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NEWS
05 November 2021

Whales’ gigantic appetites, climate fears — the week in infographics

Nature highlights three key infographics from the week in science and research.

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Climate scientists are scepticalThe momentous COP26 climate summit now under way in Glasgow, UK, represents one final opportunity for the governments of the world to craft a plan to meet their most ambitious goals for curbing climate change. Pledges are already flowing in, but the meeting has another week to run and much is still to be decided. Ahead of the summit, Nature conducted an anonymous survey of the 233 living authors of a climate-science report published in August by the Intergovernmental Panel on Climate Change, and received responses from 92 scientists — about 40% of the group. Their answers suggest strong scepticism that governments will markedly slow the pace of global warming, despite political promises made by international leaders as part of the landmark 2015 Paris climate agreement. Six in ten of the respondents, for example, said that they expect the world to warm by at least 3 °C by the end of the century, compared with conditions before the Industrial Revolution. That is far beyond the Paris agreement’s goal to limit warming to 1.5–2 °C.

Source: Nature analysis

Africa’s clinical trialsA shocking lack of COVID-19 vaccines in Africa, and the cost of existing treatments, means the continent really needs affordable, readily available COVID-19 drugs. These could reduce COVID-19 symptoms, lower the burden of disease on health-care systems and reduce deaths. The pandemic has given clinical research in Africa a boost: the Pan African Clinical Trials Registry recorded more clinical trials in 2020 than in 2019, and the number for 2021 is also on track to exceed 2019. But trials of COVID-19 drugs are still lacking in Africa, where they face infrastructure and recruitment challenges. One solution could be to establish a body to coordinate treatment trials on the continent.

Source: https://pactr.samrc.ac.za

The gluttony of whalesHow much do baleen whales, the largest known animals that have ever lived, eat? Three times as much as previously thought, report researchers who used cameras to study seven species of baleen whale. Writing in Nature, the researchers also suggest a feeding cycle involving iron and whale poo that could explain how such gluttony is possible. When whales eat iron-rich prey such as krill, they use the prey’s protein to make blubber — and defecate the iron-rich remains. Whale faeces might then provide a source of iron for microscopic marine algae called phytoplankton, and drive blooms of a type of plankton called diatoms. Diatoms, in turn, can move iron along the food chain when they are eaten by krill, which also excrete iron. Whales can further aid iron availability by mixing ocean waters through their vigorous tail movements.

doi: https://doi.org/10.1038/d41586-021-03066-5

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Identification and characterization of biotic and abiotic components in a penguin colony using RGB and multispectral camerasFigure 1 shows two image mosaics of a Chinstrap penguin (Pygoscelis antarcticus) colony, composed of 3800 pictures taken during a 29-min flight at 100 m altitude with a multispectral camera (MicaSense RedEdge-MX) using RGB bands (i.e., Red-668, Green-560 and Blue-475) (Fig. 1A) and the 10 wavelength bands covering the spectrum from visible to near-infrared light (Fig. 1B). With a resolution of 6 cm/pixel, penguin nests are clearly visible in the RGB mosaic, which are characterized by the absence of vegetation and with a predominant pink/brown color due to the abundance of guano deposition. The RGB mosaic also shows snow patches (white color), moss beds (green color) and one small lagoon with a bloom of red-pigmented greenalgae (Chlorophyceae) (Fig. 1A, upper right corner). Red algae (Chlamydomonas nivalis) patches on snow and ice are visible by zooming into a region of ice (Fig. 1A). More detailed information is obtained when the light spectrum from visible to near-infrared is used. Using the 10 wavelength bands, a thematic map was generated with the QGIS software and using a non-supervised classification method (Fig. 1B). Here it is possible to distinguish up to four species of mosses and three types of penguin guano that was verified with field observations.Figure 1Photomosaics of Vapour Col Chinstrap penguin colony on Deception Island composed of 3800 pictures taken at 100 m altitude with a 10 bands multispectral camera onboard a hexacopter, achieving 6 cm/pixel size. Panel (A): visible RGB mosaic (Red-668, Green-560 and Blue-475) with a zoom capture showing red snow patch; Panel (B): thematic map generated through non-supervised classification method.Full size imageDeception Island harbors up to 54 species of mosses, of which 13 species (including two endemics) have not been recorded elsewhere in the Antarctic. This, together with eight species of liverwort and 75 species of lichen, makes Deception Island an exceptional and unique place in Antarctica with legal protection under the Antarctic Treaty3. The use of a multispectral sensor onboard the UAV provides unique information to detect, classify and monitor moss beds without anthropogenic impacts. Antarctic moss bed health has already been assessed using multispectral sensors onboard UAVs12. Taxonomic identification would be feasible by indentifying previously each species in the field and later assigning the spectral signature using the UAV, as recently suggested by Miranda et al. (2020), who monitored lichens and mosses in the Antarctic using a combination of satellite imagery and UAVs13.Penguin guano has been suggested to be an important source of bioactive metals (e.g. Cu, Fe, Mn, Zn) for the sea surface waters, potentially fueling primary production of the Southern Ocean14. It has been suggested that the penguin species that feed mainly on Antarctic krill (Euphausia superba) (i.e., Chinstrap: Pygoscelis antarcticus, Adélie: Pygoscelis adeliae and Gentoo: Pygoscelis papua) excrete the highest concentrations of these bioactive