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

Chauhan, B. S. Grand challenges in weed management. Front. Agron. https://doi.org/10.3389/fagro.2019.00003 (2020).Article 

Google Scholar 
Oerke, E. C. Crop losses to pests. J. Agric. Sci. 144, 31–43 (2006).
Google Scholar 
Samejima, H. & Sugimoto, Y. Recent research progress in combatting root parasitic weeds. Biotechnol. Biotechnol. Equip. 32(2), 221–240 (2018).CAS 

Google Scholar 
Aly, R. Conventional and biotechnological approaches for control of parasitic weeds. In Vitro Cell. Dev. Biol. Plant 43(4), 304–317 (2007).
Google Scholar 
Fernández-Aparicio, M., Delavault, P. & Timko, M. P. Management of infection by parasitic weeds: A review. Plants 9(9), 1184 (2020).PubMed Central 

Google Scholar 
Rodenburg, J., Demont, M., Zwart, S. J. & Bastiaans, L. Parasitic weed incidence and related economic losses in rice in Africa. Agric. Ecosyst. Environ. 235, 306–317 (2016).
Google Scholar 
Weisberger, D., Nichols, V. & Liebman, M. Does diversifying crop rotations suppress weeds? A meta-analysis. PLoS One 14(7), e0219847 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ejeta, G. The Striga scourge in Africa: A growing pandemic. In Integrating New Technologies for Striga Control: Towards Ending the Witch-hunt 3–16 (World Scientific, 2007). https://doi.org/10.1142/9789812771506_0001.Chapter 

Google Scholar 
Netting, R. M. & Stone, M. P. Agro-diversity on a farming frontier: Kofyar smallholders on the Benue plains of central Nigeria. Africa 66(1), 52–70 (1996).
Google Scholar 
Pimentel, D. et al. Conserving biological diversity in agricultural and forestry systems. Bioscience 42, 354–362 (1992).
Google Scholar 
Khoshbakht, K. & Hammer, K. How many plant species are cultivated?. Genet. Resour. Crop Evol. 55(7), 925–928. https://doi.org/10.1007/s10722-008 (2008).Article 

Google Scholar 
Hajjar, R., Jarvis, D. I. & Gemmill-Herren, B. The utility of crop genetic diversity in maintaining ecosystem services. Agric. Ecosyst. Environ. 123(4), 261–270 (2008).
Google Scholar 
He, H. M. et al. Crop diversity and pest management in sustainable agriculture. J. Integr. Agric. 18(9), 1945–1952 (2019).
Google Scholar 
Ofori, F. & Stern, W. R. Cereal–legume intercropping systems. Adv. Agron. 41, 41–90 (1987).
Google Scholar 
Tanveer, M., Anjum, S. A., Hussain, S., Cerdà, A. & Ashraf, U. Relay cropping as a sustainable approach: Problems and opportunities for sustainable crop production. Environ. Sci. Pollut. Res. 24(8), 6973–6988 (2017).
Google Scholar 
Hartwig, N. L. & Ammon, H. U. Cover crops and living mulches. Weed Sci. 50(6), 688–699 (2002).CAS 

Google Scholar 
Raseduzzaman, M. D. & Jensen, E. S. Does intercropping enhance yield stability in arable crop production? A meta-analysis. Eur. J. Agron. 91, 25–33 (2017).
Google Scholar 
Davis, A. S., Hill, J. D., Chase, C. A., Johanns, A. M. & Liebman, M. Increasing cropping system diversity balances productivity, profitability and environmental health. PLoS One 7(10), e47149 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Himmelstein, J., Ares, A., Gallagher, D. & Myers, J. A meta-analysis of intercropping in Africa: Impacts on crop yield, farmer income, and integrated pest management effects. Int. J. Agric. Sustain. 15(1), 1–10 (2017).
Google Scholar 
Abson, D. J., Fraser, E. D. & Benton, T. G. Landscape diversity and the resilience of agricultural returns: A portfolio analysis of land-use patterns and economic returns from lowland agriculture. Agric. Food Secur. 2(1), 1–15 (2013).
Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571(7764), 257–260 (2019).ADS 
CAS 
PubMed 

