DOI: 10.5061/dryad.63xsj3v37
title: Resistance mixtures reduce insect herbivory in strawberry (Fragaria vesca) plantings: leaf damage and yield data

description: The transition towards more sustainable plant protection with reduced
 pesticide use is difficult, because there is no ‘silver bullet’ available
 among non-chemical tools. Integrating several plant protection approaches
 may thus be needed for efficient pest management. Recently, increasing the
 genetic diversity of plantations via cultivar mixing has been proposed as
 a possible method to reduce pest damage. However, previous studies have
 not addressed either the relative efficiency of exploiting cultivar mixing
 and intrinsic plant herbivore resistance or the potential utility of
 combining these approaches to increase cropping security. Here, using a
 full factorial experiment with 60 woodland strawberry plots, we tested for
 the relative and combined effect of cultivar mixing and intrinsic plant
 resistance on herbivore damage and yield. The experiment comprised two
 levels of diversity (“high” with ten varieties and “low” with two
 varieties), and three levels of resistance (“resistant” comprising only
 varieties intrinsically resistant against strawberry leaf beetle
 Galerucella tenella; “susceptible” with susceptible varieties only; and
 “resistance mixtures” with 50:50 mixtures of resistant and susceptible
 varieties). The experiment was carried out over two growing seasons. Use
 of resistant varieties either alone or intermixed with susceptible
 varieties in “resistance mixtures” reduced insect herbivory.
 Interestingly, resistant varieties not only reduced the mean damage in
 “resistance mixtures” by themselves being less damaged, but also protected
 intermixed susceptible varieties via associational resistance. The effect
 of higher genetic diversity was less evident, reducing herbivory only at
 the highest level of herbivore damage. In general, herbivory was lowest in
 plots with high diversity that included at least some resistant varieties,
 and highest in low diversity plots consisting only of susceptible
 varieties. Despite this, no significant difference in yield (fruit
 biomass) was found, indicating that strawberry may be relatively tolerant.
 Our results demonstrate that combined use of high genetic diversity and
 resistant varieties can help reduce pest damage and provides a useful tool
 for sustainable food production. “Resistance mixtures” may be particularly
 useful for sensitive food crops where susceptible varieties are
 high-yielding that could not be completely replaced by resistant ones.
description: Field experiment covering a total area of 130 × 40 m (i.e.,
 similar to the general size of strawberry plantation) was established at
 the Alnarp campus of the Swedish University of Agricultural Sciences in
 southern Sweden.Leaf damage data  was collected four times during 2018 and
 once in 2019 from woodland strawberry (<em>Fragaria
 vesca</em>) plants in a full factory experimental design consisting
 of two levels of genetic diveristy (i.e. number of varieties used, 10=
 high and 2 = low) and three levels of intrinsic plant resistance on the
 experimental plots (R= resistant against a generalists leaf chewing
 herbivore, Galerucella tenella, S= susceptible against this pest and M=
 resistance mixture plot, consisting of 50:50 ratio of both resistant and
 susceptible varieties).The full factorial set up resulted in the following
 treatment combinations: high diversity resistant (HR), high diversity
 susceptible (HS), high diversity mixture (HM), low diversity resistant
 (LR), low diversity susceptible (LS) and low diversity mixed (LM) plots,
 each of which was replicated ten times. The position of the varieties
 within plots were randomized. Each plot contained forty plants, and two
 replicates of each variety were grown in the same soil bag, resulting in
 2400 plants in total. Leaf damage was observed as
 number of damaged leaf (regardless of the size of the damaged area). Plant
 size (photosynthetic area) was recorded at the last scoring occasion in
 2018, measured as height x width of the plant. Position of the plot in a
 field was categorized into 4 categories to take into account potential
 edge effects: (top = 'forest' = the 4 plots in the first
 row;  bottom = 'end' = the last row i.e. 4 plots ;  right =
 'edge right' =1 vertical colum of plots; left =
 'hedge' = 1 vertical colum of plots; and 'middle'= two
 vertical colums in the middle of the field).  In order to
 investigate the effect of plot’s resistance and diversity on yield, we
 harvested all ripe fruits on a weekly basis during May and June during
 2019. The fruits were weighed on the day of harvest. For practical
 reasons, the fruits from four plants of the same variety were pooled and
 weighed resulting in ten samples per plot. Yield data were also collected
 during 2018. However, due to the natural spatiotemporal variation in
 herbivory, the arrival of herbivores in 2018 occurred after or during the
 fruit harvest. Therefore, the yield data from 2018 were not analyzed in
 relation to herbivory. For
 statistical models, we calculated the average yield per plant by dividing
 the weight of the sample box by four (as samples were harvested by
 combining fruits from four plants of the same variety) to make the dataset
 comparable to leaf damage analyses. Similarly, when using plant size as a
 covariate in yield analyses, we first calculated the average plant size
 for each variety in a plot and divided it by four to make the plant size
 data comparable to yield data.
