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Response of chlorophytes to environmental variables in field vs. forest pondsOur study demonstrated that human-originated transformation in the catchment area surrounding a small water body may influence the water conditions in terms of physical, chemical, and biological parameters as well as the ecological state of the aquatic environment in respect to green algae communities.Chlorophytes inhabiting field ponds were more abundant compared with the forest ponds. This shows that field ponds, due to the higher values of TRP and water conductivity, created favorable conditions for chlorophyte development. The high concentrations of TRP and conductivity in aquatic environments are characteristic in the case of agricultural catchments exposed to anthropogenic pressure because of the inflow from the surrounding fertilized fields42. In this type of pond, we also observed significantly higher water temperatures and pH due to the lack of trees around them compared to the forest ponds, two factors which also positively influenced the growth of chlorophytes. Both the higher light intensity and the smaller size of the field ponds cause earlier warming up than the forest ponds and give an advantage to high light tolerant species. Moreover, it is well known that an increase in temperature stimulates the release of phosphorus from the bottom sediments, so this could be another reason for the higher levels of TRP in the field ponds. Our CCA analysis showed that TRP and conductivity were the strongest determinants of the distribution of chlorophyte species in the examined water bodies. We found a large group of dominant species indicated high values of TRP (e.g. Ankistrodesmus falcatus, A. arcuatus, Monoraphidium griffithii, Pseudopediastrum boryanum, Pediastrum duplex, Scenedesmus obtusus, Scenedesmus arcuatus var. gracilis, Desmodesmus communis, Coelastrum microporum), and another group of species (e.g. Kirchneriella irregularis var. spiralis, Tetraedron minimum, Scenedesmus ecornis) that preferred high levels of conductivity.In the field ponds generally higher mean abundances of filtrators and Rotifera were observed. This could be another important factor stimulating the growth of chlorophytes and increasing their abundances by the resupply of nutrients through excretion43,44. On the other hand, the high densities of algae could be the factor that caused better zooplankton development, and therefore its abundance in field ponds was greater. Filtrating cladocerans and Rotifera also had a significant influence on the distribution of chlorophyte dominating species. However, even though the total abundance of both chlorophytes and filtering zooplankton was greater in the field ponds, CCA analysis revealed a negative relationship existing between filtrators and most dominant species of chlorophytes (e.g. Pandorina morum, Willea rectangularis, Desmodesmus armatus, Nephrochlamys willeana, Cosmarium trilobulatum). Only two chlorophyte species—Lemmermannnia tetrapedia and Tetraedron triangulare—co-occurred with cladoceran zooplankton. These latter species are very small compared to the species above and can therefore be overlooked by filtrators, which have a choice of larger and perhaps more nutritiously satisfying algae of the genus Pandorina, Crucigeniella, Cosmarium or Nephrochlamys, but still of a size suitable for zooplankton. It can also be interpreted in such a way that Crucigenia and Tetraedron are among the r-strategists that reproduce very quickly, so grazing pressure by zooplankton can stimulate their rapid development45 and thus they remain at a stable level.Specific environmental conditions prevailing in the field ponds resulted in a high number of exclusive taxa44, found only in this type of water body. Moreover, a greater diversity of the representatives of different functional groups were found here, compared to the forest ponds.Analyzing the distribution of chlorophytes in terms of phytoplankton functional groups39,40, we found that group W1 was represented by only one species, Gonium pectorale. This was especially noted in the field water bodies. This group is known to prefer small water bodies rich in organic matter from husbandry or sewage40, which suggests that the field catchment in our study migh be a supplier of these substances. It also proves that field surroundings are far more human impacted. In the field ponds we observed a higher abundance of chlorophytes belonging to the groups G (Eudorina elegans, Pandorina morum, Pandorina smithii and Volvox aureus), J (e.g. representatives of the genus Actinastrum, Chlorotetraedron, Coelastrum, Crucigenia, Desmodesmus/Scenedesmus, Golenkinia, Pediastrum, Tetraedron, Tetrastrum, Westella, Willea/Crucigeniella), W0 (genera Chlamydomonas, Chlorangiopsis, Chlamydomonadopsis, Planktococcomyxa/Coccomyxa) and X3 (Chlorella sp.), typical for shallow nutrient-rich waters (G and J), ponds with extremely high organic contents (W0), and for shallow well-mixed layers (X3), according to classification given by Padisak et al.40. Considering that nitrogen compounds had a similar level in both types of ponds it can be stated that the representatives of the above mentioned functional groups of chlorophytes associated with the field ponds were presumably dependent on higher concentrations of TRP and conductivity and not that much on nitrogen concentrations.In the forest ponds significantly higher values of water saturation were recorded