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    Physiological response and secondary metabolites of three lavender genotypes under water deficit

    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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    Spring arctic oscillation as a trigger of summer drought in Siberian subarctic over the past 1494 years

    1.Vaganov, E. A. et al. Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400(6740), 149–151. https://doi.org/10.1038/22087 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Impacts of a Warming Arctic—Arctic Climate Impact Assessment (ACIA). 144 (Cambridge University Press, 2004).3.Apps, M. J., Shvidenko, A. Z. & Vaganov, E. A. Boreal forests and the environment: A mitigation and adaptation strategies for global change. BFE. 11(1), 1–4 (2006).
    Google Scholar 
    4.Fedotov, A. P. et al. A reconstruction of the thawing of the permafrost during the last 170 years on the Taimyr Peninsula (East Siberia, Russia). Glob. Planet. Change 98–99, 139–152 (2002).
    Google Scholar 
    5.Kharuk, V. I., Dvinskaya, M. L. & Ranson, J. Fire return intervals within the northern boundary of the larch forest in Central Siberia. Int. J. Wildland Fire 22(2), 207–211. https://doi.org/10.1071/WF11181 (2011).Article 

    Google Scholar 
    6.Knorre, A. A., Kirdyanov, A. V., Prokushkin, A. S., Krusic, P. J. & Büntgen, U. Tree ring-based reconstruction of the long-term influence of wildfires on permafrost active layer dynamics in Central Siberia. Sci. Total Environ. 652, 314–319 (2019).ADS 
    Article 

    Google Scholar 
    7.Kim, J.-S., Kug, J.-S., Jeong, S.-J., Park, H. & Schaepman-Strub, G. Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation. Sci. Adv. 6(2), eaax330. https://doi.org/10.1126/sciadv.aax3308 (2020).Article 

    Google Scholar 
    8.Kirdyanov, A. V. et al. Long-term ecological consequences of forest fires in the permafrost zone of Siberia. Environ. Res. Lett. 15, 034061. https://doi.org/10.1088/1748-9326/ab7469 (2020).ADS 
    Article 

    Google Scholar 
    9.Fritts, H. C. Tree-Rings and Climate 567 (Academic Press, 1976).
    Google Scholar 
    10.Schweingruber, F. H. Tree Rings and Environment Dendroecology (Paul Haupt Publ, 1996).
    Google Scholar 
    11.Hughes, M. K., Vaganov, E. A., Shiyatov, S. G., Touchan, R. & Funkhouser, G. Twentieth-century summer warmth in northern Yakutia in a 600-year context. Holocene 9(5), 603–608 (1999).Article 

    Google Scholar 
    12.Briffa, K. R. Annual climate variability in the Holocene: Interpreting the message of ancient trees. Quat. Sci. Rev. 19, 87–105 (2000).ADS 
    Article 

    Google Scholar 
    13.Naurzbaev, M., Vaganov, E. A., Sidorova, O. V. & Schweingruber, F. H. Summer temperatures in eastern Taimyr inferred from a 2427-year late-Holocene tree-ring chronology and earlier floating series. Holocene 12(6), 727–736 (2002).ADS 
    Article 

    Google Scholar 
    14.Grudd, H. Torneträsk tree-ring width and density AD 500–2004: A test of climatic sensitivity and a new 1500-year reconstruction of north Fennoscandian summers. Clim. Dyn. 31, 843–857 (2008).Article 

    Google Scholar 
    15.Sidorova, O. V., Siegwolf, R., Saurer, M., Naurzbaev, M. M. & Vaganov, E. A. Isotopic composition (δ13C, δ18O) in Siberian tree-ring chronology. Geophys. Res. Biogeosci. 113, G02019. https://doi.org/10.1029/2007JG000473 (2008).CAS 
    Article 

    Google Scholar 
    16.Sidorova, O. V. et al. Spatial patterns of climatic changes in the Eurasian north reflected in Siberian larch tree-ring parameters and stable isotopes. Glob. Change Biol. 16, 1003–1018. https://doi.org/10.1111/j.1365-2486.2009.02008.x (2010).ADS 
    Article 

    Google Scholar 
    17.Sidorova, O. V. et al. Is the 20th century warming unprecedented in the Siberian north?. Quat. Sci. Rev. 73, 93–102. https://doi.org/10.1016/j.quascirev.2013.05.015 (2013).ADS 
    Article 

    Google Scholar 
    18.Kirdyanov, A. V., Treydte, K. S., Nikolaev, A., Helle, G. & Schleser, G. H. Climate signals in tree-ring width, density an δ13C from larches in Eastern Siberia (Russia). Chem. Geol. 252, 31–41. https://doi.org/10.1016/j.chemgeo.2008.01.023 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    19.Hilasvuori, E., Berninger, F., Sonninen, E., Tuomenvirta, H. & Jungner, H. Stability of climate signal in carbon and oxygen isotope records and ring width from Scots pine (Pinus sylvestris L.) in Finland. J. Quat. Sci. 24(5), 469–480 (2009).Article 

    Google Scholar 
    20.Loader, N. J., Young, G. H. F., Grudd, H. & McCarroll, D. Stable carbon isotopes from Torneträsk, norther Sweden provide a millennial length reconstruction of summer sunshine and its relationship to Arctic circulation. Quat. Sci. Rev. 62, 97–113 (2013).ADS 
    Article 

    Google Scholar 
    21.Churakova (Sidorova), O. V. et al. Recent atmospheric drying in Siberia is not unprecedented over the last 1500 years. Sci. Rep. 10, 15024 (2020).CAS 
    Article 

    Google Scholar 
    22.Young, G. H. F. et al. Changes in atmospheric circulation and the Arctic Oscillation preserved within a millennial length reconstruction of summer cloud cover from northern Fennoscandia. Clim. Dyn. 39, 495–507. https://doi.org/10.1007/s00382-011-1246-3 (2012).Article 

    Google Scholar 
    23.Saurer, M., Schweingruber, F., Vaganov, E. A., Shiyatov, S. G. & Siegwolf, R. Spatial and temporal oxygen isotope trends at the northern tree-line in Eurasia. Geophys. Res. Lett. https://doi.org/10.1029/2001GL013739 (2002).Article 

    Google Scholar 
    24.Saurer, M. et al. Influence of atmospheric circulation patterns on the oxygen isotope ratio of tree rings in the Alpine region. J. Geophys. Res. 117, D05118. https://doi.org/10.1029/2011JD016861 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    25.Ortega, P. et al. A model-tested North Atlantic Oscillation reconstruction for the past millennium. Nature 523, 71–74 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    26.Butzin, M. et al. Variations of oxygen-18 in West Siberian precipitation during the last 50 years. Atmos. Chem. Phys. 14, 5853–5869 (2014).ADS 
    Article 

    Google Scholar 
    27.Gagen, M. et al. North Atlantic summer storm tracks over Europe dominated by internal variability over the past millennium. Nat. Geosci. 9(8), 630–635 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    28.Thompson, D. W. & Wallace, J. M. Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Clim. 13, 1000–1016 (2000).ADS 
    Article 

    Google Scholar 
    29.Wang, J. et al. Impacts of the Siberian High and Arctic Oscillation on the East Asia winter monsoon: Driving down welling in the western Bering Sea Aquatic Ecosystem. Health Manag. 15(1), 20–30. https://doi.org/10.1080/14634988.2012.648860 (2012).Article 

