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-Karin Reich, Elena Roussanova: Der 2019 wiederaufgefundene Brief von Gauß an Humboldt vom 17. August 1832 im Umfeld der Erforschung des Magnetismus und des Erdmagnetismus
-Dagmar Hülsenberg: Anwendung naturwissenschaftlicher und kameralistischer Erkenntnisse auf die Verarbeitung von Rohstoffen durch den jungen Alexander von Humboldt
-Peter Korneffel: Alexander von Humboldt postfrisch: Die Rezeption des deutschen Naturforschers in der weltweiten Philatelie
-Jie-Oun Lee: Erzählstrategien eines transdisziplinären Naturforschers
-Eberhard Schulz-Lüpertz: Alexander von Humboldt und Ulrich Jasper Seetzen – Auf den Spuren eines Helgoland-Briefs
-Ulrich Stottmeister: Der Mineraloge August Schmidt und die Entdeckung der Ural-Diamanten 1829 Teil II: Schmidts wissenschaftlicher Diamanten-Beweis und sein weiteres Schicksal im Ural
-Petra Werner: Ernste Kunst kann nicht gedeihen ohne Gunst. Mäzene und Unterstützer des Malers Albert Berg (1825 – 1884)
-Frank Holl: Hinweis zum Beitrag von Irene Prüfer Leske in HiN, Bd. 22, Nr. 43 (2021)
Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.
Model-derived relationships between chlorophyll a (Chl-a) and nutrients and temperature have fundamental implications for understanding complex interactions among water quality measures used for lake classification, yet accuracy comparisons of different approaches are scarce. Here, we (1) compared Chl-a model performances across linear and nonlinear statistical approaches; (2) evaluated single and combined effects of nutrients, depth, and temperature as lake surface water temperature (LSWT) or altitude on Chl-a; and (3) investigated the reliability of the best water quality model across 13 lakes from perialpine and central Balkan mountain regions. Chl-a was modelled using in situ water quality data from 157 European lakes; elevation data and LSWT in situ data were complemented by remote sensing measurements. Nonlinear approaches performed better, implying complex relationships between Chl-a and the explanatory variables. Boosted regression trees, as the best performing approach, accommodated interactions among predictor variables. Chl-a-nutrient relationships were characterized by sigmoidal curves, with total phosphorus having the largest explanatory power for our study region. In comparison with LSWT, utilization of altitude, the often-used temperature surrogate, led to different influence directions but similar predictive performances. These results support utilizing altitude in models for Chl-a predictions. Compared to Chl-a observations, Chl-a predictions of the best performing approach for mountain lakes (oligotrophic-eutrophic) led to minor differences in trophic state categorizations. Our findings suggest that both models with LSWT and altitude are appropriate for water quality predictions of lakes in mountain regions and emphasize the importance of incorporating interactions among variables when facing lake management challenges.