metals15. Guano from these three congeneric penguin species has revealed the presence of microplastics across the Antarctic5. However, in order to estimate the magnitude of penguin fecal products that reach the sea, it is necessary to quantify the amount of guano excreted by the penguin colonies on land. This is possible with the multispectral reflectance data obtained from the UAV, which not only identify the guano coverage but also distinguishes different types of guano. Guano color is the result of diet, which, in turn, is related to the phase of the breeding cycle; therefore, a diet rich in krill is characterized by an excretion of pink guano, while a diet predominantly based on fish implies white guano16. Dark guano is the result of the mixture of guano with the soils that produce mud during wet precipitation.It is increasingly common in the Arctic and Antarctic to find well-developed algae blooms as highly visible red patches on the snow surface caused by red-pigmented green algae (Chlorophyceae), and that produce the phenomenon commonly-known as red snow17. These algal blooms play a crucial role in decreasing the snow-surface albedo and, consequently, accelerating the melt rate, as well as in nutrient and carbon cycling18,19. Mapping and monitoring the extent of snow algal blooms have so far been focused on satellite remote sensing; however, the spectral, temporal and spatial resolution of multi-spectral satellite imagery limits the study of most snow and ice algae18. Images taken from our UAV can enable the detection of patches of red snow on the surface snow with centimetric resolution (Fig. 1A). In addition, the image mosaic reveals the existence of a red snow bloom in a small pond located in a valley inside the colony (Supplementary Fig. S1). To the best of our knowledge, the existence of this bloom has not been previously reported and its monitoring could provide relevant information about the formation and proliferation of this bloom and its impact on cryospheric environments.As a whole, the image mosaic of the Chinstrap penguin colony in Vapour Col (the second largest breeding colony in the island with about 12,000 pairs of penguins20) may provide unique information about the different ecological niches linked to a penguin colony and their interactions. For example, the distribution and type of guano as nutrient and metal sources could be influencing the distribution and speciation of the flora in the area.3D geological formation using RGB cameraDeception Island is a complex volcanic system formed as a result of the explosive eruption of basaltic-to-andesitic magmas21. Among its multiple structures and stratigraphy, we surveyed the Murature formation, a consolidated andesitic lapilli tuff22. Using the quadcopter with a RGB camera and the software Pix4D we created a 3D photogrammetry of the Murature formation (Fig. 2; Supplementary Movie S1). The software uses a Structure from Motion photogrammetry algorithm, where obtained 3D points are interpolated to form a triangulated irregular network in order to obtain digital Surface model (DSM). This DSM is then used to project every image pixel and to calculate the georeferenced orthomosaic. For the Murature formation, the photogrammetry was generated with 843 pictures obtained from three 20-min flights at an altitude of 40 meters, taking pictures from two different angles to obtain the heights of the features (60° and 90°). With 1.4 cm/pixel resolution the resulting mosaic provides a unique view of the geological formation that will support the study of how the rocks were formed and its evolution in relation to the various geological processes that occurred on the island. 3D photogrammetry is also useful in geomorphological research. Specifically, in Deception Island morphometrics studies of landform (e.g. Crater and cone diameters, depths, slopes, heights, etc.) are useful to estimate the eruptive recurrence of the island, and in turn, for advising volcanic hazards23.Figure 23D photogrammetry of the Murature formation built with 843 RGB pictures taken from the RGB Hasselblad camera quadcopter DJI Mavic 2 Zoom at 40-m altitude, achieving 1.4 cm/pixel size.Full size imageThermal imagery to estimate animal abundance and to detect thermal anomaliesThe combination of UAV technology with a thermal-imaging camera is very useful for studying and monitoring wildlife and thermal anomalies on Deception Island. Chinstrap penguin and fur seal (Arctocephalus gazella) heat signatures were detected at Vapour Col and Baily Head, respectively (Fig. 3A, E). Figure 3A shows a mosaic from a Vapour Col section composed of 336 images taken with a thermal camera (FLIR Vue Pro R) onboard the hexacopter during a 29-min flight at 100 m altitude, whereas Fig. 3C shows one thermal picture of fur seals at Baily Head. Penguins and fur seals, with a thermal signature of 15 °C and 26 °C, respectively, are clearly identified. Penguins are highly sensitive to climate change and are considered “marine sentinels” for quantifying environmental change in the Southern Ocean24. However, the distribution and population dynamics of species such as the Chinstrap penguin are not well understood, mainly because they nest in remote and rugged areas, on-the-ground census work is difficult and sporadic25. As demonstrated for Adelia penguins26 the use of thermal imagery would allow reliable population estimates of Chinstrap penguins. Even, the use of RGB aerial images for animal counting would be far more accurate than from land-based surveys. Nevertheless, the scientific challenge is to develop a machine learning algorithm that can distinguish between animal species, based on their morphology and unique thermal fingerprint, which is only feasible using the high resolution provided by UAVs.Figure 3Thermal imagery. Panel (A): thermal mosaic of a section of Vapour Col (8.5 cm/pixel). Penguins are distinguished throughout the colony as small dots around 15 °C; Panel (B) and (D): RGB (Red-668, Green-560 and Blue-475 bands) and thermal picture of fumarole at Fumarole Bay (5.4 cm/pixel), respectively; Panel (C) and (E): RGB and Thermal image of Fur seals at Baily Head (5.4 cm/pixel), respectively.Full size imageOther useful application of