CSPs: Comparative study plot (CSP) information to be shared with all BEF-China scientists

Usage Rights

This data is Free for members.

free within BEF China

Dataset Abstract

This research is designed to collect data on diversity in subtropical forest ecosystems and relate it to ecosystem services such as primary productivity, carbon storage, prevention of soil erosion, and invasion resistance to exotic plants. This data describes the Comparative Study Sites (CSPs), used to compare the planted diversity experiment with naturally grown forests. The plots have a size of 30x30m and are situated in the Gutianshan Nature reserve, Zhejiang Province, China. Data were collected in 2008. They comprise geographical locations, elevation, inclination, exposition of the plots, an estimated age of the plot, height and cover of two tree layers, the shrub layer and the herb layer, and cover of bare soil. This data set includes environmental measures on the CSPs that are of interest for all researchers.

Dataset Design

The utilization of the forest until the 1990ies allowed us to employ a stratified sampling for establishing observational plots (in the following called Comparative Study Plots, CSPs) according to successional stages. Although most of the forest are belongs to intermediate or late successional stages, young stands are located in the peripheral buffer zone of the Gutianshan NNR, where logging has been performed until the present. Plots were assigned to five strata according to the average age of tree layer individuals (1: < 20 yrs, 2: < 40 yrs, 3: < 60 yrs, 4: < 80 yrs, 5:  80 yrs). CSP locations within strata were selected randomly; however, due to inaccessibility and excessive slopes (> 50°) of many locations, parts to the NNR had to be excluded from sampling, thus, resulting in an uneven distribution of some of the plots (Fig. 1). In total, 27 CSPs were established between May and July 2008. Species recording was performed between May and October 2008 with several visits per plot. Each CSP has a size of 30 m by 30 m, which approximates to the plot size (1 mu plot) in the BEF-Experiment at Xingangshan (Jiangxi, Fig. 1). The corners of every CSP were permanently marked with magnets and subareas of each CSP were set apart for measuring various ecosystem functioning variables, among them basic soil properties. Soil moisture was assessed on soil samples taken from five different layers of the mineral soil in 10 cm-intervals on three dates (summer and autumn 2008, spring 2009). We used mean values by averaging the soil water contents over all layers and all dates. Topsoil samples (0-5 cm) were taken in summer 2009 from four locations in each plot, pooled in a bulk sample per CSP, air-dried and then used to determine pH, both measured in H2O and 1 M KCl. A complete inventory of woody species and bamboo (Pleioblastus amarus) (> 1 m height) was carried out on the whole plot. All herbaceous species and tree recruits (i.e. seedlings and saplings ≤ 1 m height) were recorded in a central subplot of 10 m x 10 m. All individuals were identified to the species level, making use of herbarium samples and comparisons with correctly identified individuals, and counted per species. The proportion of unidentifiable individuals in a CSP ranged between 0 % and 2.3 %. These individuals were not included in the subsequent data analysis.

Spatial Extent

The Gutianshan National Nature Reserve (NNR) is located in the western part of Zhejiang Province (29º8'18" – 29º17'29" N, 118º2'14" – 118º11'12" E, Fig. 1). The Gutianshan NNR has an area of approximately 81 km2 and was initially established as a National Forest Reserve in 1975 and became a National Nature Reserve in 2001. The NNR comprises a large portion of broad-leaved forests of advanced successional stages (Hu & Yu 2008), which have not been managed since the beginning of the 1990ies, as well as young successional stages and conifer plantations, mainly of Cunninghamia lanceolata and Pinus massoniana. --- The vegetation is composed of different types of subtropical evergreen and mixed broad-leaved forests (Yu et al. 2001). Most of the stands are secondary forests, evidenced by maximum tree ages of 180 years, by agricultural terraces in almost all plots and by the presence of charcoal in almost all soil profiles. Around the Gutianshan NRR extensive deforestation has occurred during the Great Leap Forward in the 1950s, as in most parts of Southeast China. However, due to prevailing steep slopes, the Gutianshan area was only marginally usable for agricultural activities, and thus an exceptionally intact forest cover has been preserved. --- The climate at Gutianshan NNR is warm and temperate with a short dry season in November and December and with warm summers (Fig. 2). The climatic conditions are characteristic for the subtropics with an annual average temperature of 15.1°C, January minimum temperatures of -6.8°C, July maximum temperatures of 38.1°C and an accumulated temperature sum (≥ 5°C) of 5221.5 degree days.

