Ecology

Balvanera, P. et al. Bioscience 64, 49–57 (2014).Article 

Google Scholar 
Hooper, D. U. et al. Ecol. Monogr. 75, 3–35 (2005).Article 

Google Scholar 
Balvanera, P. et al. Ecol. Lett. 9, 1146–1156 (2006).Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Am. J. Bot. 98, 572–592 (2011).Article 
PubMed 

Google Scholar 
Cardinale, B. J. B. J. et al. Nature 486, 59–67 (2012).Article 
CAS 
PubMed 

Google Scholar 
Manning, P. et al. in Advances in Ecological Research (eds Eisenhauer N. et al.) 323–356 (Academic, 2019).Le Provost, G. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01918-5 (2022).Felipe-Lucia, M. R. et al. Proc. Natl Acad. Sci. USA 117, 28140–28149 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Foley, J. A. et al. Science 309, 570–574 (2005).Article 
CAS 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Ecology 94, 1697–1707 (2013).Article 
PubMed 

Google Scholar 
Teles da Mota, V. & Pickering, C. J. Outdoor Recreat. Tour. 30, 100295 (2020).Article 

Google Scholar 
Mitchell, M. G. E. et al. Trends Ecol. Evol. 30, 190–198 (2015).Article 
PubMed 

Google Scholar 
Raudsepp-Hearne, C. & Peterson, G. D. Ecol. Soc. 21, 16 (2016).Article 

Google Scholar 
Chaplin-Kramer, R. & Kremen, C. Ecol. Appl. 22, 1936–1948 (2012).Article 
PubMed 

Google Scholar  More

Seguin, R. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-00981-x (2022).Article 

Google Scholar 
Roberts, C. M. & Polunin, N. V. C. Rev. Fish Biol. Fish. 1, 65–91 (1991).Article 

Google Scholar 
Cinner, J. E. et al. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
MacNeil, M. A. et al. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Di Lorenzo, M. et al. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Rogers, A. et al. Ecology 99, 450–463 (2018).Article 

Google Scholar 
Robinson, J. P. W. et al. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar  More

McCauley, D. J. et al. Marine defaunation: animal loss in the global ocean. Science 347, 1255641 (2015).Article 

Google Scholar 
Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. 4, 158 (2017).Article 

Google Scholar 
Ceballos, G., Ehrlich, P. R. & Raven, P. H. Vertebrates on the brink as indicators of biological annihilation and the sixth mass extinction. Proc. Natl Acad. Sci. USA 117, 13596–13602 (2020).Article 
CAS 

Google Scholar 
Brandl, S. J. et al. Extreme environmental conditions reduce coral reef fish biodiversity and productivity. Nat. Commun. 11, 3832 (2020).Article 
CAS 

Google Scholar 
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).Article 
CAS 

Google Scholar 
Woodhead, A. J., Hicks, C. C., Norström, A. V., Williams, G. J. & Graham, N. A. J. Coral reef ecosystem services in the Anthropocene. Funct. Ecol. https://doi.org/10.1111/1365-2435.13331 (2019).Pereira, P. H. C. et al. Effectiveness of management zones for recovering parrotfish species within the largest coastal marine protected area in Brazil. Sci. Rep. 12, 12232 (2022).Article 
CAS 

Google Scholar 
Campbell, S. J. et al. Fishing restrictions and remoteness deliver conservation outcomes for Indonesia’s coral reef fisheries. Conserv. Lett 13, e12698 (2020).Article 

Google Scholar 
Cinner, J. E. et al. Gravity of human impacts mediates coral reef conservation gains. Proc. Natl Acad. Sci. USA 115, E6116–E6125 (2018).Article 
CAS 

Google Scholar 
Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014).Article 
CAS 

Google Scholar 
Mumby, P. J., Steneck, R. S., Roff, G. & Paul, V. J. Marine reserves, fisheries ban, and 20 years of positive change in a coral reef ecosystem. Conserv. Biol. 35, 1473–1483 (2021).Article 

Google Scholar 
Harrison, H. B. et al. Larval export from marine reserves and the recruitment benefit for fish and fisheries. Curr. Biol. 22, 1023–1028 (2012).Article 
CAS 

Google Scholar 
Kerwath, S. E., Winker, H., Götz, A. & Attwood, C. G. Marine protected area improves yield without disadvantaging fishers. Nat. Commun. 4, 2347 (2013).Article 

Google Scholar 
Di Lorenzo, M., Guidetti, P., Di Franco, A., Calò, A. & Claudet, J. Assessing spillover from marine protected areas and its drivers: a meta‐analytical approach. Fish Fish. 21, 906–915 (2020).Article 

Google Scholar 
Ban, N. C. et al. Well-being outcomes of marine protected areas. Nat. Sustain. 2, 524–532 (2019).Article 

Google Scholar 
Cinner, J. E. et al. Winners and losers in marine conservation: fishers’ displacement and livelihood benefits from marine reserves. Soc. Nat. Resour. 27, 994–1005 (2014).Article 

Google Scholar 
Gurney, G. G. et al. Biodiversity needs every tool in the box: use OECMs. Nature 595, 646–649 (2021).Article 
CAS 

Google Scholar 
Smallhorn-West, P. F. et al. Hidden benefits and risks of partial protection for coral reef fisheries. Ecol. Soc. 27, art26 (2022).Article 

Google Scholar 
Turnbull, J. W., Johnston, E. L. & Clark, G. F. Evaluating the social and ecological effectiveness of partially protected marine areas. Conserv. Biol. 35, 921–932 (2021).Article 

Google Scholar 
Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature 592, 397–402 (2021).Article 
CAS 

Google Scholar 
Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science 368, 307–311 (2020).Article 
CAS 

Google Scholar 
McShane, T. O. et al. Hard choices: making trade-offs between biodiversity conservation and human well-being. Biol. Conserv. 144, 966–972 (2011).Article 

Google Scholar 
MacNeil, M. A. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).Article 
CAS 

Google Scholar 
McClanahan, T. R. et al. Critical thresholds and tangible targets for ecosystem-based management of coral reef fisheries. Proc. Natl Acad. Sci. USA 108, 17230–17233 (2011).Article 
CAS 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Principles for estimating fish productivity on coral reefs. Coral Reefs 39, 1221–1231 (2020).Article 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).Article 

Google Scholar 
Pauly, D. & Froese, R. MSY needs no epitaph—but it was abused. ICES J. Mar. Sci. 78, 2204–2210 (2021).Article 

Google Scholar 
Rindorf, A. et al. Strength and consistency of density dependence in marine fish productivity. Fish Fish. 23, 812–828 (2022).Article 

Google Scholar 
Morais, R. A., Connolly, S. R. & Bellwood, D. R. Human exploitation shapes productivity–biomass relationships on coral reefs. Glob. Change Biol. 26, 1295–1305 (2020).Article 

Google Scholar 
Kolding, J., Bundy, A., van Zwieten, P. A. M. & Plank, M. J. Fisheries, the inverted food pyramid. ICES J. Mar. Sci. 73, 1697–1713 (2016).Article 

Google Scholar 
Morais, R. A. et al. Severe coral loss shifts energetic dynamics on a coral reef. Funct. Ecol. 34, 1507–1518 (2020).Article 

Google Scholar 
Sala, E. & Giakoumi, S. No-take marine reserves are the most effective protected areas in the ocean. ICES J. Mar. Sci. 75, 1166–1168 (2018).Article 

Google Scholar 
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).Article 

Google Scholar 
Parravicini, V. et al. Global patterns and predictors of tropical reef fish species richness. Ecography 36, 1254–1262 (2013).Article 

Google Scholar 
Morais, R. A. & Bellwood, D. R. Global drivers of reef fish growth. Fish Fish. 19, 874–889 (2018).Article 

Google Scholar 
Gislason, H., Daan, N., Rice, J. C. & Pope, J. G. Size, growth, temperature and the natural mortality of marine fish: natural mortality and size. Fish Fish. 11, 149–158 (2010).Article 

Google Scholar 
Graham, N. A. J. et al. Human disruption of coral reef trophic structure. Curr. Biol. 27, 231–236 (2017).Article 
CAS 

Google Scholar 
Froese, R. & Pauly, D. (eds.). FishBase. Version 06/2022. https://www.fishbase.org (2022).Cochrane, K. L. Reconciling sustainability, economic efficiency and equity in marine fisheries: has there been progress in the last 20 years? Fish Fish. 22, 298–323 (2021).Article 

Google Scholar 
Morais, R. A., Siqueira, A. C., Smallhorn-West, P. F. & Bellwood, D. R. Spatial subsidies drive sweet spots of tropical marine biomass production. PLoS Biol. 19, e3001435 (2021).Article 
CAS 

