Philippa Kaur

Notwithstanding constraints on the amount of hard data, according to our integrated analysis, the developmental environment and ecology of reef communities have an important impact on the appearance of reefs.Analysis of environmental conditions for reef developmentSettings of reef developmentThe F/F extinction event in Late Devonian caused the complete recession of the reef-building communities based on stromatoporoid-coral assemblages7,17. The Carboniferous is generally considered to be a sub-optimal period for the development of framed reefs. After the biological mass extinction, microorganisms and algae rebuilt new reef-building ecosystems18,19. Some short-term biological frame reefs developed with low diversity, limited reef-building organisms, small sizes, and restricted distribution20. Harsh climate and marine conditions occurred in the Mississippian, including extensive marine hypoxia, repeated glacial and interglacial climate changes, and frequent changes of sea level and seawater surface temperature, potentially hindering the recovery of Early Carboniferous metazoan reefs7,21.Metazoans gradually began to participate in reef building in Early Viséan. A large number of biogenic structures formed by corals and bryozoans began to appear, including a small number of sponge reefs/mounds in the middle and late stage of Viséan. The richness and biodiversity of the Mississippian post-zoobenthic reefs flourished in the late Viséan during which corals, bryozoans, sponges, calcareous microorganisms, and some calcareous algae became the main builders3 and large-scale reefs could also be seen in some areas although most of the Viséan metazoan reefs were tabular or laminar. Thus, the metazoan skeletal reefs in the middle to late Viséan were considered to have been resurrected due to relatively warm climatic conditions and higher sea levels after a period of complete disintegration at the end of the Devonian and recession at the beginning of the Carboniferous7.Consequently, the coral reefs in the study area were the products of shallow benthic communities thriving in relatively favourable conditions of Late Viséan-Serpukhovian, which was common for reef development at that time7. Thus, it is expected that more synchronous reefs would to be identified in southern China, or even in the study area in the future.Paleogeography of reefsLangping is located in Dian-Qian-Gui Basin22 regionally (Fig. 2), in the eastern end of Tethys tectonic domain and at the interjunction of Tethys and Pacific structure globally. The Carboniferous Dian-Qian-Gui Basin is adjacent to the Tethys Basin. During the Early Carboniferous, the continent of Gondwana was close to the equator but was separated from the northern continent by the Tethys, where the tropical currents flowed freely from east to west. The benthic warm-water organisms were distributed widely with high abundance and diversity on both sides of the shallow shelf of Tethys.Figure 2Paleogeographic map of southern China in Viséan-Serpukhovian (modified from Feng23, Yao8, and Maillet24). This figure was obtained from articles by Feng23, Yao8 and Maillet24 respectively. The author modified the picture with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/). QG Qian-Gui Basin, DQGX Dian-Qian-Gui-Xiang platform.Full size imageViséan-Serpukhovian ecosystems experienced dramatic climate changes and widespread glaciation25. However, the Viséan was also a key layer for a variety of biological structures, with abundant coral reefs and a high diversity of shallow benthic communities, peaking in the late Viséan. Newly discovered post-faunal reefs in Tianlin were mainly formed in the late Viséan-Serpukhovian period, which coincided with frequent sea level fluctuations and possible glacial changes. It seems counterintuitive that tropical coral communities developed during glacial period. However, recent studies suggest that the persistent warm ocean currents on the platform helped some coral species survive from Carboniferous glacial events24. While other areas of symbiotic reefs were poorly developed, Tianlin may provide an ecological sanctuary for corals associated with ocean currents26.Sedimentation of reef developmentAccording to the regional geological structure, the slope model for Langping paleocarbonate platform was obviously different from that of steep slope platform margin, which could be directly affected by waves. Langping palaeo-platform could be regarded as one of the small blocks (block fault barrier) separated from a large platform (continental margin sea basin)13. The relative positions of these blocks were crucial for the emergence and growth of reefs.In situ development of mud-crystalline tuffs and muddy tuffs with weak hydrodynamic conditions is common in the Langping area, and evaporites are poorly developed. There were patch reefs and reef layers in different sizes in the wide intraclast beach, where obviously developed reef beach complexes were rare. The fragments of carbonate base broken by storm in the clastic beach haven’t been observed. The study area is considered to be gentle-slope open platform27,28 based on sedimentary characteristics. It suggests that the study area was far away from the margin of steep-slope platform that directly affected by waves, and more consistent with less energetic internal environments of gentle-slope platform.On the vast platform of Langping gentle slop, deep water lead to low water energy. While in the coastal area, the water energy was relatively strong, thus coarse-grained bioclastic beach and a small amount of point reef could be developed. The beaches were irregular-shaped due to long term transportation and reformation effects of waves and water flow, showing low and gentle slope angles. Dispersed reef-beach complexes at the platform margin slightly impacted inner-platform seawater and the water flows smoothly29.Therefore, it can be assumed that Langping reefs developed along the intertidal shallows of the terrace. The seawater around Langping carbonate platform in Late Viséan-Serpukhovian was relatively shallow while the water flow was strong. Remains of crinoids, brachiopods, a few foraminifera, and solitary corals were likely broken by strong currents, and deposited in situ with a small amount of gravels and lime-mud (Fig. 3). The clastic beach was unstable, suggesting large-scale wave-resistant structures could not be formed quickly30 due to insufficient cohesive and consolidating organisms. In addition, the circumferential impact of water in extensive terraces leads to mud-lime deposition, which is detrimental to most benthic organisms. However, bondstone was more likely to be formed by some binding algaes in the platform (Fig. 4). Therefore, neither the surrounding or the inner region of the platform could provide favourable living conditions for coral reefs to develop over for a long term. The gently sloping terrace environment of Lanping resulted in significant differences in growth size, wave resistance and reef-building capacity between corals in the study area and those on the edge of the steeply sloping terrace.Figure 3Clastic beaches in the Langping. Various clastic beaches developed in the study area. Diverse composition, fragmentation degree and sorting of the clast indicate different water conditions of formation. This figure is modified by the author from field photos with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageFigure 4Algal bondstone in the Langping. Bondstone formed by various algaes living in still water. Morphology of bondstone correlates water environment and deposition of mud. Vast algal bondstones indicate deep water and high deposition rate. This figure is modified by the author from field photos with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageAt the same time, the warm climate of the late Viséan-Serpukhovian, the good circulation of seawater around the Langping platform, and the abundant supply of oxygen and nutrients were a series of favourable conditions that facilitated the growth of reef-building corals, which led to uplifts being formed on clastic beach, including patch reefs and reef layers with certain sizes. These uplifts impeded waves and provided a protected nearshore environment, though they were much smaller than those developed at the steep-slope platform margin. The inhabitants on the beach could not resist strong waves. These rises were therefore known as reef-beach complexes and could only persist where waves and currents were mild28. They were essentially different from the framework coral reefs which developed on steep-slope platform margin that reflected changed hydrodynamic conditions, nutrient sources, reef sizes, and growth rates.Another potentially favourable factor in the study area could be the deeper water area in the gentle-slope sedimentary environment, which could provide more stable conditions and reduce the damaging effects of global glacial events and large scale sea level fluctuations on reef-building communities25. The frequent fault activities in Dian-Qian-Gui Basin caused the rise and fall of equivalent sea