Varietal and intrafamilial species diversity influence aphid and yellow dwarf virus pressure within mixtures of wheat and barley

Species diversity and varietal diversity within agricultural fields can increase crop resilience to plant pathogens and insect pests. The functional differences between two species in the same family or two varieties of the same species are often less apparent than the differences between species in interfamilial polycultures (e.g., cereal-legume mixtures), but can nevertheless result in yield advantages. Intrafamilial cereal mixtures are grown for their resilience to drought, weeds, and disease in parts of northern Africa, Asia, and Europe, though they were formerly widespread in those regions. Farmers plant multiple cereal species and varieties within the same field, treating the mixture as a single crop. In the northeastern United States, we created mixtures using two varieties of wheat (Triticum aestivum) and two varieties of barley (Hordeum vulgare) to test whether species and varietal diversity would reduce the prevalence of globally important pathogens, the yellow dwarf viruses (YDVs), and their aphid vectors. YDVs are consequential viruses in agriculture, with localized outbreaks causing significant yield loss in Europe, Africa, and North America during the second half of the 20th century. This is the first experimental study of how varietal and species diversity within an intrafamilial mixture of crop species influences YDV infection prevalence. We found that the wheat varietal mixture had significantly less YDV infection than the average of the wheat varieties grown in monoculture. Aphid pressure was higher in barley monocultures and most mixtures that contained barley, though the yields of species mixtures resembled those of their higher-yielding component, wheat. Aphid pressure may not have been severe enough for us to observe intrafamilial species mixtures lowering viral prevalence, but mixing wheat and barley did not lead to higher disease prevalence or reduced yield. Our experimental results highlight the importance of diversity and identity within intrafamilial mixtures.

Methods
Experimental design

To examine the effect of intrafamilial species and varietal diversity on aphid populations and YDVs, we created mixtures using four components, including two hard red spring wheat varieties, Marquis and Glenn, and two two-row malting barley varieties, AAC Synergy and Newdale. We selected Marquis and Glenn wheat for their early maturity so that they would mature at approximately the same time as the barley varieties. All varieties were susceptible to barley yellow dwarf virus (McCallum & DePauw 2008, Mergoum et al. 2006, Legge et al. 2014, Legge et al. 2008).

We planted monoculture stands of the four components, all possible two-component mixtures, and the complete four-component mixture for a total of 11 treatments. This design resulted in: four monocultures; a two-component wheat varietal mixture; a two-component barley varietal mixture; four two-component species mixtures of 50% wheat and 50% barley (all possible combinations); and a four-component mixture composed of 25% of each variety of wheat and barley (Table 1). Hereafter, the mixtures planted in our experiment will be abbreviated to each mixture component’s first letter with a superscript denoting whether the component is a wheat (“Wh”) or barley (“Ba”). For example, the species mixture of Marquis wheat and Newdale barley will be referred to as “species mixture MWh + NBa”.

We conducted this experiment between May and August 2020 at the Cornell University Thompson Research Farm in Freeville, NY, USA. We planted treatments into 2x2m plots in a randomized block design with six replicates. Plots within a block were separated by 1.5m and experimental blocks were separated by 2m. All plots were seeded at the same overall rate (180kg/ha), with the mixtures composed of equal numbers of seeds of each component in the mixture (Woldeamlak 2001a).  To simulate typical planting methods by smallholder farmers, we broadcasted seeds by hand and tamped seeds into the soil using a land roller. Standard fertilizer for small grain crops in this location (NPK 13-13-13) was applied at a rate of 55.07kg/hectare 10 days before planting. The experimental plot was irrigated as needed. No herbicides, fungicides or pesticides were sprayed, and the plots were weeded by hand.

The growing season of summer 2020 was hot and dry, resulting in fast maturation times for all grain varieties. We planted on May 21, 2020 and harvested the experiment August 5-8, 2020.  Glenn wheat, which usually takes 64 days from planting to reach the heading stage (Mergoum et al. 2006), took only 40 days in our experiment.

