Systems Ecology for Aquatic Resource Management

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Systems Ecology for Aquatic Resource Management

Systems Ecology Beijing 2013

Systems Ecology Case Studies

Earth System

Earth is an open system (both energy and matter can cross the boundary), but we often treat it as a closed system

Hydrosphere

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Water Quantity

USGS

All water

Liquid fresh water

Water in Lakes and Rivers

1,386,000,000 km3

Relative Spheres

10,633,450 km3

(99% is ground water)

http://ga.water.usgs.gov/edu/2010/gallery/global-water-volume.html

70% of earth surface is covered by water

Hydrosphere Oceans and freshwater bodies on Earth

Jacobson et al. 2000

World Ocean Currents Water Cycle

Fig 2.15

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Importance of Water

http://www.lccdnet.org/wp-content/uploads/2011/01/importance-of-water-conservation.jpg

Importance for life, biogeochemistry, ecosystem services

Aquatic Ecosystem Services

Food Production

Waste water processing

Nutrient Recycling

Recreation

Transportation

Drinking Water

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Case Studies

1.  Shrimp Trawling in Core Sound, NC (USA) 2.  Lake Sidney Lanier, GA (USA) 3.  Neuse River Estuary, NC (USA) 4.  Cape Fear River Estuary, NC (USA)

Case Studies


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Ecosystem Impacts of Shrimp Trawling

American Fisheries Society 2012

Borrett et al. in prep.

Jeff Johnson

Joe Luckovich

Becky Deehr Stuart Borrett

Application of Throughflow Centrality

Systems Ecology and Ecoinformatics Laboratory

Shrimp – An important Fishery

FAO 2008

Shrimp images from fishwatch.gov

$425 mil yr-1

Estimated USA Economic Value

Deehr 2012 NCDMF

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Ecosystem Impacts of Shrimp Trawling

Warner et al. 2004 http://coresound.com/

Direct impacts on shrimp populations

Bycatch (5:1)

Physical disruption of benthic habitats

http://www4.ncsu.edu/

Impacts

Skimmer Trawls and Otter Trawls

TEDs, BRDs

Regulate Access (space & time)

Solutions

Gear Type

Deehr 2012

Core Sound NC and Shrimp Nursery Areas

Google Earth Image from 2011

Cor

e So

und

Nursery Areas Closed to Shrimping

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Objectives & Approach Assess the whole ecosystem impact of shrimp trawling on the Core Sound, NC ecosystem

•  Direct and indirect effects (e.g., trophic cascades) •  Focus: Relative functional importance of species (T)

•  Ecosystem understanding à adaptive management

Approach •  Construct food web based ecosystem models

•  Open and Closed to trawling •  Compare areas with Ecological Network Analysis


Food web ecosystem model •  Ecopath model

•  n = 65 compartments (g C m-2) •  2 non-living compartments: bycatch and

detritus

•  L = 614 carbon flows (g C m-2 y-1) •  e.g., feeding relationships

•  Parameterized with primary data and literature values as needed •  NCDMF Trip Ticket Program

•  Ecopath estimated trophic level corroborated with stable isotopes

http://core.ecu.edu/BIOL/luczkovichj/core_sound/core_sound.html

Atl. Croaker Atl. Menhaden

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Models for Comparisons

Spring (April, May, June)

Fall (Aug., Sept., Oct.)

