Ecology

Abbasi, N., Bahramloo, R. & Movahedan, M. Strategic planning for remediation and optimization of irrigation and drainage networks: a case study of Iran. J. Agric. Agric. Sci. Proc. 4, 211–221 (2015).
Google Scholar 
Abdelhaleem, F., Basiouny, M. & Mahmoud, A. Application of remote sensing and geographic information systems in irrigation water management under water scarcity conditions in Fayoum, Egypt. J. Environ. Manag. 299, 113683 (2021).Article 

Google Scholar 
Akbari-Alashti, H., Bozorg-Haddad, O., Fallah-Mehdipour, E. & Mariño, M. A. Multi-reservoir real-time operation rules: a new genetic programming approach. Proc. Instit. Civil Eng. Water Manag. 167(10), 561–576 (2014).Article 

Google Scholar 
Akhmouch, A. & Correia, F. N. The 12 OECD principles on water governance: when science meets policy. J. Utilities Policy. 43, 14–20 (2016).Article 

Google Scholar 
Amblard, L. & Mann, C. Understanding collective action for the achievement of EU water policy objectives in agricultural landscapes: insights from the institutional design principles and integrated landscape management approaches. J. Environ. Sci. Policy. 125, 76–86 (2021).Article 

Google Scholar 
Babaeian, F., Delavar, M., Morid, S. & Srinivasan, R. Robust climate change adaptation pathways in agricultural water management. J. Agric. Water Manag. 252, 106904 (2021).Article 

Google Scholar 
Barbosa, M. C., Alam, K. & Mushtaq, S. Water policy implementation in the state of São Paulo, Brazil: key challenges and opportunities. J. Environ. Sci. Policy. 60, 11–18 (2016).Article 

Google Scholar 
Barrett, S. M. Implementation studies: time for a revival? Personal reflections on 20 years of implementation studies. J. Public Admin. 82(2), 249–269 (2004).MathSciNet 
Article 

Google Scholar 
Baumgartner, R. J. & Korhonen, J. Strategic thinking for sustainable development. J. Sustain. Dev. 18(2), 71–75 (2010).Article 

Google Scholar 
Biswas, S. Measuring performance of healthcare supply chains in India: a comparative analysis of multi-criteria decision making methods. J. Decis. Making Appl. Manag. Eng. 3(2), 162–189 (2020).Article 

Google Scholar 
Biswas, S., Majumder, S., Pamucar, D. & Suman, D. An extended LBWA framework in picture fuzzy environment using actual score measures application in social enterprise systems. J. Enterp. Inform. Syst. (IJEIS) 17(4), 37–68 (2021).Article 

Google Scholar 
Biswas, S., Pamucar, D., Chowdhury, P. & Kar, S. A new decision support framework with picture fuzzy information: comparison of video conferencing platforms for higher education in India. J. Disc. Dyn. Nat. Soc. (2021).Bozorg-Haddad, O., Moradi-Jalal, M., Mirmomeni, M., Kholghi, M. K. H. & Mariño, M. A. Optimal cultivation rules in multi-crop irrigation areas. J. Irrig. Drain. 58(1), 38–49 (2009).Article 

Google Scholar 
Bozorg-Haddad, O., Loáiciga, H. A. & Zolghadr-Asli, B. A handbook on multi-attribute decision-making methods chapter (Wiley, 2021).MATH 
Book 

Google Scholar 
Buckley, J. J. Fuzzy hierarchical analysis. J. Fuzzy Sets Syst. 17(3), 233–247 (1985).MathSciNet 
MATH 
Article 

Google Scholar 
Chang, H. H. & Huang, W. C. Application of a quantification SWOT analytical method. J. Math. Comput. Model. 43, 158–169 (2006).MathSciNet 
MATH 
Article 

Google Scholar 
Chen, C. T. Extension of the TOPSIS for group decision-making under fuzzy environment. J. Fuzzy Sets Syst. 114(1), 1–9 (2000).MATH 
Article 

Google Scholar 
Conrad, C., Usman, M., Morper-Bush, L. & Schönbrodt-Stitt, S. Remote sensing-based assessments of land use, soil and vegetation status, crop production and water use in irrigation systems of the Aral Sea Basin. J. Water Sec. 11, 100078 (2020).Article 

Google Scholar 
David, F. R. Strategic management: concepts and cases (Prentice Hall, 2011).
Google Scholar 
Fallah-Mehdipour, E., Bozorg-Haddad, O., Beygi, S. & Mariño, M. A. Effect of utility function curvature of Young’s bargaining method on the design of WDNs. J. Water Resour. Manag. 25(9), 2197–2218 (2011).Article 

Google Scholar 
Fanghua, H. & Guanchun, C. Fuzzy multi-criteria group decision-making model based on weighted borda scoring method for watershed ecological risk management: a case study of three Gorges reservoir area of China. J. Water Resour. Manag. 24(10), 2139–2165 (2010).Article 

Google Scholar 
Gallego-Ayala, J. & Juızo, D. Strategic implementation of integrated water resources management in Mozambique: an A’WOT analysis. J. PhysChem. Earth. 36(14–15), 1103–1111 (2011).ADS 
Article 

Google Scholar 
Gao, C. Y. & Peng, D. H. Consolidating SWOT analysis with nonhomogeneous uncertain preference information. J. Knowl. Based Syst. 24, 796–808 (2011).Article 

Google Scholar 
Gosling, S. N. & Arnell, N. W. A global assessment of the impact of climate change on water scarcity. J. Clim. Change. 134, 371–385 (2016).ADS 
Article 

Google Scholar 
Gurel, M. & Tat, M. SWOT analysis: a theoretical review. J. Int. Soc. Res. 10(51), 994–1006 (2017).Article 

Google Scholar 
Hamdy, A., & Trisorio-Liuzzi, G. Water management strategies to combat drought in the semiarid regions. Water management for drought mitigation in the Mediterranean at the regional conference on arab water, Cairo, Egypt (2004).Hartmann, T. & Spit, T. Frontiers of land and water governance in urban regions. J. Water Int. 39(6), 791–797 (2014).Article 

Google Scholar 
He, L., Bao, J., Daccache, A., Wang, S. & Guo, P. Optimize the spatial distribution of crop water consumption based on a cellular automata model: a case study of the middle Heihe River basin, China. J. Sci. Total Environ. 720, 137569 (2020).ADS 
CAS 
Article 

Google Scholar 
Hwang, C.L. & Yoon, K. Methods for multiple attribute decision making. In: Multiple attribute decision making: lecture notes in economics and mathematical systems, Springer, Heidelberg, Germany, vol 186 (1981).Hwang, F. P., Chen, S. J. & Hwang, C. L. Fuzzy multiple attribute decision making: methods and applications (Springer, 1992).MATH 

Google Scholar 
Islam, M. S., Sadiq, R. & Rodriguez, M. J. Evaluating water quality failure potential in water distribution systems: a fuzzy-TOPSIS-OWA-based methodology. J. Water Resour. Manag. 27(7), 2195–2216 (2013).Article 

Google Scholar 
Karabasevic, D., Zavadskas, E. K., Turskis, Z. & Stanujkic, D. The framework for the selection of personnel based on the SWARA and ARAS methods under uncertainties. J. Inform. 27(1), 49–65 (2016).Article 

Google Scholar 
Keršuliene, V., Zavadskas, E. K. & Turskis, Z. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (Swara). J. Bus. Econ. Manag. 11(2), 243–258 (2010).Article 

Google Scholar 
Kim, S. et al. Developing spatial agricultural drought risk index with controllable geo-spatial indicators: a case study for South Korea and Kazakhstan. J. Disast. Risk Reduct. 54, 102056 (2021).Article 

Google Scholar 
Kousar, S., Zafar, A., Kausar, N., Pamucar, D. & Kattel, P. Fruit production planning in semiarid zones: a novel triangular intuitionistic fuzzy linear programming approach. J. Math. Prob. Eng. (2022).Lautze, J., de Silva, S., Giordano, M. & Sanford, L. Putting the cart before the horse: Water governance and IWRM. J. Nat. Resour. Forum Unit. Nat. Develop. 35(1), 1–8 (2011).Lee, K. L. & Lin, S. C. A fuzzy quantified SWOT procedure for environmental evaluation of an international distribution center. J. Inform. Sci. 178, 531–549 (2008).Article 

Google Scholar 
Loucks, D. P. Sustainable water resources management. Water International. Taylor & Francis, Milton Park (2000).Malczeweski, J. GIS and multicriteria decision analysis (Wiley, 1999).
Google Scholar 
Meza, I. et al. Drought risk for agricultural systems in South Africa: drivers, spatial patterns, and implications for drought risk management. J. Sci. Total Environ. 799, 149505 (2021).ADS 
CAS 
Article 

Google Scholar 
OECD. OECD principles on water governance. OECD Publishing (2015).Pahl-Wostl, C., Holtz, G., Kastens, B. & Knieper, C. Analyzing complex water governance regimes: the management and transition framework. J. Environ. Sci. Policy. 13(7), 571–581 (2010).Article 

Google Scholar 
Pahl-Wostl, C. et al. Environmental flows and water governance: managing sustainable water uses. J. Curr. Opin. Environm. Sustain. 5(3), 341–351 (2013).Article 

Google Scholar 
Pamucar, D., Torkayesh, A.E. & Biswas, S. Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach. J. Ann. Oper. Res. doi:https://doi.org/10.1007/s10479-022-04529-2(2022).Panchal, D., Chatterjee, P., Pamucar, D. & Yazdani, M. A novel fuzzy-based structured framework for sustainable operation and environmental friendly production in coal-fired power industry. J. Intell. Syst. doi: https://doi.org/10.1002/int.22507(2021).Peldschus, F., Zavadskas, E. K., Turskis, Z. & Tamosaitiene, J. Sustainable assessment of construction site by applying game theory. J. Eng. Econ. 21(3), 223–237 (2010).
Google Scholar 
Pérez-Blanco, C. & Gómez, C. Drought management plans and water availability in agriculture: a risk assessment model for a Southern European basin. J. Weather Clim. Extrem. 4, 11–18 (2014).Article 

Google Scholar 
Portoghese, I., Giannoccaro, G., Giordano, R. & Pagano, A. Modeling the impact of volumetric water pricing in irrigation districts with conjunctive use of water of surface and groundwater resources. J. Agric. Water Manag. 244, 106561 (2020).Article 

Google Scholar 
Rani, P., Mishra, A. R., Saha, A., Hezam, I. M. & Pamucar, D. Fermatean fuzzy Heronian mean operators and MEREC-based additive ratio assessment method: an application to food waste treatment technology selection. J. Intell. Syst. 37(3), 2612–2647 (2021).Article 

Google Scholar 
Rogers, P., & Hall, A.W. Effective water governance. J. Tech. Comm. Background Papers.7, Global Water Partnership (GWP) (2003).Rouillard, J. & Rinaudo, J. From State to user-based water allocations: an empirical analysis of institutions developed by agricultural user associations in France. J. Agric. Water Manag. 239, 106269 (2020).Article 

Google Scholar 
Ruzgys, A., Volvačiovas, R., Ignatavičius, Č & Turskis, Z. Integrated evaluation of external wall insulation in residential buildings using SWARA-TODIM MCDM method. J. Civil Eng. Manag. 20(1), 103–110 (2014).Article 

Google Scholar 
Saaty, T. L. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281 (1977).MathSciNet 
MATH 
Article 

Google Scholar 
Saaty, T. L. The analytic hierarchy process (McGraw-Hill, 1980).MATH 

Google Scholar 
Saaty, T. L. The analytic hierarchy process: planning, priority setting, resource allocation (RWS Publication, 1996).MATH 

Google Scholar 
Saaty, T. L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 1(1), 83–98 (2008).
Google Scholar 
Soltanjalili, M., Bozorg-Haddad, O. & Mariño, M. A. Effect of breakage level one in design of water distribution networks. J. Water Resour. Manag. 25(1), 311–337 (2011).Article 

Google Scholar 
Srdjevic, Z., Bajcetic, R. & Srdjevic, B. Identifying the criteria set for multi criteria decision making based on SWOT/PESTLE analysis: a case study of reconstructing a water intake structure. J. Water Resour. Manag. 26(12), 3379–3393 (2012).Article 

