DOI: 10.5061/dryad.7pvmcvf2j
title: Data from: Varietal and intrafamilial species diversity influence aphid and yellow dwarf virus pressure within mixtures of wheat and barley

description: 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.
description: <strong>Experimental design</strong>
 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 M<sup>Wh</sup> +
 N<sup>Ba</sup>”. 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. <strong>Aphid
 surveys</strong> 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. <strong>YDV
 sampling</strong> 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 1m<sup>2</sup> 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. <strong>Yield</strong>
 We harvested aboveground biomass from all plants within the
 innermost 1m<sup>2</sup> 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. <strong>Statistical
 analysis</strong> 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 <em>glmmTMB</em> function
 from the <em>glmmTMB</em> 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: M<sup>Wh</sup>+A<sup>Ba</sup>,
 M<sup>Wh</sup>+N<sup>Ba</sup>,
 G<sup>Wh</sup>+A<sup>Ba</sup>, and
 G<sup>Wh</sup>+N<sup>Ba</sup>. 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 1m<sup>2</sup>
 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
 (0.5m<sup>2</sup>). 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
 <em>emmeans</em> package (Lenth 2023) to conduct pairwise
 comparisons between our Tukey-adjusted Estimated Marginal Means
 (least-squares means). We used the <em>contrast()</em>
 function from <em>emmeans</em> 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.
description: # Varietal and intrafamilial species diversity influence aphid and yellow
 dwarf virus pressure within mixtures of wheat and barley
 [https://doi.org/10.5061/dryad.7pvmcvf2j](https://doi.org/10.5061/dryad.7pvmcvf2j) Dataset contents: "aphid_obs_mess.csv" :  * Should be read by "aphid_models.R" to be analyzed * This file is all the data collected from aphid observations done at the experimental plot * columns [AA, AR] are copied over from "infection_for_model.csv" to make looking at correlations in the data easier. Because these columns are averages and cumulative sums of the infection data, they only fill 66 rows. The remaining rows for these columns have been left as NA. * Abbreviations: * obs_per = observation period day (each observation period is only 1 day long) * total_plants_in_quadr = the total number of plants counted within the innermost 25cm^2 of each plot * prop_plants_occupied = the proportion of plants within the innermost 25cm^2 that had at least one aphid on them * tot_rpadi = the total number of R. padi within the innermost 25 cm^2 * tot_sav = the total number of *S. avenae* * prop_pl_occup_tot_Rpadi = the mean proportion of plants within the innermost 25cm^2 occupied by at least one *R. padi* * no_pl_occup = the number of plants within the innermost 25cm^2 occupied by at least one aphid * no_pl_NOT_occup = the number of plants within the innermost 25cm^2 that were free of aphids "infection_for_model.csv" : * Should be read by "infection_models.R" to be analyzed * This is all the data resulting from collecting leaf samples and using ELISA in the laboratory to see if they are infected with YDVs * Abbreviations: * sample = the virus sampling period (see methods) * no_inf = the number of plants infected by any YDV within the innermost 1m^2 of the plots. 30 plants were sampled at random within the innermost 1m^2 * no_NOT_inf = the number of plants without YDV infection sampled from the innermost 1m^2 of the plots. 30 plants were sampled at random within the innermost 1m^2 * pav= BYDV-PAV * rpv = CYDV-RPV "yield_plot_weights.csv" * Should be read by "yield.R" to analyze the yield data at the plot level * all weights are in grams (g) * Abbreviations: * group = Each treatment is split into two categories. "mono" means that the treatment is a monoculture, "mix" means that the treatment is a mixture * group2 = Each treatment is split into 3 categories: monoculture ("mono"), species mix("sp"), varietal mix("var") * plot_weight = the total weight of grain harvested from the innermost 1m^2 of each plot (in grams) * plot_fodder = the total weight of straw harvested from the innermost 1m^2 of each plot (in grams) * plot_full = the number of full seed heads harvested from the innermost 1m^2 of each plot * plot_tot_heads = the number of seed heads (full + incomplete) harvested from the innermost 1m^2 of each plot * mean_wt_one_head = the mean weight of one seed head from each plot (in grams) "yield_component_weights.csv" : * Should be read by "yield_by_component.R" to analyze the yield data at the level of each mixture component * all weights are in grams (g) * Abbreviations: * comp = the name of the component/variety being separated out * mix_id = the name of the mixture that the component/variety was in * comp_weight = the weight of the component's grain, separated out from whichever mixture it was in (in grams) * comp_weight_div2 = the weight of each component's grain, separated out from whichever mixture it was in, then adjusted to be on a half-plot basis. e.g. halved if it was a monoculture, multiplied by 2 if the component was part of a four-way mixture, and left alone if the component was part of a 2-component species mixture (in grams) ## Description of the data and file structure "aphid_obs_mess.csv" : Goes with "aphid_models.R" "infection_for_model.csv" : Goes with "infection_models.R" "yield_component_weights.csv" : Goes with "yield_by_component.R" "yield_plot_weights.csv" Goes with "yield.R" ## Sharing/Access information This dataset is the basis for a paper being submitted to Agronomy for Sustainable Development (submitted May 2024). It is not publicly available anywhere else except for on Dryad. All data was collected by Anna DiPaola, with the exception of the yield data, which was in part collected and recorded by undergraduate research assistant Jailyn Loor. ## Code/Software "aphid_obs_mess.csv" : Goes with "aphid_models.R" "infection_for_model.csv" : Goes with "infection_models.R" "yield_component_weights.csv" : Goes with "yield_by_component.R" "yield_plot_weights.csv": Goes with "yield.R" All the code provided is commented within the R file itself. Each R file only takes in one excel file, noted above.