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Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems., Ecology Letters, 26 (2), ISSN:1461-023X, ISSN:1461-0248

Species distribution models are commonly applied to predict species responses to environmental conditions. A wide variety of models with different properties exist that vary in complexity, which affects their predictive performance and interpretability. Machine learning algorithms are increasingly used because they are capable to capture complex relationships and are often better in prediction. However, to inform environmental management, it is important that a model predicts well for the right reasons. It remains a challenge to select a model with a reasonable level of complexity that captures the true relationship between the response and explanatory variables as good as possible rather than fitting to the noise in the data. In this study we ask: 1) how much predictive performance can we gain by using increasingly complex models, 2) how does model complexity affect the degree of overfitting, and 3) do the inferred responses differ among models and what can we learn from them? To address these questions, we applied eight models with different complexity to predict the probability of occurrence of freshwater macroinvertebrate taxa based on 2729 Swiss monitoring samples. We compared the models in terms of predictive performance during cross-validation and for generalization out of the calibration domain ("extrapolation" or transferability). We applied model agnostic tools to shed light on model interpretability. Contrary to our expectation, all models predicted similarly well during cross-validation, while no model predicted better than the null model during out-of-domain generalization on average over all taxa. Performance was best for taxa with intermediate prevalence. More complex models predicted slightly better than standard statistical models but were prone to overfitting. Overfitting indicates that a model describes not only the signal in the data but also part of the noise. This impedes the interpretation of response shapes learned by the model, because one cannot distinguish the signal from the noise. Furthermore, the strongly overfitting models learned irregular relationships and strong interactions that are ecologically not plausible. Thus, in this study, the minor gain in predictive performance from more complex models was outweighed by the overfitting. Ecological field data that is used as model input or for calibration is typically prone to different sources of variability, from sampling, the measurement process and stochasticity. We therefore call for caution when using complex data-driven models to learn about species responses or to inform environmental management. In such cases, we recommend to compare a range of models regarding their predictive performance, overfitting and response shapes to better understand the robustness of inferred responses., Ecological Modelling, 481, ISSN:0304-3800, ISSN:1872-7026, ISSN:0167-8892

Key decisions in the design of biomonitoring programs include taxonomic resolution, geographic extent, and site selection, each of which can affect our ability to infer human impacts on bio...

Effluents of wastewater treatment plants can impact microbial communities in the receiving streams. However, little is known about the role of microorganisms in wastewater as opposed to other wastewater constituents, such as nutrients and micropollutants. We aimed therefore at determining the impact of wastewater microorganisms on the microbial diversity and function of periphyton, key microbial communities in streams. We used a flow-through channel system to grow periphyton upon exposure to a mixture of stream water and unfiltered or ultra-filtered wastewater. Impacts were assessed on periphyton biomass, activities and tolerance to micropollutants, as well as on microbial diversity. Our results showed that wastewater microorganisms colonized periphyton and modified its community composition, resulting for instance in an increased abundance of Chloroflexi and a decreased abundance of diatoms and green algae. This led to shifts towards heterotrophy, as suggested by the changes in nutrient stoichiometry and the increased mineralization potential of carbon substrates. An increased tolerance towards micropollutants was only found for periphyton exposed to unfiltered wastewater but not to ultra-filtered wastewater, suggesting that wastewater microorganisms were responsible for this increased tolerance. Overall, our results highlight the need to consider the role of wastewater microorganisms when studying potential impacts of wastewater on the receiving water body., Water Research, 225, ISSN:0043-1354, ISSN:1879-2448

Recently, a plethora of studies reporting insect declines has been published. Even though the common theme is decreasing insect richness, positive trends have also been documented. Here, we analysed nationwide, systematic monitoring data on aquatic insect richness collected at 438 sites in Switzerland from 2010 to 2019. In addition to taxonomic richness, we grouped taxa in accordance with their ecological preferences and functional traits to gain a better understanding of trends and possible underlying mechanisms. We found that in general, richness of aquatic insects remained stable or increased with time. Warm-adapted taxa, common feeding guilds and pesticide-tolerant taxa showed increasing patterns while cold-adapted, rarer feeding guilds and pesticide-sensitive taxa displayed stable trends. Both climate and land-use-related factors were the most important explanatory variables for the patterns of aquatic insect richness. Although our data cover the last decade only, our results suggest that recent developments in insect richness are context-dependent and affect functional groups differently. However, longer investigations and a good understanding of the baseline are important to reveal if the increase in temperature- and pesticide-tolerant species will lead to a decrease in specialized species and a homogenization of biotic communities in the long term., Biology Letters, 18 (3), ISSN:1744-9561, ISSN:1744-957X

