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Predicting the Brown Planthopper, Nilaparvata lugens (Stål) (Hemiptera: Delphacidae) Potential Distribution Under Climatic Change Scenarios in India


Affiliations
1 Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
2 ICAR-RCER, Farming System Research Centre for Hill and Plateau Region, Ranchi 834 010, India
3 Potsdam Institute for Climate Impact Research (PIK), A Member of the Leibniz Association, Potsdam, Germany
4 Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India, India
 

The brown planthopper, Nilaparvata lugens (Stål) is the most serious pest of rice across the world. It is also known to transmit stunted viral disease; the insect alone or in combination with a virus causes the break­down of rice vascular system, leading to economic losses in commercial rice production. Despite its immense economic importance, information on its potential distribution and factors governing the present and future distribution patterns is limited. Thus, in the present study we used maximum entropy modelling with bioclimatic variables to predict the present and future potential distribution of N. lugens in India as an indicator of risk. The predictions were mapped for spatio-temporal variation and area was analysed under suitability ranges. Jackknife analysis indicated that N. lugens geographic distribution was mostly influenced by temperature-based variables that explain up to 68.7% of the distribution, with precipitation factors explaining the rest. Among individual factors, the most important for distribution of N. lugens was annual mean temperature followed by precipitation of coldest quarter and precipitation seasonality. Our results highlight that the highly suitable areas under current climate conditions are 7.3%, whereas all projections show an increase under changing climatic conditions with time up to 2090, and with emission scenarios and a corresponding decrease in low-risk areas. We conclude that climate change increa­ses the risk of N. lugens with increased temperature as it is likely to spread to the previously unsuitable areas in India, demanding adaptation strategies.

