Ecology

Wet and dry weight of aerial partsDry weight of aerial parts was significantly affected by drought stress and genotype treatments and their interactions (Table 2). With increasing drought stress the amount of dry weight of aerial parts in all genotypes was decreased. Dry functions in I2, I3, and I4 levels in H genotype (Lavandula gngustifolia cv. Hidecot) were 15.68%, 40.35% and 48.15%, respectively. In S genotype (Lavandula stricta) these amounts were 0.78%, 48.58% and 51.72%, respectively; and in M genotype (Lavandula angustifolia cv. Muneasted) they were 22.29%, 49.38% and 52.63%, respectively. Compared to the control group, the most reduction in dry weight of aerial parts was in M genotype. The highest amount of dry weight (11.40 g in plant) was observed in H genotype in drought stress of 90–100% of field capacity. The lowest amount of dry weight of aerial parts (3.07 g) was seen in S genotype in drought stress of 30–40% field capacity (Fig. 2).Table 2 Variance analysis of the effect of drought stress on enzymatic activity of antioxidant enzymes, and quantity of essential oil from different lavender genotypes.Full size tableFigure 2The effect of drought stress on dry weight of aerial parts in different lavender genotypes.Full size imageIn this study drought stress had a negative effect on biomass of lavender plants. This effect can be due to water shortage. Because drought stress cause reduction in swelling, total water potential in cell and withering, it also results in closing stomata, reduction in cell division, and cell enlargement47,48. Reduction in cell division and cell enlargement as a result of drought, reduce the leaf surface, photosynthesis and growth function of the plant. In other words, reduction in photosynthesis products, cause reduction in leaf’s surface; and reduction in transfer of assimilated materials to aerial part, as a result of drought, cause decrease in aerial yield of the plant49. In this regard, Abbaszadeh et al. (2020) reported that due to drought stress of 30% and 60% of field capacity, dry weight of aerial parts in Rosmarinus officinalis L. has decreased. While contrary to our results Rhizopoulou and Diamantoglou (1991) observed that dry weight of leaves from Marjoram plant (Origanum majorana) was increased with increased soil moisture deficiency; which can be due to differences in plant species and ecological conditions50,51.Proline content of leavesThe results of variance analysis showed that drought stress, genotype and their interactions have significantly affected proline content of leaves (Table 2). With increasing drought stress the proline content was increased. The highest amount of proline content (4.96 mg per g) was observed in H genotype in I4 drought level (30–40% of field capacity). While the lowest amount (1.08 mg per g) was observed in S genotype in irrigation of 90–100% of field capacity (Fig. 3). In each genotype separately, in I2 to I3 drought levels the amount of proline was equal, but in H and M genotypes with increasing drought stress, the amount of proline was increased, While in S genotype with increasing water deficit proline did not show a significant increase. This indicates that two genotypes (H and M) have a similar function for using these types of osmolyte to deal with this level of drought. Which this result may be exist another osmolite production as a resistance mechanism in S genotype52.Figure 3The effect of drought stress on proline content in different genotypes of lavender.Full size imageOne change that happens in biological and non-biological stresses is increasing the amount of osmolytes in plant. To prevent negative effects of drought stress, the plant increases the amount of its osmolytes including proline53. Proline is an amino acid which in addition to act as an osmolyte, plays an important role in maintaining and stabilizing membranes by adding membrane phospholipids and changing the hydrated layer around macromolecules. Proline is also recognized as a stabilizer for cellular homeostasis under stressful conditions. This is due to high ability of proline to stabilize sub-cellular structures such as proteins and cell membranes and its ability to eliminate free radicals54. In present study, increasing proline content in different genotypes of lavender as a result of drought, can be for the same reason. It is proved that in some plants, changes in amount of proline is related to their ability to tolerate and adapt with drought stress; so, the proline content can be used as an indicator to select drought-resistant plants. Hosseinpour et al. (2020), reported that in response to drought stress, accumulation of compatible metabolites such as proline can participate in water absorption. In accordance with our results, an increase in proline content in different genotypes of Calendula officinalis plant due to drought stress has been reported as well55,56. However, in some plant species, other osmolites are produced under biological stress, the most important of them is glycine betaine. So that it is probable that the relationship between glycine betaine accumulation and stress tolerance, such as drought stress, is species- or even genotype specific57. As a results, the S genotype likely produced glycine betaine under drought stress, obviously, completed studies are needed to confirm this hypothesis.Relative water content of leavesThe relative water content (RWC) of leaves was significantly affected by drought stress, genotype and their interaction (Table 2). The highest amount of RWC (87.43%) was observed in H genotype in no drought stress condition. The lowest RWC (19.60%) was observed in S genotype in 30–40% of field capacity (Fig. 4). The results of comparing average data showed that in highest level of drought stress RWC in H, S and M genotypes is 57.25%, 65.19% and 58.88%, respectively; which compared to the control group, it is decreased in all genotypes. This suggests higher resistance of H genotype to maintain RWC of leaves (Fig. 4). In all evaluated genotypes, with increasing drought, RWC was decreased.Figure 4The effect of drought stress on RWC of leaves in different lavender genotypes.Full size imageRWC is a suitable indicator for water stress in plants. Drought stress by reducing RWC and total water potential of cell, result in reduction in growth of plants. The osmoregulation mechanisms in drought-resistant plants, maintains high RWC in them. Reduction in RWC of leaves as a result of water deficiency stress, is due to reduction in amount of water in tissue, reduction in amount of water in soil, and the negative soil water potential58. Alinejad et al. (2020), reported that RWC of leaves in Datura stramonium L. plant was decreased due to drought, in a way that the highest amount of RWC (80.22%) was seen in 55% of field capacity, compared to 35% and 15% of field capacity59. Also Mohammadi et al. (2018) suggested that RWC of leaves in Thymu vulgari L. was decreased to 18.41%, after being exposed to drought60.Total phenolic and flavonoids contents in leavesDrought, genotype and their interaction had a significant effect on total phenolic content of leaves (Table 2). The results suggest that in different levels of drought, total phenolic content was different in lavender genotypes. In the highest level of drought, total phenolic content in H, S, and M genotypes was respectively increased 18.64%, 28.57% and 98.07% in comparison with the control group. The highest difference in total phenolic content compared to control group was observed in M genotype (Fig. 5).Figure 5The effect of total phenolic content of leaves in different genotypes of lavender.Full size imageTotal flavonoids content of leaves was significantly (p ≤ 0.01) affected by drought and genotype (Table 2). The results of comparing averages showed that the highest amount of total flavonoids (1.12 mg quercetin per g of fresh weight) was in H genotype, and the lowest amount (0.95 mg quercetin per g of fresh weight) was in M genotype (Table 3). Moreover, our results showed that drought level from I2 to I4 caused an increase of 12.74%, 14.61% and 15.38% in total flavonoid content of leaves, respectively. Which indicates an increase in flavonoid amount with increasing drought level (Table 3). Table 3 Comparing simple effects of genotype and drought stress on traits of lavender plant.Full size tableTotal phenolic content is related to stress-resistance, indirectly by helping cell protection, and directly as an antioxidant61. Phenolic compounds due to their reductive properties, act as a free radical remover62. Our findings are similar to those of a study on growth of Mentha piperita in drought stress54.Total antioxidant activityTotal antioxidant activity was significantly affected by drought stress and genotype (Table 2). With increasing drought, antioxidant activity in H and S genotypes was increased. The results of comparing average data showed that compared to the control group, in drought levels of I2, I3 and I4, antioxidant activity in H genotype was increased by 98.43%, 98.36% and 118.78%, respectively; and in S genotype this amounts were increased by 89.85%, 111.78%, and 131.90% respectively (Table 5). In M genotype the antioxidant activity has reached its highest amount (49.38 mg/g) in I3 level of drought, and then with increasing drought stress the antioxidant activity was decreased, in a way that in highest drought level it had the lowest antioxidant activity (23.18 mg/g). M genotype was used as control (Fig. 6). Our results indicate that in highest drought level, antioxidant activity of S genotype was more than others. Figure 6The effect of drought stress on antioxidant activity in different lavender genotypes.Full size imageAntioxidant enzymesEnzymatic activity of antioxidant enzymes in lavender leaves was significantly affected by genotype and drought stress (Table 2). Our results showed that the highest activity of SOD (304.75 μmol min−1 mg−1 protein) was observed in interaction of H genotype and I4 drought level, and the lowest activity of SOD (144.52 μmol min−1 mg−1 protein) was observed in S genotype with no drought (Fig. 7). Moreover, our observations showed that in I2 and I3 drought levels, the highest amount of SOD enzymatic activity was related to M genotype (Fig. 7). In the highest drought level, enzymatic activity of SOD was increased in H and S genotypes, and it decreased in M genotype.Figure 7The effect of drought stress on enzymatic activity of SOD, POX and CAT in different lavender genotypes.Full size imageEnzymatic activity of peroxidase (POX) enzyme was increased in all three genotypes, with increasing drought. In all drought levels, H genotype had the highest amount of POX activity, compared to other genotypes. There was no significant difference in POX activity in S and H genotypes. The results showed that the highest amount of POX activity (274.48 μmol min−1 mg−1 protein) was observed in interaction of H genotype and 30–40% field capacity, and the lowest amount (117.66 μmol min−1 mg−1 protein) was observed in interaction of S genotype and no drought condition (control) (Fig. 7).Catalase (CAT) enzyme was affected by drought, genotype and their interaction (Table 2). The results of catalase enzyme activity assessment showed that with increasing drought, catalase activity is different in H, M and S genotypes. The most different reaction in production of CAT was related to H genotype, which with increasing drought stress up to I3 level, the enzyme activity was increased. But regarding M and S genotypes, with increasing drought level, CAT activity was increased in both genotypes. In this study the highest amount of CAT (460.51 μmol min−1 mg−1 protein) was observed in interaction of S genotype with 30–40% of field capacity; and the lowest amount (157.06 μmol min−1 mg−1 protein) was observed in interaction of H genotype with 90–100% of field capacity (Fig. 7).No significant effect was observed for APX enzyme in interaction of genotype and drought (Table 2). The results of comparing average data, suggest that the highest amount of APX activity (284.96 μmol min−1 mg−1 protein) was observed in H genotype (Table 3). Also the results showed that I2, I3 and I4 drought level resulted in an increase in APX enzyme activity by 32.38%, 49.16%, and 65.53% respectively. This indicates that APX