AIM: Species distribution models (SDMs) are widely used to study geographic distributions of taxa in response to natural and anthropogenic environmental conditions. For a community, common approaches include fitting individual SDMs (iSDMs) to all taxa or directly modelling community properties such as richness. However, the parameters of iSDMs are difficult to identify for rare taxa, and community properties do not reveal taxon‐specific responses. Individual models can be combined into a hierarchical multispecies distribution model (mSDM) that constrains taxon‐specific parameters according to overarching community parameters, or a joint model (jSDM) in which interdependencies between taxa are jointly inferred. We compare how individual, hierarchical multispecies and joint SDMs differ in quality of fit, explanatory power and predictive performance, and analyse how these properties depend on the prevalence of taxa. TAXA: Presence–absence observations of 245 benthic macroinvertebrate taxa identified at a mixed taxonomic resolution. LOCATION: Four hundred and ninety‐two sites in rivers throughout Switzerland. METHODS: Individual, hierarchical and joint hierarchical generalized linear models (GLM) were developed for all taxa. Parameters were estimated using maximum likelihood estimation or Bayesian inference with Hamiltonian Markov chain Monte Carlo simulations. Predictive performance was assessed with cross‐validation. In addition, the predicted family and species richness of the models was compared with a GLM for richness. RESULTS: Individual models show a slightly higher quality of fit largely due to overfitting for rare taxa. The mSDM achieves a similar quality of fit and explanatory power, mitigates overfitting for rare taxa and considerably improves predictive performance over the whole community. The joint models further improve the quality of fit, but decrease predictive performance and increase predictive uncertainty. MAIN CONCLUSIONS: We show that even a relatively simple mSDM combines many of the analytical capabilities of iSDMs and improves predictive performance. Increasingly complex mSDMs and jSDMs provide additional analytical possibilities, but depending on the data and research questions, different levels of complexity may be appropriate.

Multi-criteria decision analysis (MCDA) requires an accurate representation of the preferences of decision-makers, for instance in the form of a multi-attribute value function. Typically, additivity or other stringent assumptions about the preferences are made to facilitate elicitation by assuming a simple parametric form. When relaxing such assumptions, parameters cannot be elicited easily with standard methods. We present a novel approach for identifying multi-attribute value functions which can have any shape. As preference information indifference statements are used that can be elicited by trade-off questions. Instead of asking one indifference statement for each pair of attributes, we ask for multiple trade-offs at different points in the attribute space. This allows inferring parameters of complex value functions despite the simplicity of the preference statements. Parameters are estimated by taking into account preference and elicitation uncertainty with a probabilistic model. Statistical inference supports identifying the most adequate preference model out of several candidate models through quantifying the uncertainty and assessing the need for non-additivity. The approach is elaborated for determining value functions by hierarchical aggregation. We apply it to an assessment of the ecological state of rivers, which is used to support environmental management decisions in Switzerland. Preference models of four experts were quantified, confirming the feasibility of the approach and the relevance of considering non-additive functions. The method suggests a promising direction for improving the representation of preferences.

Freshwater ecosystems are increasingly under threat as they are confronted with multiple anthropogenic impairments. This calls for comprehensive management strategies to counteract, or even prevent, long-term impacts on habitats and their biodiversity, as well as on their ecological functions and services. The basis for the efficient management and effective conservation of any ecosystem is sufficient knowledge on the state of the system and its response to external influence factors. In freshwater ecosystems, state information is currently drawn from ecological assessments at the reach or site scale. While these assessments are essential, they are not sufficient to assess the expected outcome of different river restoration strategies, because they do not account for important characteristics of the whole river network, such as habitat connectivity or headwater reachability. This is of particular importance for the spatial prioritization of restoration measures. River restoration could be supported best by integrative catchment-scale ecological assessments that are sensitive to the spatial arrangement of river reaches and barriers. Assessments at this scale are of increasing interest to environmental managers and conservation practitioners to prioritize restoration measures or to locate areas worth protecting. We present an approach based on decision support methods that integrates abiotic and biotic ecological assessments at the reach-scale and aggregates them spatially to describe the ecological state of entire catchments. This aggregation is based on spatial criteria that represent important ecological catchment properties, such as fish migration potential, resilience, fragmentation and habitat diversity in a spatially explicit way. We identify the most promising assessment criteria from different alternatives based on theoretical considerations and a comparison with biological indicators. Potential applications are discussed, particularly for supporting the strategic, long-term planning and spatial prioritization of restoration measures.

A wide knowledge base regarding the ecological preferences of benthic macroinvertebrates is synthesized in public databases. This knowledge can assist in disentangling the influence of mult...

