Landscape diversity is correlated with satellite-sensed primary productivity in North America
Data files
Sep 05, 2024 version files 32.41 MB
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data_CEC.csv
20.75 MB
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data_GLC.csv
11.65 MB
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data_species_richness.csv
7.71 KB
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ecoregions.csv
378 B
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README.md
5.10 KB
Nov 13, 2024 version files 32.57 MB
Abstract
Biodiversity-ecosystem functioning (BEF) experiments have established generally positive species richness-productivity relationships in plots of single ecosystem types. Here, we analyzed effects of landscape-level diversity, measured as the number of land-cover types (different ecosystems) per 250 × 250 m, across all of North America. We find that this metric is positively related to landscape-wide remotely-sensed primary production, and that a higher number of land-cover types also is associated with greater temporal stability of productivity, and with accelerated 20-year greening trends, in particular at high latitudes. Species diversity was correlated with landscape-level productivity, but the effect of species diversity and landscape diversity were independent. This indicates that diversity-functioning patterns resembling the ones at smaller scales also exist at higher levels of biological organization.
README: Landscape diversity is correlated with satellite-sensed primary productivity in North America
https://doi.org/10.5061/dryad.v41ns1s3p
CHANGELOG
date | change |
---|---|
2024-11-13 | files with data shown in figures added (data_for_fig*.csv) |
This dataset consists of the following four files:
ecoregions.csv
This files provides a textual description of the ecoregion codes used in the other files
column | content |
---|---|
ecoregion | numeric code |
ecoregion_name | plain text name of region |
data_CEC.csv
Abbreviations used:
- LC = land cover (A=agriculture, F=forest, G=grassland, S=shrubland, U=urban, W=wetland)
- EVI = enhanced MODIS vegetation index, normalized for each ecoregion
- NE = net biodiversity effect
- invCV = inverse coefficient of variation
column | content |
---|---|
id | plot identifier |
ecoregion | ecoregion code |
block | block identifier |
rich | LC type richness |
comp | LC type composition |
A_frac | area fraction covererd by agricultural land |
F_frac | area fraction covered by forest |
G_frac | area fraction covered by grassland |
S_frac | area fraction covered by shrubland |
U_frac | area fraction covered by urban land |
W_frac | area fraction covered by wetland |
alt | altitude, in meters above sea level |
gradN | north-south gradient of plot |
gradE | east-west gradient of plot |
EVIgs | growing-season integrated EVI |
EVImean | growing-season mean of EVI |
EVImax | growing-season maximum of EVI |
NE_EVIgs | NE for EVIgs |
NE_EVImean | NE for EVImean |
NE_EVImax | NE for EVImax |
invCV_EVIgs | invCV for EVIgs |
invCV_EVImean | invCV for EVImean |
invCV_EVImax | invCV for EVImax |
EVIgs_slope | temporal change in EVIgs |
EVImean_slope | temporal change in EVImean |
EVImax_slope | temporal change in EVImax |
NE_EVIgs_slope | NE for temporal change in EVIgs |
NE_EVImean_slope | NE for temporal change in EVImean |
NE_EVImax_slope | NE for temporal change in EVImax |
data_GLC.csv
Abbreviations used:
- LC = land cover (A=agriculture, F=forest, G=grassland, S=shrubland, U=urban, W=wetland)
- EVI = enhanced MODIS vegetation index, normalized for each ecoregion
- NE = net biodiversity effect
- invCV = inverse coefficient of variation
column | content |
---|---|
id | plot identifier |
ecoregion | ecoregion code |
block | block identifier |
rich | LC type richness |
comp | LC type composition |
A_frac | area fraction covererd by agricultural land |
F_frac | area fraction covered by forest |
G_frac | area fraction covered by grassland |
S_frac | area fraction covered by shrubland |
U_frac | area fraction covered by urband land |
W_frac | area fraction covered by wetland |
alt | altitude, in meters above sea level |
gradN | north-south gradient of plot |
gradE | east-west gradient of plot |
EVIgs | growing-season integrated EVI |
EVImean | growing-season mean of EVI |
EVImax | growing-season maximum of EVI |
NE_EVIgs | NE for EVIgs |
NE_EVImean | NE for EVImean |
NE_EVImax | NE for EVImax |
invCV_EVIgs | invCV for EVIgs |
invCV_EVImean | invCV for EVImean |
invCV_EVImax | invCV for EVImax |
data__species_richness.csv
column | content |
---|---|
id | plot identified |
sRare | species richness rarefied to 0.1 ha area |
data_for_fig_3a.csv
column | content |
---|---|
LCR | LC type richness |
comp | LC type composition |
EVIgs | growing-season integrated EVI |
data_for_fig_3b.csv
column | content |
---|---|
LCR | LC type richness |
comp | LC type composition |
NE_EVIgs | NE for growing-season integrated EVI |
data_for_fig_3d.csv
column | content |
---|---|
LCR | LC type richness |
comp | LC type composition |
invCV_EVIgs | invCV of growing-season integrated EVI |
data_for_fig_4a.csv
column | content |
---|---|
ecoregion | ecoregion numeric code |
EVIgs_slope_percent | linear change in EVIgs, in percent per 20 years |
data_for_fig_4b.csv
column | content |
---|---|
ecoregion | ecoregion numeric code |
NE_EVIgs_slope_percent | linear change in NE of EVIgs, in percent per 20 years |
data_for_fig_5.csv
column | content |
---|---|
comp | LC type composition |
block | block identifier |
NE | NE of EVIgs |
NE_mean | average NE for this composition |
NE_direction | direction of NE (neutral is not significant) |
order | position on abscissa for plotting |
Methods
Data was collected by processing satellite-remote sensing products collected with the MODIS instrument, at 250m pixel resolution. Land-cover type information was extracted at 30-m spatial resolution from the Commission for Environmental Cooperation’s North American Land Monitoring System’s map (CEC map, based on Landsat-7 satellite imagery), and from the global GlobeLand30 map (GLC map, based on Landsat-5 and China Environmental Disaster Alleviation Satellite (HJ-1) imagery). We focused on the land covers forest, grassland, shrubland, agriculture, wetland and urban, combining the different forest types distinguished in the CEC map.
Study plots were selected across North America to form a quasi-experimental study design with 3x6° latutude x longitude tiles that were further divided into 16 ecoregions. Within each tile x ecoregion combination, parallel experimental sub-designs spanning gradients in land-cover type richness were formed. Plots were selected so that land-cover type richness was orthogonal with the average area fraction of each land-cover type found at each richness level, and so that the richness gradient was orthogonal with important environmental factors such as altitude.
As productivity metric, we used MODIS EVI indices from the Terra satellite (years 2000-2019) and fitted harmonic time series to the data based on Fourier synthesis to model annual phenology curves.