Biologically based urban response models for the South Atlantic gulf and Tennessee River basins
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Transcript of Biologically based urban response models for the South Atlantic gulf and Tennessee River basins
Biologically based urban response models for the South
Atlantic gulf and Tennessee River basins
T.F. Cuffney, E.M. Giddings, and M.B. Gregory
North Carolina Water Science Center
Objectives:
1. Derive invertebrate response models for Atlanta, GA, Birmingham, AL, and Raleigh, NC.
2. Test and verify urban response models (ATL, BIR, RAL).
a. Comparability among models
b. Applicability of urban models to sites throughout the Southeast
– can models developed in one urban area be applied to other urban areas?
– can urban models be used to predict regional patterns in water-quality?
Denver
Portland
DallasBirmingham
Atlanta
Raleigh
Milwaukee
Boston
Salt Lake City
South Atlantic gulf and Tennessee river basins
Seattle
Sacramento
National Water-Quality Assessment (NAWQA) Program 11 Urban Stream Studies
Biological sampling sites
Birmingham Atlanta
Raleigh
0 90 180Kilometers
360
Raleigh Urban Study AreaPiedmont
Urban models: Site Characteristics
Abbrev Core urban areaDominant ecoregion
Habitat sampled Basin area (km2)
ATL Atlanta, GA Piedmont Woody snags
43-146
RAL Raleigh, NC Piedmont Riffles 5-82
BIR Birmingham, AL Central Appalachian Ridges and Valleys
Riffles 5-66
Design of urban studies
1. Urban intensity gradient defined by amultimetric index based on land-cover, population, infrastructure and socioeconomic factors associated with population density.
2. Uniform environmental setting to keep natural environmental factors as constant as possible.
% Impervious and Urban Intensity index (UII) -- Raleigh
y = -0.0547x2 + 4.1944x + 2.1458
R2 = 0.78
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% impervious surface
Urb
an in
ten
sity
(U
II)
Urban models
• Multivariate (ordination) models:– Measure ecological distances among sites
along an environmental gradient (urban) derived from differences in invertebrate assemblages.
• Multiple regression models:– Relate invertebrate assemblage metrics
(EPT) to landscape variables: land cover, land use, census variables, infrastructure.
Multivariate (ordination) models
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-1 -0.5 0 0.5 1 1.5
Ordination site scores (axis 1)
urb
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sit
y
-1 -0.5 0 0.5 1 1.5
Ordination site scores (Axis 1)
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Ordinations scores & UII
Atlanta
Atlanta urban model (Piedmont)
y = -0.0169x + 1.6651R2 = 0.81
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Urban intensity index (UII)
Ord
inat
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sco
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axis
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RTH Inverts: urban model
10% impervious surface
Ordination site scores vs. UII
Urban area a b R2
Atlanta 1.67 -0.017 0.81
Birmingham 1.84 -0.020 0.74
Raleigh 1.75 -0.015 0.74
Y = a + bXY = ordination site scoreX = urban intensity UII
Ordination model: prediction
k k ikkiki yyx /*ATL Site
score
Species abundance from ATL.
