More stories

  • in

    Mixoplankton interferences in dilution grazing experiments

    Our results show that Chl a alone is not an adequate proxy for prey growth rates in dilution grazing experiments when mixoplankton are present5,10. Chlorophyll is, in any case, a poor proxy for phototrophic plankton biomass31 because of inter-species variations, and also for the photoacclimation abilities of some species (for which very significant changes can occur within a few hours). The problem extends to the involvement of mixoplanktonic prey and grazers. Nevertheless, even very recent studies continue to rely on this parameter for quantifications of grazing despite acknowledging the dominance, both in biomass and abundance, of mixoplanktonic predators in their system30. Moreover, the detailed analysis of the species-specific dynamics revealed that different prey species are consumed at very different rates. In our experiments, and contrary to expectations (see32,33, and Fig. S1 in the Supplementary Information), C. weissflogii was only actively ingested in the ciliate experiment and, according to the results from the control bottles (Table 2), not by M. rubrum (see Fig. 4 and Fig. S1a).Certainly, it is not the first time that a negative selection against diatoms has been seen; for example, Burkill et al.34 noticed that diatoms were less grazed by protist grazers than other phytoplankton species, as assessed by a dilution technique paired with High-Performance Liquid Chromatography for pigment analysis. Using the same method, Suzuki et al.35 reported that diatoms became the dominant phytoplankton group, which suggests that other groups were preferentially fed upon. Calbet et al.36, in the Arctic, also found only occasional grazing over the local diatoms. In our study, diatoms were not only not consumed, but the presence of dinoflagellates appeared to contribute to their growth (Fig. 4), this relationship being partly dependent on the concentration of the predator (see Fig. 2c, d). This result could be a direct consequence of assimilation and use of compounds (e.g.,37,38) released by microplankton such as ammonium (e.g.,39,40) and urea (e.g.,41), which were not supplied in the growth medium, but which would have supported prey growth. Alternatively, this unexpected outcome may have been a consequence of the selective ingestion of R. salina by the two predators, relieving the competition for nutrients and light and resulting in a higher growth rate of the diatom in the presence of the predators. We cannot rule out the fact that diatoms sink faster than flagellates which, as the bottles were not mixed during most of the incubation period (although gently mixed at every sampling point), may have also involuntarily decreased ingestion rates on C. weissflogii. Still, one C. weissflogii cell contains, on average, ca. 2.5 times more Chl a than one R. salina cell (initial value excluded, see Table 3). Taken together with the preference for R. salina it is not surprising that the proportion of total Chl a represented by the diatoms increased over time, in particular in the L/D treatment (Figs. 6a, c and 7a, c).Table 3 Chl a content (pg Chl a cell−1) of the target species at each sampling point as calculated from the control bottles.Full size tableAnother factor clearly highlighted by our experiments, is that protozooplankton themselves contribute a significant portion of the total chlorophyll of the system (due to ingested Chl a), in particular at the beginning of the incubation (see Figs. 6 and 7); this being invariably ignored in a traditional dilution experiment. The high Chl a detected inside the protozooplanktonic grazers at the beginning of the incubations could suggest that the system was initially not in equilibrium, and that this was the result of superfluous feeding (e.g.,42). This would, nevertheless, be surprising since we required ca. 1 h to collect the initial samples (t = 0 h) after joining all the organisms together (see the section “Dilution grazing experiments” in the “Methods” section); previous studies, like the one on G. dominans and Oxyrrhis marina by Calbet et al.42, showed that the hunger response and consequent vacuole replenishment occurred in ca. 100 min for very high prey concentrations and it is expected to decrease at lower prey concentrations as the ones used in our study. Therefore, even if one assumes that the first 4 h of incubation are a result of superfluous feeding, after 24 h, the “estimated”, “observed”, and “from dilution slope” grazing estimates are not significantly different to those displayed in Fig. 5 (P  > 0.05 in all instances) and, therefore, we can assume that the hunger response was likely irrelevant (e.g.,43) and did not mask our results. In any case, as stated before, an actual field grazing dilution experiment also suffers from similar problems, because grazers and prey are suddenly diluted and not pre-adapted to distinct food concentrations. Nevertheless, this is not novel information, since Chl a and its degradation products have been found inside several protozooplankton species from different phylogenetic groups immediately after feeding44 and even after some days without food45. An increase in intracellular Chl a concentrations immediately after feeding has also been found in mixoplankton46,47, on which this increase is derived both from ingested prey as well as from new synthesis of their own Chl a. Additionally, several experiments with Live Fluorescently Labelled Algae (LFLA) show that predators (irrespective of their trophic mode) seem to maximise the concentration of intracellular prey shortly after the initiation of the incubation (e.g.,48; Ferreira et al., submitted). Indeed, some authors have even been able to measure photosynthesis in protozooplankton, like the ciliates Mesodinium pulex49 and Strombidinopsis sp.50.The fact that Chl a is a poor indicator of phytoplankton biomass and the inherent consequences discussed so far can be solved by the quantification of the prey community abundance (e.g.,51) by microscopy or by the use of signature pigments for each major phytoplankton group. The latter method, however, is not as thorough as the former, since rare are the cases where one pigment is exclusively associated with a single group of organisms (see52 and references therein). In any case, any pigment-based proxy is subject to the same problems, as identified by Kruskopf & Flynn31. Irrespective of the quantification method, it has been made evident that the different algae are consumed at different rates (e.g., pigments10,34,35; microscopy5,36).Prey selection in protistan grazers is a common feature (e.g.,23,26,27,28). Given the diversity of grazers in natural communities and the array of preferred prey that each particular species possesses, it is logical to think that dilution experiments will capture the net community response properly. Likewise, grazers interact with each other through toxins, competition, and intraguild predation among other factors. An example of intraguild predation could be the observed on K. armiger by G. dominans (see Figs. 2f and 4 and Table 1), which caused an average loss of ca. 18.72 pg of K. armiger carbon per G. dominans per hour in the D treatment. Interestingly, in the same treatment, a slight negative effect of K. armiger on its predator G. dominans can also be deduced (i.e., positive g, Table 1), resulting in an average loss of ca. 0.33 pg G. dominans carbon per K. armiger per hour. This could be a consequence of algal toxins, since K. armiger is a known producer of karmitoxin22, whose presence may have negative effects even on metazoan grazers21. Regarding ciliates, none of the species used is a known producer of toxic compounds, which suggests that the average loss of ca. 1.25 pg M. rubrum carbon per hour in the D treatment was due to S. arenicola predation. Altogether, it seems clear from our data that intraguild predation cannot be ignored when analysing dilution experiments (Fig. 4). Furthermore, our results clearly show that single functional responses cannot be used to extrapolate community grazing impacts, as evidenced by the differences in estimated and measured ingestion rates based on the disappearance of prey in combined grazers experiments (Fig. 5). Nevertheless, this is a relatively common procedure (e.g.,53 and references therein). Often in modelling approaches, individual predator’s functional responses have been used to extrapolate prey selectivity and community grazing responses27; in reality complex prey selectivity functions are required to satisfactorily describe prey selectivity and inter-prey allelopathic interactions54.It is, however, also evident that the measured ingestion rates in combined grazers experiments were not the same as those calculated from the slope of the dilution grazing experiment. This raises the question of why was that the case. It is well known that phytoplankton cultures, when extremely diluted, show a lag phase of different duration55 which has been attributed to the net leakage of metabolites56. Assuming that the duration of the lag phase will be dependent on the level of dilution, it seems reasonable to deduce that after ca. 24 h the instantaneous growth rates (µ) in the most diluted treatments will be lower than that of the undiluted treatments. This has consequences, not only for the estimated prey growth rates but also for the whole assessment of the grazing rate, due to the flattening of the regression line (i.e., the decrease in the computed growth rate). This artefact may be more evident in cultures acclimated to very particular conditions (as the laboratory cultures used in this study) than in nature.Another important finding of our research is the importance of light on the correct expression of the feeding activity by both mixoplankton and protozooplankton. We noticed that irrespective of the light conditions, all species exhibited a diurnal feeding rhythm (R. salina panels in Figs. 2 and 3), which is in accordance with earlier observations on protists (e.g.,29,57,58). The presence of light typically increased the ingestion rates. Additionally, the ingestion rates differed during the night period between L/D and D treatments, which implies that receiving light during the day is also vital in modulating the night behaviour of protoozoo- and mixoplankton. In particular, mixoplankton grazing is usually affected by light conditions, typically increasing (e.g.,32,59), but also sometimes decreasing(e.g.,60) in the presence of light. Different irradiance levels can also affect the magnitude of ingestion rates both in protozoo- and mixoplankton (see61 and references therein).For those reasons, we hoped for a rather consistent pattern among our protists that would help us discriminate mixoplankton in dilution grazing experiments. As a matter of fact, based on the results from Arias et al.29, we expected that in the dinoflagellate experiment, the D treatment would have inhibited only the grazing of K. armiger, enabling a simple discrimination between trophic modes. The reality did not meet the expectations since the day and night-time carbon-specific ingestion rates (as assessed using the control bottles, Table 2) of K. armiger were respectively higher and equal than those of G. dominans. Conversely, in the ciliate experiment, protozooplankton were the major grazers in our incubations regardless of the day period and light conditions. This response was not as straightforward as one would expect it to be because M. rubrum has been recently suggested to be a species complex containing at least 7 different species (62 and references therein), which hinders any possible conjecture on their grazing impact. Indeed, the uneven responses found between and within trophic modes precluded such optimistic hypothetical procedure.The D treatment in the present paper illustrated the importance of mimicking natural light conditions, a factor also addressed in the original description of the technique by Landry and Hassett1. It is crucial for the whole interpretation of the dilution technique that incubations should be conducted in similar light (and temperature) conditions as the natural ones to allow for the continued growth of the phototrophic prey. However, here we want to stress another aspect of the incubations: should they start during the day or the night? Considering our (and previous) results on diel feeding rhythms, and on the contribution of each species to the total Chl a pool, it is clear that different results will be obtained if the incubations are started during the day or the night. Besides, whether day or night, organisms are also likely to be in a very different physiological state (either growing or decreasing). Therefore, we recommend that dilution experiments conducted in the field should always be started at the same period of the day to enable comparisons (see also Anderson et al.14 for similar conclusions on bacterivory exerted by small flagellates). Ideally, incubations would be started at different times of the day to capture the intricacies of the community dynamics on a diel cycle. Nevertheless, should the segmented analysis be impossible, we argue that the right time to begin the incubations would be during the night, as this is the time where ingestion rates by protozooplankton are typically lower (e.g.,29,57,58, this study) and would, consequently, reduce their quota of Chl a in the system.Lastly, we want to stress that we are aware that our study does not represent natural biodiversity because our experiments were conducted in the laboratory with a few species. Nevertheless, we attempted to use common species of wide distribution for each major group of protists to provide a better institutionalisation of our conclusions. Further to the choice of predator and prey is their concentrations and proportions. Being a laboratory experiment designed to understand fundamental mechanisms within a dilution grazing experiment, we departed from near saturating food conditions from where we started the dilution series. In nature, the concentrations that we used may be high but are not unrealistic, and actually lower than in many bloom scenarios. We included diatoms at high concentrations, even knowing that they are not the preferred prey of most grazers34, because diatoms are very abundant in many natural ecosystems and to stress the point of food selection within the experiment. For sure, using different proportions of prey would have rendered different results. However, as previously mentioned, our aim was not to seek flaws in the dilution technique, but to understand the role of mixoplankton in these experiments and the complex trophic interactions that may occur within. Ultimately, with our choice of prey and their concentrations, we have proven that when there is no selection for a massively abundant prey, the use of Chl a as a proxy for community abundances may underestimate actual grazing rates.Some other aspects of our experiments may also be criticised because they do not fully match a standard dilution experiment. For instance, we manipulated light, adding complexity to the study. However, this manipulation enabled the deepening into the drivers of the mixoplanktonic and protozooplanktonic grazing responses. Another characteristic, perhaps awkward, of our study is that we allowed the grazers to deplete their prey before starting the experiment. One may argue this procedure does not mimic the natural previous trophic history a grazer may have in nature. Yet, in nature, when facing a dilution experiment, it is impossible to ascertain whether the organisms are encountering novel prey or not. Indeed, they (prey and predator) could have just migrated into such conditions, or be subject to famine, or just moved from a food patch. In any case, it is true that a consistent “hunger response” would have affected our initial grazing values, biasing grazing rate estimates. To overcome this artefact, we let the grazers feed for about one hour before starting the actual dilution assay (see the “Methods” section). From that point on, any dilution is, in fact, an abrupt alteration of the food scenario, which is likely more important than the previous trophic history of the grazer.In summary, with these laboratory experiments, we have presented evidence calling for a revision of the use of chlorophyll in dilution grazing experiments5,10, and we have highlighted the need to observe the organismal composition of both initial and final communities to better understand the dynamics during the dilution grazing experiments51. This approach will not incorporate mixoplanktonic activity into the dilution technique per se however if combined with LFLA (see5,17), a semi-quantitative approach to disentangle the contribution of mixoplankton to community grazing could be achieved (although not perfect). An alternative (and perhaps more elegant) solution could be the integration of the experimental technique with in silico modelling. The modelling approaches of the dilution technique have already been used, for example, to disentangle niche competition63 and to explore nonlinear grazer responses20. We believe that our experimental design and knowledge of the previously indicated data could be of use for the configuration of a dilution grazing model, which could then be validated in the field (and, optimistically, coupled to the ubiquitous application of the dilution technique across the globe). We cannot guarantee that having a properly constructed model that mimics the dilution technique will be the solution to the mixoplankton paradigm. However, it may provide a step towards that goal as it could finally shed much-needed light on the mixo- and heterotrophic contributions to the grazing pressure of a given system. To quote from the commentary of Flynn et al.6, it could provide the answer to the question of whether mixoplankton are de facto “another of the Emperor’s New Suit of Clothes” or, “on the other hand (…) collectively worthy of more detailed inclusion in models”. More

  • in

    Estimating and predicting snakebite risk in the Terai region of Nepal through a high-resolution geospatial and One Health approach