thermal cameras onboard UAVs on Deception Island is the easy and precise detection and monitoring of thermal anomalies. Figure 3B–D shows a thermal picture of one of the multiple fumaroles on the island, reaching temperatures above 90 °C. Seismic monitoring of volcanos on Deception Island has being ongoing since 1986, including many recorded volcano-tectonic earthquakes, long-period events and volcanic tremor27. There have been six documented volcanic eruptions on the island between 1841 and 197128, nowadays volcanic and geothermal activities are limited to fumaroles and hot sands. Monitoring of these fumaroles using UAVs can provide a key in surveillance for early warming systems alerting of volcano activity on the island. UAVs not only accurately detect changes in temperature but also allow the increase in monitoring frequency when required.Surface water samplingUAVs provide unique opportunities for remote sample collection from surface waters, particularly in harsh or dangerous environments. Using a surface water sampling device described in the sampling and method sections we collected filtered fresh and saline surface waters at: (1) Three locations in Crater Lake (Fig. 4A). Crater Lake is part of the Antarctic Specially Protected Area (ASPA 140) due to its exceptional botanic and ecological value3. The use of drones for water sampling avoids human disturbance through the transportation and use of infrastructure, such as inflatable boats, and the risk that they pose to the natural ecological system. (2) One and six coastal locations in the Vapour Col and Baily Head penguin colonies, respectively (Fig. 4B, C). Access to the coastal zone inhabited by penguins requires approaches by boat (often assisted by an oceanographic vessel). The approaches do not only disturb the penguins that enter and exit the colony but, due to the coastal orography and waves, also dangerously hinders such an operation. The surface water sampling device onboard the UAV allowed in-situ water collection, minimizing the risk of impact on flora and fauna, limiting water disturbance and preventing contamination in the trace metal analysis. Attached to the sampling system we included a small multiparametric instrument referenced with time and GPS position to measure ancillary parameters, such as conductivity, temperature and depth (CastAway-CTD®) (Fig. 4D). The aerial water sampling has been validated for trace metal analysis using ICP-MS by comparing metal concentrations of samples collected in a saline pond with the surface water sampling device onboard the UAV (i.e. average ± SD, n = 3; Ti: 0.20 ± 0.09; V: 1.92 ± 0.07; Cr: 1.5 ± 0.1; Mn: 19.4 ± 0.4; Fe: 11.6 ± 0.5; Cu: 1.9 ± 0.2; Zn: 0.5 ± 0.3; all values in ppb) and the traditional peristaltic pump system used from land or on boats29 (i.e. average ± SD, n = 3; Ti: 0.20 ± 0.06; V: 1.93 ± 0.09; Cr: 1.3 ± 0.1; Mn: 19.1 ± 0.3; Fe: 11.8 ± 0.3; Cu: 2.1 ± 0.4; Zn: 0.4 ± 0.3; all values in ppb).Figure 4Locations of surface water samples collected in Crater lake (A), Vapour Col (B), and Baily Head (C) using aerial water sampling device, and picture of the UAV (hexacopter) carrying, at 100 m altitude, the water sampling device and the multiparametric instrument (D). Stations at Crater lake are plotted on a mosaic composed of 3096 pictures taken during three flights of 14 min each at 120 m altitude using a quadcopter with an integrated RGB camera and a multispectral camera array with 5 bands, achieving 6.5 cm/pixel size.Full size imageDeception Island is an example of the complexity of Antarctic environments, where environmental research studies need to deal with the inter- and multi-disciplinary analysis of processes, such as volcanic and geothermal activities, limnological process from its multiple lakes and ponds, sparse and exceptional flora and diverse fauna, among other. UAV surveys on Deception Island have demonstrated that this technology may substantially contribute to the progress in environmental biological, geological and chemical studies. UAVs permit researchers to study environmental processes at smaller spatial and temporal scales compared to other remote platforms (e.g. satellites), in a more cost-effective and safer way than on foot studies. Furthermore, they are less invasive and less disturbing to wildlife and the ecosystem. The simultaneous use of multi-sensors for multiple applications and the development of algorithms based on images obtained from the drone to detect, classify and count animals in real time are the new challenges that would significantly contribute to the study of the functioning of the Antarctic ecosystem and its ongoing environmental processes. More

Climatic conditions and phenologyThe growth and development of T. durum plants was moderately differentiated by weather conditions in the analyzed years (Table 1).Table 1 The duration of growing seasons (Days), sum of temperatures (Temp.) and sum of precipitation (Prec.) during the growth and development of T. durum Desf. in the analyzed years.Full size tableThe growing seasons of 2015, 2016 and 2017 lasted 136, 132 and 145 days, respectively; the sum of temperatures was determined at 2011.3, 1895.6 and 2069.9 °C, respectively, and the sum of precipitation was determined at 366.7, 360.1 and 350.5 mm, respectively. However, a comparison of cumulative temperatures and precipitation in the phenological phases of T. durum in each year of the study indicates that temperature and precipitation could have influenced the duration of the examined phases and plant growth indicators (Fig. 1). Weather conditions were generally favorable for the growth and development of T. durum in 2015 and 2016. Cumulative temperatures and precipitation were quite similar in 2015 and 2016 up to the booting stage, but precipitation levels in successive stages were higher in 2016 than in 2015. The growing season was shortest in 2016 and longest in 2017, mainly due to low temperatures during sowing and seed germination, and high precipitation during tillering, grain formation and ripening.Figure 1Cumulative temperatures and precipitation in the phenological phases of T. durum in 2015–2017.Full size imageBiophysical parameters: LAI and SPADThe LAI denotes the area of