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Title:
Allometries: Tree species growth, biomass and crown data for detailed allometries
Access rights:
Private
Usage rights:
Brezzi Matteo, Schmid Bernhard, Baruffol Martin
Published:
No information available
Abstract:
In order to get tree growth curves we estabilshed an allometry campaign during which we carefully measured, subsampled, dried and then re-constructed the biomass of 154 trees belonging to 8 species. The sampling is done to allows estimation of biomasses for different compartments in order to see if the different tree species have different growth pattern. The neighbours are as well measured to look at a possible influence of competition on tree growth and tree biomass compartimentation
Design:
Trees were randomly chosen with the condition to look healthy and to have a particular DBH: because trees evenly located along the size range are needed to produce accurate growth curve estimate.General point:Every biomass or leaf area estimation come from an independent estimation and then has is own Standard Deviation. All the biomasses have been estimated SEPARATELY for each species in order to preserve specific differences (no pooling toward the grand mean). In all the models, the individual trees were considered as a random factor.The estimates and their standard deviation come from the posteriori distribution drawn out of the Markov chain Monte Carlo (three chains here). Each model was set up with 500'000 iterations from which the first 300'000 were discarded. Among the 200'000 valid iterations, one over twenty was kept in order to avoid the correlation between two consecutive estimates. So, each biomass estimate is the MEAN of the results given by the MCMC. The following variable is the SD of this mean (= "_sd").The model fit a value for each segment or each branch considered. The sum of them is done within the model: the final estimate gets a standard deviation that is right in confront with error propagation.The errors we provide are MODEL ERRORS. Thus they come from the variability around the estimates. Small trees for which every branch was measured in the lab have NO model error: the sd = 0.Bayesian estimation allows to include prior knowledge into the model: the priors. Here, we never used any informative priors, which means that our estimates come only from our own data.Branch biomass estimates: Branches are "branches" in our definition when their diameter is equal or below 3 cm of diameter. Estimates coming from the sampled branches are the total branch biomass, the branch wood biomass and the branch leaf biomass. . For each branch, the total fresh wood weight ant the total fresh leaf weight were measured, subsamples were dried. From the dry mass of the subsamples the total dry mass of the leaves, wood and total branch were gathered.Usually, we have 6 branches / tree which have the real data measured (real branch number can be seen in the "branch_nb" variable). As descriptor variable we have their diameter and their position in the crown. We use those predictors to predict the biomass of all the branches measured in vivo on the tree.For the branch biomass estimates, the model has this form:biomass estimate[i] ~ dnorm(yhat[i],tau[i]) yhat[i] <- c[thrd[i]]*b1[ind[i]]*pow(d[i],b2[ind[i]])tau[i]<-1/pow(sd[i],2) sd[i]<-a1+a2*d[i]where "thrd" is the branch position in the crown ( 3 levels), "ind" are the individual trees and "d" is the branch diameter. This exponential model is fitted as such "thrd" interacts with the diameter. Each trees has a proper set of estimators (it's own b1 and b2) but their are constrained to enter into a normal curve --> they are the random factors. b1[ind[i]] ~ dnorm(b1_mu, b1_tau) --> b1_mu is the average b1 estimated by the model with a precision b1_tau b2[ind[i]] ~ dnorm(b2_mu, b2_tau) --> b2_mu is the average b2 estimated by the model with a precision b2_tauA graphical inspection showed an increase of variability with increasing branch diameter, that's why here the sd is modelized as a linear function of the diameter.An idea of the amount of material really measured versus how much is predicted can be seen with the following variables: "field_bra_tot_weig" gives the real fresh weight of the branches, then "br_kg" is the wheight of the branches brought back to the lab. "p.branch.samp" is the precentage of branches that were brought back to the lab; as espected, the bigger the tree the smaller this percentage. "branch_nb" is the number of branches brought back to the lab.Branch leaf area estimates: The leaf area estimates are based on the same branches than the branch biomass estimates.To get the leaf area estimates, two models were run simultaneously; one to estimate the branch leaf biomass and one to estimate the leaf area/ gr of leaf for this particular branch. Then, these two estimates are multiplied within the model (right error at the branch level) and the resulting leaf area summed for each tree (right error at the tree level).The leaf biomass estimates are done exactly as above, so we don't show it here again. A subset of leaves were scanned and dried for each branch. From