Progress report for GNE22-305
Project Information
Monitoring insect abundance is essential for decision-making and thus a fundamental component of integrated pest management (IPM); however, these decisions are rarely based on the abundance of beneficial insects. To enhance IPM programs, monitoring tools for beneficial insects, such as pollinators and natural enemies of pests, need to be developed. While it is understood that beneficials are attracted to plant volatiles like methyl salicylate (MeSA), there are several considerations needed before volatiles can be used for monitoring. For instance, the composition of the landscape in which they live, such as the amount of non-crop habitats, can affect their response to plant volatiles. However, the combined effects of landscapes and local management practices on the response of beneficial insects to plant volatiles remains unknown. This research will determine how the landscape and local management of cranberry agroecosystems affect the beneficial insect community and subsequent ecosystem services in conjunction with the common and commercialized plant volatile MeSA. To achieve this, a trapping network will be established across 50 cranberry beds in the three largest cranberry farms in New Jersey. In each bed, sticky and pan traps baited with MeSA and unbaited traps will be used to monitor natural enemy and pollinator abundance throughout the season. In addition, sentinel eggs and exclusion cages will be placed to assess predation and pollination services. Results from these studies will help develop tools for monitoring beneficial insects that can be used in pest management decisions and conservation biocontrol.
- Investigate the effects of landscape (habitat composition) and local management practices (crop variety and pesticide usage) on response of beneficial insects to plant volatiles.
- Investigate the effects of landscape and local management practices on the ecosystem services provided by beneficial insects: biological control (predation) and pollination.
The purpose of this study is to investigate the effects of landscape composition and local management practices on plant volatile recruitment of beneficial insects, which have practical implications in integrated pest management (IPM) decision-making and biocontrol. Making decisions in IPM relies heavily on monitoring to see what insect pests are present. While many monitoring approaches are directed at pests, beneficial insects such as pollinators and natural enemies of pests are rarely considered in decision-making, despite them being important. Many beneficials are known to utilize volatile organic compounds from the plants, for example predators and parasitoids, to locate their host or prey (Price et al. 1980). Since the 1980s, entomologists have been researching these volatiles and subsequently utilizing synthetic plant volatiles to attract these beneficial insects to crops (Rodriguez-Saona et al. 2011). One such plant volatile is methyl salicylate (MeSA) that is commonly released by flowers and other plant parts particularly after insect feeding damage (Vlot et al. 2009). The attraction of beneficial insects to MeSA has been studied in multiple crops, including cranberries, and is known to attract many beneficial insects, such as hoverflies, lady beetles, lacewings, among others (Rodriguez-Saona et al. 2011). Therefore, MeSA can potentially be used to monitor beneficial insects and conservation biological control in agricultural crops. In fact, a lure called PredaLure that contains MeSA is commercially available for this purpose. However, for these lures to be successful, beneficial insects need to be present in the agroecosystem, which means further studying of the agroecosystem in the context of landscape composition and local management practices is necessary.
There are many studies about how landscapes and management practices interact to affect beneficial insect abundance and diversity (Tscharntke et al. 2012). For instance, hoverflies, which are predators as larvae and pollinators as adults, are more diverse and abundant in areas with higher floral diversity, meaning that with higher floral diversity, there is likely to be more predation of pests and pollination (Gervais et al. 2018). Thus, one can theorize that agroecosystems with higher diversity will also have higher amounts of beneficial insects. Moreover, diverse agroecosystems and non-crop habitats provide refuge for beneficials that would otherwise be killed by the pesticide regimes and toxic sprays commonly applied by farmers (Pandey et al. 2022). To date, no studies have studied the correlation that landscape composition and management practices have on beneficial insect attraction to plant volatiles. Understanding the effects of landscape factors and local management practices on the response of beneficial insects to synthetic plant volatiles will help develop new and efficient monitoring techniques for them, which could play into sustainable IPM strategies like conservation and augmentation biocontrol. As such, this proposed research directly connects to the Northeast SARE Outcome Statement, of researching key issues in sustainable agriculture. Monitoring beneficial insects will allow growers to implement these biocontrols into decision-making in IPM programs, which could lead to a decreased reliance on pesticide applications and thus a more environmentally and possibly economically friendlier approach to pest management.
Research
1. Study system
1.1 Sites
A field experiment was conducted in 2022 and 2023 to measure the response of natural enemies to MeSA. Fifty beds (sites) across the three largest commercial cranberry farms (Pine Island Cranberry Co., JJ White Native Jersey Fruits, Cutts Bros) in New Jersey, USA were chosen using a random number generator, making sure no two beds were direct neighbors, afterwhich every site was georeferenced using a Global Positioning System (GPS). Each bed was one of five cultivars (‘Ben Lear’, ‘Crimson Queen’, ‘Early Black’, ‘Mullica Queen’, and ‘Stevens’), and there were equal numbers of beds (10) per cultivar. These cultivars were chosen because they are the most commonly grown in New Jersey. The same beds were used for both 2022 and 2023. All pesticide (insecticides, fungicides, herbicides) spray records were collected per bed per month at the end of each growing season.
