Biology subjects

Fonseka, D.

Publications and source records attributed to Fonseka, D..

3 recordsLinked to original sources

bioRxiv2022

A greenhouse-based high-throughput phenotyping platform for identification and genetic dissection of resistance to Aphanomyces root rot in field pea

Aphanomyces root rot (ARR) is a devastating disease in field pea (Pisum sativum L.) that can cause up to 100% crop failure. Assessment of ARR resistance can be a rigorous, costly, time-demanding activity that is relatively low-throughput and prone to human errors. These limits the ability to effectively and efficiently phenotype the disease symptoms arising from ARR infection, which remains a perennial bottleneck to the successful evaluation and incorporation of disease resistance into new cultivars. In this study, we developed a greenhouse-based high throughput phenotyping (HTP) platform that moves along the rails above the greenhouse benches and captures the visual symptoms caused by Aphanomyces euteiches in field pea. We pilot tested this platform alongside with conventional visual scoring in five experimental trials under greenhouse conditions, assaying over 12,600 single plants. Precision estimated through broad-sense heritability (H2) was consistently higher for the HTP-indices (H2 Exg =0.86) than the traditional visual scores (H2 DSI=0.59), potentially increasing the power of genetic mapping. We genetically dissected variation for ARR resistance using the HTP-indices, and identified a total of 260 associated single nucleotide polymorphism (SNP) through genome-wide association (GWA) mapping. The number of associated SNP for HTP-indices was consistently higher with some SNP overlapped to the associated SNP identified using the visual scores. We identified numerous small-effect QTLs, with the most significant SNP explaining about 5 to 9% of the phenotypic variance per index, and identified previously mapped genes known to be involved in the biological pathways that trigger immunity against ARR, including Psat5g280480, Psat5g282800, Psat5g282880, and Psat2g167800. We also identified a few novel QTLs with small-effect sizes that may be worthy of validation in the future. The newly identified QTLs and underlying genes, along with genotypes with promising resistance identified in this study, can be useful for improving a long-term, durable resistance to ARR.

pathology↗

bioRxiv2022

Using agent-based models to predict pollen deposition in a dioecious crop

Pollination involves complex interactions between plants and pollinators, and variation in plant or pollinator biology can lead to variability in pollination services that are difficult to predict. Models that effectively predict pollination services could enhance the ability to conserve plant-pollinator mutualisms in natural systems and increase crop yields in managed systems. However, while most pollination models have focused either on effects of plant or pollination biology, few models have integrated plant-pollinator interactions. Moreover, crop management causes variation in plant-pollinator interactions and pollination services, but management is rarely considered in pollination models. Here we used extensive datasets for kiwifruit (Actinidia chinensis var. deliciosa) to develop an agent-based model to track insect-provided pollination services with variation in crop cultivars, pollinator traits, and orchard layouts. This allowed us to predict pollination outcomes in a dioecious crop under a range of management scenarios. Our sensitivity analysis indicated that flower density and the proportion of female flowers are the most important factors in successful pollination, both of which growers control via cultivar selection and cultural management practices. Our analysis also indicated that economically viable pollination services and crop yields are attained with [~]60% female flowers and a peak foraging activity of 6 to 8 bees per 1,000 open flowers with diminishing returns for additional pollinators. The quality of pollination service varied across simulated orchard layouts, highlighting the potential use of this model as a framework to screen novel orchard configurations. More broadly, linking complex plant and pollinator interactions in pollination models can help identify factors that may improve crop yields and provide a framework for identifying factors important to pollination in natural ecosystems. HIGHLIGHTS- We develop a model using extensive empirical datasets to predict pollen deposition based on the interactions between flowers and pollinators in a dioecious crop system - We conducted a thorough sensitivity analysis, and analysis of the effect of stochastic variance between model runs, which can be used to inform future design of stochastic agent-based models - Our model effectively predicted the outcomes of varying management regimes of orchard layouts and pollinator introductions on pollination in a dioecious crop - Our model can be extended for other functionally dioecious crops or plant communities where managers want to understand how their decisions impact pollination

ecology↗

bioRxiv2020

Orchard layout and plant traits influence fruit yield more strongly than pollinator behaviour and density in a dioecious crop

Mutualistic plant-pollinator interactions are critical for the functioning of both non-managed and agricultural systems. Mathematical models of plant-pollinator interactions can help understand key determinants in pollination success. However, most previous models have not addressed pollinator behavior and plant biology combined. Information generated from such a model can inform optimal design of crop orchards and effective utilization of managed pollinators like honey bees, and help generate hypotheses about the effects of management practices and cultivar selection. We expect that honey bee density per flower and male to female flower ratio will influence fruit yield. To test the relative importance of these effects, both singly and simultaneously, we utilized a delay differential equation model combined with Latin hypercube sampling for sensitivity analysis. Empirical data obtained from historical records and collected in kiwifruit orchards in New Zealand were used to parameterize the model. We found that, at realistic bee densities, the optimal orchard had 65-75% female flowers, and the most benefit was gained from the first 6-8 bees/1000 flowers, with diminishing returns thereafter. While bee density significantly impacted fruit production, plant-based parameters-flower density and male:female flower ratio-were the most influential. The predictive model provides strategies for improving crop management.

plant biology↗