Keywords

Climate Change, Maximum Entropy Modeling, Nilaparvata lugens, Potential Distribution, Rice.
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  • Lobell, D. B. and Gourdji, S. M., The influence of climate change on global crop productivity. Plant Physiol., 2012, 160(4), 1686–1697.
  • Liu, D., Mishra, A. K. and Ray, D. K., Sensitivity of global major crop yields to climate variables: a non-parametric elasticity analysis. Sci. Total Environ., 2020, 748, 141431.
  • Ceglar, A., Zampieri, M., Toreti, A. and Dentener, F., Observed northward migration of agro‐climate zones in Europe will further accelerate under climate change. Earths Future, 2019, 7(9), 1088–1101.
  • Taylor, R. A., Ryan, S. J., Lippi, C. A., Hall, D. G., Narouei‐ Khandan, H. A., Rohr, J. R. and Johnson, L. R., Predicting the fundamental thermal niche of crop pests and diseases in a changing world: a case study on citrus greening. J. Appl. Ecol., 2019, 56(8), 2057–2068.
  • Juroszek, P., Racca, P., Link, S., Farhumand, J. and Kleinhenz, B., Overview on the review articles published during the past 30 years relating to the potential climate change effects on plant pathogens and crop disease risks. Plant Pathol., 2020, 69(2), 179–193.
  • Yang, L., Huang, L. F., Wang, W. L., Chen, E. H., Chen, H. S. and Jiang, J. J., Effects of temperature on growth and development of the brown planthopper, Nilaparvata lugens (Homoptera: Delphacidae). Environ. Entomol., 2021, 50(1), 1–11.
  • Berzitis, E. A., Minigan, J. N., Hallett, R. H. and Newman, J. A., Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata). Global Change Biol., 2014, 20, 2778–2792.
  • Pandi, G. G. P., Chander, S., Pal, M. and Soumia, P. S., Impact of elevated CO2 on Oryza sativa phenology and brown planthopper, Nilaparvata lugens (Hemiptera: Delphacidae) population. Curr. Sci., 2018, 114(8), 1767–1777.
  • Rao, V. T., Nilaparvata lugens Stal (Fulgoridae: Homoptera) as a pest of paddy cultivation in North Madras and its control. Indian J. Entomol., 1950, 12, 241–248.
  • Jena, M. et al., Paradigm shift of insect pests in rice ecosystem and their management strategy. Oryza, 2018, 55, 82–89.
  • Pandi, G. G. P., Chander, S. and Pal, M., Impact of elevated CO2 on brown planthopper, Nilaparvata lugens (Stal). Indian J. Entomol., 2017, 79(1), 82–85.
  • Du, B., Chen, R., Guo, J. and He, J., Current understanding of the genomic, genetic, and molecular control of insect resistance in rice. Mol. Breed., 2020, 40, 24.
  • Pandi, G. G. P., Chander, S., Pal, M. and Pathak, H., Impact of elevated CO2 and temperature on brown planthopper population in rice ecosystem. Proc. Natl. Acad. Sci. India Sect. B, 2016, 88(1), 57–64.
  • Wu, S. F. et al., The evolution of insecticide resistance in the brown planthopper (Nilaparvata lugens Stål) of China in the period 2012–2016. Sci. Rep., 2018, 8(1), 1–11.
  • Bale, J. S. B., Masters, G. J. and Hodkinson, I. D., Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Global Change Biol., 2002, 8, 1–16.
  • Piyaphongkul, J., Pritchard, J. and Bale, J., Can tropical insects stand the heat? A case study with the brown planthopper Nilaparvata lugens (Stål). PLOS ONE, 2012, 7(1), e29409.
  • Hallman, G. J. and Denlinger, D. L., Temperature Sensitivity in Insects and Application in Integrated Pest Management, CRC Press, New York, USA, 2019, pp. 6–47.
  • Sujithra, M. and Chander, S., Simulation of rice brown planthopper, Nilaparvata lugens (Stal.) population and crop–pest interactions to assess climate change impact. Climatic Change, 2013, 121, 331–347.
  • Bentlage, B., Peterson, A. T., Barve, N. and Cartwright, P., Plumbing the depths: extending ecological niche modelling and species distribution modelling in three dimensions. Global Ecol. Biogeogr., 2013, 22(8), 952–961.
  • Kumar, S., Graham, J., West, A. M. and Evangelista, P. H., Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India. Comput. Electron. Agric., 2014, 103, 55–62.
  • Evangelista, P. H., Kumar, S., Stohlgren, T. J. and Young, N. E., Assessing forest vulnerability and the potential distribution of pine beetles under current and future climate scenarios in the Interior West of the US. For. Ecol. Manage., 2011, 262(3), 307–316.
  • Choudhary, J. S., Kumari, M., Mali, S. S., Dhakar, M. K., Das, B., Singh, A. K. and Bhatt, B. P., Predicting impact of climate change on habitat suitability of guava fruit fly, Bactrocera correcta (Bezzi) using MaxEnt modeling in India. J. Agrometeorol., 2019, 21(1), 24–30.
  • AICRIP, All India Co-ordianted Rice Improvement Programme progress report. Volume 2: entomology and plant pathology. ICAR-Indian Institute of Rice Research, Hyderabad, 2019, pp. 18–20.
  • EPPO, Global database on insect pest distribution, European and Mediterranean Plant Protection Organization, 2020; https://gd.eppo.int/taxon/NILALU/distribution (accessed on 14 December 2020).
  • CABI, Datasheet on invasive species compendium, Centre for Agriculture and Bioscience International, 2020; https://www.cabi.org/isc/datasheet/36301 (accessed on 14 December 2020).
  • Guevara, L., Gerstner, B. E., Kass, J. M. and Anderson, R. P., Toward ecologically realistic predictions of species distributions: a cross-time example from tropical montane cloud forests. Global Change Biol., 2018, 24, 1511–1522.