Google Scholar 
Stewart, R. A., Mohamed, S. & Daet, R. Strategic implementation of IT/IS projects in construction: a case study. J. Autom. Const. 11, 681–694 (2002).Article 

Google Scholar 
Thaler, T., Nordbeck, R. & Seher, W. Cooperation in flood risk management: understanding the role of strategic planning in two Austrian policy instruments. J. Environ. Sci. Policy. 114, 170–177 (2020).Article 

Google Scholar 
Thomson, J. et al. Spatial conservation action planning in heterogeneous landscapes. J. Biol. Conser. 250, 108735 (2020).Article 

Google Scholar 
Tortajada, C. Water governance: some critical issues. J. Water Resour. Develop. 26(2), 297–307 (2010).Article 

Google Scholar 
Tropp, H. Water governance: trends and needs for new capacity development. J. Water Policy. 9(2), 19–30 (2007).Article 

Google Scholar 
Van Laarhoven, P. J. & Pedrycz, W. A fuzzy extension of Saaty’s priority theory. J. Fuzzy Sets Syst. 11(1–3), 229–241 (1983).MathSciNet 
MATH 
Article 

Google Scholar 
Venot, J., Reddy, V. R. & Umapathy, D. Coping with drought in irrigated South India: Farmers’ adjustments in Nagarjuna Sagar. J. Agric. Water Manag. 97(10), 1434–1442 (2010).Article 

Google Scholar 
Vermillion, D.L. Irrigation sector reform in Asia: from patronage under participation to empowerment with partnership. In Asian Irrigation in Transition. New Delhi: Sage publications. https://www.cabdirect.org/cabdirect/abstract/20073076323(2003).Yazdani, M., Wen, Z., Liao, H., Banaitis, A. & Turskis, Z. A grey combined compromise solution (CoCoSo-G) method for supplier selection in construction management. J. Civil Eng. Manag. 25(8), 858–874 (2019).Article 

Google Scholar 
Yuksel, I. & Dagdeviren, M. Using the analytic network process (ANP) in a SWOT analysis: a case study for a textile firm. J. Inform. Sci. 177, 3364–3382 (2007).MATH 
Article 

Google Scholar 
Zadeh, L. A. Fuzzy sets. J. Inform. Control. 8(3), 338–353 (1965).MATH 
Article 

Google Scholar 
Zavadskas, E. K., Mardani, A., Turskis, Z., Jusoh, A. & Nor, K. M. Development of TOPSIS method to solve complicated decision-making problems: an overview on developments from 2000 to 2015. J. Inform. Technol. Dec. Making. 15(03), 645–682 (2016).Article 

Google Scholar 
Zuo, Q., Wu, Q., Yu, L., Li, Y. & Fan, Y. Optimization of uncertain agricultural management considering the framework of water, energy and food. J. Agric. Water Manag. 253, 106907 (2021).Article 

Google Scholar  More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Jones, J. T. et al. Top 10 plant-parasitic nematodes in molecular plant pathology. Mol. Plant Pathol. 14, 946–961. https://doi.org/10.1111/mpp.12057 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Collange, B., Navarrete, M., Peyre, G., Mateille, T. & Tchamitchian, M. Root-knot nematode (Meloidogyne) management in vegetable crop production: The challenge of an agronomic system analysis. Crop Prot. 30, 1251–1262. https://doi.org/10.1016/j.cropro.2011.04.016 (2011).Article 

Google Scholar 
Nyaku, S. T., Affokpon, A., Danquah, A. & Brentu, F. C. in Nematology–concepts, diagnosis and control (eds Mohammad Manjur Shah & Mohammad Mahamood) 153–182 (IntechOpen, 2017).Desaeger, J., Wram, C. & Zasada, I. New reduced-risk agricultural nematicides-rationale and review. J. Nematol. 52, 1 (2020).Article 

Google Scholar 
Dong, L. & Zhang, K. Microbial control of plant-parasitic nematodes: a five-party interaction. Plant Soil 288, 31–45. https://doi.org/10.1007/s11104-006-9009-3 (2006).CAS 
Article 

Google Scholar 
Singh, S., Singh, B. & Singh, A. Nematodes: A threat to sustainability of agriculture. Procedia Environ. Sci. 29, 215–216. https://doi.org/10.1016/j.proenv.2015.07.270 (2015).Article 

Google Scholar 
Oka, Y. Mechanisms of nematode suppression by organic soil amendments—A review. Appl. Soil Ecol. 44, 101–115. https://doi.org/10.1016/j.apsoil.2009.11.003 (2010).Article 

Google Scholar 
Yue, X., Li, F. & Wang, B. Activity of four nematicides against Meloidogyne incognita race 2 on tomato plants. J. Phytopathol. 168, 399–404. https://doi.org/10.1111/jph.12904 (2020).CAS 
Article 

Google Scholar 
Huang, W.-K. et al. Mutations in Acetylcholinesterase2 (ace 2) increase the insensitivity of acetylcholinesterase to fosthiazate in the root-knot nematode Meloidogyne incognita. Sci. Rep. 6, 1–9. https://doi.org/10.1038/srep38102 (2016).CAS 
Article 

Google Scholar 
Yoon, Y., Kim, E.-S., Hwang, Y.-S. & Choi, C.-Y. Avermectin: Biochemical and molecular basis of its biosynthesis and regulation. Appl. Microbiol. Biotechnol. 63, 626–634. https://doi.org/10.1007/s00253-003-1491-4 (2004).CAS 
Article 
PubMed 

Google Scholar 
Wolstenholme, A. J. & Rogers, A. Glutamate-gated chloride channels and the mode of action of the avermectin/milbemycin anthelmintics. Parasitology 131, S85–S95. https://doi.org/10.1017/S0031182005008218 (2005).CAS 
Article 
PubMed 

Google Scholar 
Haydock, P., Woods, S., Grove, I. & Hare, M. in Plant nematology (eds Roland N Perry & Maurice Moens) 459–479 (CABI, 2013).Forghani, F. & Hajihassani, A. Recent advances in the development of environmentally benign treatments to control root-knot nematodes. Front. Plant Sci. 11, 1. https://doi.org/10.3389/fpls.2020.01125 (2020).Article 

Google Scholar 
Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556. https://doi.org/10.1146/annurev.micro.62.081307.162918 (2009).CAS 
Article 
PubMed 

Google Scholar 
Mhatre, P. H. et al. Plant growth promoting rhizobacteria (PGPR): a potential alternative tool for nematodes bio-control. Biocatal. Agr. Biotechnol. 17, 119–128. https://doi.org/10.1016/j.bcab.2018.11.009 (2019).Article 

Google Scholar 
Eissa, M. F. & Abd-Elgawad, M. M. in Biocontrol agents of phytonematodes (eds Tarique Hassan Askary & Paulo Roberto Martinelli) 217–243 (CABI, 2015).Luo, T., Hou, S., Yang, L., Qi, G. & Zhao, X. Nematodes avoid and are killed by Bacillus mycoides-produced styrene. J. Invertebr. Pathol. 159, 129–136. https://doi.org/10.1016/j.jip.2018.09.006 (2018).CAS 
Article 
PubMed 

Google Scholar 
Siddiqui, I. & Shaukat, S. Systemic resistance in tomato induced by biocontrol bacteria against the root-knot nematode, Meloidogyne javanica is independent of salicylic acid production. J. Phytopathol. 152, 48–54. https://doi.org/10.1046/j.1439-0434.2003.00800.x (2004).Article 

Google Scholar 
Li, W. et al. Broad spectrum anti-biotic activity and disease suppression by the potential biocontrol agent Burkholderia ambifaria BC-F. Crop Protect. 21, 129–135. https://doi.org/10.1016/S0261-2194(01)00074-6 (2002).Article 

Google Scholar 
Khanna, K. et al. Role of plant growth promoting Bacteria (PGPRs) as biocontrol agents of Meloidogyne incognita through improved plant defense of Lycopersicon esculentum. Plant. Soil 436, 325–345. https://doi.org/10.1007/s11104-019-03932-2 (2019).CAS 
Article 

Google Scholar 
Subedi, P., Gattoni, K., Liu, W., Lawrence, K. S. & Park, S.-W. Current utility of plant growth-promoting rhizobacteria as biological control agents towards plant-parasitic nematodes. Plants 9, 1167. https://doi.org/10.3390/plants9091167 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Oka, Y. et al. New strategies for the control of plant-parasitic nematodes. Pest Manag. Sci. 56, 983–988. https://doi.org/10.1002/1526-4998(200011)56:11%3c983::AID-PS233%3e3.0.CO;2-X (2000).CAS 
Article 

Google Scholar 
Ralmi, N. H. A. A., Khandaker, M. M. & Mat, N. Occurrence and control of root knot nematode in crops: A review. Aust. J. Crop Sci. 11, 1649 (2016).Article 

Google Scholar 
Topalović, O. & Heuer, H. Plant-nematode interactions assisted by microbes in the rhizosphere. Curr. Issues Mol. Biol. 30, 75–88 (2019).Article 

Google Scholar 
Olanrewaju, O. S., Ayangbenro, A. S., Glick, B. R. & Babalola, O. O. Plant health: Feedback effect of root exudates-rhizobiome interactions. Appl. Microbiol. Biotechnol. 103, 1155–1166. https://doi.org/10.1007/s00253-018-9556-6 (2019).CAS 
Article 
PubMed 

Google Scholar 
Handley, K. M. et al. Biostimulation induces syntrophic interactions that impact C, S and N cycling in a sediment microbial community. ISME J. 7, 800–816. https://doi.org/10.1038/ismej.2012.148 (2013).CAS 
Article 
PubMed 

Google Scholar 
Tang, Y. et al. Changes in nitrogen-cycling microbial communities with depth in temperate and subtropical forest soils. Appl. Soil Ecol. 124, 218–228. https://doi.org/10.1016/j.apsoil.2017.10.029 (2018).ADS 
Article 

Google Scholar 
Babić, K. H. et al. Influence of different Sinorhizobium meliloti inocula on abundance of genes involved in nitrogen transformations in the rhizosphere of alfalfa (Medicago sativa L.). Environ. Microbiol. 10, 2922–2930 (2008).Article 

Google Scholar 
Ke, X. et al. Effect of inoculation with nitrogen-fixing bacterium Pseudomonas stutzeri A1501 on maize plant growth and the microbiome indigenous to the rhizosphere. Syst. Appl. Microbiol. 42, 248–260. https://doi.org/10.1016/j.syapm.2018.10.010 (2019).CAS 
Article 
PubMed 

Google Scholar 
Hogan, G. et al. Microbiome analysis as a platform R&D tool for parasitic nematode disease management. ISME J. 13, 2664–2680. https://doi.org/10.1038/s41396-019-0462-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Wu, Y. et al. Draft genome sequence of Stenotrophomonas maltophilia strain B418, a promising agent for biocontrol of plant pathogens and root-knot nematode. Genome Announc. 3, e00015-00015. https://doi.org/10.1128/genomeA.00015-15 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y. et al. Isolation and identification of nematicidal active substance from Burkholderia vietnamiensis B418. Plant Prot. 40, 65–69 (2014).
Google Scholar 
Li, S., Li, J., Xu, W., Chen, K. & Yang, H. Field efficacy test of biocontrol agent YKT41 and B418 against eggplant root-knot nematode disease. Shandong Sci. 24, 10–13 (2011).CAS 

Google Scholar 
Wang, Y., Wang, Z., Liu, B., Pan, M. & Li, J. Field trial of Burkholderia vietnamiensis and its composite microbial flora on cucumber root-knot nematode. Shandong Sci. 31, 39. https://doi.org/10.3976/j.issn.1002-4026.2018.01.007 (2018).Article 

