MST_allometry_deviation_data
Metadata file: swetnam_oconnor_data_note.txt
Contents:
NOTES
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Header of the data CSV file are as follows:
SITE, Plot, PFT, Species, CX, DBH, HT, EEMT
The descriptions for each column are:
SITE Study Area
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4-letter code
CATA Santa Catalina Mountains
PINA Pinaleno Mountains
VCNP Valles Caldera National Preserve
Plot
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Unique plot identifier - for plot locations contact corresponding author: Tyson L. Swetnam, tswetnam@email.arizona.edu, 1+ (520) 621-1052
PFT Plant functional type (Forest Type)
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1 Ponderosa Pine Forest
2 Mixed-Conifer Forest
3 White Fir Forest
4 Spruce-Fir Forest
5 Aspen Disclimax Mix
Species
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4-letter code Latin Common name
PIPO Pinus ponderosa Ponderosa pine
PIST Pinus strobiformis/reflexa/flexilis Arizona white pine, Limber pine
PSME Pseudotsuga menziesii Douglas-fir
ABCO Abies concolor White fir
ABLA Abies lasiocarpa va. Arizonica Sub-alpine fir/cork bark fir
PIEN Picea engelmannii Engelmann spruce
PIPU Picea pungens Blue spruce
POTR Polulus tremuloides Quaking Aspen
QUGA Quercus gambelii Gambel's oak
QUHY Quercus hypoleucoides Silverleaf oak
RONE Robinea neomexicana New Mexico Locust
CX Condition Classes
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LE Live-Excellent
LG Live-Good
LF Live-Fair
LP Live-Poor
DS* Dead-Standing
DF* Dead-Fallen
DP* Dead-stump/broken
**F Foliage / Fine branches intact
**B Bark intact
**L Only major limbs intact
**I Only bole intact
**R Rotten
DBH Diameter at breast height Measured 4.5 feet or 1.37 meters from the ground (centimeters)
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HT Height Maximum height above ground level (meters)
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EEMT Effective Energy and Mass Transfer (MegaJoules per meter square per year)
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Tree data: swetnam_plot_tree_data.csv
Age data: swetnam_oconnor_age_data.csv
Matlab Code for beginning non-linear least squares Curve Fitting:
% import the data uiopen('G:\PLOSOne_Swetnam_OConnor\data\cat_vc_pin_eemt_biomass.csv',1) %select the import selection check and allow the data to be automatically %parsed.
% Classify by health condition g = 1; h = 1; k = 1; l = 1; m = 1; for i=1:size(DBH_CM) if strcmp(CX{i},'LE'); % parses the string for 'live excellent' condition le_dbh(g,1)= DBH_CM(i); le_ht(g,1) = Height_M(i); le_spec{g,1} = Species{i}; g = g+1; elseif strcmp(CX{i},'LG'); lg_dbh(h,1)= DBH_CM(i); lg_ht(h,1) = Height_M(i); lg_spec{h,1} = Species{i}; h = h+1; elseif strcmp(CX{i},'LF'); lf_dbh(k,1)= DBH_CM(i); lf_ht(k,1) = Height_M(i); lf_spec{k,1} = Species{i}; k = k+1; elseif strcmp(CX{i},'LP'); lp_dbh(l,1)= DBH_CM(i); lp_ht(l,1) = Height_M(i); lp_spec{l,1} = Species{i}; l = l+1; elseif strcmp(CX{i},'DSF') dsf_dbh(m,1)= DBH_CM(i); dsf_ht(m,1) = Height_M(i); dsf_spec{m,1} = Species{i}; m = m+1; end end %Class by site-level structure g = 1; k = 1 ; m = 1; for