TY - GEN A1 - Kärcher, Oskar A1 - Filstrup, Christopher T. A1 - Brauns, Mario A1 - Tasevska, Orhideja A1 - Patceva, Suzana A1 - Hellwig, Niels A1 - Walz, Ariane A1 - Frank, Karin A1 - Markovic, Danijela T1 - Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1443 KW - chlorophyll a KW - nutrients KW - Ohrid-Prespa region KW - perialpine lakes KW - water temperature Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-515271 SN - 1866-8372 IS - 1 ER - TY - JOUR A1 - Kärcher, Oskar A1 - Filstrup, Christopher T. A1 - Brauns, Mario A1 - Tasevska, Orhideja A1 - Patceva, Suzana A1 - Hellwig, Niels A1 - Walz, Ariane A1 - Frank, Karin A1 - Markovic, Danijela T1 - Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions JF - Inland Waters N2 - 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. KW - chlorophyll a KW - nutrients KW - Ohrid-Prespa region KW - perialpine lakes KW - water temperature Y1 - 2020 U6 - https://doi.org/10.1080/20442041.2019.1689768 SN - 2044-2041 SN - 2044-205X VL - 10 IS - 1 SP - 29 EP - 41 PB - Taylor & Francis CY - London ER - TY - JOUR A1 - Langhammer, Maria A1 - Thober, Jule A1 - Lange, Martin A1 - Frank, Karin A1 - Grimm, Volker T1 - Agricultural landscape generators for simulation models BT - a review of existing solutions and an outline of future directions JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - There is an increasing need for an assessment of the impacts of land use and land use change (LUCC). In this context, simulation models are valuable tools for investigating the impacts of stakeholder actions or policy decisions. Agricultural landscape generators (ALGs), which systematically and automatically generate realistic but simplified representations of land cover in agricultural landscapes, can provide the input for LUCC models. We reviewed existing ALGs in terms of their objectives, design and scope. We found eight ALGs that met our definition. They were based either on generic mathematical algorithms (pattern-based) or on representations of ecological or land use processes (process-based). Most ALGs integrate only a few landscape metrics, which limits the design of the landscape pattern and thus the range of applications. For example, only a few specific farming systems have been implemented. We conclude that existing ALGs contain useful approaches that can be used for specific purposes, but ideally generic modular ALGs are developed that can be used for a wide range of scenarios, regions and model types. We have compiled features of such generic ALGs and propose a possible software architecture. Considerable joint efforts are required to develop such generic ALGs, but the benefits in terms of a better understanding and development of more efficient agricultural policies would be high. KW - Agricultural landscape KW - Field pattern KW - Agricultural landscape generator KW - Landscape simulator KW - Neutral landscape model KW - Process-based model Y1 - 2019 U6 - https://doi.org/10.1016/j.ecolmodel.2018.12.010 SN - 0304-3800 SN - 1872-7026 VL - 393 SP - 135 EP - 151 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kärcher, Oskar A1 - Frank, Karin A1 - Walz, Ariane A1 - Markovic, Danijela T1 - Scale effects on the performance of niche-based models of freshwater fish distributions JF - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog N2 - Niche-based species distribution models (SDMs) have become an essential tool in conservation and restoration planning. Given the current threats to freshwater biodiversity, it is of fundamental importance to address scale effects on the performance of niche-based SDMs of freshwater species’ distributions. The scale effects are addressed here in the context of hierarchical catchment ordering, considered as counterpart to coarsening grain-size by increasing grid-cell size. We combine fish occurrence data from the Danube River Basin, the hierarchical catchment ordering and multiple environmental factors representing topographic, climatic and anthropogenic effects to model fish occurrence probability across multiple scales. We focus on 1st to 5th order catchments. The spatial scale (hierarchical catchment order) only marginally influences the mean performance of SDMs, however the uncertainty of the estimates increases with scale. Key predictors and their relative importance are scale and species dependent. Our findings have useful implications for choosing proper species dependent spatial scales for river rehabilitation measures, and for conservation planning in areas where fine grain species data are unavailable. KW - Catchment order KW - Conservation planning KW - Danube KW - Generalized additive models KW - Species distribution modelling Y1 - 2019 U6 - https://doi.org/10.1016/j.ecolmodel.2019.05.006 SN - 0304-3800 SN - 1872-7026 VL - 405 SP - 33 EP - 42 PB - Elsevier CY - Amsterdam ER -