Ecology

Subjects and facilityWe observed two groups of Atlantic bottlenose dolphins (six different individuals in total) housed at the marine zoo “Marineland Mallorca”. One of the groups was composed of four individuals (G1) and the other was constituted by five individuals (G2). The two adult males and one of the females were the same in both groups (Table 1). Group composition changed due to the transfer of individuals to another pool of the zoo and due to the arrival of new individuals from another aquatic park.Table 1 Age, sex, group, and identification number in the network of the subject dolphins. M male, F female.Full size tableThe dolphins were kept in three outdoor interconnecting pools: the main performance pool (1.6 million liters of water), a medical pool (37.8 thousand liters of water) and a small pool (636.8 thousand liters of water). During the observational periods, the dolphins had free access to all the pools. Underwater viewing at the main and the small pool was available through the transparent walls around the rim of the pools.Ethics statementThis study was approved by the UIB Committee of Research Ethics and Marineland Mallorca. This research was conducted in compliance with the standards of the European Association of Zoos and Aquaria (EAZA). All subjects tested in this study were housed in Marineland Mallorca following the Directive 1999/22/EC on the keeping of animals in zoos. This study was strictly non-invasive and did not affect the welfare of dolphins.Behavioral observations and data collectionBehavioral data were collected in situ by APM from May to November 2016 for G1 and from November 2017 to February 2018 for G2. All observational periods were also recorded using two waterproof cameras SJCAM SJ4000. Observations were conducted at the main pool between 8:00 a.m. and 11:00 a.m. Due to the schedules and dynamics of the zoo, we were unable to collect data outside this period. Dolphin social behavior was registered and videotaped for 30 min–2 h each day. Only data from sessions that lasted at least 30 min were included in the analysis. We did not collect any data during training or medical procedures and resumed the observational session a few minutes after the end of these events.We recorded all occurrences of affiliative and aggressive interactions, the identities of the involved individuals and the identity of the dolphin initiating the contact. Aggressive contacts were defined by the occurrence of chasing, biting, and hitting, as established in previous studies37,38,39,40,41. Affiliative contacts were defined as contact swimming, synchronous breathing and swimming (at least 30″ of continuous swimming) or flipper-rubbing, as established in previous studies37,39,40,41,43.To assess the strength of the affiliative bonds in both groups, we calculated the index of affiliative relationships (IA) between dolphins following the procedure described in Yamamoto et al. For calculating the IA we recorded the relative frequencies of synchronous swimming since it is a well-defined affiliative behavior in dolphins. Data of synchronous swimming were recorded using group 0–1 sampling44 at 3-min intervals. This method consists of the observation of individuals during short periods and the recording of the occurrence (assigning to that period a 1) or non-occurrence (assigning to that period a 0) of a well-defined behavior44. For calculating the IA for each couple, the number of sampling periods in which synchronous swimming between individuals A and B occurred (XAB) was divided by the number of sampling periods in which individuals A and B were observed (YAB): (IA=frac{{X}_{AB}}{{Y}_{AB}})39,45. Therefore, the IA reflects the level of affiliation for each dolphin dyad based on the pattern of synchronous swimming. This index served to construct the general affiliative social networks of both groups of dolphins.Temporal network constructionTemporal networks can provide insight into social events such as conflicts and post-conflict interactions in which the order of interactions and the timing is crucial. Furthermore, they allow us to calculate the probabilities of the different affiliative and aggressive interactions occurring in the group.We used behavioral observations to construct temporal networks for each group. Each dolphin was treated as a node (N) with their aggressive and affiliative interactions supplying the network links. We divided the daily observations into periods of 3 min. In each period, we assigned a positive (+ 1), negative (− 1) or neutral (0) interaction to each pair of dolphins. That is, if during the period a pair of dolphins displayed affiliative interactions, we assigned a + 1 to the link between that pair of nodes, if they were involved in a conflict, we assigned a − 1, and if the pair did not engage in any interaction, we assigned to that link a 0. If during the same period, the pair displayed both aggressive and affiliative interactions we considered the last observed interaction. Therefore, we obtained an adjacency matrix (an N × N matrix describing the links in the network) for each group of dolphins. Thus, for each day we had a series of different signed networks of the group, each network representing a 3-min period.Social network analysis: time-aggregated networks and network motifsWe collapsed the temporal networks of each day in time-aggregated networks. This procedure consists in aggregating the data collected over time within specific intervals to create weighted networks. The sign and the weight of the links characterize these networks, indicating the valence and duration of the interaction respectively. Thus, they are static representations of the social structure of the group of dolphins. To obtain these time-aggregated networks we proceeded as follows:First, for each day we aggregated the values of each interaction of the temporal networks until one link qualitatively changed. We considered a qualitative change if one interaction passed from being negative (− 1) to positive (+ 1) meaning that the pair of dolphins reconciled after the conflict or vice versa, or if a new affiliation (+ 1) or aggression (− 1) took place, that is the link changed from being neutral (0) to positive