Google Scholar 
Hamilton, M. et al. Climate impacts alter fisheries productivity and turnover on coral reefs. Coral Reefs https://doi.org/10.1007/s00338-022-02265-4 (2022).Cooke, R. et al. Anthropogenic disruptions to longstanding patterns of trophic-size structure in vertebrates. Nat Ecol Evol. 6, 684–692 (2022).Article 

Google Scholar 
Eddy, T. D. et al. Energy flow through marine ecosystems: confronting transfer efficiency. Trends Ecol. Evol. 36, 76–86 (2021).Article 

Google Scholar 
Devillers, R. et al. Reinventing residual reserves in the sea: are we favouring ease of establishment over need for protection? Aquat. Conserv. Mar. Freshw. Ecosyst. 25, 480–504 (2015).Article 

Google Scholar 
Fontoura, L. et al. Protecting connectivity promotes successful biodiversity and fisheries conservation. Science 375, 336–340 (2022).Article 
CAS 

Google Scholar 
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).Article 
CAS 

Google Scholar 
Agardy, T., di Sciara, G. N. & Christie, P. Mind the gap: addressing the shortcomings of marine protected areas through large scale marine spatial planning. Mar. Policy 35, 226–232 (2011).Article 

Google Scholar 
Robinson, J. P. W. et al. Habitat and fishing control grazing potential on coral reefs. Funct. Ecol. 34, 240–251 (2020).Article 

Google Scholar 
Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).Article 

Google Scholar 
Dudley, N. et al. The essential role of other effective area-based conservation measures in achieving big bold conservation targets. Glob. Ecol. Conserv. 15, e00424 (2018).Article 

Google Scholar 
Zupan, M. et al. How good is your marine protected area at curbing threats? Biol. Conserv. 221, 237–245 (2018).Article 

Google Scholar 
Pollnac, R. et al. Marine reserves as linked social–ecological systems. Proc. Natl Acad. Sci. USA 107, 18262–18265 (2010).Article 
CAS 

Google Scholar 
McClanahan, T. R., Marnane, M. J., Cinner, J. E. & Kiene, W. E. A comparison of marine protected areas and alternative approaches to coral-reef management. Curr. Biol. 16, 1408–1413 (2006).Article 
CAS 

Google Scholar 
Smallhorn-West, P. F., Weeks, R., Gurney, G. & Pressey, R. L. Ecological and socioeconomic impacts of marine protected areas in the South Pacific: assessing the evidence base. Biodivers. Conserv. 29, 349–380 (2020).Article 

Google Scholar 
Cinner, J. E. et al. Sixteen years of social and ecological dynamics reveal challenges and opportunities for adaptive management in sustaining the commons. Proc. Natl Acad. Sci. USA 116, 26474–26483 (2019).Article 
CAS 

Google Scholar 
Wilson, S. K. et al. Habitat degradation and fishing effects on the size structure of coral reef fish communities. Ecol. Appl. 20, 442–451 (2010).Article 
CAS 

Google Scholar 
Nash, K. L. & Graham, N. A. J. Ecological indicators for coral reef fisheries management. Fish Fish. 17, 1029–1054 (2016).Article 

Google Scholar 
Brandl, S. J., Goatley, C. H. R., Bellwood, D. R. & Tornabene, L. The hidden half: ecology and evolution of cryptobenthic fishes on coral reefs. Biol. Rev. 93, 1846–1873 (2018).Article 

Google Scholar 
Willis, T. J. Visual census methods underestimate density and diversity of cryptic reef fishes. J. Fish. Biol. 59, 1408–1411 (2001).Article 

Google Scholar 
Allen, K. R. Relation between production and biomass. J. Fish. Res. Board Can. 28, 1573–1581 (1971).Article 

Google Scholar 
Leigh, E. G. On the relation between the productivity, biomass, diversity, and stability of a community. Proc. Natl Acad. Sci. USA 53, 777–783 (1965).Article 
CAS 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Cinner, J. E., Daw, T. & McClanahan, T. R. Socioeconomic factors that affect artisanal fishers’ readiness to exit a declining fishery. Conserv. Biol. 23, 124–130 (2009).Article 
CAS 

Google Scholar 
Cinner, J. E. et al. Linking social and ecological systems to sustain coral reef fisheries. Curr. Biol. 19, 206–212 (2009).Article 
CAS 

Google Scholar 
Hicks, C. C., Crowder, L. B., Graham, N. A., Kittinger, J. N. & Cornu, E. L. Social drivers forewarn of marine regime shifts. Front. Ecol. Environ. 14, 252–260 (2016).Article 

Google Scholar 
Espinosa-Romero, M. J., Rodriguez, L. F., Weaver, A. H., Villanueva-Aznar, C. & Torre, J. The changing role of NGOs in Mexican small-scale fisheries: from environmental conservation to multi-scale governance. Mar. Policy 50, 290–299 (2014).Article 

Google Scholar 
Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).Article 

Google Scholar 
Edgar, G. J. et al. Establishing the ecological basis for conservation of shallow marine life using Reef Life Survey. Biol. Conserv. 252, 108855 (2020).Article 

Google Scholar 
Selig, E. R. et al. Mapping global human dependence on marine ecosystems. Conserv. Lett. 12, e12617 (2019).Article 

Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zhang, S. et al. Sustained productivity and agronomic potential of perennial rice. Nat. Sustain. https://doi.org/10.1038/s41893-022-00997-3 (2022). More