Optima for species K: BIR model
Predicting ATL using BIR model
Eigenvalue from BIR model
Atlanta predicted by Birmingham and Raleigh models
BIRy = 0.5376x + 0.1482
R2 = 0.84
RALy = 0.6987x + 0.1365
R2 = 0.91
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Axis 1 site score
Pre
dic
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sit
e sc
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Observed Axis 1 site score
Woody snags
Riffles
Piedmont Ridge & Valley
Piedmont
Birmingham predicted by Atlanta and Raleigh models
ATLy = 0.6261x - 0.0807
R2 = 0.69
RALy = 0.4056x + 0.0461
R2 = 0.54
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Axis 1 site score
Pre
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Observed Axis 1 site score
Raleigh predicted by Atlanta and Birmingham models
ATLy = 0.6317x - 0.0132
R2 = 0.91
BIRy = 0.3397x + 0.0688
R2 = 0.82
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Axis 1 site score
Pre
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sit
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Observed Axis 1 site score
Inter-model comparisons: ordination models
1. Ordination models from Atlanta, Birmingham, and Raleigh are interchangeable.
2. Ordination models developed in one urban area can be applied to other closely allied urban areas.
Regression models
Comparison among ATL, BIR, RAL
Multiple Regression Models
• EPTr: richness of pollution intolerant forms (mayflies, stoneflies, caddisflies)
• Xi: – Land-use: (e.g., forest, developed, ag/urban
grass)• % basin area• % stream buffer
– Population density– Road density
Model: EPTr = f(Xi)
EPTr urban model variables
% basin area Census Infrastructure
City Devel Forest Grass Pop den Road den
RAL 0.11 0.01 -1.44
ATL 0.27 0.33 -3.19
BIR -1.13
% stream buffer
City Devel Forest Grass R2 P
RAL 0.13 0.47 0.81 <0.001
ATL 0.45 0.62 <0.001
BIR 0.59 <0.001
Correspondence between predicted and observed EPT richness based
on multiple regression models
ATL modeled using: BIR modeled using:ATL BIR RAL ATL BIR RAL
b 0.67 0.30 0.30 b 0.64 0.61 0.94
r2 0.66 0.51 0.33 r2 0.19 0.59 0.44
p <0.001 <0.001 <0.001 p 0.011 <0.001 <0.001
Expected = a + b * Observed
Predicted EPT richness based on multiple regression models
RAL modeled using:
ATL BIR RAL
b 0.49 0.49 0.47
r2 0.55 0.62 0.61
p <0.001 <0.001 <0.001
Expected = a + b * Observed
Inter-model comparisons: multiple regression models
1. Variability associated with estimating explanatory variables reduces the overall “fit” of the regression models.
2. Multiple regression models are not as interchangeable among urban study areas as are the ordination models.
Extrapolating urban models to South Atlantic gulf and
Tennessee (SAGT) River basins
Can urban models be used to predict regional patterns in water-quality?
Site Characteristics
Small basins (< 160 km2)
Dominant ecoregion Sites UII range
Piedmont 8 3-80
Interior Plateau 35 3-32
Mid Atlantic Coastal Plain 14 12-33
Southeastern Plains 48 4-81
Central Appalachian Ridges and Valleys 1 11
Southwestern Appalachians 2 9-16
Ordination models
Observed vs. expected site scores
Interior Plateau: Ordination
BIR:y = 0.38x + 0.20
R2 = 0.36
RALy = 0.46x + 0.09
R2 = 0.73
ATL:y = 0.48x + 0.39
R2 = 0.49
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Observed site score
Pre
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sit
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co
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ATL
BIR
RAL
MODEL:
UII: 3-32Sites: 35
Southeastern Plains: Ordination
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Expected site score
Pre
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ATL
BIR
RAL
R2
ATL: 0.002BIR: 0.003RAL: 0.074
UII: 4-81Sites: 48
Multiple regression models
Observed vs. expected EPT richness
Interior Plateau (< 160 km2)
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EPT richness
Pre
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EP
T r
ich
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ATL
RAL
BIR
R2
ATL: 0.001BIR: 0.21RAL: 0.20
EPT: multiple regression model
UII: 3-32Sites: 35
Southeastern Plains (< 160 km2)
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EPT richness
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EP
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ATL
RAL
BIR R2
ATL: 0.02BIR: 0.04RAL: 0.06
EPT: multiple regression model
UII: 4-81Sites: 48
Tolerances to urbanization differ from tolerances to general pollution
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USEPA and NCDENR tolerance values
Urb
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alu
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General pollution tolerance values: USEPA and NCDENR
Urb
an p
oll
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val
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AW
QA
(U
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Species tolerances
Conclusions1. Invertebrate responses to urbanization provide
very useful predictive models.
2. Ordination models were generally better predictors of responses than were multiple regression models using metrics.
3. Urban models based on ordination were transferable among urban areas -- models developed for one urban area are applicable to other urban areas.
4. Urban models are specific to urbanization and do not provide a general means of modeling regional responses to water quality – network design affects application.
Next steps
• Derive more generalized land-use disturbance models (forest, agriculture, urban).
• Couple biological response models with SPARROW modeling in South Atlantic gulf and Tennessee River basins.