level. More influence of sea-level fluctuations and hydrodynamic conditions would be exerted on Langping platform due to its small size. Furthermore, reef growth promoted by reef-building communities would be frequently disturbed. The sediments displaying evidence of multicycle sedimentation, different components, and diversely fragile clasts in the study area provided direct evidence of frequently changing environment.Alternatively, the sedimentary environment of Langping platform provided conditions favorable for reef-building communities to develop and reefs to grow rapidly. These factors directly or indirectly determined the ecology of reef-building communities and the general appearance of reef development in the study area.Overall, the environmental factor is the primary factor affecting the overall development trend of reefs.Inferred ecological characteristics of reef communitiesResponse of reef-building corals to hydrodynamic conditionsHydrodynamic conditions are very important factors for reef development, which directly determine the abundance and distribution of each reef-building population and are key factors influencing sedimentation and reef growth, and was particularly evident at Langping. Evidence from the fossils suggested the reef-building corals were also changed in response (Table 1). The hydrodynamic condition changes during the development of reefs are inferred based on analysis of the vertical sediments and microfacies changes of coral reefs in the study area31. How these ancient reef-building corals adapted to hydrodynamic conditions was reconstructed combining the evolution of reef-building communities with the study.Table.1 General situation of reef-building coral population in Langping.Full size tableThe Xiadong coral reef started with colonization and expansion of Diphyphyllum on the bioclastic hard substrates32,33. They grew vertically into upright clusters (Fig. 5A) and were insensitive to more sediment in a relatively calm, turbid water environment34. The relatively dense clumped Siphonodendron and massive Lithostrotion (Fig. 5B) were better suited to the turbulent water environment, becoming dominant over time, with Diphyphyllum subordinate with the continuous increase of the water energy, as indicated by the characteristics of sediment particles from fine to coarse. After flourishing for a period, the Siphonodendron–Lithostrotion assemblage eventually waned, likely due to the failure to adapt to the increasing hydrodynamic conditions. Diphyphyllum had persisted combined with Syringopora, to maintain the growth of the reef. However, this assemblage subsequently declined as a result of strong hydrodynamic conditions and finally died out in response to continuous falling of sea level. Consequently, the reefs stopped developing.Figure 5Sketch of coral cluster with upright growing morphology. Most reef-building corals in Langping grow vertically into cluster colonies. This type of morphology is very favourable for corals to get more living space and is important to reef-building. (A) Cluster coral individuals grow uprightly with certain distance between each other. (B) Polygonal columnar coral individuals grow closely to resist strong water flow. This figure is made by the author with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageThe Longjiangdong multi-layer reef was composed of three relatively independent, flat reef layers, suggesting three distinct periods of reef development. Diverse species were identified in the reef, with colonial coral Diphyphyllum contributing greatly to reef growth. Diphyphyllum clusters colonized in patchy form on substrates composed of bioclasts or lithic gravels (Fig. 6A). The first reef-building process was brief, ending under high-energy water conditions after a period of growing (Fig. 6B). Subsequently the hydrodynamic conditions became weaker and favorable. Then Diphyphyllum once again flourished. Diphyphyllum clumps in the unit grew closely together in strong currents, with larger and more sparse individuals than in the lower units. A relatively low energy environment was formed between the Diphyphyllum clusters (Fig. 6C). Subsequently, Diphyphyllum could only grow in a limited area of suitability due to the disturbance of high-energy water brought about by short-term sea-level rise and fall. Afterwards, the environment became more favourable and Diphyphyllum expanded rapidly. As a result, the upper unit of Longjiangdong coral reef was formed, in which Diphyphyllum individuals were slightly larger than those in the first two units. Finally, because the kinetic energy of the water continued to weaken, the plaster deposition forced the whole coral reef to stop growing (Fig. 6D).Figure 6Micrographs of sediments in different positions of reef. (A) Calcareous bioclastic limestone, with biological particles accounting for about 70% of the debris. Abundant and diverse organisms indicate a medium-energy environment of the subtidal zone. Samples were taken from the bioclastic beach at the reef base. (B) Slightly larger bioclastics but lower biologic content than that in (A) suggest an increasing water energy. (C) Various bioclastic particles account for about 80% of the clastic particles contained in the calcareous bio-granular rock. The obviously small benthos indicate a low-energy environment in the subtidal zone barriered by the Diphyphyllum clusters. (D) Bioclastic grainstone is mainly composed of marl, with fine clastic particles (about 30%) and bedding. Low biomass indicates a low-energy environment of the subtidal zone. This figure is modified by the author with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/). Meaning of the letters in the figure: C crinoids, BF brachiopods, F foraminifera, B bryozoan, P pelletoid, MF mollusk shell fragment.Full size imageLongjiangdong patch reef started to develop in a relatively deep water environment. Diphyphyllum initially colonized and expanded in favorable conditions with the increase of water energy. Then the reef-builders transitioned from a single coral species to an assemblage of Diphyphyllum–Caninia–Lithostrotionella. These three coral species grew independently and contribute almost equally to the structure of the reef. However, the structure and function of the coral community were not yet stable enough. It was easily influenced by the weakening hydrodynamics and the increasing sedimentation, resulting in only small patch reefs.The Xinzhai layer reef was initialized by colonization and expansion of Lonsdaleia on bioclastic beach. Large coral clusters were formed in the presence of turbulent water. With the weakening of hydrodynamic conditions, an unknown branchlike organism and Antheria communities continued to develop separately in this area. Slender branchlike organisms expanded rapidly in these low-energy water environments until they were replaced by some individual corals as hydrodynamic energy increased. Each builder was short-lived in this layer reef, departing from the reef just at the beginning of colonization and expansion, due to rapidly changed hydrodynamic conditions.The evolution of reef-building corals in these four reefs indicated that both the coral assemblages and coral individuals would constantly adapt to the changing hydrodynamic conditions in Langping as sea level rose and fell. Although this was a reactive adjustment of coral populations in response to long-term environmental impacts, it was clearly positive for the building and development of coral reefs.Impact of disturbance on reef communitiesDisturbance is a relatively discontinuous event, which is ubiquitous in nature. It may indirectly affect the composition and population structure of reef communities by changing the environmental conditions, thus affect the structure and function of reef communities, even the evolution of the reef35. The major disturbances evident in these Mississippian framework reefs were associated with frequent changes of water flow, and drastic changes of climate and weather. These seem to be most obvious in the Langping platform due to its small size, with more frequent environmental influence evident on the reef communities in the study area.The most direct effect of disturbance events on coral reefs is the disruption of continuously evolving reef communities, which is common in coral reef studies. After the interruption caused by disturbances, some communities gradually recover due to the absence of continuous disturbance, or the dominant biota may be substituted by invading communities. The winner after interruption is decided by random factors to a large extent, in a ‘Competitive lottery’36. The conditions for the emergence of ‘Competitive lottery’ also include the need for species in a community to have similar abilities to invade discontinuities and to tolerate environmental conditions.Certainly, low-intensity disturbance does not necessarily produce discontinuity, but medium-intensity disturbance without discontinuity could directly impact on community species diversity. According to the ‘Moderate disturbance hypothesis’, moderate disturbance is conducive to