or negative. If a link changed from being negative or positive to being neutral, we did not consider that this interaction has changed qualitatively. For example, if dolphins interacted positively during two periods of time, then they ceased to interact (neutral) and finally they engaged in an aggressive interaction, the total weight of the interaction in the resulting time-aggregated network would be of + 2. Therefore, a conflict or an affiliation may extend over multiple periods containing several contacts, and is considered finished when the interaction changes its valence. In this way, we obtained a series of time-aggregated networks for each day, which retain the information on the duration, timing, and ordering of the affiliative and aggressive events in the group.We examined the local-scale structure of the affiliative-aggressive social networks using motif analysis. Thus, for each group, we analyzed the network motif representation of the temporal and time-aggregated networks, identifying and recording the number of occurrences of each motif.Model of affiliative and aggressive interactionsWe built two models (a simple and a complex one) that aim to simulate the dynamics of aggressive and affiliative interactions of a group of four dolphins. These models were created using the observed probabilities of each affiliative or aggressive interaction between individuals in group G1. We only used the data of G1 since we had more hours of video recordings and, thus, more statistics of the pattern of dolphins’ interactions. Both models return affiliative/aggressive temporal networks constituted by four nodes and different aggressive, affiliative, or neutral interactions between the six possible pairs of individuals in the network. We simulated data for 20 periods of 3 min per day for a total of 80 days to mimic the empirical data time structure. We obtained one temporal network for each period (1600 temporal networks in total) and ran 100 realizations of each model.Our models work as follows: At the beginning of the simulations, all the interactions between the four nodes are neutral (0). In each period, we select a pair of nodes randomly and assign to that link a positive (+ 1) or a negative (− 1) interaction with probability p (calculated previously for each type of interaction). These interactions correspond to spontaneous aggressions and affiliations. In the complex model, if in the previous period a conflict took place, before assessing spontaneous interactions we first evaluated the different possible post-conflict contacts that could occur (reconciliation, new aggressions, and affiliations). Therefore, for reconciliations, we change the valence of the interaction from negative to positive with a certain probability. Then, we also randomly choose a pair of nodes including one of the former opponents and assign to that link a positive or negative interaction with the observed probabilities to simulate the occurrence of new affiliations (third party-affiliation) or redirected aggressions arising from the previous conflict. We keep on doing this procedure period by period. Lastly, we obtained the time-aggregated networks for the two models.The simpler model only includes the probability of aggression and affiliation between group members, whereas the complex one also includes the patterns of conflict resolution previously observed. In this way, the complex model serves to assess the influence of post-conflict management mechanisms on the observed pattern of aggressive/affiliative networks. That is, the complex model also keeps track of past actions. Thus, depending on the interaction of the previous step, the probability of the following interaction changes based on the observed pattern of conflict resolution strategies.Calculation of the observed probabilities of affiliative and aggressive interactionsFor the simple model, we calculated the probability of general aggression and affiliation per day without distinguishing between types of positive and negative interactions. Thus, we obtained the number of periods in which an aggressive or affiliative contact took place per day and divided it by the total number of periods of that day (probability of general aggression or affiliation per 3-min period). With these probabilities, we calculated the mean probability of general aggression and affiliation per period.For the complex model, we calculated the probabilities of reconciliation, new affiliations/aggressions, and spontaneous affiliations/aggressions per day. That is, the probability that former opponents exchange affiliative contacts after an aggressive encounter (reconciliation), the probabilities that a conflict may promote new affiliations (third-party affiliation) or new conflicts (redirected aggression) between one of the opponents and a bystander in the same day, and the probability of affiliative or aggressive interactions not derived from a previous conflict (spontaneous interactions). To classify affiliations and aggressions in these categories we used the temporal networks, examining the interactions that took place after a conflict between opponents and between them and bystanders. If the opponents reconciled or affiliated with a bystander after a fight, we assumed that the following affiliative or aggressive interactions were spontaneous and were not a consequence of that conflict. Thus, to calculate the number of spontaneous affiliations, we subtracted the number of reconciliations and new affiliations from the total number of affiliations per day. For spontaneous aggressions, we subtracted the number of new aggressions to the total number of aggressions per day. Then, we obtained the probability of spontaneous affiliation and aggression per period.Using the previous probabilities, we obtained the rate (r) of reconciliation, new aggression and new affiliation per minute with the following formula:({p=1-e}^{-rDelta t}). Using the same formula, we finally calculated the probability of reconciliation, new aggression and affiliation per 3-min period used in the complex model (Supplementary Table 1 for details of probabilities calculation).Network-motif analysisWe also carried out a network-motif analysis. As we