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Dryad

Disturbance amplifies sensitivity of dryland productivity to precipitation variability

Cite this dataset

Terry, Tyson et al. (2024). Disturbance amplifies sensitivity of dryland productivity to precipitation variability [Dataset]. Dryad. https://doi.org/10.5061/dryad.tx95x6b49

Abstract

Variability of the terrestrial global carbon sink is largely determined by the response of dryland productivity to annual precipitation. Despite extensive disturbance in drylands, how disturbance alters productivity-precipitation relationships remains poorly understood. Using remote-sensing to pair over 5600 km of natural gas pipeline corridors with neighboring undisturbed areas in North American drylands, we found that disturbance reduced average annual production 6-29% and caused up to a five-fold increase in the sensitivity of net primary productivity (NPP) to interannual variation in precipitation. Disturbance impacts were larger and longer-lasting at locations with higher precipitation (>450 mm mean annual precipitation). Disturbance effects on NPP dynamics were mostly explained by shifts from woody to herbaceous vegetation. Severe disturbance will amplify effects of increasing precipitation variability on NPP in drylands.

README: Disturbance amplifies sensitivity of dryland productivity to precipitation variability

https://doi.org/10.5061/dryad.tx95x6b49

Description of the data and file structure

This dataset includes remotely sensed data acquired through Google Earth Engine. All pipeline pixels were averaged to the climate pixel level, thus each row of data corresponds to multiple Landsat pixels (30m spatial resolution) either in the pipeline corridor (pipe) or in the adjacent undisturbed vegetation, that are then averaged to the climate pixel scale (1000m spatial resolution).
 

Field Meaning  
WYCoords Geographic coordinates of climate pixel  
year Year of data value  
pipenpp Annual productivity of pipeline pixels within climate pixel  
compnpp Annual productivity of pixels along undisturbed reference line parallel to the pipeline corridor, averaged to climate pixel  
WYprcp Total annual precipitation of water year (Oct-Sep)  
pipeherb Percent cover of herbaceous plants in pipeline pixel  
compherb Percent cover of herbaceous plants in undisturbed comparison pixel  
compafgc Percent cover of annual forbs and grasses in pipeline pixel  
pipeafgc Percent cover of annual forbs and grasses in undisturbed comparison pixel  
compshr Percent cover of shrubs in pipeline pixel  
pipeshr Percent cover of shrubs in undisturbed comparison pixel  
comppfgc Percent cover of perennial forbs and grasses in pipeline pixel  
pipepfgc Percent cover of perennial forbs and grasses in undisturbed comparison pixel  
YSD1 1 divided by the sqrt root of years since initial disturbance  
Pdev Annual deviations from mean precipitation  
meanWY Average annual precipitation (water year)  
YSD Years since initial disturbance  
comptree Percent cover of trees in pipeline pixel  
pipetree Percent cover of trees in undisturbed comparison pixel  
lat Latitude (decimal degrees)  
long Longitude (Decimal Degrees)  
pipeline Identity of Pipeline  

Code/Software

The code (included in the dataset) is formatted for use in R (version 4.3.2) and uses the following packages within the R framework:

  • tidyverse (version 2.0.0)
  • splitstackshape (version 1.4.8)
  • data.table (version 1.14.8)
  • lme4 (version 1.1-34)
  • MuMIn (version 1.47.5)
  • gstat (version 2.1-1)
  • sp (version2.1-2)
  • spatstat (version3.0-7)
  • viridus (version 0.6.5)
  • hrbrthemes (version 0.8.0)
  • jtools (version 2.2.2)
  • lmerTest (version 3.1-3)
  • geoR (version 1.9-2)

The needed data input for the R script is all found within the "dataWYgroups1_19.csv" file.  

Funding

United States Department of Defense, Award: 201940, SERDP

Utah State University, Award: A07339-1068, Ecology Center Research Award