Aphid surveys

We surveyed the number and species identity of aphids on every plant within the innermost 25 x 25cm quadrat of each plot. We recorded the number of plants per quadrat and the number of plants occupied by at least one aphid. We conducted four surveys, beginning as soon as aphids appeared on the young plants (3 June 2020) and surveying every other week. From these surveys, we defined two per-plot metrics of aphid pressure: number of aphids and percent of plants occupied by aphids. Percent plant occupancy was defined as the percentage of plants within a quadrat that had at least one aphid feeding on them.

YDV sampling

To estimate the proportion of plants infected with YDVs in each plot, we collected leaf samples on three sampling days (17 June, 6 July, 19 July 2020). We used a portable wooden grid to collect two plants without regard to symptoms from each 25cm x 25cm section of the center 1m2 of each plot, for a total of 30 samples per plot. We excluded the innermost 25cm x 25cm of each plot from virus sampling to avoid disturbing aphids within the aphid survey area. Each sample consisted of approximately 5g of leaf tissue and was collected into plastic bags on ice, then stored in a -20°C freezer until being tested for YDV presence.

We tested all samples for BYDV-PAV, BYDV-MAV, and CYDV-RPV using a triple-antibody sandwich, enzyme-linked immunosorbent assay (TAS-ELISA) (Agdia Inc, Elkhart, IN, USA) following D’Arcy and Hewings (1986). We homogenized leaf samples (5g) with 5mL of phosphate-buffered saline (pH 7.4) (PBS) using a leaf extraction press. We coated microtiter plates (2uL antibody/ mL coat buffer) and incubated them overnight at 4°C. After washing the coated plates twice with PBS-Tween (0.5% Tween 20 + PBS), we loaded the sap of homogenized samples into the plates alongside infected and healthy control sap and incubated the loaded plates overnight at 4°C.  Following another wash with PBS-Tween, we added alkaline phosphatase-conjugated immunoglobulin (2uL + 2uL antibody / mL conjugate buffer) to the wells and incubated the plates for four hours at 37°C. Each well received 100uL of substrate (1mg p-Nitrophenyl phosphate disodium salt hexahydrate substrate powder / mL substrate buffer) and was incubated at 37°C for 50 minutes before measuring reactions using a microtiter plate autoreader. At 405 nm, we considered samples with optical density values at least 1.75 times the mean of the OD of healthy control wells to be positive for BYDV or CYDV.

Yield

We harvested aboveground biomass from all plants within the innermost 1m2 of each plot once the entire experiment had reached maturity. We separated out the components of every mixture and separated grain heads from straw. We were unable to clearly distinguish between Newdale barley and AAC Synergy barley in mixture (Fig. 7). Since their yields did not differ significantly in monoculture, we assumed that the two varieties had equivalent yields when planted in mixture and split mixtures of the two barleys evenly. We allowed plants to air dry for 6 months before recording grain and straw weights. We also counted the number of seed heads to calculate the average weight of one seed head.

Statistical analysis

We conducted all analyses in R, version 4.0.4 (R Development Core Team 2021). All generalized linear mixed models (GLMMs) were fit using the glmmTMB function from the glmmTMB package (Brooks et al. 2022). For each of our metrics -number of aphids, percent plant occupancy, percent plant infection, yield measures- we created two separate models, one with treatment as the predictor variable and one with mixture type as the predictor variable. We grouped treatments to generate the different mixture types (see Table 1). For example, the “monoculture wheat” type included the Marquis wheat monoculture and Glenn wheat monoculture, and the “2 species mixture” mixture type included all the two-species mixtures: MWh+ABa, MWh+NBa, GWh+ABa, and GWh+NBa. In all models, the predictor variable of treatment included all possible mixtures and monocultures. To create consistent lettering within Fig. 4, we excluded the two varietal mixtures from our analysis for only that one model. None of the significant differences between treatments changed.