Google Scholar 
Saad, A.-F.S., Massoud, M. A., Ibrahim, H. S. & Khalil, M. S. Management study for the root-knot nematodes, Meloidogyne incognita on tomatoes using fosthiazate and arbiscular mycorrhiza fungus. J. Adv. Agric. Res. 16, 137–147 (2011).
Google Scholar 
Huang, W.-K. et al. Efficacy evaluation of fungus Syncephalastrum racemosum and nematicide avermectin against the root-knot nematode Meloidogyne incognita on cucumber. PLoS ONE 9, e89717. https://doi.org/10.1371/journal.pone.0089717 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jayakumar, J. & Ramakrishnan, S. Evaluation of avermectin and its combination with nematicide and bioagents against root knot nematode, Meloidogyne incognita in tomato. J. Biol. Control 23, 317–319 (2009).
Google Scholar 
Moosavi, M. & Zare, R. in Biocontrol Agents of Phytonematodes (eds Tarique Hassan Askary & Paulo Roberto Martinelli) 423–445 (CABI, 2015).Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486. https://doi.org/10.1016/j.tplants.2012.04.001 (2012).CAS 
Article 
PubMed 

Google Scholar 
Reinhold-Hurek, B., Bünger, W., Burbano, C. S., Sabale, M. & Hurek, T. Roots shaping their microbiome: Global hotspots for microbial activity. Annu. Rev. Phytopathol. 53, 403–424. https://doi.org/10.1146/annurev-phyto-082712-102342 (2015).CAS 
Article 
PubMed 

Google Scholar 
Ahemad, M. & Kibret, M. Mechanisms and applications of plant growth promoting rhizobacteria: Current perspective. J. King Saud Univ.-Sci. 26, 1–20. https://doi.org/10.1016/j.jksus.2013.05.001 (2014).Article 

Google Scholar 
Ciccillo, F. et al. Effects of two different application methods of Burkholderia ambifaria MCI 7 on plant growth and rhizospheric bacterial diversity. Environ. Microbiol. 4, 238–245. https://doi.org/10.1046/j.1462-2920.2002.00291.x (2002).Article 
PubMed 

Google Scholar 
Jo, H. et al. Response of soil bacterial community and pepper plant growth to application of Bacillus thuringiensis KNU-07. Agronomy 10, 551. https://doi.org/10.3390/agronomy10040551 (2020).CAS 
Article 

Google Scholar 
Wang, J. et al. Traits-based integration of multi-species inoculants facilitates shifts of indigenous soil bacterial community. Front. Microbiol. 9, 1692. https://doi.org/10.3389/fmicb.2018.01692 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Welbaum, G. E., Sturz, A. V., Dong, Z. & Nowak, J. Managing soil microorganisms to improve productivity of agro-ecosystems. Crit. Rev. Plant Sci. 23, 175–193. https://doi.org/10.1080/07352680490433295 (2004).CAS 
Article 

Google Scholar 
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663. https://doi.org/10.1111/1574-6976.12028 (2013).CAS 
Article 
PubMed 

Google Scholar 
Li, J. et al. Trichoderma harzianum inoculation reduces the incidence of clubroot disease in Chinese cabbage by regulating the rhizosphere microbial community. Microorganisms 8, 1325. https://doi.org/10.3390/microorganisms8091325 (2020).CAS 
Article 
PubMed Central 

Google Scholar 
Song, L. et al. Regular biochar and bacteria-inoculated biochar alter the composition of the microbial community in the soil of a Chinese fir plantation. Forests 11, 951. https://doi.org/10.3390/f11090951 (2020).Article 

Google Scholar 
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100. https://doi.org/10.1126/science.1203980 (2011).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Palaniyandi, S. A., Yang, S. H., Zhang, L. & Suh, J.-W. Effects of actinobacteria on plant disease suppression and growth promotion. Appl. Microbiol. Biotechnol. 97, 9621–9636. https://doi.org/10.1007/s00253-013-5206-1 (2013).CAS 
Article 
PubMed 

Google Scholar 
Zhou, D. et al. Rhizosphere microbiomes from root knot nematode non-infested plants suppress nematode infection. Microbial Ecol. 78, 470–481. https://doi.org/10.1007/s00248-019-01319-5 (2019).CAS 
Article 

Google Scholar 
Zou, Y. et al. Metagenomic insights into the effect of oxytetracycline on microbial structures, functions and functional genes in sediment denitrification. Ecotox. Environ. Safe. 161, 85–91. https://doi.org/10.1016/j.ecoenv.2018.05.045 (2018).CAS 
Article 

Google Scholar 
Kong, Z. et al. Seasonal dynamics of the bacterioplankton community in a large, shallow, highly dynamic freshwater lake. Can. J. Microbiol. 64, 786–797. https://doi.org/10.1139/cjm-2018-0126 (2018).CAS 
Article 
PubMed 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226. https://doi.org/10.1016/j.soilbio.2017.12.018 (2018).CAS 
Article 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365. https://doi.org/10.1016/j.apsoil.2019.103365 (2020).Article 

Google Scholar 
Schmidt, J. E., Kent, A. D., Brisson, V. L. & Gaudin, A. C. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome 7, 1–18. https://doi.org/10.1186/s40168-019-0756-9 (2019).Article 

Google Scholar 
Hu, W., Strom, N., Haarith, D., Chen, S. & Bushley, K. E. Mycobiome of cysts of the soybean cyst nematode under long term crop rotation. Front. Microbiol. 9, 386. https://doi.org/10.3389/fmicb.2018.00386 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, W.-H. & Liu, Q.-Z. Changes in fungal community and diversity in strawberry rhizosphere soil after 12 years in the greenhouse. J. Integ. Agric. 18, 677–687. https://doi.org/10.1016/S2095-3119(18)62003-9 (2019).Article 

Google Scholar 
Qiu, W. et al. Organic fertilization assembles fungal communities of wheat rhizosphere soil and suppresses the population growth of Heterodera avenae in the field. Front. Plant Sci. 11, 1225. https://doi.org/10.3389/fpls.2020.01225 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Schardl, C. L., Leuchtmann, A. & Spiering, M. J. Symbioses of grasses with seedborne fungal endophytes. Annu. Rev. Plant Biol. 55, 315–340. https://doi.org/10.1146/annurev.arplant.55.031903.141735 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edgington, S., Thompson, E., Moore, D., Hughes, K. A. & Bridge, P. Investigating the insecticidal potential of Geomyces (Myxotrichaceae: Helotiales) and Mortierella (Mortierellacea: Mortierellales) isolated from Antarctica. Springerplus 3, 1–8. https://doi.org/10.1186/2193-1801-3-289 (2014).Article 

Google Scholar 
Yi, X. et al. Comparison of the abundance and community structure of N-Cycling bacteria in paddy rhizosphere soil under different rice cultivation patterns. Int. J. Mol. Sci. 19, 3772. https://doi.org/10.3390/ijms19123772 (2018).CAS 
Article 
PubMed Central 

Google Scholar 
Duval, S. et al. Electron transfer precedes ATP hydrolysis during nitrogenase catalysis. Proc. Natl. Acad. Sci. USA 110, 16414–16419. https://doi.org/10.1073/pnas.1311218110 (2013).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pham, V. T. et al. The plant growth-promoting effect of the nitrogen-fixing endophyte Pseudomonas stutzeri A15. Arch. Microbiol. 199, 513–517. https://doi.org/10.1007/s00203-016-1332-3 (2017).CAS 
Article 
PubMed 

Google Scholar 
Ouyang, Y., Evans, S. E., Friesen, M. L. & Tiemann, L. K. Effect of nitrogen fertilization on the abundance of nitrogen cycling genes in agricultural soils: a meta-analysis of field studies. Soil Biol. Biochem. 127, 71–78. https://doi.org/10.1016/j.soilbio.2018.08.024 (2018).CAS 
Article 

Google Scholar 
Dynarski, K. A. & Houlton, B. Z. Nutrient limitation of terrestrial free-living nitrogen fixation. New Phytol. 217, 1050–1061. https://doi.org/10.1111/nph.14905 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kastl, E.-M., Schloter-Hai, B., Buegger, F. & Schloter, M. Impact of fertilization on the abundance of nitrifiers and denitrifiers at the root–soil interface of plants with different uptake strategies for nitrogen. Biol. Fert. Soils 51, 57–64. https://doi.org/10.1007/s00374-014-0948-1 (2015).CAS 
Article 

Google Scholar 
Bulgarelli, D. et al. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature 488, 91–95. https://doi.org/10.1038/nature11336 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Southey, J. in Laboratory methods for work with plants and soil nematodes (ed JF Southey) 42–44 (HMSO, 1986).Ladner, D. C., Tchounwou, P. B. & Lawrence, G. W. Evaluation of the effect of ecologic on root knot nematode, Meloidogyne incognita, and tomato plant, Lycopersicon esculenum. Int. J. Environ. Res. Public Health 5, 104–110. https://doi.org/10.3390/ijerph5020104 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Niu, D.-D. et al. Application of PSX biocontrol preparation confers root-knot nematode management and increased fruit quality in tomato under field conditions. Biocontrol Sci. Technol. 26, 174–180. https://doi.org/10.1080/09583157.2015.1085489.18 (2016).Article 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucl. Acids Res. 41, e1–e1. https://doi.org/10.1093/nar/gks808 (2013).CAS 
Article 
PubMed 

Google Scholar 
Buee, M. et al. 454 Pyrosequencing analyses of forest soils reveal an unexpectedly high fungal diversity. New Phytol. 184, 449–456. https://doi.org/10.1111/j.1469-8137.2009.03003.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Rösch, C., Mergel, A. & Bothe, H. Biodiversity of denitrifying and dinitrogen-fixing bacteria in an acid forest soil. Appl. Environ. Microbiol. 68, 3818–3829. https://doi.org/10.1128/AEM.68.8.3818-3829.2002 (2002).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Throbäck, I. N., Enwall, K., Jarvis, Å. & Hallin, S. Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE. FEMS Microbiol. Ecol. 49, 401–417. https://doi.org/10.1016/j.femsec.2004.04.011 (2004).CAS 
Article 
PubMed 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200. https://doi.org/10.1093/bioinformatics/btr381 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21. https://doi.org/10.1186/s13059-014-0550-8 (2014).CAS 
Article 

Google Scholar 
Lozupone, C. & Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235. https://doi.org/10.1128/AEM.71.12.8228-8235.2005 (2005).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124. https://doi.org/10.1093/bioinformatics/btu494 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Duarte C, Middelburg JJ, Caraco N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences. 2005;2:1–8.CAS 
Article 

Google Scholar 
Kloareg B, Quatrano RS. Structure of the cell walls of marine algae and ecophysiological functions of the matrix polysaccharides. Ocean Mar Biol Annu Rev. 1988;26:259–315.
Google Scholar 
Fletcher HR, Biller P, Ross AB, Adams JMM. The seasonal variation of fucoidan within three species of brown macroalgae. Algal Res. 2017;22:79–86.Article 

Google Scholar 
Deniaud-Bouët E, Hardouin K, Potin P, Kloareg B, Hervé C. A review about brown algal cell walls and fucose-containing sulfated polysaccharides: Cell wall context, biomedical properties and key research challenges. Carbohydr Polym. 2017;175:395–408.PubMed 
Article 
CAS 

Google Scholar 
Haug A, Larsen B, Smidsrød O. Uronic acid sequence in alginate from different sources. Carbohydr Res. 1974;32:217–225.CAS 
Article 

Google Scholar 
Bruhn A, Janicek T, Manns D, Nielsen MM, Balsby TJS, Meyer AS, et al. Crude fucoidan content in two North Atlantic kelp species, Saccharina latissima and Laminaria digitata—seasonal variation and impact of environmental factors. J Appl Phycol. 2017;29:3121–3137.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ponce NMA, Stortz CA. A comprehensive and comparative analysis of the fucoidan compositional data across the Phaeophyceae. Front Plant Sci. 2020;11:556312.PubMed 
PubMed Central 
Article 

Google Scholar 
Fleurence J. The enzymatic degradation of algal cell walls: A useful approach for improving protein accessibility? J Appl Phycol. 1999;11:313–314.CAS 
Article 

Google Scholar 
Verhaeghe EF, Fraysse A, Guerquin-Kern JL, Wu TD, Devès G, Mioskowski C, et al. Microchemical imaging of iodine distribution in the brown alga Laminaria digitata suggests a new mechanism for its accumulation. J Biol Inorg Chem. 2008;13:257–269.CAS 
PubMed 
Article 

Google Scholar 
Schiener P, Black KD, Stanley MS, Green DH. The seasonal variation in the chemical composition of the kelp species Laminaria digitata, Laminaria hyperborea, Saccharina latissima and Alaria esculenta. J Appl Phycol. 2015;27:363–373.CAS 
Article 