1.2 Landscape analysis
To measure landscape, we utilized the most recent land use/cover map provided by the New Jersey Department of Environmental Protection. Using ArcGIS Pro, we classified 6 land cover types: agriculture (which was solely cranberries, since not a lot else is farmed in those areas), barren land, forest, urban, water, and wetland. We then uploaded the coordinates of our sites and drew four spatial buffer zones around each coordinate, at 100 m, 250 m, 500 m, and 1500 m radii. Using the ZonalMetrics toolbox for ArcGIS Pro (Adamczyk and Tiede 2017), we calculated landscape composition and configuration metrics of land covers for each buffer zone. We analyzed the Shannon diversity of land covers per zone, the total edge length between land covers per zone, the largest patch index per zone, numbers of patches of each cover per zone, and the percent area of each cover per zone.
2. Beneficial insects
2.1 Natural enemies
In order to sample the natural enemy community, at each bed we set up a pair of yellow sticky traps (23 × 28 cm Unbaited Pherocon AM; Trécé Inc., Adair, Oklahoma) at the canopy level at least 10 m away from both the edge of the bed and 20 m from each other. Cranberries are a viny ground-covering plant that grows in a mat, so we drove 80 cm metal pipes bent 90° at the 60 cm mark into the ground to hang each trap. One of the traps was baited with a MeSA lure (5 g load/lure; 90 d lure; average release rate ∼35 mg/day over a 4 week period at 30 °C constant in the lab; PredaLure, AgBio Inc.), and the other trap was unbaited (control) with a square white paper (8 × 8 cm) with a hole punch that visually looks similar to the MeSA lure. The bait and paper were hung directly adjacent to their respective yellow sticky trap without touching it. Yellow sticky traps are thought to be the best sampling methods for natural enemy communities in cranberries, and can easily be used with semiochemicals (Rodriguez-Saona et al. 2011, Rodriguez-Saona et al. 2012, Rodriguez-Saona et al. 2020). Every trap was out for one week at a time per month between May to August, allowing a snapshot of the community from prebloom to preharvest (one pair of sticky traps per bed per month May-August for two years). This time period is also when the worst of the cranberry insect pests of New Jersey are active as well (Ben-Zvi and Rodriguez-Saona 2023). The MeSA lure was replaced once a month. The traps were taken back to the lab, and all trapped arthropods were counted and identified using a stereomicroscope (Nikon SMZ-U, Tokyo, Japan). The arthropods were mostly identified to family. Some arthropods were not identified to family and were put into broader groupings, notably midges and parasitoid wasps. These arthropods were characterized as natural enemies and herbivores/ non natural enemies. Counts of some very small and numerous arthropods, like thrips and whiteflies, were estimated by averaging their counts from 4 squares across the 63 squares of the sticky trap grid.
2.2 Pollinators
To sample the pollinators, we set up white pan traps (Disposable 12 oz. White Plastic Soup Bowls, Amazon) with 150 mL water and 2 mL clear scentless soap (PROVON, Office Supplies). Because cranberries are viny and grow in a mat, balancing a full pan trap is rather difficult. Therefore, we drove a 60 cm long 1.2 cm diameter metal pipe into the ground, and glued a PVC pipe connector to the bottom of each pan trap. The pan traps with the PVC connector fit snugly on the pipe and were stable, and each trap was placed at canopy level. Each site had two pan traps 20 m apart. A white MeSA-baited sachet (5 g load/lure; 90 d lure; average release rate ∼35 mg/day over a 4 week period at 30 °C constant in the lab; PredaLure, AgBio Inc.) hung above one pan trap per site from a 80 cm long metal pipe, meanwhile the other (control) trap in the site had a white square paper that visually looked like the bait. Previous studies show that Traps were set for 48 hours at a time twice during cranberry bloom (once in June and once in July) in 2022 and 2023. Afterwards, all trapped arthropods were placed in vials with 70% ethanol and counted and identified at a later date. Every arthropod was at minimum identified to family, with several genera identified – particularly within bees and syrphids. These arthropods were characterized as potential pollinators, non-pollinating herbivores, and non-pollinating predators/parasitoids (Supplementary table 1). For the sake of this study, only the potential pollinators were analyzed.
3. Ecosystem services
3.1 Predation
After the sticky traps were taken down, the MeSA lure was left in place and we set up sentinel Spodoptera frugiperda Smith (Lepidoptera: Noctuidae) egg masses in the same place. The egg masses were bought frozen from Frontier Agricultural Sciences (Delaware, USA). Spodoptera frugiperda is not a cranberry pest, however the predation of their dead egg masses can represent generalist predation ecosystem services. The egg masses arrived on a large wax paper sheet, and we cut out individual masses consisting of around 100 eggs using scalpels and scissors, making sure there was only one layer of eggs. We glued one egg mass per square green paper (8 × 8 cm) that was stapled to wooden stakes (25 cm tall, 2.5 cm wide). We set up three egg mass stakes in a circle around the MeSA bait and three around the paper bait at a 50 cm radius. The egg masses deployed for 48 hours at a time, once a month between May to August, and never overlapped with the sticky traps nor pan (three pairs of sentinel egg masses per bed month May-August for two years). We labeled the egg masses, took pictures immediately before and after the 48 hour time period in the field, and counted the eggs in all the pictures. Afterwards, we calculated predation rates by dividing the total eggs before by total eggs after.
3.2 Pollination
A field experiment investigating the effects of MeSA on pollination services was conducted in 25 of the 50 sites chosen for the pollinator study above, in 2022 and 2023. The 25 sites were randomly chosen from the 50 with a random number generator, making sure there were still an equal amount (5) of sites per cultivar.