    Our results showed that covariates at different geographical scales (national and local) may have important effects on the risk of snakebite, both for humans and animals. The results indicate that the risk of snakebite in the Terai varies at national scale between clusters and at local scale between households. The evaluation of the final models without spatial random components and the worsening of the models’ goodness of fit as a result highlighted how snakebite risk and its determining factors are indeed spatially structured.A strong association between high snakebite incidence and mortality, and poverty was established from the analysis of 138 countries affected by the disease32. In this study, we identified the PPI, an indicator for poverty, as a highly influential risk-increasing factor for humans. This not only confirms the critical role of poverty as a driver for this Neglected Tropical Disease, but also offers the possibility to use a standardized index at individual household scale for similar studies. Chaves et al.33 used the Poverty Gap, which is a simpler index expressing how far a person is from the average national poverty line, but to our knowledge, no study has used PPI for snakebite in any way. Applying PPI as a snakebite risk predictor also addresses previous expert calls for an Ecohealth approach to consider the relationship between the structural characteristics of houses, poverty, and snakebite34.Three of the survey covariates had significant effects on the odds of snakebite. Food storage and straw storage increased them, while sleeping on the floor reduce them. The effect of the first two covariates is likely to be related to prey availability, represented by rodents, which are attracted by food and shelter sources. Both food and straw are very often stored near dwellings, which in the end multiply the number of possible encounters between humans, domestic animals, and the hunting snakes20. The expected snakebite risk reduction effect by sleeping on the floor is more complex though. Previously, a higher snakebite incidence was reported among rural Hindus in Maharashtra, India, due to their custom of sleeping on the floor35,36, while in Nepal, Chappuis et al. did not find any protective effect or significant difference in snakebite cases between sleeping on a cot or on the floor37. This result, nevertheless, might be influenced again by regional customs that make sleeping on the floor more common in eastern Terai (71.1% of all affirmative answers to this question), and second, by the commonly acknowledged prevalence of kraits (Bungarus spp.) in western Terai, which are the species most commonly linked to bites to people sleeping on the floor while hunting at night inside houses22,38. This geographic separation, between the human behaviour and the distribution of the species considered to cause most bites linked to it, could explain the observed shift in the odds towards a reduction effect. This effect should be further explored in localized studies designed to capture behavioural differences in humans and snakes.For both the general human risk model and its equivalent prediction model, the covariate Distance to water had a significant risk-increasing effect. For each additional km in distance from permanent water sources, the odds of snakebite increased by 1.38 and 1.51 times, respectively. From a human perspective and in this socio-economic framework, it would be important to consider not only the distance to water, but also the path taken to get the water (or any other resource). If this path would lead a person through grasslands and open fields, this could imply an increased risk of snakebite. From an ecological perspective, there are two important aspects to consider in relation to water sources. One is, as in this study, the distance from large, constant water sources, which usually represent stable environments subject to less hydric stress. The second (not considered here) are the human-made water sources, such as ponds, reservoirs, and paddy fields that change often, are usually closer to human dwellings, and are known to attract some medically important venomous snakes (MIVS)5. Studies on snake migration and home range use have concluded that depending on species and ecological conditions, snakes can move between a few tens of meters per day and more than 10 km between seasons, while searching for water and prey resources38,39,40,41. In sub-tropical regions like the Terai, snakes living closer to continuous sources of water and vegetation should have easier access to a wider variety of prey. On the contrary, those living in agricultural areas might need to scout farther in the search for resources, encountering human-made waterbodies and prey, such as rodents42 and amphibians, abundant in this region10. Further studies considering all sources of water, and species ecology, biology and richness would be necessary to completely understand the effect of this and similar eco-physiological covariates.Another important factor was the NDVI, which is a commonly used value to express photosynthetic activity, leaf production and in summary the ‘greenness’ of the environment43. As is the case for other covariates, its interpretation depends on the study circumstances. In Iran, it was considered an indicator of prey availability for snakes and linked to snake habitat suitability14. Elevated NDVI values have been associated with higher number of hospitalizations in Nigeria and northern Ghana, in particular during the periods of high agricultural activity, which is also related to higher snake-human contact and higher snakebite incidence43. In our study, its ‘protective’ effect can indeed be the consequence of better access to prey associated with healthier ecosystems, explained in the Terai by the higher NDVI values of the multiple dense forests distributed along the region. In addition, the averaged NDVI values for agricultural areas should be lower than those for perennial forests, because they include the highs and lows of production and harvest.Environmental drivers like temperature and precipitation are common factors in geospatial analyses of snakebite13,14,17,44. They are found in many cases to be the main factors modulating the incidence or risk of snakebite, while varying in importance according to study conditions. For example, in Iran, precipitation seasonality was the most prevalent climatic covariate determining the habitat suitability leading to snakebite risk14, while in Mozambique, temperature seasonality was the predominant covariate13. Despite the Terai’s sub-tropical climate, the range of the average minimum temperature of the coldest month (BIO6) was 1.8–10.9 °C. For our snakebite risk analysis in animals, an increase of 10 °C of BIO6 between any two points represented an increase in the odds of snakebite of 23.41 times. For snakes, this range could be the difference between total lethargy and partial activity45, which could lead to increased numbers of snakebites. In addition, and according to the production and holding practices of domestic animals in the Terai, this temperature range can also represent the difference between animals (mainly ruminants) being kept in sheds when at the lower range limits, or being let out of them at the upper limits, which would again increase the chances of encounters with snakes.Similarly, for the animal model, pig density and sheep density, significantly influenced the variation in the risk of snakebite for animals in the Terai. This could be due to the conditions in which the animals and their feed are kept, favouring environments that are beneficial for either snakes or their prey. At more local scales, rather than the distribution, the presence of other animal species could instead be the factor associated with higher snakebite rates12. However, since the available data on domestic animal density was produced more than 10 years ago, and the animal population has grown substantially in the last years in Nepal, this outcome should be interpreted with caution.For the animal risk, the possession of an animal shed also significantly increased the odds of snakebite. Similar to straw storage, animal sheds and similar constructions offer some shelter and at the same time attract small (prey) animals, both of which are likely to attract snakes, increasing snakebite risk for the animals using the shed. If in addition, the sheds function as poultry coops, the snake hunting behaviour might be instead targeted towards chicks and chickens12. Mitigation measures such as raising the coop’s floor or securing openings with fine metal mesh have been suggested to reduce this risk12.The human modification of terrestrial systems was the only non-significant covariate in the animal risk model. However, as its strong, risk-reducing effect still seems to explain a lot of the response variation, it was retained. Its change in one unit, i.e., going from a pristine to fully modified environment, decreased the odds of snakebite by 0.13 (equivalent to 7.69 times), which agrees with previous national survey results from Sri Lanka21.For our human risk prediction model, four covariates were either significant or helped to explain the changes in the response. Distance to water and NDVI were clearly significant, and precipitation of the driest quarter (BIO17) and the mean annual temperature (BIO1) helped to explain some of the response variation with convincing, unambiguous effects. For BIO17, an increase of 100 mm of rain during the driest months of the year represented an odds-reduction effect equivalent to 8.33 times. This agrees with the results of distance to water, suggesting that the additional availability of resources during water shortage periods, i.e., almost four times more rain (BIO17 range: 18–71 mm), could locally improve ecological conditions for snakes also leading to less scouting and fewer human encounters. Previous studies have analysed the multilevel ecological effects of droughts, e.g., reducing snake prey and leading snakes to engage in riskier behaviours46,47. For BIO1, the protective effect was weaker. An increase of 10 °C represented a reduction of the odds of snakebite equivalent to 3.57 times. Average temperatures for specific locations are difficult to interpret, since they might depend on mild highs and lows, strong highs and lows, or relative combinations of both. Thus, despite having a relatively important effect on the response, this effect still might be the consequence of confounding and unknown interactions.Several other evaluated covariates, for both humans and animals, showed a negligible effect on describing the response, were not significant while having very large uncertainties, or both. Consequently, they were discarded as predicting factors. For the list of baseline covariates evaluated, see supplementary Table S1. For a complete list of available survey covariates, see Alcoba et al.27.Some of our discarded covariates have been important in other studies, for example, to quantify snakebite risk based on reclassification methods of covariates such as habitat suitability, species presence, or envenoming severity13,14,17,44,48. These methods are especially relevant when one species (or very few) is the cause of most snakebite cases, and has differentiated optimal and sub-optimal habitats. In Nepal, and particularly in the Terai, there are at least two, and sometimes more than 10 MIVS with overlapping distributions49. Thus, it could be said that practically the whole region offers suitable habitat for multiple MIVS. In addition, the impossibility of reliably identifying the species having bitten the surveyed victims hindered the use of single species in the analysis. In our analysis, species richness was removed, as it showed almost no effect on the response. A recent meta-analysis reported an equivalent result at global scale, finding no significant difference between the number of venomous snake species in tropical and temperate locations, while the number of snakebites is clearly higher in tropical areas50. These results suggested that high incidence of snakebite is unrelated to species richness, but instead related to other factors like the number of people working in agricultural environments21,32,50. Another important driver of snakebite incidence has been population density50. In our study, however, any possible effect from population density on the risk was diminished by the random selection of households at specific numbers during study design. This was later confirmed by the minimal effect of population density as covariate in the human risk analysis.This study presents a few limitations. For instance, despite the capacity of the INLA method to borrow strength from neighbouring observations and areas, the selection of adequate covariates with enough explanatory power still depends greatly on the number of snakebite cases, which even for a national scale study like this remains small. Also, some of the covariates with the strongest explanatory power came from our household survey, which prevented their use for generalized spatial prediction models. Concerning the animal risk analysis, due to the small number of snakebite cases we opted to aggregate all animal species and consider a grouped response. Thus, for a spatial analysis of animal risk, it was not worth it to consider each species, since that would dilute further an already sparse dataset in individual models and selection processes. Moreover, the data gathered for animals was dependent on the random selection of (human) households and unrelated to the current distribution of animal populations. This, in addition to the possible number of dry bites that go unnoticed, might be responsible for the low number of animal victims recorded (even combined across all species), making a more detailed analysis unfeasible.Despite the large number of covariates examined during our analysis, very few were useful to predict snakebite risk along the Terai. It is possible that confounders or other difficult-to-measure covariates could better explain the complex relationship between the ecology and biology of MIVS, socio-economic factors, human behavioural traits, and the circumstances around domestic animal keeping. This needs to be further explored, following a recent call for an overarching One Health and Ecohealth approach to better understand the drivers for snakebite risk, incidence, and mortality under specific situations34.In conclusion, snakebite is a multi-factorial disease and there is no possible universal approach to model its risk. Each model should be individually designed for each set of socio-economical, geographic, ecological, cultural, and environmental circumstances19. To better understand and address the snakebite problem, it is necessary to approach it, whenever possible, with local data collected at a national scale, so that the conclusions drawn can fuel appropriate national public health policies and actions. As long as people work, live, and keep their domestic animals in close contact with natural environments with MIVS, the risk of snakebite will be present. However, better understanding of the factors influencing that risk at the most granular scale possible, and the estimation of the populations at risk, can help to better target prevention and mitigation measures. For humans, this evidence can channel efforts towards improved access to treatment through the optimized stockpiling of antivenom, and the improvement, relocation or new construction of treating facilities, but more importantly, towards community education and sensitization in preventive campaigns51. Part of that preventive and educative efforts can be done at household level, by promoting and facilitating the use of protective equipment such as rubber boots, or the guidance on how to improve and adapt their immediate surroundings to make them ecologically less attractive for snakes and their prey. For domestic animals, this information could help better target awareness-raising activities for animal owners and implement mitigation strategies. For animals at higher risk, tailored interventions such as the improvement of housing conditions, regular cleaning of sheds and surrounding areas (e.g., from food waste and surrounding vegetation), and using light when animals are walked out of the enclosure at night could be deployed specifically as snakebite prevention measures52. It is also important to highlight that many of the factors analysed in this study affect most directly the snakes themselves, not only as snakebite agents, but also as a diverse group of species, differently affected by ecological, climatic and environmental factors in a multiplicity of settings shared with humans and domestic animals. It is therefore necessary to further investigate how those factors influence the behavioural and ecological traits of MIVS in order to truly understand this disease from a One Health viewpoint. At stake is the reduction of snakebite envenoming incidence rates in humans and animals, and of its possible long-term sequelae on human populations. More

  • in

    Phage co-transport with hyphal-riding bacteria fuels bacterial invasion in a water-unsaturated microbial model system

    1.Muok AR, Briegel A. Intermicrobial hitchhiking: how nonmotile microbes leverage communal motility. Trends Microbiol. 2021;29:542–50.CAS 
    PubMed 

    Google Scholar 
    2.Kohlmeier S, Smits THM, Ford RM, Keel C, Harms H, Wick LY. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ Sci Technol. 2005;39:4640–6.CAS 
    PubMed 

    Google Scholar 
    3.Simon A, Bindschedler S, Job D, Wick LY, Filippidou S, Kooli WM, et al. Exploiting the fungal highway: development of a novel tool for the in situ isolation of bacteria migrating along fungal mycelium. FEMS Microbiol Ecol. 2015;91:fiv116.PubMed 

    Google Scholar 
    4.Deveau A, Bonito G, Uehling J, Paoletti M, Becker M, Bindschedler S, et al. Bacterial–fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol Rev. 2018;42:335–52.CAS 
    PubMed 

    Google Scholar 
    5.Harms H, Schlosser D, Wick LY. Untapped potential: exploiting fungi in bioremediation of hazardous chemicals. Nat Rev Microbiol. 2011;9:177.CAS 
    PubMed 

    Google Scholar 
    6.Otten W, Hall D, Harris K, Ritz K, Young IM, Gilligan CA. Soil physics, fungal epidemiology and the spread of Rhizoctonia solani. N. Phytol. 2001;151:459–68.
    Google Scholar 
    7.Sun B, Chen X, Zhang X, Liang A, Whalen JK, McLaughlin NB. Greater fungal and bacterial biomass in soil large macropores under no-tillage than mouldboard ploughing. Eur J Soil Biol. 2020;97:103155.CAS 

    Google Scholar 
    8.Otto S, Bruni EP, Harms H, Wick LY. Catch me if you can: dispersal and foraging of Bdellovibrio bacteriovorus 109J along mycelia. ISME J. 2017;11:386–93.PubMed 

    Google Scholar 
    9.Kjeldgaard B, Listian SA, Ramaswamhi V, Richter A, Kiesewalter HT, Kovács ÁT. Fungal hyphae colonization by Bacillus subtilis relies on biofilm matrix components. Biofilm. 2019;1:100007.PubMed 
    PubMed Central 

    Google Scholar 
    10.Narr A, Nawaz A, Wick LY, Harms H, Chatzinotas A. Soil viral communities vary temporally and along a land use transect as revealed by virus-like particle counting and a modified community fingerprinting approach (fRAPD). Front Microbiol. 2017;8:1975.PubMed 
    PubMed Central 

    Google Scholar 
    11.Rosner A, Gutstein R. Adsorption of actinophage Pal 6 to developing mycelium of Streptomyces albus. Can J Microbiol. 1981;27:254–7.CAS 
    PubMed 

    Google Scholar 
    12.Ghanem N, E. Stanley C, Harms H, Chatzinotas A,Y, Wick L. Mycelial effects on phage retention during transport in a microfluidic platform. Environ Sci Technol. 2019;53:11755–63.CAS 
    PubMed 

    Google Scholar 
    13.Dennehy JJ. What ecologists can tell virologists. Annu Rev Microbiol. 2014;68:117–35.CAS 
    PubMed 

    Google Scholar 
    14.Hurst CJ, Gerba CP, Cech I. Effects of environmental variables and soil characteristics on virus survival in soil. Appl Environ Microbiol. 1980;40:1067–79.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Yeager JG, Brien RT. Enterovirus inactivation in soil. Appl Environ Microbiol. 1979;38:694–701.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Schwartz DA, Lindell D. Genetic hurdles limit the arms race between Prochlorococcus and the T7-like podoviruses infecting them. ISME J. 2017;11:1836–51.PubMed 
    PubMed Central 

    Google Scholar 
    17.Shan J, Ramachandran A, Thanki AM, Vukusic FBI, Barylski J, Clokie MRJ. Bacteriophages are more virulent to bacteria with human cells than they are in bacterial culture; insights from HT-29 cells. Sci Rep. 2018;8:5091.PubMed 
    PubMed Central 

    Google Scholar 
    18.Chaudhry W, Lee E, Worthy A, Weiss Z, Grabowicz M, Vega NM, et al. Mucoidy, a general mechanism for maintaining lytic phage in populations of bacteria. FEMS Microbiology Ecology. 2020;96:fiaa162.19.Yu Z, Schwarz C, Zhu L, Chen L, Shen Y, Yu P. Hitchhiking behavior in bacteriophages facilitates phage infection and enhances carrier bacteria colonization. Environ Sci Technol. 2020;55:2462–72.PubMed 

