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NEON Data in the Classroom: Quantifying Spatial Patterns

Author(s): Kusum Naithani

University of Arkansas, Fayetteville, AR 72701

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Students build on fundamental concepts of spatial patterns and combine this knowledge with the open-data from the National Ecological Observatory Network to quantify spatial autocorrelation and complexity.

Licensed under CC Attribution-NonCommercial 4.0 International according to these terms

Version 1.0 - published on 23 Jul 2020 doi:10.25334/AJBF-AZ49 - cite this


Quantifying spatial patterns is the first step to understanding reciprocal interactions of spatial patterns and underlying ecological processes, the main focus of landscape ecology. This open education resource (OER) is developed in partnership with the National Ecological Observatory Network’s (NEON) Spring 2018 Data Education Faculty Mentoring Network. The main goal of this OER is to train students in landscape scale data analysis for quantifying spatial patterns.  We used raster images from the Harvard Forest (HARV) and the San Joaquin Experimental Range sites (SJER) for this lesson, but it can be adapted for any spatial dataframe including field and remotely sensed data. The specific learning objectives are to: 1) develop informed hypothesis about spatial patterns and complexity using raster data, 2) design a spatial sampling grid and extract data from raster images, (3) quantify spatial autocorrelation of the raster images using semi-variograms, (4) quantify the spatial complexity of the raster images using fractals calculated by semi-variograms, and (5) compare and evaluate different indices for quantifying spatial patterns.

This OER was implemented in an upper level undergraduate course titled “Introduction to Landscape Ecology” at the University of Arkansas. Following discussion of spatial patterns and diverse tools to quantify them, students were given the data, R code (SP-Tutorial.rmd file), and specific questions embedded in the SP-Tutorial.rmd file related to the data, method, and ecological inference of the spatial patterns. Students were able to follow and reproduce the output from the R code. Furthermore, a discussion of applications of semivariograms for predictive and exploratory analyses in ecology before implementing this exercise was helpful in understanding the concept and context of these tools. In conclusion, using open data products generated by NEON and other networks has great potential for enriching students’ learning when integrated with foundational ecological concepts.

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