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    Effect of irrigation and bio-fertilizers on morphological and bio-chemical traits of milk thistle

    Abstract

    The milk thistle plant strengthens the body’s immune system and is one of the top-selling medicinal plants in the world in terms of turnover. Considering the water crisis in Iran and especially in the northwest of Iran, which has led to the drying up of Lake Urmia, the cultivation of crops with low water requirements is considered important under the conditions of water scarcity. The cultivation of medicinal plants will solve the environmental problems of the region, change the cropping pattern from high-consumption to low-consumption crops, increase farmers’ income and ultimately improve the living conditions of farmers in the region. Given the significance of milk thistle, an experiment was conducted to assess the effect of irrigation type and biofertilizers on its seed yield, oil yield, and fatty acids. So, a split-plot experiment was conducted in Miandoab County at the south of West Azerbaijan province, Iran in 2016–2017 based on a randomized complete block design with 10 treatments and three replications. The main plot was assigned to irrigation regime (at two levels of full irrigation and no irrigation) and the sub-plot to biofertilizer (at five levels of control, nitrogen biofertilizer, phosphorus biofertilizer, potash biofertilizer, and NPK biofertilizer). Based on the results, the interaction of irrigation × biofertilizer was significant for biological yield, capitule number, and DPPH radical scavenging at the P < 0.01 probability level and for seed yield per plant and plant height at the P < 0.05 probability level. But, this interaction was insignificant for the other traits including harvest index, crown diameter, branch number, oil yield, phenol and flavonoid content, superoxide radical scavenging, nitric oxide radical scavenging, and chain breaking. In addition, it was found that most morphological and biochemical traits of the thistles were affected by irrigation and biofertilizers so that the biofertilizers improved them versus the control. Given the significant role of biofertilizers in the development of sustainable agriculture, they can be a good candidate in case there is a need for nutrient supplements for this plant. Under drought stress conditions, the combination of bio-fertilizers (NPK(b)) and irrigation helps the plant to tolerate drought stress easily and also improves the medicinal properties of the plant.