photosynthetic tissue per unit ground surface area (m2 m−2). The LAI is directly associated with plant canopy, and it is an indicator of net primary production, water and nutrient use, and the carbon balance. SPAD is a measure of leaf greenness that is directly associated with chlorophyll content and nitrogen sufficiency.The main effects of LAI and SPAD were analyzed separately in the framework of the Zadoks scale to reveal the significant effects of years, nitrogen rates and sowing density, and an absence of significant effects associated with the application of the growth regulator (see Tables 1.1–1.6 in the Supplementary Information). In the analyzed years, LAI and SPAD were similar in the 2nd node detectable stage (Z32), but they differed in the stem elongation stage (Z45) and the ear emergence and heading stage (Z59), when LAI values were higher and leaf greenness values were lower in 2016 and 2017 than in 2015. These findings can be attributed to moderate temperatures and precipitation in 2015, and high precipitation in the critical growth stages in the remaining years. The general trend associated with the nitrogen rate was similar across the examined growth stages, i.e. a significant increase in LAI and SPAD values with nearly identical effects were noted in treatments with nitrogen rates of 80 and 120 kg ha−1. A similar trend was observed in sowing density. In treatments with a sowing density of 450 and 550 germinating seeds per m2, LAI values continued to increase, whereas SPAD values were below those noted in the treatment with a sowing density of 350 germinating seeds per m2. The only significant interaction was observed between years and nitrogen rates.The average values of LAI continued to increase in successive growth stages and were determined at 1.30 at Z32, 1.75 at Z45, and 1.99 at Z59. In turn, leaf greenness was significantly lower in the stem elongation stage (Z45) than in the preceding (Z32) and subsequent (Z59) stages.The significant effect of the years × growth stages interaction for LAI and SPAD values resulted from similar means in stage Z32 in all years, as well as higher LAI values and lower SPAD values in subsequent growth stages in 2016 and 2017 than in 2015. In 2015, the increase in the nitrogen rate induced only a rising trend in LAI and SPAD values, whereas significant differences were observed in 2016 and 2017. To summarize, it should be noted that in successive Zadoks growth stages, the interactions between years, nitrogen rates and sowing density exerted significant effects on LAI and SPAD values, whereas the effects of year × nitrogen rate interactions were significant only in selected growth stages.Contribution of different sources of variation to physiological and biophysical parameters of plant growthThe calculated eta-squares η2 provide information about the contribution of different sources of variation to physiological variables (Table 2). The experimental years and agronomic factors (33.1% and 38.6%), growth stages, and interactions with other factors (32.5% and 39.3%) and random factors (34.4% and 22.1%) made similar contributions to the variation in the LAI and chlorophyll content. The variation in the net photosynthetic rate was related mostly to variations across years (32.6%) and the interactions between growth stages and other factors (24.3%). The variation in the transpiration rate was attributed mostly to the main effects of growth stages (45.8%) and the year × growth stage interaction (16.1%). Instantaneous WUE was strongly determined by variation in agronomic factors and growth stages (22.3% and 21.1%, respectively).Table 2 Eta-square (η2) values for the sources of variation in the leaf area index (LAI), chlorophyll content (SPAD), net photosynthetic ratio (Pn), transpiration rate (E) and instantaneous water use efficiency (WUE).Full size tableIt is worth noting that the variation in agronomic factors made a considerable contribution to the total variation in LAI (22.3%) and SPAD (11.8%), but only a marginal contribution to the net photosynthetic rate (0.4%) and transpiration (2.0%).Photosynthetic indicators— net photosynthetic rate, transpiration rate, and instantaneous water use efficiencyThe effects of the net photosynthetic rate (Pn), transpiration rate (E) and instantaneous WUE were highly differentiated in successive growth stages, and relatively small differences were noted for agronomic factors (see Tables 2.1–2.9 in the Supplementary Information). At the same time, the analyzed photosynthetic indicators differed in successive stages of growth. The net photosynthetic rate was similar in the 2nd node detectable stage (Z32) and the stem elongation stage (Z45) at 29.7 μmol CO2 m–2 s–1, and it was 15% higher at the end of the heading stage (Z59) than in the preceding stages. The transpiration rate continued to increase by 60% on average in successive stages of growth and development, from 1.59 H2O m–2 s–1 in stage Z32, to 2.52 mmol H2O m–2 s–1 in stage Z45, and 4.06 mmol H2O m–2 s–1 in stage Z59.An analysis of the results noted in different growth stages across years revealed significant year × growth stage and growth regulator × growth stage interactions (Fig. 2).Figure 2Mean values and standard error of photosynthesis indicators for year × growth stage (upper) and growth regulator × growth stage interactions (GR 0—without growth regulator, GR 1—with growth regulator).Full size imageThe year × growth stage interaction resulted from differences in the rates of photosynthesis and transpiration in the analyzed growth stages across years. In 2015, the net photosynthetic rate was similar in the first two growth stages, and it increased by around 30% at the end of the heading stage (Z59). In 2016, the photosynthetic rate continued to increase in successive growth stages. In 2017, the net photosynthetic rate was around 10% higher in the 2nd node detectable stage (Z32) than in the stem elongation stage (Z45) and at the end of the heading stage (Z59). The transpiration rate increased significantly in successive stages of plant growth and development, and the only exception was noted in 2015, when the analyzed parameter was similar in stages Z32 and Z45. The WUE index was highest in stage Z32, and