Philippa Kaur

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Infection properties of clade A and clade B T7-like cyanophagesWe set out to test the hypothesis that the phylogenetic separation of T7-like cyanophages into two major clades reflects differences in their infection physiology. To do this we investigated a suite of infection properties of three pairs of clade A and B phages, each pair infecting the same Synechococcus host (Table 1) to allow us to control for variability in host genetics and physiology. These six cyanophages are representatives of 3 clade A and 2 clade B cyanophage subclades (SI Appendix, Table S1).Table 1 Summary of infection physiology of three pairs of clade A and clade B cyanophages infecting the same Synechococcus hosts.Full size tableWe began by investigating adsorption kinetics and the length of time taken to produce new phages in the infection cycle, the latent period, from phage growth curve experiments. In all three pairs of phages, adsorption was 7–15-fold more rapid in the clade A phage versus the clade B phage (Fig. 1, Table 1). Furthermore, the clade A phage had a faster infection cycle with a latent period that was 3-5-fold shorter than the clade B phage on the same host (Fig. 1a–c) (Table 1). To determine how representative these findings are for a greater diversity of T7-like cyanophages we report the latent period of nine additional non-paired phages that infect a variety of hosts and span the diversity of this cyanophage genus, measured here and taken from the literature (SI Appendix, Table S1). These phages showed the same pattern as observed between phage pairs, although one clade A phage had a relatively long latent period (see SI Appendix, Table S1). Overall, the 5 clade A phages representative of 5 subclades had a significantly shorter latent period (3.3 ± 3.6 h, n = 5 phages (mean ± SD) than the 10 clade B phages from 7 subclades (7.7 ± 2.0 h, n = 10 phages) (Kruskal-Wallis: χ2 = 4.72, df = 1; p = 0.029, n = 15). No significant differences in the length of the latent period were found for clade B phages that infected Synechococcus and Prochlorococcus (Kruskal-Wallis: χ2 = 1.13, df = 1; p = 0.29, n = 10).Fig. 1: Comparison of the infection physiology between pairs of clade A and clade B T7-like cyanophage infecting the same Synechococcus host.a–c Cyanophage growth curves, d–f burst sizes, g–i virulence as the percentage of lysed host cells, j–l decay as loss of infectivity, m–o plaque sizes. a, d, g, j, m Clade A Syn5 phage and clade B S-TIP37 phage infecting WH8109. b, e, h, k, n Clade A S-CBP42 phage and clade B S-RIP2 phage infecting WH7803. c, f, i, l, o Clade A S-TIP28 phage and clade B S-TIP67 phage infecting CC9605. The host strain is shown at the right of the panels. Red and blue lines or bars show results for clade A and clade B phages, respectively. a–c, g–I Error bars indicate standard deviations. d–f Burst size results are for single cells. j–l The solid line shows the fitted multi-level linear model. m–o The time after infection at which plaques were photographed appears above the images. *p value  More

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More than three billion people rely on the ocean to make a living, most of whom are in developing countries. For some 17% of the world’s population, fisheries and aquaculture provide the main source of animal protein. For the least-developed countries, fish contributes about 29% of animal protein intake; in other developing countries, it accounts for 19%1.As the global population increases, the demand for seafood is expected to rise, too. Already, Africa and Asia have seen fish production double over the past few decades. Globally, fish consumption is set to rise by around 15% by 20302.Although ocean ecosystems are strained by climate change, overfishing and more, studies nevertheless suggest that seafood can be expanded sustainably to meet future food demands3. Last year, international efforts promoting this approach included the Blue Food Assessment (a joint initiative of 25 research institutions) and the United Nations Food Systems Summit.Success will depend on small-scale fisheries. Small operations tend to deliver both food and income directly to the people who need them most, and locals have a strong incentive to make their practices sustainable. What’s more, these fisheries can be remarkably efficient. Almost everything that hand-to-mouth fisheries catch is consumed. By contrast, around 20% of the fish caught by industrial fleets is estimated to be wasted, mainly because of unwanted by-catch4. So, whereas large-scale operators land more fish, small-scale fisheries provide a larger share of the fish that is actually consumed.Small fishers rarely have the right resources to expand their operations, or even to