Data from: Megagrazer loss drives complex landscape-scale biophysical cascades
Data files
Jan 22, 2025 version files 4.65 MB
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albedo_1000mGrid.csv
3.37 MB
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burntarea_1000mGrid.csv
241.25 KB
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copernicus_landcover_1000mGrid.csv
354.34 KB
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data_all.csv
72.08 KB
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firefrequency_1000mGrid.csv
7.46 KB
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grid_1000m.dbf
24.97 KB
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grid_1000m.prj
145 B
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grid_1000m.shp
152.42 KB
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grid_1000m.shx
8.07 KB
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README.md
3.32 KB
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woodycover_MODIS_1000mGrid.csv
421.87 KB
Abstract
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.
README: Megagrazer loss drives complex landscape-scale biophysical cascades
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
Methods
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.