Gert Everaert

Development and application of an integrated ecological modelling framework to analyze the impact of wastewater discharges on the ecological water quality of rivers

Modelling is an effective tool to investigate the ecological state of water resources. In developing countries, the impact of sanitation infrastructures (e.g. wastewater treatment plants) is typically assessed considering the achievement of legal physicochemical quality standards, but ignoring the ecological water quality (EWQ) of the receiving river. In this paper, we developed a generic integrated ecological modelling framework quantifying the impact of wastewater discharges on the EWQ of the Cauca river (Colombia). The framework is flexible enough to be used in conjunction with different approaches/models and integrates a hydraulic and physicochemical water quality model with aquatic ecological models. Two types of ecological models were developed, habitat suitability models for selected macroinvertebrate groups and ecological assessment models based on a macroinvertebrate biotic index. Four pollution control scenarios were tested. It was found that the foreseen investments in sanitation infrastructure will lead to modest improvements of the EWQ, with an increase lower than six units of the ecological index BMWP-Colombia. Advanced investments, such as the collection and treatment of all wastewater produced by the cities of Cali, Yumbo and Palmira and upgrading of the treatment systems should be considered to achieve a good EWQ. The results show that the integration of ecological models in hydraulic and physicochemical water quality models (e.g. MIKE 11) has an added value for decision support in river management and water policy. The integration of models is a key aspect for the success in environmental decision making. The main limitation of this approach is the availability of physicochemical, hydraulic and biological data that are collected simultaneously. Therefore, a change in the river monitoring strategy towards collection of data which include simultaneous measurements of these variables is required.

Comparison of the Abiotic Preferences of Macroinvertebrates in Tropical River Basins

We assessed and compared abiotic preferences of aquatic macroinvertebrates in three river basins located in Ecuador, Ethiopia and Vietnam. Upon using logistic regression models we analyzed the relationship between the probability of occurrence of five macroinvertebrate families, ranging from pollution tolerant to pollution sensitive, (Chironomidae, Baetidae, Hydroptilidae, Libellulidae and Leptophlebiidae) and physical-chemical water quality conditions. Within the investigated physical-chemical ranges, nine out of twenty-five interaction effects were significant. Our analyses suggested river basin dependent associations between the macroinvertebrate families and the corresponding physical-chemical conditions. It was found that pollution tolerant families showed no clear abiotic preference and occurred at most sampling locations, i.e. Chironomidae were present in 91%, 84% and 93% of the samples taken in Ecuador, Ethiopia and Vietnam. Pollution sensitive families were strongly associated with dissolved oxygen and stream velocity, e.g. Leptophlebiidae were only present in 48%, 2% and 18% of the samples in Ecuador, Ethiopia and Vietnam. Despite some limitations in the study design, we concluded that associations between macroinvertebrates and abiotic conditions can be river basin-specific and hence are not automatically transferable across river basins in the tropics.

Multidecadal Field Data Support Intimate Links between Phytoplankton Dynamics and PCB Concentrations in Marine Sediments and Biota

We analyzed three decades of field observations in the North Sea with additive models to infer spatiotemporal trends of chlorophyll a concentration, sediment organic carbon content, and polychlorinated biphenyls (PCBs) concentrations in mussels and sediments. By doing so, we separated long-term changes in PCB concentrations from seasonal variability. Using the inferred seasonal variability, we demonstrated that phytoplankton blooms in spring and autumn correspond to the annual maxima of the organic carbon content (r = 0.56; p = 0.004) and the PCB concentrations in sediments (r = 0.57; p = 0.004). Furthermore, we found a negative correlation between the PCB concentrations in sediments and in blue mussels (Mytilus edulis; r = -0.33, p = 0.012), which is probably related to the cleansing of the dissolved PCB phase driven by sinking organic matter during phytoplankton blooms and the filter-feeding behavior of the blue mussel. The present research demonstrates the role of seasonal phytoplankton dynamics in the environmental fate of PCBs at large spatiotemporal scales.