i=1:size(DBH_CM) if strcmp(VarName1{i},'PINA'); pina.cx{g,1} = CX{i}; pina.dbh(g,1) = DBH_CM(i); pina.eemt(g,1) = EEMT(i); pina.ht(g,1) = Height_M(i); pina.inner(g,1) = Inner_plot(i); pina.pft(g,1) = PFT(i); pina.plot(g,1) = Plot(i); pina.spec{g,1} = Species{i}; pina.sg(g,1) = Specific_Gravity(i); g = g+1; elseif strcmp(VarName1{i},'VCNP') vcnp.cx{k,1} = CX{i}; vcnp.dbh(k,1) = DBH_CM(i); vcnp.eemt(k,1) = EEMT(i); vcnp.ht(k,1) = Height_M(i); vcnp.inner(k,1) = Inner_plot(i); vcnp.pft(k,1) = PFT(i); vcnp.plot(k,1) = Plot(i); vcnp.spec{k,1} = Species{i}; vcnp.sg(k,1) = Specific_Gravity(i); k = k+1; elseif strcmp(VarName1{i}, 'CATA') cata.cx{m,1} = CX{i}; cata.dbh(m,1) = DBH_CM(i); cata.eemt(m,1) = EEMT(i); cata.ht(m,1) = Height_M(i); cata.inner(m,1) = Inner_plot(i); cata.pft(m,1) = PFT(i); cata.plot(m,1) = Plot(i); cata.spec{m,1} = Species{i}; cata.sg(m,1) = Specific_Gravity(i); m=m+1; end end %Categorize PINA g = 1; h = 1; k = 1; l = 1; m = 1; for i=1:size(pina.dbh) if strcmp(pina.cx{i},'LE'); pina_le_dbh(g,1)= pina.dbh(i); pina_le_ht(g,1) = pina.ht(i); pina_le_spec{g,1} = pina.spec{i}; g = g+1; elseif strcmp(pina.cx{i},'LG'); pina_lg_dbh(h,1)= pina.dbh(i); pina_lg_ht(h,1) = pina.ht(i); pina_lg_spec{h,1} = pina.spec{i}; h = h+1; elseif strcmp(pina.cx{i},'LF'); pina_lf_dbh(k,1)= pina.dbh(i); pina_lf_ht(k,1) = pina.ht(i); pina_lf_spec{k,1} = pina.spec{i}; k = k+1; elseif strcmp(pina.cx{i},'LP'); pina_lp_dbh(l,1)= pina.dbh(i); pina_lp_ht(l,1) = pina.ht(i); pina_lp_spec{l,1} = pina.spec{i}; l = l+1; elseif strcmp(pina.cx{i},'DSF') pina_dsf_dbh(m,1)= pina.dbh(i); pina_dsf_ht(m,1) = pina.ht(i); pina_dsf_spec{m,1} = pina.spec{i}; m = m+1; end end %Categorize VCNP g = 1; h = 1; k = 1; l = 1; m = 1; for i=1:size(vcnp.dbh) if strcmp(vcnp.cx{i},'LE'); vcnp_le_dbh(g,1)= vcnp.dbh(i); vcnp_le_ht(g,1) = vcnp.ht(i); vcnp_le_spec{g,1} = vcnp.spec{i}; g = g+1; elseif strcmp(vcnp.cx{i},'LG'); vcnp_lg_dbh(h,1)= vcnp.dbh(i); vcnp_lg_ht(h,1) = vcnp.ht(i); vcnp_lg_spec{h,1} = vcnp.spec{i}; h = h+1; elseif strcmp(vcnp.cx{i},'LF'); vcnp_lf_dbh(k,1)= vcnp.dbh(i); vcnp_lf_ht(k,1) = vcnp.ht(i); vcnp_lf_spec{k,1} = vcnp.spec{i}; k = k+1; elseif strcmp(vcnp.cx{i},'LP'); vcnp_lp_dbh(l,1)= vcnp.dbh(i); vcnp_lp_ht(l,1) = vcnp.ht(i); vcnp_lp_spec{l,1} = vcnp.spec{i}; l = l+1; elseif strcmp(vcnp.cx{i},'DSF') vcnp_dsf_dbh(m,1)= vcnp.dbh(i); vcnp_dsf_ht(m,1) = vcnp.ht(i); vcnp_dsf_spec{m,1} = vcnp.spec{i}; m = m+1; end end %Categorize CATA g = 1; h = 1; k = 1; l = 1; m = 1; for i=1:size(cata.dbh) if strcmp(cata.cx{i},'LE'); cata_le_dbh(g,1)= cata.dbh(i); cata_le_ht(g,1) = cata.ht(i); cata_le_spec{g,1} = cata.spec{i}; g = g+1; elseif strcmp(cata.cx{i},'LG'); cata_lg_dbh(h,1)= cata.dbh(i); cata_lg_ht(h,1) = cata.ht(i); cata_lg_spec{h,1} = cata.spec{i}; h = h+1; elseif strcmp(cata.cx{i},'LF'); cata_lf_dbh(k,1)= cata.dbh(i); cata_lf_ht(k,1) = cata.ht(i); cata_lf_spec{k,1} = cata.spec{i}; k = k+1; elseif strcmp(cata.cx{i},'LP'); cata_lp_dbh(l,1)= cata.dbh(i); cata_lp_ht(l,1) = cata.ht(i); cata_lp_spec{l,1} = cata.spec{i}; l = l+1; elseif strcmp(cata.cx{i},'DSF') cata_dsf_dbh(m,1)= cata.dbh(i); cata_dsf_ht(m,1) = cata.ht(i); cata_dsf_spec{m,1} = cata.spec{i}; m = m+1; end end