compared to the field ponds. Moreover, the lack of inflow of fertilizers from the catchment area resulted in lower TRP concentrations, which along with lower water temperatures, pH and conductivity in the forest ponds may have contributed to the reduced abundance of chlorophytes compared to the field water bodies. RDA analysis showed that some dominant chlorophyte species (e.g. Closterium moniliferum, Closterium tumidulum, Cosmarium trilobulatum and Mougeotia sp.) were associated with this type of small water body. At the same time the abundance of these species was smaller in the field ponds. We also found that chlorophyte diversity (Shannon–Weaver index) was greater in the forest ponds. This suggests that water bodies located within the forested area, usually more natural ponds being less exposed to anthropogenic pressure, are characterized by greater biodiversity. Moreover, in this type of water body we found many exclusive species39, not reported from the field ponds. Interestingly, about the half of these taxa belonged to desmids, which prefer lower pH and conductivity46, conditions typical for forest ponds. This could be also a reason for the dominance of desmid species with the highest abundance/frequency, associated with forest ponds.Taking into consideration the phytoplankton functional groups39,40 our study showed that the chlorophytes associated with forest ponds prefer mesotrophic waters (from the group TD: Cladophora glomerata, Geminella turfosa, Geminella planctonica, Microspora sp., Netrium digitus, Oedogonium sp., Oocystidium ovale, Spirogyra sp. Zygnema sp. and those belonging to the group N: mainly genera Closterium, Cosmarium, Euastrum, Micrasterias, Staurastrum, Staurodesmus, Xanthidium). This explains their greater share in the less fertile forest ponds. Another group associated with the forest ponds – T (Mougeotia sp., Binuclearia lauterbornii) contains species tolerant to light deficiency, so they were able to develop well in the more shaded water bodies located in the forest catchment.Chlorophyte community structure in two types of habitats (open water vs. macrophyte-dominated zone)In our study, the type of habitat (open water and macrophyte-dominated zones) also had a significant structuring effect on chlorophytes. There were a group of species linked to the open water zone (Pandorina morum, Nephrochlamys willeana, Oocystis lacustris, Scenedesmus armatus, Scenedesmus intermedius and Desmodesmus communis), being negatively related to vegetated stations at the same time. Generally, we found here a higher mean abundance of chlorophytes compared to the macrophyte-dominated zones, possibly due to the higher values of nutrients such as NH4 and TRP, the conditions favouring the development of many algae species. The results of the CCA analysis with habitats confirmed the high importance of both nutritional factors in structuring the distribution of chlorophyte species. There was a group of species associated with a rise in the concentration of ammonium (e.g. Scenedesmus arcuatus var. gracilis, Pediastrum duplex, Closterium moniliferum, Closterium tumidulum, Cosmarium trilobulatum, Willea rectangularis) as well as with phosphates (Monoraphidium tortile, Scenedesmus ecornis, Tetradesmus lagerheimii and Tetraedron minimum). Generally, high abundance of chlorophytes in the open water area was accompanied by a small-sized fraction of zooplankton–rotifers. Therefore, rotifers had a lower impact on the distribution of chlorophytes than filtrators. The increasing numbers of cladocerans contributed to the lowering abundance of some chlorophytes, such as Monoraphidium tortile, Scenedesmus ecornis, Tetradesmus lagerheimii or Tetraedron minimum. This shows that filtrators, whose densities were significantly higher among macrophytes, were able to control the development of some chlorophyte species much more efficiently than small-bodied rotifers.The effect of habitat was also visible in the case of phytoplankton functional groups39,40. We found that representatives of the group N (e.g. Closterium, Cosmarium, Euastrum, Micrasterias, Staurastrum) had a significantly higher mean abundance in the open water zones compared to the macrophyte-dominated zones. Interestingly, according to Padisak et al.40 group N prefers less fertile (mesotrophic) conditions, which is inconsistent with our results. However, we think that their association with the open water sites could be connected rather with the place/level where they live in the water column, rather than with the trophic state of water. The above mentioned chlorophytes taxonomically belong to desmids, which are mostly benthic organisms. Their greater quantitative share in the samples from the open water areas could be an effect of the intensive water mixing in the shallow ponds due to the lack of macrophytes. Neustupa et al.47 confirm that desmids are able to form tychoplanktonic communities due to water movements. In the samples collected from the macrophyte-dominated stations the mean abundance of desmids was generally lower, probably because of the macrophyte stabilizing effect. Aquatic plants are known to reduce turbidity and stabilize bottom sediments48, so they can prevent any intensive water mixing in ponds. In the examined open water stations, we also found a higher mean abundance of chlorophytes typical for shallow nutrient-rich waters (group G: Eudorina, Pandorina, Volvox and group K: Radiococcus) and/or for ponds with extremely high organic contents (group W0: e.g. Chlamydomonas), which proves that the sites lacking macrophytes were more fertile. Additionally, clearly more representatives from the codon J and X1 (typical for waters with high trophic levels) and a greater diversity of the representatives of different functional groups were recorded in the open water area compared to the macrophyte-dominated zones.The macrophyte-dominated stations had more abundant communities of filtrators, as aquatic plants are known to provide a profitable shelter for zooplankton49. Cladoceran predominance among macrophytes may have been a force reducing green algae numbers. The chlorophytes of the investigated ponds were mostly small- or medium-size species. Their size distribution makes them a high quality food for zooplankton, particularly for cladoceran filtrators. According to RDA analysis apart from pond size, the presence of filtrators significanly reduced the abundance of several chlorophyte dominating species. The lower algae abundance among macrophytes compared to the open water zone could also be explained by competition between algae and macrophytes for light and nutrients37,50 and/or with the secretion of allelopathic substances e.g. by Ceratophyllum demersum51 inhibiting algal development. Our studies demonstrated that among chemical factors which clearly differentiated the two types of analysed habitat, TRP and NH4 significantly influenced the distribution of chlorophyte dominating species. The lower levels of these parameters in macrophyte-dominated zones suggest that the nutrient uptake by aquatic plants in the investigated water bodies was high. There are many reports on the decrease of nutrient concentrations by macrophytes30,37,52, which are consistent with our observations. Despite lower, compared to the open water zone, chlorophyte densities within the macrophyte-dominated zones there was a group of species (e.g. Mougeotia sp., Pediastrum tetras, Scenedesmus obtusus, Monoraphidium contortum) that selectively chose vegetated stands. Furthermore, we found a great number29 of exclusive chlorophyte species for macrophyte-dominated zones. Half of these taxa belong to desmids, which are often periphytic organisms associated with aquatic macrophytes53,54.Preference towards macrophyte-dominated stations was also documented for two phytoplankton functional groups (T: Mougeotia sp. and Binuclearia lauterbornii and TD: e.g., Spirogyra sp., Zygnema sp., Cladophora glomerata, Oedogonium sp.) and one group which occurred exlusively among vegetated sites (MP—Ulothrix). Interestingly, all the representatives of these groups had a similar filamentous morphological form, which suggests that many of them are of epithytic origin, coexisting within aquatic plants. Two more groups—X2 (Pseudodidymocystis/Didymocystis, Pteromonas) and W1 (Gonium pectorale) were clearly affected by the presence of macrophytes. According to Padisak et al.40, codons TD and X2 indicate mesoeutrophic conditions and their higher abundances in the macrophyte-dominated zones also proves that plants contribute to lowering the trophic levels in the examined ponds. On the other hand, the relatively high abundance of the representative of the group W1 in these habitats suggests that macrophytes could enrich ponds with organic matter during the process of their decomposition.Concluding, our results prove that different types of catchment area (field and forest) as well as different types of habitats (open water zone and macrophyte-dominated zone) create distinct, specific conditions (dependent on some physical–chemical and biological variables) for the occurrence of chlorophytes in small water bodies. We conclude that cosmopolitan chlorophytes undoubtedly respond to the level of habitat heterogeneity, contributing to the ecological assessment of small water bodies. Chlorophytes in particularl react to the level of human transformation in the ponds’ vicinities. This is why we suggest using them for water quality evaluation in ponds. This interdisciplinary research significantly broadens the knowledge, not only about the response of chlorophytes to physical–chemical parameters of water, but also about the food preferences of zooplankton for which green algae are the basic food, and vice versa about the impact of zooplankton on microalgae communities. The analyses provide valuable information on chlorophytes-zooplankton interactions and also about the relationships between chlorophytes and macrophytes. Received data emphasize the high value of field ponds, underestimated habitats particularly vulnerable to destruction in the agricultural landscape. The research will help to better understand the functioning of poorly studied small water bodies, which will contribute to the preservation of their biodiversity and protection against degradation. They will also be useful in the management of small water bodies based on the specificity of chlorophyte occurrence in various habitats and catchment type ponds. Moreover, these results are important in a broader context, as the interactions between the studied organisms and the physico-chemical parameters of water in small bodies of water are to some extent universal, so the analyses will broaden the knowledge about the functioning of larger bodies of water. More