Statistical species distribution models (SDMs) are widely used to quantify how taxa respond to environmental conditions and to predict their distribution. However, the application of SDMs to freshwater fish taxa is complicated by the active dispersal of fish taxa through river networks, and the species- and habitat-dependent observation process (i.e., the sampling method and effort) required to accurately sample their distributions. Many studies have applied presence-absence models (PAMs) to fish taxa, while more recent studies have proposed zero-inflated models (ZIMs) to account for count observations with many zeroes. However, relatively few studies have incorporated the observation process into the model structure, which would facilitate the combination of data from various monitoring programs that differ in their observation process. In this study, we use conceptual models to identify potentially dominant natural and anthropogenic environmental conditions with a direct, mechanistic effect on the distributions of freshwater fish taxa in Switzerland, a region with a large range of environmental conditions, from alpine streams that are mainly affected by hydromorphological alterations to lowland streams in densely populated areas with intensive agricultural land use. Moreover, numerous barriers impede fish migration along the entire river network. Using combined data from two fish monitoring programs in Switzerland, we applied an exhaustive cross-validation procedure to select a set of environmental variables with the highest (out-of-sample) predictive performance for the PAM and ZIM for fish density (individuals/m2) of the seven most prevalent fish taxa (Salmo spp., Cottus spp., Squalius spp., Barbatula spp., Barbus spp., Phoxinus spp., Gobio spp.). We used these variables to develop a PAM and ZIM for each taxon that accounts for differences in sampling methods and sampling effort. We quantified the quality of fit during calibration using all samples and predictive performance during 5-fold cross-validation of each model. Results show that stream temperature and stream morphology within the accessible habitat commonly appear among the best predictive presence-absence models for multiple taxa. Spatial variables that account for migration barriers and quantify morphological conditions within the accessible habitat were selected for 6 out of 7 taxa. The selected PAMs performed well for all taxa with an intermediate prevalence (10–40%), with an explanatory power ( D 2 ) of between 0.32 - 0.37 during calibration using all samples and only minor decreases in explanatory power during cross-validation ( D 2 = 0.34 – 0.44). As expected, the PAM for the highly prevalent Salmo spp. (91%) failed to predict the few absence data points. By contrast, the ZIM model performed best for Salmo spp., with a standardized likelihood ratio of 1.56. For all other taxa besides Barbus spp. the ZIM models also had likelihood ratios above one, indicating a better predictive performance than the null model. We hope this study stimulates the development and application of fish species distribution models based on prior knowledge of causally linked environmental variables and incorporating observation errors to improve their predictive performance. This can facilitate learning from biomonitoring data to support management.

Wastewater treatment plants (WWTPs) play an important role in retaining organic matter and nutrients but to a lesser extent micropollutants. Therefore, treated wastewater is recognized as a major source of multiple stressors, including complex mixtures of micropollutants. These can potentially affect microbial communities in the receiving water bodies and the ecological functions they provide. In this study, we evaluated in flow-through channels the consequences of an exposure to a mixture of stream water and different percentages of urban WWTP effluent, ranging from 0% to 80%, on the microbial diversity and function of periphyton communities. Assuming that micropollutants exert a selective pressure for tolerant microorganisms within communities, we further examined the periphyton sensitivity to a micropollutant mixture extracted from passive samplers that were immersed in the wastewater effluent. As well, micropollutants in water and in periphyton were comprehensively quantified. Our results show that micropollutants detected in periphyton differed from those found in water, both in term of concentration and composition. Especially photosystem II inhibitors accumulated in periphyton more than other pesticides. Although effects of other substances cannot be excluded, this accumulation may have contributed to the observed higher tolerance of phototrophic communities to micropollutants upon exposure to 30% and 80% of wastewater. On the contrary, no difference in tolerance was observed for heterotrophic communities. Exposure to the gradient of wastewater led to structural differences in both prokaryotic and eukaryotic communities. For instance, the relative abundance of cyanobacteria was higher with increasing percentage of wastewater effluent, whereas the opposite was observed for diatoms. Such results could indicate that differences in community structure do not necessarily lead to higher tolerance. This highlights the need to consider other wastewater constituents such as nutrients and wastewater-derived microorganisms that can modulate community structure and tolerance. By using engineered flow-through channels that mimic to some extent the required field conditions for the development of tolerance in periphyton, our study constitutes a base to investigate the mechanisms underlying the increased tolerance, such as the potential role of microorganisms originating from wastewater effluents, and different treatment options to reduce the micropollutant load in effluents., Water Research, 203, ISSN:0043-1354, ISSN:1879-2448

Predicting the composition and dynamics of communities is a challenging but useful task to efficiently support ecosystem management. Community ecology has developed a number of promising theories, including food webs, metabolic theory, ecological stoichiometry, and environmental filtering. Their joint implementation in a mechanistic modeling framework should help us to bring community ecology to a new level by improving its predictive abilities. One of the challenges lies in the proper consideration of model uncertainty. In this paper, we contribute to this challenging task by modeling the temporal dynamics of macroinvertebrate communities in a stream subjected to hydropeaking in Switzerland. To this end, we extended the mechanistic model Streambugs regarding flood-induced drift processes and the use of trait information to define performance filters. Model predictions without any calibration were in the right order of magnitude but did not reflect the dynamics of most of the invertebrate taxa well. Bayesian inference drastically improved the model fit. It revealed that a large share of total model output uncertainty can be attributed to observation errors, which exceeded model parameter uncertainty. Observed and simulated community-aggregated traits helped to identify and understand model deficits. The combination of different ecological theories and trait information in a single mechanistic modeling framework combined with Bayesian inference can thus help to predict responses of communities to environmental changes, which can support ecosystem management.

