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    Impacts of the US southeast wood pellet industry on local forest carbon stocks

    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

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    Numerical simulation and parameter optimization of earth auger in hilly area using EDEM software

    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

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    Current global population size, post-whaling trend and historical trajectory of sperm whales

    Selection of surveys and extraction of dataWe selected published surveys that produced estimates of sperm whale population size or density (see Supplementary Information for methodology; surveys listed in Table 1). We extracted: the type of survey (ship, aerial; acoustic, visual), the years of data collection; the coordinates of the boundary of the study area; the estimates of g(0) and CV (g(0)) used to correct for availability bias, if given; and an estimate of sperm whale population or density in study area with CV. From these we calculated for each survey the survey area with waters greater than 1000 m deep (typical shallow depth limit of sperm whales3). When no value of g(0) was used (8 ship visual surveys) we corrected the population/density estimate using an assumed generic value of g(0) and recalculated the CV to include uncertainty in g(0) (as in Eq. 1 of8). Three ship visual surveys did calculate a single g(0) estimate: 0.62 (CV 0.35)32; 0.57 (CV 0.28)35; 0.61 (CV 0.25)37. These are consistent and suggest a generic g(0) = 0.60 (CV 0.29), also agreeing with g(0) = 0.60 estimated from pooled surveys in the California Current10.Global habitat of sperm whalesTo extrapolate sperm whale densities from surveyed study areas to the sperm whales’ global habitat, we created a one-degree latitude by one-degree longitude grid. We removed the following grid points as not being prime sperm whale habitat1,3,40: points on land or with central depths less than 1000 m; largely ice-covered points in the Beaufort Sea, and the waters north of Svalbard and Russia; the Black Sea and Red Sea both of which have shallow entrances that appear not to be traversable by sperm whales.Generally, food abundance is a good predictor of species distribution. However, this is not possible for sperm whales as we have no good measures of the abundance or distribution of most of their prey, deep-water squid57. Instead, oceanographic measures have been used to describe sperm whale distributions over various spatial scales with a moderate level of success13,14. We follow this approach. Measures that might predict sperm whale density were collected for each grid point, some at just the surface, others at the surface, 500 m depth, 1000 m depth or an average of the measures at the different depths (Supplementary Table S2). Water depth was the strongest predictor in Mediterranean encounters, when compared to slope and distance to shore13. Temperature and salinity have been used as predictors for the distribution of fish and larger marine animals, which could translate into prey availability and thus density for sperm whales58,59. Primary productivity and dissolved oxygen generally dictate the biomass of wildlife in an area, while nitrate and phosphate levels limit the amount of primary productivity in an area60. Eddy kinetic energy is a measure of the dynamism of physical oceanography which is becoming a commonly used predictor of cetacean habitat61. We did not use: latitude and longitude as these primarily describe the general geographic distribution of the study areas, and geographic aggregates of sperm whale catches62 as these proved to have no predictive power. The mean values of the 14 predictor measures were calculated over calendar months for each grid point, and then over the grid points in each study area.To obtain predictors of the sperm whale density at each grid point, we then made quadratic regressions of the density of sperm whales in each study area (i), d(i), on the mean values of the predictor measures, weighting each study area by its surface area. Because the surveys were conducted over different time periods, the densities were corrected based on the estimated trajectory of global sperm whale populations by multiplying d(i) by the ratio of the global population in 1993 over that in the mid-year of the survey (as in Fig. 4). Predictor variables were selected using forward stepwise selection based upon reduction in AIC.Sperm whale population sizeThe population of sperm whales globally, N, was then calculated as follows:$$N=sum_{k}dleft(kright)cdot aleft(kright),$$
    (1)
    where a{k} are the parameters of the regression; the summation is over k, the grid points; d(k) is the estimated sperm whale density at grid point k from the habitat suitability model; and a(k) is the area of the 1° cell centred on grid point k. Population estimates for other ocean areas (North Atlantic, North Pacific, Southern Hemisphere) were calculated similarly.The CVs of these population estimates were calculated following the methodology in8, (although there is an error in Eq. (3) of8 such that the squareroot symbol covers both the numerator and denominator rather than just the numerator). The error due to uncertain density estimates for the different surveys is:$$CVleft({D}_{T}right)=frac{sqrt{sum_{i}{left(CV({n}_{i})cdot {n}_{i}right)}^{2}}}{sum_{i}{n}_{i}}.$$
    (2)
    This is combined with the uncertainty in the extrapolation process (output from the linear models), CV(extrap.), to give an overall CV for the population estimate:$$CVleft(Nright)=sqrt{{CV({D}_{T})}^{2}+{CV(mathrm{extrap}.)}^{2}.}$$
    (3)
    Post-whaling trend in population sizeWe compiled a database of series of surveys producing population estimates of the same study area during the period 1978 (by which time most commercial sperm whaling had ceased) and 2022. Each series had to span at least 10 years, and all of the surveys in the series had to be comparable in terms of area covered throughout the time span. There also had to have been at least 3 surveys for a data set to be included.The data consisted of the survey area, A, the estimated population in area A in year y (for multi-year surveys, y would be the midpoint of the data collection years), nE(A,y), and the provided CV of that estimate, CV(nE(A,y)). The data series used for these analyses are summarized in Table 3.For each survey area, A, we calculated the trend in logarithmic population size, r(A), over time using weighted linear regression:$${text{Log}}left( {n_{E} left( {A,y} right)} right) , sim {text{ constant}}left( A right) , + rleft( A right) cdot y. left[ {{text{weight }} = { 1}/left( {{1} + {text{ CV}}left( {n_{E} left( {A,y} right)} right)} right)^{{2}} } right]$$
    (4)
    Table 3 also includes other published estimates of sperm whale population trends, from sighting rates or mark-recapture analyses of photoidentification data, with these estimates also having to span at least 10 years of data collection, and include data collected in three or more different years.Population trajectoryTo examine possible trajectories of the global sperm whale population following the start of commercial whaling in 1712, we used a variant of the theta-logistic, a population model that has been employed in other recent analyses of the population trajectories of large cetaceans45,63. The theta-logistic model is:$$nleft(y+1right)=nleft(yright)+rcdot nleft(yright)left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-fleft(yright)cdot cleft(yright).$$
    (5)