    Google Scholar 
    20.Tarafder AK, von Kügelgen A, Mellul AJ, Schulze U, Aarts DGAL, Bharat TAM. Phage liquid crystalline droplets form occlusive sheaths that encapsulate and protect infectious rod-shaped bacteria. Proc Natl Acad Sci. 2020;117:4724–31.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Callaway RM, Ridenour WM. Novel weapons: invasive success and the evolution of increased competitive ability. Front Ecol Environ. 2004;2:436–43.
    Google Scholar 
    22.Granato ET, Meiller-Legrand TA, Foster KR. The evolution and ecology of bacterial warfare. Curr Biol. 2019;29:521–37.
    Google Scholar 
    23.Gama JA, Reis AM, Domingues I, Mendes-Soares H, Matos AM, Dionisio F. Temperate Bacterial viruses as double-edged swords in bacterial warfare. PLoS One. 2013;8:e59043.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Dragoš A, Andersen AJC, Lozano-Andrade CN, Kempen PJ, Kovács ÁT, Strube ML. Phages carry interbacterial weapons encoded by biosynthetic gene clusters. Curr Biol. 2021;31:3479–89.PubMed 

    Google Scholar 
    25.Pyšek P, Bacher S, Kühn I, Novoa A, Catford JA, Hulme PE, et al. Macroecological framework for invasive aliens (MAFIA): disentangling large-scale context dependence in biological invasions. NeoBiota. 2020;62:407–61.
    Google Scholar 
    26.Blackburn TM, Pyšek P, Bacher S, Carlton JT, Duncan RP, Jarošík V, et al. A proposed unified framework for biological invasions. Trends Ecol Evol. 2011;26:333–9.PubMed 

    Google Scholar 
    27.Richardson DM, Pyšek P. Plant invasions: merging the concepts of species invasiveness and community invasibility. Prog Phys Geogr Earth Environ. 2006;30:409–31.
    Google Scholar 
    28.Williamson M. Explaining and predicting the success of invading species at different stages of invasion. Biol Invasions. 2006;8:1561–8.
    Google Scholar 
    29.Demerec M, Adelberg EA, Clark AJ, Hartman PE. A proposal for a uniform nomenclature in bacterial genetics. Genetics 1966;54:61–76.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Dechesne A, Wang G, Gülez G, Or D, Smets BF. Hydration-controlled bacterial motility and dispersal on surfaces. Proc Natl Acad Sci. 2010;107:14369–72.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Maurhofer M, Keel C, Schnider U, Voisard C, Haas D, Defao G. Influence of enhanced antibiotic production in Pseudomanas fluorescens strain CHA0 on its disease suppressive capacity. Phytopathol. 1992;82:190–5.CAS 

    Google Scholar 
    32.Schamfuß S, Neu TR, van der Meer JR, Tecon R, Harms H, Wick LY. Impact of mycelia on the accessibility of fluorene to PAH-degrading bacteria. Environ Sci Technol. 2013;47:6908–15.PubMed 

    Google Scholar 
    33.Bichet MC, Chin WH, Richards W, Lin Y-W, Avellaneda-Franco L, Hernandez CA, et al. Bacteriophage uptake by mammalian cell layers represents a potential sink that may impact phage therapy. iScience. 2021;24:102287.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Lu F, Wu S-H, Hung Y, Mou C-Y. Size effect on cell uptake in well-suspended, uniform mesoporous silica nanoparticles. Small. 2009;5:1408–13.CAS 
    PubMed 

    Google Scholar 
    35.Fortier L-C, Moineau S Phage production and maintenance of stocks, including expected stock Lifetimes. In: Clokie MRJ, Kropinski AM, editors. Bacteriophages: Methods and Protocols, Volume 1: Isolation, Characterization, and Interactions. Totowa: Humana Press; 2009. p. 203–19.36.Mazzocco A, Waddell TE, Lingohr E, Johnson RP Enumeration of Bacteriophages Using the Small Drop Plaque Assay System In: Clokie MRJ, Kropinski AM, editors. Bacteriophages: Methods and Protocols, Volume 1: Isolation, Characterization, and Interactions Totowa: Humana Press; 2009. p. 81–85.37.Kropinski AM, Mazzocco A, Waddell TE, Lingohr E, Johnson RP Enumeration of Bacteriophages by Double Agar Overlay Plaque Assay In: Clokie MRJ, Kropinski AM, editors. Bacteriophages: Methods and Protocols, Volume 1: Isolation, Characterization, and Interactions. Totowa: Humana Press; 2009. p. 69–76.38.Thanki AM, Taylor-Joyce G, Dowah A, Yakubu Nale J, Malik D, Rebecca Jane Clokie M. Unravelling the Links between Phage Adsorption and Successful Infection in Clostridium difficile. Viruses. 2018;10:441.39.Nair RR, Fiegna F, Velicer GJ. Indirect evolution of social fitness inequalities and facultative social exploitation. Proc R Soc B Biol Sci. 2018;285:20180054.
    Google Scholar 
    40.Postma M, Goedhart J. PlotsOfData—A web app for visualizing data together with their summaries. PLOS Biol. 2019;17:e3000202.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Wood M. Statistical inference using bootstrap confidence intervals. Significance. 2004;1:180–2.
    Google Scholar 
    42.Cumming G, Finch S. Inference by eye: confidence interval and how to read pictures of data. Am Psychol. 2005;60:170–80.PubMed 

    Google Scholar 
    43.Frada MJ, Schatz D, Farstey V, Ossolinski JE, Sabanay H, Ben-Dor S, et al. Zooplankton may serve as transmission vectors for viruses infecting algal blooms in the ocean. Curr Biol. 2014;24:2592–7.CAS 
    PubMed 

    Google Scholar 
    44.Frada MJ, Vardi A. Algal viruses hitchhiking on zooplankton across phytoplankton blooms. Commun Integr Biol. 2015;8:e1029210.PubMed 
    PubMed Central 

    Google Scholar 
    45.Totsche KU, Kögel-Knabner I. Mobile organic sorbent affected contaminant transport in soil: numerical case studies for enhanced and reduced mobility. Vadose Zo J. 2004;3:352–67.CAS 

    Google Scholar 
    46.Reche I, D’Orta G, Mladenov N, Winget DM, Suttle CA. Deposition rates of viruses and bacteria above the atmospheric boundary layer. ISME J. 2018;12:1154–62.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Lehmann K, Lehmann R, Totsche KU. Event-driven dynamics of the total mobile inventory in undisturbed soil account for significant fluxes of particulate organic carbon. Sci Total Environ. 2021;756:143774.CAS 
    PubMed 

    Google Scholar 
    48.Storms ZJ, Sauvageau D. Modeling tailed bacteriophage adsorption: insight into mechanisms. Virology. 2015;485:355–62.CAS 
    PubMed 

    Google Scholar 
    49.Shan Y, Harms H, Wick LY. Electric field effects on bacterial deposition and transport in porous media. Environ Sci Technol. 2018;52:14294–301.CAS 
    PubMed 

    Google Scholar 
    50.Junier P, Cailleau G, Palmieri I, Vallotton C, Trautschold OC, Junier T, et al. Democratization of fungal highway columns as a tool to investigate bacteria associated with soil fungi. FEMS Microbiol Ecol. 2021;97:fiab003.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Furuno S, Remer R, Chatzinotas A, Harms H, Wick LY. Use of mycelia as paths for the isolation of contaminant-degrading bacteria from soil. Micro Biotechnol. 2012;5:142–8.CAS 

    Google Scholar 
    52.Jiang F, Zhang L, Zhou J, George TS, Feng G. Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. N. Phytol. 2021;230:304–15.CAS 

    Google Scholar 
    53.Jansa J, Hodge A. Swimming, gliding, or hyphal riding? On microbial migration along the arbuscular mycorrhizal hyphal highway and functional consequences thereof. N. Phytol. 2021;230:14–16.
    Google Scholar 
    54.Zhang Y, Kastman EK, Guasto JS, Wolfe BE. Fungal networks shape dynamics of bacterial dispersal and community assembly in cheese rind microbiomes. Nat Commun. 2018;9:336.PubMed 
    PubMed Central 

    Google Scholar 
    55.Ping D, Wang T, Fraebel DT, Maslov S, Sneppen K, Kuehn S. Hitchhiking, collapse, and contingency in phage infections of migrating bacterial populations. ISME J 2020;14:2007–18.PubMed 
    PubMed Central 

    Google Scholar 
    56.Testa S, Berger S, Piccardi P, Oechslin F, Resch G, Mitri S. Spatial structure affects phage efficacy in infecting dual-strain biofilms of Pseudomonas aeruginosa. Commun Biol. 2019;2:405.PubMed 
    PubMed Central 

    Google Scholar 
    57.May T, Tsuruta K, Okabe S. Exposure of conjugative plasmid carrying Escherichia coli biofilms to male-specific bacteriophages. ISME J. 2011;5:771–5.CAS 
    PubMed 

    Google Scholar 
    58.Abedon ST. Phage “delay” towards enhancing bacterial escape from biofilms: a more comprehensive way of viewing resistance to bacteriophages. AIMS Microbiol. 2017;3:186.PubMed 
    PubMed Central 

    Google Scholar 
    59.Adams MH Bacteriophages (Interscience Publishers, Inc., New York – London, 1959)60.Schrader HS, Schrader JO, Walker JJ, Bruggeman NB, Vanderloop JM, Shaffer JJ, et al. Effects of host starvation on bacteriophage dynamics. Bact Oligotrophic Environ Starvation-Survival Lifestyle. 1997; 368-85.61.Schrader HS, Schrader JO, Walker JJ, Wolf TA, Nickerson KW, Kokjohn TA. Bacteriophage infection and multiplication occur in Pseudomonas aeruginosa starved for 5 years. Can J Microbiol. 1997;43:1157–63.CAS 
    PubMed 

    Google Scholar 
    62.Łoś M, Golec P, Łoś JM, Węglewska-Jurkiewicz A, Czyż A, Węgrzyn A, et al. Effective inhibition of lytic development of bacteriophages λ, P1 and T4 by starvation of their host, Escherichia coli. BMC Biotechnol. 2007;7:13.PubMed 
    PubMed Central 

    Google Scholar 
    63.Bryan D, El-Shibiny A, Hobbs Z, Porter J, Kutter EM. Bacteriophage T4 infection of stationary phase E. coli: life after log from a phage perspective. Front Microbiol. 2016;7:1391.PubMed 
    PubMed Central 

    Google Scholar 
    64.Yin J. A quantifiable phenotype of viral propagation. Biochem Biophys Res Commun. 1991;174:1009–14.CAS 
    PubMed 

    Google Scholar 
    65.Chatterjee A, Willett JLE, Dunny GM, Duerkop BA. Phage infection and sub-lethal antibiotic exposure mediate Enterococcus faecalis type VII secretion system dependent inhibition of bystander bacteria. PLOS Genet. 2021;17:e1009204.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Berthold T, Centler F, Hübschmann T, Remer R, Thullner M, Harms H, et al. Mycelia as a focal point for horizontal gene transfer among soil bacteria. Sci Rep. 2016;6:36390.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Lee KL, Hubbard LC, Hern S, Yildiz I, Gratzl M, Steinmetz NF. Shape matters: the diffusion rates of TMV rods and CPMV icosahedrons in a spheroid model of extracellular matrix are distinct. Biomater Sci. 2013;1. https://doi.org/10.1039/C3BM00191A.68.Hudson P, Greenman J. Competition mediated by parasites: biological and theoretical progress. Trends Ecol Evol. 1998;13:387–90.CAS 
    PubMed 

    Google Scholar 
    69.Sax DF, Stachowicz JJ, Brown JH, Bruno JF, Dawson MN, Gaines SD, et al. Ecological and evolutionary insights from species invasions. Trends Ecol Evol. 2007;22:465–71.PubMed 

    Google Scholar 
    70.Wagner PL, Waldor MK. Bacteriophage control of bacterial virulence. Infect Immun. 2020;70:3985–93.
    Google Scholar 
    71.Chantrey J, Dale TD, Read JM, White S, Whitfield F, Jones D, et al. European red squirrel population dynamics driven by squirrelpox at a gray squirrel invasion interface. Ecol Evol. 2014;4:3788–99.PubMed 
    PubMed Central 

    Google Scholar 
    72.Essl F, Bacher S, Genovesi P, Hulme PE, Jeschke JM, Katsanevakis S, et al. Which taxa are alien? Criteria, applications, and uncertainties. Bioscience 2018;68:496–509.
    Google Scholar 
    73.Seebens H, Gastner MT, Blasius B. The risk of marine bioinvasion caused by global shipping. Ecol Lett. 2013;16:782–90.CAS 
    PubMed 

    Google Scholar 
    74.Seebens H, Essl F, Blasius B. The intermediate distance hypothesis of biological invasions. Ecol Lett. 2017;20:158–65.PubMed 

    Google Scholar 
    75.Hulme PE, Bacher S, Kenis M, Klotz S, Kühn I, Minchin D, et al. Grasping at the routes of biological invasions: a framework for integrating pathways into policy. J Appl Ecol. 2008;45:403–14.
    Google Scholar 
    76.Liebhold AM, Brockerhoff EG, Garrett LJ, Parke JL, Britton KO. Live plant imports: the major pathway for forest insect and pathogen invasions of the US. Front Ecol Environ. 2012;10:135–43.
    Google Scholar  More

  • in

    Artefactual depiction of predator–prey trophic linkages in global soils

    1.Wall, D. H., Bardgett, R. D. & Kelly, E. Biodiversity in the dark. Nat. Geosci. 3(5), 297–298 (2010).ADS 
    CAS 

    Google Scholar 
    2.Eisenhauer, N., Bonn, A. & Guerra, C. A. Recognizing the quiet extinction of invertebrates. Nat. Commun. 10(1), 1–3 (2019).
    Google Scholar 
    3.Koch, A. et al. Soil security: Solving the global soil crisis. Global Pol. 4(4), 434–441 (2013).
    Google Scholar 
    4.Wall, D. H., Nielsen, U. N. & Six, J. Soil biodiversity and human health. Nature 528(7580), 69–76 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nat. Commun. 11(1), 1–13 (2020).
    Google Scholar 
    6.Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Zou, K., Thébault, E., Lacroix, G. & Barot, S. Interactions between the green and brown food web determine ecosystem functioning. Funct. Ecol. 30(8), 1454–1465 (2016).
    Google Scholar 
    8.Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42, S3–S15 (2006).
    Google Scholar 
    9.de Vries, F. T. et al. Soil food web properties explain ecosystem services across European land use systems. Proc. Natl. Acad. Sci. 110(35), 14296–14301 (2013).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Adhikari, K. & Hartemink, A. E. Linking soils to ecosystem services—A global review. Geoderma 262, 101–111 (2016).ADS 
    CAS 

    Google Scholar 
    11.Cameron, E. K. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33(5), 1187–1192 (2019).PubMed 

