Relationship of Salt Marsh Vegetation Zonation to Spatial ......61 marsh were higher than those...
Transcript of Relationship of Salt Marsh Vegetation Zonation to Spatial ......61 marsh were higher than those...
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ONLINE SUPPLEMENT TO :
Relationship of Salt Marsh Vegetation Zonation to
Spatial Patterns in Soil Moisture, Salinity and Topography
Kevan B. Moffett1*, David A. Robinson2 , and Steven M. Gorelick1
1 Dept. of Environmental Earth System Science, Stanford University, Stanford, California 94305,
USA
2 Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd LL57 2UW,
UK
Contents:
I. Complete vegetation maps
II. EMI signal depth calculation
III. ECa uncertainty determination
IV. Soil sampling results and ECa relationship to edaphic conditions
V. Archie’s Law parameter estimations: f, φ, and σs
VI. Cross-correlation of geographic and edaphic metrics
VII. Literature cited in supplement
* Corresponding author. E-mail: [email protected]. Fax: 650-498-5099.
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I.
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II. EMI signal depth calculation 1
Many portable EMI devices, like the DUALEM-1S used in this study, include two pairs 2
of electrodes in orthogonal horizontal and vertical configurations. Due to the difference in the 3
directionality of the induced and received magnetic fields, these two configurations result in 4
different signal penetration depths. In terrestrial environments, 70% of the apparent bulk soil 5
electrical conductivity signal measured with the electrodes in the horizontal configuration (ECaH) 6
by the DUALEM-1S typically originates above sH = 0.76 m depth; 70% of the vertical signal 7
(ECaV) originates above sV = 1.59 m (Abdu and Robinson 2007). In the highly conductive salt 8
marsh environment a “skin effect” due to soil currents travelling more rapidly and with less 9
resistance through highly conductive near-surface sediments reduces the effective signal depths 10
(Callegary and others 2007). We fit logarithmic curves to the data of Callegary and others (2007, 11
after correcting their reversal of their vertical and horizontal data labels) and extrapolated their 12
calculations of the skin effect to the range of bulk conductivities measured in the salt marsh 13
(Eqns. S1 and S2). The asymptotic logarithmic shape of the data by Callegary and others (2007) 14
gave us good confidence that the large extrapolation required was reasonable. The units of these 15
regressions are: depth in m and conductivity in mS/m. 16
( ) 757.0ln049.0 +−= HH ECas (R2 = 0.96) (Eqn. S1) 17
( ) 599.1ln148.0 +−= VV ECas (R2 = 0.98) (Eqn. S2) 18
Using these regressions and ECaH = 1364 mS/m and ECaV = 1453 mS/m, the medians of the 19
temperature-corrected ECa data from this study (dry and wet conditions pooled), we calculate 20
effective support depths of 0-0.40 m (sH, shallow) and 0-0.52 m (sV, deep). Only the shallow 21
data, more relevant to the root zone (of comparable depth), was presented in the paper. 22
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III. ECa uncertainty determination 24
The stated accuracy of the Electromagnetic Induction (EMI) geophysical instrument used 25
in this study (DUALEM-1S, Dualem, Inc., Milton, ON, Canada) was 0.001 dS/m. Despite this 26
very high accuracy, we took the conservative approach of determining the in situ measurement 27
uncertainty based on two types of successive field measurements of ECa obtained at multiple test 28
locations. Based on these two types of tests, described presently, we estimated the measurement 29
uncertainty for this study as 0.01 dS/m. 30
The first method we used to estimate ECa measurement uncertainty was to stand in one 31
location and collect a streaming series of 1479 ECa measurements, one measurement every one 32
to two seconds. The 95% confidence level for the mean of these measurements (horizontal 33
electrode configuration) was 0.01 dS/m. 34
The second method we used to roughly confirm this ECa measurement uncertainty was to 35
identify, from the locations of all the measurement points within each survey, pairs of points 36
nearly-overlapping measurement points created either by temporary pauses during the streaming 37
survey or by the intersection of survey traverses in different directions. To identify such pairs, 38