    IntroductionMilk thistle (Silybum marianum L.) is an annual herbaceous plant species originated from the east of the Mediterranean region1, but it is extensively distributed in hot and arid regions. Today, this plant species has provoked interests due to its significance in pharmaceutical industries (treatment of liver diseases) and plant oil production2,3. It is used in the treatment of hepatic disorders, hepatitis (A), high blood cholesterol content, some toxicities, bilious precipitations and stones, migraine, spleen diseases, and alcoholic cirrhosis and has antiviral, anti-coagulation and anti-thrombotic activities4.The medicinal plants the black cumin (Nigella sativa L.), the echinacea purpureae (L.) moench and the milk thistle (Silybum marianum L.) are effective in strengthening the body’s immunity and in treating hepatitis diseases. It should be noted that black cumin is not very well known in the world and has a low turnover, and the water consumption of echinacea is also higher5,6. Due to the reasons mentioned, the milk thistle was selected for the present study. Oil, food, cosmetics, and forage uses are some other applications of thistle. Drought stress is one of the most important and destructive abiotic stresses influencing plant production. It is estimated that about 26% of all arable lands of the world are located in arid regions7. On the other hand, we have witnessed the loss of crop quality and soil fertility in the recent decades due to the inappropriate and excessive use of fertilizers and chemical pesticides and the crisis of the environment pollution, especially the pollution of water and soil resources, which entails the contamination of food and endangers the safety of human communities. Furthermore, the inadequate supply of nutrients8 and irrigation water9 dramatically disturb plant growth and physiology. Hence, reductions in yields and quality associated the unsuitable water and fertilization management10. So, the focus has dramatically been shifted towards finding approaches to improving soil and crop quality and removing pollutants. The prevention of the excessive application of chemical inputs and sustainable food production along with environmental conservation are issues that have drawn the attention of most researchers and crop producers11,12. In this respect, biofertilizer-based sustainable farming has been considered more than ever to stop or reduce the consumption of chemical inputs13,14.In a study on the combined effect of drought stress and biofertilizer inoculation on the quantitative and qualitative traits of thistle, Mohammadpour Vashvaei et al.15 reported that the simple and interactive effects of drought stress and biofertilizers were significant for all traits. It has also been reported that the use of biofertilizers induced plant growth by increasing nutrient availability and thereby it increased yield components (capitule number per plant, seed number per capitule, and 1000-seed weight), which resulted in the enhancement of thistle yield16. Volaii et al.17 found the positive impact of biofertilizer and vermicompost on yield and yield components of thistle and stated that the concurrent use of the two biofertilizers was most effective.In another study, Eskandari Nasrabadi et al.13 revealed promising results as to the reduction of chemical fertilizer use in the cultivation of thistle and reported the need to consider more use of biological and organic fertilizers. Based on the results of a four-year experiment by Haban et al.18 about the negative impact of chemical fertilizers on thistle seed yield in the long run, it was recommended to use biofertilizers. However, Yazdani Biuki et al.19 reported that none of the morphological traits and yield components of thistle was influenced by different fertilizer treatments. They hypothesized that thistle may not easily lend itself to the effect of fertilizers.The milk thistle plant strengthens the body’s immune system and is one of the top-selling medicinal plants in the world in terms of turnover. Considering the water crisis in Iran and especially in the northwest of Iran, which has led to the drying up of Lake Urmia, the cultivation of crops with low water requirements deficit irrigation tactic is considered important under the conditions of water scarcity20. However, irrigating plants with water less than normal level undoutly results in decline in crop productivity and quality21. Therefore, under these circumstances, unconventional strategies should be adopted. The cultivation of such crops will solve the environmental problems of the region, change the cropping pattern from high-consumption to low-consumption crops, increase farmers’ income and ultimately improve the living conditions of farmers in the region. If this scientific research is not carried out, the northwestern region of Iran will be deserted and devoid of inhabitants in the next 40 years due to salt storms in Lake Urmia. This is because toxic salt particles are dispersed in the atmosphere and are the most important factor in the occurrence of respiratory diseases, various types of cancer and skin and digestive problems. These particles also have an impact on eye health, heart health and high blood pressure. Since milk thistle is of high importance due to its drought stress adaptation and resistance and nowadays the use of biofertilizer to curb on the use of chemical fertilizer and to increase crop yields is important for the movement towards sustainable agriculture, which can be much more important under no-irrigation conditions22 and since little research has addressed the effect of combined use of no-irrigation conditions and biofertilizers on medicinal plants23 including milk thistle, the present study focused on the effect of bio-fertilizers on the quality and quantity of thistle under no-irrigation conditions. Bio-fertilizers effectively increase the plant’s tolerance to drought stress, prevent a reduction in grain yield and improve the plant’s antioxidant properties (phenolic and flavonoid compounds).Results and discussionThe results of ANOVA are presented in Table 1 for morphological traits and in Table 2 for biochemical traits. Accordingly, the interaction of irrigation × biofertilizer was significant for biological yield, capitule number, and DPPH radical scavenging at the P < 0.01 level and for seed yield per plant and plant height at the P < 0.05 level. But, it was not significant for the other traits including harvest index, crown diameter, branch number, oil yield, phenol and flavonoid content, superoxide radical scavenging, nitric oxide radical scavenging, and chain breaking.Table 1 The results of the analysis of variance for the recorded morphological traits based on a split-plot experiment.Full size tableTable 2 The results of the analysis of variance for the recorded biochemical traits based on a split-plot experiment.Full size tableSeed and biological yield per plantBased on the comparison of the means, the treatment of the P biofertilizer to the fully-irrigated plants was related to the highest seed yield (20.74 g), and the non-irrigated plants that were not treated with a biofertilizer produced the lowest one (10.99 g). The interaction of ‘P biofertilizer × full irrigation’, which produced the highest seed yield per plant, did not differ significantly only from the treatments of the NPK biofertilizer and the N biofertilizer to the full irrigation conditions. Among the fully-irrigated plants, those treated with the P biofertilizer exhibited the highest seed yield (20.74 g) and those not treated with a biofertilizer exhibited the lowest one (17.68 g) so that only P biofertilizer was grouped with the control in a separate statistical group, and the other treatments did not show a significant difference from the control. Under the no-irrigation conditions, the highest and lowest seed yields were 16.78 and 10.99 g obtained from the NPK biofertilizer and no-biofertilizer, respectively. In general, the bio-fertilizer treatment with full irrigation and without irrigation increased grain yield by about 15 and 35%, respectively, compared to the control treatment. In these conditions, in addition to the NPK biofertilizer, the P biofertilizer differed from the control significantly too whereas the N biofertilizer and the K biofertilizer were in the same statistical group with the control. So, it can be concluded that the NPK fertilizer could partially alleviate the adverse impacts of drought stress. In other words, although when less water is consumed, which naturally induces drought stress and seed yield per plant is decreased, the application of the NPK biofertilizer can partially mitigate the negative impacts on the yield. This may partially be attributed to the positive effect of biofertilizers on improving plant nutritional status under stressful conditions24,25. It seems that the separate use of the N and K biofertilizers entailed responses similar to the control.In a similar finding, Mohammadpour Vashvaei et al.15 attributed the decline in seed yield of milk thistle under water deficit conditions to the reduction of photosynthesis and assimilation in the plants and the decline in the nutrients mobilized from the leaves to the seeds. Volaii et al.17 reported the positive effect of biofertilizer and vermicompost on yield and yield components of milk thistle and stated that the combined use of the two biofertilizers had the strongest impact on the traits. In another study, Eskandari Nasrabadi et al.13 found promising results as to the decrease in chemical fertilizer use in the cultivation of milk thistle. They emphasized that more attention should be given to the use of biofertilizers and organic fertilizers.The interaction of ‘NPK biofertilizer × full irrigation’ had the highest and ‘no biofertilizer × no irrigation’ had the lowest biological yield per plant (132 and 86.2 g plant−1, respectively). Under the full irrigation conditions, in addition to the NPK biofertilizer, the N biofertilizer produced a considerably high biological yield of 127 g plant−1. As expected, the lowest biological yield (116 g plant−1) was obtained from the full irrigation of the control plants, which was in the same statistical group with the P and K biofertilizers. Under no irrigation conditions, the NPK biofertilizer had the highest biological yield of 118 g plant−1 followed by the N biofertilizer, which produced 102 g plant−1 of biological yield. The other treatments did not differ from the control significantly (Fig. 1). In general, the bio-fertilizer treatment with full irrigation and without irrigation increased biological yield by about 11.5 and 27%, respectively, compared to the control treatment. It has been documented that low water supply reduced the availability of nutrients and uptake26, while reactive oxygen species are stimulated which damage the cell membrane with reduction of photosynthesis efficiency27.Sanchez et al.28 reported for Plantago major L. and P. lanceolota L. that the application of biofertilizers enhanced biological yield. Rodriguez et al.29 attributed the decline in biological yield of the stressed plants to the dry weight of their roots, stems, and leaves. Consistently, Anwar et al.30 observed that the application of NPK fertilizer + vermicompost increased the biological yield of basil versus the control. Yazdani Biuki et al.19 reported that the changes in the biological yield and harvest index of milk thistle in response to chemical, biological, and organic fertilizers versus the control were statistically significant.Fig. 1The results of means comparison for the interaction of irrigation × biofertilizer for biological yield per plant. Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageHarvest indexIt was found that the P biofertilizer had the highest harvest index of 16.3% and the N biofertilizer had the lowest one of 13.8%. The other treatments were placed in the same statistical group with the N biofertilizer despite some small variations (Fig. 2). This is consistent with the results of Darzi et al.31 for fennel and Rezaei-Chiyaneh et al.32 for Lallemantia as to the increase in yield and yield components of these plant species in response to the treatment of the biological phosphate fertilizer.Fig. 2The results of means comparison for the simple effect of biofertilizer on harvest index. Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imagePlant heightThe treatment of N biofertilizer to the fully-irrigated plants was associated with the highest plant height of 178 cm and the control treatment under no-irrigation conditions was associated with the lowest plant height of 111 cm. Under the full irrigation conditions, the NPK and P biofertilizers were grouped in the same statistical category with the N biofertilizer, whereas the treatment of no biofertilizer had the lowest plant height of 153 cm and was significantly different from the other treatments. Under no irrigation conditions, the NPK biofertilizer had the highest plant height (138 cm) and the N biofertilizer had the second highest plant height, but the other treatments did not differ from the control significantly (Fig. 3a). In general, the bio-fertilizer treatment with full irrigation and without irrigation increased plant height by about 14 and 20%, respectively, compared to the control treatment. The morphological changes in medicinal plants in response to drought stress have been subject to extensive studies. Our findings are in agreement with the findings as to the decline in the plant height of basil33 and savory34 under drought stress. The significant impact of fertilizer treatments on plant height of milk thistle has already been reported35.Crown diameterThe mean crown diameter was higher in the full irrigation conditions (23.3 mm) than in the no-irrigation conditions (19.3 mm). The NPK biofertilizer exhibited the highest crown diameter of 22.6 mm followed by the N biofertilizer in the second rank (22.4 mm). These two treatments were significantly different from the control (19.3 mm) whereas this difference was not observed in the P and K biofertilizers (Fig. 3b). The quantity and quality of a plant’s vegetative growth depend on cell division, elongation, and differentiation, and all these are influenced by drought stress36. For the same reason, the crown diameter was lower in the no-irrigation conditions than in the full-irrigation conditions. Since the crown is a sink of assimilates, the improvement of nutritional conditions by the studied fertilizers increased assimilates and this increased stem diameter.Capitule numberBased on the results of the comparison of means, when drought stress was applied, almost five capitules were decreased from all plants. All treatments, including the control and biofertilizers, produced their highest capitule number (15 capitules) in the full irrigation conditions and they were all categorized in the same statistical group. However, the differences between the fertilizer treatments with one another and with the control were more considerable in the no-irrigation conditions so that the NPK biofertilizer was related to the highest number of capitules (13.3 capitules) and the control to the lowest number (8.7 capitules). Also, the N and P biofertilizers were placed in the same group and had an intermediate number of capitules. Only, the K biofertilizer did not differ from the control significantly (Fig. 3c). In general, the bio-fertilizer treatment with full irrigation and without irrigation increased capitule number by about 2 and 35%, respectively, compared to the control treatment. Irrigation is effective in increasing the number of capitules per plant due to its effect on increasing moisture and water uptake by the plants and improving nutrient uptake and retention capacity. On the other hand, drought stress limited plant growth and reduced the number of auxiliary branches, thereby reducing the number of capitules per plant.Volaii et al.17 concluded that organic and biological fertilizers were influential on most growth traits of milk thistle including the number of capitules per plant and capitule diameter. Mohammadpour Vashvaei et al.15 mentioned the number of capitule per plant to be the most important seed component. They revealed that this trait was significantly (P < 0.01) influenced by drought stress, biofertilizer, and their interaction. Most researchers agree that this trait is genetic and is influenced by genotype37.Fig. 3The results of means comparison for the interaction of irrigation × biofertilizer for plant height (a), crown diameter (b) and capitule number (c). Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageNumber of auxiliary branchesThe plants fully irrigated produced more branches (3 auxiliary branches) than those not irrigated (2.69 auxiliary branches). On the other hand, the application of the NPK and N biofertilizers was related to the highest number of auxiliary branches (3.11 and 3 auxiliary branches, respectively) and the control had the lowest number (2.46 auxiliary branches). But, the P and K biofertilizers were in between these two extremes (Fig. 4a). The increase in the number of auxiliary branches in the full irrigation conditions is related to the improved vegetative growth of the plant. The increased number of auxiliary branches in the fertilizer treatments can be caused by the improved growth conditions due to the uptake of nutrients, especially N. A study on the effect of biofertilizers (nitrogen and phosphate solubilizing microorganisms) on the quantitative and qualitative yield of milk thistle revealed that although biofertilizers enhanced some recorded traits, there were no significant differences among the fertilizer treatments in the number of branches per plant, capitule diameter, the number of capitule per plant, and 1000-seed weight17.Oil yield per plantAccording to Fig. 4b, oil yield was higher under the full irrigation conditions (5.2 g plant−1) than the no-irrigation conditions (3.5 g plant−1). Among the biofertilizer treatments, the NPK biofertilizer (4.94 g plant−1) and the P biofertilizer (4.83 g plant−1) had the maximum and the no-biofertilizer treatment had the minimum oil yield (3.68 g plant−1). A study reported that the soil application of various organic and chemical fertilizers was not influential on yield components and morphological traits of milk thistle, but they influenced oil percentage and seed silymarin and silybin contents so that the plants treated with compost, Azotobacter, and their mixture produced the highest seed oil percentage. In addition, the plants treated with chemical fertilizer had the lowest silybin content19. Finally, the acceptable oil percentage of milk thistle implies its high medicinal and nutritional significance. Based on our results, it can be considered a source of plant oil production.Fig. 4The results of means comparison for the simple effect of irrigation and biofertilizer on the number of auxiliary branches (a) and oil yield per plant (b)..Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageTotal phenol and flavonoid contentIn the present study, the null hypothesis as to the equal total phenol content of the different treatments was supported. In contrast, some research has shown than the accumulation of phenol compounds is very sensitive to nutrient stress so that total phenol content usually increases with the decrease in N content of the medium, but higher amounts of N in the medium usually stimulate plant growth and hinder phenol synthesis38. Mazarie et al.39 found that as drought stress was intensified, the phenol content of common sage was increased. The mean phenol content of milk thistle in the present study was 30.3 mg quercetin per g dry matter.Based on the observations and the comparison of means, flavonoid content was the maximum in the plants treated with the NPK biofertilizer (2.59 mg quercetin g−1 DM) and then the K biofertilizer (2.56 mg quercetin g−1 DM), but the N and P biofertilizers did not differ from the control (2.27 mg quercetin g−1 DM) significantly (Fig. 5). Since silymarin, the active ingredient of milk thistle, is a flavonoid compound composed of five flavonolignans, it can be concluded that the application of NPK and K biofertilizers increased silymarin content. Also, studies on the relationship between the accumulation of flavonolignans and the vegetative traits of milk thistle revealed a positive significant relationship between the accumulation of these compounds and plant height. Similarly, the increase in plant height and flavonoid content in the plants treated with the NPK biofertilizer in the present study supports it.Fig. 5The results of means comparison for the simple effect of biofertilizer on total flavonoid content. Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageDPPH radical scavengingAccording to the results of the means comparison in Fig. 6a, the capacity of inhibiting DPPH radical was increased by almost 5% under the no-irrigation conditions. Also, the results showed that under the full irrigation conditions, this capacity did not vary among the fertilizer treatments significantly. However, under the no-irrigation conditions, the treatments of K (58.8%), NPK (57.6%), and P biofertilizers (55.7%) had the highest inhibiting capacity and they were in the same statistical group, the N biofertilizer (51.6%) was in the intermediate group, and no-biofertilizer application (44.3%) exhibited the lowest DPPH radical scavenging capacity. The difference in antioxidant activity may be associated with the difference in phenol content and other active compounds. The percent scavenging of DPPH radical in thyme was measured by Mehran et al.40 to be 66.6%. The results of a study showed that drought stress increased DPPH free radicals scavenging41. Studies have generally reported the positive effect of biofertilizer on increasing antioxidant properties, e.g. DPPH free radicals scavenging42.Superoxide radical scavengingSuperoxide radical scavenging was higher under the full irrigation conditions (77.2%) than the no-irrigation conditions (72.3%). Also, among the biofertilizer treatments, it was the highest (81.4%) in the plants treated with the NPK biofertilizer followed by those treated with the K biofertilizer (79.7%). The plants treated with the P biofertilizer were at an intermediate level (75.3%), and those treated with the N biofertilizer (70.3%) and those not treated at all (70.0%) had the lowest scavenging capacity (Fig. 6b). Superoxide anion (O2−) is a reduced form of molecular oxygen that is a free radical composed of the electron transport systems of mitochondria. Some electrons that pass through the mitochondria chain reaction react with oxygen directly and form superoxide anion. Free radicals are highly reactive and mostly damage proteins and break down DNA strings43. With respect to the effect of biofertilizers on superoxide radical scavenging, it has been shown that biofertilizers increase this antioxidant trait42.Nitric oxide radical scavengingBased on the comparison of the means (Fig. 6c), the full irrigation conditions had higher nitric oxide radical scavenging (26.9%) than the no-irrigation conditions (27.3%). In addition, the trend of nitric oxide radical scavenging capacity for different biofertilizers (Fig. 6c) was similar to that of superoxide radical scavenging capacity. Nitric oxide radical scavenging capacity was the highest in the plants treated with the K biofertilizer (28.1%) and the lowest in those not treated with a biofertilizer (26.2%). Except for the NPK biofertilizer, all treatments were in the same statistical group with the control. In general, the bio-fertilizer treatment with full irrigation and without irrigation increased DPPH radical scavenging by about 7 and 25%, respectively, compared to the control treatment. Marcocci et al.44 concluded that nitric oxide radical scavengers compete with conducting oxygen to reduce nitrite oxide generation not only in the plants of the Lamiaceae but also in other plant species.Fig. 6The results of means comparison for the simple effect of irrigation and biofertilizer on DPPH radical scavenging (a), superoxide radical scavenging (b) and nitric oxide radical scavenging (c). Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageChain-breaking activityThe treatments of the NPK (16.0%), K (15.8%), and P biofertilizers (15.7%) had the highest chain-breaking activity with slight differences, respectively. They were in the same statistical group. But, the treatments of the N biofertilizer (13.6%) and no biofertilizer (13.0%) had the minimum chain breaking and were placed in the same statistical group (Fig. 7). In a study on the effect of humic acid on the antioxidant properties of thyme under the ecological conditions of Urmia, Iran, Taghipour et al.45 reported that the highest total phenol content, DPPH radical scavenging percentage, and chain-breaking activity were obtained in the first harvest from the application of humic acid and the highest DPPH radical scavenging percentage and chain-breaking activity were obtained in the second harvest from the treatment of humic acid.Fig. 7The results of means comparison for the simple effect of biofertilizer on chain-breaking activity. Note: N(b): nitrogen bio-fertilizer, P(b): phosphate bio-fertilizer, K(b): potash bio-fertilizer, NPK(b): complete bio-fertilizers.Full size imageMaterials and methodsThe present study was carried out at the agricultural research station of Miandoab city located in the south of West Azarbaijan province in Iran (Lat. 36°58’ N., Long. 46°06’ E., Alt. 1314 m.) during 2017. The climate of the Miandoab region is relatively hot in the summer, and relatively cold in the winter. The average rainfall in the region amounts to 289 mm. Table 3 contains meteorological parameters information related to the months of the experiment. To determine its physical and chemical characteristics and estimate fertilizer requirements46,47, the soil at the study site was randomly sampled from a depth of 0–30 cm before sowing. Then, they were mixed and sent to a laboratory. The results are presented in Table 4.Table 3 Meteorological information related to the months of the experiment in the crop year.Full size tableTable 4 Physicochemical properties of soil at the study site.Full size tableThe field trial was carried out a split-plot experiment based on a randomized complete block design with 10 treatments and 3 replications. The main plot was assigned to irrigation at two levels (I1: irrigation water with crop water requirement (CWR) of 100% and I2: no-irrigation conditions) and the sub-plot was assigned to bio-fertilizer at five levels (control, nitrogen bio-fertilizer- N(b), phosphate bio-fertilizer- P(b), potash bio-fertilizer- K(b), and complete bio-fertilizers- NPK(b)). In this study, the irrigation technique employed was the surface irrigation (furrow method) three irrigations with a water depth of 100 mm were conducted in the sensitive areas of the milk thistle plant, including the vegetative period, the pre-flowering period, and the seed filling period. As a result, the water requirement of this plant is 300 mm or 3000 m3 per ha. On May 15, May 30, and June 15, irrigation was done with an interval of 15 days. Furthermore, the growth period for this plant was four months (late March to mid-July). It should also be mentioned that the milk thistle plant from the research farm of Urmia University named after Dr. Amir Rahimi has been registered in the herbarium of the Urmia Agricultural Research Centre of West Azerbaijan Province with the international code 11,071-WESTA. In general, previous research experiments have shown that the amount of heavy elements in agricultural soils in the current research area is not too critical48,49.Azotobacter 1 contains the bacteria of the O4 strain of Azotobacter vinelandii, which fixes atmospheric N actively into the forms that are absorbable by plants. One 100-g container of Azotobacter 1 can be an effective replacement for 30–50 kg chemical N fertilizer. The phosphate biofertilizer contained two phosphate-solubilizing bacteria that decompose insoluble phosphorus compounds of soil by two mechanisms – the secretion of organic acids and enzyme phosphatase. Then, this nutrient becomes available to plants. Based on the amount of soil absorbable phosphorus, each package of this biofertilizer can replace 50–100% of the chemical phosphate fertilizer demand of plants. The biofertilizer Pota-Barvar-2 contains two potassium solubilizing bacteria that decompose insoluble potassium in the root zone and release its ions, thereby optimizing potassium uptake. So, it can be a replacement for at least 50% of potassium chemical fertilizers.The biofertilizers used in the study were applied to the seeds during sowing, for which the biofertilizers were diluted to the amount required to dampen the seeds for 1 ha. Then, the seeds were spread on a piece of plastic or a clean surface, and the diluted solution was applied to them with a sprayer. Then, the seeds were mixed with the solutions well. The seeds used in the experiment were from the local landrace of Western Europe, which has been domesticated and is extensively produced at the farms of this region. After a tillage operation in the autumn and land preparation in February 2017, a total of 20 plots were built, each with an area of 6 m2. The seeds were sown on rows with an inter-row spacing of 60 cm and an on-row spacing of 25 cm. They were sown at a depth of 3–5 cm in Late-March, 2017 after inoculation with the biofertilizers. After the germination and growth of the plants, the extra plants were thinned at the 2-4-leaf stage and the empty parts of the plots were re-planted on April 10, 2017. At the same time, the weeds were removed by hand. Each plot contained five sowing rows spaced by 2 m.It should be noted that in the full irrigation treatment, the irrigation of the plots was initiated one month after sowing and repeated every 15 days. Also, the plants were sown at the physiological maturity stage four months after sowing. Since the maturity of the plants and plant constituent parts was not concurrent, the harvest was performed at several stages after the drying and yellowing of the plants. The harvested plants were sun-dried for several days until they lost their moisture. Then, the plants of each plot were weighed and crushed to have their seeds separated. Then, the remaining foliage was separated from the seeds by screening and airing and the crop of each plot was poured into separate packages and weighed.To determine morphological traits and yield components, 10 plants were randomly harvested from each plot to measure the traits like plant height, crown diameter, capitule number, and the number of auxiliary branches. Also, to determine seed yield, two marginal rows and 0.5 m from each side of the plots were eliminated as the marginal effect and the plants of the remaining area were harvested to find out their seed yield. The ratio of the economical part (seed) to total dry weight (biological yield) was taken as the seed harvest index. Indeed, the harvest index was calculated by the following equation and expressed in percent:$${text{Harvest index }}={text{ }}frac{{{text{Seed yield}}}}{{{text{Biological yield}}}}$$
    (1)
    To find out the oil yield, the ground seeds were oven-dried at 75 °C for 24 h. Then, 2.5 g of each sample was wire-wrapped in a piece of thin cloth and was then placed in an oil extraction device. The samples were boiled in 300 cc of n-hexane for 6 h. Then, the seeds wrapped in the cloth were oven-dried again at 75 °C for 24 h. Then, its weight was placed in the equation of oil calculation to determine the oil percentage. Afterward, the oil samples were poured into micro-tubes and were sent to a laboratory to be analyzed. Oil yield per plant was calculated by the following equation:$${text{Oil yield }}={text{ Seed yield per plant }} times {text{ Oil percent}}$$
    (2)
    To estimate the flavonoid content of different parts, 10 µL of the plant extract was first diluted with 1 mL of distilled water and it was then added with 0.075 mL of sodium nitrite (5%). Five minutes after the reaction, 0.15 mL of aluminum chloride (10%) was added, and 6 min after the reaction, 0.5 mL of sodium hydroxide (1 mol L−1) was incorporated and was adjusted to a final volume of 3 mL. Finally, its absorption was read at 510 nm and the total flavonoid content was determined by the quercetin standard curve. To find out DPPH stable radical scavenging rate, firstly 10 µL of the extract was mixed with 2 mL of methanol solution (0.004%). Then, the absorption of the solution was read at 517 nm after 30 min of incubation (at the darkness at room temperature). DPPH scavenging activity was calculated by the following Eqs50,51.:$${text{Percent DPPH radical suppression }}={text{ }}frac{{{{text{A}}_{{text{blank}}}}{text{ – }}{{text{A}}_{{text{sample}}}}}}{{{{text{A}}_{{text{blank}}}}}} times 100$$
    (3)
    In which Ablank represents the extract-free reaction mixture absorption and Asample represents the extract-containing reaction mixture absorption.Superoxide free radical scavenging was measured by the procedure described in Beauchamp and Fridovich52. So, 9 mL of tris-HCL buffer solution (pH = 8.2, 50 mmol L−1) was poured into a test tube and it was placed in a water bath at 25 °C for 20 min. Then, 40 µL of pyrogallol solution (45 mmol L−1 pyrogallol in 10 mmol L−1 hydrochloric acid), already incubated at 25 °C, was injected into the upper part of the test tube with a µL syringe, and they were mixed. The solution was incubated at 25 °C for 3 min, and immediately after that, one drop of ascorbic acid was poured into the mixture to stop the reaction. The mixture absorbance was read at 320 nm as A0 after 5 min. A0 represents the pyrogallol auto-oxidation rate. The auto-oxidation rate A1 was calculated by the same procedure with the only difference being the addition of 50 µL of tris buffer to the extract. At the same time, a control blank of the reaction compound was considered as A2. Percent radical scavenging was calculated by the following equation:$${text{Percent scavening of superoxide radicals}}=frac{{{A_0} – ({A_1} – {A_2})}}{{{A_0}}} times 100$$
    (4)
    To collect free nitric radicals, 10 µL of the extract was added with 0.5 mL of phosphate-buffered saline (10 mmol) and 2 mL of sodium nitroprusside (10 mmol). It was, then, incubated at 25 °C for 150 min. Then, 0.5 mL of this solution was mixed with 1 mL of sulfanilic acid (0.33% in glacier acetic acid 10%) and was rested for 5 min for the reaction to complete. Then, 1 mL of naphthyl ethylene diamine dihydrochloride (0.1%) was added and the mixture was kept at 25 °C for 30 min during which a pink color formed in the solution. Next, the absorbance was read at 540 nm to determine percent inhibition by the following Eqs44,53.:$${text{Percent scavenging of free nitric oxide radicals}}=frac{{{{text{A}}_{{text{blank}}}}{text{ – }}{{text{A}}_{{text{sample}}}}}}{{{{text{A}}_{{text{sample}}}}}} times 100$$
    (5)
    In which Ablank represents the extract-free reaction mixture absorbance and Asample represents the extract-containing reaction mixture absorbance.Chain breaking activity was measured by the DPPH reagent and the method described in Brand-Williams et al.54 with slight modifications. So, 10 µL of the extract was mixed with 1.9 mL of methanol solution (0.004%) of DPPH. Then, its absorbance was read at time zero and again 30 min after incubation at the darkness and room temperature at 515 nm. The reaction speed was calculated by the following equation:$$Abs – 3 – Abs0 – 3= – 3kt$$
    (6)
    There were more than two treatments groups in the present study. Therefore, an analysis of variance was performed to investigate the presence or absence of treatment differences overall. If treatment differences were significant, a comparison of means was performed based on Duncan’s test. The test of normality of experimental error distribution and the analysis of variance (ANOVA) for all traits were performed in the SAS 9.4 software package. The comparison of the means for all traits was carried out by Duncan’s multiple range test at the P < 0.05 probability level. Finally, all graphs were drawn in MS-Excel.ConclusionsThe milk thistle is of high importance due to its drought stress adaptation and resistance and nowadays the use of biofertilizer to curb on the use of chemical fertilizer and to increase crop yields is important for the movement towards sustainable agriculture, which can be much more important under no-irrigation conditions and since little research has addressed the effect of combined use of no-irrigation conditions and biofertilizers on medicinal plants including milk thistle, the present study focused on the effect of bio-fertilizers on the quality and quantity of thistle under no-irrigation conditions. Bio-fertilizers effectively increase the plant’s tolerance to drought stress, prevent a reduction in grain yield. In the present study, bio-fertilizers were examined to determine whether they influence the plant’s tolerance to drought stress and whether, on the other hand, the yield of the plant is reduced.The results showed that most morphological and biochemical traits of milk thistle were affected by the irrigation conditions and biofertilizers. The use of biofertilizers improved these traits versus the control. Given the significant role of biofertilizers in the development of sustainable agriculture, they can be a good candidate in case there is a need for nutrient supplements for this plant. Under drought stress conditions, the combination of bio-fertilizers (NPK(b)) and irrigation helps the plant to tolerate drought stress easily and also improves the plant’s antioxidant properties (phenolic and flavonoid compounds). Although the absence of irrigation results in a decrease in milk thistle yield, the utilization of bio-fertilizers, specifically complete bio-fertilizer and phosphate, can effectively compensate for the decrease in yield by enhancing nutritional conditions. It is recommended that the milk thistle plant be considered for cultivation and exploitation in arid and semi-arid areas and rain-fed areas. In addition to its medicinal properties, it is recommended to utilize the milk thistle plant in the food industry and animal husbandry.

