2014 Annual Report to the US Army Corps of Engineers, Savannah District

Transcription

1 2014 Annual Report to the US Army Corps of Engineers, Savannah District Wetland Vegetation Communities and Interstitial Salinity Conditions in the Upper Savannah River Tidal Floodplain During the Savannah Harbor Expansion Project Pre-construction Phase This report covers the 2014 pre-construction phase of the (SHEP). Impetus for this wetland monitoring comes partially from The Wetlands Interagency Coordination Team, who declared the freshwater and oligohaline marshes upstream of the harbor as the highest priority wetland resource in the Savannah River Basin. Members of the Wetland Interagency Coordination Team include representatives from Georgia, South Carolina, US Environmental Protection Agency, US Fish and Wildlife Service, and the National Oceanic and Atmospheric Administration National Marine Fisheries Service. This monitoring effort was funded by the US Army Corps of Engineers, Savannah District (USACE) with William Bailey, Planning Division Chief. Funding was routed through the Cooperative Ecosystems Studies Units (CESU) as cooperative agreement # W912H between USACE, CESU, and Clemson University. The official start date was 04 December Monitoring includes: discrete surveys of marsh vegetation at 12 areas; annual surveys of three tidal freshwater forest areas; and hourly measurements of belowground salinity, aboveground salinity, and water level at all 15 areas. All marsh monitoring areas are within the boundaries of the Savannah National Wildlife Refuge (SNWR). Two of the three tidal freshwater forest monitoring areas are on SNWR land, while the third is located on land owned by the Georgia Department of Natural Resources and managed by SNWR. Scope and intensity of monitoring, including monitoring area locations, was agreed upon by William Bailey (USACE), Chuck Hayes (SNWR), Jamie Duberstein (Clemson University), William Conner (Clemson University), and Wiley Kitchens (University of Florida). Common names of herbaceous vegetation (i.e., marsh grasses) and trees are used throughout this report. Latin names are provided in Appendix B and Table 3 for grasses and tree, respectively. 1

2 Table of Contents Site Selection...2 Map of Study Area...3 Hydrologic Monitoring...4 Marsh Monitoring...18 Tidal Forest Monitoring...23 Remote Sensing...27 Literature Cited...32 Appendix A: Coordinates...33 Appendix B: Latin Names of Herbaceous Vegetation...36 Site Selection Drs. Jamie Duberstein, William Conner, and Wiley Kitchens met with USACE personnel in January 2014 to discuss the locations of the marsh monitoring areas. Priority was placed on retaining the seven areas previously monitored by Kitchens ( ) so that comparisons to historic site-specific conditions would be more accurate. Determining the locations of the five new marsh monitoring area involved some debate: William Bailey urged for placement of an area more upstream, but Duberstein claimed that the areas suggested by Bailey would be restricted to sites immediately adjacent to tidal creeks and dominated by giant cutgrass, which is not very responsive to changes in salinity. In the end, all involved parties agreed that the more downstream area suggested by Duberstein would be monitored, so long as three tidal freshwater forest areas (upstream of marshes) would also be monitored (Figure 1; coordinates in Appendix A). 2

3 Figure 1. Locations of the marsh and tidal freshwater forest monitoring areas associated with SHEP. Yellow circles indicate marsh areas that have been monitored prior to SHEP. White circles indicate new SHEP marsh monitoring areas. 3

4 Salinity and Water Level Monitoring Methods Water monitoring stations are located in each of the (12) marsh and (3) tidal freshwater forest monitoring areas. Stations consist of a platform stabilizing wells that are designed to house autonomous water sensors, one for aboveground monitoring and one for belowground (i.e., root zone or interstitial) monitoring (Figure 2). Though the wells are the same diameter and length above and below ground, water enters the wells differently depending upon whether they are constructed for aboveground or belowground salinity measurements. Water is free to move between belowground and aboveground sections of wells. Water depth and belowground salinity are measured with Aquatroll 200 sensors (In-Situ Inc.). Aquatroll 200 sensors are suspended from the top of the well to a depth of approximately 12 ½ in (36 cm) below the soil surface. The belowground section of the belowground well has thin slits cut into it for water exchange (i.e., wellpoint pvc) while the aboveground section is made of common schedule 40 pvc that does not allow water to enter. Water depth sensors are programmed to assume a specific gravity of 0.999, and measurements are accurate for any water level above the depth that the belowground sensor hangs: -14 in (-36 cm), relative to the soil surface. Because the sensors are not vented to the atmosphere, water depth data must first be corrected for barometric pressure; we collect this information via Barotrolls (In-Situ Inc.). Barotrolls were deployed at the extreme upstream and downstream monitoring areas: Swamp 1 and Back 4, respectively. Water levels are corrected using data from the Barotroll that is closest, provided the closest sensor is functioning correctly. Once water depth data are pressure-corrected, they are post-processed to have zero represent the marsh/forest soil surface. For tidal freshwater forest areas the soil surfaces are the hollows, not hummocks (microtopographical high spots). The final post-processing involves addition/subtraction of a constant that is visually determined from hydrographs (of pressure-corrected data) made using SigmaPlot (ver. 12.5, Systat Software, Inc.). The hydrographs are used to identify changes in the rate at which water drains from the floodplain; water drains slower in a soil medium versus surface flooding. Aboveground salinity is measured with Aquatroll 100 sensors that are suspended to a depth of approximately 2 ½ in (6 cm) above the soil surface. The belowground sections of the aboveground monitoring wells are used primarily for support and stability because the area of interest lies entirely above the soil surface. These belowground sections are made of common schedule 40 pvc with holes drilled in them to allow some water exchange to occur belowground, and allow proper drainage. The aboveground portion of the aboveground monitoring well is made of wellpoint pvc to permit free exchange of water into the aboveground portion of the well. Aboveground sections of all wells extend approximately 4 ft (125 cm) above the soil surface, allowing them to remain above high water during most tidal cycles. Therefore, wells should be sufficiently high to allow interfacing with the sensors even during most high tides. Note that sensors will continue to operate accurately and safely even if wells are over-topped. However, interfacing protocols prevent opening the wells housing the belowground sensors if the estuary flood stage exceeds the tops of the wells. Tops of the wells are sealed with a locking cap, to which the suspension wires are attached. The locking caps help ensure better precision on water level readings between periods of interfacing with the sensors (e.g., when downloading data). 4