Google Scholar 
Deniaud-Bouët E, Kervarec N, Michel G, Tonon T, Kloareg B, Hervé C. Chemical and enzymatic fractionation of cell walls from Fucales: Insights into the structure of the extracellular matrix of brown algae. Ann Bot. 2014;114:1203–1216.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Michel G, Tonon T, Scornet D, Cock JM, Kloareg B. Central and storage carbon metabolism of the brown alga Ectocarpus siliculosus: Insights into the origin and evolution of storage carbohydrates in Eukaryotes. N. Phytol. 2010;188:67–81.CAS 
Article 

Google Scholar 
Mann K. Ecology of coastal waters—A systems approach, Berkeley: University of California Press; 1982.Egan S, Harder T, Burke C, Steinberg P, Kjelleberg S, Thomas T. The seaweed holobiont: Understanding seaweed-bacteria interactions. FEMS Microbiol Rev. 2013;37:462–476.CAS 
PubMed 
Article 

Google Scholar 
Kirchman DL. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol. 2002;39:91–100.CAS 
PubMed 

Google Scholar 
Thomas F, Hehemann JH, Rebuffet E, Czjzek M, Michel G. Environmental and gut Bacteroidetes: The food connection. Front Microbiol. 2011;2:93.PubMed 
PubMed Central 
Article 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–611.CAS 
PubMed 
Article 

Google Scholar 
Wietz M, Wemheuer B, Simon H, Giebel HA, Seibt MA, Daniel R, et al. Bacterial community dynamics during polysaccharide degradation at contrasting sites in the Southern and Atlantic Oceans. Environ Microbiol. 2015;17:3822–3831.CAS 
PubMed 
Article 

Google Scholar 
Arnosti C, Wietz M, Brinkhoff T, Hehemann J-H, Probant D, Zeugner L, et al. The biogeochemistry of marine polysaccharides: sources, inventories, and bacterial drivers of the carbohydrate cycle. Ann Rev Mar Sci. 2020;13:9.1–9.28.
Google Scholar 
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:490–495.Article 
CAS 

Google Scholar 
Barbeyron T, Brillet-Guéguen L, Carré W, Carrière C, Caron C, Czjzek M, et al. Matching the diversity of sulfated biomolecules: Creation of a classification database for sulfatases reflecting their substrate specificity. PLoS One. 2016;11:1–33.Article 
CAS 

Google Scholar 
Tang K, Lin Y, Han Y, Jiao N. Characterization of potential polysaccharide utilization systems in the marine Bacteroidetes Gramella flava JLT2011 using a multi-omics approach. Front Microbiol. 2017;8:220.PubMed 
PubMed Central 

Google Scholar 
Zhu Y, Chen P, Bao Y, Men Y, Zeng Y, Yang J, et al. Complete genome sequence and transcriptomic analysis of a novel marine strain Bacillus weihaiensis reveals the mechanism of brown algae degradation. Sci Rep. 2016;6:38248.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Bordron P, Eveillard D, Michel G. Gene expression analysis of Zobellia galactanivorans during the degradation of algal polysaccharides reveals both substrate-specific and shared transcriptome-wide responses. Front Microbiol. 2017;8:1808.PubMed 
PubMed Central 
Article 

Google Scholar 
Ficko-Blean E, Préchoux A, Thomas F, Rochat T, Larocque R, Zhu Y, et al. Carrageenan catabolism is encoded by a complex regulon in marine heterotrophic bacteria. Nat Commun. 2017;8:1685.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Koch H, Dürwald A, Schweder T, Noriega-Ortega B, Vidal-Melgosa S, Hehemann JH, et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 2019;13:92–103.CAS 
PubMed 
Article 

Google Scholar 
Bunse C, Koch H, Breider S, Simon M, Wietz M. Sweet spheres: succession and CAZyme expression of marine bacterial communities colonizing a mix of alginate and pectin particles. Environ Microbiol. 2021;23:3130–3148.CAS 
PubMed 
Article 

Google Scholar 
Hehemann JH, Arevalo P, Datta MS, Yu X, Corzett CH, Henschel A, et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat Commun. 2016;7:12860.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gralka M, Szabo R, Stocker R, Cordero OX. Trophic interactions and the drivers of microbial community assembly. Curr Biol. 2020;30:R1176–R1188.CAS 
PubMed 
Article 

Google Scholar 
Jiménez DJ, Dini-Andreote F, DeAngelis KM, Singer SW, Salles JF, van Elsas JD. Ecological insights into the dynamics of plant biomass-degrading microbial consortia. Trends Microbiol. 2017;25:788–796.PubMed 
Article 
CAS 

Google Scholar 
Kang S, Kim JK. Reuse of red seaweed waste by a novel bacterium, Bacillus sp. SYR4 isolated from a sandbar. World J Microbiol Biotechnol. 2015;31:209–217.PubMed 
Article 

Google Scholar 
Jonnadula R, Verma P, Shouche YS, Ghadi SC. Characterization of Microbulbifer strain CMC-5, a new biochemical variant of Microbulbifer elongatus type strain DSM6810T isolated from decomposing seaweeds. Curr Microbiol. 2009;59:600–607.CAS 
PubMed 
Article 

Google Scholar 
Martin M, Barbeyron T, Martin R, Portetelle D, Michel G, Vandenbol M. The cultivable surface microbiota of the brown alga Ascophyllum nodosum is enriched in macroalgal-polysaccharide-degrading bacteria. Front Microbiol. 2015;6:1487.PubMed 
PubMed Central 
Article 

Google Scholar 
Dogs M, Wemheuer B, Wolter L, Bergen N, Daniel R, Simon M, et al. Rhodobacteraceae on the marine brown alga Fucus spiralis are abundant and show physiological adaptation to an epiphytic lifestyle. Syst Appl Microbiol. 2017;40:370–382.CAS 
PubMed 
Article 

Google Scholar 
Brunet M, le Duff N, Fuchs B, Amann R, Barbeyron T, Thomas F. Specific detection and quantification of the marine flavobacterial genus Zobellia on macroalgae using novel qPCR and CARD-FISH assays. Syst Appl Microbiol. 2021;44:126269.CAS 
PubMed 
Article 

Google Scholar 
Barbeyron T, L’Haridon S, Corre E, Kloareg B, Potin P. Zobellia galactanovorans gen. nov., sp. nov., a marine species of Flavobacteriaceae isolated from a red alga, and classification of [Cytophaga] uliginosa (ZoBell and Upham 1944) Reichenbach 1989 as Zobellia uliginosa gen. nov., comb. nov. Int J Syst Evol Microbiol. 2001;51:985–997.CAS 
PubMed 
Article 

Google Scholar 
Barbeyron T, Thiébaud M, Le Duff N, Martin M, Corre E, Tanguy G, et al. Zobellia roscoffensis sp. nov. and Zobellia nedashkovskayae sp. nov., two flavobacteria from the epiphytic microbiota of the brown alga Ascophyllum nodosum, and emended description of the genus Zobellia. Int J Syst Evol Microbiol. 2021;71:004913.Nedashkovskaya OI, Suzuki M, Vancanneyt M, Cleenwerck I, Lysenko AM, Mikhailov VV, et al. Zobellia amurskyensis sp. nov., Zobellia laminariae sp. nov. and Zobellia russellii sp. nov., novel marine bacteria of the family Flavobacteriaceae. Int J Syst Evol Microbiol. 2004;54:1643–1648.CAS 
PubMed 
Article 

Google Scholar 
Nedashkovskaya O, Otstavnykh N, Zhukova N, Guzev K, Chausova V, Tekutyeva L, et al. Zobellia barbeyronii sp. nov., a new member of the family Flavobacteriaceae, isolated from seaweed, and emended description of the species Z. amurskyensis, Z. laminariae, Z. russellii and Z. uliginosa. Diversity. 2021;13:520.CAS 
Article 

Google Scholar 
Chernysheva N, Bystritskaya E, Stenkova A, Golovkin I. Comparative genomics and CAZyme genome repertoires of marine Zobellia amurskyensis KMM 3526T and Zobellia laminariae KMM 3676T. Mar Drugs. 2019;17:661.CAS 
PubMed Central 
Article 

Google Scholar 
Chernysheva N, Bystritskaya E, Likhatskaya G, Nedashkovskaya O, Isaeva M. Genome-wide analysis of PL7 alginate lyases in the genus Zobellia. Molecules. 2021;26:2387.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Barbeyron T, Thomas F, Barbe V, Teeling H, Schenowitz C, Dossat C, et al. Habitat and taxon as driving forces of carbohydrate catabolism in marine heterotrophic bacteria: Example of the model algae-associated bacterium Zobellia galactanivorans DsijT. Environ Microbiol. 2016;18:4610–4627.CAS 
PubMed 
Article 

Google Scholar 
Potin P, Sanseau A, Le Gall Y, Rochas C, Kloareg B. Purification and characterization of a new k‐carrageenase from a marine Cytophaga‐like bacterium. Eur J Biochem. 1991;201:241–247.CAS 
PubMed 
Article 

Google Scholar 
Lami R, Grimaud R, Sanchez-Brosseau S, Six C, Thomas F, West NJ, et al. Marine bacterial models for experimental biology. In: Boutet A, Schierwater B, editors. Handbook of Marine Model Organisms in Experimental Biology. London: Taylor & Francis Ltd; 2021.Dudek M, Dieudonné A, Jouanneau D, Rochat T, Michel G, Sarels B, et al. Regulation of alginate catabolism involves a GntR family repressor in the marine flavobacterium Zobellia galactanivorans DsijT. Nucleic Acids Res. 2020;48:7786–7800.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Lundqvist LCE, Jam M, Jeudy A, Barbeyron T, Sandström C, et al. Comparative characterization of two marine alginate lyases from Zobellia galactanivorans reveals distinct modes of action and exquisite adaptation to their natural substrate. J Biol Chem. 2013;288:23021–23037.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Thomas F, Barbeyron T, Tonon T, Génicot S, Czjzek M, Michel G. Characterization of the first alginolytic operons in a marine bacterium: from their emergence in marine Flavobacteriia to their independent transfers to marine Proteobacteria and human gut Bacteroides. Environ Microbiol. 2012;14:2379–94.CAS 
PubMed 
Article 

Google Scholar 
Jam M, Flament D, Allouch J, Potin P, Thion L, Kloareg B, et al. The endo-β-agarases AgaA and AgaB from the marine bacterium Zobellia galactanivorans: Two paralogue enzymes with different molecular organizations and catalytic behaviours. Biochem J. 2005;385:703–713.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hehemann JH, Correc G, Thomas F, Bernard T, Barbeyron T, Jam M, et al. Biochemical and structural characterization of the complex agarolytic enzyme system from the marine bacterium Zobellia galactanivorans. J Biol Chem. 2012;287:30571–30584.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labourel A, Jam M, Jeudy A, Hehemann JH, Czjzek M, Michel G. The β-glucanase ZgLamA from Zobellia galactanivorans evolved a bent active site adapted for efficient degradation of algal laminarin. J Biol Chem. 2014;289:2027–2042.CAS 
PubMed 
Article 

Google Scholar 
Labourel A, Jam M, Legentil L, Sylla B, Hehemann JH, Ferrières V, et al. Structural and biochemical characterization of the laminarinase ZgLamCGH16 from Zobellia galactanivorans suggests preferred recognition of branched laminarin. Acta Crystallogr. 2015;D71:173–184.
Google Scholar 
Dorival J, Ruppert S, Gunnoo M, Orłowski A, Chapelais-Baron M, Dabin J, et al. The laterally-acquired GH5 ZgEngAGH5_4 from the marine bacterium Zobellia galactanivorans is dedicated to hemicellulose hydrolysis. Biochem J. 2018;475:3609–3628.PubMed 
Article 