    Data availability

    The datasets generated and analyses during the current study are not publicly available due to the work on the data and subsequent studies, but are available from the corresponding author on reasonable request.
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    Download referencesAcknowledgementsThe authors are thankful to the Office of Vice Chancellor for Research and Technology, Urmia University. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Author informationAuthors and AffiliationsDepartment of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, IranJafar Ghaffarzadeh, Reza Amirnia & Amir RahimiAgricultural Engineering Research Department, West Azerbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, IranAfshin KhorsandAuthorsJafar GhaffarzadehView author publicationsSearch author on:PubMed Google ScholarReza AmirniaView author publicationsSearch author on:PubMed Google ScholarAmir RahimiView author publicationsSearch author on:PubMed Google ScholarAfshin KhorsandView author publicationsSearch author on:PubMed Google ScholarContributionsJ.G. conceived the idea and wrote the manuscript. R.A. and A.R. reviewed the collected data, and prepared the equipment and materials. J.G. and A.K. was responsible for editing, original data and text preparation. All authors took responsibility for the integrity of the data that is present in this study.Corresponding authorCorrespondence to
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    Palaeometabolomes yield biological and ecological profiles at early human sites

    AbstractThe science of metabolic profiling exploits chemical compound byproducts of metabolism called metabolites1 that explain internal biological functions, physiological health and disease, and provide evidence of external influences specific to an organism’s habitat. Here we assess palaeometabolomes from fossilized mammalian hard tissues as a molecular ecological strategy to provide evidence of an ancient organism’s relationship with its environment. From eastern, central and southern African Plio-Pleistocene localities of palaeoanthropological significance, we study six fossils from Olduvai Gorge, Tanzania, one from the Chiwondo Beds, Malawi, and one from Makapansgat, South Africa. We perform endogeneity assessments by analysing palaeometabolomes of palaeosols and the effects of owl digestion on rodent bones to enable prudent ecological inferences. Diagenesis is indicated by metabolites of collagenase-producing bacteria2, whereas the preservation of peptides including those of collagen are identified by proteomics. Endogenous metabolites document biological functions and exogenous metabolites render environmental details including soil characteristics and woody cover, and enable annual minimum and maximum rainfall and temperature reconstructions at Olduvai Gorge, supporting the freshwater woodland and grasslands of Olduvai Gorge Bed I3,4,5, and the dry woodlands and marsh of Olduvai Gorge Upper Bed II6. All sites denote wetter and/or warmer conditions than today. We infer that metabolites preserved in hard tissues derive from an extravasated vasculature serum filtrate that becomes entombed within developing mineralized matrices, and most probably survive palaeontological timeframes in the nanoscopic ‘pool’ of structural-bound water that occurs in hard tissue niches7.

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    Fig. 1: Representative images of fossil specimens.Fig. 2: PLS-DA of the top 100 most variable metabolites.Fig. 3: Bone ultrastructure and metabolite niches.Fig. 4: Average metabolite molecular masses of extant and fossil hard tissues.

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    Buried, not erased: palynofloras in ultra-high-pressure metamorphic rocks

    Article
    Open access
    14 October 2025

    Data availability

    The raw mass spectrometry metabolomics data generated during this study are available at MassIVE (UCSD) (https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) under the deposition number MSV000097146. Proteomics LC–MS data (proteomics raw mass spectrometry data, peak lists and results) that support the findings of this study are deposited to the ProteomeXchange Consortium via the MassIVE partner repository and can be retrieved with the accession code MSV000097173 and with the dataset identifier PXD061016.
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    Download referencesAcknowledgementsFunding for this project was provided by The Leakey Foundation (grant number Spring202310420) to T.G.B. We thank all institutions, which provided and supported sampling: the CMCK Karonga, Malawi, NMHN Paris, France, and the Senckenberg Research Institute and the Natural History Museum Frankfurt, Germany. We express our gratitude to the Werner Reimers Foundation in Bad Homburg, Germany, which provides the Gustav Heinrich Ralph von Koenigswald collection as a permanent loan for scientific research to the Senckenberg Research Institute and Natural History Museum Frankfurt. Thanks go to the New York University Grossman School of Medicine’s Applied Bioinformatics Laboratories for access to QIAGEN IPA. The Zeiss Gemini 300 FE-SEM used for evaluating bone microanatomy was provided courtesy of the National Institutes of Health S10 Shared Instrumentation Program, grant number 1S10OD026989-01; mass spectrometry instrumentation, National Institutes of Health grant numbers S10 OD023659 and S10 RR027990. We thank P. Ausili and L. Kaleel for assistance with metabolite annotations and data organization. N.R. and M.D.M. are members of the Fonds de Recherche du Québec–Santé (FRQS) Centre for Structural Biology Research at McGill University, and the FRQS Network for Intersectorial Research in Sustainable Oral and Bone Health. M.D.M. is the Canada Research Chair in Biomineralisation, and N.R. is a William Dawson Scholar at McGill University. A.S. is supported by the French government in the framework of the University of Bordeaux’s IdEx ‘Investments for the Future’ programme/GPR ‘Human Past’. This project is an outcome of the 2010 Max Planck Research Award to T.G.B., endowed by the German Federal Ministry of Education and Research to the Max Planck Society and the Alexander von Humboldt Foundation in respect of the Hard Tissue Research Program in Human Paleobiomics.Author informationAuthors and AffiliationsDepartment of Molecular Pathobiology, New York University College of Dentistry, New York, NY, USATimothy G. Bromage, Bin Hu & Shoshana YakarDepartment of Anthropology, New York University, New York, NY, USATimothy G. BromageSenckenberg Research Institute and Natural History Museum, Frankfurt am Main, GermanyTimothy G. Bromage, Ottmar Kullmer, Friedemann Schrenk & Jülide KubatInstitute of Systematics and Evolution of Biodiversity, National Museum of Natural History, Paris, FranceChristiane DenysDepartment of Neuroscience and Physiology and Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USAChristopher Lawrence De Jesus, Hediye Erdjument-Bromage & Thomas A. NeubertInstitute of Ecology, Evolution and Diversity, Goethe University, Frankfurt am Main, GermanyOttmar Kullmer & Friedemann SchrenkEarth and Life History, Hessisches Landesmuseum Darmstadt, Darmstadt, GermanyOliver SandrockFaculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, CanadaMarc D. McKee, Natalie Reznikov & Daniel J. BussDepartment of Anatomy and Cell Biology, McGill University, Montreal, Quebec, CanadaMarc D. McKee & Natalie ReznikovDepartment of Bioengineering, McGill University, Montreal, Quebec, CanadaNatalie Reznikov & Eran IttahDepartment of Earth and Planetary Sciences, Rutgers, the State University of New Jersey, New Brunswick, NJ, USAGail M. AshleyEurofins Lancaster Laboratories PSS, Lancaster, PA, USASher B. Poudel & Antoine SouronUniversité de Bordeaux, CNRS, Ministère de la Culture, PACEA, UMR 5199, Pessac, FranceDaniel J. BussDepartment of Dental Hygiene and Dental Assisting, New York University College of Dentistry, New York, NY, USASasan RabiehAuthorsTimothy G. BromageView author publicationsSearch author on:PubMed Google ScholarChristiane DenysView author publicationsSearch author on:PubMed Google ScholarChristopher Lawrence De JesusView author publicationsSearch author on:PubMed Google ScholarHediye Erdjument-BromageView author publicationsSearch author on:PubMed Google ScholarOttmar KullmerView author publicationsSearch author on:PubMed Google ScholarOliver SandrockView author publicationsSearch author on:PubMed Google ScholarFriedemann SchrenkView author publicationsSearch author on:PubMed Google ScholarMarc D. McKeeView author publicationsSearch author on:PubMed Google ScholarNatalie ReznikovView author publicationsSearch author on:PubMed Google ScholarGail M. AshleyView author publicationsSearch author on:PubMed Google ScholarBin HuView author publicationsSearch author on:PubMed Google ScholarSher B. PoudelView author publicationsSearch author on:PubMed Google ScholarAntoine SouronView author publicationsSearch author on:PubMed Google ScholarDaniel J. BussView author publicationsSearch author on:PubMed Google ScholarEran IttahView author publicationsSearch author on:PubMed Google ScholarJülide KubatView author publicationsSearch author on:PubMed Google ScholarSasan RabiehView author publicationsSearch author on:PubMed Google ScholarShoshana YakarView author publicationsSearch author on:PubMed Google ScholarThomas A. NeubertView author publicationsSearch author on:PubMed Google ScholarContributionsT.G.B. conceived the study and led the work. C.D., J.K., O.K., F.S. and O.S. prepared and provided fossil samples and extant representatives of African species. G.M.A. and C.D. provided palaeosols. S.Y. and S.B.P. prepared and provided the extant laboratory mouse material, A.S. provided likelihoods of plant resources based on isotopic evidence. B.H. undertook the histology and light and electron microscopy. S.R. produced the fossil bone extracts. T.A.N., C.L.D.J. and H.E.-B. performed the metabolomics and proteomics and their relevant bioinformatics. M.D.M., N.R., D.J.B. and E.I. elaborated the interpretation of the bone ultrastructural metabolite niches. T.G.B. and S.R. annotated the metabolite lists from online databases, and T.G.B. interpreted the metabolic and ecological profiles and wrote the manuscript, with valuable contributions from all co-authors.Corresponding authorCorrespondence to
    Timothy G. Bromage.Ethics declarations