Case study areaThe studied catchment (2 621 km2) is located in the central-western part of Poland, and constitutes a part of the Oder River basin. The Wełna River (118 km) discharges to the Warta River near the town of Oborniki18, with an average flow rate of 8.1 m3s−1 (1980–2019) in this profile19. The natural conditions in this catchment favour the development of intensive agriculture, which covers almost 72% of this area (1888 km2) and contributes to the high consumption of mineral fertilizers20. Forest areas cover another 22% of this catchment (589 km2), while urbanised ones only 4% (93 km2) (Fig. 1). The Wełna River catchment is inhabited by approx. 230,000 people, of which only approx. 74% is served by wastewater treatment facilities21.Figure 1Localisation of the Wełna River catchment with its land use forms and nutrient sources. This figure was created using ArcGIS 10.2.1 for Desktop available at https://www.esri.com/en-us/home. Licence granted to Institute of Meteorology and Water Management.Full size imageInput dataBoth the mass-balance method and the modelling method require a similar amount and type of input data (Supplementary Table S1). Basic information on the Wełna River daily flow rates and nutrient concentrations in the closing profile of the catchment (Oborniki) has been obtained from the state monitoring services (Institute of Meteorology and Water Management—National Research Institute—IMGW-PIB13 and State Environmental Monitoring22—SEM) (Supplementary Table S1). They have formed the basis for the estimation of the share of individual sources in the mass-balance method, as well as for the calibration of the Macromodel DNS/SWAT in the modelling method. Other data, such as maps of elevation, river network and soil maps, as well as meteorological data, necessary for the development of an accurate representation of the studied catchment area on the Macromodel DNS/SWAT digital platform, were also obtained from state repositories. Data on the land use comes from the Corine Land Cover8, while detailed information on nutrient sources has been obtained mostly from the Local Data Bank of statistical information. The utilisation of the collected database has been presented in Fig. 2, and described in the following text. The comparison of the results for nutrient loads from both method was based on the year 2017, which was characterised by the maximum amount of monitoring data for both flows (365 measurements) (IMGW-PIB) and total nitrogen (TN) and total phosphorus (TP) (12 measurements–SEM). The average air temperature in 2017 in Poland was 1.5 °C higher than the long-term average (1971–2000) and was over 10 °C which resulted from the warm autumn and the end of the year. The time of the snow cover presence was shorter than the long-term data, and the rest of the year was classified as thermally normal.Figure 2Methodology diagram with relevant chapters marked in grey ovals (green—steps and data used for Mass Balance method, blue—steps data used for Modelling method, green/blue—steps and data used for both methods).Full size imageIn terms of precipitation, 2017 was assessed as wet, similarly due to rainy autumn and summer. In the Wełna River catchment area, the annual rainfall was about 770 mm, however high variability of precipitation conditions in particular months, from extremely wet to very dry, should be noted23. Therefore hydrologically, 2017 was considered normal with the flows only slightly lower than the long-term average.Mass-balance methodThe first method used for the quantification of sources and loads in the studied area was the static mass-balance method. It is widely used by the Polish administration authorities responsible for water management17. This method is based on the assumption that the sum of the nutrient loads in the river’s closing profile (selected based on access to the monitoring data) and its retention in the catchment equals the emission of nutrients in a given time. Such assumption allows the apportioning of the river loads among identified sources and the estimation of their contribution to the total loads, based on known or assumed values of their retention.River load calculationThe total load of nutrients discharged from the catchment was calculated using the daily flow rate and nutrient concentrations in the closing profile of the catchment area (Oborniki, Fig. 1) from the SEM (Supplementary Table S1). The daily river load was calculated using the following Eq. 5:$${L}_{river}=0.0864sum_{t=1}^{n=365}{({Q}_{t}cdot {C}_{t})}_{t}$$
(1)
where: Lriver is the annual load [kga-1], n is the number of days, t is the consecutive day, Ct is the concentration [mg L-1], Qt is the mean daily flow rate [Ls-1], and 0.0864 is the unit conversion.Due to the fact that the flow rate is measured daily and nutrient concentrations only 12 times a year, the linear interpolation method5 was used to obtain the daily concentration values:$${x}_{k}={x}_{a}+kcdot frac{{x}_{b}-{x}_{a}}{n+1}$$
(2)