5 Hourly measurements from belowground sensors (Aquatroll 200) include: Pressure (PSI) Temperature ( C) Depth (cm) Specific Conductivity (µs) Salinity (PSU) Total Dissolved Solids (ppt) Hourly measurements from aboveground sensors (Aquatroll 100) include: Temperature ( C) Specific Conductivity (µs) Salinity (PSU) Total Dissolved Solids (ppt) Hourly measurements from barometer pressure sensors (Barotroll) include: Pressure (PSI) Temperature ( C) A B Figure 2. Water monitoring stations used to house In-Situ Aquatroll sensors. Separate wells are used aboveground and belowground water monitoring. Wells extend roughly 2 ½ - 12 ½ in (6 36 cm) below the soil surface (A). Holes are cut in the station platform to allow vegetation to grow up and stabilize the units (B). 5

6 Monitoring stations and the associated sensors were deployed to marsh locations on 02 April 2014, while tidal forest water monitoring stations and sensors were deployed on 01 August Locations of water monitoring stations within marsh areas were chosen to best represent the hydrologic conditions at each marsh area, often coinciding with the middle point of the middle transect (e.g., at Back 1: see Figure 9). Water monitoring stations within tidal forest areas were located between the two survey plots (see Figure 11). Results All aboveground salinity, belowground salinity, and depth sensors deployed in marsh monitoring areas operated successfully and accurately starting 02 April 2014, logging hourly through Data presented in this report end at 16 December 2014, coinciding with the most recent download. Sensors located at tidal freshwater forest monitoring areas began logging hourly on 01 August 2014, though the belowground sensor at Swamp 3 reported faulty data due to siltation of the belowground monitoring well. Therefore, we have no data to report for belowground salinity and water depth at Swamp 3 in year 2014; aboveground salinity measurements at Swamp 3 were not affected. The two barometric pressure sensors deployed in early April provided similar measurements up to a point in early May when the barometric pressure sensor at Back 4, the most downstream marsh monitoring area, was damaged. Because of the data loss associated with the Back 4 barometric pressure sensor, water depth readings from all sensors were corrected using barometric pressure data from the sensor located at Swamp 1, the most upstream tidal forest monitoring area. Discharge measured at the USGS water monitoring station near Clyo, GA was relatively high in January and February, compared to the 79-year median values (Figure 3). High flows at the Clyo gage likely contributed to the fairly fresh estuarine conditions during the early growing season (Figure 4), conditions that preceded the June synoptic marsh sample. Monthly and annual averages of aboveground and belowground salinity measured in each marsh/tidal forest monitoring area for the period of record are provided in Table 1. Hourly measurements of the water level at each of the monitoring areas were collected and postprocessed as described above. Lunar phases are detectable when viewing long time periods (Figure 5). Individual area hydrographs were made from data collected June 2014 in order to facilitate comparisons between marsh monitoring areas (Figures 6-8). This time period was chosen because it captures a full moon spring tide, making extremes in water levels easier to detect. Water levels during this time period were not unusually high, as compared to other times in 2014 for which we have site-specific data (see Figure 5). Unfortunately, water level data are not available for tidal freshwater forest monitoring areas during that time period; alternate time periods (e.g., October December) result in less pronounced hydrographs for marsh monitoring areas so they are not shown in this report. 6

7 Figure 3. Daily discharge rates (blue) versus daily average river discharge rate for the last 79 years (yellow) at USGS gage near Clyo, GA. 7

8 Figure 4. Average belowground salinity at marsh monitoring areas during the 2014 early growing season (02 April 30 June). These were soil salinities that preceded the June synoptic vegetation sample. 8

9 Table 1. Average, maximum, and minimum salinity (psu: practical salinity units) measured via sensors at above- and below-ground locations in (12) marsh monitoring areas and (3) tidal forest areas. Statistics based on hourly measurements starting 01 April 2014 for marsh areas, 01 August 2014 for tidal freshwater forest areas, and concluding 16 December 2014 (all). Measurements taken during dry well conditions were removed from calculations of summary statistics. Aboveground Salinity (psu) Belowground Salinity (psu) Area Month Avg. (std. err) Max Min Avg (std. err) Max Min Back 1 April 0.05 (0.00) (0.00) May 0.09 (0.01) (0.00) June 0.10 (0.01) (0.00) July 0.20 (0.01) (0.00) August 0.14 (0.02) (0.00) September 0.07 (0.01) (0.00) October 0.09 (0.03) (0.00) November 0.19 (0.04) (0.00) December 0.16 (0.01) (0.00) Annual 0.10 (0.01) (0.00) Back 2 April 0.05 (0.00) (0.00) May 0.14 (0.01) (0.00) June 0.18 (0.01) (0.00) July 0.16 (0.01) (0.00) August 0.12 (0.01) (0.00) September 0.12 (0.01) (0.00) October 0.11 (0.01) (0.00) November 0.15 (0.02) (0.00) December 0.23 (0.02) (0.00) Annual 0.13 (0.00) (0.00) Back 3 April 0.08 (0.00) (0.01) May 0.43 (0.05) (0.01) June 0.21 (0.03) (0.01) July 0.13 (0.02) (0.01) August 0.30 (0.04) (0.01) September 0.15 (0.01) (0.01) October 0.25 (0.03) (0.00) November 0.33 (0.05) (0.00) December 0.25 (0.04) (0.01) Annual 0.22 (0.01) (0.01)