the distances between all points within a given survey were calculated and filtered for points 39
within 15 cm. Among a total of 5859 data pairs (2024 from dry conditions, 3835 from wet 40
conditions), the median ECa difference within the pairs was 0.015 dS/m. The result changed 41
little for a small increase in the distance filter: a distance filter of 20 cm resulted in a total of 42
85336 pairs (11910 from dry conditions, 73426 from wet conditions), and a median difference of 43
0.016 dS/m. Although this value was higher than the uncertainty determined using the first 44
method, we also award it lower overall confidence because the points in question were not 45
identical, but rather separated somewhat in space, time, and potentially in instrument orientation. 46
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Given this additional uncertainty, we felt this second method sufficiently confirmed the 47
magnitude of the uncertainty estimated by the first method to proceed with our analysis. 48
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IV. Soil sampling results and ECa relationship to edaphic conditions 50
Salt marsh soil properties were determined from 23 cores from 0-30 cm depth (Table S1). 51
Empirical relationships have shown ECa to increase with increasing clay content (Domsch and 52
Giebel 2004; Rhoades and others 1999), increasing soil water content (Malicki and Walczak 53
1999; Rhoades and others 1999), or increasing solution conductivity (Malicki and Walczak 1999, 54
Rhoades and others 1999; Sam and Ridd 1998), though not for as high values of these properties 55
as occur in salt marshes. To establish the specific relationships between ECa and edaphic 56
variables for the salt marsh environment, we conducted linear regression analyses (Figure S2). 57
The relationship between temperature-corrected (Reedy and Scanlon 2003) ECa and soil paste 58
extract conductivity (ECe) for our data was essentially an extrapolation of that reported by 59
Rhoades and others (1999) for a glacial till. The pore water conductivity (ECw) values of our 60
marsh were higher than those reported previously in the EMI literature (Friedman 2005; Rhoades 61
and others 1999) but spanned a smaller range compared to our ECa range, resulting in a 62
shallower ECw/ECa regression slope. Although soil paste extract conductivity (ECe) represents 63
the total soluble soil salinity, related to the osmotic potential in the soil, the dissolved salt 64
incident upon a plant root membrane as part of the transpiration stream is better represented by 65
the pore water solute conductivity (ECw); we defined an interstitial electrical conductivity index 66
(IECI) as the difference between these two parameters to represent the excess soil salinity above 67
the background pore water level. Variance in ECa was more closely related to variance in IECI 68
than to variance in ECw (Table S2), highlighting the importance of considering the effects of 69
interstitial salinity in clayey salt marsh soils. 70
The relationship between ECa and volumetric water content (θ) for our data was roughly 71
a linear extrapolation of that reported by Malicki and others (1999) for a silty loam with high 72
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solution conductivity (11.7 dS/m). The relationship of ECa to clay content for our data was 73
unique: the clay contents of our soils were beyond the range of those used by Domsch and 74
Giebel (2004) to compare ECa and clay fraction; our soils were likely more similar to the 75
gleysols excluded from their regression analysis than to the soils they included. The fit of the 76
linear relationship between ECa and clay fraction was strongly influenced by a few data points 77
associated with the margin of the marsh closest to the bay, which unsurprisingly contained 78
smaller clay fractions. In summary, variability in ECa values mostly reflected variability in soil 79
water content (θ) and in the total salt content of the soil (ECe). These results establish the overall 80
utility of ECa data as a proxy for these zonation-relevant edaphic variables and confirm the 81
applicability of EMI methods to the salt marsh environment. 82
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Table S1. Root Zone Soil Core and Pore Water Characteristics
Characteristic Statistic
Average Std. Dev.