    Google Scholar 
    30.Buermann, W. et al. Interannual covariability in Northern Hemisphere air temperatures and greenness associated with El-Nino-Southern Oscillation and the Arctic Oscillation. J. Geophys. Res. 108(D13), 4396. https://doi.org/10.1029/2002JD002630 (2003).Article 

    Google Scholar 
    31.Baltzer, H. et al. Impact of the Arctic Oscillation pattern on interannual forest fire variability in Central Siberia. Geophys. Res. Lett. https://doi.org/10.1029/2005GL022526 (2005).Article 

    Google Scholar 
    32.Zhang, J. et al. Analysis of the positive Arctic Oscillation index event and its influence in the winter and spring of 2019/2020. Front. Earth Sci. https://doi.org/10.3389/feart.2020.580601 (2021).Article 

    Google Scholar 
    33.Zielinski, G. A. Use of paleo-records in determining variability within the volcanism- climate system. Quat. Sci. Rev. 19, 417–438 (2000).ADS 
    Article 

    Google Scholar 
    34.Panuyshkina, I. P. & Arbatskaya, M. K. Dendrochronological approach to study flammability of forests in Evenkia (Siberia). Sib. Ecol. J. 2, 167–173 (1999).
    Google Scholar 
    35.Valendik, E. N., Kisilyakhov, E. K., Rizova, V. A., Ponamarev, E. I. & Danilova, I. V. Large fires in taiga landscape of Central Siberia. Geogr. Nat. Resour. 14(1), 52–59 (2014).
    Google Scholar 
    36.Naulier, M. et al. A millennial summer temperature reconstruction for northeastern Canada using oxygen isotopes in subfossil trees. Clim. Past. 11, 1153–1164. https://doi.org/10.5194/cp-11-1153-2015 (2015).Article 

    Google Scholar 
    37.Churakova (Sidorova), O. V. et al. Siberian tree-ring and stable isotope proxies as indicators of temperature and moisture changes after major stratospheric volcanic eruptions. Clim. Past. https://doi.org/10.5194/cp-2018-70.y (2019).Article 

    Google Scholar 
    38.Furyaev, V. V., Vaganov, E. A., Tchebakova, N. M. & Valendik, E. N. Effects of fire and climate on successions and structural changes in the Siberian boreal forest. Eurasian J. For. Res. 2, 1–15 (2001).
    Google Scholar 
    39.Keller, K. M. et al. 20th-century changes in carbon isotopes and water-use efficiency: Tree-ring based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    40.D’Arrigo, R. D., Cook, E. R., Mann, M. E. & Jacoby, G. C. Tree-ring reconstructions of temperature and sea-level pressure variability associated with the warm-season Arctic Oscillation since AD 1650. Geophys. Res. Lett. 30(11), 1549. https://doi.org/10.1029/2003GL017250 (2003).ADS 
    Article 

    Google Scholar 
    41.Kress, A. et al. Swiss tree rings reveal warm and wet summers during medieval times. Geophys. Res. Lett. 41, 1732–1737. https://doi.org/10.1002/2013GL059081 (2014).ADS 
    Article 

    Google Scholar 
    42.Büntgen, U. et al. Recent European drought extremes beyond Common Era background variability. Nat. Geosci. 14, 190–196. https://doi.org/10.1038/s41561-021-00698-0 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    43.Kodera, K. & Kuroda, Y. Regional and hemispheric circulation patterns in the northern hemisphere winter, or the NAO and AO. Geophys. Res. Lett. 30(18), 2003. https://doi.org/10.1029/2003GL017290 (1934).Article 

    Google Scholar 
    44.Abaimov, A. P., Bondarev, A. I., Ziryanova, O. A. & Shitova, S. A. Forest Krasnoyarsk Polar (Nauka, 1997).
    Google Scholar 
    45.Ary-Mas Natural Conditions, Flora and Vegetation. (eds. Norin, B.N.) (Nauka, Leningrad, 1978).46.Ogi, M., Yamazaki, K. & Tachibana, Y. The summertime annular mode in the Northern Hemisphere and its linkage to the winter mode. J. Geophys. Res. 109, D20114 (2004).ADS 
    Article 

    Google Scholar 
    47.Gagen, M. H., McCarroll, D., Loader, N. J., Robertson, I. & Jalkanen, R. Exorcising the ‘segment length curse’ summer temperature reconstruction since AD 1640 using non de-trend stable carbon isotope ratios from line trees in northern Finland. Holocene 17, 433–444 (2007).ADS 
    Article 

    Google Scholar 
    48.Boettger, T. et al. Wood cellulose preparation methods and mass spectrometric analyses of δ13C, δ18O, and nonexchangeable δ2H values in cellulose, sugar, and starch: An inter-laboratory comparison. Anal. Chem. 15, 4603–4612 (2007).Article 

    Google Scholar 
    49.Weigt, R. B. et al. Comparison of δ18O and δ13C values between tree-ring whole wood and cellulose in five species growing under two different site conditions. Rapid Commun. Mass Spectrom. 29(29), 2233–2244. https://doi.org/10.1002/rcm.7388 (2015).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Francey, R. J. et al. A 1000-year high precision record of δ13C in atmospheric CO2. Tellus B51, 170–193 (1999).ADS 
    Article 

    Google Scholar  More

  • in

    Novel attempt at discrimination of a bullet-shaped siphonophore (Family Diphyidae) using matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-ToF MS)

    1.Pugh, P. The vertical distribution of the siphonophores collected during the SOND cruise, 1965. J. Mar. Biol. Assoc. U.K. 54, 25–90 (1974).Article 

    Google Scholar 
    2.Grossmann, M. M., Collins, A. G. & Lindsay, D. J. Description of the eudoxid stages of Lensia havock and Lensia leloupi (Cnidaria: Siphonophora: Calycophorae), with a review of all known Lensia eudoxid bracts. Syst. Biodivers. 12, 163–180 (2014).Article 

    Google Scholar 
    3.Dunn, C. W. & Wagner, G. P. The evolution of colony-level development in the Siphonophora (Cnidaria: Hydrozoa). Dev. Genes. Evol. 216, 743–754 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Karas, M. & Hillenkamp, F. Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal. Chem. 60, 2299–2301 (1988).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Clark, A. E., Kaleta, E. J., Arora, A. & Wolk, D. M. Matrix-assisted laser desorption ionization–time of flight mass spectrometry: a fundamental shift in the routine practice of clinical microbiology. Clin. Microbiol. Rev. 26, 547–603 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    6.Dvorak, V. et al. Identification of phlebotomine sand flies (Diptera: Psychodidae) by matrix-assisted laser desorption/ionization time of flight mass spectrometry. Parasit. Vectors 7, 1–7 (2014).Article 

    Google Scholar 
    7.Rossel, S. & Arbizu, P. M. Revealing higher than expected diversity of Harpacticoida (Crustacea: Copepoda) in the North Sea using MALDI-TOF MS and molecular barcoding. Sci. Rep. 9, 1–14 (2019).CAS 
    Article 

    Google Scholar 
    8.Feltens, R., Görner, R., Kalkhof, S., Gröger-Arndt, H. & von Bergen, M. Discrimination of different species from the genus Drosophila by intact protein profiling using matrix-assisted laser desorption ionization mass spectrometry. BMC Evol. Biol. 10, 1–15 (2010).Article 
    CAS 

