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ARS Home » Midwest Area » Madison, Wisconsin » Vegetable Crops Research » Research » Research Project #425451

Research Project: Cranberry Genetic Improvement and Insect Pest Management

Location: Vegetable Crops Research

2017 Annual Report

1a. Objectives (from AD-416):
Objective 1: Develop and apply genomic and genetic tools to map and characterize the genetic bases of the key cranberry traits that determine yield. Objective 2: Based on horticultural, genetic, and genomic information, formulate and apply breeding approaches for genetically improving cranberry yield. Objective 3: Determine the development thresholds of key arthropod pests (cranberry fruitworm and Sparganothis fruitworm) to better predict the developmental status of populations in the field. Objective 4: Develop novel, innovative IPM strategies to reduce pesticide use and sustain cranberry yield, quality, and profitability. Objective 5: Develop alternative cranberry production practices that improve water conservation and decrease plant disease.

1b. Approach (from AD-416):
Objective 1: Next-generation sequencing technology will be used to characterize the cranberry genome. The resultant data will be used to discover and mine molecular markers such as SSRs and SNPs. We will then develop high-resolution genetic maps using the developed markers based on several available half-sib F1 mapping populations. Phenotyping will involve collecting data on yield-related traits and other horticultural measurements, including total fruit weight, percent rotten fruit, average berry weight, and fruit quality parameters. These traits will be localized in the linkage map described above. Information derived from the high resolution cranberry linkage map with yield-related will be used to plan strategic crosses. Objective 2: Prior to creating cranberry hybrids, horticultural, genetic, and genomic information will be carefully considered to ensure that strategic crosses are accomplished. A microsatellite marker based fingerprinting assay will be developed for the true-to-type verification of the cranberry cultivars. We will also characterize known cranberry diversity from the breeding programs and collections and samples sent in by growers. Pedigree information will be evaluated in the light of marker information to determine the most likely genotypes or genetic pools associated with each named cultivar and their associated horticultural performance. A series of cranberry hybrids with complementary genetic pools will be created and evaluated. Objective 3: The temperature-specific development rates and degree-day (DD) accumulations associated with cranberry fruitworm (CFW) and Sparganothis fruitworm (SFW) will be determined. Larval growth rates will be measured over a wide range of controlled temperatures. Growth rates will be plotted against temperature, and models will be fit to the dynamic. From these models, the lower and upper development thresholds will be isolated. The thresholds will then be used to generate degree-day (DD) accumulations that can be linked to discrete biological events, such as flight initiation in the field, adult lifespan, ovipositional period, and egg-hatch periods. DD accumulations represent key developmental benchmarks, helping to optimize pest management in the cranberry system. Objective 4: novel insect pest management approaches will be investigated. Two primary tactics will be explored within the cranberry system: pheromone-based mating disruption and trophic position measurement. In partnership with private industry, as well as Wisconsin cranberry growers, the first ever 3-species mating disruption program will be deployed at large scales within commercial marshes. Population suppression of the target pests will be assayed and compared with conventional pest management approaches. Studies of arthropod trophic position will be conducted using stable isotopic analysis of amino acids. Trophic position estimation will reveal the lifetime trophic tendencies of carnivorous species, thereby providing empirical evidence as to which species are actually beneficial for cranberry production.

3. Progress Report:
The progress reported relates to Objectives 1 and 2. We are studying the yield traits in cranberry using traditional data collection methods. We are also developing high-throughput data collection and visualization software to massively collect and understand data. Molecular tools are being applied to study and identify cranberry genes by mapping those genes found to control important cranberry traits, mainly yield traits. We have developed several high density molecular maps and a concomitant composite map that will be used to map many traits of economic importance. In the future, these efforts will help breed high yielding cranberry cultivars more efficiently by allowing markers-assisted breeding. This progress relates to Objectives 3 and 4, and milestones 8-10. The ARS continues to advance the practice of integrated pest management (IPM) in United States cranberries by investigating explicitly the intersection between crop production technology, arthropod biology, and agroecology. Now in its 6th year of research and development, the multi-species pheromone-based mating disruption program in cranberries has shown that it significantly reduces berry infestation rates. Optimal deployment systems continue to be refined. After retrofitting drones to deliver the pheromone-loaded paraffin emulsion to cranberry beds, the deployment system is now focusing on emulsion extruding devices that are mounted to the boom-arms of Wisconsin spray equipment. Other ARS studies are building on the discovery of a new species of insect-eating nematode in Wisconsin. Field studies have shown that this native nematode can control flea beetle populations as well as the best commercial insecticides. Insect phenology models among the top cranberry pests are being used to generate pest emergence data across Wisconsin, and published in trade journals on a biweekly basis. Finally, ARS work continues to reveal the importance of bee-microbe symbioses among native pollinators of cranberries.

