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Biophysical and Land-use Controls on Biodiversity: Regional to Continental Scales. Andrew Hansen and Linda Phillips Montana State University Curt Flather Colorado State University. Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences May 1-2, 2008. - PowerPoint PPT Presentation
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Andrew Hansen and Linda PhillipsAndrew Hansen and Linda PhillipsMontana State UniversityMontana State University
Curt FlatherCurt FlatherColorado State UniversityColorado State University
Biophysical and Land-use Controls on Biophysical and Land-use Controls on Biodiversity: Regional to Continental Biodiversity: Regional to Continental
ScalesScales
Joint Workshop on NASA Biodiversity, Terrestrial Ecology, and Related Applied Sciences
May 1-2, 2008
Human Land Human Land UseUse
(Land use,Home
density)
Current Current Biodiversity Biodiversity
ValueValue
Biophysical Biophysical PotentialPotential
(i.e. Energy,Habitat
structure)
Conservation Conservation Priority/StrategiesPriority/Strategies
Research Questions:Research Questions:
1: Which biophysical predictor variables are most strongly related to 1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?bird biodiversity potential in areas without intense human land use?
2: How are these patterns of biodiversity modified due to land use?2: How are these patterns of biodiversity modified due to land use?
3: What geographic areas are highest priorities for conservation based 3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?on biodiversity modification resulting from land use change?
Ecosystem Energy as a Framework for Conservation?Ecosystem Energy as a Framework for Conservation?
Hawkins et al. 2003
Key HypothesisKey HypothesisPrimary productivity, and the factors that drive it (climate, soils, topography), ultimately influence:
disturbance and succession resources for organisms species distributions and demographies community diversity responses to habitat fragmentation, land use, exotics effectiveness of conservation
Conservation CategoryConservation Category Low EnergyLow Energy Medium EnergyMedium Energy High EnergyHigh Energy
Conservation Zones Protect high energy places Protect more natural areas Protect low energy places
Disturbance Use fire, flooding, logging judiciously in hotspots
Similar to “Descending” Use disturbance to break competitive dominance Use shifting mosaic harvest pattern Maintain structural complexity
Landscape Pattern Maintain connectivity due to migrations
Manage for patch size and edge
Sensitive Species Many species with large home ranges and low population sizes due to energy limitations
Forest interior species
Exotics High exotics likely due to productivity and high land use
Protected Area Size Large Smaller Smaller
Land Use Low overall High overall Moderate overall
Focused on hot spots Emphasize “backyard” conservation
More random across landscape
Plan development outside of hotspots
Apply restoration
Framework for Classifying Ecosystems for ConservationFramework for Classifying Ecosystems for Conservation
Focus of This TalkFocus of This Talk
1. Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?
Which MODIS energy products best explain patterns of bird diversity across North America?
Does the relationship between birds and energy (slope and sign) differ between places of low, medium, and high energy?
History of Predictor Variables Used to Explain History of Predictor Variables Used to Explain Species Energy PatternsSpecies Energy Patterns
NDVI = (NIR - red) / (NIR + red)NDVI = (NIR - red) / (NIR + red)
Latitude (MacArthur 1972)
Evapotranspiration (Currie 1987, Hawkins et al 2003)
Ambient temperature (Acevedo and Currie, 2003)
Water/Energy Balance (Hawkins et al 2003)
1960’s
1970’s
1980’s
Remote Sensing advances
1990’s
1999
present
MODIS Land Surface Product Development
NDVINDVIEVIEVIGPP (simulated from fpar, climate, cover type)GPP (simulated from fpar, climate, cover type)NPPNPP
AVHRR
Thematic Mapper
Precipitation (Chown et al., 2003)
----------------------------------
Strength of relationship
with bird richness
Vegetated coverLow vegetation Dense vegetation
Vegetation index
Vegetation productivityhigh
low
Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment
Not complete vegetation cover(backscatter)
Dense vegetation(saturation)
Does NDVI have limitations that higher order products address?Does NDVI have limitations that higher order products address?