    Google Scholar 
    9.Mazzeo, M. F. et al. Fish authentication by MALDI-TOF mass spectrometry. J. Agric. Food Chem. 56, 11071–11076 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.Kaufmann, C., Schaffner, F., Ziegler, D., Pflueger, V. & Mathis, A. Identification of field-caught Culicoides biting midges using matrix-assisted laser desorption/ionization time of flight mass spectrometry. Parasitology 139, 248–258 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Laakmann, S. et al. Comparison of molecular species identification for North Sea calanoid copepods (Crustacea) using proteome fingerprints and DNA sequences. Mol. Ecol. Resour. 13, 862–876 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Lassen, S., Wiebring, A., Helmholz, H., Ruhnau, C. & Prange, A. Isolation of a Nav channel blocking polypeptide from Cyanea capillata medusae–a neurotoxin contained in fishing tentacle isorhizas. Toxicon 59, 610–616 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Lazcano-Pérez, F., Arellano, R. O., Garay, E., Arreguín-Espinosa, R. & Sánchez-Rodríguez, J. Electrophysiological activity of a neurotoxic fraction from the venom of box jellyfish Carybdea marsupialis. Comp. Biochem. Physiol. C: Toxicol. Pharmacol. 191, 177–182 (2017).
    Google Scholar 
    14.Helmholz, H., Naatz, S., Lassen, S. & Prange, A. Isolation of a cytotoxic glycoprotein from the Scyphozoa Cyanea lamarckii by lectin-affinity chromatography and characterization of molecule interactions by surface plasmon resonance. J. Chromatogr. B 871, 60–66 (2008).CAS 
    Article 

    Google Scholar 
    15.Suzuki, R. & Shimodaira, H. Pvclust: an R package for assessing the uncertainty in hierarchical clustering. Bioinformatics 22, 1540–1542 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143 (1993).Article 

    Google Scholar 
    17.Park, N. & Lee, W. Four new records of family Diphyidae (Hydrozoa: Siphonophorae) in Korean waters. J. Spec. Res. 9, 131–146 (2020).
    Google Scholar 
    18.Karger, A., Bettin, B., Gethmann, J. M. & Klaus, C. Whole animal matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry of ticks–are spectra of Ixodes ricinus nymphs influenced by environmental, spatial, and temporal factors?. PLoS ONE 14, e0210590 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Tayri-Wilk, T. et al. Mass spectrometry reveals the chemistry of formaldehyde cross-linking in structured proteins. Nat. Commun. 11, 1–9 (2020).Article 
    CAS 

    Google Scholar 
    20.Rossel, S. & Martínez Arbizu, P. Effects of sample fixation on specimen identification in biodiversity assemblies based on proteomic data (MALDI-TOF). Fron. Mar. Sci. 5, 1–13 (2018).Article 

    Google Scholar 
    21.Zheng, L., He, J., Lin, Y., Cao, W. & Zhang, W. 16S rRNA is a better choice than COI for DNA barcoding hydrozoans in the coastal waters of China. Acta Oceanol. Sin. 33, 55–76 (2014).Article 
    CAS 

    Google Scholar 
    22.Yeom, J., Park, N., Jeong, R. & Lee, W. Integrative description of cryptic Tigriopus species from Korea using MALDI-TOF MS and DNA barcoding. Front. Mar. Sci. 8, 495 (2021).Article 

    Google Scholar 
    23.Peter, S. Molecular characterization and phylogenetic analysis of Diphyes dispar (Siphonophora: Diphyidae) from the Laccadive Sea, off the south-west coast of Arabian Sea, Indian Ocean. Int. J. Fish Aquat. Stud. 4, 30–35 (2016).
    Google Scholar 
    24.Dunn, C. W., Pugh, P. R. & Haddock, S. H. Molecular phylogenetics of the Siphonophora (Cnidaria), with implications for the evolution of functional specialization. Syst. Biol. 54, 916–935 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Munro, C. et al. Improved phylogenetic resolution within Siphonophora (Cnidaria) with implications for trait evolution. Mol. Phylogenet. Evol. 127, 823–833 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Totton, A. K. Siphonophora of the Indian Ocean together with systematic and biological notes on related specimens from other oceans. Disc Rep 27, 1–162 (1954).
    Google Scholar 
    27.Totton, A. K., Bargmann, H. E. & British Museum (Natural History). A synopsis of the Siphonophora. (British Museum (Natural History), 1965).28.Mapstone, G. M. Siphonophora (Cnidaria, Hydrozoa) of Canadian Pacific waters. (NRC Research Press, 2009).29.Nishiyama, E. Y., Araujo, E. M. & Oliveira, O. M. Species of Lensia (Cnidaria: Hydrozoa: Siphonophorae) from southeastern Brazilian waters. Zoologia (Curitiba) 33, 2337 (2016).Article 

    Google Scholar 
    30.MALDIquantForeign: Import/Export routines for MALDIquant (2015).31.Gibb, S. & Strimmer, K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics 28, 2270–2271 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Palarea-Albaladejo, J., Mclean, K., Wright, F. & Smith, D. G. MALDIrppa: quality control and robust analysis for mass spectrometry data. Bioinformatics 34, 522–523 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Gibb, S. & Strimmer, K. Species Identification using MALDIquant manual. http://www.strimmerlab.org/software/maldiquant/. (2015).34.Ahdesmäki, M. & Strimmer, K. Feature selection in omics prediction problems using cat scores and false nondiscovery rate control. Ann. Appl. Stat. 4, 503–519 (2010).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    35.vegan: Community Ecology Package. R package version 2.5-2. 2018 (2018).36.Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Schuchert, P. DNA barcoding of some Pandeidae species (Cnidaria, Hydrozoa, Anthoathecata). Rev. Suisse Zool. 125, 101–127 (2018).
    Google Scholar 
    38.Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Collins, A. G. Towards understanding the phylogenetic history of Hydrozoa: hypothesis testing with 18S gene sequence data. Scientia Marina (2000).40.Strychar, K. B., Hamilton, L. C., Kenchington, E. L. & Scott, D. B. Cold-water Corals and Ecosystems 679–690 (Springer, Berlin, 2005).Book 

    Google Scholar 
    41.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Thompson, J. D., Higgins, D. G. & Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Kumar, S., Stecher, G. & Tamura, K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    44.Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9, 772–772 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Guindon, S. & Gascuel, O. A simple, fast and accurate method to estimate large phylogenies by maximum-likelihood. Syst. Biol. 52, 696–704 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974).ADS 
    MathSciNet 
    MATH 
    Article 

    Google Scholar 
    48.Yamaoka, K., Nakagawa, T. & Uno, T. Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J. Pharmacokinet. Biopharm. 6, 165–175 (1978).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Hurvich, C. M. & Tsai, C.-L. Regression and time series model selection in small samples. Biometrika 76, 297–307 (1989).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    50.Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    51.Young, N. D. & Healy, J. GapCoder automates the use of indel characters in phylogenetic analysis. BMC Bioinform. 4, 6 (2003).Article 

    Google Scholar 
    52.Swofford, D. L. & Sullivan, J. Phylogeny inference based on parsimony and other methods using PAUP*. Phylogenet. Handb. Pract. Approach DNA and Protein Phylogeny 7, 160–206 (2003).
    Google Scholar 
    53.Trifinopoulos, J., Nguyen, L.-T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Ronquist, F. et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

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    Large-bodied birds are over-represented in unstructured citizen science data

    1.Pocock, M. J., Tweddle, J. C., Savage, J., Robinson, L. D. & Roy, H. E. The diversity and evolution of ecological and environmental citizen science. PLoS ONE 12, e0172579 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    2.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294 (2017).Article 