survive. If they do scale up, they might lose some of their current advantages or engage in the same harmful practices as do large commercial fisheries. Managed with care, however, small fisheries could provide win–wins for livelihoods and the environment. Making this happen should be high on the agenda at the UN Ocean Conference in Lisbon this month.As someone who has studied food security and policymaking for decades, here I suggest ways to support and strengthen artisanal fishing operations.Small reformsThe potential and importance of small-scale fisheries has been increasingly recognized over the past decade. In 2014, the UN Food and Agriculture Organization (FAO) provided voluntary guidelines to support sustainable small-scale fisheries, aimed at improving food security and eradicating poverty. A forthcoming report by the FAO, Duke University in Durham, North Carolina, and the non-profit organization WorldFish, headquartered in Penang, Malaysia, will conclude a remarkable initiative to collate case studies, questionnaire results and data sets to help get fishers a seat at policymakers’ tables. The UN General Assembly has declared 2022 the International Year of Artisanal Fisheries and Aquaculture.Most nations already have management policies for marine ecosystems that provide for small-scale fisheries. In India, Indonesia, Malaysia and Sri Lanka, for example, there is a ban on trawling within about 8 kilometres of the coastline to prevent industrial fishers from scooping up large catches, which protects those regions for local fishers. Countries such as Costa Rica ease access by exempting small-scale fisheries from licences, and Angola exempts subsistence and artisanal fishers from paying licensing fees5.But this is not enough. Small-scale fishers’ rights to access are often poorly defined, ineffectively enforced or unfairly distributed4. The boundaries of exclusive economic zones (EEZs) — the parts of the coast belonging to a given nation — are often poorly policed, and large-scale vessels regularly swoop in and take sea life through bottom trawling, something that small fishers seldom practice. Large-scale bottom-trawlers account for 26% of the global fisheries catch, with more than 99% of that occurring in the EEZs of coastal countries6. Even when there are well-meaning policies to protect local fishers, foreign vessels can take advantage. For instance, a 2018 investigation by the Environmental Justice Foundation in London found that around 90% of Ghana’s industrial fishing fleet was linked to Chinese ownership, despite Ghanaian laws expressly forbidding foreign ownership or control of its boats. Clearer definitions of the terms fisher, fishing and fishing vessel to make provisions for small-scale operators could help, in part, to avoid such abuse.Government subsidies also require reform. One estimate found that large-scale fishers receive about three-and-a-half times more subsidies than small-scale fishers do7. This widens the existing advantages of large operations in terms of vessels and gear, infrastructure (including cold storage), processing capacity and access to cheap fuel. By giving large-scale fishers the capacity to catch even more, it can have the perverse effect of encouraging overfishing8. Instead, subsidies and other funds should be directed towards small-scale fishers to let them expand their access to markets, while keeping them from adopting the negative practices of large-scale operations.More for consumptionThe total global loss and waste from fisheries is estimated at between 30% and 35% annually1. This could increase as smaller operations broaden their markets. A 2015 estimate of the Volta Basin coast in West Africa attributed 65% of fish-production losses to a lack of technology and good manufacturing practices, and to a lack of infrastructure such as decent roads and cold storage9. The study found that fish were rarely lost to physical damage during the process; most waste resulted from spoilage. Such losses limit the sale of fish locally and to distant markets.Public and private investment in cold-storage facilities and processing equipment (such as for drying, fermentation, pickling or smoking) could help. Current funding for fishery conservation projects comes from development partners, regional banks, the World Bank, private foundations and other agencies — with some entities also providing microloans to small-scale fisheries — but these efforts are uncoordinated and inadequate.One promising strategy is to pair international or national funding with direct contracts for feeding programmes linked to schools, hospitals and similar facilities. Such arrangements would provide small fisheries with large, consistent markets and storage infrastructure that boosts local consumption and does not incentivize overfishing.