To analyze percent of plant occupancy by aphids, we used a binomial GLMM with a logit link. Our first model included sampling period as an interaction with percent occupancy, but since the interaction between sampling period and treatment was not significant in our ANOVA (Type II Wald Chi-square), we based all comparisons on a final model that included sampling period as an additive fixed effect. In this final model, block and plot number nested in block were also random effects, and we accounted for variation in plant density among plots by including the number of plants per quadrat as an offset. The number of aphids was analyzed using a negative binomial distribution GLMM with a log link. Sampling period had a non-significant interaction with the number of aphids when assessing the initial model with ANOVA so it was included as an additive fixed effect. The final model included random effects of block and plot nested within block, and the number of plants per quadrat as an offset. Since aphid species abundance varied across sampling periods, we also ran occupancy and aphid abundance models separately for the two most abundant aphid species.

We analyzed the percent of YDV-infected plants sampled from the innermost 1m2 of each plot using a binomial GLMM with a logit link and sampling period as a fixed effect. Our model included block and plot number nested in block as random effects and an offset by the number of plants per quadrat. To analyze yield metrics (grain, straw, number of seed heads, seed head weight), we used a Gaussian LMM with a log link and included block as a random effect.

We tracked the outcome of planting each variety in different mixture contexts, and we adjusted the yield of each variety to the equivalent of a half-plot area (0.5m2). Since the initial seeding rates were the same for every variety, this allowed us to compare the yield of an equal number of seeds of each variety grown over the same land area among monocultures and mixtures. We compared the monoculture yield means to the combined yield mean of the three species mixtures for each variety (Fig. 7). For each variety, we also compared the monoculture yield means to the individual yield means of the three species mixtures separately (Fig. S1).

For each of these models, we used the emmeans package (Lenth 2023) to conduct pairwise comparisons between our Tukey-adjusted Estimated Marginal Means (least-squares means). We used the contrast() function from emmeans to build our custom contrasts so that we could compare each mixture to the unweighted average of its components. For example, to determine whether mixtures had higher or lower percent aphid occupancy than their components grown in monoculture, we tested whether the mean percent occupancy in a mixture is significantly different than the unweighted average of its two components’ mean percent occupancy.

农田内的物种多样性与品种多样性可提升作物对植物病原菌及虫害的抗逆性。同科内两个物种或同一物种的两个品种之间的功能差异，通常不如科间混作体系（如禾本科-豆科混播）中不同物种种群间的差异显著，但仍可带来产量增益。在北非、亚洲与欧洲部分地区，科内禾本科作物混播种植已被用于提升作物对干旱、杂草与病害的抗逆性，尽管该种植方式此前在这些区域曾广泛普及。农户会在同一块农田中种植多种禾本科作物与品种，将混播组合视作单一作物进行管理。在美国东北部，我们利用两个普通小麦（Triticum aestivum）品种与两个大麦（Hordeum vulgare）品种构建混播组合，以验证物种与品种多样性是否可降低全球重要病原——黄矮病毒（Yellow dwarf viruses, YDVs）及其蚜虫介体的侵染发生率。YDVs是农业生产中危害严重的病毒，20世纪后半叶在欧洲、非洲与北美曾引发局部暴发，造成显著产量损失。本研究是首个针对科内作物混播组合中的品种与物种多样性如何影响YDV侵染发生率的实验性研究。我们发现，小麦品种混播组的YDV侵染率显著低于单播小麦品种的平均侵染水平。大麦单播组及多数含大麦的混播组的蚜虫压力更高，尽管物种混播组的产量与其高产出组分——小麦的产量相当。蚜虫压力可能尚未达到足以让我们观测到科内物种混播降低病毒侵染率的程度，但小麦与大麦混播并未引发更高的病害发生率或产量下降。本实验结果凸显了科内混播组合中多样性与组分特性的重要性。

## 材料与方法

### 1. 实验设计
为探究科内物种与品种多样性对蚜虫种群及YDVs的影响，我们构建了包含4个组分的混播组合：2个硬红春小麦品种Marquis与Glenn，以及2个二棱酿造大麦品种AAC Synergy与Newdale。我们选择早熟的Marquis与Glenn小麦，使其成熟期与大麦品种大致同步。所有供试品种均对大麦黄矮病毒（Barley yellow dwarf virus, BYDV）敏感（McCallum & DePauw 2008, Mergoum et al. 2006, Legge et al. 2014, Legge et al. 2008）。