Experiment results and regression modelThe simulation experiment results based on the design scheme are presented in Table 4, including 24 analysis factors and 7 zero-point experiments for estimating the errors. Quadratic multiple regression analysis of the results in Table 4 was performed using the Design-Expert software, and the regression models between the influencing factors and evaluation indices were established as follows:$$ Y_{{1}} = {1767.57} – {64.29}X_{{1}} + {117.46}X_{{2}} + {324.46}X_{{3}} + {107.87}X_{{4}} – {21.81}X_{{1}} X_{{2}} + {17.94}X_{{1}} X_{{3}} – {41.44}X_{{1}} X_{{4}} + {16.69}X_{{2}} X_{{3}} – {41.19}X_{{2}} X_{{4}} + {73.56}X_{{3}} X_{{4}} + {23.2}{X_{{1}}^{{2}}} – {82.42}{{X_{{2}}}^{{2}}} – {13.17}{{X_{{3}}}^{{2}}} – {53.67}{{X_{{4}}}^{{2}}} $$$$ Y_{{2}} = {1968.14} + {636.42}X_{1} + {34.42}X_{2} + {66}X_{3} + {115.17}X_{{4}} + {28.63}X_{{1}} X_{{2}} + {9.13}X_{{1}} X_{{3}} – { 45.87}X_{{1}} X_{{4}} + {1}0X_{{2}} X_{{3}} + {30.5}X_{{2}} X_{{4}} – {1.75}X_{{3}} X_{{4}} + {55.03}{X_{{1}}^{{2}}} – {8.1}{{X_{{2}}}^{{2}}} – {72.72}{{X_{{3}}}^{2}} + {61.03}{{X_{{4}}}^{{2}}} $$Table 4 Experiment schemes and results.Full size tableThe relationship between the actual values of the efficiency of conveying-soil and the distance of throwing-soil and the predicted values of the regression model is shown in Fig. 7. It can be seen from Fig. 7 that the actual values are basically distributed on the predicted curve, consistent with the trend of the predicted values, and linearly distributed.Figure 7Scatter plot. (a) Scatter plot of actual and predicted distance of throwing-soil. (b) Scatter plot of actual and predicted efficiency of conveying-soil.Full size imageVariance analysis and discussionThe F-test and analysis of variance (ANOVA) were performed on the regression coefficients in the regression models of the evaluation indices Y1 and Y2, and the results are shown in Table 5. According to the significance values P of the lack of fitting in the regression models of the objective functions Y1 and Y2 in Table 5, PL1 = 0.1485  > 0.05 and PL2 = 0.2337  > 0.05 (both were not significant), indicating that no loss factor existed in the regression analysis, and the regression model exhibited a high fitting degree.Table 5 ANOVA results of regression model.Full size tableAccording to the ANOVA, the significance values P of each influencing factor in the test could be determined28. For the evaluation index Y1, the factors X1, X2, X3, X4, X3X4, X22, X42 had extremely significant influences, while the factors X1X4, X2X4 had a significant influence. For the evaluation index Y2, the factors X1, X3, X4, X1X4, X12, X32, X42 had extremely significant influences, and the factors X2, X1X4 had a significant influence. Within the level range of the selected factors, according to the F value of each factor as shown in Table 5, the weight of the factors affecting the efficiency of conveying-soil is feeding speed  > helix angle of auger  > rotating speed of auger  > slope angle. And the weight of the factors affecting the distance of throwing-soil is slope auger  > rotating speed of auger  > feeding speed  > helix angle of auger.In addition, it is obvious that there are interactions between the feeding speed and rotating speed of the auger, slope auger and rotating speed of auger, helix angle of the auger and rotating speed of the auger on the efficiency of conveying-soil Y1. For the distance of throwing-soil Y2, there is an interaction between the slope angle and the rotating speed of the auger.Analysis of response surfaceThe fitting coefficient of the efficiency of conveying-soil is R2 = 0.9714, R2adjust = 0.9263, R2pred = 0.8082, the difference between R2adjust and R2pred is less than 0.2. The fitting coefficient of the distance of throwing-soil is R2 = 0.9873, R2adjust = 0.9742, R2pred = 0.9355, the difference between R2adjust and R2pred is smaller than 0.2. It is indicated that the response surfaces of the two models established have good consistency and predictability for the experimental results29.The response surface is created directly using the Design-Expert software. After entering the data, select “Analysis” module. In the “Model-Graph” menu bar, select “3D-surface” to switch to the 3D view. To express the interactive influence of each factor on the efficiency of conveying-soil Y1 and distance of the throwing-soil Y2, the above two quadratic regression equations of the evaluation indices were subjected to the dimensionality reduction treatment. Two of the factors was set to level 0, while the other two underwent interaction effect analysis to study the influence law on the evaluation indices Y1 and Y2, and the corresponding response surfaces were generated, as illustrated in Fig. 8.Figure 83D response diagram effect of evaluation indices. (a) Effect of interaction between X1 and X2 on efficiency of conveying-soil. (b) Effect of interaction between X2 and X4 on efficiency of conveying-soil. (c) Effect of interaction between X3 and X4 on efficiency of conveying-soil. (d) Effect of interaction between X3 and X4 on distance of throwing-soil.Full size imageIt can be seen in Fig. 8a, when the slope angle was constant, the efficiency of conveying-soil increased with the rotating speed of the auger to a certain value, then the efficiency increase changed more gently. The reasons for this phenomenon are described as follows. On the one hand, the greater the kinetic energy of the soil when leaving the original position, and the thinner the soil was cut, resulting in the smaller the probability of blockage in the spiral blade space. On the other hand, the centrifugal force of soil arriving at the pit mouth is greater, so it does not obstruct in the pit mouth. However, if the rotation speed of the auger was too high and the soil layer cut was too thin, the subsequent soil’s driving effect to the front would be weakened, or even the flow would be interrupted, so the vertical rising speed of the soil would be reduced. When the rotational speed of the auger was constant, the efficiency of conveying-soil decreased with the increase of slope and then slightly increased. With the increase of slope, the time of slope cutting process increased, and there was more soil backfilling on the side of high altitude, which leaded to the reduction of soil discharge efficiency. However, with the increase of slope, the amount of soil slide at the pit mouth was increased, improving the efficiency of soil discharge. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the slope angle, indicating that the rotating speed of auger X4 had a more significant influence than the slope angle X1.As can be seen in Fig. 8b, when the helix angle of the auger was fixed, the efficiency of conveying-soil continued to increase with the increase of the rotation speed. When the rotating speed of auger was fixed, the efficiency of conveying-soil increased with the increase of the helix angle and tends to decrease when it reached a certain value. The spiral blades space was the channel of soil movement. This phenomenon was caused by the increase of the gap between the two spiral blades with the increase of the helix angle of the auger, the soil was not easy to produce blockage. Meanwhile, the movement distance of soil was shorter, and the soil with higher kinetic energy was discharged more quickly from the pit. When reaching the pit mouth, the angle of soil throwing was larger and the soil backfilling rate was reduced. However, if the helix angle of auger was too large, the upward support ability and friction of the spiral blade surface to the soil would be reduced. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the helix angle than the rotating speed of the auger, indicating that the helix angle of the auger X2 had a more significant influence than the rotating speed of the auger X4.When the feeding speed was fixed, the efficiency of throwing-soil continued to increase with the increase of the rotating speed. When the rotating speed of auger was fixed, the efficiency of the throwing-soil with the increase of the feeding speed (see in Fig. 8c). The phenomenon was caused by the faster the feeding speed of the auger, the thickness of soil cut per unit time increased. Furthermore, the subsequent driving force of soil increased, and the soil kinetic energy increased. However, in the actual production, excessive feeding speed would cause soil blockage on the surface of spiral blades. The reason is due to in the simulation process, the soil would not stop moving because of blockage. Further analysis demonstrated that the response surface for Y1 changed more rapidly in the direction of the rotating speed than in that of the feeding speed, indicating that the rotating speed of auger X4 had a more significant influence than the feeding speed X3.When the slope was fixed, the distance of the throwing-soil increased with the increase of rotation speed of the auger, and the increase amplitude increased gradually, as shown in Fig. 8d. The reason for this phenomenon was that the soil had more kinetic energy when it left its original position and the centrifugal force it received when it reaching the pit mouth is greater. When the rotation speed was too low, the soil layer was thin and the subsequent soil driving force was insufficient, resulting in the soil mass per unit area at the pit mouth was light and then the kinetic energy was small. When the rotating speed of auger was fixed, the distance of the throwing-soil increased continuously with the increase of the slope. As the slope increased, the time of soil swipe down process increased and then the rolling distance on the slope increased. Further analysis demonstrated that the response surface for Y2 changed more rapidly in the direction of the slope angle than in that of the rotating speed of auger, indicating that the slope angle X1 had a more significant influence than the rotating speed X3.Comprehensive optimal designAs relative importance and influencing rules of various experimental factors on evaluation indexes were different from each other, evaluation indexes should be taken into comprehensive consideration30. The optimization equation is obtained by the Design-Expert software multi-objective optimization method with Y1 and Y2 as the optimization objective function.$$25le {X}_{1}le 45$$$$10le {X}_{2}le 22$$$$0.04le {X}_{3}le 0.1$$$$30le {X}_{4}le 120$$$${{Y}_{1}}_{mathrm{max}}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$$${{Y}_{2}}_{min}({X}_{1},{X}_{2},{X}_{3},{X}_{4})$$In practice, the best combination of parameters needs to be selected according to the terrain slope. When the slope was fixed, the Design-Expert software was applied to optimize and solve the above mathematical model. The optimal combination of working parameters affecting the efficiency of conveying-soil Y1 and distance of throwing-soil Y2 for the auger were obtained and are shown in Table 6. If the ground preparation was required before the digging operation, the digging parameters can be designed according to values of Group 6 in Table 6.Table 6 Optimal parameter combinations of several terrain slopes.Full size tableDisturbance of soilA soil disturbance is defined as the loosening, movement and mixing of soil caused by an auger passing through the soil16. In the interface of the EDEM Analyst, add a “Clipping plane” to show the movement of the auger inside the pit. The kinetic energy, soil particle velocity vector, and velocity value of soil particles is observed when the auger in the middle of the soil bin31,32, as shown in Fig. 9.Figure 9The disturbance of the soil effect by spiral blade.Full size imageThe soil was lifted to the surface and then dropped to the lower side. In addition to the volume occupied by the spiral blades, the disturbed area also included the out-of-pit disturbed area caused by the compression of the cutting end of the spiral blade, as shown in the lower left corner of the auger.The kinetic energy and velocity of soil decreased firstly and then increased along the opposite direction of the auger feeding. The cutting end of the auger and the soil-throwing section occurred in the region with high kinetic energy and velocity. This was because the maximum kinetic energy was obtained at the cutting end of the auger, which was gradually consumed in the process of rising. After reaching the dumping end, the soil lost the restraint of the pit wall. When the centrifugal force of soil lost the reaction force, the kinetic energy of soil increased. Too much kinetic energy, however, can cause the soil to spread too far, causing subsequent trouble. The kinetic energy of the soil at the cutting end was related to the rotational speed of the auger. The spiral angle affected the angle between the force and gravity, and then the kinetic energy consumption in the process of soil increased.Verification experimentsTo verify the accuracy of the optimization model for auger working, as well as to evaluate the rationality of the working parameter combination optimized by the virtual experiment, performance verification tests were carried out on the EDEM software. According to the optimized process parameter setting test (as shown in Table 6), the relative error between the theoretical value and the experimental value was obtained. The verification test results are summarized in Table 7. The average relative errors of the efficiency of conveying-soil and the distance of throwing-soil between the Theoretical value and text value were only 4.4%, 9.1%. The simulation model is fairly accurate. The field performance verification experiments were carried out in slope. Figure 10 illustrates the field test and working conditions.Table 7 Results and comparison of validation test.Full size tableFigure 10Operation diagram at the experiment site.Full size image More

