Philippa Kaur

Global fire activity is changing in many areas as temperatures increase and land use intensifies1,2,3,4,5. This is sparking an increase in attention given to fire activity and fire ecology. However, the availability of data for spatially delineated fire events is limited or non-existent in many countries6, with most global fire data coming from satellite-based active fire detections7,8 and gridded burned area products9,10. The lack of products containing delineated events has led to many global studies about fire ecology that are computationally-intensive, coarse-scale trend analyses1,4.A key advantage of datasets like Monitoring Trends in Burn Severity (MTBS)11 or the Fire Occurrence Dataset12 lies in their ease of use. Since its inception in 2007 MTBS has been cited 947 times in peer-reviewed studies according to a Google Scholar search at the time of this writing, despite documented limitations for scientific use of some facets of the product13. The MTBS dataset is regularly updated, easy to find on the internet, and it is free, fast and easy to download and use. Many environmental scientists and resource managers do not have the computational budget or expertise in big data or remote sensing to deal with the challenges one must overcome to process large fire datasets. This is especially true for cases when all that is needed is a shapefile of fire perimeters that can be used to map fire history. Other global fire perimeter datasets have been produced from satellite-derived burned area products14,15, but these are only available in yearly or monthly global shapefiles. Often field-based studies of fire effects require an entire time series over study areas that are only a few hundred km in diameter16 or a single ecoregion17. The end user who wants to understand the fire history for their region would have to download yearly shapefiles with a global extent, clip all of those shapefiles to their area of interest, and then combine them into one shapefile, just to get started. We suspect that the lack of accessible fire perimeter datasets that are easy to download and use contributes to a disparity in research, where fire ecology studies are conducted mostly in developed countries that have either research infrastructure capable of handling big data or longer-term government records, or temperate forested regions that have substantial tree-ring records18.There are two existing global perimeter products, the Global Fire Atlas (GFA) (Andela et al.14) and the Global Wildfire Information System (GWIS) (Artes et al.15). Both were created by applying spatiotemporal flooding algorithms to the MODIS MCD64 Burned Area Product. These algorithms assign burned pixels from the MCD64 products using a moving window whose size is defined by spatial and temporal parameters. They are created as monthly or yearly slices of the entire globe, and they can be subsetted. These products are extremely valuable for global scale studies. But when we look at how those products delineate known fire events we see a consistent problem in that they both seem to over-segment events in ways that appear unrealistic. This inconsistent event delineation is not problematic for coarse-scale or regional estimates of burned area or fire seasonality, but can lead to unrealistic estimates for number of fire events and event-level characteristics like fire size and spread rate. In Fig. 1 we illustrate this with an example of the 2013 Rim Fire in California, United States, which was unmistakably a single event that burned about 90,000 ha over the course of three months. Figure 2 illustrates how the day-to-day progression of the Rim Fire was a steady progression from a single ignition in late August. Table 1 shows how the differences in event delineation propagate to calculations of burned area and number of events. In the GFA, the Rim Fire is delineated as one large event of 804.5 km2, and 13 additional events totaling 88.7 km2. in GWIS it is delineated as one event of 878 km2 and 47 additional events totalling 20 km2. With FIRED, there is one event of 892 km2 and 2 single pixel events totalling less than one km2. One cause for potential differences is how one defines a “fire event”. Large fires often have multiple ignition sources. The Global Fire Atlas algorithm and others19, for example, search for local minima to identify various ignition locations that may begin as small patches, only to later form a large complex and in the end described with a single fire perimeter. The choice of outside sources for optimizing the spatial-temporal parameters, the method of optimization, and the intent of the final product’s meaning (defining events as single ignition patches vs contiguous burned area) all lead to different outcomes in the final events that are delineated. Another likely source of this discrepancy is that GWIS and GFA are calibrated to create a single global product. Because different geographical areas have different types of fire regimes, they have fires that grow at different rates and to different sizes, and occur in greater or fewer frequencies, and so the spatial and temporal parameters that work well for defining a fire event in one area may result in over- or under-segmentation in other areas. Here, we decided upon an approach of creating many regional products across the globe, rather than one product for everywhere on earth.Fig. 1Comparison of global fire event products performance for the 2013 Rim Fire (a). In the FIRED product (b), the Rim fire was classified as one very large event with two single pixel events. The Global Fire Atlas (GFA, c) and Global Wildfire Information System (GWIS, d) each delineated a very large event, with 13 and 47 smaller events, respectively.Full size imageFig. 