龚玲春1,
果晓玉1,
于淼2,,
1.沈阳农业大学土地与环境学院 沈阳 110161
2.沈阳农业大学理学院 沈阳 110161
基金项目: 沈阳农业大学土地与环境学院青年创新项目20140102
详细信息
作者简介:边振兴, 研究方向为农地利用与保护、农业景观生态学。E-mail:zhx-bian@263.net
通讯作者:于淼, 研究方向为数理统计与景观生态学。E-mail:yumiao77@163.com
中图分类号:P901计量
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被引次数:0
出版历程
收稿日期:2018-03-07
录用日期:2018-06-20
刊出日期:2019-01-01
Effect of agricultural landscape composition on natural enemy population of corn borer
BIAN Zhenxing1,,GONG Lingchun1,
GUO Xiaoyu1,
YU Miao2,,
1. College of Land and Environment, Shenyang Agricultural University, Shenyang 110161, China
2. College of Science, Shenyang Agricultural University, Shenyang 110161, China
Funds: the Youth Innovation Project of Land and Environment College of Shenyang Agricultural University20140102
More Information
Corresponding author:YU Miao, E-mail:yumiao77@163.com
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摘要
摘要:高比例和多样的非耕作生境带来的农田景观高异质性是维持农业生物多样性的关键，推测提高非耕作生境组成能够促进农田中玉米螟天敌数量和种类增加。为揭示不同非耕作生境组成对玉米螟天敌数量的影响，本文选取东北平原玉米种植典型县——辽宁省昌图县，按照5个非耕作生境比例（0~10%、10%~20%、20%~30%、30%~40%和40%~50%）选取20个直径为600 m的样区。陷阱法采集玉米螟天敌，并用体视显微镜鉴别到科。主成分分析法筛选主要非耕作生境类型，逐步回归模型对非耕作生境组成（比例和类型）与玉米螟天敌数量进行拟合。并采用"赤池信息准则（AIC）"模型筛选，评估不同非耕作生境组成对玉米螟天敌数量的影响。研究结果表明，随着非耕作生境比例增加，玉米螟天敌数量呈先增后减的趋势，非耕作生境比例为20%~30%时天敌数量达最大值。非耕作生境比例20%~30%时，非耕作生境组成与玉米螟天敌数量关系最优模型（AIC=4.24，为最小值）是Y=b0-b1D+b2PC2，最优模型表明PC2（β=3.787，P=0.005）代表以林地为主的非耕作生境组成与玉米螟天敌数量呈显著正相关，景观多样性指数D（β=-3.173，P=0.005）与玉米螟天敌数量呈显著负相关。该比例的非耕作生境组成与玉米螟天敌数量关系的其他模型表明，以草地和村落为主（PC1）的非耕作生境与玉米螟天敌呈显著正相关（β=1.957，P=0.000）。而其他比例下，所有关系模型说明农田周边林地生境和其他作物耕地有利于提高农田中天敌数量。最适宜玉米螟天敌聚集的非耕作生境比例为20%~30%，以林地为主或村落、草地为主的非耕作生境，以及分布有其他作物的耕作生境能够提高玉米螟天敌数量。研究结果可为今后从景观生态角度构建适宜非耕作生境组成来防治玉米螟提供理论依据和方法。
关键词:农业景观/
非耕作生境/
玉米田/
玉米螟/
天敌
Abstract:The agricultural landscape with high proportion and diversity of non-cropped habitats is the key to maintaining agricultural biodiversity. It is a hypothesis that the natural enemy population and types of corn borer could increase by complex and high composition of non-cropped habitats in cornfields. To clarify the relationship between non-cropped habitats proportion and natural enemy, this study investigated the effects of composition of non-cropped habitats on natural enemy population of corn borer in Changtu County in the Northeast China Plain. Twenty plots (φ=600 m) were selected and classed into five groups based on the proportions of non-cropped habitats (0-10%, 10%-20%, 20%-30%, 30%-40% and 40%-50%). The samples of natural enemies of corn borer were collected by the pitfall trap method. The families of enemies were identified using stereomicroscope. Then principal component analysis was used to classify the main types of non-cropped habitats in the region. Stepwise regression model was used to fit relation curve of non-cropped habitat composition (ratio and type) and population