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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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LandS: Vegetation modeling based on Ellenberg's ecological indicator values.

Quintana Rumohr1, Volker Grimm2, Gottfried Lennartz1

  • 1Gaiac Research Institute for Ecosystem Analysis and Assessment at RWTH Aachen University, Kackertstraße 10, Aachen 52072, Germany.

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|December 11, 2023
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Summary

LandS is an enhanced grassland simulation model incorporating environmental factors for broader applicability. This improved model aids in predicting species composition across diverse Central European landscapes.

Keywords:
LandS Model (Landscape Succession Model)Modeling environmental conditionsPlant sociologySemi-natural grasslandSpecies compositionVegetation development

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Area of Science:

  • Ecology
  • Computational Biology
  • Vegetation Science

Background:

  • The GraS Model simulates grassland development using Ecological Indicator Values (EIVs).
  • Existing EIVs focused on management practices, limiting site applicability.
  • A need existed for a model adaptable to various environmental conditions.

Purpose of the Study:

  • To introduce LandS, an upgraded GraS Model with expanded EIVs for enhanced grassland simulation.
  • To increase the model's versatility for diverse Central European sites and environmental conditions.
  • To improve model implementation through version control and flexible data input.

Main Methods:

  • Integrated Ellenberg's EIVs for environmental factors (light, moisture, temperature, pH, nitrogen).
  • Restructured model implementation with version control and external data input files.
  • Conducted simulation experiments to demonstrate model behavior and interactions.

Main Results:

  • LandS demonstrates improved versatility across varied environmental gradients (e.g., moisture, soil pH).
  • The model effectively simulates grassland vegetation development using updated EIVs.
  • Enhanced implementation facilitates easy adaptation to new study sites and species compositions.

Conclusions:

  • LandS provides a more robust tool for simulating grassland dynamics in Central Europe.
  • The inclusion of Ellenberg's EIVs enhances predictive capabilities for species occurrence and composition.
  • The flexible design supports broader ecological research and landscape-specific applications.