a higher level of community diversity37. In environmental conditions with moderate intensity of disturbance, most species will not disappear entirely. The dominant pioneer species will also be restrained by disturbance to a certain extent, so large number of species can coexist, attaining the highest diversity35.The reef-builders in Langping are diverse compared with the Late Carboniferous reefs in Ziyun County10, which also developed in Dian-Qian-Gui Basin. More than 4 reef-building corals are identified in Xiadong reef, while 4 and 3 are in Xinzhai layer reef, Longjiangdong patch reef respectively. These reef-building corals, mostly Diphyphyllum, Lithostrotion, Siphonodendron and Lonsdaleia, were distributed irregularly in the reefs. Their ecological niche and function were likely similar and none of them was obviously dominant in the community (Fig. 7). This is in line with ‘Competitive lottery’ theory and the ‘Moderate disturbance hypothesis’.Figure 7Different species occupied the discontinuity surface irregularly. (A) Different reef-builders colonized and grew on the same hard substrate. (B) and (C) show detailed morphology of colony corals of (A). (D) Colony corals and a large number of individual corals grew together in a limited area, indicating equal colonization on the newly formed discontinuity surface. This figure is modified by the author from field photos with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageThe stability of a classical reef ecosystem includes the ability to withstand external disturbances and the ability to return to its original state once the disturbance is removed37,38. It is generally accepted that communities with high diversity are always more stable although ecosystem stability is not absolutely correlated to biodiversity35.There have been no reef-building corals with strong resistance and rapid recovery ability in the communities in Langping. None of these corals succeeded in developing into dominant species that can build reef shelves, which made the reefs in Langping mostly appear in the form of small patch reefs or reef layers. However, formation of the large reef in Xiadong Village, patch reef in Longjiangdong Village, and layer reef in Xinzhai Village were all related to their relatively high diversity of reef-building corals. Compared with the situation where only one reef-building organism dominated the Bianping large coral reef, Wengdao large phylloid algal reef and Ivanovia cf. manchurica patch reefs in Ziyun County10, Guizhou province, the different coral assemblages in Langping area could effectively adapt to changing hydrodynamic conditions and maintain reef growth.Species diversity increased by disturbance stabilized the ecosystemas shown during the construction of coral reefs in Langping.Effects of non-reef-builders on reef-building coralsBesides reef-building corals, there were a large number of reef-dwellers and off-reef organisms in the study area. Reef-dwellers referred to the species that didn’t directly contribute to reef growth in the community, mainly including various benthos and algaes39. Off-reef organisms are not part of the reef-building community, but also play an important role in participating in energy flow and providing organic matter to the reef ecosystem40.Common reef-dwelling organisms include crinoids, brachiopods, gastropods, various algae, foraminifera, bryozoans and individual corals. Crinoids were overwhelmingly dominant in numbers in the reef samples studied here.Carboniferous echinodermata in Guangxi Province reached its peak in Middle-to-Late Mississippian. In terms of amount and distribution, thick limestone with echinodermata debris in the carbonate platform were often dominated by crinoids41,42,43,44,45,46. The large number of crinoids in Langping excluded other metazoans and restricted the development of benthic reef-builders in Late Viséan-Serpukhovian in Langping, leading to poorly developed reef-building communities.Microorganisms and algaes had limited success in stablishing on the moving clastic beach in frequently disturbed water. There has not been obvious evidence of extensive “algal turf” in the coastal area of Langping platform. Only a few corals bonded by algal mats were observed47 (Fig. 8). In addition to their significant contribution to primary productivity, macroalgae were considered to play an important role in two aspects of coral reef ecosystems. One was to promote reef construction by its own binding and consolidation48,49. The other was to create a good condition for zoobenthos larvae to dwell and develop, thereby improving species diversity50. The limited productivity of algae in Langping constrained coral reef trophic inputs, which may then have limited populations of dependent metazoans. As a result, algaes and other metazoans were unable to achieve a variety of reef-building patterns, such as bonding, bounding, entanglement51,52. The reef framework in the study area was not stable in the presence of strong water flow, and the biological communities could not deal with frequent environmental changes, which were directly related to poor development of calcareous algae.Figure 8Micrographs of microbes and algaes. (A) Encrustations (indicated by black arrows) with distinct thickness around coral clusters formed by microbe and algal mats through bonding mud. The encrustations were formed before the clastic deposition (indicated by white arrows), showing the corals were living then. Microbes and algaes inside of the dense coral clusters had little impact on corals. (B) Single polarized micrograph showed clear and smooth boundaries of coral individuals without encrustation or drilling hole made by microbes or algaes. Few corals surrounded by bonding algaes could be observed in Langping, indicating that algaes were poorly developed between coral clusters. This figure is modified by the author from field photos with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageInfluence of coral morphology on reef developmentThe accumulation of reef structure had obvious impact on communities. Large reef structures could support abundance and diverse biota by modifying local environments and creating diverse conditions. Consequently the reef-building communities thrived between disturbances, stabilizing reef construction. In terms of large reef, the framework-building corals would play a key role in reef construction regardless of which kind of patterns was adopted. Therefore, reef-building corals with large size, rapid growth vertically, and strong resistance would become the biggest contributors to reef frame construction.The main reef-building corals in Langping were composed of Diphyphyllum, Lithostrotion, Siphonodendron, and Lonsdaleia, etc., being the dominant builders. These corals were similar in morphology such as cluster colony, thick and strong skeleton, and densely packed individuals (Fig. 9), which enabled them to resist water flow. At the same time, the upright colonies were adaptable to relatively calm water, being insensitive to mud deposition. The ecological characteristics of the Langping corals matched the gently sloping environment, the deep water environment and the rapidly changing energy of the currents. These cluster corals were able to colonize hard substrates and expand rapidly, thus altering the surrounding environment. The visible carbonate uplifts were formed with a large amount of benthos grouped into reef-building communities. These distinct uplifts constructed by coral clusters in different water conditions are composed of coral reefs of different sizes and appearance in the study area.Figure 9Main reef-building corals in the study area. (A) Diphyphyllum, (B) Lithostrotion, (C) Siphonodendron, (D) Lonsdaleia. (A) Rapidly grew clusters of main reef-building corals. The strong individuals are packed tightly when growing to support each other. This figure is modified by the author from field photos with CorelDRAW (version 2022, and the URL link: https://www.coreldraw.com/cn/).Full size imageThe complex and diverse local environments formed by large coral reefs can significantly increase benthic populations and improve reef species diversity. As a result, the nutrient flow in the community becomes complicated, and nutrients could be recycled effectively by reducing loss caused by water flow. Therefore, the overall productivity of large coral reef communities was always high. Complex trophic structure satisfied most of the benthos in the community with sufficient nutrients and inorganic salts.The morphology of reef-building corals in Langping enabled them to become predominant species in various water environments, which promoted the continued domed growth of coral reefs and facilitates the development of reef-building communities that form a variety of reefs. It suggests that the morphology of reef-building corals was a key prerequisite for reef development.In conclusion, coral reef communities are always constrained and influenced by environmental conditions. However, the ecology of the inhabitants is also an important factor in the formation of coral reefs. More