where: xk is the interpolated concentration value, xa is the first of the two measured concentration values between which the concentrations are interpolated, xb is the second of the two measured concentration values between which the concentrations are interpolated, k is the consecutive number of missing value and n is number of missing values.Source apportionmentFor the mass-balance method, data on nutrient loads for source apportionment (emission inventory) was collected in order to proceed with further calculations. The calculations were performed for 2017, due to the availability of river monitoring data and the nutrient sources were divided into 7 categories, based on the HELCOM guidelines5: municipal (MWS) and industrial (INS) point sources, municipal diffuse sources (SCS), forestry (MFS), agriculture (AGS), natural background (NBS) and atmospheric deposition (ATS). The category of “unknown sources” (UKS) was taken into account, in order to include possible discrepancies in nutrients load apportionment, and to cover eventual differences between calculated river load and inventoried emission.The MWS loads were calculated on the basis of the number of inhabitants served by the wastewater treatment plants (WWTPs)21. In the Wełna River catchment, 151 771 inhabitants were served by the 12 WWTPs covered by the National Wastewater Treatment Program (NWTP)24, which provides information on the total discharge volume from each facility. For 5 of these plants, information on influent/effluent nutrient concentrations was also available, allowing the direct calculation of discharged loads. For the remaining seven facilities, the loads were calculated on the basis of the mean influent concentration information, available for the WWTPs covered by the NWTP (80 mgL−1 and 12 mgL−1 for TN and TP, respectively), and approximated nutrient reduction level in non-biological WWTPs. This reduction level, based on data from the NWTP, was set at 65% for TN and 35% for TP24. Another 19 350 inhabitants of this catchment were connected to the small WWTPs, not included in the NWTP. This part of the MWS load was calculated using the mean daily wastewater outflow (0.12 m3day−1 per person), the same mean nutrient concentrations and reduction levels as presented above. Additionally, the remaining 25% of the catchment’s population (58,000) is not connected to any WWTP and uses septic tanks and other types of individual wastewater treatment systems. The load from this source was expressed as SCS, and calculated using unit loads set on 11 gday−1 per person and 1.6 gday-1 per person for TN and TP, respectively17. The industrial nutrient input information (INS) was gathered directly from the Statistics Poland office database21.The AGS load was calculated using nitrate and phosphate concentrations in shallow groundwater (90 cm below the ground surface), from 22 sampling points located on agricultural areas in the Wełna River catchment17. Concentrations were recalculated to TN and TP respectively and averaged. Thus, the obtained mean concentrations were 8.25 mgL−1 of TN and 1.92 mgL−1 of TP. Subsequently, load values were calculated by multiplying these concentrations by the outflow from agricultural areas, calculated as a share of the total catchment outflow respective to the agricultural use of the area. The calculated loads were multiplied by coefficients reflecting the share of monitored outflow (groundwaters and tile drainage) from the agricultural runoff (1.11 for TN and 4.17 for TP)25. Subsequently, the natural background (NBS) was subtracted from the AGS load.The load corresponding to NBS was calculated using the total catchment outflow and nutrient concentration values reflecting conditions in undisturbed areas of pre-human activity, set as 0.15 mgL−1 and 0.02 mgL−1, for TN and TP respectively17. The MFS load was also calculated in a similar way, using nutrient concentrations set to represent forest catchment as 0.31 mgL−1 and 0.038 mgL−117 and the outflow calculated as the share of the total catchment area, respective for the catchment part covered by forest. Also in this case, the NBS load has been subtracted. As for the ATS load, data on pollutant deposition into the ground from precipitation was taken from the SEM network26. This data was based on precipitation and its chemistry measurements taken from 22 monitoring stations covering the entire territory of Poland. The total load from the point and diffuse sources was calculated by adding the loads mentioned above. The eventual difference between the river load (“River load calculation” Section) and inventoried emission (“Mass-balance method” Section) accounted for the other sources (UKS).Load apportionmentThe contribution of each source to the calculated river load was calculated based on a simplified equation modified from HELCOM5:$${L}_{river}=DP+LOD-RP-RD$$
(3)
where: Lriver is the river load [kga−1], DP is the load from point sources (MWS and INS) [kga−1], LOD is the load from diffuse sources (SCS, ATS, MFS, AGS and, NBS) [kga−1], RP is the point source retention [kga−1] and RD is the diffuse source retention [kga−1].In the adopted mass-balance method, it is assumed that nutrient load from the point sources (DP) is introduced directly into the river bed phase, while load from the diffuse sources (LOD) is discharged into both phases of the catchment, land and river bed ones. In both phases, self-purification processes are taking place, resulting in the reduction of nutrient loads on the way from the source to the catchment closing profile. However, due to the limited amount of data, the self-purification processes in the river have been omitted, therefore the point source retention (RP) equalled 0 kga−1. Subsequently, the diffuse source retention (RD) has been estimated on the basis of the difference between each nutrient load of the river (Lriver) and the point sources (LOD). The remaining river load has been then attributed proportionally to the contribution of the particular diffuse sources to the total source apportionment (emission inventory).Modelling methodThe digital platform, the Macromodel DNS with the SWAT module27,28,29,30,31,32, was used for comparison for the nutrient