Aafke M. Schipper

Sensitivity of Polar and Temperate Marine Organisms to Oil Components

Potential contamination of polar regions due to increasing oil exploitation and transportation poses risks to marine species. Risk assessments for polar marine species or ecosystems are mostly based on toxicity data obtained for temperate species. Yet, it is unclear whether toxicity data of temperate organisms are representative for polar species and ecosystems. The present study compared sensitivities of polar and temperate marine species to crude oil, 2-methyl-naphthalene, and naphthalene. Species sensitivity distributions (SSDs) were constructed for polar and temperate species based on acute toxicity data from scientific literature, reports, and databases. Overall, there was a maximum factor of 3 difference in sensitivity to oil and oil components, based on the means of the toxicity data and the hazardous concentrations for 5 and 50% of the species (HC₅ and HC₅₀) as derived from the SSDs. Except for chordates and naphthalene, polar and temperate species sensitivities did not differ significantly. The results are interpreted in the light of physiological characteristics, such as metabolism, lipid fraction, lipid composition, antioxidant levels, and resistance to freezing, that have been suggested to influence the susceptibility of marine species to oil. As a consequence, acute toxicity data obtained for temperate organisms may serve to obtain a first indication of risks in polar regions.

How Many Environmental Impact Indicators Are Needed in the Evaluation of Product Life Cycles

Numerous indicators are currently available for environmental impact assessments, especially in the field of Life Cycle Impact Assessment (LCIA). Because decision-making on the basis of hundreds of indicators simultaneously is unfeasible, a nonredundant key set of indicators representative of the overall environmental impact is needed. We aimed to find such a nonredundant set of indicators based on their mutual correlations. We have used Principal Component Analysis (PCA) in combination with an optimization algorithm to find an optimal set of indicators out of 135 impact indicators calculated for 976 products from the ecoinvent database. The first four principal components covered 92% of the variance in product rankings, showing the potential for indicator reduction. The same amount of variance (92%) could be covered by a minimal set of six indicators, related to climate change, ozone depletion, the combined effects of acidification and eutrophication, terrestrial ecotoxicity, marine ecotoxicity, and land use. In comparison, four commonly used resource footprints (energy, water, land, materials) together accounted for 84% of the variance in product rankings. We conclude that the plethora of environmental indicators can be reduced to a small key set, representing the major part of the variation in environmental impacts between product life cycles.

Modeling the influence of environmental heterogeneity on heavy metal exposure concentrations for terrestrial vertebrates in river floodplains

To analyze the influence of environmental heterogeneity on heavy metal exposure concentrations for terrestrial vertebrates in river floodplains, a spatially explicit exposure model has been constructed (SpaCE-model: Spatially explicit cumulative exposure model). This model simulates the environmental use of individual organisms by selecting model cells to be foraged in within a multicelled, heterogeneous landscape. Exposure durations and exposure concentrations are calculated for the selected cells, whereby exposure concentrations are dependent on the availability and contaminant concentrations of different diet items in each cell. The model was applied to a selection of 10 terrestrial vertebrate species, including six small mammalian and four top predator species. It was parameterized for cadmium contamination in a 285-ha, embanked floodplain area along the Rhine River in The Netherlands. Simulations of 1,000 individuals for each species resulted in intraspecies variation in exposure concentrations of between 11 and 39%, with the smallest values generally corresponding to the species with the largest home ranges. Comparison of the model results with cadmium concentrations measured in four of the species from the study area showed that the predicted variation accounted for 12 to 16% of the variation in the measurements. This indicates that environmental heterogeneity governs a minor part of the variation in metal exposure concentrations that can actually be observed in river floodplains.

