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Spatial landscape model to characterize biological diversity using R statistical computing environment.

Hariom Singh1, R D Garg1, Harish C Karnatak2

  • 1Geomatics Engineering Group, Civil Engineering Department, Indian Institute of Technology Roorkee, Roorkee, India.

Journal of Environmental Management
|October 9, 2017
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Summary
This summary is machine-generated.

A new Spatial Biodiversity Model (SBM) aids rapid biodiversity assessment. This scale-independent tool identifies high-disturbance, high-richness areas using spatial data and parallel computation.

Keywords:
BiodiversityBiological richnessDisturbance index mapMCDMParallel computationSBM

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Area of Science:

  • Ecology
  • Environmental Science
  • Geographic Information Systems (GIS)

Background:

  • Urbanization and population growth are driving global forest degradation and biodiversity loss.
  • There is a critical need for advanced tools to rapidly assess and monitor biodiversity.
  • Existing methods may lack the flexibility for diverse spatial scales and resolutions.

Purpose of the Study:

  • To develop and implement a novel Spatial Biodiversity Model (SBM) for characterizing biological diversity.
  • To create a scale, resolution, and location-independent solution for spatial biodiversity richness modeling.
  • To provide a robust and adaptable tool for global biodiversity assessment.

Main Methods:

  • Development of a platform-independent, parallel computation-based Spatial Biodiversity Model (SBM).
  • Implementation on the R statistical computing platform using open-source software.
  • Integration of multi-criteria decision-making (MCDM) techniques with sensitivity analysis for input derivation and model validation.

Main Results:

  • The SBM effectively identifies areas of high disturbance and high biological richness.
  • Outputs include key landscape indices such as forest fragmentation (FR) and disturbance index (DI).
  • Model stability and robustness were demonstrated through spatial sensitivity analysis and regression validation.

Conclusions:

  • The Spatial Biodiversity Model (SBM) offers a flexible and powerful approach for biodiversity assessment.
  • The model's applicability extends globally, with successful testing in the Indian landscape (Uttarakhand).
  • SBM provides crucial data for conservation planning and management in degraded forest ecosystems.