did not consider the identities or sex of the nodes in these models, we grouped the obtained motifs into equivalent categories considering the pattern of interactions between nodes. We also classified the motifs obtained from the real data of G1 into those equivalent categories. Finally, we compared the pattern of equivalent network motifs of the observed social network of dolphins and the ones of the two models. To do so we calculated the Spearman’s rank correlation coefficient (rs), defined as a nonparametric measure of the statistical dependence between the rankings of two variables: ({r}_{s}=frac{covleft({rg}_{X}{rg}_{Y}right)}{{sigma }_{{rg}_{X}}}{sigma }_{{rg}_{Y}}); rgX and rgY are the rank variables; cov (rgX rgY) is the covariance of the rank variables, and σrgX and σrgY are the standard deviations of the rank variables. Therefore, this coefficient allows us to assess the statistical dependence between the motif ranking of the real data and the one of each model.Computational implementationsAll the models, network construction, visualization and motif analysis were generated and implemented using MATLAB R2018b. More

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat1. Many correlational studies associate spillover with changes in land use or other anthropogenic stressors2,3, although the mechanisms underlying the observed correlations have not been identified4. One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behavior, and viral dynamics. We present 25 years of data on land-use change, bat behavior, and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviors that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of 25 years. Our long-term study identifies the mechanistic connections among habitat loss, climate, and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics. More

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Model validationBased on the time-series validations of water temperature and DO concentration, model accuracy improved gradually, despite several discrepancies at the beginning of the simulation (Supplementary Fig. S1). The model is primarily driven by a set of boundary data, including wind speed, solar radiation, and precipitation data24,25. From this perspective, more high-quality boundary data promotes better numerical reproducibility. However, meteorological data collection was challenging due to the early observation equipment limitations and low observational accuracy compared to current data. The temporal inconsistency of accuracy in observational data has been eliminated to a large extent by fitting a regression curve24. Spatial resolution is the other issue. Possessing spatially constant values for all boundary conditions complicates the numerical reproducibility of variations on finer scales.The relationship between turnovers and the curve shape of water temperature versus DO concentration is theoretically sound27,28. In the last stage of stratification in the lake, water temperature and DO concentration near the bottom are more likely to slightly increase due to thermal diffusion and DO supplies from the upper water. If a turnover occurs, the whole column of water is mixed strongly (Supplementary Fig. S3). Bottom water temperature decreases due to surface water cooling, and DO concentration increases, due to surface water replenishment and increased oxygen solubility. If the turnover fails, only the partial column of water is mixed, causing a delay in the timing of deep-water renewal (Supplementary Fig. S3). However, the upper water in later months, like that in March, has been rapidly warmed, resulting in an increase in the bottom water temperature. For example, in 2007 and 2016, the simulated water temperature and DO concentration fluctuated within a limited range in February and then skyrocketed in March, after mixing with the warmed surface water (blue points in Supplementary Fig. S4). On the other hand, explicit definitions of turnover timing are challenging. The threshold used to judge turnover timing is reliable because the results matched the observation. The turnover timing varied by 36 days in Lake Biwa during the simulation period, which is comparable to that observed in other lakes, such as approximately 21 days in Heiligensee, Germany over a 17-year timespan29, 16 days in Lake Washington over a 40-year timespan30, and 28 days in Blelham Tarn over a 41-year timespan31.Variables affecting the mixing regimeDetermining variables that affect the mixing regime is essential to improve understanding and enable future projections16,17,18. Air temperature, wind speed, cloud cover, precipitation, water density, and lake transparency are all potential variables. We, here, compared the above variables to the turnover timing in Lake Biwa. The meteorological inputs in this study provided data for air temperature, wind speed, cloud cover, and precipitation. Water density and particulate organic carbon (POC) concentration representing lake transparency were the model’s outputs. The annual averages and cold season (November–April) values of the above variables were calculated over the simulation period (Supplementary Fig. S6). Annual averages illustrate general long-term warming trends18, while cold season values particularly determine the timing of turnover17. However, in Lake Biwa, air temperature during the cold season fluctuated greatly compared to the annual averages. A random forest analysis17 has been conducted between the turnover timing and the above two variable sets (cold season values versus annual averages) in Lake Biwa, and the cold season values better explained the turnover timing (35.39% versus 18.48%). The results agree with the conclusion drawn from the previous sensitivity tests, which indicated the relative importance of air temperature and solar radiation during winter based on 40 scenarios32.The importance of variables was estimated based on the random forest analysis using the cold season data (Fig. 4a). Wind speed dominates the timing of turnover, which is consistent with the previous studies17,25. The POC concentration, the difference in water density between the surface and bottom, and cloud cover have moderate effects on the timing of