enzymatic activity increases with increasing drought level (Table 3).Using physiological and biochemical mechanisms to reduce effects of stress shows that to overcome drought, oxidative stress and to eliminate ROS, plants will increase the amount of antioxidant content55. One of major mechanisms to cope with oxidative stress in plants, is activation of antioxidant enzymes61. Findings of the present study indicates that different lavender genotypes showed partial resistance against drought. In this research, increased activity of antioxidant enzymes in lavender genotypes under drought condition, was considered as an important drought-resistance factor. Among all antioxidant enzymes, SOD can have a good response against drought stress. In a way that H, S, and M genotypes of lavender in the highest level of drought stress (I4), showed an increased amount of SOD, by 57.42%, 35.85% and 60.69% compared to normal conditions (Fig. 7).In this study, the minimum enzymatic changes were related to the POX enzyme and the highest enzymatic changes were related to the CAT enzyme. Moreover, it was observed that the highest amount of catalase enzymatic activity was in H genotype. In a way that in plants under drought stress CAT activity was increased up to I3 drought level; but, after this level with increasing drought (I4 drought level) CAT enzymatic activity was decreased. CAT and POX are among important plants enzymes which can protect plant cells against free radicals63. In this study, in drought period, enzymatic activity of CAT and POX was increased, this means that lavender genotypes, in the face of stress produce antioxidant enzymes to protect themselves. While in H genotype compared to other genotypes, in high drought stress, CAT activity was decreased which this response indicates the different function of this genotype in dealing with ROS. Enzymatic response to drought condition was different in various lavender genotypes. Generally, the negative effect of drought is shown by production of reactive oxygen species (ROS). Increased enzymatic activity of antioxidant enzymes, particularly CAT and POX can reduce the negative effects of drought64, 65. In this regard, increased activity of antioxidant enzymes in different genotypes of Calendula officinalis plant was reported to56.Malondialdehyde (MDA) contentReaction of different lavender genotypes under drought stress was different in terms of malondialdehyde (MDA) production and accumulation (Table 2). With increasing drought, MDA content was significantly increased in M and H genotypes. The highest amount of MDA in these genotypes was 14.34 and 9.50 nmolg − 1 FM respectively, which was observed in drought level of 30–40% of field capacity. This indicates a significant increase in MDA content with increasing drought (Fig. 8). While the process of production and accumulation of MDA in S genotype was different at various drought levels. For S genotype, in first level of drought (I2), MDA content was increased which showed the vulnerability of the cell membrane at this drought level. But with increasing drought, gradually, the S genotype plants adapted to the dry environment, which in this level cell membrane damage was not obvious. Then, increasing in drought stress resulted in increased MDA content. Generally, in I2 and I3 drought levels, lavender genotypes underwent varying degrees of damage, which in M and H genotypes followed by increasing enzymatic activity, and in S genotype it resulted in decreased enzymatic activity. But in the highest level of drought (I4), the cell membrane was seriously damaged and in all three genotypes and MDA content was significantly increased (Fig. 8).Figure 8The effect of drought stress on MDA content in different lavender genotypes.Full size imageMembrane lipid peroxidation due to the accumulation of active oxygen species leads to cell damage and death. In plants this lipid peroxidation happens under drought stress66. MDA is the final product of membrane peroxidation and membrane processes. Simultaneously with peroxidation, the MDA content increases significantly67. So the MDA content can be considered as an indicator of drought-resistance in plants. Among lavender genotypes, in the highest level of drought, MDA content in M genotype was significantly increased compared to others genotypes; whish suggests that M genotype is more vulnerable in comparison with the two other genotypes. An increase in MDA content under drought stress, was reported in Thymus species as well66.Quantity and quality of essential oilMutual interaction between drought stress and genotype had a significant effect on percentage and yield of essential oil in lavender plants (Table 2). Our findings suggested a different essential oil percentage for each genotype in various levels of drought stress. With increasing drought to I3 level, the essential oil percentage was increased in M and H genotypes, but after that with increasing drought to a higher level (I4), essential oil percentage in these genotypes was decreased. While in S genotype, increasing essential oil percentage totally had an upward trend (Fig. 9).Figure 9The effect of drought stress on essential oil percent in different lavender genotypes.Full size imageEvaluation of essential oil percentage in different levels of drought, showed that in I2 drought level, the highest amount of essential oil (0.81%) was observed in H genotype; and in I3 and I4 drought levels, the highest amounts of essential oil were 1.29% and 1.68% respectively, which were observed in S genotype. Moreover, our results suggest that the highest difference in essential oil percentage in the studied genotypes compared to the control, was related to S genotype (Fig. 9). Totally, the highest percentage of essential oil was observed in S genotype in I4 drought level. This shows the high capacity of this genotype to produce essential oil under drought stress.Essential oil yield was significantly affected by genotype and drought. The results showed that the essential oil yield in S genotype was different from the others. So that the highest yield of essential oil (0.055 g per plant) was observed in this genotype in I3 drought level. While in H and M genotypes the highest amounts were 0.068 g and 0.065 g respectively, which were gained in I2 drought level (Fig. 10). Results of comparing average data showed that the highest yield of essential oil at I2 and I3 levels was obtained with 151/85% and 122.22% difference compared to the control, respectively, and they gained from H genotype. This indicates the high potential of H genotype to maintain biomass and produce essential oil in drought stress. Also our results suggest that in the highest drought level (I4), the highest essential oil yield (0.046 g per plant) was observed in M genotype (Fig. 10).Figure 10The effect of drought stress on essential oil yield in different lavender genotypes.Full size imagePrincipal component analysis (PCA)PCA analysis was performed to identify susceptibility of genotypes in irrigation regimes. According to physiological traits in the PCA analysis (Fig. 11a, b), the first factor (PC1) explains about 90% of the total variance of variables, and the second factor (PC2) about 8%.Figure 11Principal component analysis (PCA) for genotypes (a) and physiological traits (b) based on water status calculated for physiological traits. (R 4.0.4 packages, https://rstudio.com/products/rstudio/).Full size imageThe results of PCA analysis of different irrigation regimes showed that in the first component, which shows 89.91% of changes, the best traits are antioxidant enzymes CAT, SOD, APX, while in the second component, with 8.10% changes, only the trait Catalase is the best trait. Also, in total, the first and second components, which show 98.01% of the changes, show CAT as the most effective trait (Fig. 11a).The results of PCA analysis in lavender genotypes showed that the first and second main components could explain 98.91% of the existing changes. So that the first main component with 91.13% and the second component with 7.78% had a share in the total variation. Therefore, using these two components and ignoring other components will only cause the loss of a small part of about 1.09% of the data changes (Fig. 11b). These two principal components include peroxidase, ascorbate peroxidase, and superoxide. Physiological responses of Lavandula genotypes (L. angustifolia cv. Hidcote, L. angustifolia cv. Munstead, and L. stricta) submitted to drought stress were evaluated through principal component analysis (PCA), and the results are illustrated in Fig. 11a. Lavandula stricta presents higher levels of CAT activity than L. angustifolia cv. Hidcote and L. angustifolia cv. Munstead. In addition, APX and CAT increase in stress-treated in 30–40% FC. This result shows that L. stricta exhibits the most affected physiological changes while trying to adjust to changes in the water status of the environment, under the imposed conditions and shows the highest resistance.The results of analysis of essentials oils from H, S and M genotypes is shown is Tables 4, 5 and 6. The trend of changes in essential oils composition is described in all three genotypes. By studying the mass spectra and the Kovats retention index, 23 compounds were identified in the H genotype’s essential oil (Table 4). The yield of H genotype essential oil from I1 to I4 drought levels was 99.89%, 82.78%, 81.09% and 82.85%, respectively. The main components of H genotype essential oil in I1 to I4 drought levels, include 1.8-Cineol compounds (5.94%, 7.73%, 4.24% and 3.50%), Linalool (23.20%, 16.30%, 11.90% and 10.57%), Camphor (3.41%, 4.65%, 2.32% and 2.87%), Borneol (4.89%, 3.34%, 3.65% and 3.01%), Bornyl formate (27.32%, 16.04%, 19.45% and 20.03%), Lavandulyl acetate (1.40%, 4.21%, 6 and 8.35%), Caryophyllene oxide (10.92%, 11.77%, 12.16% and 19.91%), α-Muurolene (4.38%, 3.20%, 1.20% and 0%) (Table 4). The results of grouping the essential oil compounds showed that the amount of hydrocarbon monoterpenes from I1 to I4 drought level were 12.88%, 8.86%, 8.53% and 6.06%, respectively. The amount of oxygen monoterpenes was 64.76%, 50.70%, 43.32% and 42.45%; and hydrocarbon sesquiterpene compounds were 13.12%, 11.45%, 13.03% and 13.96%. The amount of oxygen sesquiterpene compounds were 10.92%, 11.77%, 16.21%, and 19.91%; which shows that increasing drought level, result in decreasing monoterpene compounds, and increasing sesquiterpene compounds.Table 4 Chemical composition of essential oils extracted from Lavandula angustifolia cv. Hidcote plants under different irrigation regime.Full size tableTable 5 Chemical composition of essential oils extracted from Lavandula stricta plants under different irrigation regime.Full size tableTable 6 Chemical composition of essential oils extracted from Lavandula angustifolia cv. Munstead plants different irrigation regime.Full size tableHeat map for the essential oil profile in Lavandula angustifolia cv. Hidcote corresponding to the different irrigation regime The similar discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 22 rows and 4 columns were achieved. α- pinene, β-Pinene, δ-3-Carene, type of Cymene, 1,8-Cineol, Camphor and Linalool from the main compounds, peaked at control. Moreover, lavandulyl acetate, Myrtenyl acetate, caryophyllene oxide, camphene and γ-Cadinene revealed highest percentage at 30–40% FC, Some compounds, such as Camphor and Linalyl acetate, are at the levels of the intermediate irrigation regime (Fig. 12). It is remarkable that as the water limit increases, the amount of monoterpene compounds decreases and the amount of sesquiterpene compounds increases.Figure 12Heatmap for the essential oil profile in aerial parts of Lavandula angustifolia cv. Hidcote corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageWith evaluation of the essential oil from S genotype, 18 compounds were identified (Table 5). The amount of essential oil in I1 to I4 drought levels was 99.41%, 98.48%, 99.53% and 99.93% respectively (Table 5). Among identified compounds in S genotype the followings were accounted for the highest amount of components in the essential oil in I1 to I4 levels respectively; Linalool (32.60%, 28.45%, 20.12% and 19.12%), decanal (10.26%, 15.21%, 18.56% and 19.27%), 1-Decanol (8.01%, 10.31%, 17.88% and 