Artisanal fishers at a fish-processing cooperative in Santa Rosa de Salinas, Ecuador.Credit: Camilo Pareja/AFP/Getty

Other strategies pair local fishers with conservation efforts. As fishing operations scale up, fish entrails and other waste cannot simply be thrown into the sea: care must be taken not to contaminate the environment. One option is to fund ecosystem-restoration projects that also benefit local fisheries. For example, the Mikoko Pamoja (Mangroves Together) project in Gazi Bay, Kenya, restores and conserves degraded mangrove forests, which act as nurseries for young fish. The restoration thus earns saleable carbon credits while enhancing nearby fishery grounds for the local community.Consumers could support small fisheries by buying local, because shorter supply chains mean more income for the fishers. The use of ecolabels — which seek to promote sustainably managed fisheries by certifying that a product has a reduced environmental impact — could also encourage consumer adoption, and help consumers to make informed choices.However, such certification is costly to obtain and maintain, and requires compliance, monitoring and reporting. Certification can distort market opportunities, effectively excluding small enterprises from entering international markets. These programmes can also have unintended consequences: most certification programmes focus on environmental sustainability and pay less attention to social responsibility elements, such as fairness in access to resources, markets and wages.Instead, simple incentive programmes could be implemented by funders, managers and local governments trying to promote sustainable fisheries. For example, local markets could display a rating system for individual fishers or small entrepreneurs. This could include various elements of sustainability other than environmental ones — such as providing information on the type of fishing gear, location of the catch and freshness. Promoting the rating as a social responsibility concept would inform consumers of the need to support sustainable fisheries. The rating system could be conducted by community members trained in inspection and enforcement.Local controlDiverse efforts are needed to protect small fisheries’ access and to boost local consumption and reduce waste, and must be tailored to local community conditions. The 2021 UN Food Systems Summit was a ‘people’s summit’ that elevated roles for Indigenous peoples and civil-society groups, yet the voice of fishing communities was notably absent.Few governments take an integrated approach to the development, implementation and enforcement of policies. For example, policies governing urban development tend not to consider the implications on the ocean, fish and fishers. In the late 2000s, for instance, fishers were initially denied access to traditional public fishing zones along the beach front in Durban, South Africa, following upgrades to the port and the development of a private marina and hotel. (Fishers later reclaimed some of the zones after protests and engagement with the authorities10.)Cooperatives can help on several fronts: by coordinating fishing activities, sharing information (about weather, sea conditions or fish movement) and advocating effectively for human and social rights. For instance, CoopeSoliDar, a small-scale fisheries management cooperative in San José, Costa Rica, has helped to strengthen collective action to sustainably use molluscs, alleviate poverty and strengthen the representation of women and young people in community decision-making. Governments can help by creating a legal framework to establish cooperatives and include them in decisions to manage marine resources.Local communities can also stand up for themselves. For example, a class action by a group of 5,000 artisanal fishers in South Africa in 2004 argued against a policy they said did not give them recognition or access to food and fishing rights that were established in the country’s constitution. The court ruled in the group’s favour in 2007, and the resulting legal framework granted small-scale fishers collective community fishing rights, recognizing community members as bona fide fishers11.Integrated inputsSmall fisheries do not operate in isolation. Unlike terrestrial resources, the ocean is an extensive, global commons without clear territorial boundaries. Issues as diverse as climate change, ocean acidification, overfishing and pollution by nutrients and plastics and other chemicals all affect local fishers. But such system interactions get scant attention when fisheries policies focus on a single seafood stock or individual fishing area.Whereas the concept of integrated land management has been part of the development agenda for a few decades, integrated marine management is only now emerging. To work, it must involve all relevant stakeholders, including small-scale fishers.A context-specific strategy in the Seychelles is a