brian roberts, lumcon - · pdf filedr. brian roberts, lumcon ecosystem ecologist and...

Dr. Brian Roberts, LUMCONEcosystem Ecologist and Biogeochemist

Associate Professor and REU Program DirectorCWC Executive [email protected]

http://robertsresearchlab.weebly.com/11 August 2015

Louisiana salt marsh biogeochemistry and microbial community dynamics following the

Deepwater Horizon oil spill

Wetland Biogeochemistry and Microbial Ecology SubprojectCo‐Pis: Anne Bernhard (Connecticut College) and Anne Giblin (MBL)Post‐docs: John Marton (2012‐2014); Ari Chelsky and Troy Hill (2015 – present)RAs: Anya Hopple (LUMCON) 2012‐2013

Shauna‐kay Rainford (LUMCON) 2012‐2013Matthew Rich (LUMCON) 2013‐2014Hillary Sullivan (LUMCON) 2014‐2015Becky Forgrave (LUMCON) 2014‐2015Sam Setta (LUMCON) 2015‐ presentRon Scheuermann (LUMCON) 2015‐present

Grad students: Tiffany Warner (LUMCON/LSU ) 2012‐2013Lindsey Green (LUMCON/LSU) 2013‐2014Natalie Ceresnak (LUMCON/LSU) 2015‐present

Undergraduates: Aaron Marti (Univ. Wisconsin‐Steven Point) 2012 REUShanina Halbert (Haverford College) 2012 REUSara Mack (Univ. Maryland) 2013 REUTierra Moore (Rice University) 2013 REURegina Bledsoe (Nicholls State University) 2013‐2015Mia Dawson (Oberlin College) 2014 REUBrian Donnelly (Villanova University) 2014 REUKristen Chatelain (LA Tech University) 2015 REUSam Fortin (Eckerd College) 2015 REUKatie Baudoin (Univ Louisiana at Lafayette) 2015 intern

Insects/spidersInfauna

•Δ pop/comm

Birds •Δ pop/comm

Rats•Δ pop/comm

Microbes•Δ pop/comm

Plants •↓production•↓roots•↓soil strength

Fish•Δ pop/comm

InvertsInfauna

•Δ pop/comm

Oil• Toxicity• Physical

impacts

↑Erosion

Phytoplankton •Δ pop/comm

Biogeochemistry Δ process rates / gas fluxes

Δ mineralizationΔ oil degradation

Direct impacts of oiling Indirect impacts of oilingNote: Δ pop/comm indicates tracking impacts in biomass, species composition/diversity and production

Insects/spidersInfauna

•Δ pop/comm

Birds •Δ pop/comm

Rats•Δ pop/comm

Microbes•Δ pop/comm

Plants •↓production•↓roots•↓soil strength

Fish•Δ pop/comm

InvertsInfauna

•Δ pop/comm

Oil• Toxicity• Physical

impacts

N + P pollution

Other stressors

↑Erosion

Physical forcings• Hydrology • Sediment delivery

Climate change• Δ salinity• Δ inundation• Δ vegetation

Phytoplankton •Δ pop/comm

Biogeochemistry Δ process rates / gas fluxes

Δ mineralizationΔ oil degradation

Direct impacts of oiling Indirect impacts of oiling Influences on oil effectsNote: Δ pop/comm indicates tracking impacts in biomass, species composition/diversity and production

Overall Objectives •Improve understanding of temporal and spatial patterns in marsh biogeochemical process rates, associated microbial communities, and factors regulating rates

To date, our research activities focused on evaluating:•C cycle: soil respiration & greenhouse gas fluxes (CO2, CH4, N2O)

plant production and decomposition dynamics•N cycle: nitrification, denitrification, dissimilatory nitrate reduction to ammonium (DNRA), and anammox rates

•P cycle: Phosphorus sorption rates•Fe cycle: Fe reduction rates•relationships between biogeochemical process rates, microbial communities and abiotic variables in oiled and unoiled marshes

•Evaluate the impact of oil exposure on marsh biogeochemical processes and associated microbial communities

Rationale Biogeochemical pathways are carried out by different groups

Autotrophs•Photoautotrophs get energy from the sun (light)•Chemoautotrophs get energy from reduced inorganic compounds

Heterotrophs•Get Carbon and energy from reduced organic compounds

Rationale Chemoautrophic pathways are often more susceptible to pollutants than heterotrophic pathways because these pathways are carried out by a more limited number of organisms under a relatively narrow set of environmental conditions.

