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Title:
CSPs: Comparative study plot (CSP) information to be shared with all BEF-China scientists
Access rights:
Free for members
Usage rights:
free within BEF China
Published:
Ecology monograph paper, doi: 10.1890/09-2172.1
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.
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.
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).
Measurement cirumstances:
No information available
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)

Filter:
Dataset column

Name:
date
Definition:
year of samplingDate time information
Unit:
No information available
Datagroup:
Date time information
Keywords:
date
Values:
2008
Contributors:

Dataset column

Name:
CSP
Definition:
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.
Unit:
No information available
Datagroup:
BEF research plot name
Keywords:
CSP, location
Values:
CSP05
CSP01
CSP02
CSP04
CSP03
Contributors:

Dataset column

Name:
Elevation
Definition:
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
Datagroup:
Elevation
Keywords:
elevation, co-variable
Values:
345
348
251
310
309
Contributors:
No information available

Dataset column

Name:
slope_mean
Definition:
Calculated plot mean slope inclination from geomorphological maps
Unit:
°
Datagroup:
Inclination
Keywords:
slope inclination
Values:
27.8
15.1
19.2
13.777777777777779
26.444444444444443
Contributors:
No information available

Dataset column

Name:
slope_sd
Definition:
standard deviation of plot mean inclination
Unit:
degree
Datagroup:
Standard deviation
Keywords:
slope inclination
Values:
12.576874722194612
10.119817897853414
1.5275252316519468
2.6193722742502854
11.05792827693225
Contributors:
No information available

Dataset column

Name:
aspect_mean
Definition:
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:
°
Datagroup:
Aspect
Keywords:
slope aspect
Values:
104.32822140818539
148.92323987500768
183.77602838428052
177.60944155383575
178.9850563181652
Contributors:
No information available

Dataset column

Name:
aspect_sd
Definition:
standard deviation of plot mean aspect
Unit:
degree
Datagroup:
Standard deviation
Keywords:
aspect
Values:
13.703203194062977
22.34800513095817
15.500537625084709
112.24972160321825
25.796825201489202
Contributors:
No information available

Dataset column

Name:
aspect_northness
Definition:
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
Datagroup:
Aspect
Keywords:
slope aspect; northness
Values:
-0.050711685913604886
0.02410060211644457
-0.002895137873659055
-0.03355433700042952
-0.05931145397168294
Contributors:
No information available

Dataset column

Name:
aspect_eastness
Definition:
eastness of aspect calculated as: sin((aspect in degree*PI)/180); these values are continous and can be used in linear models
Unit:
rad
Datagroup:
Aspect
Keywords:
slope aspect; eastness
Values:
-0.029965347396781083
0.04171101150594651
0.017713182567503954
-0.06975647374412483
-0.06585643082005667
Contributors:
No information available

Dataset column

Name:
Coordinates_N
Definition:
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
Datagroup:
Latitude
Keywords:
latitude, location
Values:
29.21483
29.23885
29.21713
29.21489
29.2145
Contributors:
No information available

Dataset column

Name:
Coordinates_E
Definition:
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
Datagroup:
Longitude
Keywords:
longitude, location
Values:
118.08084
118.09966
118.08389
118.08803
118.09066
Contributors:
No information available

Dataset column

Name:
Coordinates_ValueComment
Definition:
comment to measured values in the columns coordinatesHelper
Unit:
No information available
Datagroup:
Helper
Keywords:
No information available
Values:
coordinates helpful, correct
not visited
coordinates were misleading
Contributors:
No information available

Dataset column

Name:
successional_stage
Definition:
successional age of the CSP, updated by Helge Bruelheide, Goddert v Oheimb, Karin NadrowskiSuccessional age of a forest plot
Unit:
No information available
Datagroup:
Successional age of a forest plot
Keywords:
successional age, explanatory
Values:
2
5
3
1
4
Contributors:
No information available

