DOI: 10.6084/m9.figshare.988687.v9
title: Data, code, and figures from Dunnette et al. 2014, Biogeochemical impacts of wildfires over four millennia in a Rocky Mountain subalpine watershed. New Phytologist In Press (doi: 10.1111/nph.12828)

description: # Data, code, and figures from Dunnette et al. 2014, Biogeochemical impacts of wildfires over four millennia in a Rocky Mountain subalpine watershed. _New Phytologist_ In Press (doi: 10.1111/nph.12828). Figshare.<br>http://dx.doi.org/10.6084/m9.figshare.988687 ## Overview<br>The repository includes data and script needed to recreate the analyses in: Dunnette, P.V., P.E. Higuera, K.K. McLauchlan, K.M. Derr, C.E. Briles, and M.H. Keefe. 2014. Biogeochemical impacts of wildfires over four millennia in a Rocky Mountain<br>subalpine watershed. _New Phytologist_ In Press doi: 10.1111/nph.12828 The archive includes seven folders, with contents described below. Most data files are in .csv<br>format; some are in .xls format. Scripts and functions are written for MATLAB software<br>(www.mathworks.com), and each is fully commented, including dependencies. For some scripts<br>or functions, the MATLAB statistics and curve fitting toolboxes are required; all other functions<br>required are provided in this archive. ####CHICKAREE LAKE BIOGEOCHEMICAL DATA: CH10_biogeochemData.csv #####Includes the following raw data (by column):<br>*1. core_ID: Sediment core identification number<br>*2. drive_ID: Drive identifier<br>*3. top_sam_#: Top sample number<br>*4. bot_sam_#: Bottom sample number<br>*5. top_cm: Top depth of sample (cm)<br>*6. bot_cm: Bottom depth of sample (cm)<br>*7. top_age: Top age of sample (cal. yr before CE 1950)<br>*8. bot_age: Bottom age of sample (cal. yr before CE 1950)<br>*9. d15N: Nitrogen isotopic composition (?15N; ‰)<br>*10. %N: Percent Nitrogen (by weight)<br>*11. d13C: Carbon isotopic composition (?13C; ‰)<br>*12. %C: Percent Carbon (by weight)<br>*13. C:N_atomic_ratio: Ratio of %C to %N (atomic)<br>*14. bulk_density: Bulk density (dry g wet cm-3)<br>*15. C_acc: Carbon accumulation rate (g cm-2 yr-1)<br>*16. MS_SI : Magnetic susceptibility (SI units) #####Missing Values: NaN #####Checksum values:<br>*CH10_biogeochemData.csv: 635 rows (with headers), 16 columns<br>*Column 3 (top_sam_#): 58551<br>*Column 5 (top_cm): 201836<br>*Column 10 (%N): 626.83<br>*Column 15 (C_acc): 341.58 ========================================================= **CHICKAREE LAKE BIOGENIC SILICA DATA: CH10_BSiData.csv** Includes the following raw data (by column):<br>1. top_cm: top depth of sample (cm)<br>2. bot_cm: bottom depth of sample (cm)<br>3. top_age: top age of sample (cal. yr before CE 1950)<br>4. bot_age: bottom age of sample (cal. yr before CE 1950)<br>5. %BSi: Percent biogenic silica (by weight)<br>6. d15NAIR: Nitrogen isotopic composition (?15N; ‰)<br>7. %N: Percent Nitrogen (by weight)<br>8. d13CVPDB_17O_corrected: Carbon isotopic composition (?13C; ‰)<br>9. %C: Percent Carbon (by weight)<br>10. C:N_atomic_ratio: Ratio of %C to %N (atomic)<br>11. bulk_density: Bulk density (dry g wet cm-3) Missing Values: None Checksum values:<br>CH10_BSiData.csv: 41 rows (with headers), 11 columns<br>Column 3 (top_cm): 69935<br>Column 5 (%BSi): 1042.45<br>Column 10 (C:N): 591.52 **CHICKAREE LAKE LOSS-ON-IGNITION DATA: CH10_LOI_Data.csv** Includes the following raw data (by column):<br>1. core_ID: Sediment core identification number<br>2. drive_ID: Drive identifier<br>3. top_sam_#: Top sample number<br>4. bot_sam_#: Bottom