turnover. However, air temperature is less important, which is contrary to the turnover mechanism17,24,32. A re-confirmation was conducted of the relationship between turnover timing and air temperature (Fig. 4b and Supplementary Fig. S7). The cool air generally encourages an early turnover, albeit with several anomalies. The turnover timing between 1976 and 1990 remained constant independent of climate change, and the period coincidently had a substantial nutrient fluctuation (Fig. 3). As a result, it is essential to investigate the nutrient status further.Figure 4Analysis results of the relationship between potential variables and turnover timing: (a) the importance of variables importance using a random forest analysis, and (b) the relationship between the cold season air temperature and the timing of turnover. Variable importance is calculated using the percentage increase in mean square error (MSE) and the increase in node purity. Higher values illustrate the greater importance of the variable. Variables include air temperature (AT), precipitation (pptn.), cloud cover (CC), the difference in density (DD), POC, and wind speed (WS).Full size imageLake nutrient concentrationsBecause phosphorus is the limiting nutrient in Lake Biwa and DIP concentrations can be effectively limited by regulating external loadings as practiced (Fig. 3), DIP concentrations become the focus of this discussion for nutrient status. However, the DIP concentrations disproportionately responded to the external loadings of total phosphorus (TP) in Lake Biwa. Although external TP loading itself fails to determine lake phosphorus concentrations due to the hydrodynamics of lakes33, Lake Biwa exhibited insignificant changes in the inflow rate or the retention time (and see an example of the surface flow in Supplementary Fig. S8). Therefore, it can be assumed that the hydraulic loading remained constant, and the input nutrient concentrations were proportionate to the external nutrient loadings in Lake Biwa. This finding contradicts a recent meta-analysis that highlighted a deterministic relationship between input nutrient concentrations and lake nutrient concentrations, based on steady-state mass balance models6. The possible reason is the dynamics of the lake’s ecosystem22, which have been considered in this study. For example, the surface DIP concentrations were almost nonexistent regardless of the external TP loadings in Lake Biwa, supporting that phosphorus is the limiting nutrient in Lake Biwa34,35. The low DIP concentrations at the surface may be caused by the rapid recycling of phosphorus because the amount of phosphorus available for phytoplankton is easily affected by the feedback mechanism between phytoplankton photosynthesis and the phosphorus released from the water35,36.Hypoxia and strategiesThe variations in DO concentration are the public’s top concern as it relates to hypoxia, a key indicator of water quality. Lake bottom, among all water depths, is more sensitive to small changes in oxygen conditions12. In Lake Biwa, the annual minimum DO concentrations ranged from 2 to 5.5 mg/L over the last 60 years (Supplementary Fig. S9). The decrease in DO concentrations in the early period, typically till the 1980s, was mainly caused by nutrient enrichments (Fig. 3). The nutrient enrichment-induced heavy eutrophication eventually accelerates the rate of DO depletion2. After eutrophication was controlled in the 1980s, climate change became the dominant stressor23. There remains much uncertainty surrounding the relationship between climatic variables-related turnover timing and hypoxia in Lake Biwa12. We, therefore, first investigate the relationship between hypoxia and turnover timing, and then concentrate on nutrients to alleviate hypoxia.Although the relationship between turnover timing and DO concentrations is quite weak (R2 = 0.10), there is a general decrease in DO concentrations with increasing turnover timing (Fig. 5a). On the other hand, a linear relationship has been found between DIP concentrations and DO concentrations, with an R2 of 0.67 (Fig. 5b). The slope of –0.841 μgP/mgDO means an increase in DIP concentrations by approximately 0.841 μgP/L causes a decrease in DO concentrations by 1 mg/L. Note that the simulation results were compared over the whole period, and eutrophication-induced hypoxia differs theoretically from climate-induced hypoxia. Additional testing has been conducted to distinguish the effects of two stressors (eutrophication- and climate-induced hypoxia; Supplementary Fig. S10). Before 1980 when eutrophication progressed, the annual minimum DO concentrations and the DIP concentrations had a stronger linear relationship (R2 = 0.89). Although waste-water treatment has improved conditions in the lake, climate change induced alteration of turnover timing may adversely influence water quality. However, the relationship weakened dramatically with an R2 of 0.10 after 1980, when climate change dominated hypoxia. The lower R2 value indicates that climate-related hypoxia is more complex as concluded previously37,38. The two possibilities are as follows. First, there can be a legacy of hypoxia related to eutrophication. The DO recovery at the bottom of Lake Biwa was complicated by the low DO concentration in 1980 and the delayed timing of turnover; similar phenomena have been observed in the Lake of Zurich22. Second, ecosystem dynamics could help explain the difficulty in predicting hypoxia at the bottom. Phytoplankton fully exploits phosphorus at the surface, as explained above, then the death and sinking of the surface phytoplankton are accompanied by the sedimentation of phosphorus to the bottom as modeled. Bacteria break down the sinking phytoplankton, releasing phosphorus and consuming DO in the process. Additional DO consumption lowers the bottom DO concentration, which in turn encourages phosphorus release from the sediment in a low DO environment22. Such unfavorable feedback between DIP and DO concentrations are strengthened by prolonged stratification and eventually accelerates the development of hypoxia. However, future research is necessary because this numerical model simplified the relationship between water and sediment. The sinking of organic carbon into sediment is integrated in the model, and due to the decomposition of organic carbon in the sediment, nutrients are released into and oxygen is depleted in the water. Despite that, the trends between DO and DIP concentrations stay the same under climate change (Fig. 5b), and thus controlling lake phosphorus is beneficial to the Lake Biwa hypoxia.Figure 5The linear regression results of the relationship: (a) between turnover timing and annual minimum concentration of DO, (b) between the annual minimum concentration of DO and annual average concentration of DIP. The simulation results at the monitoring station were used for analysis.Full size image More