Closed Least impact

Open Most impact


ENA Statistics (response variables) Example Network

~T = (Tj) =nX

i=1

fij + yj

FCI =Cycled

TST

APL =TSTP

zi

TST =X

Tj Like GDP

Like multiplier effect

Recycling

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Centralization characterizes the concentration or dispersion of the centrality (throughflow activity)

FC =

Pni (Tmax

� Tj)

n� 1

Where Tmax is the maximum value f Tj

Lower FC à More dispersed Higher FC à More centralized

Interpretation


TST, FCI, and APL

Fall > Spring Open > Closed in Spring Closed > Open in Fall

There is more TST per unit input in the Spring-Closed model APL is less with Trawling

Cycling is less in the Fall Cycling is less with Trawling

Tota

l Sys

tem

Th

rou

gh

flow


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Throughflow Centrality - Spring Closed to Trawling

Open to Trawling

Units: gC m-2 yr-1

The two sites have a difference in the magnitude and distribution of throughflow in the ecosystem

N90% TST (n = 3) Detritus, Meiofauna, Phytoplankton

N90% TST (n = 8) Detritus, Phytoplankton, Meiofauna, Atlantic Menhaden, Benthic Bacteria, Benthic Macroalgae, Sea Cucumbers, Benthic Microalgae

Impact (Δt) = TOpen - TClosed


52+, 11- Net difference: -217 Fall

Impact of Trawling on T

Showing differences greater than 0.5 gC m-2 yr-1

More species have an increase in throughflow than decline Net impact is negative in Fall

45+, 16-

Impact differs by season; bigger in Fall

Net difference: +25 Spring


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Centralization

Ecosystems open to trawling are less centralized.

The ecosystems are more

centralized in the Fall

Units of dispersion are the same as T

The disturbance spreads the activity across more species.


Summary & Discussion •  Detritus and Phytoplankton are consistently two of the

most central nodes –  Meiofauna and Atlantic Menhaden

•  Trawling appears to stimulate throughflow activity in more compartments than are reduced

•  Magnitude of negative impacts were greater with trawling in Fall, less in Spring –  Suggests closing nursery areas may be appropriate

•  Shrimp trawling tends to de-centralize the ecosystem activity –  Unexpected consequence

•  Network analysis shows whole ecosystem impacts (direct and indirect) of Shrimp trawling


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Case Studies


Lake Sidney Lanier, GA, USA

http://www.mylakelanier.com/xsites/Agents/dianathomsen/content/uploadedFiles/lakelanier1.jpg

Metropolitan Population 2011 5,457,831 (Wikipedia)

Atlanta

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Lake Sidney Lanier, GA, USA •  150 km2 Reservoir, 1958 •  Uses

– Drinking Water Supply – Waste Water Discharge – Recreation – Navigation

•  Challenges – Competing demands, stakeholders – Small watershed, sensitive to environmental

variation – Eutrophication, excess Phosphorus – Data limitations

Project Goals •  Characterize Lake Lanier ecosystem •  Construct a phosphorus model to evaluate

system state – Consider data uncertainty

•  Apply Network Environ Analysis – Whole system indicators – Characterize sensitivity of indicators to model

uncertainty

Borrett & Osidele. 2007. Ecological Modelling

Kaufman & Borrett. 2010. Ecological Modelling

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Conceptual Model: P in Reservoir

Modified from Osidele & Beck (2004) Consistent currency: Phosphorus Units: Flow: mg P m-2 d-1 Storage: mg P m-2

Model Construction •  Constant Structure

(conceptual Model) à –  11 compartments –  26 within system connections –  5 boundary inputs, 4 boundary losses

•  Identify Plausible Model Parameterizations –  Used Regionalized Sensitivity Analysis to identify

parameterizations that fit known empirical data for SRP, phytoplankton, fish, and detritus.

–  Donor controlled transfer functions –  Monte Carlo Simulations (500, 1000, 5000)

•  Randomly select parameters from uniform distribution •  Range based on biological knowledge •  Compare model behavior to observed system behavior

Variable Flow and Storage à Plausible parameterizations

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Examples of Plausible Model Simulations

Range defined by empirical observations

Epilimnion SRP Phytoplankton

Larval-juvenile Fish

Plausible models generated behavior within known constraints.

