@article{KaercherFilstrupBraunsetal.2020,
  author    = {K{\"a}rcher, Oskar and Filstrup, Christopher T. and Brauns, Mario and Tasevska, Orhideja and Patceva, Suzana and Hellwig, Niels and Walz, Ariane and Frank, Karin and Markovic, Danijela},
  title     = {Chlorophyll a relationships with nutrients and temperature, and predictions for lakes across perialpine and Balkan mountain regions},
  series = {Inland Waters},
  volume    = {10},
  journal   = {Inland Waters},
  number    = {1},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {2044-2041},
  doi       = {10.1080/20442041.2019.1689768},
  pages     = {29 -- 41},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}
@article{KaercherFrankWalzetal.2019,
  author    = {K{\"a}rcher, Oskar and Frank, Karin and Walz, Ariane and Markovic, Danijela},
  title     = {Scale effects on the performance of niche-based models of freshwater fish distributions},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {405},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2019.05.006},
  pages     = {33 -- 42},
  year      = {2019},
  abstract  = {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.},
  language  = {en}
}
@article{MarkovicWalzKaercher2019,
  author    = {Markovic, Danijela and Walz, Ariane and K{\"a}rcher, Oskar},
  title     = {Scale effects on the performance of niche-based models of freshwater fish distributions: Local vs. upstream area influences},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {411},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2019.108818},
  pages     = {11},
  year      = {2019},
  abstract  = {Niche-based species distribution models (SDMs) play a central role in studying species response to environmental change. Effective management and conservation plans for freshwater ecosystems require SDMs that accommodate hierarchical catchment ordering and provide clarity on the performance of such models across multiple scales. The scale-dependence components considered here are: (a) environment spatial structure, represented by hierarchical catchment ordering following the Strahler system; (b) analysis grain, that included 1st to 5th order catchments; and (c) response grain, the grain at which species respond most, represented by local and upstream catchment area effects. We used fish occurrence data from the Danube River Basin and various factors representing climate, land cover and anthropogenic pressures. Our results indicate that the choice of response grain local vs. upstream area effects and the choice of analysis grain, only marginally influence the performance of SDMs. Upstream effects tend to better predict fish distributions than corresponding local effects for anthropogenic and land cover factors, in particular for species sensitive to pollution. Key predictors and their relative importance are scale and species dependent. Consequently, choosing proper species dependent spatial scales and factors is imperative for effective river rehabilitation measures.},
  language  = {en}
}