    Google Scholar 
    3.Chandler, M. et al. Involving citizen scientists in biodiversity observation. In The GEO Handbook on Biodiversity Observation Networks 211–237 (Springer, 2017).4.McKinley, D. C. et al. Citizen science can improve conservation science, natural resource management, and environmental protection. Biol. Cons. 208, 15–28 (2017).Article 

    Google Scholar 
    5.Pereira, H. M. et al. Monitoring essential biodiversity variables at the species level. In The GEO Handbook on Biodiversity Observation Networks 79–105 (Springer, 2017).6.Wiggins, A. & Crowston, K. From conservation to crowdsourcing: A typology of citizen science. in 2011 44th Hawaii International Conference on System Sciences 1–10 (IEEE, 2011).7.Haklay, M. Citizen science and volunteered geographic information: Overview and typology of participation. In Crowdsourcing Geographic Knowledge 105–122 (Springer, 2013).8.Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Welvaert, M. & Caley, P. Citizen surveillance for environmental monitoring: Combining the efforts of citizen science and crowdsourcing in a quantitative data framework. Springerplus 5, 1890 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Callaghan, C. T., Rowley, J. J., Cornwell, W. K., Poore, A. G. & Major, R. E. Improving big citizen science data: Moving beyond haphazard sampling. PLoS Biol. 17, e3000357 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Bonter, D. N. & Cooper, C. B. Data validation in citizen science: A case study from project FeederWatch. Front. Ecol. Environ. 10, 305–307 (2012).Article 

    Google Scholar 
    12.Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560 (2016).Article 

    Google Scholar 
    13.Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120 (2017).Article 

    Google Scholar 
    14.Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720 (2013).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Sullivan, B. L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biol. Cons. 169, 31–40 (2014).Article 

    Google Scholar 
    16.Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves?. PLoS ONE 10, e0139600 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Tiago, P., Ceia-Hasse, A., Marques, T. A., Capinha, C. & Pereira, H. M. Spatial distribution of citizen science casuistic observations for different taxonomic groups. Sci. Rep. 7, 1–9 (2017).CAS 
    Article 

    Google Scholar 
    18.Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149 (2016).Article 

    Google Scholar 
    19.Callaghan, C. T. et al. Three frontiers for the future of biodiversity research using citizen science data. Bioscience 71, 55–63 (2021).
    Google Scholar 
    20.Ward, D. F. Understanding sampling and taxonomic biases recorded by citizen scientists. J. Insect Conserv. 18, 753–756 (2014).Article 

    Google Scholar 
    21.Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 1–14 (2017).CAS 
    Article 

    Google Scholar 
    22.Martı́n-López, B., Montes, C., Ramı́rez, L. & Benayas, J. What drives policy decision-making related to species conservation? Biol. Conserv. 142, 1370–1380 (2009).23.Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol 8, e1000385 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    24.Aceves-Bueno, E. et al. The accuracy of citizen science data: A quantitative review. Bull. Ecol. Soc. Am. 98, 278–290 (2017).Article 

    Google Scholar 
    25.Davies, T. K., Stevens, G., Meekan, M. G., Struve, J. & Rowcliffe, J. M. Can citizen science monitor whale-shark aggregations? Investigating bias in mark–recapture modelling using identification photographs sourced from the public. Wildl. Res. 39, 696–704 (2013).Article 

    Google Scholar 
    26.Crall, A. W. et al. Assessing citizen science data quality: An invasive species case study. Conserv. Lett. 4, 433–442 (2011).Article 

    Google Scholar 
    27.van Strien, A. J., van Swaay, C. A. & Termaat, T. Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Appl. Ecol. 50, 1450–1458 (2013).Article 

    Google Scholar 
    28.Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. 422, 108927 (2020).Article 

    Google Scholar 
    29.Tiago, P., Pereira, H. M. & Capinha, C. Using citizen science data to estimate climatic niches and species distributions. Basic Appl. Ecol. 20, 75–85 (2017).Article 

    Google Scholar 
    30.Sullivan, B. L. et al. Using open access observational data for conservation action: A case study for birds. Biol. Cons. 208, 5–14 (2017).Article 

    Google Scholar 
    31.Callaghan, C. T. et al. Citizen science data accurately predicts expert-derived species richness at a continental scale when sampling thresholds are met. Biodivers. Conserv. 29, 1323–1337 (2020).Article 

    Google Scholar 
    32.Birkin, L. & Goulson, D. Using citizen science to monitor pollination services. Ecol. Entomol. 40, 3–11 (2015).Article 

    Google Scholar 
    33.Delaney, D. G., Sperling, C. D., Adams, C. S. & Leung, B. Marine invasive species: Validation of citizen science and implications for national monitoring networks. Biol. Invasions 10, 117–128 (2008).Article 

    Google Scholar 
    34.Schultz, C. B., Brown, L. M., Pelton, E. & Crone, E. E. Citizen science monitoring demonstrates dramatic declines of monarch butterflies in western north america. Biol. Cons. 214, 343–346 (2017).Article 

    Google Scholar 
    35.Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Cons. 173, 144–154 (2014).Article 

    Google Scholar 
    36.Isaac, N. J., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060 (2014).Article 

    Google Scholar 
    37.Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297 (2012).Article 

    Google Scholar 
    38.Bonney, R. et al. Next steps for citizen science. Science 343, 1436–1437 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    39.Jordan, R. C., Gray, S. A., Howe, D. V., Brooks, W. R. & Ehrenfeld, J. G. Knowledge gain and behavioral change in citizen-science programs. Conserv. Biol. 25, 1148–1154 (2011).PubMed 
    Article 

    Google Scholar 
    40.Crall, A. W. et al. The impacts of an invasive species citizen science training program on participant attitudes, behavior, and science literacy. Public Underst. Sci. 22, 745–764 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    41.Jordan, R. C., Ballard, H. L. & Phillips, T. B. Key issues and new approaches for evaluating citizen-science learning outcomes. Front. Ecol. Environ. 10, 307–309 (2012).Article 

    Google Scholar 
    42.Evans, C. et al. The neighborhood nestwatch program: Participant outcomes of a citizen-science ecological research project. Conserv. Biol. 19, 589–594 (2005).Article 

    Google Scholar 
    43.Haywood, B. K., Parrish, J. K. & Dolliver, J. Place-based and data-rich citizen science as a precursor for conservation action. Conserv. Biol. 30, 476–486 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Pocock, M. J. et al. A vision for global biodiversity monitoring with citizen science. In Advances in Ecological Research vol. 59, 169–223 (Elsevier, 2018).45.Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv. 18, 61 (2017).Article 

    Google Scholar 
    46.Isaac, N. J. & Pocock, M. J. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531 (2015).Article 

    Google Scholar 
    47.Angulo, E. & Courchamp, F. Rare species are valued big time. PLoS ONE 4, e5215 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    48.Booth, J. E., Gaston, K. J., Evans, K. L. & Armsworth, P. R. The value of species rarity in biodiversity recreation: A birdwatching example. Biol. Cons. 144, 2728–2732 (2011).Article 

    Google Scholar 
    49.Rowley, J. J. et al. FrogID: Citizen scientists provide validated biodiversity data on frogs of australia. Herpetol. Conserv. Biol. 14, 155–170 (2019).
    Google Scholar 
    50.Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers recording behaviour. Sci. Rep. 6, 33051 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Garrard, G. E., McCarthy, M. A., Williams, N. S., Bekessy, S. A. & Wintle, B. A. A general model of detectability using species traits. Methods Ecol. Evol. 4, 45–52 (2013).Article 