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

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

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

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Protecting an overfished lobster species helps the crustaceans to grow big, according to an analysis of European lobsters in marine sanctuaries1.

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doi: https://doi.org/10.1038/d41586-022-03708-2

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Conservation biology 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

Ringø, E. et al. Probiotics, lactic acid bacteria and bacilli: Interesting supplementation for aquaculture. J. Appl. Microbiol. 129, 116–136 (2020).PubMed 

Google Scholar 
Borges, N. et al. Bacteriome structure, function, and probiotics in fish larviculture: The good, the bad, and the gaps. Annu. Rev. Anim. Biosci. 9, 423–452 (2021).CAS 
PubMed 

Google Scholar 
Aguilar-Toalá, J. E. et al. Postbiotics: An evolving term within the functional foods field. Trends Food Sci. Technol. 75, 105–114 (2018).
Google Scholar 
Salminen, S. et al. The International Scientific Association of Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of postbiotics. Nat. Rev. Gastroenterol. Hepatol. 18, 649–667 (2021).PubMed 
PubMed Central 

Google Scholar 
Dash, G. et al. Evaluation of paraprobiotic applicability of Lactobacillus plantarum in improving the immune response and disease protection in giant freshwater prawn, Macrobrachium rosenbergii (de Man, 1879). Fish Shellf. Immunol. 43, 167–174 (2015).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Effects of partial substitution of fish meal by soybean meal with or without heat-killed Lactobacillus plantarum (LP20) on growth performance, digestibility, and immune response of Amberjack, Seriola dumerili Juveniles. Biomed. Res. Int. 2015, 514196 (2015).PubMed 
PubMed Central 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Immune responses and stress resistance in red sea bream, Pagrus major, after oral administration of heat-killed Lactobacillus plantarum and vitamin C. Fish Shellf. Immunol. 54, 266–275 (2016).CAS 

Google Scholar 
Dawood, M. A. O., Koshio, S., Ishikawa, M. & Yokoyama, S. Interaction effects of dietary supplementation of heat-killed Lactobacillus plantarum and β-glucan on growth performance, digestibility and immune response of juvenile red sea bream, Pagrus major. Fish Shellf. Immunol. 45, 33–42 (2015).CAS 

Google Scholar 
Tung, H. T. et al. Effects of heat-killed Lactobacillus plantarum supplemental diets on growth performance, stress resistance and immune response of juvenile Kuruma shrimp Marsupenaeus japonicus bate. Aquac. Sci. 57, 175–184 (2009).CAS 

Google Scholar 
Van Nguyen, N. et al. Evaluation of dietary heat-killed Lactobacillus plantarum strain L-137 supplementation on growth performance, immunity and stress resistance of Nile tilapia (Oreochromis niloticus). Aquaculture 498, 371–379 (2019).
Google Scholar 
Yang, H. et al. Effects of dietary heat-killed Lactobacillus plantarum L-137 (HK L-137) on the growth performance, digestive enzymes and selected non-specific immune responses in sea cucumber, Apostichopus japonicus Selenka. Aquac. Res. 47, 2814–2824 (2016).CAS 

Google Scholar 
Singh, S. T., Kamilya, D., Kheti, B., Bordoloi, B. & Parhi, J. Paraprobiotic preparation from Bacillus amyloliquefaciens FPTB16 modulates immune response and immune relevant gene expression in Catla catla (Hamilton, 1822). Fish Shelf. Immunol. 66, 35–42 (2017).CAS 

Google Scholar 
Kamilya, D., Baruah, A., Sangma, T., Chowdhury, S. & Pal, P. Inactivated probiotic bacteria stimulate cellular immune responses of catla, Catla catla (Hamilton) in vitro. Probiot. Antimicrob. Proteins 7, 101–106 (2015).CAS 

Google Scholar 
Wang, J. et al. Supplementation of heat-inactivated Bacillus clausii DE 5 in diets for grouper, Epinephelus coioides, improves feed utilization, intestinal and systemic immune responses and not growth performance. Aquac. Nutr. 24, 821–831 (2018).CAS 

Google Scholar 
Giri, S. S. et al. Effects of dietary heat-killed Pseudomonas aeruginosa strain VSG2 on immune functions, antioxidant efficacy, and disease resistance in Cyprinus carpio. Aquaculture 514, 734489 (2020).CAS 