Species distribution models (SDMs) are often criticised for lacking explicit linkage to ecological concepts. We aim to improve the ecological basis of SDMs by integrating prior knowledge about ecological preferences of organisms. Additionally, we aim to support a systematic, data-driven review of such prior knowledge by confronting it with independent monitoring data using Bayesian inference. We developed a series of multi-species distribution models (MSDMs) with increasing complexity to predict the probability of occurrence of taxa at sampling sites based on habitat suitability functions that are parameterized with prior ecological knowledge. We subsequently assessed the models` predictive performance with 3-fold cross-validation. So far, if ecological preferences or functional traits have been used in SDMs, they were mainly used as fixed inputs without considering their uncertainty. We take the additional step of considering uncertainty about preference parameters by including them as uncertain prior information that is subsequently updated with Bayesian inference. We apply the series of models in a case study on macroinvertebrates in Swiss streams. We analyse differences in the quality of fit, changes in predictive performance, and the potential to learn about the parameters from the data. We consider ecological preferences for natural and human modified environmental factors including temperature, flow velocity, organic matter concentration, insecticide pollution, and substratum. Results indicate that updating prior knowledge on ecological preferences with Bayesian inference, rather than using it as fixed input, improves model fit and predictive performance. For example, the predictive performance measured by the deviance for validation data improves by 17 % and the explanatory power increases 3.8 times from a model that treats ecological preferences as fixed scores to a model that treats them as uncertain parameters. The spatial distribution of many taxa, including rare taxa with frequencies of occurrence down to about 5 %, which are difficult to model with SDMs that do not consider prior information, can be captured by the new models. Integrating prior knowledge as uncertain parameters in a Bayesian framework establishes ecological interpretable links between taxa and their environment and supports a systematic revision and complementation of databases on ecological preferences, even in case of poor or missing prior knowledge. Model outputs need to be carefully interpreted by modellers and experts on ecological preferences. Increased exchange between these research fields will benefit further integration of ecological preferences into SDMs.

Surface water quality management requires foresighted decision making regarding long-term investments. It should consider multiple objectives (e.g. related to different pollutants and costs), integrate multiple sources of pollution (point and diffuse sources), and external conditions that change over time (climate, population and land-use changes). Multi-attribute value theory can support such decisions, especially the development of an assessment method. Integrated surface water quality assessment methods including micropollutants are currently lacking or in development in many countries. Important steps for the development of such an immission oriented and integrated surface water quality assessment method are discussed in this paper and exemplified for organic micropollutants. The proposed assessment method goes beyond simple pass-fail criteria for single substances. It provides a continuous assessment on a scale from zero to one based on five color-coded water quality classes and suggestions for the visualization of assessment results. It takes into account the toxicity of the micropollutants and their mixture to aquatic organisms by comparing measured concentrations to environmental quality standards (EQS). The focus of this paper is on aggregation over multiple substances and time. Advantages and disadvantages of different aggregation methods are discussed as well as their implications for practice. The consequences of different aggregation methods are illustrated with didactical examples and by an application of the proposed water quality assessment method to pesticide monitoring data from Switzerland. Recommendations are provided that account for the purpose of the assessment. Furthermore, the paper illustrates how the proposed method can facilitate dealing with uncertainty and a transparent communication of monitoring results to support water quality management decisions.

Understanding and predicting the ecological consequences of different management alternatives is becoming increasingly important to support environmental management decisions. Ecological models could contribute to such predictions, but in the past this was often not the case. Ecological models are often developed within research projects but are rarely used for practical applications. In this synthesis paper, we discuss how to strengthen the role of ecological modeling in supporting environmental management decisions with a focus on methodological aspects. We address mainly ecological modellers but also potential users of modeling results. Various modeling approaches can be used to predict the response of ecosystems to anthropogenic interventions, including mechanistic models, statistical models, and machine learning approaches. Regardless of the chosen approach, we outline how to better align the modeling to the decision making process, and identify six requirements that we believe are important to increase the usefulness of ecological models for management support, especially if management decisions need to be justified to the public. These cover: (i) a mechanistic understanding regarding causality, (ii) alignment of model input and output with the management decision, (iii) appropriate spatial and temporal resolutions, (iv) uncertainty quantification, (v) sufficient predictive performance, and (vi) transparent communication. We discuss challenges and synthesize suggestions for addressing these points. © 2019 The Author(s) This paper was initialized during a special session on Ecological Modelling at the 10th Symposium for European Freshwater Science 2017 ( http://www.sefs10.cz/ ) and further developed during the AQUACROSS project, funded by European Union's Horizon 2020 research and innovation programme (Grant agreement No. 642317 ). SD, SDL and MF were partly funded by the “GLANCE” project (Global Change Effects in River Ecosystems; 01 LN1320A) through the German Federal Ministry of Education and Research ( BMBF ). SDL has received additional funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 748625 . JML acknowledges the support of the Spanish Government through María de Maeztu excellence accreditation 2018–2021 (Ref. MDM-2017-0714 ).