this we obtain a mean area/weight at the branch level that can be modeled.We did not find any pattern for the leaf area / gr of leave with the branch diameter, so this variable does not appear in our model. In contrast, leaves tend to have a lower surface / gr when being situated higher in the crown, so "thrd" is in our model. As before, the individuals are the random factor.area[i]~dnorm(yhatar[i],tauar) yhatar[i] <- c2[thrd[i]]*b9[ind[i]]where "thrd" is the branch position in the crown ( 3 levels) and "ind" are the individual treesSegments biomasses: The segments are the part of the wood belonging to the stem or branches with a diameter above 3 cm. The whole volume of the tree is recorded by "segment". In some of those segments, a slice was take and a real density gathered. This density is here modelled, applied to the segment, then multiplied by the segment volume, which give the segment biomass and their error (segment level) and summed within the model to have the biomasses with the right error term at the individual level.segment_density]~dnorm(yhat[i],tau[i]) yhat[i]<-b0[indiv[i]]+b1[s_or_b[i]]+b2[indiv[i]]*llength[i] + b3[indiv[i]]*larea[i] + b4[indiv[i]]*relpos[i] tau[i]<-1/pow(sd,2) biom[i]<- yhat[i]*segvol[i]Where "indiv" is the individual marker, "s_or_b" is a two level factor to indicate if the segment is from the Stem or a Branch, "llength" is the log of the total length of the stem or the branch where the segment is situated, "larea" is the log of the average sectional area of the segment, "relpos" is the relative position of the segment within the branch or the stem. Then, to get the biomass "biom" of the segment "i", we multiply it by its volume = "segvol".Here, to avoid a super complicated model but still letting a lot of individual freedom for the estimates, we don't make interactions but still having different parameter estimates for all the variables at the individual level. . As before, the individuals are the random factor.To know how much biomass was really measured versus estimated, you may look at "sl_kg" that gives the total dry weight of the slices brought back to the lab, "slice_nb" that gives the number of slices brought back to the lab and "p.seg.samp" wich is the percentage of dry biomass really measured compared to the biomass predicted.Dead Attached Material(DAM) biomasses: The DAM were less extensively sampled, so we have only one density value per individual. For some (strange) reasons that occur in the field, quite a certain number of trees don't have a proper sample. So we decided to calculate an average DAM density at the species level (so, here there is no "[indiv]" marker). The individual estimates were used to asses the "noise" around that mean.In the field, we measured the diameter and the length of the DAM. The DAM volume is estimated applying the formula of a cone (might not be the best).Within the WinBUGS model, the density is applied to each DAM which give us its biomass and the error. Then the different DAM are summed at the individual level which provide the error at the individual level. Here the error reflects our uncertainty around the specific mean.the model could not be simpler:DAM_density[i]~dnorm(mu,tau)Total living biomass (tot_bio), total biomass (tot_bio_big) and total wood biomass (total_wood_biomass): To get these estimates with the right error, we re-used the models described above and we made them run in parallel and then we summed their estimates. "tot_bio_big" is obtained by having three models running, one for the DAM, one for the total branch biomass and one for the total segment biomass. Their estimates are summed inside the model. Similarly, the total living biomass is obtain by summing the total segment biomass and the total branch biomass. The total wood biomass is obtained by summing the total segment biomass with the woody part of the branches biomass. ----- data describe among others: variables that describe the tree shape, the place where it was growing and its social situation;
Spatial extent:
According to Google Map, the site is N 29.113494, E 118.008817. The forest is an exploited plantation with about 80% of the trees being Cunninghamia lanceolata. The 20% remaining is naturally present there and comes from the surrounding natural forest. The site is situated at the end of a small valley with relatively strong slope
Temporal extent:
Time needed to sample the 154 trees
Taxonomic extent:
154 trees belonging to 8 different species (n, DBH size range in cm): 2 coniferous: Pinus massoniana (19, 2.9 – 23.2) and Cunninghamia lanceolata (17, 1 – 17.7), 3 deciduous: Liquidambar formosana (15, 2.6 – 37.5), Sassafras tzumu (20, 5.1 – 27.7) and Alniphyllum fortunei (21, 2.4 – 18.7), 3 evergreens: Castanopsis fargesi (25, 2.5 – 27.3), Castanopsis sclerophylla (16, 2.7 – 16.5) and Schima superba (21, 1.2 – 23.1).
Measurement cirumstances:
No information available
Data analysis:
Exponential curves according to species and other covariates; This data are not "raw", to get the biomasses from the raw data one needs to many steps, so here we provide the processed data and the methods required to get them