    Competing interests
    The authors declare no competing interests.

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    Nature thanks Yolanda Fernández-Jalvo, Jasmina Wiemann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

    Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended data figures and tablesExtended Data Fig. 1 Polarised light images of specimen M-D. Mus sp. or Dendromus sp., Bed I, Level FLKN1 M3.a. Birefringent brightness at top and bottom is attributed to heavily crystalised bone domains, while the central curvilinear arc of brightness is attributed to collagen. FW = 1.547 mm; b) Detail of bright bone lamellae and osteocyte lacunae interspersed among them. FW = 0.324 mm.Extended Data Fig. 2 Polarised light image of specimen Sm. Saccostomus cf. mearnsi, Bed I, Level FLKN1.a. Birefringent brightness is attributed to collagen. FW = 1.343 mm; b) Detail of bright domains and osteocyte lacunae interspersed among them. FW = 0.324 mm.Extended Data Fig. 3 Polarised light image of specimen Gg. Gerbilliscus gentryi, Bed I, Level FLKN1 M1.a. Birefringent brightness is attributed to collagen. FW = 3.922 mm; b) Detail of bright domains and osteocyte lacunae interspersed among them. FW = 0.324 mm.Extended Data Fig. 4 Polarised light image of specimen Gi. Gerbilliscus sp. indet, Bed I, Level DK.a. Birefringent brightness is attributed to collagen. FW = 4.506 mm; b) Detail of bright domains and osteocyte lacunae interspersed among them. FW = 0.324 mm.Extended Data Fig. 5 Polarised light image of specimen Xi. Xerus cf. inauris, Bed I, Level FLKN1 M3.a. Birefringent brightness is attributed to collagen. FW = 4.434 mm; b) Detail of bright domains and osteocyte lacunae interspersed among them. FW = 0.324 mm.Extended Data Fig. 6 BSE-SEM image of specimen M-D. Mus sp. or Dendromus sp., Bed I, Level FLKN1 M3.a. Macroscopic view of the bone fragment. FW = 1.515 mm; b. Detail of bone and osteocyte lacunae and their associated canaliculi. Large cracks are due to diagenesis or preparation. FW = 0.25 mm.Extended Data Fig. 7 BSE-SEM image of specimen Sm. Saccostomus cf. mearnsi, Bed I, Level FLKN1.a. Macroscopic view of the bone fragment. FW = 1.329 mm; b. Detail of bone illustrating diagenetically disorganized bony structure but preserving some vascular canals and osteocyte lacunae. FW = 0.25 mm.Extended Data Fig. 8 BSE-SEM image of specimen Gg. Gerbilliscus gentryi, Bed I, Level FLKN1 M1.a. Macroscopic view of the bone fragment. FW = 3.726 mm; b. Detail of bone illustrating a vascular canal, mineralization density variation, and osteocyte lacunae and their associated canaliculi. FW = 0.25 mm.Extended Data Fig. 9 BSE-SEM image of specimen Gi. Gerbilliscus sp. indet, Bed I, Level DK.a. Macroscopic view of the bone fragment. FW = 4.261 mm; b. Detail of bone illustrating a vascular canal (bottom), mineralization density variation, and osteocyte lacunae and their associated canaliculi. Large cracks are due to diagenesis or preparation. FW = 0.25 mm.Extended Data Fig. 10 BSE-SEM image of specimen Xi, Xerus cf. inauris, Bed I, Level FLKN1 M3.a. Macroscopic view of the bone fragment. FW = 4.211 mm; b. Detail of bone illustrating a vascular canal, mineralization density variation, and osteocyte lacunae and their associated canaliculi. FW = 0.25 mm.Supplementary informationSupplementary InformationSupplementary Figs 1–10.Reporting SummarySupplementary Table 1Detailed list of samples.Supplementary Table 2Calcium/phosphate ratios from the study sample.Supplementary Table 3UM-HET3 and C57BL-6J mice and diets.Supplementary Table 4Olduvai Gorge Mus sp. or Dendromus sp., extant MNHN CD, palaeosols.Supplementary Table 5Olduvai Gorge Saccostomus cf. mearnsi, extant MNHN 1991-817, palaeosols.Supplementary Table 6Olduvai Gorge Gerbilliscus gentryi, extant MNHN NC, palaeosols.Supplementary Table 7Olduvai Gorge Gerbilliscus sp. indet, MNHN NC, palaeosols.Supplementary Table 8Olduvai Gorge Xerus cf. inauris, MNHN 2005-575, palaeosols.Supplementary Table 9Olduvai Gorge, Kolpochoerus majus, von Koenigswald 1854.Supplementary Table 10Chiwondo Beds, Elephas recki shungurensis, EE, palaeosol HCRP-RC-11.Supplementary Table 11Makapansgat, Bovidae, EW, palaeosol TF12.Supplementary Table 12Site metabolite comparisons.Supplementary Table 13OG soil carbonates.Supplementary Table 14Statistical tests.Supplementary Table 15Fossil Bone Proteomics.Supplementary Table 16Summary statistics.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleBromage, T.G., Denys, C., De Jesus, C.L. et al. Palaeometabolomes yield biological and ecological profiles at early human sites.
    Nature (2025). https://doi.org/10.1038/s41586-025-09843-wDownload citationReceived: 05 August 2022Accepted: 31 October 2025Published: 17 December 2025Version of record: 17 December 2025DOI: https://doi.org/10.1038/s41586-025-09843-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    CH4 and N2O emissions increased following the conversion of aquaculture ponds to rice monoculture and rice–shrimp coculture fields in southeast China

    AbstractIn recent years, numerous aquaculture ponds in southeast China have been transformed into rice paddies or rice–shrimp fields. This shift in land use can potentially alter the biogeochemical cycling of carbon and nitrogen, thereby influencing CH4 and N2O emissions. However, the exact impacts and factors driving these changes remain unclear. Herein, a two-year field experiment was conducted to evaluate and compare CH4 and N2O emissions from shrimp ponds (SP), alongside reclaimed rice monoculture (RM) and rice–shrimp coculture (RS) fields that were converted from shrimp ponds. The findings showed that converting aquacultural wetlands to RM significantly increased annual emissions, with CH4 rising dramatically from 103 to 490 kg/(ha·yr) (a 375.7% increase) and N2O increasing from 4.22 to 7.39 kg/(ha·yr) (a 75.1% increase). However, further converting RM into RS notably reduced annual emissions, with CH4 decreasing from 490 to 189 kg/(ha·yr) and N2O from 7.39 to 4.32 kg/(ha·yr), corresponding to reductions of 61.4% and 41.5%, respectively. This agricultural land use change significantly impacted the reliance of CH4 and N2O fluxes on both biotic and abiotic variables across the three wetland systems, stemming from diverse agricultural practices. Furthermore, the scaled global warming potential (SGWP) and net ecosystem economic profit (NEEP)-SGWP of RM (24.1 t CO2-eq/(ha·yr) and 125 kg CO2-eq per $/(ha·yr)) were obviously higher than those of RS (9.66 t CO2-eq/(ha·yr) and 4.76 kg CO2-eq $/(ha·yr)) and SP (5.78 t CO2-eq/(ha·yr) and 1.1 kg CO2-eq per $/(ha·yr)), respectively. The results highlight that the conversion of aquaculture SP to RM and further to RS coculture can drastically reduce greenhouse gas emissions while enhancing economic benefits, thereby addressing environmental and profitability issues arising from the reclamation of SP.

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    All data generated or analyzed during this study are included in this published article and its supplementary information files.
    AbbreviationsSP:
    Shrimp ponds
    RM:
    Rice monoculture
    RS:
    Rice–shrimp coculture
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    Download referencesAcknowledgementsThe project was supported by the Huzhou Public-Welfare Applied Research Project (No. 2022GZ24), Zhejiang Key Research and Development Project of China (No. 2022C02027) .Author informationAuthors and AffiliationsAgriculture Ministry Key Laboratory of Healthy Freshwater Aquaculture, Key Laboratory of Fish Health and Nutrition of Zhejiang Province, Zhejiang Institute of Freshwater Fisheries (Zhejiang Freshwater Fishery Environmental Monitoring Station), Number 999, Hangchangqiao Road, Wuxing District, Huzhou, 313001, Zhejiang, ChinaMei Liu, Dan Zhou, Songbao Zou, Meng Ni & Julin YuanDepartment of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USAMinpeng HuCollege of Resource and Environment, Anhui Science and Technology University, Chuzhou, 313000, ChinaYu ZhangAgricultural and Rural Office of Zhongguan Town, Huzhou, 313220, ChinaBin HeAuthorsMei LiuView author publicationsSearch author on:PubMed Google ScholarMinpeng HuView author publicationsSearch author on:PubMed Google ScholarDan ZhouView author publicationsSearch author on:PubMed Google ScholarSongbao ZouView author publicationsSearch author on:PubMed Google ScholarYu ZhangView author publicationsSearch author on:PubMed Google ScholarBin HeView author publicationsSearch author on:PubMed Google ScholarMeng NiView author publicationsSearch author on:PubMed Google ScholarJulin YuanView author publicationsSearch author on:PubMed Google ScholarContributionsMei Liu: methodology, data curation, writing-original draft. Minpeng Hu: conceptualization, methodology. Dan Zhou and Songbao Zou: data curation, investigation. Yu Zhang and Bin He: investigation, visualization. Meng Ni: prepared figures and tables. Julin Yuan: supervision, writing-original draft. All authors reviewed the manuscript.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleLiu, M., Hu, M., Zhou, D. et al. CH4 and N2O emissions increased following the conversion of aquaculture ponds to rice monoculture and rice–shrimp coculture fields in southeast China.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-28979-3Download citationReceived: 16 March 2025Accepted: 13 November 2025Published: 17 December 2025DOI: https://doi.org/10.1038/s41598-025-28979-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsReclaimed landRice–shrimp cocultureCH4 and N2O emissionsEnvironmental factors More

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    … of the Year

    December is a time when ‘… of the Year’ pieces appear in all kinds of publications. For this year only, Nature Plants is joining the trend.

