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王海军, 王洪铸, 赵永晶, 潘保柱, 舒凤月, 冯伟松, 梁小民. 长江中下游浅水湖群底栖动物资源量宏观格局与预测模型[J]. 水生生物学报, 2019, 43(S1): 18-26. DOI: 10.7541/2019.163
引用本文: 王海军, 王洪铸, 赵永晶, 潘保柱, 舒凤月, 冯伟松, 梁小民. 长江中下游浅水湖群底栖动物资源量宏观格局与预测模型[J]. 水生生物学报, 2019, 43(S1): 18-26. DOI: 10.7541/2019.163
WANG Hai-Jun, WANG Hong-Zhu, ZHAO Yong-Jing, PAN Bao-Zhu, SHU Feng-Yue, FENG Wei-Song, LIANG Xiao-Min. MACRO-PATTERNS AND PREDICTIVE MODELS OF ZOOBENTHOS ON MID-LOWER YANGTZE SHALLOW LAKES[J]. ACTA HYDROBIOLOGICA SINICA, 2019, 43(S1): 18-26. DOI: 10.7541/2019.163
Citation: WANG Hai-Jun, WANG Hong-Zhu, ZHAO Yong-Jing, PAN Bao-Zhu, SHU Feng-Yue, FENG Wei-Song, LIANG Xiao-Min. MACRO-PATTERNS AND PREDICTIVE MODELS OF ZOOBENTHOS ON MID-LOWER YANGTZE SHALLOW LAKES[J]. ACTA HYDROBIOLOGICA SINICA, 2019, 43(S1): 18-26. DOI: 10.7541/2019.163

长江中下游浅水湖群底栖动物资源量宏观格局与预测模型

MACRO-PATTERNS AND PREDICTIVE MODELS OF ZOOBENTHOS ON MID-LOWER YANGTZE SHALLOW LAKES

  • 摘要: 长江泛滥平原是世界上最重要的湿地之一。近几十年来, 该区域许多湖泊面临着渔业资源过度利用和人为富营养化等各种问题。建立区域尺度的湖泊生态系统定量管理平台是解决这些问题的重要基础。研究针对国际上缺少区域尺度高预测力资源量预测模型这一问题, 利用底栖动物这一重要生物类群, 开展了基于长江中下游46个中小型湖泊105湖次实地调查的区域比较研究。结果显示该区域湖泊寡毛类密度和生物量分别为(403±225) ind./m2和(1.12±0.39) g/m2, 螺类密度和生物量分别为(82±20) ind./m2和(26.38±3.99) g/m2, 摇蚊密度和生物量为(356±62) ind./m2和(1.86±0.58) g/m2, 总计密度和生物量为(847±248) ind./m2和(29.41±3.97) g/m2。环境分析表明, 影响底栖动物现存量的主要因子是水深、透明度、水温、总磷、浮游藻类叶绿素a和沉水植物生物量, 并据此构建了一系列底栖动物资源量预测模型。模型对底栖动物密度的解释率总体优于生物量。在系列简单回归模型中, 对各类群密度和总密度的最优解释率为18%—33%, 对各类群和总生物量的解释率为7%—18%; 在多元回归模型中, 对各类群密度和总密度的解释率为46%—49%, 对各类群和总生物量的解释率为16%—55%。若考虑样本量大这一因素, 模型的解释率明显优于过去已有工作。尽管多元回归模型相对于简单回归模型解释率普遍升高, 但模型百分误差率没有明显改变。其中密度和生物量简单回归模型百分误差率的分别为76%—171%和115%—1034%, 多元回归模型的百分误差率分别为88%—114%和141%—1015%。因此, 在实际应用中, 建议选择变量少而预测能力相当的简单回归模型。

     

    Abstract: The Yangtze floodplain is one of the most important wetlands in the world. For decades, many lakes in this region have suffered from multiple stressors, such as over-exploitation fishery resources and cultural eutrophication. To solve these problems, it is necessary to establish a quantitative lake ecosystem management platform at a regional scale. Recognizing the lack of regional scale zoobenthos models with high predictive power, we carried out 105 lake-time investigations on 46 small-to medium-sized lakes along the mid-lower Yangtze River in this study. The results showed that the density and biomass of zoobenthos of these lakes were (847±248) ind./m2 (mean±SE) and (29.41±3.97) g/m2, respectively. The density and biomass of oligochaetes, gastropods and chironomids were (403±225) ind./m2 and (1.12±0.39) g/m2, (82±20) ind./m2 and (26.38±3.99) g/m2, and (356±62) ind./m2 and (1.86±0.58) g/m2, respectively. Further analyses showed that water depth, Secchi depth, water temperature, total phosphorus, phytoplankton chlorophyll a, and submersed macrophyte biomass were the important factors affecting the standing crops of zoobenthos. A series of models were therefore established. The explanation of variations in zoobenthos density among lakes was generally better than that of biomass. The explantion percentage of simple regression models were between 18%—33% for density and 7%—18% for biomass. In the multiple regression models, the explanation percentage was 46%—49% for density and 16%—55% for biomass. Considering the large sampling size of this study, the explanation of these research models was significantly higher than that of previous studies. When comparing the percentage predictive errors (PPE), the simple models and multiple models showed similar results: PPE was 76%—171% for density and 115%—1034% for biomass in simple models, and 88%—114% for density and 141%—1015% for biomass in multiple models. Therefore, these simple regression model with relatively few variables and good predictive power are suggested in practical application.

     

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