Heavy-metal concentrations in small mammals from a diffusely polluted floodplain: importance of species- and location-specific characteristics.

The soil of several floodplain areas along large European rivers shows increased levels of heavy metals as a relict from past sedimentation of contaminants. These levels may pose risks of accumulation in food webs and toxicologic effects on flora and fauna. However, for floodplains, data on heavy-metal concentrations in vertebrates are scarce. Moreover, these environments are characterised by periodical flooding cycles influencing ecologic processes and patterns. To investigate whether the suggested differences in accumulation risks for insectivores and carnivores, omnivores, and herbivores are reflected in the actual heavy-metal concentrations in the species, we measured the current levels of Zn, Cu, Pb, and Cd in 199 specimens of 7 small mammal species (voles, mice, and shrews) and in their habitats in a diffusely polluted floodplain. The highest metal concentrations were found in the insectivorous and carnivorous shrew, Sorex araneus. Significant differences between the other shrew species, Crocidura russula, and the vole and mouse species was only found for Cd. The Cu concentration in Clethrionomys glareolus, however, was significantly higher than in several other vole and mouse species. To explain the metal concentrations found in the specimens, we related them to environmental variables at the trapping locations and to certain characteristics of the mammals. Variables taken into account were soil total and CaCl(2)-extractable metal concentrations at the trapping locations; whether locations were flooded or nonflooded; the trapping season; and the life stage; sex; and fresh weight of the specimens. Correlations between body and soil concentrations and location or specimen characteristics were weak. Therefore; we assumed that exposure of small mammals to heavy-metal contamination in floodplains is significantly influenced by exposure time, which is age related, as well as by dispersal and changes in foraging and feeding patterns under influence of periodic flooding.

Some models describing the distribution of eggs of the parasitePseudeucoila bochei (Hym., Cynip.) over its hosts, larvae ofDrosophila melanogaster.

Ovipositing females of the cynipid waspPseudeucoila bochei discriminate between parasitized and unparasitized hosts, which results in a far more uniform distribution of eggs over the hosts than would be obtained if oviposition were random (Fig. 1,a 0-f 0).For the description of the distributions a few models were worked out, which rest on the assumption that the hosts are probed at random. The total number of effective probes made in a larva during the experiment is a random variable with a Poisson distribution and an expectation λ. The chance that at a certain probe an egg will be laid (δ) is dependent on the number of eggs present (j); 1=δ0<δ1≧δ2≧δ3.... In model I it was assumed that the female had only the ability to distinguish parasitized from unparasitized hosts. The chance that an egg will be laid in an unparasitized host when it is probed, δ0, is considered to be equal to 1, while δ1=δ2=...=δ n <1 (Fig. 2, 1,a 1-f 1). When the mean number of eggs present in a host was larger than about 1.1, this model did not describe the distribution of eggs satisfactorily (Fig. 3).It seemed that the ovipositing female is not only able to distinguish parasitized from unparasitized hosts, but also to distinguish thenumber of eggs present in a host. In model II it was assumed that the wasp could distinguish between hosts with 0, 1, and 2 or more eggs: the chance that an egg would be laid in a host containing 2, 3, 4, ... eggs was, hence, the same in this model δ1<δ2=δ3=...=δ n (Fig. 2, 1,c 2,e 2,f 2). This model described the distributions of eggs much better (Figs. 4 and 5), but at mean numbers of eggs per host above 2 it was apparently inadequate.Two other models were then tried, in which the chance δ that an egg would be laid in a host decreased with the number of eggs already present (j). In model III (Fig. 2) the chance decreased according to the function δ j =δ/j (δ0=1, δ<1). Fig. 1,d 3,e 3,f 3, gives some examples. In model IV the chance δ j =δ j (δ0=1, δ<1) (see Fig. 1,d 4,e 4,f 4).From the comparison of Figs. 6 and 7 it is clear that model IV gives the best description of the distributions of eggs found.The value of these models is discussed, and plans for both an approach through experimental analysis and simulation models are given. In an Appendix the mathematical derivation of the models is presented.