    In the last month of the year, publications traditionally try to identify a singular thing, person, or event that has been of particular prominence in the previous 11 months. These are different from annual awards such as the Oscars, Grammys, or Royal Society Science Book Prize (won this year by Masud Husain’s Our Brains, Our Selves, a book about neurology and the nature of self), as they do not necessarily reward an outstanding achievement but rather look for something that can serve as a reflection of the year.The best known, and where the idea originated, is the Time Person of the Year, which was started in 1927 by the editors of Time magazine to fill the slow news days between Christmas and the start of the new year. The first recipient was Charles Lindbergh, who had made the first solo non-stop transatlantic flight.In 1989, Science attempted something similar, but rather than focus on a person they decided to select a ‘Molecule of the Year’, choosing DNA polymerase, which, given its central role in the polymerase chain reaction (PCR), is a molecule whose effect on science (certainly on biology) would be difficult to overestimate. However, Molecule of the Year only lasted until 1996, when it was relaunched as ‘Breakthrough of the Year’.None of these yearly breakthroughs have been directly related to plant research. Possibly the closest came in 2002, when RNA interference (RNAi) by small RNAs was cited, as some of the crucial studies in that area had taken place in plants. However, last year, one of the runners-up for the 2024 Breakthrough of the Year was RNA pesticides, following the US Environmental Protection Agency’s approval of the insecticide Calantha for use against the Colorado potato beetle. The spray delivers small RNA molecules that specifically prevent expression of a component of the beetle’s proteasome (PSMB5), causing the accumulation of non-functional proteins and thus the death of the beetles.At the time of writing, the 2025 Breakthrough of the Year is yet to be announced; however, the ‘Word of the Year’ has been. There are actually a number of different words of the year as all of the major dictionaries like to name their own. Most of the words chosen for 2025 have some relationship to social media or artificial intelligence. Collins English Dictionary has selected ‘vibe coding’, which is the process of asking a large language model (LLM) to write computer code for a specific application and then using the LLM to fix bugs and further ‘improve’ the code so that the final result has had no direct human involvement and no-one is quite sure what is actually in the code or how it works. The Macquarie Dictionary of Australian English has chosen ‘AI slop’: images, text or other forms of digital content that have been lazily and quickly produced without depth or quality, something we have thankfully not seen too much of at Nature Plants so far.The Cambridge Dictionary has opted for ‘parasocial’, a word relating to the phenomenon where people see famous individuals or celebrities in the media so much that they develop a personal relationship with them and believe it to be reciprocated. And finally, the Oxford English Dictionary has chosen ‘rage bait’: internet content the sole purpose of which is to cause outrage in consumers in order to increase online traffic, engagement and ultimately, revenue — like ‘AIP slop’, not something we are intentionally creating at Nature Plants.There are already quite a number of ‘Plants of the Year’, but these are to do with horticulture rather than plant science and are more of a prize. For example, the Royal Horticultural Society named the Philadelphus variety Petite Perfume Pink as its plant of 2025 back in May at its annual Chelsea Flower Show, citing it as the first truly pink-flowered Philadelphus.If we can’t name a Plant of the Year, we wondered whether we could suggest a Nature Plants Word of the Year: a word that represents a change or development in some field of plant biology in 2025. Our best suggestion is ‘ground-truthing’.Ground-truthing is the practice of validating results and conclusions derived from large-scale proxy data, often from remote sensing, by direct measurements at representative locations. It isn’t a word that has appeared much in what we have published. In fact, it has appeared only once in our published corpus, in a paper published back in 2017 about the predicted effects of climate change on coffee in Ethiopia1. However, ground-truthing has been turning up more and more during peer review, with reviewers increasingly requesting — often strongly requesting — the inclusion of field data that validate the ecological relationships inferred from remotely-sensed estimates of vegetation parameters, especially those related to functional traits. Such data are generally obtained from one of NASA’s satellite-mounted Moderate Resolution Imaging Spectroradiometers (MODIS) or from the Tropospheric Monitoring Instrument (TROPOMI).This July, we published a study using remotely-sensed sun-induced chlorophyll fluorescence (SIF) data from TROPOMI to look at photosynthesis on an ecosystem scale and how it is related to tree species richness2. The researchers validated their results with near-infrared measurements from MODIS and combined these with diversity data from 967 ground plots to show that species richness is positively correlated with ecosystem photosynthesis.A further example of ground-truthing came in a paper we published in September identifying how trade-offs in leaf acclimation strategies feed-in to drivers of vegetation greening3. The researchers used measures of leaf area index and season length derived from MODIS data products. They ground-truthed the negative correlations that emerged using data from the USA National Phenology Network and the Pan European and northern Eurasia Phenological databases, combining field measurements on leaf mass area, specific leaf area and leaf dry matter content.We are not at a point where ground-truthing is a requirement for the publication of studies based on remotely-sensed proxy data; but the rising frequency with which it is mentioned in reviewer comments shows its importance is becoming increasingly recognized.

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    Nat. Plants 11, 2439 (2025). https://doi.org/10.1038/s41477-025-02203-6Download citationPublished: 17 December 2025Version of record: 17 December 2025Issue date: December 2025DOI: https://doi.org/10.1038/s41477-025-02203-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    Environmental DNA reveals the distinct genetic diversity and evolutionary pathways of the Chinese Minnow Rhynchocypris oxycephalus in Korean freshwater systems

    AbstractEstablishing an environmental DNA (eDNA) reference library at regional and local scales is essential not only for accurate biodiversity assessment but also for comprehensive long-term monitoring. To date, genetic diversity studies of the Chinese minnow (Rhynchocypris oxycephalus) have largely been restricted to China, leaving substantial knowledge gaps across its broader distribution, including South Korea. Hence, the present study identified suitable regions for guiding eDNA surveys and non-invasive sampling, based on documented occurrences retrieved from the IUCN Geospatial Conservation Assessment Tool (GeoCAT). The newly designed primer pairs successfully amplified long mitochondrial fragments (~ 1 kb) of the cytochrome b (Cytb) and 16S ribosomal RNA (16S rRNA) genes. The generated sequences revealed 29 haplotypes from 41 Cytb sequences and 13 haplotypes from 21 16S rRNA sequences, corresponding to high intraspecific genetic diversity (5.57% for Cytb and 2.46% for 16S rRNA), thereby indicating potential cryptic diversity of R. oxycephalus in South Korea. The phylogenetic analyses, combined with multiple species delimitation methods, resolved several putative molecular operational taxonomic units and highlighted a distinct genetic clade in the Seomjin River basin, likely driven by microhabitat-specific evolutionary processes. In addition, the shared haplotypes across catchments of different river basins indicate either ongoing gene flow or anthropogenic influences contributing to genetic admixture of R. oxycephalus. The time-calibrated phylogenetic analyses also suggest that historical geographic changes and ancient river networks, from the Early Miocene to the Late Pliocene, likely facilitated the diversification of R. oxycephalus across China, the Korean Peninsula, and Japan. Overall, this study represents the first eDNA-based assessment of R. oxycephalus diversity in South Korea, while also providing new evolutionary insights from a broader geographic context in China and Japan. Given the complexity of multiple river networks in South Korea, further investigations using multiple genetic markers are recommended to enhance understanding of this cyprinid species phylogeography in the region.

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    Data availability

    The generated sequencing data has been deposited in NCBI GenBank (https://www.ncbi.nlm.nih.gov/) with the following accession numbers PQ330261 to PQ330301 and PQ333020 to PQ333040.
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    Download referencesAcknowledgementsThe authors wish to express their profound gratitude to the laboratory colleagues of the Molecular Physiology Laboratory, Department of Marine Biology, Pukyong National University for their support during this research.FundingThis work was supported by Dongwon Research Foundation in 2024 (202404170001) and the corresponding author (Hyun-Woo Kim) was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2021-NR060118).Author informationAuthors and AffiliationsIndustry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan, 48513, Republic of KoreaGyurim BangResearch Center for Marine Integrated Bionics Technology, Pukyong National University, Busan, 48513, Republic of KoreaSoo Rin Lee, Ah Ran Kim & Hyun-Woo KimMarine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan, 48513, Republic of KoreaSoo Rin Lee, Ah Ran Kim & Hyun-Woo KimInstitute of Marine Life Science, Pukyong National University, Busan, 48513, Republic of KoreaHye-Eun KangDepartment of Marine Biology, Pukyong National University, Busan, 48513, Republic of KoreaHsu Htoo & Hyun-Woo KimDepartment of Zoology, Bodoland University, Kokrajhar, 783370, IndiaImon AbedinAdvance Tropical Biodiversity, Genomics, and Conservation Research Group, Department of Biology, Faculty of Science and Technology, Airlangga University, Surabaya, 60115, Republic of IndonesiaMuhammad Hilman Fu’adil AminMajor of Biomedical Engineering, Division of Smart Healthcare, Pukyong National University, Busan, Republic of KoreaMyunggi YiMolecular Ecology and Evolution Laboratory, Department of Biological Science, Sangji University, Wonju-si, 26339, Kangwon-State, Republic of KoreaHyuk Je LeeOcean and Fisheries Development International Cooperation Institute, College of Fisheries Science, Pukyong National University, Busan, 48513, Republic of KoreaShantanu KunduInternational Graduate Program of Fisheries Science, Pukyong National University, Busan, 48513, Republic of KoreaShantanu KunduAdvance Tropical Biodiversity, Genomics, and Conservation Research Group, Department of Biology, Faculty of Science and Technology, Airlangga University, Surabaya, 60115, IndonesiaHyun-Woo KimAuthorsGyurim BangView author publicationsSearch author on:PubMed Google ScholarSoo Rin LeeView author publicationsSearch author on:PubMed Google ScholarHye-Eun KangView author publicationsSearch author on:PubMed Google ScholarAh Ran KimView author publicationsSearch author on:PubMed Google ScholarHsu HtooView author publicationsSearch author on:PubMed Google ScholarImon AbedinView author publicationsSearch author on:PubMed Google ScholarMuhammad Hilman Fu’adil AminView author publicationsSearch author on:PubMed Google ScholarMyunggi YiView author publicationsSearch author on:PubMed Google ScholarHyuk Je LeeView author publicationsSearch author on:PubMed Google ScholarShantanu KunduView author publicationsSearch author on:PubMed Google ScholarHyun-Woo KimView author publicationsSearch author on:PubMed Google ScholarContributionsS.K. and H.-W.K. conceived and supervised the study. G.B., S.R.L., and H.H. performed the experiments. G.B., H.-E.K., and A.R.K. performed the data analyses. M.Y., H.J.K., I.A. and M.H.F.A. contributed to the data analyses. G.B., S.K., and H.-W.K. wrote the manuscript. All authors read and approved the final manuscript.Corresponding authorsCorrespondence to
    Shantanu Kundu or Hyun-Woo Kim.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Ethical accordance for procedures
    The study is entirely based on environmental water samples; therefore, no animal samples were collected from the wild, and no special permissions were required for this research.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleBang, G., Lee, S.R., Kang, HE. et al. Environmental DNA reveals the distinct genetic diversity and evolutionary pathways of the Chinese Minnow Rhynchocypris oxycephalus in Korean freshwater systems.
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-32073-zDownload citationReceived: 03 June 2025Accepted: 08 December 2025Published: 17 December 2025DOI: https://doi.org/10.1038/s41598-025-32073-zShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    Circovirus infection in Croatian population of Eurasian griffon vultures (Gyps fulvus)

    AbstractCircoviruses cause diseases in a wide range of hosts, including avian species. One of the most studied circoviruses is beak and feather disease virus (BFDV), which has spread worldwide due to global trade of birds and characteristics of the virus that allow it to persist in the environment. The Eurasian griffon vulture (Gyps fulvus) is one of the largest birds in the world and is among several vulture species that are considered endangered today, and currently the only remaining vulture species in Croatia. Several juvenile Eurasian griffon vultures undergoing rehabilitation at the Beli Visitor Centre and Rescue Centre for Griffon Vultures on the island of Cres, Croatia, had feather lesions similar to those caused by BFDV in psittacine birds. Signs included partially retained feather sheaths, discoloration and circumferential constrictions of the feather shafts, gnawed feather tips, keratin and egg deposits near the feather shafts, as well as feather breakage and spontaneous feather loss. Feather samples were collected for molecular detection of viral pathogens and macroscopic detection of ectoparasites; blood samples were taken for complete blood count (CBC), biochemical analysis and oxidative stress analysis. The molecular analysis confirmed a circovirus infection, which was supported by the blood analyses indicating a viral infection. The collected ectoparasites were identified as chewing lice – Colpocephalum turbinatum and Falcolipeurus quadripustulatus, which have previously been detected in Eurasian griffon vultures. To our knowledge, this is the first report of circovirus infection in Eurasian griffon vultures. Considering that circoviruses are highly contagious, very resistant and easily transmissible, it is extremely important to perform continuous monitoring in order to improve the breeding and survival conditions of Eurasian griffon vulture populations worldwide.

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    Data availability

    The sequence generated and analysed during the current study is available in the GenBank repository, under the accession number PV780460.
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    Siniša Faraguna.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleLozica, L., Faraguna, S., Lukač, M. et al. Circovirus infection in Croatian population of Eurasian griffon vultures (Gyps fulvus).
    Sci Rep (2025). https://doi.org/10.1038/s41598-025-32905-yDownload citationReceived: 09 August 2025Accepted: 13 December 2025Published: 17 December 2025DOI: https://doi.org/10.1038/s41598-025-32905-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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    KeywordsCircovirusBFDVEurasian griffon vultureGyps fulvusFeather lesion More

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    Vegetation carbon use efficiency response to drought in the Manas River Basin of Xinjiang

    Abstract

    Carbon Use Efficiency (CUE), quantified as the ratio of net primary production to gross primary production (NPP/GPP), serves as a crucial indicator of ecosystem carbon sequestration capacity. However, understanding of its spatiotemporal dynamics and drought response mechanisms in arid inland basins remains limited. This study investigates the CUE characteristics in the Manas River Basin, a representative arid endorheic basin in Xinjiang, China, using MODIS satellite data (2001-2020). Results demonstrate that the multi-year mean CUE of the basin was 0.50 (±0.12), with coniferous forests exhibiting the highest values and croplands the lowest. Seasonal analysis revealed CUE in spring and autumn significantly exceeded that in winter (p<0.01). Spatially, 57.99% of the basin displayed low CUE fluctuation, primarily distributed in grassland and woodland areas. Future trend projections indicate divergent persistence patterns between plain and desert grasslands. Drought response analysis identified a dominant 3-month lag effect, with forests showing greater drought resistance and longer response lags compared to croplands (1.2 months longer, p=0.03). The ecosystem maintains high resilience, regulated by the interactive effects of vegetation type, irrigation practices, and climate gradients. These findings establish a mechanistic framework for understanding carbon cycling processes in arid lands under climate stress, providing scientific basis for global dryland ecosystem management.

