big data informing lake ecology: case on nutrient and ... · big data informing lake ecology: case...
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Big data informing lake ecology: Case study on nutrient and water color effects on lake primary
production
C. Emi Fergus, Andrew O. Finley, Pat A. Soranno, Tyler Wagner
Michigan Inland Lakes ConventionApril 2016
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Lakes in the landscape
Michigan over 10,000 inland lakes (>4 ha in size)
U.S. estimated over 120,000 inland lakes
MI Lake and Stream Associations, Inc.
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Lakes in the landscape
Michigan over 10,000 inland lakes (>4 ha in size)
U.S. estimated over 120,000 inland lakes
How can we effectively study and manage them?
MI Lake and Stream Associations, Inc.
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Landscape limnology
The spatially explicit study of lakes, streams, and wetlands as they interact with freshwater, terrestrial, and human landscapes …to affect lake characteristics. Soranno et al. 2010
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Principles
Soranno et al. 2010. BioScience
Patch characteristics
Patch context
Patch connectivity & directionality
Spatial scale & hierarchy
www.fw.msu.edu/~LLRG
Landscape limnology
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http://michpics.wordpress.com/2009/12/19/michigan‐farming‐and‐other‐success‐stories/
http://www.visitusa.com/maine
Landscape limnology
Riparianchar.
Soils
Land cover/use
Geology
Climate
Region
alLocal
Scale an
d hierarchy
Lake characteristics
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Novel questions and perspectives
• Regional variation• Broad‐scale disturbance effects• Prediction• Temporal trends
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Harnessing ‘Big Data’ to address lake questions
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Harnessing ‘Big Data’ to address lake questions
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Overall Goals
• More holistic understanding of lake ecology
• Provide information to guide management and conservation action
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Case study
Lake nutrient and water color effects on lake primary production
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Drivers of lake primary production
Nutrients Light
PhytoplanktonBulk of 1°
production in lakes
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TP ~ Chlorophyll a relationship
https://www.iisd.org/ela/ecosystem‐experimentation
Phosphorus
Chloroph
yll a
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Revisiting the TP ~ Chlorophyll relationship
TP ~ CHL
Spatial variation in relationships
Wagner et al. 2011
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Colored dissolved organic carbon (water color)
http://www.ualberta.ca/ERSC08/water/climate/impacts6.htmhttp://recon.sccf.org/definitions/cdom.shtml
• Humic substances primarily from surrounding landscape
• Alters physical, chemical, and biological environment
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Colored dissolved organic carbon (Water Color)
• Nutrients bound to humic compounds
Light
• Weakens light• Shades algae
O OH
O PP
P
Negative effects Positive effects
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Nutrient‐water color paradigm
ColorTP
Chlorophyll
Light environment
Nutrient Light
( + )
(–)
( + )
(+)
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Important to understand in time of global change
Nutrient
DOC
http://www.peak‐light.com/black‐clough
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Landscape nutrient and carbon sources
http://www.garthlenz.com/industrial‐landscape/agriculture/Ches‐Lancaster‐8436
http://blogs.ubc.ca/thearodgers/2015/05/03/impacts‐of‐climate‐change‐on‐carbon‐emissions‐from‐canadian‐peatlands/
• Agriculture –nutrient source
• Wetlands & Forest –carbon source
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Agriculture
Spatial Nutrient‐water color paradigm
ColorTP
Chlorophyll
Light environment
Nutrient Light
( + )
(–)
( + )
( + )
Wetlands
( + )( + )
Landscape variables
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Agriculture
Spatial Nutrient‐water color paradigm
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
( – )
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Agriculture
Spatial Nutrient‐water color paradigm
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
LakeConnectivity
( – ) Lake( + )
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1) Do TP and Water Color effects on Chlorophyll vary over space?
2) If so, are there lake and landscape variables that account for variation in these relationships?
Research questions
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Subset of lakes from LAGOSN = 779 lakesComplete records for Chl, TP, Color, and lake depth
LAGOS: Lake Multi‐Scaled Geospatial Databasehttp://csilimno.cse.msu.edu
Soranno, et al. 2015. Gigascience
Lake database
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Spatially‐varying coefficient model
Co‐authors: quantitative ecologists with mad statistical skills
CHL
TP
CHL
Color
?
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Spatially‐varying coefficient model
= Intercept, TP, and Water Color
Spatially‐varying coefficients
CHL
CHL
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Spatially‐varying coefficient model
Spatially‐varying coefficients
Stationary coefficients
Hypothesized landscape & lake variables• Lake depth• Catchment: Lake Area ratio• Agriculture• Wetland• Lake connectivity type (isolated vs. drainage)
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Q1) Spatial variation in TP & Color effects?