Filter:
Dataset column

Name:
tree_code
Definition:
unique tree code
Unit:
No information available
Datagroup:
Tree identifier for harvested trees
Keywords:
tree, object
Values:
103
101
105
107
104
Contributors:

Dataset column

Name:
species_acronym
Definition:
species_acronym
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
alf
cul
caf
cas
lif
Contributors:
No information available

Dataset column

Name:
species
Definition:
species name
Unit:
No information available
Datagroup:
Scientific plant species name
Keywords:
species, taxon, allometries
Values:
Cunninghamia lanceolata
Castanopsis sclerophylla
Castanopsis fargesii
Liquidambar formosana
Alniphyllum fortunei
Contributors:
No information available

Dataset column

Name:
slope
Definition:
the slope where the tree was; Instrumentation: compass Recta type armée Suisse DP6G (derived from datagroup)
Unit:
Degree
Datagroup:
Inclination
Keywords:
inclination, slope
Values:
12
16
14
10
13
Contributors:

Dataset column

Name:
exposition
Definition:
exposition of the slope from North; Instrumentation: compass Recta type armée Suisse DP6G (derived from datagroup)
Unit:
Degree
Datagroup:
Aspect
Keywords:
exposition, aspect
Values:
0
190
197
10
20
Contributors:

Dataset column

Name:
geoform
Definition:
description of the ground form; Instrumentation: none; Source: Christian Wirth, personal comunication
Unit:
none
Datagroup:
Curvature
Keywords:
curvature
Values:
concave
convex
slope
Contributors:

Dataset column

Name:
dbh
Definition:
diameter at brest high of the tree; Instrumentation: caliper; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Centimeters
Datagroup:
Diameter at breast height
Keywords:
dbh, size
Values:
10.4
1
10.1
10.3
10.2
Contributors:
No information available

Dataset column

Name:
heigh
Definition:
vertical height of the standing tree; Instrumentation: Haglöf Vertex IV Hypsometer; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Plant height
Keywords:
height, size
Values:
10.3
10.7
10.5
10.4
10.6
Contributors:

Dataset column

Name:
crw_strt
Definition:
vertical height of the crown start (measured on the standing tree); Instrumentation: Haglöf Vertex IV Hypsometer; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Crown architecture
Keywords:
crown
Values:
0.9
0.8
0.4
1
0.1
Contributors:

Dataset column

Name:
bas_d1
Definition:
basal diameter of the tree (at 10 cm above the ground); Instrumentation: caliper; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Centimeters
Datagroup:
Basal diameter
Keywords:
basal diameter
Values:
10.9
10.7
11
10.3
10
Contributors:

Dataset column

Name:
bas_d2
Definition:
second basal diameter of the tree, measured perpenduculary of the first one (at 10 cm above the ground); Instrumentation: caliper; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Centimeters
Datagroup:
Basal diameter
Keywords:
basal diameter
Values:
10.4
10.9
10.7
10.1
11
Contributors:

Dataset column

Name:
social_sta
Definition:
social status of the tree; Instrumentation: none; Source: Christian Wirth, personal comunication
Unit:
none
Datagroup:
Tree social status
Keywords:
social status
Values:
4
5
3
2
Contributors:

Dataset column

Name:
crw_n
Definition:
crown projection toward the North (measured on the standing tree); Instrumentation: ruler; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Crown architecture
Keywords:
crown
Values:
0.3
0.2
-0.3
0.4
0
Contributors:

Dataset column

Name:
crw_s
Definition:
crown projection toward the South (measured on the standing tree); Instrumentation: ruler; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Crown architecture
Keywords:
crown
Values:
0.2
0.6
0.4
0.5
-0.5
Contributors:

Dataset column

Name:
crw_e
Definition:
crown projection toward the East (measured on the standing tree); Instrumentation: ruler; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Crown architecture
Keywords:
crown
Values:
-0.1
0.1
0.15
0.05
0.2
Contributors:

Dataset column

Name:
crw_w
Definition:
crown projection toward the West (measured on the standing tree); Instrumentation: ruler; Source: Snowdon, P. et al. (2002). Protocol for Sampling Tree and Stand Biomass. Office (pp. 1–76). Canberra, Australia
Unit:
Meters
Datagroup:
Crown architecture
Keywords:
crown
Values:
0.7
0
0.8
0.4
0.6
Contributors:

Dataset column

Name:
tot_bio_big
Definition:
total dry standing biomass including the dead attached material
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
response variable, biomass
Values:
0.183229019666667
1.00702335333333
1.02266464333333
0.513031193333333
0.365365278333333
Contributors:

Dataset column

Name:
tot_bio_big_sd
Definition:
total dry standing biomass including the dead attached material standard deviation of the estimate (see sampling description)
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.0381755880348408
0.0548726710659948
0.0365129535044636
0.0561967243493172
0.0233461183333973
Contributors:

Dataset column

Name:
tot_bio
Definition:
total dry biomass excluding the dead attached material
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.513031193333333
0.365365278333333
100.941464333333
0.98988923
0.183229019666667
Contributors:

Dataset column

Name:
tot_bio_sd
Definition:
total dry biomass excluding the dead attached material standard deviation of the estimate (see sampling description)
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.0527099073814094
0.0548726710659948
0.0435399993116495
0.0233461183333973
0.036764732970587
Contributors:

