DOI: 10.5061/dryad.08kprr59f
title: Prioritizing accuracy or efficiency: Comparing general allometric models for perennial bunchgrass species

description: Aboveground biomass is important, yet difficult to estimate in dryland
 ecosystems due to high spatial heterogeneity and the variability of
 graminoid growth form and density. Allometric relationships are one method
 of estimating aboveground biomass of forage resources. These models use
 growth characteristics such as height or diameter to predict biomass.
 While allometry in forest ecosystems is common, biomass estimation in
 grasslands and shrublands is primarily based on harvesting or percent
 cover. Cover estimates vary among researchers and require double sampling
 at each site to create a relationship between cover and biomass.
 Multi-species (general) allometric models for high value forage groups,
 like perennial bunchgrasses, could increase the efficiency of biomass
 estimation by eliminating the need for destructive sampling. While some
 general models exist, few studies focus on the application of these models
 to locations outside the training populations. We tested the applicability
 of a general bunchgrass model to locations not included in model training
 against general models developed using biomass samples from our focal
 sites. We found that our general bunchgrass model trained on data we
 collected in 2019 made accurate predictions at 76% of sites and that this
 model outperformed a general bunchgrass model trained on data collected at
 different sites and by a different research group, which made accurate
 predictions at 64% of our sites. Despite the loss in accuracy, our study
 highlights the potential value in further developing general allometric
 equations for perennial grasses through the development of a grass
 database. This database may lead to the development of general models with
 higher confidence in extrapolation beyond the training populations
 increasing both efficiency and accuracy for land managers.
description: Data included in this repositroy are from two separate
 methodlogies. The first dataset icnludes the biomass of harvested
 bunchgrass species collected across Wyoming, US. The second is the cover
 and density of bunchgrasses from 10 quadrats across 21 sites. 
description: # README
 [https://doi.org/10.5061/dryad.08kprr59f](https://doi.org/10.5061/dryad.08kprr59f) These data were collected to develop and test general allometric equations for perennial bunchgrasses native to big sagebrush plant communities in Wyoming. We compared general allometric models developed using a previous sampling date by Smith et al. (2021) to various models developed from the harvested biomass data we collected in 2019. We found that general models that were trained using biomass data collected at the sampling sites had the lowest prediction error and were the closest to site level biomass. However, the best model from Smith et al. (2021) only lost high accuracy estimates at 3 sites compared to the number of high accuracy predictions made by the best model. ### General Information Year and location of data collection: 6/2019 - 8/2021 on public land managed by the BLM and USFS across Wyoming Funding sources: Yale School of the Environment ### Data and File Overview File List: 1. Plot_Data.csv 2. Bunchgrass2018.csv 3. Bunchgrass2019.csv 4. Bunchgrass_Pooled.csv 5. BunchgrassSamples2019.csv 6. FieldCover2019.csv 7. BunchgrassAllometry_Data Analysis_4Dryad.Rmd Information for: [Plot_Data.csv] 1. Number of variables: 8 2. Number of rows: 23 3. Variable List: 1. Plot ID: Arbitrary label for each of our sampling locations 2. Elevation (m): Elevation for each of our sampling location in meters 3. MAT (C): mean annual temperature extracted from PRISM ([https://prism.oregonstate.edu/) ](https://prism.oregonstate.edu/)) in degrees Celsius 4. Temp.range (__C): The range of temperatures each of our sites experiences on average over 30 years 5. MAP (mm): Mean annual precipitation in millimeters for each of our sites. Also extracted from PRISM. 6. Basin: The general mountain basin where each site was located. 