DOI: 10.5061/dryad.9s4mw6mwt
title: Salinity, water table, and subsurface resistivity data from migrating marsh–forest ecotones

description: Upland habitats are converting to salt marsh at unprecedented rates due to
 sea level rise. While increasing salinity is understood to be the main
 driver of this conversion of upland to marsh, the factors that influence
 salinity in the zone of change (i.e., the marsh-forest ecotone) are
 unclear, especially in the northeastern USA, where marsh migration studies
 are less common. This study examined spatial and temporal patterns in
 salinity across the marsh-forest ecotone and potential drivers using a
 combination of groundwater well measurements and geophysical surveys.
 Across three study sites, groundwater salinity was influenced by multiple
 factors, including topography, weather events, and tidal cycles.
 Electrical resistivity tomography (ERT) showed differing patterns among
 sites: at the Massachusetts site, there was a sharper transition from low
 to high resistivity moving from the marsh to the forest. Whereas at the
 New Jersey and New York sites, there was a low resistivity (high salinity)
 layer overlaying high resistivity. The presence of this deeper fresh layer
 contrasts with the ‘salt wedge’ configuration found in open water
 estuarine systems and suggests that trees may be able to survive saltwater
 inundation by accessing this deeper reserve of freshwater. Salinity
 dynamics were occasionally driven by storm surges and hydraulic gradients,
 but the magnitude and direction of these effects were not consistent
 across events. Finally, sites with greater soil hydraulic conductivity
 exhibited lower water tables and enhanced tidal advection. This pattern
 suggests that well-drained soils characteristic of the Pine Barrens
 Ecoregion may facilitate efficient drainage but simultaneously heighten
 susceptibility to saltwater intrusion. These findings underscore the need
 to consider local hydrologic and soil conditions when predicting the pace
 of marsh migration and the resilience of coastal forests under rising sea
 levels.
description: To examine temporal and spatial variability across the
 mash-forest ecotone, we used a combination of geophysical surveys
 (electrical resistivity imaging) and groundwater well salinity
 meters. <em>Study Sites</em>
 Three locations showing signs of forest retreat were selected
 within the Atlantic coastal pine barrens ecoregion: Egg Harbor, New Jersey
 (39.3259°, -74.6502°), Pine Neck, New York (40.8422°, -72.5644°), and
 Waquoit Bay, Massachusetts (41.5562°, -70.5057°). These sites differ in
 tide range, slope, and vegetation composition along the marsh-forest
 transition but share common characteristics of the pine barrens ecoregion,
 which is known for its sandy, fast-draining soils (Forman 2012).
 At Waquoit Bay, the study focused on the Sage Lot Pond marsh, a
 back-barrier marsh within the Waquoit Bay National Estuarine Research
 Reserve. Common species along the ecotone included <em>Iva
 frutescens</em>, <em>Baccharis halimifolia</em>,
 <em>Toxicodendron radicans</em>, <em>Phragmites
 australis</em>, <em>Nyssa sylvatica</em>, and
 <em>Pinus rigida</em>. Pine Neck is located in The Nature
 Conservancy’s Pine Neck Sanctuary along Shinnecock Bay in East Quogue, NY.