Rationale Chemoautrophic pathways are often more susceptible to pollutants than heterotrophic pathways because these pathways are carried out by a more limited number of organisms under a relatively narrow set of environmental conditions.

We hypothesized that:• ammonium oxidation (nitrification), methane oxidation, and

anammox (if present) will be most impacted in oiled sediments• denitrification, dissimilatory nitrate reduction to ammonium

(DNRA), and methane production will be impacted much less.

• Largely abiotic processes (e.g., phosphorus sorption) will be least impacted

2012‐2014 Study Sites• 3 Regions• Paired oiled & unoiled sites (2 sets per region)

• 4 plots (5,10,15,20m)• 48‐52 total plots

2012 Sites depicted: some shifting/adding of sites has occurred

General sampling approach

High spatial resolution sampling: 12‐13 sites sampled on three campaigns per year (May/Jun, July, Sep/Oct)• Subset of biogeochemical rates and associated microbial community parameters

High temporal resolution sampling: 4 sites in Terrebonne Bay (two sets of paired oiled/control sites)• Sampled monthly (May –Sep) in 2012, bimonthly in 2013, and seasonally in 2014‐

2015 for biogeochemical rates, microbial community, soil / water characterization

Intensive sampling: subset of above sites and/or control sites used for intensive field sampling or experiments• Examples include GHG salinity manipulation experiments (TB and WB sites);

Spartina‐Avicennia comparison studies (WB only); plant production / decomposition (TB plus LUMCON sites); oil exposure chamber experiments (LUMCON sites), Salinity gradient studies (includes WB), subhabitat variability studies (LUMCON sites), marsh mesocosm oiling experiments (start in 2016)…

LUMCON

Terrebonne Bay marsh sites

Distances apartTB1 and TB2: 0.25 kmTB3 and TB4: 1.8 kmPairs: ~4.0 km

Red = Oiled SitesWhite = Unoiled Sites

Marsh edge

20m15m10m5m

TB2

•2012: Monthly (May‐Sep) 2013: Bi‐monthly (Mar‐Nov)

•2014‐2015: Seasonally‐began 2 years post‐spill

•Flux rates determined for 4 plots (5, 10, 15 & 20m from edge)

•Vented, static chamber method (floating chamber method when water depth > 15cm)

•Rates calculated from changes in concentration (5 time points) during each incubation

Soil greenhouse gas fluxes: Methods

Greenhouse gas fluxes

varied with time:•June CO2 & N2O peaks

•Stronger in unoiled sites•No seasonal CH4 pattern

varied with oil status:•Oiled sites were:

• Lower in CO2• Higher in CH4• Lower in N2O

Unoiled Oiled

net C

O2 f

lux

(m

ol m

-2 h

-1)

0

5000

10000

15000

20000

Unoiled Oiledne

t CH

4 flu

x (

mol

m-2

h-1

)

-200

0

200

400

600

800

Unoiled Oiled

net N

2O fl

ux (

mol

m-2

h-1

)

-2

0

2

4

p = 0.032

p = 0.007

p = 0.002

May June July August September0

2000

4000

6000

8000

10000unoiled marshesoiled marshes

oil status: p < 0.001month: p < 0.001

ab

a

bc

bcc

* *

May June July August September0

100

200

300

400oil status: p = 0.003month: p = 0.242 *

May June July August September-2

-1

0

1

2

3oil status: p < 0.001month: p < 0.001

***ab ab

bc

cd

Roberts and Marton (In review)

Abundance of methane‐oxidizing bacteria tended to be higher in unoiled than oiled sites

pmoA

gen

e copies gdw

‐11.00E+07

1.10E+08

2.10E+08

3.10E+08

4.10E+08

5.10E+08

6.10E+08

7.10E+08

TB WB EB

unoiledoiled

Across 3 regions – July 2012

Why are net CH4 fluxes higher from soils in oiled marshes?