Published

Ecology monograph paper, doi: 10.1890/09-2172.1

Temporal Extent

Most of the data were collected in 2008, while some additional data were also collected in 2009.

Taxonomic Extent

The forest is representative for Chinese mixed broad-leaved forests (Wu 1980, Hu & Yu 2008, Legendre et al. 2009), with evergreen species dominating the forest in number of individuals (Yu et al. 2001) but with approximately similar proportions of deciduous species in terms of species number (Lou and Jin 2000). A total of 1426 seed plant species of 648 genera and of 149 families has been recorded as occurring naturally in the NNR. About 258 of the species are woody (Lou and Jin 2000).

Data Analysis

Data can be used as covariates in other data sets. Successional age (Age of the 5th largest tree) and diersity of tree species are the main explanatory variables (species number)

Paper proposal submissions

Published

2016

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2012

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2010

Data columns available in the raw data part of this dataset

date
year of samplingDate time information
Data group: Date time information
Keywords: date
Values
2008
* Böhnke, M. also contributed to this column.
CSP
CSP nameBEF research plot name Reasearch plots of the Biodiversity - Ecosystem functioning experiment (BEF-China). There are three main sites for research plots in the BEF Experiment: Comparative Study Plots (CSP) in the Gutianshan Nature Reserve, having a size of 30x30m^2, measured on the ground.
Data group: BEF research plot name
Keywords: CSP, location
Values
CSP04
CSP01
CSP05
CSP03
CSP02
* Böhnke, M. also contributed to this column.
Elevation
Elevation (in m above sea level)of the sampled plot, measured with geko201 GPS; Elevation;; ; GIS, Hypsometer, Interpolation from map (derived from datagroup); Instrumentation: GIS, Hypsometer, Interpolation from map (derived from datagroup)
Unit: meters
Data group: Elevation
Keywords: elevation, co-variable
Values
251
345
309
310
348
slope_mean
Calculated plot mean slope inclination from geomorphological maps
Unit: °
Data group: Inclination
Keywords: slope inclination
Values
15.1
19.2
13.777777777777779
27.8
26.444444444444443
slope_sd
standard deviation of plot mean inclination
Unit: degree
Data group: Standard deviation
Keywords: slope inclination
Values
10.119817897853414
2.6193722742502854
11.05792827693225
1.5275252316519468
12.576874722194612
aspect_mean
mean aspect in degree calculated from geomorphological maps and SRTM 90 (only CSP15, 24). MOD(360+ARCTAN2(x;y)*(180/pi);360); y = Sinus, x = Cosinus (in degree); for y > 0, x >= y: Arctan2 = Arctan(y/x); x <= -y: Arctan2 = Arctan(y/x) + Pi; all others : Arctan2 = Pi/2 – Arctan(x/y);; for y < 0, x >= -y: Arctan2 = Arctan(y/x); x <= y: Arctan2 = Arctan(y/x) - Pi ; all others: Arctan2 = -Arctan(x/y) - Pi/2
Unit: °
Data group: Aspect
Keywords: slope aspect
Values
178.9850563181652
148.92323987500768
177.60944155383575
183.77602838428052
104.32822140818539
aspect_sd
standard deviation of plot mean aspect
Unit: degree
Data group: Standard deviation
Keywords: aspect
Values
15.500537625084709
22.34800513095817
25.796825201489202
112.24972160321825
13.703203194062977
aspect_northness
northness of aspect calculated as: cos((aspect in degree*PI)/180); these values are continous and can be can be used in linear models
Unit: rad
Data group: Aspect
Keywords: slope aspect; northness
Values
-0.002895137873659055
-0.050711685913604886
-0.05931145397168294
-0.03355433700042952
0.02410060211644457
aspect_eastness
eastness of aspect calculated as: sin((aspect in degree*PI)/180); these values are continous and can be used in linear models
Unit: rad
Data group: Aspect
Keywords: slope aspect; eastness
Values
-0.06585643082005667
0.04171101150594651
-0.029965347396781083
0.017713182567503954
-0.06975647374412483
Coordinates_N