Google Scholar 
Gaudin, A. C. et al. Increasing crop diversity mitigates weather variations and improves yield stability. PLoS One 10(2), e0113261 (2015).PubMed 
PubMed Central 

Google Scholar 
Bowles, T. M. et al. Long-term evidence shows that crop-rotation diversification increases agricultural resilience to adverse growing conditions in North America. One Earth 2(3), 284–293 (2020).ADS 

Google Scholar 
Chauhan, B. S., Singh, R. G. & Mahajan, G. Ecology and management of weeds under conservation agriculture: A review. Crop Prot. 38, 57–65 (2012).
Google Scholar 
Nichols, V., Verhulst, N., Cox, R. & Govaerts, B. Weed dynamics and conservation agriculture principles: A review. Field Crop Res. 183, 56–68 (2015).
Google Scholar 
Banik, P., Midya, A., Sarkar, B. K. & Ghose, S. S. Wheat and chickpea intercropping systems in an additive series experiment: Advantages and weed smothering. Eur. J. Agron. 24(4), 325–332 (2006).
Google Scholar 
Workayehu, T. & Wortmann, C. S. Maize–bean intercrop weed suppression and profitability in Southern Ethiopia. Agron. J. 103(4), 1058–1063 (2011).
Google Scholar 
Haugaard-Nielsen, H., Ambus, P. & Jensen, E. S. Interspecific competition, N use and interference with weeds in pea barley intercropping. Field Crop Res. 70, 101–109 (2001).
Google Scholar 
Jensen, E. S. Intercropping of Cereals and Grain Legumes for Increased Production, Weed Control, Improved Product Quality and Prevention of N-losses in European Organic Farming Systems, Final Report on Intercrop Project (QLK5-CT-2002-02352) (Risø National Laboratory, 2006).Arlauskienė, A., Šarūnaitė, L., Kadžiulienė, Ž, Deveikytė, I. & Maikštėnienė, S. Suppression of annual weeds in pea and cereal intercrops. Agron. J. 106(5), 1765–1774 (2014).
Google Scholar 
Szumigalski, A. & van Acker, R. Weed suppression and crop production in annual intercrops. Weed Sci. 53(6), 813–825 (2005).CAS 

Google Scholar 
Stoltz, E. & Nadeau, E. Effects of intercropping on yield, weed incidence, forage quality and soil residual N in organically grown forage maize (Zea mays L.) and faba bean (Vicia faba L.). Field Crop Res. 169, 21–29 (2014).
Google Scholar 
Sauerborn, J., Müller-Stöver, D. & Hershenhorn, J. The role of biological control in managing parasitic weeds. Crop Prot. 26(3), 246–254 (2007).
Google Scholar 
Jamil, M., Rodenburg, J., Charnikhova, T. & Bouwmeester, H. J. Pre-attachment Striga hermonthica resistance of New Rice for Africa (NERICA) cultivars based on low strigolactone production. New Phytol. 192(4), 964–975. https://doi.org/10.1111/j.1469-8137.2011.03850.x (2011).Article 
CAS 
PubMed 

Google Scholar 
Yoneyama, K. et al. Nitrogen deficiency as well as phosphorus deficiency in sorghum promotes the production and exudation of 5-deoxystrigol, the host recognition signal for arbuscular mycorrhizal fungi and root parasites. Planta 227(1), 125–132. https://doi.org/10.1007/s00425-007-0600-5 (2007).Article 
CAS 
PubMed 

Google Scholar 
Sauerborn, J. Legumes used for weed control in agroecosystems in the tropics. Plant Res. Dev. 50, 74–82 (1999).
Google Scholar 
Ejeta, G. & Butler, L. G. Host-parasite interactions throughout the Striga life cycle, and their contributions to Striga resistance. Afr. Crop Sci. J. 1(2), 75–80. https://doi.org/10.4314/acsj.v1i2.69889 (1993).Article 