4. Accomplishments
1. Cranberry carbohydrate management. Cranberry plants produce both low-growing branches and vertical branches, known as uprights. Uprights are either reproductive (fruiting) or vegetative (nonfruiting) in a given year. Vegetative uprights only produce leaves, whereas fruiting uprights can produce flowers and thus have the potential to contribute to next years crop. Previous research has demonstrated that individual cranberry uprights exhibit biennial (every other year) bearing tendencies. Specifically, reproductive uprights have a lower probability of developing and setting fruit the following year relative to vegetative uprights. This study evaluated and compared carbohydrate concentrations across cranberry cultivars that differ in biennial fruiting tendencies. Vegetative uprights generally had greater concentrations of carbohydrates relative to reproductive uprights, while roots had the lowest concentration across all cultivars. Concentrations of carbohydrates in cranberry reproductive uprights were lowest at late bloom/early fruit set and bud development. These findings support the explanation that carbohydrate limitation in reproductive uprights may contribute to biennial fruiting by reducing the potential for return bloom. This research contributes to developing better cultivars through breeding for resource allocation for increased return bloom. The return bloom characteristic has the potential to enhance yields by circumventing traditional biennial fruiting tendencies.

2. New cranberry fingerprinting methods. Cranberry is in need of inexpensive high-throughput genetic fingerprinting methods for research and germplasm purity testing for agricultural purposes. In this study, we developed sixteen molecular marker panels, which can be used for high-throughput deoxyribonucleic acid (DNA) fingerprinting in cranberry. The panels contained a total of 61 molecular markers which easily separated important commercial cranberry cultivars. In addition, a subset of these panels were used to genotype (characterize with molecular markers) seedlings extracted from fruits in a cranberry bed planted with the cultivar Stevens. The seedlings were determined to be either self-pollinated or cross-pollinated using presence/absence of Stevens inherited molecular markers. This research provides the first quantitative evidence that the majority of seeds in commercial cranberries are self-pollinated. Therefore, the efficient and powerful DNA fingerprinting made possible by the presented molecular marker panels represents an important and applicable resource in the cranberry industry for assessing the purity of grower and licensed propagator cranberry vines, protecting intellectual property rights, assisting growers in determining genetic purity of existing beds, and for enabling genetic research and analysis of genetic diversity in cultivated, breeding and wild cranberry germplasm.

3. Development of a cranberry composite map. Cranberry is a recently domesticated, but economically important, fruit crop with limited molecular resources. New genetic resources could accelerate genetic gain in cranberry through characterization of its genetic features and by enabling molecular-assisted breeding. To increase the availability of cranberry genomic resources, we used a sequencing approach to simultaneously discover thousands of molecular markers in the cranberry genetic code within three inter-related cranberry populations, whose pedigrees trace to seven wild cranberry selections that represent the genetic base of the commercial cranberry industry. Genetic maps were constructed for the three cranberry populations, which were merged to create the first high-density cranberry composite map containing 6073 markers in 12 chromosomes. Collectively, the results presented represent an important contribution to the current understanding of cranberry genetic structure and to the availability of molecular tools for future genetic research and breeding efforts in cranberry.

