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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

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Front Earth Sci    2012, Vol. 6 Issue (1) : 57-65    https://doi.org/10.1007/s11707-012-0307-1
RESEARCH ARTICLE
Ecology of testate amoebae in Dajiuhu peatland of Shennongjia Mountains, China, in relation to hydrology
Yangmin QIN1,2(), Richard J PAYNE3, Yansheng GU1, Xianyu HUANG4, Hongmei WANG1
1. State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China; 2. Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130012, China; 3. School of Science and the Environment, Manchester Metropolitan University, Manchester M1 5GD, UK; 4. State Key Laboratory of Geological Processes and Mineral Resource, China University of Geosciences, Wuhan 430074, China
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Abstract

This study investigates the testate amoeba communities of a large peatland in Central China. The ecology and seasonal variability of testate amoeba communities were studied during 2009–2010. Investigation of environmental controls using ordination showed that the relationship between testate amoeba communities and depth to water table (DWT) and pH are extremely weak. The small proportion of variance explained by water table depth here (only 1.9% in the full data) shows that the hydrological control is weaker than we expected in this peatland, and weaker than any study we are aware of using a similar methodology. Attempts to develop species-environment (transfer function) models or identify indicator species for future palaeoecological studies were unsuccessful. Previous large-scale studies of peatland testate amoeba ecology have been largely restricted to Europe and North America and results have been relatively consistent among studies. Our results contrast with this consensus and suggest that at least in minerotrophic peatlands in China testate amoeba communities may be primarily controlled by different environmental variables. In China, testate amoebae have been relatively little studied but may prove to be valuable for a variety of applications in palaeoecology and biomonitoring and much further work is required.

Keywords testate amoebae      ecology      minerotrophic      Dajiuhu peatland      China     
Corresponding Author(s): QIN Yangmin,Email:qymcug@yahoo.com.cn   
Issue Date: 05 March 2012
 Cite this article:   
Yangmin QIN,Richard J PAYNE,Yansheng GU, et al. Ecology of testate amoebae in Dajiuhu peatland of Shennongjia Mountains, China, in relation to hydrology[J]. Front Earth Sci, 2012, 6(1): 57-65.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-012-0307-1
https://academic.hep.com.cn/fesci/EN/Y2012/V6/I1/57
DataExplanatory variables*Co-variables% variance explainedP
AllDWT, pH, Time-14.70.001
AllDWTTime, pH1.90.004
AllpHDWT, Time(1.0)ns
AllTimeDWT, pH9.60.001
2009DWT, pH-6.50.001
2009DWTpH2.40.023
2009pHDWT4.00.001
Jul-2010DWT, pH, Plants-31.70.019
Jul-2010DWTPlants, pH(3.7)ns
Jul-2010pHDWT, Plants(3.8)ns
Jul-2010PlantsDWT, pH19.70.015
Sep-2010DWT, pH, Plants-29.70.008
Sep-2010DWTPlants, pH13.00.007
Sep-2010pHDWT, Plants(6.5)ns
Sep-2010PlantsDWT, pH(13.1)ns
All 2010DWT, pH, Plants, Time-27.00.001
All 2010DWTPlants, pH, Time4.60.01
All 2010pHDWT, Plants, Time(1.8)ns
All 2010PlantsDWT, pH, Time16.20.001
All 2010TimePlants, pH, DWT5.40.005
Tab.1  Results of redundancy analysis of square root-transformed testate amoeba data, showing % variance explained by environmental variables and -value determined by 999 Monte Carlo permutations. Analyses were conducted using five different combinations of samples from the three sampling times
Fig.1  PCA ordination based on full data set. Plot (a) shows samples and plot (b) shows selected major species (>1% total) well-fitted by the ordination
ModelRMSEPjackRMSEPbootRjack2Rboot2MaxBiasjackMaxBiasboot
Weighted averaging (tolerance downweighting, inverse regression)*10.510.70.080.1030.431.1
Maximum likelihood11.412.50.140.3324.728.4
Tab.2  Transfer function performance of model structures tested in this study showing root mean squared error of prediction (RMSEP), and maximum bias (MaxBias) assessed by boot-strap (‘boot’) or jack-knife (‘jack’) cross-validation for DWT
Fig.2  Performance of testate amoeba-hydrology transfer functions developed using (a) weighted averaging and (b) maximum likelihood. See Table 2 for details
Fig.3  Relative abundance of four testate amoeba taxa along the hydrological gradient. (a) ; (b) type; (c) ; (d)
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