Ecology

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

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

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

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Summary of sequencing dataWe obtained 168 million and 180 million 250 bp reads from Asian Badger and Northern Hog Badger, respectively. After removing transcripts and unigenes below 200 bp, we obtained 335,772 transcripts and 285,159 unigenes belonging to Asian Badger and 413,917 transcripts and 362,075 unigenes belonging to Northern Hog Badger (Table 1). Next, we analysed the length distribution of the unigenes and transcripts in these two species (Fig. 1). Their N50 of transcript length is longer than 1000 bp, and their N50 of unigene length is longer than 600 bp. The average GC content of the transcriptome data of Asian Badge was 52.71%, a value slightly higher than that of the Northern Hog Badger, which was 52.12% (Table 1).Table 1 Summary of the transcriptome of Asian Badgers and Northern Hog Badgers.Full size tableFigure 1Length and quantity distribution of transcripts and unigenes.Full size imageFunctional annotation and classification of the assembled unigenesThe success rate of annotation of these research data in the seven databases is shown in Table 2. In total, 34,150 (ZH) and 31,632 (GH) unigenes had GO terms (Table 2). Among them, there were three GO items related to digestion: positive regulation of the digestive system process (GH and ZH both have one gene), digestive tract development (GH and ZH both have four genes), and digestion (GH has five genes, ZH has three). Next, we compared the GO terms of Asian Badger and Northern Hog Badger transcriptomes and found that the distributions pattern of gene functions from these two species were particularly similar (Fig. 2). This predictable result indicates that there is no bias in the construction of the libraries from the Asian Badger and Northern Hog Badger. For both species, in the three main partitions (cellular component, molecular function, and biological process) of the GO classification, ‘Cellular process’, ‘Binding’ and ‘Metabolic process’, terms were principal individually (Fig. 2). In total, 8915 (ZH) and 10,203 (GH) unigenes had KOG terms (Table 2). In addition, 15,667 (ZH) and 17,823 (GH) were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (Table 2) and grouped into 32 subclasses. Interestingly, the digestive system subcategory contains 695 and 611 unigenes in Asian Badger and Northern Hog Badger, respectively, involving 9 pathways, namely, bile, gastric acid, pancreatic, salivary secretion, carbohydrate, protein, vitamin, fat digestion and absorption, and mineral absorption.Table 2 Gene annotation success rate statistics.Full size tableFigure 2GO term Top20 for GH and ZH.Full size imageAnalysis of orthologous genesThe transcriptome evolution of different species can be understood by comparing transcriptome data. We analysed the possible orthologous genes between the transcriptome of Asian Badger and Northern Hog Badger obtained in this study. We selected a total of 5227 homologous gene pairs from these four species. After 5227 pairs of homologous genes were optimized and screened, 943 orthologous gene pairs were obtained (Supplementary Table S1).To explore whether the genes related to small intestinal digestion in Asian Badger and Northern Hog Badger have undergone adaptive evolution. We can predict the genes that affect the evolution of the two species through selection pressure on orthologous genes12. We selected 473 orthologous gene pairs with Ka/Ks  > 1 called divergent orthologous genes from the Ka/ks analysis results. We obtained 1263 orthologous gene pairs with Ka/Ks  More

Overview of the FluxNet methodThe FluxNet approach is based on a mechanistic model, which includes multiple species/types of phytoplankton, bacteria, dissolved and particulate organic matter (DOM, POM), inorganic nutrients, micronutrients and inhibitors (see Table 1). For phytoplankton—bacteria carbon flux, which is the focus here, phytoplankton produce organic carbon by exudation and death. For exudation, living phytoplankton produce total DOM at constant and photosynthesis-proportional rates (ke, ef), with a composition defined by an exudation fraction (Fe) for each DOM species. These parameters vary by phytoplankton type. For example, for green algae (gre), the constant exudation rate is kegre and the fraction of glucose-containing HMW DOM (gl2) is Fegre,gl2. For one phytoplankton