Model Sample Size Plausible Models

500 14

1000 18

5000 90

Total 122

Next: Apply ENA to models

NEA Whole System Indicators

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Indicator Results

14, 18, 90 models

1.   90 models is sufficient sample size of plausible models,

2.   Considerable uncertainty remains

3.   Network non-locality, aggradation, homogenization, and amplification are present

4.   Average Phosphorus recycling 39%

Conclusions

Indicator Correlations

Borrett & Osidele 2007

Not all Indicators are independent!

Indicator redundancy? Significance?

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Statistical Factor Analysis

2 key latent factors Explain 80% variation

Borrett & Osidele 2007

Conclusions (1) •  Small variability in the ecosystem indicators lets us

–  circumvent part of the modeling and data uncertainty –  draw more robust conclusions regarding the condition of

the Lake Lanier ecosystem

•  FCI, Indirect/Direct, IFI, AGG, and HMG are robust to to model uncertainty

•  Internal processes heavily influence phosphorus flow and storage –  Well developed ecosystem –  P is well mixed (HMG) –  Changing system dynamics would be difficult by simply

changing the nutrient inputs

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Conclusions (2) •  Common indicator

variation can be mapped onto two factors

•  Tentative Interpretation – Factor 1

•  system integration •  Network growth •  Growth Form II

– Factor 2 •  Boundary growth •  Growth Form 0

Case Studies


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Temporal Variation of Indirect Effects

in the Neuse River Estuary

Neuse River Estuary, NC

http://www.coe.uncc.edu/~jdbowen/neem/

(Christian and Thomas 2003)

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Objectives

Q: Should the 30% decrease in N loading rapidly alter the system’s trophic state?

H1: Indirect effects are dominant If indirect effects are dominant, then we would not expect rapid change

H2: Indirect effects vary seasonally; moderate inter-

annual variation discrete-time series analysis

Throughflow Decompositions & Indirect Flows

TST = Boundary + Direct + Indirect

Indirect/TST

Indirect/Direct

TST = non-Cycled + Cycled 2. (Finn 1976)

Finn Cycling Index = Cycled/TST

1.

Network Environ Analysis

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TST and FCI

Sp S F W Sp S F W Sp S F W Sp S F W

Tota

l Sys

tem

Thr

ough

flow

(mm

ol N

m-2

sea

son-1

)

4000

8000

12000

16000

20000

24000

Finn

Cyc

ling

Inde

x

0.00

0.25

0.50

0.75

1.00

TST FCI

1985 1986 1987 1988 1989

Sp S F W Sp S F W Sp S F W Sp S F W

Indirect/Direct

0

25

50

75

100

125

150

175

1985 1986 1987 1988 1989

Indirect Effects

Indirect Flows Dominate

H1: Indirect flows are dominant

System Currency Indirect/Direct Sourcecold spring energy 1.02 Higashi & Patten 1989oyster reef, SC energy 1.58forest, Puerto Rico nitrogen 6.14Lake Lanier, GA phosphorus 7.45 Borrett & Osidele 2007

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Temporal Variation

Little Temporal Variation

Spring Summer Fall Winter

Indirect/TST

0.0

0.5

1.0

Seasons

BA A A

yc1985 yc1986 yc1987 yc19880.0

0.5

1.0

Years A A A A

H2: Indirect flows vary seasonally; moderate inter-annual variation

Summary & Conclusions

•  Indirect flow dominates direct

•  Stable ecosystem organization

•  N load reduction will not have rapid effect on trophic state of estuary

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Case Studies


Hines et al. 2012, Hines et al. in review

Nutrients in Estuarine Waters

Eutrophication

Recycling and removal of nutrients

(Costanza et al., 1998)

usbr.gov oceanservice.noaa.gov

eutrophication.yolasite.com

Ecosystem Services

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Nitrogen Cycling and Removal