leading example of such integration. Communities, financing partners and the government worked together to create the Seychelles Marine Spatial Plan Initiative, which protects 30% of the archipelago’s waters and boosts climate resilience. The Seychelles faces significant threats from rising sea levels and warmer air and water temperatures that put fisheries, infrastructure, tourism and its rich biodiversity at risk.In an example in the Coral Triangle region (encompassing Indonesia, Malaysia, Papua New Guinea, the Philippines, the Solomon Islands and East Timor), local communities gave their input to a marine protection plan. This led to a greater understanding of how practices such as overfishing and taking undersized stock sustains marine and coastal resources, and how managing these helps to address food security, climate change and threats to marine biodiversity. Such cooperation between fishing communities and governments in managing marine protected areas is essential to the preservation of future fish stocks (see go.nature.com/3xvkqxj).Fishers should be actively engaged in relevant meetings held by the UN and national and local councils, so that they can weigh in on matters that affect fishing access, their livelihoods and environmental concerns. Both fishers and organizers must help to build empowerment mechanisms to make sure their voices are heard, such as providing translation services and scheduling meetings at accessible locations. This is important not just for the fishers’ human rights, but also because much can be learnt from artisanal fishers’ local knowledge.Moves that would, for instance, restrict the fishing season or areas so that stocks or biodiversity can recover should include compensation mechanisms that will secure fishers’ cooperation and livelihoods. Social-protection measures such as food and income assistance can also help to tide fishers over.When fish swim in schools, they move more efficiently, forage better and are protected from predators. The same might be said for small-scale fishers, but those networks should extend to local and international communities, too. Collaborative problem-solving and an integrated food system can deliver seafood protein, sustainably, to a world that increasingly needs it. More

Six biological replicates each were sampled from four fruit species (blueberries, cherries, raspberries, and strawberries) at four developmental stages. Developmental stages were based on fruit pigmentation ranging from unripe (green) to fully ripe (red/purple/navy; Fig. S1) throughout June to September in 2018. Ten fruits (except blueberries N = 20) were collected for each species per replicate, and this was replicated six times for each ripening stage for each fruit at different sites.Quantitative analysis of fungal communitiesMetabarcoding analysis is generally not quantitative, but the addition of 265 P. cucumerina cells to sub-samples prior to DNA extraction served as an internal standard to attempt an estimation of the size of fungal populations. One replicate spiked with the internal standard of the strawberry stage 3 samples was removed due to poor sequence quality leaving 96 non-spiked and 95 spiked samples which produced a total of 38,445,395 reads that clustered into 1712  > 97% identity Amplicon Sequence Variants (ASV), which from here-in we call phylotypes (Table S1). Blast searches across all phylotypes for matches to the P. cucumerina internal standard’s ITS sequence generated from Sanger sequencing revealed one phylotype that matched with 100% identity. Plectosphaerella cucumerina was naturally present in 21 of the 95 non-spiked samples and comprised of a total of 444 reads. Cherry was the only fruit where the internal standard was reliably recovered: 23 of 24 spiked samples and only one of 24 non-spiked samples contained the internal standard phylotype. After internal standard DNA read normalisation, the mean (± SE) number of fungal cells from each of the useable 23 pairs of cherry replicates was 307,323 (± 39,090) cells. The range of phylotype cell abundance across all cherry samples was 3.9 million for an Aureobasidium phylotype to 3 cells for a phylotype taxonomically assigned no higher level than kingdom. There was no significant change in total fungal cell numbers across cherry maturation stage (Kruskal–Wallis, chi-squared = 2.63, P = 0.45; Fig. S2), but fruit surface areas also increased significantly (Kruskal–Wallis, chi-squared = 19.70, P = 0.0002, Fig. S2). When cell numbers were normalised for surface area this revealed that absolute fungal population sizes remained static across cherry maturation stages (Kruskal–Wallis, chi-squared = 2.49, P = 0.48; Fig. 1A). However, there was a significant change in absolute Saccharomycetales cell numbers when normalised for cherry surface area across maturation (Kruskal–Wallis, chi-squared = 15.30, P = 0.002): stage 1 had significantly greater absolute Saccharomycetales cell