type the total DOM production varies in time with the photosynthesis rate, but the composition is constant. Phytoplankton die by a general death function and inhibition. The death function is time-variable (a bell-shaped function with a maximum at a specific time of year) and does not differentiate between various death mechanisms like zooplankton grazing or viral lysis, but presumably it represents mostly grazing in this case. Upon death, the phytoplankton biomass is converted to POM and DOM, where e.g., the content of chrysolaminarin (chr) for the diatom Rhizosolenia styliformis (rst) is defined by a composition fraction (Fxrst,chr). POM dissolves to DOM at a first-order rate. Bacteria consume DOM using Monod-level kinetics, where e.g. the affinity for Polaribacter (pol) for chrysolaminarin is defined by a half-saturation constant (Kshpol,chr).Table 1 Model components.Full size tableThe novel aspect is the upscaling to hundreds of state variables and thousands of parameters, which is accompanied by several conceptual and practical modeling challenges. To balance mass and account for the action of unobserved components, cryptic or hypothetical species are included [17], like DOM types d01-d15, which may represent e.g., threonine [18]. To simulate a diverse community with a smaller number of drivers (“paradox of the plankton”) and control chaos, interaction via micronutrients and inhibitors, as well as dormancy is included [19,20,21,22]. Parameters are optimized/calibrated to minimize the discrepancy between the model and observations. Which parameters are optimized and the corresponding ranges is based on available information (complete model equations and parameters are in Table S1–S25). For example, the constant DOM production rate (ke) is optimized for all phytoplankton, with a range adopted from a previous modeling study [23]. For rst (Rhizosolenia styliformis), the exudation fractions for most DOM components, like the cryptic species d01 (Ferst,d01), are optimized. Others, like glucose-containing HMW DOM (Ferst,gl2), are fixed based on literature (Table S14). The optimization is challenging because of the many components, nonlinear interactions, and resulting local optima in the objective function. We developed an optimization routine customized for microbial ecosystems with a number of key features.First, the method mimics natural speciation, where a coarse-grained model is gradually de-lumped to a finer resolution, a strategy also used in manual model development [13, 24, 25]. This is illustrated in Fig. 1, which shows how the model starts with just one component in each ecological compartment (Fig. 1E). This model is optimized until a threshold is reached, and then all species are de-lumped/split into two, followed by another round of optimization and so on. During the course of the optimization, with time or model runs, the number of components and parameters increase, and the total error generally decreases, although there can be a transient increase when new species are introduced (Fig. 1A, B). This way the optimization routine works with a smaller model on average and computational effort can be directed to a smaller set of parameters corresponding to newly introduced species, and the performance increases (Fig. 1C).Fig. 1: FluxNet inference method illustration.A Number components and optimized parameters. B Error for entire model (Total) and selected individual observations (rst = R. styliformis, pol = Polaribacter, lam = particulate chrysolaminarin). Best of 128 replicate runs. C Diversification of chrysolaminarin uptake affinity (max. heterotrophy rate/half-saturation constant). D Method performance with and without de-lumping. E Network corresponding to different de-lump levels. See Table 1 for component names and abbreviations.Full size imageAt each de-lumping level, the new species generally inherits the parameter values (i.e., the genome [26]) from the old species. Subsequent optimization then diversifies the population. This is illustrated in Fig. 1C, which shows the uptake affinity of all bacteria species for chr. However, different parameter values can also be specified for the new species, and then they are adopted and overwrite those inherited from the old species. This is used, for example, to assign species-specific cell sizes or prevent species from taking up a substrate. In Fig. 1C, those species that are not capable of assimilating chr, like rei (Reinekea), have an affinity equal to 0. The method thus allows for natural and automated