Google Scholar 
Shabanzadeh, S. et al. Growth performance, intestinal histology, and biochemical parameters of rainbow trout (Oncorhynchus mykiss) in response to dietary inclusion of heat-killed Gordonia bronchialis. Fish Physiol. Biochem. 42, 65–71 (2016).CAS 
PubMed 

Google Scholar 
Wu, X. et al. Use of a paraprobiotic and postbiotic feed supplement (HWF™) improves the growth performance, composition and function of gut microbiota in hybrid sturgeon (Acipenser baerii × Acipenser schrenckii). Fish Shelf. Immunol. 104, 36–45 (2020).CAS 

Google Scholar 
Mora-Sánchez, B., Balcázar, J. L. & Pérez-Sánchez, T. Effect of a novel postbiotic containing lactic acid bacteria on the intestinal microbiota and disease resistance of rainbow trout (Oncorhynchus mykiss). Biotechnol. Lett. 42, 1957–1962 (2020).PubMed 

Google Scholar 
Xiong, J. et al. The temporal scaling of bacterioplankton composition: High turnover and predictability during shrimp cultivation. Microb. Ecol. 67, 256–264 (2014).PubMed 

Google Scholar 
Martins, P. et al. Seasonal patterns of bacterioplankton composition in a semi-intensive European seabass (Dicentrarchus labrax) aquaculture system. Aquaculture 490, 240–250 (2018).
Google Scholar 
Offret, C. et al. Protective efficacy of a Pseudoalteromonas strain in European abalone, Haliotis tuberculata, infected with Vibrio harveyi ORM4. Probiot. Antimicrob. Proteins 11, 239–247 (2019).
Google Scholar 
Richards, G. P. et al. Mechanisms for Pseudoalteromonas piscicida-induced killing of vibrios and other bacterial pathogens. Appl. Environ. Microbiol. 83, e00175-e217 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin, S.-R., Chen, Y.-H., Tseng, F.-J. & Weng, C.-F. The production and bioactivity of prodigiosin: Quo vadis?. Drug Discov. Today 25, 828–836 (2020).CAS 
PubMed 

Google Scholar 
Vynne, N. G., Månsson, M., Nielsen, K. F. & Gram, L. Bioactivity, chemical profiling, and 16S rRNA-based phylogeny of Pseudoalteromonas strains collected on a global research cruise. Mar. Biotechnol. 13, 1062–1073 (2011).CAS 

Google Scholar 
Lovejoy, C., Bowman, J. P. & Hallegraeff, G. M. Algicidal effects of a novel marine Pseudoalteromonas isolate (class Proteobacteria, gamma subdivision) on harmful algal bloom species of the genera Chattonella, Gymnodinium, and Heterosigma. Appl. Environ. Microbiol. 64, 2806–2813 (1998).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Franks, A. et al. Inhibition of fungal colonization by Pseudoalteromonas tunicata provides a competitive advantage during surface colonization. Appl. Environ. Microbiol. 72, 6079–6087 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hjelm, M. et al. Selection and identification of autochthonous potential probiotic bacteria from turbot larvae (Scophthalmus maximus) rearing units. Syst. Appl. Microbiol. 27, 360–371 (2004).PubMed 

Google Scholar 
Sorieul, L. et al. Survival improvement conferred by the Pseudoalteromonas sp. NC201 probiotic in Litopenaeus stylirostris exposed to Vibrio nigripulchritudo infection and salinity stress. Aquaculture 495, 888–898 (2018).
Google Scholar 
Pruitt, K. D., Tatusova, T. & Maglott, D. R. NCBI reference sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 33, D501–D504 (2005).CAS 
PubMed 

Google Scholar 
Yan, Y.-Y., Xia, H.-Q., Yang, H.-L., Hoseinifar, S. H. & Sun, Y.-Z. Effects of dietary live or heat-inactivated autochthonous Bacillus pumilus SE5 on growth performance, immune responses and immune gene expression in grouper Epinephelus coioides. Aquac. Nutr. 22, 698–707 (2016).CAS 

Google Scholar 
Louvado, A. et al. Humic substances modulate fish bacterial communities in a marine recirculating aquaculture system. Aquaculture 544, 737121 (2021).CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 1, 5 (2019).
Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 1–17 (2018).
Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6. 2019. (2020).R. Core Team. R: A Language and Environment for Statistical Computing (Version 2120) (R Foundation for Statistical Computing, 2012).
Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wemheuer, F. et al. Tax4Fun2: Prediction of habitat-specific functional profiles and functional redundancy based on 16S rRNA gene sequences. Environ. Microbiome 15, 11 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Setiyono, E. et al. An Indonesian marine bacterium, Pseudoalteromonas rubra, produces antimicrobial prodiginine pigments. ACS Omega 5, 4626–4635 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
PubMed 

Google Scholar 
Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—roseobacters are the most active component. Front. Microbiol. 8, 1771 (2017).PubMed 
PubMed Central 

Google Scholar 
Sieber, C. M. K. et al. Unusual metabolism and hypervariation in the genome of a gracilibacterium (BD1-5) from an oil-degrading community. MBio 10, e02128-e2219 (2022).
Google Scholar 
Jaffe, A. L., Castelle, C. J., Matheus Carnevali, P. B., Gribaldo, S. & Banfield, J. F. The rise of diversity in metabolic platforms across the Candidate Phyla Radiation. BMC Biol. 18, 69 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, H. N. & Piñeiro, S. Ecology of the predatory Bdellovibrio and like organisms. In Predatory Prokaryotes 213–248 (Springer, 2006).
Google Scholar 
Johnke, J. et al. Multiple micro-predators controlling bacterial communities in the environment. Curr. Opin. Biotechnol. 27, 185–190 (2014).CAS 
PubMed 

Google Scholar 
Rice, T. D., Williams, H. N. & Turng, B.-F. Susceptibility of bacteria in estuarine environments to autochthonous bdellovibrios. Microb. Ecol. 35, 256–264 (1998).CAS 
PubMed 

