# Lac Noir, Quebec 1,000 Year Pollen Data and Climate Reconstructions #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # 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: http://ncdc.noaa.gov/paleo/study/18735 # # Original_Source_URL: ftp://ftp.ncdc.noaa.gov/pub/data/paleo/paleolimnology/northamerica/canada/pq/lacnoir2013ms.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleolimnology #-------------------- # Contribution_Date # Date: 2015-03-20 #-------------------- # Title # Study_Name: Lac Noir, Quebec 1,000 Year Pollen Data and Climate Reconstructions #-------------------- # Investigators # Investigators: Paquette, N.; Gajewski, K. #-------------------- # Description_and_Notes # Description: High-resolution (decadal scale) pollen and sediment physical properties record from Lac Noir, southwestern Quebec, # spanning the past 1,000 years. Chronology is based on varve counts except for the upper 6.5 cm, which is based on 210Pb. # To calculate the pollen percentages, use the basic sum, which is situated at the end of the pollen counts. Aquatics were # calculated separately, use the column named Aquatics and Basic sum to calculate aquatic %. To calculate pollen influx, # calculate (pollen concentration * interval thickness)/years per sample, situated beside the sums. The formulas for # calculating concentration and influx are also provided in the charcoal tab. # # Additional Reference: Paquette, N., 2012. # Climatic change causes abrupt shifts in forests, inferred from a high-resolution lacustrine record, Southwestern Quebec, Canada. # MSc, University of Ottawa, Ontario, Canada. # # Lac Noir Water Chemistry Data # Depth (m) temp (°C) Conductivity (µmHOS) pH DO (mg/L) Saturation level(DO) % DO # 0 21 72 8.2 8.6 8.9 96.63 # 2 17 72 8.3 9.65 86.01 # 4 12 60 8.2 10.76 76.21 # 6 7 60 5.8 12.12 47.85 # 8 6 58 3.5 12.43 28.16 # 10 5.4 58 1.2 12.6 9.52 # 12 4 0.7 13.09 5.35 # 14 3.9 0.4 13.09 3.06 # 16 4.1 6.9 0.5 13.09 3.82 # # #-------------------- # Publication # Authors: Nathalie Paquette, Konrad Gajewski # Published_Date_or_Year: 2013-09-01 # Published_Title: Climatic change causes abrupt changes in forest composition, inferred from a high-resolution pollen record, southwestern Quebec, Canada # Journal_Name: Quaternary Science Reviews # Volume: 75 # Edition: # Issue: # Pages: 169-180 # DOI: 10.1016/j.quascirev.2013.06.007 # Online_Resource: # Full_Citation: # Abstract: A pollen profile from a lake with varved sediments sampled at continuous 10-year intervals and spanning the past 1000 years was analyzed to understand the effects of climate change and anthropogenic activity on forests in southwestern Quebec. Pollen assemblages were dominated by arboreal taxa, primarily Pinus, Tsuga, Betula and Fagus. Between 990 and 1560 AD, pollen accumulation rates and percentages of hardwoods (Betula, Fagus, Acer, Ulmus, Tilia) and Tsuga were relatively high. At 1560 AD, PARs of many hardwood taxa (Fagus, Acer, Betula, Fraxinus, Ulmus) and Tsuga abruptly decreased, some remaining low for the remainder of the record (Tsuga, Fagus, Acer), but others increasing after 50 years (Betula, Fraxinus). An increase in non-arboreal pollen between 1810 and 2010 AD was caused by European settlement of the area. The transition in the pollen assemblages beginning at 1560 AD and a climate reconstruction based on these data shows an abrupt climate cooling had a significant impact on the pollen accumulation rates of the region within a couple of decades. A synthesis of this record with other high-resolution and well-dated pollen data from the conifer-hardwood forest of eastern North America shows consistent results across the whole area, indicating that very-high resolution pollen data can provide insight into multi-decadal climate variability and its impact on forest vegetation. #------------------ # Funding_Agency # Funding_Agency_Name: Natural Sciences and Engineering Research Council of Canada # Grant: Discovery Grant #------------------ # Site_Information # Site_Name: Lac Noir # Location: North America>Canada>Quebec # Country: Canada # Northernmost_Latitude: 45.7755 # Southernmost_Latitude: 45.7755 # Easternmost_Longitude: -75.1351 # Westernmost_Longitude: -75.1351 # Elevation: 176 m #------------------ # Data_Collection # Collection_Name: LacNoir2013MS # Earliest_Year: 988 # Most_Recent_Year: 1658 # Time_Unit: AD # Core_Length: m # Notes: #------------------ # Chronology: # # Chronology is based on varve counts except for the upper 6.5 cm, which is based on 210Pb. # # Table 1 # Pb-210 Activity and estimated age of sediments from Lac Noir, Quebec. Age was # estimated with a linear regression model. Cs-137 peak occurs at 11.5 cm, maximum # Cs-137 input of year 1963 which occurred 46 years before 2010. # # Depth (cm) Total Pb-210 activity (DPM/g) Age (yr) Total Cs-137 activity (DPM/g dry wt) # 0.5 45.9 1.8 – # 2.5 36.3 8.8 – # 4.5 29.3 15.9 – # 7.5 22.4 26.5 – # 8.5 – – 6.32 # 9.5 – – 8.62 # 10.5 25.5 41.7 20.87 # 11.5 – 46 24.58 # 12.5 – – 8.38 # 13.5 – – 2.89 # 14.5 16.8 58.9 −0.23 # 16.5 – – 3.13 # 19.5 8 80.5 – # 23.5 6.6 97.7 – # 27.5 2.6 – – # 32.5 1.5 – – # 36.5 1.5 – – # # # Table 2 # Radiocarbon age determinations and calibrated ages BP (before 1950) using CALIB # 6.0 from Lac Noir, southwestern Quebec. Both samples are based on plant material # (leaf fragments, seeds, fiber strands) picked from the sediment. # # # Laboratory no. Depth (cm) 13C/12C ratio Conventional radiocarbon age Calibrated 14C age ranges (probability distribution) Sigma # BETA-276987 41-42 -27.2permil 580 +/- 40 BP 540-562 (0.33) 1s # 593-637 (0.67) # 528-573 (0.34) 2s # 577-653 (0.65) # BETA-276988 77-78 -26.5permil 980 +/- 40 BP 799-813 (0.13) 1s # 826-866 (0.43) # 1901-924 (0.43) # 794-958 (1.00) 2s # # #---------------- # Variables # # Data variables follow that are preceded by "##" in columns one and two. # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) # ## age_calBP2010 age, , , Calendar years before AD2010, , , , ,N ## susc_cgs Magnetic susceptibility, , , CGS, , lake sediment, , ,N # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # age_calBP2010 susc_cgs 352 0.51 362 0.51 372 0.60 382 0.59 392 0.52 402 0.52 412 0.61 422 0.80 432 1.07 442 1.46 452 1.63 472 1.58 482 1.61 492 1.97 502 2.40 512 2.96 532 3.12 542 2.99 552 2.95 572 3.01 582 3.03 602 2.96 612 2.93 622 2.89 632 3.05 642 3.18 662 3.14 672 3.00 682 3.12 692 3.15 702 2.88 712 2.58 722 2.44 742 2.33 752 2.33 762 2.32 782 2.31 792 2.24 812 2.17 822 2.27 832 2.46 842 2.89 852 3.22 862 3.05 872 2.54 882 2.27 892 2.16 902 2.29 912 2.72 932 3.18 942 3.31 952 3.07 962 2.93 972 2.99 982 2.98 992 2.88 1002 2.84 1012 2.70 1022 2.50