    Data availability

    Remote sensing datasets generated and/or analysed during this study are available at https://www.resdc.cn, https://ladsweb.modaps.eosdis.nasa.gov and https://earthengine.google.com/ in the repository. The Xinjiang forest survey data underpinning this study’s findings are obtainable from the Forestry Department. However, restrictions apply to the availability of these data, which were utilised under licence for the present study and are therefore not publicly accessible. They may be obtained from the corresponding author upon reasonable request.
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    Download referencesFundingThis work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (2023D01A49); the National Natural Science Foundation of China (42261013). Author informationAuthor notesJingjing Kong and Mei Zan have contributed equally to this work.Authors and AffiliationsSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, ChinaJingjing Kong, Mei Zan, Zhizhong Chen, Shunfa Yang, Jia Zhou & Lili ZhaiXinjiang Laboratory of Lake Environment and Resources in the Arid Zone, Urumqi, ChinaMei ZanAuthorsJingjing KongView author publicationsSearch author on:PubMed Google ScholarMei ZanView author publicationsSearch author on:PubMed Google ScholarZhizhong ChenView author publicationsSearch author on:PubMed Google ScholarShunfa YangView author publicationsSearch author on:PubMed Google ScholarJia ZhouView author publicationsSearch author on:PubMed Google ScholarLili ZhaiView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualisation, J. K. and M. Z.; methodology,J. K. and S. Y.; software, J. K. and J. Z.; validation, J. K. and Z. C.; formal analysis, M. Z. and L. Z.; investigation, M. Z. and S. Y.; resources, Z. C. and M Z.; data curation, M Z. and J K.; writing—original draft preparation, C. Z. and M. Z.; writing—review and editing, M. Z. and C. Z.; visualisation, J. K., and Z. C.; supervision, M. Z.; project administration, M. Z. and J. Z.; funding acquisition, M. Z. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
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    Reprints and permissionsAbout this articleCite this articleKong, J., Zan, M., Chen, Z. et al. Vegetation carbon use efficiency response to drought in the Manas River Basin of Xinjiang.
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    Current protected areas provide limited benefits for European river biodiversity

    AbstractProtected areas are a principal conservation tool for addressing biodiversity loss. Such protection is especially needed in freshwaters, given their greater biodiversity losses compared to terrestrial and marine ecosystems. However, broad-scale evaluations of protected area effectiveness for freshwater biodiversity are lacking. Here, we provide a continental-scale analysis of the relationship between protected areas and freshwater biodiversity using 1,754 river invertebrate community time series sampled between 1986 and 2022 across ten European countries. Protected areas primarily benefited poor-quality communities (indicative of higher human impacts) that were protected, or that gained protection, across a substantial proportion of their upstream catchment. Protection had little to no influence on moderate- and high-quality communities, although high-quality communities potentially provide less scope for effect. Our results reveal the overall limited effectiveness of current protected areas for freshwater biodiversity, likely because they are typically designed and managed to achieve terrestrial conservation goals. Broadly improving effectiveness for freshwater biodiversity requires catchment-scale management approaches involving larger and more continuous upstream protection, and efforts to address remaining stressors. These approaches would also benefit connected terrestrial and coastal ecosystems, thus generally helping bend the curve of global biodiversity loss.

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    The recovery of European freshwater biodiversity has come to a halt