    Here, n(y) is the population of sperm whales in year y, r is the maximum potential rate of increase of a sperm whale population, and θ describes how the rate of increase varies with population size relative to its basal level before whaling in 1711, n(1711). The recorded catch in year y is c(y) and f(y) is a correction for bias in recorded catches.Whaling reduced the proportion of large breeding males64, likely disrupted the social cohesion of the females3, and may have had other lingering effects which reduced pregnancy or survival, and thus the rate of increase. Poaching has been found to reduce the reproductive output of African elephants, Loxodonta Africana, which have a similar social system to the sperm whales3, and this effect lingers well beyond the effective cessation of poaching46. There is some evidence for these effects of what we call “social disruption” on sperm whale population dynamics20,46,65. We added a term to the theta-logistic to account for such effects:$$nleft(y+1right)=nleft(yright)left[1+rcdot left(1-{left(frac{nleft(yright)}{nleft(1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}right]-f(y)cdot c(y).$$
    (6)

    Here, (frac{sum_{t=y-T}^{y}f(t)cdot c(t)}{nleft(y-Tright)}) is the proportion of the population killed over the last T years, and q is the reduction in the rate of increase when almost all the whales have been killed. This reduction is modelled to fall linearly as the proportion killed declines to zero.The global sperm whale population has some geographic structure18. Females appear to rarely move between ocean basins, and males seem to largely stay within one basin. Furthermore, sperm whaling was progressive, moving from ocean area to ocean area as numbers were depleted4. We model this by assuming K largely separate sperm whale subpopulations of equal size. Exploitation in 1712 starts in subpopulation 1 and moves to subpopulations 1 and 2 when the population 1 falls to α% of its initial value, and so on for the other ocean areas. The catch in each year in each area being exploited is pro-rated by the sizes of the different subpopulations being exploited. The population model for subpopulation k, which is one of the KE subpopulations being exploited in year y, is:$$nleft(k,y+1right)=nleft(k,yright)left[1+rcdot left(1-{left(frac{nleft(k,yright)}{nleft(k,1711right)}right)}^{theta }right)-qcdot frac{sum_{t=y-T}^{y}C(k,t)}{nleft(k,y-Tright)}right]-Cleft(k,yright),$$
    (7)
    where the estimated catch in year y in subpopulation k is given by: (Cleft(k,yright)=f(y)cdot c(y)cdot n(k,y)/sum_{{k}^{mathrm{^{prime}}}= More