sample number<br>5. top_cm: top depth of sample (cm)<br>6. bot_cm: bottom depth of sample (cm)<br>7. top_age: top age of sample (cal. yr before CE 1950)<br>8. bot_age: bottom age of sample (cal. yr before CE 1950)<br>9. d15N: Nitrogen isotopic composition (?15N; ‰)<br>10. %N: Percent nitrogen (by weight)<br>11. d13C: Carbon isotopic composition (?13C; ‰)<br>12. %C: Percent carbon (by weight)<br>13. C:N_atomic_ratio: Ratio of %C to %N (atomic)<br>14. bulk_density: Bulk density (dry g wet cm-3)<br>15. LOI_550: Loss on ignition at 550 C (% organic matter; multiply by 100)<br>16. LOI_1000: Loss on ignition at 1000 C (% organic matter; multiply by 100) Missing Values: None Checksum values:<br>CH10_LOI_Data.csv: 124 rows (with headers), 16 columns<br>Column 3 (top_samp_#): 12010<br>Column 5 (top_cm): 31833.7<br>Column 10 (%N): 161.05<br>Column 15 (LOI_550): 38.63 =========================================================<br>**CHICKAREE LAKE CHARCOAL DATA: CH10_charData.csv, CH10_charParams.csv,<br>CH10_charResults.csv** Three files provide the raw input data, the parameters used, and the output data for charcoal<br>analysis via the program CharAnalysis (see ‘Materials and Methods’ in main text, and web site<br>https://sites.google.com/site/charanalysis/). CH10_charData.csv<br>Includes the following raw data (by column):<br>1. cmTop: top depth (cm) of the sample<br>2. cmBot: bottom depth (cm) of the sample<br>3. ageTop (yr BP): estimated age at top of sample (cal. yr before CE 1950)<br>4. ageBot (yr BP): estimated age at bottom of sample (cal. yr before CE 1950)<br>5. charVol (cm3): volume of sediment subsample from which charcoal was prepared (cm3)<br>6. charCount (#): pieces of charcoal counted in the sample (#) Missing values: None Checksum values:<br>CH10_charData.csv: 1202 rows (with headers), 6 columns<br>Column 3 (ageTop (yr BP)): 2446519<br>Column 6 (charCount (#)): 38218<br>CH10_charParams.csv<br>See CharAnalysis User’s Guide for description of parameters file, available at the web<br>site linked to above.<br>Missing values: -9999 (column 3) or blank cell (all others)<br>Checksum values: CH10_charParams.csv: 26 rows (with headers), 5 columns<br>Column 3 (Parameters): -39779 CH10_charResults.csv<br>Includes the following derived data (reflecting interpolation):<br>1. cmTop_i: top depth (cm) of interpolated sample<br>2. ageTop_i: bottom depth (cm) of interpolated sample<br>3. charCount_i: pieces of charcoal in interpolated sample<br>4. charVol_i: volume of interpolated sample<br>5. charCon_i: charcoal concentration in interpolated sample (pieces/cm3)<br>6. charAcc_i: charcoal accumulation rate, based in interpolated concentration and age<br>(pieces/cm2*yr)<br>7. charBkg: background charcoal, Cback, smoothed based on methods selected in<br>*_charParams.csv file, (pieces/cm2*yr)<br>8. charPeak: peak charcoal, Cpeak, based on methods selected in *_charParams.csv file,<br>(pieces/cm2*yr)<br>9. thresh1: threshold value (pieces/cm2*yr) based on first threshold entered in<br>*_charParams.csv file<br>10. thresh2: same as thresh1, but for second threshold entered<br>11. thresh3: same as thresh1, but for third threshold entered<br>12. threshFinalPos: positive threshold value (pieces/cm2*yr), based on fourth threshold<br>value entered in *charParams.csv file<br>13. threshFinalNeg: negative threshold value (pieces/cm2*yr), based on fourth threshold<br>value entered in *charParams.csv file<br>14. SNI: signal-to-noise index values, based on threshdFinalPos