Google Scholar 
Carsky, R. J., Singh, L. & Ndikawa, R. Suppression of Striga hermonthica on sorghum using a cowpea intercrop. Exp. Agric. 30(3), 349–358. https://doi.org/10.1017/s0014479700024467 (1994).Article 

Google Scholar 
Hsiao, A. I., Worsham, A. D. & Moreland, D. E. Effects of temperature and dl-strigol on seed conditioning and germination of witchweed (Striga asiatica). Ann. Bot. 61(1), 65–72. https://doi.org/10.1093/oxfordjournals.aob.a087528 (1988).Article 
CAS 

Google Scholar 
Patterson, D. T. Effects of Environment on Growth and Reproduction of Witchweed and the Ecological Range of Witchweed (Monograph Series of the Weed Science Society of America, 1990).Stewart, G. R. & Press, M. C. The physiology and biochemistry of parasitic angiosperms. Annu. Rev. Plant Biol. 41(1), 127–151. https://doi.org/10.1146/annurev.pp.41.060190.001015 (1990).Article 
CAS 

Google Scholar 
Anil, L., Park, R. H. P. & Miller, F. A. Temperate intercropping of cereals for forage: A review of the potential for growth and utilization with particular reference to the UK. Grass Forage Sci. 53, 301–317 (1998).
Google Scholar 
Mamolos, A. & Kalburtji, K. Significance of allelopathy in crop rotation. J. Crop Prod. 4, 197–218 (2001).
Google Scholar 
Khan, T. D., Chung, M. I., Xuan, T. D. & Tawata, S. The exploitation of crop allelopathy in sustainable agricultural production. J. Agron. Crop Sci. 191(3), 172–184 (2005).
Google Scholar 
Cissoko, M., Boisnard, A., Rodenburg, J., Press, M. C. & Scholes, J. D. New Rice for Africa (NERICA) cultivars exhibit different levels of post-attachment resistance against the parasitic weeds Striga hermonthica and Striga asiatica. New Phytol. 192(4), 952–963 (2011).CAS 
PubMed 

Google Scholar 
Rodenburg, J. et al. Do NERICA rice cultivars express resistance to Striga hermonthica (Del.) Benth. and Striga asiatica (L.) Kuntze under field conditions?. Field Crop Res. 170, 83–94 (2015).
Google Scholar 
Randrianjafizanaka, M. T., Autfray, P., Andrianaivo, A. P., Ramonta, I. R. & Rodenburg, J. Combined effects of cover crops, mulch, zero-tillage and resistant varieties on Striga asiatica (L.) Kuntze in rice-maize rotation systems. Agric. Ecosyst. Environ. 256, 23–33 (2018).
Google Scholar 
Rodenburg, J. et al. Genetic variation and host–parasite specificity of Striga resistance and tolerance in rice: The need for predictive breeding. New Phytol. 214(3), 1267–1280. https://doi.org/10.1111/nph.14451 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nickrent, D. L. & Musselman, L. J. Introduction to parasitic flowering plants. Plant Health Instr. 13(6), 300–315 (2004).
Google Scholar 
Parker, C. Parasitic weeds: A world challenge. Weed Sci. 60(2), 269–276 (2012).CAS 

Google Scholar 
Shai Vaingast 2014. im2graph. Retrieved from: https://www.im2graph.co.il/free-downloads/windows-3264bit/ (2014).Google Maps 2021. https://maps.google.com [Accessed February 2021–December 2022].Kambach, S. et al. Consequences of multiple imputation of missing standard deviations and sample sizes in meta-analysis. Ecol. Evol. 10(20), 11699–11712 (2020).PubMed 
PubMed Central 

