# Waccamaw/Savannah River Wetlands Late Holocene Multiproxy Sediment Data #----------------------------------------------------------------------- # World Data Service for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program # National Centers for Environmental Information (NCEI) #----------------------------------------------------------------------- # Template Version 3.0 # Encoding: UTF-8 # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/26690 # Description: NOAA Landing Page # Online_Resource: https://www1.ncdc.noaa.gov/pub/data/paleo/paleolimnology/northamerica/usa/southcarolina/waccamaw2017pollen11-11-3-1.txt # Description: NOAA location of the template # # Original_Source_URL: # Description: # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleolimnology # # Dataset DOI: # # Parameter_Keywords: geochemistry, physical properties, population abundance #-------------------- # Contribution_Date # Date: 2019-05-01 #-------------------- # File_Last_Modified_Date # Date: 2019-05-01 #-------------------- # Title # Study_Name: Waccamaw/Savannah River Wetlands Late Holocene Multiproxy Sediment Data #-------------------- # Investigators # Investigators: Jones, M.C.; Bernhardt, C.E.; Krauss, K.W.; Noe, G.B. #-------------------- # Description_Notes_and_Keywords # Description: Multiproxy (pollen, plant macrofossils, sediment accretion, and carbon accumulation) data from river wetlands sediment cores. # Cores are from 2 transects ranging from tidal freshwater forested wetlands (TFFW) to oligohaline marsh, along the Waccamaw and Savannah # Rivers (South Carolina and Georgia, USA) for the late Holocene (~6,000 - 1,500 years BP). #-------------------- # Publication # Authors: Miriam C. Jones, Christopher E. Bernhardt, Ken W. Krauss, Gregory B. Noe # Published_Date_or_Year: 2017-12-01 # Published_Title: The Impact of Late Holocene Land Use Change, Climate Variability, and Sea Level Rise on Carbon Storage in Tidal Freshwater Wetlands on the Southeastern United States Coastal Plain # Journal_Name: Journal of Geophysical Research Biogesciences # Volume: 122 # Edition: # Issue: 12 # Pages: 3126-3141 # Report_Number: # DOI: 10.1002/2017JG004015 # Online_Resource: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017JG004015 # Full_Citation: # Abstract: This study examines Holocene impacts of changes in climate, land use, and sea level rise (SLR) on sediment accretion, carbon accumulation rates (CAR), and vegetation along a transect of tidal freshwater forested wetlands (TFFW) to oligohaline marsh along the Waccamaw River, South Carolina (four sites) and along the Savannah River, Georgia (four sites). We use pollen, plant macrofossils, accretion, and CAR from cores, spanning the last 1,500-6,000 years to test the hypothesis that TFFW have remained stable throughout the late Holocene and that marshes transitioned from TFFW during elevated SLR during the Medieval Climate Anomaly, with further transformation resulting from colonial land use change. Results show low and stable accretion and CAR through much of the Holocene, despite moderate changes associated with Holocene paleoclimate. In all records, the largest observed change occurred within the last ~400 years, driven by colonial land clearance, shifting terrigenous sediment into riparian wetlands, resulting in order-of-magnitude increases in accretion and C accumulation. The oligohaline marshes transitioned from