DOI: 10.5061/dryad.zs7h44jhx
title: Data from: Megagrazer loss drives complex landscape-scale biophysical cascades

description: Wild animals can modulate ecosystem-climate feedbacks, e.g. through
 impacts on vegetation and associated carbon dynamics. However, vegetation
 cover and composition also affect land surface albedo, which is an
 important component of the global energy budget. We currently know very
 little about the influence of wild animals on land surface albedo and the
 resulting climate forcing of these albedo changes. Leveraging a unique,
 ecosystem-scale, semi-experimental approach, we study how the loss of the
 world’s largest, terrestrial grazer, white rhinoceros (Ceratotherium
 simum), affected the coupling between fire dynamics, woody encroachment
 and surface albedo in Hluhluwe-iMfolozi Park (HiP), South Africa. Our path
 analysis revealed that areas in the park where more rhinos had been
 removed showed a stronger increase in burnt area and woody encroachment
 compared to areas with fewer rhinos removed, which were both related to a
 decrease in surface albedo. Increasing burnt area was further associated
 with higher rates of woody encroachment, indirectly reinforcing the
 negative effect of rhino loss on albedo. Our study demonstrates that
 removing megagrazers in HiP were related to complex ecosystem-wide
 cascades with measurable impacts on land cover and surface albedo and
 consequences on climate forcing. This highlights the importance of
 restoring functional ecosystems by reinstating trophic processes.
description: We assessed changes in woody plant, fire and short-wave albedo
 dynamics along a gradient of rhino loss, including legal and illegal
 removals at a 1 km² spatial scale. Due to sensitivity around rhino
 poaching, our permit prohibits sharing or displaying spatial data on rhino
 removals or counts. See details for the experimental design in the methods
 section of of the article.  We obtained data on mean
 annual rainfall, burnt area, woody cover and surface albedo across the
 park between 2010 and 2019. From these products we derived mean annual
 rainfall, rate of change in woody encroachment, mean annual burnt area,
 fire frequency and rate of change in annual surface albedo for each 1x1 km
 grid cell.
description: # Megagrazer loss drives complex landscape-scale biophysical cascades
 [https://doi.org/10.5061/dryad.zs7h44jhx](https://doi.org/10.5061/dryad.zs7h44jhx) ## Description of the data and file structure We assessed changes in woody plant, fire and short-wave albedo dynamics along a gradient of rhino loss, including legal and illegal removals at a 1 km² spatial scale. Due to sensitivity around rhino poaching, our permit prohibits sharing or displaying spatial data on rhino removals and counts. **Here is the description of the datasets:** **data.all.csv** encompasses all processed data for analysis. Variables: cell_id, burntarea.mean (mean annual burnt area expressed in ha),  fire_frequency (expressed in number of years a fire intersected the grid cell), modis_woody.mean (mean annual woody cover expressed in ha),  modis_woody.coefficient (rate of change in mean annual woody cover), albedo.mean (mean annual short wave surface albedo), albedo.coefficient (rate of change in mean annual short wave surface albedo), canopy (binary, 0=no canopy cover, 1=canopy cover), and rainfall (long-term mean annual rainfall expressed in mm/year). **burntarea_1000mGrid.csv** encompasses the burnt area per grid cell for each year between 2010-2019 accessed with Google Earth Engine Code Editor from FireCCI51: MODIS Fire_cci Burned Area Pixel Product, Version 5.1. Variables: id (unique grid cell identification), year (year), burntcover (area burnt expressed in ha). **firefrequency_1000mGrid.csv** encompasses the number of years that at least one burnt pixel (250 m) intersected the grid cell computed with Google Earth Engine Code Editor from FireCCI51: MODIS Fire_cci Burned Area Pixel Product, Version 5.1. Variables: id (unique grid cell identification), firefreq (number of years that at least one burnt pixel (250 m) intersected the grid cell) **woodycover*_*MODIS*_*1000mGrid.csv** encompasses the mean tree cover per grid cell for each year between 2010-2019 accessed with Google Earth Engine Code Editor from MOD44B.006 Terra Vegetation Continuous Fields Yearly Global 250m. Variables: id (unique grid cell identification), year (year), tree (tree cover estimate expressed in percentage). **copernicus_landcover_1000mGrid.csv** encompasses the area of different landcover types per grid cell for each year between 2015-2019 accessed with Google Earth Engine Code Editor from  Copernicus Global Land Cover Layers: CGLS-LC100 Collection 3 at 100 m resolution Variables: id (unique grid cell identification), year (year), bare (bare cover estimate expressed in percentage), crop (crop cover estimate expressed in percentage), grass (grass cover estimate expressed in percentage), shrub (shrub cover estimate expressed in percentage), tree (tree cover estimate expressed in percentage), urban (urban cover estimate expressed in percentage). **albedo_1000mGrid.csv** encompasses mean annual short-wave albedo measurements between 2010-2019 accessed with Google Earth Engine Code Editor from MCD43A3.006 MODIS Albedo Daily 500m. Variables:  id (unique grid cell identification), year (year), month (the number of the month in a calendar year), albedo (mean annual short-wave albedo). **grid_1000m.shp** encompasses the grid used for obtaining all data and conducting all analyses