Google Scholar 
Nakagawa, S. & Freckleton, R. P. Missing inaction: The dangers of ignoring missing data. Trends Ecol. Evol. 23(11), 592–596 (2008).PubMed 

Google Scholar 
Idris, N. R. N. & Robertson, C. The effects of imputing the missing standard deviations on the standard error of meta analysis estimates. Commun. Stat. Simul. Comput. 38(3), 513–526. https://doi.org/10.1080/03610910802556106 (2009).Article 
MathSciNet 
MATH 

Google Scholar 
van Buuren, S. & Groothuis-Oudshoorn, K. mice: Multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).
Google Scholar 
van Buuren, S. Flexible Imputation of Missing Data (CRC Press, 2018).MATH 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315. https://doi.org/10.1002/joc.5086 (2017).Article 

Google Scholar 
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691(10), 4–9 (2012).
Google Scholar 
Reuter, H. I., Nelson, A. & Jarvis, A. An evaluation of void filling interpolation methods for SRTM data. Int. J. Geogr. Inf. Sci. 21(9), 983–1008 (2007).
Google Scholar 
CGIAR—Consortium for Spatial Information. http://srtm.csi.cgiar.org © 2004–2021. Accessed September 19, 2021, via: http://srtm.csi.cgiar.org/srtmdata/.QGIS Development Team. QGIS Geographic Information System http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2020).Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82(13), 26. https://doi.org/10.18637/jss.v082.i13 (2017).Article 

Google Scholar 
Song, C., Peacor, S. D., Osenberg, C. W. & Bence, J. R. An assessment of statistical methods for non-independent data in ecological meta-analyses. Ecology 101(12), e03184. https://doi.org/10.1002/ecy.3184 (2020).Article 
PubMed 

Google Scholar 
Del Rey, A. C. compute.es: Compute Effect Sizes. R package version 0.2-2. https://cran.r-project.org/package=compute.es (2013).R Core Team. R: A language and environment for statistical computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 0.4. 3 (2015)Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
Liebman, M. & Dyck, E. Crop rotation and intercropping strategies for weed management. Ecol. Appl. 3(1), 92–122 (1993).PubMed 

Google Scholar 
Pumariño, L. et al. Effects of agroforestry on pest, disease and weed control: A meta-analysis. Basic Appl. Ecol. 16(7), 573–582 (2015).
Google Scholar 
Kuyah, S., Whitney, C. W., Jonsson, M., Sileshi, G. W., Öborn, I., Muthuri, C. W. & Luedeling, E. Agroforestry delivers a win-win solution for ecosystem services in sub-Saharan Africa. A meta-analysis (2019).Evidente, A., Fernandez-Aparicio, M., Andolfi, A., Rubiales, D. & Motta, A. Trigoxazonane, a mono-substituted trioxazonane from Trigonella foenum-graecum root exudates, inhibits Orobanche crenata seed germination. Phytochemistry 68, 2487–2492 (2007).CAS 
PubMed 

Google Scholar 
Khan, Z. R. et al. Control of witchweed Striga hermonthica by intercropping with Desmodium spp., and the mechanism defined as allelopathic. J. Chem. Ecol. 28(9), 1871–1885 (2002).CAS 
PubMed 

Google Scholar 
Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. 13(1), 4–21 (2022).
Google Scholar 
Bakker, A. et al. Beyond small, medium, or large: Points of consideration when interpreting effect sizes. Educ. Stud. Math. 102(1), 1–8 (2019).
Google Scholar 
Scott, D. et al. Mapping the drivers of parasitic weed abundance at a national scale: A new approach applied to Striga asiatica in the mid-west of Madagascar. Weed Res. 60(5), 323–333 (2020).
Google Scholar 
Scott, D. et al. Identifying existing management practices in the control of Striga asiatica within rice–maize systems in mid-west Madagascar. Ecol. Evol. 11(19), 13579–13592 (2021).PubMed 
PubMed Central 