10 Table 1 (cont d). Average, maximum, and minimum salinity at above- and below-ground locations in marsh and tidal freshwater forest monitoring areas. Aboveground Salinity (psu) Belowground Salinity (psu) Area Month Avg. (std. err) Max Min Avg (std. err) Max Min Back 3.5 April 0.12 (0.01) (0.02) May 0.38 (0.04) (0.02) June 0.35 (0.03) (0.01) July 0.25 (0.03) (0.01) August 0.55 (0.05) (0.02) September 0.42 (0.03) (0.01) October 0.52 (0.05) (0.01) November 0.46 (0.05) (0.00) December 0.36 (0.04) (0.00) Annual 0.41 (0.01) (0.01) Back 4 April 0.38 (0.03) (0.02) May 0.58 (0.05) (0.02) June 0.84 (0.07) (0.01) July 0.70 (0.06) (0.01) August 0.93 (0.07) (0.01) September 0.96 (0.06) (0.01) October 0.90 (0.07) (0.04) November 0.85 (0.08) (0.01) December 0.92 (0.10) (0.01) Annual 0.80 (0.02) (0.01) Front 1 April 0.04 (0.00) (0.00) May 0.16 (0.01) (0.00) June 0.18 (0.02) (0.00) July 0.14 (0.04) (0.00) August 0.14 (0.03) (0.00) September 0.10 (0.03) (0.00) October 0.07 (0.02) (0.01) November 0.14 (0.02) (0.00) December 0.20 (0.02) (0.00) Annual 0.13 (0.01) (0.00)

11 Table 1 (cont d). Average, maximum, and minimum salinity at above- and below-ground locations in marsh and tidal freshwater forest monitoring areas. Aboveground Salinity (psu) Belowground Salinity (psu) Area Month Avg. (std. err) Max Min Avg (std. err) Max Min Front 2 April 0.05 (0.00) (0.00) May 0.48 (0.05) (0.01) June 0.43 (0.05) (0.04) July 0.35 (0.04) (0.04) August 0.51 (0.05) (0.02) September 0.36 (0.03) (0.01) October 0.50 (0.05) (0.04) November 0.51 (0.06) (0.03) December 0.38 (0.05) (0.01) Annual 0.43 (0.02) (0.01) Middle 1 April 0.06 (0.00) (0.00) May 0.17 (0.03) (0.00) June 0.18 (0.03) (0.00) July 0.18 (0.05) (0.00) August 0.26 (0.05) (0.00) September 0.12 (0.02) (0.00) October 0.25 (0.04) (0.00) November 0.31 (0.05) (0.00) December 0.32 (0.05) (0.00) Annual 0.20 (0.01) (0.00) Middle 2 April 0.06 (0.01) (0.00) May 0.22 (0.04) (0.01) June 0.17 (0.04) (0.01) July 0.10 (0.04) (0.01) August 0.08 (0.03) (0.01) September n/a a n/a a n/a a 0.57 (0.01) October n/a a n/a a n/a a 0.78 (0.01) November 0.29 (0.09) (0.02) December 0.43 (0.07) (0.01) Annual 0.19 (0.02) (0.01) a Salinity was not measured due to lack of aboveground flooding. 11

12 Table 1 (cont d). Average, maximum, and minimum salinity at above- and below-ground locations in marsh and tidal freshwater forest monitoring areas. Aboveground Salinity (psu) Belowground Salinity (psu) Area Month Avg. (std. err) Max Min Avg (std. err) Max Min Middle 3 April 0.15 (0.02) (0.00) May 0.23 (0.08) (0.01) June n/a a n/a a n/a a 0.88 (0.01) July n/a a n/a a n/a a 1.03 (0.02) August n/a a n/a a n/a a 0.99 (0.03) September n/a a n/a a n/a a 1.02 (0.01) October n/a a n/a a n/a a 1.21 (0.02) November 0.46 (0.45) b (0.02) December 0.63 (0.19) b (0.01) Annual 0.33 (0.07) (0.01) Middle 4 April 0.06 (0.01) (0.02) May 0.23 (0.04) (0.02) June 0.48 (0.07) (0.02) July 0.32 (0.05) (0.02) August 0.62 (0.08) (0.02) September 0.45 (0.04) (0.01) October 0.34 (0.05) (0.01) November 0.60 (0.09) (0.01) December 0.64 (0.09) (0.00) Annual 0.42 (0.02) (0.01) Middle 5 April 0.14 (0.01) (0.01) May 0.26 (0.03) (0.01) June 0.33 (0.03) (0.01) July 0.28 (0.03) (0.01) August 0.44 (0.04) (0.01) September 0.46 (0.03) (0.00) October 0.46 (0.04) (0.01) November 0.47 (0.04) (0.00) December 0.47 (0.05) (0.00) Annual 0.39 (0.01) (0.00) a Salinity was not measured due to lack of aboveground flooding. b High standard errors coincide with field notes reporting extensive hog damage. 12

