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Potential distribution prediction of two destructive rice weevils in China based on an ensemble model
Published online by Cambridge University Press: 07 October 2025
Abstract
Rice water weevils (RWWs) (Lissorhoptrus oryzophilus) and rice weevils (RW) (Echinocnemus squameus) (both Coleoptera: Curculionidae) are major rice pests that cause significant economic losses in China. Understanding their potential distribution areas is crucial for effective management. This study used the Biomod2 package in R to simulate and predict the current and future potential distributions, changes in suitable areas, shifts in distribution centres, and overlaps under climate change for both pests under three greenhouse gas emission scenarios. By 2023, the suitable areas for RWWs and RWs were 538.52 × 104 km2 and 376.05 × 104 km2, respectively. The suitable area for the former pest expanded southwestward and northeastward across China, whereas the latter spread mainly into Northeast China. The suitable area for RWWs is projected to remain stable, whereas that for RWs is expected to decline. The distribution centroid of RWWs is anticipated to shift toward southeastern or southwestern Shaanxi, whereas RWs are likely to migrate toward central-eastern or northeastern Shaanxi. The niche overlap between the two pests is high (Schoener’s D = 0.658, I = 0.816), with overlap concentrated in central, eastern, and southern China. The key factors influencing their distributions include precipitation of the wettest month (Bio13), mean temperature of the warmest quarter (Bio10), and precipitation of the driest month (Bio14). This study provides a theoretical basis for the prediction of the potential distribution of both pests, which offers valuable insights for the development of effective pest control strategies in China.
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- © The Author(s), 2025. Published by Cambridge University Press.
References
Abdulwahab, UA, Hammill, E and Hawkins, CP (2022) Choice of climate data affects the performance and interpretation of species distribution models. Ecological Modelling 471, 110042.CrossRefGoogle Scholar
Aghaee, MA and Godfrey, LD (2014) A century of rice water weevil (Coleoptera: Curculionidae): A history of research and management with an emphasis on the United States. Journal of Integrated Pest Management 5, 1–14.CrossRefGoogle Scholar
Alamgir, M, Mukul, SA and Turton, SM (2002) Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate. Applied Geography 60, 10–19.CrossRefGoogle Scholar
Albuquerque, F, Benito, B, Macı´as-rodrı´guez, MA and Gray, C (2018) Potential changes in the distribution of Carnegiea gigantea under future scenarios. PeerJ 6, e5623.CrossRefGoogle ScholarPubMed
Allouche, O, Tsoar, A and Kadmon, R (2006) Assessing the accuracy of species distribution models: Prevalence, kappa, and the true skill statistic (TSS). Journal of Applied Ecology 43, 1223–1232.CrossRefGoogle Scholar
Araújo, MB and New, M (2007) Ensemble forecasting of species distributions. Trends in Ecology and Evolution 22, 42–47.CrossRefGoogle ScholarPubMed
Araújo, MB, Thuiller, W and Pearson, RG (2006) Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography 33, 1712–1728.CrossRefGoogle Scholar
Azrag, AG, Mohamed, SA, Ndlela, S and Ekesi, S (2022) Predicting the area suitability of the invasive white Mango scale, Aulacaspis tubercularis (Newstead, 1906) (Hemiptera: Diaspididae) using bioclimatic variables. Pest Management Science 78, 4114–4126.CrossRefGoogle ScholarPubMed