    Google Scholar 
    52.Denis, T. et al. Biological traits, rather than environment, shape detection curves of large vertebrates in neotropical rainforests. Ecol. Appl. 27, 1564–1577 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Sólymos, P., Matsuoka, S. M., Stralberg, D., Barker, N. K. & Bayne, E. M. Phylogeny and species traits predict bird detectability. Ecography 41, 1595–1603 (2018).Article 

    Google Scholar 
    54.Wood, C., Sullivan, B., Iliff, M., Fink, D. & Kelling, S. eBird: Engaging birders in science and conservation. PLoS Biol 9, 1001220 (2011).Article 
    CAS 

    Google Scholar 
    55.GBIF.org (3rd December 2019). GBIF occurrence download. https://doi.org/10.15468/dl.lpwczr56.Gilfedder, M. et al. Brokering trust in citizen science. Soc. Nat. Resour. 32, 292–302 (2019).Article 

    Google Scholar 
    57.Callaghan, C., Lyons, M., Martin, J., Major, R. & Kingsford, R. Assessing the reliability of avian biodiversity measures of urban greenspaces using eBird citizen science data. Avian Conserv. Ecol. 12, 66 (2017).
    Google Scholar 
    58.Johnston, A. et al. Best practices for making reliable inferences from citizen science data: Case study using eBird to estimate species distributions. BioRxiv 574392 (2019).59.Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles: Ecological archives E096–269. Ecology 96, 3109–3109 (2015).Article 

    Google Scholar 
    60.Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).62.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
    Article 

    Google Scholar 
    63.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    64.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

    Google Scholar 
    65.Johnston, A. et al. Species traits explain variation in detectability of UK birds. Bird Study 61, 340–350 (2014).Article 

    Google Scholar 
    66.Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).Article 

    Google Scholar 
    67.Henckel, L., Bradter, U., Jönsson, M., Isaac, N. J. & Snäll, T. Assessing the usefulness of citizen science data for habitat suitability modelling: Opportunistic reporting versus sampling based on a systematic protocol. Divers. Distrib. 26, 1276–1290 (2020).Article 

    Google Scholar 
    68.Caley, P., Welvaert, M. & Barry, S. C. Crowd surveillance: Estimating citizen science reporting probabilities for insects of biosecurity concern. J. Pest. Sci. 93, 543–550 (2020).Article 

    Google Scholar 
    69.Périquet, S., Roxburgh, L., le Roux, A. & Collinson, W. J. Testing the value of citizen science for roadkill studies: A case study from South Africa. Front. Ecol. Evol. 6, 15 (2018).Article 

    Google Scholar 
    70.Nakagawa, S. & Freckleton, R. P. Model averaging, missing data and multiple imputation: A case study for behavioural ecology. Behav. Ecol. Sociobiol. 65, 103–116 (2011).Article 

    Google Scholar 
    71.Schlossberg, S., Chase, M. & Griffin, C. Using species traits to predict detectability of animals on aerial surveys. Ecol. Appl. 28, 106–118 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Aristeidou, M., Scanlon, E. & Sharples, M. Profiles of engagement in online communities of citizen science participation. Comput. Hum. Behav. 74, 246–256 (2017).Article 

    Google Scholar 
    73.Troscianko, J., Skelhorn, J. & Stevens, M. Quantifying camouflage: How to predict detectability from appearance. BMC Evol. Biol. 17, 1–13 (2017).Article 

    Google Scholar 
    74.Schuetz, J. G. & Johnston, A. Characterizing the cultural niches of North American birds. Proc. Natl. Acad. Sci. 22, 10868–10873 (2019).Article 
    CAS 

    Google Scholar 
    75.Lišková, S. & Frynta, D. What determines bird beauty in human eyes?. Anthrozoös 26, 27–41 (2013).Article 

    Google Scholar 
    76.Tulloch, A. I., Possingham, H. P., Joseph, L. N., Szabo, J. & Martin, T. G. Realising the full potential of citizen science monitoring programs. Biol. Cons. 165, 128–138 (2013).Article 

    Google Scholar 
    77.Kobori, H. et al. Citizen science: A new approach to advance ecology, education, and conservation. Ecol. Res. 31, 1–19 (2016).CAS 
    Article 

    Google Scholar 
    78.Callaghan, C. T., Poore, A. G., Major, R. E., Rowley, J. J. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B 286, 20191487 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    79.Pacifici, K. et al. Integrating multiple data sources in species distribution modeling: A framework for data fusion. Ecology 98, 840–850 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Robinson, O. J. et al. Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models. Divers. Distrib. 26, 976–986 (2020).Article 

    Google Scholar 
    81.van Strien, A. J., Termaat, T., Groenendijk, D., Mensing, V. & Kery, M. Site-occupancy models may offer new opportunities for dragonfly monitoring based on daily species lists. Basic Appl. Ecol. 11, 495–503 (2010).Article 

    Google Scholar 
    82.Van der Wal, R. et al. Mapping species distributions: A comparison of skilled naturalist and lay citizen science recording. Ambio 44, 584–600 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    83.Dennis, E. B., Morgan, B. J., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    84.Stoudt, S., Goldstein, B. R. & De Valpine, P. Identifying charismatic bird species and traits with community science data. bioRxiv. https://doi.org/10.1101/2021.06.05.446577 More

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    Coral conservation strikes a balance

    NATURE INDEX
    24 September 2021

    Coral conservation strikes a balance

    Australia–Fiji collaboration matches community needs with reef protection.

    Clare Watson

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    Clare Watson

    Clare Watson is a freelance writer in Wollongong, Australia.

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    A spear fisherman catches reef fish, a cultural mainstay on Mali Island in Fiji.Credit: Juergen Freund/naturepl.com

    Coral reefs are under threat, and so too are the livelihoods of more 500 million people who depend on them. Global climate change is causing longer and more frequent marine heatwaves, leading to widespread and repeated coral bleaching. Overfishing and pollution exacerbate the problem, adding pressure to these marine biodiversity hotspots that sustain coastal communities.Reef-management programmes that limit or prohibit fishing and other commercial activities are bound to be ineffective if local communities are not involved in their design and management, says Sangeeta Mangubhai, a coral-reef ecologist in Fiji. “If people haven’t been engaged in the management [of conservation strategies], they’re not as likely to understand what the rules are, or they might not comply with it,” she says. Initiatives that are designed to protect coral reefs without incorporating insights from local communities may also affect them in unintended ways, she adds.
    Nature Index 2021 Science cities
    In collaboration with environmental social scientist, Georgina Gurney, Mangubhai is identifying the conditions that support both conservation outcomes and the wellbeing of coastal communities who often have cultural practices and spiritual ties to the sea. Their work explores the social factors that influence coral-reef-management programmes, such as the perceived fairness of payment schemes that direct tourism revenue back to the communities who manage local reefs (G. G. Gurney et al. Environ. Sci. Policy 124, 23–32; 2021).“First and foremost, it’s an ethical and moral issue,” says Gurney. “Conservation should not impinge on the wellbeing of people; it should promote the wellbeing of people.”Based at James Cook University (JCU) in Townsville, a city on the northeastern coast of Queensland, Australia, Gurney has close access to the Great Barrier Reef, which contains the world’s largest coral reef ecosystem. The university has long-standing ties with researchers in nearby Pacific island nations, such as Papua New Guinea, Fiji and New Caledonia.Townsville was the second most-prolific city in the 82 high-quality natural-sciences journals tracked by the Nature Index for research related to the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14) in 2015–20, with a Share of 15.59, 52% of which is attributed to JCU. Beijing, placed first by output related to SDG14, had a Share of 17.88 for the same period. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

