GIS spatial data extraction (Plantae)

Local Knowledge Graph (Top 75 entities)

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Knowledge graph centered on GIS spatial data extraction (Plantae) with 75 nodes and 218 connections. Top connected: Rocky Mountain Biological Laboratory, Gothic, Two-form Bumble Bee, Buff-tailed Bumble Bee, Yellow-fronted Bumble Bee.

Method synopsis

Integration of field GPS coordinates with multiple spatial datasets (elevation, slope, land cover, climate) to characterize environmental conditions at population locations. Uses established spatial datasets and climate reanalysis products.

Synthesized from method descriptions across 4 papers 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 — A pathogen's spatial range is not constrained by geographical features in the flax rust pathosystem (2023), Ecology and Evolution. The protocol was originally introduced by Vegetation pattern dependency along the southwest-facing hillslope of the upper East River Watershed (2011). Implementations in other papers (listed below) may differ.

Process GPS data and create sampling ribbons
The GPS file for each transect was cleaned using a GPX Editor to eliminate irregular path deviations (e.g., when backtracking). A rectangular ribbon extending 2.5 m to either side of the trail was created, and points were regularly sampled from the ribbon (1/m²). Using the flax population GPS coordinates (with the population end coordinates set back by 10 m to account for the sampling methodology), each sampled point was classified as either no flax, healthy flax, or diseased flax. The distance from a population to the nearest neighboring population and to the nearest diseased population was calculated.
Quantities: 2.5 m corridors on each side of trail, points sampled at 1/m² density, 10 m buffer for population end coordinatesDuration: Data processing conducted after field sampling completionConditions: Post-field data processing and spatial analysis
Equipment: GPX Editor software, GPS data processing tools
Extract spatial and climate data
Rocky Mountain Biological Laboratory spatial datasets encoding raster data for elevation, slope aspect, slope grade, and land cover were aggregated in R. For additional analyses, PRISM Climate Group's 30-year average temperature raster data with 800 m spatial resolutions was used. For each sampled point location, the raster data from each of the spatial datasets was extracted and included for analyses. For each population, the mean elevation, slope aspect and slope grade, mode land-cover type, and average annual temperature were extracted.