13 Table 1 (cont d). Average, maximum, and minimum salinity at above- and below-ground locations in marsh and tidal freshwater forest monitoring areas. Aboveground Salinity (psu) Belowground Salinity (psu) Area Month Avg. (std. err) Max Min Avg (std. err) Max Min Swamp 1 August 0.05 (0.00) (0.00) September 0.05 (0.00) (0.00) October 0.05 (0.00) (0.00) November 0.04 (0.00) (0.00) December 0.05 (0.00) (0.00) Annual 0.05 (0.00) (0.00) Swamp 2 August 0.13 (0.03) (0.00) September 0.08 (0.01) (0.00) October 0.12 (0.01) (0.00) November 0.11 (0.01) (0.00) December 0.12 (0.01) (0.00) Annual 0.11 (0.01) (0.00) Swamp 3 August 0.22 (0.04) n/a c n/a c n/a c September 0.11 (0.01) n/a c n/a c n/a c October 0.25 (0.03) n/a c n/a c n/a c November 0.26 (0.03) n/a c n/a c n/a c December 0.21 (0.03) n/a c n/a c n/a c Annual 0.20 (0.01) n/a c n/a c n/a c c Missing data resulting from monitoring well siltation. 13

14 Figure 5. Hydrographs (water levels) from the period of record (03 April 16 December, 2014) at the Back 3 marsh monitoring area. Zero depth represents marsh surface. Dashed vertical lines indicate the period of time used for comparing multiple areas (Figures 6 8). 14

15 Figure 6. Hydrographs of water levels at Back River marsh monitoring areas June Zero depth represents ground surface. 15

16 Figure 7. Hydrographs of water levels at Middle River marsh monitoring areas June Zero depth represents ground surface. 16

17 Figure 8. Hydrographs of water levels at Front River marsh monitoring areas June Zero depth represents ground surface. 17

18 Freshwater and Oligohaline Marsh Monitoring Please see Appendix B for Latin names of herbaceous (marsh) plants. Methods The twelve marsh monitoring areas encompass much of the expanse of oligohaline and freshwater tidal marsh area (within SNWR) that experiences natural tidally driven hydrologic fluctuations. The inner marsh habitat is the target for monitoring, as opposed to locations directly adjacent to tidal creeks. Therefore, in each area, three transects are laid out perpendicular to a tidal creek and extend into the inner marsh zone. Three vegetation sample collection points are located along each transect at distances of 65, 230, and 394 ft (20, 70, and 120 m) from the tidal creek (Figure 9), making for nine vegetation collection points per area and totaling 108 points throughout the SNWR. Figure 9. Three transects situated within the Back 1 area. Three sample points make up each transect. The water monitoring station is located at the vegetation collection point in the middle of the center transect. 18

19 Four synoptic marsh vegetation sample events were conducted in 2014: April, June, August, and October. Synoptic sample events typically lasted two to three days. One cardinal direction (north, south, east, or west) was chosen prior to the start of each synoptic sampling. The cardinal directions were different for each synoptic sample. Sampling protocol involved hiking to the collection point, marked with a pvc pole, turning the collector s back to the cardinal direction, and throwing a hoop behind their back approximately10-30 ft (3-10 m). The collector then went to where the hoop landed, laid the hoop flat on the soil surface, and cut the vegetation from the 2.69 ft 2 (0.25 m 2 ) circular areas. Vegetation was cut at the soil surface and placed in an individual bag for transport to the laboratory. Vegetation that was not attached to the soil (i.e., wrack) was not collected. Back at the laboratory, vegetation samples were then sorted by species, number of stems of each species were tallied, then each species (in each sample) was placed in a paper bag and oven dried at 212 F (100 C) for at least 5 days. Once all water was removed from the vegetation, dry weight of each species in each sample was measured. This methodology yielded data for a) the number of individual plants of each species and b) the collective dry weight of that species; these data were determined for each of 108 sample points. A method of representing the community composition at each sample point must be flexible to inherent differences of plot structure. For example, a sample may be comprised of many stems of small stature species with a relatively small collective biomass (e.g., sand spikerush). Conversely, a sample could include only a few stems of large stature species that have a large cumulative biomass (e.g., cattail). The oligohaline and freshwater marshes of the Savannah River floodplain have this structure. Importance values (IV; Curtis and McIntosh 1950) are an optimal way of dealing with large differences in structural diversity, while still accurately representing the importance of a species in a sample. Stem counts and dry weight data from synoptic samples of marsh vegetation were used in computing importance values for each species in each sample. Computational methodology for IV was similar to that of Curtis and McIntosh (1950), but with exclusion of the Relative Frequency term (see Kent and Coker 1992); this IV formulation is identical to that of prior analyses performed by Kitchens and others (e.g., Kitchens et al. 2003; Wetzel and Kitchens 2007): IV = (Relative Density + Relative Dominance)/2 Importance values were used as the basis of iterative community analyses performed on the June 2014 synoptic sample. The full suite of community analysis included: cluster analyses, outlier analyses, indicator species analyses, and ordinations; these analyses were performed using the software program PC-ORD version 6.0 (McCune and Mefford 2011). Hierarchical and agglomerative clustering of sample units was performed using a Sørensen distance measure in combination with a flexible beta (β = -0.25) linkage method (McCune and Grace 2002) to compute dissimilarity values (from IV using matrix algebra) for each species in each sample. The cluster analysis places each sample into one of several groups, with group size defined by the user. These groups are also commonly called clusters, and after the analyses are complete all members of a group are considered the same community. Therefore, groups can be thought of as either clusters and/or communities. Group memberships from the cluster analyses were used to classify samples during indicator species analyses and aid in interpreting ordinations. 19