All methods were performed in accordance with the local relevant guidelines, regulations and legislation.InstrumentsLI-6400 photosynthesis system (LI-6400 Inc., Lincoln, NE, USA) and PAM-2500 portable chlorophyll fluorescence apparatus (PAM-2500, Walz, Germany) were used in the study.MaterialsAbout 90 living E. brevicornu plants were collected from Taihang Mountains in October 2018. The E. brevicornu was not in endangered or protected. The collection of these E. brevicornu plants was permitted by local government. These plants were averagely planted in nine plots of 2 m2. The roots of E. pubescens were planted 6–8 cm below ground. These plots were placed on farmland near Taihang Mountains and covered with sunshade net (about 70% light transmittance). These plants were timely irrigated after planting to ensure that they grew well but not fertilized.Determination of photosynthetic characteristicsThe photosynthetic characteristics of mature leaves on the E. brevicornu plants were determined between June 6–8, 2019 with the Li-6400 photosynthesis system. The diurnal variation of photosynthesis in three leaves of three plants was determined. When the light response curve was determined, the temperature of the leaf chamber was set at 28 °C, and the concentration of CO2 in the leaf chamber was set at 400 µmol mol−1. When determining the CO2 response curve, the light intensity in the leaf chamber was set at 1000 µmol m−2 s−1, and the temperature of the leaf chamber was set at 28 °C. The light response curve and CO2 response curve were determined three times in three leaves of three different plants.Determination of chlorophyll fluorescence characteristicsThe fluorescence characteristics of chlorophyll in E. brevicornu leaves were determined with PAM-2500 portable chlorophyll fluorescence apparatus between June 8–9, 2019. The leaves underwent dark adaptation for 30 min before determining slow kinetics of chlorophyll fluorescence. Then the light curves of chlorophyll fluorescence were determined. All of these determinations were repeated three times on three mature leaves of three plants.The data was analysed with SPSS (Statistical Product and Service Solutions, International Business Machines Corporation, USA). The light response curves were fitted with following modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{LCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. LCP is the light compensation point. E is the apparent quantum yield. M and N are parameters. The dark respiration rate under the LCP is calculated according to E·LCP. The light saturation point (LSP) is calculated according to (((M + N) ·(1 + N·LCP)/M)½)/−1)/N.The net photosynthetic rate under the light saturation point (LSP) can be calculated according to the above model.The CO2 response curves were fitted with below modified rectangular hyperbola model11,12.$${text{Photo}}, = ,{text{E}}cdotleft( {{1} – {text{M}}cdot{text{PAR}}} right)cdotleft( {{text{PAR}} – {text{CCP}}} right)/({1}, + ,{text{N}}cdot{text{PAR}})$$PAR is the value of light intensity in leaf chamber of Li-6400 photosynthesis system. Photo is net photosynthetic rate. CCP is CO2 compensation point. E is also the apparent quantum yield. M and N are parameters. The dark respiration rate under the CO2 calculated according to E·CCP. The CO2 saturation point (CSP) is calculated according to (((M + N) ·(1 + N·CCP)/M)½)/−1)/N.The net photosynthetic rate under the CO2 saturation point (CSP) can be alternatively calculated according to the above model.The light curves of chlorophyll fluorescence were fitted according to the below model of Eilers and Peeters12,13.$${text{ETR}}, = ,{text{PAR}}/({text{a}}cdot{text{PAR}}^{{2}} , + ,{text{b}}cdot{text{PAR}}, + ,{text{c}})$$ETR is the electron transport rate of photosynthetic system II. PAR is fluorescence intensity. The letters a, b and c are parameters. More