Google Scholar 
Feng, S. et al. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol. Ecol. 93, fix020 (2017).
Google Scholar 
Duarte, L. N. et al. Bacterial and microeukaryotic plankton communities in a semi-intensive aquaculture system of sea bass (Dicentrarchus labrax): A seasonal survey. Aquaculture 503, 59–69 (2019).
Google Scholar 
Hu, L. et al. Reclassification of the taxonomic framework of orders cellvibrionales, oceanospirillales, pseudomonadales, and alteromonadales in class gammaproteobacteria through phylogenomic tree analysis. mSystems 5, e00543-e620 (2021).
Google Scholar 
Garrity, G. M., Bell, J. A. & Lilburn, T. Oceanospirillalesord. Nov. in Bergey’s Manual® of Systematic Bacteriology 270–323 (Springer, 2005).
Google Scholar 
Thomas, F., Hehemann, J.-H., Rebuffet, E., Czjzek, M. & Michel, G. Environmental and gut bacteroidetes: The food connection. Front. Microbiol. 2, 93 (2011).PubMed 
PubMed Central 

Google Scholar 
Rajilić-Stojanović, M. & De Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047 (2014).PubMed 

Google Scholar 
Rosenberg, E. The family Chitinophagaceae. In The Prokaryotes: Other Major Lineages of Bacteria and The Archaea (eds Rosenberg, E. et al.) (Springer, 2014).
Google Scholar 
Beckmann, A., Hüttel, S., Schmitt, V., Müller, R. & Stadler, M. Optimization of the biotechnological production of a novel class of anti-MRSA antibiotics from Chitinophaga sancti. Microb. Cell Fact. 16, 1–10 (2017).
Google Scholar 
Loudon, A. H. et al. Interactions between amphibians’ symbiotic bacteria cause the production of emergent anti-fungal metabolites. Front. Microbiol. 5, 441 (2014).PubMed 
PubMed Central 

Google Scholar 
Taylor, J. D. & Cunliffe, M. Coastal bacterioplankton community response to diatom-derived polysaccharide microgels. Environ. Microbiol. Rep. 9, 151–157 (2017).CAS 
PubMed 

Google Scholar 
González, J. M. & Whitman, W. B. Oceanospirillum and related genera. In The Prokaryotes: A Handbook on the Biology of Bacteria Volume 6: Proteobacteria: Gamma Subclass (eds Dworkin, M. et al.) 887–915 (Springer, 2006).
Google Scholar 
Kleindienst, S., Paul, J. H. & Joye, S. B. Using dispersants after oil spills: Impacts on the composition and activity of microbial communities. Nat. Rev. Microbiol. 13, 388–396 (2015).CAS 
PubMed 

Google Scholar 
Williams, T. J. et al. The role of planktonic Flavobacteria in processing algal organic matter in coastal East Antarctica revealed using metagenomics and metaproteomics. Environ. Microbiol. 15, 1302–1317 (2013).CAS 
PubMed 

Google Scholar 
Gobet, A. et al. Evolutionary evidence of algal polysaccharide degradation acquisition by Pseudoalteromonas carrageenovora 9T to adapt to macroalgal niches. Front. Microbiol. 10, 2740 (2018).
Google Scholar 
Beier, S., Rivers, A. R., Moran, M. A. & Obernosterer, I. The transcriptional response of prokaryotes to phytoplankton-derived dissolved organic matter in seawater. Environ. Microbiol. 17, 3466–3480 (2015).CAS 
PubMed 

Google Scholar 
Müller, O., Seuthe, L., Bratbak, G. & Paulsen, M. L. Bacterial response to permafrost derived organic matter input in an Arctic fjord. Front. Mar. Sci. 5, 263 (2018).
Google Scholar 
Fernández-Álvarez, C. & Santos, Y. Identification and typing of fish pathogenic species of the genus Tenacibaculum. Appl. Microbiol. Biotechnol. 102, 9973–9989 (2018).PubMed 

Google Scholar 
Wirth, J. S. & Whitman, W. B. Phylogenomic analyses of a clade within the roseobacter group suggest taxonomic reassignments of species of the genera Aestuariivita, Citreicella, Loktanella, Nautella, Pelagibaca, Ruegeria, Thalassobius, Thiobacimonas and Tropicibacter, and the proposal. Int. J. Syst. Evol. Microbiol. 68, 2393–2411 (2018).CAS 
PubMed 

Google Scholar 
Luo, H. & Moran, M. A. Evolutionary ecology of the marine Roseobacter clade. Microbiol. Mol. Biol. Rev. 78, 573–587 (2014).PubMed 
PubMed Central 

Google Scholar 
Shahina, M. et al. Luteibaculum oceani gen. nov., sp. Nov., a carotenoid-producing, lipolytic bacterium isolated from surface seawater, and emended description of the genus Owenweeksia Lau et al. 2005. Int. J. Syst. Evol. Microbiol. 63, 4765–4770 (2013).CAS 
PubMed 

Google Scholar 
Davidov, Y. & Jurkevitch, E. Diversity and evolution of Bdellovibrio-and-like organisms (BALOs), reclassification of Bacteriovorax starrii as Peredibacter starrii gen. nov., comb. nov., and description of the Bacteriovorax–Peredibacter clade as Bacteriovoracaceae fam. nov. Int. J. Syst. Evol. Microbiol. 54, 1439–1452 (2004).CAS 
PubMed 

Google Scholar 
Müller, F. D., Beck, S., Strauch, E. & Linscheid, M. W. Bacterial predators possess unique membrane lipid structures. Lipids 46, 1129–1140 (2011).PubMed 

Google Scholar 
Kandel, P. P., Pasternak, Z., van Rijn, J., Nahum, O. & Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 89, 149–161 (2014).CAS 
PubMed 

Google Scholar 
Cao, H., Wang, H., Yu, J., An, J. & Chen, J. Encapsulated bdellovibrio powder as a potential bio-disinfectant against whiteleg shrimp-pathogenic vibrios. Microorganisms 7, 25 (2019).
Google Scholar 
Jafarian, N., Sepahi, A. A., Naghavi, N. S., Hosseini, F. & Nowroozi, J. Using autochthonous Bdellovibrio as a predatory bacterium for reduction of Gram-negative pathogenic bacteria in urban wastewater and reuse it. Iran. J. Microbiol. 12, 556–564 (2020).PubMed 
PubMed Central 

Google Scholar 
Cosgrove, L., McGeechan, P. L., Handley, P. S. & Robson, G. D. Effect of biostimulation and bioaugmentation on degradation of polyurethane buried in soil. Appl. Environ. Microbiol. 76, 810–819 (2010).ADS 
CAS 
PubMed 

Google Scholar 
Cruz, A. et al. Microbial remediation of organometals and oil hydrocarbons in the marine environment. In Marine Pollution and Microbial Remediation 41–66 (Springer, 2017).
Google Scholar 
Zhao, J., Chen, M., Quan, C. S. & Fan, S. D. Mechanisms of quorum sensing and strategies for quorum sensing disruption in aquaculture pathogens. J. Fish Dis. 38, 771–786 (2015).CAS 
PubMed 