Google Scholar 
Rubiales, D. & Fernández-Aparicio, M. Innovations in parasitic weeds management in legume crops. A review. Agron. Sustain. Dev. 32(2), 433–449 (2012).CAS 

Google Scholar 
Bir, M. S. H. et al. Weed population dynamics under climatic change. Weed Turfgrass Sci. 3(3), 174–182 (2014).
Google Scholar 
Mohamed, K. I., Bolin, J. F., Musselman, L. J. & Townsend, P. A. Genetic diversity of Striga and implications for control and modelling future distributions. In Integrating New Technologies for Striga Control—Towards Ending the Witch-Hunt (eds Ejeta, G. & Gressel, J.) 71–84 (World Scientific, 2007).
Google Scholar 
Mandumbu, R., Mutengwa, C. S., Mabasa, S. & Mwenje, E. Predictions of the Striga scourge under new climate in southern Africa. J. Biol. Sci. 17, 192–201. https://doi.org/10.3923/jbs.2017.194.201 (2017).Article 

Google Scholar 
Mudereri, B. T. et al. Multi-source spatial data-based invasion risk modelling of Striga (Striga asiatica) in Zimbabwe. GIScience Remote Sens. 57(4), 553–571. https://doi.org/10.1080/15481603.2020.1744250 (2020).Article 

