Ripley's K function (Pinaceae)

Knowledge graph centered on Ripley's K function (Pinaceae) with 31 nodes and 109 connections. Top connected: Gothic, Crested Butte, Engelmann Spruce, Abies lasiocarpa, Pinus albicaulis.

Method synopsis

Point pattern analysis using Ripley's K function to test for clustering, randomness, or regularity in tree spatial distributions by comparing observed patterns to simulated random distributions.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Reconstruction and spatial analysis of alpine treeline in the Elk Mountains, Colorado, USA (2016), Physical Geography. Implementations in other papers (listed below) may differ.

1. Spatial pattern analysis using Ripley's K function
   The spatial arrangement of trees in contemporary and reconstructed stands were quantified using Ripley's K function to analyze tree locations as points in two-dimensional space. The K function is a second-order point pattern analysis technique based on calculating the mean number of points occupying circular plots of variable radii surrounding each point in the study area. K functions were calculated for all possible radii using the x, y coordinates of all trees in the transects. Two pieces of information were used to determine if the spatial arrangement of trees was random, clustered, or dispersed. First, a theoretical K function was calculated for each transect assuming complete spatial randomness (CSR). Second, an envelope of K functions was produced for each transect using 99 simulations of tree distributions in an area with the same spatial dimensions as the transect. Observed K functions were compared to theoretical functions to assess whether spatial patterns of trees were significantly different from CSR.
   Quantities: K functions calculated for all possible radii. Used 99 simulations to create envelope. Analysis conducted for 1900, 1960, and 2013 time periods.Duration: Not specifiedConditions: Computational analysis using statistical software
   Equipment: spatstat package, R statistical software

Typical Equipment

• spatstat package
• R statistical software

Output Measurements

• spatial clustering index
• significance of spatial pattern

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used