Wet core bulk density (g/cm3)* 1.33 0.19
Core water fraction (g H20 / g total)* 0.60 0.09
Volumetric water content [θ] (cm3 H2O/cm3 total)* 0.83 0.15
Sand fraction* 2.64% 6.95%
Silt fraction* 35.54% 5.04%
Clay fraction* 61.82% 9.44%
Textural ClasS8 Clay
Paste extract electrical conductivity [ECe] (dS/m)* 68.94 15.01
Pore water electrical conductivity [ECw] (dS/m)** 57.2 7.0
Interstitial electrical conductivity index [IECI] (dS/m)** 14.25 12.43
* N (0-30 cm depth soil cores) = 23, ** N (ECw @ -30 cm depth) = 17
85 Table S2. ECa - Soil Properties Correlation Matrix
ECa ECe ECw† IECI† θ Clay Silt Sand
ECa 1
ECe 0.674*** 1
ECw† 0.526* 0.596* 1
IECI† 0.675** 0.891*** 0.167 1
θ 0.437* 0.413 0.286 0.400 1
Clay 0.509* 0.513* 0.631* 0.341 0.346 1
Silt -0.548* -0.644** -0.313 -0.627* -0.358 -0.695*** 1
Sand -0.293 -0.230 -0.632* -0.020 -0.210 -0.854*** 0.219 1
N = 23, † N = 17for ECw samples * significant at p < 0.05, ** significant at p < 0.005, *** significant at p < 0.0005
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Table S3. Parameters of Linear Relationships between ECa and Salt Marsh Soil Properties (Illustrated in Figure S2), with 95% Confidence Intervals
Dependent Variable Slope Intercept
R2 Lower 95% Value Upper 95% Lower 95% Value Upper 95%
ECe (dS/m) 2.11 4.21 6.30 -24.15 7.04 38.23 0.45
IECI (dS/m) 1.28 3.23 5.17 -60.72 -32.27 -3.82 0.46
ECw (dS/m) 0.16 1.42 2.69 18.12 36.66 55.20 0.28
θ (fraction) 0.00 0.03 0.05 0.04 0.42 0.80 0.19
Clay (fraction) 0.00 0.02 0.04 0.10 0.32 0.55 0.26
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Figure S2. Relationships between apparent bulk soil electrical conductivity (ECa) and related 90
soil properties for salt marsh sediment cores 0-30 cm deep and pore water samples from -30 cm. 91
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V. Archie’s Law parameter estimations: f, φ, and σs 93
Archie’s Law is an empirical model that relates temperature-corrected apparent bulk soil 94
electrical conductivity data (ECa) to the pore water fluid conductivity (ECw), a formation factor 95
(f = 1/F = φm/a, with porosity φ), soil saturation (S), and the mineral surface conductivity (σs) 96
(Kirsch 2006). 97
sn
m
sn S
aECwSfECwECa σϕσ +⋅⋅=+⋅⋅= (Eqn. S3) 98
Surface conductivity is important in soils with large clay fractions, such as our salt marsh, but 99
had not been tabulated for saline, clay, salt marsh soils. The parameters σs and f (and φ) are 100
difficult to measure but can be estimated in multiple ways. We compare three methods here and 101
test the uncertainty in the salt marsh clay soil parameter estimates. 102
Method 1 103
Rhoades and others (1989) proposed an empirical relationship between σs (dS/m) and the 104
volumetric clay content (VCC) of a soil. 105
021.031.2 −⋅= VCCsσ (Eqn. S4) 106
We calculated σs for each of 23 cores taken from 30-60 cm depth based on their clay mass 107
fraction and a clay bulk density of 1.05 g/cm3. We used cores from this depth interval because 108
they were below the water table and so were saturated, permitting the assignment of S = 1. Using 109
these values of σs, a = 1, m = 2, n = 2, and S = 1, we solved Eqn. S3 for φ for each core. 110
( ) ( )2SECwECaa s ⋅−⋅= σϕ (Eqn. S5) 111
The resulting averages were σs = 1.595 ± 0.406 dS/m (µ ± 1σ) and φ = 0.417 ± 0.036, which are 112
reasonable for clay soils. The corresponding average formation factor value was f = 0.175 ± 113
0.030. Use of a different m value yielded no difference in the saturation changes estimated by 114
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the differential EMI calculation because the values of σs and f used in evaluating equation E1 115
were not affected by (insensitive to) the value of m. 116
Method 2 117
Pore water samples were collected from below the water table (60 cm depth) at 16 118
locations concurrently with coring and ECa measurements. A simple linear regression between 119
the ECw (dS/m) of these samples and the coincident temperature-corrected ECa (dS/m) yielded 120
an equation in the form of Eqn. S3 (for S = 1). The slope of the regression was f = 0.223 (φ = 121
0.472 for f=ϕ ) with intercept σs = 2.479 dS/m. These parameter estimates were uncertain 122