 The marsh-forest ecotone was dominated by <em>Phragmites
 australis</em>, mixed with <em>Smilax</em> spp.,
 <em>Iva frutescens</em>, <em>Baccharis
 halimifolia</em>, and <em>Nyssa sylvatica</em>. The Egg
 Harbor site, located along the Great Egg Harbor River in the Tuckahoe
 Wildlife Management Area, was dominated by tall and dense stands of
 <em>Phragmites australis</em>, with scattered
 <em>Juniperus virginiana</em> and <em>Nyssa
 sylvatica</em>. Topographic slopes across the
 ecotones were calculated in ArcGIS Pro (Esri Inc., Redlands, CA) by
 averaging slope values along transects derived from Digital Elevation
 Models (Molino et al. 2021). Tidal range estimates were obtained using
 NOAA’s VDatum tool (Tolkova et al. 2023). Waquoit Bay has a tidal range of
 0.6 m and an average ecotone slope of 3.45 %. Pine Neck has a 0.8 m tidal
 range and an average slope of 2.1 %. Egg Harbor has the highest tidal
 amplitude (1.1 m; Tolkova et al. 2023) and the lowest average slope (1.8
 %). <em>Groundwater Wells</em>
 Groundwater wells installed as part of a previous study (Payne et
 al. in submission) were utilized to measure water table height and
 salinity. Two wells were used at each site – one in the forest and one in
 the marsh or <em>Phragmites</em>/scrub shrub zone. Wells were
 constructed from 1 m sections of 3.8 cm inner diameter polyvinyl chloride
 (PVC) with a screened interval at 0.5 m – 1.0 m of depth that was covered
 with pantyhose to prevent infilling of sediment (Jobe and Gedan 2021).
 Boreholes were dug using a post hole digger or auger and were backfilled
 with sand after inserting the wells. The top ~15 cm was capped with
 concrete to prevent gaps acting as preferential flow pathways (Montalto et
 al., 2006). The well tops extended above the marsh surface by ~10 cm and
 were open on the top for Payne et al. in submission). To limit artifacts
 on salinity due to direct precipitation or overtopping, well tops were
 extended and capped with downward-facing PVC elbows. At each site, the
 lower well was outfitted with a HOBO U20 water level logger (Onset Corp.,
 Bourne, MA) and a HOBO U24 Conductivity/Salinity logger. The upper well
 was outfitted with a Meter Hydros 21 Water Level/Conductivity/Salinity
 meter (Meter Group, Inc. USA, Pullman, WA). Another water level logger was
 installed in the tidal creek at each site. Well
 elevations were measured during leaf-off using a survey-grade Leica GNSS
 GS14 (Leica Geosystems, Heerbrug, Switzerland). For Pine Neck and Harbor,
 measurements from 20-40 minute static occupations were post-processed
 using NOAA’s Online Positioning User Service (OPUS) (https://geodesy.noaa.gov/OPUS/). For Waquoit Bay, elevations were determined from Real-Time Network (RTN) measurements. Reference water elevations were measured periodically throughout the deployment. For the HOBO loggers, data were corrected for barometric pressure using weather station data from the closest airport and converted to NAVD88 using Hoboware Pro (version 3.17.19, Onset Computer Corp., Bourne, MA). <em>Soil Characteristics</em> Stratigraphy was assessed by coring the top 1.5 meters of soil, describing the layers and subsampling sediments. At each well site along the east transects, cores were collected using either Russian peat borer for organic sediments (Aquatic Research Instruments, Hope, ID, USA), or an Edelman or Riverside auger (Eijkelkamp, Wilmington, NC, USA) for sandy sediments. Cores were subsampled at varying depths based on the layer thicknesses and amount of sediment available for analyses. For each core, two to five subsamples were analyzed for particle size distribution, three to four for organic content, and one to three for saturated hydraulic conductivity. The goal was to obtain at least four subsamples from the two primary layers: an organic rich surface layer and a deeper outwash sand layer. This contact between organic soils and outwash sands was also assessed for the western transects via probing.  