balancing method described in “Mass-balance method” Section. This advanced dynamic tool tracks nitrogen and phosphorus migration paths in the river basin taking into account their spatial and temporal variability. For this purpose, it takes into account a very extensive input database, similar to that used in the mass balance method (Supplementary Table S1). Natural and anthropogenic processes that affect the transport and transformation of nutrients, are also part of this platform. The SWAT module (version 2012) is a tool which operates in the spatial information system (GIS) and is fully integrated with it. Using the digital elevation model (DEM), the SWAT module divided the entire analysed Wełna River catchment into a total of 225 sub-catchments of an average area of 11.5 km2. The subsequent use of data on land use (forests, agriculture and urbanised areas) and the types of soils (31 classes) allowed the authors to identify a total of 2824 hydrological response units (HRUs), homogeneous in terms of vegetation, soil and topography33. Afterwards, a simulation of soil water content, evapotranspiration, surface runoff, primary and underground flows was carried out in accordance with the water balance Eq. (4), which represents the basis for the quantitative component and the HRU.$${SW}_{t}={SW}_{0}+sum_{i=1}^{t}({R}_{day}-{Q}_{surf}-{E}_{a}-{W}_{seep}-{Q}_{gw})$$
(4)
where: SWt is the final soil water content (mm H2O), SW0 is the initial soil water content (mm H2O), Rday is the amount of precipitation (mm H2O), Qsurf is amount of surface runoff (mm H2O), Ea is the amount of evapotranspiration (mm H2O), Wseep is the amount of water entering the vadose zone from the soil profile (mm H2O), Qgw is the amount of return flow (mm H2O).The model directs all runoff flows generated by each HRU through the channel network, thus simulating a catchment. The water balance equation also represents a basis for the simulation, transport and transformation of nutrients required for the quantitative component of the model. This tool models forms of nitrogen, organic and inorganic , different forms of phosphorus in soil34, as well as organic nitrogen and phosphorus forms associated with plant residues, microbial biomass and soil humus35,36,37,38. Final results of simulations are produced by the SWAT model as all the forms of nitrogen and phosphorus (in kilograms of N and P per a time unit, respectively) are then summed up to give TN and TP values. To verify that the model properly predicts TN and TP values its results are calibrated with the TN and TP values resulting from SEM, as described in Sect. 2.4.1. Moreover, the particular forms of nitrogen and phosphorus have also been compared with the modelling results (Supplementary Table S4). A detailed overview of the migration and transformation pathways of nitrogen and phosphorus forms in the catchment has been presented in the Supplementary Information (Sect. S1), while mathematical description of these processes is included in the theoretical documentation of the SWAT model39.Similarly, as in the case of the mass-balance method, diffuse sources of nutrients from agriculture (AGS), forestry (MFS) or urban areas (URB) in SWAT were simulated in the land phase of the catchment. In the land phase, the model simulates both the infiltration of nutrients into the soil (fertilization, plant biomass, precipitation) and their removal from it (volatilization, denitrification, erosion, surface runoff). Additionally, changes in the distribution of nutrients in the soil (uptake by plants) and the low mobility of phosphorus itself are also taken into account39,40,41.Pollutants from municipal and industrial point sources (MWS, INS) are introduced directly into the river bed phase. The exception here is the nutrient load from municipal diffuse sources (SCS) which, reduced as a result of the self-purification processes taking place in the land phase, is also treated in the model as point sources. The SCS nutrient load mainly derives from leaking or illegally emptied septic tanks. For this purpose, septic tanks have been divided into three types: leaky, partially illegally emptied, and sealed septic tanks, legally emptied. The loads from the legally emptied tanks are included in the effluents from WWTPs reported in the catchment. While for the remaining types, their loads are calculated using factors depending on their effectiveness in nutrient removal (15 – 50%). The final nutrient load derived from these types of facilities is then calculated, taking into consideration the number of inhabitants using the different types of septic tanks and the average chemical composition of wastewater21.The load of nutrients from the atmospheric deposition (ATS) affects both land and river phases due to the presence of two deposition mechanisms in the SWAT module, i.e., wet and dry deposition. The model also allows for the determination of nutrient loads generated as a result of natural processes of nitrogen and phosphorus transformation and transport in the soil, with the omission of all anthropogenic pressure—natural background (NBS).Calibration, verification and validationThe SWAT module for the Wełna River has been calibrated, verified and validated using the SWAT-CUP software42. For the quantitative component (water circulation in the catchment), the implemented daily flow data (source: IMGW-PIB) for the period of 18 years (2001–2018) came from the water gauge stations on the Wełna River (Pruśce and Kowanówko) and