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    The supply of multiple ecosystem services requires biodiversity across spatial scales

    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

    Google Scholar 
    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tilman, D., Isbell, F. & Cowles, J. M. Biodiversity and ecosystem functioning. Annu. Rev. Ecol. Evol. Syst. 45, 471–493 (2014).Article 

    Google Scholar 
    Hector, A. et al. Plant diversity and productivity experiments in European grasslands. Science 286, 1123–1127 (1999).Article 
    CAS 
    PubMed 

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

    Google Scholar 
    Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    van der Plas, F. et al. Towards the development of general rules describing landscape heterogeneity–multifunctionality relationships. J. Appl. Ecol. 56, 168–179 (2019).Article 

    Google Scholar 
    Jochum, M. et al. The results of biodiversity–ecosystem functioning experiments are realistic. Nat. Ecol. Evol. 4, 1485–1494 (2020).Article 
    PubMed 

    Google Scholar 
    Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    van der Plas, F. et al. Biotic homogenization can decrease landscape-scale forest multifunctionality. Proc. Natl Acad. Sci. USA 113, E2549–E2549 (2016).
    Google Scholar 
    Isbell, F. et al. High plant diversity is needed to maintain ecosystem services. Nature 477, 199–202 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hautier, Y. et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality. Nat. Ecol. Evol. 2, 50–56 (2018).Article 
    PubMed 

    Google Scholar 
    Srivastava, D. S. & Vellend, M. Biodiversity–ecosystem function research: is it relevant to conservation? Annu. Rev. Ecol. Evol. Syst. 36, 267–294 (2005).Article 

    Google Scholar 
    Isbell, F. et al. Linking the influence and dependence of people on biodiversity across scales. Nature 546, 65–72 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mori, A. S., Isbell, F. & Seidl, R. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol. Evol. 33, 549–564 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chase, J. M. & Knight, T. M. Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecol. Lett. 16, 17–26 (2013).Article 
    PubMed 

    Google Scholar 
    Chase, J. M. et al. Embracing scale-dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).Article 
    PubMed 

    Google Scholar 
    Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
    PubMed 

    Google Scholar 
    Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hagan, J. G., Vanschoenwinkel, B. & Gamfeldt, L. We should not necessarily expect positive relationships between biodiversity and ecosystem functioning in observational field data. Ecol. Lett. 24, 2537–2548 (2021).Article 
    PubMed 

    Google Scholar 
    Brose, U. & Hillebrand, H. Biodiversity and ecosystem functioning in dynamic landscapes. Philos. Trans. R. Soc. B 371, 20150267 (2016).Article 

    Google Scholar 
    Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).Article 

    Google Scholar 
    Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes-eight hypotheses. Biol. Rev. 87, 661–685 (2012).Article 
    PubMed 

    Google Scholar 
    Ricotta, C. On beta diversity decomposition: trouble shared is not trouble halved. Ecology 91, 1981–1983 (2010).Article 
    PubMed 

    Google Scholar 
    Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gonthier, D. J. et al. Biodiversity conservation in agriculture requires a multi-scale approach. Proc. R. Soc. Lond. B 281, 20141358 (2014).
    Google Scholar 
    Flynn, D. F. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).Article 
    PubMed 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Allan, E. et al. Land use intensification alters ecosystem multifunctionality via loss of biodiversity and changes to functional composition. Ecol. Lett. 18, 834–843 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Le Provost, G. et al. Land-use history impacts functional diversity across multiple trophic groups. Proc. Natl Acad. Sci. USA 117, 1573–1579 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Adl, S. M., Coleman, D. C. & Read, F. Slow recovery of soil biodiversity in sandy loam soils of Georgia after 25 years of no-tillage management. Agric. Ecosyst. Environ. 114, 323–334 (2006).Article 

    Google Scholar 
    Le Provost, G. et al. Contrasting responses of above- and belowground diversity to multiple components of land-use intensity. Nat. Commun. 12, 3918 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    James, L. A. Legacy effects. Oxford Bibliographies in Environmental Science https://doi.org/10.1093/OBO/9780199363445-0019 (2015).Lamy, T., Liss, K. N., Gonzalez, A. & Bennett, E. M. Landscape structure affects the provision of multiple ecosystem services. Environ. Res. Lett. 11, 124017 (2016).Article 