of natural enemies of corn borer. Then the optimal model was selected based on the Akaike's Information Criterion (AIC) for effects of different proportions of non-cropped habitats on natural enemy population of corn borer. Results showed that natural enemy population of corn borer increased at first and then decreased with increasing proportion of non-cropped habitats. Natural enemy population of corn borer was highest[with the optimal model of AICminimum value=4.24, Y=b0-b1D + b2PC2, PC2 (β=3.787, P=0.005)] when the proportion of non-cropped habitats was 20%-30%. This optimal model implied that there was significant positive correlation between natural enemy population of corn borer and the composition of non-cropped habitats dominated by woodland. There was also significantly negative correlation between natural enemy population of corn borer and the landscape diversity index D. Other models[PC1 (β=1.957, P=0.000)] for 20%-30% proportion of non-cropped habitats implied that significant positive correlation existed between natural enemy population of corn borer and the composition of non-cropped habitats dominated by grassland and village. Under other proportions of non-cropped habitats, the optimal models suggested that corn filed neighboring woodland or other crops fields was beneficial for increase of natural enemy population of corn borer. In conclusion, the study showed that natural enemy population of corn borer was highest when the proportion of non-cropped habitats was 20%-30%. Non-cropped habitats dominated by woodland, grassland, village or in intercropped patterns attracted natural enemies of corn borer in farmlands with different proportions of non-cropped habitats. The study further provided theoretical basis for the prevention and control of corn borer by ways of landscape ecology.
Key words:Agricultural landscape/
Non-cropped habitat/
Corn field/
Corn borer/
Natural enemy
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图1研究区样点分布图
Figure1.Distribution of corn field sampling sites in the study area
下载: 全尺寸图片幻灯片
图2玉米田中300 m半径的样方陷阱布设示意图
Figure2.Layout of the 300 m radius pitfall traps in corn field
下载: 全尺寸图片幻灯片
图3不同非耕地生境比例下农田景观类型的主成分分析
Figure3.Principal component analysis for farmland landscape types with different proportions of non-cropped habitats
下载: 全尺寸图片幻灯片
图4夏季和秋季不同非耕作生境比例的玉米螟天敌数量分析特征
Figure4.Quantitative features of corn borer' natural enemies in corn fields with different proportions of non-cropped habitats in summer and autumn
下载: 全尺寸图片幻灯片
图5夏季和秋季不同非耕作生境比例的玉米螟天敌数量分析特征
同一季节不同字母表示处理间差异显著。
Figure5.Quantitative features of corn borer' natural enemies in cornfields with different proportions of non-cropped habitats in summer and autumn
Different letters in the same season show significant differences at 0.05 level.