The effects of extreme drought on soil biochemical propertiesAs shown in Fig. 1A, the range of SOC during the early, midterm and late extreme drought experiments, were 73.53–251.44 g kg−1, 54.75–256.16 g kg−1, and 66.37–282.16 g kg−1, respectively. Concomitantly, DOC was 171.85–323.74 mg kg−1, 158.15 – 504.62 mg kg−1, and 166.63–418.43 mg kg−1, MBC was 247.80 – 461.69 mg kg−1, 257.90–450.98 mg kg−1, and 264.10–458.15 mg kg−1, respectively (Fig. 1B, C). The variation ranges of soil TN were 3.50–16.60 g kg−1, 4.70–34.5 g kg−1, and 6.70–32.50 g kg−1, respectively (Fig. 1D). Similarly, the variation ranges of NH4+ were 5.96–12.03 g kg−1, 5.39–12.59 g kg−1, and 5.74–13.03 g kg−1, NO3− were 2.27–8.79 mg kg−1, 5.07–9.62 mg kg−1, and 5.09–9.52 mg kg−1, respectively (Fig. 1E, F). The changes of SOC and NH4+ with soil depth were significantly different in different extreme drought periods and decreased significantly with the increase of soil depth (Table 1, P  More

We investigated whether ricefield mosquito larval control and/or rice cultivation practices are associated with malaria vector densities through a systematic review and meta-analysis. Forty-seven experimental studies were eligible for inclusion in the qualitative analysis and thirty-three studies were eligible for the meta-analysis. It was demonstrated that the use of fish, chemical and biological larvicides in rice fields were effective in controlling larval malaria vector densities at all developmental stages. Intermittent irrigation, however, could only significantly reduce late-stage larvae. Based on a limited number of studies, meta-analyses on other forms of larval control such as monomolecular surface films (MSFs), neem, copepods and Azolla failed to demonstrate any consistent reduction in anopheline numbers. Similarly, rice cultivation practices such as plant variety and density, type of levelling and pesticide application were not generally associated with reduced malaria vectors. Nonetheless, in one study, minimal tillage was observed to reduce average numbers of larvae throughout a cropping season. In another study, herbicide application increased larval abundance over a 4-week period, as did one-time drainage in a third study.
Despite their different modes of action, the use of chemical and bacterial larvicides and MSFs were all relatively effective measures of larval control in rice fields, varying between a 57% to 76% reduction in vector abundance compared to no larviciding. Their effects were highest (often reaching 100% reduction) only shortly following application but did not persist for longer than two weeks. These larvicides mostly had short residual half-lives because they were applied to paddy water which was naturally not completely stagnant: there was a small but constant process of water loss (through drainage, evapotranspiration and percolation) and replacement through irrigation. Hence, even with a residual formulation, weekly re-application would be needed for sustained control47,40,41,50. This would be very labour- and cost-intensive to scale-up, to ensure that larvicides are evenly distributed across vast areas (even at plot/sub-plot level) throughout at least one 5-month long rice-growing season per year42,51. Aerial application (including unmanned aerial vehicles), although widely used in the US and Europe, is unlikely to be a feasible delivery system for smallholders in SSA, even in large irrigation schemes26,27,48,49. Furthermore, if synthetic organic chemicals were to be considered for riceland malaria vector control, their management in the current landscape of insecticide resistance across Africa must be considered.Biological control using fish was found to be, in general, slightly more effective than (chemical, bacterial and MSF) larviciding. The degree of effectiveness was dependent on the fish species and their feeding preferences: surface-feeding, larvivorous species provided better anopheline control than bottom-feeding selective feeders4,43. Selecting the most suitable fish for local rice fields is not straightforward; many criteria need to be considered4,52,53. Generally, fish were well-received by rice farmers, perceived to contribute to increased yield by reducing weeds and pests and providing fertiliser through excrement43,44. This was reportedly also observed in Guangxi, China, where a certain proportion of the field had to be deepened into a side-trench where the fish could take shelter when the fields were drained. Even with this reduction in rice production area, carp rearing still increased yields by 10% and farmer’s income per hectare by 70%53. Unfortunately, none of the eligible studies in this review had included yield or water use as an outcome. Future entomological studies need to measure these critical agronomic variables so that studies of vector control in rice can be understood by, and transferred to, agronomists. In SSA, irrigated rice-fish farming can be scaled up provided that an inventory of fish species suitable for specific locations is available and that water is consistently available in fields (an important limiting factor in African irrigation schemes)54. Lessons can be learnt from successful large-scale rice-fish systems in Asia, where they have served as win–win solutions for sustainable food production and malaria control16,55.Overall, there was only limited evidence that intermittent irrigation is effective at reducing late-instar anopheline larvae in rice fields. This finding contrasts with prior reviews, which found mixed