21.34%), Kessane (2.44%, 4.43%, 9.99% and 11.50%), Hexadecane (1.26%, 5.77%, 6.10% and 11.9%), 2-methyl-1-hexadecanol (11.1%, 9.32%, 8.15% and 2.37%) and Hexahydrofarnesyl acetone (6.8%, 6.34%, 3.78% and 1.26%) (Table 5). The most obvious point was the high percentage of Linalool, decanal and 1-Decanol in the S genotype. With increasing drought, Linalool compounds were decreased and decanal and 1-Decanol compounds were increased. The grouping of essential oil components also showed that among the 18 compounds identified, the following were the highest in I1 to I4 drought levels, respectively; 3 hydrocarbon monoterpenes with total of (5.34%, 5.44%, 4.57% and 4.34%), 6 oxygen monoterpenes with total of (60.49%, 61.03%, 59.57% and 60.45%), 3 hydrogen sesquiterpenes with total of (5.69%, 10.27%, 11.85% and 15.24%) and 6 oxygen sesquiterpenes with total of (27.89%, 28.09%, 29.44% and 18.32%). With increasing drought, the amounts of hydrocarbon monoterpenes and oxygen sesquiterpenes were decreased; while the amount of hydrocarbon sesquiterpenes was increased. Also the highest amount of oxygen monoterpenes, by 61.03%, was seen in I2 drought level.Heat map for the essential oil profile in Lavandula stricta corresponding to the different irrigation regime The parallel discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 18 rows and 4 columns were achieved. α- pinene, Amyl isovalerate, Citronellol, β-Ionone and Linalool from the main compounds, peaked at control. Moreover, α-Thujene, decanal, 1-Decanol, Sesquiphellandrene, Kessane and Hexadecane revealed highest percentage at 30–40% FC (Fig. 13). These results confirm the results obtained from the Lavandula angustifolia cv. Hidcote so that as the water limit increases, the amount of monoterpene compounds decreases and the amount of Sesquiterpene compounds increases.Figure 13Heatmap for the essential oil profile in aerial parts of Lavandula stricta corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageEssential oil yield in M genotype from I1 to I4 drought levels was obtained 99.90%, 98.38%, 93.08% and 87.04% (Table 6). As it is shown in Table 6, analysis of the essential oil from M genotype included 27 compounds which its major part was consisted of Camphor (16.82%, 16.32%, 17.11% and 18.30%), Borneol (44.96%, 42.80%, 37.54% and 30.99%) and Caryophyllene oxide (14.68%, 15.21%, 15.90% and 17.21%) from I1 to I4 drought levels, respectively. comparison of essential oil components (Table 6) showed that from 27 identified compounds in M genotype, the followings were the most prevalent from I1 to I4 levels respectively, including hydrocarbon monoterpene with total of (17.82%, 17.45%, 13.91% and 9.96%), 12 total oxygen monoterpene compounds with total of (65.95%, 62.05%, 56.96% and 50.42%), 4 hydrocarbon sesquiterpenes with total of (1.58%, 23.23%, 5.42% and 8.09%) and 2 oxygen sesquiterpenes with total of (14.91%, 15.65%, 16.79% and 18.37%). The highest drought level resulted in 31.76% and 17.23% increase in Camphor and Caryophyllene oxide. It also caused 31.07% decrease in Borneol compared to the control (Table 6). Totally, with increasing drought level, monoterpene compounds were decreased and sesquiterpene compounds were increased in lavender genotypes.The major components of essential oil were different in various lavender genotypes in the highest level of drought (I4). In this study in H genotype, the compounds Linalool, Bornyl formate and Caryophyllene oxide; in S genotype the compounds Linalool, decanal, 1-Decanol, Kessane and Hexadecane; and in M genotype the compounds Camphor, Borneol and Caryophyllene oxide, were the most prevalent components of essential oil. In this study, Borneol compound was not observed in S genotype. regarding the fact that essential oil extraction was performed on flowering branches in all three genotypes, and they were studied under similar drought conditions; and also comparing the results of this study with finding of other studies shows that the difference in types and percentage of essential oil’s components can be due to the effect of genetic differences; and to some extent, environmental factors on essential oil in different genotypes.A total comparison of essential oil analysis results for different lavender genotypes under drought stress showed that oxygen monoterpenes are the most prevalent components of the essential oil, which will decrease with increasing drought level. Sarker et al. (2012) reported that the essential oil of lavender (Lavandula angustifolia) contains high amounts of linalool and linalool acetate, along with scares amount of other monoterpenes68. A study by Hassan et al. (2014) showed that the compounds carvacrol, phenol-2-amino-4, 6-bis, trans-2-caren-4-ol, and n-hexadecanoic acid are the main constituents of Lavandula stricta plants which were collected from the Shaza Mountains in southern Saudi Arabia69. Total results from essential oil analysis in this study showed that Linalool was the main ingredient of essential oils in H and S genotypes. This compound is an oxygen monoterpene with a density of 0.85 and a pleasant smell, and is the main component of the essential oil from lavender plant. While in M genotype, Borneol was the main component of the essential oil, which is a circular monoterpene compound with density of Mohammadnejad ganji et al. (2017) suggested that the difference in natural quality of the essential oil from lavender plants is related to intrinsic factors (genetic or heredity capabilities and maturity), and external factors including sunlight, water, heat, pressure, latitude, and soil which affect plant growth and essential oil production70.Heat map for the essential oil profile in Lavandula angustifolia cv. Munstead corresponding to the different irrigation regime The parallel discrimination was also supported by the heatmap constructed for essential compounds. Accordingly, 18 rows and 4 columns were achieved. α- pinene, Tricycle, Camphene, Thuja-2,4(10)-diene, δ-3-Carene, ρ-Cymene, Borneol and limonene from the main compounds, peaked at control. Moreover, Camphor, α-Santalene, γ-Cadinene, δ-Cadinene, Caryophyllene oxide, α-Muurolene and Ledene oxide-(II) revealed highest percentage at 30–40% FC (Fig. 14). The results showed that the composition of the compounds was similar to the previous two genotypes and the water limit increases, the amount of monoterpene compounds decreases and the amount of Sesquiterpene compounds increases.Figure 14Heatmap for the essential oil profile in aerial parts of Lavandula angustifolia cv. Munstead corresponding to irrigation regimes (CIMminer, https://discover.nci.nih.gov/cimminer/oneMatrix.do).Full size imageEssential oils are generally in the group of terpenoids and The structure of terpenoids consists of two main precursors, isopentenyl pyrophosphate (IPP) and its isomer, dimethylallyl pyrophosphate (DMAPP). These compounds are synthesized via the cytosolic pathway of mevalonic acid (MVA) or plasticity of methylerythritol phosphate (MEP)71. The MVA pathway is primarily responsible for the synthesis of Sesquiterpenoids and triterpenoids, while the MEP pathway is used for the biosynthesis of monoterpenoids, diterpenoids and tetraterpenoids72. Monoterpenes and Sesquiterpenes are the main constituents of essential oils that play a role in aroma, flavor, photosynthetic pigments and antioxidant activities73.In drought conditions, the amount of these isoprenes does not decrease in relation to the mediators of the MEP pathway and in contrast sometimes increases. Therefore, sesquiterpene compounds increase in drought conditions because most of these compounds are synthesized through the MVA pathway74. Another reason for the decrease in MEP path flux is the location of this path, which has a significant impact in drought conditions. In this case, plastids are not able to provide the required IPP of this path, so most monoterpene compounds are reduced75.Also, since the quality of the essential oil is due to the presence of linalool and linalyl acetate76. According to the results obtained from heatmaps related to essential oils, three genotypes are identified, the highest amount of linalool amount in S genotype was remained under mind- (I2) till severe-drought (I4) condition. This indicates more compatibility with maintaining the desired quality of drought conditions in this plant than the other two commercial genotypes. And then the H genotype is in the second stage due to the presence of important compounds.Comparing the grouping created in the heat maps related to the essential oil of 3 genotypes, it is clear that the two genotypes S and H were divided into two groups I1, I2 and I3, I4 in the genotype. But in the genotype M, the results were divided into I4 and I3 groups I2 were divided into genotypes. This can be due to differences in the resistance mechanism of plants in different genotypes, so in genotypes S and H of the plant through increasing sesquiterpene compounds showed resistance to drought stress, while in genotype M increased resistance to drought levels through higher monoterpene compounds. Another conclusion that can be drawn from these heat maps is that in genotypes S and H, the rate of drought resistance in the first and second levels of drought with the third and fourth levels has shown more changes in the type of essential oil compounds, while in the third genotype (M) these changes in the last level drought has been most evident.At a glance, it seems Genotype S has a different mechanism in reducing the negative effects of drought compared to genotypes M and H, So that, among the enzymatic and non-enzymatic mechanisms, it tends to use the enzymatic pathway more. In association with the production of “proline”, drought stress index osmolyte, genotype S has a different trend from genotypes H and M and this osmolyte in this plant has a lower production flux compared to other genotypes. Also, due to the fact that the production of soluble sugars in this plant has been moderate compared to other genotypes, it is expected this genotype replace proline with another osmolyte or uses an enzymatic mechanism to deal with drought, as the results of antioxidant enzyme “catalase” related to genotype S had the highest value with a significant difference under drought stress, while, in the H and M genotypes, the SOD enzyme was responsive to drought.On the other hand, the high resistance of genotype S can be attributed to the greater activation of the pathway of essential oil compounds. Because by examining the constituents of the essential oil (monoterpene and sesquiterpene), it can be concluded that genotype H and then M at high drought levels still retain the ability to produce monoterpene compounds, while in genotype S with increasing drought, the amount of semi-heavy compounds (sesquiterpene) has increased significantly (Fig. 15), this can confirm the existence of a different resistance mechanism in the S genotype. Because some structural compounds of the membrane, such as sterols, are made from the mevalonic acid (MVA) pathway of acetyl coenzyme A origin. For this reason it seems that S genotype by setting up terpenoid pathways involved in the production of steroids another solution to drought is by preserving its plasma membrane. Steroids are derivatives of triterpenes that, along with phospholipids, are major components of plasma membranes70. Also, the study of MDA content as the final product of membrane lipid peroxidation in genotypes at the fourth level of drought (the most severe drought) showed the M genotype is most sensitive to drought. In this way, the two genotypes S and H have almost equal MDA content, so that it can be said that with a small difference from genotype S, genotype H has less composition.Figure 15The amount of monoterpenes and sesquiterpene compounds in different genotypes under irrigation regimes.Full size imageContinuous production of isoprene under drought conditions shows that despite the reduction in the synthesis of osmolyte and relative increasing of MDA (with very little difference from genotype H) that occurs under these conditions, the function of this pathway is essential for the S genotype. Isoprene has long been used to protect plants from drought, high temperatures and oxidative stress are recommended77. Of course, it was showed which is possible with increasing drought, sufficient isoprene is not produced to counteract and launch defense pathways and instead used as a general signal to increase drought tolerance78,79.Reasons such as further activation of terpenoid skeletal pathways towards the production of semi-heavy (sesquiterpene) compounds, production of steroids via the MVA pathway could be a reason for lower susceptibility of S genotype and high resistance of this genotype through these mechanisms compared to other genotypes. In contrast, on the one hand, H genotype using proline production, soluble sugar levels and decreased MDA in response to stress caused by drought and on the other hand, the ability to produce substances important monoterpenes, such as Linalool and Linalyl acetate, with the aim of using medicine and aromatherapy76, It (H genotype) can be considered as a cultivar with high commercial value and significant resistance to M genotype. More