expansion of the model to very large scale, yet provides a way to constrain/curate it based on available information.Second, the routine includes multi-parameter optimization (Nelder-Mead simplex method) on selected subsets of dependent parameters, like those involved in the production and consumption of chrysolaminarin (chr) or directly affecting the photosynthesis of the diatom R. styliformis (rst). Dependence between parameters, like max. photosynthesis rate and nutrient half-saturation constant, are explicitly considered. Also, Monte Carlo scans are performed on selected parameter sets at various points in the process.Application to Helgoland time seriesThe FluxNet method is applied to a four-year time series at Helgoland [27], including near-daily observations of 15 phytoplankton and 38 heterotrophic bacteria types (e.g., species, strains) and various bulk and auxiliary parameters (e.g., Chlorophyll a, DAPI, temperature, nitrate+nitrite, ammonium, phosphate, light extinction) (Tables S19 and S20). Data from more focused studies characterizing DOM and POM are also included [28, 29] (Table S21).In addition to the time-series data, the model is informed by literature information. Model parameters, incl. general properties like phytoplankton exudation fraction or bacteria growth efficiency, are constrained based on past models and data. Also, constraints are implemented for parameters controlling composition, exudation and utilization for the specific components included in the model. Those were based on a literature meta-analysis, where we searched primarily for studies with strains from Helgoland, but included strains from other locations if necessary. These constraints include, for example, for the phytoplankton storage polysaccharide chrysolaminarin, the typical content (~30% for diatoms, none for dinoflagellates) and ability of bacteria to assimilate it (yes for Polaribacter, no for Roseobacters and Reinekea) (Tables S4 and S11). Imposing constraints from the literature generally results in a worse agreement with the observations, but also increased realism of the model. Removing the constraints of phytoplankton composition (Table S4) significantly improves the agreement with observations, but also predicts substantial glycogen content of diatoms (e.g., Fxmhe,gly+ply = 0.19). Removing uptake constraints by bacteria (Table S11) reduces the error, but not significantly, suggesting that there is enough flexibility of the model to reproduce the observations even with this constraint. However, that model also includes features that disagree with literature, like substantial uptake of chr by s11 (Kshs11,chr = 25 L/mmolC/d).Carbon fluxes through and within in the ecosystemThe final model includes 210 components and their behavior and interaction are described by a total of 8200 calibrated parameters of 50 different parameter types (e.g., the composition of each of the 53 microbes is described by 76 fractions Fx, or 4000 total parameters) (Fig. 1), and it constitutes a mass-balancing, mechanistically-constrained, quantitative representation of the ecosystem. It reproduces many of the observed patterns of summary parameters like Chlorophyll a (chl), total bacteria (dap), particulate chrysolaminarin (lam), various high-molecular weight (HMW) DOM compounds, as well as absolute concentrations of individual phytoplankton and bacteria species (Fig. 2A–C). Only subset of the hundreds of model components is shown in Fig. 2B, C, which were selected based on (a) importance (e.g., rst is the dominant OM producer in 2009), (b) availability of data (e.g., chrysolaminarin, [29]) and (c) illustration of co-blooming (panel B) and succession (panel C). All model-data comparisons are presented in the SI (Fig. S1). The model under-predicts total DOM (doc), probably because a large fraction of observed DOM is more refractory allochthonous material, which is not considered in the model.Fig. 2: FluxNet model results and comparison to data.A All model types lumped. Phytoplankton (chl, μgChla/L), POM (poc, incl. microbes, μmolC/L ×0.1*), DOM (doc, μmolC/L ×0.1*), bacteria (dap, 1e6/mL ×3*). Gray shading are spring blooms, defined as the first time of the year the phytoplankton exceeds 3 µgChla/L plus 28 days. B Selected types for 2009 spring bloom. Rhizosolenia styliformis (rst, centric diatom, 1e6/L ×1.2*), Thalassiosira nordenskioeldii (tno, centric diatom, 1e6/L ×0.05*), particulate chrysolaminarin (lam = phr + phytoplankton content, μmolC/L ×0.002*), dissolved chrysolaminarin (chr, μmolC/L ×0.002*, no data available), Polaribacter (pol, DAPI × CARD-FISH, 1e6/mL ×0.1*), NS3a marine group (ns3, 1e6/mL ×0.2*). C Selected types for 2010 spring bloom. Mediopyxis helysia (mhe, centric diatom, 1e6/L), Thalassiosira nordenskioeldii (tno, centric diatom, 1e6/L), glucose-containing HMW DOM (glc, μmolC/L ×0.01*), arabinose-containing HMW DOM (ara, μmolC/L ×3*), Reinekea (rei, 1e6/mL ×5*), Alteromonas (alt, 1e6/mL ×1.5*). Lines are model and symbols are data [27,28,29]. *Individual concentration series scaled to illustrate dynamics. See Fig. S1 for all model-data comparisons. Upside-down triangles mark various bloom stages for networks in (D) and Fig. 4A. D Inferred carbon flux network. Nodes are components. Size indicates in/outflux (μmolC/L/d), color varied randomly within each ecological compartment. Lines are fluxes. Thickness is proportional to log flux (μmolC/L/d), colored based on the source node, lines below a threshold distance are colored gray to highlight most important fluxes. Italic numbers are total fluxes (μmolC/L/d). Flux cut off is 0.01%. See Table 1 for component names and abbreviations. See Movie S1.Full size imageIt is important to understand that the model was calibrated to these observations, so this is not a prediction per se. The main information produced by this analysis (emergent property) are the mass fluxes. Predicted ecosystem-level fluxes can be compared to independent estimates, which were not used as input here. For the period 2009–2012, the gross primary production rate in the model is 28 (±1.2 standard deviation) mmolC/m2/d. Uncertainty of fluxes and parameters are based on top 5% of 128 replicate runs, as in [23]. This flux compares well to a regional estimate of 29 (26–33) mmolC/m2/d for the Transitional East Region of the North Sea for the same period [30]. At the end of March, the bacterial production rate in the model is 0.32 (±0.041), 0.14 (±0.017), 0.20 (±0.025) and 0.45 (±0.057) μmolC/L/d for the 4 years, respectively. This is consistent with measurements of 0.20 μmolC/L/d in 1992 ~30 km from Helgoland [31].These comparisons provide confidence in other aggregate fluxes predicted by the model. The C, N and P fluxes to the sediment bed, via settling of phytoplankton and POM, are 5.8 (±0.91) mmolC/m2/d, 0.87 (±0.14) mmolN/m2/d and 0.054 (±0.0085) mmolP/m2/d, which constitute 20%, 16% and 18% of the input via photosynthesis (C) or external input (N, P) (see Fig. S2). External “new” input of N is 0.66 μmolN/L/d, which is 6.0 time higher than the 0.11 (±0.023) μmolN/L/d released or “recycled” by bacteria.The resulting flux network includes quantitative carbon fluxes between all components at each time point, like 28 days into the 2009 spring bloom (Fig. 2D, Dataset S1 list all fluxes). The dominant source of organic matter is rst at 0.36 (±0.19) μmolC/L/d, 30% of which is dissolved and particulate chrysolaminarin (chr + phr). These instantaneous fluxes exhibit a higher uncertainty than the integrated fluxes discussed in the previous paragraph, which can be explained by small timing differences (Table S26). The DOM is consumed by a diverse consortium of bacteria, mostly Polaribacter (pol) at 0.46 (±0.22) μmolC/L/d, 35% of which is chr. chr has a through-flux of 0.25 (±0.049) μmolC/L/d and a turnover time of 8.8 (±2.0) days. In the model, phytoplankton and bacteria interact via DOM, but the carbon flux can be traced and used to quantify phytoplankton – bacteria associations. Here, the carbon flux via all DOM types from rst to pol is 0.27 (±0.20) μmolC/L/d, 58% of carbon to pol, making this the second-strongest (after ns3) microbial linkage in the system at this time. This who produces/consumes how much of what when information is the main output of the FluxNet method, and it is critical for moving our understanding of microbial ecosystem functioning beyond bulk parameters like respiration and photosynthesis rates towards a higher resolution.Whereas the 2009 spring bloom illustrates co-blooming of phytoplankton and bacteria, the 2010 bloom shows succession of phytoplankton, DOM and bacteria. Several factors control this pattern in the model. Reinekea (rei) is negative for chrysolaminarin (chr) based on literature (Table S11), but is predicted to have a relatively high affinity for other glucose-containing DOM (gl2) (khrei / Kshrei,gl2 = 63 (±22) L/mmolC/d). A substantial fraction of gl2 is produced relatively early by phytoplankton exudation, and it is the primary substrate for rei at bloom stage 14 days. Alteromonas (alt) is predicted to have a low affinity for gl2 (khalt / Kshalt,gl2 = 0.015 (±0.0097) L/mmolC/d), but it is positive for chr based on literature and predicted to have a high affinity (khalt / Kshalt,chr = 52 (±4.7) L/mmolC/d). Chr is a death (i.e. grazing) product of phytoplankton and produced relatively later in the bloom, and it is the primary substrate for alt at this time. The substrate spectra of bacteria emerge in the analysis, within literature constraints, and can be considered a prediction testable with modern experimental techniques [6].Oligotrophic and copiotrophic carbon processingThe network includes concentrations and fluxes for each bacteria type, and a natural question is to what extend they are correlated. There is increasing awareness that high abundance may not necessarily mean high importance and vice versa, including the over-proportional role of rare species in biogeochemical cycles [32]. In the model, there is a strong correlation between concentration and carbon flux of bacteria, but for the same concentration there is also about an order of magnitude variation in flux (Fig. 3). The spread reflects differences in growth rates during the bloom periods. Some species, like the oligotroph SAR11 (s11), have consistently lower flux and others, like the copiotroph Polaribacter (pol), have consistently higher flux. There are also some, like the cryptic alphaproteobacteria (alx), that go in different directions in different years.Fig. 3: Correlation between spring bloom abundance and importance.Concentration and carbon flux for all model bacteria types during spring bloom periods (see Fig. 2 caption for definition). Lines: All(dashed)/Olig.(thick)/Copi.(thin), log Flux = –0.93/–1.03/–0.81 + 0.93/1.00/0.94 × log Conc., R2 = 0.88/0.92/0.92.Full size imageIt is important to realize that, in dynamic systems, microbial interactions and the corresponding networks are not static [3, 33]. The dynamics of the entire Helgoland flux network over the four-year period is illustrated in an animation, which shows the production of DOM and POM during and after phytoplankton blooms and later blooming of bacteria (Movie S1). These features are also evident in the phytoplankton – DOM – bacteria interactions at two selected time points during the 2009 spring bloom (Fig. 4A, B). At the onset of the bloom, the oligotroph SAR11 (s11) consumes the most DOM, primarily the cryptic species d08, which comes mostly from grazing death of green algae (gre) and exudation by rst. After 28 days the copiotroph pol dominates, which consumes primarily chr, a death product of mostly rst. SAR11 continues to be a major carbon processor in the early parts of the bloom, which was unexpected, because it is an inferior competitor at this time (growth rate s11 = 0.051 vs. pol = 0.15 1/d, bloom average), but can be explained by the higher biomass concentration (s11 = 0.68 vs. pol = 0.13 μmolC/L, bloom start). The flux is proportional to concentration and growth rate, and neither measure alone is a good proxy for the importance of a species [4]. Across all four years, oligotrophic bacteria, defined based on below-average growth rates (literature classifications are often ambiguous), dominate carbon processing for the first 18 days, generally past the phytoplankton peak (Fig. 4C).Fig. 4: Carbon processing during the course of blooms.A Phytoplankton—DOM—bacteria carbon flux network for the start and +28 days of 2009 spring bloom. See Fig. 2 legend. Flux cut off is 0.3%. B Cell concentrations, growth rate and relative carbon processing for s11 and pol for 2009 spring bloom. C Fraction of DOM processed by oligotrophic bacteria and exudate fraction in DOM pool for all blooms. Oligotrophs are defined based on literature as shown in Table S23 or based on below-average growth rates (kg). For the later, the oligotrophic fraction or weight given for type i, is based on fOLIi = kgAVEn / (kgAVEn + kgin), n = 5. kg is the net growth rate calculated from biomass change, plus dilution rate.Full size imageThe use of d08 by s11 and chr by pol in 2009 suggests are more general pattern, i.e., use of exudation products earlier by oligotrophs and death (i.e., grazing) products later by copiotrophs. Across all years, the fraction of DOM produced by exudation decreases during the course of the bloom (Fig. 4C), a common feature of phytoplankton blooms [33]. This is reflected in the diet of these bacterial groups, i.e., for oligotrophs (vs. copiotrophs), exudates make up a higher portion of the diet (27 vs. 18%), and