May July August September

MO

B a

bund

ance

(p

moA

gen

e co

pies

gdw

-1)

0

1e+8

2e+8

3e+8

4e+8

5e+8

6e+8Unoiled Oiled

Terrebonne marshes - 2012

Anne Bernhard, unpublished data

Soil C:N10 12 14 16 18 20 22

unoiled: r2=0.11, p=0.03oiled: r2=0.17, p=0.009

Soil total N (%)0.0 0.5 1.0 1.5 2.0


Soil organic C (%)5 10 15 20 25 30

net N

2O fl

ux (

mol

m-2

d-1

)

-2

0

2

4 unoiled: r2=0.06, p=0.11oiled: r2=0.01, p=0.50

all: r2 =0.11, p=0.002all: r2 =0.25, p<0.0001all: r2 =0.06, p=0.03

Soil organic C (%)5 10 15 20 25 30

net C

O2 f

lux

(m

ol m

-2 d

-1)

0

5000

10000

15000

20000

unoiled; r2=0.35, p<0.0001oiled: r2<0.01, p=0.98

Soil total N (%)0.0 0.5 1.0 1.5 2.0

unoiled: r2=0.36, p<0.0001oiled: r2=0.04, p=0.25

Soil water content (%)60 65 70 75 80 85 90


all: r2 =0.24, p<0.0001 all: r2 =0.27, p<0.0001 all: r2 =0.14, p<0.001

What controls soil GHG fluxes?Soil properties

•CO2 fluxes positively related to soil C, N & H2O content (unoiled marshes only)•N2O fluxes related to soil N, C & C:N (‐); similar in oiled & unoiled marshes•CH4 fluxes not significantly related to any soil properties

Water depth•Water depth has a strong influence on net GHG fluxes

• CO2 and CH4 fluxes significantly higher when water depth ≤ 10 cm for both unoiled and oiled sites

•N2O significant (p = 0.017) when all plots combined

CO2 CH4 N2Omean med mean med mean med

≤ 10cm: 4440 3241 80.8 28.7 0.71 0.71

> 10cm: 699 526 25.3 7.5 0.10 0.51

a)

< 10 cm > 10 cm < 10 cm > 10 cm

net C

O2 f

lux

(m

ol m

-2 h

-1)

0

2000

4000

6000

8000

10000

12000

14000

16000

18000Unoiled plots Oiled plots

b)

< 10 cm > 10 cm < 10 cm > 10 cm

net C

H4 f

lux

(m

ol m

-2 h

-1)

-200

0

200

400

600


c)

Water depth (cm)

< 10 cm > 10 cm < 10 cm > 10 cm

net N

2O fl

ux (

mol

m-2

h-1

)

-2

0

2


p < 0.001 p < 0.001

p < 0.001 p = 0.035

p = 0.413 p = 0.176

What controls soil GHG fluxes?

May Jun Jul Aug Sep Mean Unoiled Oiled

CO

2 equ

ival

ents

(mg

m-2

h-1

)

0

50

100

150

200

250

300

CO2 CH4 N2O

81%

15%

4%

91%

4%

5%

66%

33%

1%

2012

Soil radiative forcing• CO2 dominated soil contributions to radiative forcing in TB• CH4 contribution significantly greater in oiled marshes


Nitri

ficat

ion

Pote

ntia

l(n

M N

O3-

N dr

y g-1

d-1

)

0

2000

4000

6000

8000

10000

12000

PIS Est Sed

s

New Eng

land S

alt M

arsh

Georgi

a Tida

l Cree

k Salt

Mars

h

Portug

al Inte

rtidal

Sandy

Flat

Denmark

Est Sed

s

2012

2013

Marton et al. (2015); Marton et al. In prep.

Nitrificatio

n po

tential (nm

olgdw

‐1d‐

1 )

Nitrification

Louisiana salt marsh soils have highest rates of nitrification potential in literature

Terrebonne Bay(2013)

MonthMarch May July Sept Nov

Nitri

ficat

ion

Pote

ntia

l(n

M N

O3-

N dr

y g-1

d-1

)0

500

1000

1500

2000

2500

3000

3500A A B B B

a

ba

b

a

a

a

a

a

a

Terrebonne Bay(2012-2013)

May-2012

June

-2012Ju

ly-2012

Aug-20

12Sep

t-2012

Mar-2013

May-2013

July-

2013

Sept-201

3Nov-2

013

Nitri

ficat

ion

Pote

ntia

l(n

M N

O3-

N dr

y g-1

day

-1)

0

500

1000

1500

2000

2500

3000

1.00E+04

1.00E+05

1.00E+06

1.00E+07 oiledunoiled * *

TB WB EB

Nitr

ifica

tion

pote

ntia

l (nm

ol g

dw-1

d-1

)

0

200

400

600

800

1000

1200

1400

1600

1800

Unoiled plotsOiled plots

Bacterial amoA

gen

e copies gdw

‐1

Nitrification

Marton et al (2015); Marton et al (In prep.)