Bachiller, E, Gim´enez, J, Albo-Puigserver, M, Pennino, MG, Marí-Mena, N, Esteban, A, Lloret-Lloret, E, Bellido, JM and Coll, M (2021) Trophic niche overlap between round sardinella (Sardinella aurita) and sympatric pelagic fish species in the Western Mediterranean. Ecology and Evolution 11, 16126–16142.CrossRefGoogle ScholarPubMed
Bellard, C, Bertelsmeier, C, Leadley, P, Thuiller, W and Courchamp, F (2012) Impacts of climate change on the future of biodiversity. Ecology Letters 15, 365–377.CrossRefGoogle ScholarPubMed
Broennimann, O, Fitzpatrick, M, Pearman, P, Petitpierre, B, Pellissier, L, Yoccoz, N, Thuiller, W, Fortin, M-J, Randin, C, Zimmermann, N, Graham, C and Guisan, A (2012) Measuring ecological niche overlap from occurrence and spatial environmental data. Global Ecology and Biogeography 21, 481–497.CrossRefGoogle Scholar
Brown, JL (2014) Sdmtoolbox: A python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods in Ecology and Evolution 5, 694–700.CrossRefGoogle Scholar
Chen, H, Chen, Z and Zhou, Y (2005) Rice water weevil (Coleoptera: Curculionidae) in mainland China: Invasion, spread and control. Crop Protection 24, 695–702.CrossRefGoogle Scholar
Cola, V, Broennimann, O, Petitpierre, B, Breiner, FT, DAmen, M, Randin, C, Engler, R, Pottier, J, Pio, D, Dubuis, A, Pellissier, L, Mateo, RG, Hordijk, W, Salamin, N and Guisan, A (2017) ecospat: An R package to support spatial analyses and modelling of species niches and distributions. Ecography 40, 774–787.CrossRefGoogle Scholar
De Souza Muñoz, ME, De Giovanni, R, De Siqueira, MF, Sutton, T, Brewer, P, Pereira, RS, Canhos, DAL and Canhos, VP (2011) openModeller: A generic approach to species’ potential distribution modelling. GeoInformatica 15, 111–135.CrossRefGoogle Scholar
Deutsch, CA, Tewksbury, JJ, Tigchelaar, M, Battisti, DS, Merrill, SC, Huey, RB and Naylor, RL (2018) Increase in crop losses to insect pests in a warming climate. Science 61, 916–919.CrossRefGoogle Scholar
Dormann, CF, Calabrese, JM, Guillera-Arroita, G, Matechou, E, Bahn, V, Bartoń, K and Hartig, F (2018) Model averaging in ecology: A review of Bayesian, information-theoretic, and tactical approaches for predictive inference. Ecological Monographs 88, 485–504.CrossRefGoogle Scholar
Elith, J and Leathwick, JR (2009) Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40, 677–697.CrossRefGoogle Scholar
Faleiro, FV, Nemésio, A and Loyola, R (2018) Climate change likely to reduce orchid bee abundance even in climatic suitable sites. Global Change Biology 24, 2272–2283.CrossRefGoogle ScholarPubMed
Fick, SE and Hijmans, RJ (2017) WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37, 4302–4315.CrossRefGoogle Scholar
Garcia, K, Lasco, R, Ines, A, Lyon, B and Pulhin, F (2013) Predicting geographic distribution and area suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography 44, 12–22.CrossRefGoogle Scholar
González-Moreno, P, Diez, JM, Ibáñez, I, Font, X and Vilà, M (2014) Plant invasions are context dependent: Multiscale effects of climate, human activity and area. Diversity and Distributions 20, 720–731.CrossRefGoogle Scholar
Guisan, A, Thuiller, W and Zimmermann, NE (2017) Habitat Suitability and Distribution Models: With Applications in R. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Guo, QH and Liu, Y (2010) ModEco: An integrated software package for ecological niche modelling. Ecography 33, 637–642.CrossRefGoogle Scholar
Hastie, T, Tibshirani, R and Friedman, J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York, NY: Springer Science & Business Media.CrossRefGoogle Scholar