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Morphology and light-emitting behavior of the undescribed Japanese Terebellidae wormIn 2016, some of the present authors were exploring shallow coastal waters (depth less than 1 m) to observe the ecological behaviors of marine animals in the Noto Peninsula, when they discovered unknown violet-light-emitting worms. At the sampling point, the worms were living in small holes (a few centimeters in diameter) or in cracks in rocks covered by sand at the shallow sea bottom (Supplementary Fig. S1). We successfully video-recorded their emission of violet light from the whole tentacle stretching into sea water when stimulated by air bubbling at night (Fig. 1A–C; Supplementary Videos S1 and S2). The violet-light emission consisted of rapid flashes with variable duration in the order of milliseconds (Supplementary Video S3), as observed for the worm P. perplexus in response to stimulation17. From our morphological observation, we identified the violet-light-emitting worm as a member of Polycirrus on the basis of the following characteristics18: (1) a sheetlike prostomium covering the upper lip; (2) avicular unicini on some neuropodia; (3) no branchiae. The specimens also have the following characteristics: (1) neurochaetae beginning on last notochaetigerous segment, chaetiger 14; (2) uncini with a long neck and concave base; (3) notopodial pre- and post-chaetal lobes both similar shape. These characters are also found in Polycirrus disjunctus Hutchings and Glasby18; however some of the characters in parapodial lobes and chaetae have differentiation. Thus, we concluded that this species should be treated as an undescribed species. Further comparative observation is needed to describe the species. At this time we treated the Polycirrus species observed in this study as Polycirrus sp. ISK. Application of an electric pulse also caused clear light emission from the tentacles of the living worm (Fig. 1D; Supplementary Video S3), and the luminescence spectrum showed that its λmax was 444 nm or slightly longer, depending on the individual (Fig. 2A). We also found that light emission was efficiently induced by the addition of KCl solution and observed the time course of light emission with rapid fluctuations with variable duration in the order of milliseconds for up to 30 s (Fig. 2B). The flash pattern was similar to that observed in a study of P. perplexus17. In the genus Polycirrus, P. medius and P. nervosus in Japan have been described18,19. However, the morphological features of the species in the present study differed from these species on the basis of our observations described above.Figure 1Photographs of Polycirrus sp. ISK. (A) Polycirrus sp. ISK in its natural habitat with bright-field illumination. (B) Bioluminescence of Polycirrus sp. ISK in its natural habitat without bright-field illumination. The worms were stimulated by air bubbling from SCUBA gear. (C) A single worm with stretched tentacles. Tentacles are indicated by white arrows. (D) The worm with light emission at the tentacles. This worm was stimulated by an electric shock. Scale bars = 100 mm for A and B, 10 mm for C and D. Each photograph was extracted from the videos recorded with the following settings: sensitivity, ISO 51200 or 11 lx; white balance, 4300 K or 5800 K; shutter speed, 1/30 s or 1/60 s; iris, F1.8-3.5; frame rate, 29.97 fps or 60 i; frame size, 1920 × 1080 pixels. Original high quality videos are available at https://youtu.be/KEsU0kWAEfg and https://youtu.be/24dxvPlBDB0Full size imageFigure 2Luminescence spectra and KCl-induced light emission of Polycirrus sp. ISK. (A) Spectrum analysis of Polycirrus sp. ISK using a living worm stimulated by an electric shock. The luminescence spectra were obtained from two different individuals. The λmax represented in closed circles and open circles were 444 nm and 446 nm, respectively. (B) Typical light-emission signal of a living worm soaked in 667 mM KCl. The black line indicates luminescence intensity after adding KCl solution, and the gray line indicates luminescence intensity before adding KCl solution.Full size imageJapanese Polycirrus spp. have not been described as luminous worms according to our review of the literature and web pages. In addition, the number of reports for new Polycirrus spp. from all over the world has been increasing, but a limited number of species are known to emit light13,17,18. Our finding of KCl-induced light emission from Polycirrus sp. ISK suggested that we can easily test the light-emitting ability of Polycirrus spp. by luminescence measurement just after adding KCl solution. A spectrum pattern has been reported for only one species, P. perplexus collected in California17, and it would be necessary for further understanding of these species to examine the light-emitting abilities and to compare light-emitting behaviors and spectrum patterns. The color of bioluminescence is often related to habitat, and light in the blue range is typical for pelagic species20. Thus, one of the points to be focused on is the ecological function of the violet-light emission of this worm inhabiting in a shallow coastal water environment. In P. perplexus, deterring predation is a possible function of luminescence based on that species’ habitat and its violet-light emission17,21. As shown in Supplementary Videos S1, S2, which are the first video records of in situ light emission of a Polycirrus species, the air bubble-stimulated luminescence of Polycirrus sp. ISK in its natural habitat also seemed to deter predation, but this explanation is still speculative.Differentially expressing genes between the tentacles and the rest of bodyA few years after discovering this worm, we found it difficult to collect enough of them to conduct common biochemical and chemical analyses because we did not find a place densely inhabited by hundreds of the worms whose wet weight was a few tens of milligrams (e.g. 16.5, 29.8, or 31.8 mg). Next, we conducted RNA-Seq analysis. In luminous animals with strong light emission, such as firefly or syllid polychaetes (Syllidae), luciferase expression is high especially at the luminous organ or in the whole body22,23. On the other hand, the light emission of Polycirrus sp. ISK was not so strong compared to that of fireflies, and the light-emitting area was limited to the tentacles. In addition, the genetic information related to the tentacles responsible for various ecological functions is still limited. Thus, in the present study we decided to purify RNA from the tentacles and the rest of body separately (Fig. 1C) and performed RNA-Seq analysis followed by a computational analysis using the MASER pipeline24. By de novo assembly, 110,775 contigs were predicted; 26.1% of them showed more than twice the expression level in the tentacles than in the rest of body, whereas 20.8% showed more than twice the expression in the rest of body than in the tentacles. When we performed a blastX search to the NCBI nr database for the contigs longer than 300 bp, 35.6% showed significant homology with registered genes with e-values of less than 1e−10. The average length for these contigs was 1384 bp, and half of them were in the range of 463–1863 bp (Supplementary Fig. S2). In the assembled sequence, we found the cytochrome oxidase subunit I (COI) gene and tried to construct a phylogenetic tree. However, the obtained phylogenetic tree was unreliable due to the low bootstrap values as shown in Supplementary Fig. S3.To focus on the tissue-specific genes, we first picked up genes with high expression levels based on high fpkm values (over 1000) and then ranked these genes based on the tissue-specificity judged by the comparison of fpkm values in tentacles and the rest of body. In tentacle-specific genes, we found that some genes coding for lectin(-like) domains were ranked in the top eight as shown in Supplementary Table S1. Of the top eight genes in the rest of body-specific genes (Supplementary Table S2), seven exhibited no similarity to any genes, and the remaining gene exhibited significant similarity to a hypothetical protein of Capitella teleta, which is a Polychaetes species with whole-genome information available25. Recently, TPM is preferably used to normalize expression level, and the value is used for statistical differential expression analysis26, and we also calculated TPM for tissue-specific genes (Supplementary Table S3).As we were unable to conduct statistical differential expression analysis due to no biological/technical replication resulted from difficulties in the sample collection, we simply compared TPM value between the tentacle and the rest of body samples. The ratio of TPM (tentacle/rest of body) was calculated, and then top 100 genes (Fig. 3A), which were highly expressing in the tentacle, were selected. Similarly, top 100 genes highly expressing in the rest of body were selected using the ratio of TPM (rest of body/tentacle) (Fig. 3B). These gene lists were annotated by gene ontology (GO) terms and analyzed using WEGO program27. WEGO results showed different expression patterns for the tentacle and the rest of body. In the tentacle, GO terms including cell adhesion, biological adhesion, small molecular binding, positive regulation of biological process, regulation of response to stimulus, carbohydrate binding, and immune response were significantly higher (Fig. 3C, D). In the rest of body, GO terms including hydrolase activity, catalytic activity, localization, and establishment of localization were significantly higher. In the top 100 genes highly expressing in the tentacle, we found 21 genes annotated as a gene coding for fucolectin by blast search (Supplementary Table S4). Fucolectin is a fucose-binding lectin involved in the innate immunity of diverse invertebrate species28. However, its function in invertebrates remains unclear, and no information is available for Terebellidae, including sequence information. Fucolectin was first identified in eel with mRNA distribution mainly in liver and gill28. In sea cucumber, expression of the fucolectin gene is confirmed in respiratory trees, muscle, and tentacle29. We were not able to see whether this gene was expressed in the respiratory organ of Polycirrus sp. ISK because a characteristic of the genus Polycirrus is the absence of branchiae18. Nevertheless, the tentacle-specific expression of fucolectin was consistent with the observation in sea cucumber, and the high expression of such proteins involved in innate immunity seemed reasonable because tentacles stretching out of their bodies can be damaged by attack of predators and thus are threatened by infectious bacteria and other pathogens11, as is the respiratory organ. In addition, localization of antimicrobial compounds in Terebellidae worms is suggested to be of antiseptic importance in damage by predation14. This study would provide indispensable information about the ecological meaning of Polycirrus sp. ISK’s life in future genetic studies.Figure 3WEGO analysis of highly expressing genes in the tentacle and the rest of body. (A) Box plot graph for the distribution of TPM value for top 100 genes highly expressing in the tentacle. Corresponding genes in each part are colored in the same gradation color according to the TPM value (red to blue form higher to lower value). (B) Box plot graph for the distribution of TPM value for