description: The plant damage was scored 4 times in 2018, but due to the very
 low level of herbivory in the first scoring occasions (obs time1), this
 time point was not analysed in the publication.  Yield data is only from
 year 2019, as  due to the natural spatiotemporal variation in herbivory,
 the arrival of herbivores in 2018 occurred after or during the fruit
 harvest  For practical reasons, the fruits from four plants of the same
 variety were pooled and weighed resulting in ten samples per
 plot.  For statistical models, we calculated the
 average yield per plant by dividing the weight of the sample box by four
 (as samples were harvested by combining fruits from four plants of the
 same variety) to make the dataset comparable to leaf damage analyses.
 Similarly, when using plant size as a covariate in yield analyses, we
 first calculated the average plant size for each variety in a plot and
 divided it by four to make the plant size data comparable to yield
 data. For leaf damage data Year=
 year of sampling<br> running number= running number of the
 row<br> edge= position of the plot in the field<br> block= id
 number of the block<br> plot= id number of the plot<br> bag=
 id number of the soil bag where 2 plants of the same genotypes were
 grown<br> resistance= resistance of the plot, i.e. if the plot
 contained only resistant or susceptible plants or their 50:50
 mixture<br> diversity= high = 10 genotypes, low = 2
 genotypes<br> plot2= id code for the plot including the level of
 diversity and resistance<br> plant_res= plant resistance, resist or
 susceptible against the strawberry leaf beetle, Galerucella
 tenella<br> observation_time= date of the data collection<br>
 obs_time= number for scoring occasion<br> period= time in the
 growing season when leaf damage was estimated<br> genotype= genotype
 id of the plant<br> plant id= code for plant id, including plot and
 genotype and id number<br> plant id2= more comprehensive id code for
 a plant, including block, genotype and plant number<br> damage
 leaves= number of insect damaged (leaf chewing marks) on the
 plant<br> plant area= plant size calculated at the end of the
 growing season in 2018, obs_time 4. Area describes the photosynthethic
 area height x width cm of the plant (cm2) For yield
 data edge= position of the plot in the field (same as
 above)<br> resistance= resistance of the plot, i.e. if the plot
 contained only resistant or susceptible plants or their 50:50
 mixture<br> diversity= high = 10 genotypes, low = 2
 genotypes<br> block= id number of the block<br> plot= id code
 for the plot including the level of diversity and resistance<br>
 genotype= id code for genotype (i.e. variety)<br> weight 1-4= for
 pracitcal reasons, woodland strawberry fruits from the same genotype were
 collected in maximum 5 plastic boxes, these columns show the weight of
 each box (g)<br> total weight= summarized weight per genotype from
 all the fruits<br> area2018= plant photosynthetic area (size)
 measured at the end of previous growing season in 2018, measured as plant
 height x widht<br> average_weight_plant= average yield per plant
 calculated by dividing the total yield weight by 4 (i.e. the number of
 plants per genotype from which the fruits were collected
 together)<br> average_area= average photosynthetic area for plant,
 calculated by the total area per genotype, divided by 4 (number of plants
 where fruits were collected together)