latitude, measured with geko 201, mind that some of the coordinates were not helpful in finding the plot. It is not clear, if they all refer to the south-west corner of the plot.Latitude
Unit: WGS 84, Dezimalgrad: hddd.ddddd degree
Data group: Latitude
Keywords: latitude, location
Values
29.21483
29.21489
29.21713
29.23885
29.2145
Coordinates_E
longitude, measured with geko 201, mind that some of the coordinates were not helpful in finding the plot. It is not clear, if they all refer to the south-west corner of the plot.Longitude
Unit: WGS 84, Dezimalgrad: hddd.ddddd degree
Data group: Longitude
Keywords: longitude, location
Values
118.08084
118.08389
118.09066
118.08803
118.09966
Coordinates_ValueComment
comment to measured values in the columns coordinatesHelper
Data group: Helper
Values
coordinates helpful, correct
not visited
coordinates were misleading
successional_stage
successional age of the CSP, updated by Helge Bruelheide, Goddert v Oheimb, Karin NadrowskiSuccessional age of a forest plot
Data group: Successional age of a forest plot
Keywords: successional age, explanatory
Values
3
5
1
4
2
tree_age_max5
plot age as estimated by the age of the 5th largest treeSuccessional age of a forest plot
Unit: years
Data group: Successional age of a forest plot
Keywords: successional age, explanatory
Values
115.542037959348
106.200269215238
105.769524835106
21.7205276618657
101.300551891237
T1_height
height of highest tree layer. Height of vegetation layers was estimated as mean value from four different spots within the CSP, with simultaneous estimates by four different researchers.Vegetation layer height
Unit: meter
Data group: Vegetation layer height
Keywords: tree layer, height, co-variable
Values
20
25
18
22
15
T1_cover
cover of highest tree layer, cover of vegetation layers was estimated as a mean of four different spots within the CSP, with simultaneous estimates by four different researchers. Note that climbers were excluded from estimation.; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: tree layer, cover, co-variable
Values
25
15
10
0
20
T2_height
height of intermediate tree layer. Height of vegetation layers was estimated as mean value from four different spots within the CSP, with simultaneous estimates by four different researchers.Vegetation layer height
Unit: meter
Data group: Vegetation layer height
Keywords: tree layer, height, co-variable
Values
14
18
12
10
15
T2_cover
cover of intermediate tree layer, cover of vegetation layers was estimated as a mean of four different spots within the CSP, with simultaneous estimates by four different researchers. Note that climbers were excluded from estimation.; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: tree layer, cover, co-variable
Values
15
25
10
30
20
SL_height
height of shrub layerVegetation layer height
Unit: meter
Data group: Vegetation layer height
Keywords: height, shrub layer, co-variable
Values
5
4
3
6
SL_cover
cover of shrub layer; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: cover, shrub layer, co-variable
Values
15
25
30
20
10
HL_height
height of herb layer. The her layer was defined to be 1 m and was not measured.Vegetation layer height
Unit: meter
Data group: Vegetation layer height
Keywords: herb layer
Values
1
HL_cover
cover of herb layer. Cover of the herb layer was estimated by Sabine Both in the central plot of the CSP.; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: herb layer, cover, co-variable
Values
1
20
25
2
15
Open_soil
soil cover. Estimated in the central plot of the CSP.; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: soil, cover, co-variable
Values
5
0
1
2
7
fallen_wood
fallen wood cover estimation. Estimated in the central plot of the CSP.; Datagroup description: Estimated cover when looking from above. Cover can be estimated in percent, but there are also ordinal classifications of cover, such as Londo or Braun Blanquet.
Unit: %
Data group: Vegetation layer cover
Keywords: cover, woody debris, co-variable
