TY - JOUR A1 - Balint, Miklos A1 - Marton, Orsolya A1 - Schatz, Marlene A1 - Düring, Rolf-Alexander A1 - Grossart, Hans-Peter T1 - Proper experimental design requires randomization/balancing of molecular ecology experiments JF - Ecology and evolution N2 - Properly designed (randomized and/or balanced) experiments are standard in ecological research. Molecular methods are increasingly used in ecology, but studies generally do not report the detailed design of sample processing in the laboratory. This may strongly influence the interpretability of results if the laboratory procedures do not account for the confounding effects of unexpected laboratory events. We demonstrate this with a simple experiment where unexpected differences in laboratory processing of samples would have biased results if randomization in DNA extraction and PCR steps do not provide safeguards. We emphasize the need for proper experimental design and reporting of the laboratory phase of molecular ecology research to ensure the reliability and interpretability of results. KW - batch effect KW - bias KW - DNA extraction KW - environmental DNA KW - laboratory practice KW - lake community KW - metabarcoding KW - nondemonic intrusions KW - PCR KW - sediment Y1 - 2018 U6 - https://doi.org/10.1002/ece3.3687 SN - 2045-7758 VL - 8 IS - 3 SP - 1786 EP - 1793 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Francke, Till A1 - Baroni, Gabriele A1 - Brosinsky, Arlena A1 - Foerster, Saskia A1 - Lopez-Tarazon, José Andrés A1 - Sommerer, Erik A1 - Bronstert, Axel T1 - What Did Really Improve Our Mesoscale Hydrological Model? BT - a Multidimensional Analysis Based on Real Observations JF - Water resources research N2 - Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases. KW - modeling KW - sensitivity analyses KW - model enhancement KW - sediment Y1 - 2018 U6 - https://doi.org/10.1029/2018WR022813 SN - 0043-1397 SN - 1944-7973 VL - 54 IS - 11 SP - 8594 EP - 8612 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Bálint, Miklós A1 - Márton, Orsolya A1 - Schatz, Marlene A1 - Düring, Rolf-Alexander A1 - Grossart, Hans-Peter T1 - Proper experimental design requires randomization/balancing of molecular ecology experiments T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Properly designed (randomized and/or balanced) experiments are standard in ecological research. Molecular methods are increasingly used in ecology, but studies generally do not report the detailed design of sample processing in the laboratory. This may strongly influence the interpretability of results if the laboratory procedures do not account for the confounding effects of unexpected laboratory events. We demonstrate this with a simple experiment where unexpected differences in laboratory processing of samples would have biased results if randomization in DNA extraction and PCR steps do not provide safeguards. We emphasize the need for proper experimental design and reporting of the laboratory phase of molecular ecology research to ensure the reliability and interpretability of results. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 616 KW - batch effect KW - bias KW - DNA extraction KW - environmental DNA KW - laboratory practice KW - lake community KW - metabarcoding KW - nondemonic intrusions KW - PCR KW - sediment Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-423878 SN - 1866-8372 IS - 616 ER -