because single point-samples of pore water were related to ECa measurements of an overlying 123
volume in each case, inherently assuming sediment and solution homogeneity. The simplicity of 124
the method enhanced its general utility, however. 125
Method 3 126
Sen and others (1988) proposed a complex formulation of Archie’s Law describing ECa (S/m) 127
for clay-bearing sandstones in terms of ECw (S/m), the soil cation exchange capacity (CEC, 128
meq/g), grain density (ρg = 2.65 g/ml), a formation factor (f = 1/F = φm, with porosity φ), and 129
empirical parameters m, A = 1.93*m, C = 0.7, and E ~ 0. [Note that our study uses standard units 130
of dS/m and cmolc/kg; we provide the conversion from the units used by Sen and others (1988) 131
where applicable.] 132
QEQCECw
QAECwECa m ⋅+
⋅+⋅+⋅⋅= 1ϕ (Eqn. S6a) 133
( ) ϕϕρ −⋅⋅= 1gCECQ (Eqn. S6b) 134
The parameter Q represents volume-normalized CEC. For unsaturated conditions, one would 135
replace Q with Q/S and f with nSf ⋅ (Kirsch 2006). A parameter for mineral surface 136
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conductivity (σs), analogous to that in equation E1, can be algebraically extracted from Eqn. S6b 137
(for EQ = 0). 138
⋅+⋅⋅⋅=
QCECw
QAfECwsσ (Eqn. S7) 139
Because φ, m, and CEC were unknown, there was one excess degree of freedom in Eqns. S6a 140
and S6b. We addressed this uncertainty by parameter estimation and sensitivity analysis. 141
Because measurements of CEC for the salt marsh soils were unavailable, we solved 142
equations S7 and S6b for CEC using the values for f, φ, and σs found in the above empirical 143
regression and an observed average value of ECw = 5.285 S/m (52.85 dS/m) for the marsh pore 144
water. We tested the sensitivity of the result to multiple m values (Table S4). The CEC estimates 145
were very close to the 11.14 cmolc/kg CEC of Stege Marsh, a San Francisco Bay salt marsh 146
similar to our study area and located 53 km to the north (Córdova-Kreylos and Scow 2007). 147
Table S4. Sensitivity of CEC Estimates to m Values
Parameter Test 1 Test 2 f 0.223 same φ 0.472 same σs 0.2479 S/m (2.479 dS/m) same
ECw 5.285 S/m (52.85 dS/m) same
m 2 2.34 CEC 0.1011 meq/g (10.11 cmolc/kg) 0.1068 meq/g (10.68 cmolc/kg)
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With a CEC estimate, Eqn. S6 can be solved in terms of an φ polynomial and the 149
resulting value of φ used to calculate f and σs. 150
For gCECAa ρ⋅⋅= and gCECCc ρ⋅⋅= : 151
( ) ( ) ( )( ) ( )ECwECacECaECwECacacacECw mm ⋅−−⋅⋅++⋅+−−⋅= + ϕϕϕ 10 (Eqn. S8) 152
We tested the sensitivity of this method to uncertainty in CEC and m (Table S5) using the 153
ECa and ECw data for the same subset of cores as above. Halving CEC from 0.1 meq/g to 0.05 154
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meq/g yielded a change in the average saturation difference (∆S) calculated by the Q-DEMI 155
methodology of 0.6%, that is, only 10% of the 6.2% average saturation change estimated using 156
CEC = 0.1 meq/g. Changing m from 2 to 2.34 yielded parameter estimates that were unrealistic 157
for clay sediments, so we elected to use m = 2 in the remaining calculations. 158
Table S5. Sensitivity of φ, f, and to CEC and m Values
Parameter Test 1 Test 2 Test 3
ECa and ECw from core data same same
ρg 2.65 g/ml
(2605 kg/m3) same same
m 2 2 2.34
CEC 0.10 meq/g
(10 cmolc/kg) 0.05 meq/g
(5 cmolc/kg) 0.05 meq/g
(5 cmolc/kg)
φ (µ ± 1σ) 0.396 ± 0.037 0.422 ± 0.035 0.017 ± 0.086
f (µ ± 1σ) 0.158 ± 0.028 0.179 ± 0.028 0.006 ± 0.029
σs (µ ± 1σ) 0.2307 ± 0.0124 S/m (2.307 ± 0.124 dS/m)
0.1211 ± 0.0042 S/m (1.211 ± 0.042 dS/m)
0.0041 ± 0.0212 S/m (0.041 ± 0.212 dS/m)
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Summary 160
The three methods, described above, for estimating the parameters in Archie’s Law are 161
compared in Table S6 (Method 3 using CEC = 0.1 meq/ml and m = 2). The results are 162
comparable from all three methods. This study used the parameters from Method 2 because the 163
simplicity of Method 2 lends it the greatest potential to be widely applied in future studies. 164
Table S6. Comparison of Archie’s Law Parameter Estimates