4. Cranberry genetic data visualization. Visualization of data from any stage of genetic and genomic research is one of the most useful approaches for detecting potential errors, ensuring accuracy and reproducibility, and presentation of the resulting data. Therefore, we developed a software package for plotting a variety of genetic data types in a concise manner for data exploration and presentation. The program is very simple, requires minimal coding experience, even for complex figures that incorporate high-dimensional genetic information, and allows simultaneous analysis and visual exploration of genomic and genetic data. The program is also very flexible in formatting and configuration, automatable, and provides publication quality figures. This software tool is useful for any species and freely available for genetic and genomic researchers with little computational expertise.

5. Cranberry high-throughput phenotyping methods. In crop breeding programs, massive trait data collection is key for the efficient evaluation and selection of new cultivars and varieties. In these cases, multiple populations with numerous individuals are constantly being evaluated for traits requiring a considerable investment in time and money. The need for new approaches to massively acquire trait data will continue to increase in coming years. We developed a software, called GiNA, for image-based horticultural trait data collection such as shape and color data. The GiNA image analysis framework is highly accessible and freely available to scientists and groups working in major and minor crop research programs. The application and use of this software is simple, but very helpful in terms of the massive amount of high-quality measurements that can be generated. Although many image-based trait collection technologies are available, they are not easy-to-use and optimize, and they are not economically accessible for scientists that commonly face limitations related with massive trait data collection activities. Trait data collection using software such as GiNA can lead to an accelerated progress in crop improvement and a more efficient characterization of traits of interest for both science and industry.

6. Phenology models have been validated by field trapping of the respective moth flights. Specifically, the temperature-mediated development thresholds of the cranberry fruitworm, Acrobasis vaccinii, have allowed us to generate degree-day accruals based on current weather, and these are used to provide predictions for growers as to when the moth flights will start, as well as when it reaches its peak. This service has allowed growers to time their sprays and plan future control strategies. The degree-day accruals have also been calculated for sparganothis fruitworm. All degree-days are tabulated by pest species, along with their corresponding color-coded maps of Wisconsin (heat maps of pest development). All pest projection information has been published bi-weekly in trade journals widely available to growers.

7. There were two significant accomplishments in the USDA-ARS pheromone mating disruption program in Wisconsin cranberries: 1) a three-species mating disruption blend was applied to commercial acreage; 2) the mating disruption materials were applied via an unmanned aerial vehicle (UAV). The mating disruption system deployed in cranberries represents the first time for any crop that three different pest species were targeted with a single pheromone blend. Further, this work was the first use of fully autonomous UAVs to deploy pest control materials. Pest suppression was demonstrated for all three pest species; thus, the research program was successful in mechanizing the deployment of pheromones via UAVs, as well as demonstrating the potential impact that the top insect pests of cranberries could be controlled by disrupting mating, thereby decreasing fruit damage and increasing crop yields.

8. Newly discovered native nematode species were demonstrated to be highly virulent biological control agents against some of the most troublesome pests of cranberries (sparganothis fruitworm, cranberry fruitworm, and flea beetles). Field trials using large sods from commercial cranberry marshes revealed that Oscheius wisconsinensis (the most promising nematode among the recently discovered species) can effectively track down, subdue, and kill cranberry pests in the soil. The nematodes represent an effective “green” bio-insecticide that can reduce chemical use by growers.