its tributary (the Flinta River-Ryczywół) (Fig. 1). The qualitative component (nitrogen and phosphorus concentration in the catchment) was gathered from the SEM stations localised at the Wełna River (Oborniki and Rogoźno) (Fig. 1) and covered a period of 13 years (2005–2018). Three statistical measures, coefficient of determination (R2)43, percent bias (PBIAS)44, and Kling-Gupta efficiency (KGE)45, have been used to indicate the Wełna River model performance (Supplementary Tables S2 and S3). In terms of the quantitative component, the calibration and verification coefficients R2, KGE and PBIAS classified the model performance generally as good and very good for the main river (Wełna), and satisfactory and good for its tributary (Flinta). During the validation procedure, all coefficient values rated the model performance for daily flow simulations as very good. In terms of qualitative components, the model performance for TN simulations can be considered as very good or good, according to the all-applied coefficients. Lower model performance, mostly satisfactory, was observed for TP mainly due to the variability of phosphorus temporal distribution patterns (Supplementary Table S2). The entire process was described in detail in Orlińska-Woźniak et al46.Variant scenariosIn order to determine the contribution of individual sources to the total load of nutrients in the profile closing the analysed catchment, a final simulation of the model was used and subjected to calibration, verification and validation procedures, and called the baseline scenario (A0). Subsequent so-called variant scenarios (A1–A5), i.e. model simulations, were developed. In variant scenarios the values of selected parameters were changed in relation to the A0 scenario. This was used both in the river bed phase for point sources (A1) and for individual diffuse sources (A2–5), thus imitating surface changes for particular types of land use in the land phase of the catchment (Fig. 3).Figure 3Variant analysis diagram for assessment of nutrient loads (L) for particular modelling scenarios and sources described in the text: MWS, INS, SCS—point sources, URB—urban, AGS—agricultural, MFS—forest.Full size imageIn the A1 scenario, all parameterized and aggregated point sources (MWS, INS, SCS) for each relevant sub-basin (LMWS,INS,SCS), were removed from the model to calculate their contribution to the total nutrient loads in the closing profile of the studied catchment (LA1).In the next two scenarios (A2 and A3), concerning urban and agricultural land use, their surface areas (5 663 ha and 192 917 ha, respectively) were successively replaced by the forest land use. This procedure was based on the assumption that the forest is the primary type of land use for this catchment area47. In order to completely eliminate the influence of these areas, the nutrient loads from the relevant surface area occupied by forest land use were subtracted, in order to estimate the contribution of urban and agricultural land (LURB and LAGS, respectively).The change in land use from urbanised and agricultural areas to forest areas increased their percentage of the catchment area to almost 100%, thus the original image of the catchment area and the nutrient load at its mouth. On this basis, in scenario A4, the nutrient load from forests LMFS, which currently occupy only 20% of the catchment area (A0), flowing to the closing profile, was calculated from the proportion.The A5 scenario is the difference between the nutrient load from the A0 scenario and the sum of the remaining loads from the subsequent variant scenarios (A1–A4). In this way, both the natural background (NBS) and atmospheric deposition (ATS) were taken into account. More

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Kyoto University microbiome researcher Hiroyuki Ogata says that the recent work2,3 further connects RNA viruses and the carbon pump, which affects the Earth’s biogeochemical cycles and thus its climate. And it sheds light on the diversity, evolution and ecology of RNA viruses, which has not previously been possible through applying the techniques of traditional DNA-based metagenomics. The team found many new lineages at the phylum-level by using “highly sensitive” computational approaches.It’s possible to assess the ecosystem impact of viruses by inferring auxiliary metabolic genes (AMGs). AMGs hint at the ways RNA viruses manipulate the physiology of their hosts as they seek to maximize production of more virus through the host. As Jian explains, labs have identified a variety of AMGs that are encoded by DNA viruses and, he says, it’s “well-recognized” that AMGs probably play a role in marine ecosystems. It was unknown if AMGs could be found in RNA viruses, which the recent Science paper2 has now established, he says. Jian sees this work as providing “a very important foundational dataset” for exploring questions connected to AMGs. “In my opinion, if more long-sequence or complete marine RNA virus genomes can be obtained in the future, and they can be further connected with specific hosts, it will greatly promote the understanding of the ecological impact of RNA viruses in the oceans.”To tease out AMGs, the scientists used a variety of tools, such as viral identification software for both DNA and RNA viruses, says Wainaina. The ones for DNA viruses are available on Cyverse, and the protocols for the tools from the Sullivan lab are on protocols.io. One method for RNA viruses is in progress and