浅水湖泊底栖动物栖息地模拟
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摘要
为研究白洋淀水生生物栖息地适宜度,分别于2016年春、夏、秋3季进行了实地考察和采样,结合野外观测和实验室测量得到环境和生物信息。基于14个采样点的数据,通过构建广义可加模型(GAMs)模拟和预测底栖动物的分布。使用Margalef指数作为响应变量,采用向前逐步回归筛选解释环境因子,通过评价剩余偏差来判断模型的表现。水深、水温、底泥氨氮、底泥有机质和沉水植物生物量被用于建立最终优化的广义可加模型。Margalef指数对环境因子的响应曲线表明该指数与水深和底泥氨氮浓度成线性负相关,与水温和底泥有机质浓度成线性正相关,而与沉水植物生物量是单峰关系。结果表明,预测值与实测值呈现显著的强相关性(Pearson R~2=0.847,P<0.001),均方误差较小(MSE=0.013),模型性能表现良好。
To explore the habitat suitability of aquatic communities in the Baiyangdian Lake, field investigations and samplings are done in the spring, summer, and autumn of 2016. The environmental and biological information are obtained combing in-situ measurements and in-lab testings. Based on data of 14 sampling sites, generalized additive models(GAMs)are established to simulate and predict the distribution of macroinvertebrates. Using the Margalef index as a response variable,explanatory environmental variables are selected by stepwise regression, and model performances are evaluated by residual deviations. Finally, water depth,water temperature, ammonium nitrogen in sediment, organic matter in sediment, and biomass of submerged macrophytes are included in the optimized GAM. The response curves of the Margalef index to those five environmental factors showes that, the index have negative linear relations with water depth and ammonium nitrogen in sediment, positive linear relations with water temperature and organic matter in sediment, while a unimodal relation with biomass of submerged macrophytes.The prediction showes that a significant high correlation exist between the predicted values and measured values(Pearson R~2=0.847, P<0.001), with a low mean square error(MSE=0.013). The results confirm a good predicting performance of the optimized model.
To explore the habitat suitability of aquatic communities in the Baiyangdian Lake, field investigations and samplings are done in the spring, summer, and autumn of 2016. The environmental and biological information are obtained combing in-situ measurements and in-lab testings. Based on data of 14 sampling sites, generalized additive models(GAMs)are established to simulate and predict the distribution of macroinvertebrates. Using the Margalef index as a response variable,explanatory environmental variables are selected by stepwise regression, and model performances are evaluated by residual deviations. Finally, water depth,water temperature, ammonium nitrogen in sediment, organic matter in sediment, and biomass of submerged macrophytes are included in the optimized GAM. The response curves of the Margalef index to those five environmental factors showes that, the index have negative linear relations with water depth and ammonium nitrogen in sediment, positive linear relations with water temperature and organic matter in sediment, while a unimodal relation with biomass of submerged macrophytes.The prediction showes that a significant high correlation exist between the predicted values and measured values(Pearson R~2=0.847, P<0.001), with a low mean square error(MSE=0.013). The results confirm a good predicting performance of the optimized model.
引文
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[26] LI D,ERICKSON R A,TANG S,et al.Structure and spatial patterns of macrobenthic community in Tai Lake,a large shallow lake,China[J].Ecological Indicators,2016,61(2):179- 187.
[27] TAKAMURA N,ITO T,UENO R,et al.Environmental gradients determining the distribution of benthic macroinvertebrates in Lake Takkobu,Kushiro wetland,northern Japan[J].Ecological Research,2009,24(2):371- 381.
[28] FREEMAN S M,ROGERS S I.A new analytical approach to the characterisation of macro-epibenthic habitats:linking species to the environment[J].Estuarine Coastal & Shelf Science,2003,56(3):749- 764.
[2] DONOHUE I,JACKSON A L,PUSCH M T,et al.Nutrient enrichment homogenizes lake benthic assemblages at local and regional scales[J].Ecology,2009,90(12):3470- 3477.
[3] BAH A.A review of statistical methods for the evaluation of aquatic habitat suitability for instream flow assessment[J].River Research & Applications,2006,22(5):503- 523.
[4] AUSTIN M P.Spatial prediction of species distribution:an interface between ecological theory and statistical modelling[J].Ecological Modelling,2002,157(2):101- 118.
[5] GEVREY M,PARK Y S,VERDONSCHOT P F M,et al.Predicting Dutch macroinvertebrate species richness and functional feeding groups using five modelling techniques[M].Berlin:Springer Berlin Heidelberg,2005.
