边振兴^1,,
张宇飞¹,
杨祎博¹,
于淼^2,,
1.沈阳农业大学土地与环境学院 沈阳 110866
2.沈阳农业大学理学院 沈阳 110866
基金项目: 辽宁省自然科学基金项目01064219009

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@syau.edu.cn

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

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出版历程

收稿日期:2020-01-09
录用日期:2020-03-19
刊出日期:2020-10-01

Effects of agricultural landscape pattern on qualitative food web structure of corn pest-predatory natural enemies

BIAN Zhenxing^1,,
ZHANG Yufei¹,
YANG Yibo¹,
YU Miao^2,,
1. College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
2. College of Science, Shenyang Agricultural University, Shenyang 110866, China
Funds: the Natural Science Foundation of Liaoning Province01064219009

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Corresponding author:YU Miao, E-mail:yumiao77@163.com

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摘要
摘要:农业过度集约化带来的农业景观均质化已成为农田生物多样性降低的主要原因之一。为研究农业景观格局对害虫-捕食性天敌定性食物网结构的影响，本文选择辽宁省昌图县为研究区，选取8个典型田块为样区，在分析定性食物网结构的基础上，采用回归分析和最优模型确定食物网参数与景观指数之间的关系。结果表明：1）互作丰度（IR）与各景观指数无显著相关性。2）连接密度（LD）与蔓延度指数（CONTAG，x₁）、聚集度指数（AI，x₂）呈显著正相关（P < 0.05），最优模型为：LD=-64.621+0.780x₁+0.739x₂。农业景观中非耕作斑块越聚集，玉米害虫-捕食性天敌定性食物网结构越复杂。3）连接性（C）与CONTAG（x₁）、香农多样性指数（SHDI，x₃）呈显著正相关（P < 0.05），而与香农均匀度指数（SHEI，x₄）呈显著负相关（P < 0.05），最优模型为：C=-178.500+1.831x₁-106.808x₄。景观类型越多样，且同类斑块连接度越好，害虫与捕食性天敌的相互作用越频繁，也越有利于复杂食物网结构的维持。4）普遍性（G）与景观形状指数（LSI，x₅）、斑块结合指数（COHESION，X₇）、AI（x₂）呈显著正相关（P < 0.05），而与斑块密度（PD，x₆）呈显著负相关（P < 0.05），最优模型为：G=-2 994.798+26.891x₂+27.090x₅-0.491x₆+2.851x₇。非耕作斑块破碎化程度越低，天敌的搜寻行为和聚集行为越强，越有利于食物网结构的稳定。5）易损性（V）与SHEI（x₄）呈显著正相关，而与CONTAG（x₁）呈显著负相关（P < 0.05），最优模型为：V=8.411+5.351x₄。斑块类型在景观中分布越均匀，害虫多样性越高，群落结构也越复杂。总体而言，农业景观异质性越强越有利于玉米害虫-捕食性天敌定性食物网的构建和抗干扰性的增强。而利用田间数据构建食物网矩阵的方法可以成为研究如何增强农业景观异质性的有力工具。
关键词:农业景观格局/
景观异质性/
土壤节肢动物/
捕食性天敌/
害虫/
定性食物网
Abstract:The homogenization of agricultural landscape caused by excessive agricultural intensification has been one of the main reasons for the reduction of farmland biodiversity. Research on the impact of the agricultural landscape pattern on the qualitative food web structure of pest-predatory natural enemies was conducted in Changtu County, Liaoning Province, where eight typical fields were sampled. Regression analysis and optimal model determined the relationship between food web parameters and landscape indexes. The results showed an insignificant correlation between food web interaction richness (IR) and landscape indexes. However, a significant positive multiple correlations between food web linkage density (LD) and contagion index (CONTAG, x₁) and aggregation index (AI, x₂) were observed. The corresponding optimal model was: LD=-64.621+0.780x₁+0.739x₂. The complexity of the qualitative food web structure of the corn pest-predatory natural enemies was dependent on the degree of concentration of the non-cultivated patches in the agricultural landscape. Furthermore, food web connectance (C) was significantly positively correlated with CONTAG, (x₁) and Shannon diversity index (SHDI, x₃), but was significantly negatively correlated with Shannon evenness index (SHEI, x₄). The corresponding optimal model was: C=-178.500+1.831x₁-106.808x₄; the more diverse the landscape types, the better the connectivity of the same patches; the more frequent the interaction between pests and predatory natural enemies, the more beneficial it is to maintain the complex food web structure. Food web generality (G) was significantly positively correlated with landscape shape index (LSI, x₅), cohesion index (COHESION, x₇), and AI (x₂); however, it was significantly negatively correlated with patch density (PD, x₆). The corresponding optimal model was: G=-2 994.798+26.891x₂+27.090x₅-0.491x₆+2.851x₇; the lower the degree of non-cultivated patch fragmentation, the stronger the search and aggregation behavior of natural enemies, which is beneficial and increases the stability of the food web structure. Food web vulnerability (V) was significantly positively correlated with the SHEI (x₄), but was significantly negatively correlated with CONTAG (x₁). The corresponding optimal model was: V=8.411+5.351x₄; the more evenly distributed patch types in the landscape, the higher the pest diversity and the increased complexity of the community structure. In general, the construction of the qualitative food web of corn pest-predatory natural enemies and the enhancement of anti-interference largely depends on the strength of the heterogeneity of the agricultural landscape. The use of field data in the construction of a food web matrix is a method that can be a powerful resource for studying ways to enhance the heterogeneity of agricultural landscapes.
Key words:Agricultural landscape pattern/
Landscape homogenization/
Soil arthropods/
Predatory natural enemy/
Pest/
Qualitative food web

