The European Water Framework Directive requires that member states assess all their surface waters based on a number of biological elements, including macroinvertebrates. Since 1989, the Flemish ...Environment Agency has been using the Belgian Biotic Index for assessing river water quality based on macroinvertebrates. Throughout the years, the Belgian Biotic Index has proven to be a reliable and robust method providing a good indication of general degradation of river water and habitat quality. Since the Belgian Biotic Index does not meet all the requirements of the Water Framework Directive, a new index, the Multimetric Macroinvertebrate Index Flanders (MMIF) for evaluating rivers and lakes was developed and tested. This index was developed in order to provide a general assessment of ecological deterioration caused by any kind of stressor, such as water pollution and habitat quality degradation. The MMIF is based on macroinvertebrate samples that are taken using the same sampling and identification procedure as the Belgian Biotic Index. The index calculation is a type-specific multimetric system based on five equally weighted metrics, which are taxa richness, number of Ephemeroptera, Plecoptera and Trichoptera taxa, number of other sensitive taxa, the Shannon–Wiener diversity index and the mean tolerance score. The final index value is expressed as an Ecological Quality Ratio ranging from zero for very bad ecological quality to one for very good ecological quality. The MMIF correlates positively with dissolved oxygen and negatively with Kjeldahl nitrogen, total nitrogen, ammonium, nitrite, total phosphorous, orthophosphate and biochemical and chemical oxygen demand. This new index is now being used by the Flemish Environment Agency as a standard method to report about the status of macroinvertebrates in rivers and lakes in Flanders within the context of the European Water Framework Directive.
Aquatic habitat suitability models have increasingly received attention due to their wide management applications. Ecological expert knowledge has been frequently incorporated in such models to link ...environmental conditions to the quantitative habitat suitability of aquatic species. Since the formalisation of problem-specific human expert knowledge is often difficult and tedious, data-driven machine learning techniques may be helpful to extract knowledge from ecological datasets. In this paper, both expert knowledge-based and data-driven fuzzy habitat suitability models were developed and the performance of these models was compared. For the data-driven models, a hill-climbing optimisation algorithm was applied to derive ecological knowledge from the available data. Based on the available ecological expert knowledge and on biological samples from the Zwalm river basin (Belgium), habitat suitability models were generated for the mayfly
Baetis rhodani (Pictet 1843). Data-driven models appeared to outperform expert knowledge-based models substantially, while a step-forward model selection procedure indicated that physical habitat variables adequately described the mayfly habitat suitability in the studied area. This study has important implications on the application of expert knowledge in ecological studies, especially if this knowledge is extrapolated to other areas. The results suggest that data-driven models can complement expert knowledge-based approaches and hence improve model reliability.
To facilitate decision support in the ecosystem management, ecological expertise and site-specific data need to be integrated. Fuzzy logic can deal with highly variable, linguistic, vague and ...uncertain data or knowledge and, therefore, has the ability to allow for a logical, reliable and transparent information stream from data collection down to data usage in decision-making. Several environmental applications already implicate the use of fuzzy logic. Most of these applications have been set up by trial and error and are mainly limited to the domain of environmental assessment. In this article, applications of fuzzy logic for decision support in ecosystem management are reviewed and assessed, with an emphasis on rule-based models. In particular, the identification, optimisation, validation, the interpretability and uncertainty aspects of fuzzy rule-based models for decision support in ecosystem management are discussed.
This paper presents an integrated modelling approach to simulate and assess ecological effects of physical habitat changes in rivers. An ecohydraulic simulation tool was created by combining a 1D ...hydraulic model based on HEC-RAS software and the fish habitat module of CASiMiR, a fuzzy logic-based ecohydraulic modelling system. This tool was applied on a river stretch commonly occurring in Belgium and elsewhere in Europe. In particular the effect of weir removal on habitat suitability for bullhead (
Cottus gobio L.) was simulated. Physical conditions of the studied stretch after weir removal were simulated with a hydraulic model. CASiMiR linked these conditions to ecological expert knowledge to calculate habitat suitability for three life stages of bullhead at four different flow rates based on fuzzy logic. Results indicated that after weir removal, habitat suitability increased significantly for all life stages and all flow rates. The presented approach is promising regarding fish community assessment and ecological river engineering.
In recent years, fuzzy models have been acknowledged as a suitable approach for species distribution modelling due to their transparency and their ability to incorporate the ecological gradient ...theory. Specifically, the overlapping class boundaries of a fuzzy model are similar to the transitions between different environmental conditions. However, the need for ecological expert knowledge is an important constraint when applying fuzzy species distribution models. Moreover, the consistency of the ecological preferences of some fish species across different rivers has been widely contested. Recent research has shown that data-driven fuzzy models may solve this ‘knowledge acquisition bottleneck’ and this paper is a further contribution. The aim was to analyse the brown trout (
Salmo trutta fario L.) habitat preferences based on a data-driven fuzzy modelling technique and to compare the resulting fuzzy models with a commonly applied modelling technique, Random Forests. A heuristic nearest ascent hill-climbing algorithm for fuzzy rule optimisation and Random Forests were applied to analyse the ecological preferences of brown trout in 93 mesohabitats. No significant differences in model performance were observed between the optimal fuzzy model and the Random Forests model and both approaches selected river width, the cover index and flow velocity as the most important variables describing brown trout habitat suitability. Further, the fuzzy model combined ecological relevance with reasonable interpretability, whereas the transparency of the Random Forests model was limited. This paper shows that fuzzy models may be a valid approach for species distribution modelling and that their performance is comparable to that of state-of-the-art modelling techniques like Random Forests. Fuzzy models could therefore be a valuable decision support tool for river managers and enhance communication between stakeholders.