Dataset column

Name:
tree_age_max5
Definition:
plot age as estimated by the age of the 5th largest treeSuccessional age of a forest plot
Unit:
years
Datagroup:
Successional age of a forest plot
Keywords:
successional age, explanatory
Values:
106.200269215238
115.542037959348
21.7205276618657
101.300551891237
105.769524835106
Contributors:
No information available

Dataset column

Name:
T1_height
Definition:
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
Datagroup:
Vegetation layer height
Keywords:
tree layer, height, co-variable
Values:
15
22
25
18
20
Contributors:
No information available

Dataset column

Name:
T1_cover
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
tree layer, cover, co-variable
Values:
0
25
10
20
15
Contributors:
No information available

Dataset column

Name:
T2_height
Definition:
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
Datagroup:
Vegetation layer height
Keywords:
tree layer, height, co-variable
Values:
15
18
10
14
12
Contributors:
No information available

Dataset column

Name:
T2_cover
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
tree layer, cover, co-variable
Values:
15
10
20
25
30
Contributors:
No information available

Dataset column

Name:
SL_height
Definition:
height of shrub layerVegetation layer height
Unit:
meter
Datagroup:
Vegetation layer height
Keywords:
height, shrub layer, co-variable
Values:
6
3
5
4
Contributors:
No information available

Dataset column

Name:
SL_cover
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
cover, shrub layer, co-variable
Values:
25
30
15
10
20
Contributors:
No information available

Dataset column

Name:
HL_height
Definition:
height of herb layer. The her layer was defined to be 1 m and was not measured.Vegetation layer height
Unit:
meter
Datagroup:
Vegetation layer height
Keywords:
herb layer
Values:
1
Contributors:
No information available

Dataset column

Name:
HL_cover
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
herb layer, cover, co-variable
Values:
2
25
15
1
20
Contributors:
No information available

Dataset column

Name:
Open_soil
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
soil, cover, co-variable
Values:
5
1
2
0
7
Contributors:
No information available

Dataset column

Name:
fallen_wood
Definition:
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:
%
Datagroup:
Vegetation layer cover
Keywords:
cover, woody debris, co-variable
Values:
15
2
20
1
10
Contributors:

Dataset column

Name:
N_individuals
Definition:
Number of tree and shrub individuals
Unit:
count
Datagroup:
Abundance
Keywords:
abundance, density, explanatory
Values:
1135
1195
207
1233
245
Contributors:
No information available

Dataset column

Name:
N_adult_species
Definition:
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
Datagroup:
Taxonomic biodiversity
Keywords:
tree layer, explanatory
Values:
32
30
29
25
27
Contributors:
No information available

Dataset column

Name:
N_herb_species
Definition:
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
Datagroup:
Taxonomic biodiversity
Keywords:
herb layer, biodiversity, explanatory
Values:
11
2
3
12
21
Contributors:
No information available

Dataset column

Name:
rarefy_100
Definition:
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
Datagroup:
Taxonomic biodiversity
Keywords:
biodiversity, rarefied diversity, explanatory
Values:
18.11426524
15.25412562
16.77861972
16.70766334
14.1498851
Contributors:
No information available

Dataset column

Name:
Fdleaf
Definition:
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
Unit:
No information available
Datagroup:
Functional biodiversity
Keywords:
functional biodiversity, explanatory
Values:
0.27
0.31
0.28
0.32
0.33
Contributors:
No information available

Dataset column

Name:
FD_Qall_traits
Definition:
Functional diversity all tree traitsFunctional biodiversity
Unit:
No information available
Datagroup:
Functional biodiversity
Keywords:
functional biodiversity, explanatory
Values:
0.3125552
0.3102272
0.2880409
0.2944587
0.3143548
Contributors:
No information available

Dataset column

Name:
t_cover
Definition:
cumulative tree layer cover; Datagroup description: Vegetation layer cover
Unit:
%
Datagroup:
Vegetation layer cover
Keywords:
cover, co-variable, trees
Values:
20.95
23.5
32.5
28
36
Contributors:
No information available