TFFW ~300-500 years ago, coincident with colonial land clearance. Postcolonial decreases in CAR and accretion occur because of watershed reforestation over the last century. All sites show evidence of recent (decades to century) swamp forest decline due to increasing salinity and tidal inundation from SLR. This study suggests that allochthonous sediment input during colonialization helped maintain TFFW but that current SLR rates are too high for TFFW to persist, although higher accretion rates in oligohaline marshes increase the resilience of tidal wetlands as they transition from TFFW to marsh. #------------------ # Funding_Agency # Funding_Agency_Name: United States Geological Survey # Grant: Climate and Land Use Change R&D #------------------ # Site_Information # Site_Name: Waccamaw11-11-3-1 # Location: North America>United States Of America>Georgia # Country: United States Of America # Northernmost_Latitude: 33.422823 # Southernmost_Latitude: 33.422823 # Easternmost_Longitude: -79.207996 # Westernmost_Longitude: -79.207996 # Elevation: #------------------ # Data_Collection # Collection_Name: Waccamaw11-11-3-1pollen # Earliest_Year: 1030 # Most_Recent_Year: -61 # Time_Unit: Cal. Year BP # Core_Length: 0.86 # Notes: Butler Island, moderately salt impacted TFFW (tidal freshwater forested wetlands). Percent compressed 37.1; original depth 140 #------------------ # Chronology_Information # Chronology: # Lab_ID depth_cm age_14C 14C error Material dated # WW9340 9-10 >Modern seeds # WW9341 24-25 185 30 seeds # Beta-353945 31-32 220 30 Bulk organic, picked free of roots # Beta-352947 39-40 40* 30 Bulk organic, picked free of roots * date rejected # WW9342 51-52 520 30 seeds # WW9343 74-75 1180 30 seeds # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: one per line, shortname-tab-variable components (what, material, error, units, seasonality, data type,detail, method, C or N for Character or Numeric data, free text) # ## depth_cm depth, , , cm, , , , ,C, Depths are midpoints ## Pinus Pinus, sediment, , count, ,Pollen,,,N, ## Quercus Quercus, sediment, , count, ,Pollen,,,N, ## Carya Carya, sediment, , count, ,Pollen,,,N, ## Liquidambar Liquidambar, sediment, , count, ,Pollen,,,N, ## Celtis Celtis, sediment, , count, ,Pollen,,,N, ## Myrica Myrica, sediment, , count, ,Pollen,,,N, ## Salix Salix, sediment, , count, ,Pollen,,,N, ## Nuphar Nuphar, sediment, , count, ,Pollen,,,N, ## Nyssa Nyssa, sediment, , count, ,Pollen,,,N, ## Magnolia Magnolia, sediment, , count, ,Pollen,,,N, ## Ilex Ilex, sediment, , count, ,Pollen,,,N, ## Amaranthaceae Amaranthaceae, sediment, , count, ,Pollen,,,N, ## Poaceae Poaceae, sediment, , count, ,Pollen,,,N, ## Ambrosia Ambrosia, sediment, , count, ,Pollen,,,N, ## Asteraceae indet Asteraceae indet, sediment, , count, ,Pollen,,,N, ## TCT TCT, sediment, , count, ,Pollen,,,N, ## Ericaceae Ericaceae, sediment, , count, ,Pollen,,,N, ## Cyperaceae Cyperaceae, sediment, , count, ,Pollen,,,N, ## Nymphaea Nymphaea, sediment, , count, ,Pollen,,,N, ## Typha Typha, sediment, , count, ,Pollen,,,N, ## Fabaceae Fabaceae, sediment, , count, ,Pollen,,,N, ## Apiaceae Apiaceae, sediment, , count, ,Pollen,,,N, ## Myriophyllum Myriophyllum, sediment, , count, ,Pollen,,,N, ## Sagittaria Sagittaria, sediment, , count, ,Pollen,,,N, ## Polygonaceae Polygonaceae, sediment, , count, ,Pollen,,,N, ## Triporate Triporate, sediment, , count, ,Pollen,,,N, ## Tricolporate