Hill, MP (1988) The role of temperature in the distribution of insect pests. Environmental Entomology 17, 617–622.Google Scholar
Hill, MP, Bertelsmeier, C, Clusella-Trullas, S, Garnas, J, Robertson, MP and Terblanche, JS (2016) Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change. Biological Invasions 18, 1105–1119.CrossRefGoogle Scholar
Hongoh, V, Berrang-Ford, L, Scott, ME and Lindsay, LR (2012) Expanding geographical distribution of the mosquito, Culex pipiens, in Canada under climate change. Applied Geography 33, 53–62.CrossRefGoogle Scholar
Iturbide, M, Bedia, J, Herrera, S, Del Hierro, O, Pinto, M and Manuel Gutierrez, J (2015) A framework for species distribution modelling with improved pseudoabsence generation. Ecological Modelling 312, 166–174.CrossRefGoogle Scholar
Jiang, MX (2009) Distribution and expansion of the rice water weevil in China. In Hao, WF, Guo, JY, Zhang, F, et al. (eds), Studies on Biological Invasions in China. Beijing: Science Press, pp. 96–97.Google Scholar
Jin, Z, Yu, WT, Zhao, HY, Xian, XQ, Jing, KT, Yang, N, Lu, XM and Liu, WX (2022) Potential global distribution of invasive alien species, Anthonomus grandis Boheman, under current and future climate using optimal Maxent model. Agriculture 12, 1759.CrossRefGoogle Scholar
Johnson, SN, Anderson, EA, Dawson, G and Griffiths, DW (2008) Varietal susceptibility of potatoes to wireworm herbivory. Agricultural and Forest Entomology 10, 167–174.CrossRefGoogle Scholar
Lake, TA, Briscoe Runquist, RD and Moeller, DA (2020) Predicting range expansion of invasive species: Pitfalls and best practices for obtaining biologically realistic projections. Diversity and Distributions 26, 1767–1779.CrossRefGoogle Scholar
Lantschner, MV, de la Vega, G and Corley, JC (2019) Predicting the distribution of harmful species and their natural enemies in agricultural, livestock and forestry systems: An overview. International Journal of Pest Management 65, 190–206.CrossRefGoogle Scholar
Lawler, JJ (2009) Climate change adaptation strategies for resource management and conservation planning. Annals of the New York Academy of Sciences 1162, 79–98.CrossRefGoogle ScholarPubMed
Lázaro-Lobo, A, Ramirez-Reyes, C, Lucardi, RD and Ervin, GN (2021) Multivariate analysis of invasive plant species distributions in southern US forests. Landscape Ecology 36, 3539–3555.CrossRefGoogle Scholar
Li, ZZ, Du, W, Duan, XN, Hou, ZZ, Sheng, YB, Wu, P, Shi, YY, Lu, ZJ, Zhou, XL, Sun, YP and Cao, SJ (2025) Preliminary study on occurrence and field control effects of invasive pest Lissorhoptrus oryzophilus in Ningxia. Journal of Agriculture 15, 36–40.Google Scholar
Liu, CL, Wolter, C, Xian, WW and Jeschke, JM (2020) Species distribution models have limited spatial transferability for invasive species. Ecology Letters 23, 1682–1692.CrossRefGoogle ScholarPubMed
Luo, M, Wang, H and Lyv, Z (2017) Evaluating the performance of species distribution models Biomod2 and MaxEnt using the giant panda distribution data. The Journal of Applied Ecology 28, 4001–4006.Google ScholarPubMed
Meynard, CN, Migeon, A and Navajas, M (2013) Uncertainties in predicting species distributions under climate change: A case study using Tetranychus evansi (Acari: Tetranychidae), a widespread agricultural pest. PLoS ONE 8, 1–10.CrossRefGoogle ScholarPubMed
Mugo, R and Saitoh, SI (2020) Ensemble modelling of skipjack tuna (Katsuwonus pelamis) areas in the Western North Pacific using satellite remotely sensed data: A comparative analysis using machine-learning models. Remote Sensing 12, 2591.CrossRefGoogle Scholar