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图1研究区土地利用类型和样区分布
Figure1.Distribution of land use types and sampling areas in the study area


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图2典型采样区田块的土地利用现状图
Figure2.Present land use map of field of the typical sampling areas


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图3样区内土壤动物样点布设示意图
Figure3.Map of sample points for soil arthropod in the sampling area


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图42018年和2019年玉米田害虫-捕食性天敌定性食物网参数值
图中数据为平均值±标准误差, 数据来源于8个样区内1 056个陷阱布设点, 其中2018年和2019年各528个。合并代表两年数据的总平均值。
Figure4.Qualitative metrics of pest-predatory natural enemies in the tested maize fields across 2018 and 2019
The data was mean±standard error, which was came from a total of 1 056 study sites whereas 528 each in 2018 and 2019. Pooled represents the total average of the two-year data.


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表1研究区8个样区田块的景观格局指数
Table1.Landscape pattern parameters of 8 fields of the sampling areas in the study area

田块序号 Number of sampling area	LPI	ED	PD	CONTAG	AI	LSI	COHESION	SHDI	SHEI
1	87.049	32.044	5.728	83.773	98.617	2.545	99.545	0.496	0.277
2	97.416	6.682	2.228	93.146	99.700	1.423	99.860	0.131	0.119
3	90.145	28.639	3.819	89.326	98.692	2.394	99.544	0.272	0.169
4	94.135	21.448	4.137	86.745	98.990	2.076	99.665	0.238	0.216
5	83.630	38.537	8.274	86.746	98.377	2.832	99.113	0.480	0.218
6	83.748	38.951	7.956	81.197	98.333	2.851	99.368	0.569	0.318
7	84.815	35.736	8.592	84.616	98.426	2.709	99.424	0.507	0.260
8	81.874	47.034	10.820	83.375	97.999	3.209	99.250	0.606	0.276
LPI:最大斑块指数; ED:边界密度; PD:斑块密度; CONTAG:蔓延度指数; AI:聚集度指数; LSI:景观形状指数; COHESION:斑块结合指数; SHDI:香农多样性指数; SHEI:香农均匀度指数。LPI: largest patch index; ED: edge density; PD: patch density; CONTAG: contagion index; AI: aggregation index; LSI: landscape shape index; COHESION: cohesion index; SHDI: Shannon’s diversity index; SHEI: Shannon’s evenness index.