N2

NH4 NO3

NH4

NO2

NO3 NO2

N2 Atmosphere

Water

Sediment

DNRA

Fixation

Nitrification

Nitrification

Anammox

De

nitrifica

tion

Simplified Nitrogen Cycle

Upstream

Coupled Nitrification-Denitrification Coupled DNRA-Anammox

Coupled processes à indirect pathways

Complications

Sea level rise

treehugger.com Average rise of 1.7mm year-1

(IPCC, 2007)

earthtrends.wri.org, IPCC 2007

Salt water intrusion (Giese et al. 1985)

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Effects on Microbes

arizona.edu

Hypotheses H1: Coupled nitrification-denitrification will be higher in the oligohaline portion of the estuary compared to polyhaline sites

H2: Coupled DNRA-anammox will be lower in the oligohaline portion of the estuary compared to polyhaline sites

H3: Microbial nitrogen removal capacity will be higher in the oligohaline portion of the estuary compared to polyhaline sites

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Study Sites

Wilmington

Wilmington

Conceptual Model

Hines et al. 2012

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Wilmington

Conceptual Model

Low Network Model

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

3.1

5.2

136.

5

1.7

2.1

1.9

9.8 7.4

119.

2

16.0

850.

0 5.5

39.0

146.7

150.0

14.1

144.0

119.2

212.8

109.0 82.0

853.9

257.1

172.

0 0.3

3.9

276.0

1008.6

3.9e-5

1159.6 10

80.0

6.0

104.

1

130.0

5.0

(a) (b)

(c)

(d) (e)

3.9e

-7

79.0

173.

2

1238

.2

1020.0

1160.0

3.9e-5

1 2 3 4

5 6 7 8

10.5 27.6 1.0e-6 55.9

682.6 4.2 1.0e-5 7110.0

nmol N cm-3 d-1 Summer

Hines et al. 2012

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Low Network Model

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

3.1

5.2

136.

5

1.7

2.1

1.9

9.8 7.4

119.

2

16.0

850.

0 5.5

39.0

146.7

150.0

14.1

144.0

119.2

212.8

109.0 82.0

853.9

257.1

172.

0 0.3

3.9

276.0

1008.6

3.9e-5

1159.6 10

80.0

6.0

104.

1

130.0

5.0

(a) (b)

(c)

(d) (e)

3.9e

-7

79.0

173.

2

1238

.2

1020.0

1160.0

3.9e-5

1 2 3 4

5 6 7 8

10.5 27.6 1.0e-6 55.9

682.6 4.2 1.0e-5 7110.0

Nitrification DNRA Denitrification Anammox Boundary

nmol N cm-3 d-1

Coupled NOx à NH4 NH4 à NOx NOx à N2 NH4 à NOx NOx à N2

NOx + NH4 à N2

Summer

Coupled NOx + NH4 à N2

NOx à NH4

Hines et al. 2012

Low Network Model

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

3.1

5.2

136.

5

1.7

2.1

1.9

9.8 7.4

119.

2

16.0

850.

0 5.5

39.0

146.7

150.0

14.1

144.0

119.2

212.8

109.0 82.0

853.9

257.1

172.

0 0.3

3.9

276.0

1008.6

3.9e-5

1159.6 10

80.0

6.0

104.

1

130.0

5.0

(a) (b)

(c)

(d) (e)

3.9e

-7

79.0

173.

2

1238

.2

1020.0

1160.0

3.9e-5

1 2 3 4

5 6 7 8

10.5 27.6 1.0e-6 55.9

682.6 4.2 1.0e-5 7110.0


Hines et al. 2012

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High Network Model

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

3.1

5.4

55.3

0.7

7.3

1.7

0.5 1.4

119.

2

0.5

423.

8 1.5

104.4

146.7

100.0

7.7

77.5

119.2

186.2

53.2 159.5

425.8

253.2

136.

7 7.8e

-3

2.0

132.4

308.9

3.9e-5

1246.8 10

06.9

127.

3

32.7

72.5

3.6

(a) (b)

(c)

(d) (e)

3.9e

-7

39.1

345.

5

975.