they have a higher affinity for exudates (39 vs. 35 L/mmolC/d), which is also consistent with experimental evidence from another system [7].After the model was developed, while this paper was in peer review, metaproteomic data for the Helgoland Island spring bloom in 2016 were published that suggest that algal storage compounds (e.g., chrysolaminarin) are used throughout the bloom, whereas cell wall-related compounds (e.g., fucose-containing) are used at later bloom stages [34]. Our model also predicts an increase in the consumption of cell-wall vs. storage compounds at later bloom stages (Fig. 5), which validates our outcomes, although a direct comparison is not possible because of the different time.Fig. 5: Consumption of cell wall vs. storage compounds during the course of blooms.Total consumption (all bacteria) in µmolC/L/d of cell wall compounds divided by storage compounds. Cell wall compounds = man (mannan) + glo (glucoromannan) + fcs (FCSP). Storage compounds = chr (chrysolaminarin) + gly (glycogen) + sta (starch). Averages for all four years.Full size imagePhytoplankton functional similarity decouples them from bacteriaAn important question is to what extent the patterns recur from year to year [27]. We compare networks of phytoplankton producers, DOM exchanged and bacteria consumers, as well as phytoplankton – bacteria interactions quantified in absolute (μmolC/L/d moving between phytoplankton X to bacteria Y) and relative (% of carbon for bacteria Y supplied by phytoplankton X) terms (Fig. 6A). All networks show significant similarity so there is recurrence from year to year. The recurrence is higher for DOM than phytoplankton, suggesting that different phytoplankton produce similar DOM, which is expected considering similar composition (e.g., chr in diatoms). There are no phytoplankton producers that recur in the top quartile every year, but chr and others are in the top quartile of DOM exchanged (produced and consumed) every year. The recurrence is lower for bacteria consumers suggesting factors beyond DOM shape the bacteria community.Fig. 6: Recurring patterns and comparison of FluxNet and LSA methods.A Similarity of networks for spring blooms. Error bars are 95% confidence limits. Bray-Curtis similarity was calculated as 1 – Bray-Curtis dissimilarity. Text on top of symbols lists components that recur in the top quartile every year, listed in order of average rank. B Carbon flux networks for top recurring bacterial consumer, top four DOM sources and top coupled phytoplankton. (C&D) LSA network (showing top 15% of significant local similarity scores) and sample time series.Full size imageAn important question is how specific interactions are and how tightly networks are interconnected [35, 36], which depends on the mechanisms of interaction and will affect the recurrence. Consistent with the relatively low recurrence of phytoplankton producers, phytoplankton—bacteria coupling shows relatively low recurrence, i.e. low specificity. The primary substrate for the consumer pol is mostly chr and gl2, although it does change from year to year with varying DOM, consistent with the known assimilation capabilities of pol (Polaribacter) [37] (Fig. 6B). However, the primary associated phytoplankton for pol is different each year, although it is always a diatom. The de-coupling of phytoplankton production and bacteria consumption was also concluded from the lower recurrence of phytoplankton and higher recurrence of bacteria abundance in the same dataset [27]. It suggests that carbon processing is resilient to changes in phytoplankton, which may arise from factors like species invasion or climate change.The above discussion focused on one-way/commensal (phytoplankton  > DOM  > bacteria) interactions, but the network also includes specific two-way/mutualistic phytoplankton-bacteria interactions. Phaeocystis (pha) has the highest exudation fraction and Bacteroidetes nvi the highest affinity for DOM d04, whereas nvi has the highest exudation fraction and pha the highest requirement for micronutrient m15. Such mutualism is observed in other systems and the interactions predicted here can be tested experimentally [20]. Alternatively, experimentally-observed interactions could be used as input to the method, as constraints.Robustness of the analysisTo understand the effect of some of the choices made in the model structure we repeated the analysis with added or removed components or processes. Models without micronutrients or inhibitors produce significantly worse fit