    Google Scholar 
    12.Phillips, H. R., Heintz-Buschart, A. & Eisenhauer, N. Putting soil invertebrate diversity on the map. Mol. Ecol. 29(4), 655–657 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    13.El Mujtar, V., Muñoz, N., Mc Cormick, B. P., Pulleman, M. & Tittonell, P. Role and management of soil biodiversity for food security and nutrition; where do we stand?. Glob. Food Sec. 20, 132–144 (2019).
    Google Scholar 
    14.Schuldt, A. et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat. Commun. 9(1), 2989 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Eisenhauer, N. et al. Priorities for research in soil ecology. Pedobiologia 63, 1–7 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    16.Brose, U. & Scheu, S. Into darkness: Unravelling the structure of soil food webs. Oikos 123(10), 1153–1156 (2014).
    Google Scholar 
    17.Phillips, H. R. et al. Red list of a black box. Nat. Ecol. Evol. 1(4), 1–1 (2017).
    Google Scholar 
    18.Hairston, N. G., Smith, F. E. & Slobodkin, L. B. Community structure, population control, and competition. Am. Nat. 94(879), 421–425 (1960).
    Google Scholar 
    19.Vidal, M. C. & Murphy, S. M. Bottom-up vs top-down effects on terrestrial insect herbivores: A meta-analysis. Ecol. Lett. 21(1), 138–150 (2018).PubMed 

    Google Scholar 
    20.Wagg, C., Bender, S. F., Widmer, F. & van der Heijden, M. G. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc. Natl. Acad. Sci. 111(14), 5266–5270 (2014).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536(7617), 456–459 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    22.Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4(1), 1–23 (1973).
    Google Scholar 
    23.Allesina, S. & Tang, S. Stability criteria for complex ecosystems. Nature 483(7388), 205–208 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    24.Crowther, T. W. et al. Biotic interactions mediate soil microbial feedbacks to climate change. Proc. Natl. Acad. Sci. 112(22), 7033–7038 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Maran, A. M. & Pelini, S. L. Predator contributions to belowground responses to warming. Ecosphere 7(9), e01457 (2016).
    Google Scholar 
    26.Geisen, S., Wall, D. H. & van der Putten, W. H. Challenges and opportunities for soil biodiversity in the Anthropocene. Curr. Biol. 29(19), R1036–R1044 (2019).CAS 
    PubMed 

    Google Scholar 
    27.Rooney, N., McCann, K., Gellner, G. & Moore, J. C. Structural asymmetry and the stability of diverse food webs. Nature 442(7100), 265–269 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    28.Murphy, S. M., Lewis, D. & Wimp, G. M. Predator population size structure alters consumption of prey from epigeic and grazing food webs. Oecologia 192(3), 791–799 (2020).ADS 
    PubMed 

    Google Scholar 
    29.Scheu, S. Plants and generalist predators as links between the below-ground and above-ground system. Basic Appl. Ecol. 2, 3–13 (2001).
    Google Scholar 
    30.Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304(5677), 1629–1633 (2004).ADS 
    CAS 
    PubMed 

    Google Scholar 
    31.de Vries, F. T. & Wallenstein, M. D. Below-ground connections underlying above-ground food production: A framework for optimising ecological connections in the rhizosphere. J. Ecol. 105(4), 913–920 (2017).
    Google Scholar 
    32.Wu, T., Ayres, E., Bardgett, R. D., Wall, D. H. & Garey, J. R. Molecular study of worldwide distribution and diversity of soil animals. Proc. Natl. Acad. Sci. 108(43), 17720–17725 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Symondson, W. O. C., Sunderland, K. D. & Greenstone, M. H. Can generalist predators be effective biocontrol agents?. Annu. Rev. Entomol. 47(1), 561–594 (2002).CAS 
    PubMed 

    Google Scholar 
    34.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5(10), eaax0121 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Karp, D. S. et al. Crop pests and predators exhibit inconsistent responses to surrounding landscape composition. Proc. Natl. Acad. Sci. 115(33), E7863–E7870 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Johnson, S. N. et al. New frontiers in belowground ecology for plant protection from root-feeding insects. Appl. Soil. Ecol. 108, 96–107 (2016).
    Google Scholar 
    37.Veen, C. et al. Applying the aboveground-belowground interaction concept in agriculture: Spatio-temporal scales matter. Front. Ecol. Evol. 7, 300 (2019).
    Google Scholar 
    38.Birkhofer, K., Wise, D. H. & Scheu, S. Subsidy from the detrital food web, but not microhabitat complexity, affects the role of generalist predators in an aboveground herbivore food web. Oikos 117(4), 494–500 (2008).
    Google Scholar 
    39.Birkhofer, K. et al. Organic farming affects the biological control of hemipteran pests and yields in spring barley independent of landscape complexity. Landsc. Ecol. 31(3), 567–579 (2016).
    Google Scholar 
    40.van der Putten, W. H. et al. Empirical and theoretical challenges in aboveground–belowground ecology. Oecologia 161(1), 1–14 (2009).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Kleijn, D. et al. Ecological intensification: Bridging the gap between science and practice. Trends Ecol. Evol. 34(2), 154–166 (2019).PubMed 

    Google Scholar 
    42.Bender, S. F., Wagg, C. & van der Heijden, M. G. An underground revolution: Biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31(6), 440–452 (2016).PubMed 

    Google Scholar 
    43.Gagic, V. et al. Combined effects of agrochemicals and ecosystem services on crop yield across Europe. Ecol. Lett. 20(11), 1427–1436 (2017).PubMed 

    Google Scholar 
    44.Briones, M. J. The serendipitous value of soil fauna in ecosystem functioning: The unexplained explained. Front. Environ. Sci. 6, 149 (2018).
    Google Scholar 
    45.Kaya, H. K. & Gaugler, R. Entomopathogenic nematodes. Annu. Rev. Entomol. 38(1), 181–206 (1993).
    Google Scholar 
    46.Ferris, H. & Tuomisto, H. Unearthing the role of biological diversity in soil health. Soil Biol. Biochem. 85, 101–109 (2015).CAS 

    Google Scholar 
    47.Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Change Biol. 21, 973–985 (2015).ADS 

    Google Scholar 
    48.Bender, S. F. & van der Heijden, M. G. Soil biota enhance agricultural sustainability by improving crop yield, nutrient uptake and reducing nitrogen leaching losses. J. Appl. Ecol. 52(1), 228–239 (2015).CAS 

    Google Scholar 
    49.De Vries, F. T. et al. Land use alters the resistance and resilience of soil food webs to drought. Nat. Clim. Change 2, 276–280 (2012).ADS 

    Google Scholar 
    50.Bastida, F. et al. Climatic vulnerabilities and ecological preferences of soil invertebrates across biomes. Mol. Ecol. 29(4), 752–761 (2020).PubMed 

    Google Scholar 
    51.Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: The bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).
    Google Scholar 
    52.Polis, G. A. Complex trophic interactions in deserts: An empirical critique of food-web theory. Am. Nat. 138(1), 123–155 (1991).
    Google Scholar 
    53.Polis, G. A. & Strong, D. R. Food web complexity and community dynamics. Am. Nat. 147(5), 813–846 (1996).
    Google Scholar 
    54.Lavelle, P. et al. Ecosystem engineers in a self-organized soil: A review of concepts and future research questions. Soil Sci. 181(3/4), 91–109 (2016).ADS 
    CAS 

    Google Scholar 
    55.Nielsen, U. N. et al. The enigma of soil animal species diversity revisited: The role of small-scale heterogeneity. PLoS ONE 5(7), e11567 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Heinen, R., van der Sluijs, M., Biere, A., Harvey, J. A. & Bezemer, T. M. Plant community composition but not plant traits determine the outcome of soil legacy effects on plants and insects. J. Ecol. 106(3), 1217–1229 (2018).
    Google Scholar 
    57.Ramirez, K. S., Geisen, S., Morriën, E., Snoek, B. L. & van der Putten, W. H. Network analyses can advance above-belowground ecology. Trends Plant Sci. 23(9), 759–768 (2018).CAS 
    PubMed 

    Google Scholar 
    58.Boyer, S., Snyder, W. E. & Wratten, S. D. Molecular and isotopic approaches to food webs in agroecosystems. Food Webs 9, 1–3 (2016).
    Google Scholar 
    59.Casey, J. M. et al. Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. Methods Ecol. Evol. 10(8), 1157–1170 (2019).
    Google Scholar 
    60.Choate, B. A. & Lundgren, J. G. Invertebrate communities in spring wheat and the identification of cereal aphid predators through molecular gut content analysis. Crop Prot. 77, 110–118 (2015).
    Google Scholar 
    61.Furlong, M. J. Knowing your enemies: Integrating molecular and ecological methods to assess the impact of arthropod predators on crop pests. Insect Sci. 22(1), 6–19 (2015).PubMed 

    Google Scholar 
    62.Eitzinger, B., Rall, B. C., Traugott, M. & Scheu, S. Testing the validity of functional response models using molecular gut content analysis for prey choice in soil predators. Oikos 127(7), 915–926 (2018).
    Google Scholar 
    63.Barberán, A., Bates, S. T., Casamayor, E. O. & Fierer, N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 6(2), 343–351 (2012).PubMed 

    Google Scholar 
    64.Morriën, E. Understanding soil food web dynamics, how close do we get?. Soil Biol. Biochem. 102, 10–13 (2016).
    Google Scholar 
    65.Digel, C., Curtsdotter, A., Riede, J., Klarner, B. & Brose, U. Unravelling the complex structure of forest soil food webs: Higher omnivory and more trophic levels. Oikos 123(10), 1157–1172 (2014).
    Google Scholar 
    66.Toscano, B. J., Hin, V. & Rudolf, V. H. Cannibalism and intraguild predation community dynamics: Coexistence, competitive exclusion, and the loss of alternative stable states. Am. Nat. 190(5), 617–630 (2017).PubMed 

    Google Scholar 
    67.Coleman, D. C. & Wall, D. H. Soil fauna: Occurrence, biodiversity, and roles in ecosystem function. Soil Microbiol. Ecol. Biochem. 4, 111–149 (2015).
    Google Scholar 
    68.Brussaard, L. Biodiversity and ecosystem functioning in soil. Ambio 26, 563–570 (1997).
    Google Scholar 
    69.Briar, S. S. et al. The distribution of nematodes and soil microbial communities across soil aggregate fractions and farm management systems. Soil Biol. Biochem. 43, 905–914 (2011).CAS 

    Google Scholar 
    70.Oelbermann, K. & Scheu, S. Trophic guilds of generalist feeders in soil animal communities as indicated by stable isotope analysis (15N/14N). Bull. Entomol. Res. 100(5), 511 (2010).CAS 
    PubMed 

    Google Scholar 
    71.Cohen, J. E., Pimm, S. L., Yodzis, P. & Saldaña, J. Body sizes of animal predators and animal prey in food webs. J. Anim. Ecol. 62, 67–78 (1993).
    Google Scholar 
    72.Nielsen, U. N., Wall, D. H. & Six, J. Soil biodiversity and the environment. Annu. Rev. Environ. Resour. 40, 63–90 (2015).
    Google Scholar 
    73.Veresoglou, S. D., Halley, J. M. & Rillig, M. C. Extinction risk of soil biota. Nat. Commun. 6(1), 1–10 (2015).
    Google Scholar 
    74.Ruf, A. A maturity index for predatory soil mites (Mesostigmata: Gamasina) as an indicator of environmental impacts of pollution on forest soils. Appl. Soil. Ecol. 9(1–3), 447–452 (1998).
    Google Scholar 
    75.Zak, D. R., Holmes, W. E., White, D. C., Peacock, A. D. & Tilman, D. Plant diversity, soil microbial communities, and ecosystem function: Are there any links?. Ecology 84(8), 2042–2050 (2003).
    Google Scholar 
    76.Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169(4), 587–596 (2017).CAS 
    PubMed 

    Google Scholar 
    77.Barnes, A. D. et al. Energy flux: The link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 33(3), 186–197 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    78.Heinen, R., Biere, A., Harvey, J. A. & Bezemer, T. M. Effects of soil organisms on aboveground plant-insect interactions in the field: Patterns, mechanisms and the role of methodology. Front. Ecol. Evol. 6, 106 (2018).
    Google Scholar 
    79.Rillig, M. C. et al. The role of multiple global change factors in driving soil functions and microbial biodiversity. Science 366(6467), 886–890 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Wardle, D. A., Hyodo, F., Bardgett, R. D., Yeates, G. W. & Nilsson, M. C. Long-term aboveground and belowground consequences of red wood ant exclusion in boreal forest. Ecology 92(3), 645–656 (2011).PubMed 

    Google Scholar 
    81.Preisser, E. L. & Strong, D. R. Climate affects predator control of an herbivore outbreak. Am. Nat. 163(5), 754–762 (2004).PubMed 

    Google Scholar 
    82.Hamilton, J. et al. Elevated atmospheric CO2 alters the arthropod community in a forest understory. Acta Oecol. 43, 80–85 (2012).ADS 

    Google Scholar 
    83.Zaller, J. G. et al. Future rainfall variations reduce abundances of aboveground arthropods in model agroecosystems with different soil types. Front. Environ. Sci. 2, 44 (2014).
    Google Scholar 
    84.Koltz, A. M., Classen, A. T. & Wright, J. P. Warming reverses top-down effects of predators on belowground ecosystem function in Arctic tundra. Proc. Natl. Acad. Sci. 115(32), E7541–E7549 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    85.Santonja, M. et al. Plant litter mixture partly mitigates the negative effects of extended drought on soil biota and litter decomposition in a Mediterranean oak forest. J. Ecol. 105(3), 801–815 (2017).
    Google Scholar 
    86.Garratt, M. P. et al. Enhancing soil organic matter as a route to the ecological intensification of European arable systems. Ecosystems 21(7), 1404–1415 (2018).CAS 

    Google Scholar 
    87.Smith-Ramesh, L. M. Predators in the plant–soil feedback loop: Aboveground plant-associated predators may alter the outcome of plant–soil interactions. Ecol. Lett. 21(5), 646–654 (2018).PubMed 

    Google Scholar 
    88.Gurr, G. M., Wratten, S. D., Landis, D. A. & You, M. Habitat management to suppress pest populations: Progress and prospects. Annu. Rev. Entomol. 62, 91–109 (2017).CAS 
    PubMed 

    Google Scholar 
    89.Rypstra, A. L., Carter, P. E., Balfour, R. A. & Marshall, S. D. Architectural features of agricultural habitats and their impact on the spider inhabitants. J. Arachnol. 27, 371–377 (1999).
    Google Scholar 
    90.Von Berg, K., Thies, C., Tscharntke, T. & Scheu, S. Changes in herbivore control in arable fields by detrital subsidies depend on predator species and vary in space. Oecologia 163(4), 1033–1042 (2010).ADS 

    Google Scholar 
    91.Rowen, E., Tooker, J. F. & Blubaugh, C. K. Managing fertility with animal waste to promote arthropod pest suppression. Biol. Control 134, 130–140 (2019).
    Google Scholar 
    92.Perović, D. J. et al. Managing biological control services through multi-trophic trait interactions: Review and guidelines for implementation at local and landscape scales. Biol. Rev. 93(1), 306–321 (2018).PubMed 

    Google Scholar 
    93.Roger-Estrade, J., Anger, C., Bertrand, M. & Richard, G. Tillage and soil ecology: Partners for sustainable agriculture. Soil Tillage Res. 111(1), 33–40 (2010).
    Google Scholar 
    94.Dias, T., Dukes, A. & Antunes, P. M. Accounting for soil biotic effects on soil health and crop productivity in the design of crop rotations. J. Sci. Food Agric. 95(3), 447–454 (2015).CAS 
    PubMed 

