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Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

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Digital elevation model (DEM) error significantly impacts environmental variables and predictive habitat models. This uncertainty affects statistical significance, model selection, prediction accuracy, and spatial predictions, highlighting the need for error consideration.

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Area of Science:

  • Geospatial analysis
  • Ecological modeling
  • Environmental science

Background:

  • Digital Elevation Model (DEM) error effects on habitat models are understudied.
  • DEM errors can propagate to environmental variables and influence model outcomes.

Purpose of the Study:

  • To investigate the impact of DEM error on environmental variables.
  • To assess DEM error's influence on predictive habitat model components and results.

Main Methods:

  • Performed error analysis on a DEM to create multiple error realizations.
  • Developed environmental variables from DEM error realizations for habitat modeling.
  • Evaluated effects on generalized additive models (GAMs) and generalized linear models (GLMs).

Main Results:

  • DEM error significantly affected statistical significance of environmental variables.
  • Species response curves, model selection, and coefficients/standard errors were impacted.
  • Prediction accuracy (Cohen's kappa, AUC) and spatial extent of predictions were greatly affected.

Conclusions:

  • DEM error introduces substantial uncertainty into habitat modeling.
  • Reliability of model interpretation and prediction accuracy are compromised by DEM error.
  • Sensitivity of DEM-derived variables to DEM error must be considered in modeling.