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The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt

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  • Schmidt, Heiko
  • Radinger, Johannes
  • Teschlade, Daniel
  • Stoll, Stefan

Abstract

Species Distribution Models (SDM) are frequently used in ecological research, but the effects of model extent and granularity on model outcomes are rarely addressed. In freshwater SDMs, two different approaches are commonly used to define the granularity of the models, i.e. to subdivide entire river systems into appropriate spatial modelling units: river reaches on the actual river network or subcatchments. We built maximum entropy (MaxEnt) SDMs for barbel (Barbus barbus) and grayling (Thymallus thymallus) in the River Ruhr catchment in Germany with identical environmental predictor data and analysed the difference between these two approaches. All models performed well (AUC > 0.9) and geographic predictors (e.g. distance to the river source) dominated over hydromorphological predictors in explaining species distributions. There was high agreement between the two model setups in river parts of low and high habitat suitability but considerably lower agreement in river parts of medium habitat suitability. In these medium suitable river parts, model results were spatially very heterogeneous and alternated at fine spatial scales especially in models based on river reaches. Increasing model regularization, a setting to control overfitting, had a smoothing effect on the environmental variables in the river reach models similar to the coarser subcatchment granularity. A restriction of the spatial extent led to a shift in predictor contributions to the model and an increase of the importance of hydromorphologic predictors by ca. 45 %. Restricting the model extend to the natural core distributional area of a given fish species might therefore be considered beneficial for the application of SDMs in a management context. By decreasing the weight of fixed geographic predictors in the model, predictors relating to hydromorphological river structures (i.e. which are accessible for restoration projects) gain importance. We conclude that SDM setups based on river reaches and subcatchments can both give valuable complementary information about the distribution of species. The high-resolution model based on river reaches might better discover individual local habitat features, whereas the subcatchment model might better account for the minimum spatial requirements of a fish population.

Suggested Citation

  • Schmidt, Heiko & Radinger, Johannes & Teschlade, Daniel & Stoll, Stefan, 2020. "The role of spatial units in modelling freshwater fish distributions: Comparing a subcatchment and river network approach using MaxEnt," Ecological Modelling, Elsevier, vol. 418(C).
  • Handle: RePEc:eee:ecomod:v:418:y:2020:i:c:s0304380020300089
    DOI: 10.1016/j.ecolmodel.2020.108937
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    References listed on IDEAS

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    1. Johannes Radinger & Christian Wolter & Jochem Kail, 2015. "Spatial Scaling of Environmental Variables Improves Species-Habitat Models of Fishes in a Small, Sand-Bed Lowland River," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    2. Radinger, Johannes & Hölker, Franz & Wolter, Christian, 2017. "Assessing how uncertainty and stochasticity affect the dispersal of fish in river networks," Ecological Modelling, Elsevier, vol. 359(C), pages 220-228.
    3. Kuemmerlen, Mathias & Schmalz, Britta & Guse, Björn & Cai, Qinghua & Fohrer, Nicola & Jähnig, Sonja C., 2014. "Integrating catchment properties in small scale species distribution models of stream macroinvertebrates," Ecological Modelling, Elsevier, vol. 277(C), pages 77-86.
    4. Pletterbauer, Florian & Graf, Wolfram & Schmutz, Stefan, 2016. "Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis," Ecological Modelling, Elsevier, vol. 327(C), pages 95-104.
    5. Anderson, Robert P. & Gonzalez, Israel, 2011. "Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent," Ecological Modelling, Elsevier, vol. 222(15), pages 2796-2811.
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    1. Keretz, Shay S. & Woolnough, Daelyn A. & Morris, Todd J. & Roseman, Edward F. & Zanatta, David T., 2024. "Habitat modelling of native freshwater mussels distinguishes river specific differences in the Detroit and St. Clair rivers of the Laurentian Great Lakes," Ecological Modelling, Elsevier, vol. 487(C).
    2. Schartel, Tyler E. & Cao, Yong, 2024. "Background selection complexity influences Maxent predictive performance in freshwater systems," Ecological Modelling, Elsevier, vol. 488(C).
    3. Ping He & Yu Gao & Longfei Guo & Tongtong Huo & Yuxin Li & Xingren Zhang & Yunfeng Li & Cheng Peng & Fanyun Meng, 2021. "Evaluating the Disaster Risk of the COVID-19 Pandemic Using an Ecological Niche Model," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    4. Arayaselassie Abebe Semu & Tamrat Bekele & Ermias Lulekal & Paloma Cariñanos & Sileshi Nemomissa, 2021. "Projected Impact of Climate Change on Habitat Suitability of a Vulnerable Endemic Vachellia negrii (pic.serm.) kyal. & Boatwr (Fabaceae) in Ethiopia," Sustainability, MDPI, vol. 13(20), pages 1-16, October.
    5. Carina Seliger & Melanie Haslauer & Günther Unfer & Stefan Schmutz, 2021. "aquaZone: An Integrative Tool for Sustainable Fish Farm Zoning," Sustainability, MDPI, vol. 13(3), pages 1-25, January.

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