    Georgina Gurney and Sangeeta Mangubhai at a fish market in Suva, Fiji.Credit: Isabelle Gurney

    According to Gurney, successful conservation programmes should evaluate social factors alongside ecological outcomes, such as fish stocks and coral health, although this is rarely the case. With Mangubhai and other collaborators, Gurney has developed a framework that combines 90 social and ecological indicators, from coral cover and fish biomass to household incomes derived from the reef, equitable benefit-sharing and conflicts occurring over marine resources (G. G. Gurney et al. Biol. Conserv. 240, 108298; 2019).In principle, the framework standardizes how outcomes of coral-reef programmes are evaluated to improve data collection and enable cross-country comparisons. It has been adopted by the New York-based non-governmental organization, the Wildlife Conservation Society (WCF), and its partners in 7 countries and more than 130 communities across Africa, Asia and the Pacific.Besides improving conservation efforts, Mangubhai, who leads the WCF’s Fiji programme, says the partnership gives equal footing to local conservation scientists and policymakers, empowering them to direct independent research. “If you have these meaningful collaborations, the outcome is going to have so much more of an impact on the ground,” she says.Incorporating an understanding of the social factors that influence coral-reef conservation into marine-management strategies translates to respect for local traditional cultural practices of Indigenous Fijians, says Mangubhai. Temporary closures called tabu, which are used to maintain the productivity of their customary fishing grounds, are a good example. “It’s a real merging of traditional knowledge and other best practices, such as size limits on fish catch, to help communities achieve the outcomes they want for themselves,” she says.

    doi: https://doi.org/10.1038/d41586-021-02409-6This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

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    Rising tide of floating plastics spurs surge in research

    NATURE INDEX
    24 September 2021

    Rising tide of floating plastics spurs surge in research

    Strong government policies and research insights are essential to deliver on a pledge to clean up the sea.

    Michael Eisenstein

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    Michael Eisenstein

    Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

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    A jellyfish swims beneath a slick of floating plastic debris in the Indian Ocean near Sri Lanka.Credit: Alex Mustard/naturepl.com

    Many stories have been written about the ‘Great Pacific garbage patch’, a name evoking a vast Sargasso Sea of plastic bottles and bags. But the reality is that much of this debris has been broken down into a murky suspension of ‘microplastics’ spanning an area three times the size of France.
    Nature Index 2021 Science cities
    These plastic flecks introduce long-lasting chemical pollution into marine and coastal ecosystems, says Daoji Li, an oceanographer at East China Normal University in Shanghai. In 2020, Li and his colleagues found that microplastic debris is highly concentrated in even the deepest underwater trenches (G. Peng et al. Water Res. 168, 115121; 2020). Staving off this influx of pollutants is a target of the United Nations’ Sustainable Development Goal (SDG) Life below water (SDG14), with its aim to “prevent and significantly reduce marine pollution of all kinds” by 2025.Between 4.8 million and 12.7 million tonnes of plastic waste entered the oceans in 2010, according to a study in Science, and those numbers are expected to increase dramatically by 2050 without improvements to waste-management infrastructure (J. Jambeck et al. Science 347, 768–771; 2015). Scientists in China, which is a major producer and importer of plastic waste, are taking the lead in amelioration. According to the 2021 UNESCO Science Report, floating plastic debris was the fastest-growing area of SDG-related research in 2012–19 (see ‘A buoyant field’). Publications from the Chinese mainland on the topic jumped from 7 in the period 2012–15 to 286 in 2016–19, placing it third by volume after the United States and United Kingdom. Much of this work has come from investigators in Beijing, the top-ranked city in the Nature Index for SDG14-related research. (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

    Source: UNESCO

    Li is sceptical that much can be done to eliminate existing plastic pollution. “But what we can do is stop them entering to the ocean,” he says. His team has developed a monitoring framework that outlines ‘gold-standard’ technologies and assays for detecting and quantifying microplastic contamination.Government action is essential to stem the flow of plastic debris. UNESCO reports that 127 countries have adopted legislation to regulate plastic bags. In 2020, China launched an ambitious effort to ban plastic bags nationwide by 2022 and cut single-use plastic in restaurants by one-third by 2025 — although the COVID-19 pandemic created a surge in demand for delivery that derailed this effort.Despite the many hurdles to overcome, Li feels positive about the future. “I am pretty confident that we could meet the target set for SDG14,” he says, “but when we realize those challenges, we should keep going.”

    Source: UNESCO

    doi: https://doi.org/10.1038/d41586-021-02408-7This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

    Related Articles

    Sustainable Development Goals research speaks to city strengths and priorities

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    How cities are collaborating to help safeguard oceans

    NATURE INDEX
    24 September 2021

    How cities are collaborating to help safeguard oceans

    Despite missed deadlines in 2020 for key targets in marine conservation, momentum for these Sustainable Development Goals is growing.

    Michael Eisenstein

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    Michael Eisenstein

    Michael Eisenstein is a freelance writer in Philadelphia, Pennsylvania.

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    Bart Shepherd, co-leader of the Hope for Reefs initiative, guides fish into a decompression chamber while on expedition in Vanuatu.Credit: Luiz Rocha/California Academy of Sciences

    For about 30 minutes each year, vast colonies of corals in the waters of Palau, an island nation in the western Pacific, erupt in an almost perfectly synchronized mass-spawning event. Releasing buoyant packages of sperm and egg cells into the water to be fertilized by neighbouring colonies, these hermaphroditic species must make the most of rare opportunities to seed new life.In one of the world’s few indoor coral-culturing labs, Rebecca Albright and her team at the California Academy of Sciences in San Francisco are recreating the seasonal and lunar shifts that trigger such an event. The aim is to create multiple spawning systems that can be studied under controlled conditions. “Corals are notorious for being fickle animals to keep in captivity,” says Albright, a coral biologist and co-leader of Hope for Reefs, a global initiative to research and restore crucial coral-reef systems. “Most only sexually reproduce once a year, so you have to simulate all these environmental cues to elicit that.”
    Nature Index 2021 Science cities
    Strategies for cultivating and transplanting healthy corals into depleted areas are a crucial part of strengthening populations against what Albright describes as the “one-two punch effect” of climate change. Rising temperatures cause coral bleaching and death, while ocean acidification caused by increased levels of carbon dioxide makes corals less resilient and prevents regrowth. “If we are able to cap warming at 1.5  °C, we’re still going to lose 90% of reefs by 2050,” she says. “And if we edge towards 2 °C, we risk losing 97% to 99%.”Of the United Nations’ 17 Sustainable Development Goals (SDGs), Life below water (SDG14) and other SDGs related to environmental sustainability — Responsible consumption and production (SDG12), Climate action (SDG13) and Life on land (SDG15) — were the weakest in both donor funding and outcomes, attracting less than US$25 billion between them in 2000–13, according to the 2021 UNESCO Science Report (see go.nature.com/3zlojva). SDGs that are more directly related to economic growth — Industry, innovation and infrastructure (SDG9) and Sustainable cities and communities (SDG11) — by comparison, received $130 billion and $147 billion, respectively, over the same period.James Leape, co-director of Stanford University’s Center for Ocean Solutions in California, notes that four of the ten targets for SDG14, which aims to “conserve and sustainably use the oceans, seas and marine resources”, were due in 2020. All were missed. These include controlling the global damage wrought by illegal and unregulated fishing, which remains largely unchecked, and implementing scientifically grounded strategies for restoring affected fish stocks.But there are signs of momentum. The amount of ocean being conserved and managed within marine protected areas (MPAs), for example, has increased from 0.9% to 7.7% since 2000, says Leape. MPAs are regions in which fishing, mining and other activities are restricted. Efforts are under way to further expand the number of MPAs globally.Coastal collaborationsAs the world’s leading fishing nation, responsible for 15% of the reported global wild fish catch, China has ramped up efforts to designate new MPAs. Since 1980, China has designated more than 270 MPAs, comprising about 5% of its national waters. But it’s a long way off efforts by countries such as the United States, which has more than 1,000 MPAs that cover about 26% of its waters, and the United Kingdom, with 371 MPAs comprising 38% of its seas. In a 2019 Nature correspondence, fisheries researchers Yunzhou Li and Yiping Ren, from the Ocean University of China in Qingdao and Yong Chen from the University of Maine, Orono, say that effective monitoring and strict enforcement will also be essential to the success of China’s efforts (see Nature 573, 346; 2019).In a city-based analysis by the Nature Index, Beijing had the greatest output related to SDG14 in the 82 natural-sciences journals tracked by the index in 2015–20, with a Share of 17.88, followed by the coastal city of Townsville in northeastern Queensland, Australia (Share 15.59) and the Boston metropolitan area (Share 13.66). The San Francisco Bay Area, second only to Beijing in output related to all 17 SDGs, had the sixth-highest Share for SDG14 (13.24). (For more information on the analyses used in this article, see ‘A guide to Nature Index’.)