Review Publications
DeVetter, L.W., Beaver, E., Colquhoun, J., Zalapa, J., Harbut, R. 2016. Comparison of nonstructural carbohydrates across cranberry cultivars. European Journal of Horticultural Science. 81(6):321-326. doi: 10.17660/eJHS.2016/81.6.5.
Schlautman, B., Bolivar-Medina, J., Hodapp, S., Zalapa, J. 2017. Cranberry SSR multiplexing panels for DNA horticultural fingerprinting and genetic studies. Scientia Horticulturae. 219:280-286. doi. 10.1016/j.scienta.2017.03.005.
Blanke, C.M., Chikaraishi, Y., Takizawa, Y., Steffan, S.A., Dharampal, P.S., Vander Zanden, M.J. 2017. Comparing compound-specific and bulk stable nitrogen isotope trophic discrimination factors across multiple freshwater fish species and diets. Canadian Journal of Fisheries and Aquatic Sciences. 74(8):1291-1297. doi: 10.1139/cjfas-2016-0420.
Takizawa, Y., Dharampal, P.S., Steffan, S.A., Takano, Y., Ohkouchi, N., Chikaraishi, Y. 2017. Intra-trophic isotopic discrimination of 15N/14N for amino acids in autotrophs: Implications for nitrogen dynamics in ecological studies. Ecology and Evolution. 7(9):2916-2924. doi: 10.1002/ece3.2866.
Schlautman, B., Covarrubias-Pazaran, G., Diaz-Garcia, L., Iorizzo, M., Polashock, J., Grygleski, E., Vorsa, N., Zalapa, J. 2017. Construction of a high-density American cranberry (Vaccinium macrocarpon Ait.) composite map using genotyping-by-sequencing for multi-pedigree linkage mapping. Genes, Genomes, and Genomics. 7(4):1177-1189. doi: 10.1534/g3.116.037556.
Diaz-Garcia, L., Covarrubias-Pazaran, G., Schlautman, B., Zalapa, J. 2017. SOFIA: An R package for enhancing genetic visualization with Circos. Journal of Heredity. 108(4):443-448. doi: 10.1093/jhered/esx023.
Steffan, S.A., Singleton, M.E., Sojka, J., Chasen, E.M., Deutsch, A.E., Zalapa, J.E., Guedot, C. 2017. Flight synchrony among the major moth pests of cranberries in the Upper Midwest, USA. Insects. 8(1):26. doi: 10.3390/insects8010026.
Pauli, J.N., Steffan, S.A., Newsome, S.D. 2015. Response to Pilaar Birch and Graham. Bioscience. 65(10):953-954. doi: 10.1093/biosci/biv134.
Hirsch, H., Brunet, J., Zalapa, J.E., von Wehrden, H., Hartmann, M., Kleindienst, C., Schlautman, B., Kosman, E., Wesche, K., Renison, D., Hensen, I. 2017. Intra- and interspecific hybridization in invasive Siberian elm. Biological Invasions. 19(6):1889-1904. doi: 10.1007/s10530-017-1404-6.
Diaz-Garcia, L., Covarrubias-Pazaran, G., Schlautman, B., Zalapa, J. 2016. GiNA, an efficient and high-throughput software for horticultural phenotyping. PLoS One. 11(8). doi: 10.1371/journal.pone.0160439.
Steffan, S.A., Chasen, E.M., Deutsch, A.E., Mafra-Neto, A. 2017. Multi-species mating disruption in cranberries (Ericales:Ericaceae): Early evidence using a flowable emulsion. Journal of Insect Science. 17(2):54. doi: 10.1093/jisesa/iex025.
Pauli, J.N., Newsome, S.D., Cook, J.A., Harrod, C., Steffan, S.A., Baker, C.J. O., Ben-David, M., Bloom, D., Bowen, G.J., Cerling, T.E., Cicero, C., et al. 2017. Why we need a centralized repository for isotopic data. Proceedings of the National Academy of Sciences. 114(12):2997-3001. doi: 10.1073/pnas.1701742114.
Steffan, S.A., Chikaraishi, Y., Dharampal, P.S., Pauli, J.N., Guedot, C., Ohkouchi, N. 2017. Unpacking brown food-webs: Animal trophic identity reflects rampant microbivory. Ecology and Evolution. 7(10):3532-3541. doi: 10.1002/ece3.2951.
McMahan, E.E., Steffan, S.A., Guedot, C. 2017. Population densities of lepidopteran pests in selected cranberry cultivars in Wisconsin. Journal of Economic Entomology. 110(3):1113-1119. doi: 10.1093/jee/tow274.
Daverdin, G., Johnson-Cicalese, J., Zalapa, J.E., Vorsa, N., Polashock, J.J. 2017. Mapping and identification of fruit rot resistance QTL in American cranberry using GBS. Molecular Breeding. doi: 10.1007/s11032-017-0639-3.