rMultivariateMatching: A package that uses multivariate matching to find representative simulation sites and generate high-resolution maps
The goals of rMultivariateMatching are a) to use multivariate matching for cost-effective selection of optimal sites for simulation and b) to interpolate simulation output from spatially sparse simulations to create high-resolution datasets, as described in:
Renne, R. R., Schlaepfer, D. R., Palmquist, K. A., Lauenroth, W. K., & Bradford, J. B. In preparation. Estimating complex ecological variables at high spatial resolution using cost-effective multivariate matching algorithms.
Simulation models are valuable tools for estimating ecosystem structure and function under various climatic and environmental conditions and disturbance regimes, but computational requirements can restrict the number of feasible simulations. Thus, simulation models are often run at coarse scales or for representative points and these results can be difficult to use in decision-making. In this package, we have developed cost-effective methods for interpolating multivariate and time series simulation output to high resolution maps. This package includes tools for:
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Interpolation of complex multivariate and timeseries data to high-resolution maps
You can find examples of the site selection and interpolation methods in print here.
To install the rMultivariateMatching package, please run the following
code.
Note that you may need to install devtools and rmarkdown to install
the package and view the vignettes.
# install.packages(c("devtools","rmarkdown"))
devtools::install_github("DrylandEcology/rMultivariateMatching", build_vignettes = TRUE)There are four vignettes (referenced below) that provide step-by-step guides for using the package. You can view these vignettes by running the following code. Click on the ‘HTML’ links to view each vignette.
library(rMultivariateMatching)
browseVignettes("rMultivariateMatching")The site selection method (kpoints) is designed to select optimal
sites for simulation from within a study area, for the purpose of
applying the interpolatePoints function to interpolate sparse or low
spatial resolution simulation output to create high spatial resolution
maps of output variables across the study area. The site selection
method is meant to balance the cost of adding more sites (in terms of
computational or other resources) with the benefit of representing a
larger proportion of the study area.
Importantly, the site selection method could also be used to select an optimal set of sites for field sampling.
Below is an example of site selection using the kpoints function to
find 50 points across drylands in the state of Wyoming. Please see the
Selecting points with rMultivariateMatching vignette for a more
detailed workflow of how to apply these methods.
library(rMultivariateMatching)
# Load targetcells data for Target Cells
data(targetcells)
# Create data frame of potential matching variables for Target Cells
allvars <- makeInputdata(targetcells)
# Select six matching variables
matchingvars <- allvars[,c("cellnumbers","x","y","bioclim_01","bioclim_04",
"bioclim_09","bioclim_12","bioclim_15","bioclim_18")]
# Solve kpoints for k = 200
results1 <- kpoints(matchingvars,criteria = c(0.7,42,3.3,66,5.4,18.4),
klist = 50, n_starts = 10, min_area = 50, iter = 50,
raster_template = targetcells[[1]],
verify_stop = FALSE, savebest = FALSE)Animation of the kpoints algorithm searching for a solution with 50
points across Wyoming drylands. In the figure on the left, colors
represent areas matched to the sites selected in the current iteration
(which are represented by the black points). The figure on the right
shows the proportion of the study area that is represented by the points
in each iteration.
Interpolation is accomplished by assigning output from each simulated
site to all sites in the study area that have been matched to it. There
are three vignettes that provide detailed workflows of how to apply
these methods, depending on matching method and whether or not your
simulation sites were selected using kpoints.
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For interpolating simulation output from sites selected using
kpoints, please refer to the Matching and interpolation with kpoints vignette. -
For interpolating simulation output from sites that were not selected using
kpoints, please refer to the Matching and interpolation without kpoints vignette. -
For interpolating simulation output using a two-step matching process (as described in Renne et al.) using sites that were not selected using
kpoints, please refer to the Two-step matching and interpolation without kpoints vignette.
Interpolation of DryPROP for the state of Wyoming. DryPROP refers to the proportion of days that all layers in an ecologically relevant portion of the soil are dry when soil temperature at 50 cm is >5 degrees Celsius. Simulation output data used in the interpolation are from Bradford et al. (2019).
In Renne et al., we suggest four methods of evaluating matching:
Matching quality for an example using simulated sites from Bradford et al. (2019). Values ≤ 1 represent high quality matching.
2. Calculate the standard deviation of differences between simulated sites and matched sites for a set of variables relevant to the project
Standard deviation of differences between simulated and matched sites using simulated sites from Bradford et al. (2019).
Distance (km) between simulated sites and matched sites using simulated sites from Bradford et al. (2019).
Average distance between the simulated site that is matched to a given location and the simulated sites that are matched to the eight adjacent neighbors of that location using simulated sites from Bradford et al. (2019).
Errors estimated with leave-one-out cross-validation for six output variables from Bradford et al. (2019).
In the site selection example in Renne et al., our goal was to project the impacts of climate change, wildfire, and livestock grazing on big sagebrush (Artemisia tridentata) plant communities in the western United States using STEPWAT2, an individual-based, gap dynamics plant simulation model (Palmquist et al., 2018a; Palmquist et al., 2018b). We chose a set of six climate variables that capture the major drivers of plant community structure in drylands as matching variables. We used the site selection interpolation methods described in Renne et al. to generate the maps of simulation output for a recent publication investigating the projected impacts of climate change on big sagebrush plant communities:
Palmquist, K. A., Schlaepfer, D. R., Renne, R. R., Torbit, S. C., Doherty, K. E., Remington, T. E., Watson, G., Bradford, J. B., & Lauenroth, W. K. (2021). Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients. Glob. Change Bio., 27(20), 5169-5185. https://doi.org/10.1111/gcb.15776
Interpolated datasets from the project are available from the US Geological Survey Sciencebase:
Renne, R. R., Palmquist, K. A., Schlaepfer, D. R., Lauenroth, W. K., and Bradford, J. B. (2021). High-resolution maps of big sagebrush plant community biomass using multivariate matching algorithms: U.S. Geological Survey data release. https://doi.org/10.5066/P9MNKWS4.
Bradford, J. B., Schlaepfer, D. R., Lauenroth, W. K., Palmquist, K. A., Chambers, J. C., Maestas, J. D., & Campbell, S. B. (2019). Climate-driven shifts in soil temperature and moisture regimes suggest opportunities to enhance assessments of dryland resilience and resistance. Front. Ecol. Evol. 7:358. https://doi.org/10.3389/fevo.2019.00358
Palmquist, K. A., Bradford, J. B., Martyn, T. E., Schlaepfer, D. R., & Lauenroth, W. K. (2018a). STEPWAT2: an individual-based model for exploring the impact of climate and disturbance on dryland plant communities. Ecosphere, 9(8). https://doi.org/10.1002/ecs2.2394
Palmquist, K. A, Schlaepfer, D. R., Martyn, T. E., Bradford, J. B., & Lauenroth, W. K. (2018b). DrylandEcology/STEPWAT2: STEPWAT2 Model Description (Palmquist, et al., 2018 Ecosphere). Zenodo. https://doi.org/10.5281/zenodo.1306924