我们设置了4个组分的单播小区、所有可能的双组分混播小区以及完整的四组分混播小区，共11个处理。该设计包含：4个单播处理；1个双组分小麦品种混播处理；1个双组分大麦品种混播处理；4个50%小麦+50%大麦的双物种类混播处理（所有可能的组合）；以及1个由小麦与大麦各品种按25%比例组成的四组分混播处理（见表1）。后续我们将用各组分首字母结合上标标识混播组合的作物类型：小麦（"Wh"）或大麦（"Ba"）。例如，Marquis小麦与Newdale大麦的物种类混播组合将记为"物种混播 MWh + NBa"。

本实验于2020年5月至8月在美国纽约州弗里维尔市康奈尔大学汤普森试验农场开展。我们采用随机区组设计，将各处理种植于2×2m的小区中，共设6次重复。区组内小区间距为1.5m，区组间间距为2m。所有小区的总播种量统一为180kg/ha，混播组合中各组分的种子数量均等（Woldeamlak 2001a）。为模拟小农的典型种植方式，我们采用人工撒播，并通过镇压器将种子压入土中。播种前10天，按55.07kg/ha的用量施用当地小粒作物专用复合肥（NPK 13-13-13）。试验小区按需进行灌溉，未喷施任何除草剂、杀菌剂或杀虫剂，杂草均采用人工拔除。

2020年夏季生长季炎热干燥，所有谷类品种的成熟周期均大幅缩短。我们于2020年5月21日播种，2020年8月5日至8日完成收获。通常从播种至抽穗期需64天的Glenn小麦（Mergoum et al. 2006），在本实验中仅用了40天便完成抽穗。

### 2. 蚜虫调查
我们对每个小区最内侧25×25cm样方内的所有植株进行蚜虫数量与物种鉴定调查。我们记录每个样方内的植株总数以及至少有1头蚜虫寄生的植株数。我们共开展4次调查，首次调查于蚜虫首次出现在幼苗上时开展（2020年6月3日），之后每两周调查1次。基于上述调查结果，我们定义了两个反映小区蚜虫压力的指标：蚜虫总数以及有蚜虫寄生的植株占比。植株寄生率定义为样方内至少有1头蚜虫取食的植株所占百分比。

### 3. YDV采样
为估算每个小区内感染YDVs的植株比例，我们分别于2020年6月17日、7月6日与7月19日三次采集叶片样本。我们利用便携式木质网格，在每个小区中心1m²区域内的每个25cm×25cm分区采集2株无症状植株，每个小区共采集30份样本。为避免干扰蚜虫调查区域内的蚜虫种群，我们将每个小区最内侧的25cm×25cm区域排除在病毒采样范围之外。每份样本约含5g叶片组织，装入塑料袋后置于冰上，随后保存于-20℃冰箱中，直至开展YDV检测。

我们采用三重抗体夹心酶联免疫吸附试验（triple-antibody sandwich enzyme-linked immunosorbent assay, TAS-ELISA）（Agdia Inc, 印第安纳州埃尔克哈特市，美国）对所有样本进行BYDV-PAV、BYDV-MAV与CYDV-RPV的检测，检测流程参照D’Arcy和Hewings（1986）的方法。我们使用叶片提取压榨器将5g叶片样本与5mL磷酸盐缓冲液（pH 7.4，PBS）混匀。将包被缓冲液稀释的抗体（2μL抗体/mL包被缓冲液）加入微孔板，4℃下过夜孵育。用PBS-Tween（0.5% Tween 20 + PBS）洗涤包被后的微孔板两次，随后将匀浆样本的汁液加入微孔板，同时设置感染与健康对照汁液，4℃下过夜孵育。再次用PBS-Tween洗涤后，向孔中加入碱性磷酸酶标记的免疫球蛋白（2μL抗体+2μL/mL结合缓冲液），37℃下孵育4小时。每孔加入100μL底物（1mg对硝基苯基磷酸二钠盐六水合物底物粉末/mL底物缓冲液），37℃下孵育50分钟后，使用微孔板自动读数仪读取反应结果。在405nm波长下，光密度值至少为健康对照孔均值1.75倍的样本，判定为BYDV或CYDV阳性。