Relationships with soil properties

Soil organic C (%)5 10 15 20 25 30

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000MayJuneJulyAugustSeptember

r2 = 0.18p = 0.0001

• Soil organic C explains highest % of variance in nitrification

July

Soil organic C (%)5 10 15 20 25 30

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000r2 = 0.35p = 0.015

June

Soil organic C (%)5 10 15 20 25 30

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000r2 = 0.40p = 0.005

•pattern driven by June and July

•Other significant relationships: soil OM, N, water content, bulk density

Seasonally in Terrebonne Bay

July 2012

Soil organic C (%)0 5 10 15 20 25

Pote

ntia

l nitr

ifica

tion

rate

s(n

mol

N g

dw-1

d-1

)0

1000

2000

3000

4000

5000

6000Oiled: r2 = 0.08; p = 0.14Unoiled: r2 = 0.27; p = 0.009

July 2012

Soil organic C (%)0 5 10 15 20 25

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000r2 = 0.17p = 0.0021

Relationships with soil properties

• Soil organic C explains highest % of variance in nitrification

July 2012

Soil organic C (%)0 5 10 15 20 25

Pote

ntia

l nitr

ifica

tion

pote

ntia

l(n

mol

N g

dw-1

d-1

)

0

1000

2000

3000

4000

5000

6000TerrebonneWestern BaratariaEastern Barataria

r2 = 0.35, p = 0.015r2 = 0.51, p <0.001r2 = 0.41, p = 0.007

•Stronger relationships within region•Stronger relationship in unoiled marshes•Other significant relationships: soil OM, C, N, TP, and C:N

Spatially across LA coast

AOB abundance (amoA copies gdw-1)

0 1e+6 2e+6 3e+6 4e+6 5e+6

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000r2 = 0.19p = 0.0013

•Nitrification increased with AOB abundance


0 1e+6 2e+6 3e+6 4e+6 5e+6

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000

Terrebonne BayWest Barataria BayEast Barataria Bay

r2 = 0.21, p = 0.0779r2 = 0.32, p = 0.0096r2 = 0.13, p = 0.1789

•Stronger relationship within TB and WB

r2 = 0.12, p = 0.090r2 = 0.29, p = 0.0035


0 1e+6 2e+6 3e+6 4e+6 5e+6

Pote

ntia

l nitr

ifica

tion

rate

(nm

ol N

gdw

-1 d

-1)

0

1000

2000

3000

4000

5000

6000

UnoiledOiled

•Stronger relationship in oiled marshes

Nitrification and microbial abundancesExample: Ammonia Oxidizing Bacteria in July 2012

•Explains less of variation than expected (in comparison to NE marshes)

Stronger separation between regions than

with oil status

PCA Axis 1: positive loadings of SOM, organic C, total N

PCA Axis 2: positive loadings of nitrification potential, AOA/AOB abundances

July 2012

WB distinct from TB and EB; EB largely intermediate but with some overlap with TB

Marton et al. (2015)

Pearson product-moment correlation coefficents(r) between nitrification and microbial abundance

Nitrification and microbial abundances

C

C

C

C

Bernhard et al. (In review) Marton et al. (2015)

TB: AOB only

WB: AOB and AOA

EB: AOA only

All: AOB and AOA

Bernhard et al. (In review)

Nitrifying communities

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

TRF414 (11)

TRF316

TRF296 (3,4,5,7,8,9,11, 12,13,14) TRF283

TRF170 (4)

TRF119 (1,2)

TRF83 (6,10)

TRF73

unoiled oiled unoiled oiled unoiled oiled

TB WB EB

Rel

ativ

e Ab

unda

nce

of T

RF

A

TRF492 (1)

TRF472 TRF462 (4) TRF403 (6,11,12,14) TRF343 TRF336 (2, 5, 16) TRF315 TRF278 (3, 8, 10)