In densely populated areas, surface waters are affected by many sources of pollution. Besides classical pollutants like nutrients and organic matter that lead to eutrophication, micropollutants from various point- and non-point sources are getting more attention by water quality managers. For cost-effective management an integrated assessment is needed that takes into account all relevant pollutants and all sources of pollution within a catchment. Due to the difficulty of identifying and quantifying sources of pollution and the need for considering long-term changes in boundary conditions, typically substantial uncertainty exists about the consequences of potential management alternatives to improve surface water quality. We therefore need integrated assessment methods that are able to deal with multiple objectives and account for various sources of uncertainty. This paper aims to contribute to integrated, prospective water management by combining a) multi-criteria decision support methods to structure the decision process and quantify preferences, b) integrated water quality modelling to predict consequences of management alternatives accounting for uncertainty, and c) scenario planning to consider uncertainty from potential future climate and socio-economic developments, to evaluate the future cost-effectiveness of water quality management alternatives at the catchment scale. It aims to demonstrate the usefulness of multi-attribute value functions for water quality assessment to i) propagate uncertainties throughout the entire assessment procedure, ii) facilitate the aggregation of multiple objectives while avoiding discretization errors when using categories for sub-objectives, iii) transparently communicate the results. We show how to use such multi-attribute value functions for model-based decision support in water quality management. We showcase the procedure for the Mönchaltorfer Aa catchment on the Swiss Plateau. We evaluate ten different water quality management alternatives, including current practice, that tackle macro- and micropollutants from a wide spectrum of agricultural and urban sources. We evaluate costs and water quality effects of the alternatives under four different socio-economic scenarios for the horizon 2050 under present and future climate projections and visualize their uncertainty. While the performance of alternatives is catchment specific, the methods can be transferred to other places and other management situations. Results confirm the need for cross-sectoral coordination of different management actions and interdisciplinary collaboration to support the development of prospective strategies to improve water quality., Graphical abstract Image 1, Highlights • Model based decision support is needed for integrative water quality management. • Multi-criteria decision support methods help developing water quality assessments. • We evaluate alternatives that reduce agricultural and urban sources of pollution. • We illustrate tradeoffs between costs and water quality including uncertainty. • Scenario analysis helps evaluating robustness of outcomes to future developments.

Summary The community assembly of macroinvertebrates in streams depends on the regional taxon pool, dispersal limitations, local habitat conditions and biotic interactions. By integrating existing knowledge about these processes from theoretical ecology in a mechanistic model, we can test our mechanistic understanding and disentangle multiple stressor effects on community assembly. To assess to which degree we can predict the community composition of macroinvertebrates, we integrated these processes in the mechanistic food web model Streambugs and tested it on 36 sites in the Glatt catchment on the Swiss plateau. The model predicts the observation probability of taxa from a regional taxon pool at each site taking into account uncertain knowledge on parameters, environmental conditions at the sites and sampling errors. We use allometric scaling according to the metabolic theory of ecology, ecological stoichiometry and autecological data from trait databases that include the current knowledge on habitat requirements of the different taxa to parameterize their growth, respiration and death. Without any calibration, for the majority of taxa at the 36 sites, the difference between the observed and predicted relative frequency of occurrence is

Using macroinvertebrates as ecological indicators for different stressors has a long tradition. However, when applied to field data, one often observes correlations between different macroinvertebrate indices that can be attributed to both correlations of stressors and inherent correlations due to the sensitivity of taxa to different stressors. Ignoring the source of any given correlation leads to ambiguous conclusions about the impact of different stressors. Here, we demonstrate how to distinguish the causes of correlation by means of Monte Carlo simulations. We assessed to which degree trait-based indices are stressor-specific and whether this depends on the pool of taxa and its taxonomic resolution. Therefore, we (1) analysed the frequencies of “sensitive” and “insensitive” taxa for pairwise combinations of different indices, (2) analysed the inherent correlation of indices with random samples from different taxon pools derived from field samples and from a complete species list of a whole ecoregion, and (3) compared this inherent correlation with the actual correlation of the field samples. We exemplified this approach by analysing two existing indices (SPEAR pesticides , Saprobic Index) and new indices for temperature, flow and pH stress. We used these new indices to illustrate our approach while in-depth testing of their applicability was not the focus of our study. We found strong correlations between several indices in our study area at the Swiss Plateau. The probability that this correlation is only due to inherent correlation in the taxa sensitivities was low (maximum of 0.34). The problem of inherent correlation between indices is more severe for the smaller taxon pool with lower taxonomic resolution. Correlation in the sensitivity of different taxa to different stressors leads to an inherent correlation in trait-based indices, which weakens their explanatory power. Our results highlight the importance of correlation analyses when using trait-based indices to guide ecosystem-management, especially in regions with reduced biodiversity.

Ecological assessment requires the integration of many physical, chemical, and/or biological quality elements. The choice of the aggregation method of such partial assessments into an overall assessment can considerably affect the assessment outcome – an issue that has been controversially discussed within the scientific community for the last decade. Current practice often considers only two different aggregation methods, the weighted arithmetic mean (additive aggregation) and the one-out, all-out method (minimum aggregation). However, both have important drawbacks. Additive aggregation compensates a bad status of one quality element by a number of elements featuring good status. Minimum aggregation can lead to overly pessimistic assessment results, since only the quality element in the worst status is considered. Here, we introduce a toolbox containing current and new aggregation methods, demonstrate and discuss their properties with simple, didactical examples, and suggest in which situations best to use them. Then, we illustrate the consequences of selected aggregation schemes for ecological river assessment with the case study of the Swiss Modular Concept of stream assessment (SMC), which we apply to ten river reaches in the Monchaltdorfer Aa catchment in Switzerland. To be able to do so, we used multi-criteria decision analysis, i.e., multi-attribute value theory, to arrange the SMC quality elements into an objectives hierarchy, and to translate their individual assessments into value functions. Our case study revealed that choosing the most appropriate aggregation method particularly matters, if objectives with significantly different qualities are aggregated. We argue that redundant objectives (i.e., quality elements), often found at the lower levels of the objectives hierarchy, should best be aggregated additively allowing for compensation to increase the statistical significance of the results. Further, we suggest that complementary sub-objectives that often occur at higher levels may be optimally aggregated with a mixture of additive and minimum aggregation. Such a mixed method will allow some compensation, but nevertheless penalize for very bad states. Since here we compare commonly used aggregation methods with some which we believe have never been discussed in an assessment context before, our study concurrently informs ecological assessment in theory and in practice.