Google Scholar  More

Study siteWe conducted laboratory studies at the field site in Finca Irlanda, which is a 300-hectare organic shaded coffee farm located at 1100-m altitude, in the municipality of Tapachula, the state of Chiapas in Southern Mexico (92° 20′ 29″ W and 15° 10′ 65″ N). For the laboratory experiments, all organisms were freshly collected from Finca Irlanda or reared in the lab from insects collected from the field close by. The lab and field work was performed with a permit from the farm owner the Peters family.Ant aggression testTo examine the effect of phorid flies (P. lascinosus) on the aggressivity of ants (A. sericeasur) towards the parasitoids of the beetle larvae (A. orbigera), we conducted an ant aggression test with two treatments: with and without phorids. In the first treatment, a small coffee branch containing two leaves with scale insects (C. viridis) and 20 ant workers were both introduced into a one-liter plastic container. This was done to mimic as much as possible field conditions where the ants are tending scale insects. After waiting for at least 15 min for the ants to calm down and start tending the scale insects, one third- or fourth-instar larva of the beetle was introduced. In the second treatment, all settings were the same except for the addition of 3–4 phorid flies. Once the two treatments were established, one female parasitoid wasp (H. shuvakhinae) was released into each container. During a forty-minute trial, each time that a parasitoid wasp encountered an ant worker, the response of the ant individual was recorded. Ant responses to parasitoids were classified into two categories: (1) the ant ignores the wasp; (2) the ant attacks the wasp. All insects were used for a single replicate and then discarded. A total of four replicates were completed for both the presence and absence of phorids. For each trial, we calculated the proportion of actions (either aggressive or none) by ants when encountering the parasitoid wasp in the treatments with and without phorid flies. We used R36 to conduct a two-sample Mann–Whitney U test on the proportion of ant actions.Parasitism experiments and analysesTo examine the parasitoid wasp’s host preference and the effect of the 1st degree and the 2nd degree HOIs on the beetle’s parasitism and sex ratio, we conducted a laboratory experiment in insect tents (60 cm × 60 cm × 60 cm) with three treatments: (1) no ants (no HOIs but only the wasp and the beetle larvae), (2) ants (1st degree HOI), and (3) ants and phorids (1st and 2nd degree HOIs) (Fig. 1-B). We randomly assigned insect tents to each treatment in each trial, and the tents for each treatment were also shuffled in each trial. All beetle larvae used for these experiments were reared in the laboratory for at least two generations from freshly collected beetle adults. In each tent we placed a coffee branch with 4–6 leaves infested with approximately 100 scale insects inside a plastic container at the center of an insect tent. The set up for the three treatments of species combinations were as follows: (1) 4–5 third or fourth instar beetle larvae and a parasitoid wasp; (2) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, and about 60–80 ant workers; (3) 4–5 third or fourth instar beetle larvae, a parasitoid wasp, about 60–80 ant workers and 3–4 phorid flies. Organism densities in these treatments were close to those observed in the field. To allow for acclimation, we introduced organisms into the tents in the following order: first, we introduced the coffee branch containing scales, immediately followed by the ants (in treatments 2 and 3). After the ants settled down and started tending the scale insects, we introduced the beetle larvae. Once the larvae began moving on the coffee leaves, we introduced the phorids (in treatment 3). When the three treatments were established, and the organisms exhibit normal behavior, we released one lab-reared female parasitoid wasp (H. shuvakhinae) in each tent (treatments 1, 2, and 3). We allowed the organisms to interact for 24 h. After 24 h, we collected all beetle larvae in each treatment and reared them with sufficient scale insects as food, until beetle adults emerged or parasitism symptoms appeared (parasitized larvae turned into hardened black mummies). The treatments of no HOI and 1st + 2nd degree HOI were repeated for 10 consecutive times, and the treatment of 1st degree HOI was repeated for 11 consecutive times, with new individuals of each organism. We recorded parasitism instances and beetle sexes upon emergence. To estimate the sex ratio without parasitoid influence, 78 randomly selected beetle individuals were reared on coffee leaves with scale insects without any interaction with other organisms.To analyze the effect of the parasitoid, the ant and the phorid fly on the parasitism rate and the sex ratio of the beetle, we developed a nested model, starting from$$logitleft(widehat{P}(S)right)=a+bA$$where (widehat{P}(S)) is the probability of an individual being parasitized, A is a binary variable, standing for the absence (0) and presence (1) of ants, a is the baseline probability of parasitism, and b is the magnitude of parasitism altered by ants in the logistic function. We further hypothesized that phorid attacks modify the strength of the interaction modification that ants exert upon the host-parasitoid interaction. Therefore,$$b=g+hP$$where P is another binary variable, standing for the presence (1) and absence (0) of phorids. Substituting b, we obtain the following function,$$logitleft(widehat{P}(S)right)=a+gA+hAP$$where g represents the effect of ants on the parasitism rate of A. orbigera larvae, and h represents the effect of the fly’s facilitation, via interfering with the ant’s interference on the parasitism rate of A. orbigera larvae. We used binary responses (1: survival; 0: parasitized) of all available beetle individuals across the three treatments. We performed model selection based on the Akaike Information Criterion (AIC) and likelihood ratio tests. For the latter, we started model selection by fitting the full model and preceding each step by eliminating the term that had the least significance (the greatest p-value) on the explanation of the dependent variable. The analysis was performed with the application of the bbmle package in R. By doing this, we determined the maximum likelihood estimates of survival probability of the beetle, (widehat{P}(S)), in the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates.The same idea applies to the sex ratio of the beetle under the influence of various organisms. We developed the following equation,$$logitleft(widehat{P}(F|S)right)= r+mA+nAP$$where (widehat{P}(F|S)) is the probability of a parasitism survivor being female. A and P are both binary variables. Respectively, they represent the ant and the phorid fly, and the numeric attributes, 0 and 1, denote their absence and presence. As before, model selection and parameter estimates were conducted with AIC. By doing this, we determined (widehat{P}(F|S)), the estimate of being a female beetle given survival, for the three treatments: (1) A = 0, AP = 0 (no HOI); (2) A = 1, AP = 0 (one HOI: ant interference) and (3) A = 1, AP = 1 (interacting HOIs: phorid interference with ant interference), and errors associated with these estimates. We employed the mle2 function in the bbmle package in R to estimate the female probability (1) in the absence of HOI (the beetle and the parasitoid alone), (2) in the presence of the 1st degree HOI (the beetle, the parasitoid and the ant), and (3) in the presence of the 1st and the 2nd degree HOIs (the beetle, the parasitoid, the ant and the phorid fly).Probabilities of per capita female and per capita male survival from parasitism under the influence of ant and the phorid flyTo test whether the sex ratio of beetle survivors’ population is due to sex-differential survival probability, Bayes’ theorem was employed. Per capita female survival probability from parasitism in each treatment of the parasitism experiment was derived based on (widehat{P}(F)), (widehat{P}left(F|Sright),) and (widehat{P}(S)), and per capita male survival probability was derived based on (widehat{P}(M)), (widehat{P}left(M|Sright),) and (widehat{P}(S)). According to the Central Limit Theorem, the estimates of proportions, (widehat{P}left(S|Fright)) and (widehat{P}left(S|Mright)), are approximately normally distributed,$$widehat{P}left(S|Fright)sim Nleft(widehat{P}left(S|Fright), sqrt{frac{widehat{P}(S|F)times left(1-widehat{P}left(S|Fright)right)}{{n}^{*}}}right)$$$$widehat{P}left(S|Mright)sim Nleft(widehat{P}left(S|Mright), sqrt{frac{widehat{P}(S|M)times left(1-widehat{P}left(S|Mright)right)}{{n}^{*}}}right)$$with means (widehat{P}left(S|Fright)) and (widehat{P}(S|M)), and standard deviations (sqrt{frac{widehat{P}left(S|Fright)times (1-widehat{P}left(S|Fright))}{{n}^{*}}}) and (sqrt{frac{widehat{P}left(S|Mright)times (1-widehat{P}left(S|Mright))}{{n}^{*}}}), where (widehat{P}(S|F)) and (widehat{P}(S|M)), respectively, are the population proportions of females and males. Here we employ n*, the smallest sample size among those of the three variables in the Bayesian formulas for males and females. Since the three variables have different sample sizes, n* guarantees a conservative estimate of standard error, and thus confidence interval, of each derived probability. More