TRF196 (2,4,8,9) TRF187 (3, 11)

TRF130 (3, 7, 10, 13) TRF127 (3, 13)TRF115

TRF98 (3,13)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

unoiled oiled unoiled oiled unoiled oiled

TB WB EB

A

Rel

ativ

e ab

unda

nce

AOA

AOB

Table 4. Pearson's correlation coefficients (r) describing significant relationships between individual TRFs and potential nitrification rates in oiled and unoiled sites from the three regions. Only TRFs with coefficients that were significant (P ≤ 0.05) are shown. The sum of the relative abundances of TRFs that were correlated with rates was calculated for each region. Relative abundances that correspond with signficant correlations between AOA or AOB abundances and potential nitrification rates reported in Marton et al. (2015) are indicated with asterisks (*).

TRF

TB WB EB unoiled oiled unoiled oiled unoiled oiled

AOA 73 0.82 0.95

83 0.81 0.92 0.83

119 0.81 0.64

170 0.70

296 0.90

414 0.89 0.94

Sum of

AOA TRFs

15.9% 0 0 98.3%* 0 28.0%*

AOB 98 0.83

115 0.90

127 0.82 0.95

130 0.85

187 0.95

196 0.88 0.78

278 0.59

336 0.91

403 0.97

492 0.89

Sum of

AOB TRFs

19.8% 62.1%* 6.4% 65.1%* 0 8.7%

Marton and Roberts (2014)

PSI (

mg

P/10

0 g)

0

40

80

120

160

200

PSI (

mg

P/10

0 g

soil)

0

20

40

60

80

100

120

140

1 2 3 4Terrebonne West

BaratariaEast

Barataria

1 2 3 41 2 3 4 5

a

a

aa

a a

ab

bb

a

a

aa

A A A

•No differences between regions•No differences between oiled and unoiled marshes

•High variability in PSI•~order of magnitude•at least 5x within regions

Phosphorus sorption

All Marshes

PSI (

mg

P/10

0 g

soil)

0

50

100

150

200

Unoiled Marshes

PSI (

mg

P/10

0 g

soil)

0

50

100

150

200

Oiled Marshes

Fe (mg/g)0 1 2 3 4

PSI (

mg

P/10

0 g

soil)

0

50

100

150

200

Terrebonne

r2 = 0.95p < 0.001

Western Barataria

r2 = 0.51p < 0.001

Eastern Barataria

Fe (mg/g)0 1 2 3 4

r2 = 0.89p < 0.001

r2 = 0.88p < 0.001

r2 = 0.71p < 0.001 (a) (d)

(b) (e)

r2 = 0.55p < 0.001 (c) (f)

Phosphorus sorption

•Strongest correlation with Fe•Also significant relationships with:

•Plant‐available PO4 (r2=0.34, p<0.001)•Al (r2=0.29, p<0.001)•Soil total P (r2=0.19, p=0.001)•Oxalate‐extractable PO4 (r2=0.15, p=0.005)•Soil organic C (r2=0.14, p=0.007)•Soil total N (r2=0.08, p=0.038)


Soil P Storage Capacity

(0.15 – PSR) x (Fe + Al)

How much more P could be stored in the soil?


P Saturation RatioTheoretical estimate of P

saturation of soils (based on ratio of extractable P to Fe + Al) • 0.15 considered eutrophication “tipping” point (only exceeded in TB)


• As phosphorus sorption sites became less available (indicated by increase in PSR), PSI decreased exponentially

• As the soil storage capacity for phosphorus (SPSC) increased, PSI also increased

Marsh position and greenhouse gas fluxes

Distance from marsh edge5 m 10 m 15 m 20 m

net C

O2 f

lux

(m

ol m

-2 h

-1)

0

2000

4000

6000

8000

10000

12000

unoiledoiled

oil status: p < 0.001position: p = 0.001interaction: p = 0.002

A

BBB

* Distance from marsh edge5 m 10 m 15 m 20 m

net C

H4 f

lux

(m

ol m

-2 h

-1)

0

50

100

150

200

250oil status: p = 0.004position: p = 0.74interaction: p = 0.59

* *

Stronger pattern in unoiled marshes:•Unoiled: r2 = 0.69, p = 0.02•Oiled: r2 = 0.18, p = 0.29