2The two primary outputs FIREDpy provides are a daily- and event-level product. Panel a shows the default single event polygon. In b, each day has a separate polygon, with associated statistics generated, within each event. Panel c shows the daily perimeters derived from the airborne infrared by the incident management team for comparison.Full size imageTable 1 Rim fire comparison.Full size tableBesides the ease of access and use, the advantage of the FIRED product lies in the user’s ability to use the open-source software, FIREDpy, to tailor the spatial and temporal parameters of the moving window algorithm in order to realistically delineate events for their region of interest. In Fig. 3, we illustrate this by comparing the three products for a pair of small fires in Florida. In this case, the FIRED product that was created with a larger moving window (5 pixels and 11 days) over-aggregated the events, but it only required one line of code at command line to recreate the product with a smaller moving window (1 pixel and 5 days) to get more realistic results.Fig. 3Product comparison for two small events in Florida, the Moonshine Bay and Sour Orange fires (outlined) that both ignited in February of 2007 and were delineated by MTBS. In b the firedpy product that was optimized for the entire United States with a moving window of 5 pixels, 11 days resulted in aggregation of the two fires delineated by MTBS, but also several smaller fires nearby. In b, it was re-ran with a window of one pixel and five days, for a more realistic result. Delineations by the Global Fire Atlas (c) and the Global Wildfire Information System (d) are shown for comparison.Full size imageHere, we present a collection of regionally-tailored fire perimeter datasets for every country in the world with significant fire activity20, which we created with the open source algorithm, FIREDpy21. Each dataset is either a single country or a broader region, depending on the data volume. These datasets differ from other similar efforts14,15 in that each dataset created by FIREDpy is a single file containing a collection of polygons that is generated for the entire time series, rather than monthly or yearly aggregations with a global extent. Furthermore, we have generated the data products at a spatial extent land managers and ecologists would typically use to do regional-scale research, and we adjusted the spatial and temporal parameters for each country to yield realistic event delineations. We also made every effort to ensure that download sizes are reasonable (  More

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Analysis of changes in ecosystem services value in the YRBThe results showed that from 1990 to 2020, the total ecosystem services value in the YRB showed a dynamic trend of decrease-increase–decrease, with overall increasing trend, and a total increase of 31.85 × 1010 USD, with an average annual increase of 1.14 × 1010 USD (Table 2). This changing trend is consistent with land use cover change in the area. In 30a, YRB cultivated land decreased by 8663 km2, due to rapid urbanization. In addition, after year 2000, China began to implement the policy of returning farmland to forest and grassland on a large scale, which accelerated the reduction of cultivated land. Results again showed that the forest area increased by 30,933,093 km2, indicating that the implementation of “returning farmland to forest and grassland”policy achieved great results, thus increased the value of ecosystem services generated by forest land by 167.66 × 1010 USD. Grassland increased by 738 km2, as corresponding ESV increased by 28.73 × 1010 USD, while unused land decreased by 8131 km2, with 9.52 × 1010 USD ESV decrease. In general, the ecological protection and management measures in the YRB have achieved remarkable results, and ecosystem service values has been significantly improved due to forest and grassland increase.Table 2 The value of ecosystem services in the YRB from 1990 to 2020.Full size tableIn terms of ecosystem service structure in the YRB (Table 3), the relative proportions of various ESVs did not change significantly, resulting in relatively stable ESV structure. Soil conservation and waste disposal are the most important among them, accounting for about 37% of ESV’s total value. The YRB ecosystem, as can be seen, emphasises the importance of soil conservation and waste disposal in the basin, with Climate regulation, Biodiversity conservation, and Entertainment accounting for only 11.99 percent of the total. Various services have changed to varying degrees during the study period. Waste disposal and climate regulation, for example, have suffered losses of 22.23 × 1010 USD and 20.29 × 1010 USD, respectively.The rest of the services showed an upward trend, among which the value of the Food production service increased the most, which was 19.03 × 1010 USD, owing to the obvious increase of the forest land and grassland area in the YRB.Table 3 The value of individual ecosystem functions in the YRB from 1990 to 2020.Full size tableSpatial distribution and variation characteristics of ecosystem services in the YRBThe total ESV value of the study area and changes in the value of each service could not reflect their spatial differences. To describe the temporal and spatial distribution pattern of ESV in the study area, the natural breakpoint method was used. This method was further used to classify ESV with reference to existing studies, and divided the area into four levels: low-value, lower-value, higher-value, and high-value areas. Takin the three-level watershed of the YRB as the statistical unit for analysis, the result showed that the higher the level, the higher the ESV. As shown in Fig. 2, from 1990 to 2020, the spatial characteristics of ESV were relatively stable. The YRB’s upper reaches, from Shizuishan to the north bank of Hekou Town, the Fenhe River Basin, from Hekou Town to Longmen, and the Jinghe River Basin are all rich in high-values. The forest and grassland are relatively concentrated in the above-mentioned areas, the ESV coefficient is high, and the watershed area is large, resulting in a high total ESV. The higher value areas are mainly distributed in the areas from Longyang Gorge to Lanzhou main stream basin, the Daxia River and Tao River basin, and the Wei River basin. The area above Baoji Gorge and the inflow area fall in the transition zone between the high-value area and the lower-value area. For example, the transition area between the Loess and Qinghai-Tibet Plateaus is a higher-value area. The lower-value area mainly includes the Huangshui River Basin, the Datong River