To look at pollination we set up two cages (30 cm x 30 cm x 30 cm metal grid wrapped in a thin white fabric at the base and open at the top) in each of the chosen sites 20 m apart. These cages were open to allow pollinators to enter. One of the cages was baited with the MeSA sachet, and the other had the paper square. The cages were set up before bloom (beginning of June) and closed at the beginning of fruit set (end of July) to prevent potential herbivores. Cranberries are a pollinator dependent crop, thus yield was used as a proxy for pollination. All the formed fruits were collected from each cage at the end of the season (September) and were counted, then 10 berries per sample were measured and weighed. This experiment was repeated for 2022 and 2023 in the same location.
4. Statistical analysis
4.1 Natural enemies
4.1.1 Natural enemies with methyl salicylate
The response variables in the community ecology study were the organism count. Natural enemies and herbivores were mostly identified to family. We also further calculated total abundances and Shannon diversity of natural enemies at each sire, as well as that of herbivores at each site.
For the plant level, we began with Hellinger transforming the natural enemy count data using “decostand” function, testing for dispersion homogeneity using the “betadisper” and “permutest” functions, then running a Permutational Multiple Analysis of Variance (PERMANOVA) to look at the effects of MeSA on the natural enemy community with 999 permutations on a Bray-Curtis distances using “adonis2” function in RStudio (ver. 4.2; R Core Team 2024) all with the “vegan” package (Oksanen et al. 2025). After that, we looked at one natural enemy taxa at a time and ran generalized linear mixed models (GLMMs) on the MeSA lure with random factors of bed nested in farm and month nested in year, using the “glmer” function in the “lme4” package in RStudio (Bates et al. 2015). When looking at one natural enemy family at a time we did not look at Hellinger transformed data, rather raw data. The distributions used for the GLMMs were either Poisson, Negative Binomial or Poisson-Lognormal, and that was determined with the “overdisp_fun” function from the “PsychHelperFunctions” package (Huff and Papenmeier 2022). We also then looked at the effect of MeSA with the same random factors on the total abundance and Shannon diversity of natural enemies using a Poisson-Lognormal distributed GLMM and a Gamma log link distributed GLMM respectively. Afterwards, we repeated this same exact process except with the herbivore count data.
4.1.2 Natural enemies and local management
Our local management variables consisted of the number of insecticides sprayed per bed, the number of fungicides sprayed per bed, the number of herbicides sprayed per bed, and the cultivar per bed. Our response variables were still the natural enemy counts and random factors were still bed nested in farm and month nested in year, except now we were looking at each local management factor, and the interactions of cultivar and each pesticide amount individually as fixed factors. We similarly ran GLMMs that either had Poisson, Negative Binomial, or Poisson-Lognormal distributions for each natural enemy taxon, and we now chose best fit models by eliminating insignificant fixed variables and interactions until we achieved the lowest Akaike’s Information Criterion (AIC) value. The AIC value does not inherently mean anything, rather it is used in model comparison and selection (CITATION). Other than just knowing how management affects the natural enemies, we also wanted to know how MeSA is involved. Therefore, we ran another round of GLMMs for every natural enemy taxon with the interaction of the MeSA lure and each of the local management variables as explanatory factors and the same random factors as before. Again we removed insignificant interactions and variables as per the AIC value.
4.1.3 Landscape level natural enemies
Lastly for the landscape level, the explanatory variables were the all landscape factors per buffer zone (Shannon diversity of land covers, the total edge length between land covers, the largest patch index, numbers of patches of each land cover, and the percent area of each land cover), year, and the random site nested in farm. We ran a redundancy analysis (RDA) with the “rda” function in the “vegan” package (Oksanen et al. 2025), which is similar to a principal component analysis, but it considers a constrained environment, i.e the landscape variables. For this analysis we looked at each buffer zone separately. We ran several rounds of RDA, removed the highly correlated variables, and performed forward selection using the “ordiR2step” function from the “vegan” package (Oksanen et al. 2025). The RDA reported the most important landscape variables for the natural enemies as a whole at each buffer zone. To look at individual taxa singularly, we ran a random-forest analysis one buffer zone at a time, using 500 runs the “Boruta” function in the “Boruta” package (Kursa and Rudnicki 2010) which selects the most important explanatory variable for each response. We checked for correlation between variables using the “ggcorrplot” function from the package “ggcorrplot” (Kassambara and Patil 2023). Then, we went on to individual GLMMs for each natural enemy taxon, using only the uncorrelated important landscape variables, and the random site nested in the farm and month nested in year, and proceeded to choose the best fit models using AIC and run estimated marginal means post-hoc tests. We ran additional models to see if there was an interaction between any of the landscape variables and MeSA.
4.2 Pollinators
4.2.1 Pollinators and MeSA
The response variables in the pollinator study were the pollinator counts. Pollinators had been identified at minimum to family, with some to species. For the analyses, we grouped species that were in the same genus together, therefore we can represent a diversity of insects that do not range in identification from family to species. Our pollinator taxa were either genera (such as Bombus spp.), monotypically represented genera (such as Apis mellifera L.), or family (such as Muscidae). For the total list of species caught, see Supplementary Table 1. We also further calculated Shannon diversity of pollinators at each site, as well as total abundance of pollinators at each site.