Different pattern by oil status:•Oiled: CH4 ↓; r2 = 0.80, p = 0.10•Unoiled: CH4 ↑; r2 = 0.95, p = 0.03

N2O: no spatial pattern with distance (p = 0.65 / 0.73 for unoiled / oiled marshes, respectively)


Terrebonne W. Barataria E. Barataria

Nitri

ficat

ion

Pote

ntia

l(n

M N

O3-

N dr

y g-1

d-1

)

0

1000

2000

3000

4000

50005m10m15m20m


AOA

Abun

danc

e(a

moA

cop

ies

dry

g-1)

0.0

5.0e+7

1.0e+8

1.5e+8

2.0e+8

2.5e+8

3.0e+8

3.5e+8


AOB

Abun

danc

e(a

moA

cop

ies

dry

g-1)

0

5e+5

1e+6

2e+6

2e+6

3e+6

3e+6

TB WB EB

Tota

l nitr

ate

redu

ctio

n (n

mol

gw

w-1

)

0

200

400

600

800

1000

1200

1400

1600

18005 m10 m15 m20 m

TB WB EB

nirS

gen

e cop

ies g

dw-1

0

1e+11

2e+11

3e+115 m10 m15 m20 m

Marsh position and N cycle

; Roberts et al (unpublished data)Marton et al. (2015)

Marsh position and P sorption

Terrebonne

Distance from edge (m)0 5 10 15 20 25

PSI (

mg

P/10

0 g

soil)

0

50

100

150

200r2 = 0.47p = 0.0032

Western Barataria


Eastern Barataria


N.S. N.S.



Soil

Org

anic

C (%

)

0

5

10

15

20

y = 0.8216x ‐ 4.9901R² = 0.9597

0

5

10

15

20

0 5 10 15 20 25

Relativ

e elevation (cm)

Distance from the marsh edge (m)

y = ‐0.5195x + 2.3875R² = 0.995

‐12

‐10

‐8

‐6

‐4

‐2

00 5 10 15 20 25

Relativ

e elevation (cm)

Distance from marsh edge (m)

y = ‐0.0094x + 0.125R² = 0.3237

‐5

‐3

‐1

1

3

5

0 10 20

Relativ

e elevation (cm)

Distance from marsh edge (m)

Terrebonne western Barataria eastern BaratariaMarsh position, elevation, and soil properties


Fe (m

g/g)

0

1

2

3

4r2 = 0.32p = 0.021


Al (m

g/g)

0.0

0.5

1.0

1.5

2.0


Plan

t-ava

ilabl

e PO

4-P

(g/

g)

0

10

20

30

40r2 = 0.31p = 0.026

r2 = 0.29p = 0.031

Terrebonne Bay marshes

Distance from marsh edge (m)0 5 10 15 20

Soil

wat

er c

onte

nt (%

)

70

75

80

85

90r2 = 0.97p = 0.02


Soil

tota

l N (%

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4r2 = 0.98p < 0.01


Soil

tota

l P (

g gd

w-1

)

400

450

500

550

600

650

700r2 = 0.86p = 0.07


Conclusions• Oiled sites were lower in CO2 and N2O and higher in CH4 fluxes

• Effects seen at least 4 years post‐spill

• Nitrification rates did not show consistent responses to oil• No effects in 2012 (Marton et al. 2015) but some differences in 2013 in some regions

• AOA/AOB abundances do not show consistent oil responses• No overall AO community differences but some differences in correlations of individual populations with nitrification rates between oiled and unoiled sites

• Biogeochemical process rates display high spatial variability within Louisiana salt marshes

• Related to variability in soil properties which appear to be, at least in part, regulated by differences in elevation and hydrology

• Phosphorus sorption did not show strong response to oil

Small-scale measurements not detecting same response signals as whole-system measurements

Detecting impacts and recovery from oiling complicated due to differences in:

•timing, spatial distribution & extent of oiling•overall & specific compound degradation rates•loss of habitat as a result of oiling•decreasing amounts of residual oil across the coast

Re‐oiling events (e.g. Hurricane Isaac) have added to the complexity

When making management decisions we need to remember that our coastal ecosystems are faced with multiple stressors that not only are likely to influence the functioning of the systems but also how they will respond to future oil spills…

Thanks!

brian roberts, lumcon - · pdf filedr. brian roberts, lumcon ecosystem ecologist and...

Documents