GPP NPP
What is the shape of the species energy relationship?What is the shape of the species energy relationship?
What is the shape of the relationship?What is the shape of the relationship?Why?Why?
energy energy
richn
ess
richn
ess
Hypothesis:
More individuals hypothesis More individuals hypothesis (Wright, 1983, Preston, 1962; MacArthur & Wilson,
1963, 1967)
Hypothesis:
Competitive exclusion Competitive exclusion (MacArthur and Levins, 1964, 1967; Grime, 1973 1979,
Rosenzweig 1992)
Energy as a framework for conservationEnergy as a framework for conservation
Energy
Bio
div
ersi
ty
Energy
Bio
div
ersi
ty
Energy
Bio
div
ersi
ty
Identify and manage Identify and manage hotspots judiciouslyhotspots judiciously
Protect harsh placesProtect harsh places
But most of But most of landscape is high in landscape is high in diversity, so more diversity, so more options for multiple options for multiple use such as shifting use such as shifting mosaic approach to mosaic approach to forest management; forest management;
If slope and sign vary among energy levels, conservation strategies should differ among low, intermediate, and high energy places.
Response dataResponse data Bird richness from BBS data for years 2000-2005, estimated Bird richness from BBS data for years 2000-2005, estimated richness using COMDYNrichness using COMDYN
Subset of routes (1838) to represent terrestrial natural routes Subset of routes (1838) to represent terrestrial natural routes (exclude human dominated land uses, water impacted)(exclude human dominated land uses, water impacted)
MethodsMethods
• Survey unit is a roadside route• 39.4 km in length• 50 stops at 0.8 km intervals• Birds tallied within 0.4 km• 3 minute sampling period
• Water birds, hawks, owls, and nonnative species excluded in this analysis
Predictor dataPredictor data Calculate both Calculate both breeding season averages breeding season averages for NDVI, EVI and for NDVI, EVI and
GPP and GPP and annual averages annual averages of NDVI, EVI, and GPP, NPPof NDVI, EVI, and GPP, NPP
MethodsMethods
Annual Average MODIS GPPAnnual Average MODIS GPP
NDVI
Enhanced Vegetation Index
Gross Primary Production
Net Primary Production
MODIS MODIS products usedproducts used
0% h
erba
ceou
s
100%
her
bace
ous
0% bare ground
100% bare ground
0% tree 100% tree50%
50%50%
Statistical analysisStatistical analysis Stratify BBS routes by vegetation life from and density Stratify BBS routes by vegetation life from and density
(MODIS VCF)(MODIS VCF)Perform correlation analyses between predictors across Perform correlation analyses between predictors across
vegetative strata and regression analysis between predictor vegetative strata and regression analysis between predictor and response variables across strata.and response variables across strata.
MethodsMethods
Statistical analysisStatistical analysis Perform regression analysis with linear, polynomial, spline Perform regression analysis with linear, polynomial, spline
and breakpoint spline modelsand breakpoint spline models
Perform simple linear regression analysis of four quartiles Perform simple linear regression analysis of four quartiles of GPP to determine slopes and significanceof GPP to determine slopes and significance
Assess and control for effects of spatial correlation on Assess and control for effects of spatial correlation on significance levels and coefficients using generalized least significance levels and coefficients using generalized least squares analyses.squares analyses.
MethodsMethods
variable time modeloverall
rankdelta aic-R from
best overall r2 adj r2
GPP annual quadratic 1 31.625 0.5353 0.5346
NDVI annual quadratic 2 72.939 0.5212 0.5205
NPP annual quadratic 3 96.321 0.513 0.5123
EVI annual quadratic 4 180.654 0.4824 0.4816
NDVI BS linear 5 288.095 0.3561 0.3556
NDVI BS quadratic 6 309.786 0.4406 0.4398
NDVI annual linear 7 331.438 0.4219 0.4215
NPP annual linear 8 374.89 0.4035 0.4031
EVI BS linear 9 395.62 0.4296 0.4292
EVI BS quadratic 10 395.62 0.3954 0.3945
EVI annual linear 11 410.694 0.3878 0.3874
GPP annual linear 12 411.244 0.3876 0.3872
GPP BS linear 13 416.29 0.376 0.3756
GPP BS quadratic 14 416.29 0.3863 0.3854
Results: Best Predictor?Results: Best Predictor?