    Google Scholar 
    95.Tamburini, G., De Simone, S., Sigura, M., Boscutti, F. & Marini, L. Conservation tillage mitigates the negative effect of landscape simplification on biological control. J. Appl. Ecol. 53(1), 233–241 (2016).
    Google Scholar 
    96.Pretty, J. et al. Global assessment of agricultural system redesign for sustainable intensification. Nat. Sustain. 1(8), 441–446 (2018).
    Google Scholar 
    97.Swift, M. J., Heal, O. W., Anderson, J. M. & Anderson, J. M. Decomposition in Terrestrial Ecosystems Vol. 5 (University of California Press, 1979).
    Google Scholar 
    98.van Straalen, N. M., Butovsky, R. O., Pokarzhevskii, A. D., Zaitsev, A. S. & Verhoef, S. C. Metal concentrations in soil and invertebrates in the vicinity of a metallurgical factory near Tula (Russia). Pedobiologia 45(5), 451–466 (2001).
    Google Scholar 
    99.Birkhofer, K. et al. Methods to identify the prey of invertebrate predators in terrestrial field studies. Ecol. Evol. 7(6), 1942–1953 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    100.Potapov, A. M., Tiunov, A. V. & Scheu, S. Uncovering trophic positions and food resources of soil animals using bulk natural stable isotope composition. Biol. Rev. 94(1), 37–59 (2019).
    Google Scholar  More

  • in

    Comparative quantification of local climate regulation by green and blue urban areas in cities across Europe

    Climate change and the urban heat island effect threaten the sustainability of rapidly growing urban settlements and urban population worldwide1. Such threats may be ameliorated by the ecosystem service of local climate regulation provided by green–blue urban areas (natural, restored, or (re)constructed ecosystems, such as forested land, wetlands, parks)2,3,4. The spatiotemporal relationships existing between natural ecosystems and human societies form the basis of the ecosystem service framework, used to represent such benefits from nature to human well-being5,6. Areas of ecosystem service provision (nature contribution of some supply) and ecosystem service use (human beneficiaries with some ecosystem service demand) in a landscape are then often connected by some form of carrier flow, which can be natural (air and water movement) or depend on human-made infrastructure (e.g., pipelines for water, road network and vehicles for human movement)7,8. Additionally, ecosystem service relevance is scale-dependent, e.g., with carbon sequestration being globally relevant, while recreational areas provide mostly local and regional benefits9,10,11. Over each scale of relevance, it is essential to distinguish the supply and demand sides of spatial ecosystem services relationships2, and the degree to which potential supply (left, Fig. 1) can actually reach and fulfill some actual demand (right, Fig. 1). This may be referred to as the degree of realization of ecosystem service supply and demand12. Conceptually, we define a potential as the hypothetical maximum capacity for a service (supply) or need (demand). In contrast, a realized service quantifies the actual ecosystem service, after consideration of proper spatial flow connections between natural ecosystems and humans. For example, for a city, only part of its total potential ecosystem service demand (Pd) may be actually fulfilled (referred to as the realized ecosystem service demand, Rd, right in Fig. 1) by only part (the realized supply part, Rs, left in Fig. 1) of the city’s total potential ecosystem service supply (Ps). Thus, Rd measures the part of the human demand (for the ecosystem service) actually fulfilled, while Rd quantify the part of the supply used to provide the ecosystem service. The Methods section describes and discusses in further detail this and other term definitions used in the analysis, the relationships between terms, and the calculation methods employed to quantify them.Figure 1Spatial flow dependence of ecosystem services and studied city locations. Schematic of potential and realized supply and demand of flow-dependent ecosystem service (for explanation, see “Methods”).Full size imageIn practice, implementing the concept of ecosystem services into urban landscape management and decision making is still problematic5, with one reason being the challenge to link spatially disaggregated areas of service provision with the human beneficiaries13. In addition, considerable ambiguity still remains, conceptually and in practice, regarding the distinction and quantification of potential and realized ecosystem services supply and demand14. For example, without consideration of the spatial relationship between supply and demand (implicitly or explicitly), it becomes difficult to determine or quantify, in practice, if an actual ecosystem service exists. To contribute to its resolution, we here investigate the degree of supply and demand realization for the urban ecosystem service of local climate regulation using comparative quantitative indicators in and across 660 cities of different sizes and in different parts of Europe (Fig. 2).Figure 2Studied city locations. Map of the European study region and locations of the cities studied. See Supplementary Table 1 for further city data.Full size imageThe potential of green–blue urban areas for cooling cities is generally well established, and has been studied using direct observations15,16, remote sensing17 or modelling based approaches18,19. The regulation of local urban air temperatures by such areas can increase thermal comfort and decrease health risks related to urban heat island (UHI) effects20,21 for urban populations. The UHI effects relate to often-observed higher ambient air temperatures in urban environments compared to their close surroundings20,21. The spatial extents of cities in this study are then considered according to their respective administrative unit definitions.The investigation focuses on urban realization of this ecosystem service because the proportion of the global human population living in urban areas is steadily rising22, and cities are critical for both climate change mitigation and societal adaptation to warming23,24. For adaptation, cities need to handle exacerbated urban warming by UHI effects and provide livable environments for their residents while avoiding detrimental consequences from competing development interests25,26. The UHI effects emphasize the importance of local climate regulation as an essential urban ecosystem service, the actual realization of which depends on city function and form, with the latter including the spatial distribution of green–blue urban areas, as well as temporal changes in this by growing urbanization. The degree to which such growth leads to replacement of moist soils and vegetative cover with paved and impervious surfaces also affects urban surface energy and radiation balances27, and associated land surface temperatures at local human scale, although the relationship with air temperature is complex27. For example the proportion of vegetation in a particular area will regulate the resulting ratio of sensitive to latent heat flux (known as Bowen ratio), which will in turn affect properties of the urban climate27.In reality, a city’s climate consists of a variety of smaller-scale microclimates, which can be modified and leveraged through deliberate design20. This emphasizes the importance of good city planning28, including for conservation, restoration, and construction of new urban green–blue areas29,30. Such areas can provide various services to urban populations, e.g., urban flood mitigation12 and more general health31 and well-being32 benefits, including cooling required to mitigate UHI effects. The latter can be achieved, e.g., by enhanced latent heat flux associated with higher evapotranspiration from green areas and evaporation from blue areas. Through the flow of air and its lateral heat advection, green–blue urban areas can also cool surrounding built parts of the city that would commonly have a demand for such ecosystem service of local climate regulation2. How to measure and predictively quantify the zones of influence of such air cooling by green–blue areas is still a challenging research question, but such zones are reported to be in the range of several hundred meters29,33,34.The aim of the indicators developed and used in this study is to quantify actual realized urban ecosystem service supply in terms of its fulfillment of some actual demand for that ecosystem service of the urban human population. Over each city, such realization and associated indicator values depend both on local conditions (such as natural land-cover areas that can supply the considered ecosystem service) and overall urban form and spatial configuration of the natural and built areas in the urban landscape. At larger scales spanned by multiple cities (such as those over Europe studied in this paper, Fig. 2), the quantitative indicators can be used to detect main ecosystem service realization patterns, similarities and differences among cities. This is done by quantifying indicator statistics across the cities, and assessing ecosystem service realization patterns in terms of how these statistics depend on city characteristics, or associated country or sub-region characteristics, such as population density or socio-economic measures like Human Development Index (HDI) and GDP per capita.A few studies have evaluated spatial dependencies of ecosystem services35,36 and mostly focused on multiple services in a specific study area. Our comparative multi-city study aims instead at revealing possible overarching statistical patterns of the spatially dependent ecosystem service of local climate regulation, and its realization in and across European urban systems. While this urban ecosystem service is important per se, the dependence of its realization on spatial proximity to green–blue areas may also provide useful guidance for further study of other urban ecosystem services that depend on the spatial distribution of green–blue areas and their proximity to human needs within cities2,12,32.Previous multi-city explorations of urban socio-economic growth and human-made infrastructure have revealed and quantified various statistical cross-city patterns37,38,39. Our study hypothesizes that such patterns may also emerge in the cross-city statistics of ecosystem service realization indicators related to green–blue city areas and their provision to urban populations. Identification of such quantitative ecosystem service indicator patterns can increase fundamental understanding of urban ecosystem service conditions, as well as projection capabilities for changes in these conditions under city growth, e.g., in terms of population density, HDI, and GDP per capita.To explore and test the main study hypothesis, we compile and synthesize for all 660 European cities (Fig. 2) high-resolution datasets for city morphology (e.g., land cover) and bio-physical characteristics (e.g. degree of imperviousness, vegetation type and vegetation density), based on previous study reports of the relevance of these parameters for the ecosystem service of local climate regulation2,12, along with city-scale measures of human population, city area, and resulting population density ratio (Supplementary Table 1). Using these data, we evaluate and map total potential ecosystem service supply and demand in each city (Figs. 1, 2, Supplementary Figures 1–3, Methods), and further apply a model of radially decaying ecosystem service supply and demand realization at 20 m resolution (Supplementary Figure 2–3, Methods) to also account for the spatial influence reach of local climate regulation from each location in the city. Furthermore, for comparative multi-city analysis, we quantify a set of directly comparable ecosystem service realization indicators for each city (explained further below) and their resulting statistics across all 660 cities over Europe, and comparatively for cities in different European countries and sub-regions.Indicator definitions and calculationsFor each of the 660 cities, we consider and calculate two basic metrics of urban ecosystem service realization: the ratio of realized to potential ecosystem service supply (Rs/Ps), and the ratio of realized to potential ecosystem service demand (Rd/Pd). For each discretized city pixel within a city, we first calculate its local net potential ecosystem service supply (Ps) or demand (Pd) directly from the urban morphology and bio-physical data (Supplementary Figure 1). For each net supply pixel, we further calculate (as illustrated bottom right in Supplementary Figure 2) that pixel’s ecosystem service realized supply contributions to the surrounding net demand pixels within its spatial influence radius (top, Supplementary Figure 2). Analogously, for each net demand pixel, we calculate the contributions to fulfilling (realizing) its ecosystem service demand from the surrounding net supply pixels that have that net demand pixel within their spatial influence radius. For each pixel of any type, we thus calculate its realized ecosystem service supply Rs or demand Rd in relation to its potential net local supply Ps or demand Pd, respectively (Supplementary Figure 2; see also Supplementary Figure 3 and Supplementary Information for further calculation and mapping details). We further calculate comparative indicators of city-average relative realized ecosystem service supply and demand, Rs/Ps and Rd/Pd, respectively, from the sums of local Rs, Rd, Ps and Pd over all pixels in the city. The city-average supply indicator Rs/Ps thus quantifies the average degree of realized (actually used) ecosystem service supply from all green–blue areas over the whole city (left in Fig. 1). Analogously, the city-average demand indicator Rd/Pd quantifies the average degree of realized (actually fulfilled) ecosystem service demand over each city (right in Fig. 1). For further cross-city comparison, we also calculate indicators for how large area fraction of total city area has a relatively high degree of ecosystem service supply and demand realization, respectively. Local Rs/Ps ≥ 0.5 and Rd/Pd ≥ 0.5 are then selected as illustrative thresholds for such relatively high degree of ecosystem service supply and demand realization, respectively, with the area fractions calculated from the number of pixels with Rs/Ps ≥ 0.5 or Rd/Pd ≥ 0.5 relative to the total number of pixels in each city.Based on the power-law relationships with population density results found for both previous city-average and city-fraction indicators of ecosystem service realization, we also have an opportunity to project indicator values for future scenarios of changed population density, as$$r_{i} = frac{Ri}{{Pi}} = Ai cdot left( {PD} right)^{beta i} le 1$$
    (1)
    where index i = d represents demand and i = s supply. Furthermore, for city-average indicators, Ri and Pi represent realized and potential ecosystem service, respectively, while for area-fraction indicators, they represent city area with high degree of ecosystem service realization (≥ 0.5) and total city area, respectively. The constraint of (r_{i} le 1) is due to the upper limit of Ri ≤ Pi for both indicator types, with Ai the scale factor and βi the exponent of a power law relationship ri with population density (denoted PD). Based on Eq. (1), a relative measure of ecosystem service realization effectiveness can be estimated from the demand fulfillment ((r_{d})) relative to the supply use ((r_{s})), as:$$Effectiveness = frac{{r_{d} }}{{r_{s} }} = frac{{Ad cdot left( {PD} right)^{beta d} }}{{As cdot left( {PD} right)^{beta s} }} = frac{Ad}{{As}}PD^{{left( {beta d – beta s} right)}}$$
    (2a)
    with$$r_{d} = Ad cdot left( {PD} right)^{beta d} quad ifquad r_{d} le 1,,,,,r_{d} = 1quad otherwise$$
    (2b)
    $$r_{s} = As cdot left( {PD} right)^{beta s} quad if,r_{s} le 1,,,,r_{s} = 1quad otherwise.$$
    (2c) More

  • in

    Mammalian gut metabolomes mirror microbiome composition and host phylogeny

    1.Ley RE, Hamady M, Lozupone C, Turnbaugh PJ, Ramey RR, Bircher JS, et al. Evolution of mammals and their gut microbes. Science. 2008;320:1647–51.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Song SJ, Sanders JG, Delsuc F, Metcalf J, Amato K, Taylor MW, et al. Comparative analyses of vertebrate gut microbiomes reveal convergence between birds and bats. mBio. 2020;11:e02901–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Godon J-J, Arulazhagan P, Steyer J-P, Hamelin J. Vertebrate bacterial gut diversity: size also matters. BMC Ecol. 2016;16:12.PubMed 
    PubMed Central 

    Google Scholar 
    4.Lutz HL, Jackson EW, Webala PW, Babyesiza WS, Kerbis Peterhans JC, Demos TC, et al. Ecology and host identity outweigh evolutionary history in shaping the bat microbiome. mSystems. 2019;4:e00511–19.PubMed 
    PubMed Central 

    Google Scholar 
    5.Nishida AH, Ochman H. Rates of gut microbiome divergence in mammals. Mol Ecol. 2018;27:1884–97.PubMed 
    PubMed Central 

    Google Scholar 
    6.Groussin M, Mazel F, Sanders JG, Smillie CS, Lavergne S, Thuiller W, et al. Unraveling the processes shaping mammalian gut microbiomes over evolutionary time. Nat Commun. 2017;8:14319.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    7.Lim SJ, Bordenstein SR. An introduction to phylosymbiosis. Proc Biol Sci. 2020;287:20192900.PubMed 
    PubMed Central 

    Google Scholar 
    8.Ross AA, Müller KM, Weese JS, Neufeld JD. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc Natl Acad Sci USA. 2018;115:E5786–E5795.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Ochman H, Worobey M, Kuo C-H, Ndjango J-BN, Peeters M, Hahn BH, et al. Evolutionary relationships of wild hominids recapitulated by gut microbial communities. PLoS Biol. 2010;8:e1000546.PubMed 
    PubMed Central 

    Google Scholar 
    10.Amato KR, G Sanders J, Song SJ, Nute M, Metcalf JL, Thompson LR, et al. Evolutionary trends in host physiology outweigh dietary niche in structuring primate gut microbiomes. ISME J. 2018;13:576–87.PubMed 
    PubMed Central 