    Google Scholar 
    Alsterberg, C. et al. Habitat diversity and ecosystem multifunctionality—the importance of direct and indirect effects. Sci. Adv. 3, e1601475 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity—ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

    Google Scholar 
    Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).Article 
    PubMed 

    Google Scholar 
    Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

    Google Scholar 
    Bullock, J. M., Aronson, J., Newton, A. C., Pywell, R. F. & Rey-Benayas, J. M. Restoration of ecosystem services and biodiversity: conflicts and opportunities. Trends Ecol. Evol. 26, 541–549 (2011).Article 
    PubMed 

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

    Google Scholar 
    Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16, 894–908 (2013).Article 

    Google Scholar 
    Fischer, M. et al. Implementing large-scale and long-term functional biodiversity research: The Biodiversity Exploratories. Basic Appl. Ecol. 11, 473–485 (2010).Article 

    Google Scholar 
    Blüthgen, N. et al. A quantitative index of land-use intensity in grasslands: Integrating mowing, grazing and fertilization. Basic Appl. Ecol. 13, 207–220 (2012).Article 

    Google Scholar 
    Vogt, J. et al. Eleven years’ data of grassland management in Germany. Biodivers. Data J. 7, e36387 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).Article 
    PubMed 

    Google Scholar 
    Linders, T. E. W. et al. Stakeholder priorities determine the impact of an alien tree invasion on ecosystem multifunctionality. People Nat. 3, 658–672 (2021).Article 

    Google Scholar 
    Nathan, R. Long-distance dispersal of plants. Science 313, 786–788 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Manning, P. et al. Grassland management intensification weakens the associations among the diversities of multiple plant and animal taxa. Ecology 96, 1492–1501 (2015).Article 

    Google Scholar 
    Clough, Y. et al. Density of insect-pollinated grassland plants decreases with increasing surrounding land-use intensity. Ecol. Lett. 17, 1168–1177 (2014).Article 
    PubMed 

    Google Scholar 
    Vickery, J. A. et al. The management of lowland neutral grasslands in Britain: effects of agricultural practices on birds and their food resources. J. Appl. Ecol. 38, 647–664 (2001).Article 

    Google Scholar 
    López-Jamar, J., Casas, F., Díaz, M. & Morales, M. B. Local differences in habitat selection by Great Bustards Otis tarda in changing agricultural landscapes: implications for farmland bird conservation. Bird. Conserv. Int. 21, 328–341 (2011).Article 

    Google Scholar 
    Wells, K., Böhm, S. M., Boch, S., Fischer, M. & Kalko, E. K. Local and landscape-scale forest attributes differ in their impact on bird assemblages across years in forest production landscapes. Basic Appl. Ecol. 12, 97–106 (2011).Article 

    Google Scholar 
    Bommarco, R., Lindborg, R., Marini, L. & Öckinger, E. Extinction debt for plants and flower-visiting insects in landscapes with contrasting land use history. Divers. Distrib. 20, 591–599 (2014).Article 

    Google Scholar 
    Kuussaari, M. et al. Extinction debt: a challenge for biodiversity conservation. Trends Ecol. Evol. 24, 564–571 (2009).Article 
    PubMed 

    Google Scholar 
    Lee, M., Manning, P., Rist, J., Power, S. A. & Marsh, C. A global comparison of grassland biomass responses to CO2 and nitrogen enrichment. Philos. Trans. R. Soc. B 365, 2047–2056 (2010).Article 
    CAS 

    Google Scholar 
    Smith, P. Do grasslands act as a perpetual sink for carbon? Glob. Change Biol. 20, 2708–2711 (2014).Article 

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

    Google Scholar 
    Bradford, M. A. et al. Discontinuity in the responses of ecosystem processes and multifunctionality to altered soil community composition. Proc. Natl Acad. Sci. USA 111, 14478–14483 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schaub, S. et al. Plant diversity effects on forage quality, yield and revenues of semi-natural grasslands. Nat. Commun. 11, 768 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mace, G. M., Norris, K. & Fitter, A. H. Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol. 27, 19–26 (2012).Article 
    PubMed 