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表2定性食物网参数计算公式及意义
Table2.Calculation formula and significance of qualitative food web parameters

食物网参数 Food web parameters	计算公式 Calculation formula	指数意义 Meaning of metrics
互作丰度?Interaction richness	L	反映食物网中发生天敌-害虫相互作用的频繁程度?Reflect the frequency of natural enemy-pest interactions in the food web
连接密度?Linkage density	L/S	反映捕食作用范围的广泛程度?Reflect the breadth of predation range
连接性?Connectance	L/S2	反映食物网结构复杂程度?Reflect the complexity of food web structure
普遍性?Generality	L/Senemy	反映天敌多样性?Reflect the diversity of natural enemies
易损性?Vulnerability	L/Sprey	反映害虫多样性?Reflect the diversity of pest
L代表食物网中实际发生的捕食关系数量; S代表食物网中总物种数; Senemy代表捕食性天敌物种数; Sprey代表害虫的物种数。L represents the number of actual predation relationships in the food web; S represents the total number of species in the food web; Senemy represents the number of predatory natural enemies; Sprey represents the number of species of pests.

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表3调查玉米田土壤节肢动物类型及数量统计表
Table3.Types and quantities of arthropods in the tested maize fields

		土壤节肢动物类型?Soil arthropod type		数量 Number	优势度 Dominance
昆虫纲 Insecta	直翅目 Orthoptera	蟋蟀科?Gryllidae	黄脸油葫芦?Teleogryllus emma	12 109	+++
			棺头蟋?Doenitzi stein	550	++
		蝗科?Acrididae	笨蝗?Haplotropis brunneriana Saussure	4	+
			中华蚱蜢?Acrida cinerea	14	+
			大垫尖翅蝗 Epacromius coerulipes	48	+
			宽翅曲背蝗?Pararcyptera microptera meridionalis	6	+
			短额附蝗?Atractomorpha sinensis	18	+
			短星翅蝗?Calliptamus abbreviatus	25	+
			棉蝗?Chondracris rosea	1	+
			辽宁金色蝗?Chrysacris	3	+
	鞘翅目 Coleoptera	步甲科?Carabidae	中华婪步甲?Harpalus sinicus	2 608	++
			附边青步甲?Chlaenius prostenus	658	++
			麻青步甲?Chlaenius junceus	4 846	+++
			蠋步甲?Dolichus halensis	529	++
			大星步甲?Calosoma maximoviczi	1 522	++
			毛婪步甲?Harpalus griseus	195	+
			中华金星步甲?Calosoma chinense	513	++
			赤褐婪步甲?Harpalus rubefactu	1 115	++
		虎甲科?Cicindelidae	细虎甲?Cicindelagracilis	15	+
			斜斑虎甲?Cicindela germanica	4	+
		埋葬甲科 Staphylinoidea	日负葬甲?Nicrophorus japonicuw	18	+
			双斑葬甲?Ptomascopus plagiatus	1	+
		叶甲科Chrysomeloidea	双斑长跗萤叶甲?Monolepta hieroglyphica	14	+
			刺松隐翅虫 Staphylinidae（Pinophilus punctatissimus）	21	+
		粪金龟科?Geotrupidae	戴锤角粪金龟?Bolbotrypes davidis	30	+
昆虫纲 Insecta	鞘翅目 Coleoptera	金龟科?Scarab	婪嗡蜣螂?Onthophagus lenzi	130	+
		花金龟科?Cetoniidae	白星花金龟?Protaetia brevitarsis	5	+
		鳃金龟科?Melolonthidae	额臀大黑鳃金龟?Holotrichia convespyga	5	+
			华北大黑鳃金龟?Holotrichia oblita	2	+
		蜉金龟科?Aphodiidae	黑蜉金龟?Aphodius breviusculus	2	+
		瓢虫科?Coccinellidae	异色瓢虫?Harmonia axyridis	23	+
			龟纹瓢虫?Propylaea japonica	1	+
			黄斑盘瓢虫?Lemnia saucia	3	+
		拟步甲科?Tenebrionindae	沙潜?Opatrum subaratum	1 797	++
		鞘翅目幼虫?Coleoptera		4	+
	半翅目 Hemiptera	长蝽科?Lygaeidae	角红长蝽?Lygaeus hanseni	1	+
		红蝽科?Pyrrhocoridae	地红蝽?Pyrrhocoris tibialis	125	+
	革翅目 Dermaptera	蠼螋科?Labiduridae	蠼螋?Titanolabis colossea	3	+
	鳞翅目 Lepidoptera	螟蛾科?Pyralidae 鳞翅目幼虫?Lepidoptera	亚洲玉米螟?Ostrinia furnacalis	3	+
				14	+
	膜翅目 Hymenoptera	叶蜂科?Tenthredinidae	叶蜂?Tenthredinidae	2	+
		蚁科?Formicidae		5 089	+++
蛛形纲 Arachnida	蜘蛛目 Araneae	幽灵蛛科?Pholcidae		2 863	++
		皿蛛科?Linyphiidae		73	+
		漏斗蛛科?Agelenidae		320	+
		狼蛛科?Lycosidae		68	+
		转蛛科?Trochanteriidae		234	+
		栉足蛛科?Ctenidae		1	+
		近管蛛科?Anyphaenidae		1	+
		逸蛛科?Zoropsidae		3	+
		狼栉蛛科?Zoridae		19	+
		弱斑蛛科?Ischnothyreus		27	+
软甲纲 Malacostraca	等足目 Isopoda	潮虫科?Onsicidae	鼠妇 Armadillidium vulgare	20	+
总计?Sum				35 705
+++表示个体数占总捕获量的10%以上, ++表示个体数占总捕获量的1%~10%, +表示个体数占总捕获量的1%以下。“+++”, “++” and “+” indicate that the number of individuals accounts for more than 10%, 1%-10%, and less than 1% of the total capture, respectively.