results (regardless of larval stage) but emphasised that success was site-specific4,17,56. This contrast is presumably due to the inclusion criteria of our systematic review. These reviews excluded studies in various geographical settings and some older studies that reported successful anopheline control with intermittent irrigation but lacked either a contemporaneous control arm, adequate replication or adequate differentiation between culicines and anophelines16,57,50,51,52,61. It seems, from our review, that intermittent irrigation does not prevent the recruitment of early instars (and in one case, may have encouraged oviposition31) but tends to prevent their development into late-stage immatures. This important conclusion is, however, based only on four studies; more evidence is urgently needed where future trials should consider the basic principles of modern trials with adequate replication, controls and differentiation between larval instars and species.Generally, it is observed that drainage, passive or active, did not reliably reduce overall numbers of mosquito immatures. In India and Kenya, closer inspection revealed that soils were not drying sufficiently, so any stranded larvae were not killed31,46. Highlighted by van der Hoek et al.29 and Keiser et al.17, water management in rice fields is very dependent on the physical characteristics of the soil and the climate and is most suited to places that not only favour rapid drying, but also have a good control of water supply17,56. Moreover, repeated drainage, although directed against mosquitoes, can also kill their aquatic predators62. Since mosquitoes can re-establish themselves in a newly flooded rice field more quickly than their predators, intermittent irrigation with more than a week between successive drying periods can permit repeated cycles of mosquito breeding without any predation pressure. Its efficacy against malaria vectors is therefore highly reliant on the timing of the wetting and drying periods. Further site-specific research on timing, especially with regards to predator–prey interactions within the rice agroecosystem, is required to find the perfect balance.Another limitation in intermittent irrigation is that it cannot be applied during the first two to three weeks following transplanting, because rice plants must remain flooded to recover from transplanting shock. Unfortunately, this time coincides with peak vector breeding. Thus, other methods of larval control would be required to fill this gap. To agronomists, intermittent irrigation provides benefits to farmers, as it does not penalise yield but significantly reduces water consumption. Nonetheless, farmer compliance seems to be variable, especially in areas where water availability is inconsistent and intermittent irrigation would potentially require more labour31,32,39. Importantly, rice farmers doubted their ability to coordinate water distribution evenly amongst themselves, suggesting that there may be sharing issues, as in the “tragedy of the commons”63. Instead, they said that they preferred to have an agreed authority to regulate water46.No general conclusions could be made on the effect on malaria vectors of other rice cultivation practices (apart from water management) because only one study was eligible for each practice. Nevertheless, these experiments on pesticide application, tillage and weed control, as well as another study on plant spacing (not eligible since glass rods were used to simulate rice plants), do illustrate that small changes in agronomic inputs and conditions can have considerable effects on mosquito densities, not just rice yield36,38,64. Moreover, in partially- or shallowly-flooded plots, the larvae are often concentrated in depressions (usually footprints), suggesting that rice operations which leave or remove footprints (e.g. hand-weeding, drum seeders, levelling) will influence vector breeding4.Our study has some important limitations. First, in most trials, the units of intervention were replicate plots of rice, and success was measured as a reduction in larval densities within treated plots. This design focuses on the identification of effective and easy-to-implement ways of growing rice without growing mosquitoes, on the assumption that higher vector densities are harmful. However, from a public health perspective, the need for epidemiological outcomes is often, and reasonably, stressed22,65. Nonetheless, from a farmers’ perspective, it is also important to consider whether the vectors emerging from their rice fields significantly contribute to the local burden of malaria and to determine how this contribution can be minimised. There is evidence that riceland vectors do increase malaria transmission, since human biting rates are much higher in communities living next to rice schemes than their non-rice counterparts66 and that additional riceland vectors may intensify transmission and malaria prevalence in rice communities15. Hence, when investigating how rice-attributed malaria risk can be minimised, mosquito abundance as measured in the experimental rice trials is a useful indicator of potential impact on epidemiological outcomes.Second, larval density was not always separated into larval developmental stages. This can be misleading because some interventions work by reducing larval survival (but not by preventing oviposition) and development to late instars and pupae. Therefore, an intervention could completely eliminate late-stage larvae and pupae but have little effect on the total number of immatures. This was illustrated in our meta-analyses of intermittent irrigation in Table 3 and Supplementary Table 5, and could have been the case for some studies that failed to demonstrate consistent reductions in overall anopheline numbers but did not differentiate between larval instars34,45,67,60,69. We infer that when monitoring mosquito immatures in rice trials, it is important to distinguish between larval instars and pupae. Pupae should always be counted separately since its abundance is the most direct indicator of adult productivity70.Third, experimental trials rarely reported the timing of intervention application or accounted for different rice-growing