Google Scholar 
Groisillier A, Labourel A, Michel G, Tonon T. The mannitol utilization system of the marine bacterium Zobellia galactanivorans. Appl Environ Microbiol. 2015;81:1799–1812.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fournier JB, Rebuffet E, Delage L, Grijol R, Meslet-Cladière L, Rzonca J, et al. The vanadium iodoperoxidase from the marine Flavobacteriaceae species Zobellia galactanivorans reveals novel molecular and evolutionary features of halide specificity in the vanadium haloperoxidase enzyme family. Appl Environ Microbiol. 2014;80:7561–7573.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Grigorian E, Groisillier A, Thomas F, Leblanc C, Delage L. Functional characterization of a L-2-haloacid dehalogenase from Zobellia galactanivorans DsijT suggests a role in haloacetic acid catabolism and a wide distribution in marine environments. Front Microbiol. 2021;12:725997.PubMed 
PubMed Central 
Article 

Google Scholar 
Zhu Y, Thomas F, Larocque R, Li N, Duffieux D, Cladière L, et al. Genetic analyses unravel the crucial role of a horizontally acquired alginate lyase for brown algal biomass degradation by Zobellia galactanivorans. Environ Microbiol. 2017;19:2164–2181.CAS 
PubMed 
Article 

Google Scholar 
Zablackis E, Perez J. A partially pyruvated carrageenan from hawaiian Grateloupia filicina (Cryptonemiales, Rhodophyta). Bot Mar. 1990;33:273–276.CAS 
Article 

Google Scholar 
Filisetti-Cozzi T, Carpita N. Measurement of uronic acids without interference from neutral sugars. Anal Biochem. 1991;197:15162.Article 

Google Scholar 
Blumenkrantz N, Asboe-Hansen G. New method for quantitative determination of uronic acids. Anal Biochem. 1973;54:484–489.CAS 
PubMed 
Article 

Google Scholar 
Cumashi A, Ushakova NA, Preobrazhenskaya ME, D’Incecco A, Piccoli A, Totani L, et al. A comparative study of the anti-inflammatory, anticoagulant, antiangiogenic, and antiadhesive activities of nine different fucoidans from brown seaweeds. Glycobiology. 2007;17:541–552.CAS 
PubMed 
Article 

Google Scholar 
Jung SY, Oh TK, Yoon JH. Tenacibaculum aestuarii sp. nov., isolated from a tidal flat sediment in Korea. Int J Syst Evol Microbiol. 2006;56:1577–1581.CAS 
PubMed 
Article 

Google Scholar 
ZoBell C. Studies on marine bacteria. I. The cultural requirements of heterotrophic aerobes. J Mar Res. 1941;4:75.
Google Scholar 
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 
Article 

Google Scholar 
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14:417–419.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vallenet D, Calteau A, Dubois M, Amours P, Bazin A, Beuvin M, et al. MicroScope: An integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis. Nucleic Acids Res. 2020;48:D579–D589.CAS 
PubMed 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–359.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–2079.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Thomas F, Barbeyron T, Michel G. Evaluation of reference genes for real-time quantitative PCR in the marine flavobacterium Zobellia galactanivorans. J Microbiol Methods. 2011;84:61–6.CAS 
PubMed 
Article 

Google Scholar 
Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29:24–26.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.Article 
CAS 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. 2018. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.Lex A, Gehlenborg N, Strobelt H. UpSet: Visualization of intersecting sets. IEEE Trans Vis Comput Graph. 2014;20:1983–1992.PubMed 
PubMed Central 
Article 

Google Scholar 
Krassowski M. krassowski/complex-upset. 2020. https://doi.org/10.5281/zenodo.3700590.Murtagh F, Legendre P. Ward’s hierarchical clustering method: clustering criterion and agglomerative algorithm. J Classif. 2014;31:274–295.Article 

Google Scholar 
Wickham H Use R! ggplot2: Elegant graphics for data analysis. 2nd ed. London: Springer; 2016.Kidby DK, Davidson DJ. Ferricyanide estimation of sugars in the nanomole range. Anal Biochem. 1973;55:321–325.CAS 
PubMed 
Article 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. DbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen X, Hu Y, Yang B, Gong X, Zhang N, Niu L, et al. Structure of lpg0406, a carboxymuconolactone decarboxylase family protein possibly involved in antioxidative response from Legionella pneumophila. Protein Sci. 2015;24:2070–2075.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Datta MS, Schwartzman J, Cermak N, Schmitz D, Barrere J, et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr Biol. 2019;29:1528–1535.e6.CAS 
PubMed 
Article 

Google Scholar 
Pollak S, Gralka M, Sato Y, Schwartzman J, Lu L, Cordero OX. Public good exploitation in natural bacterioplankton communities. Sci Adv. 2021;7:eabi4717.Pontrelli S, Szabo R, Pollak S, Schwartzman J, Ledezma D, Cordero OX, et al. Metabolic cross-feeding structures the assembly of polysaccharide degrading communities. Sci Adv. 2022;8:eabk3076.Holdt SL, Kraan S. Bioactive compounds in seaweed: Functional food applications and legislation. J Appl Phycol. 2011;23:543–597.CAS 
Article 

Google Scholar 
Kawamura-Konishi Y, Watanabe N, Saito M, Nakajima N, Sakaki T, Katayama T, et al. Isolation of a new phlorotannin, a potent inhibitor of carbohydrate-hydrolyzing enzymes, from the brown alga Sargassum patens. J Agric Food Chem. 2012;60:5565–5570.CAS 
PubMed 
Article 

Google Scholar 
Garbary DJ, Brown NE, MacDonell HJ, Toxopeux J. Ascophyllum and its symbionts — A complex symbiotic community on North Atlantic shores. Algal and Cyanobacteria Symbioses. 2017:547–572.Pluvinage B, Grondin JM, Amundsen C, Klassen L, Moote PE, Xiao Y, et al. Molecular basis of an agarose metabolic pathway acquired by a human intestinal symbiont. Nat Commun. 2018;9:1043.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Reintjes G, Arnosti C, Fuchs BM, Amann R. An alternative polysaccharide uptake mechanism of marine bacteria. ISME J. 2017;11:1640–1650.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hollants J, Leliaert F, de Clerck O, Willems A. What we can learn from sushi: A review on seaweed-bacterial associations. FEMS Microbiol Ecol. 2013;83:1–16.CAS 
PubMed 
Article 

Google Scholar 
Thomas F, Le Duff N, Wu TD, Cébron A, Uroz S, Riera P, et al. Isotopic tracing reveals single-cell assimilation of a macroalgal polysaccharide by a few marine Flavobacteria and Gammaproteobacteria. ISME J. 2021;15:3062–3075.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Datta MS, Sliwerska E, Gore J, Polz MF, Cordero OX. Microbial interactions lead to rapid micro-scale successions on model marine particles. Nat Commun. 2016;7:11965.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Enke TN, Leventhal GE, Metzger M, Saavedra JT, Cordero OX. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat Commun. 2018;9:2743.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sichert A, Cordero OX. Polysaccharide-bacteria Interactions from the lens of evolutionary ecology. Front Microbiol. 2021;12:705082.PubMed 
PubMed Central 
Article 

Google Scholar 
Sichert A, Corzett CH, Schechter M, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–1039.CAS 
PubMed 
Article 

Google Scholar 
Reisky L, Préchoux A, Zühlke MK, Bäumgen M, Robb CS, Gerlach N, et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat Chem Biol. 2019;15:803–812.CAS 
PubMed 
Article 

Google Scholar 
Mabeau S, Kloareg B, Joseleau J-P. Fractionation and analysis of fucans from brown algae. Phytochemistry. 1990;29:2441–2445.CAS 
Article 

Google Scholar 
Küpper FC, Kloareg B, Guern J, Potin P. Oligoguluronates elicit an oxidative burst in the brown algal kelp Laminaria digitata. Plant Physiol. 2001;125:278–291.PubMed 
PubMed Central 
Article 

Google Scholar 
Küpper FC, Müller DG, Peters AF, Kloareg B, Potin P. Oligoalginate recognition and oxidative burst play a key role in natural and induced resistance of sporophytes of Laminariales. J Chem Ecol. 2002;28:2057–2081.PubMed 
Article 

Google Scholar 
Leonard S, Hommais F, Nasser W, Reverchon S. Plant–phytopathogen interactions: bacterial responses to environmental and plant stimuli. Environ Microbiol. 2017;19:1689–1716.PubMed 
Article 

Google Scholar 
Sato K, Naito M, Yukitake H, Hirakawa H, Shoji M, McBride MJ, et al. A protein secretion system linked to bacteroidete gliding motility and pathogenesis. PNAS. 2010;107:276–281.CAS 
PubMed 
Article 

Google Scholar 
Eckroat TJ, Greguske C, Hunnicutt DW. The type 9 secretion system is required for Flavobacterium johnsoniae biofilm formation. Front Microbiol. 2021;12:660887.PubMed 
PubMed Central 
Article 

Google Scholar 
Xie S, Tan Y, Song W, Zhang W, Qi Q, Lu X. N-glycosylation of a cargo protein C-terminal domain recognized by the type IX secretion system in Cytophaga hutchinsonii affects protein secretion and localization. Appl Environ Microbiol. 2022;88:e0160621.PubMed 
Article 

Google Scholar  More

Whitfield, A. E., Falk, B. W. & Rotenberg, D. Insect vector-mediated transmission of plant viruses. Virology 479–480, 278–289. https://doi.org/10.1016/j.virol.2015.03.026 (2015).CAS 
Article 
PubMed 

Google Scholar 
Nault, L. R. Arthropod transmission of plant viruses: A new synthesis. Ann. Entomol. Soc. Am. 90, 521–541. https://doi.org/10.1093/aesa/90.5.521 (1997).Article 

Google Scholar 
Maluta, N., Fereres, A. & Lopes, J. R. S. Plant-mediated indirect effects of two viruses with different transmission modes on Bemisia tabaci feeding behavior and fitness. J. Pest Sci. 92, 405–416. https://doi.org/10.1007/s10340-018-1039-0 (2019).Article 

Google Scholar 
Scheirs, J. & De Bruyn, L. Integrating optimal foraging and optimal oviposition theory in plant–insect research. Oikos 96, 187–191. https://doi.org/10.1034/j.1600-0706.2002.960121.x (2002).Article 

Google Scholar 
Pyke, G. H. Optimal foraging theory: A critical review. Annu. Rev. Ecol. Syst. 15, 523–575. https://doi.org/10.1146/annurev.es.15.110184.002515 (1984).Article 

Google Scholar 
Hurd, H. Manipulation of medically important insect vectors by their parasites. Annu. Rev. Entomol. 48, 141–161. https://doi.org/10.1146/annurev.ento.48.091801.112722 (2003).CAS 
Article 
PubMed 

Google Scholar 
Moore, J. Parasites and the Behavior of Animals (Oxford University Press, 2002).
Google Scholar 
Eigenbrode, S. D., Bosque-Pérez, N. A. & Davis, T. S. Insect-borne plant pathogens and their vectors: Ecology, evolution, and complex interactions. Annu. Rev. Entomol. 63, 169–191. https://doi.org/10.1146/annurev-ento-020117-043119 (2018).CAS 
Article 
PubMed 

Google Scholar 
Mauck, K., Bosque-Pérez, N. A., Eigenbrode, S. D., De Moraes, C. M. & Mescher, M. C. Transmission mechanisms shape pathogen effects on host–vector interactions: Evidence from plant viruses. Funct. Ecol. 26, 1162–1175. https://doi.org/10.1111/j.1365-2435.2012.02026.x (2012).Article 

Google Scholar 
Blanc, S. & Michalakis, Y. Manipulation of hosts and vectors by plant viruses and impact of the environment. Curr. Opin. Insect. Sci. 16, 36–43. https://doi.org/10.1016/j.cois.2016.05.007 (2016).Article 
PubMed 

Google Scholar 
Moreno-Delafuente, A., Garzo, E., Moreno, A. & Fereres, A. A plant virus manipulates the behavior of its whitefly vector to enhance its transmission efficiency and spread. PLoS ONE 8, e61543. https://doi.org/10.1371/journal.pone.0061543 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ng, J. C. K. & Falk, B. W. Virus-vector interactions mediating nonpersistent and semipersistent transmission of plant viruses. Annu. Rev. Phytopathol. 44, 183–212. https://doi.org/10.1146/annurev.phyto.44.070505.143325 (2006).CAS 
Article 
PubMed 