a significant interaction was noted due to the correlation between the net photosynthetic rate and the transpiration rate in the remaining stages. Water use efficiency was similar in stages Z32 and Z45 in 2015, and in stages Z45 and Z59 in 2016, whereas significantly lower values in successive stages of plant growth were noted in 2017.The growth regulator was the only agronomic factor that induced significant differences in the net photosynthetic rate across the examined growth stages. Photosynthesis indicators were similar regardless of the application of the growth regulator, and significant interactions resulted mainly from varied disproportions between the end of heading and the stem elongation stage in treatments with and without the application of the growth regulator.It should be noted that the interactions between growth stages and nitrogen rates and sowing density were not significant, which implies that the effects of the interactions between increasing nitrogen rates and sowing density on photosynthetic indicators in successive growth stages were similar to the average values of photosynthetic indicators in the corresponding growth stages (Supplementary Information).Agronomic traitsThe means for yield components and yield are presented in Tables 3.1–3.8 of the Supplementary Information. Stem length was differentiated by the nitrogen rate and nitrogen rate × year interaction. Nitrogen rates of 80 and 120 kg N per ha increased stem length by 11% and 13%, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction resulted from the fact that the nitrogen-induced increase in stem length was smaller in 2015 (0.07 cm per 1 kg of nitrogen) than in 2016 and 2017 (0.09 cm per 1 kg of nitrogen). In 2015, ear length was similar to that noted in the remaining years, and only in 2017, ear length was 7% higher than in 2016. Ear length and the number of kernels per ear increased with a rise in nitrogen rate and decreased with a rise in sowing density.Grain weight per ear and 1000 kernel weight were highest in 2015 and significantly lower in the following years. Grain weight per ear increased only in response to the nitrogen rate of 120 kg ha−1, but 1000 kernel weight was not affected. Both traits decreased with a rise in sowing density. The significant year × nitrogen rate and year × sowing density interactions for both traits can be largely attributed to the magnitude of differences between years, rather than an increase or a decrease in this trend.The biological yield (grain and straw) differed across years and nitrogen rates. In 2016, the biological yield was similar to that noted in 2015 and significantly higher (by 30%) than that noted in 2017. The biological yield increased by 28% and 35% in response to nitrogen rates of 80 and 120 kg ha−1, respectively, relative to the unfertilized control. The significant year × nitrogen rate interaction was associated with variations in nitrogen use efficiency, and the difference between maximal biological yield was determined at 0.5 t ha−1 in 2015, 2.3 t ha−1 in 2016, and 2.8 t ha−1 in 2017.Grain yield was similar in 2015 (4.94 t ha−1) and 2016 (5.38 t ha−1), and it was significantly lowest in 2017 (3.87 t ha−1). Straw yield was highest in 2016 (2.86 t ha−1), and it exceeded the values noted in the remaining years by 16%. The harvest index was similar in 2015 and 2016, and it was 9% lower in 2017. Grain yield increased by 30% and 36%, whereas straw yield increased by 20% and 35% in response to the nitrogen rates of 80 and 120 kg ha−1, respectively. A minor increase in grain yield (3%) was observed in treatments with a sowing density of 550 seeds m−2 relative to the remaining sowing densities.Path modellingA simple correlation analysis of manifest variables in all phenological stages revealed significant correlations between the LAI and leaf greenness (SPAD) only in stage Z32, as well as a very strong correlation between the net photosynthetic rate and the transpiration rate, which was positive in stages Z32 and Z45 and negative in stage Z59. No simple correlations were noted between the indicators of physiological processes (Pn, E, WUE) and biophysical parameters (LAI, SPAD). AAll correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary information).All correlations between the manifest variables of yield components and biological yield were statistically significant, excluding the correlation between stem length and ear length (Supplementary Information). The outer and inner PLS-PM models well fit the data, and their goodness of fit was determined at 0.973 and 0.786, respectively. The outer weights provide information about the relative importance of a manifest variable for the corresponding latent variable (for details please see the Supplementary Information). Outer weights that exceed 0.3 are considered meaningful. By the same token, loading estimates represent the correlations between a latent variable and the corresponding manifest variables. Loadings higher than 0.7 capture more than 50% of the variability contributed by a latent variable to the corresponding manifest variable. In general, both indicators in the outer model, i.e. outer weights and loadings, exceeded the thresholds, which indicates that manifest variables were strongly related with latent variables. Growth regulators (({w}_{GR}) = − 0.007) and the length of the growing season (({w}_{DAYS})=0.197) provided the only evidence for the low explanatory value of latent variable A (agronomic factors).In the inner model, all equations that regressed latent variables well fit the data and were statistically significant (Table 3). The latent variables expressed by the value of R2 increased in successive stages of T. durum growth and development, from 0.218 in physiological processes in stage Z32 (Table 3, Eq. 1) to 0.698 and 0.708 in yield components and Biological Yield, respectively (Table 3, Eqs. 7 and 8). It is worth noting that in successive stages of growth, the value of physiological processes was relatively lower in comparison with biophysical parameters.Table 3 Parameters of regression models for latent variables.Full size tableThe analysis of path coefficients (βi) revealed that agronomic factors (A) and climate conditions (CC) in stages Z32, Z45 and Z59 exerted a specific influence on physiological processes (PP) and biophysical parameters (BP) of T. durum plants. Agronomic factors directly determined physiological processes in all stages and biophysical parameters in stages Z32 and Z59. At the same time, climate conditions did not exert a direct influence on physiological processes in any stage, but directly affected biophysical parameters in all stages. All of the modeled parameters, i.e. agronomic factors, climate conditions and physiological processes, significantly influenced biophysical parameters in stages Z32 and Z59, but not Z45. Consequently, it can be stated that agronomic factors were the main determinant of variability in physiological processes (photosynthesis, transpiration) in a model evaluating the impact of agricultural practices on yield and the manifest variables associated with T. durum growth and development. At the same time, physiological processes made a significant but negative contribution to biophysical parameters. A one unit increase in photosynthesis processes with constant values of agronomic factors and climate conditions implies a decrease of − 0.382, − 0.065 and − 0.395 in biophysical parameters in stages Z32, Z45 and Z59, respectively.The performance of every preceding latent variable in terms of its total impact on the target latent variable, i.e. the biological yield of T. durum (IPMA – Importance-Performance Map Analysis), was analyzed to highlight latent variables associated with agricultural practices that improve biological yield. The total effect (importance) of preceding latent variables (A, CC32, PP32, BP32, CC45, PP45, BP45, CC59, PP59, BP59, YC and CC) on the anticipated performance of the specific target (Biological Yield) is presented in Fig. 3.Figure 3Importance-Performance Map Analysis presenting the impact of latent variables on biological yield (A—agronomic factors, YC—yield components, CC32, CC45, CC59—climate conditions in growth stages, PP32, PP45, PP59—physiological processes, BP32, BP45, BP59—biophysical parameters in the phenological stages of plant growth and development Z32, Z45 and Z59, CC—climate conditions for the entire growing season).Full size imageThe importance and performance of latent variables that influenced the biological yield of T. durum varied. The biological yield of T. durum was affected mostly by agronomic factors (A), followed by yield components (YC) and biophysical parameters (BP) in growth stages Z59 (BP59) and Z32 (BP32), climate conditions in stage Z59 (CC59), and climate conditions in stage Z32 (CC32). A one unit increase in the above latent variables led to an increase of 0.575, 0.422, 0.234, 0.203 and 0.109 units in biological yield, respectively. At the same time, the performance scores of these latent variables were determined at 53.1, 53.5, 67.1, 59.6 and 61.8, respectively (scores closer to 100 denote higher performance). The remaining latent variables, in particular climate conditions for the entire growing season and physiological processes in stage Z32, were characterized by low importance and exerted a relatively small effect on biological yield performance.The results of the importance-performance analysis clearly indicate that latent variables have considerable potential to optimize the agricultural conditions for the growth and development of T. durum plants. More

Direct nucleotide sequencingReadable electropherograms were obtained from both direction in COI fragments of all three individuals of the Japanese spiny lobster. COI sequences determined by direct nucleotide sequencing ranged from 807 to 864 bp and have been deposited in International Nucleotide Sequence Database Collection (INSDC) under accession numbers of LC571524‒LC571526. No stop codon was observed in these sequences (designated by PJK1-direct, PJK2-direct, and PJK3-direct). No indel was observed between these sequences. All nucleotide substitutions at 19 variable sites observed between these sequences were transition at the 3rd position of a codon, and all substitutions were synonymous. The mean Kimura two parameter (K2P) distance between these three haplotypes was 1.510 ± 0.352% SE and that between these sequences and a reference sequence of P. japonicus (NC_004251) was 1.087 ± 0.270%, which were all well within the range reported for Japanese spiny lobster samples collected in Japan and Taiwan9,10.Electropherograms obtained by forward primer for 12S fragments were not readable, while those by reverse primer were readable in all individuals. 12S sequences determined by direct nucleotide sequencing using reverse primer alone ranged from 551 to 570 bp and have been deposited in INSDC under accession numbers of LC605705‒LC605707. Of nine variable sites, eight were transition and one was indel. The mean K2P distance between these three haplotypes (designated by PJK1-12Sdirect, PJK2-12Sdirect, and PJK3-12Sdirect) was 0.970 ± 0.338%, and that between these sequences and a reference sequence of P. japonicus was 0.835 ± 0.282%.Electropherograms obtained by both primers for Dloop fragments were readable only in one individual (PJK2). This Dloop sequence determined by direct nucleotide sequencing was 762 bp and deposited in INSDC under accession number of LC605749. K2P distance between this haplotype (designated by PJK2-Dloopdirect) and a reference sequence of P. japonicus was 3.666%. No indel was observed between the two sequences, and 25 of 27 variable sites were transition.Phylogenetic analysis of clones, heteroplasmy and NUMTsAmong the 36–42 positive COI clones examined per individual, sequences (809–892 bp) of 22–31 clones per individual (75 clones in total) were successfully determined. After alignment, both ends of all sequences were trimmed to fit the shortest sequence obtained by direct nucleotide sequencing, yielding 774–810 bp sequences. Eleven clones of PJK1 were identical to PJK1-direct, as well as seven of PJK2 to PJK2-direct and three of PJK3 to PJK3-direct. These dominant haplotypes (807 bp) were determined to be genuine COI haplotypes of each individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC 571527, LC571533 and LC571538). Nucleotide sequences of the remaining 54 clones were