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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
unoiled: r2=0.25, p=0.0011oiled: r2=0.22, p=0.0023
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
unoiled: r2=0.29, p=0.0004oiled: r2=0.006, p=0.634
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
800Unoiled plots Oiled plots
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
4Unoiled plots Oiled plots
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
Roberts and Marton (In review)
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
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
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
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
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)
Marton and Roberts (2014)
Soil P Storage Capacity
(0.15 – PSR) x (Fe + Al)
How much more P could be stored in the soil?
Marton and Roberts (2014)
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)
Marton and Roberts (2014)
• 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)
Roberts and Marton (In review)
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
Terrebonne W. Barataria E. Barataria
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
Terrebonne W. Barataria E. Barataria
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
Distance from edge (m)0 5 10 15 20 25
Eastern Barataria
Distance from edge (m)0 5 10 15 20 25
N.S. N.S.
Marton and Roberts (2014)
Terrebonne W. Barataria E. Barataria
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
Distance from edge (m)0 5 10 15 20 25
Fe (m
g/g)
0
1
2
3
4r2 = 0.32p = 0.021
Distance from edge (m)0 5 10 15 20 25
Al (m
g/g)
0.0
0.5
1.0
1.5
2.0
Distance from edge (m)0 5 10 15 20 25
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
Distance from marsh edge (m)0 5 10 15 20
Soil
tota
l N (%
)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4r2 = 0.98p < 0.01
Distance from marsh edge (m)0 5 10 15 20
Soil
tota
l P (
g gd
w-1
)
400
450
500
550
600
650
700r2 = 0.86p = 0.07
Roberts and Marton (In review)
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!