Google Scholar 
Tyc, O., Song, C., Dickschat, J. S., Vos, M. & Garbeva, P. The ecological role of volatile and soluble secondary metabolites produced by soil bacteria. Trends Microbiol. 25, 280–292 (2017).CAS 
PubMed 

Google Scholar  More

Meat, Milk and More: Policy Innovations to Shepherd Inclusive and Sustainable Livestock Systems in Africa (Malabo Montpellier Panel, 2020).Value of Agricultural Production (FAO, accessed August 25, 2022); https://www.fao.org/faostat/en/#data/QVJayne, T. & Sanchez, P. A. Agricultural productivity must improve in sub-Saharan Africa. Science 372, 1045–1047 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Dangal, S. R. S. et al. Methane emission from global livestock sector during 1890–2014: magnitude, trends and spatiotemporal patterns. Glob. Change Biol. 23, 4147–4161 (2017).Article 
ADS 

Google Scholar 
Mottet, A. et al. Climate change mitigation and productivity gains in livestock supply chains: insights from regional case studies. Reg. Env. Change 17, 129–141 (2016).Article 

Google Scholar 
Valin, H. et al. Agricultural productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food security? Environ. Res. Lett. 8, 035019 (2013).Article 
ADS 
CAS 

Google Scholar 
González-Quintero, R. et al. Yield gap analysis to identify attainable milk and meat productivities and the potential for greenhouse gas emissions mitigation in cattle systems of Colombia. Agric. Syst. 195, 103303 (2022).Article 

Google Scholar 
Crops and Livestock Products (FAO, accessed August 17,2022); https://www.fao.org/faostat/en/#data/QCLLedo, J. et al. Persistent challenges in safety and hygiene control practices in emerging dairy chains: the case of Tanzania. Food Control 105, 164–173 (2019).Article 

Google Scholar 
Häsler, B. et al. Integrated food safety and nutrition assessments in the dairy cattle value chain in Tanzania. Glob. Food Sec. 18, 102–113 (2018).Article 

Google Scholar 
Supply Utilization Accounts (FAO, accessed August 26, 2022); https://www.fao.org/faostat/en/#data/SCLMichael, S. et al. Tanzania Livestock Master Plan (International Livestock Research Institute, 2018).Tanzania Livestock Sector Analysis (2016/2017–2030/2031) (United Republic of Tanzania Ministry of Livestock and Fisheries, 2017); https://www.mifugouvuvi.go.tz/uploads/projects/1553602287-LIVESTOCK%20SECTOR%20ANALYSIS.pdfNicholson, C. et al. Assessment of Investment Priorities for Tanzania’s Dairy Sector: Report on Activities and Accomplishments (International Livestock Research Institute, 2021).Chagunda, M. G. C., Romer, D. A. M. & Roberts, D. J. Effect of genotype and feeding regime on enteric methane, non-milk nitrogen and performance of dairy cows during the winter feeding period. Livest. Sci. 122, 323–332 (2009).Article 

Google Scholar 
Notenbaert, A. et al. Towards environmentally sound intensification pathways for dairy development in the Tanga region of Tanzania. Reg. Environ. Change 20, 138 (2020).Yesuf, G. A. et al. Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector. Env. Res. Lett. 16, 064032 (2021).Article 
CAS 

Google Scholar 
GLS (Greening Livestock Survey) (International Livestock Research Institute, 2019); https://data.ilri.org/portal/dataset/greeninglivestockIntended Nationally Determined Contributions (United Republic of Tanzania, 2021); https://unfccc.int/sites/default/files/NDC/2022-06/TANZANIA_NDC_SUBMISSION_30%20JULY%202021.pdfNdung’u, P. W. et al. Farm-level emission intensities of smallholder cattle (Bos indicus; B. indicus–B. taurus crosses) production systems in highlands and semi-arid regions. Animal 16, 100445 (2022).Article 
PubMed 

Google Scholar 
Goopy, J. P. et al. Severe below-maintenance feed intake increases methane yield from enteric fermentation in cattle. Br. J. Nutr. 123, 1239–1246 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goopy, J. P. et al. A new approach for improving emission factors for enteric methane emissions of cattle in smallholder systems of East Africa—results for Nyando, Western Kenya. Agric. Syst. 161, 72–80 (2018).Article 

Google Scholar 
Supporting Low Emissions Development in the Tanzanian Dairy Cattle Sector—Reducing Enteric Methane for Food Security and Livelihoods (FAO, 2019).Gerssen-Gondelach, S. J. et al. Intensification pathways for beef and dairy cattle production systems: impacts on GHG emissions, land occupation and land use change. Agric. Ecosyst. Environ. 240, 135–147 (2017).Article 

Google Scholar 
Havlik, P. et al. Climate change mitigation through livestock system transitions. Proc. Natl Acad. Sci. USA 111, 3709–3714 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herrero, M. et al. Greenhouse gas mitigation potentials in the livestock sector. Nat. Clim. Change 6, 452–461 (2016).Article 
ADS 

Google Scholar 
Dizyee, K., Baker, D. & Omore, A. Upgrading the smallholder dairy value chain: a system dynamics ex-ante impact assessment in Tanzania’s Kilosa district. J. Dairy Res. 86, 440–449 (2019).Article 
CAS 
PubMed 

Google Scholar 
Simões, A. R. P., Nicholson, C. F., Novakovicc, A. M. & Protil, R. M. Dynamic impacts of farm-level technology adoption on the Brazilian dairy supply chain. Int. Food Agribus. Manag. Rev. 23, 71–84 (2020).Article 

Google Scholar 
Rahimi, J. et al. Heat stress will detrimentally impact future livestock production in East Africa. Nat. Food. 2, 88–96 (2021).Article 