    Google Scholar 
    Peter, S., Le Provost, G., Mehring, M., Müller, T. & Manning, P. Cultural worldviews consistently explain bundles of ecosystem service prioritisation across rural Germany. People Nat. 4, 218–230 (2022).Article 

    Google Scholar 
    Emmerson, M. et al. How agricultural intensification affects biodiversity and ecosystem services. Adv. Ecol. Res. 55, 43–97 (2016).Article 

    Google Scholar 
    Gonzalez, A. et al. Scaling-up biodiversity–ecosystem functioning research. Ecol. Lett. 23, 757–776 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl Acad. Sci. USA 100, 12765–12770 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, B. J. et al. Spatial covariance between biodiversity and other ecosystem service priorities. J. Appl. Ecol. 46, 888–896 (2009).Article 

    Google Scholar 
    Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

    Google Scholar 
    Metzger, J. P. et al. Considering landscape-level processes in ecosystem service assessments. Sci. Total Environ. 796, 149028 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Costanza, R. et al. Twenty years of ecosystem services: how far have we come and how far do we still need to go? Ecosyst. Serv. 28, 1–16 (2017).Article 

    Google Scholar 
    DeFries, R. & Nagendra, H. Ecosystem management as a wicked problem. Science 356, 265–270 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272 (2018).Article 
    PubMed 

    Google Scholar 
    Schenk, N. et al. Assembled ecosystem measures from grassland EPs (2008–2018) for multifunctionality synthesis—June 2020. Version 40. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27087 (2022).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, HAI, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27568 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, Alb, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27569 (2020).Michael Scherer-Lorenzen, M. & Mueller, S. Acoustic diversity index based on environmental sound recordings on all forest EPs, SCH, 2016. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27570 (2020).Penone, C. et al. Assembled RAW diversity from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27707 (2021).Penone, C. et al. Assembled species information from grassland EPs (2008–2020) for multidiversity synthesis—November 2020. Version 3. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27706 (2021).Junge, X., Schüpbach, B., Walter, T., Schmid, B. & Lindemann-Matthies, P. Aesthetic quality of agricultural landscape elements in different seasonal stages in Switzerland. Landsc. Urban Plan. 133, 67–77 (2015).Article 

    Google Scholar 
    Lindemann-Matthies, P., Junge, X. & Matthies, D. The influence of plant diversity on people’s perception and aesthetic appreciation of grassland vegetation. Biol. Conserv. 143, 195–202 (2010).Article 

    Google Scholar 
    Haines-Young, R. & Potschin, M. B. Common International Classification of Ecosystem Services (CICES) V5.1 and Guidance on the Application of the Revised Structure. https://cices.eu/content/uploads/sites/8/2018/01/Guidance-V51-01012018.pdf (2018)Byrnes, J. E. et al. Investigating the relationship between biodiversity and ecosystem multifunctionality: challenges and solutions. Methods Ecol. Evol. 5, 111–124 (2014).Article 

    Google Scholar 
    Neyret, M. et al. Assessing the impact of grassland management on landscape multifunctionality. Ecosyst. Serv. 52, 101366 (2021).Article 

    Google Scholar 
    Ferraro, D. M. et al. The phantom chorus: birdsong boosts human well-being in protected areas. Proc. R. Soc. B 287, 20201811 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Graves, R. A., Pearson, S. M. & Turner, M. G. Species richness alone does not predict cultural ecosystem service value. Proc. Natl Acad. Sci. USA 114, 3774–3779 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chan, K. M. A., Satterfield, T. & Goldstein, J. Rethinking ecosystem services to better address and navigate cultural values. Ecol. Econ. 74, 8–18 (2012).Article 

    Google Scholar 
    Villamagna, A. M., Angermeier, P. L. & Bennett, E. M. Capacity, pressure, demand, and flow: a conceptual framework for analyzing ecosystem service provision and delivery. Ecol. Complex. 15, 114–121 (2013).Article 

    Google Scholar 
    Bolliger, R., Prati, D., Fischer, M., Hoelzel, N. & Busch, V. Vegetation Records for Grassland EPs, 2008–2018. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24247 (2020).Le Provost, G. & Manning, P. Cover of all vascular plant species in representative 2×2 quadrats of the major surrounding homogeneous vegetation zones in a 75-m radius of the 150 grassland EPs, 2017–2018. Version 4. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/27846 (2021).Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence–absence data. J. Anim. Ecol. 72, 367–382 (2003).Article 