top 100 genes highly expressing in the rest of body. Each gene is colored as in (A). (C) WEGO analysis of top 100 genes highly expressing in the tentacle (orange bar) and the rest of the body (blue bar). (D) P-values from Chi-square tests obtained by WEGO analysis. CC cellular component, MF molecular function, BP biological process.Full size imageTranscripts coding for luciferase-like genes in the wormTo find genes similar to the known luciferase, which is an enzyme oxidizing a specific compound called luciferin to emit light, from related species in polychaetes, we performed a blastX analysis against the Odontosyllis luciferase sequence using our RNA-Seq data. We found a gene coding for a protein that exhibited similarity to Odontosyllis luciferase, but the e-value was more than 1e−10 (Supplementary Fig. S4). In addition, the top hit for this gene analyzed by blastX was annotated to code an uncharacterized protein of Saccoglossus kowalevskii (Hemichordata), and its specific function was not predicted. Other hits were for genes from Chordata, Mollusca, and other phyla but there was no hit from Annelida. This result would suggest that the light-emission system of Polycirrus sp. ISK differs from that of the genus Odontosyllis, although further experiments using high purity Odontosyllis luciferase and the substrate will be necessary to confirm this. In further blastX analyses of representative luciferases, photoproteins, and a putative luciferase [luciferases from the ostracod Cypridina noctiluca (Accession number: BAD08210.1), the copepod Gaussia princeps (AAG54095.1), the deep-sea shrimp Oplophorus gracilirostris (BAB13775.1 and BAB13776.1), the firefly Photinus pyralis (AAA29795.1), the sea pansy Renilla reniformis (AAA29804.1); photoproteins from the hydrozoan jellyfish Aequorea victoria (AAA27720.1), the hydroid Clytia gregaria (CAA49754.1), the hydroid Obelia geniculate (AAL86372.1); a putative luciferase from the tunicate Pyrosoma atlanticum30 sequences using our RNA-Seq data], we found some tissue-nonspecific genes whose sequences exhibited similarity to firefly luciferase (FLuc) or Renilla luciferase-like protein (RLuc-like) sequences with an e-values of less than 1e−10 and percent identity of more than 50%. FLuc is a member of the acyl-adenylate-forming superfamily of enzymes responsible for firefly luciferin-dependent bioluminescence, which is found in terrestrial luminous beetles emitting light ranging from green to red31. Previously, a putative acyl-CoA synthetase protein was found in the luminous organ of firefly squid emitting blue light32, but there is no clear biochemical evidence that such protein is responsible for firefly squid’s bioluminescence. On the other hand, RLuc is responsible for coelenterazine-dependent bioluminescence, which is found in marine luminous organisms belonging to various taxa. An RLuc-like protein is found to be localized in luminous organs of the brittle star Amphiura filiformis, as revealed by taking advantage of the cross reactivity of anti-RLuc antibody to A. filiformis RLuc-like protein33. A recent study reported that recombinant RLuc-like protein found in P. atlanticum exhibited luciferase activity to coelenterazine30. However, an RLuc-like protein from sea urchin Strongylocentroutus purpuratus is confirmed to exhibit dehalogenase activity to various substrates but no luciferase activity to coelenterazine34. Therefore, it is suspected that Polycirrus sp. ISK possesses a luminescence system using an RLuc-like enzyme.Coelenterazine content in the wormTo investigate whether Polycirrus sp. ISK possesses not only a Renilla luciferase homologous gene but also coelenterazine, we analyzed an ethanolic extract of Polycirrus sp. ISK by UPLC with a UV–visible detector (Fig. 4). The obtained UPLC chromatogram did not show a peak corresponding to that of authentic coelenterazine. When further checking the chromatogram, we found the peak at a retention time similar to those of authentic coelenteramide and coelenteramine, which can be formed from coelenterazine. However, the absorption spectrum obtained by UPLC analysis and the mass spectrum obtained by MS/MS analysis were not identical to those of authentic coelenteramide or coelenteramine (Fig. 4 and Supplementary Figs. S5 and S6). In addition, when the worm extract was mixed with a recombinant RLuc, we did not detect luminescence using a luminometer. These results suggested that the luminescence system in the worm was independent of coelenterazine, although a RLuc homologous gene was found. Similarly, the existence of an RLuc homologous gene was reported in P. atlanticum, which has been suggested to use a coelenterazine-independent luminescence system relying on bacterial bioluminescent symbionts30,35. We also mixed the worm extract with a recombinant cypridinid luciferase, but we did not detect luminescence using a luminometer. This result was consistent with Harvey’s observation for P. caliendrum16. To examine whether the luminescence system is based on luciferin–luciferase reaction, which is found in various luminous animals including some syllid Odontosyllis spp.23,36,37,38,39, we prepared two different extracts of the worm using 100 mM HEPES–NaOH buffer (pH 7.4) and methanol, and subsequently subjected a mixture of the two to luminescent measurement. As a result, no light emission was detected from the mixture of the buffer and methanolic extracts of the worm. This result was also consistent with Harvey’s observation for P. caliendrum16. However, there is still a possibility that the light emission is based on luciferin–luciferase reaction, because luciferin–luciferase reaction found in fireflies or luminous mushrooms requires a cofactor such as ATP or NADPH, and we did not test all possible conditions due to the limitation of the number of collected specimens. In addition, extraction of luciferin and luciferase in the active form is sometimes difficult, as shown in previous studies37. Further studies using hundreds or more of the specimens must be performed to elucidate the mechanism underlying the violet-light emission.Figure 4Comparison of the ethanolic extract of Polycirrus sp. ISK with CTZ, CTMD, and CTM. (A) UPLC analysis of (a) the extract, (b) authentic CTZ, (c) authentic CTMD, and (d) authentic CTM using a multiwavelength detector. The black solid line indicates detection at 333 nm, and the blue solid line indicates detection at 435 nm. The compound between the red vertical dashed lines was collected for MS/MS analysis. (B) Absorption spectra of the compound from the extract, CTZ, CTMD, and CTM obtained at retention times of (a) 9.65, (b) 10.89, (c) 9.47, and (d) 9.27 shown in (A). CTZ coelenterazine, CTMD coelenteramide, CTM coelenteramine. These chemical structures are shown in Supplementary Fig. S5.Full size image More

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Water useMany potato physiological features (photosynthesis intensity, leaf water potential) morphological and agronomic features as the Soil Plant Analysis Development (SPAD) and dry matter content can be used as indicators of potato water stress. In this result water consumption and the average daily amount of water used for irrigation differed over the growing season, but differences also occurred between varieties and the humidity level (Table 1). When irrigating the Julinka variety at all stages of the growing season, regardless of the established pF values, water consumption per pot was higher. The average dose of water supplied per pot was 9.7%, 30.7% and 26.6% greater than for the Denar variety, at humidity levels 1, 2 and 3, respectively. The highest water consumption was observed during the potato growth period from BBCH 40/400 to 69/609 and ranged from 0.39 l/pot /day (level 1) to 0.99 l/ pot/day (level 3).Table 1 Water consumption per pot within potato growing stages (in liters) and average consumption of water per pot (in brackets).Full size tableThe highest water consumption in both potato varieties occurred in July (11–18 July). Analyzing the remaining two months of the irrigation period, it can be seen that in June the plants used less water than in July. Seasonal irrigation doses in mid-early potato of studies of Rolbiecki et al. (2015)9 ranged from 40 to 170 mm, and the highest daily values of field water consumption (over 3 mm) occurred in July, similar to the results in this research.Depending on the irrigation system, water consumption efficiency in potato varies from 5.4 to 12 kg m−316,24. Drip irrigation is one of the most effective methods and ranged from 6.3 to 8.6 kg m−3 (Sharma 2007)25. Different values for average WUE index’ in potato cultivation were obtained by Ati et al. (2012)26, and indicated value ranged from 5.9 to 12.2 kg m−3. In present research, average WUE index’ for the Denar variety was from 0.00 l day−1 in the 1st period to 0.79 l day−1 in the 5th harvest period, while for the Julinka it was from 0.49 to 0.92 l day−1, respectively.In the research by Zin El Abedin et al. (2019)27 the amount of water used for irrigating potato amounted to 1505 mm and 1062 mm for FI (full irrigation) and PRD (partial root zone drying) variants, respectively. The use of 50% of water consumption in the PRD reduced water productivity (WP), as compared to water stress in the form of excess FI and deficit irrigation (DI). A large amount of water in conditions of water deficit causes losses due to evaporation and leads to degradation of the soil environment. In turn, in this research the highest water consumption in both varieties was found at level 3, 39.60 l for the Denar variety and 50.15 l for the Julinka variety.Pszczółkowski et al. (2009)28 showed that early potato varieties water requirements in the period from May 1 to August 31 amounted to 336.4 mm, with greatest requirements in July (108—119.6 mm). In our research, the amount of water used depended on the assumed humidity level and amounted from 19.60 × 103 to 39.60 × 103 cm3 for the Denar variety and between 21.50×103 to 50.15 × 103 cm3 for the Julinka (Table 1).Total potato and tuber massThe total weight of plants aboveground—(stems with leaves) and underground (tubers, stolons and roots) was greater in water humidity level 1 than in humidity levels 2 and 3 (Table 2). Administration of increased amounts of water in the later stages of potato growth resulted in inhibition of biomass growth, mainly for the Julinka variety. At the 5th harvest time, at humidity level 3, the total weight and the weight of tubers were 59.2% and 54.7% lower than those obtained at level 1, respectively. At the same time, the difference for Denar was 11.9% and 18.8%, respectively. Begum et al. (2015, 2018)16,22 and Reyes-Cabrera et al. (2016)5 showed that the production of total and commercial tuber yield was strongly dependent on the total biomass production and its structure.Table 2 Potato total biomass and tuber increase depending on water humidity level (g per plant).Full size tableA three-factor analysis of variance showed that the total weight as well as the weight of potato tubers differed significantly by the humidity level and the variety. A significant effect was found for humidity level on the total weight and tuber weight for the Denar variety and tuber weight for the Julinka variety (Table 3).Table 3 Variance analysis for total biomass and tuber of potato depending on factors (significance verified by the Fisher test).Full size tableAnalysis of variance showed a significant impact of the variety on potato plant weight, while it did not show significant interaction of weight and weight of tubers between measurement dates. No significant effect was obtained for interaction between the factors studied (Table 3).Wang et al. (2009)29, concluded that the use of irrigation significantly contributed to an increase total and commercial tubers of medium-early Folva variety yield and its quality. Ossowski et al. (2013)30, shown that irrigation had a significant effect on medium-early potato varieties: Barycz, Mors, Triada tuber yield. When using drip irrigation, yield increased by 26%. In turn, Mazurczyk et al. (2007)31 showed that drip irrigation increased the tuber yield from 29.4–37.5 to 45.1–54.4 t·ha−1.Over the period from the 1st to the 5th harvest date, the total plant biomass increased from 3.5-fold (Julinka—level 