phases, or “days after transplantation”, in the outcome. Both aspects are important to consider since an intervention may be suited to control larvae during certain growth phases but not others. This is illustrated by Djegbe et al.38, where, compared to deep tillage, minimal tillage could significantly reduce larvae during the early stages of rice cultivation but not during tillering and maturation38. In contrast, other interventions, such as Azolla and predatory copepods, took time to grow and accumulate, and were more effective during the later stages of a rice season45,67,71. This differentiation is important because it can identify components that could potentially form a complementary set of interventions against riceland malaria vectors, each component being effective at different parts of the season. Since rice fields, and hence the dynamics of riceland mosquito populations, vary from place to place, this set of interventions must also be robust. Special attention must be paid to the early stages of rice cultivation, particularly the first few weeks after transplanting (or sowing), since, with many vector species, a large proportion of adult mosquitoes are produced during this time.Fourth, the analysis of entomological counts is often inadequate. Many studies failed to provide the standard deviation (or any other measure of error) for larval counts and could not be included in the quantitative analysis. Often, due to the extreme (and not unexpected) variability of larval numbers, sample sizes were insufficient to calculate statistically significant differences between treatments. Fifth, a high risk of bias was found across both CTS and CITS studies, including high heterogeneity and some publication bias. Study quality was, in general, a shortcoming and limited the number of eligible studies for certain interventions, including intermittent irrigation. Moreover, there are conspicuous a priori reasons for bias in such experimental trials: trial locations are frequently chosen to maximise the probability of success.Finally, few studies were conducted in African countries, where the relationship between rice and malaria is most important because of the efficiency, and the “rice-philic” nature, of the vector An. gambiae s.l.15. In particular, there was a lack of studies on the effectiveness and scalability of biological control and rice cultivation practices. There is also very little information (particularly social science studies) on the views and perspectives of African rice farmers on mosquitoes in rice and interventions to control them72,73.In the future, as malaria declines (particularly across SSA), the contribution of rice production to increased malaria transmission is likely to become more conspicuous15. Unless this problem is addressed, rice growing will probably become an obstacle to malaria elimination. Current default methods of rice production provide near-perfect conditions for the larvae of African malaria vectors. Therefore, we need to develop modified rice-growing methods that are unfavourable to mosquitoes but still favourable for the rice. Although larviciding and biological control may be appropriate, their unsustainable costs remain the biggest barrier to uptake amongst smallholder farmers. Future investigations into riceland vector control should pay more attention to interventions that may be useful to farmers.Supported by medical entomologists, agronomists should lead the research task of identifying cultivation methods that achieve high rice productivity whilst suppressing vector productivity. Rice fields are a major global source of greenhouse gases, and agronomists have responded by successfully developing novel cultivation methods that minimise these emissions while maintaining yield. We need the same kind of response from agronomists, to achieve malaria control co-benefits within rice cultivation. At present, only a few aspects of rice cultivation have been investigated for their effects on mosquitoes, and the potential of many other practices for reducing anopheline numbers are awaiting study. Due to the spatial and temporal heterogeneity of rice agroecosystems, it is likely that no single control method can reduce mosquito numbers throughout an entire cropping season and in all soil types and irrigation methods. Thus, effective overall control is likely to come from a combination of local, site-specific set of complementary methods, each of which is active and effective during a different phase of the rice-growing season. More

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Automated tracking of four social insect speciesFifty queenright colonies were used in the tracking experiments (Table 1). Honeybee colonies (subspecies A. mellifera carnica) were housed in the campus apiary of the University of Lausanne. Colonies of L. niger were raised from single mated queens collected on campus. T. nylanderi colonies were collected from the University of Lausanne campus, and L. acervorum colonies collected from Anzeindaz, Switzerland. These four species were chosen because of their abundance and easy availability in Switzerland, and because they – or closely-related species – have previously been used as model systems for the study of spatial organisation in social insects20,21,23,27,44. The colony sizes used in our experiments (Table 1) fell within the natural range of sizes experienced by these species in nature, either as recently founded colonies (L. niger colonies are founded by a single queen and progressively grow from a few workers to mature sizes of up to 40,000 workers over the course of several years; new honeybee colonies are founded by swarms counting 2400–41,000 bees61) or as mature colonies (all colonies of T. nylanderi and L. acervorum used in our experiments were mature colonies collected whole from the field).In all species, a paper tag bearing a unique two-dimensional barcode was glued to the thorax of individuals to allow automated tracking of their movements (Fig. S1). In the ants, tagging of all individuals was performed in a single session two days before the beginning of the experiment, whilst in the bees, newly-emerged workers (one-day-old or less) were tagged every 3 days