For the plant level, we began with Hellinger transforming the pollinator count data using “decostand” function, testing for dispersion homogeneity using the “betadisper” and “permutest” functions, then running a Permutational Multiple Analysis of Variance (PERMANOVA) to look at the effects of MeSA on the pollinator community with 999 permutations on a Bray-Curtis distances using “adonis2” function in RStudio (ver. 4.2; R Core Team 2024) all with the “vegan” package (Oksanen et al. 2025). After that, we looked at one pollinator taxa at a time and ran generalized linear mixed models (GLMMs) with MeSA with random factors of site nested in farm and month nested in year, using the “glmer” function in the “lme4” package in RStudio (Bates et al. 2015). When looking at one pollinator at a time we did not look at Hellinger transformed data, rather raw data. The distributions used for the GLMMs were either Poisson or Negative Binomial, and that was determined using the “overdisp_fun” function from the “PsychHelperFunctions” package (Huff and Papenmeier 2022). We also looked at the effects of MeSA on the total pollinator abundance and Shannon diversity of pollinators by running a Negative Binomial distributed GLMM and a Gamma log link shited by 0.1 GLMM respectively, both with the same random factors as earlier. Estimated marginal means post-hoc tests were run using the “emmeans” function in the “emmeans” package (Lenth 2025).
4.2.2 Pollinators and local management
Moving onto the field level, our explanatory variables were the number of sprays per pesticide type (insecticide, herbicide, and fungicides), cultivar, interactions, and again site nested in farm and month nested in year as random factors. We ran with the “glmer” function GLMMs for each pollinator taxon (Bates et al. 2015), and then chose best fit models by eliminating insignificant variables and looking for the lowest Akaike’s Information Criterion (AIC). By itself, the AIC value does not mean anything, but it is used in model comparison and selection (CITATION). Afterwards, we ran estimated marginal means post-hoc tests.
Since we also wanted to know how MeSA can affect the pollinator’s behavior towards the different management tactics, we ran another separate round of GLMMs. This time our explanatory variables were each of the field level variables interacting with MeSA, and the same random factors. We similarly chose the best fit models using AIC and ran estimated marginal means post-hoc tests.
4.2.3 Landscape level pollinators
Lastly for the landscape level, the explanatory variables were the all landscape factors per buffer zone (Shannon diversity of land covers, the total edge length between land covers, the largest patch index, numbers of patches of each land cover, and the percent area of each land cover), year, and the random site nested in farm. We ran a redundancy analysis (RDA) with the “rda” function in the “vegan” package (Oksanen et al. 2025), which is similar to a principal component analysis, but it considers a constrained environment, i.e the landscape variables. For this analysis we looked at each buffer zone separately. We ran several rounds of RDA, removed the highly correlated variables, and performed forward selection using the “ordiR2step” function from the “vegan” package (Oksanen et al. 2025). The RDA reported the most important landscape variables for the pollinators as a whole at each buffer zone. To look at individual taxa singularly, we ran a random-forest analysis one buffer zone at a time, using 500 runs the “Boruta” function in the “Boruta” package (Kursa and Rudnicki 2010) which selects the most important explanatory variable for each response. We checked for correlation between variables using the “ggcorrplot” function from the package “ggcorrplot” (Kassambara and Patil 2023). Then, we went on to individual GLMMs for each pollinator taxon, using only the uncorrelated important landscape variables, and the random site nested in the farm and month nested in year, and proceeded to choose the best fit models using AIC and run estimated marginal means post-hoc tests. We ran additional models to see if there was an interaction between any of the landscape variables and MeSA.
4.3 Predation
In the predation study, the response variable was the proportion of eggs eaten. We did not count the three egg masses at each location as individual replicates, instead we calculated the predation rates from the combined egg loss at each site. Predation rates then underwent an arcsine square-root transformation before analyzing under 0.1 shifted Gamma log link distributed GLMMs. Initially, our fixed variable was just the presence or absence of MeSA. Then, we looked at the field level and whether the pesticide regime or cultivar affected predation. We additionally looked to see if there was an interaction between the management and MeSA. Lastly, we investigated the landscape variables by selecting the most important with the “Boruta” function, and then seeing if there was any influence. We then also looked to see if there was an interaction between the landscape factors and MeSA. The random factors were bed nested infarm and month nested in year. All GLMMs were followed by AIC model selection and then estimated marginal means post-hoc tests.
4.4 Pollination
We approached the statistics very similarly for the pollination study, except the response variables were now yields (berry count, average berry volume, and average berry weight per sample) as a proxy for pollination. On the plant level, our explanatory variable was MeSA, with the year and the site nested in the farm as random factors, and we ran G/LMMs for each yield measurement. Berry count was run with a Negative Binomial distribution, the volume had a normal distribution, and the mass was run as 0.1 shifted Gamma distribution with a log link. After that, we looked at the field level, using the pesticide regiment, cultivar, and the random effects; however, since these cultivars have been bred to differ in yields (Vorsa and Johnson-Cicalese 2012), we ran the cultivar and pesticide models separately. Subsequently, we looked at the landscape variables. We selected the most important landscape variables, we ran a random-forest analysis one buffer zone at a time with the “Boruta” function from the “Boruta” package (Kursa and Rudnicki 2010) followed by a correlation matrix using “ggcorrplot” from package “ggcorrplot” to ensure no cross-talk (Kassambara and Patil 2023). All the models were refined using the AIC values and had estimated marginal means post-hoc tests run.