3) to 7.2-fold (Julinka—level 1). On the first harvest, Denar did not produce tubers at levels 1 and 3, and for level 2 its weight was the lowest (6 g from a pot). The increase in tuber weight to the last harvest date was the highest for level 2: 23.9- and 22.9-fold, in Denar and Julinka varieties, respectively. At level 3, the growth dynamics of tubers was the lowest: 11.7 times for the Julinka and 9.1 times for the Denar variety (measured from the second harvest date). The highest total biomass increases and tuber weight was found between the 3rd, 4th and 5th dates when humidity was at levels 1 and 2, and between the 3rd and 4th dates at level 3.Kumari et al. (2011, 2018)1,2 concluded that drip irrigation significantly contributed to an increase in potato tuber yield 18% greater than with other irrigation methods. Xu et al. (2010)32 achieved higher yields using the same irrigation system (40–48 t ha−1), and potato tuber weight was reduced under the slight water stress. Potato reacts to stress when soil water tension exceeds 20 kPa24. In a study by Amer et al. (2016)33 potato tuber yield also decreased with the application of excessive irrigation, resulting in greater stress, increased vegetative growth and potential leaching of nutrients from the root zone.Changes also occur in the quality of potato tubers, such as the shape, skin smoothness and chemical composition34.In the research carried out by Zin El-Abedin et al. (2019)27 differences were found in potato tuber yield depending on the irrigation variant. At FI, the highest tuber yields of 31.77–35.91 Mg ha−1 were obtained. Water deficiency reduced tuber yield, in DI variants, by 53.24–65.15% as compared to the FI. Similar results were obtained by Kumari et al. (2011)1. In the present research, the tuber weight of the Denar variety in the fifth term in level 1, increased by 26% compared to the irrigation at level 2 and was a 24% increase for the Julinka variety under similar conditions. At humidity level 3 there was a decrease in total biomass by 12% and 59% (for Denar and Julinka, respectively) in comparison obtained at level 1. In the research Liu et al. (2006)35 the aboveground biomass reached the highest values in excess water conditions.Potato varieties react differently to the humidity of the soil. Mahmood et al. (2016)36 response of potato varieties diversity to soil water deficit, also Hassanapanah (2010)17 showed the reaction of potato varieties to stress conditions. In our study, a higher total and tubers weight was found for the Julinka variety than for the Denar variety.Regardless of the humidity level and variety, the trends in the biomass yield structure were similar (Fig. 3). A downward trend from the 1st to 5th harvest period was shown for roots and stolons. This varied from 5 to 18% at the beginning of the study to 2–5% by the 5th period. It should be noted that under level 3, especially for the Denar variety, the percentage of roots and stolons was at a constant, low level. The percentage of stems with leaves decreased from 68–90% at the first harvest time to 40–55% at 5th. The dynamics of the decline in the share of stems and leaves was highest at humidity level 3. The tuber percentage was from 0 to 20% for the 1st period to 40–60% for the 5th period.Figure 3Potato biomass structure changes depending on humidity level and tuber harvest term (percentage).Full size imageThe Denar variety, regardless of the humidity level, was characterized by a greater share of stems and leaves. For the Julinka, the tuber percentage at the last harvest was at the same or higher than in the case of stems and leaves. At humidity levels 2 and 3, tubers accounted up to 60% of the harvested biomass.The growth of stem and stolon biomass was noticeable at all stages of potato development (Table 2); greater dynamics were found in the growth of tuber mass (Fig. 3). Under level 3, the growth of the biomass of stems with leaves and stolons was slower than in level 2 of water was used.Water use efficiencyAverage daily doses of water used for the Denar and Julinka varieties in potato harvesting periods are shown in Fig. 4. The volume of water was determined each time for the corresponding level of humidity (1, 2 and 3). Based on the data obtained, a proportional increase in water consumption was found for both potato varieties. The most intensive increase in water consumption was noted at humidity level 3. The W index corresponding to the average daily dose of water calculated for the Denar variety varied from 0.40 l day−1 in the 1st period (O1) to 0.79 l day−1 in the 5th harvest period (O5), whereas for the Julinka it was from 0.49 l day−1 (O1) to 0.92 l day−1 (O5). The W values for the level 3 changed for the Denar variety from 0.23 l day−1 in (O1) to 0.38 l day−1 (O5), while for the Julinka from 0.28 l day-1 (O1) to 0.28 l day-1, respectively (O5). The difference in the intensity of water consumption increase for humidity levels was expressed by varying the values of simple directional coefficients approximating empirical data. The highest values of these coefficients were obtained for the humidity level 1. The directional coefficient for the Denar was 0.0077 day−1, and for the Julinka variety 0.009 day−1. For humidity level 3, these values are 4 and 6 times lower: 0.002 day−1 (Denar) and 0.0014 day−1 (Julinka), respectively.Figure 4Average daily water consumption for potato varieties, at three soil humidity levels (1, 2, 3) and in each of five growing stages (O1), (O2), (O3), (O4), (O5).Full size imageThe average daily water consumption throughout the growing season calculated from potato planting is shown in Fig. 5. The average daily water use was the highest for both varieties at humidity level 3. Index W1 for the Denar was 0.53 l day−1, while for Julinka was higher—0.70 l day−1. The water consumption for the humidity level 1 was about 2 times lower: for the Denar—0.27 l day−1 and for Julinka—0.29 l day−1.Figure 5Average daily water consumption for potato varieties, at three soil humidity levels (1, 2, 3), cumulative calculation from potato planting.Full size imageAhmadi et al. (2017)37 used various irrigation schedule strategies for water demand measurements at evapotranspiration. Water demand has been fully or partially satisfied in static and dynamic modes. The research presents dynamics of vapor pressure deficit (VPD) throughout the growing season. The value of VPD in the first days after planting the potato was about 0.5 kPa while in 70 days maximum value was noted (2.5 kPa), and at the end of the growing season (after 150 days) about 1.5 kPa. Due to the shorter potato growing season in present research, no decrease in water demand was noticed up to about 70 days and, as in the results of the research presented by Ahmadi et al. (2017)37, a steady increase in water demand was noted. Similar results were obtained by King et al. (2020)38 and the largest water deficit was found in the middle of vegetation, after 70–80 days after planting35,39.Values for average daily increase in potato tuber weight (index W2) in individual vegetation periods are presented below (Fig. 6). No approximation of functional models to empirical data is possible; hence, the conclusions are based on a description. In the 1st period, i.e. until day 24 (O1), tuber weight gains were smaller than in the other periods. Depending on the humidity level, these amounted to 2.0 to 3.5 g day−1 for the Denar variety, and 2.7 to 3.9 g day−1 for the Julinka. The differences for Denar were 1.5 g day−1 and for Julinka 1.2 g day−1. In the 2nd irrigation period (O2), average daily increase in potato tuber weight was the highest, from 5.9 g day−1 for level 2 to 7.9 g day−1 for level 3. Average daily tuber weight gain was 13% higher for level 1 than for level 2.Figure 6Average daily potato varieties tuber increase, at three soil humidity levels (1, 2, 3), in each of five potato growing stages (O1), (O2), (O3), (O4), (O5).Full size imageThe average daily weight gain of tubers of potato varieties (W3), calculated incrementally from the beginning of the experiment (Fig. 7). For the entire growing season, this indicator for the Denar variety was the highest for the humidity level 1st (5.7 g day−1), at the level 3rd (5.1 g day−1) and the lowest at the level 2nd (4.3 g day−1). The average daily weight gain of potato tubers of the Julinka was definitely highest for the first humidity level (8.1 g day−1).Figure 7Average daily potato varieties tuber increase, at three soil humidity levels (1, 2, 3), cumulative calculation from potato planting.Full size imageThe ratio of the average daily water consumption to the average weight gain of potato tuber (W4) for individual periods is given in Fig. 8. For humidity level 1 for Denar and Julinka varieties, the values decreased with the growing period of vegetation. In the period (O1), 0.079 l of water was used for the Denar variety and 0.075 l for the Julinka for an increase in potato tuber weight of 1 g. In the next stages of the growing season, this index ranged from 0.35 to 0.45 l g−1 for the Denar variety, for the Julinka it was definitely smaller and range from 0.25 to 0.34 l g−1. At humidity level 1, Julinka used less water than Denar to produce the same weight of tubers. At humidity level 2, the volume of water used at the beginning of growth was also the largest for the Denar variety (0.159 l g−1). This amount was two times higher than the volume at level 1. In subsequent periods, the indicator changed and ranged from 0.059 to 0.105 l g−1. For the Julinka variety, water consumption varied in individual periods from 0.085 to 0.113 l g−1 and showed no trend. At humidity level 3, Denar used the greatest amount of water, as compared to levels 1 and 2, and showing no trend. The Julinka variety used even more water at the same humidity level. This amount ranged from 0.164 to 0.298 l g−1 and, unlike in previous cases, it showed an upward trend with plant development.Figure 8Ratio of average daily water consumption to average daily tuber mass increase dependent on three soil humidity levels (1), (2), (3), in each of five potato growing stages (O1), (O2), (O3), (O4), (O5).Full size imageJovanovic et al. (2010)40 divided the potato growing season into five stages related to growth phases. There were no increases in the weight of leaves and stems, while the tuber weight, regardless of the irrigation method (PRD and FI), increased steadily. The weight of tubers in the last harvest, as compared to the first, increased five-fold. A similar relationship was obtained in the work of Shahnazari et al. (2007)41. This research also took account of different levels of humidity using the strategies of PRD and FI, also considering soil retention characteristics (pF curve). The research showed a clear steady increase in potato tuber weight in each harvest.The ratio of the average daily water consumption to the average weight gain of potato tuber varieties calculated cumulatively from the planting (Fig. 9). The W5 value (0.114 l g−1) for the Denar variety at the end of the growing season was the highest for the 3rd humidity level and was about two times higher than at level 1. Water consumption efficiency for the Denar variety was the highest at humidity level 1. The sequence of W5 values is similar for the Julinka, with the difference that for the 3rd level it was 0.205 l g−1; i.e. six times higher than the indicator for level 1. Water consumption efficiency for the Julinka variety was definitely highest at humidity level 1.Figure 9Ratio of average daily water consumption to average daily tuber mass increase dependent on three soil humidity levels (1), (2), (3), cumulative calculation from potato planting.Full size imageBadr et al. (2010)42 analyzed the tuber yield, using two irrigation systems: surface and subsurface drip line. The total volume of water applied during the growing season was the differentiating factor. Results showed that as the volume of water applied during the growing season increased, the yield increased. When the subsurface line was used, applying 75 mm of water during the growing season, the total yield was approx. 27.5 t ha−1, and 32.5 t ha−1 for 325 mm. The effect of water amount on increase in yield was greater for the surface drip line. After applying 75 mm, the yield was 17.5 t ha−1, and 40 t ha−1 (for 325 mm). Similar results were obtained in the work of Linker et al. (2016)43. Regardless of the frequency, amount and total size of irrigation treatments, a proportional increase in the size of crops was observed with increasing doses of water.Shahnazari et al. (2007)41 planned several harvest dates (H0–H4) throughout the entire growing season, analyzing the irrigation efficiency indicator (average WUE index’). Regardless of the irrigation technique, and taking into account, above all, the amount of water administered, the value of the average WUE index’ indicator was the highest in the period H2–H3, similar results were found in our own research. More