下载: 全尺寸图片幻灯片
图6非生境比例为20%~30%时最优模型中景观变量与玉米螟天敌数量的相关性分析
非耕作生境比例20%~30%上D+PC2为所有区间最优模型。
Figure6.Relationship between quantity of corn borer's natural enemies and landscape variables in the best fit model in corn field with 20%-30% non-cropped habitats
The best fit model is D+PC2 under 20%-30% non-cropped habitats
下载: 全尺寸图片幻灯片
图7非耕作生境比例与玉米螟天敌数量的关系
Figure7.Relationship between proportion of non-cropped habitats and quantity of corn borer' s natural enemies
下载: 全尺寸图片幻灯片表120个调查区域采集的玉米螟天敌种类及数量
Table1.Quantities and types of natural enemies of corn borer collected in 20 investigated corn fields
| 门Phylum | 纲Classes | 目Orders | 科Families | 占捕获量比Proportion (%) |
| 节肢动物门 Arthropoda | 昆虫纲 Insecta | 鞘翅目 Coleoptera | 步甲科Carabidae | 36.00 |
| 瓢虫科Coccinellidae | 2.32 | |||
| 隐翅甲科Staphylinidae | 2.10 | |||
| 膜翅目 Hymenoptera | 蚁科Formicinae | 41.76 | ||
| 姬蜂科Lchneumqnidae | 3.11 | |||
| 革翅目Dermaptera | 蠼螋科Labiduridae | 2.35 | ||
| 半翅目Hemiptera | 花蝽科Anthocoridae | 2.64 | ||
| 脉翅目Neuroptera | 草蛉科Chrysopidae | 2.31 | ||
| 蛛形纲 Arachnida | 蛛形目 Aranelida | 蟹蛛科Thomisidae | 3.74 | |
| 球蛛科Theridiidae | 2.37 |
下载: 导出CSV表2不同非耕作生境比例下不同景观变量与玉米螟天敌数量分布的模型分析
Table2.Models for quantities of corn borer' natural enemies in corn field and different landscape variables under different proportions of non-cropped habitats
| 非耕作生境比例 Non-cropped habitats proportion (%) | 模型 Model | 残差平方和 Sp2 | 样本量 Sample size | 阶数 Ki | 赤池信息准则 AIC | 调整R2 Adjusted R2 |
| 0-10 | Y=b0-b1D** | 86.265 | 8 | 3 | 43.66 | 0.620 |
| Y=b0+b1PC1** | 48.856 | 8 | 3 | 39.11 | 0.785 | |
| Y=b0+b1PC2* | 119.411 | 8 | 3 | 46.26 | 0.475 | |
| Y=b0+b1PC3** | 47.410 | 8 | 3 | 38.87 | 0.791 | |
| Y=b0+b1PC1*+b2PC2** | 48.856 | 8 | 4 | 41.11 | 0.785 | |
| Y=b0+b1PC1**+b2PC3** | 47.410 | 8 | 4 | 40.87 | 0.791 | |
| Y=b0+b1PC2**+b2PC3** | 47.410 | 8 | 4 | 40.87 | 0.791 | |
| Y=b0+b1PC1**+b2PC2*+b3PC3** | 47.410 | 8 | 5 | 42.87 | 0.791 | |
| Y=b0-b1D**+b2PC1** | 48.856 | 8 | 4 | 41.11 | 0.785 | |
| Y=b0-b1D**+b2PC2* | 86.265 | 8 | 4 | 45.66 | 0.620 | |
| Y=b0-b1D**+b2PC3** | 47.410 | 8 | 4 | 40.87 | 0.791 | |
| Y=b0-b1D**+b2PC1**+b3PC2* | 48.856 | 8 | 5 | 43.11 | 0.785 | |
| Y=b0-b1D**+b2PC1**+b3PC3** | 47.410 | 8 | 5 | 42.87 | 0.791 | |
| Y=b0-b1D**+b2PC2*+b3PC3** | 47.410 | 8 | 5 | 42.87 | 0.791 | |
| Y=b0-b1D**+b2PC1**+b3PC2*+b4PC3** | 47.410 | 8 | 6 | 44.87 | 0.791 | |
| 10-20 | Y=b0-b1D** | 58.985 | 8 | 3 | 40.62 | 0.762 |
| Y=b0+b1PC1** | 40.933 | 8 | 3 | 37.70 | 0.835 | |
| Y=b0+b1PC2** | 69.396 | 8 | 3 | 41.92 | 0.719 | |
| Y=b0+b1PC1**+b2PC2** | 40.933 | 8 | 4 | 39.70 | 0.835 | |