Soil organic content was measured using Loss on Ignition (LOI), where soils were ignited for four hours at 550°C and the mass loss was used to calculate percent organic matter (Heiri et al., 2001). Particle size distribution was measured in triplicate on 14 – 16 samples per site using a laser granulometer (LS 13-320, Beckman Coulter, Brea, CA, U.S.A.) after pretreatment with concentrated hydrogen peroxide where necessary to remove coarse organic material such as peat and dispersal using a 5 % solution of sodium hexametaphosphate (Gray et al., 2010). Distributions were post-processed with Gradistat.v8 software (Blott & Pye 2001), including bin aggregation to texture classes and statistical description. Saturated hydraulic conductivity was measured on 10 - 14 samples per site utilizing a benchtop K<sub>sat</sub> instrument (Meter instruments, Pullman, WA, USA), which uses the falling head method to measure saturated hydraulic conductivity (K<sub>sat</sub>) over a range of 0.01 to 5,000 cm day <sup>-1</sup> (Trifunovic et al., 2018). The area-normalized flow rate through saturated sediment samples were measured to determine K<sub>sat</sub> using the following equation:  K<sub>sat</sub> = (A<sub>burette</sub>/A<sub>sample</sub>)L*b (Equation 1) where <em>A<sub>sample</sub></em> <sub>and</sub> A<sub>burette</sub> are the cross-sectional areas of the sample and burette (in sq. cm); <em>L</em> is the sample depth in cm; and <em>b</em> is the coefficient of the fitted exponential function describing the falling head over time (s<sup>-1</sup>). <em>Electrical Resistivity Surveys</em> To measure subsurface salinity patterns and seasonal variability, we conducted electrical resistivity surveys in the summer and winter of 2023 using a Syscal Pro 96 (Iris Instruments, Orleans, France). Resistivity is the inverse of conductivity and is also commonly used as a proxy for salinity. Resistivity surveys are conducted by transmitting an electrical current through the subsurface and measuring the strength of the returning current using a series of electrodes placed into the ground. For our surveys, electrodes were placed at 1 m intervals along an 80 m transect aligning with the groundwater wells, starting in the marsh and ending in the forest. We used a mixed array to maximize the depth of study in the salty soils. Contact resistance checks were performed prior to the survey start, and electrodes with high values were adjusted or wetted to improve contact with the soil.  Salinity was measured from groundwater wells and soil samples to validate resistivity measurements. For the summer survey, water samples were collected from 4-5 groundwater wells along the resistivity transect. Wells were pumped out and allowed to refill before the samples were collected and stored in capped bottles. Conductivity and salinity were measured from the samples at the lab using the YSI Pro 30 water quality sonde. For the winter resistivity surveys, salinity was measured from 1.5 m deep soil cores that were collected at each well location using either a Russian peat borer for organic sediments (Aquatic Research Instruments, Hope, ID, USA), or an Edelman or Riverside auger (Eijkelkamp, Wilmington, NC, USA) for sandy sediments. Cores were subsampled at the top, middle, and bottom (exact depths varied based on sediment availability) and salinity was measured on 5:1 (of water to dry soil) soil slurries using a YSI Pro 30 (Xylem Inc., Yellow Springs, Ohio, USA).