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All experimental procedures were permitted by the Institutional Animal Care and Usage Committees of the Grassland Science College of Gansu Agricultural University (GSC-IACUC-2015-0011). Our experiments were conducted according to their guidelines, which are in accordance with the Guide for the Care and Use of Laboratory Animals (the Constitution of Experimental Animal Ethics Committee of Gansu Agricultural University). All experiments were performed in accordance with ARRIVE guidelines.Animals and laboratory conditionsAdults of both sexes (three males and three females) were captured in April 2015. Specifically, the animals were captured in Mayin Tan (37°12′N, 102°46′E; Tianzhu Tibetan Autonomous County, China) using live traps25 set at fresh surface mounds. The individuals were then transported to the laboratory and housed in an acrylic box with a pipeline covered with soil. The box and pipeline were covered by black cloth to simulate the dark environment of plateau zokors. The temperature in the room was maintained between 20 and 25 °C. Food was supplied daily and consisted of potatoes, lettuce and carrot. After three days of acclimatization to the laboratory the animals were used in the different experiments. Our laboratory is located 2 km away from the field site. At the end of the experiments all animals were returned to the capture site in good health.Laboratory testing arenaThe experimental setup for the laboratory experiments was as follows (Fig. 1): A transparent Perspex tube (8 cm × 8 cm × 80 cm) was joined to the side of the dark acrylic box (40 cm × 40 cm × 40 cm). A rubber stopper was inserted into one end of the tube to avoid effects from the external environment. Treatment apparatus was placed into the rubber stopper (see “Laboratory treatment apparatus” section, below), and, to avoid the apparatus being damaged by the animals, wire mesh (8 cm × 8 cm × 0.5 cm) was placed about 15 cm from one end of the tube. A mercury thermometer was inserted into the tube in the middle to monitor the tube’s temperature. Between experiments with different animals, the box and tube were wiped with 95% alcohol and then with distilled water.Figure 1Schematic drawing of the setup used to test burrow-sealing behavior in plateau zokors in the laboratory. (1) acrylic box covered with soil 30 cm in depth; (2) experimental animal; (3) mercury thermometer; (4) transparent Perspex tube; (5) the pipe’s support clip; (6) wire mesh (8 cm × 8 cm × 0.5 cm); (7) rubber stopper (8 cm × 8 cm × 5 cm).Full size imageLaboratory treatment apparatusA rubber stopper with seven holes was used for plugging one end of the tube (Fig. 2). The oxygen concentration, light, temperature, sound and gas flow were considered in this design.Figure 2Schematic diagram of the rubber stopper used to simulate the entrance plug of the burrow. (1) power supply; (2) light bulb switch; (3) electric wire switch; (4) oxygen cylinder; (5) in situ three-parameter soil gas analyzer; (6) voice recorder; (7) negative pressure drainage device; (8) rubber plug; (9) LED bulb; (10) the iron rod; (11) heating cord; (12) AVOXIVY speaker with 5 cm diameter.Full size imageOxygen treatmentTo avoid the oxygen that was delivered into the tube causing the gas to flow too strongly, become drier, and create a sound, a steel oxygen cylinder and thin hose (0.3 cm in diameter) were selected, and one end of the hose was connected directly to the oxygen cylinder with a humidifier bottle, while the opposite end was inserted into the rubber stopper (Fig. 2). Before beginning the experiment, we allowed the oxygen cylinder to sit for two hours at laboratory temperature to remove any temperature effects. A three-parameter soil gas analyzer (13.05.03Pro, Shanghai SAFE Biotech Co., Ltd, China) was used to monitor the oxygen concentration in the tube (Fig. 2).Light treatmentThe average light intensity—that is, 360 Lux from 8:00 am to 8:00 pm—was measured in the field. One end of a wire was connected to an LED light (1 Watt), and the other end to the power supply (Fig. 2).Temperature treatmentThe temperature in the burrow entrance in the field was about 3 °C warmer than that at a tunnel depth of 10 cm. As such, one end of a wire was connected to a heater strip and the other end to the electrical power supply (Fig. 2). A thermometer was inserted into the tube to monitor the temperature inside the tube (Fig.1). During the experiment period in the laboratory, we switched on or off to make sure the relatively constant temperature inside the tube. The temperature range inside the tube was 3.2 ± 0.27 °C .Sound treatmentWhen a burrow is opened, wind whistle can be produced around the burrow entrance. Accordingly, a voice recorder (PCM-D50, frequency response 50 Hz–40 kHz, Sony, Japan) was placed at the burrow entrance in the field to record the burrow-entrance sound, the duration of which was 30 min. In the laboratory, the two ends of a wire were connected to an AVOXIVY loudspeaker (diameter: 5 cm; impedance: 4 Ω; 50 Hz–20 kHz) and a voice recorder, respectively (Fig. 2). The recorded sound was played back with a 60 dB sound pressure level, as measured at the burrow entrance in the field (XL2 sound level meter, Nti Audion, Switzerland). The sound was repeatedly played within one hour.Gas flow treatmentTo avoid ambient atmosphere entering the tube, a negative pressure drainage ball with plastic tube (12 cm long, 2 cm in diameter) connected the tube through a rubber stopper (Fig. 2). The tunnel gas was inhaled by the ball, then we pinched the ball to blow the gas into the tunnel as gas flow treatment.Field treatment apparatusFor the field experiment, the apparatus consisted of a tube (40 cm long, 8 cm in diameter) and an alarm device. The alarm device was made up of a loudspeaker, two slide rails (15 cm long), two metal plates (approximately 7 cm in length and 3 cm in width), and three coiled metal springs (5 cm long, 2 cm in diameter). The three springs were joined to one of the metal plates, while the other metal plate was fixed on the slide rails. The two metal plates were touched by the plateau zokor when it was plugging, which triggered the alarm device, thus enabling us to know whether or not burrow-sealing behavior was occurring (Fig. 3). The aluminum tube with an oxygen device was embedded into the burrow. The soil covering the tube served as an excellent insulator, buffering the tube from the aboveground temperature (Fig. 4A). A steel oxygen cylinder and thin hose (0.3 cm in diameter) were applied by connecting one end of the hose directly to the oxygen cylinder with a humidifier bottle, and then the opposite end of the hose was inserted into the tube (Fig. 4A). A three-parameter soil gas analyzer (13.05.03Pro, Shanghai SAFE Biotech Co., Ltd, China) was used to monitor the oxygen concentration in the tube (Fig. 4A). Allowing sunlight to enter the burrow, a glass bottle, open at one end but closed at the other, was embedded into the burrow. We also used soil to cover the bottle, and there was a 5 cm gap at the surface (Fig. 4B). The aluminum tube with high thermal conductivity was embedded into the burrow. Again, we used soil to cover the bottle and retained a 20 cm gap (Fig. 4B).Figure 3Schematic drawing of the apparatus used to test the burrow-sealing behavior of plateau zokor in the field. (1) tube; (2) loudspeaker; (3) slide rail; (4) metal plate; (5) coiled metal springs.Full size imageFigure 4(A) Schematic drawing of the apparatus used in the oxygen treatment placed in the tunnel of the plateau zokor. (B) Schematic drawing of the apparatus used for the temperature and light treatments placed in the tunnel of the plateau zokor. (1) tunnel of the plateau zokor; (2) oxygen cylinder; (3) three-parameter soil gas analyzer; (4) plateau zokor.Full size imageProcedureIn the laboratory experiment, we tested three males and three females for their responses to each treatment. To avoid generating stress and habituation to treatments, zokors were tested for 12 h each day and there was one hour interval between treatments, and five days interval between round of testing for the same individual (Table 1). We performed a control experiment in which a rod was inserted into the burrow but no further treatment was applied, which allowed us to evaluate whether it was the treatment that was causing the burrow-sealing behavior. Before beginning treatment experiment, each zokor was tested 24 times (12 h × 2 days) under the control experiment. We determined the rod movement as occurrence of burrow-sealing behavior.Table 1 Times of the experiments for each treatment in the laboratory simulation.Full size tableIn the field experiment, we tested three zokors (one male, two females), and six zokors were caught in the cold season and warm season (three males and three females, respectively). We then fastened radio collars (Ag357, Biotrack, Ltd., UK) to each captured individual to allow us to track the position in foraging tunnels of each zokor. Each zokor was used three times in the experiments under each treatment, and, after finishing each experiment, we changed the position of the foraging tunnel to ensure the test tunnel was not an abandoned tunnel. According to radio-tracking data, the straight-line distance between the test tunnel and the nest for each treatment was about 5 m. We conducted a control experiment that whether plateau zokor move to the test tunnel or not during the time between treatments. In the cold season, from 4 October 2015 to 2 November 2015, the burrow-sealing behavior of each zokor was tested under different treatments during their active time (12:00–18:00) and inactive time (09:00–11:00) for a total of 27 days (Table 2). The same was done in the warm season but for a total of 18 days from 15 May 2016 to 5 June 2016, in which the active time was 14:00–20:00 and the inactive time was 08:00–13:00 (Table 2).Table 2 Times of the experiments for each treatment in the active and inactive periods of plateau zokors during the warm and cold season.Full size tableData analysisThe occurrence of burrow-sealing was recorded as “1”, and non-sealing was recorded as “0”. The frequency of burrow-sealing was the number of times the burrow was sealed divided by the total number of experiments for each treatment26, and we considered the frequency for each individual as a replicate. The latency to reseal the burrow was the period from the start of the treatment to the sealing of the burrow, and we considered each instance of latency to reseal the burrow as a repeat. The latency to reseal the burrow for non-sealing under each treatment was unavailable data and was therefore removed. The presence of a normal distribution in the initial data was determined using the Kolmogorov–Smirnov test. All data followed a normal distribution. A comparison of males and females in their frequency of sealing the burrow and in their latency to reseal the burrow under each treatment was performed with an independent-samples T-test. Multiple comparisons were made for the frequency of burrow-sealing and the latency to reseal the burrow under different treatments by using the least significant difference method at the significance level of P = 0.05. In the field experiment, the number of replicates was fewer than three for frequency and the latency to reseal the burrow, we did not conduct multiple comparisons.Preliminary statistical analysis of the data was performed using Excel 2013 and SPSS 19.0. All the figures and tables were produced in GraphPad Prism 8.0 and Excel 2013. More