Google Scholar 
Mbululo, Y. & Nyihirani, F. Climate characteristics over southern highlands Tanzania. Atmos. Clim. Sci. 2, 454–463 (2012).
Google Scholar 
Kihoro, E. M., Schoneveld, G. C. & Crane, T. A. Pathways toward inclusive low-emission dairy development in Tanzania: producer heterogeneity and implications for intervention design. Agric. Syst. 190, 103073 (2021).Mruttu, H. et al. Animal Genetics Strategy and Vision for Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Agricultural Sample Survey 2018/19 Report on Livestock and Livestock Characteristics (Private Peasant Holdings) (Central Statistical Agency, 2019).2019/20 National Sample Census of Agriculture Main Report (Tanzania National Bureau of Statistics, 2022).Robinson, T. P. et al. Global Livestock Production Systems (FAO, 2011).Herrero, M. et al. Biomass use, production, feed efficiencies and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baseline Study of the Tanzania Dairy Value Chain (United Republic of Tanzania Ministry of Agriculture, Livestock and Fisheries, 2016).Mbwambo, N., Nandonde, S., Ndomba, C. & Desta, S. Assessment of Animal Feed Resources in Tanzania (Tanzania Ministry of Agriculture, Livestock and Fisheries and ILRI, 2016).Hartung, C., Lerer, A., Anokwa, Y., Tseng, C., Brunette, W., & Borriello, G. Open data kit: tools to build information services for developing regions. Proc. 4th ACM/IEEE International Conference on Information and Communication Technologies and Development (Association for Computing Machinery, 2010).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).https://www.r-project.orgRufino, M. C. et al. Lifetime productivity of dairy cows in smallholder farming systems of the central highlands of Kenya. Animal 3, 1044–1056 (2009).Article 
CAS 
PubMed 

Google Scholar 
Hawkins, J. et al. Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector. Sci. Rep. 11, 4190 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Python Software Foundation (Python Software Foundation, 2019); https://www.python.org/psf/Kashoma, I. P. B. et al. Predicting body weight of Tanzania shorthorn zebu cattle using heart girth measurements. Livest. Res. Rural. Dev. 23, Table 1 (2011).Galukande, E. B., Mahadevan, P. & Black, J. G. Milk production in East African zebu cattle. Anim. Sci. 4, 329–336 (1962).Article 

Google Scholar 
Gillah, K. A., Kifaro, G. C. & Madsen, J. Effects of pre partum supplementation on milk yield, reproduction and milk quality of crossbred dairy cows raised in a peri urban farm of Morogoro town Tanzania. Livest. Res. Rural. Dev. 26 (2014).Njau, F. B. C., Lwelamira, J. & Hyandye, C. Ruminant livestock production and quality of pastures in the communal grazing land of semi-arid central Tanzania. Livest. Res. Rural. Dev. 8, Table 4 (2013).Mwambene, P. L. et al. Selecting indigenous cattle populations for improving dairy production in the Southern Highlands and Eastern Tanzania. Livest. Res. Rural. Dev. 26 (2014).Rege, J. E. O. et al. Cattle of Kenya: Uses, Performance, Farmer Preferences, Measures of Genetic Diversity and Options for Improved Use (International Livestock Research Institute, 2001).Beffa, L. M. Genotype × Environment Interaction in Afrikaner Cattle. PhD thesis, Univ. of the Free State (2005).Meaker, H. J., Coetsee, T. P. N. & Lishman, A. W. The effects of age at 1st calving on the productive and reproductive-performance of beef-cows. S. Afr. J. Anim. Sci. 10, 105–113 (1980).
Google Scholar 
Chenyambuga, S. W. & Mseleko, K. F. Reproductive and lactation performances of Ayrshire and Boran crossbred cattle kept in smallholder farms in Mufindi district, Tanzania. Livest. Res. Rural. Dev. 21, 100 (2009).
Google Scholar 
Ojango, J. M. K. et al. Dairy production systems and the adoption of genetic and breeding technologies in Tanzania, Kenya, India and Nicaragua. Anim. Genet. Resour. 59, 81–95 (2016).Article 

Google Scholar 
Feedipedia—Animal Feed Resources Information System (FAO, accessed 2021); https://www.feedipedia.org/Lukuyu, B. et al. (eds) Feeding Dairy Cattle in East Africa (East Africa Dairy Development Project, 2012).Rubanza, C. D. K. et al. Biomass production and nutritive potential of conserved forages in silvopastoral traditional fodder banks (Ngitiri) of Meatu District of Tanzania. Asian-Aust. J. Anim. Sci. 19, 978–983 (2006).Article 

Google Scholar 
Food Balances (2010-) (FAO, accessed September 29, 2021); http://www.fao.org/faostat/en/#data/FBSCrop Data for the United Republic of Tanzania (FAO, accessed September 22, 2021); http://www.fao.org/faost at/en/#data/QCGilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data. 5, 180227 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
2014/15 Annual Agricultural Sample Survey Report (The United Republic of Tanzania, 2016).Basic Data for Livestock and Fisheries (The United Republic of Tanzania Ministry of Livestock and Fisheries, 2013).IPCC Guidelines for National Greenhouse Gas Inventories Vol. 4 Agriculture, Forestry and Other Land Use (IPCC, 2006).2019 Refinement to the IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer L. A.) (IPCC, 2019).Fertilizers by Nutrient (FAO, accessed July 6, 2022); https://www.fao.org/faostat/en/#data/RFNHutton, M. O. et al. Toward a nitrogen footprint calculator for Tanzania. Env. Res. Lett. 12, 034016 (2017).Article 

Google Scholar 
Tanzania Fertilizer Assessment (International Fertilizer Development Center, 2012); http://tanzania.countrystat.org/fileadmin/user_upload/countrystat_fenix/congo/docs/Tanzania%20Fertilizer%20Assessment%202012.pdfA Common Carbon Footprint Approach for the Dairy Sector: The IDF Guide to Standard Life Cycle Methodology (International Dairy Federation, 2015); https://www.fil-idf.org/wp-content/uploads/2016/09/Bulletin479-2015_A-common-carbon-footprint-approach-for-the-dairy-sector.CAT.pdfBruzzone, L., Bovolo, F. & Arino, O. European Space Agency land cover climate change initiative. ESA LC CCI data: high resolution land cover data via Centre for Environmental Data Analysis; https://climate.esa.int/en/projects/high-resolution-land-cover/ (2021)Characteristics of Markets for Animal Feeds Raw Materials in the East African Community: Focus on Maize Bran and Sunflower Seed Cake (Kilimo Trust, 2017).Ngunga, D. & Mwendia, S. Forage Seed System in Tanzania: A Review Report (Alliance of Biodiversity and CIAT, 2020).Nkombe, B.M. Investigation of the Potential for Forage Species to Enhance the Sustainability of Degraded Rangeland and Cropland Soils. MSc thesis, Ohio State Univ. (2016).Producer Prices (FAO, accessed 2021); http://www.fao.org/faostat/en/#data/PP More