all different one another, in which 20 haplotypes were observed in PJK1, 14 in PJK2, and 20 in PJK3 (LC571541–LC571577, OK429332–OK429343, LC654683-LC654687).Phylogenetic tree constructed using three genuine COI haplotypes, 57 unique haplotypes and eight sequences of reference lobster species is shown in Fig. 1. Haplotypes detected from P. japonicus were segregated into four groups (designated by A, B, C and D). Among the outgroup species used, Australian rock lobster (P. cygnus) that morphologically and genetically belongs to the P. japonicus group11,12, appeared to be the closest kin to all haplotypes detected from P. japonicus. All haplotypes in group A were of the same length (807 bp), and no indel was observed. Three distinct clades (designated by c-I to c-III) were observed in group A, in which 14 haplotypes from PJK1, 11 from PJK2 and 11 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). PJK1-C25 was outlier, having 10 nucleotide differences from the genuine COI sequence. The numbers of variable nucleotide sites between haplotypes within c-I, c-II and c-III were 20, 15 and 26, respectively, of which nonsynonymous nucleotide substitutions were observed at 11, 13 and 10 sites. Stop codon was observed only in one haplotype (PJK3-C1). The mean K2P distance between different haplotypes within these clades ranged from 0.320 ± 0.075 to 0.561 ± 0.103%. The mean K2P distances between three clades ranged from 1.343 ± 0.339 to 2.178 ± 0.464%. Although group A must be composed of sequences containing those caused by Taq polymerase error or true heteroplasmic sequences as well as genuine haplotypes, it is difficult to determine the former two categories. All of the non-genuine haplotypes in group A had singleton difference one another, supporting the occurrence of Taq polymerase error. We determined haplotypes (marked with dagger in Fig. 1) differed by less than two substitutions from the genuine haplotype to be due to Taq polymerase error. This criterion may be reasonable, since Taq polymerase-mediated errors were estimated to occur approximately at a frequency of 7.2 × 10−5 per bp per cycle13 to one mutation per 10,000 nucleotides per cycle14. When Taq polymerase error is taken into account, these K2P distances within and between clades and number of haplotypes are likely to be somewhat overestimated. PJK1-C25, two (PJK1-C5 and PJK1-C60) in c-I clade, one (PJK2-C26) in c-II, and five (PJK3-C1, PJK3-C5, PJK3-C26, PJK3-C31, PJK3-C34) in c-III differed by 3 to 10 nucleotides from their genuine haplotypes, which were determined to be heteroplasmic haplotypes.Figure 1Neighbor-joining phylogenetic (NJ) tree showing relationships among 57 different haplotypes of cytochrome oxidase subunit I (COI) or COI-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and COI sequences of eight congeneric species derived from the GenBank database. Haplotypes detected from the same individual of the Japanese spiny lobster share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Stop codons were observed in haplotypes carrying asterisk. Haplotypes carrying dagger differ from the corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D ranged from 774 to 810 bp. K2P distance between haplotypes of groups A and B ranged from 7.169 to 8.177% with a mean of 7.754 ± 0.973%, that between A and C ranged from 12.073 to 17.392% with a mean of 14.521 ± 1.151%, and that between A and D ranged from 17.472 to 23.880% with a mean of 21.042 ± 1.600%. Multiple stop codons were observed in a haplotype of group B, in five of eight haplotypes of group C, and all haplotypes of group D. Three haplotypes in group C had no stop codon but differed in four to 10 deduced amino acids from the genuine haplotypes. BLAST homology search revealed no identical sequence for haplotypes in groups B to D but indicated that the closest species were P. japonicus or P. cygnus with moderate similarity (83–89% homology). Therefore, all haplotypes of groups B to D (LC571565–LC571570, LC571572–LC571577, LC654683-LC654687) were determined to be NUMTs.Among the 30–35 positive 12S clones examined per individual, sequences (772–806 bp) of 25–27 clones per individual (77 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 731–765 bp sequences. Thirteen clones of PJK1 were identical one another, as well as 12 of PJK2 and three of PJK3, and these were identical to PJK1-12Sdirect, PJK2-12Sdirect and PJK3-12Sdirect, respectively. These dominant haplotypes ranging from 761 to 762 bp in size were determined to be genuine 12S haplotypes of the individual, and representative sequences of these three genuine haplotypes were deposited in INSDC (LC605708‒LC605710). Nucleotide sequences of the remaining 49 clones were all different one another, in which 12 haplotypes were observed in PJK1, 23 in PJK2, and 14 in PJK3 (LC605711‒LC605748, OK429126–OK429131, LC654678-LC654682).Since incorporation of all eight Panulirus species sequences made sequence alignment ambiguous because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 2). Haplotypes detected from P. japonicus were segregated into three groups (designated by A to C). Sequence size of haplotypes in group A ranged from 760 to 762 bp. Three distinct clades (s-I to s-III) were observed in group A, in which 12 haplotypes each from PJK1, PJK2 and PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within s-I, s-II and s-III were 24, 17 and 16, respectively. Of these variable sites, transversion was observed at five, one and three sites, and indel was observed at one, zero and one sites, respectively. The mean K2P distances between different haplotypes within these clades ranged from 0.345 ± 0.081 to 0.519 ± 0.101%. The mean K2P distances between three clades ranged from 0.936 ± 0.275 to 1.371 ± 0.359%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Five haplotypes in s-I clade and two haplotypes in s-III clade differed by three to six nucleotides from their genuine haplotypes, which were determined to be heteroplasmic copies.Figure 2Neighbor-joining phylogenetic (NJ) tree showing relationships among 52 different haplotypes of clones of 12S rDNA (12S) or 12S-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and 12S rDNA sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in