    Residents in the coastal town of Maroantsetra, in northeastern Madagascar, display their catch.Credit: Rebecca Gaal

    Many small island states face serious threats from the rapid decline of their coral reefs, which represent one of the world’s most diverse ecosystems. Gildas Todinanahary, a marine biologist at the Fisheries and Marine Science Institute at the University of Toliara in Madagascar, says the percentage of live coral cover surrounding the island nation has dropped from more than 80% in the 1980s to less than 10%, on average, today. “Decades ago, they used to say there will always be fish in the sea,” says Todinanahary. “Now they say there are no more fish.” This has jeopardized the livelihood of the fishing communities on the island’s western shore, he says.Christopher Golden, an ecologist and epidemiologist at the Harvard School of Public Health in Boston, is working with Todinanahary and his colleagues to deploy a series of small tiered platforms, designed to mimic the cracks and crevices of the reef, into healthy coral communities along the Madagascar coast. Once colonized, these structures are transported into degraded reefs in an effort to repopulate them. “If we can create a healthier reef, we can then rehabilitate some of the fish populations, and that will lead to improved fish-catch and greater access to seafood as a nutritional resource,” says Golden.Todinanahary is enthusiastic about the potential for seeding new reefs in barren coastal stretches, but says education and outreach to fishing communities will be key to ensuring that those restoration efforts endure. “It’s important to help communities change their habits and activities,” he says — for example, by providing training for alternative livelihoods such as aquaculture.Buy-in from community leaders is also crucial to the success of partnerships between researchers in leading science cities and colleagues in low- and middle-income maritime nations in SDG-related projects. In 2016, the government of Palau invited Leape and his team at Stanford to develop a strategy for turning 80% of its exclusive economic zone, a 370-km radius surrounding the island, into a protected area where fishing is prohibited. The initiative went into effect in January 2020. “We’re using satellite tracking to understand the patterns of use of the sanctuary by large pelagic species, and using DNA analysis to monitor biodiversity in the sanctuary,” says Leape. Palau’s programme has helped to motivate other island nations in the region to extend marine protection and conservation efforts as part of the Micronesia Challenge, an initiative to conserve 50% of marine resources and 30% of terrestrial resources by 2030.Golden’s research emphasizes both the sustainability and food-security sides of the fisheries-management coin, with routine health assessments of communities in places such as Madagascar and the Republic of Kiribati, an island nation in the central Pacific Ocean, coupled with close monitoring of the ecological health of their surrounding waters. To help this effort, Golden and his colleagues developed the Aquatic Food Composition Database, which compiles detailed nutritional information on more than 3,700 local plant and animal species to provide ecologically grounded guidance to local fishers. “We can look at what type of resilience there might be if we lose access to one species and have to focus on another,” says Golden. “We can understand the type of nourishment that people are actually getting from their catch.”Stanford’s Center for Ocean Solutions is also leveraging new technologies to guide sustainable fishing practices that benefit small-scale fishers, whose livelihood SDG14 aims to safeguard. “Their catches account for about two-thirds of the seafood we eat, and 90% of the fishery jobs,” says Leape. The centre is partnering with ABALOBI, an organization in South Africa founded by fisheries researcher Serge Raemaekers, from the University of Cape Town. ABALOBI has designed a mobile app toolbox to help fishers track specific fish populations, coordinate boats and crews, and bring catches to market. Leape is hopeful that early pilot testing in Africa and the Indian Ocean will pave the way for broader deployment in the near future.In parallel, Leape’s team is working on strategies to crack down on illegal fishing — currently estimated to account for roughly 20% of the global catch. This is being achieved partly through tools such as the satellite-based fishery monitoring efforts of Global Fishing Watch, a website run by Google in partnership with conservation non-profit organizations Oceana and SkyTruth. But technology is only part of the solution. Leape sees a crucial role for aggressive government enforcement and getting major corporations to engage in closer oversight of fishing practices. “We’ve been using Global Fishing Watch and other data sources to understand the patterns and areas for illegal fishing,” he says. “We’re working with these partners to try to translate that data into a more concerted effort to crack the problem.”

    doi: https://doi.org/10.1038/d41586-021-02407-8This article is part of Nature Index 2021 Science cities, an editorially independent supplement produced with the financial support of third parties. About this content.

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    1.MacArthur, R. H. & Wilson, E. The theory of Island Biogeography. (Princeton University Press, 1967).2.Losos, J. B. & Schluter, D. Analysis of an evolutionary species–area relationship. Nature 408, 847 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Losos, J. B. & Ricklefs, R. E. The Theory Of Island Biogeography Revisited. (Princeton University Press, 2009).5.Onstein, R. E. et al. Frugivory-related traits promote speciation of tropical palms. Nat. Ecol. Evol. 1, 1903 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Bush, M. B. & Whittaker, R. J. Krakatau: colonization patterns and hierarchies. J. Biogeogr. 18, 341–356 (1991).7.Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

    Google Scholar 
    8.Hoeksema, J. D. et al. Evolutionary history of plant hosts and fungal symbionts predicts the strength of mycorrhizal mutualism. Commun. Biol. 1, 116 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Duchicela, J., Bever, J. D. & Schultz, P. A. Symbionts as filters of plant colonization of islands: tests of expected patterns and environmental consequences in the galapagos. Plants 9, 74 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    10.Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Chaudhary, V. B., Nolimal, S., Sosa‐Hernández, M. A., Egan, C. & Kastens, J. Trait‐based aerial dispersal of arbuscular mycorrhizal fungi. New Phytol. 228, 238–252 (2020).12.Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis (Academic press, 2008).13.Oneto, D. L., Golan, J., Mazzino, A., Pringle, A. & Seminara, A. Timing of fungal spore release dictates survival during atmospheric transport. Proc. Natl Acad. Sci. USA 117, 5134–5143 (2020).CAS 
    Article 