Sample collection and isolation of V. cholerae O1 possessing the CT geneTwenty-three patients (patient numbers 9 to 31) who were diagnosed with cholera were examined in this study. The diagnosis was confirmed by the isolation of V. cholerae O1 from the stool of each patient. The age of patients, date of hospital admission, stool sampling date, pathogen isolated and medicines administered to the patients as treatments are described in Supplementary Table S1. Twenty-one of the stool samples were collected on the first day of hospitalization, while the remaining two stool samples were collected on the second day (patient number 29) and fourth day (patient number 10) of hospitalization. All patients had not been given any antibiotics and the samples of diarrheal stool were taken during severe diarrhea.To confirm the presence of the CT gene (ctx) in these 23 isolates, we examined the presence of ctxA in these isolates by PCR. The PCR to detect ctxA was performed as reported by Keasler and Hall6. In this PCR, amplification was performed in 30 cycles. The size of the amplified ctxA fragment was 302 bp. The target fragment was amplified from each of the V. cholerae O1 isolates. This indicated that all of the V. cholerae O1 isolates from the 23 cholera patients possessed ctxA.CT production from the isolatesThe production of CT from these 23 isolates was examined by detecting secreted CT in the medium. The 23 isolates were cultured statically in AKI medium7, and the secreted CT in the culture supernatants was measured using the GM1-ganglioside enzyme-linked immunosorbent assay (ELISA) method8. The detection limit of CT by the ELISA method used is 1.0 ng ml−1. All the samples examined were found to have CT above this concentration (Fig. 1). This shows that all isolates examined are toxigenic V. cholerae O1.Figure 1Amount of cholera toxin produced by V. cholerae O1 isolated from patients with diarrhea. Twenty-three strains of V. cholerae O1 were isolated from 23 patients with diarrhea. These isolates were cultured statically in AKI medium7 at 37 °C for 24 h. After removing the cells by centrifugation, the CT in the culture supernatants was measured using a GM1-ganglioside ELISA method8. The samples indicated by blue circle are isolates obtained by bacterial culture from two patients (patient 12 and patient 18), who are focused on in this study.Full size imageAnalysis of the stool samples of patients diagnosed with cholera diseaseMetagenomic sequencing analysisThe primary objective of this metagenomic analysis is to show the proportion of V. cholerae living in the diarrhea stool. Subsequently, if the number of V. cholerae infected in the intestinal tract is small, it is required to clarify the etiological microorganisms that cause diarrhea in that patient. For this analysis, it is necessary to investigate the presence of pathogenic microorganisms other than V. cholerae in the stool. To do this, we need to analyze the gene reads obtained by metagenomic analysis with a comprehensive manner. Therefore, we planned to obtain reads with the Burrows-Wheeler Alignment tool (BWA) with default parameters, a matching software with the ability to fulfill these objectives9 (http://bio-bwa.sourceforge.net).However, we were concerned that the genes derived from organisms other than V. cholerae in the stool were counted mistakenly as genes derived from V. cholerae in the analysis using BWA. We therefore first examined genes in stool from people unrelated to cholera disease to ensure that the analysis method we planned to use in this study would correctly detect genes from V. cholerae in stool. For this analysis we used DNA sequences reported by the NIH Human Microbiome Project (https://www.hmpdacc.org/hmp/hmp/hmasm2/). The genes we have analyzed are DNA derived from feces of 20 healthy individuals (10 males and 10 females). The results are shown in Supplementary Table S2.The number of reads analyzed in this analysis varied from sample to sample. The largest number obtained after quality filtering was 60,975,797. The lowest number was 10,301,809. However, the number of reads detected as originating from V. cholerae was very small (12 reads or less) in all samples, and none of them were detected in 7 samples. This very small number shows that the analytical method used is suitable for detecting the genes from V. cholerae in these samples.Therefore, we analyzed DNA and RNA samples from prepared diarrheal stool by the method using BWA. All raw sequencing data obtained were deposited into the DDBJ Sequence Read Archive under the accession code PRJDB10675. This number can be searched not only from DDBJ but also from EMBL and GenBank.Diarrheal stools are mostly composed of liquid, and their properties are very different from those of normal stools. The origin of the nucleic acids in diarrheal stools varies from patient to patient and is not constant. One sample may contain many genes derived from human cells, while another sample may contain many genes derived from microorganisms. To clarify the nature of the reads we obtained, we determined the proportion of reads of bacterial origin to the total number of reads in the samples analyzed, and presented this proportion in order of patient age (Fig. 2a). The ratios were not consistent, indicating that the cells of eukaryotic origin and microorganisms existing in the stool of patients with diarrhea varied from person to person.Figure 2Age of patient and the ratio of the number of read detected by metagenomic sequencing analysis of their stools. The DNA in the stool samples from 23 patients who were diagnosed with cholera disease were extracted using a commercially available kit. Patient ages are listed in Supplementary Table S1. The extracted DNA were investigated by a metagenomic sequencing analysis to clarify the origin of individual DNA. The origin of the DNA sequences was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads originating from ctxA in each sample are shown in Supplementary Table S3 (the data from DNA sample). The age of each patient and the ratio of the number of reads from all bacteria to the total number of reads after filtering (a) and the ratio of the number of reads from V. cholerae to the number of reads from all bacteria (b) were calculated. The horizontal axis of these figures shows the age of each patient and is the same arrangement in both (a) and (b). The numbers in parentheses indicate the sample numbers. This sample number is also the patient’s number.Full size imageThis result implied that it was difficult to detect V. cholerae in a sample with a small number of read derived from bacteria. Therefore, it was unclear whether the data obtained by the analysis was suitable for the detection of V. cholerae. In order to examine whether the data shown in Fig. 2a can be used to clarify the infection status of V. cholerae, the ratio of the reads from V. cholerae to the reads of all bacteria in the sample was calculated (Fig. 2b). As a result, the reads from V. cholerae were detected even in samples with a low ratio of bacterial genes, as seen in patients 13, 25, and 29. Conversely, some patients, such as patients 10, 18, and 17, had a high proportion of bacterial genes but a low detection rate of the read from V. cholerae (Fig. 2a,b). From these results, we thought that the data obtained are useful for analyzing the infection status of V. cholerae in the intestinal tract of the examined patients. The data also showed that patient age did not affect the intestinal retention of V. cholerae.In order to more clearly illustrate the presence of V. cholerae in the diarrheal stools of the patients examined, the ratio of reads from V. cholerae to total reads for each sample which was determined in Fig. 2a was sorted in descending order. The results are shown in Fig. 3a. The ratio (percentage) in each patient is indicated by the blue bar in the figure. The numbers in parentheses after the sample number, with D as the first letter, indicate the order from lowest to highest percentage obtained. As shown in Fig. 3a, the percentage of V. cholerae that the patients carried in their stools varied from 0.003% (sample 12(D1)) to 38.337% (sample 28(D23)).Figure 3The ratios of DNA and RNA derived from V. cholerae in stool samples. The DNA and RNA in the stool samples from 23 patients who were diagnosed with cholera disease were extracted using a commercially available kit. Subsequently, the RNA samples were treated with DNase I to remove DNA from the samples. Reverse-transcribed DNA was prepared from these RNA samples using random primers and reverse transcriptase. The extracted DNA and reverse-transcribed DNA were investigated by a metagenomic sequencing analysis to clarify the origin of individual DNA and RNA. The origin of the DNA sequences was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads originating from ctxA in each sample are shown in Supplementary Tables S3 (the data from DNA) and S4 (the data from RNA). The percentages of reads of DNA from V. cholerae and from ctxA relative to the total reads are presented by blue bar and red bar in panel a, respectively. The percentages of reads of DNA from V. cholerae relative to the total bacterial reads are presented in panels b. Samples are arranged in ascending order of the ratio of reads from V. cholerae to the total reads in the DNA analysis. The ranking of each sample is presented by the numbers in parentheses starting with the letter D. The samples in these panels are arranged in the order of the D number. Similarly, the ratio of reads from V. cholerae to the total RNA reads and the total bacterial RNA are presented in panels c and d, respectively. The samples indicated by red circle are samples from the diarrheal stools of a patients who are focused on in this analysis.Full size imageHowever, what we want to reveal in this study is the presence of toxigenic V. cholerae producing CT. The genes presented by blue bar in Fig. 3a appear to contain the genes derived from toxigenic V. cholerae, but it cannot be concluded that they are. It is highly possible that other genes derived from such as V. cholerae not possessing ctx or bacteria having the same gene sequence as V. cholerae, are included. So, in order to examine the existence of V. cholerae possessing ctx, we examined the number of reads derived from ctxA (Supplementary Table S3). In the samples with D number 8 or more, the gene derived from ctxA was detected in all the samples except one sample (D9). The ratio of read from ctxA to the number of reads from total DNA is shown by the red bar in Fig. 3a. The ratio of the number of reads derived from ctxA to the total DNA was correlated with the ratio of the number of reads derived from the V. cholerae gene to the total DNA (Fig. 3a). From these results, it seems that most of the genes of V. cholerae detected in Fig. 3a are derived from V. cholerae possessing ctx.Furthermore, the ratio of the number of reads of V. cholerae to the number of reads derived from total bacteria which was obtained in Fig. 2a, was arranged in the order used for the array in Fig. 3a (the order of the ratio of the number of reads from V. cholerae to the number of reads from total DNA) (Fig. 3b). From this arrangement of Fig. 3b, it can be seen that the sample with a large D head number has a large proportion of V. cholerae in the bacteria. The highest value was obtained from sample 24 (D22). The sample showed that 95.917% of the bacteria was V. cholerae.On the other hand, in many samples with small D numbers, this ratio is small, but there are exceptions. For example, in samples 25 (D3), 29 (D8) and 13 (D11), the presence of V. cholerae is clear. Although not as clear as these three samples, the presence of V. cholerae in other samples such as 22 (D4), 21 (D5), 9 (D10) and 11(D12) is evident, although in small quantities (Fig. 3b). Therefore, it was considered that these patients were infected with V. cholerae. These results seem to accurately reflect the actual state of V. cholerae in the stool. Therefore, it was considered that the infection status of V. cholerae in the patient could be inferred from the obtained data.As shown in Fig. 3b, in the samples of 18 (D2), 12 (D1), 17 (D7), 10 (D9)) and 23 (D6), the ratio of the read from V. cholerae to the read from total bacteria is very low at 0.032%, 0.118%, 0.225%, 0.244% and 0.285%, respectively. It was unknown whether these patients were infected with V. cholerae and developed diarrhea due to the infection with V. cholerae. Therefore, further examination was needed to determine if these patients were infected with V. cholerae. These five samples are marked by red circles in Fig. 3a,b.Subsequently, we examined the ratio of the reads of RNA of V. cholerae to clarify the expression of the genes of V. cholerae in the intestinal lumen of these patients. RNA samples were prepared by different methods from the patient’s stool and the RNA in these samples was analyzed by metagenomic sequencing analysis. The ratio of the number of reads derived from the RNA of V. cholerae to the number of reads derived from total RNA and to the number of reads derived from total bacterial RNA in the sample was determined. The results are shown in Fig. 3c,d, respectively. Samples that had fewer reads for genes derived from V. cholerae in the previous analysis of DNA reads (Fig. 3a,b)were also indicated with a red circle in Figs. 3c,d. These samples also had low amounts of RNA read from V. cholerae. In particular, the ratio of RNA read from V. cholerae to total bacterial RNA in samples 12 (D1) and 18 (D2) was low, 0.038% and 0.236%, respectively (Supplementary Table S4, Fig. 3d). Judging from these low values, it is doubtful that these two patients, patients 12 and 18, had diarrhea due to infection with V. cholerae.Detection of ctxA by PCRSubsequently, we amplified ctxA in the DNA samples extracted from the stool samples by PCR, in order to reconfirm the presence of ctx in stool samples. The PCR was performed using the same conditions used for the detection of ctxA in the isolates as described above in the “Sample collection and isolation of V. cholerae O1 possessing the CT gene” section of the “Results”. Amplification in this PCR was also done for 30 cycles.From the results of metagenomic sequencing shown in Fig. 3, we found that the samples from patient 12 (D1) and patient 18 (D2) contained few genes derived from V. cholerae O1. The results obtained by PCR are shown in Fig. 4. The samples from the two patients, 12 (D1) and 18 (D2), are indicated by blue circle. No distinct bands corresponding to ctxA were detected in the lanes analyzed sample 12(D1). Meanwhile, a very faint band was visible in the lane where the sample from 18(D2) was analyzed. However, it often happens that small amounts of sample are mixed into adjacent lanes when adding the sample to be analyzed in agar electrophoresis. Hence, we concluded that the amount of ctxA in these two samples amplified by PCR was very low. This supports our inference that the diarrhea in these two patients was not caused by the infection with V. cholerae O1.Figure 4PCR to detect ctxA in the stool samples of diarrhea patients. DNA was extracted from the stool samples of 23 patients who were diagnosed with cholera disease. PCR to amplify ctxA in these DNA samples was performed using the specific primers ctcagacgggatttgttaggcacg and tctatctctgtagcccctattacg6, and the products were analyzed by agarose gel electrophoresis. The sample numbers are the same as the numbers shown in the footnotes of Fig. 3. Numbers beginning with D in parentheses show the order of the content of DNA from V. cholerae among these samples. The samples indicated by blue circle are samples from the diarrheal stools of patients (patients 12 and 18), who are focused on in this study. S: the size marker for gel electrophoresis; N: the negative control in which DNA was not added to the reaction mixture; P: the positive control in which DNA prepared from V. cholerae O1 N1696128 was added to the reaction mixture.Full size imageSimilarly, clear bands were not detected in samples 9(D10), 10(D9), 13(D11), 22(D4), and 25(D3). The results of metagenomic analysis of these samples showed that the number of read from V. cholerae was low and ctxA was either not detected (samples 10(D9), 22(D4) and 25(D3)) or was detected but in small amounts (samples 9(D10) and 13(D11) (Supplementary Table S3, Fig. 3a).The amount of sample added to the reaction solution in the PCR reaction was as small as 5 µl, and it is not clear whether this small volume of solution contained the necessary amount of ctxA for the amplification in PCR. It is also possible that the sample contained substances that would inhibit amplification by PCR. For these reasons, we believe that no clear band corresponding to ctxA appeared in this PCR. However, it is clear from the results of Fig. 3b,d that these samples, (9(D10), 10(D9), 13(D11), 22(D4), and 25(D3)) contain the gene derived from V. cholerae (ctx). Therefore, we considered these four patients to be patients infected with V. cholerae.The levels of CT and proteolytic activity in the stool samplesFrom the genetic studies in Figs. 3 and 4, it was inferred that V. cholerae O1 was not involved in the onset of the diarrhea in two patients (12(D1) and 18(D2)). However, this inference was based on amplification and analysis of genetic sample prepared from diarrhea stool of patients. There is no proof that the sample procurement and the analysis of sample was done reliably with high probability. Hence, we thought that it was necessary to analyze samples adjusted from different perspectives by different means.Then, we challenged to measure the amount of CT. CT is the toxin responsible for the diarrhea caused by V. cholerae O1. CT is released into the intestinal lumen, where it acts on the intestinal cells of patients to induce diarrhea. Thus, we measured the CT content in the stool samples. In addition, we also measured the proteolytic activity in the stool samples, because CT is sensitive to proteolytic activity, and we were concerned that the CT would be degraded by proteases during storage outside of the body.The CT content and the proteolytic activity in the stool samples of the 23 cholera patients were measured by the GM1-ganglioside ELISA method and the lysis of casein, respectively8,10, and the results are presented in Fig. 5a,b, respectively.Figure 5The levels of CT and proteolytic activity in the stool samples. Twenty-three stool samples of patients who were diagnosed with cholera disease were centrifuged at 10,000×g for 10 min. The CT content of the supernatants was determined using a GM1-ganglioside ELISA method8 (a). The proteolytic activity of the supernatants was determined by the lysis of casein10 (b). The sample numbers are the same as the numbers shown in the footnotes of Fig. 3. Numbers beginning with D in parentheses show the order of the content of DNA from V. cholerae among these samples. The samples in this figure are arranged in the order of the D numbers. A bar indicating the amount of CT is not drawn in the figure for the sample whose CT amount was below the detection limit. From the tests shown in Figs. 2, 3 and 4, samples of two patients who are unlikely to have diarrhea caused by the infection with V. cholerae are marked with a blue circle. O.D.: optical density.Full size imageProteolytic activity was detected in all samples, although there were differences in the strengths of the activity. It was also found that high protease activity was not associated with decreased levels of CT in the samples, e.g., sample 11(D12) showed the highest protease activity among the samples examined, and the amount of CT in that sample was also high. Therefore, we considered that the proteolytic activity had almost no influence on the amount of CT in this study. Furthermore, the fact that protease activity was found in all samples indicated that these samples were collected and stored without any significant denaturation.The ELISA method used in this assay can accurately detect CT at concentrations above 1.0 ng ml−1, but it is impossible to accurately determine the concentration of CT at concentrations below 1.0 ng ml−1. Therefore, we treated samples containing less than 1.0 ng ml−1 of CT as containing no CT.As described above, we considered that the diarrhea in the two patients (12(D1) and 18(D2)) was not due to the infection with V. cholerae O1 from the genetic analysis. The analysis of CT in stool samples showed that the CT concentrations of these two samples were below the detection limit (Fig. 5a). This indicates that the number of V. cholerae O1 in the intestinal lumen of these patients, (12(D1) and 18(D2)), was extremely low at the time of sampling.Investigation of diarrheagenic microorganisms in diarrheal stoolIt was shown that diarrhea in patients 12 (D1) and 18 (D2) may have been caused by infection with microorganisms other than V. cholerae. Then we examined the data of metagenomic sequencing of these two patients to reveal the infected diarrhea-causing microorganisms (DDBJ Sequence Read Archive under the accession code PRJDB10675). As a result, we found that that DNA from the two bacteria, Streptococcus pneumoniae and Salmonella enterica was abundant in the stools of patients 12(D1) and 18(D2), respectively.The ratios of DNA read of St. pneumoniae in DNA samples of patient 12(D1) to the total DNA and to the total bacterial DNA are 0.095% and 3.988%, respectively. These ratios of V. cholerae in this patient, 12 (D1), are 0.003% and 0.118%, respectively. And those of S. enterica in the stools of patients 18(D2) are 0.536% and 1.118%, respectively. And these ratios of V. cholerae in this patient, 18 (D2), are 0.015% and 0.032%, respectively (Supplementary Table 2).These two bacteria, St. pneumoniae and S. enterica, are bacteria that are not detected as normal intestinal bacteria. As shown, these ratios of DNA of each bacteria in diarrheal stool are much higher than these of V. cholerae. Therefore, these two bacteria are considered to be related to these patients’ symptom, respectively.Nonetheless, toxigenic V. cholerae O1 was also isolated from these two patients in laboratory bacteriology tests. It is likely that some of the very few V. cholerae O1 in the intestinal tract were extruded with the diarrhea and were subsequently detected by the enrichment culture for V. cholerae. This indicated that V. cholerae O1 may cause subclinical infections in residents of the Kolkata region of India. With this subclinical infection, the number of V. cholerae O1 inhabiting the intestinal tract might be small.Surveillance of patient samples where no diarrhea-causing microorganisms were detectedTo detect people with a subclinical infection of V. cholerae O1, we further analyzed the specific-pathogen-free stool samples of diarrhea patients. “Specific-pathogen-free stool sample” refers to the stool samples in which no etiological agent of diarrhea, including V. cholerae, was detected by our bacterial examination in the laboratory.The number of samples examined in this analysis was 22 (samples number 1001 to 1022). All 22 diarrhea patients examined were inpatients at ID hospital, Kolkata. From the 22 patients, 20 patient stool samples were collected on the 1st day of hospitalization, and the stools of the remaining two patients (patients 1004 and 2022) were collected on the 2nd day of hospitalization. Antibiotics were used in a limited manner in these patients. Ofloxacin was the only antibiotic administered, and only four patients (patients 1001, 1011, 1012, and 1021) were administered with it (Supplementary Table S1).DNA and RNA were extracted from the stool samples, and the DNA and RNA were analyzed by a metagenomic sequencing analysis using the same method used in the analysis of diarrheal stools from cholera patients.Reads of the genes from V. cholerae were detected in every sample, although the value varied from sample to sample (Supplementary Tables S5 and S6). Although reads of the genes from V. cholerae were detected in every sample, we do not believe that every stool sample examined contained V. cholerae. In the metagenomic analysis, if the base sequence of a read was common to multiple bacteria, the read was recognized as being derived from those multiple bacteria. Therefore, even if a bacterium is not present in the sample, the reads in common with other bacteria are counted as the reads of those bacteria, i.e., if a read from bacteria other than V. cholerae is homologous to a corresponding gene of V. cholerae, its detection indicates that one gene derived from V. cholerae was found in the sample. The total number of such reads is finally counted as the number of reads of V. cholerae. Therefore, it is unclear whether bacteria presenting a low read count are present in the sample. In order to solve these problems, not only the reads derived from V. cholerae but also the reads derived from ctxA were searched for in the sample.In addition, as described above, other DNA present in diarrheal stool, such as food-derived DNA, might hinder the analysis of the bacteria in the stool. As such, we determined four relative values of the number of reads from the genes of V. cholerae: the ratio of DNA reads of V. cholerae to the total DNA; the ratio of the DNA reads of V. cholerae to the total bacterial DNA; the ratio of the RNA reads of V. cholerae to the total RNA; and the ratio of the RNA reads of V. cholerae to the total bacterial RNA. Furthermore, we determined the relative value of the number of reads from ctxA to the total DNA (Supplementary Tables S5 and S6). These ratios are also shown in Fig. 6a–d.Figure 6The ratio of DNA and RNA derived from V. cholerae in stool samples of the specific-pathogen-free patients. The stool samples from 22 diarrheal patients in which no etiological agent of diarrhea, including V. cholerae, was detected by our bacterial examination in the laboratory were analyzed in this examination. The extraction of DNA and RNA, and the preparation of reverse-transcribed DNA samples from the RNA samples were performed in the same manner as in Fig. 2. The origin of the reads obtained in this analysis was assigned by mapping to a database that included human and microorganism sequences. The obtained numbers of total reads, total bacterial reads, reads originating from V. cholerae, reads from ctxA in each sample are shown in Supplementary Tables S5 (the data from DNA) and S6 (the data from RNA). The percentages of reads of DNA from V. cholerae (blue bar in a) and of reads of DNA from ctxA (red bar in a) relative to the total DNA reads, and the percentages of reads of DNA from V. cholerae relative to the total bacterial DNA reads (b) are presented. Similarly, the results obtained from the RNA samples are presented in (c) and (d). The (c) and (d) show the percentages of reads of RNA from V. cholerae relative to the total RNA reads and to the reads of total bacterial RNA, respectively. The samples indicated by green circles are the samples of interest in this manuscript, as described in the text.Full size imageThe ratios of the number of reads derived from DNA of V. cholerae and the number of reads derived from ctxA to the number of reads of total DNA genes in these samples are shown by the blue and red bars in Fig. 6a, respectively. Reads from ctxA were detected in samples 1004, 1006, 1010, 1017 and 1018. This indicates that V. cholerae possessing ctx were alive in these samples; 1004, 1006, 1010, 1017 and 1018.The ratio of V. cholerae to total bacterial DNA in these samples was examined. The results are shown in Fig. 6b. The proportion of DNA of V. cholerae to total bacteria DNA in the stool of patients 1004, 1006, 1010, 1017, and 1018 is 28.633%, 0.234%, 73.068%, 2.282%, and 2.774%, respectively (Fig. 6b).In addition, the read of RNA from V. cholerae was examined. The ratio of the RNA to total RNA and to total bacterial RNA was calculated. RNA derived from V. cholerae was reliably detected in 4 of the 5 samples (1004, 1010, 1017, 1018). The ratio of the remaining one sample (1006) were low (Fig. 6c,d). However, it has been shown that the sample (1006) contains the read from DNA of ctxA (Supplementary Table S5). Therefore, we considered these five samples to be those containing toxigenic V. cholerae.As antibiotics were not administered to these five patients, the effects of antibacterial agents could be disregarded in our examination of the bacterial species in the stools. Among these 5 samples, the ratio of samples 1004 and 1010 examined in this examination was high and comparable to those of the samples of the cholera patients (Figs. 3 and 6). We considered that the diarrhea of the patients 1004 and 1010, might have been caused by the infection with V. cholerae O1.On the other hand, the samples of patients 1006, 1017 and 1018 did not show high values that could indicate that the diarrhea was caused by the infection with V. cholerae. It is probable that the diarrhea of these three patients (1006, 1017 and 1018) was caused by the actions of factors other than V. cholerae O1, and that a small number of V. cholerae inhabits the intestinal tract as a form of subclinical infection; this would explain why a gene derived from V. cholerae was detected by the metagenomic sequencing analysis. These results support the hypothesis that subclinical infections of V. cholerae occur in Kolkata. More