Google Scholar 
Stafford, C. A., Walker, G. P. & Ullman, D. E. Infection with a plant virus modifies vector feeding behavior. Proc. Natl. Acad. Sci. 108, 9350–9355. https://doi.org/10.1073/pnas.1100773108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Rajabaskar, D., Bosque-Pérez, N. A. & Eigenbrode, S. D. Preference by a virus vector for infected plants is reversed after virus acquisition. Virus Res. 186, 32–37. https://doi.org/10.1016/j.virusres.2013.11.005 (2014).CAS 
Article 
PubMed 

Google Scholar 
Su, Q. et al. Manipulation of host quality and defense by a plant virus improves performance of whitefly vectors. J. Econ. Entomol. 108, 11–19. https://doi.org/10.1093/jee/tou012 (2015).Article 
PubMed 

Google Scholar 
Chen, G. et al. Virus infection of a weed increases vector attraction to and vector fitness on the weed. Sci. Rep. 3, 2253. https://doi.org/10.1038/srep02253 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Wei, J. et al. Vector development and vitellogenin determine the transovarial transmission of begomoviruses. Proc. Natl. Acad. Sci. 114, 6746–6751. https://doi.org/10.1073/pnas.1701720114 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ogada, P. A., Moualeu, D. P. & Poehling, H.-M. Predictive models for tomato spotted wilt virus spread dynamics, considering Frankliniella occidentalis specific life processes as influenced by the virus. PLoS ONE 11, e0154533. https://doi.org/10.1371/journal.pone.0154533 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shoemaker, L. G. et al. Pathogens manipulate the preference of vectors, slowing disease spread in a multi-host system. Ecol. Lett. 22, 1115–1125. https://doi.org/10.1111/ele.13268 (2019).Article 
PubMed 

Google Scholar 
Shelton, A. M. & Badenes-Perez, F. R. Concepts and applications of trap cropping in pest management. Annu. Rev. Entomol. 51, 285–308. https://doi.org/10.1146/annurev.ento.51.110104.150959 (2006).CAS 
Article 
PubMed 

Google Scholar 
Bennett, C. W. The Curly Top Disease of Sugarbeet and Other Plants (The American Phytopathological Society, 1971).Book 

Google Scholar 
Chen, L.-F. & Gilbertson, R. L. Chapter 17: Transmission of curtoviruses (beet curly top virus) by the beet leafhopper (Circulifer tenellus). In Vector-Mediated Transmission of Plant Pathogens (ed. Brown, J. K.) 243–262 (The American Phytopathological Society of America, 2016).Chapter 

Google Scholar 
Creamer, R. Chapter 37: Beet curly top virus transmission, epidemiology, and management. In Applied Plant Virology (ed. Awasthi, L. P.) 521–527 (Academic Press, 2020).Chapter 

Google Scholar 
Gilbertson, R. L., Melgarejo, T. A., Rojas, M. R., Wintermantel, W. M. & Stanley, J. Beet curly top virus (Geminiviridae). In Encyclopedia of Virology 4th edn (eds Bamford, D. H. & Zuckerman, M.) 200–212 (Academic Press, 2021).Chapter 

Google Scholar 
Hudson, A., Richman, D. B., Escobar, I. & Creamer, R. Comparison of the feeding behavior and genetics of beet leafhopper, Circulifer tenellus, populations from California and New Mexico. Southwest. Entomol. 35, 241–250, 210 (2010).Article 

Google Scholar 
Soto, M. J. & Gilbertson, R. L. Distribution and rate of movement of the curtovirus Beet mild curly top virus (Family Geminiviridae) in the beet leafhopper. Phytopathology 93, 478–484. https://doi.org/10.1094/phyto.2003.93.4.478 (2003).Article 
PubMed 

Google Scholar 
Prager, S. M., Lewis, O. M., Michels, J. & Nansen, C. The influence of maturity and variety of potato plants on oviposition and probing of Bactericera cockerelli (Hemiptera: Triozidae). Environ. Entomol. 43, 402–409. https://doi.org/10.1603/en13278 (2014).Article 
PubMed 

Google Scholar 
Prager, S. M., Vaughn, K., Lewis, M. & Nansen, C. Oviposition and leaf probing by Bactericera cockerelli (Homoptera: Psyllidae) in response to a limestone particle film or a plant growth regulator applied to potato plants. Crop Prot. 45, 57–62 (2013).CAS 
Article 

Google Scholar 
McBryde, M. C. A method of demonstrating rust hyphae and Haustoria in unsectioned leaf tissue. Am. J. Bot. 23, 686–688 (1936).Article 

Google Scholar 
Backus, E. A., Hunter, W. B. & Arne, C. N. Technique for staining leafhopper (Homoptera: Cicadellidae) salivary sheaths and eggs within unsectioned plant tissue. J. Econ. Entomol. 81, 1819–1823. https://doi.org/10.1093/jee/81.6.1819 (1988).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical computing, Vienna, Austria, 2019).Stafford, C. A., Walker, G. P. & Creamer, R. Stylet penetration behavior resulting in inoculation of beet severe curly top virus by beet leafhopper, Circulifer tenellus. Entomol. Exp. Appl. 130, 130–137. https://doi.org/10.1111/j.1570-7458.2008.00813.x (2009).Article 

Google Scholar 
Chen, L.-F., Brannigan, K., Clark, R. & Gilbertson, R. L. Characterization of curtoviruses associated with curly top disease of tomato in California and monitoring for these viruses in beet leafhoppers. Plant Dis. 94, 99–108. https://doi.org/10.1094/pdis-94-1-0099 (2010).CAS 
Article 
PubMed 

Google Scholar 
Rojas, M. R. et al. World management of geminiviruses. Annu. Rev. Phytopathol. 56, 637–677. https://doi.org/10.1146/annurev-phyto-080615-100327 (2018).CAS 
Article 
PubMed 

Google Scholar 
Schoonhoven, L. M., Van Loon, B., van Loon, J. J. & Dicke, M. Insect-plant biology (Oxford University Press, 2005).
Google Scholar 
Mauck, K. E., Kenney, J. & Chesnais, Q. Progress and challenges in identifying molecular mechanisms underlying host and vector manipulation by plant viruses. Curr. Opin. Insect. Sci. 33, 7–18. https://doi.org/10.1016/j.cois.2019.01.001 (2019).Article 
PubMed 

Google Scholar 
Pelosi, P., Iovinella, I., Felicioli, A. & Dani, F. R. Soluble proteins of chemical communication: An overview across arthropods. Front. Physiol 5, 320. https://doi.org/10.3389/fphys.2014.00320 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Pelosi, P., Zhou, J. J., Ban, L. P. & Calvello, M. Soluble proteins in insect chemical communication. Cell. Mol. Life Sci. 63, 1658–1676. https://doi.org/10.1007/s00018-005-5607-0 (2006).CAS 
Article 
PubMed 

Google Scholar 
Matsuo, T., Sugaya, S., Yasukawa, J., Aigaki, T. & Fuyama, Y. Odorant-binding proteins OBP57d and OBP57e affect taste perception and host-plant preference in Drosophila sechellia. PLoS Biol. 5, e118. https://doi.org/10.1371/journal.pbio.0050118 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Li, Z. et al. Mouthparts enriched odorant binding protein AfasOBP11 plays a role in the gustatory perception of Adelphocoris fasciaticollis. J. Insect Physiol. 117, 103915. https://doi.org/10.1016/j.jinsphys.2019.103915 (2019).CAS 
Article 
PubMed 

Google Scholar 
Waris, M. I. et al. Silencing of chemosensory protein gene NlugCSP8 by RNAi induces declining behavioral responses of Nilaparvata lugens. Front. Physiol. 9, 379. https://doi.org/10.3389/fphys.2018.00379 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hu, K. et al. Odorant-binding protein 2 is involved in the preference of Sogatella furcifera (Hemiptera: Delphacidae) for rice plants infected with the Southern rice black-streaked dwarf virus. Fla. Entomol. 102, 353–358. https://doi.org/10.1653/024.102.0210 (2019).CAS 
Article 

Google Scholar 
Brentassi, M. E., Machado-Assefh, C. R. & Alvarez, A. E. The probing behaviour of the planthopper Delphacodes kuscheli (Hemiptera: Delphacidae) on two alternating hosts, maize and oat. Aust. Entomol. 58, 666–674. https://doi.org/10.1111/aen.12383 (2019).Article 

Google Scholar 
Milenovic, M., Wosula, E. N., Rapisarda, C. & Legg, J. P. Impact of host plant species and whitefly species on feeding behavior of Bemisia tabaci. Front. Plant Sci. 10, 1. https://doi.org/10.3389/fpls.2019.00001 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Stafford, C. A. & Walker, G. P. Characterization and correlation of DC electrical penetration graph waveforms with feeding behavior of beet leafhopper, Circulifer tenellus. Entomol. Exp. Appl. 130, 113–129. https://doi.org/10.1111/j.1570-7458.2008.00812.x (2009).Article 

Google Scholar 
Mauck, K. E., Chesnais, Q. & Shapiro, L. R. Evolutionary determinants of host and vector manipulation by plant viruses. In Advances in Virus Research (ed. Malmstrom, C. M.) 189–250 (Academic Press, 2018).
Google Scholar 
Chesnais, Q. et al. Virus effects on plant quality and vector behavior are species specific and do not depend on host physiological phenotype. J. Pest Sci. 92, 791–804 (2019).Article 

Google Scholar  More

Díaz, S. et al. Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. https://doi.org/10.5281/zenodo.3553579 (2019).Fei, S. et al. Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017).ADS 
Article 

Google Scholar 
Jetz, W. et al. Monitoring plant functional diversity from space. Nat. Plants 2, 16024 (2016).Article 

Google Scholar 
HyspIRI Mission Concept Team. HyspIRI Final Report. https://hyspiri.jpl.nasa.gov/downloads/reports_whitepapers/HyspIRI_FINAL_Report_1October2018_20181005a.pdf. Jet Propulsion Laboratories, California Institute of Technology, Pasadena, CA, USA (2018).Turner, W. Sensing biodiversity. Science 346, 301–302 (2014).ADS 
CAS 
Article 

Google Scholar 
Ustin, S. L. & Middleton, E. M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1 (2021).Article 

Google Scholar 
Cawse-Nicholson, K. et al. NASA’s surface biology and geology designated observable: a perspective on surface imaging algorithms. Remote Sens. Environ. 257, 112349 (2021).ADS 
Article 

Google Scholar 
Stavros, E. N. et al. ISS Observations Offer Insights Into Plant Function. Nature Ecology and Evolution 1, https://doi.org/10.1038/s41559-017-0194 (2017).Rast, M., Nieke, J., Adams, J., Isola, C. & Gascon, F. Copernicus Hyperspectral Imaging Mission for the Environment (Chime). IEEE International Geoscience and Remote Sensing Symposium IGARSS, 108–111, https://doi.org/10.1109/IGARSS47720.2021.9553319 (2021).Cogliati, S. et al. The PRISMA imaging spectroscopy mission: overview and first performance analysis. Remote Sens. Environ. 262, 112499 (2021).ADS 
Article 

Google Scholar 
Asner, G. P. et al. Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation. Science 355, 385–389 (2017).ADS 
CAS 
Article 

Google Scholar 
Meireles, J. E. et al. Leaf reflectance spectra capture the evolutionary history of seed plants. N. Phytologist 228, 485–493 (2020).Article 

Google Scholar 
Schweiger, A. K. et al. Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function. Nat. Ecol. Evolution https://doi.org/10.1038/s41559-018-0551-1 (2018).Article 

Google Scholar 
Cavender-Bares, J. et al. Harnessing plant spectra to integrate the biodiversity sciences across biological and spatial scales. Am. J. Bot. 104, 966–969 (2017).Article 

Google Scholar 
Laliberté, E., Schweiger, A. K. & Legendre, P. Partitioning plant spectral diversity into alpha and beta components. Ecol. Lett. 23, 370–380 (2020).Article 

Google Scholar 
Rocchini, D. et al. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges. Ecol. Inform. 5, 318–329 (2010).Article 

Google Scholar 
Gholizadeh, H. et al. Detecting prairie biodiversity with airborne remote sensing. Remote Sens. Environ. 221, 38–49 (2019).ADS 
Article 

