Influence of Freshwater Sediment Characteristics on Persistence of Fecal Indicator Bacteria

Extended persistence of enteric bacteria in coastal sediments and potential remobilization of pathogens during natural turbulence or human activities may induce an increased risk of human infections. In this study, the effect of sediment characteristics such as particle grain size and nutrient and organic matter contents on the survival of fecal indicator bacteria (FIB) including total coliforms, Escherichia coli, and Enterococcus was investigated. The experimentation was carried out for 50 days in microcosms containing lake water and different contaminated freshwater sediments in continuous-flow and batch conditions. Results of this study revealed: (1) extended FIB survival in sediments up to 50 days, (2) higher growth and lower decay rates of FIB in sediments with high levels of organic matter and nutrients and small (mainly silt) grain size, and (3) longer survival of Enterococcus sp. compared to E. coli and total coliforms. FIB survival in sediments and possible resuspension are of considerable significance for the understanding of permanent microbial pollution in water column and therefore human risk during recreational activities.     

Long-term adaptation potential of Central European mountain forests to climate change: a GIS-assisted sensitivity assessment

An ecological risk assessment is described for determining the adaptation potential of the approximately 11 000 Swiss Forest Inventory points (FIP) to a hypothetically changing climate. The core of the study is a spatially explicit forest community model that generates estimates of the potential natural vegetation for the entire potential forest area of Switzerland under today's as well as under altered climate regimes. The model is based on the Bayes formula. The probabilities of the communities occurring along ecological gradients are derived from empirical data featuring the relationships between quasi-natural vegetation types and measured site variables. Bioclimatological input variables are the quotient between July temperature and annual precipitation (model version A) or mean annual temperature (model version B). Other site variables include aspect, acidity of top soil and, to account for continentality, geographical region. Climate change scenarios are defined as follows: ‘Moderate climate change’ implies an increase of the mean annual temperature of 4°C to 1.4°C depending on the region (model version B) or an increase of the July temperature of 1.5°C (model version A). ‘Strong climate change’ implies an increase of the mean annual temperature of 2°C to 2.8°C (model version B) or an increase of the July temperature of 3.0°C (model version A).

The simulation experiment showed that the geographical distribution of 15 potential natural forest types (distinguished on the basis of floristic affinities) varies considerably with changing temperature. Under moderate warming 30–55% of the FIP change their potential natural vegetation type, whereas under strong climate change the values increase to 55–89% depending on the model version used. In the ecological risk assessment the existing tree species composition on any FIP was compared with the expected tree species composition under today's as well as under altered climate regimes. A major finding indicated that, under the current climate conditions, approximately 25–30% (depending on the model version used) of all FIP must be considered as poorly adapted, i.e. less than 20% of the actual basal area consists of tree species that are expected as dominating taxa. This definition applies for trees with a diameter at breast height (DBH) ≥ 12 cm. Moderate warming increases the percentage of poorly adapted FIP by 5–10% (relative to all FIP considered), strong warming leads to a 10–30% increase of poorly adapted FIP (relative to all FIP considered). If trees with a DBH < 12cm are considered, the percentage of FIP that have to be classified as poorly adapted is reduced significantly. There are strong regional differences as exhibited in risk maps of 10 km × 10 km resolution.     

Additive partitioning of plant species diversity in an agricultural mosaic landscape

In this paper, we quantify the effects of habitat variability and habitat heterogeneity based on the partitioning of landscape species diversity into additive components and link them to patch-specific diversity. The approach is illustrated with a case study from central Switzerland, where we recorded the presence of vascular plant species in a stratified random sample of 1'280 quadrats of 1 m² within a total area of 0.23 km². We derived components of within- and between-community diversity at four scale levels (quadrat, patch, habitat type, and landscape) for three diversity measures (species richness, Shannon index, and Simpson diversity). The model implies that what we measure as within-community diversity at a higher scale level is the combined effect of heterogeneity at various lower levels. The results suggest that the proportions of the individual diversity components depend on the habitat type and on the chosen diversity aspect. One habitat type may be more diverse than another at patch level, but less diverse at the level of habitat type. Landscape composition apparently is a key factor for explaining landscape species richness, but affects evenness only little. Before we can test the effect of landscape structure on landscape species richness, several problems will have to be solved. These include the incorporation of neighbourhood effects, the unbiased estimation of species richness components, and the quantification of the contribution of a landscape element to landscape species richness.