20 Indicator species analyses (Dufrêne and Legendre 1997) were performed on varying numbers of groups. It is generally understood that there are at least five communities within the study area: a diverse mix with sand spikerush in the freshwater marshes, dense giant cutgrass near the creek banks and within some interior marsh zones, softstem bulrush dominated marshes, marshes containing softstem bulrush with considerable presence of other species, and marshes dominated by smooth cordgrass. It is also generally understood that the Savannah River marshes are fairly dynamic, changing in community composition in response to interstitial (belowground) salinity and other environmental variables. Indicator analyses based on groups sizes ranging from three to eight were performed, but results for fewer than five and greater than seven groups were not realistic and could not be interpreted logically. Therefore, only group sizes between five and seven were considered realistic. Non-metric multidimensional scaling ordinations were performed utilizing a Sørensen distance measure. Comparisons of final stress versus the number of axes indicated that the optimal dimensionality of the ordination was achieved using two axes; the final stress with a 2- dimensional solution was and final instability Three ordination graphs (five through seven groups) of samples in species space were generated, each depicting community membership of the samples at each group level, although only the final six-community scenario is presented in this report. Three outlier analyses were performed on the dataset. Outlier analyses included identification of species with a dissimilarity greater than two standard deviations from the average (n = 1 species, stiff marsh bedstraw), identification of species found in only 1 plot (n = 10 species), and identification of entire samples with an average distance greater than two standard deviations from the overall average (n = 5 samples). Per results of each outlier analysis, outliers were removed from the original (full) dataset and IV recomputed. The resultant three (modified) datasets were then used in the full suite of community analyses (i.e., cluster analysis, indicator species analysis, and ordination) and results compared to each other and results of analyses without species or samples removed. Care was taken to strongly consider removing outliers during multivariate analysis, especially since Wetzel and Kitchens (2007) and Kitchens et al. (2003) both removed species (species with occurrences in <1% and <5% of total number of plots, respective of each analysis). After comparing results of the analyses done on each dataset, it was determined that removal of outlier species and/or outlier plot samples did not change the interpretation of the results, so neither were removed from the dataset. Results presented herein represent analyses performed on the full dataset all species, all sample plots. Results Results of the analyses performed on the data obtained from the June 2014 synoptic marsh vegetation sampling event indicate that there were six discernable and interpretable marsh communities that existed throughout the monitoring points during the SHEP pre-construction phase. The six communities (Table 2) include: a fresh mix dominated by sand spikerush, a giant cutgrass dominated community, a softstem bulrush dominated community, a mix of softstem bulrush and several other common species, a cattail dominated community, and a community containing primarily softstem bulrush and smooth cordgrass. Indicator species analyses gave overall higher indicator values for species when six groups were considered, versus scenarios where either a greater or lesser number of groups/communities were considered. Ordination 20

21 graphs bolstered the notion of six communities by showing the greatest community separation when samples (n = 108) were broken into six groups, though some mixing is still evident in the Bulrush and Bulrush mix communities (Figure 10). A total of 54 species were identified throughout the 108 samples collected in the June 2014 synoptic sampling event. The greatest species richness was found in the Fresh mix, which averaged 12 species and reached as many as 19 species within a single 0.25 m 2 sample area. Table 2. The six marsh communities that existed within the 108 monitoring points in June Maximum species richness refers to the highest number of unique species found within a single 2.69 ft 2 (0.25 m 2 ) sample area. Average salinity reflects soil salinity conditions leading up the June 2014 synoptic marsh sampling event. 21

22 Figure 10. Non-metric multidimensional scaling ordination of Savannah River marsh monitoring samples (n = 108) in species space. Samples are separated into six groups and named (as communities) based on the dominant species, with the exception of the Cordgrass Community that was dominated by softstem bulrush; smooth cordgrass was the second most dominant species (See Table 2). 22