will be soon available on Cyverse, he says. DNA viral identification tools include VirSorter2, a pipeline for identifying viral sequence from metagenomics data, and the protocol for using this and other tools are also on protocols.io. To identify AMGs from viral sequence that had been identified through VirSorter, the team used use DRAM-v, a software tool from the lab of microbiome researcher Kelly Wrighton at Colorado State University. Her group had created Distilled and Refined Annotation of Metabolism (DRAM), a framework to resolve metabolic information from microbial data. The companion tool DRAM-v is for viruses and can be applied to metagenomic data sets for annotating metagenomics-based assembled genomes, for example through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, and to contiguous viral sequences identified by VirSorter.The hunt for AMGs is one instance in which the team needed to determine in each case whether a sequence was likely ‘stolen’ from host cells, says Dominguez-Huerta. RNA viral genomes are less than 40 kilobases long and usually have complicated genomic organization, both in a structural genomics sense related to the physical arrangement of genes along the viral genome and in a functional sense in terms of transcription and translation: there are overlapping genes, frameshifts and more, all of which makes this kind of annotation difficult. And sometimes information in the annotation databases is wrong and indicates that a match is cellular when it is in fact viral. Thus, to find AMGs, “we don’t have a defined clean methodology automated in a pipeline yet,” he says. It remains a time-consuming task. Assigning putative function to the protein sequences encoded by AMGs also involves checking the literature and comparing different annotation sources.Dominguez-Huerta says he and the team were glad they could assemble AMG functionalities to suggest the range of ways in which RNA viruses manipulate the metabolisms of their hosts—from photosynthesis to central carbon metabolism to vacuolar digestion and RNA repair. This overview let them see how some AMGs are repeated across different viruses across the oceans. Finding AMGs in long-read sequence is what he calls a “fire test” for the lab. To avoid ‘false AMGs’ from unreliable matches, they use BLASTP, the Basic Alignment Search Tool that compares a protein query sequence to a protein database.“I am fascinated by the ability of viruses to metabolic reprogram not only their hosts but more importantly at the ecosystem level,” says Wainaina. It is probable that the AMGs the team identified “are a central cog in microbial metabolism networks.” Current and future modeling efforts will hopefully provide insights into the ecosystem roles of viruses—both DNA viruses and RNA viruses—and on a global scale both within the ocean ecosystem and beyond.Host inference is challenging, says Dominguez-Huerta, because, for example, viruses with RNA genomes do not share genetic information with their host genomic DNA the way dsDNA viruses do when they infect bacteria. That means there is no clear signal to be derived from the host genome to help one guess the possible host. But sometimes RNA viruses do integrate into host genomes, and those, likely more accidental, events were sufficient for the scientists to capture some signal to infer hosts. “We also performed statistical co-occurrence analytics using abundances to infer the hosts with certain success,” he says.Unlike dsDNA viruses, RNA viruses infect mostly eukaryotes, from protists and fungi to invertebrates and fish larvae; only a minority infect bacteria. Overall, the team has been able to capture “a picture of dsDNA viruses infecting prokaryotes and RNA viruses infecting eukaryotes in the oceans, complementing each other in their marine hosts,” says Dominguez-Huerta. The fact that the scientists can infer “that RNA viruses can steal genes from the host,” in the form of AMGs, to then reprogram host metabolism matters not only as scientists complete the picture of how viruses directly tune the activity of hosts during infection, but also in regard to how this influences biogeochemical cycles, he says. “We think that these AMGs are incorporated into the RNA virus genomes from cellular mRNA transcripts by non-homologous recombination,” he says. This gives, in his view, a new picture of RNA viruses, which, despite their small genome sizes, can squeeze in protein-coding genes. Such proteins could be sufficient to boost the production of virus particles per infected cell, perhaps increasing viral fitness in the difficult conditions of the oligotrophic open ocean and letting the viruses better propagate in the environment.More generally, says Dominguez-Huerta, capturing RNA from ocean samples is difficult, because RNA is physically fragile and degrades rapidly. When digging into metatranscriptomic data, which include the RNA from plankton and RNA from other organisms, less than 1% of this RNA is likely to be viral RNA, he says. Previously, some labs have first purified RNA from samples, enriched it for replicating RNA viruses and then applied a method called dsRNA-seq to recover dsRNA virus sequence and replicate sequences from single-stranded RNA viruses. For future ocean RNA virus projects, he says that the lab is currently working on a wet-lab method to purify RNA virus particles from seawater to solve the challenges of obtaining viral RNA for analysis. 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