over the 21 days prior to the beginning of the experiment (Supplementary Note 1).Tagged colonies were kept in glass observation nests with a single entrance (internal nest dimensions, A. mellifera: 69 × 45 × 4 cm, L. niger: 70 × 40 × 8 mm; L. acervorum: 63 × 42 × 2 mm, T. nylanderi: 63 × 42 × 1.5 mm). The honeybee observation nests also included a 64 × 44 cm wooden frame enclosing a double-sided wax comb containing honey, pollen, and developing brood20. Bees were free to move between both sides of the comb. In all species, individuals were allowed to freely exit and enter the nest. Ants were provided with ad libitum food (Drosophila, sugar solution) and water in a foraging arena, while bees foraged on natural resources outside. Both the ant and honeybee observation nests were exposed to diurnal cycles of temperature and light (Supplementary Note 1).High resolution digital video cameras operating at two frames per second were used to identify the location and orientation of each tag across successive images22. All colonies were continuously tracked for three days, which corresponded to the inter-cohort time in the honeybee colonies. The trajectories of each worker, and the physical contacts between workers (Fig. S18 and Supplementary Note 14) were extracted using an existing software pipeline62.Building bipartite site-visit networksTo quantify the spatial preferences of individual ants and bees, the interior of the nest was discretised into a regular hexagonal lattice (Fig. 1a, b). Because the worker body lengths of our four study species span an order of magnitude (from ~ 1.5 mm for T. nylanderi to ~ 15 mm for A. mellifera), the width of the hexagonal bins were defined as 1/4 of the mean worker body-length.To characterise the spatial preferences of different individuals to different parts of the nest, we counted the number of times ({n}_{i}^{s}) that each individual i visited each hexagonal site s. A visit by individual i to site s began when i crossed the border into s, and was terminated when i crossed the border out of s, regardless of the amount of time spent inside. To prevent stationary individuals located on the border between two adjacent sites from rapidly accumulating many single-frame visits to the two sites, successive visits to a same site were only counted when at least 20s elapsed between the end of the previous visit and the start of the next.The site-visit data were used to construct a bipartite network, in which individuals (layer 1) were connected by undirected edges to the sites (layer 2) they visited (Fig. 1c, d). Because individuals typically made multiple visits to the same sites, each edge i–s was weighted according to the total number of times individual i visited site s, that is, ({n}_{i}^{s}).Partitioning site-visit networks into modulesThe extent to which the site-visit networks were partitioned into discrete ‘modules’ (i.e., set of workers with similar space-use patterns and the set of sites that they exhibit strong ties to) was assessed using the DIRTLPAwb+ algorithm for partitioning weighted bipartite networks39. This algorithm searches for the partition that maximises the number and strength of the links within modules, whilst minimizing connections between modules. The number of modules was not specified a priori by the user, but was identified by the algorithm. All site-visit networks had positive modularity (Fig. S3), indicating that they could be partitioned into a set of well-separated modules (Figs. 1e–h, S2, and S4–S5). The modules in each partition were then assigned functional labels according to the following rules. First, the module whose sites were on average closest to the nest entrance was labelled ‘forager’ module. Second, the module or modules with the greatest spatial overlap with the brood pile in the ant colonies or the broodnest(s) in the honeybee colonies were labelled ‘nurse’ module(s). After defining the forager and nurse modules, the remaining modules (if any) were labelled as follows. If there was only one module remaining after identifying the nurse and forager modules, as was typically the case in honeybee colonies, it was labelled ‘peripheral’. If there were two modules remaining, as was typically the case in ant colonies, then the module whose sites were on average closer to the nest borders (i.e., to the periphery of the nest) was labelled ‘peripheral’, and the remaining module labelled ‘intermediate’. In some cases, the DIRTLPAwb+ algorithm identified five or more modules (9.0% of all iterations across all species and colonies). In these cases, the supernumerary modules never contained more than 1 or 2 individuals, and as they could not be unequivocally assigned using our labelling scheme, they were left unclassified for these iterations.Validating network modulesAs a network constructed by a purely random process could exhibit apparent modular structure by chance, we tested whether the discovered modules represent statistically significant entities. To do so, we produced 1000 null model random networks for each observed network using an established permutation method for bipartite networks63 (Supplementary Note 2). Comparisons between the maximum modularity of the observed networks with that of the corresponding random networks showed that, in all four species, the observed modularity was significantly greater than expected by chance (Fig. S3).Constructing worker task profilesA unique labour profile for each ant and each honeybee was constructed by estimating the activity of each worker in the following four tasks:1. Entrance guarding: workers were classed as guarding when they were (i) within two body lengths of the entrance, (ii) roughly facing the entrance, i.e., with a body alignment diverging from the direct heading to the entrance by no more than π/2 radians, and (iii) ‘on station’ at the entrance, as defined by trajectory coordinates with an associated first passage time (ref. 64; time taken for the individual to pass beyond a circle centred on its current location with a radius of two body-lengths) in excess of 500s.2. Patrolling: workers were classed as patrolling65 when they were (i) active, and (ii) ‘roaming’, as defined by first passage times of More