numbers than stage 4 (P = 0.0007; Fig. 1B). Six individual Saccharomycetales yeast phylotypes from the genera Debaryomyces, Saccharomyces, Kodamaea, one from the family Pichiaceae, and phylotypes with  > 97% homology to M. pulcherrima and Metschnikowia gruessii, had significantly greater abundances on ripening stage 1 than 4 (P values span 0.045 to 0.006).Figure 1Absolute fungal cell abundances on cherry epicarp. Number of total fungal (A) and Saccharomycetales yeasts (B) cells per mm2 of cherry epicarp (N = 6 except, stage 3 and 4, N = 5) at four ripening stages (1, unripe/green fruit; 2, de-greening fruit; 3, ripening fruit; and 4, fully ripe/harvest fruit) estimated from DNA read abundances normalised to DNA abundances from the deliberate addition of 265 live Plectosphaerella cucumerina cells prior to DNA extraction. Different lower-case letters above bars show significant differences between ripening stages at P  > 0.05, Dunn’s comparisons post-hoc with Benjamini–Hochberg multiple comparison correction.Full size imageOverview of fungal diversity across all fruit samplesThe P. cucumerina internal standard phylotype was removed from all samples, and the sequence data normalised and analysed. A total of 1712 fungal phylotypes was revealed, comprising seven phyla, 25 classes, 96 orders, 197 families, and 280 genera. The most abundant and diverse phylum was Ascomycota, comprising 92.2% of reads and 57.3% of phylotypes, followed by Basidiomycota (7.7% reads and 33.6% phylotypes), Zygomycota (0.1% and 1.1%), Chytridiomycota ( > 0.1% and 0.7%), Mucoromycota ( > 0.1% and 0.3%), Glomeromycota and Rozellomycota (both  > 0.1% and 0.1%; Fig. S3A). A phylotype from the Cladosporium genus was the most common phylotype across all samples, comprising 60.8% of reads. A total of 87 phylotypes from the order Saccharomycetales (budding yeasts) was detected, comprising 1,792,782 DNA reads (4.7% of the total) spanning 10 families and 25 genera. Metschnikowia was the most abundant Saccharomycetales genus (40.0% of Saccharomycetales reads), followed by Hanseniaspora (38.2%), then Pichia (5.2%), with the remaining genera contributing fewer than 3% each. Candida was the most diverse genus within the order Saccharomycetales accounting for 21.8% of phylotypes, despite only comprising 2.4% of reads, followed by Metschnikowia (11.5%), Hanseniaspora (8.0%) and Pichia (6.9%), with each of the remaining genera contributing fewer than 3.5% of phylotypes each (Fig. S3B). The most common Saccharomycetales yeast across all samples was a phylotype from the genus Hanseniaspora with  > 97% homology to H. uvarum and comprised 38.2% of the total Saccharomycetales reads (Fig. S3B).The effect of fruit species and ripening stage on epicarp fungal communitiesWe analysed differences in three biodiversity metrics to evaluate the effect of fruit species and maturation stage on fungal communities: differences in the absolute numbers of phylotypes (richness); differences in the types of phylotypes (i.e. presences/absences); and differences in the relative abundances of phylotypes (community composition) following Morrison-Whittle et al.14 and Morrison‐Whittle and Goddard37.
Fungal phylotype richnessPhylotype richness was not normally distributed (Shapiro-Wilks, P = 0.008) but square root transformation allowed the data to conform to the assumptions of ANOVA. There was a significant effect of both fruit type and ripening stage on the number of fungal phylotypes, including an interaction between the two (F3,175 = 18.58, P = 1.65 × 10–10; F3,175 = 5.00, P = 0.002 and F9,175 = 6.69, P = 3.25 × 10–8 respectively). Comparisons of effect sizes revealed fruit type (ω2 = 0.30) had a 4.4 times greater effect than ripening stage (ω2 = 0.068) on fungal phylotype richness. Disregarding ripening stage, cherry (mean ± SE number of phylotypes = 98 ± 4.1) had significantly more fungal phylotypes than blueberry (68 ± 3.7), raspberry (72 ± 2.9) and strawberry (76 ± 3.2) (Tukey’s HSD, P  0.05) and there was a significant effect of ripening stage on the number of fungal phylotypes for cherry, raspberry, and strawberry (one-way ANOVA: F3,44 = 4.33, P = 0.0093; F3,44 = 13.56, P = 2.11 × 10–6 and F3,44 = 13.86, P = 1.84 × 10–6, respectively, Fig. 2), but not blueberry (F3,44 = 2.27, P = 0.055). On cherries phylotype numbers increased during ripening, but raspberry and strawberry had greater numbers at intermediate stages of fruit maturation (Fig. 2).Figure 2Number of observed phylotypes across fruit types and maturation stages. Number of fungal phylotypes across four ripening stages (1, unripe/green fruit; 2, de-greening fruit; 3, ripening fruit; and 4, fully ripe/harvest fruit) for blueberry, cherry, raspberry and strawberry (N = 12 except N = 11 for strawberry stage 3). Numbers of fungal phylotypes differ across ripening stages for cherry, raspberry and strawberry but not blueberry (ANOVA, P values shown). Where significant, different lowercase