description: # Conductivity, water table elevation, and subsurface resistivity data
 from migrating marsh–forest ecotones This dataset includes water level,
 specific conductivity, electrical resistivity, and soil data collected at
 marsh–forest ecotones in Egg Harbor, NJ; Waquoit Bay, MA; and Pine Neck,
 NY. ## Files Included ### 1. Water Level and Specific Conductivity Data
 Files: * `WaterLevels_SPC_metadata.csv` * `EH_2024EDT.csv` * `PN_2024.csv`
 * `WB_2024EDT.csv` #### WaterLevels_SPC_metadata.csv This file contains
 metadata for 6 wells along marsh–upland ecotones and 3 tidal channel
 loggers. Missing values are filled with `-999`. **Fields:** * `Well_ID` —
 Names of wells * `location` — Site name: `Waquoit`, `Pine_Neck`, or
 `Egg_Harbor` * `latitude` — Decimal degrees north * `longitude` — Decimal
 degrees west (negative) * `installation_year` — Year wells were installed
 * `inner_diameter` — Inner diameter of wells (m) * `outer_diameter` —
 Outer diameter of wells (m) * `well_top_elevation` — Elevation of top of
 well relative to NAVD88 (m) * `soil_elevation` — Elevation of soil surface
 relative to NAVD88 (m) #### EH_2024EDT.csv Water level and specific
 conductivity measurements at Egg Harbor, NJ. **Fields:** * `DT_UTC-4` —
 Date and time (MM/DD/YYYY HH:MM) * `value` — Measurement (water level in m
 NAVD88 or specific conductivity in mS/cm) * `well_id` — `T5E` (upper),
 `T3E` (lower), `Creek` (tidal logger) * `measure` — `level` for water
 level, `CT` for specific conductivity Missing values are filled with
 `-999`. #### PN_2024.csv Water level and specific conductivity
 measurements at Pine Neck, NY. **Fields:** * `DT_UTC-4` — Date and time
 (MM/DD/YYYY HH:MM) * `value` — Measurement (water level in m NAVD88 or
 specific conductivity in mS/cm) * `well_id` — `P5E` (upper), `P4E`
 (lower), `Creek` (tidal logger) * `measure` — `level` or `CT` Missing
 values are filled with `-999`. #### WB_2024EDT.csv Water level and
 specific conductivity measurements at Waquoit Bay, MA. **Fields:** *
 `DT_UTC-4` — Date and time (MM/DD/YYYY HH:MM) * `value` — Measurement
 (water level in m NAVD88 or specific conductivity in mS/cm) * `well_id` —
 `WB4E` (upper), `WB3E` (lower), `Creek` (tidal logger) * `measure` —
 `level` or `CT` Missing values are filled with `-999`. --- ### 2.
 Electrical Resistivity Topography (ERT) Files: * `EH_ERT_2023.08.21.csv` *
 `EH_ERT_2023.12.08.csv` * `PN_ERT_2023.07.22.csv` *
 `PN_ERT_2023.12.13_BothHalves.csv` * `WB_ERT_2023.07.20B_HighSpeed_v2.csv`
 * `WB_ERT_2023.12.16_both.csv` These files contain apparent resistivity
 measurements along transects spanning the marsh to forest ecotones.
 **Common Fields:** * `El-array` — Type of electrode array * `xA, xB` —
 Current electrode positions (m) * `xM, xN` — Potential electrode positions
 (m) * `Rho (Ohm·m)` — Apparent resistivity * `Dev. Rho (%)` — Measurement
 deviation (%) * `SP (mV)` — Spontaneous potential (if available) * `VMN
 (mV)` — Measured voltage between M and N * `IAB (mA)` — Current injected *
 `Time (ms)` — Pulse duration * `Coef. k (m)` — Geometric factor * `Metal
 factor` — Quality index (if available) * `yA, yB, yM, yN` — Y-coordinates
 of electrodes * `zA, zB, zM, zN` — Elevations of electrodes (m) * `Depth
 A/B/M/N` — Depths of electrodes in ground (m) * `Stack` — Number of
 stacked measurements * `Rs-Check` — Contact resistance check (Ω) * `Vab,
 Pab, Rab` — Voltage, power, and resistance measurements * `Latitude,
 Longitude, UTM` — GPS coordinates * `Name` — Survey or line name *
 `Channel` — Measurement index * `Overload` — Flag (1 = overload, 0 =
 valid) * `Tx-Bat, Tx Temp, Rx-Bat, Rx Temp` — Battery voltages and
 temperatures * `Date` — Date and time of measurement * `Gapfiller, Synch`