River monitoring and assessment programs are important tools to quantify the condition of river ecosystems, identify deficits, and provide preliminary indication of how to improve them. But, they are limited in delivering comparable assessment results across national or transnational borders, aggregating site-specific assessments into broader scale assessments, and supporting river management decisions. We present a multi-criteria decision analysis approach for improving the comparability of ecological assessment methods of different origin and for combining these assessments into a joint procedure. The approach consists of seven consecutive steps. The most central ones concern the hierarchical allocation of ecological assessment endpoints, and the harmonization of the scoring procedure of attributes (ecological indicators or assets) to a common scale from 0 to 1. We demonstrate the approach integrating three programs developed to assess the hydromorphological river condition in Switzerland, Germany, and the USA. In our example, the integrated assessment produces comparable results for the whole range from natural to impacted rivers, while data continuity with original assessments was maintained. Our approach provides a common assessment standard due to the definition of the minimum amount of information required, is flexible regarding measurement and assessment endpoints, and bridges the gap between river quality assessment and management.

Catchments are complex systems where water quantity, quality and the provided ecological services are determined by interacting physical, chemical, biological, economic, and social factors. The awareness of these interactions led to the prevailing catchment management paradigm of Integrated Water Resources Management. The design and evaluation of solutions for integrated water resources management requires to predict changes of local or regional water quality, which requires integrated approach for modeling too. On one hand, integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allow for mapping of global change processes – as climate change, population growth, migration, and socio-economic development – to the regional and local contexts. On the other hand, models have to be sufficiently simple and fast enough to apply proper methods of uncertainty analysis, which can consider model structure deficits and propagate errors through the chain of submodels. Here, we present an integrated catchment model satisfying both objectives. The conceptual "iWaQa" model was developed to support the integrated management of small streams. It can predict both traditional water quality parameters like nutrients and a wide set of organic micropollutants originating from plant and material protection products. Due to the model's simplicity, it allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future water quality in a small catchment with mixed land use in the Swiss Plateau. The focus of our study is the change of water quality over the next decades driven by climate change, population growth or decline, socio-economic development and the implementation of management strategies for improving water quality. Our results indicate that input and model structure uncertainties are the most influential factors on certain water quality parameters and in these cases the uncertainty of modeling is already very high for the present conditions. Nevertheless, a proper quantification of today's uncertainty can make the management fairly robust for the foreseen range of possible evolution into the next decades. With a time-horizon of 2050, it seems that human land use and management decisions have a larger influence on water quality than climate change. However, the analysis of single climate model chains indicates that the importance of climate grows when a certain climate prediction is considered instead of the ensemble forecast.

Modeling community dynamics of aquatic invertebrates is an important but challenging task, in particular in ecotoxicological risk assessment. Systematic parameter estimation and rigorous assessment of model uncertainty are often lacking in such applications. We applied the mechanistic food web model Streambugs to investigate the temporal development of the macroinvertebrate community in an ecotoxicological mesocosm experiment with pulsed contaminations with the insecticide thiacloprid. We used Bayesian inference to estimate parameters and their uncertainty. Approx. 85% of all experimental observations lie within the 90% uncertainty intervals indicating reasonably good fits of the calibrated model. However, a validation with independent data was not possible due to lacking data. Investigation of vital rates and limiting factors in the model yielded insights into recovery dynamics. Inclusion of the emergence process and sub-lethal effects turned out to be potentially relevant model extensions. Measurements of food source dynamics, individual body size (classes), and additional knowledge on sub-lethal effects would support more accurate modeling. This application of a process-based, ecotoxicological community model with uncertainty assessment by Bayesian inference increased our process understanding of toxicant effects in macroinvertebrate communities and helped identifying potential improvements in model structure and experimental design.

Point-source measures have been suggested to decrease pharmaceuticals in water bodies. We analyzed 68 and 50 alternatives, respectively, for a typical Swiss general and psychiatric hospital to decrease pharmaceutical discharge. Technical alternatives included reverse osmosis, ozonation, and activated carbon; organizational alternatives included urine separation. To handle this complex decision, we used Multiple-Criteria Decision Analysis (MCDA) and combined expert predictions (e.g., costs, pharmaceutical mass flows, ecotoxicological risk, pathogen removal) with subjective preference-valuations from 26 stakeholders (authorities, hospital-internal actors, experts). The general hospital contributed ca. 38% to the total pharmaceutical load at the wastewater treatment plant, the psychiatry contributed 5%. For the general hospital, alternatives removing all pharmaceuticals (especially reverse osmosis, or vacuum-toilets and incineration), performed systematically better than the status quo or urine separation, despite higher costs. They now require closer scrutiny. To remove X-ray contrast agents, introducing roadbags is promising. For the psychiatry with a lower pharmaceutical load, costs were more critical. Stakeholder feedback concerning MCDA was very positive, especially because the results were robust across different stakeholder-types. Our MCDA results provide insight into an important water protection issue: implementing measures to decrease pharmaceuticals will likely meet acceptance. Hospital point-sources merit consideration if the trade-off between costs and pharmaceutical removal is reasonable.