Vegetation characteristics and eco-hydrological processes in a pristine mire in the Ob River valley (Western Siberia)

Relations between vegetation characteristics and eco-hydrological processes were assessed in a pristine mire in the valley of the Ob River (Western Siberia). Along a transect from the terrace scarp to the river, field data were collected on vegetation composition, peat stratigraphy, peat chemistry, hydrology and hydrochemistry. Based on floristic composition, eight vegetation communities were distinguished. Hydraulic head measurements were used to obtain an indication of groundwater flow directions. The water balance of the mire was calculated with a two-dimensional steady-state numerical groundwater model. Water types were defined based on cluster analysis of hydrochemical data. The results revealed that the dominant hydrological factor in the Ob mire is the discharge of groundwater, which supplies about threefold more water than net precipitation. Although the discharge flux decreases with increasing distance from the terrace scarp, high water levels and a “groundwater-like” mire water composition were observed in the major part of the study site. Precipitation and river water play only a minor role. Despite dilution of discharging groundwater with rainwater, spatial differences in pH and solute concentrations of the surficial mire water are small and not reflected in the vegetation composition. Although small amounts of silt and clay were found in the peat in the proximity of the river, indicating the occurrence of river floods in former times, no river-flood zone could be recognized based on hydrochemical characteristics or vegetation composition. A comparison of the Ob mire with well-studied and near-natural mires in the Biebrza River valley (Poland) revealed substantial differences in both vegetation characteristics and the intensity and spatial pattern of eco-hydrological processes. Differences in the origin and ratios of water fluxes as well as a dissimilar land use history would seem to be key factors explaining the differences observed.

Contaminant exposure in relation to spatio-temporal variation in diet composition: A case study of the little owl (Athene noctua).

We assessed dietary exposure of the little owl Athene noctua to trace metal contamination in a Dutch Rhine River floodplain area. Diet composition was calculated per month for three habitat types, based on the population densities of six prey types (earthworms, ground beetles and four small mammal species) combined with the little owls functional response to these prey types. Exposure levels showed a strong positive relationship with the dietary fraction of earthworms, but also depended on the dietary fraction of common voles, with higher common vole fractions resulting in decreasing exposure levels. Spatio-temporal changes in the availability of earthworms and common voles in particular resulted in considerable variation in exposure, with peaks in exposure exceeding a tentative toxicity threshold. These findings imply that wildlife exposure assessments based on a predefined, average diet composition may considerably underestimate local or intermittent peaks in exposure.

Plant communities in relation to flooding and soil contamination in a lowland Rhine River floodplain

Using canonical correspondence analysis (CCA), relationships were investigated between plant species composition and flooding characteristics, heavy metal contamination and soil properties in a lowland floodplain of the Rhine River. Floodplain elevation and yearly average flooding duration turned out to be more important for explaining variation in plant species composition than soil heavy metal contamination. Nevertheless, plant species richness and diversity showed a significant decrease with the level of contamination. As single heavy metal concentrations seemed mostly too low for causing phytotoxic effects in plants, this trend is possibly explained by additive effects of multiple contaminants or by the concomitant influences of contamination and non-chemical stressors like flooding. These results suggest that impacts of soil contamination on plants in floodplains could be larger than expected from mere soil concentrations. In general, these findings emphasize the relevance of analyzing effects of toxic substances in concert with the effects of other relevant stressors.

Review: Receptor-oriented approaches in wildlife and human exposure modelling: A comparative study

Five human and five wildlife receptor-oriented exposure models were compared with the aim to identify similarities, differences and areas where both fields can learn from each other. Similarities were revealed in exposure endpoints, chemical stressors and extent of model validation. Differences were observed in effect endpoints, non-chemical stressors, exposure routes and media, and parameter uncertainty assessment. The most outstanding differences related to the simulation of behaviour and the representation of individuals and space. Whereas human models use static individuals and obtain activity patterns from databases, activity patterns for wildlife tend to be an emerging property of dynamic individuals, resulting from interactions with the surrounding environment. These differences can be explained by differences in legislation, dominant exposure routes and data availability. Wildlife models can serve as a source of inspiration for human models with respect to the simulation of emergent activity patterns and the inclusion of non-chemical stressors. Human models can act as a source of inspiration for wildlife models when it comes to including multiple exposure routes and the compilation and use of activity data. These adaptations could result in a more holistic approach, in line with current environmental policy that advances towards a more integrated health approach.