Values
10
15
2
20
1
* Böhnke, M. also contributed to this column.
N_individuals
Number of tree and shrub individuals
Unit: count
Data group: Abundance
Keywords: abundance, density, explanatory
Values
1233
1135
245
1195
207
N_adult_species
Number of tree and shrub species. Trees were counted when they exceeded 1m height. This data is aggregated from the raw data provided by Martin Böhnke.Biodiversity; Taxon diversity can be given as species richness, or other diversity indices. We also use rarefied species richness, shannon diversity index, and phylogenetic diversity indices.;; Source: Oksanen, J.; Kindt, R.; Legendre, P.; O'Hara, B.; Simpson, G. L.; Solymos, P.; Stevens, M. H. H. & Wagner, H. vegan: Community Ecology Package 2008, Ricotta, C. A semantic taxonomy for diversity measures Acta Biotheoretica, 2007, 55, 23-33 ., "Dray, S. an (derived from datagroup)
Unit: count
Data group: Taxonomic biodiversity
Keywords: tree layer, explanatory
Values
29
27
32
30
25
N_herb_species
Number of herb species, excluding woody species and climbers. Herbs were considered those plants that are less then 1 m high. This data is aggregated data from Sabine Both.Biodiversity; Taxon diversity can be given as species richness, or other diversity indices. We also use rarefied species richness, shannon diversity index, and phylogenetic diversity indices.;; Source: Oksanen, J.; Kindt, R.; Legendre, P.; O'Hara, B.; Simpson, G. L.; Solymos, P.; Stevens, M. H. H. & Wagner, H. vegan: Community Ecology Package 2008, Ricotta, C. A semantic taxonomy for diversity measures Acta Biotheoretica, 2007, 55, 23-33 ., "Dray, S. an (derived from datagroup)
Unit: count
Data group: Taxonomic biodiversity
Keywords: herb layer, biodiversity, explanatory
Values
12
2
11
21
3
rarefy_100
estimate of species number by randomly drawing 100 individuals (rarefaction) Rarefaction curves show the increase in species number with an increase of sampled individuals. R uses rarefy() from the package vegan to estimated species number for a given number of individuals. To compare different plots, the number of individuals should be smaller than the minimum number of individuals.; ; Oksanen, J.; Kindt, R.; Legendre, P.; O'Hara, B.; Simpson, G. L.; Solymos, P.; Stevens, M. H. H. & Wagner, H. vegan: Community Ecology Package 2008, Ricotta, C. A semantic taxonomy for diversity measures Acta Biotheoretica, 2007, 55, 23-33 ., "Dray, S. an (derived from datagroup); Source: Oksanen, J.; Kindt, R.; Legendre, P.; O'Hara, B.; Simpson, G. L.; Solymos, P.; Stevens, M. H. H. & Wagner, H. vegan: Community Ecology Package 2008, Ricotta, C. A semantic taxonomy for diversity measures Acta Biotheoretica, 2007, 55, 23-33 ., "Dray, S. an (derived from datagroup)
Unit: count
Data group: Taxonomic biodiversity
Keywords: biodiversity, rarefied diversity, explanatory
Values
18.11426524
15.25412562
16.70766334
14.1498851
16.77861972
Fdleaf
functional diversity of tree traits; The Functional Diversity (FD) was measured for each CSP by calculating Rao’s Quadratic Entropy Rao. The index takes the relative abundances of species and the pairwise functional differences between species into account. Based on leaf traits only.Functional biodiversity
Data group: Functional biodiversity
Keywords: functional biodiversity, explanatory
Values
0.31
0.32
0.28
0.27
0.33
FD_Qall_traits
Functional diversity all tree traitsFunctional biodiversity
Data group: Functional biodiversity
Keywords: functional biodiversity, explanatory
Values
0.2880409
0.3143548
0.2944587
0.3102272
0.3125552
t_cover
cumulative tree layer cover; Datagroup description: Vegetation layer cover
Unit: %
Data group: Vegetation layer cover
Keywords: cover, co-variable, trees
Values
32.5
28
23.5
36
20.95
sum_cover
cumulative tree and shrub layer cover; Datagroup description: Vegetation layer cover
Unit: %
Data group: Vegetation layer cover
Keywords: cover, co-variable, trees, shrub
Values
42.625
43.3
31.6
38.8
39.25