    Google Scholar 
    Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143 (2010).Article 

    Google Scholar 
    Ostrowski, A., Lorenzen, K., Petzold, E. & Schindler, S. Land use intensity index (LUI) calculation tool of the Biodiversity Exploratories project for grassland survey data from three different regions in Germany since 2006, BEXIS 2 module. Zenodo https://doi.org/10.5281/zenodo.3865579 (2020).Thiele, J., Weisser, W. & Scherreiks, P. Historical land use and landscape metrics of grassland EP. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/25747 (2020).Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

    Google Scholar 
    Westphal, C., Steckel, J. & Rothenwöhrer, C. InsectScale / LANDSCAPES – Landscape heterogeneity metrics (grassland EPs, radii 500 m–2000 m, 2009) – shape files. Version 2. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/24046 (2019).Fahrig, L. et al. Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecol. Lett. 14, 101–112 (2011).Article 
    PubMed 

    Google Scholar 
    Sirami, C. et al. Increasing crop heterogeneity enhances multitrophic diversity across agricultural regions. Proc. Natl Acad. Sci. USA 116, 16442–16447 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gessler, P. E., Moore, I. D., Mckenzie, N. J. & Ryan, P. J. Soil–landscape modelling and spatial prediction of soil attributes. Int. J. Geogr. Inf. Syst. 9, 421–432 (1995).Article 

    Google Scholar 
    Zinko, U., Seibert, J., Dynesius, M. & Nilsson, C. Plant species numbers predicted by a topography-based groundwater flow index. Ecosystems 8, 430–441 (2005).Article 
    CAS 

    Google Scholar 
    Moeslund, J. E. et al. Topographically controlled soil moisture drives plant diversity patterns within grasslands. Biodivers. Conserv. 22, 2151–2166 (2013).Article 

    Google Scholar 
    Keddy, P. A. Assembly and response rules: two goals for predictive community ecology. J. Veg. Sci. 3, 157–164 (1992).Article 

    Google Scholar 
    Myers, M. C., Mason, J. T., Hoksch, B. J., Cambardella, C. A. & Pfrimmer, J. D. Birds and butterflies respond to soil-induced habitat heterogeneity in experimental plantings of tallgrass prairie species managed as agroenergy crops in Iowa, USA. J. Appl. Ecol. 52, 1176–1187 (2015).Article 

    Google Scholar 
    Carvalheiro, L. G. et al. Soil eutrophication shaped the composition of pollinator assemblages during the past century. Ecography 43, 209–221 (2020).Article 

    Google Scholar 
    Schöning, I., Klötzing, T., Schrumpf, M., Solly, E. & Trumbore, S. Mineral soil pH values of all experimental plots (EP) of the Biodiversity Exploratories project from 2011, Soil (core project). Version 8. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/14447 (2021).Sørensen, R., Zinko, U. & Seibert, J. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol. Earth Syst. Sci. 10, 101–112 (2006).Article 

    Google Scholar 
    Le Provost, G. et al. Aggregated environmental and land-use covariates of the 150 grassland EPs used in ‘Contrasting responses of above- and belowground diversity to multiple components of land-use intensity’. Version 5. Biodiversity Exploratories Information System https://www.bexis.uni-jena.de/ddm/data/Showdata/31018 (2021).R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2020).Grace, J. B. Structural equation modeling for observational studies. J. Wildl. Manag. 72, 14–22 (2008).Article 

    Google Scholar 
    Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge University Press, 2006).Rosseel, Y. Lavaan: an R package for structural equation modeling and more. Version 0.5–12 (BETA). J. Stat. Softw. 48, 1–36 (2012).Article 

    Google Scholar 
    Le Bagousse-Pinguet, Y. et al. Phylogenetic, functional, and taxonomic richness have both positive and negative effects on ecosystem multifunctionality. Proc. Natl Acad. Sci. USA 116, 8419–8424 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

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    Switch to perennial rice promotes sustainable farming

    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

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    Surprising effects of cascading higher order interactions

    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

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    Crop diversification and parasitic weed abundance: a global meta-analysis

    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

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    Source apportionment of soil heavy metals with PMF model and Pb isotopes in an intermountain basin of Tianshan Mountains, China

    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