7. Latitude: coordinate in WGS84 8. Longitude: coordinate in WGS84 Information for: [Bunchgrass2018.csv] 1. Number of variables: 10 2. Number of rows: 522 3. Variable List: 1. Artifact of data processing 2. Site: Same as Plot ID 3. Species.Name: the scientific name for each of the samples 4. Plant: The number of a each sample of a given species within a given site 5. mass: the dried biomass in grams 6. Diameter: the diameter of the harvested individual in centimeters 7. Height: the heigh of the harvested individual in centimeters 8. Tillers: the number of tillers for the harvested individual 9. Sample.Basin: the geographic basin where the site was located 10. volume: the cylindrical volume of the harvested individual calculated using the height and diameters in cm^3 Information for: [Bunchgrass2019.csv] 1. Number of variables: 14 2. Number of rows: 177 3. Variable List: 1. Artifact of data processing 2. Date: sampling date 3. Site: same as Plot ID 4. Quadrat: The quadrat at a given site each sample was a part of 5. Type: the functional type of the species sampled 6. Species: the four letter species code for the sample 7. Sample: indicates if the sample was the first sample (s1) of a given species of part of the total biomass of the samples collected (tb) 8. mass: the dried biomass of the sample in grams 9. Cover: the cover following Peet's recommended cover classes (see methods) 10. Density: the number of individuals after the first sample included for total biomass samples 11. Height: the height of the s1 samples in centimeters 12. Diameter: the basal diameter of the given sample in centimeters 13. Tillers: the number of tiller for a given sample 14. volume: the cylindrical volume of the harvested individual calculated using the height and diameters in cm^3 Information for: [Bunchgrass_Pooled.csv] 1. Number of variables: 9 2. Number of rows: 607 3. Variable List: 1. Artifact of data processing 2. Site: same as Plot ID 3. Species: the four letter species code for the sample 4. mass: the dried biomass of the sample in grams 5. Height: the height of the s1 samples in centimeters 6. Diameter: the basal diameter of the given sample in centimeters 7. Tillers: the number of tiller for a given sample 8. volume: the cylindrical volume of the harvested individual calculated using the height and diameters in cm^3 9. year: the year the samples were collected either 2018(6) or 2019 Information for: [BunchgrassSamples2019.csv] 1. Number of variables: 97 2. Number of rows: 14 3. Variable List: same at Bunchgrass2019 -- this dataset is filter to only include the individual samples Information for: [FieldCover2019.csv] 1. Number of variables: 10 2. Number of rows: 2347 3. Variable List: 1. Date: Sampling date 2. Quadrat #: the number of the quadrat within each plot where 1-3 were harvested for biomass and 4-10 were measured for prediction. 3. Species: the raw species information recorded in the field (n/a values indicate that the species level was not accounted for) 4. Plant Type: the functional group or non-living component 5. Cover: the cover following the same scale detailed in the methods section of the protocol 6. Density: the number of individuals of a given species within a quadrat (n/a values indicate that this sample was part of a functional group where density is unaccounted for (e.g., bare ground)) 7. Height (cm): the height of each individual in centimeters (n/a values indicate that this sample was a group where  Height, Basal Diameter and Tiller # were not measure for each individual) 8. Basal Diameter (cm): the basal diameter of a given individual in centimeters (n/a values indicate that this sample was a group where  Height, Basal Diameter and Tiller # were not measure for each individual) 9. Tiller #: the number of tillers for measured individuals (n/a values indicate that this sample was a group where  Height, Basal Diameter and Tiller # were not measure for each individual) 10. Site: the Plot ID ### Sharing/Access information Data was collected in the field from June to August of 2019. The data collected by Smith et al. (2021), which was used to develop our Pooled Models and retrain the models from their manuscript can be found at: [https://doi.org/10.1016/j.rama.2021.07.009](https://doi.org/10.1016/j.rama.2021.07.009 "Persistent link using digital object identifier") ### Code/Software Methods for processing the data: Raw data were processed in Excel and in R. Software-specific information needed to interpret the data: Analyses were conducted in R v.3.6.3 (R Core Team. 2021 R: a language and environment for statistical computing. Vienna, Austria. See [https://www.R-project.org/.)](https://www.R-project.org/.\))