To evaluate the role of the benthic community within headwater stream ecosystems, it is crucial to understand the mechanisms of the processes dominating their turnover rates and temporal dynamics. To analyze the benthic community dynamics of an intermittent Mediterranean stream (Fuirosos, Spain), we developed a mechanistic model that describes the most important ecosystem components (five functional feeding groups of invertebrates, periphyton, and benthic organic matter) under consideration of important transformation processes. The model is an extension of that developed for application to the prealpine River Sihl (Switzerland). A combination of prior knowledge from the literature and information from field observations within a Bayesian framework is used to constrain plausible ranges of the estimated model parameters and to evaluate the uncertainty of the model outcome. The Bayesian inference resulted in a realistic description of most of the main features of the system, offered insights into the degree of information the data contains about model parameters, and also made it possible to quantify the dependence structure of the parameter estimates. Local and global sensitivity analyses revealed that rate parameters for growth and death of invertebrate functional feeding groups are most influential on the model results and additional information on these parameters would be most helpful to reduce output uncertainty. The study clearly points out the importance of organic matter dynamics, as allochthonous organic matter is the main energy source in this Mediterranean headwater stream. Hydrological disturbances of the system such as droughts and floods lead to complex colonization patterns which would require additional data to support a more detailed description by an extended model. Furthermore, the study shows that it is possible to deal with a complex ecological model with eight state variables and more than 60 parameters in a Bayesian framework.

Background, aim, and scope Contaminated mine drainage water has become a major hydrogeological and geochemical problem. Release of soluble metal contaminants and acidity from mining sites can pose serious chemical risks to surface and groundwater in the surrounding environment, and it is an important socio-economic factor addressed by working groups like SUITMA Morel and Heinrich (J Soils Sediments 8:206–207, 2008). The release of Zn and Pb from sulfide-bearing flotation residues of a small scale mine in Western Germany is investigated with focus on metal transfer to soil solution. Total contents of the soil material as well as soil water sampled with suction cups were analyzed. The influence of pH on leaching behavior was investigated with pHstat tests. Isotopic analyses helped assessing seepage water velocity. The aim of this study was the assessment of the environmental behavior of zinc and lead caused by the weathering of sulfide-bearing mine tailings. Especially, we address in this paper the dissolution of sphalerite (ZnS) in contrast to the well-known dissolution processes of pyrite (FeS2).

Summary 1. Most of the existing river ecosystem models describe only part of the ecosystem, have site-specific features, and lack an adequate treatment of uncertainty. 2. We developed a dynamic model of the functional groups of non-filamentous and filamentous algae, scrapers, collector-gatherers and predators of the benthos community in rivers. This model is based on relatively universal model formulations and is intended to be a first submodel for a more complete model of the river ecosystem and the biogeochemical processes in the river. 3. The model was tested for the River Sihl in Switzerland. To take advantage of prior knowledge of model parameters, to account for their non-identifiability given the measured data, and to consider uncertainty of model parameters and results, Bayesian inference was used to update the prior probability distribution of the model parameters. 4. The calibrated model showed very good agreement of model results with measured data. None of the posterior parameter marginals showed a conflict with the prior. Some posterior marginals (specific growth rates of the functional groups, specific death rates, parameters characterizing self- and mutual shading of algae, and the critical level of discharge for catastrophic loss) were significantly narrower than the priors, indicating considerable gain of information from the measurements. For other parameters (half-saturation concentrations of algae and invertebrate growth, parameters characterizing temperature dependence of invertebrate growth rates, colonization rates and most coefficients of catastrophic loss), no significant information could be gained from the data. This clearly demonstrated the need for a Bayesian analysis. 5. The model seems to be a promising step towards a more comprehensive model of the river ecosystem and the biogeochemical processes in the river. However, more efforts are required: In particular, extension of the model by a description of the dynamics of benthic biofilms and nutrient dynamics and application of the (extended) model to more rivers to learn about the dependence of model parameters on river characteristics. The difficulty of finding adequate data sets for testing such and extended models demonstrates the need for comprehensive measurement campaigns that get information of periphyton, benthic invertebrates, benthic biofilms, suspended and sedimented organic particles, suspended organisms and nutrient concentrations and transformation rates in the water column.

Sulfide-bearing mine tailings are a serious environmental problem around the world. In this study, the vertical distribution and speciation of Zn and Pb in the fine-grained flotation residues of a former sulfide ore mine in Germany were investigated to assess the inorganic weathering processes that effect the environmental risk arising from this site. Total metal contents were determined by X-ray fluorescence spectroscopy (XRF). Mobilizable fractions of Zn, Pb, Fe, and Mn were quantified by sequential chemical extractions (SCE). Furthermore, the speciation of Zn was analyzed by Zn K-edge extended X-ray absorption fine structure spectroscopy (EXAFS) to identify the residual Zn species. The variations in pH and inorganic C content show an acidification of the topsoil to pH 5.5. EXAFS results confirm that Zn is mainly bound in sphalerite in the subsoil and weathering reactions lead to a redistribution of Zn in the topsoil. A loss of 35% Zn and S from the topsoil compared with the parent material with 10 g kg-1 Zn and neutral pH has been observed. If acidification proceeds it will lead to a significant release of Zn, S, and Pb to the ground water. In contrast to Zn, Pb is enriched in the mobile fraction of the topsoil by more than a factor of two compared with the subsoil which contains a total of 2 g kg-1 Pb. Thus, the high bioavailability of Pb and the potential for Pb uptake by plants and animals currently represent the most severe threat for environmental health.