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表42018年和2019年不同景观格局指数对玉米田定性食物网参数影响的单变量回归分析结果
Table4.Effects of landscape pattern parameters on qualitative food web parameters from univariate regression analysis of the tested maize fields in 2018 and 2019

景观格局指数 Landscape pattern parameter	食物网参数 Food web parameter	P (2018)	P (2019)	效应 Effect
斑块密度 Patch density	互作丰度?Interaction richness	0.336	0.232	-
	连接密度?Linkage density	0.329	0.176	-
	连接性?Connectance	0.139	0.051	-
	普遍性?Generality	0.045*	0.039*	-
	易损性?Vulnerability	0.155	0.108	+
最大斑块指数 Largest patch index	互作丰度?Interaction richness	0.325	0.261	+
	连接密度?Linkage density	0.321	0.211	+
	连接性?Connectance	0.160	0.103	+
	普遍性?Generality	0.097	0.064	+
	易损性?Vulnerability	0.223	0.157	-
景观形状指数 Landscape shape index	互作丰度?Interaction richness	0.362	0.195	-
	连接密度?Linkage density	0.373	0.152	-
	连接性?Connectance	0.238	0.089	-
	普遍性?Generality	0.048*	0.045*	+
	易损性?Vulnerability	0.270	0.212	+
蔓延度指数 Contagion index	互作丰度?Interaction richness	0.045*	0.466	-
	连接密度?Linkage density	0.002**	0.049*	+
	连接性?Connectance	0.047*	0.043*	+
	普遍性?Generality	0.268	0.192	+
	易损性?Vulnerability	0.038*	0.042*	-
斑块结合指数 Cohesion index	互作丰度?Interaction richness	0.472	0.145	+
	连接密度?Linkage density	0.435	0.120	+
	连接性?Connectance	0.177	0.115	+
	普遍性?Generality	0.049*	0.041*	+
	易损性?Vulnerability	0.121	0.288	+
香农多样性指数 Shannon’s diversity index	互作丰度?Interaction richness	0.114	0.435	-
	连接密度?Linkage density	0.125	0.357	-
	连接性?Connectance	0.030*	0.042*	+
	普遍性?Generality	0.182	0.122	-
	易损性?Vulnerability	0.345	0.061	+
香农均匀度指数 Shannon’s evenness index	互作丰度?Interaction richness	0.097	0.409	+
	连接密度?Linkage density	0.054	0.048*	-
	连接性?Connectance	0.001**	0.048*	-
	普遍性?Generality	0.317	0.223	-
	易损性?Vulnerability	0.029*	0.028*	+
边界密度 Edge density	互作丰度?Interaction richness	0.362	0.195	-
	连接密度?Linkage density	0.373	0.152	-
	连接性?Connectance	0.238	0.089	-
	普遍性?Generality	0.071	0.045*	-
	易损性?Vulnerability	0.270	0.212	+
聚集度指数 Aggregation index	互作丰度?Interaction richness	0.391	0.167	+
	连接密度?Linkage density	0.041*	0.046*	+
	连接性?Connectance	0.297	0.079	+
	普遍性?Generality	0.038*	0.036*	+
	易损性?Vulnerability	0.030*	0.234	-
“*”和“**”表示该景观指数对相应的食物网参数有显著(P < 0.05)和极显著(P < 0.01)影响。“+”和“-”分别代表景观正负效应。“*” and “**” indicate significant effect at P < 0.05 and P < 0.01, “+” and “-” indicate positive and negative effects of landscape land use type, respectively.