Dataset column

Name:
sum_cover
Definition:
cumulative tree and shrub layer cover; Datagroup description: Vegetation layer cover
Unit:
%
Datagroup:
Vegetation layer cover
Keywords:
cover, co-variable, trees, shrub
Values:
39.25
31.6
43.3
42.625
38.8
Contributors:
No information available

No information available


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Filter:
8 bruelheide medium

Helge
Bruelheide

Owner of:
53 Datasets

Involved in:
7 Projects

E-Mail Profile
3 nadrowski medium

Karin
Nadrowski

Owner of:
44 Datasets

Involved in:
1 Projects

E-Mail Profile
No information available

Filter:

Scale-dependent diversity patterns affect spider assemblages of two contrasting forest ecosystems

Abstract:
. Spiders are important generalist predators in forests. However, differences in assemblage structure and diversity can have consequences for their functional impact and conservation approaches. Such differences are particularly evident across lat...

Id: 56
Users: 4
Datafiles: 1
Attachments: 2
Board: Final
State: Accepted
Created at: 2012-05-11
Updated at: 2017-02-09

Horizontal, but not vertical canopy structure is related to stand functional diversity in a subtropical slope forest (DOI: 10.1007/s11284-011-0887-3

Abstract:
In biodiversity research, niche complementarity is seen as one mechanism with which species coexistence is maintained. Niche complementarity also results in a more efficient use of the total resource pool, and thus, species-rich communities should...

Id: 22
Users: 15
Datafiles: 4
Attachments: 2
Board: Final
State: Accepted
Created at: 2011-01-12
Updated at: 2015-04-25

Tree morphology responds to neighbourhood competition and slope in species-rich forests in subtropical China, doi:10.1016/j.foreco.2010.08.015

Abstract:
A) Species-specific differences in growth responses of crown and stem. Data of projected crown area, crown displacement and stem inclination are compared. B) The morphological response of crown and stem is influenced by biotic and abiotic param...

Id: 6
Users: 14
Datafiles: 3
Attachments: 2
Board: Final
State: Accepted
Created at: 2010-04-13
Updated at: 2015-04-25

Community assembly of ectomycorrhizal fungi along a subtropical secondary forest succession

Abstract:
 Environmental selection and dispersal limitation are two of the primary processes structuring biotic communities in ecosystems, but little is known about these processes in shaping the soil microbial community during secondary forest succession....

Id: 50
Users: 26
Datafiles: 16
Attachments: 1
Board: Final
State: Accepted
Created at: 2012-02-23
Updated at: 2015-04-25

Tree diversity promotes functional dissimilarity and maintains functional richness despite species loss in predator assemblages

Abstract:
The effects of species loss on ecosystems depend on the functional characteristics of the species, i.e. the functional diversity, in a community. However, how functional diversity is impacted by environmental changes in natural ecosystems is only ...

Id: 64
Users: 14
Datafiles: 3
Attachments: 1
Board: Final
State: Accepted
Created at: 2012-12-05
Updated at: 2017-02-09

Functional and phylogenetic dissimilarity of woody plants drive insect herbivory in highly diverse forests

Abstract:
The degree of herbivore damage a plant species experiences has been shown to often be affected by the density of conspecific plant neighbors. However, host spectra of many herbivores often comprise a suite of related plant species, and generalist ...

Id: 69
Users: 19
Datafiles: 7
Attachments: 1
Board: Accept
State: Accepted
Created at: 2013-01-19
Updated at: 2015-02-09

Species diversity and population density affect genetic structure and gene dispersal in a subtropical understory shrub. doi:10.1093/jpe/rtr029

Abstract:
A) Description of population genetic structure of understorey shrub Ardisia crenata in CSPs with microsatellite markers B) Effect of population density on small scale genetic structure C) Test for effects of environmental variables (e.g. succes...

Id: 14
Users: 9
Datafiles: 2
Attachments: 2
Board: Prep
State: Accepted
Created at: 2010-05-03
Updated at: 2013-01-19

Estimation of throughfall erosivity in a highly diverse forest ecosystem using sand-filled splash cups. doi:10.1007/s12583-010-0132-y

Abstract:
A) application of the splash cup technique under forest vegetation B) compare rainfall erosivity and throughfall erosivity [11] Geißler, C., Kühn, P., She, X., Scholten, T. (2010): Estimation of throughfall erosivity in a highly diverse forest...