European Commission Directorate General for Research and Innovation. A sustainable Bioeconomy for Europe: Strengthening the Connection Between Economy, Society and the Environment: Updated Bioeconomy Strategy (Directorate General for Research and Innovation, 2018).
Google Scholar 
Teitelbaum, L., Boldt, C. & Patermann, C. Global Bioeconomy Policy Report (IV): A Decade of Bioeconomy policy (International Advisory Council on Global Bioeconomy, 2020).
Google Scholar 
European Parliament; European Council. Directive (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the promotion of the use of energy from renewable sources (2018). (Online). http://data.europa.eu/eli/dir/2018/2001/oj.European Parliament; European Council. Directive 2009/28/EC on the Promotion of the Use of Energy from Renewable Sources (2009). (Online). http://data.europa.eu/eli/dir/2009/28/oj.Glasenapp, S., & McCusker, A. Wood energy data: the joint wood, in Wood Energy in the ECE Region: Data, Trends and Outlook in Europe, the Commonwealth of Independent States and North America, Geneva, United Nations’ Economic Commission for Europe: ECE/TIM/SP/42, 12–29 (2018).Eurostat. Wood Products—Production and Trade (2021). (Online). https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Wood_products_-_production_and_trade#Wood-based_industries. Accessed 10 9 2021.Food and Agriculture Organization of the United Nations. FAOSTAT: Forestry Production and Trade (2021). (Online). http://www.fao.org/faostat/en/#data. Accessed 13 September 2021.The Intergovernmental Panel on Climate Change. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (PCC Task Force on National Greenhouse Gas Inventories, 2019).
Google Scholar 
European Parliament; European Council. Commission Delegated Regulation (EU) 2019/807 of 13 March 2019 Supplementing Directive (EU) 2018/2001 of the European Parliament and of the Council as Regards the Determination of High Indirect Land-Use Change-Risk (2018) (Online). fttps://eur-lex.europa.eu/eli/reg_del/2019/807/oj.de Oliveira Garcia, W., Amann, T. & Hartmann, J. Increasing biomass demand enlarges negative forest nutrient budget areas in wood export regions. Sci. Rep. 8, 5280 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Searchinger, T. et al. Europe’s renewable energy directive poised to harm global forests. Nat. Commun. 9, 3741 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galik, C. S. & Abt, R. C. Sustainability guidelines and forest market response: An assessment of European Union pellet demand in the southeastern United States. GCB Bioenergy 8, 658–669 (2016).
Google Scholar 
Favero, A. D. & Sohngen, B. Forests: Carbon sequestration, biomass energy, or both?. Sci. Adv. 6(13), eaay6792 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Cowie, A. et al. Applying a science-based systems perspective to dispel misconceptions about climate effects of forest bioenergy. GCB-Bioenergy 13, 1210–1231 (2021).
Google Scholar 
Camia, A, Jonsson, G. J. R., Robert, N., Cazzaniga, N., Jasinevičius, G., Avitabile, V., Grassi, G., Barredo, J., & Mubareka, S. The Use of Woody Biomass for Energy Production in the EU (European Commission, Joint Research Center, 2021).Aguilar, F. X., Mirzaee, A., McGarvey, R., Shifley, S. & Burtraw, D. Expansion of US wood pellet industry points to positive trends but the need for continued monitoring. Sci. Rep. 10, 18607 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dale, V., Parish, E., Kline, K. & Tobin, E. How is wood-based pellet production affecting forest conditions in the southeastern United States?. For Ecol Manag 396, 143–14 (2017).
Google Scholar 
Ceccherini, G. et al. Abrupt increase in harvested forest area over Europe after 2015. Nature 583, 72–77 (2020).ADS 
CAS 
PubMed 