Mulcahy, M, Wilson, B and Reagan, T (2022) Spatial distribution of Lissorhoptrus oryzophilus (Coleoptera: Curculionidae) in rice. Environmental Entomology 51, 108–117.CrossRefGoogle ScholarPubMed
Mulieri, PR and Patitucci, LD (2019) Using ecological niche models to describe the geographical distribution of the myiasis-causing Cochliomyia hominivorax (Diptera: Calliphoridae) in Southern South America. Parasitology Research 118, 1077–1086.CrossRefGoogle ScholarPubMed
Müller, C (2013) African lessons on climate change risks for agriculture. Annual Review of Nutrition 33, 395–411.CrossRefGoogle ScholarPubMed
Müller, FA, Dias, NP, Gottschalk, MS, Garcia, FRM and Nava, DE (2019) Potential distribution of Bactrocera oleae and the parasitoids Fopius arisanus and Psyttalia concolor, aiming at classical biological control. Biological Control 132, 144–151.CrossRefGoogle Scholar
Naimi, B and Araújo, MB (2016) SDM: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375.CrossRefGoogle Scholar
Nair, RR and Peterson, AT (2023) Mapping the global distribution of invasive pest Drosophila suzukii and parasitoid Leptopilina japonica: Implications for biological control. PeerJ 11, e15222.CrossRefGoogle ScholarPubMed
Pecl, GT, Araújo, MB, Bell, JD, Blanchard, J, Bonebrake, TC, Chen, IC, Clark, TD, Colwell, RK, Danielsen, F, Evengard, B, Falconi, L, Ferrier, S, Frusher, S, Garcia, RA, Griffis, RB, Hobday, AJ, Janion-Scheepers, C, Jarzyna, MA, Jennings, S and Williams, SE (2017) Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355, 1–9.CrossRefGoogle ScholarPubMed
Peterson, A, Papeş, M and Eaton, M (2007) Transferability and model evaluation in ecological niche modelling: A comparison of GARP and Maxent. Ecography 30, 550–560.CrossRefGoogle Scholar
Peterson, AT, Papeş, M and Soberón, J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecological Modelling 213, 63–72.CrossRefGoogle Scholar
Porter, JH, Parry, ML and Carter, TR (1991) The potential effects of climatic change on agricultural insect pests. Agricultural and Forest Meteorology 57, 221–240.CrossRefGoogle Scholar
Qi, GJ, Gao, Y, Huang, DC and Lyu, LH (2012) Invasion dynamics and suitability analysis of Lissorhoptrus oryzophilus (Coleoptera: Curculionidae) in China based on MAXENT. Journal of Plant Protection 39, 129–136.Google Scholar
Ramasamy, M, Das, B and Ramesh, R (2022) Predicting climate change impacts on potential worldwide distribution of fall armyworm based on CMIP6 projections. Journal of Pest Science 95, 841–854.CrossRefGoogle Scholar
Rathore, MK and Sharma, LK (2023) Efficacy of species distribution models (SDMs) for ecological realms to ascertain biological conservation and practices. Biodiversity and Conservation 32, 3053–3087.CrossRefGoogle Scholar
Runquist, BRD, Lake, TA, Tiffin, P and Moeller, DA (2019) Species distribution models throughout the invasion history of Palmer amaranth predict regions at risk of future invasion and reveal challenges with modelling rapidly shifting geographic ranges. Scientific Reports 9, 2426.CrossRefGoogle Scholar
Runquist, RDB, Lake, TA and Moeller, DA (2021) Improving predictions of range expansion for invasive species using joint species distribution models and surrogate co-occurring species. Journal of Biogeography 48, 1693–1705.CrossRefGoogle Scholar
Saito, T, Hirai, K and Way, MO (2005) The rice water weevil, Lissorhoptrus oryzophilus Kuschel (Coleoptera: Curculionidae). Applied Entomology and Zoology 40, 31–39.CrossRefGoogle Scholar