Dataset column

Name:
tot_wood_bio
Definition:
total dry wood biomass
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, wood, dry weight
Values:
0.835006626666667
0.933126776666667
0.244039089016667
0.392902148333333
0.131414159833333
Contributors:

Dataset column

Name:
tot_wood_bio_sd
Definition:
total dry wood biomass estimates standard deviation (see sampling details)
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, dry weight
Values:
0.0370585212769948
0.043074155425042
0.0382163692981812
0.0401769113665396
0.0146875639726168
Contributors:

Dataset column

Name:
tlbiomass
Definition:
total leaf dry biomass of the small branches
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, leaf, dry weight
Values:
0.0488727315133333
0.0625853693033333
0.0213221541079407
0.0247012378983333
0.0790891907558767
Contributors:

Dataset column

Name:
tlbiomass_sd
Definition:
total leaf dry biomass of the small branches standard deviation of the estimate (see sampling description), indipendent estimates of the tree biomass or of the tree component biomass and their associated error
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, leaf
Values:
0.0171096660056617
0
0.00948064263379279
0.0141229249980643
0.0207986236502255
Contributors:

Dataset column

Name:
tlfarea
Definition:
total leaf fresh area, indipendent estimates of the tree biomass or of the tree component biomass and their associated error
Unit:
CentimetersSquared
Datagroup:
Leaf area
Keywords:
leaf
Values:
113791.455
1040183.77
105339.231666667
101954.043333333
106295.3635
Contributors:

Dataset column

Name:
tlfarea_sd
Definition:
total leaf fresh area standard deviation of the estimate (see sampling description), indipendent estimates of the tree biomass or of the tree component biomass and their associated error
Unit:
CentimetersSquared
Datagroup:
Leaf area
Keywords:
leaf
Values:
10024.2631107316
102552.884454243
10139.1876292698
10056.9839574142
0
Contributors:

Dataset column

Name:
tdambio
Definition:
total dry biomass of the dead attached material, indipendent estimates of the tree biomass or of the tree component biomass and their associated error
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.002139311
0
0.0018272328
0.0017140617
0.00190497996666667
Contributors:

Dataset column

Name:
tdambio_sd
Definition:
total dry biomass of the dead attached material standard deviation of the estimate (see sampling description), indipendent estimates of the tree biomass or of the tree component biomass and their associated error
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.00013009448584222
0
0.000291719474569235
0.00013547046553382
0.000195800381588775
Contributors:

Dataset column

Name:
sl_kg
Definition:
total dry weight of the slice sampled in order to estimate the segment wood; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass
Values:
0.00856
0.00349
0.02097
0
0.02116
Contributors:

Dataset column

Name:
slice_nb
Definition:
number of slice sampled; Instrumentation: none; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
dimentionless
Datagroup:
Sample size
Keywords:
No information available
Values:
10
0
13
11
12
Contributors:

Dataset column

Name:
p.seg.samp
Definition:
percentage of the total segment biomass for which we have a real biomass measure; Instrumentation: none; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
dimentionless
Datagroup:
Helper
Keywords:
No information available
Values:
0.674124248139835
0.827611823934838
1.0494343391189
0.731975089051266
0.9590675863808
Contributors:

Dataset column

Name:
field_bra_tot_weig
Definition:
total fresh mass of the small branches weighted in the field; Instrumentation: field electronical dynamometer; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, branch
Values:
0.385
0.35
0.62
0.69
0.53
Contributors:

Dataset column

Name:
br_kg
Definition:
total fresh weight of the branches sampled (brought back to the lab); data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
Kilograms
Datagroup:
Above and below ground biomass measurement
Keywords:
biomass, branch
Values:
0.22789
0.32038
0.38696
0.34703
0.28111
Contributors:

Dataset column

Name:
branch_nb
Definition:
number of branches sampled; Instrumentation: none; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
dimentionless
Datagroup:
Sample size
Keywords:
No information available
Values:
2
1
3
5
4
Contributors:

Dataset column

Name:
p.branch.samp
Definition:
percentage of the total small branch biomass for which we have a real detailed biomass measure.; Instrumentation: none; data to quantifiy the amount of the tree really sampled in order to estimate the precision of the biomass estimate
Unit:
dimentionless
Datagroup:
Helper
Keywords:
No information available
Values:
10.0240708812261
100
11.9072774717309
10.7740208408193
11.9609736842105
Contributors:

No information available


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74 brezzi medium

Matteo
Brezzi

Owner of:
6 Datasets

Involved in:
1 Projects

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15 schmid medium

Bernhard
Schmid

Owner of:
30 Datasets

Involved in:
2 Projects

E-Mail Profile
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