Google Scholar 
FORISK Consulting. U.S. Wood Bioenergy Database (2020). (Online). https://forisk.com/. Accessed 2020.Domke, G. et al. Toward inventory-based estimates of soil organic carbon in forests of the United States. Ecol. Appl. 27(4), 1223–1235 (2017).CAS 
PubMed 

Google Scholar 
Python Org. Python Programming Language (2022) (Online). https://www.python.org/. Accessed 1 January 2018.STATA. Stata: statistical software for data science (2022) (Online). https://www.stata.com/. Accessed 1 January 2018.QGIS. Free and Open Source Geographic Information System (2021). (Online). https://qgis.org/en/site/.US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program (2020). (Online). https://www.fia.fs.fed.us/.Burrill, E. A., Wilson, A. M., Turner, J. A., Pugh, S. A., Menlove, J., Christiansen, G., Conkling, B., & David, W. The Forest Inventory and Analysis Database: Database Description and User Guide Version 8.0 for Phase 2 (US Department of Agriculture, US Forest Service, 2018).Ahmed, M. et al. Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. J. Environ. Manag. 199, 158–171 (2017).
Google Scholar 
Timilsina, N. et al. A framework for identifying carbon hotspots and forest management drivers. J. Environ. Manag. 114, 293–302 (2012).
Google Scholar 
Coulston, J., Ritters, K., McRoberts, R., Reams, G. & Smith, W. True versus perturbed forest inventory plot locations for modeling: A simulation study. Can. J. For. Res. 36, 801–807 (2006).
Google Scholar 
Anselin, L. Spatial effects in econometric practice in environmental and resource economics. Am. J. Agric. Econ. 83(3), 705–710 (2001).MathSciNet 