to the data (Fig. 7A), highlighting the need for a two-way interaction between phytoplankton and bacteria to maintain diversity. Models with more micronutrients or inhibitors are similar to the basecase. Together, these results provide some justification for the complexity (i.e., number of parameters) in the basecase model. The analysis including osmotrophy (aka absorbotrophy, i.e., phytoplankton can perform heterotrophy) produces a better fit to the observations, but that model was not adopted as basecase, because the osmotrophy process is poorly constrained and includes some probably unrealistic features/fluxes, like significant exudation and uptake of the same substance by one phytoplankton species. Importantly, excluding the runs with worse fit to the observations, the main conclusions (as shown in Figs. 4C and 5A) are the same, confirming that the results are reproducible and robust to some of the choices made in model structure.Fig. 7: Reproducibility of main results.A Total error for runs with different models. “w” or “b” indicates performance is significantly worse (open bars, think lines) or better than basecase, p  ns9 interaction ranks in the top 1% for LSA and FluxNet (relative interaction). However, the lack of mechanistic constraints is evident. One of the strongest links for the 2009 spring bloom (rank 13%) is between the diatom Chaetoceros debilis (cde) and Roseobacters (ros) (Fig. 6C, D). The shifted peaks line up nicely, but the bacteria biomass is higher than that of the phytoplankton and genome analysis suggests ros do not assimilate chrysolaminarin [37], which is a major death product of diatoms. Considering this, growth yield and other competing consumers, it is unlikely that cde is a major source of carbon to ros.Summary and outlookModern observational tools are generating high-resolution descriptions of the components of microbial ecosystems, and an ongoing grand challenge is to use these data to understand how systems function. Our method predicts dynamic mass fluxes between marine phytoplankton and bacteria, which provides insights into the functioning of the ecosystem. Specifically, it showed that there is a strong correlation between concentration and flux of bacteria during blooms, but oligotrophs are relatively less important than copiotrophs. However, due to their higher biomass, they are major carbon processors during early phases of blooms, well past the peak. Oligotrophs grow preferentially on exudation products, which are more abundant earlier in the bloom. Also, our results suggest that phytoplankton are functionally similar in terms of what organic carbon species they produce, and that this decouples them from bacteria.FluxNet is an inference method for microbial time series data that serves the same general purpose as existing empirical inference methods, like LSA [38]. In general, both approaches have strengths and weaknesses (see Introduction) and may complement each other. A main advantage of FluxNet is that it produces quantitative concentrations and fluxes, and associated conclusions (e.g., preferential use of exudates by oligotrophs). Also, it is constrained by mass balance and additional information from the literature (i.e., beyond the time series data), which make the results more realistic.The existing FluxNet code can readily be applied at a higher resolution (microdiversity), explicit representation of other ecosystem components, like viruses and zooplankton, and more processes, like photoheterotrophy and mixotrophy. It may also be applied to understand other microbial ecosystems, like the human gut or wastewater treatment plants. For an inference method it is important to be applicable to various types of observations, including modern environmental -omics observations, like transcript, protein and metabolite levels, and the present model will have to evolve in this direction [39]. The present model includes a relatively simple representation of the various processes, and the current biological understanding supports increasing the mechanistic realism (and complexity). For example, the present version assumes constant composition of DOM produced by phytoplankton, but observations show that it changes with physiology and interaction with bacteria [18, 40]. Also, the model assumes simple first-order dissolution of POM to DOM and direct utilization by bacteria, whereas break-down of especially polysaccharides is often mediated by extracellular enzymes [41]. More

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