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    09 August 2023

    IntroductionBiodiversity is in crisis owing to human-induced global change1,2,3. Extensive actions have been implemented to address these losses, including legislation and agreements to expand the cover of protected areas (PAs), such as the EU Habitats Directive (92/43/EEC) and the Kunming-Montreal Global Biodiversity Framework, which sets a target of 30% global PA coverage by 20304. PAs restrict or reduce human activity in designated locations, such as national parks, nature reserves, or marine sanctuaries, with the aim of maintaining and restoring biodiversity. Whether PAs generally achieve this aim remains unclear. Several broad-scale (i.e., global or continental) studies have investigated the effectiveness of terrestrial and marine PAs, providing insights into their potential for reducing biodiversity loss5,6,7, exploitation8, and habitat loss9,10. However, similar broad-scale perspectives are currently lacking for freshwaters. This knowledge gap is particularly concerning given that freshwater ecosystems harbor a disproportionate amount of global biodiversity by area, and this biodiversity is declining faster compared to terrestrial and marine ecosystems11,12,13. Evidence of PA effectiveness for individual freshwater ecosystems, or freshwaters in individual regions, is currently mixed14, with some PAs generally benefitting freshwater biodiversity15,16 whereas others exhibit little to no effect17,18,19. This variability highlights the need for research that evaluates the general effectiveness of PAs for freshwater biodiversity at broader spatial scales.Inland (i.e., non-marine) PAs may broadly fail to protect freshwater biodiversity because their boundaries and management typically prioritize terrestrial habitats and charismatic taxa20,21, lack explicit goals for freshwaters, and neglect the needs of freshwater taxa22,23,24. For example, most inland PAs are small, with ~85% less than 10 square kilometers25. However, many freshwater ecosystems, particularly larger rivers, can extend across tens to hundreds of kilometers with catchments encompassing thousands of square kilometers26. Thus, local river communities can be impacted by upstream terrestrial pollutants and other inputs across broad spatial scales27,28,29. Small-scale protection of a river site can therefore be compromised by inputs arriving from upstream, unprotected areas30,31. Small PAs may also only succeed at protecting local habitat, while other key upstream and downstream habitats used by mobile freshwater taxa remain unprotected32,33,34.Evaluating the benefits of inland PAs for freshwaters requires appropriate counterfactuals, i.e., unprotected areas, for comparison. Studies often rely on spatial comparisons of protected and unprotected sites17,19,35,36, but this may produce biased results due to spatial biases in PA placement. For example, PAs tend to be designated in less impacted, forested, higher elevation areas with little human development37,38, which may already have high and/or stable biodiversity compared to unprotected sites. These biases often cannot be fully controlled, which makes it difficult to distinguish the effects of protection from pre-existing differences between sites39. An alternative approach is to incorporate a temporal component into the spatial comparisons, specifically by comparing the rate of biodiversity change between protected and unprotected sites6. This method better evaluates PA effectiveness by using earlier years within sites as the baseline, thus helping determine whether establishing or expanding protection affected biodiversity, and whether biodiversity was lost (or gained) at a faster rate in unprotected sites. However, such temporal comparisons are hindered by the scarcity of high-resolution time-series data.To address the need for broad-scale, temporal evaluations of PA effectiveness for freshwater biodiversity, we examine 1,754 time series of river invertebrate communities collected between 1986 and 2022 across ten European countries (Fig. 1 and Supplementary Table 1). We focus on river invertebrates because they are key components of freshwater biodiversity that provide important ecosystem functions and services40, and they exhibit consistent compositional responses to human pressures41. These taxa are therefore commonly used as bioindicators and have been monitored globally for decades, including in countries across Europe. Consequently, analysis of European, long-term river invertebrate community data can address the need for broad-scale, temporal evaluations of PA effectiveness for freshwater biodiversity.Fig. 1: Locations of the 1754 sampled European river sites.Sites are in Belgium, Czechia, Denmark, Finland, France, Hungary, Lithuania, Spain, Sweden, and the UK. Sites are colored based on the presence of a protected area in the full upstream catchment (no = red; yes = blue). Point sizes for sites with upstream protected areas are based on the proportion of the catchment covered by protected areas.Full size imageWe first quantify biodiversity change as site-specific temporal changes in invertebrate abundance, taxonomic richness, and ecological quality (a measure of human impacts based on similarity to communities in least-impacted conditions; see “Methods” section). We then determine whether the rate of biodiversity change differs between sites with and without upstream PAs (as in ref. 6 for terrestrial and marine ecosystems), under the expectation that protection would better maintain biodiversity and lead to greater increases in biodiversity through time. To compare the effects of protection close to a river site versus across its broader catchment, we investigate relationships at four progressively larger upstream distances, ranging from PAs up to 1 km upstream (i.e., the immediate vicinity of a site) 10 km, 100 km, and the full upstream catchment. Lastly, for sites with upstream PAs, we determine whether biodiversity change depends on the amount of PA cover, or the degree of PA gain, and whether it varies with river size and initial ecological quality. Regarding river size, as discussed above, larger rivers integrate inputs across longer distances, thus potentially exposing their communities to cumulative pollutants from rural and urban sources, so we expect that biodiversity in larger rivers primarily responds to PA cover that spans larger upstream scales. Regarding ecological quality, PAs tend to be designated in already less impacted areas (i.e., better initial ecological quality), and we expect that the effectiveness of such PAs differs from those established in poorer quality sites, which generally have lower biodiversity42 and thus more scope for improvement.Here, we show that upstream PAs primarily benefit poor-quality communities where PAs encompass a larger proportion of the catchment. These communities exhibit much higher rates of biodiversity recovery than likely would have occurred in the absence of protection. In contrast, PAs have little to no effect on biodiversity in moderate- and high-quality communities, although the latter group may have been unaffected because human impacts in such rivers are generally low regardless of protection status. Our results underscore the need to broadly improve PA effectiveness in freshwaters by ensuring PA design and management explicitly consider freshwater biodiversity and integrate the needs of freshwater ecosystems.ResultsProtected and unprotected sitesProtected and unprotected sites only differed in the rate of ecological quality change (represented as the Ecological Quality Ratio; EQR), and only when protections encompassed smaller upstream scales, specifically when PAs were within 1-km (based on a significant Likelihood ratio test or LRT, n = 1754, L = 23.4, df = 1, P < 0.001) and 10-km upstream distances from a river site (LRT, n = 1754, L = 5.97, df = 1, P = 0.039; Fig. 2a–c). However, these changes were always greater (i.e., better) in unprotected sites, which was the opposite of our expectations. For example, the rate of EQR change for protected sites at the 1-km upstream scale was +1.1% year−1, whereas it was +1.9% year−1 for unprotected sites (Fig. 2c). For all other metrics and upstream scales, we found no differences in biodiversity change between protected and unprotected sites (Fig. 2a–c; Supplementary Table 2), with similar proportions of sites in these groups both gaining and losing biodiversity (Supplementary Fig. 1).Fig. 2: Overall effects of protected areas on river biodiversity.Rate of temporal change in a, d abundance, b, e richness, and c, f ecological quality (as the Ecological Quality Ratio; EQR) in (a–c) protected and unprotected sites, and (d–f) in sites that gained and did not gain upstream PA cover, for the 1-km, 10-km, 100-km, and full upstream scales. Points show the predicted group mean based on the respective linear mixed model, with lines as 95% confidence intervals. Asterisks indicate significant differences between groups based on Likelihood Ratio Tests and corrected for multiple comparisons using the Benjamini–Hochberg false discovery rate (c, 1 km: P < 0.001, 10 km: P = 0.021; e, 1 km: P = 0.039). Numbers in (c, f) indicate the number of sites out of 1754 total in each group, and these same sample sizes apply to (a, b) and (d, e).Full size imageFor sites with upstream PAs, higher PA cover was related to greater increases in taxon richness and ecological quality, but the nature of these relationships depended on initial ecological quality and upstream scale, as evidenced by significant PA cover*ecological quality interactions from generalized additive mixed models (Supplementary Table 3), and differences in effect sizes among upstream scales. Richness primarily increased with higher PA cover close to a site (i.e., at smaller upstream scales), and primarily in initially degraded communities (i.e., lower initial ecological quality; Fig. 3). For example, considering richness at the 1-km upstream scale and an initially poor ecological quality of 0.2 (i.e., 20% similarity to reference conditions), increasing PA cover from <1% to 100% almost tripled the rate at which richness increased, from +2.8% year−1 to +8.2% year−1 (Fig. 3a). These effects weakened as the upstream scale and initial ecological quality increased (Fig. 3b–d) to the point that, at the full upstream scale and an initially high ecological quality of 0.8, increasing PA cover from <1% to 100% only increased the rate at which richness increased from +0.7% to +1.1% year−1 (Fig. 3d). Similar to richness, ecological quality also increased with higher PA cover primarily in initially poorer quality communities. However, the effect of upstream scale was the opposite to that observed for richness, with greater improvements in ecological quality when PA cover increased at larger upstream scales (Supplementary Fig. 2), indicating ecological quality primarily responded to the amount of protection across the catchment.Fig. 3: Effects of the amount of protected area on taxon richness.Relationship between increasing the amount of protected area (PA) cover and the rate of temporal change in richness for only sites with PAs at the a 1-km, b 10-km, c 100-km, and d full upstream scales. Lines show the best-fit relationships, with shaded areas as 95% confidence intervals, based on generalized additive mixed models. Line and shading color illustrate how relationships depend on initial ecological quality (as the initial Ecological Quality Ratio, EQR) using examples of 0.2 (red), 0.4 (orange), 0.6 (light blue), and 0.8 (dark blue), which respectively indicate higher to lower human impacts.Full size imageIncreasing PA cover did not affect the rate of change in abundance at any upstream scale, and we found no evidence for effects of river size (Supplementary Table 3).Sites that gained and did not gain protected areasSimilar to the protected and unprotected sites, sites that gained and did not gain PA cover only differed in the rate of ecological quality change, and only at the 1-km upstream scale (LRT, n = 1754, L = 7.79, df = 1, P = 0.021), with greater increases in sites that did not gain PA cover (Fig. 2d–f). For all other metrics and upstream scales, we found no differences in biodiversity change between sites that gained and did not gain PA cover (Fig. 2d–f; Supplementary Table 2), with similar proportions of sites in these groups both gaining and losing biodiversity (Supplementary Fig. 1).For sites that gained upstream PAs, higher gains translated to greater increases in richness and ecological quality, primarily in initially poorer quality communities and only at larger upstream scales (richness: full only, Supplementary Fig. 3; EQRs: 10 km, 100 km, and full, Fig. 4). These increases were stronger for ecological quality and weaker for richness. For example, at the full upstream scale and an initially poor ecological quality of 0.2, increasing the rate of PA gain from <1% year−1 to the maximum observed value of 7.5% year−1 more than tripled the rate of EQR gain, from +4.1% year−1 to +14% year−1 (Fig. 4d). The rate of richness gain almost doubled under the same conditions, from +2.3% year−1 to +4.5% year−1 (Supplementary Fig. 3). Furthermore, as initial ecological quality increased, we found some instances where higher PA gains translated to slightly lower rates of increase in both ecological quality and richness. Using the full upstream scale as an example and an initially high ecological quality of 0.8, increasing the rate of PA gain from <1% to 7.5% year−1 decreased the rate of EQR gain from +0.32% to +0.19% year−1 (Fig. 4d).Fig. 4: Effects of the rate of protected area gain on ecological quality.Relationship between gain of upstream protected area (PA) cover and the rate of temporal change in ecological quality (represented as the Ecological Quality Ratio; EQR) for only sites that gained PA cover at the a 1-km, b 10-km, c 100-km, and d full upstream scales. Lines show the best-fit relationships, with shaded areas as the 95% confidence intervals, based on generalized additive mixed models. Line and shading color illustrate how relationships depend on initial ecological quality using example initial EQRs of 0.2 (red), 0.4 (orange), 0.6 (light blue), and 0.8 (dark blue), which respectively indicate higher to lower human impacts. Black lines and grey shading indicate non-significant (P > 0.05) relationships based on Wald-type tests. Best-fit relationships are shown up to the maximum rate of PA gain observed at each upstream scale.Full size imageIncreasing PA gain did not affect the rate of change in abundance at any upstream scale, and we found no evidence for effects of river size (Supplementary Table 3).DiscussionA principal question for evaluating the effectiveness of protection for nature conservation is to determine what would have happened in its absence39. Our results show that, broadly speaking, the same changes in river invertebrate biodiversity occurred regardless of the presence or degree of upstream protection, although PAs improved biodiversity outcomes in a subset of poor-quality communities that had or gained PA cover across a larger proportion of their upstream catchment. Additionally, some rivers lost invertebrate biodiversity during our 1986 to 2022 study period, which occurred in a comparable proportion of protected and unprotected sites. We therefore found no consistent evidence that inland PAs have generally benefited European river invertebrate biodiversity, suggesting that PAs may have not benefited water or habitat quality, given that invertebrates are key indicators of both43. These findings provide continental-scale support for similar results from individual freshwater ecosystems and specific regions for invertebrates18,31, other taxonomic groups (e.g., fish17,18,19), and water quality18. This conclusion should not be misconstrued as suggesting that PAs are ineffective, particularly given that it is based on a subset of total freshwater biodiversity and does not address whether PAs achieved the terrestrial conservation goals they are typically designed and managed for, such as reducing habitat loss9,10. We also found that PAs increased the rate of improvement in biodiversity and ecological quality for some river invertebrate communities, and other studies have shown PAs benefiting certain, individual freshwater ecosystems and taxonomic groups14,44. Our findings do, however, highlight the need to broadly improve the capacity of inland PAs to support freshwater biodiversity.Our results for poor-quality communities (e.g., around an initial EQR of 0.2 or 20% similarity to reference conditions) suggest that PAs led to greater increases in biodiversity in these sites than would have occurred without protection. The lesser influence of protection on higher quality communities potentially reflects the already low human impacts in these sites, thus biodiversity remained high and stable regardless of protection status. This explanation fits with our results showing low PA effectiveness in high-quality communities (e.g., initial EQR around 0.8) where biodiversity was likely already high, and may explain why protection was sometimes associated with lower biodiversity gains, which may occur if PAs are placed in areas with a lower scope for improvement (e.g., remote, forested catchments37,38). However, it does not explain why PAs were less effective for moderate-quality communities (e.g., initial EQR around 0.4–0.6), which have considerable potential for further improvement. A more likely explanation for these communities is that current approaches to implementing inland PAs, which typically focus on management of terrestrial habitats23, can address some stressors affecting poor-quality rivers, but not other stressors that may be more relevant in higher quality ecosystems. For example, land-use change and pollution are among the principal stressors driving freshwater biodiversity loss45. PAs have some capacity to address these stressors by reducing the human activities that cause them, such as deforestation, urban expansion, intensive agriculture, and tourism10,46. Doing so can subsequently improve water and habitat quality in hydrologically connected rivers47,48. However, as communities recover, other unaddressed stressors may become more relevant, such as upstream flow alterations or climate change49, thereby limiting PA effectiveness. Maximizing the benefits of PAs for freshwater biodiversity requires that existing management regimes consider both terrestrial- and freshwater-focused actions23,50, and set specific goals to address the most important stressors in each freshwater ecosystem. Preventing degradation, including in higher quality rivers, also requires conservation actions beyond establishing PAs, such as better wastewater treatment, habitat restoration, and further improvements to land management practices, including reducing micropollutants11,51.PA benefits in initially poor-quality communities varied among upstream scales and community metrics, suggesting that the spatial scale of protection determined which community components were affected. Richness primarily responded to the amount of PA cover close to a site (i.e., at smaller upstream scales), whereas ecological quality primarily responded when protection encompassed and expanded across the broader catchment (i.e., at larger upstream scales). Abundance exhibited no response to protection whatsoever. Increases in richness that neither affect abundance nor substantially alter compositional metrics, such as ecological quality, can occur when only numerically rare species increase52. Similarly, compositional changes may not affect richness or abundance if new taxa replace previous taxa53. Our results could therefore be explained by protection at smaller scales primarily increasing rare taxa, and protection across larger scales producing stronger compositional recovery by replacing tolerant with sensitive taxa. Increasing rare taxa can provide some benefits, including potentially diversifying and stabilizing ecosystem functions54,55, but may represent a less desirable outcome compared to substantially improving invertebrate ecological quality, which is a principal indicator of European river health. Therefore, our results suggest that protecting the broader catchment, or at least a large proportion of the catchment and a river’s lateral buffer zones, may elicit greater biodiversity benefits. This conclusion supports the value of catchment-scale rather than local-scale approaches to freshwater protection22,56, including PAs that are configured to protect and connect key longitudinal (upstream to downstream), lateral (riparian and floodplain), and vertical habitats (surface to groundwater)14,33,57.An additional solution to improving PA effectiveness for freshwaters could be to further limit human activities within current PA boundaries, given that many still permit continued human use58, such as land development and resource extraction. Designating stricter PAs that do not allow such activities may reduce human impacts46, thus potentially benefiting downstream freshwaters. However, evidence that the strictness of a PA’s designation determines its conservation benefits is equivocal59, including in freshwater ecosystems16,44. Stricter protection can also counterintuitively lead to worse conservation outcomes by disenfranchising local communities and promoting illegal use of protected resources60. Integrating terrestrial with freshwater approaches to PA design and management may be an alternative approach for improving freshwater conservation outcomes14,50. Freshwater-focused PAs (e.g., Ramsar wetlands or river PAs61) can be designed based on the distribution of both terrestrial and freshwater biodiversity while accounting for habitat connectivity and downstream impacts22,30,50. Effective, adequately funded, and co-produced management is also key to PA effectiveness14,60. We therefore advocate that freshwater ecosystems would further benefit from inclusion in PA management priorities that integrate the freshwater needs of local communities and stakeholders.Inland PAs are increasing globally, supported by the 30% by 2030 coverage target set by the Kunming-Montreal Global Biodiversity Framework4. These PAs typically prioritize the needs of terrestrial habitats and taxa, raising questions about their benefits for freshwater biodiversity. Our findings, based on European river invertebrate communities, show that PAs have benefited certain freshwater communities, specifically poor-quality communities where protection encompassed a larger proportion of the upstream catchment. All other communities exhibited more limited (or no) effects of protection, although the lack of effect in high-quality communities may have occurred because these communities are less impacted regardless of whether they are protected or not. Improving overall PA effectiveness, particularly in impacted rivers, requires design and management strategies that explicitly integrate the needs of freshwater ecosystems14,57, including actions that address multiple stressors and continuous coverage that extends over larger upstream distances and lateral buffer zones. Accordingly, a holistic, catchment-scale framework for managing freshwaters is required14,22,23,62. Such a framework would better support freshwater biodiversity, including aquatic invertebrates and the ecosystem functions they provide (e.g., prey, nutrient cycling, and decomposition40), and would benefit terrestrial ecosystems via aquatic-terrestrial linkages63 and marine ecosystems via freshwater-marine linkages64. Consequently, improving freshwater protection is a critical issue relevant to all ecosystems and is essential to bend the curve of global biodiversity loss.MethodsRiver invertebrate biodiversityWe collated river invertebrate time series from ref. 42 and from data provided by European freshwater researchers and managers. We defined the following criteria for data inclusion: (i) time series must span a duration of ≥10 years with ≥7 individual sampling years to enable robust estimation of biodiversity change; (ii) within a time series, samples in different years must be collected using the same methods and from the same three-month season; (iii) data were available at the community-level with taxa identified to a consistent taxonomic level through time (if inconsistent levels were used then taxa were adjusted to the most temporally consistent level); and (iv) ecological quality values could be calculated for each community following methods compliant with the EU Water Framework Directive (see Supplementary Table 4). These criteria allowed the inclusion of data from ten European countries (Fig. 1). Included data encompassed 1754 sites and 24,245 individual years collected between 1986 and 2022. Included time series spanned a mean total duration of 19.7 ± 5.7 years (mean ± SD) with 13.8 ± 5.5 sampling years (Supplementary Table 1). Taxonomic resolution varied among sites, with 57% (993 sites) identified only to the family level or higher, and 43% (761 sites) identified to a mixed resolution, typically a combination of families, genera, and species, with some classified to intermediate (e.g., subfamily) or higher levels (e.g., Oligochaeta at subclass). These taxonomic differences among sites did not influence our results (see Supplementary Fig. 4). Identifications higher than species level introduce some uncertainty, given that we cannot detect potential species shifts occurring within these groups. However, such identifications still reliably reflect overall community responses to environmental change65,66 and are common in invertebrate research in which many taxa cannot be reliably identified to the species level.We quantified biodiversity for each site and year based on three community metrics: (i) abundance (total number of individuals), (ii) richness (total number of taxa), and (iii) ecological quality, quantified as the Ecological Quality Ratio (EQR). Ecological quality is commonly used in Europe as a community-based indicator of human impacts, particularly organic pollutants and general environmental degradation67. It reflects the compositional similarity of sensitive and tolerant taxa to expected values from least-impacted reference communities, which are defined based on country-specific criteria (Supplementary Table 4). EQRs are a continuous observed-to-expected ratio of this similarity, ranging from 0 (low similarity indicating high human impacts) to 1 (equal to reference conditions indicating low human impacts), although EQRs for some communities can be above 1, reflecting conditions better than the average reference state. We chose EQRs over other compositional metrics, such as temporal β-diversity, because they provide meaningful information not just about whether communities changed, but also how they changed.Rates of temporal change in each community metric were quantified for each site by relating site-specific abundance, richness, and EQRs to sampling year using the gls function from the nlme package in R68,69, then extracting the slope of this relationship. We included a first-order autoregressive structure in each model to control for temporal autocorrelation between successive years. All slopes were converted to percent change per year by log-transforming all metrics prior to modeling, then exponentiating the slopes, subtracting 1, and multiplying by 100. This transformation ensured all rates of biodiversity change had the same units across sites and metrics.Protected areas and upstream scalesWe obtained vectorial cartographic polygons for inland PAs from Protected Planet25. We excluded 2% of European PAs (accounting for 6% of total cover) for which the year of establishment was unknown. We further excluded all point data due to analytical errors that arise when inferring the dimensions of PAs with unknown boundaries70. The majority of point data in our included countries (1171 points out of 1247 total or 94%) were natural monuments in Sweden. These PAs have a registered area of 0 km2 because they are individual features, such as a single tree or rock formation, thus contributing marginally to total protected area cover. Of the remaining 76 excluded points, 20 were Ramsar wetlands with a total area of 296 km2, and 50 were large biosphere reserves with a total area of 94,188 km2. To fill the biosphere information gap, we used data on European biosphere boundaries from ref. 71, although we excluded the outer transition zones, which are not considered protected. Polygons for all included PAs were dissolved into a single layer, with no distinctions made between different PA types (discussed further in Supplementary Note 1).In addition to the PA polygons, for each site, we produced upstream polygons representing four different spatial scales. The four scales included: lateral buffer zones extending up the main channel and all tributaries to (i) 1-, (ii) 10-, and (iii) 100-km longitudinal distances; and (iv) the full upstream contributing area, i.e., the upstream catchment including all terrestrial areas that drain into a site. Each upstream scale was selected to account for the potential effects of PAs at progressively greater distances from each site, with the full upstream scale also accounting for PA effects outside the lateral buffer zones. Upstream distances were delineated using the Hydrography90m river network and the hydrographr package72,73. Buffer zone widths for 1–100-km upstream scales were quantified as 100 m multiplied by the Strahler order of each segment, plus half the predicted river width based on its order from ref. 74 (see Fig. 5). We used this approach because human activities adjacent to a river typically exert the strongest influence on local communities, and 100-m lateral buffer zones effectively capture these effects75,76,77. Additionally, our data encompassed sites from rivers of Strahler orders 1–11, representing small streams to very large rivers, and larger lateral areas are needed to capture the larger surface and ground water inputs to higher-order rivers78. Lastly, including river width as part of the buffer zones captured PAs that encompass rivers. The full upstream contributing area was delineated using the get_upstream_catchment function from the hydrographr package.Fig. 5: Illustrated example of the four upstream scales.A river site (red circle) and the upstream scales captured by lateral buffer zones (dashed lines) extending up to 1-km (purple), 10-km (pink), and 100-km (yellow) upstream distances. The solid black outline represents the full upstream contributing area. Note the decline in buffer zone thickness from higher (larger) to lower (smaller) order rivers.Full size imageBased on the PA and upstream polygons, we calculated the percent of each upstream scale covered by PAs to represent both the presence (>0% cover) and degree (total % cover) of protection. We also calculated the rate of temporal change in percent PA cover to capture biodiversity responses to PA expansion. PA cover was calculated for the year before the first and last year of each invertebrate time series, which allowed invertebrate communities ≥1 year to respond to environmental changes resulting from PA establishment. Percent PA cover was quantified as the mean percent cover between the first and last years (always ranging from 0 to 100% across sites). Temporal changes in percent PA cover (% year−1) for each site were quantified as the slope of the relationship between PA cover and year, which ranged from 0 to 9% year−1 depending on the upstream scale (1 km: 0–9%; 10 km: 0–8.6%; 100 km: 0–7.5%; Full: 0–7.5% year−1). PA cover changes were only neutral or positive because Protected Planet data cannot represent PA cover declines79, although PA cover has generally increased globally58.In addition to PA cover, we quantified the size (in km2) of each full upstream area to represent river size, given that larger rivers have larger upstream areas. Size was calculated based on the number of 90 m by 90 m pixels in the full upstream area, derived from the Hydrography90m river network. Sites primarily encompassed medium- to larger-sized rivers, with 671 sites out of 1754 total having an upstream catchment size between 10 and 100 km2 and 701 sites between 100 and 1000 km2, with the remainder comprised of very small (135 sites <10 km2) and very large rivers (247 sites >1000 km2).Statistical analysesWe conducted two sets of analyses: (1) categorical comparisons of changes in abundance, richness, and EQRs between protected and unprotected sites (i.e., those with and without upstream PAs), and between sites that did or did not gain upstream PA cover; and (2) for sites with upstream PAs, we related the rate of biodiversity change to the amount of upstream PA cover and the rate of PA gain using regression. The first set of analyses provided a broad overview of the effects of having or gaining any upstream PA cover. The second set determined whether the degree of protection, or its rate of gain, influenced biodiversity change. We also used the second set of analyses to test the influence of river size and initial ecological quality (detailed below). These temporal trend comparisons have some strengths compared to other potential approaches, such as before-and-after comparisons or spatial comparisons of protected and environmentally similar unprotected sites. Specifically, using temporal trends of percent biodiversity change enables comparison of sites that differ in total biodiversity, and allows for variation in protection timing (e.g., sites can be already protected at the start of their time series or can become protected later). Temporal analyses also allow for changes in protection effectiveness through time, such as lagged effects, and capture the potential compounding effects of establishing multiple PAs in subsequent years.For our first set of analyses, we compared sites with upstream PAs (>0% cover; ‘protected’) versus those without (0% cover; ‘unprotected’), and those that gained upstream PAs (>0% cover year−1; ‘gain’) versus those that did not (0% cover year−1; ‘no gain’). Sites were assigned to these categories separately for each upstream scale, given that sites could change assignments across scales (e.g., an upstream PA is present within 10 km but not 1 km). We then related the continuous, site-level rates of biodiversity change to these categories using linear mixed models (LMMs) conducted in the lme4 package80. Additionally, each model included fixed continuous terms for site latitude and longitude to control for broad-scale spatial trends, a fixed continuous term for time-series length to control for slope differences among shorter to longer time series, and a random intercept term designating the provider of each dataset to control for differences in sampling methods among providers (see Supplementary Table 1). We tested the significance (P < 0.05) of the fixed categorical PA term by dropping it from each model and comparing the reduced versus fuller models using Likelihood ratio tests81. We ran separate models for each upstream scale. To control for conducting multiple models using the same response variables, we corrected all P-values using the Benjamini–Hochberg false discovery rate82.For our second set of analyses, we used generalized additive mixed models (GAMMs) to relate biodiversity change to the amount of upstream PA cover for sites with upstream PAs, and to the rate of PA gain for sites that gained upstream PAs. Models were conducted in the mgcv package83. PA cover and rates of gain were converted to proportions and square-root transformed prior to analyses to produce a more even distribution of values. To determine the influence of river size, we included an interaction between the PA term and a continuous term for the size of the full upstream area (log10-transformed km2). To determine the influence of initial ecological quality, we included an interaction between the PA term and a continuous term for the EQR averaged across the first three sampling years to represent the initial status of the community. The individual PA, river size, and quality terms were modeled as fixed smoothed terms using thin-plate regression splines, with all fixed interactions modeled using tensor product smooths. Additionally, we included the same fixed and random control variables as for the LMMs. All smoothed terms used the default basis dimensions, and we checked model diagnostics, including the need for higher basis dimensions, using the gam.check function. All GAMMs used a Gaussian distribution (identity link function). The significance (P < 0.05) of interactions, and if needed the individual PA term, were determined using Wald-type tests83. We corrected all P-values as above when conducting multiple models using the same response variables.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