letters represent significant differences in phylotype numbers within each fruit (P  97% homology to Metschnikowia kunwiensis and H. uvarum on raspberry; and phylotypes with  > 97% homology to Kalmanozyma fusiformata (Ustilaginaceae smut fungi) and Podosphaera aphanis on strawberry.Twenty-four of the 195 indicator phylotypes belonged to the Saccharomycetales budding yeasts (Table S13). There were no Saccharomycetales indicator phylotypes for cherry, and just one for blueberry, a fungal phylotype with  > 97% homology to Metschnikowia koreensis. Raspberry had 15 Saccharomycetales indicator phylotypes: three with  > 97% homology to the Metschnikowia and, Candida genera, two Pichia and Schwanniomyces, and one each from Hanseniaspora, Barnettozyma, Debaryomyces, Candida, Geotrichum and Martiniozyma. There were eight indicator phylotypes for strawberry; two Candida and one from each of the Metschnikowia, Starmerella, Kodamaea and Hyphopichia genera and the Pichiaceae family, and a phylotype assigned to the no higher level than fungal kingdom (with  > 97% homology to deposit from Candida genus). The dynamics of Saccharomycetales yeast indicator phylotypes abundances across maturation for raspberry and strawberry is shown in Fig. 6.Figure 6Dynamics of changes in the proportion of budding yeast indicator phylotypes. Mean proportion of reads for the Saccharomycetales budding yeast indicator phylotypes that are significantly overrepresented on (A) raspberry and (B) strawberry (P  97% homology identified by manual Blast searches.Full size imageDifferences of yeast known to be attractive to D. suzukii
Yeast from the Hanseniaspora, Pichia, Saccharomyces, Candida and Metschnikowia genera and their combinations are attractive to D. suzukii27,28,30,31, and phylotypes belonging to these genera were recovered here. The combined relative read abundances of all phylotypes assigned to these genera were significantly different between fruit types and ripening stages (Kruskal–Wallis chi-squared = 60.54, P = 4.51 × 10–13; chi-squared = 10.11, P = 0.018, respectively). Raspberry had the highest relative abundance of yeast genera known to be attractive to D. suzukii (mean ± SE = 21,539 ± 4339) and this was significantly greater than on the other fruits (P  97% homology to H. uvarum as over-represented on raspberry generally, and especially at later stages (Fig. 6A).Differences of Botrytis cinerea, known to be repulsive to D. suzukii
The relative read abundances of B. cinerea were significantly different between fruit types and ripening stages (Kruskal–Wallis chi-squared = 73.45, P = 7.80 × 10–16; Kruskal–Wallis chi-squared = 23.81, P = 2.74 × 10–5, respectively). Raspberry had the lowest relative abundance of B. cinerea (mean ± SE = 800 ± 136) and this was significantly lower than strawberry (1994 ± 292) and blueberry (5990 ± 1305) (P  More

Experimental design and crop managementField experiments were conducted at Chengyang Agricultural Experimental Station, Qingdao, China (36°18′ 11″/N, 120°21′ 13″/E) in 2019 and 2020. The soil type in the field was brown loam that contained 22.76 g kg−1 organic matter, 82.39 mg kg−1 alkali-hydrolysable N, 25.10 mg kg−1 Olsen-P and 94.89 mg kg−1 exchangeable K. The test cultivars of maize were Jinhai5 with strong lodging resistance and Xundan20 with weak lodging resistance, which were repeated four times in plots laying out in randomized block designs. Plant density was 7.5 plants / m2 with the row spacing of 60 cm. the plot consisted of 8 rows length of 15 m. Two–three seeds per hole were manually sowed at 5 cm on 20 April 2019 and 24 April 2020, and the seedlings were thinned to the target planting density at V2, and harvested on 10 September and 14 September, respectively. Fertilization and irrigation management followed local production practices in maize.Sampling and measurementPlant samples were taken at V8, V12, R1, R2 and R6. Ten typical plants of each tested cultivars were selected to be subjected to mechanical and above-ground morphological measurements at each sampling. The other three maize plants were used to measure morphological traits of roots. Xundan20 was seriously damaged due to the storm in the late stage of maize growth in 2020, resulting in the missing data for physiological maturity.Determination of leaf area vertical distributionLeaf area of expanded leaves each was computed by the coefficient method: Single leaf area = length * width * 0.75. Leaf area for unexpanded leaves was estimated by the leaf weight method. Leaf area per plant was the sum of all individual green leaf areas. Leaf height is the height from the ground to the leaf collar position of maize.Determination of max root side-pulling resistanceSample plants were surrounded with water-proof steel devices inserted into underground, and watered to soil moisture over saturation at one day before mechanical testing. When measured, due to the limited space, all leaves of sample plants