ABSTRACT
Climate change and the global economy impose new challenges in the management of food-producing trees and require studying how to model plant physiological responses, namely growth dynamics and phenology. Hazelnut (Corylus avellana L.) is a multi-stemmed forest species domesticated for nut production and now widely spread across different continents. However, information on stem growth and its synchronization with leaf and reproductive phenology is extremely limited. This study aimed at (i) defining the sequencing of radial growth phases in hazelnut (onset, maximum growth and cessation) and the specific temperature triggering stem growth; and (ii) combining the stem growth phases with leaf and fruit phenology. Point dendrometers were installed on 20 hazelnut trees across eight orchards distributed in the Northern and Southern hemisphere during a period of three growing seasons between 2015 and 2018. The radial growth variations and climatic parameters were averaged and recorded every 15 min. Leaf and reproductive phenology were collected weekly at each site. Results showed that stem radial growth started from day of year 84 to 134 in relation to site and year but within a relatively narrow range of temperature (from 13 to 16.5 °C). However, we observed a temperature-related acclimation in the cultivar Tonda di Giffoni. Maximum growth always occurred well before the summer solstice (on average 35 days) and before the maximum annual air temperatures. Xylogenesis developed rapidly since the time interval between onset and maximum growth rate was about 3 weeks. Importantly, the species showed an evident delay of stem growth onset with respect to leaf emergence (on average 4-6 weeks) rarely observed in tree species. These findings represent the first global analysis of radial growth dynamics in hazelnut, which is an essential step for developing models on orchard functioning and management on different continents.
Subject(s)
Corylus , Forests , Plant Leaves/physiology , Seasons , Temperature , TreesABSTRACT
Impedance flow cytometry (IFC) is a versatile lab-on-chip technology which enables fast and label-free analysis of pollen grains in various plant species, promising new research possibilities in agriculture and plant breeding. Hazelnut is a monoecious, anemophilous species, exhibiting sporophytic self-incompatibility. Its pollen is dispersed by wind in midwinter when temperatures are still low and relative humidity is usually high. Previous research found that hazelnut can be characterized by high degrees of pollen sterility following a reciprocal chromosome translocation occurring in some cultivated genotypes. In this study, IFC was used for the first time to characterize hazelnut pollen biology. IFC was validated via dye exclusion in microscopy and employed to (i) follow pollen hydration over time to define the best pre-hydration treatment for pollen viability evaluation; (ii) test hazelnut pollen viability and sterility on 33 cultivars grown in a collection field located in central Italy, and two wild hazelnuts. The accessions were also characterized by their amount and distribution of catkins in the tree canopy. Pollen sterility rate greatly varied among hazelnut accessions, with one main group of highly sterile cultivars and a second group, comprising wild genotypes and the remaining cultivars, producing good quality pollen. The results support the hypothesis of recurring reciprocal translocation events in Corylus avellana cultivars, leading to the observed gametic semi-sterility. The measured hazelnut pollen viability was also strongly influenced by pollen hydration (R adj 2 = 0.83, P ≤ 0.0001) and reached its maximum at around 6 h of pre-hydration in humid chambers. Viable and dead pollen were best discriminated at around the same time of pollen pre-hydration, suggesting that high humidity levels are required for hazelnut pollen to maintain its functionality. Altogether, our results detail the value of impedance flow cytometry for high throughput phenotyping of hazelnut pollen. Further research is required to clarify the causes of pollen sterility in hazelnut, to confirm the role of reciprocal chromosome translocations and to investigate its effects on plant productivity.
ABSTRACT
High-quality pollen is a prerequisite for plant reproductive success. Pollen viability and sterility can be routinely assessed using common stains and manual microscope examination, but with low overall statistical power. Current automated methods are primarily directed towards the analysis of pollen sterility, and high throughput solutions for both pollen viability and sterility evaluation are needed that will be consistent with emerging biotechnological strategies for crop improvement. Our goal is to refine established labelling procedures for pollen, based on the combination of fluorescein (FDA) and propidium iodide (PI), and to develop automated solutions for accurately assessing pollen grain images and classifying them for quality. We used open-source software programs (CellProfiler, CellProfiler Analyst, Fiji and R) for analysis of images collected from 10 pollen taxa labelled using FDA/PI. After correcting for image background noise, pollen grain images were examined for quality employing thresholding and segmentation. Supervised and unsupervised classification of per-object features was employed for the identification of viable, dead and sterile pollen. The combination of FDA and PI dyes was able to differentiate between viable, dead and sterile pollen in all the analysed taxa. Automated image analysis and classification significantly increased the statistical power of the pollen viability assay, identifying more than 75,000 pollen grains with high accuracy (R2 = 0.99) when compared to classical manual counting. Overall, we provide a comprehensive set of methodologies as baseline for the automated assessment of pollen viability using fluorescence microscopy, which can be combined with manual and mechanized imaging systems in fundamental and applied research on plant biology. We also supply the complete set of pollen images (the FDA/PI pollen dataset) to the scientific community for future research.