Id: 21
Users: 9
Datafiles: 2
Attachments: 2
Board: Prep
State: Accepted
Created at: 2010-10-06
Updated at: 2013-01-19

Tree species traits but not diversity mitigate stem breakage in a subtropical forest following a rare and extreme ice storm

Abstract:
Future climates are likely to include extreme events, which in turn have great impacts on ecological systems. In this study, we investigated possible effects that could mitigate stem breakage caused by a rare and extreme ice storm in a Chinese sub...

Id: 7
Users: 33
Datafiles: 11
Attachments: 1
Board: Final
State: Accepted
Created at: 2010-03-16
Updated at: 2016-12-10

Tree species richness strengthens relationships between ants and the functional composition of spider assemblages in a highly diverse forest

Abstract:
In species-rich ecosystems such as (sub)tropical forests, higher trophic level interactions often play key functional roles. Plant species loss may alter these interactions, but particularly among predators intraguild interactions might modify net...

Id: 116
Users: 6
Datafiles: 2
Attachments: 1
Board: Submit
State: Accepted
Created at: 2014-06-18
Updated at: 2015-01-28

Tree diversity promotes predator but not omnivore ants in a subtropical Chinese forest

Abstract:
1. Epigeic ants are functionally important arthropods in tropical and subtropical forests, particularly by acting as predators. High predation pressure has been hypothesized to be a mechanism facilitating high diversity across trophic levels. 2. ...

Id: 86
Users: 9
Datafiles: 2
Attachments: 1
Board: Final
State: Accepted
Created at: 2013-06-20
Updated at: 2015-08-30

Tree phylogenetic diversity promotes host-parasitoid interactions

Abstract:
Evidence from grassland experiments suggests that a plant community’s phylogenetic diversity (PD) is a strong predictor of ecosystem processes, even stronger than species richness per se. This has, however, never been extended to species-rich fore...

Id: 148
Users: 12
Datafiles: 2
Attachments: 0
Board: Final
State: Accepted
Created at: 2015-02-11
Updated at: 2018-07-26

Impact of species diversity, stand age and environmental factors on leaf litter decomposition in subtropical forests in China

Abstract:
Tree diversity is considered to influence decomposition either by changing environmental conditions or by non-additive litter mixture effects. Thus, we examined the influence of tree species richness, forest age and environmental factors on single...

Id: 164
Users: 14
Datafiles: 7
Attachments: 0
Board: Prep
State: Accepted
Created at: 2016-01-08
Updated at: 2016-01-08

Biodiversity Promotes Tree Growth during Succession in Subtropical Forest

Abstract:
Losses of plant species diversity can affect ecosystem functioning, with decreased primary productivity being the most frequently reported effect in experimental plant assemblages, including tree plantations. Less is known about the role of biodiv...

Id: 100
Users: 18
Datafiles: 7
Attachments: 1
Board: Final
State: Accepted
Created at: 2013-11-27
Updated at: 2015-11-27

Ant community structure during forest succession in a subtropical forest in South-East China

Abstract:
Understanding how communities respond to environmental gradients is critical to predict responses of species to changing habitat conditions such as in regenerating secondary habitats after human land use. In this study, ground-living ants were sam...

Id: 101
Users: 10
Datafiles: 2
Attachments: 0
Board: Final
State: Accepted
Created at: 2013-12-04
Updated at: 2017-02-09

Woody plant phylogenetic diversity mediates bottom-up control of arthropod biomass in a species-rich forest

Abstract:
Effects of global change are predicted to cause non-random species loss in plant communities, with important consequences for ecosystem functioning. However, little is known about how plant diversity and the loss of this diversity influence the st...

Id: 102
Users: 13
Datafiles: 4
Attachments: 0
Board: Accept
State: Accepted
Created at: 2013-12-12
Updated at: 2015-02-25
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