    Google Scholar 
    11.Moeller AH, Caro-Quintero A, Mjungu D, Georgiev AV, Lonsdorf EV, Muller MN, et al. Cospeciation of gut microbiota with hominids. Science. 2016;353:380–2.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Brooks AW, Kohl KD, Brucker RM, van Opstal EJ, Bordenstein SR. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 2016;14:e2000225.PubMed 
    PubMed Central 

    Google Scholar 
    13.Delsuc F, Metcalf JL, Wegener Parfrey L, Song SJ, González A, Knight R. Convergence of gut microbiomes in myrmecophagous mammals. Mol Ecol. 2014;23:1301–17.CAS 
    PubMed 

    Google Scholar 
    14.Muegge BD, Kuczynski J, Knights D, Clemente JC, González A, Fontana L, et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 2011;332:970–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. 2009;457:480–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature. 2012;486:207–14.
    Google Scholar 
    17.Weimer PJ. Redundancy, resilience, and host specificity of the ruminal microbiota: implications for engineering improved ruminal fermentations. Front Microbiol. 2015;6:296.PubMed 
    PubMed Central 

    Google Scholar 
    18.Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.CAS 
    PubMed 

    Google Scholar 
    19.Nelson MB, Martiny AC, Martiny JBH. Global biogeography of microbial nitrogen-cycling traits in soil. Proc Natl Acad Sci USA. 2016;113:8033–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 

    Google Scholar 
    21.Inkpen SA, Andrew Inkpen S, Douglas GM, Brunet TDP, Leuschen K, Ford Doolittle W, et al. The coupling of taxonomy and function in microbiomes. Biol Philos. 2017;32:1225–43.
    Google Scholar 
    22.Krautkramer KA, Fan J, Bäckhed F. Gut microbial metabolites as multi-kingdom intermediates. Nat Rev Microbiol. 2021;19:77–94.CAS 
    PubMed 

    Google Scholar 
    23.Turnbaugh PJ, Gordon JI. An invitation to the marriage of metagenomics and metabolomics. Cell. 2008;134:708–13.CAS 
    PubMed 

    Google Scholar 
    24.Moya A, Ferrer M. Functional redundancy-induced stability of gut microbiota subjected to disturbance. Trends Microbiol. 2016;24:402–13.CAS 
    PubMed 

    Google Scholar 
    25.Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat Biotechnol. 2016;34:828–37.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Wilson DE, Reeder DM Mammal species of the world: a taxonomic and geographic reference. 2005. JHU Press.27.Jami E, Israel A, Kotser A, Mizrahi I. Exploring the bovine rumen bacterial community from birth to adulthood. ISME J. 2013;7:1069–79.PubMed 
    PubMed Central 

    Google Scholar 
    28.Stevenson DM, Weimer PJ. Dominance of Prevotella and low abundance of classical ruminal bacterial species in the bovine rumen revealed by relative quantification real-time PCR. Appl Microbiol Biotechnol. 2007;75:165–74.CAS 
    PubMed 

    Google Scholar 
    29.Caporaso JG, Gregory Caporaso J, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
    PubMed 

    Google Scholar 
    32.Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Gawlik-Dziki U, Dziki D, Baraniak B, Lin R. The effect of simulated digestion in vitro on bioactivity of wheat bread with Tartary buckwheat flavones addition. LWT. 2009;42:137–43.CAS 

    Google Scholar 
    34.Melnik AV, da Silva RR, Hyde ER, Aksenov AA, Vargas F, Bouslimani A, et al. Coupling targeted and untargeted mass spectrometry for metabolome-microbiome-wide association studies of human fecal samples. Anal Chem. 2017;89:7549–59.CAS 
    PubMed 

    Google Scholar 
    35.Giavalisco P, Li Y, Matthes A, Eckhardt A, Hubberten H-M, Hesse H, et al. Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high-resolution mass spectrometry. Plant J. 2011;68:364–76.CAS 
    PubMed 

    Google Scholar 
    36.Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat Protoc. 2006;1:387–96.CAS 
    PubMed 

    Google Scholar 
    37.Hochberg U, Degu A, Toubiana D, Gendler T, Nikoloski Z, Rachmilevitch S, et al. Metabolite profiling and network analysis reveal coordinated changes in grapevine water stress response. BMC Plant Biol. 2013;13:184.PubMed 
    PubMed Central 

    Google Scholar 
    38.Shabat SKB, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016;10:2958–72.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Pluskal T, Castillo S, Villar-Briones A, Orešič M MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11;1–11.40.Nothias L-F, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17:905–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.da Silva RR, Wang M, Nothias L-F, van der Hooft JJJ, Caraballo-Rodríguez AM, Fox E, et al. Propagating annotations of molecular networks using in silico fragmentation. PLoS Comput Biol. 2018;14:e1006089.PubMed 
    PubMed Central 

    Google Scholar 
    42.Ernst M, Kang KB, Caraballo-Rodríguez AM, Nothias L-F, Wandy J, Chen C, et al. MolNetEnhancer: enhanced molecular networks by integrating metabolome mining and annotation tools. Metabolites. 2019;9:144.CAS 
    PubMed Central 

    Google Scholar 
    43.Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, et al. ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform. 2016;8:61.PubMed 
    PubMed Central 

    Google Scholar 
    44.Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Kessner D, Chambers M, Burke R, Agus D, Mallick P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics. 2008;24:2534–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, et al. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data. Nat Biotechnol. 2021;39:169–73.CAS 
    PubMed 

    Google Scholar 
    47.Kiela PR, Ghishan FK. Physiology of intestinal absorption and secretion. Best Pr Res Clin Gastroenterol. 2016;30:145–59.CAS 

    Google Scholar 
    48.Karasov WH, Diamond JM. Interplay between physiology and ecology in digestion. Bioscience. 1988;38:602–11.CAS 

    Google Scholar 
    49.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.
    Google Scholar 
    51.Wickham H, ggplot2: elegant graphics for data analysis. Springer; 2016.52.Hulsen T, de Vlieg J, Alkema W. BioVenn—a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008;9:488.PubMed 
    PubMed Central 

    Google Scholar 
    53.Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 
    54.Anderson MJ, Walsh DCI. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr. 2013;83:557–74.
    Google Scholar 
    55.Galili T. dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics. 2015;31:3718–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Hedges SB, Dudley J, Kumar S. TimeTree: a public knowledge-base of divergence times among organisms. Bioinformatics. 2006;22:2971–2.CAS 
    PubMed 

    Google Scholar 
    58.Kumar S, Stecher G, Suleski M, Hedges SB. TimeTree: a resource for timelines, timetrees, and divergence times. Mol Biol Evol. 2017;34:1812–9.CAS 
    PubMed 

    Google Scholar 
    59.Baker FB. Stability of two hierarchical grouping techniques case I: sensitivity to data errors. J Am Stat Assoc. 1974;69:440–5.
    Google Scholar 
    60.De Cáceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.
    Google Scholar 
    61.Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, et al. Proposed minimum reporting standards for chemical analysis chemical analysis working group (CAWG) metabolomics standards initiative (MSI). Metabolomics. 2007;3:211–21.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Jarmusch AK, Wang M, Aceves CM, Advani RS, Aguirre S, Aksenov AA, et al. ReDU: a framework to find and reanalyze public mass spectrometry data. Nat Methods. 2020;17:901–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Ridlon JM, Kang D-J, Hylemon PB. Bile salt biotransformations by human intestinal bacteria. J Lipid Res. 2006;47:241–59.CAS 
    PubMed 

    Google Scholar 
    64.Winston JA, Theriot CM. Diversification of host bile acids by members of the gut microbiota. Gut Microbes. 2019;11:1–14.65.Quinn RA, Melnik AV, Vrbanac A, Fu T, Patras KA, Christy MP, et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature. 2020;579:123–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Haslewood GA. Bile salt evolution. J Lipid Res. 1967;8:535–50.CAS 
    PubMed 

    Google Scholar 
    67.Hofmann AF, Hagey LR, Krasowski MD. Bile salts of vertebrates: structural variation and possible evolutionary significance. J Lipid Res. 2010;51:226–46.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Hofmann AF. Bile acids: the good, the bad, and the ugly. N. Physiol Sci. 1999;14:24–29.CAS 

    Google Scholar 
    69.Bergman EN. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev. 1990;70:567–90.CAS 
    PubMed 

    Google Scholar 
    70.Engelhardt W von, Rechkemmer G. The physiological effects of short-chain fatty acids in the hind gut. Fibre in human and animal nutrition. 1983. The Royal Society of New Zealand, Palmerston North, New Zealand, pp 149-55.71.Reichardt N, Duncan SH, Young P, Belenguer A, McWilliam Leitch C, Scott KP, et al. Phylogenetic distribution of three pathways for propionate production within the human gut microbiota. ISME J. 2014;8:1323–35.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Clemens ET, Stevens CE. Sites of organic acid production and patterns of digesta movement in the gastro-intestinal tract of the raccoon. J Nutr. 1979;109:1110–6.CAS 
    PubMed 

    Google Scholar 
    73.Schwab C, Cristescu B, Boyce MS, Stenhouse GB, Gänzle M. Bacterial populations and metabolites in the feces of free roaming and captive grizzly bears. Can J Microbiol. 2009;55:1335–46.CAS 
    PubMed 

    Google Scholar 
    74.Schwab C, Gänzle M. Comparative analysis of fecal microbiota and intestinal microbial metabolic activity in captive polar bears. Can J Microbiol. 2011;57:177–85.CAS 
    PubMed 

    Google Scholar 
    75.Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    76.Tofalo R, Cocchi S, Suzzi G. Polyamines and gut microbiota. Front Nutr. 2019;6:16.PubMed 
    PubMed Central 

    Google Scholar 
    77.Matsumoto M, Kibe R, Ooga T, Aiba Y, Kurihara S, Sawaki E, et al. Impact of intestinal microbiota on intestinal luminal metabolome. Sci Rep. 2012;2:233.PubMed 
    PubMed Central 

    Google Scholar 
    78.Pugin B, Barcik W, Westermann P, Heider A, Wawrzyniak M, Hellings P, et al. A wide diversity of bacteria from the human gut produces and degrades biogenic amines. Micro Ecol Health Dis. 2017;28:1353881.
    Google Scholar 
    79.Nakamura A, Ooga T, Matsumoto M. Intestinal luminal putrescine is produced by collective biosynthetic pathways of the commensal microbiome. Gut Microbes. 2019;10:159–71.CAS 
    PubMed 

    Google Scholar 
    80.Aura A-M, O’Leary KA, Williamson G, Ojala M, Bailey M, Puupponen-Pimiä R, et al. Quercetin derivatives are deconjugated and converted to hydroxyphenylacetic acids but not methylated by human fecal flora in vitro. J Agric Food Chem. 2002;50:1725–30.CAS 
    PubMed 

    Google Scholar 
    81.Booth AN, Deeds F, Jones FT, Murray CW. The metabolic fate of rutin and quercetin in the animal body. J Biol Chem. 1956;223:251–7.CAS 
    PubMed 

    Google Scholar 
    82.Jaganath IB, Mullen W, Edwards CA, Crozier A. The relative contribution of the small and large intestine to the absorption and metabolism of rutin in man. Free Radic Res. 2006;40:1035–46.CAS 
    PubMed 

    Google Scholar 
    83.Mena P, Calani L, Bruni R, Del Rio D. Bioactivation of high-molecular-weight polyphenols by the gut microbiome. Diet-Microbe Interactions in the Gut. Academic Press; 2015. pp 73–101.84.Serra A, Macià A, Romero M-P, Reguant J, Ortega N, Motilva M-J. Metabolic pathways of the colonic metabolism of flavonoids (flavonols, flavones and flavanones) and phenolic acids. Food Chem. 2012;130:383–93.CAS 

    Google Scholar 
    85.Peng X, Zhang Z, Zhang N, Liu L, Li S, Wei H. In vitro catabolism of quercetin by human fecal bacteria and the antioxidant capacity of its catabolites. Food Nutr Res. 2014;58:23406.86.Feng X, Li Y, Brobbey Oppong M, Qiu F. Insights into the intestinal bacterial metabolism of flavonoids and the bioactivities of their microbe-derived ring cleavage metabolites. Drug Metab Rev. 2018;50:343–56.CAS 
    PubMed 

    Google Scholar 
    87.Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science. 2019;364:1055.
    Google Scholar 
    88.Maini Rekdal V, Nol Bernadino P, Luescher MU, Kiamehr S, Le C, Bisanz JE, et al. A widely distributed metalloenzyme class enables gut microbial metabolism of host- and diet-derived catechols. Elife. 2020;9:e50845.PubMed 
    PubMed Central 

    Google Scholar 
    89.Davenport ER, Sanders JG, Song SJ, Amato KR, Clark AG, Knight R. The human microbiome in evolution. BMC Biol. 2017;15:127.PubMed 
    PubMed Central 

    Google Scholar 
    90.Steiner CC, Ryder OA. Molecular phylogeny and evolution of the Perissodactyla. Zool J Linn Soc. 2011;163:1289–303.
    Google Scholar 
    91.McKenzie VJ, Song SJ, Delsuc F, Prest TL, Oliverio AM, Korpita TM, et al. The effects of captivity on the mammalian gut microbiome. Integr Comp Biol. 2017;57:690–704.PubMed 
    PubMed Central 

    Google Scholar 
    92.Frankel JS, Mallott EK, Hopper LM, Ross SR, Amato KR. The effect of captivity on the primate gut microbiome varies with host dietary niche. Am J Primatol. 2019;81:e23061.PubMed 

    Google Scholar 
    93.Dührkop K, Nothias L-F, Fleischauer M, Reher R, Ludwig M, Hoffmann MA, et al. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nat Biotechnol. 2021;39:462–71.PubMed 

    Google Scholar 
    94.Tripathi A, Vázquez-Baeza Y, Gauglitz JM, Wang M, Dührkop K, Nothias-Esposito M, et al. Chemically informed analyses of metabolomics mass spectrometry data with Qemistree. Nat Chem Biol. 2021;17:146–51.CAS 
    PubMed 

    Google Scholar 
    95.Hehemann J-H, Correc G, Barbeyron T, Helbert W, Czjzek M, Michel G. Transfer of carbohydrate-active enzymes from marine bacteria to Japanese gut microbiota. Nature. 2010;464:908–12.CAS 
    PubMed 

    Google Scholar 
    96.Pudlo NA, Pereira GV, Parnami J, Cid M, Markert S, Tingley JP, et al. Extensive transfer of genes for edible seaweed digestion from marine to human gut bacteria. bioRxiv. 2020. https://doi.org/10.1101/2020.06.09.142968.97.Scheline RR Metabolism of higher terpenoids. CRC Handbook of Mammalian Metabolism of Plant Compounds. CRC Press; 1991. pp 197–241.98.Saha JR, Butler VP Jr, Neu HC, Lindenbaum J. Digoxin-inactivating bacteria: identification in human gut flora. Science. 1983;220:325–7.CAS 
    PubMed 

    Google Scholar 
    99.Koppel N, Bisanz JE, Pandelia M-E, Turnbaugh PJ, Balskus EP. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. Elife. 2018;7:e33953.PubMed 
    PubMed Central 

    Google Scholar 
    100.Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19:29–41.CAS 
    PubMed 

    Google Scholar 
    101.Ridlon JM, Kang DJ, Hylemon PB, Bajaj JS. Bile acids and the gut microbiome. Curr Opin Gastroenterol. 2014;30:332–8.PubMed 
    PubMed Central 