| Y=b0-b1D**+b2PC1** | 40.933 | 8 | 4 | 39.70 | 0.835 | |
| Y=b0-b1D**+b2PC2** | 58.985 | 8 | 4 | 42.62 | 0.762 | |
| Y=b0-b1D**+b2PC1**+b3PC2** | 40.933 | 8 | 5 | 41.70 | 0.835 | |
| 20-30 | Y=b0-b1D** | 2.977 | 8 | 3 | 16.73 | 0.978 |
| Y=b0+b1PC1** | 2.676 | 8 | 3 | 15.87 | 0.981 | |
| Y=b0+b1PC2** | 14.355 | 8 | 3 | 29.31 | 0.896 | |
| Y=b0+b1PC1**+b2PC2** | 2.676 | 8 | 4 | 17.87 | 0.981 | |
| Y=b0-b1D**+b2PC1** | 2.676 | 8 | 4 | 17.87 | 0.981 | |
| Y=b0-b1D**+b2PC2** | 0.487 | 8 | 4 | 4.24 | 0.996 | |
| Y=b0-b1D**+b2PC1**+b3PC2** | 2.676 | 8 | 5 | 19.87 | 0.981 | |
| 30-40 | Y=b0+b1D** | 23.527 | 8 | 3 | 33.27 | 0.652 |
| Y=b0+b1PC1** | 23.834 | 8 | 3 | 33.37 | 0.648 | |
| Y=b0+b1PC2** | 23.569 | 8 | 3 | 33.28 | 0.651 | |
| Y=b0+b1PC1**+b2PC2** | 23.569 | 8 | 4 | 35.28 | 0.651 | |
| Y=b0+b1D**+b2PC1** | 9.379 | 8 | 4 | 27.91 | 0.834 | |
| Y=b0+b1D**+b2PC2** | 23.527 | 8 | 4 | 35.27 | 0.625 | |
| Y=b0+b1D**+b2PC1**+b3PC2** | 9.379 | 8 | 5 | 29.91 | 0.834 | |
| 40-50 | Y=b0+b1D* | 33.086 | 8 | 3 | 35.99 | 0.451 |
| Y=b0+b1PC1* | 29.523 | 8 | 3 | 35.08 | 0.510 | |
| Y=b0+b1PC2** | 19.809 | 8 | 3 | 31.89 | 0.671 | |
| Y=b0+b1(PC3*) | 28.391 | 8 | 3 | 34.77 | 0.529 | |
| Y=b0+b1PC1*+b2PC2** | 19.809 | 8 | 4 | 33.89 | 0.671 | |
| Y=b0+b1PC1*+b2(PC3*) | 19.809 | 8 | 4 | 33.89 | 0.671 | |
| Y=b0+b1PC2**+b2(PC3*) | 28.391 | 8 | 4 | 36.77 | 0.529 | |
| Y=b0+b1PC1*+b2PC2**+b3(PC3*) | 19.809 | 8 | 5 | 35.89 | 0.671 | |
| Y=b0+b1D*+b2PC1* | 8.911 | 8 | 4 | 27.50 | 0.822 | |
| Y=b0+b1D*+b2PC2** | 19.809 | 8 | 4 | 33.89 | 0.671 | |
| Y=b0+b1D*+b2(PC3*) | 28.391 | 8 | 4 | 36.77 | 0.529 | |
| Y=b0+b1D*+b2PC1*+b3PC2** | 19.809 | 8 | 5 | 35.89 | 0.671 | |
| Y=b0+b1D*+b2PC1*+b3(PC3*) | 28.391 | 8 | 5 | 38.77 | 0.529 | |
| Y=b0+b1D*+b2PC2**+b3(PC3*) | 19.809 | 8 | 5 | 35.89 | 0.671 | |
| Y=b0+b1D*+b2PC1*+b3PC2**+b4(PC3*) | 19.809 | 8 | 6 | 37.89 | 0.671 | |
| 列出了所有模型。其中字体加粗表示为最优模型, AIC值最小; 括号表示自变量和天敌数量呈负相关关系。Y:天敌数量; D:景观多样性指数; PC1:主成分1; PC2:主成分2; PC3:主成分3。**:在0.01水平(单侧)上显著相关; *:在0.05水平(单侧)上显著相关。All the models are shown. Bold characters and numbers indicate the best model with the minimum AIC. Independent variables in parentheses are negatively related with quantities of corn borer’ natural enemies. D represents Simpson D; PC1 represents principal component 1; PC2 represents principal component 2; PC3 represents principal component 3. ** represents significant correlation at 0.01 level (unilateral); * represents significant correlation at 0.05 level (unilateral). | ||||||
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