Site, meadow, and fertilisationThe grassland utilized in this study was located in Sedico (BL: 420 m a.s.l., eastern Italian Pre-Alps), where the annual mean temperature is 10.6 °C and the annual rainfall is approximately 1366 mm (389, 326, 401, and 250 mm in spring, summer, autumn, and winter, respectively). The site was level and had an alluvial calcareous substratum. The soil was sandy-loam textured with 12.2% gravel content, 14.6% total carbonate content, and a pH of 7.5. Since 1977, a section of the meadow has been used for a fertilisation trial organised as three completely randomized blocks with 24 m2 plots and twenty-seven treatments obtained by combining three levels of yearly N, P, and K applications per ha: 0, 96, and 192 kg N as ammonium nitrate; 0, 54, and 108 kg P2O5 as triple superphosphate; and 0, 108, and 216 kg K2O as K sulphate. Since 2010, the grassland has been cut twice per year and surveyed for seed production in three treatments: no fertilisation (000), fertilization with no N and intermediate levels of P and K (011), and fertilisation with the highest nutrient rates (222).The vegetation of the three treatments (Annex 1) corresponded to the following meadow types: type 000, vegetation intermediate between a poor-soil form of the Arrhenatherum elatius meadow (Ar0) and a Bromus erectus meadow (Br), with high species richness and low legume abundance; type 011, an Ar0 meadow with high species richness and legume abundance; and type 222, a grass-rich form of the Arrhenatherum elatius meadow with low species richness and legume abundance.Plant sampling and laboratory analysisDuring the two growth periods within each of the years from 2012 to 2017, fertile shoots were sampled from the three fertilization treatments. 15–30 shoots (5–10 in each plot) from each flowering species were collected at the optimal seed maturation stage (most fruits/inflorescences still intact, i.e., no seed shedding). At the sub-plot level, all fertile shoots were collected at the time of meadow mowing on one 1-m2 sub-plot per plot. Collected shoots were put separately per species into porous paper bags, dried, and preserved in a refrigerator until laboratory analysis.During the autumn and winter after collection, the 15–30 shoots of each species were analysed for the number of compound and/or simple inflorescences and the flowers per simple inflorescence or shoot. For species with flowers or inflorescences too numerous to be rapidly counted, an inflorescence length or diameter which could be related to the number of flowers was also measured (e.g., the panicle length in grasses). In sample flowers, intact fruits, or simple inflorescences, the number of ovules per flower and the number of ovules transformed to seed were observed under a binocular microscope. Mature seeds were weighed and tested for germinability and viability according to ISTA19. Germination trials were performed with three seed samples per species, which were placed on filter paper in petri-dishes and moved to a germinator for 4 weeks (8 h light/25 °C and 16 h darkness/15 °C) with weekly observation and extraction of germinated seeds. At the end of the germination test, seeds that had not germinated were checked for viability with the tetrazolium test. Total viability was calculated as the sum of germinability and viability of non-germinated seeds.All shoots collected on the sub-plots were counted and measured for the number of inflorescences and flowers. When inflorescences and flowers were too numerous to be counted rapidly (e.g., in all grasses), only the same length/diameter measured on the 15–30 shoot samples was recorded.A more detailed description of the laboratory analyses is available in Scotton20.Data analysisThe value of each reproductive trait was calculated for each year and growth period at the plot level for each species. The values of the traits describing the size of the reproductive system were obtained from the shoots collected on the sub-plots. However, for species with too many flowers per shoot, a relation was calculated between the flowers per shoot and the length/diameter of the inflorescences measured on the 15–30 shoot samples. This relationship was then used to calculate the flower number for each shoot. The number of ovules per flower, the portion of ovules transformed to seed (ovule site utilisation, i.e. the filled seed/ovule ratio), the 1000-seed weight, germinability, and viability were calculated from the results of lab analyses of the 15–30 shoot samples.Because all the species collected were not always present in the six study years, only the thirty-two species (fifteen grasses and seventeen forbs: Table 1) found in at least three of the study years were considered in this paper to obtain enough reliable results. For all grasses, data were available only for the first growth period. For four forbs (see Annex 3) data were available for the first and second growth periods: in these cases, the average values of the two periods were used in the analyses.Table 1 Species studied for reproductive traits in a grassland fertilisation trial in the Italian eastern Alps.Full size tableThe statistical analyses were performed at the levels of individual species and the two grassland functional groups (grasses and forbs). Nine main reproductive traits describing the whole process of gamic reproduction were considered: number of simple inflorescences per shoot, flowers per simple inflorescence, ovules per flower, ovules and viable seeds per shoot, OSU (ovule site utilization), percent viability, germinability, and seed weight. Percent dormancy (the difference between percent viability and germinability) and the shoot density recorded in the subplots were also considered in some analyses.Only sixteen species were present in all of the fertilisation treatments, presenting a challenge in the tests that included all of the species together because a balanced among-treatments comparison was only possible by discarding the data from species not present in all of the treatments. To overcome this issue, we assumed that due to symbiotic N-fixation, the high presence of legumes in the 011 treatment (fertilization with P and K) was equivalent to a yearly N fertilisation of about 3.5 kg/ha per percent point of legume abundance in the species composition21,22. Therefore, treatment 011 (30% more legume abundance than in treatment 222: Annex 1) was regarded as an intermediate N addition of 105 kg per ha per year (from 3.5 kg N × 30% legume abundance). The values of the reproductive traits were then calculated for two fertilisation levels, low (LowFert) and high (HighFert). For species present in 000 and 011, LowFert was 000 and HighFert was 011. For species present in 011 and 222, LowFert was 011 and HighFert was 222. For species present in three fertilisation treatments, LowFert was 000 and HighFert was the average between 011 and 222. Statistical analysis considering only the species present in all fertilisation treatments yielded a similar pattern of fertilisation effects to those found in analysis of the two separate fertilisation levels. The analysis of the two fertilisation levels was therefore utilized because it was representative of a larger number of species.Statistical analyses (see summary in Annex 2) were conducted with three main aims: 1. studying the fertilisation effect on the reproductive behaviour of individual species and the two species groups of grasses and forbs; 2. finding species biological and ecological traits explaining their response to fertilisation; and 3. identifying multispecies correlations among reproductive traits and the possible effects of fertilisation on their patterns.For the first aim, the fertilisation effect was tested for the reproductive traits of each individual species through application of a mixed linear model under a repeated measure approach. In the model, fertilisation treatment, year, and block were input as class factors, and a plot identifier was used as a random factor. In case of significant fertilisation effects, the among-treatment differences were tested using the Tukey multiple comparison adjustment. Prior to performing the mixed model, data were checked for homoscedasticity and normality and, if necessary, log-transformed.From the individual species mixed models, a table was calculated containing the frequency of cases with fertilisation effects (three levels: no, positive, or negative) for each reproductive trait and species group. To check if grasses and forbs differed for the obtained frequencies, for each trait a chi-square test on the frequency table “fertilisation effect x species group” was performed.In a following set of analyses, the effect of the grassland functional group (grasses or forbs) on the multi-year means of each reproductive trait was tested with general linear models (GLM). Prior to the analysis, the data were sometimes log-transformed to mitigate homoscedasticity and normality problems. In these analyses, species were considered as replicates within the species group (therefore not included as a class factor) and the fertilisation level was input as a class factor. The effect of the fertilisation level on each reproductive trait was tested separately for the two species groups. In this case, the GLM included both fertilisation level and species as class factors.For the second aim, possible biological and ecological traits explaining the species response to fertilisation were investigated by relating the percent variation due to fertilisation in two important size traits (ovule and viable seed number per shoot: variables Y) to the following explanatory (X) variables: average values of the nine reproductive traits, the seven Ellenberg bioindicator values23, and the percent variation of shoot density. The relationships were fitted according to a linear regression approach for grasses and forbs together or separately and checked for the parametric assumptions of residual normality and homoscedasticity. For the percent variation due to fertilisation of individual species of OSU, seed germinability, viability and weight, one-way analyses of variance were performed where three traits of the species reproductive biology (type of reproduction, breeding system and pollen vector4: Annex 1) were used as categorical factors. A GLM approach was also used in this case.For the third aim, multispecies correlations were analysed by in-pairs relating the reproductive trait values of individual species averaged across fertilisation treatments and years. Fertile shoot density recorded in the subplots. was used as a supplementary characteristic. Nonlinear relationships were made linear with a log-transformation. Because the purpose of the analysis was not to predict one trait from the other but to efficiently summarise the relationships between traits, the standardised major axis (SMA) approach was used instead of the linear regression method24. The analyses were performed for grasses and forbs both together and separately. In order to verify if fertilisation could affect the characteristics of the evaluated relationships, a second set of SMA analyses were performed by separating the two fertilisation levels and the lines obtained were tested for common slope and elevation according to Warton et al.24.The year effect will be reported in a forthcoming paper and is therefore not discussed here, despite its inclusion in the statistical analyses.We used SAS/STAT software 12.325 with procedures MIXED, GLM, REG, and UNIVARIATE, and R 3.0.026 with package SMATR.Additional statementsThe experimental research and the collection of plant material was done according to relevant institutional, national, and international guidelines and legislation. No grassland species considered in the research is included in the list of endangered species according to the IUCN, European Union, Italian national and regional classifications. The collection of plant specimen was done with permission of the grassland owner during the hay-making operations for forage production which do not need a special permission from the concerned local authorities. The plant species were identified by the first author, Michele Scotton. A voucher specimen of each plant species considered in the research was stored in the laboratory of the authors’ Department (DAFNAE) and the authors have provided an ID number for each voucher specimen. More