### 4. 产量测定
待整个实验小区的作物完全成熟后，我们收获每个小区最内侧1m²区域内的所有植株地上生物量。我们分离混播组合中的各组分，并将谷穗与秸秆分开。由于我们无法清晰区分混播小区中的Newdale大麦与AAC Synergy大麦（见图7），且二者在单播条件下的产量无显著差异，我们假设两个大麦品种在混播条件下的产量相当，并将含两个大麦品种的混播小区产量平均分配。我们将植株风干6个月后，记录谷粒与秸秆重量。我们还统计了穗头数量，以计算单穗平均重量。

### 5. 统计分析
我们所有分析均在R 4.0.4版本中完成（R Development Core Team 2021）。所有广义线性混合模型（Generalized Linear Mixed Model, GLMM）均通过glmmTMB包的glmmTMB函数拟合（Brooks et al. 2022）。针对我们的4个指标——蚜虫总数、植株寄生率、植株感染率、产量指标，我们分别构建两个独立模型：一个以处理为预测变量，另一个以混播类型为预测变量。我们通过分组处理得到不同的混播类型（见表1）。例如，"小麦单播"类型包含Marquis小麦单播与Glenn小麦单播，"双物种类混播"类型包含所有双物种类混播组合：MWh+ABa、MWh+NBa、GWh+ABa与GWh+NBa。在所有模型中，处理作为预测变量包含所有可能的混播与单播组合。为使图4的标注保持统一，我们仅在该单个模型中排除了两个品种混播处理。处理间的显著差异结果未发生改变。

为分析蚜虫植株寄生率，我们采用带有logit连接函数的二项式GLMM。我们的首个模型将采样时期与寄生率设置为交互项，但由于ANOVA（Type II Wald卡方检验）结果显示采样时期与处理的交互项不显著，我们最终采用仅将采样时期作为加性固定效应的模型。在该最终模型中，区组以及嵌套于区组内的小区编号作为随机效应，同时我们将样方内植株数量作为偏移项，以校正不同小区间的植株密度差异。蚜虫总数的分析采用带有log连接函数的负二项分布GLMM。初始模型的ANOVA结果显示，采样时期与蚜虫总数的交互项不显著，因此将采样时期作为加性固定效应纳入最终模型。最终模型包含区组以及嵌套于区组内的小区编号作为随机效应，并将样方内植株数量作为偏移项。由于蚜虫物种丰度随采样时期发生变化，我们还针对两种优势蚜虫物种分别开展了寄生率与蚜虫丰度的模型分析。

我们采用带有logit连接函数的二项式GLMM，结合作为固定效应的采样时期，分析每个小区最内侧1m²区域内的YDV感染植株比例。该模型包含区组以及嵌套于区组内的小区编号作为随机效应，并以样方内植株数量作为偏移项。针对产量指标（谷粒重量、秸秆重量、穗头数量、单穗重量），我们采用带有log连接函数的高斯线性混合模型（Linear Mixed Model, LMM），并将区组作为随机效应。

我们追踪了每个品种在不同混播情境下的种植结果，并将每个品种的产量校正至0.5m²半小区面积的等效产量。由于所有品种的初始播种量一致，该校正方式可让我们在相同土地面积、相同种子数量的条件下，比较单播与混播小区中各品种的产量。我们将各品种的单播平均产量与其对应的三种双物种类混播组合的总平均产量进行比较（见图7）。针对每个品种，我们还分别将其单播平均产量与三种双物种类混播组合中的单品种产量进行比较（见图S1）。

针对每个模型，我们使用emmeans包（Lenth 2023）进行Tukey校正的估计边际均值（Estimated Marginal Means, least-squares means，最小二乘均值）的两两比较。我们使用emmeans包中的contrast()函数构建自定义对比，以比较每个混播组合与其单播组分的未加权平均值。例如，为确定混播组合的蚜虫寄生率是否显著高于或低于其单播组分的平均水平，我们检验混播小区的平均寄生率与两个单播组分的平均寄生率是否存在显著差异。