The Application of Predictive Modelling for Determining Bio-Environmental Factors Affecting the Distribution of Blackflies (Diptera: Simuliidae) in the Gilgel Gibe Watershed in Southwest Ethiopia

Blackflies are important macroinvertebrate groups from a public health as well as ecological point of view. Determining the biological and environmental factors favouring or inhibiting the existence of blackflies could facilitate biomonitoring of rivers as well as control of disease vectors. The combined use of different predictive modelling techniques is known to improve identification of presence/absence and abundance of taxa in a given habitat. This approach enables better identification of the suitable habitat conditions or environmental constraints of a given taxon. Simuliidae larvae are important biological indicators as they are abundant in tropical aquatic ecosystems. Some of the blackfly groups are also important disease vectors in poor tropical countries. Our investigations aim to establish a combination of models able to identify the environmental factors and macroinvertebrate organisms that are favourable or inhibiting blackfly larvae existence in aquatic ecosystems. The models developed using macroinvertebrate predictors showed better performance than those based on environmental predictors. The identified environmental and macroinvertebrate parameters can be used to determine the distribution of blackflies, which in turn can help control river blindness in endemic tropical places. Through a combination of modelling techniques, a reliable method has been developed that explains environmental and biological relationships with the target organism, and, thus, can serve as a decision support tool for ecological management strategies.

Species distribution models for sustainable ecosystem management

Abstract Reactions to ongoing loss of biodiversity include a variety of restoration actions and are characterised by high costs and uncertainty. Related decision-making can be supported by developing species distribution models (SDMs) that link predictors (both abiotic and biotic) with biotic response variables (e.g., abundance, occurrence, etc.). SDMs can fill in the gaps of current ecological knowledge and predict the potential impact of environmental (including climate) change on species distributions. As climate change already resulted in species shifting their range and an increased risk of extinction, invasion, and disease propagation, SDMs can act as a valuable tool to estimate future species distributions and their effects on ecosystem functioning and related services. Among the variety of modelling techniques used to predict future species distributions, five modelling techniques are selected: decision trees, generalised linear models, artificial neural networks, fuzzy logic, and Bayesian belief networks. The unique advantages of each modelling technique allow the modeller to choose the most appropriate technique in each particular situation. In turn, each modelling technique is characterised by specific drawbacks and is restricted by the limited ecological knowledge related to biotic interactions. Gathering additional ecological knowledge provides the possibility to go beyond simple pattern recognition and to establish more ecologically sound models.

Development and assessment of an integrated ecological modelling framework to assess the effect of investments in wastewater treatment on water quality

Worldwide, large investments in wastewater treatment are made to improve water quality. However, the impacts of these investments on river water quality are often not quantified. To assess water quality, the European Water Framework Directive (WFD) requires an integrated approach. The aim of this study was to develop an integrated ecological modelling framework for the River Drava (Croatia) that includes physical-chemical and hydromorphological characteristics as well as the ecological river water quality status. The developed submodels and the integrated model showed accurate predictions when comparing the modelled results to the observations. Dissolved oxygen and nitrogen concentrations (ammonium and organic nitrogen) were the most important variables in determining the ecological water quality (EWQ). The result of three potential investment scenarios of the wastewater treatment infrastructure in the city of Varaždin on the EWQ of the River Drava was assessed. From this scenario-based analysis, it was concluded that upgrading the existing wastewater treatment plant with nitrogen and phosphorus removal will be insufficient to reach a good EWQ. Therefore, other point and diffuse pollution sources in the area should also be monitored and remediated to meet the European WFD standards.