    Google Scholar 
    14.Roper, M., Pepper, R. E., Brenner, M. P. & Pringle, A. Explosively launched spores of ascomycete fungi have drag-minimizing shapes. Proc. Natl Acad. Sci. USA 105, 20583–20588 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. New Phytol. 209, 1705–1719 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems- a journey towards relevance? New Phytol. 157, 475–492 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Martino, E. et al. Comparative genomics and transcriptomics depict ericoid mycorrhizal fungi as versatile saprotrophs and plant mutualists. New Phytol. 217, 1213–1229 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.McCORMICK, M. K. et al. Limitations on orchid recruitment: not a simple picture. Mol. Ecol. 21, 1511–1523 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Selosse, M. A. et al. Saprotrophic fungal symbionts in tropical achlorophyllous orchids: finding treasures among the ‘molecular scraps’? Plant Signal. Behav. 5, 349–353 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Smith, G. R., Finlay, R. D., Stenlid, J., Vasaitis, R. & Menkis, A. Growing evidence for facultative biotrophy in saprotrophic fungi: data from microcosm tests with 201 species of wood‐decay basidiomycetes. New Phytol. 215, 747–755 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Lindahl, B. D. & Tunlid, A. Ectomycorrhizal fungi–potential organic matter decomposers, yet not saprotrophs. New Phytol. 205, 1443–1447 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Peay, K. G., Schubert, M. G., Nguyen, N. H. & Bruns, T. D. Measuring ectomycorrhizal fungal dispersal: macroecological patterns driven by microscopic propagules. Mol. Ecol. 21, 4122–4136 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Pither, J., Pickles, B. J., Simard, S. W., Ordonez, A. & Williams, J. W. Below‐ground biotic interactions moderated the postglacial range dynamics of trees. New Phytol. 220, 1148–1160 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.van der Heijden, M. G., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    25.Chaudhary, V. B. et al. MycoDB, a global database of plant response to mycorrhizal fungi. Sci. Data 3, 160028 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Pyšek, P. et al. Facultative mycorrhizal associations promote plant naturalization worldwide. Ecosphere 10, e02937 (2019).Article 

    Google Scholar 
    27.Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. New Phytol. 199, 41–51 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Steidinger, B. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Bueno, C. G. et al. Plant mycorrhizal status, but not type, shifts with latitude and elevation in Europe. Glob. Ecol. Biogeogr. 26, 690–699 (2017).Article 

    Google Scholar 
    30.Cameron, D. D., Leake, J. R. & Read, D. J. Mutualistic mycorrhiza in orchids: evidence from plant–fungus carbon and nitrogen transfers in the green‐leaved terrestrial orchid Goodyera repens. New Phytol. 171, 405–416 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Dearnaley, J. D. Further advances in orchid mycorrhizal research. Mycorrhiza 17, 475–486 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Davison, J. et al. Microbial island biogeography: isolation shapes the life history characteristics but not diversity of root-symbiotic fungal communities. ISME J. 12, 2211–2224 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Koziol, L. & Bever, J. D. Mycorrhizal feedbacks generate positive frequency dependence accelerating grassland succession. J. Ecol. 107, 622–632 (2019).Article 

    Google Scholar 
    34.Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).Article 
    CAS 

    Google Scholar 
    35.Koziol, L. et al. The plant microbiome and native plant restoration: the example of native mycorrhizal fungi. BioScience 68, 996–1006 (2018).36.Lu, M. & Hedin, L. O. Global plant–symbiont organization and emergence of biogeochemical cycles resolved by evolution-based trait modelling. Nat. Ecol. Evol. 3, 239 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Zotz, G. The systematic distribution of vascular epiphytes–a critical update. Bot. J. Linn. Soc. 171, 453–481 (2013).Article 

    Google Scholar 
    38.Zotz, G. Vascular epiphytes in the temperate zones–a review. Plant Ecol. 176, 173–183 (2005).Article 

    Google Scholar 
    39.Taylor, A., Weigelt, P., König, C., Zotz, G. & Kreft, H. Island disharmony revisited using orchids as a model group. New Phytol. 223, 597–606 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Razanajatovo, M. et al. Autofertility and self‐compatibility moderately benefit island colonization of plants. Glob. Ecol. Biogeogr. 28, 341–352 (2019).Article 

    Google Scholar 
    41.van Kleunen, M. et al. The Global Naturalized Alien Flora (Glo NAF) database. Ecology 100, e02542 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Pysek, P. et al. Naturalized alien flora of the world: species diversity, taxonomic and phylogenetic patterns, geographic distribution and global hotspots of plant invasion. Preslia 89, 203–274 (2017).Article 

    Google Scholar 
    43.Weigelt, P., König, C. & Kreft, H. GIFT–A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

    Google Scholar 
    44.Kalwij, J. M. Review of ‘The Plant List, a working list of all plant species’. J. Veg. Sci. 23, 998–1002 (2012).Article 

    Google Scholar 
    45.Byng, J. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20 (2016).Article 

    Google Scholar 
    46.Maherali, H. et al. Mutualism persistence and abandonment during the evolution of the mycorrhizal symbiosis. Am. Nat. 188, E113–E125 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Brundrett, M. C. Mycorrhizal associations and other means of nutrition of vascular plants: understanding the global diversity of host plants by resolving conflicting information and developing reliable means of diagnosis. Plant Soil 320, 37–77 (2009).CAS 
    Article 

    Google Scholar 
    48.Gerdemann, J. Vesicular-arbuscular mycorrhiza and plant growth. Annu. Rev. Phytopathol. 6, 397–418 (1968).Article 

    Google Scholar 
    49.Bueno, C. G., Gerz, M., Zobel, M. & Moora, M. Conceptual differences lead to divergent trait estimates in empirical and taxonomic approaches to plant mycorrhizal trait assignment. Mycorrhiza 29, 1–11 (2018).Article 
    CAS 

    Google Scholar 
    50.Brundrett, M. C. & Tedersoo, L. Evolutionary history of mycorrhizal symbioses and global host plant diversity. New Phytol. 220, 1108–1115(2018).51.Vrålstad, T. Are ericoid and ectomycorrhizal fungi part of a common guild? New Phytol. 164, 7–10 (2004).52.Vrålstad, T., Fossheim, T. & Schumacher, T. Piceirhiza bicolorata–the ectomycorrhizal expression of the Hymenoscyphus ericae aggregate? New Phytol. 145, 549–563 (2000).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Danielson, J. J. & Gesch, D. B. Global multi-resolution terrain elevation data 2010 (GMTED2010). Report No. 2331-1258, (US Geological Survey, 2011).55.Center for International Earth Science Information Network – CIESIN – Columbia University, U. N. F. a. A. P.-F., and Centro Internacional de Agricultura Tropical – CIAT. Gridded Population of the World, Version 3 (GPWv3): Population Count Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). (2005).56.Tuanmu, M. N. & Jetz, W. A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr. 23, 1031–1045 (2014).Article 

    Google Scholar 
    57.Weigelt, P. & Kreft, H. Quantifying island isolation–insights from global patterns of insular plant species richness. Ecography 36, 417–429 (2013).Article 

    Google Scholar 
    58.Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    59.Triantis, K. A., Economo, E. P., Guilhaumon, F. & Ricklefs, R. E. Diversity regulation at macro‐scales: species richness on oceanic archipelagos. Glob. Ecol. Biogeogr. 24, 594–605 (2015).Article 

    Google Scholar 
    60.Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).Article 

    Google Scholar 
    61.Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST. 27, 716–748 https://doi.org/10.1007/s11749-018-0599-x (2018).Article 

    Google Scholar 
    62.R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2019).63.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    64.Delavaux, C. et al. Mycorrhizal Types Influence Island Biogeography of Plants: associated data. Zenodo https://doi.org/10.5281/zenodo.5179626 (2021). More