Parameter
Method 1 (using VCC)
Method 2 (linear regression)
Method 3 (using CEC)
average st. dev. average st. dev
φ 0.417 0.036 0.472 -- 0.396 0.037
f 0.175 0.030 0.223 r2 = 0.20
0.158 0.028
σs (dS/m) 1.595 0.406 2.479 2.307 0.124
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VI. Cross-correlation of geographic and edaphic metrics 166
One aspect of the study was the assessment of the spatial correlation between each of the 167
six metrics used to represent abiotic salt marsh ecosystem structures. Testing the correlation 168
between the metrics for all 2256 marsh locations yielded the correlation structure of Table S7. 169
Some of these correlation coefficients, when pertinent to the main scientific discussion, were 170
included in the paper; all are provided here for reference. 171
Notably, elevation was poorly correlated with any of the other metrics. ECa values were 172
slightly more strongly correlated with the distance to a main tidal channel than to the nearest 173
channel of any size. The magnitude and spatial distribution of ECa values from dry and wet 174
conditions were similar (strongly and significantly correlated). The difference in the sign of the 175
correlation between ∆ECa and dry or wet ECa values was due to the dual nature of the edaphic 176
change between these two conditions, involving both water and salt dynamics in the root zone 177
(see Q-DEMI methodology description in the paper). 178
Table S7. Geographic and Edaphic Metrics’ Correlation Matrix (N = 2256)
Elevation Dist. to main
channel Dist. to any
channel ECa dry ECa wet ∆ECa
Elevation 1
Dist. to main channel
-0.068* 1
Dist. to any channel
0.185** 0.428** 1
ECa dry -0.004 0.537** 0.440** 1
ECa wet 0.061* 0.405** 0.357** 0.831*** 1
∆ECa -0.118** 0.217** 0.134** 0.272** -0.309** 1
* significant at p < 0.005, ** significant at p < 0.0005, *** significant at p << 0.0005
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VII. Literature cited in supplement 180
Abdu H, Robinson DA, Jones SB. 2007. Comparing bulk soil electrical conductivity 181
determination using the DUALEM-1S and EM38-DD electromagnetic induction instruments. 182
Soil Science Society of America Journal 71: 189-196. 183
Callegary JB, Ferré TPA, Groom RW. 2007. Vertical spatial sensitivity and exploration depth of 184
low-induction-number electromagnetic-induction instruments. Vadose Zone Journal 6: 158-185
167. 186
Córdova-Kreylos AL, Scow KM. 2007. Effects of ciprofloxacin on salt marsh sediment 187
microbial communities. International Society for Microbial Ecology Journal 1: 585-595. 188
Domsch H, Giebel A. 2004. Estimation of soil textural features from soil electrical conductivity 189
recorded using the EM38. Precision Agriculture 5: 389-409. 190
Friedman SP. 2005. Soil properties influencing apparent electrical conductivity: a review. 191
Computers and Electronics in Agriculture 46: 45-47. 192
Kirsch R. 2006. Petrophysical properties of permeable and low-permeable rocks. Kirsch R, 193
editor. Groundwater Geophysics. New York: Springer. 194
Malicki MA, Walczak RT. 1999. Evaluating soil salinity status from bulk electrical conductivity 195
and permittivity. European Journal of Soil Science 50: 505-514. 196
Reedy RC, Scanlon BR. 2003. Soil water content monitoring using electromagnetic induction. 197
Journal of Geotechnical and Geoenvironmental Engineering 129: 1028-1039. 198
Rhoades JD, Chanduvi F, Lesch S. 1999. Soil salinity assessment: methods and interpretation of 199
electrical conductivity measurements. Irrigation and Drainage Paper 57, FAO, Rome, Italy. 200
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Rhoades JD, Manteghi NA, Shouse PJ, Alves WJ. 1989. Soil electrical conductivity and soil 201
salinity: new formulations and calibrations. Soil Science Society of America Journal 53: 433-202
439. 203
Sam R, Ridd P. 1998. Spatial variations of groundwater salinity in a mangrove-salt flat system, 204
Cocoa Creek, Australia. Mangroves and Salt Marshes 2: 121-132. 205
Sen PN, Goode PA, Sibbit A. 1988. Electrical conduction in clay bearing sandstones at low and 206
high salinities. Journal of Applied Physics 63: 4832-4840. 207