Additive modelling reveals spatiotemporal PCBs trends in marine sediments

We developed generalised additive mixed models (GAMMs) to infer spatiotemporal trends of environmental PCB concentrations from an extensive dataset (n=1219) of PCB concentrations measured between 1991 and 2010 in sediments of the Belgian Coastal Zone (BCZ) and the Western Scheldt estuary. A GAMM with time, geographical zone, periodicity and the organic carbon - water partition coefficient as covariates explained 49% of the variability in the log transformed PCB sediment concentrations. The time trends unraveled two to threefold PCB concentration decreases in the BCZ during the last 20 years. However, in the Western Scheldt estuary, time trends were spatially heterogeneous and not significantly decreasing. These results demonstrate that international efforts to cut down emissions of PCBs have been effective to reduce concentrations in open water ecosystems like the BCZ but had little effect in the urbanised and industrialised area of the Scheldt estuary.

Realistic environmental mixtures of hydrophobic compounds do not alter growth of a marine diatom

In this paper we determine whether a realistic mixture of hydrophobic chemicals affects the growth dynamics of a marine diatom and how this effect compares to the effect of temperature, light regime and nutrient conditions. To do so, we examine the specific growth rate of Phaeodactylum tricornutum in a 72 h algal growth inhibition test using a full factorial design with three nutrient regimes, two test temperatures, three light intensities and three chemical exposures. Passive samplers were used to achieve exposure to realistic mixtures of organic chemicals close to ambient concentrations. Nutrient regime, temperature and time interval (24, 48 and 72 h) explained 85% of the observed variability in the experimental data. The variability explained by chemical exposure was about 1%. Overall, ambient concentrations of hydrophobic compounds present in Belgian coastal waters, and for which the passive samplers have affinity, are too low to affect the intrinsic growth rate of P. tricornutum.

Relative contribution of persistent organic pollutants to marine phytoplankton biomass dynamics in the North Sea and the Kattegat

In this paper, we use concentrations of persistent organic pollutants (POPs) and of chlorophyll a to infer POP-induced effects on marine primary production in the Kattegat and the North Sea between the 1990s and the 2000s. To do so, we modelled phytoplankton dynamics using four classical drivers (light and nutrient availability, temperature and zooplankton grazing) and tested whether extending this model with a POP-induced phytoplankton growth limitation term improved model fit to observed chlorophyll a concentrations. Including monitored concentrations of PCBs and pesticides did not lead to a better model fit, suggesting that POP-induced growth limitation of marine phytoplankton in the North Sea and the Kattegat is small compared to the limitations caused by the classical drivers. In an attempt to more fully represent the multitude of POPs in the marine environment, the monitored concentrations were multiplied with a factor 10 and 100. Under these two configurations, region-specific contributions of POPs in the phytoplankton growth limitation were found. The inferred contribution of POPs to phytoplankton growth limitation was ca. 1% in Belgian marine waters, but in the Kattegat POPs explained ca. 10% of the phytoplankton growth limitation. These results suggest that there are regional differences in the contribution of POPs to the phytoplankton growth limitation.

Development and selection of decision trees for water management: Impact of data preprocessing, algorithms and settings

In the present research, we found that different preprocessing options and parameterizations of classification and regression trees alter their model fit and have a direct effect on their applicability for end-users. We found that, in terms of applicability, classification trees react different to pruning than regression trees. Indeed, in case of high pruning levels, classification focus on the extreme values of the response variable, whereas regression tree are more likely to predict the intermediate values. Furthermore, when applying cross-validation with a high number of folds, modellers are likely to find one model that outperforms the other models in terms of reliability. Models were assessed based on the determination coefficient, the percentage of Correctly Classified Instances and the Cohens Kappa statistic for each parameterization. We found positive correlations (R-2 > 0.70) between the statistical criteria and we found a non-linear negative relation between the model fit and the level of pruning. Therefore, environmental modellers should make use of an exhaustive list of model parameterizations to develop and compare environmental models in a transparent and objective manner. General methodological guidelines derived from the present research may help modellers to efficiently select statistical and ecological relevant models that are meeting the needs of users. The validity of our conclusion should be further tested for other datasets and scientific domains as our findings are based on one set of freshwater data.