are removed in order to improve the measurement accuracy. The defoliated stalks were immobilized by a pair of lengthwise steel clamps to prevent stalks from bending (Fig. 7). After the digital pole dynamometer18 with a 1.5 m long slider and a main unit was linked to the stalks at a height of 80 cm away from the ground, the operator by hand pulled at a slow and uniform speed until the roots were pulled out. Records of load force, declination angle and sensor position were automatically stored in main unit during this operation. The peak value of forces, extracted from records, was taken as the max root side-pulling resistance.Figure 7Schematic diagram for measuring max root side-pulling resistance.Full size imageRoot anti-lodging indexBased on the method of Cui et al.6, the force value comparison is changed to the moment value comparison to calculate root anti-lodging index:$${text{AL}}_{root} = M_{root} / , M_{wind} = F_{root} / , F_{wind}$$
(1)
where M root is the root failure moment, M wind is the wind resultant moment. Root anti-lodging index indicates the ability of plants to resist root lodging. The larger its value is, the stronger the resistance is, and vice versa.$${text{M}}_{root} = F , *d$$
(2)
where F is the max root side-pulling resistance, d is moment arm, i.e., the length of force arm. As a component of root anti-lodging index, the root failure moment represents the ability of the root system to resist lateral pulling. The greater its value is, the better the resistance is, and vice versa.With the base of the stem as the fulcrum,$${text{M}}_{wind} = sum 0.{5}CA_{i} rho V^{2} h_{i}$$
(3)
where C is coefficient of air resistance, ρ is air mass density ,V is the wind speed , Ai is the area of a single leaf , hi is the height of leaf, ∑ represents to sum up over all leaves. C value is set to be 0.219. When encountering wind speed at grade 6 or higher, maize is more prone to lodging. Unless stated explicitly, the following analysis was limited to the upper wind speed for grade 6 wind20.Root morphological traitsThe number and length of all primary nodal roots were measured. Root-soil balls each of two or three tested plants were obtained after lateral root-pulling testing. The images of the three frontal sides, 120 degrees apart from each other, of the root-soil balls were taken using a digital camera. Ball volumes were then evaluated by considering them to be rotationally symmetric. Average volumes were used for further analysis.Single root tensile resistanceRoots after counting the number of nodal roots were used to measure the single root tensile resistance. First, clean the dust off roots. Then, diameters of roots were determined with a vernier caliper. Single root tensile resistance was measured by HF-500 digital push–pull apparatus. Fixed the upper and lower ends of the root, then one end moved slowly and uniformly, the other end was still until the root breaks. The peak tension force displayed by the instrument was taken as the single root tensile resistance.Statistical analysisBased on variance analysis, the Tukey method was used to compare the differences among means. The logarithmic transformation of variables was carried out to improve the homogeneity of error variance if appropriate.The substantive effect or influence of various factors on the response variable can be expressed by effect size of factors, which can be calculated under the framework of variance analysis. Effect size is the proportion of the effect of a certain factor in the total effect, which is a dimensionless number21,22,23.The formula for calculating effect size of factors is:$$omega^{2} = frac{{df_{effect} times left( {MS_{effect} – MS_{error} } right)}}{{SS_{total} + MS_{error} }}$$
(4)
where df is the degree of freedom, MS represents mean square.Two conceptual models were used when dealing with effect size. One model was of components, i.e., taking the logarithm of both sides of Eq. (1):$${text{LOG}}left( {{text{AL}}_{{{text{root}}}} } right) , = {text{ LOG}}left( {{text{M}}_{{{text{root}}}} } right) , + {text{ LOG}}left( {{text{M}}_{{{text{wind}}}} } right)$$
(5)
where LOG denotes logarithmic transformation.The other was the factorial model, i.e.,$${text{factors affecting AL}}_{{{text{root}}}} = {text{ wind grade }} + {text{ cultivar }} + {text{ growth stage}}$$
(6)
Experimental research and field studies on plants including the collection of plant materialThe authors declare that the cultivation of plants and carrying out study in Chengyang Agricultural Experimental Station complies with all relevant institutional, national and international guidelines and treaties.Statement of permissions and/or licenses for collection of plant or seed specimensThe authors declare that the seed specimens used in this study are publicly accessible seed materials and we were given explicit written permission to use them for this research. More

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