1

381.1

1255.1

3.9e-5

1 2 3 4

5 6 7 8

2.9 15.1 1.0e-6 49.28

107.9 14.3 1.0e-5 3724.4


Polyhaline Model Parameterization Evaluation

77% of parameters were high or medium quality

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Environs are the internal system environments - Input & Output for each node - sub-networks

ENA Environ Analysis

A

C

B

D

E

Input history of material in a given output

Analysis performed using NEA.m (Fath & Borrett, 2006) Patten 1978, Patten 1982

Input Environs

Denitrification Environ at Low

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

3.8e-3

6.4e-3

0.2

12.0e-3

16.0

e-3

43.0e-3

0.2 0.2

2.7

82.0e-3

4.4 0.3

2.0

7.7

7.9

7.5

77.1

3.1

5.5

2.8 2.1

10.3

3.1

172.

0

0.2

(b)

1.0

92.5

64.8

7.7

6.0

3.9e-5

1 2 3 4

5 6 7 8

10.5 27.6 1.0e-6 55.9

682.6 4.2 1.0e-5 7110.0

nmol N cm-3 d-1

Sub-Network

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W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

W- NH4

W- NOx

W- M

W- ON

S- NH4

S- NOx

S- M

S- ON

Hypothesis Testing H1: Coupled nitrification-denitrification will be higher in the oligohaline portion of the estuary compared to polyhaline sites

Hypothesis Testing H1: Coupled nitrification-denitrification will be higher in the oligohaline portion of the estuary compared to polyhaline sites

Uncoupled

Coupled to nitrification

De

nitr

ific

atio

n (

nm

ol N

cm

-3 d

-1)

% C

ou

ple

d N

itrifi

ca

tion

-De

nitr

ific

atio

n

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Hypothesis Testing H2: Coupled DNRA-anammox will be lower in the oligohaline portion of the estuary compared to polyhaline sites

Uncoupled

Coupled to DNRA

An

am

mo

x (n

mo

l N c

m-3

d-1

)

% C

ou

ple

d D

NR

A-a

na

mm

ox

Findings H1: Coupled nitrification-denitrification will be higher in the oligohaline portion of the estuary compared to polyhaline sites

H2: Coupled DNRA-anammox will be lower in the oligohaline portion of the estuary compared to polyhaline sites

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Hypothesis Testing H3: Microbial nitrogen removal capacity will be higher in the oligohaline portion of the estuary compared to polyhaline sites

Uncoupled denitrification

Denitrification coupled to nitrification

Uncoupled anammax

Anammox coupled to DNRA

N R

em

ova

l (n

mo

l N c

m-3

d-1

)

% N

Re

mo

ved

Wilmington

Implications

•  Rising sea level may result in a decreased coupling of nitrification-denitrification

•  DNRA coupled to anammox may become more important

•  Overall N2 removal may

not be significantly altered

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Additional Examples

Urban Water Metabolism Beijing, China Sarmato, Italy

Bodini and Bondavalli 1992 Zhang et al. 2010

Considered options to improve system sustainability Scenario Analysis

Evaluate changes from 2003 to 2007 Apply Utility analysis to characterize changing nature of relationships.

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Hydrology Okefenokee Swamp, Georgia, USA

Uplands Lowlands

Surfa

ce

Su

b-S

urfa

ce

Patten & Matis 1982

Characterizes flow of water through the system Origin & Fate

Mao et al. 2013

Virtual Water Trade Virtual water = water embodied in food

Yang et al. 2012

Analyzed complex interdependencies among regions

Used control and utility analysis (ENA)

“Virtual water is useful as it globalizes perspectives on water scarcity, ecological sustainability, food production, and consumption”

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Summary Examples of using systems ecology and ecological network analysis to study water resources

–  Water quality –  Water quantity –  Aquatic ecosystem structure and function –  Ecosystem services

•  Model Construction & Evaluation •  Systems Analysis •  Applications of ENA are still a frontier of the

science

Systems Ecology for Aquatic Resource Management

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