Quantifying Biodiversity Losses Due to Human Consumption: A Global-Scale Footprint Analysis

It is increasingly recognized that human consumption leads to considerable losses of biodiversity. This study is the first to systematically quantify these losses in relation to land use and greenhouse gas (GHG) emissions associated with the production and consumption of (inter)nationally traded goods and services by presenting consumption-based biodiversity losses, in short biodiversity footprint, for 45 countries and world regions globally. Our results showed that (i) the biodiversity loss per citizen shows large variations among countries, with higher values when per-capita income increases; (ii) the share of biodiversity losses due to GHG emissions in the biodiversity footprint increases with income; (iii) food consumption is the most important driver of biodiversity loss in most of the countries and regions, with a global average of 40%; (iv) more than 50% of the biodiversity loss associated with consumption in developed economies occurs outside their territorial boundaries; and (v) the biodiversity footprint per dollar consumed is lower for wealthier countries. The insights provided by our analysis might support policymakers in developing adequate responses to avert further losses of biodiversity when population and incomes increase. Both the mitigation of GHG emissions and land use related reduction options in production and consumption should be considered in strategies to protect global biodiversity.

Contrasting changes in the abundance and diversity of North American bird assemblages from 1971 to 2010

Although it is generally recognized that global biodiversity is declining, few studies have examined long-term changes in multiple biodiversity dimensions simultaneously. In this study, we quantified and compared temporal changes in the abundance, taxonomic diversity, functional diversity, and phylogenetic diversity of bird assemblages, using roadside monitoring data of the North American Breeding Bird Survey from 1971 to 2010. We calculated 12 abundance and diversity metrics based on 5-year average abundances of 519 species for each of 768 monitoring routes. We did this for all bird species together as well as for four subgroups based on breeding habitat affinity (grassland, woodland, wetland, and shrubland breeders). The majority of the biodiversity metrics increased or remained constant over the study period, whereas the overall abundance of birds showed a pronounced decrease, primarily driven by declines of the most abundant species. These results highlight how stable or even increasing metrics of taxonomic, functional, or phylogenetic diversity may occur in parallel with substantial losses of individuals. We further found that patterns of change differed among the species subgroups, with both abundance and diversity increasing for woodland birds and decreasing for grassland breeders. The contrasting changes between abundance and diversity and among the breeding habitat groups underscore the relevance of a multifaceted approach to measuring biodiversity change. Our findings further stress the importance of monitoring the overall abundance of individuals in addition to metrics of taxonomic, functional, or phylogenetic diversity, thus confirming the importance of population abundance as an essential biodiversity variable.

How to address data gaps in life cycle inventories: A case study on estimating co2 emissions from coal-fired electricity plants on a global scale

One of the major challenges in life cycle assessment (LCA) is the availability and quality of data used to develop models and to make appropriate recommendations. Approximations and assumptions are often made if appropriate data are not readily available. However, these proxies may introduce uncertainty into the results. A regression model framework may be employed to assess missing data in LCAs of products and processes. In this study, we develop such a regression-based framework to estimate CO2 emission factors associated with coal power plants in the absence of reported data. Our framework hypothesizes that emissions from coal power plants can be explained by plant-specific factors (predictors) that include steam pressure, total capacity, plant age, fuel type, and gross domestic product (GDP) per capita of the resident nations of those plants. Using reported emission data for 444 plants worldwide, plant level CO2 emission factors were fitted to the selected predictors by a multiple linear regression model and a local linear regression model. The validated models were then applied to 764 coal power plants worldwide, for which no reported data were available. Cumulatively, available reported data and our predictions together account for 74% of the total worlds coal-fired power generation capacity.