Results
1. Natural enemies
1.1 Natural enemy and pest communities (sticky traps) and MeSA
We caught 418173 natural enemy individuals across 28 taxa (mostly families), and 1632406 herbivorous individuals across 30 taxa. We found that both natural enemies (F = 8.4419, df = 1, p = 0.001) and herbivores (F = 7.0198, df = 1, PERMANOVA p = 0.002) were affected by MeSA as a whole, and that both natural enemies (F = 0.0005, df = 1, p = 0.986) and herbivores (F = 0.0753, df = 1, p = 0.782) had a homogenous dispersion by MeSA. The total abundance (χ² = 8.5727, df = 1, p = 0.0034) and Shannon diversity (χ² = 6.2833, df = 1, p = 0.0122) of natural enemies both increased with the presence of the MeSA lure, while the total herbivore count marginally increased (χ² = 3.7206, df = 1, p = 0.0537) and diversity marginally decreased (χ² = 2.8981, df = 1, p = 0.0887). Of our natural enemies, we specifically found Syrphidae, Dolichopodidae, Tachinidae, Tabanidae, Empididae, Coccinellidae, Anthocoridae, Pentatomidae, Araneae, and Neuroptera to be significantly attracted to MeSA, while Formicidae was significantly repelled and Phlaeothripidae was marginally repelled (Table 1).
1.2 Natural enemies and local management
Insecticides significantly impacted Tachinidae (χ² = 4.6236, df = 1, p = 0.0315), Anthocoridae (χ² = 9.1491, df = 1, p = 0.0025), Phlaeothripidae (χ² = 6.2833, df = 1, p = 0.0022), parasitoid wasps (χ² = 8.2869, df = 1, p = 0.004), spiders (χ² = 4.7398, df = 1, p = 0.0295), and marginally Tettigoniidae (χ² = 3.6945, df = 1, p = 0.0564). Interestingly, only Tachinidae and Tettigoniidae have a negative relationship with insecticides, while Anthocoridae, Phlaeothripidae, parasitoid wasps, and spiders all increase with increasing insecticides. Fungicides also had many positive relationships with natural enemies, specifically with Syrphidae (χ² = 9.1279, df = 1, p = 0.0025), Tachinidae (χ² = 13.876, df = 1, p = 0.0002), Cantharidae (χ² = 17.6398 df = 1, p < 0.0001), Lampyridae (χ² = 4.3945, df = 1, p = 0.0361), Phlaeothripidae (χ² = 4.1093, df = 1, p = 0.0426), and parasitoid wasps (χ² = 24.4068, df = 1, p < 0.0001), while negatively affecting Neuroptera (χ² = 13.8109, df = 1, p = 0.0002). As a whole, increasing fungicides led to an increasing total abundance of natural enemies (χ² = 10.5254, df = 1, p = 0.0012) but decreasing Shannon diversity of natural enemies (χ² = 5.0684, df = 1, p = 0.0244). Herbicides had a positive impact on Neuroptera (χ² = 4.3878, df = 1, p = 0.0362), and a negative impact on Staphylinidae (χ² = 3.9845, df = 1, p = 0.0459), parasitoid wasps (χ² = 4.066, df = 1, p = 0.0438), and marginally Formicidae (χ² = 3.6582, df = 1, p = 0.0558).
Cultivar was also significant more many natural enemies including Empididae (χ² = 10.7561, df = 4, p = 0.0294), Coccinellidae (χ² = 23.8127, df = 4, p < 0.0001), Lampyridae (χ² = 9.7443, df = 4, p = 0.045), Miridae (χ² = 10.023, df = 4, p = 0.04), Anthocoridae (χ² = 16.1984, df = 4, p = 0.0028), parasitoid wasps (χ² = 28.0678, df = 4, p < 0.0001), and to natural enemy abundance as a whole (χ² = 9.5996, df = 4, p = 0.0477). Specifically, Coccinellidae had higher counts in ‘Mullica Queen’ than ‘Stevens’, Miridae and parasitoids had higher counts in ‘Stevens’ than ‘Crimson Queen’, and there were also more parasitoid wasps in both ‘Ben Lear’ and ‘Stevens’ than ‘Mullica Queen’. Cultivar additionally had several significant interactions with the pesticide type for many natural enemies. Notably, increasing insecticides led to more Tabanidae catches in ‘Crimson Queen’ but less in ‘Early Black’ and ‘Mullica Queen’ (χ² = 9.9572, df = 4, p = 0.0412); more Empididae in ‘Early Black’ than ‘Mullica Queen’ (χ² = 15.0883, df = 4, p = 0.0045); more Coccinellidae in each of the cultivars except for less in ‘Mullica Queen’ (χ² = 10.4618, df = 4, p = 0.0333); more Anthocoridae in ‘Ben Lear’ but less in ‘Early Black’ (χ² = 11.636, df = 4, p = 0.0203); more Phlaeothripidae in ‘Stevens’ (χ² = 21.9715, df = 4, p = 0.0002); more spiders in ‘Crimson Queen’ (χ² = 13.426, df = 4, p = 0.0094); and overall lower Shannon diversity but specifically lower in ‘Stevens’ and ‘Early Black’ (χ² = 14.58577, df = 4, p = 0.0056). Increasing fungicides led to lower Dolichopodidae counts in all the cultivars except for ‘Stevens’ (χ² = 10.6639, df = 4, p = 0.0306); less Empididae in ‘Early Black’ than ‘Mullica Queen’ (χ² = 10.0291, df = 4, p = 0.0399); less Coccinellidae in ‘Mullica Queen’ and ‘Crimson Queen’ but more in the other cultivars (χ² = 103.322, df = 4, p = 0.0353); more Cantharidae in ‘Ben Lear’ (χ² = 38.3065, df = 4, p < 0.0001); more Cleridae in ‘Early Black’ (χ² = 11.8122, df = 4, p = 0.0188), more Anthocoridae in ‘Early Black’ than ‘Crimson Queen’ (χ² = 28.695, df = 4, p = 0.0003); less Formicidae in ‘Early Black’ than ‘Mullica Queen’ and ‘Crimson Queen’ (χ² = 9.4883, df = 4, p = 0.0499); more Phlaeothripidae in ‘Crimson Queen’ (χ² = 10.644, df = 4, p = 0.0309); more spiders in ‘Crimson Queen’ but less in ‘Mullica Queen’ (χ² = 20.5469, df = 4, p = 0.0004); more parasitoid wasps (χ² = 28.695, df = 4, p < 0.0001) and overall natural enemy abundance (χ² = 10.0925, df = 4, p = 0.0391) in ‘Ben Lear’, ‘Mullica Queen’, and ‘Stevens’ than the other two; and generally lower Shannon diversity of natural enemies especially in ‘Mullica Queen’ other than ‘Early Black’ which had higher diversity (χ² = 9.7712, df = 4, p = 0.0445). Lastly, increasing herbicides led to more Dolichopodidae especially in ‘Early Black’ then ‘Ben Lear’ (χ² = 17.9693, df = 4, p = 0.0013); and more Tachinidae especially in ‘Mullica Queen’ (χ² = 22.6794, df = 4, p = 0.0001).