The plots of Igeo, PERI, and PLI of HMs in the topsoil of the tourist area of Sayram Lake (Fig. 5) reveal the degree of HM pollution and eco-risk in this study area on the one hand and, on the other hand, indicate the direction for the relevant agencies to target soil environmental protection and HM pollution prevention and control measures. In this study, the Igeo results showed that Cd was the most highly enriched HM, and Pb, Zn, Cd, and Ni were slightly enriched in a few sample sites. The unnatural accumulation of these elements is usually closely associated with human activities in the area34. Tourism is the main economic activity in the district, and published studies have reported that tourism infrastructure construction (e.g., roads, buildings, etc.) and tourism wastes (e.g., plastic bags, batteries, hotel wastewater) release Cd into the soil35. Additionally, the accumulation of Pb, Zn, Cu and Ni in soils is usually associated with traffic emissions36. The PERI showed that the study area was at low risk overall, with only point ss04 exhibiting medium risk; however, this result was caused by the abnormally high Cd concentration value (Fig. 4) at point ss04 (Cd (concentration): 1.08 mg/kg, Cd (background): 0.34 mg/kg). This anomalous concentration value has a large influence on the PERI calculated based on the measured concentration, the background value and the toxicity coefficient. Therefore, references to this point can be appropriately removed when considering eco-risk. The PLI of each sampling point was greater than 1 and less than 2, which means that the area was in a moderately contaminated state. In general, the degree of soil HM contamination in this area was low; however, due to HM toxicity, bioaccumulation, and persistence37, the HM contamination of this area still requires sustained attention.Figure 5Contamination and ecological risk indices: (a) geoaccumulation index (Igeo) of HMs; (b) ecological risk of individual HMs; (c) potential ecological risk index (PERI) of HMs; (d) pollution load index (PLI) of HMs.Full size imageCorrelation analysis is an efficient way to reveal correlations among HMs through Pearson correlation coefficients, and HMs with significant correlations may originate from the same source38. As shown in Table S5, the elemental pairs Cd-Cu (p  More