Landscape Ecology

, Volume 31, Issue 1, pp 67–84

Data, data everywhere: detecting spatial patterns in fine-scale ecological information collected across a continent

  • Kevin M. Potter
  • Frank H. Koch
  • Christopher M. Oswalt
  • Basil V. IannoneIII
Research Article

DOI: 10.1007/s10980-015-0295-0

Cite this article as:
Potter, K.M., Koch, F.H., Oswalt, C.M. et al. Landscape Ecol (2016) 31: 67. doi:10.1007/s10980-015-0295-0



Fine-scale ecological data collected across broad regions are becoming increasingly available. Appropriate geographic analyses of these data can help identify locations of ecological concern.


We present one such approach, spatial association of scalable hexagons (SASH), which identifies locations where ecological phenomena occur at greater or lower frequencies than expected by chance. This approach is based on a sampling frame optimized for spatial neighborhood analysis, adjustable to the appropriate spatial resolution, and applicable to multiple data types.


We divided portions of the United States into scalable equal-area hexagonal cells and, using three types of data (field surveys, aerial surveys, satellite imagery), identified geographic clusters of forested areas having high and low values for (1) invasive plant diversity and cover, (2) mountain pine beetle-induced tree mortality, and (3) wildland forest fire occurrences.


Using the SASH approach, we detected statistically significant patterns of plant invasion, bark beetle-induced tree mortality, and fire occurrence density that will be useful for understanding macroscale patterns and processes associated with each forest health threat, for assessing its ecological and economic impacts, and for identifying areas where specific management activities may be needed.


The presented method is a “big data” analysis tool with potential application for macrosystems ecology studies that require rigorous testing of hypotheses within a spatial framework. This method is a standard component of annual national reports on forest health status and trends across the United States and can be applied easily to other regions and datasets.


Big data Ecological monitoring Hotspots Invasive plants Mountain pine beetle Wildfire 

Funding information

Funder NameGrant NumberFunding Note
US Forest Service
  • 14-CS-11330110-042
National Science Foundation
  • 1241932

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Kevin M. Potter
    • 1
  • Frank H. Koch
    • 2
  • Christopher M. Oswalt
    • 3
  • Basil V. IannoneIII
    • 4
  1. 1.Department of Forestry and Environmental ResourcesNorth Carolina State UniversityResearch Triangle ParkUSA
  2. 2.Eastern Forest Environmental Threat Assessment Center, Southern Research Station, USDA Forest ServiceResearch Triangle ParkUSA
  3. 3.Forest Inventory and Analysis, Southern Research Station, USDA Forest ServiceKnoxvilleUSA
  4. 4.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA

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