Tricolporate, sediment, , count, ,Pollen,,,N, ## Tricolpate Tricolpate, sediment, , count, ,Pollen,,,N, ## Trilete Trilete, sediment, , count, ,Pollen,,,N, ## Monolete Monolete, sediment, , count, ,Pollen,,,N, ## Mint Mint, sediment, , count, ,Pollen,,,N, ## Osmunda Osmunda, sediment, , count, ,Pollen,,,N, ## Crumpled Crumpled, sediment, , count, ,Pollen,,,N, ## Betula Betula, sediment, , count, ,Pollen,,,N, ## Ulmus Ulmus, sediment, , count, ,Pollen,,,N, ## Fraxinus Fraxinus, sediment, , count, ,Pollen,,,N, ## Acer Acer, sediment, , count, ,Pollen,,,N, ## Alnus Alnus, sediment, , count, ,Pollen,,,N, ## Juglans Juglans, sediment, , count, ,Pollen,,,N, ## Ostrya/Carpinus Ostrya/Carpinus, sediment, , count, ,Pollen,,,N, ## Rosaceae Rosaceae, sediment, , count, ,Pollen,,,N, ## Coylus Coylus, sediment, , count, ,Pollen,,,N, ## Ipomea Ipomea, sediment, , count, ,Pollen,,,N, ## Liriodendron Liriodendron, sediment, , count, ,Pollen,,,N, ## Parthenocissus Parthenocissus, sediment, , count, ,Pollen,,,N, ## Onagraceae Onagraceae, sediment, , count, ,Pollen,,,N, ## Fagus Fagus, sediment, , count, ,Pollen,,,N, ## Juglans Juglans, sediment, , count, ,Pollen,,,N, ## Pontedaria Pontedaria, sediment, , count, ,Pollen,,,N, ## Castanea-like Castanea-like, sediment, , count, ,Pollen,,,N, ## Tsuga Tsuga, sediment, , count, ,Pollen,,,N, ## Plantus Plantus, sediment, , count, ,Pollen,,,N, ## Total Total, sediment, , count, ,Pollen,,,N, ## Weight (g) Weight, sediment, , g, ,Pollen,,,N, ## Lycopodium (exotic) Lycopodium (exotic), sediment, , count, ,Pollen,,,N, # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # depth_cm Pinus Quercus Carya Liquidambar Celtis Myrica Salix Nuphar Nyssa Magnolia Ilex Amaranthaceae Poaceae Ambrosia Asteraceae indet TCT Ericaceae Cyperaceae Nymphaea Typha Fabaceae Apiaceae Myriophyllum Sagittaria Polygonaceae Triporate Tricolporate Tricolpate Trilete Monolete Mint Osmunda Crumpled Betula Ulmus Fraxinus Acer Alnus Juglans Ostrya/Carpinus Rosaceae Coylus Ipomea Liriodendron Parthenocissus Onagraceae Fagus Juglans Pontedaria Castanea-like Tsuga Plantus Total weight (g) Lycopodium (exotics) 0-1 110 28 4 11 0 16 0 0 21 0 0 7 21 1 80 0 0 0 0 3 0 1 0 2 1 0 1 1 0 5 0 1 0 0 4 0 1 6 0 0 0 0 0 0 0 2 1 0 0 0 0 2 330 4.45 4 3-4 141 19 11 14 0 10 0 0 7 0 0 3 15 3 77 0 0 1 0 4 0 0 0 1 7 0 1 1 1 2 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 5 0 0 0 1 0 0 326 2.67 5 5-6 80 24 1 8 0 12 0 0 16 0 1 4 20 4 106 0 0 0 0 4 0 4 0 0 7 3 1 1 0 0 0 0 0 0 1 0 0 3 0 0 1 0 0 0 0 5 0 0 0 0 0 0 306 4.52 6 8-9 152 9 8 13 0 5 0 0 22 0 0 5 18 1 30 0 1 1 0 14 0 1 0 1 14 0 3 2 3 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 307 0.96 32 10-11 76 24 4 6 0 18 0 0 10 0 0 4 34 3 40 0 0 1 1 20 2 2 0 4 3 0 0 3 0 1 0 0 2 0 0 1 2 69 0 1 1 0 0 0 0 1 0 0 0 0 0 0 333 2 8 13-14 139 11 5 6 0 7 0 0 12 1 0 12 13 4 30 0 0 4 0 22 0 3 0 0 1 2 2 2 0 3 0 1 3 0 1 1 0 10 1 2 2 0 0 0 0 0 0 0 0 0 0 0 300 1.12 44 15-16 72 9 2 1 0 7 0 0 3 0 0 1 14 6 25 0 0 1 0 4 0 0 0 1 2 0 1 2 0 2 0 1 0 0 1 0 0 142 0 0 0 0 0 0 0 0 0 0 0 0 1 0 298 5.12 7 18-19 164 8 3 2 0 3 0 0 8 0 0 1 13 9 77 0 0 5 0 0 0 0 0 5 7 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 307 4.52 3 20-21 150 14 3 3 0 2 0 0 5 1 0 1 43 9 47 1 0 10 0 1 0 2 0 3 1 0 0 2 0 0 0 0 0 0 3 0 1 2 0 0 0 0 0 2 0 0 0 0 3 0 1 0 310 6.98 1 23-24 192 6 3 4 0 7 0 0 5 0 0 0 16 6 55 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 300 5.07 4 25-26 131 