0
5
10
15
20
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85
NDVI
GP
P
Vegetation classesClass 1 Class 2Class 3Class 4Class 5Class 6Class 7Class 8Class 9Class 10
Correlation between NDVI and GPP across vegetation classes
Results: Best Predictor?Results: Best Predictor?
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
Per
cen
t o
f m
axim
um
R2
0
10
20
30
40
50
60
70
80
90
100
Co
rrelation
coefficien
t
NDVI
GPP
NPP
NDVI/GPP
NDVI/NPP
GPP/NPP
Vegetated surface gradientbare cover----------------------------------------------- herbaceous cover----------------------------------------- forested cover
Results: Best Predictor?Results: Best Predictor?
Strength of relationship
with bird richness
Vegetated coverLow vegetation Dense vegetation
Vegetation index
Vegetation productivityhigh
low
GPP NPP
Intrepertation: Best PredictorIntrepertation: Best Predictor
1. Annual formulation better than breeding season for all predictors
2. Results suggest that GPP better represents primary productivity and bird richness than NDVI in low and high vegetation areas
3. GPP should be used especially in desert areas (bare ground) and dense forests (SE and PNW)
4. Results help explain differences in past studies on predictors and strength of relationships: will depend on vegetation density of samples.
Vegetation Coninuous fieldsVegetation Coninuous fields
Blue gradient - bare groundRed gradient - forest cover Green gradient - herbaceous cover
R2 = 0.5346
0
0.5
1
1.5
2
2.5
0 5 10 15 20 25
GPP (gC/m2)
Bir
d r
ich
ne
ss
(lo
g)
Results: Slope and Shape?Results: Slope and Shape?
Results: Slope and Shape?Results: Slope and Shape?
a=0.083p<.001
a=0.013p<.001
a=0.005p<.036
a= - 0.018p<.001
Results: Slope and Shape?Results: Slope and Shape?
Variable (annual) model overall rank
delta aic-R from best overall r2 adj r2
GPP Spline (cubic) 1 0 0.5484 0.5464
GPP Breakpoint (linear) 2 26.304 0.5384 0.5371
GPP Quadratic 3 31.625 0.5353 0.5346
NDVI Quadratic 4 72.939 0.5212 0.5205
NDVI Spline (cubic) 5 74.416 0.5234 0.5214
NDVI Breakpoint (linear) 6 83.322 0.519 0.5176
NPP Spline (cubic) 7 91.33 0.5176 0.5155
NPP Quadratic 8 96.321 0.513 0.5123
NPP Breakpoint (linear) 9 101.476 0.5126 0.5112
EVI Spline (cubic) 10 180.609 0.4854 0.4832
EVI Quadratic 11 180.654 0.4824 0.4816
EVI Breakpoint (linear) 12 186.134 0.4818 0.4803
Results: Slope and Shape?Results: Slope and Shape?
Results: Slope and Shape?Results: Slope and Shape?
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
0 2 4 6 8 10 12 14 16 18 20
GPP (gC/m2)
rich
nes
s (l
og
)
Results: Slope and Results: Slope and Shape?Shape?