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表5景观格局指数与定性食物网参数关系的多元回归分析及基于AICc准则的最优模型筛选结果
Table5.Multivariate regression analysis and the best model selection to evaluate landscape effects on qualitative food web metrics in maize fields

响应变量 Response variables	模型方程 Model	残差平方和 Sp2	样本量 Sample size n	阶数 Ki	校正的AIC值 AICc	调整R2 Adjusted R2
连接密度(LD) Linkage density	LD=b0+b1x1+b2x2	0.407	8	4	2.808	0.443
	LD=b0+b1x1	0.728	8	3	5.460	0.004
	LD=b0+b2x2	0.598	8	3	3.887	0.182
连接性(C) Connectance	C=b0+b1x1+b3x3+b4x4	2.206	8	5	18.329	0.531
	C=b0+b1x1+b3x3	4.555	8	4	22.130	0.032
	C=b0+b1x1+b4x4	2.474	8	4	17.247	0.475
	C=b0+b3x3+b4x4	4.328	8	4	21.721	0.081
	C=b0+b1x1	4.688	8	3	20.360	0.004
	C=b0+b3x3	4.625	8	3	20.252	0.017
	C=b0+b4x4	4.707	8	3	20.392	0.019
普遍性(G) Generality	G=b0+b2x2+b5x5+b6x6+b7x7	0.918	8	6	13.316	0.914
	G=b0+b2x2+b5x5+b6x6	1.358	8	5	14.448	0.873
	G=b0+b2x2+b5x5+b7x7	2.480	8	5	19.266	0.769
	G=b0+b2x2+b6x6+b7x7	5.097	8	5	25.029	0.524
	G=b0+b5x5+b6x6+b7x7	5.702	8	5	25.927	0.468
	G=b0+b2x2+b5x5	2.863	8	4	18.415	0.733
	G=b0+b2x2+b6x6	5.260	8	4	23.281	0.509
	G=b0+b2x2+b7x7	5.116	8	4	23.059	0.523
	G=b0+b5x5+b6x6	5.783	8	4	24.039	0.460
	G=b0+b5x5+b7x7	5.767	8	4	24.017	0.462
	G=b0+b6x6+b7x7	6.199	8	4	24.595	0.422
	G=b0+b2x2	5.260	8	3	21.281	0.509
	G=b0+b5x5	5.824	8	3	22.096	0.457
	G=b0+b6x6	6.213	8	3	22.613	0.420
	G=b0+b7x7	7.077	8	3	23.655	0.340
易损性(V) Vulnerability	V=b0+b1x1+b4x4	2.913	8	4	18.553	0.334
	V=b0+b1x1	3.656	8	3	18.371	0.164
	V=b0+b4x4	3.537	8	3	18.106	0.191
表中粗体行代表最优模型(AICc值最小)。模型中的x1-x7分别代表景观蔓延度指数(CONTAG)、聚集度指数(AI)、香农多样性指数(SHDI)、香农均匀度指数(SHEI)、景观形状指数(LSI)、斑块密度(PD)、斑块结合指数(COHESIOH)。Bold lines are the best models. x1-x7 in the model represent the landscape indexes of contagion index (CONTAG), aggregation index (AI), Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI), landscape shape index (LSI), patch density (PD), and cohesion index (COHESIOH).

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