Spatial Non-Stationarity in Global Patterns of Biodiversity:
Mapping the Drivers of Terrestrial Vertebrate Species Richness
Bill Morris
University of Vermont
Dept. of Plant & Soil Science
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Purpose:
To identify regional patterns within the suite of
factors that influence levels of biodiversity
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Question 1:
Are relationships explaining Terrestrial Vertebrate
Species Richness (TVSR) spatially non-stationary?
Question 2:
Where are spatial patterns of TVSR driven by similar
combinations of factors?
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Input Data: Terrestrial Species Richness
Reported at the Ecoregion Level
Data: Global Ecoregions, provided by the World Wildlife Fund
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Known Influences
On Species Richness
Biophysical Variables:
Mean Elevation
Elevation Range
Mean Annual Temperature
Annual Temperature Range
Total Annual Precipitation
Percent Tree Cover
Percent Herbaceous Cover
Anthropogenic Variables:
Human Population Density
Total Protected Area
Percent Urban Cover
Percent Cropland Cover
Percent Ag Mosaic Cover
Spatial Resolutions Range from
500m to 5km
MODIS VCF Tree Cover
Landscan Population Density
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Data Preparation:
1. Acquired input variable datasets at global scale
and best-available spatial resolution
Human Population Density
Data: Landscan 2008, provided by Oak Ridge National Laboratory
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Data Preparation:
2. Overlaid WWF ecoregion polygons, excluding
polar zones and ecoregions <10km2
Ecoregion Polygons
Data: Global Ecoregions, provided by World Wildlife Fund
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Data Preparation:
3. Extracted variable means for each ecoregion
polygon (“Zonal Statistics”)
Mean Human Population Density Per Ecoregion
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Mean Annual Temperature (C)
Annual Temperature Range
Biophysical Variables:
Total Annual Precipitation (mm)
Mean Elevation (m)
Within-Ecoregion Elevation Range
Percent Tree Cover
Percent Herbaceous Vegetation Cover
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Anthropogenic Variables:
Percent Cropland Cover
Percent Agricultural Mosaic Cover
Percent Urban Cover
Human Population Density (per sqkm)
Percent Protected Area
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Answering Question 1:
X Axis = Average Annual Temperature
X Axis = Annual Temperature Range
Dot size = Ecoregion Area
X Axis = Herbaceous vegetation Cover
X Axis = Cropland Cover
Y Axis = TVSR
Are relationships
explaining Terrestrial
Vertebrate Species
Richness (TVSR) spatially
non-stationary?
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Answering Question 1:
AiC results show that Geographically-Weighted Regression provides
better models than Global Regression for these comparisons.
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Answering Question 2:
Where are spatial patterns of TVSR driven by similar
combinations of factors?
Geographically-Weighted Regression (GWR) on the
attributes and locations of the ecoregion polygons
“GWR is a method of analyzing spatially-varying relationships.
[It is] is a technique for exploratory spatial data analysis.”
- From Fotheringham et al. http://ncg.nuim.ie/ncg/GWR/
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From Fotheringham, Brundson and Charlton. 2002. Geographically Weighted Regression
GWR in Action
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Methods:
Unsupervised classification of ecoregions into Biodiversity
Driver Zones (BDZs) – for each ecoregion polygon:
Biophysical
Variables
Anthropogenic
Variables
TVSR
GWR
2-Step
Clustering
Algorithm
Output
Parameters
BDZ
Membership
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•Significant Negative Explanatory Variables:
•Mean Annual Temp (0.25)
•Precipitation total (0.25)
•Ag Mosaic Density (0.19)
•Significant Positive Explanatory Variables:
•Mean Elevation (0.16)
•Elevation Range (0.18)
•Temp annual range (0.23)
BDZ #3: Hot to Trot
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•Significant Negative Explanatory Variables:
•Ag density (0.26)
•Urban Density (0.26)
•Ag Mosaic Density (0.25)
•Human Population Density (0.24)
•Mean Annual Temp (0.33)
•Significant Positive Explanatory Variables:
•Mean Elevation (0.27)
•Elevation Range (0.28)
•Tree Cover Density (0.23)
•Herbaceous Cover Density (0.23)
•Precipitation total (0.30)
•Protected Area (0.26)
•Temp annual range (0.45)
BDZ #5: Don’t Crowd Me
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Biodiversity Driver Zones
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A Step Further:
The variation in explanatory dynamics between identified Biodiversity
Driver Zones suggests that divergent, decentralized strategies may be
appropriate for conservation and management of the world’s biodiversity
resources.