The presence or absence of the MeSA bait also had several significant interactions with several of the management factors. For example, MeSA interacting with the number of fungicides significantly affected Syrphidae (χ² = 5.3252, df = 1, p = 0.021), Phoridae (χ² = 5.1641, df = 1, p = 0.0231), Cantharidae (χ² = 5.1152, df = 1, p = 0.0237), Cleridae (χ² = 7.8504, df = 1, p = 0.0051), Anthocoridae (χ² = 4.2981, df = 1, p = 0.0382), Formicidae (χ² = 8.7649, df = 1, p = 0.0031), Neuroptera (χ² = 8.7457, df = 1, p = 0.0031), and Megaloptera (χ² = 23.6866, df = 1, p < 0.0001). Syrphidae had a more positive relationship with fungicides in the baited traps, meanwhile Cantharidae and Cleridae had more positive relationships in the unbaited traps. Formicidae had a more negative fungicide relationship in the unbaited traps, and Neuroptera and Megaloptera had more negative relationships with the baited traps. Insecticides had an interaction in Odonata (χ² = 4.6427, df = 1, p = 0.0312) and marginally in parasitoid wasps (χ² = 3.6308, df = 1, p = 0.0567) in which both insect groups did better with increasing insecticides when a MeSA bait was present. Similarly, increasing herbicides increased Dolichopodidae (χ² = 5.1802, df = 1, p = 0.0228) in the presence of MeSA. Certain cultivars were more attractive to specific natural enemies with MeSA, such as baited ‘Stevens’, ‘Early Black’, and ‘Ben Lear’ attracting more Empididae (χ² = 19.6767, df = 4, p = 0.0006) and baited ‘Mullica Queen’ and ‘Crimson Queen’ attracting more Neuroptera (χ² = 9.8708, df = 4, p = 0.0426) than those same cultivars unbaited. Unbaited ‘Early Black’ however attracted more Cleridae (χ² = 15.5297, df = 4, p = 0.0037) than baited ‘Early Black’.
2. Pollinators
2.1 Pollinator community (pan traps) and MeSA
We caught 121000 individual arthropods over the course of this study, 11145 of which we believe could be potential pollinators. We found that as a whole, pollinators were impacted by MeSA (F = 2.6819, df = 1, p = 0.011) and had homogeneous dispersions with and without MeSA (F = 2.3794, df = 1, p = 0.134). The total abundance of pollinators caught were higher in the MeSA baited traps than the unbaited ones (χ² = 23.196, df = 1, p < 0.0001), however there was not difference in Shannon diversity (χ² = 1.1504, df = 1, p = 0.2835). Upon looking at each individual pollinator taxa, MeSA significantly attracted A. mellifera (χ² = 22.913, df = 1, p < 0.0001), Cerceris spp. (χ² = 8.946, df = 1, p = 0.0028), and Toxomerus marginatus Say (χ² = 27.077, df = 1, p < 0.0001), and marginally attracted Tiphiidae (χ² = 3.6009, df = 1, p = 0.0578). However, MeSA repelled Agapostemon spp. (χ² = 3.8441, df = 1, p = 0.0499), Megachile spp. (χ² = 5.0084, df = 1, p = 0.0252), Lampyridae (χ² = 5.2849, df = 1, p = 0.0215), and marginally repelled Vespidae (χ² = 3.4778, df = 1, p = 0.0622) (Table 1).