M1: CHL ~ Intercept + TP + Color
Spatially‐varying
CHL
MNULL: CHL ~ Intercept + TP + Color
Non‐spatialCH
L Vs.
Evaluated using model fit criteria G = goodness of fit; P = penalty; D = model criteria
Gelfand and Gohosh 1998
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Results: Q1
Model G P DNull 5456.0 5435.2 10891.31 4736.4 4502.9 9239.4
Lower is better
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Results: Q1
Model G P DNull 5456.0 5435.2 10891.31 4736.4 4502.9 9239.4
M1 Intercept
Lower is better
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Results: Q1
Model G P DNull 5456.0 5435.2 10891.31 4736.4 4502.9 9239.4
M1 InterceptNull Model Intercept
Lower is better
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Results: Q1
M1: Chl ~ TP Slopes
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Results: Q1
M1: Chl ~ TP Slopes M1: Chl ~ Color Slopes
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Conclusions: Q1
Lake Chlorophyll exhibits spatial variation even after accounting for TP & Color
• Landscape, lake, & other spatial variables may explain remaining spatial variation
TP effects on Chlorophyll vary over space but Color effects were not significant for most lakes
• TP is primary driver of lake productivity in Upper Midwest and NE U.S.
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Q2) Lake & landscape drivers of variation
Agriculture
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
LakeConnectivity
( – ) Lake( + )
M1: CHL ~ Intercept + TP + Color
Spatially‐varying
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Q2) Lake & landscape drivers of variation
Agriculture
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
LakeConnectivity
( – ) Lake( + )
M1: CHL ~ Intercept + TP + Color
Spatially‐varying
M2: CHL ~ Intercept + TP + Color + Depth + CA:LK + AGR + WET
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Q2) Lake & landscape drivers of variation
Agriculture
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
LakeConnectivity
( – ) Lake( + )
M3: CHL ~ Intercept + TP + Color + Depth + CA:LK + AGR + WET + Connectivity
Spatially‐varying
M1: CHL ~ Intercept + TP + Color
M2: CHL ~ Intercept + TP + Color + Depth + CA:LK + AGR + WET
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Results: Q2
M G P D1 4736.4 4502.9 9239.42 4667.8 4508.4 9176.23 4593.7 4495.0 9088.8
M3: CHL ~ Intercept + TP + Color + Depth + CA:LK + AGR + WET + Connectivity
Spatially‐varying
M1: CHL ~ Intercept + TP + Color
M2: CHL ~ Intercept + TP + Color + Depth + CA:LK + AGR + WET
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Model 3: Global parameter estimates
Spatially‐varying Fixed over space
β0TPβ1
Colorβ2
Depthβ3
CA:LKβ4
AGRβ5
WETβ6
Lake Typeβ7
‐0.48(‐0.6 – ‐0.3)
0.68 (0.5 – 0.7)
0.01(‐0.09 – 0.13)
‐0.01 (‐0.01 – ‐0.01)
‐0.0003(‐0.0005 –‐0.0001)
0.51 (0.2 – 0.8)
0.13(‐0.34 – 0.65)
0.22 (0.1 – 0.3)
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Comparing model with lake & landscape covariates
MODEL 3: Intercept
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Comparing model with lake & landscape covariates
MODEL 3: InterceptMODEL 1: Intercept
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Comparing model with lake & landscape covariates
MODEL 3: TP SlopeMODEL 1: TP Slope
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Comparing model with lake & landscape covariates
MODEL 1: Color Slope MODEL 3: Color Slope
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Conclusions: Q2
Hypothesized lake and landscape variables account for spatial variation
Agriculture
ColorTP
Light environment
Nutrient Light
Chlorophyll
Wetlands
( + )( + )
Landscape variables
Lake variables
LakeDepth
LakeConnectivity
( – ) Lake( + )
• To a great deal for Chlorophyll
• Moderately for CHL~TP
• And less for CHL~Color
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Conclusions: Q2
BUT spatial variation remains• Scale of variation remaining – help identify
potential predictors to consider for future models
Chlorophyll remaining variation CHL~TP Effects
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Big data informing lake ecology
• Evaluate existing theory• Help meet management and conservation goals
• Assess lake water quality and ecological health• Set regional restoration targets
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AcknowledgementsFunding: NSF Macrosystems Ecology
Database support: Ed Bissell
Special thanks: CSI Limnology Team, MSU Limnology Lab