 — Acquisition flags Each file corresponds to a specific site and date: *
 `EH_ERT_2023.08.21.csv` — Egg Harbor, NJ, 8/21/23 *
 `EH_ERT_2023.12.08.csv` — Egg Harbor, NJ, 12/8/23 *
 `PN_ERT_2023.07.22.csv` — Pine Neck, NY, 7/22/23 *
 `PN_ERT_2023.12.13_BothHalves.csv` — Pine Neck, NY, 12/13/23 *
 `WB_ERT_2023.07.20B_HighSpeed_v2.csv` — Waquoit Bay, MA, 7/20/23 *
 `WB_ERT_2023.12.16_both.csv` — Waquoit Bay, MA, 12/16/23 --- ### 3. Soil
 Data Files: * `SoilData.csv` * `Ksat.csv` * `Particle_Size_compiled.csv`
 #### SoilData.csv Contains soil salinity and organic matter from soil
 cores collected at well locations. **Fields:** * `Site` — `EH`, `WB`, or
 `PN` * `Station` — Location order from marsh (1) to forest (5) * `Sample
 Well` — Well where soil was collected (`T1E`–`T5E`, `WB1E`–`WB5E`,
 `P1E`–`P5E`) * `Depth` — Sample depth from surface (cm) * `Depth_mid` —
 Mid-point of sample depth * `Tin #` — Sample tin identifier * `tin wt` —
 Mass of tin (g) * `wet wt` — Mass before drying (g) * `initial dry wt, 2nd
 dry wt` — Mass after drying (g) * `water wt` — Mass of water lost (g) *
 `soil wt` — Mass of dry soil (g) * `water:soil` — Ratio of water to soil *
 `SPC` — Specific conductivity (µS/cm) * `sal PSU` — Salinity (Practical
 Salinity Units) * `tin wt_2` — Mass of tin used for Loss on Ignition (g) *
 `pre-muffle, post-muffle` — Mass before/after ignition (g) * `organic wt`
 — Organic mass (g) * `sediment wt` — Mass of sediment (g) * `%OM` —
 Percent organic matter #### Ksat.csv Contains benchtop saturated hydraulic
 conductivity measurements from soil cores. **Fields:** * `Site` — `EH`,
 `WB`, or `PN` * `Well_ID` — Well where soil was sampled * `Latitude,
 Longitude` — Sample location (decimal degrees) * `Depth_range` — Depth
 range of sample (cm) * `Depth` — Mid-point of sample depth range (cm) *
 `Layer` — Soil layer determined visually * `Sample_Date` — Date of
 collection * `Average_Ksat_cm/d` — Average saturated hydraulic
 conductivity at 10°C of trials * `STDEV_Ksat_cm/d` — Standard deviation of
 trials #### Particle_Size_compiled.csv Contains particle size distribution
 collected from soil cores using a laser granulometer. **Fields:** * `Site`
 — `EH`, `WB`, or `PN` * `Well_ID` — Well where soil was sampled * `Depth`
 — Depth range of sample * `Layer` — Soil layer determined visually *
 `Mean` — Mean particle diameter of the sample * `Median` — Median particle
 diameter of the sample * `Mode` — Modal particle diameter of the sample *
 `D_0_0` — Characteristic particle diameter as reported by the particle
 size analysis software * `Mean_Median_Ratio` — Ratio of the mean particle
 diameter to the median particle diameter, used as an indicator of
 distribution skewness. * `SD` — Standard deviation of the particle size
 distribution * `Variance` — Variance of particle diameters * `CV` —
 Coefficient of variation (standard deviation divided by the mean) *
 `Skewness` — Measure of asymmetry of the particle size distribution *
 `Kurtosis` — Measure of the peakedness or tail weight of the particle size
 distribution * `d10` — Particle diameter at which 10% of the sample mass
 is finer * `d50` — Particle diameter at which 50% of the sample mass is
 finer (median grain size) * `d90` — Particle diameter at which 90% of the
 sample mass is finer * `Specific_Surface_Area` — Specific surface area of
 particles, calculated or instrument-derived (units specified in Methods) *
 `R_R_Dm` — Rosin–Rammler characteristic particle diameter parameter (Dm),
 representing the scale of the distribution * `R_R_n` — Rosin–Rammler
 distribution shape parameter (n), describing the spread or uniformity of
 particle sizes --- ## Sharing/Access Information No publicly accessible
 locations are available. ## Code/Software None included.