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https://open.uns.ac.rs/handle/123456789/20345
Title: | Assessing the ecological impact of chemical pollution on aquatic ecosystems requires the systematic exploration and evaluation of four lines of evidence | Authors: | Backhaus T Brack Werner Van den Brink PJ Deutschmann B. Hollert H Posthuma L Segner H Seiler TB Teodorovic Ivana Focks A |
Issue Date: | 2019 | Journal: | Environmental Sciences Europe | Abstract: | © 2019, The Author(s). The aim of the European Water Framework Directive is to ensure good ecological status for all European surface waters. However, although current monitoring strategies aim to identify the presence and magnitude of ecological impacts, they provide little information on the causes of an ecosystem impairment. In fact, approaches to establish causal links between chemical pollution and impacts on the ecological status of exposed aquatic systems are largely lacking or poorly described and established. This is, however, crucial for developing and implementing appropriately targeted water management strategies. In order to identify the role of chemical pollution on the ecological status of an aquatic ecosystem, we suggest to systematically combine four lines of evidence (LOEs) that provide complementary evidence on the presence and potential ecological impact of complex chemical pollution: (1) component-based methods that allow a predictive mixture risk modeling; (2) effect-based methods; (3) in situ tests; (4) field-derived species inventories. These LOEs differ systematically in their specificity for chemical pollution, data demands, resources required and ecological relevance. They complement each other and, in their combination, allow to assess the contribution of chemical pollution pressure to impacts on ecological structure and function. Data from all LOEs are not always available and the information they provide is not necessarily consistent. We therefore propose a systematic, robust and transparent approach to combine the information available for a given study, in order to ensure that consensual conclusions are drawn from a given dataset. This allows to identify critical data gaps and needs for future testing and/or options for targeted and efficient water management. | URI: | https://open.uns.ac.rs/handle/123456789/20345 | ISSN: | 2190-4715 | DOI: | 10.1186/s12302-019-0276-z |
Appears in Collections: | PMF Publikacije/Publications |
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