    Google Scholar 
    102.Begley M, Gahan CGM, Hill C. The interaction between bacteria and bile. FEMS Microbiol Rev. 2005;29:625–51.CAS 
    PubMed 

    Google Scholar 
    103.Lee M-T, Le HH, Johnson EL. Dietary sphinganine is selectively assimilated by members of the mammalian gut microbiome. J Lipid Res. 2021;62:100034.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    104.Johnson EL, Heaver SL, Waters JL, Kim BI, Bretin A, Goodman AL, et al. Sphingolipids produced by gut bacteria enter host metabolic pathways impacting ceramide levels. Nat Commun. 2020;11:2471.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Towards a unified understanding of human–nature interactions

    1.Gaston, K. J. et al. Personalised ecology. Trends Ecol. Evol. 33, 916–925 (2018).
    Google Scholar 
    2.Soga, M. & Gaston, K. J. The ecology of human–nature interactions. Proc. R. Soc. B 287, 20191882 (2020).
    Google Scholar 
    3.Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14, 20180082 (2018).
    Google Scholar 
    4.Mace, G. M. Whose conservation? Science 345, 1558–1560 (2014).CAS 

    Google Scholar 
    5.Soga, M. & Gaston, K. J. Extinction of experience: the loss of human–nature interactions. Front. Ecol. Environ. 14, 94–101 (2016).
    Google Scholar 
    6.Hartig, T., Mitchell, R., De Vries, S. & Frumkin, H. Nature and health. Annu. Rev. Public Health 35, 207–228 (2014).
    Google Scholar 
    7.Chippaux, J. P. Incidence and mortality due to snakebite in the Americas. PLoS Negl. Trop. Dis. 11, e0005662 (2017).
    Google Scholar 
    8.Markevych, I. et al. Exploring pathways linking greenspace to health: theoretical and methodological guidance. Environ. Res. 158, 301–317 (2017).CAS 

    Google Scholar 
    9.Bratman, G. N. et al. Nature and mental health: an ecosystem service perspective. Sci. Adv. 5, eaax0903 (2019).
    Google Scholar 
    10.Marselle, M. R. et al. Pathways linking biodiversity to human health: a conceptual framework. Environ. Int. 150, 106420 (2021).CAS 

    Google Scholar 
    11.Hanski, I. et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc. Natl Acad. Sci. USA 109, 8334–8339 (2012).CAS 

    Google Scholar 
    12.Rook, G. A. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc. Natl Acad. Sci. USA 110, 18360–18367 (2013).CAS 

    Google Scholar 
    13.Tzoulas, K. et al. Promoting ecosystem and human health in urban areas using Green Infrastructure: a literature review. Landsc. Urban Plann. 81, 167–178 (2007).
    Google Scholar 
    14.Balmford, A. et al. A global perspective on trends in nature-based tourism. PLoS Biol. 7, e1000144 (2009).
    Google Scholar 
    15.Nisbet, E. K., Zelenski, J. M. & Murphy, S. A. The nature relatedness scale: linking individuals’ connection with nature to environmental concern and behavior. Environ. Behav. 41, 715–740 (2009).
    Google Scholar 
    16.Chawla, L. Childhood nature connection and constructive hope: a review of research on connecting with nature and coping with environmental loss. People Nat. 2, 619–642 (2020).
    Google Scholar 
    17.Shanahan, D. F. et al. Nature-based interventions for improving health and wellbeing: the purpose, the people and the outcomes. Sports 7, 141 (2019).
    Google Scholar 
    18.Chapman, B. K. & McPhee, D. Global shark attack hotspots: identifying underlying factors behind increased unprovoked shark bite incidence. Ocean Coast. Manag. 133, 72–84 (2016).
    Google Scholar 
    19.Penteriani, V. et al. Human behaviour can trigger large carnivore attacks in developed countries. Sci. Rep. 6, 20552 (2016).CAS 

    Google Scholar 
    20.Ives, C. D. et al. Reconnecting with nature for sustainability. Sustain. Sci. 13, 1389–1397 (2018).
    Google Scholar 
    21.Cox, D. T. C. & Gaston, K. J. Human-nature interactions and the consequences and drivers of provisioning wildlife. Phil. Trans. R. Soc. B 373, 20170092 (2018).
    Google Scholar 
    22.Michie, S., Van Stralen, M. M. & West, R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement. Sci. 6, 42 (2011).
    Google Scholar 
    23.Soga, M., Evans, M. J., Cox, D. T. & Gaston, K. J. Impacts of the COVID‐19 pandemic on human–nature interactions: pathways, evidence and implications. People Nat. 3, 518–527 (2021).
    Google Scholar 
    24.Shaw, L. M., Chamberlain, D. & Evans, M. The house sparrow Passer domesticus in urban areas: reviewing a possible link between post-decline distribution and human socioeconomic status. J. Ornith. 149, 293–299 (2008).
    Google Scholar 
    25.Gaston, K. J. & Evans, K. L. Birds and people in Europe. Proc. R. Soc. B 271, 1649–1655 (2004).
    Google Scholar 
    26.Soga, M. & Gaston, K. J. Shifting baseline syndrome: causes, consequences, and implications. Front. Ecol. Environ. 16, 222–230 (2018).
    Google Scholar 
    27.Pauly, D. Anecdotes and the shifting baseline syndrome of fisheries. Trends Ecol. Evol. 10, 430 (1995).CAS 

    Google Scholar 
    28.Kellert, S. R. & Wilson, E. O. The Biophilia Hypothesis (Island, 1993).29.Balling, J. D. & Falk, J. H. Development of visual preference for natural environments. Environ. Behav. 14, 5–28 (1982).
    Google Scholar 
    30.Ulrich, R. S. in The Biophilia Hypothesis (eds Kelbert, S. R. & Wilson, E. O.) 73–137 (Island, 1993).31.Fukano, Y. & Soga, M. Why do so many modern people hate insects? The urbanization-disgust hypothesis. Sci. Total Environ. 777, 146229 (2021).CAS 

    Google Scholar 
    32.Pergams, O. R. & Zaradic, P. A. Is love of nature in the US becoming love of electronic media? 16-year downtrend in national park visits explained by watching movies, playing video games, internet use, and oil prices. J. Environ. Manag. 80, 387–393 (2006).
    Google Scholar 
    33.Kesebir, S. & Kesebir, P. A growing disconnection from nature is evident in cultural products. Perspect. Psychol. Sci. 12, 258–269 (2017).
    Google Scholar 
    34.Soga, M. et al. How can we mitigate against increasing biophobia among children during the extinction of experience? Biol. Conserv. 242, 108420 (2020).
    Google Scholar 
    35.Soga, M., Yamanoi, T., Tsuchiya, K., Koyanagi, T. F. & Kanai, T. What are the drivers of and barriers to children’s direct experiences of nature? Landsc. Urban Plann. 180, 114–120 (2018).
    Google Scholar 
    36.Pett, T. J., Shwartz, A., Irvine, K. N., Dallimer, M. & Davies, Z. G. Unpacking the people–biodiversity paradox: a conceptual framework. BioScience 66, 576–583 (2016).
    Google Scholar 
    37.Balding, M. & Williams, K. J. Plant blindness and the implications for plant conservation. Conserv. Biol. 30, 1192–1199 (2016).
    Google Scholar 
    38.Gerl, T., Randler, C. & Neuhaus, B. J. Vertebrate species knowledge: an important skill is threatened by extinction. Int. J. Sci. Educ. 43, 928–948 (2021).
    Google Scholar 
    39.Cheng, J. C. H. & Monroe, M. C. Connection to nature: children’s affective attitude toward nature. Environ. Behav. 44, 31–49 (2012).
    Google Scholar 
    40.Pyle, R. M. The Thunder Tree: Lessons from an Urban Wildland (Houghton Mifflin, 1993).41.Wells, N. M. & Lekies, K. S. Nature and the life course: pathways from childhood nature experiences to adult environmentalism. Child. Youth Environ 16, 41663 (2006).
    Google Scholar 
    42.Wilson, E. O. in The Biophilia Hypothesis (Island, 1993).43.Nisbet, E. K., Zelenski, J. M. & Murphy, S. A. Happiness is in our nature: exploring nature relatedness as a contributor to subjective well-being. J. Happiness Stud. 12, 303–322 (2011).
    Google Scholar 
    44.Lin, B. B. et al. How green is your garden? Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landsc. Urban Plann. 157, 239–246 (2017).
    Google Scholar 
    45.Uitto, A., Juuti, K., Lavonen, J. & Meisalo, V. Students’ interest in biology and their out-of-school experiences. J. Biol. Educ. 40, 124–129 (2006).
    Google Scholar 
    46.Pretty, J. et al. Green exercise in the UK countryside: effects on health and psychological well-being, and implications for policy and planning. J. Environ. Plann. Manag. 50, 211–231 (2007).
    Google Scholar 
    47.Strachan, D. P. Family size, infection and atopy: the first decade of the ‘hygiene hypothesis’. Thorax 55, S2–S10 (2000).
    Google Scholar 
    48.Mills, J. G. et al. Urban habitat restoration provides a human health benefit through microbiome rewilding: the Microbiome Rewilding Hypothesis. Restor. Ecol. 25, 866–872 (2017).
    Google Scholar 
    49.Ulrich, R. S. et al. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 11, 201–230 (1991).
    Google Scholar 
    50.Kaplan, R. & Kaplan, S. The Experience of Nature: A Psychological Perspective (Cambridge Univ. Press, 1989).51.Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H. & Gaston, K. J. Psychological benefits of greenspace increase with biodiversity. Biol. Lett. 3, 390–394 (2007).
    Google Scholar 
    52.Kuo, F. E. Nature-deficit disorder: evidence, dosage, and treatment. J. Policy Res. Tour. Leis. Events 5, 172–186 (2013).
    Google Scholar 
    53.Louv, R. Last Child in the Woods (Algonquin Books, 2005).54.Mygind, L. et al. Mental, physical and social health benefits of immersive nature-experience for children and adolescents: a systematic review and quality assessment of the evidence. Health Place 58, 102136 (2019).
    Google Scholar 
    55.Nyhus, P. J. Human–wildlife conflict and coexistence. Annu. Rev. Environ. Res. 41, 143–171 (2016).
    Google Scholar 
    56.von Döhren, P. & Haase, D. Ecosystem disservices research: a review of the state of the art with a focus on cities. Ecol. Indic. 52, 490–497 (2015).
    Google Scholar 
    57.Geffroy, B., Samia, D. S., Bessa, E. & Blumstein, D. T. How nature-based tourism might increase prey vulnerability to predators. Trends Ecol. Evol. 30, 755–765 (2015).
    Google Scholar 
    58.Richardson, M. et al. The green care code: how nature connectedness and simple activities help explain pro‐nature conservation behaviours. People Nat. 2, 821–839 (2020).
    Google Scholar 
    59.Van der Wal, A. J., Schade, H. M., Krabbendam, L. & Van Vugt, M. Do natural landscapes reduce future discounting in humans? Proc. R. Soc. B 280, 20132295 (2013).
    Google Scholar 
    60.Zelenski, J. M., Dopko, R. L. & Capaldi, C. A. Cooperation is in our nature: nature exposure may promote cooperative and environmentally sustainable behavior. J. Environ. Psychol. 42, 24–31 (2015).
    Google Scholar 
    61.Barua, M., Bhagwat, S. A. & Jadhav, S. The hidden dimensions of human-wildlife conflict: health impacts, opportunity and transaction costs. Biol. Conserv. 157, 309–316 (2013).
    Google Scholar  More

  • in

    Carbon assimilating fungi from surface ocean to subseafloor revealed by coupled phylogenetic and stable isotope analysis

    1.Doney S, Abbott MR, Cullen JJ, Karl DM, Rothstein L. From genes to ecosystems: the ocean’s new frontier. Ecol Environ. 2004;2:457–66.
    Google Scholar 
    2.Field CB, Behrenfeld MJ, Randerson JT, Falkowski P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science. 1998;281:237–40.CAS 
    PubMed 

    Google Scholar 
    3.Eppley RW, Petersen BJ. Particulate organic matter flux and planktonic new production in the deep ocean. Nature. 1979;282:677–80.
    Google Scholar 
    4.Ducklow H, Steinberg DK, Buessler KO. Upper ocean carbon export and the biological pump. Oceanography. 2001;14:56–58.
    Google Scholar 
    5.Carlson C, Ducklow H. Dissolved organic carbon in the upper ocean of the central equatorial Pacific Ocean, 1992: Daily and finescale vertical variations. Deep Sea Res II. 1995;42:639–56.CAS 

    Google Scholar 
    6.Cho BC, Azam F. Major role of bacteria in biogeochemical fluxes in the ocean’s interior. Nature. 1988;332:441–3.CAS 

    Google Scholar 
    7.Duarte CM, Cebrian J. The fate of marine autotrophic production. Limnol Oceanogr. 1996;41:1758–66.CAS 

    Google Scholar 
    8.Ducklow H. The bacterial component of the oceanic euphotic zone. FEMS Microbiol Ecol. 1999;30:1–30.CAS 

    Google Scholar 
    9.Herndl GJ, Reinthaler T. Microbial control of the dark end of the biological pump. Nat Geosci. 2013;6:718–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Worden AZ, Follows MJ, Giovannoni SJ, Wilken S, Zimmerman AE, Keeling PJ. Environmental science. Rethinking the marine carbon cycle: factoring in the multifarious lifestyles of microbes. Science. 2015;347:1257594.PubMed 

    Google Scholar 
    11.Grossart HP, Rojas-Jimenez K. Aquatic fungi: targeting the forgotten in microbial ecology. Curr Opin Microbiol. 2016;31:140–5.PubMed 

    Google Scholar 
    12.Richards TA, Jones MD, Leonard G, Bass D. Marine fungi: their ecology and molecular diversity. Ann Rev Mar Sci. 2012;4:495–522.PubMed 

    Google Scholar 
    13.Burgaud G, Arzur D, Durand L, Cambon-Bonavita MA, Barbier G. Marine culturable yeasts in deep-sea hydrothermal vents: species richness and association with fauna. FEMS Microbiol Ecol. 2010;73:121–33.CAS 
    PubMed 

    Google Scholar 
    14.Burgaud G, Le Calvez T, Arzur D, Vandenkoornhuyse P, Barbier G. Diversity of culturable marine filamentous fungi from deep-sea hydrothermal vents. Environ Microbiol. 2009;11:1588–1600.PubMed 

    Google Scholar 
    15.Redou V, Navarri M, Meslet-Cladiere L, Barbier G, Burgaud G. Species richness and adaptation of marine fungi from deep-subseafloor sediments. Appl Environ Microbiol. 2015;81:3571–83.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Hyde KD, Jones EBG, Leao E, Pointing SB, Poonyth AD, Vrjmoed LLP. Role of fungi in marine ecosystems. Biodivers Conserv. 1998;7:1147–61.
    Google Scholar 
    17.Jones EB. Marine fungi: some factors influencing biodiversity. Fungal Diversity. 2000;4:53–73.
    Google Scholar 
    18.Priest T, Fuchs B, Amann R, Reich M. Diversity and biomass dynamics of unicellular marine fungi during a spring phytoplankton bloom. Environ Microbiol. 2021;23:448–63.CAS 
    PubMed 

    Google Scholar 
    19.Gutierrez MH, Jara AM, Pantoja S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ Microbiol. 2016;18:1646–53.PubMed 