23 Tidal Forest Monitoring Please see Table 3 for Latin names of trees and shrubs. Methods Three tidal freshwater forest monitoring areas were established in October These areas experience daily tidal hydrological fluctuations, and have flora indicative of healthy tidal freshwater forest ecosystems, given some differences in tree species dominance. The tidal freshwater forest areas we are monitoring are named Swamp 1, Swamp 2, and Swamp 3 with numbers indicating increasing distance downstream (i.e., Swamp 3 is most downstream, and nearest the shipping port). Swamp 1 is the most upstream area, and has been monitored by cooperators of a Global Climate Change Research and Development project (GCC) led by USGS National Wetlands Research Center (NWRC); key cooperators include Jamie Duberstein and William Conner from Clemson University, and Ken Krauss and Nicole Cormier of the NWRC. Belowground salinity conditions at Swamp 1 have remained fresh (salinity < 0.2 ppt) since 2004, and tree growth has been consistently positive with no significant correlation to salinity between Two adjacent 65 x 82 ft = 5330 ft 2 (20 x 25 m = 0.05 ha) representative plots were established within each monitoring area (Figure 11). All plots are 132 ft (40 m) from tidal channels. Surveys of tidal freshwater forest monitoring plots were performed in October 2014, with the exception of shrubs (trees 3.93 in [10 cm]) at Swamp 1, which were inventoried in 2012 as part of the ongoing USGS GCC study; shrubs there show no signs of distress since that survey. Complete surveys for all areas included identification (to species, except ash) of all non-vine woody vegetation 4.5 ft (1.4 m) tall, with measurements of the bole/trunk diameter taken at breast height (dbh: 4.5 ft above the ground, or above the buttress when pertinent). Measurements were taken with handheld calipers, standard fiberglass d-tapes, and small 0.02 inch steel d-tapes, depending upon the size of the tree. All trees with dbh 3.94 in (10 cm) were fixed with an aluminum identification tag via an aluminum nail; dbh of tagged trees will be measured annually. Because baldcypress are found within all three tidal freshwater forest monitoring areas, ten representative baldcypress trees per plot were fit with dendrometer bands (Figure 12) to monitor growth. Dendrometer bands were installed on trees in Swamp 2 and Swamp 3 in November 2014, and circumference growth will be measured monthly. Note that a short settling period is often necessary to allow the band to become responsive to radial swelling (i.e., growth). Increases in circumference will be used to calculate basal area increments of that tree. Trees at Swamp 1 were previously fit with dendrometer bands as part of the USGS GCC project, and measurements have been made by project personnel at timescales of monthly, then later quarterly, starting in Because the USGS GCC dendrometer bands were nearly stretched to their maximums, trees at Swamp 1 were fit with an additional dendrometer band in December Overlaps in data between bands of the same tree are necessary to ensure a continuous record of growth. Banded trees were 8.15 dbh 22.4 in. Annual litter productivity is another measure of tidal forest health. Leaf and other tree debris (e.g., seed, bark, branches) are collectively referred to as tree litter. The USGS GCC project has collected tree litter measurements at Swamp 1 since 2005 using five litter traps within each 23

24 plot in an X-pattern (Figure 11). A litter trap is a 2.69 ft 2 (0.25 m 2 ) elevated box that collects leaves and stems (Figure 13). Litter is collected monthly, oven dried to stable weight, sorted by annual versus long term growth (e.g., bark and branches versus seeds and leaves), and weighed. Though litter has been collected at Swamp 1 at varying intervals since 2004, measurements of tree litter productivity for all three SHEP tidal freshwater forest monitoring areas will begin in February 2015 as all boxes are emptied at that time. Litter collected in boxes prior to February would be indicative of a small portion of the 2014 growth season, and data could be more misleading than informative. Litter traps will be monitored on a monthly basis beginning March Figure 11. Tidal freshwater forest monitoring area Swamp 2 (see Figure 1 for location). Each tidal freshwater forest monitoring area has two 65 x 82 ft (20 x 25 m) plots with five litter traps each (10 traps per area). Four pvc wells for synoptic salinity sampling are positioned on the outside corners of the plots. A water monitoring station and associated sensors is located between the two plots. 24

25 A B Figure 12. Dendrometer bands were installed on baldcypress trees (A) and will be used to measure monthly growth (B). Figure 13. A litter trap with a monthly sample. Results The data compiled from the three tidal freshwater forest sites were typical of healthy ecosystems. The dominant species were baldcypress, water tupelo, and swamp tupelo, and differences between the three monitoring areas exist as shifts in species dominance (see Table 3). Swamp 1 is dominated mostly by baldcypress, has nearly equal occupation by water tupelo, and the most swamp tupelo of the three areas (~ 17% of the basal area). Swamp 2 is also dominated by baldcypress, but has somewhat less basal area of swamp tupelo and no water tupelo. Total basal area for Swamp 2 is relatively low, averaging 49 m 2 /ha, versus 69 and 58 m 2 /ha for Swamp 1 and Swamp 3, respectively. Swamp 3 contains the fewest number of species (eight species in 2014), is dominated mostly by water tupelo with baldcypress co-dominant (but to a lesser extent than Swamp 1 and 2), and there are very few swamp tupelo. 25

26 Table 3. Community composition of the three tidal freshwater forest monitoring sites. Basal Area (m 2 /ha) Percent of Community (%) Latin Name Common Name Swamp 1 Swamp 2 Swamp 3 Swamp 1 Swamp 2 Swamp 3 Acer rubrum L. red maple Alnus serrulata (Ait.) Willd. hazel alder Baccharis halimifolia L. eastern baccharis Betula nigra L. river birch Carpinus caroliniana Walt. American hornbeam Cephalanthus occidentalis L. buttonbush Cornus foemina P. Mill. swamp dogwood Fraxinus spp. ash Ilex decidua Walt. deciduous holly Liquidambar styraciflua L. sweetgum Morella cerifera (L.) Small waxmyrtle Nyssa aquatica L. water tupelo Nyssa biflora Walt. swamp tupelo Persea palustris (Raf.) Sarg. swamp bay Planera aquatica J.F. Gmel. water elm Quercus laurifolia Michx. laurel oak Quercus nigra L. water oak Taxodium distichum (L.) L.C. Rich. baldcypress Triadica sebifera (L.) Small Chinese tallow Ulmus americana L. American elm Viburnum dentatum L. arrowwood Total

