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Title:
CSPs: Tree species stem breaking probability deviation CSPs
Access rights:
Free for public
Usage rights:
Permission is granted to anybody to access, use and publish all open for public data freely. The commercial use of any data is prohibited. The quality and completeness of data cannot be guaranteed. Users employ these data at their own risk. In order to make attribution of use for owners of the data possible, the identifier of ownership of data must be retained with every data record. Users must publicly acknowledge, in conjunction with the use of the data, the data owners. Cite the data as follows: Nadrowski, K. (2013): Deviations from stem breaking probabilities at species level. BEF-China data portal (Accessed through URL http://china.befdata.biow.uni-leipzig.de/datasets/327)
Published:
No information available
Abstract:
The great 2008 ice storm destroyed considerable areas in the Gutianshan Nature Reserve. In the Comparative Study Plots (CSPs) we quantified the amount of wood downed by the ice storm (coarse woody debris data). Combining data on living and killed trees, we estimated a general unimodal stem breaking probability using a Ricker function. Here we give the averaged deviations from that unimodal function for each species. Positive deviations indicate increased risk to break compared with the general expectation, and negative values indicate a lower risk to break.
Design:
"In the 30x30m CSPs the central of 9 subplots was mapped in greater detail than the remaining 8 plots, resulting in more tree stems <10cm in the central plot only. We checked for duplicated stems based on the stemID. There is an attached files with an R script, with which the data were generated. ---- We used the Power Rickerts function (Bolker 2007) to estimate size dependent mortality due to the ice storm for all individuals. The function describes a uni-modal curve through the origin and returning to zero mortality risk for very large individuals:p=(p ) ̂(x/x ̂ e^((1-x/x ̂ )^α ) )The three parameters of the Power Rickerts function are maximum mortality ((p ) ̂), diameter at maximum mortality (x ̂), and a scaling factor increasing the slope of the curve reaching maximum mortality (α). We used Bayesian inference and Gibbs Sampling via Markov Chain Monte Carlo (JAGS: Plummer, 2003) with uninformative priors to derive mortality risk estimates for each tree individual and to estimate model parameters and their confidence intervals.
Spatial extent:
Comparative Study sites, BEF China Experiment, Gutianshan, China 29°08'-29°17'N 118°02'-118°11'E
Temporal extent:
Living stems in 2008, killed stems in 2008 and early 2009.
Taxonomic extent:
Tree species names as in the reference list for tree species.
Measurement cirumstances:
No information available
Data analysis:
No information available

Filter:
Dataset column

Name:
species
Definition:
species name; Datagroup description: Scientific plant species name; Datagroup description: Scientific plant species name; Source: Flora of China, Helge Bruelheide (derived from datagroup)
Unit:
No information available
Datagroup:
Scientific plant species name
Keywords:
species, explanatory
Values:
Camellia chekiangoleosa
Alniphyllum fortunei
Camellia fraterna
Albizia kalkora
Adinandra millettii

Dataset column

Name:
spec_fam
Definition:
combination of species an family name; Datagroup description: Helper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
Adinandra millettii:Theaceae
Alniphyllum fortunei:Styracaeae
Camellia chekiangoleosa:Theaceae
Camellia fraterna:Theaceae
Albizia kalkora:Fabaceae
Contributors:
No information available

Dataset column

Name:
family
Definition:
family name
Unit:
No information available
Datagroup:
Plant family name
Keywords:
family, taxon, explanatory
Values:
Elaeocarpaceae
Ebenaceae
Daphniphyllaceae
Aquifoliaceae
Anacardiaceae
Contributors:
No information available

Dataset column

Name:
fix_int
Definition:
general intecepts for deviations form expected breaking risk; Datagroup description: Helper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
-786.9786256
Contributors:
No information available

Dataset column

Name:
rand_family
Definition:
family effect on top of intercept; Datagroup description: Helper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
109.2303709
131.6553805
127.7812639
-103.367196
122.0830152
Contributors:
No information available

Dataset column

Name:
rand_spec_in_fam
Definition:
species within familiy effect; Datagroup description: Helper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
100.711451
-10.84664744
100.1699536
-108.9443843
106.2957885
Contributors:
No information available

Dataset column

Name:
rand_spec_stem_break
Definition:
species specific deviation from expected breaking risk, sum of intercept + family + spec in family
Unit:
No information available
Datagroup:
Deviation from expected stem breaking probability
Keywords:
response variable, stem break
Values:
-1084.23999
-1048.523531
-1016.321439
-1014.451898
-1003.106769
Contributors:
No information available

Dataset column

Name:
mixed_m_res
Definition:
residuals from the random component model with species and plots as random effects, to control phylogenetic independence of the model; Datagroup description: Helper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
-103.199347
113.1017029
1003.289224
-111.4491255
-0.071830465
Contributors:
No information available

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3 nadrowski medium

Karin
Nadrowski

Owner of:
44 Datasets

Involved in:
1 Projects

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