Basin, the basin below Lanzhou, and the Guanzhong Plain area. Thus, the unused land in this area is widely distributed. Due to the large area of construction land in the Guanzhong Plain, the ecosystem service value has shrunk. The low-value area is found in the YRB’s lower reaches, which contains the most extensive and large area of construction land in the basin, has a poor ecosystem service function, and is also the YRB’s most economically developed area. In terms of changes in the value of watershed ecosystem services, the number of watersheds at the ESV level did not change significantly between 1990 and 2020. The average ESV of the watershed is 40.52 × 1010 USD. There were 7 high-value, 5 higher-value, 12 lower-value, and 5 low-value watersheds respectively. Figure 2Spatial distribution of ESV changes in YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageThe hotspot analysis revealed the spatial agglomeration characteristics and ESV evolution law in the YRB from 1990 to 2020 (Fig. 3). In most of the YRB, the ESV accumulation characteristics were not significant in space, and significant areas were dominated with high and low ESV accumulation. The Maqu-Longyangxia River Basin, Daxia River and Tao River Basin, the Datong River Basin, and Fen River Basin were the five core areas where ESV had the highest value. The Inner River, YRB’s northern and eastern margins, and the lower reaches are primarily low-value agglomeration areas. The high-value agglomeration area and low-value agglomeration area did not change significantly in space from 1990 to 2020, but the number of grids in each decreased from 647 to 627 and 699 to 681, respectively. In general, the YRB’s high-value agglomeration areas are strewn about, whereas the low-value agglomeration areas are scattered.Figure 3Spatial agglomeration characteristics of ESV in the YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageFrom 1990 to 2020, the barycenter coordinates of the ESV in the YRB remained stable between 106.78°–106.94° E and 36.40°–36.65° N (Fig. 4). During the study period, the ESV barycenter coordinates showed a transfer trajectory of first to southwest, then to northeast, and then to southwest. From the perspective of overall transfer direction, ESV barycenter shifted from northeast of Huanxian County to southwest from 1990 to 2020. The ESV in the northeast decreased, while that in southeast increased. From 1995 to 2000 and from 2000 to 2005, the migration distance of ESV barycenter in the YRB was longer by 16.33 km and 15.75 km, respectively, while the barycenter migration distance of ESV from 2005 to 2020 was shorter.Figure 4Barycenter coordinates of ecosystem services in the YRB from 1990 to 2020.Full size imageResponse of ecosystem services to land-use changeThe area of land use type change in the YRB increased by 64,356 km2 between 1990 and 2020. Each land type’s area has changed to varying degrees. Cultivated land and construction land are the two land types that have seen the most changes. The area of cultivated land has shrunk by 8663 km2, while the area of construction land has grown by 13,109 km2. In comparison to water, forest, and grassland, unused land has undergone significant transformations. However, in comparison to 1990, it shrunk by 8131 km2 in 2020. The forest increased by 3093 km2 while grassland increased by 738 km2. Ecosystem services are significantly impacted by changes in land use types. Using the spatial analysis method, the researcher introduces a resilience index to reflect ESV’s response to land-use change in this paper. During 1990–2000 and 2000–2010, average elasticity of ESV change in the YRB relative to land use change was 0.27 and 0.44, respectively, but dropped to 0.04 during 2010–2020. This indicates that the disturbance capacity of land-use change on ecosystem services was low between 1990 and 2000, but increased between 2000 and 2010. Land-use change has had less of an impact on ecosystem services since 2010. The range of changes in land land-use types was wide during this time, but the average elasticity index was low because there were so many different types of land land-use changes, such as the conversion of forest land and cultivated land to construction land, and the conversion of forest land and water area to cultivated land. The decrease in ESV caused by the change in land use per unit area was minor. Furthermore, the forest land and grassland in the river basin have been effectively increased, as ESV has increased. Overall, the value of ecosystem services has remained relatively constant.Accurate spatial statistics on the elasticity index from 1990 to 2020 was carried out (Fig. 5). The elastic index of the upper YRB and Loess Plateau is higher, and the impact of land use change on ecosystem services is more apparent in this region, according to the findings. This is mainly due to the implementation of large-scale ecological engineering measures in response to vegetation degradation in the upper reaches of the YRB and soil erosion in the middle reaches (Loess Plateau), by the Chinese government. In addition, Lanzhou New District, Guanzhong Plain, and the lower Yellow River region also showed higher elasticity index. The above-mentioned regional development and construction, as well as human activities, have resulted in a rapid increase in construction land, resulting in a significant decline in ecosystem services and a higher resilience index as a result of rapid urbanisation.Figure 5Spatial distribution of elastic coefficients in the YRB from 1990 to 2020.Full size imageIn general, the land use types in the YRB have changed dramatically, and land type conversion is very common. The conversion of ecological land to urban construction land, as well as the conversion of unused and cultivated land to ecological land, has resulted in significant changes in ecosystem service value. This demonstrates that the basin’s ecological construction projects have yielded positive environmental results. 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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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

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