27 Vegetation Community Classification from Remote Sensing Imagery Remote sensing (RS) has been used in ecology for more than two decades (Cohen and Goward 2004), and introduced an important, extensive source of data for land cover change analyses. Using traditional pixel-based methods and medium-resolution imagery, ecological RS has been used to classify landscapes into discrete land cover types, track landscape changes over time, and analyze ecosystem structure and function (see Cohen and Goward 2004 for a review). However, tracking subtle community changes along a salinity gradient can be difficult due to similar phenologies and spectral signatures (Miao et al. 2007, Newton et al. 2009). Newer, more advanced RS techniques are being introduced that make these types of classifications more feasible. We use one of these advanced techniques, texture analysis, along with a pixel-based analysis to augment and expand the current field effort by providing a pre-construction, landscape-level classification of vegetation communities along a salinity gradient. Through this classification it is theoretically possible to infer river salinities from the vegetation zones on a larger scale than previously available through field sample analyses. Methods The World View 2 satellite was used to acquire imagery from within the SNWR boundaries on 01 June 2014 (Figure 14). Imagery included 8-band multispectral satellite imagery with 6.56 ft (2 m) resolution, and panchromic satellite imagery with 20 in (50 cm) resolution. The multispectral image was pan-sharpened to 20 in (50 cm) resolution using ERDAS Imagine (Hexagon Spatial, Norcross, Georgia, USA). We used Feature Analyst (Overwatch Systems, Austin, Texas, USA) extension for ArcGIS 10.1 (ESRI, Redlands, California, USA) to classify the World View 2 imagery. Feature Analyst is an ArcGIS add-on that uses machine learning algorithms (neural networks and genetic algorithms) along with texture and spectral characteristics to classify user-defined categories of land cover. It integrates manual and task-specific automated approaches through an iterative cycle of automated modeling and correction by the user (Blundell and Opitz 2006). We supplied at least 10 training polygons for each class that included the characteristic spectral and texture characteristics of that class. We used field data for training polygons as well as areas that were delineated by an expert with knowledge of the area and its vegetation dynamics. Feature Analyst allows an initial training and then subsequent re-training, where the accuracy of each class (indicating which polygons are correct and which are incorrect) can be assessed and re-classified, refining the current model. After the first iteration, we realized that a communitylevel classification, visually, had a very high error rate and subsequent iterations would not likely improve the classification at that level. However, when vegetation type was color-coded by salinity tolerance (Table 4), a pattern was evident along the river (Figure 15). To enhance that pattern, we clustered the data using class information in surrounding pixels with a k-means unsupervised classification in ERDAS Imagine to group the species patches into three salinity zones: fresh, oligohaline, and mesohaline. This provided a generalization of the detailed species classification and reduced the amount of error from small patches that were misclassified. We performed an accuracy analysis of the three salinity zones using field points obtained in October 2014, generally classifying those points into the plant communities from the field data analysis of the June 2014 synoptic marsh sample (see Table 2). 27

28 Results Results from the initial classification, re-class, and generalizing classification were generally good (Figure 16). The overall accuracy of classifying the three salinity zones (fresh, oligohaline, and mesohaline) was 78% (Table 5). The textural and spectral characteristics of the SNWR vegetation communities are very similar, and it was difficult to distinguish them, even with 20 in (50 cm) resolution imagery. The community level classifications were very poor, but when grouped in a cluster analysis, the RS analysis was able to provide a landscape-level map of salinity zones with good accuracy (78%). While the analysis was difficult, the resulting product is very important to distinguish changes in vegetation zones along the Savannah River. The ability to assess changes at landscape level is critical during construction activities. Table 4: Reclassification table for salinity tolerances of vegetation species/communities along the Savannah National Wildlife Refuge. 0 = least tolerant, 5 = most tolerant. Initial class Salinity tolerance class Species/Community 0 4 bulrush 1 1 fresh mix 2 1 pickerelweed 3 1 shrub 4 5 cordgrass 5 1 trees 6 3 cattail 7 2 cutgrass 8 0 river (water) Table 5: Confusion matrix and accuracy analysis for salinity zone map of Savannah National Wildlife Refuge. Confusion matrix lists how many pixels were classified properly by class. Producer s accuracy represents the probability of a reference pixel being classified correctly; user s accuracy is the probability that a pixel classified on the map accurately represents the current vegetation configuration. Field Accuracy Image Raster Fresh Oligohaline Mesohaline Row Total Overall Producers Users Fresh % 88% 77% Oligohaline % 76% Mesohaline % 84% Column Total

29 Figure 14: Pan-sharpened World View 2 satellite image of Savannah National Wildlife Refuge (true color). Clouds are visible at bottom of image. 29

30 Figure 15: Reclassified image of World View 2 satellite image of Savannah National Wildlife Refuge. An upstream/downstream salinity gradient is evident in the vegetation pattern. 30

31 Figure 16: Generalized vegetation zones from remote sensing analysis of Savannah National Wildlife Refuge. 31

32 Literature Cited Blundell J, Opitz D (2006) Object recognition and feature extraction from imagery: The Feature Analyst approach. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36: C42. Cohen WB, Goward SN (2004) Landsat's role in ecological applications of remote sensing. BioScience 54: Curtis JT, McIntosh RP (1950) The interrelations of certain analytic and synthetic phytosociological characters. Ecology 31: Dufrêne M, Legendre P (1997) Species assemblages and indicator species: The need for a flexible asymmetrical approach. Ecological Monographs 67: Kent M, Coker P (1992) Vegetation description and analysis a practical approach. West Sussex, England: John Wiley & Sons Ltd. Kitchens WM, Dusek ML, Brush JM, Wetzel P, Malloy K, Dusek R, Graves C, Berryman SD, Webb R, Duberstein J, Loftin CS, McCloskey JR (2003) Tidal wetland resource utilization studies. Final report to the United States Army Corps of Engineers. Miao X, Gong P, Swope S, Pu R, Carruthers R, Anderson GL (2007) Detection of yellow starthistle through band selection and feature extraction from hyperspectral imagery. Photogrammetric Engineering and Remote Sensing 73: McCune B, Grace JB (2002) Analysis of ecological communities. MjM Software Design. Gleneden Beach, OR, USA. McCune B, Mefford MJ (2011) PC-ORD. Multivariate analysis of ecological data. Version 6.0. MjM Software Inc. Gleneden Beach, OR, USA. Newton AC, Hill RA, Echeverría C, Golicher D, Benayas JMR, Cayuela L, Hinsley SA (2009) Remote sensing and the future of landscape ecology. Progress in Physical Geography 33: Wetzel PR, Kitchens WM (2007) Vegetation change from chronic stress events: Detection of the effects of tide gate removal and long-term drought on a tidal marsh. Journal of Vegetation Science 18:

33 Appendix A Locations of Savannah River marsh monitoring sample points and swamp monitoring areas. Datum is WGS84. Coordinates are in the UTM system. Marsh Sample Point Zone Easting Northing Status Water Monitor? BACK 1 2A 17S Historic BACK 1 2B 17S Historic BACK 1 2C 17S Historic BACK 1 4A 17S Historic BACK 1 4B 17S Historic Yes BACK 1 4C 17S Historic BACK 1 5A 17S Historic BACK 1 5B 17S Historic BACK 1 5C 17S Historic BACK 2 2A 17S Historic BACK 2 2B 17S Historic BACK 2 2C 17S Historic BACK 2 3A 17S Historic BACK 2 3B 17S Historic Yes BACK 2 3C 17S Historic BACK 2 4A 17S Historic BACK 2 4B 17S Historic BACK 2 4C 17S Historic BACK 3 2A 17S Historic BACK 3 2B 17S Historic BACK 3 2C 17S Historic BACK 3 3A 17S Historic BACK 3 3B 17S Historic Yes BACK 3 3C 17S Historic BACK 3 4A 17S Historic BACK 3 4B 17S Historic BACK 3 4C 17S Historic BACK 3.5 1A 17S New BACK 3.5 1B 17S New Yes BACK 3.5 1C 17S New BACK 3.5 2A 17S New BACK 3.5 2B 17S New BACK 3.5 2C 17S New BACK 3.5 3A 17S New BACK 3.5 3B 17S New BACK 3.5 3C 17S New BACK 4 2A 17S Historic BACK 4 2B 17S Historic BACK 4 2C 17S Historic 33

34 Appendix A Marsh Sample Point Zone Easting Northing Status Water Monitor? BACK 4 5A 17S Historic BACK 4 5B 17S Historic BACK 4 5C 17S Historic BACK 4 6A 17S Historic BACK 4 6B 17S Historic Yes BACK 4 6C 17S Historic FRONT 1 1A 17S Historic FRONT 1 1B 17S Historic Yes FRONT 1 1C 17S Historic FRONT 1 2A 17S Historic FRONT 1 2B 17S Historic FRONT 1 2C 17S Historic FRONT 1 3A 17S Historic FRONT 1 3B 17S Historic FRONT 1 3C 17S Historic FRONT 2 1A 17S New FRONT 2 1B 17S New FRONT 2 1C 17S New FRONT 2 2A 17S New FRONT 2 2B 17S New Yes FRONT 2 2C 17S New FRONT 2 3A 17S New FRONT 2 3B 17S New FRONT 2 3C 17S New MIDDLE 1 2A 17S Historic MIDDLE 1 2B 17S Historic Yes MIDDLE 1 2C 17S Historic MIDDLE 1 5A 17S Historic MIDDLE 1 5B 17S Historic MIDDLE 1 5C 17S Historic MIDDLE 1 6A 17S Historic MIDDLE 1 6B 17S Historic MIDDLE 1 6C 17S Historic MIDDLE 2 3A 17S Historic MIDDLE 2 3B 17S Historic MIDDLE 2 3C 17S Historic MIDDLE 2 4A 17S Historic MIDDLE 2 4B 17S Historic Yes MIDDLE 2 4C 17S Historic MIDDLE 2 5A 17S Historic MIDDLE 2 5B 17S Historic MIDDLE 2 5C 17S Historic 34

35 Appendix A Marsh Sample Point Zone Easting Northing Status Water Monitor? MIDDLE 3 1A 17S New MIDDLE 3 1B 17S New MIDDLE 3 1C 17S New MIDDLE 3 2A 17S New MIDDLE 3 2B 17S New Yes MIDDLE 3 2C 17S New MIDDLE 3 3A 17S New MIDDLE 3 3B 17S New MIDDLE 3 3C 17S New MIDDLE 4 1A 17S New MIDDLE 4 1B 17S New Yes MIDDLE 4 1C 17S New MIDDLE 4 2A 17S New MIDDLE 4 2B 17S New MIDDLE 4 2C 17S New MIDDLE 4 3A 17S New MIDDLE 4 3B 17S New MIDDLE 4 3C 17S New MIDDLE 5 1A 17S New MIDDLE 5 1B 17S New MIDDLE 5 1C 17S New MIDDLE 5 2A 17S New MIDDLE 5 2B 17S New Yes MIDDLE 5 2C 17S New MIDDLE 5 3A 17S New MIDDLE 5 3B 17S New MIDDLE 5 3C 17S New Swamp Area Zone Easting Northing Status Water Monitor? Swamp 1 17S New Yes Swamp 2 17S New Yes Swamp 3 17S New Yes 35