    Data availability

    All data needed to repeat our analyses are publicly available from https://doi.org/10.6084/m9.figshare.25245430.
    Code availability

    All code needed to repeat our analyses is publicly available from https://doi.org/10.6084/m9.figshare.25245430.
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    Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, New York, 2017).Download referencesAcknowledgementsThis study was facilitated by the Senckenberg Synthesis Center on Freshwater Biodiversity Change in Europe and its data providers. We thank Marie-Josée Létourneau for the artwork in Fig. 5 and for permitting its use. J.S.S. and P.H. received funding from the EU Horizon 2020 project eLTER PLUS (#871128). P.H. received funding from the Frontiers Research Foundation (Frontiers Planet Prize). N.J.B. received funding from the Research Council of Lithuania (#S-MIP-24-61) and acknowledges the Lithuanian EPA. D.C.-M. received funding from the EU Horizon 2020 research and innovation programme (#10106238). S.D. received funding from the Leibniz Competition (#J45/2018). A.M.G.-F. acknowledges the grant RYC2023-045780-I funded by MICIU/AEI/10.13039/501100011033 and by ESF+. L.K. received funding from Formas (#2023-00284) and sourced data from the Swedish University of Agricultural Sciences database (Miljödata). A.L. received funding from the Spanish Ministry of Science and Innovation (#PID2020-115830GB-100). P.P. was funded by the Czech Science Foundation (#GA23-05268S), and thanks to CHMI and the Povodí enterprises for the provided data.FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthors and AffiliationsDepartment of River Ecology and Conservation, Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, GermanyJames S. Sinclair & Peter HaaseSchool of Science and Technology, Nottingham Trent University, Nottingham, UKRachel StubbingtonDivision of Biology, Kansas State University, Manhattan, KS, USAEllen A. R. WeltiFreshwater and Marine Solutions, Finnish Environment Institute, Oulu, FinlandJukka AroviitaState Scientific Research Institute Nature Research Centre, Vilnius, LithuaniaNathan J. BakerSHE2 Research Group, FEHM-Lab (Freshwater Ecology, Hydrology and Management), Institute of Environmental Assessment and Water Research (IDAEA), CSIC, Barcelona, SpainMiguel Cañedo-ArgüellesDepartment of Hydrobiology, University of Pécs, Pécs, HungaryZoltán CsabaiHUN-REN Balaton Limnological Research Institute, Tihany, HungaryZoltán CsabaiInstitute of Aquatic Ecology, HUN-REN Centre for Ecological Research, Budapest, HungaryDavid Cunillera-MontcusíIFREMER–DYNECO/LEBCO, Centre de Bretagne, Plouzané, FranceDavid Cunillera-MontcusíLeibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, GermanySami DomischINRAE, UR RiverLy, centre de Lyon-Villeurbanne, Villeurbanne, Cedex, FranceMartial Ferréol & Mathieu FlouryUniversity of Paris-Saclay, INRAE, UR HYCAR, Antony, FranceMathieu FlouryDepartment of Animal Sciences and Aquatic Ecology, Ghent University, Ghent, BelgiumMarie Anne Eurie Forio & Peter L. M. GoethalsIHCantabria – Instituto de Hidráulica Ambiental de la Universidad de Cantabria, Santander, SpainAlexia M. González-FerrerasNature Solutions, Finnish Environment Institute, Oulu, FinlandKaisa-Leena HuttunenEcology and Genetics Research Unit, University of Oulu, Oulu, FinlandKaisa-Leena Huttunen & Timo MuotkaDepartment of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, SwedenRichard K. JohnsonDepartment of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, SwedenLenka KuglerováDepartment of Plant Biology and Ecology, University of the Basque Country (UPV/EHU), Leioa, Bilbao, SpainAitor LarrañagaOulanka Research Station, University of Oulu Infrastructure Platform, Kuusamo, FinlandRiku PaavolaWater, Energy and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, Oulu, FinlandRiku PaavolaDepartment of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech RepublicPetr PařilDepartment of Ecoscience, Aarhus University, Aarhus, DenmarkJes J. RasmussenResearch Center One Health Ruhr, University Alliance Ruhr & Faculty for Biology, University of Duisburg-Essen, Essen, GermanyRalf B. SchäferFlanders Environment Agency (VMM), Aalst, BelgiumRudy VannevelDepartment of Tisza Research, Institute of Aquatic Ecology, HUN-REN Centre for Ecological Research, Debrecen, HungaryGábor VárbíróSchool of Life Sciences, University of Essex, Colchester, UKMartin WilkesFaculty of Biology, University of Duisburg-Essen, Essen, GermanyPeter HaaseAuthorsJames S. SinclairView author publicationsSearch author on:PubMed Google ScholarRachel StubbingtonView author publicationsSearch author on:PubMed Google ScholarEllen A. R. WeltiView author publicationsSearch author on:PubMed Google ScholarJukka AroviitaView author publicationsSearch author on:PubMed Google ScholarNathan J. BakerView author publicationsSearch author on:PubMed Google ScholarMiguel Cañedo-ArgüellesView author publicationsSearch author on:PubMed Google ScholarZoltán CsabaiView author publicationsSearch author on:PubMed Google ScholarDavid Cunillera-MontcusíView author publicationsSearch author on:PubMed Google ScholarSami DomischView author publicationsSearch author on:PubMed Google ScholarMartial FerréolView author publicationsSearch author on:PubMed Google ScholarMathieu FlouryView author publicationsSearch author on:PubMed Google ScholarMarie Anne Eurie ForioView author publicationsSearch author on:PubMed Google ScholarPeter L. M. GoethalsView author publicationsSearch author on:PubMed Google ScholarAlexia M. González-FerrerasView author publicationsSearch author on:PubMed Google ScholarKaisa-Leena HuttunenView author publicationsSearch author on:PubMed Google ScholarRichard K. JohnsonView author publicationsSearch author on:PubMed Google ScholarLenka KuglerováView author publicationsSearch author on:PubMed Google ScholarAitor LarrañagaView author publicationsSearch author on:PubMed Google ScholarTimo MuotkaView author publicationsSearch author on:PubMed Google ScholarRiku PaavolaView author publicationsSearch author on:PubMed Google ScholarPetr PařilView author publicationsSearch author on:PubMed Google ScholarJes J. RasmussenView author publicationsSearch author on:PubMed Google ScholarRalf B. SchäferView author publicationsSearch author on:PubMed Google ScholarRudy VannevelView author publicationsSearch author on:PubMed Google ScholarGábor VárbíróView author publicationsSearch author on:PubMed Google ScholarMartin WilkesView author publicationsSearch author on:PubMed Google ScholarPeter HaaseView author publicationsSearch author on:PubMed Google ScholarContributionsJ.S.S. and P.H. conceived the study. J.A., S.D., and R.B.S. contributed to planning and methods. J.S.S. and P.H. wrote most of the manuscript with contributions from all authors, particularly R.S. and E.A.R.W. J.A., N.J.B., M.C.-A., Z.C., D.C.-M,. M.Fe., M.Fl., M.A.E.F., P.L.M.G., A.M.G.-F., K.-L.H., R.K.J., L.K., A.L., T.M., R.P., P.P., J.J.R., R.V., G.V., and M.W. provided invertebrate data or contributed to calculating ecological quality values for their respective countries.Corresponding authorCorrespondence to
    James S. Sinclair.Ethics declarations

    Competing interests
    Since April 16th, 2025, Miguel Cañedo-Argüelles has been seconded to the ERC Executive agency. The views expressed in this paper are purely those of the author. They do not necessarily reflect the views or official positions of the European Commission, the ERC Executive Agency, or the ERC Scientific Council. The other authors declare no competing interests.

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    Reprints and permissionsAbout this articleCite this articleSinclair, J.S., Stubbington, R., Welti, E.A.R. et al. Current protected areas provide limited benefits for European river biodiversity.
    Nat Commun 16, 11146 (2025). https://doi.org/10.1038/s41467-025-67125-5Download citationReceived: 27 March 2025Accepted: 21 November 2025Published: 17 December 2025Version of record: 17 December 2025DOI: https://doi.org/10.1038/s41467-025-67125-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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