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
0 2 4 6 8 10 12 14 16 18 20
GPP (gC/m2)
rich
nes
s (l
og
)
energy energy
richn
ess
richn
ess
More Individuals Hypothesis
Predicts higher habitat heterogeneity in areas
of high richness
Competitive Exclusion Hypothesis
Predicts high canopy cover in overstory and
lower habitat heterogeneity
Interpretation: Interpretation: Slope and ShapeSlope and Shape
5000 10000 15000
510
15
Annual Gross Primary Productivity
Bir
d S
pe
cie
s R
ich
ne
ss
Coast RangeSpringfieldCle ElumGoldforkYellowstone
Breakpoint GPP=12266; R-squared = 0.46Quadratic Model; R-squared = 0.44Quadratic 95% confidence bands
3000 4000 5000 6000 7000 8000 90005
10
15
Breeding Season NDVI
Bir
d S
pe
cie
s R
ich
ne
ss
Coast RangeSpringfieldCle ElumGoldforkYellowstone
Cubic Model; R-squared = 0.49Cubic 95% confidence bandsBreakpoint NDVI=8007; R-squared = 0.48
Disturbance Effects and Ecosystem EnergyDisturbance Effects and Ecosystem Energy
Diversity increases with disturbance under high energy and decreases under low energy.
HighLow
Lo
wH
igh
Divers
ity
Landscape Productivity
Inte
nsi
ty o
f D
istu
rba
nce
SpringfieldSpringfield
Cle ElumCle Elum
Disturbance Frequency
Spec
ies
Div
ersi
ty
Cle Elum
Springfield
High Low
60 70 80 90 100
81
01
21
41
61
8
% of Landscape Occupied by Closed Canopy Forest
Bir
d R
ich
ne
ss
Site: SpringfieldR2=.16P-value <.01
High Low
40 50 60 70 80 90
68
10
12
14
% of Landscape Occupied by Closed Canopy Forest
Bir
d R
ich
ne
ss
Site: Cle ElumR2=.30P-value <.01
High Low
Huston 1994.
McWethy et al. in review.
Human Land Human Land UseUse
(Land use,Home
density)
Current Current Biodiversity Biodiversity
ValueValue
Biophysical Biophysical PotentialPotential
(i.e. Energy,Habitat
structure)
Conservation Conservation Priority/StrategiesPriority/Strategies
Next Steps:Next Steps:
1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?
2: How are these patterns of biodiversity modified due to land use?
3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?
Human Land Human Land UseUse
(Land use,Home
density)
Current Current Biodiversity Biodiversity
ValueValue
Biophysical Biophysical PotentialPotential
(i.e. Energy,Habitat
structure)
Conservation Conservation Priority/StrategiesPriority/Strategies
Next Steps:Next Steps:
1: Which biophysical predictor variables are most strongly related to bird biodiversity potential in areas without intense human land use?
2: How are these patterns of biodiversity modified due to land use?
3: What geographic areas are highest priorities for conservation based on biodiversity modification resulting from land use change?
Vegetation structure from ELVS/GLAS
Balmford et al. 2001
Vertebrates and NPP
This study.
Humans and NPP
Next StepsNext Steps
Environmental gradient
bio
div
ers
ity
Fine scale studies, local scale
Broader scale studies, regional scale
Broadest scale studies, covering entire environmental gradient
Energy thresholds where limiting factors for organismsChange and cause change SER
Does the shape of the relationship vary with Does the shape of the relationship vary with energy levels (geographically)?energy levels (geographically)?
Is the negative portion of the unimodal Is the negative portion of the unimodal relationship real?relationship real?
Nugget .002Sill .006So using GLS, enter (800000, .25)
NDVI = (NIR - red) / (NIR + red)
Do higher order MODIS products help us answer these questions? Do higher order MODIS products help us answer these questions?
Strength of relationship
with bird richness
Vegetated coverLow vegetation Dense vegetation
Vegetation index
Vegetation productivityhigh
low
Phillips, L.B., Hansen, A.J. & Flather, C.H. (in press), Remote Sensing of Environment
Not complete vegetation cover(backscatter)
Dense vegetation(saturation)
Does NDVI have limitations that higher order products address?Does NDVI have limitations that higher order products address?
GPP NPP
Results: Best Predictor?Results: Best Predictor?
This slide corresponds to green cells in previous slide