Department of Geology, Vienna University, Vienna, Austria Metal mobility at contaminated sites can be assessed by soil water investigations or by leaching tests. Leaching tests are usually carried out in open contact with the atmosphere disregarding possible changes of redox conditions. This can affect the original metal speciation and distribution, particularly when anoxic samples are investigated. In this study, the applicability of common leaching tests (the German S4 test (S4), ammonium nitrate extraction (AmmN), and saturation soil extraction (SSE)) is tested for the assessment of zinc release from sulfide-bearing flotation residues of a former ore mine. Results are compared to soil solution samples obtained by centrifugation and suction cups. The influence of sample storage on S4 leaching test results is investigated in a long-term study to assess oxidation kinetics. Within the first 200 days the release of zinc increases with a slope of 0.1 mmol kg -1 d -1 or 6.0 mg kg -1 d -1 , respectively. Since oxidation of the sulfide-bearing samples leads to a significant overestimation of metal release, a feasible modification for the conduction of leaching tests for anoxic material is proposed where oxidation is prevented efficiently. The modified SSE is found to be the only of the tested leaching procedures, which can be recommended for the assessment of current soil water concentrations at anoxic sites if direct investigation of the soil water is impossible due to technical reasons.

Goal, Scope and Background The suitability of leaching tests for the assessment of soil water concentrations is a controversial issue. The aim of this paper is to review and critically discuss the applicability and comparability of leaching tests in the scope of groundwater risk assessment of inorganic contamination and to discuss soil water sampling methods as alternative.

Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems. Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses. Synthesis and applications. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground-truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network-based perspective.

For the first time, we combine concepts of theoretical food web modeling, the metabolic theory of ecology, and ecological stoichiometry with the use of functional trait databases to predict the coexistence of invertebrate taxa in streams. We developed a mechanistic model that describes growth, death, and respiration of different taxa dependent on various environmental influence factors to estimate survival or extinction. Parameter and input uncertainty is propagated to model results. Such a model is needed to test our current quantitative understanding of ecosystem structure and function and to predict effects of anthropogenic impacts and restoration efforts. The model was tested using macroinvertebrate monitoring data from a catchment of the Swiss Plateau. Even without fitting model parameters, the model is able to represent key patterns of the coexistence structure of invertebrates at sites varying in external conditions (litter input, shading, water quality). This confirms the suitability of the model concept. More comprehensive testing and resulting model adaptations will further increase the predictive accuracy of the model.

[1] In the absence of model deficiencies, simulation results at the correct parameter values lead to an unbiased description of observed data with remaining deviations due to observation errors only. However, this ideal cannot be reached in the practice of environmental modeling, because the required simplified representation of the complex reality by the model and errors in model input lead to errors that are reflected in biased model output. This leads to two related problems: First, ignoring bias of output in the statistical model description leads to bias in parameter estimates, model predictions and, in particular, in the quantification of their uncertainty. Second, as there is no objective choice of how much bias to accept in which output variable, it is not possible to design an “objective” model calibration procedure. The first of these problems has been addressed by introducing a statistical (Bayesian) description of bias, the second by suggesting the use of multiobjective calibration techniques that cannot easily be used for uncertainty analysis. We merge the ideas of these two approaches by using the prior of the statistical bias description to quantify the importance of multiple calibration objectives. This leads to probabilistic inference and prediction while still taking multiple calibration objectives into account. The ideas and technical details of the suggested approach are outlined and a didactical example as well as an application to environmental data are provided to demonstrate its practical feasibility and computational efficiency.

Decisions in environmental management can be challenging amongst other things due to two major sources of uncertainty. Predictions of consequences of different management alternatives can be very uncertain. Furthermore uncertainty exists regarding the subjective preferences of decision makers and stakeholders. For a transparent decision process it is important to disentangle these different elements and the related sources of uncertainty by separating the prediction of consequences from their valuation. Predictions of consequences should be estimated as objectively as possible based on the current state of knowledge; the preference functions which are used to valuate these consequences must be elicited carefully to reflect the subjective preferences of each stakeholder. We incorporate uncertainty in decision support by applying the multi attribute value and utility theory. We propagate uncertainty in the prediction of consequences to the valuation (numerically implemented by Monte Carlo simulation) and incorporate the risk attitude of the stakeholders to discriminate between uncertain alternatives. Furthermore we address uncertainties inherent in the model of the subjective preference structure by sensitivity analysis to evaluate the robustness of model results against a variation in parameters of the preference model. We illustrate this procedure with a case study on the improvement of water quality in a river catchment of the Swiss Plateau. Different management options to reduce point and non point sources will be evaluated in this study. For deriving predictions of outcomes expert knowledge as well as mathematical models can be used. The case study is integrated in a framework for multi criteria water management (MCWM) which allows us to use existing assessment procedures to evaluate the ecological status of aquatic ecosystems.