values<br>15. threshGOF: P value from KS goodness-of-fit test between fitted noise distribution and<br>empirical data below the sample-specific threshold<br>16. peaks1: samples that exceed thresh1 values are identified by “1”; only the first sample is<br>identified<br>17. peaks2: same as peaks1, but for the second threshold value<br>18. peaks3: same as peaks1, but for the third threshold value<br>19. peaksFinal: same as peaks1, but for the final threshold value<br>20. peaksInsig.: peaks that exceeded threshFinalPos (the final threshold), but did not pass<br>the minimum-count test are identified with a “1”<br>21. peakMag: peak magnitude is the total pieces of charcoal accumulated in a given peak<br>(pieces/cm2*peak); if a peak is only one sample long, then peak magnitude is simply<br>CHAR minus the positive threshold. If a peak is more than one sample long, then each<br>sample exceeding threshFinal is summed<br>22. smPeak Frequ (peaks 1ka-1): frequency of peaks (from peaksFinal) smoothed over time,<br>as set in *_charParams.csv file<br>23. smFRIs (yr*fire-1): fire return intervals (from peaksFinal) smoothed over time, as set in<br>*_charParams.csv file<br>Missing values: NaN Checksum values:<br>CH10_charResults.csv: 458 rows (with headers), 23 columns<br>Column 2 (age Top_i): 1014540<br>Column 6 (char Acc_i): 753.8938<br>Column 19 (peaks Final): 36 =========================================================<br>**CHICKAREE LAKE MAGNETIC SUSCEPTIBILITY DATA: CH10_MS_charData.csv,<br>CH10_MS_charParams.csv, CH10_MS_charResults.csv** Three files provide the raw input data, the parameters used, and the output data for peak<br>analysis of magnetic susceptibility (MS) data, using the program CharAnalysis (see ‘Materials<br>and Methods’ in main text, and web site https://sites.google.com/site/charanalysis/). The<br>column headers for all files are the same as for charcoal analysis (CH10_char* files), but NOTE<br>that the units for MS have been manipulated to be able to be used in CharAnalysis (see 7-9<br>below). CH10_MS_charData.csv Includes the following raw data (by column):<br>1. cmTop: top depth (cm) of the sample<br>2. cmBot: bottom depth (cm) of the sample<br>3. ageTop (yr BP): estimated age at top of sample (cal. yr before CE 1950)<br>4. ageBot (yr BP): estimated age at bottom of sample (cal. yr before CE 1950)<br>5. dummyVar: dummy variable set to 1, to facilitate use in CharAnalysis program<br>6. MS_shifted_trans: Shifted MS data (from col. 8), transformed by dividing by the<br>sediment accumulation rate (cm/yr). Thus, when these transformed values are multiplied<br>by the sediment accumulation rate (cm/yr), as done in CharAnalysis, the result is the<br>transformed MS values (column 8).<br>7. MS_raw_SI: Raw magnetic susceptibility measurements (SI units)<br>8. MS_shifted: Raw MS values, shifted by adding the minimum values in MS_raw_SI to<br>each value, such that the minimum value becomes 0. CharAnalysis cannot work with<br>negative data. Missing values: None Checksum values:<br>CH10_MS_charData.csv: 1477 rows (with headers), 8 columns<br>Column 3 (ageTop (yr BP)): 3970165<br>Column 6 (setRate/MS): 0.3626761 =========================================================<br>CHICKAREE LAKE CHRONOLOGY DATA: CH10_210Pb_data.xls,<br>CH10_ageDepthData.xls, CH10_ageDepthData.csv, CH10_radiometricSamples.csv, and<br>CH10_14Cdates.zip. These files provide the chronology data presented in Table S1 (CH10_radiometricSamples.csv),<br>the age-depth data presented in Figure 