2.2 Pollinators and local management
Insecticides, fungicides, and herbicides all had different effects on different pollinators. Increasing insecticides positively impacted many Dipteran pollinators, such as T. marginatus (χ² = 10.3423, df = 1, p = 0.0013), Eupeodes americanus Wiedemann (χ² = 8.8751, df = 1, p = 0.0029), Chalcosyrphus sp. (χ² = 3.9819, df = 1, p = 0.0460), Dolichopodidae (χ² = 13.543, df = 1, p = 0.0002), Muscidae (χ² = 12.4308, df = 1, p = 0.0004), in addition to Agapostemon splendens Lepeletier (Hymenoptera: Halictidae) (χ² = 4.6665, df = 1, p = 0.0308) and marginally Mordellidae (χ² = 3.5889, df = 1, p = 0.0582). Interestingly, increasing insecticides also led to an increase in the Shannon diversity of pollinators (χ² = 10.259, df = 1, p = 0.0014). However, increasing insecticides negatively impacted Megachile spp. (χ² = 3.9453, df = 1, p = 0.0470), Bombus spp. (χ² = 4.3881, df = 1, p = 0.0362), and Cerceris spp. (χ² = 7.1459, df = 1, p = 0.0075). Fungicides negatively affected A. mellifera (χ² = 8.6938, df = 1, p = 0.0032), Ammophila spp. (χ² = 5.6127, df = 1, p = 0.0178), and Dolichopodidae (χ² = 3.922, df = 1, p = 0.0477), as well as marginally negatively impacting Xylocopa virginica L. (Hymenoptera: Apidae) (χ² = 3.7352, df = 1, p = 0.0533). Fungicides positively impacted Tachinidae (χ² = 5.1534, df = 1, p = 0.0232), and marginally Chrysididae (χ² = 3.5961, df = 1, p = 0.0579) and Mordellidae (χ² = 3.539, df = 1, p = 0.0599). The total pollinator abundance decreased with fungicides (χ² = 4.5058, df = 1, p = 0.0338). Interestingly, herbicides positively influenced pollinators, specifically Ammophila spp. (χ² = 7.1757, df = 1, p = 0.0074) and Tiphiidae (χ² = 6.9843, df = 1, p = 0.0082).
Cultivar was also an important factor for several pollinators including A. mellifera (χ² = 10.8963, df = 4, p = 0.0278), Bombus spp. (χ² = 15.8457, df = 4, p = 0.0032), T. marginatus (χ² = 21.7845, df = 4, p = 0.0002), Dielis plumipes Drury (Hymenoptera: Scoliidae) (χ² = 12.5282, df = 4, p = 0.0138), Tiphiidae (χ² = 12.1543, df = 4, p = 0.0162), and Tachinidae (χ² = 20.4661, df = 4, p = 0.0004), as well as for the total abundance of pollinators (χ² = 10.1531, df = 4, p = 0.0379), where there were more A. mellifera and pollinators in total in ‘Mullica Queen’ than ‘Ben Lear’, and specifically more Tachinidae in each of ‘Crimson Queen’, ‘Early Black’, ‘Stevens’ than ‘Ben Lear’. There were also strong interactions between fungicides and cultivars for many pollinators such as Bombus spp. (χ² = 16.0895, df = 4, p = 0.0029), T. marginatus (χ² = 23.6143, df = 4, p < 0.0001), D. plumipes (χ² = 12.9696, df = 4, p = 0.0114), and marginally for Megachile spp. (χ² = 9.2815, df = 4, p = 0.0544). In these interactions, increasing fungicides led to lower Bombus spp. catch most notably in ‘Early Black’ then ‘Crimson Queen’; overall less T. marginatus particularly in ‘Mullica Queen’, with an exception of higher counts in ‘Crimson Queen’; as well as less D. plumipes in ‘Early Black’ and ‘Stevens’ but more in ‘Crimson Queen’. Additionally, there were interactions between cultivar and insecticides in which more insecticides led to more Muscidae in ‘Stevens’ (χ² = 18.3105, df = 4, p = 0.0011); and more Tachinidae in ‘Crimson Queen’ and ‘Stevens’ (χ² = 13.4705, df = 4, p = 0.0092). When herbicides increased there was also an increase in T. marginatus in ‘Crimson Queen’ and ‘Mullica Queen’, but a decrease in ‘Ben Lear’ (χ² = 8.666, df = 4, p = 0.0341).
The MeSA bait also had some interactions with the local management. There were higher catches of Lasioglossum spp. with increasing insecticides in the unbaited traps (χ² = 4.5683, df = 1, p = 0.0326); increasing fungicides had lower A. mellifera counts in the unbaited traps (χ² = 4.1986, df = 1, p = 0.0406); and increasing herbicides had more Bombus spp. in the baited traps but less in the unbaited traps (χ² = 5.7119, df = 1, p = 0.0169). In terms of a MeSA by cultivar interaction, baited traps in beds of ‘Mullica Queen’ had more Lasioglossum spp. than the unbaited traps of the same cultivar, however unbaited traps in ‘Early Black’ beds caught more of that genus than the baited in the same beds (χ² = 13.6696, df = 4, p = 0.0084). There were also higher Bombus spp. counts in the unbaited ‘Crimson Queen’ traps than the baited ones (χ² = 9.7553, df = 4, p = 0.0448). Baited traps in beds of ‘Ben Lear’, ‘Crimson Queen’, and ‘Early Black’ all had higher catches of T. marginatus than the corresponding unbaited traps (χ² = 10.7532, df = 4, p = 0.0295).
3. Predation
The predation study is currently being analyzed so the results are not complete.
4. Pollination
4.1 Pollination services and MeSA
We found that the cages baited with MeSA resulted in higher berry count than those without the MeSA (χ² = 4.4291, df = 1, p = 0.03533). Baited cages had on average 67.18 ± 5.08 cranberries, while unbaited cages had 55.78 ± 4.37. However, MeSA did not have any influence on either volume or mass.