[6] YI Y,CHENG X,WIEPRECHT S,et al.Comparison of habitat suitability models using different habitat suitability evaluation methods[J].Ecological Engineering,2014,71:335- 345.
[7] YI Y,SUN J,ZHANG S,et al.Assessment of Chinese sturgeon habitat suitability in the Yangtze River (China):comparison of generalized additive model,data-driven fuzzy logic model,and preference curve model[J].Journal of Hydrology,2016,536:447- 456.
[8] MU?OZ-MAS R,MARTíNEZ-CAPEL F,SCHNEIDER M,et al.Assessment of brown trout habitat suitability in the Jucar River Basin (SPAIN):Comparison of data-driven approaches with fuzzy-logic models and univariate suitability curves[J].Science of the Total Environment,2012,440:123- 131.
[9] YI Y,CHENG X,YANG Z,et al.Evaluating the ecological influence of hydraulic projects:a review of aquatic habitat suitability models[J].Renewable and Sustainable Energy Reviews,2017,68:748- 762.
[10] YI Y,SUN J,ZHANG S.A habitat suitability model for Chinese sturgeon determined using the generalized additive method[J].Journal of Hydrology,2016,534:11- 18.
[11] MILNER A M,BRITTAIN J E,CASTELLA E,et al.Trends of macroinvertebrate community structure in glacier-fed rivers in relation to environmental conditions:a synthesis[J].Freshwater Biology,2001,46(12):1833- 1847.
[12] MARGALEF R.Information theory in ecology[J].General Systems.1958,3:36- 71.
[13] 赵晓辉,孙中孚.白洋淀干淀原因分析[J].河北水利,2006(11):38.
[14] XU MQ,ZHU J,HUANG YY,et al.The Ecological Degradation and Restoration of Baiyangdian Lake,China[J].Journal of Freshwater Ecology,1998,13(4):433- 446.
[15] 高彦春,王晗,龙笛.白洋淀流域水文条件变化和面临的生态环境问题[J].资源科学,2009,31(9):1506- 1513.
[16] 杨柳燕.中国湖泊水生态系统区域差异性[M].北京:科学出版社,2013.
[17] XU MQ,ZHU J,HUANG YY,et al.The ecological degradation and restoration of Baiyangdian Lake,China[J].Journal of Freshwater Ecology,1998,13(4):433- 446.
[18] 谢松,黄宝生,王宏伟,等.白洋淀底栖动物多样性调查及水质评价[J].水生态学杂志,2010,3(1):43- 48.
[19] HASTIE T,TIBSHIRANI R J.Generalized additive models[M].London:Chapman and Hall,1990.
[20] SIMON N.Generalized additive models:an introduction with R[M].London:Chapman & Hall/CRC,2006.
[21] 国家环境保护总局,国家质量监督检验检疫总局.地表水环境质量标准:GB 38382—2002[S].北京:中国环境科学出版社,2002.
[22] 郑丙辉,许秋瑾,朱延忠.湖泊营养盐控制标准制订方法的初步研究[J].环境科学,2009,30(9):2497- 2501.
[23] 段学花,王兆印,徐梦珍.底栖动物与河流生态评价[M].北京:清华大学出版社,2010.
[24] BEAUGER A,LAIR N,REYES-MARCHANT P,et al.The distribution of macroinvertebrate assemblages in a reach of the River Allier (France),in relation to riverbed characteristics[J].Hydrobiologia,2006,571(1):63- 76.
[25] SAVAGE A A.Density dependent and density independent relationships during a twenty-seven year study of the population dynamics of the benthic macroinvertebrate community of a chemically unstable lake[J].Hydrobiologia,1996,335(2):115- 131.
[26] LI D,ERICKSON R A,TANG S,et al.Structure and spatial patterns of macrobenthic community in Tai Lake,a large shallow lake,China[J].Ecological Indicators,2016,61(2):179- 187.
[27] TAKAMURA N,ITO T,UENO R,et al.Environmental gradients determining the distribution of benthic macroinvertebrates in Lake Takkobu,Kushiro wetland,northern Japan[J].Ecological Research,2009,24(2):371- 381.
[28] FREEMAN S M,ROGERS S I.A new analytical approach to the characterisation of macro-epibenthic habitats:linking species to the environment[J].Estuarine Coastal & Shelf Science,2003,56(3):749- 764.