Data-driven environmental models are mainly assessed on the basis of their model fit and only limited attention is given to their applicability for end-users. In this paper, we present the ...applicability index (API) that scores decision trees in terms of their interpretability and applicability for end-users. The API integrates two criteria, viz. the simplicity of the model and its ability to predict the classes of the response variable. We developed 10,000 decision trees with different parameterizations and assessed the use of API for model selection with two different datasets. The API reduced the number of decision trees that were retained only based on statistical criteria from 2,806 to 173 and from 1,117 to 784, respectively. The models that were retained were more easily interpretable, equally statistically reliable but less complex. Conventional statistical criteria such as Cohen’s kappa and the number of correctly classified instances were only moderately correlated with the API (r=0.26 and r=0.49, respectively). This indicates that the API is a useful complement to the existing statistical criteria available for model selection. The API was tested for two datasets consisting of water quality data in lowland rivers in Belgium and the Netherlands, hence its validity needs to be tested for other types of data and modelling domains.
•The applicability index (API) quantifies the applicability of decision trees.•To do so, the API integrates the simplicity submetric and the binary submetric.•The value of the API is illustrated on the basis of two water quality datasets.•The API is a complement to existing statistical criteria for model selection.
Ecological expert knowledge is often based on qualitative rules consisting of linguistic terms such as ‘low’, ‘moderate’ or ‘high’. Since fuzzy systems transform these rules and terms into a ...mathematical framework, they allow implementing this expert knowledge in ecological models. However, the development of a reliable knowledge base is complex and time consuming. Recent research has shown that complementing fuzzy systems by data-driven techniques can solve this knowledge acquisition bottleneck. In this paper, a heuristic nearest ascent hill-climbing algorithm for rule base optimisation is applied to construct a fuzzy rule-based habitat suitability model for spawning European grayling (
Thymallus thymallus L.) in the Aare river (Bern, Switzerland). Optimisation of the fuzzy rule-based model was based on two different training criteria, the weighted correctly classified instances
(
CC
I
w
)
and Cohen's Kappa. The ecological relevance of the results was assessed by comparing the optimised rule bases with a rule base derived from ecological expert knowledge. Optimisation based on Kappa appeared to generate acceptable results (CCI
=
0.70; Kappa
=
0.32) and was more practical than optimisation based on
CC
I
w
since the latter required fine tuning of a weight parameter, which accounted for the species prevalence. The optimal rules showed 74% similarity with the rules derived from expert knowledge, while 84% of all model errors was due to false positive predictions of the model. These errors might be due to the impact of variables, which were not included in this study on grayling presence and thus are not necessarily a model error. The habitat suitability model optimised in this paper is able to predict the effect of different impacts on the river system and to select the optimal restoration option. Hence, it could be a valuable decision support tool for river managers and ease the discussion between stakeholders.
The amount of animal manure produced in Flanders—Belgium by intensive animal farming generates a surplus that needs to be treated in order to achieve quality objectives set by the Nitrates Directive ...(91/676/EEC) and the European Water Framework Directive (2000/60/EU). After the physical separation and biological nitrification/denitrification processes, the liquid fraction of manure can be cost-efficiently and effectively treated by constructed wetlands (CWs). However, current discharge criteria limits do not evaluate whether nutrient loads from specific point sources (such as CWs) affect the water quality of their receiving waterway. Thus, we investigated whether a site-specific analysis, based on local environmental conditions, would yield more relevant discharge thresholds. In the present study, a standardized framework was developed for environmental impact assessment (EIA) of effluents from CWs on the water quality of receiving watercourses. This framework was tested as a case study on a manure treatment installation located in Langemark—Belgium. The effect of different impact scenarios on water quality and flow of the effluent and the receiving waterway was studied. Standardized EIA guidelines and sensitivity analyses were applied to determine the expected impacts of total nitrogen (TN), total phosphorous (P), chlorides (Cl⁻) and sulphates (SO₄²⁻) on the receiving watercourse. From this study, we concluded that the methodology currently applied requires adaptation when assessing the discharge from wetlands as current estimations of impact are overly conservative when compared with actual impact. In addition, results showed that expected impact might be mitigated by differentiating discharge limits between dry and wet periods.
In this study we developed and applied a multimetric index to assess the biological water quality of rivers in Vietnam as a complementary tool to the traditional physical–chemical analysis. ...Macroinvertebrate samples were collected at 15 monitoring sites in the Cau river basin, Northern of Vietnam. Eighteen candidate metrics were tested for their range, stability, sensitivity and responsiveness to anthropogenic impacts. The MMI was calculated as the arithmetic mean of five metrics that were retained being the Biological Monitoring Working Party (BMWP) – Viet, total number of taxa, Margalef index, number of ephemeroptera, plecoptera and trichoptera (EPT) and percent of insects. The MMI is split up in five water quality classes, ranging from class one (high biological status) to class five (bad biological status). The study demonstrated that the multimetric approach is suitable for application in the Vietnamese national monitoring and assessment program.
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