10 1 1 0 1 0 0 4 6 0 1 51 7 72 0 0 8 0 1 0 0 0 1 1 0 1 2 0 1 2 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 306 7.06 1 28-29 111 10 2 2 0 3 0 0 6 0 0 0 61 7 119 0 0 1 0 0 0 0 0 4 1 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 330 5 1 30-31 97 5 1 4 0 1 1 0 4 0 0 1 130 2 25 0 0 1 0 1 0 0 0 4 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 280 6.99 2 33-34 169 3 1 4 0 0 0 0 7 0 0 0 55 2 72 0 0 1 0 0 0 0 0 6 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 323 5.06 4 35-36 106 7 1 1 0 1 0 0 4 0 1 2 135 6 63 0 0 2 0 1 0 0 0 5 0 0 0 0 0 5 0 0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 1 0 0 0 346 6 2 38-39 92 7 4 4 0 3 0 0 18 0 0 0 71 4 71 0 0 0 0 0 0 0 0 11 3 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 292 7.31 1 40-41 58 8 0 1 0 1 0 0 4 0 0 3 137 2 30 0 0 3 0 0 1 0 0 1 0 0 1 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 254 7.27 2 43-44 75 3 1 1 0 2 0 0 19 0 0 5 145 2 72 0 0 0 0 1 0 0 0 4 1 1 1 0 0 1 0 0 1 0 0 0 1 3 0 0 0 0 0 0 0 0 0 0 2 0 0 0 341 7.24 1 45-46 60 10 1 0 0 1 0 0 5 0 0 5 175 4 34 0 0 4 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 302 7.2 1 48-49 135 4 8 5 0 2 0 0 42 0 1 1 91 1 13 0 2 1 0 0 0 0 0 0 2 0 0 0 0 1 0 0 0 0 4 0 0 0 0 1 0 0 0 0 2 0 0 0 0 0 0 0 316 50-51 153 15 7 2 0 2 0 0 58 0 1 2 61 3 6 1 0 3 0 0 0 0 0 0 0 0 0 0 0 3 0 0 3 1 0 2 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 326 10.09 2 53-54 94 9 4 0 0 2 0 0 63 0 0 1 103 3 7 0 0 1 0 1 0 0 0 0 2 1 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 294 10.07 1 55-56 130 16 1 0 0 2 0 0 49 0 0 2 78 1 5 0 0 5 0 0 0 0 0 0 1 0 0 1 0 1 0 0 4 0 0 2 2 2 1 3 0 0 0 0 1 1 0 0 0 0 0 0 308 10.09 2 58-59 129 9 7 5 0 5 0 0 51 0 0 1 106 4 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 2 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 328 10.04 1 60-61 143 14 4 1 0 6 0 0 61 1 0 2 54 3 4 1 0 6 0 0 0 0 0 0 2 1 0 1 0 1 0 1 2 1 1 3 1 5 0 1 0 0 0 2 1 1 0 0 0 3 0 0 327 10.12 1 63-64 100 7 5 3 0 2 0 0 86 0 1 0 85 0 3 0 0 2 0 0 1 0 0 0 4 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 303 10 1 65-66 156 18 11 2 0 4 0 0 73 1 1 2 27 0 7 0 0 3 0 0 0 0 0 1 1 0 0 0 0 2 0 2 1 0 0 1 1 5 0 2 0 0 0 1 0 0 0 0 0 0 0 0 322 9.94 1 68-69 170 12 10 2 0 0 0 0 72 0 0 2 23 1 10 0 0 6 0 2 1 0 0 0 1 0 0 1 0 0 0 0 3 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 320 3.87 2 70-71 165 20 2 3 0 3 0 0 53 0 0 4 24 0 32 0 0 4 0 0 1 0 0 0 4 0 0 2 0 0 0 1 2 0 0 2 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 328 3.98 4 73-74 204 19 5 4 0 3 0 0 29 0 0 2 15 1 23 0 1 1 0 2 3 0 0 1 2 0 1 0 1 0 0 0 3 0 0 2 1 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 325 3.95 5 75-76 174 18 8 6 0 4 0 0 25 0 0 4 23 0 31 0 0 0 0 0 1 0 0 0 2 2 0 0 0 1 0 0 2 0 0 0 0 6 0 1 1 0 1 0 0 0 0 0 1 0 0 0 311 5 1 78-79 217 15 11 3 0 3 0 0 27 0 0 4 11 0 22 0 0 3 0 1 1 0 0 0 2 0 1 0 0 2 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 326 3.98 1 80-81 186 33 8 9 0 6 0 0 26 0 0 6 29 1 26 1 0 2 0 0 0 0 0 1 1 2 0 3 0 1 0 0 0 0 3 2 1 6 0 0 0 0 0 0 0 0 1 0 1 0 0 0 355 5.01 1 83-84 177 21 6 7 0 3 0 0 30 0 1 3 31 0 27 0 0 3 0 1 0 0 0 3 0 0 0 0 0 1 0 0 0 0 1 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 318 4.04 6 85-86 137 33 8 3 0 1 0 0 22 0 0 6 36 1 46 0 0 1 0 1 2 1 0 0 0 0 1 3 0 1 0 0 0 1 2 1 0 6 0 0 0 0 0 0 1 0 0 1 2 0 0 0 317 4.99 3 86-88 146 34 6 5 0 6 0 0 16 0 1 5 45 1 32 0 0 0 0 0 1 0 0 0 0 1 2 2 0 4 0 0 2 0 4 0 0 8 0 1 0 0 0 0 0 0 0 0 2 0 1 0 325 7.11 2