Google Scholar 
Strange-Olesen, A., Bager, S., Kittler, B., Price, W., & Aguilar, F. Environmental Implications of Increased Reliance of the EU on Biomass from the South East US (European Commission Report ENV.B.1/ETU/2014/0043, 2015).Spelter, H., & Toth, D. North America’s Wood Pellet Sector (U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2009).Goerndt, M., Aguilar, F. & Skog, K. Drivers of biomass co-firing in US coal-fired power plants. Biomass Bioenerg. 58, 158–167 (2013).
Google Scholar 
US Department of Agriculture, Forest Service. Forest Inventory and Analysis National Program: Timber Products Output Studies (2022). (Online). https://www.fia.fs.fed.us/program-features/tpo/. Accessed 2022.Sonter, L. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8(1013), 66. https://doi.org/10.1038/s41467-017-00557-w (2017).CAS 

Google Scholar 
Mirzaee, A., McGarvey, R., Aguilar, F. & Schliep, E. Impact of biopower generation on eastern US forests. Environ. Dev. Sustain. https://doi.org/10.1007/s10668-022-02235-4 (2022).
Google Scholar 
Brandeis, C., Taylor, M., Abt, K., & Alderman, D. Status and Trends for the U.S. Forest Products Sector: A Technical Document Supporting the Forest Service 2020 RPA Assessment (US Department of Agriculture, Forest Service Southern Research Station, Forest Inventory and Analysis, 2021).US Environmental Protection Agency. Emissions & Generation Resource Integrated Database (eGRID) (2021) (Online). https://www.epa.gov/egrid.US Department of Transportation. Ports: ArcGIS Online (2021) (Online). https://data-usdot.opendata.arcgis.com/datasets/usdot::ports/about.US Census Bureau. TIGER/Line Shapefiles (2021) (Online). https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.US Census Bureau. Population and Housing Units Estimates Datasets (2021) (Online). https://www.census.gov/programs-surveys/popest/data/data-sets.html.McCann, P. The Economics of Industrial Location: A Logistics-Costs Approach (Springer, 1998).Singh, D., Cubbage, F., Gonzalez, R. & Abt, R. Locational determinants for wood pellet plants: A review and case study of North and South America. BioResources 11(3), 7928–7952 (2016).
Google Scholar 
Boukherroub, T., LeBel, L. & Lemieux, S. An integrated wood pellet supply chain development: Selecting among feedstock sources and a range of operating scales. Appl. Energy 198, 385–400 (2017).
Google Scholar 
Heckman, J., Ichimura, H. & Todd, P. Matching as an econometric evaluation estimator: Evidence from evaluating a JobTraining Programme. Rev. Econ. Stud. 64(4), 605–654 (1997).MATH 

Google Scholar 
Caliendo, M. & Kopeinig, S. Some practical guidance for the implementation of propensity score matching. J. Econ. Surv. 22(1), 31–72 (2008).
Google Scholar 
Woo, H., Eskelson, B. & Monleon, V. Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest. Ecol. Appl. 31(3), e02283 (2021).PubMed 

Google Scholar 
Morreale, L., Thompson, J., Tang, X., Reinmann, A. & Hutyra, L. Elevated growth and biomass along temperate forest edges. Nat. Commun. 12(7181), 66 (2021).
Google Scholar 
Isard, W. The general theory of location and space-economy. Q. J. Econ. 63(4), 476–506 (1949).
Google Scholar 
Aguilar, F. X. Spatial econometric analysis of location drivers in a renewable resource-based industry: The U.S. South Lumber Industry. For. Policy Econ. 11(3), 184–193 (2009).
Google Scholar 
Aguilar, F. X. Conjoint analysis of industry location preferences: evidence from the softwood lumber industry in the US. Appl. Econ. 66, 3265–3274 (2010).
Google Scholar 
Aguilar, F. X., Goerndt, M., Song, N. & Shifley, S. Internal, external and location factors influencing cofiring of biomass with coal in the US northern region. Energy Econ. 34, 1790–1798 (2012).
Google Scholar 
Ferraro, P. J. et al. Estimating the impacts of conservation on ecosystem services and poverty by integrating modeling and evaluation. Proc. Natl. Acad. Sci. 112(24), 7420–7425 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, D. & Pearse, P. Forest Economics 412 (UBC Press, 2011).
Google Scholar 
Villalobos, L., Coria, J. & Nordén, L. Has forest certification reduced forest degradation in Sweden?. Land Econ. 94, 220–238 (2018).
Google Scholar 
Wooldridge, J. Econometric Analysis of Cross Section and Panel Data (MIT Press, 2010).Blackman, A., Corral, L., Lima, E. & Asner, G. Titling indigenous communities protects forests in the Peruvian Amazon. PNAS 114(16), 4123–4128 (2016).ADS 

Google Scholar 
Abt, K. L., Abt, R. C., Galik, C. S., & Skog, K. E. Effect of Policies on Pellet Production and Forests in the U.S. South: A Technical Document Supporting the Forest Service Update of the 2010 RPA Assessment USDA (Forest Service GTR Srs-202, 2014).Hardie, P. Parks, P. Gottleib and D. Wear, “Responsiveness of rural and urban land uses to land rent determinants in the U.S. South,” Land Economics, vol. 76, no. 4, pp. 659–673, 2000.Parish, E., Herzberger, A., Phifer, C. & Dale, V. Transatlantic wood pellet trade demonstrates telecoupled benefits. Ecol. Soc. 23(1), 28 (2018).
Google Scholar 
Titus, B. et al. Sustainable forest biomass: A review of current residue harvesting guidelines. Energy Sustain. Soc. 11, 66. https://doi.org/10.1186/s13705-021-00281-w (2021).
Google Scholar 
Jandl, R. et al. How strongly can forest management influence soil carbon sequestration?. Geoderma 137(3), 253–268 (2007).ADS 
CAS 

Google Scholar 
Nave, L., Vance, E., Swanston, C. & Cepas, P. S. Harvest impacts on soil carbon storage in temperate forests. For. Ecol. Manag. 259, 857–866 (2010).
Google Scholar 
Mayer, M. et al. Tamm review: Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis. For. Ecol. Manag. 466, 118127 (2020).
Google Scholar 
Berryman, E., Hatten, J., Page-Dumroese, D. S., Heckman, K. A., D’Amore, D. V., Puttere, J., & Domke, G. M. Soil carbon in Forest and Rangeland Soils of the United States Under Changing Conditions 9–31 (Springer, 2020).Nave, L. E. et al. Land use and management effects on soil carbon in US Lake States, with emphasis on forestry, fire, and reforestation. Ecol. Appl. 66, 2356 (2021).
Google Scholar 
Cao, B., Domke, G. M., Russell, M. B. & Walters, B. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Sci. Total Environ. 654, 94–106 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Coulston, J. & Wear, D. From sink to source: Regional variation in U.S. forest carbon futures. Sci. Rep. 5, 66. https://doi.org/10.1038/srep16518 (2015).
Google Scholar 
Röder, M., Whittaker, C. & Thornley, P. How certain are greenhouse gas reductions from bioenergy? Life cycle assessment and uncertainty analysis of wood pellet-to-electricity supply chains from forest residues. Biomass Bioenerg. 79, 50–63 (2015).
Google Scholar 
Hanssen, S., Duden, A., Junginger, M., Dale, D. & D. vander Hilst,. Wood pellets, what else? Greenhouse gas parity times of European electricity from wood pellets produced in the south-eastern United States using different softwood feedstocks. GC-Bioenergy 9(9), 1406–1422 (2017).CAS 

Google Scholar 
Picciano, P., Aguilar, F., Burtraw, D. & Mirzaee, A. Environmental and socio-economic implications of woody biomass co-firing at coal-fired power plants. Resour. Energy Econ. 6, 66 (2022).
Google Scholar 
Hetchner, S., Schelhas, J., & Brosius, J. Forests as Fuel: Energy, Landscape, Climate, and Race in the U.S. South (Lexington Books, 2022).Coulston, J., Wear, D. & Vose, J. Complex forest dynamics indicate potential for slowing carbon accumulation in the southeastern United States. Sci. Rep. 5, 8002 (2015).ADS 
PubMed 
PubMed Central 

Google Scholar 
Palahí, M. et al. Concerns about reported harvests in European forests. Nature 592, E15–E17 (2021).PubMed 

Google Scholar  More