group B varied from 731 to 762 bp. K2P distance between groups A and B ranged from 1.336 to 7.445% with a mean of 3.449 ± 0.398%, and those between a reference sequence of P. japonicus and groups A and B were 0.864 ± 0.236% and 3.189 ± 0.410%, respectively. Sequence size of haplotypes in group C varied from 744 to 765 bp. K2P distance between groups A and C ranged from 3.104 to 22.434% with a mean of 12.049 ± 0.901%, and those between a reference sequence of P. japonicus and group C ranged from 3.951 to 21.287% with a mean of 11.764 ± 0.901%. BLAST homology search indicated that the closest species for haplotypes in groups B and C was P. japonicus or P. cygnus with moderate to high similarity (84–98% homology). Therefore, all 13 haplotypes (LC605741‒LC605748, LC654678-LC654682) in groups B and C were determined to be NUMTs.Among the 36–49 positive Dloop clones examined per individual, sequences (777–893 bp) of 26–38 clones per individual (92 clones in total) were successfully determined. After alignment, primer sequences were trimmed, yielding 736–853 bp sequences. Three clones (821 bp) of PJK1 were identical one another and determined to be genuine haplotype of this individual. Nine clones (813 bp) of PJK2 were identical to PJK2-Dloopdirect and determined to be genuine haplotype of this individual. Three clones (821 bp) of PJK3 were identical one another and determined to be genuine haplotype of this individual. Representative sequences of these three genuine haplotypes were deposited in INSDC (LC605750‒LC605752). Nucleotide sequences of the remaining 78 clones were all different one another, in which 25 haplotypes were observed in PJK1, 17 in PJK2, and 36 in PJK3 (LC605753‒LC605815, LC654419-LC654430, LC654675-LC654677).Incorporation of all eight Panulirus species sequences made sequence alignment considerably unreliable because of multiple indels, reference sequences of P. japonicus and of closely related P. cygnus were used for constructing phylogenetic tree (Fig. 3). Haplotypes detected from P. japonicus were segregated into four groups (designated by A to D). Sequence size of haplotypes in group A ranged from 812 to 822 bp. Three distinct clades (d-I to d-III) were observed in group A, in which 17 haplotypes from PJK1, 13 from PJK2 and 15 from PJK3 were cohesively clustered together with their corresponding genuine haplotypes (bold italic). The numbers of variable nucleotide sites between haplotypes within d-I, d-II and d-III were 27, 61 and 28, respectively, of which indels were observed at five, two and four sites and transversion was observed at 0, six and six sites. The mean K2P distance between different haplotypes within these clades ranged from 0.340 ± 0.067 to 1.097 ± 0.139%. The mean K2P distance between these three clades ranged from 7.577 ± 0.951 to 8.770 ± 0.984%. Haplotypes differed by less than two substitutions (including indel) from the genuine haplotypes are marked with dagger. Eight haplotypes in d-I clade, three in d-II clade, and four in d-III clade differed by three to five nucleotides from the genuine haplotype were determined to be heteroplasmic copies.Figure 3Neighbor-joining phylogenetic (NJ) tree showing relationships among 80 different haplotypes of control region (Dloop) or Dloop-like sequences obtained from the Japanese spiny lobster (Panulirus japonicus), and control region sequences of P. japonicus and P. cygnus derived from the GenBank database. Haplotypes detected from the same lobster individual share the same color. Genuine mtDNA haplotype is shown in bold italic and number of clones examined is shown in parenthesis. Haplotypes carrying dagger differ from corresponding genuine mtDNA haplotype by less than two nucleotides (including indel). The bootstrap values greater than 60% (out of 1000 replicates) are shown at the nodes.Full size imageSequence size of haplotypes in groups B to D largely varied from 736 to 853 bp. K2P distances between group A and others ranged from 14.748 ± 1.030% (A vs B) to 61.619 ± 3.045% (A vs D), whereas that between haplotypes of group A and a reference sequence of P. japonicus was much smaller (6.333 ± 0.663%). BLAST homology search revealed no identical sequence for haplotypes in groups B to D and indicated that the closest species for haplotypes in groups B and C was P. japonicus with low to moderate similarity (74–88% homology). On the other hand, no significantly similar sequence was found for haplotypes in group D. Therefore, all 31 haplotypes (LC605788‒LC605815, LC654675-LC654677) in groups B to D were determined to be NUMTs.Impact of heteroplasmy and NUMTs for direct nucleotide sequencingPartial electropherogram obtained by direct nucleotide sequencing for COI amplicon of PJK3 is shown in Fig. 4 (top). Peak signals of this electropherogram are readable, but there are a number of sites where two (asterisk) or three (dagger) signals overlap. Alignment of a genuin haplotype (PJK-C7) and nine NUMTs sequences, corresponding to this partial electropherogram, is shown in Fig. 4 (bottom). At the sites where plural peaks overlap, different NUMT haplotypes were observed to share the same nucleotide different from the PJK3-direct. Heteroplasmic copies in COI determined in this study may have little negative impact on direct nucleotide sequencing, since nucleotides different from the genuine haplotypes were all unique to each heteroplasmic haplotype. Thus, the plural peaks at a site were composed of signals from genuine plus NUMT haplotypes, and the intensity of each peak was positively related to the copy numbers of these haplotypes. Frequent failure to obtain readable electropherograms in 12S and Dloop regions by direct sequencing may be due to extensive indels observed in the NUMT haplotypes.Figure 4A part of electropherogram obtained by direct nucleotide sequencing for COI region of PJK3 (top), and corresponding sequences from genuine haplotype (PJK3-C7) and nine NUMT haplotypes (see Fig. 1) are aligned (bottom). Apparent double (asterisk) and triple (dagger) peaks are observed at seven and five sites, respectively, which are comprised of signals from genuine and NUMT haplotypes.Full size image More

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