4.2 Pollination services and local management
The pesticides that significantly affected berry count were fungicides (χ² = 19.3693, df = 1, p < 0.0001) and herbicides (χ² = 5.3155, df = 1, p = 0.02114), of which both led to higher amounts of berries. With increasing insecticides, there was increasing volume (χ² = 10.0764, df = 1, p = 0.0015) and mass (χ² = 10.2377, df = 1, p = 0.0014) of the berries. Similarly, increasing herbicides also resulted in bigger (χ² = 16.393, df = 1, p < 0.0001) and heavier (χ² = 4.5407, df = 1, p = 0.0331) fruit.
Cultivar very clearly impacted berry count (χ² = 27.5, df = 4, p < 0.0001), average volume (χ² = 178.19, df = 4, p < 0.0001), and average mass (χ² = 135.31, df = 4, p < 0.0001). ‘Mullica Queen’ had more berries than either ‘Ben Lear’ or ‘Crimson Queen’, and ‘Stevens’ also had more than ‘Crimson Queen’. ‘Crimson Queen’ had the most volume and mass, being larger than every other cultivar other than ‘Mullica Queen’. ‘Mullica Queen’ was statistically similar to ‘Stevens’ in size and weight, and both were heavier than ‘Ben Lear’ although ‘Stevens’ was comparable in size to ‘Ben Lear’. In turn, ‘Ben Lear’ was both bigger and heavier than ‘Early Black’.
Only insecticides significantly interacted with MeSA to affect berry count (χ² = 13.5069, df = 1, p = 0.0002), in which increasing insecticides led to more berries in the baited cages. Methyl salicylate marginally interacted with cultivar to impact berry count (χ² = 9.3328, df = 4, p = 0.0533) and significantly interacted to impact berry size (χ² = 9.8437, df = 4, p = 0.0431), where MeSA led to increased berry count and size specifically in ‘Crimson Queen’.
Methyl salicylate (MeSA) can be used to attract many beneficial insects. Previous studies have shown that many natural enemies, notably Syrphidae, Coccinellidae, and Neuroptera, will come to a MeSA lure (Rodriguez-Saona et al. 2012). Our study corroborated this, showing increased levels of several natural enemy families at the MeSA baited traps. Syrphidae, whose larvae are predaceous, also are active pollinators, which doubles their ecosystem services. We found that MeSA also attracts honeybees, Apis mellifera, which has never been shown before. In the context of applied agriculture that relies on pollinators, discovering a potential field tested bee attractant can be monumental in increasing yields. We found that MeSA was able to increase pollination services, as seen by the heightened fruit count.
This study also aimed to see the relationships that local management, such as insecticides, fungicides, herbicides, and cultivar, have on the beneficial insect community, and those further interactions with MeSA. We found that all these factors are unique to the beneficial insect taxon. One interesting trend in the pollinator/pollination study we found was that specifically the variety Crimson Queen had more and bigger fruits when exposed to the MeSA lure. At the same time, when faced with increasing fungicides and herbicides, the numbers of the most commonly found syrphid, Toxomerus marginatus, increased in that variety, especially when the lure was present.
Education & Outreach Activities and Participation Summary
Participation Summary:
The ideas in this proposed research are novel and applicable to cranberry growers in New Jersey and the USA, my target audience. Since all the research will be done at commercial farms, there will be an opportunity for regular communication with growers. All the farms on which the research will be conducted have a long history in New Jersey, with the youngest one being over a century old and one of them being amongst the biggest cranberry producers in the world. Thus, this research will likely have profound impact on the whole cranberry industry in New Jersey and the USA.
By working directly with growers, I will be providing them with information on my research progress. Information on biological control and pollination services in cranberries will be provided to growers through various media sources. Factsheets will be written on how to identify and conserve natural enemies of herbivores and pollinators. Another factsheet was already written on New Jersey cranberry pests. This information will be given to growers also through bogs published in the Rutgers Plant & Pest Advisory.
Another important method of outreach is by attending and presenting at grower meetings and scientific conferences. I presented findings from this research at the American Cranberry Growers Association winter meeting, the North American Cranberry Research and Extension Workers conference, and at the Entomological Society of America branch and national meetings. I also plan on writing scientific articles including a meta-analysis about how and what synthetic herbivore-induced plant volatiles are used to attract natural enemies of cultivated crops as well as a synthesis literature review of the past thirty years of integrated pest management practices against insect pests of cranberries (which was published in 2023). These publications will be directed at both scientists and farmers, who can hopefully learn how to utilize plant volatiles and see the history of cranberry pest management.
2023 Update
I wrote and published a literature review on cranberry pest management from the past thirty years as well as a factsheet on cranberry pests of New Jersey, which was distributed at the American Cranberry Growers Association in January 2024. I also attended and presented at the North American Cranberry Research and Extension Workers meeting in August 2023 and the International Society of Chemical Ecology meeting in July 2023.
Advances in cranberry insect pest management: A literature synthesis
Project Outcomes
My research will theoretically help farmers be more sustainable. Results from my research will suggest to farmers methods to consider their landscape when managing their land, allowing for greater conservation of beneficial insects. Furthermore, since I am also looking at ecosystem services, I will be able to inform the farmers on how much the beneficial insects are actually providing.
During the course of this project so far I learned how to talk and work with farmer and how to use ArcGISPro. These two skills helped me realize how unique landscape ecology is and how important it is to approach problems from a large scale, since surrounding habitats may have some strong impacts on sustainability and integrated pest and pollinator management.