Shang, HW, Cheng, JA, Jiang, MX, Tang, QY and Gu, YQ (2003) Survival of the rice water weevil, Lissorhoptrus oryzophilus, in early rice grain in Zhejiang Province. Acta Entomologica Sinica 46, 190–195.Google Scholar
Sillero, N (2011) What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods. Ecological Modelling 222, 1343–1346.CrossRefGoogle Scholar
Skendžić, S, Zovko, M, Živković, IP, Lešić, V and Lemić, D (2021) The impact of climate change on agricultural insect pests. Insects 12, 440.CrossRefGoogle ScholarPubMed
Staley, JT, Hodgson, CJ, Mortimer, SR, Morecroft, MD, Masters, GJ, Brown, VK and Taylor, ME (2007) Effects of summer rainfall manipulations on the abundance and vertical distribution of herbivorous soil macroinvertebrates. European Journal of Soil Biology 43, 189–198.CrossRefGoogle Scholar
Stout, MJ, Riggio, MR, Li, Z and Roberts, R (2002) Flooding influences ovipositional and feeding behavior of the rice water weevil (Coleoptera: Curculionidae). Journal of Economic Entomology 95, 715–721.CrossRefGoogle ScholarPubMed
Tepa-Yotto, GT, Gouwakinnou, GN, Fagbohoun, JR, Tamò, M and Sæthre, MG (2021) Horizon scanning to assess the bioclimatic potential for the alien species Spodoptera eridania and its parasitoids after pest detection in West and Central Africa. Pest Management Science 77, 4437–4446.CrossRefGoogle ScholarPubMed
Thomas, CD, Cameron, A, Green, RE, Bakkenes, M, Beaumont, LJ, Collingham, YC, Erasmus, BF, De Siqueira, MF, Grainger, A, Hannah, L, Hughes, L, Huntley, B, Van Jaarsveld, AS, Midgley, GF, Miles, L, Ortega-Huerta, MA, Peterson, AT, Phillips, OL and Williams, SE (2004) Extinction risk from climate change. Nature 427, 145–148.CrossRefGoogle ScholarPubMed
Thuiller, W (2003) Biomod: Optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9, 1353–1362.CrossRefGoogle Scholar
Thuiller, W, Lafourcade, B, Engler, R and Miguel, B (2009) BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373.CrossRefGoogle Scholar
Tindall, KV, Bernhardt, JL, Stout, MJ and Beighley, DH (2013) Effect of depth of flooding on the rice water weevil, Lissorhoptrus oryzophilus, and yield of rice. Journal of Insect Science (Online) 13, 62.CrossRefGoogle ScholarPubMed
Tingley, MW, Monahan, WB, Beissinger, SR and Moritz, C (2009) Birds track their Grinnellian niche through a century of climate change. Proceedings of the National Academy of Sciences 106, 19637–19643.CrossRefGoogle ScholarPubMed
Vermeulen, SJ, Aggarwal, PK, Ainslie, A, Angelone, C, Campbell, BM, Challinor, AJ, Hansen, J, Ingram, J, Jarvis, A, Kristjanson, P, Lau, C, Nelson, G, Thornton, P and Wollenberg, E (2012) Options for support to agriculture and food security under climate change. Environmental Science & Policy 15, 136–144.CrossRefGoogle Scholar
Vilà, M, Espinar, JL, Hejda, M, Hulme, PE, Jarošík, V, Maron, JL, Pergl, J, Schaffner, U, Sun, Y and Pyšek, P (2011) Ecological impacts of invasive alien plants: A meta-analysis of their effects on species, communities, and ecosystems. Ecology Letters 14, 702–708.CrossRefGoogle ScholarPubMed
Walther, GR, Roques, A, Hulme, PE, Sykes, MT, Pyšek, P, Kühn, I, Zobel, M, Bacher, S, Botta-Dukát, Z, Bugmann, H, Czúcz, B, Dauber, J, Hickler, T, Jarošík, V, Kenis, M, Klotz, S, Minchin, D, Moora, M, Nentwig, W, Ott, J, Panov, VE, Reineking, B, Robinet, C, Semenchenko, V, Solarz, W, Thuiller, W, Vilà, M, Vohland, K and Settele, J (2009) Alien species in a warmer world: Risks and opportunities. Trends in Ecology and Evolution 24, 686–693.CrossRefGoogle Scholar
Wan, FH and Yang, NW (2016) Invasion and management of agricultural alien insects in China. Annual Review of Entomology 61, 77–98.CrossRefGoogle ScholarPubMed