Google Scholar 
Wang, R. et al. Influence of species richness, evenness, and composition on optical diversity: a simulation study. Remote Sens. Environ. 211, 218–228 (2018).ADS 
Article 

Google Scholar 
Féret, J.-B. & Asner, G. P. Mapping tropical forest canopy diversity using high‐fidelity imaging spectroscopy. Ecol. Appl. 24, 1289–1296 (2014).Article 

Google Scholar 
Draper, F. C. et al. Imaging spectroscopy predicts variable distance decay across contrasting Amazonian tree communities. J. Ecol. 107, 696–710 (2019).Article 

Google Scholar 
Wang, R., Gamon, J. A., Cavender‐Bares, J., Townsend, P. A. & Zygielbaum, A. I. The spatial sensitivity of the spectral diversity–biodiversity relationship: an experimental test in a prairie grassland. Ecol. Appl. 28, 541–556 (2018).Article 

Google Scholar 
Rossi, C. et al. Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species-rich grasslands. Remote Sens. Ecol. Conserv. https://doi.org/10.1002/rse2.244 (2021).Article 

Google Scholar 
Finderup Nielsen, T., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evolution 14, 450–453 (1999).CAS 
Article 

Google Scholar 
Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).ADS 
Article 

Google Scholar 
Rocchini, D. et al. Measuring β‐diversity by remote sensing: a challenge for biodiversity monitoring. Methods Ecol. Evolution 9, 1787–1798 (2018).Article 

Google Scholar 
Chadwick, K. D. & Asner, G. P. Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry. Ecol. Lett. 21, 978–988 (2018).Article 

Google Scholar 
Felsenstein, J. Phylogenies and the comparative method. American Naturalist, 1-15, https://doi.org/10.1086/284325 (1985).Wang, R. & Gamon, J. A. Remote sensing of terrestrial plant biodiversity. Remote Sens. Environ. 231, 111218 (2019).ADS 
Article 

Google Scholar 
Schimel, D. S., Asner, G. P. & Moorcroft, P. Observing changing ecological diversity in the Anthropocene. Front. Ecol. Environ. 11, 129–137 (2013).Article 

Google Scholar 
NEON (National Ecological Observatory Network). Spectrometer orthorectified surface directional reflectance—mosaic, RELEASE-2021 (DP3.30006.001). https://doi.org/10.48443/qeae-3×15. Dataset accessed from https://data.neonscience.org on March (2021).Richter, R. & Schläpfer, D. Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction. Int. J. Remote Sens. 23, 2631–2649 (2002).Article 

Google Scholar 
Asner, G. P. & Martin, R. E. Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests. Front. Ecol. Environ. 7, 269–276 (2009).Article 

Google Scholar 
Rüfenacht, D., Fredembach, C. & Süsstrunk, S. Automatic and accurate shadow detection using near-infrared information. IEEE Trans. pattern Anal. Mach. Intell. 36, 1672–1678 (2013).Article 

Google Scholar 
NEON (National Ecological Observatory Network). High-resolution orthorectified camera imagery mosaic, RELEASE-2021 (DP3.30010.001). https://doi.org/10.48443/4e85-cr14. Dataset accessed from https://data.neonscience.org on March 3 (2021).Feilhauer, H., Asner, G. P., Martin, R. E. & Schmidtlein, S. Brightness-normalized partial least squares regression for hyperspectral data. J. Quant. Spectrosc. Radiat. Transf. 111, 1947–1957 (2010).ADS 
CAS 
Article 

Google Scholar 
NEON (National Ecological Observatory Network). Plant presence and percent cover, RELEASE-2021 (DP1.10058.001). https://doi.org/10.48443/abge-r811. Dataset accessed from https://data.neonscience.org on March 3 (2021).NEON (National Ecological Observatory Network). Woody plant vegetation structure, RELEASE-2021 (DP1.10098.001). https://doi.org/10.48443/e3qn-xw47. Dataset accessed from https://data.neonscience.org on March 3 (2021).Schweiger, A. K. NEON_crown_area (1.0.0). https://doi.org/10.5281/zenodo.6383923 (2022).R Foundation for Statistical Computing. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2019).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography 42, 1353–1359 (2019).Article 

Google Scholar 
Smith, S. A. & Brown, J. W. Constructing a broadly inclusive seed plant phylogeny. Am. J. Bot. 105, 302–314 (2018).Article 

Google Scholar 
Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).CAS 
Article 

Google Scholar 
NEON (National Ecological Observatory Network). Plant foliar traits, RELEASE-2021 (DP1.10026.001). https://doi.org/10.48443/za0d-wn97. Dataset accessed from https://data.neonscience.org on March 3 (2021).Legendre, P. & De Cáceres, M. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16, 951–963 (2013).Article 

Google Scholar 
Dray, S. & Dufour, A.-B. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: Linear and nonlinear mixed effects models. R package version 3.1-152 (2021).NEON (National Ecological Observatory Network). LAI—spectrometer—mosaic, RELEASE-2021 (DP3.30012.001). https://doi.org/10.48443/h2rb-pj34. Dataset accessed from https://data.neonscience.org on March 3 (2021). More

Our study combining field data and aerial imagery analysis clearly showed that the spotted souslik avoids close coexistence with another burrowing species, i.e. the European mole, in the period of low population abundance. This is the first study on this subject described in the available literature, as attention has been paid mainly to other parameters of the habitat so far14,18,20. The present results can (1) make a new contribution to the knowledge of the ecology of burrowing mammals and their interspecies relationships, (2) contribute to better designs of conservation and assessment of the quality of habitats of endangered burrowing mammals, and (3) indicate new possibilities of using remote sensing and deep learning methods in ecology and conservation. Below we will try to address each of these issues.The interaction between underground animals is not a new idea in ecology (e.g.22); however, this issue has not been analyzed for the mole and the souslik so far. This was probably related to the fact that the potential negative or positive relationships between these species are not intuitively obvious. The spatial distribution of underground tunnels of these animals is completely different: the mole builds an extensive network of horizontal tunnels close to the ground surface, while the souslik usually builds one deep nest burrow with a vertical entrance and possibly a small number of shallow safety burrows near the nest burrow. Moreover, the food preferences of the souslik and the mole differ, i.e. the former is mainly a herbivore, while the latter is an obligatory predator. There are also clear differences in the annual cycle: the mole is active all year round, and the souslik hibernates in an underground nest for about half a year from October to March. Thus, it seems that the emergence of competitive relationships between these two species is unlikely. Our study shows, however, that these species avoid each other in space, which raises the question of the mechanism of this relationship. Based on the knowledge of the biology of both species, some hypothetical mechanisms can be proposed.Although they are colonial animals, sousliks inhabit burrows alone (except for mother and offspring) and they have a strong behavioural trait of a negative reaction to the presence of other animals in their burrows and their close vicinity14,23. The negative reaction to other sousliks is a reflection of the intraspecific competition in the population and the territoriality of individuals. It is regulated by odour signals and the social structure of the population30,31. Koshev32 described aggressive reactions of free-ranging European sousliks to other vertebrate species that appeared near burrows: towards the reptile Lacerta trilineata, the bird Corvus frugilegus, and the mammal Mustela nivalis. Theoretically, the mole can get into the souslik’s burrow unintentionally when digging new tunnels. For souslik, the presence of moles in their nest burrow means a violation of its strictly defended territory and is probably a highly stressful episode. It can therefore be assumed that sousliks should choose places outside areas of frequent occurrence of other burrowing mammals to set up a nest burrow.It remains an open question whether avoidance of areas where the mole is often present may be important for the souslik during winter hibernation. Theoretically, the presence of moles in souslik burrows during hibernation may disturb this process and cause waking up and energy-consuming increases in metabolism, which may reduce winter survival. It is also unknown whether the mole can be a predator for the souslik during winter hibernation. Remains of rodent species were found in the digestive tracts of moles33; therefore, at least theoretically, the mole may use such a food source. On the other hand, remains of vertebrates, including the remains of moles, were sometimes found in the stomachs of sousliks18. The relationship between the souslik and the mole may therefore be more complex and require further research focused on this issue. It is possible that the moles can avoid the souslik colonies as well. This scenario seems also realistic, since the moles home ranges are likely much more dynamic than that of sousliks, that likely benefit from dwelling within an existing colony of the conspecifics.The spotted souslik protection requires the designation of special areas of conservation16. A number of various conservation activities are also routinely undertaken for this species, including regular monitoring of the population size, habitat monitoring, mowing, reduction of predation risk, and application of more invasive methods such as reintroduction. Similar activities are also performed for a closely related species, i.e. the European souslik Spermophilus citellus, in Europe. Importantly, in the current guidelines of souslik conservation, the issue of the competition with other species and its impact on spatial distribution is not considered. In turn, there is evidence in the literature that interspecies interactions may be important for the souslik population21. In periods of low abundance, when the survival of the population is at risk, the sousliks may have different habitat preferences than in periods of the abundant population20. It seems, therefore, that nowadays, when the souslik most often forms small populations, more attention should be paid to a wider range of factors and threats that may determine longer term population trends or the health condition, survival, and abundance of their colonies.Our study indicates that, in the period of low population abundance, the presence of other burrowing species may be an important factor determining the distribution of sousliks. This observation shows that in addition to the assessment of the area and condition of the habitat the presence of other potentially competitive species should also be taken into account in the analysis of population survival. In such a case, the actual area of habitats suitable for sousliks in a given location may turn out to be much lower than assumed. In our study area, the habitat suitable for the souslik was reduced from 105 ha to approx. 65 ha, i.e. by nearly 38%, but it probably is even smaller (compare Fig. 8). This observation has consequences for improvement of the reintroduction methods of sousliks (or other burrowing mammals), which are constantly of scientific interest20,34,35. Our results indicate that the reintroduction of sousliks should be carried out in places where there is the lowest probability of competition for resources including even shelter or space with other burrowing species and where adequate space for the settlement of the population is ensured.So far, investigations of the distribution of small burrowing mammals have been based on laborious field studies involving site inspections by trained observers (e.g.36,37,38). Our results show that, in certain conditions, high-resolution imagery can be successfully used to support studies of the distribution of such animals. As reported by other authors (e.g.7,10,12), however, such animals must produce clear signs of their presence in the environment. Evidence of the presence of the European mole, i.e. mounds of soil, in short vegetation habitats has shown that remote sensing can detect moles and their area of occupancy successfully. The advantage of these markers of the presence of moles is that the mounds are redundant and quite durable and can be visible in the environment for up to several months.By combining field research and remote sensing, it is also possible to study more sophisticated ecological issues, e.g. interspecies interactions. In this work, the remote estimation of the distribution of moles facilitated estimation of the actual habitat available to the souslik and excluded areas with the lowest probability of its occurrence. As a result, the population may be monitored more economically. Since the conservation guidelines recommend monitoring souslik populations by means of laborious inspections of transects, the indication of areas with no burrows may significantly reduce the amount of fieldwork without negative consequences for the accuracy of results. Some areas of the souslik occurrence are large, e.g. Świdnik (105 ha) or Pastwiska nad Huczwą (150 ha), and every 10 ha to be monitored means one day’s work for one observer (according to the calculations presented in the results). Our study showed that when the area of the occurrence of moles is excluded from the monitoring (Fig. 8), the error in estimating the size of the souslik population will be relatively small (0.9–8.7%). At the same time, the time devoted to the research can be limited by 14% or 38%, respectively. This suggests that our method can contribute to improved monitoring and management of these protected species, especially that souslik monitoring requires considerable research effort and has to be carried out twice a year.However, mole mounds may be underestimated by remote sensing, which can be seen in Fig. 7. Small mole mounds that are easily identified during field research may not be noticed by remote sensing. Such underestimation does not constitute a critical threat to the determination of the mole area according to the scheme shown in Fig. 8, since its marks are highly redundant. However, since there is currently little research on this subject, we recommend combining field research and remote sensing in assessments similar to ours. Finally, it is worth noting that, for a better understanding of the issue of the interactions between souslik and other burrowing species, it is advisable to use another remote sensing technique—telemetry. Telemetry studies are successfully conducted in Bulgarian souslik populations34 and their combination with studies of habitat selectivity dependent on other burrowing species may provide new and valuable insight into this issue. More

Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).CAS 
PubMed 
Article 

Google Scholar 
Zuleta, D., Duque, A., Cardenas, D., Muller-Landau, H. C. & Davies, S. J. Drought-induced mortality patterns and rapid biomass recovery in a terra firme forest in the Colombian Amazon. Ecology 98, 2538–2546 (2017).PubMed 
Article 

Google Scholar 
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).CAS 
PubMed 
Article 

Google Scholar 
Powers, J. S. et al. A catastrophic tropical drought kills hydraulically vulnerable tree species. Glob. Chang. Biol. 26, 3122–3133 (2020).PubMed 
Article 

Google Scholar 
Bennett, A. C. et al. Resistance of African tropical forests to an extreme climate anomaly. Proc. Natl Acad. Sci. USA 118, e2003169118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 
PubMed 
Article 

Google Scholar 
McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, (2020).Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 
Article 

Google Scholar 
Matthews, H. D. et al. An integrated approach to quantifying uncertainties in the remaining carbon budget. Commun. Earth Environ. 2, 7 (2021).Article 

Google Scholar 
Girardin, C. A. J. et al. Nature-based solutions can help cool the planet—if we act now. Nature 593, 191–194 (2021).CAS 
PubMed 
Article 

Google Scholar 
Friedlingstein, P. et al. Earth Syst. Sci. Data 14, 1917–2005 (2022)
Google Scholar 
Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).CAS 
PubMed 
Article 

Google Scholar 
Rowland, L. et al. Death from drought in tropical forests is triggered by hydraulics not carbon starvation. Nature 528, 119–122 (2015).CAS 
PubMed 
Article 

Google Scholar 
Lloyd, J. & Farquhar, G. D. Effects of rising temperatures and [CO2] on the physiology of tropical forest trees. Phil. Trans. R. Soc. B 363, 1811–1817 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
O’Sullivan, O. S. et al. Thermal limits of leaf metabolism across biomes. Glob. Chang. Biol. 23, 209–223 (2017).PubMed 
Article 

Google Scholar 
Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).PubMed 
Article 

Google Scholar 
Rifai, S. W., Li, S. & Malhi, Y. Coupling of El Niño events and long-term warming leads to pervasive climate extremes in the terrestrial tropics. Environ. Res. Lett. 14, 105002 (2019).CAS 
Article 

Google Scholar 
Rifai, S. W. et al. ENSO drives interannual variation of forest woody growth across the tropics. Phil. Trans. R. Soc. B 373, 20170410 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Smith, M. N. et al. Empirical evidence for resilience of tropical forest photosynthesis in a warmer world. Nat. Plants 6, 1225–1230 (2020).CAS 
PubMed 
Article 

Google Scholar 
Malhi, Y. et al. Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc. Natl Acad. Sci. USA 106, 20610–20615 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McDowell, N., Allen, C. D. & Anderson‐Teixeira, K. Drivers and mechanisms of tree mortality in moist tropical forests. New Phytol. 219, 851–869 (2018).PubMed 
Article 

Google Scholar 
McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178, 719–739 (2008).PubMed 
Article 

Google Scholar 
Bauman, D. et al. Tropical tree growth sensitivity to climate is driven by species intrinsic growth rate and leaf traits. Glob. Chang. Biol. 28, 1414–1432 (2022).PubMed 
Article 

Google Scholar 
Esquivel-Muelbert, A. et al. Tree mode of death and mortality risk factors across Amazon forests. Nat. Commun. 11, 5515 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Chang. Biol. 25, 3793–3802 (2019).PubMed 
Article 

Google Scholar 
Aguirre-Gutiérrez, J. et al. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecol. Lett. 22, 855–865 (2019).PubMed 
Article 

Google Scholar 
Aguirre-Gutiérrez, J. et al. Long-term droughts may drive drier tropical forests towards increased functional, taxonomic and phylogenetic homogeneity. Nat. Comm. 11, 3346 (2020).Article 

Google Scholar 
Meir, P., Mencuccini, M. & Dewar, R. C. Drought-related tree mortality: addressing the gaps in understanding and prediction. New Phytol. 207, 28–33 (2015).PubMed 
Article 

Google Scholar 
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).CAS 
PubMed 
Article 

Google Scholar 
Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).Article 

Google Scholar 
Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).CAS 
PubMed 
Article 

Google Scholar 
Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol. 36, 520–532 (2021).PubMed 
Article 

Google Scholar 
Phillips, O. L. et al. Drought–mortality relationships for tropical forests. New Phytol. 187, 631–646 (2010).PubMed 
Article 

Google Scholar 
Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Change 9, 384–388 (2019).Article 

Google Scholar 
Lingenfelder, M. & Newbery, D. M. On the detection of dynamic responses in a drought-perturbed tropical rainforest in Borneo. Plant Ecol. 201, 267–290 (2009).Article 

Google Scholar 
McDowell, N. G. et al. The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends Ecol. Evol. 26, 523–532 (2011).PubMed 
Article 

Google Scholar 
Zuleta, D. et al. Individual tree damage dominates mortality risk factors across six tropical forests. New Phytol. 233, 705–721 (2022).PubMed 
Article 

Google Scholar 
Fontes, C. G. et al. Dry and hot: the hydraulic consequences of a climate change-type drought for Amazonian trees. Phil. Trans. R. Soc. B 373, 20180209 (2018).Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett. 12, 351–366 (2009).PubMed 
Article 

Google Scholar 
Peters, J. M. R. et al. Living on the edge: a continental-scale assessment of forest vulnerability to drought. Glob. Chang. Biol. 27, 3620–3641 (2021).PubMed 
Article 

Google Scholar 
Yang, J., Cao, M. & Swenson, N. G. Why functional traits do not predict tree demographic rates. Trends Ecol. Evol. 33, 326–336 (2018).PubMed 
Article 

Google Scholar 
Espírito-Santo, F. D. B. et al. Size and frequency of natural forest disturbances and the Amazon forest carbon balance. Nat. Commun. 5, 3434 (2014).PubMed 
Article 

Google Scholar 
Chambers, J. Q. et al. The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape. Proc. Natl Acad. Sci. USA 110, 3949–3954 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rifai, S. W. et al. Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237 (2016).PubMed 
Article 

Google Scholar 
López, J., Way, D. A. & Sadok, W. Systemic effects of rising atmospheric vapor pressure deficit on plant physiology and productivity. Glob. Chang. Biol. 27, 1704–1720 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to droughttextendashfire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Phillips, O. L. et al. Pattern and process in Amazon tree turnover, 1976–2001. Phil. Trans. R. Soc. Lond. B 359, 381–407 (2004).CAS 
Article 

Google Scholar 
Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).Article 

Google Scholar 
Andrus, R. A., Chai, R. K., Harvey, B. J., Rodman, K. C. & Veblen, T. T. Increasing rates of subalpine tree mortality linked to warmer and drier summers. J. Ecol. 109, 2203–2218 (2021).Article 

Google Scholar 
Murphy, H. T., Bradford, M. G., Dalongeville, A., Ford, A. J. & Metcalfe, D. J. No evidence for long-term increases in biomass and stem density in the tropical rain forests of Australia. J. Ecol. 101, 1589–1597 (2013).Article 

Google Scholar 
Bennett, A. C., McDowell, N. G., Allen, C. D. & Anderson-Teixeira, K. J. Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015).PubMed 
Article 

Google Scholar 
Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Anderegg, W. R. L. et al. Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc. Natl Acad. Sci. USA 113, 5024–5029 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Taylor, T. C., Smith, M. N., Slot, M. & Feeley, K. J. The capacity to emit isoprene differentiates the photosynthetic temperature responses of tropical plant species. Plant Cell Environ. 42, 2448–2457 (2019).CAS 
PubMed 
Article 

Google Scholar 
Arellano, G., Zuleta, D. & Davies, S. J. Tree death and damage: a standardized protocol for frequent surveys in tropical forests. J. Veg. Sci. 32, e12981 (2021).Article 

Google Scholar 
Bradford, M. G., Murphy, H. T., Ford, A. J., Hogan, D. L. & Metcalfe, D. J. Long-term stem inventory data from tropical rain forest plots in Australia. Ecology 95, 2362 (2014).Article 

Google Scholar 
Johnson, D. J. et al. Climate sensitive size-dependent survival in tropical trees. Nat. Ecol. Evol. 2, 1436–1442 (2018).PubMed 
Article 

Google Scholar 
Needham, J., Merow, C., Chang-Yang, C.-H., Caswell, H. & McMahon, S. M. Inferring forest fate from demographic data: from vital rates to population dynamic models. Proc. Biol. Sci. 285, 20172050 (2018).PubMed 
PubMed Central 

Google Scholar 
Lewis, S. L. et al. Tropical forest tree mortality, recruitment and turnover rates: calculation, interpretation and comparison when census intervals vary. J. Ecol. 92, 929–944 (2004).Article 

Google Scholar 
Reeves, J., Chen, J., Wang, X. L., Lund, R. & Lu, Q. Q. A review and comparison of changepoint detection techniques for climate data. J. Appl. Meteorol. Climatol. 46, 900–915 (2007).Article 

Google Scholar 
Clark, J. S., Bell, D. M., Kwit, M. C. & Zhu, K. Competition-interaction landscapes for the joint response of forests to climate change. Glob. Chang. Biol. 20, 1979–1991 (2014).PubMed 
Article 

Google Scholar 
Oliva, J., Stenlid, J. & Martínez-Vilalta, J. The effect of fungal pathogens on the water and carbon economy of trees: implications for drought-induced mortality. New Phytol. 203, 1028–1035 (2014).CAS 
PubMed 
Article 

Google Scholar 
Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556 (1987).Article 

Google Scholar 
Yanoviak, S. P. et al. Lightning is a major cause of large tree mortality in a lowland neotropical forest. New Phytol. 225, 1936–1944 (2020).PubMed 
Article 

Google Scholar 
Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Seeking the ‘point of no return’ in the sequence of events leading to mortality of mature trees. Plant Cell Environ. 44, 1315–1328 (2020).PubMed 
Article 

Google Scholar 
Aragão, L. E. O. C. et al. Spatial patterns and fire response of recent Amazonian droughts. Geophys. Res. Lett. 34, L07701 (2007).Article 

Google Scholar 
Malhi, Y. et al. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests. Glob. Chang. Biol. 21, 2283–2295 (2015).PubMed 
Article 

Google Scholar 
Hutchinson, M. F., Xu, T., Kesteven, J. L., Marang, I. J. & Evans, B. J.ANUClimate v2.0, NCI Australia. https://doi.org/10.25914/60a10aa56dd1b (2021).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Carscadden, K. A. et al. Niche breadth: causes and consequences for ecology, evolution, and conservation. Q. Rev. Biol. 95, 179–214 (2020).Article 

Google Scholar 
Swenson, N. G. et al. A reframing of trait–demographic rate analyses for ecology and evolutionary biology. Int. J. Plant Sci. 181, 33–43 (2020).Article 

Google Scholar 
Morueta-Holme, N. et al. Habitat area and climate stability determine geographical variation in plant species range sizes. Ecol. Lett. 16, 1446–1454 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Brum, M. et al. Hydrological niche segregation defines forest structure and drought tolerance strategies in a seasonal Amazon forest. J. Ecol. 107, 318–333 (2019).Article 

Google Scholar 
Chitra-Tarak, R. et al. The roots of the drought: hydrology and water uptake strategies mediate forest-wide demographic response to precipitation. J. Ecol. 106, 1495–1507 (2018).Article 

Google Scholar 
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Modell. 275, 73–77 (2014).Article 

Google Scholar 
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
PubMed 
Article 

Google Scholar 
Duursma, R. A. Plantecophys—an R package for analysing and modelling leaf gas exchange data. PLoS ONE 10, e0143346 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).PubMed 
Article 

Google Scholar 
Bloomfield, K. J. et al. The validity of optimal leaf traits modelled on environmental conditions. New Phytol. 221, 1409–1423 (2019).CAS 
PubMed 
Article 

Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and STAN (CRC Press, 2020).“RStan: the R interface to Stan.” R package version 2.21.2. http://mc-stan.org/ (Stan Development Team, 2020).Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).PubMed 
PubMed Central 
Article 

Google Scholar  More