2 (CH10_ageDepthData.csv), and the input files used in<br>the MATLAB functions CRSModel.m and MCAgeDepth.m (*.xls), publically available at<br>http://code.google.com/p/crsmodel/ and http://code.google.com/p/mcagedepth/, as used in<br>Higuera et al. (2009, Ecological Monographs, 79:201–219). See the user manuals for each<br>program at these web sites for details on the two .xls files. The .zip archive includes the .B00<br>files output from CALIB (see Materials and Methods in main text) and needed to run<br>MCAgeDepth. The age-depth data output from MCAgeDepth is described below. CH10_ageDepthData.csv Includes the following raw data (by column):<br>1. Top depth of sample (cm)<br>2. Calibrated age at sample top (cal yr BP)<br>3. Upper 95% confidence intervals for data in column two<br>4. Lower 95% confidence intervals for data in column two<br>5. Sedimentation rate (cm/yr)<br>6. Upper 95% confidence intervals for data in column five<br>7. Lower 95% confidence intervals for data in column five<br>8. Sample resolution (yr/sample)<br>9. Upper 95% confidence intervals for data in column eight<br>10. Lower 95% confidence intervals for data in column eight Missing values: None Checksum values: CH10_AgeDepthData.csv: 1596 rows (with headers), 10 columns<br>Column 1 (sampleCm): 642150.9<br>Column 2 (calAge): 4679522<br>Column 5 (sedAcc): 211.236<br>Column 8 (sampleRes): 6860.85 ========================================================= CHICKAREE LAKE POLLEN DATA: CH10_PollenCounts.xls, CH10_pollenPercentages.csv. Raw pollen data needed to use in the function pollenDiagram_ROMO.m, to create Fig. S2<br>(*.xls), and pollen percentages (*.csv). CH10_PollenCounts.xls Includes the following raw data (by column):<br>1. Sample_ID : Identification of sample, by lake (CH), year (07 or 10), core (1 or 2), and<br>drive (A, B, C…).<br>2. Sample_number: Sample number for each drive<br>3. Top_cm: Top depth of each sample (cm)<br>4. age_yrBP: Age of each sample, in calibrated years before present (CE 1950)<br>5. Pinus hap: Haploxilon Pinus pollen grains counted<br>6. Pinud dip: Diploxilon Pinus pollen grains counted<br>7. Pinus undiff: Undifferentiated Pinus pollen grains counted<br>8. Picea: Picea pollen grains counted<br>9…112: Columns heads are the taxonomic identification for each pollen grain counted<br>111. Charcoal: NOT COUNTED<br>112. EU: Exotic pollen grains, added as spike. Missing values: NaN. Checksum values: CH10_PollenData.xls: 46 rows (with headers), 113 columns<br>Column 2 (sample_number): 6143<br>Column 4 (age_yrBP): 86683<br>Column 10 (Pseudotsuga/Larix): 42<br>Column 113 (EU): 8793 CH10_pollenpercentages.csv Includes the following raw data (by column):<br>1. Sample_ID : Identification of sample, by lake (CH), year (07 or 10), core (1 or 2), and<br>drive (A, B, C…).<br>2. Sample_number: Sample number for each drive<br>3. Top_cm: Top depth of each sample (cm)<br>4. age_yrBP: Age of each sample, in calibrated years before present (CE 1950)<br>5. Pinus hap: Haploxilon Pinus pollen grains counted<br>6. Pinud dip: Diploxilon Pinus pollen grains counted<br>7. Pinus undiff: Undifferentiated Pinus pollen grains counted<br>8. Picea: Picea pollen grains counted<br>9…107: Columns heads are the taxonomic identification for each pollen grain counted Missing values: None. Checksum values: CH10_PollenData.xls: 46 rows (with headers), 106 columns<br>Column 2 (sample_number): 6143<br>Column 4 (age_yrBP): 86683<br>Column 10 (Pseudotsuga/Larix): 12<br>Column 102 (Nuphar): 13.19