Outcomes of the hydromorphology integration in the Water Framework Directive: A review based on science mapping

Spatial variability of the Po River food web and its comparison with the Danube River food web

Freshwater ecosystems are experiencing unprecedented pressure globally. To address environmental challenges, systematic and comparative studies on ecosystems are needed, though mostly lacking, especially for rivers. Here, we describe the food web of the Po River (as integrated from the white literature and monitoring data), describe the three river sections using network analysis, and compare our results with the previously compiled Danube River food web. The Po River food web was taxonomically aggregated in five consecutive steps (T1-T5) and it was also analyzed using the regular equivalence (REGE) algorithm to identify structurally similar nodes in the most aggregated T5 model. In total, the two river food webs shared 30 nodes. Two network metrics (normalized degree centrality [nDC]) and normalized betweenness centrality [nBC]) were compared using Mann-Whitney tests in the two rivers. On average, the Po River nodes have larger nDC values than in the Danube, meaning that neighboring connections are better mapped. Regarding nBC, there were no significant differences between the two rivers. Finally, based on both centrality indices, Carassius auratus is the most important node in the Po River food web, whereas phytoplankton and detritus are most important in the Danube River. Using network analysis and comparative methods, it is possible to draw attention to important trophic groups and knowledge gaps, which can guide future research. These simple models for the Po River food web can pave the way for more advanced models, supporting quantitative and predictive-as well as more functional-descriptions of ecosystems.

Science maps for exploration, navigation, and reflection-A graphic approach to strategic thinking

The world of science is growing at an unprecedented speed with more and more scholarly papers produced each year. The scientific landscape is constantly changing as research specialties evolve, merge or become obsolete. It is difficult for researchers, research managers and the public alike to keep abreast with these changes and maintain a true and fair overview of the world of science. Such an overview is necessary to stimulate scientific progress, to maintain flexible and responsive research organizations, and to secure collaboration and knowledge exchange between different research specialties and the wider community. Although science mapping is applied to a wide range of scientific areas, examples of their practical use are sparse. This paper demonstrates how to use a topical, scientific reference maps to understand and navigate in dynamic research landscapes and how to utilize science maps to facilitate strategic thinking. In this study, the research domain of biology at Aarhus University serves as an example. All scientific papers authored by the current, permanent staff were extracted (6,830 in total). These papers were used to create a semantic cognitive map of the research field using a co-word analysis based on keywords and keyword phrases. A workflow was written in Python for easy and fast retrieval of information for topic maps (including tokens from keywords section and title) to generate intelligible research maps, and to visualize the distribution of topics (keywords), papers, journal categories, individual researchers and research groups on any scale. The resulting projections revealed new insights into the structure of the research community and made it possible to compare researchers or research groups to describe differences and similarities, to find scientific overlaps or gaps, and to understand how they relate and connect. Science mapping can be used for intended (top-down) as well as emergent (bottom-up) strategy development. The paper concludes that science maps provide alternative views of the intricate structures of science to supplement traditional bibliometric information. These insights may help strengthen strategic thinking and boost creativity and thus contribute to the progress of science.

A new index for the assessment of hydromorphology in transitional and coastal waters around Ireland

In assessing the overall status of individual water bodies the EU Water Framework Directive (WFD) requires member states to assess both ecological and chemical status. The ecological status of transitional and coastal (TraC) waters is based on the assessment of specific biological elements as well as supporting chemical, physico-chemical and hydromorphological elements. Hydromorphology of TraC waters is one of the basic features of marine and coastal ecosystems controlling the presence of biota. Human induced hydromorphological alterations and pressures can damage the ecology and functioning of aquatic ecosystems. Thirteen metrics were developed and combined to form a hydromorphological index, the Hydromorphological Quality Index (HQI). The index categorises a water body into 5 classes. Semi-qualitative and quantitative criteria were used to assign a morphological classification directly related to that of the WFD, i.e., high, good, moderate, poor and bad. Thirty-three transitional and coastal water bodies were assessed using HQI.