    Google Scholar 
    20.Gutierrez MH, Pantoja S, Tejos E. The role of fungi in processing marine organic matter in the upwelling ecosystem off Chile. Mar Biol. 2011;158:205–19.
    Google Scholar 
    21.Bochdansky AB, Clouse MA, Herdl GJ. Eukaryotic microbes, principally fungi and labyrinthulomycetes, dominate biomass on bathypelagic marine snow. ISME J. 2017;11:362–73.PubMed 

    Google Scholar 
    22.Becker S, Tebben J, Coffinet S, Wittshire K, Iversen MH, Harder T, et al. Laminarin is a major molecule in the marine carbon cycle. Proc Natl Acad Sci USA. 2020;117:6599–607.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat Microbiol. 2017;2:17065.CAS 
    PubMed 

    Google Scholar 
    24.Hassett BT, Gradinger R. Chytrids dominate arctic fungal communities. Environ Microbiol. 2016;18:2001–9.CAS 
    PubMed 

    Google Scholar 
    25.Lavik G, Stuhrmann T, Bruchert V, Van der Plas A, Mohrholz V, Lam P, et al. Detoxification of sulphidic African shelf waters by blooming chemolithotrophs. Nature. 2009;457:581–4.CAS 
    PubMed 

    Google Scholar 
    26.Ortega-Arbulu AS, Pichler M, Vuillemin A, Orsi WD. Effects of organic matter and low oxygen on the mycobenthos in a coastal lagoon. Environ Microbiol 2019;21:374–88.CAS 
    PubMed 

    Google Scholar 
    27.Orsi WD, Morard R, Vuillemin A, Eitel M, Wörheide G, Milucka J, et al. Anaerobic metabolism of Foraminifera thriving below the seafloor. ISME J. 2020;14:2580–94.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Orsi WD, Vuillemin A, Rodriguez P, Coskun OK, Gomez-Saez GV, Lavik G, et al. Metabolic activity analyses demonstrate that Lokiarchaeon exhibits homoacetogenesis in sulfidic marine sediments. Nat Microbiol. 2020;5:248–55.CAS 
    PubMed 

    Google Scholar 
    29.Dittmar T, Koch B, Hertkorn N, Kattner G. A simple and efficient method for the solid-phase extraction of dissolved organic matter (SPE-DOM) from seawater. Limnology and Oceanography. Methods. 2008;6:230–5.CAS 

    Google Scholar 
    30.Green NW, Perdue EM, Aiken GR, Butler KD, Chen H, Dittmar T, et al. An intercomparison of three methods for the large-scale isolation of oceanic dissolved organic matter. Mar Chem. 2014;161:14–19.CAS 

    Google Scholar 
    31.Riedel T, Dittmar T. A method detection limit for the analysis of natural organic matter via Fourier transform ion cyclotron resonance mass spectrometry. Anal Chem. 2014;86:8376–82.CAS 
    PubMed 

    Google Scholar 
    32.Merder J, Freund JA, Feudel U, Hansen CT, Hawkes JA, Jacob B, et al. ICBM-OCEAN: processing ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Anal Chem. 2020;92:6832–8.CAS 
    PubMed 

    Google Scholar 
    33.Koch BP, Dittmar T. From mass to structure: an aromaticity index for high resolution mass data of natural organic matter. Rapid Commun Mass Spectrom. 2006;20:926–32.CAS 

    Google Scholar 
    34.Koch BP, Dittmar T. Erratum: from mass to structure: an aromaticity index for high resolution mass data of natural organic matter. Rapid Commun Mass Spectrom. 2016;20:250–250.
    Google Scholar 
    35.Oksanen J, Blanchen FG, Friendly M, Kindt R, Legendre R, McGlinn D, et al. Vegan: community ecology package. R package version 2 4-3 2017. (https://CRAN.R-project.org/package=vegan). Accessed June 2020.36.Hansen CT, Niggemann J, Giebel HA, Simon M, Bach W, Dittmar T. Biodegradability of hydrothermally altered deep-sea dissolved organic matter. Mar Chem. 2019;217. https://doi.org/10.1016/j.marchem.2019.103706.37.Orsi WD, Smith JM, Liu S, Liu Z, Sakamoto CM, Wilken S, et al. Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean. ISME J. 2016;10:2158–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and rusts. Mol Ecol. 1993;2:113–8.CAS 
    PubMed 

    Google Scholar 
    39.White TJ, Bruns S, Lee S, Taylor J “Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics”. In: M Innis, D Gelfand, K Sninsky, T White, editors. PCR Protocols: a guide to methods and applications. Academic Pres, New York, NY; 1990. pp. 315–22.40.Tedersoo L, Lindahl B. Fungal identification biases in microbiome projects. Environ Microbiol Rep. 2016;8:774–9.PubMed 

    Google Scholar 
    41.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
    PubMed 

    Google Scholar 
    42.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2019;47:D259–D264.CAS 
    PubMed 

    Google Scholar 
    43.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Coskun OK, Pichler M, Vargas S, Gilder S, Orsi WD. Linking uncultivated microbial populations and benthic carbon turnover by using quantitative stable isotope probing. Appl Environ Microbiol. 2018;84:e01083–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Chemidlin Prevost-Boure N, Christen R, Dequiedt S, Mougel C, Lellevre M, Jolivet C, et al. Validation and application of a PCR primer set to quantify fungal communities in the soil environment by real-time quantitative PCR. PLoS One. 2011;6:e24166.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    46.Banos S, Lentendu G, Kopf A, Wubet T, Glockner FO, Reich M. A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms. BMC Microbiol. 2018;18:190.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.CAS 
    PubMed 

    Google Scholar 
    48.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.CAS 
    PubMed 

    Google Scholar 
    49.Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The marine microbial eukaryote transcriptome sequencing project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.PubMed 
    PubMed Central 

    Google Scholar 
    50.Tatusov RL, Koonin EV, Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631–7.CAS 
    PubMed 

    Google Scholar 
    51.Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Guindon S, Dufayard JF, Lefort V, Anisimova M, Hordijk W, Gascuel O, et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol. 2010;59:307–21.CAS 
    PubMed 

    Google Scholar 
    53.Gouy M, Guindon S, Gascuel O. SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol Biol Evol. 2010;27:221–4.CAS 
    PubMed 

    Google Scholar 
    54.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–495.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Tamames J, Puente-Sanchez F. SqueezeMeta, a highly portable, fully automatic metagenomic analysis pipeline. Front Microbiol. 2018;9:3349.PubMed 

    Google Scholar 
    56.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.CAS 
    PubMed 

    Google Scholar 
    58.Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.CAS 
    PubMed 

    Google Scholar 
    59.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–596.CAS 
    PubMed 

    Google Scholar 
    61.Edgar RC. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinforma. 2004;5:1–19.
    Google Scholar 
    62.Guillard RRL, Hargraves PE. Stichochrysis immobilis is a diatom, not a chrysophyte. Phycologia. 1993;32:234–6.
    Google Scholar 
    63.Inthorn M, Wagner T, Scheeder G, Zabel M. Lateral transport controls distribution, quality and burial of organic matter along continental slopes in high-productivity areas. Geology. 2006;34:205–8.CAS 

    Google Scholar 
    64.Hungate BA, Mau RL, Schwartz E, Caporaso JG, Dijkstra P, van Gestel N, et al. Quantitative microbial ecology through stable isotope probing. Appl Environ Microbiol. 2015;81:7570–81.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Igarza M, Dittmar T, Graco M, Niggemann J. Dissolved organic matter cycling in the coastal upwelling system off central Peru during an “El Niño” year. Front Mar Sci. 2019;6:198.
    Google Scholar 
    66.Kuypers MM, Lavik G, Woebken D, Schmid M, Fuchs BM, Amann R, et al. Massive nitrogen loss from the Benguela upwelling system through anaerobic ammonium oxidation. Proc Natl Acad Sci USA. 2005;102:6478–83.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Wright JJ, Konwar KM, Hallam SJ. Microbial ecology of expanding oxygen minimum zones. Nat Rev Microbiol. 2012;10:381–94.CAS 
    PubMed 

    Google Scholar 
    68.Rossel PE, Stubbins A, Hach PF, Dittmar T. Bioavailability and molecular composition of dissolved organic matter from a diffuse hydrothermal system. Mar Chem. 2015;177:257–66.CAS 

    Google Scholar 
    69.Schmidt F, Koch BP, Goldhammer T, Elvert M, Witt M, Lin Y, et al. Unraveling signatures of biogeochemical processes and the depositional setting in the molecular composition of pore water DOM across different marine environments. Geochim Cosmochim Acta. 2017;207:57–80.CAS 

    Google Scholar 
    70.Gruninger RJ, Puniya AK, Callaghan TM, Edwards JE, Youssef N, Dagar SS, et al. Anaerobic fungi (phylum Neocallimastigomycota): advances in understanding their taxonomy, life cycle, ecology, role and biotechnological potential. FEMS Microbiol Ecol. 2014;90:1–17.CAS 
    PubMed 

    Google Scholar 
    71.Jones MD, Richards TA, Hawksworth DL, Bass D. Validation and justification of the phylum name Cryptomycota phyl. nov. IMA Fungus. 2011;2:173–5.PubMed 
    PubMed Central 

    Google Scholar 
    72.Spatafora JW, Chang Y, Benny GL, Lazarus K, Smith ME, Berbee ML, et al. A phylum-level phylogenetic classification of zygomycete fungi based on genome-scale data. Mycologia. 2016;108:1028–46.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    73.Morand SC, Bertignac M, Iltis A, Kolder ICRM, Pirovano W, Jourdain R, et al. Complete genome sequence of Malassezia restricta CBS 7877, an opportunist pathogen involved in dandruff and seborrheic dermatitis. Microbiol Resour Announc. 2019;8:e01543–18.PubMed 
    PubMed Central 

    Google Scholar 
    74.Buckley DH, Huangyutitham V, Hsu SF, Nelson TA. Stable isotope probing with 15N achieved by disentangling the effects of genome G+C content and isotope enrichment on DNA density. Appl Environ Microbiol. 2007;73:3189–95.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    75.Tedersoo L, Sanchez-Ramirez S, Kõljalg U, Bahram M, Döring M, Schigel D, et al. High-level classification of the Fungi and a tool for evolutionary ecological analyses. Fungal Diversity. 2018;90:135–59.
    Google Scholar 
    76.Walsh EA, Kirkpatrick JB, Rutherford SD, Smith DC, Sogin M, D’Hondt S, et al. Bacterial diversity and community composition from seasurface to subseafloor. ISME J. 2016;10:979–89.PubMed 

    Google Scholar 
    77.Karpov SA, Mamkaeva MA, Aleoshin VV, Nassonova E, Lilje O, Gleason FH. Morphology, phylogeny, and ecology of the aphelids (Aphelidea, Opisthokonta) and proposal for the new superphylum Opisthosporidia. Front Microbiol. 2014;5:112.PubMed 
    PubMed Central 

    Google Scholar 
    78.Jones MD, Forn I, Gadelha C, Egan MJ, Bass D, Massana R, et al. Discovery of novel intermediate forms redefines the fungal tree of life. Nature. 2011;474:200–3.CAS 
    PubMed 

    Google Scholar 
    79.Chang Y, Wang S, Sekimoto S, Aerts AL, Choi C, Clum A, et al. Phylogenomic analyses indicate that early Fungi evolved digesting cell walls of algal ancestors of land plants. Genome Biol Evol. 2015;7:1590–601.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Loron CC, Francois C, Rainbird RH, Turner EC, Borensztajn S, Javaux EJ. Early fungi from the Proterozoic era in Arctic Canada. Nature. 2019;570:232–5.CAS 
    PubMed 

    Google Scholar 
    81.Lyons TW, Reinhard CT, Planavsky NJ. The rise of oxygen in Earth’s early ocean and atmosphere. Nature. 2014;506:307–15.CAS 
    PubMed 

    Google Scholar 
    82.Passow U. Production of transparent exopolymer particles (TEP) by phyto- and bacterioplankton. Mar Ecol Prog Ser. 2002;236:1–12.
    Google Scholar 
    83.Takahashi E, Ledauphin J, Goux D, Orvain F. Optimising extraction of extracellular polymeric substances (EPS) from benthic diatoms: comparison of the efficiency of six EPS extraction methods. Mar Freshw Res. 2009;60:1201–10.CAS 

    Google Scholar 
    84.de Brouwer JFC, Wolfstein K, Stal J. Physical characterization and diel dynamics of different fractions of extracellular polysaccharides in an axenic culture of a benthic diatom. Eur J Phycol. 2002;37:37–44.
    Google Scholar 
    85.Bass D, Howe A, Brown N, Barton H, Demidova M, Michelle H, et al. Yeast forms dominate fungal diversity in the deep oceans. Proc R Soc B. 2007;274:3069–77.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    86.Amend A. From dandruff to deep-sea vents: Malassezia-like fungi are ecologically hyper-diverse. PLoS Pathog. 2014;10:e1004277.PubMed 
    PubMed Central 

    Google Scholar 
    87.Meeboon J, Takamatsu S. Microidium phyllanthi-reticulati sp. nov. on Phyllanthus reticulatus. Mycotaxon. 2017;132:289–97.
    Google Scholar 
    88.Lueders T, Wagner B, Claus P, Friedrich MW. Stable isotope probing of rRNA and DNA reveals a dynamic methylotroph community and trophic interactions with fungi and protozoa in oxic rice field soil. Environ Microbiol. 2004;6:60–72.CAS 
    PubMed 

    Google Scholar 
    89.Kjeldsen KU, Schreiber L, Thorup CA, Boesen T, Bjerg JT, Yang T, et al. On the evolution and physiology of cable bacteria. Proc Natl Acad Sci USA. 2019;116:19116–25.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Dyksma S, Bischof K, Fuchs BM, Hoffmann K, Meier D, Meyerdierks A, et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J. 2016;10:1939–53.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Middelburg JJ. Chemoautotrophy in the ocean. Geophys Res Let. 2011;38:94–97.
    Google Scholar 
    92.Starzynska-Janiszewska A, Dulinski R, Stodolak B. Fermentation with edible Rhizopus strains to enhance the bioactive potential of hull-less pumpkin oil cake. Molecules. 2020;25:5782.CAS 
    PubMed Central 

    Google Scholar 
    93.Dubovenko AG, Dunaevsky YE, Belozersky MA, Oppert B, Lord JC, Elpidina EN. Trypsin-like proteins of the fungi as possible markers of pathogenicity. Fungal Biol. 2010;114:151–9.CAS 
    PubMed 

    Google Scholar 
    94.Arnosti C, Wietz M, Brinkhoff T, Hehemann JH, Probandt D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2021;13:81–108.CAS 
    PubMed 

    Google Scholar 
    95.Rossel PE, Bienhold C, Hehemann JH, Dittmar T, Boetius A. Molecular composition of dissolved organic matter in sediment porewater of the arctic deep-sea observatory HAUSGARTEN (Fram Strait). Front Mar Sci. 2020;7:428.
    Google Scholar 
    96.Fenchel T, Finlay BJ. Ecology and evolution in anoxic worlds. In: RM May, PH Harvey, editors. Oxford Series in Ecology and Evolution. Oxford University Press, Oxford; 1–288, 1995.97.Lynd LR, Weimer PJ, van Zyl WH, Pretorius IS. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol Mol Biol Rev. 2002;66:506–77.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More