Wang, RL, Li, Q, Feng, CH and Shi, ZP (2017) Predicting potential ecological distribution of Locusta migratoria tibetensis in China using MaxEnt ecological niche modeling. Acta Ecologica Sinica 37, 8556–8566.Google Scholar
Wang, ZJ, Shang, HW, Li, DM and Cheng, JA (2005) Spatial pattern analysis of spread for the rice water weevil in Zhejiang and Fujian provinces, East China. Journal of Biomathematics 20, 257–263.Google Scholar
Warren, DL, Glor, RE and Turelli, M (2008) Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883.CrossRefGoogle ScholarPubMed
Warren, DL, Glor, RE and Turelli, M (2010) ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33, 607–611.CrossRefGoogle Scholar
Wisz, MS, Pottier, J, Kissling, WD, Pellissier, L, Lenoir, J, Damgaard, CF, Dormann, CF, Forchhammer, MC, Grytnes, JA, Guisan, A, Heikkinen, RK, Høye, TT, Kühn, I, Luoto, M, Maiorano, L, Nilsson, MC, Normand, S, Öckinger, E, Schmidt, NM and Svenning, JC (2013) The role of biotic interactions in shaping distributions and realised assemblages of species: Implications for species distribution modelling. Biological Reviews 88, 15–30.CrossRefGoogle ScholarPubMed
Wu, T, Lu, Y, Fang, Y, Xiaoge, X, Li, L, Li, W, Jie, W, Zhang, J, Liu, Y, Zhang, L, Zhang, F, Zhang, Y, Wu, F, Li, J, Chu, M, Wang, Z, Shi, X, Liu, X, Wei, M and Liu, X (2019) The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geoscientific Model Development 12, 1573–1600.CrossRefGoogle Scholar
Xin, XG, Wu, TW, Zhang, J, Zhang, F, Li, WP, Zhang, YW, Lu, YX, Fang, YJ, Jie, WH and Zhang, L (2019) Introduction of BCC models and its participation in CMIP6. Climate Change Research 15, 533–539.Google Scholar
Yan, YW, Wang, YC, Feng, CC, Wan, PH and Chang, K (2017) Potential distributional changes of invasive crop pest species associated with global climate change. Applied Geography 82, 83–92.CrossRefGoogle Scholar
Zhai, BP, Zheng, XH, Shang, HW and Cheng, JA (1999) Effect of wind on the flight of rice water weevil. Chinese Agricultural Meteorology 20, 24–27.Google Scholar
Zhang, L, Chen, X and Xin, X (2019) Overview and review of CMIP6 scenario model comparison program (ScenarioMIP). Progress in Climate Change Research 15, 519–525.Google Scholar
Zhang, L, Yu, HY and Xu, ZX (1994) Rice water weevil and plant quarantine (I). Plant Quarantin 8, 215–219.Google Scholar
Zhang, XY, Zhao, J, W, M, Li, ZP, L, S and C, H (2022) Potential distribution prediction of Amaranthus palmeri S. Watson in China under current and future climate scenarios. Ecology and Evolutio 12, e9505.CrossRefGoogle Scholar
Zhang, YJ, Hu, J, Wang, CB, Wang, YQ, Ji, ML, Fang-Zhou, M and Lu, YQ (2024) Estimating global geographical distribution and ecological niche dynamics of Ammannia coccinea under climate change based on Biomod2. Scientific Reports 14, 30579.CrossRefGoogle ScholarPubMed
Zhao, GH, Cui, XY, Sun, JJ, Li, TT, Wang, Q, Ye, XZ and Fan, BG (2021) Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and Maxent models. Ecological Indicators 132, 108256.CrossRefGoogle Scholar
Ziska, LH, Blumenthal, DM, Runion, GB, Hunt, ER and Diaz-Soltero, H (2011) Invasive species and climate change: An agronomic perspective. Climatic Change 105, 13–42.CrossRefGoogle Scholar
Zou, L, Stout, MJ and Dundand, RT (2004) The effects of feeding by the rice water weevil, Lissorhoptrus oryzophilus Kuschel, on the growth and yield components of rice, Oryza sativa. Agricultural and Forest Entomology 6, 47–53.CrossRefGoogle Scholar
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