DOI: 10.5061/dryad.mw6m9066p
title: Data and code from: Global change affects large herbivore forage biomass through gradual successional shifts and abrupt disturbances

description: Forage availability is a key factor regulating large herbivore
 populations. Global changes in land use and climate may affect the
 spatiotemporal distribution of forage across the ranges of large
 herbivores, especially in mountain ecosystems. We test two synergistic
 hypotheses for how landscape and climate changes from 2001 to 2023 have
 affected forb and graminoid biomass at the peak of the growing season
 within and across ecoregions of the eastern slopes of the Rocky Mountains
 in Alberta, Canada. The successional change hypothesis posits that the
 encroachment of woody vegetation into previously herbaceous communities
 has reduced forage biomass. The disturbance hypothesis proposes that
 abrupt community shifts caused by fire and timber harvesting have
 increased forage biomass. We used remote sensing to quantify temporal
 changes in land cover and disturbances, NDVI greenness and phenology
 indices, and spring climate. We then used in-situ vegetation data to
 parameterize generalized linear and gradient boosted regression tree
 models of forb and graminoid biomass to predict annual peak forb and
 graminoid biomass. We found that herbaceous land cover declined while
 shrub and forest area increased, and the percent of annual biomass within
 herbaceous areas declined from woody encroachment. Disturbance effects
 varied, with rising forage biomass in conjunction with increased area of
 logged forests in the foothills ecoregion, while burned areas declined and
 had a reduced contribution to the percent of annual biomass. We found that
 spring became warmer across the study area and ended earlier in the
 alpine, suggesting the effects of long-term climatic shifts may be
 strongest at higher ecoregions. Disturbance frequency, succession, and
 climate together shaped forage biomass in space and time. Increased
 prescribed fire and other ecological restoration actions may be needed to
 ensure that shifts in forage biomass do not threaten large herbivore
 persistence in the face of global change.
description: # Data and code from: Global change affects large herbivore forage biomass
 through gradual successional shifts and abrupt disturbances Dataset DOI:
 [10.5061/dryad.mw6m9066p](10.5061/dryad.mw6m9066p) ## Description of the
 data and file structure This repository contains R code and data files for
 "Global change affects large herbivore forage biomass through gradual
 successional shifts and abrupt disturbances". An overview of the data
 collection & analysis is available in the main manuscript.  ### Files
 and variables #### File: Biomass_Plot_Data.zip **Description:** Raw data
 from the forage biomass plots in .csv form  #### File: code.zip
 **Description:** R code for all analysis:  1.
 integrating_fire_and_cutblocks_to_landcover_mar162024.RMD is the file that
 merges our fire polygons from Parks Canada and Alberta into our
 time-varying landcover raster based on burn year and type of landcover
 inside the burn.  2. spatial_covariate_extraction_jan132025.RMD extracts
 all spatial variables from our rasters at vegetation point locations for
 the year of sampling  3.
 prepping_spatial_covars_forpredictions_jan142025.RMD this scales, centers,
 and aligns rasters as needed to generate spatial model predictions for
 each year of our study period 4. preping_data_for_modelling_jan132025.RMD
 scales and centers covariates, and uses AIC to test among correlated
 covariates.  5. regression_modelling_jan142025.RMD is analytical code for
 conducting GLM model fitting and selection, and generating summary figures
 and tables.  6. GBM regression jan142025.RMD is analytical code for
 gradient boosting machine learning analysis and generation of summary
 tables and figures.  7. model_validation_jan142025.RMD is code for
 conducting model validation based on withheld training data and YHT long
 term data  8. spatial_model_predictions_2001to2023_jan142025.RMD generates
 spatial model predictions based on the top models from both methods and
 from script 4.  9. changes_in_landcover_NDVI_and_climate_Jan192025.RMD
 quantifies changes in the time-varying landcover map, NDVI vegetation
 indices and climate patterns 10. changes_in_foragebiomass_jan192025.RMD
 quantifies changes in forage (based on predicted values) over time   ####
 File: Spatial_Data.zip **Description:** Spatial data needed to conduct
 analyses. Includes time-varying landcover, phenology indices, and
 topographic covariates. Can be downloaded and opened in R using the
 terra() package.  ## Code/software R version 4.4.0 (2024-04-24 ucrt) --
 "Puppy Cup" RStudio version 2024.4.2.764 -- "Chocolate
 Cosmos" ## Access information Data was derived from the following
 sources: * Time-varying landcover for our study area was derived from:  *
 Hermosilla, T., Wulder, M. A., White, J. C., & Coops, N. C. (2022).
 Land cover classification in an era of big and open data: Optimizing
 localized implementation and training data selection to improve mapping
 outcomes. *Remote Sensing of Environment*, *268*, 112780.
 [https://doi.org/10.1016/j.rse.2021.112780](https://doi.org/10.1016/j.rse.2021.112780) Hermosilla, T., Wulder, M. A., White, J. C., Coops, N. C., & Hobart, G. W. (2018). Disturbance-informed annual land cover classification maps of Canada’s forested ecosystems for a 29-year Landsat time series. *Canadian Journal of Remote Sensing*, *44 *(1), 67–87. [https://doi.org/10.1080/07038992.2018.1437719](https://doi.org/10.1080/07038992.2018.1437719) * NDVI phenology metrics were derived from MODIS MOD13Q1 - MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid following methods used in the following papers:  * Merkle, J. A., Monteith, K. L., Aikens, E. O., Hayes, M. M., Hersey, K. R., Middleton, A. D., Oates, B. A., Sawyer, H., Scurlock, B. M., & Kauffman, M. J. (2016). Large herbivores surf waves of green-up during spring. *Proceedings of the Royal Society B: Biological Sciences*, *283*( 1833), 20160456. [https://doi.org/10.1098/rspb.2016.0456](https://doi.org/10.1098/rspb.2016.0456) * Beck, P. S. A., Atzberger, C., Høgda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. *Remote Sensing of Environment*, *100*(3), 321–3 * Bischof, R., Loe, L. E., Meisingset, E. L., Zimmermann, B., Van Moorter, B., & Mysterud, A. (2012). A migratory northern ungulate in the pursuit of spring: Jumping or surfing the green wave? *The American Naturalist*, *180*(4), 407–424. [https://doi.org/10.1086/667590](https://doi.org/10.1086/667590)