for a better understanding of the habitat selection process but are also intended to help improve the conservation of animals. Usually, habitat selection by larger animals is assessed by radio-tracking or visual observation studies, where the chosen habitat is determined for some animals at a set of specific points in time. Hence the resulting data often have the following structure: a categorical variable indicating the habitat type selected by an animal at a specific point in time is repeatedly observed and will be explained by covariates. These may either describe properties of the habitat types currently available and/or properties of the animal. In this paper, we present a general approach to the analysis of such data in a categorical regression setup. The proposed model generalizes and improves upon several of the approaches previously discussed in the literature. In particular, it accounts for changing habitat availability due to the movement of animals within the observation area. It incorporates both habitat- and animal-specific covariates, and includes individual-specific random effects to account for correlations introduced by the repeated measurements on single animals. Furthermore, the assumption that the effects are linear can be dropped by including the effects in nonparametric manner based on a penalized spline approach. The methodology is implemented in a freely available software package. We demonstrate the general applicability and the potential of the proposed approach in two case studies: The analysis of a songbird community in South-America and a study on brown bears in Central Europe.     

Contribution of tobacco smoke to environmental benzene exposure in Germany

The concentrations of environmental tobacco smoke (ETS) constituents including benzene were measured in the living rooms of 10 nonsmoking households and 20 households with at least one smoker situated in the city and suburbs of Munich. In the city, the median benzene levels during the evening, when all household members were at home, were 8.1 and 10.4 μg/m³ in nonsmoking and smoking homes, respectively. The corresponding levels of 3.5 and 4.6 μg/m³ were considerably lower in the suburbs. Median time-integrated 1-week benzene concentrations in the city were 10.6 μg/m³ in nonsmoking homes and 13.1 μg/m³ in smoking homes. In the suburbs, the corresponding values were 3.2 and 5.6 μg/m³. While the benzene concentrations in nonsmoking homes located in the city were significantly higher (p < 0.05) than in suburban nonsmoking households, no difference was found between smoking and nonsmoking households located either in the city or in the suburbs. Individual exposures to benzene and to specific markers for tobacco smoke of all household members (82 nonsmokers and 32 smokers) were determined by questionnaire, personal monitoring, and biomonitoring. Within the city, the benzene exposure determined by personal samplers was 11.8 μg/m³ for nonsmokers living in nonsmoking homes and 13.3 μg/m³ for nonsmokers in smoking homes. The corresponding values for nonsmokers living in the suburbs were 5.9 and 6.9 μg/m³, respectively. Neither difference was statistically significant. Nonsmokers living in nonsmoking households in the city had significantly higher exposure to benzene compared to their counterparts living in the suburbs (personal samplers: 11.8 vs 5.9 μg/m³, p < 0.001; benzene in exhalate: 2.4 vs. 1.1 μg/m³, p < 0.05; trans,trans-muconic acid excretion in urine: 92 vs. 54 μg/g creatinine, p < 0.05). Nonsmokers from all households with smokers were significantly more exposed to benzene than nonsmokers living in the nonsmoking households (personal samplers: 13.2 vs. 7.0 μg/m³, p < 0.05; benzene in exhalate: 2.6 vs. 1.8 μg/m³, p < 0.01; trans,trans-muconic acid excretion in urine: 73 vs. 62 μg/g creatinine), but the contribution of ETS to the total benzene exposure was relatively low compared to that from other sources. Analysis of variance showed that at most 15% of the benzene exposure of nonsmokers living in smoking homes was attributable to ETS. For nonsmokers living in nonsmoking households benzene exposure from ETS was insignificant.