  • Fourcade, Y., Engler, J. O., Rödder, D. and Secondi, J., Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLOS ONE, 2014, 9(5), e97122.
  • Hortal, J., Roura-Pascual, N., Sanders, N. J. and Rahbek, C., Understanding (insect) species distributions across spatial scales. Ecography, 2010, 33, 51–53.
  • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A., Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol., 2005, 25(15), 195–204.
  • Moss, R. H. et al., The next generation of scenarios for climate change research and assessment. Nature, 2010, 463(7282), 747–756.
  • Taylor, K. E., Stouffer, R. J. and Meehl, G. A., An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 2012, 93, 485–498.
  • Gao, T., Xu, Q., Liu, Y., Zhao, J. and Shi, J., Predicting the potential geographic distribution of Sirex nitobei in China under climate change using maximum entropy model. Forests, 2021, 12, 151.
  • Rajpoot, R. et al., Climate models predict a divergent future for the medicinal tree Boswellia serrata Roxb. in India. Global Ecol. Conserv., 2020, 23, e01040.
  • Wei, J., Zhang, H., Zhao, W. and Zhao, Q., Niche shifts and the potential distribution of Phenacoccus solenopsis (Hemiptera: Pseudococcidae) under climate change. PLOS ONE, 2017, 12(7), e0180913.
  • Warren, D. L., Glor, R. E. and Turelli, M., ENMTools: a toolbox for comparative studies of environmental niche models. Ecography, 2010, 33(3), 607–611.
  • Phillips, S. J., Anderson, R. P. and Schapire, R. E., Maximum entropy modeling of species geographic distributions. Ecol. Model., 2006, 190(3–4), 231–259.
  • Elith, J., Graham, C. H., Anderson, R. P., Dudik, M. and Ferrier, S., Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 2006, 29, 129–151.
  • Pearson, R. G., Raxworthy, C. J., Nakamura, M. and Peterson, A. T., Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr., 2007, 34, 102–117.
  • Pearce, J. and Ferrier, S., An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecol. Model., 2000, 128, 127–147.
  • Qin, Y., Wang, C., Zhao, Z., Pan, X. and Li, Z., Climate change impacts on the global potential geographical distribution of the agricultural invasive pest, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae). Climatic Change, 2019, 155, 145–156.
  • Chemura, A., Musundire, R. and Chiwona-Karltun, L., Modelling habitat and spatial distribution of the edible insect Henicus whellani Chop (Orthoptera: Stenopelmatidae) in south-eastern districts of Zimbabwe. J. Insects Food Feed, 2018, 4(4), 229–238.
  • Liu, C., Newell, G. and White, M., The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites. Ecography, 2019, 42(3), 535–548.
  • Fois, M., Cuena-Lombraña, A., Fenu, G. and Bacchetta, G., Using species distribution models at local scale to guide the search of poorly known species: review, methodological issues and future directions. Ecol. Model., 2018, 385, 124–132.
  • West, A. M., Kumar, S., Brownb, C. S., Stohlgren, T. J. and Bromberg, J., Field validation of an invasive species Maxent model. Ecol. Inform., 2016, 36, 126–134.
  • Warren, D. L. and Seifert, S. N., Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl., 2011, 21, 335–342.
  • Ali, M. P., Huang, D. and Nachman, G., Ahmed, N., Begum, M. A. and Rabbi, M. F., Will climate change affect outbreak patterns of planthoppers in Bangladesh? PLOS ONE, 2014, 9(3), 1–10.
  • Hu, G. et al., Outbreaks of the brown planthopper Nilaparvata lugens (Stål) in the Yangtze River Delta: immigration or local reproduction? PLOS ONE, 2014, 9, e88973.
  • Yadav, D. S., Chander, S. and Selvaraj, K., Agro-ecological zoning of brown planthopper Nilaparvata lugens (Stal) incidence on rice (Oryza sativa L.). J. Sci. Ind. Res., 2010, 69, 818–822.
  • Thomson, L. J., Macfadyen, S. and Hoffmann, A. A., Predicting the effects of climate change on natural enemies of agricultural pests. Biol. Control, 2010, 52(3), 296–306.
  • Pandi, G. G. P., Chander, S. and Soumia, P. S., Elevated CO2 reared brown plant hopper as prey on feeding potential of wolf spider Pardosa pseudoannulata. Indian J. Entomol., 2018, 80(1), 127–130.

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  • Predicting the Brown Planthopper, Nilaparvata lugens (Stål) (Hemiptera: Delphacidae) Potential Distribution Under Climatic Change Scenarios in India

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Authors

Govindharaj Guru-Pirasanna-Pandi
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
Jaipal Singh Choudhary
ICAR-RCER, Farming System Research Centre for Hill and Plateau Region, Ranchi 834 010, India
Abel Chemura
Potsdam Institute for Climate Impact Research (PIK), A Member of the Leibniz Association, Potsdam, Germany
G. Basana-Gowda
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
Mahendran Annamalai
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
Naveenkumar Patil
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
Totan Adak
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India
Prakash Chandra Rath
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753 006, India, India

Abstract


The brown planthopper, Nilaparvata lugens (Stål) is the most serious pest of rice across the world. It is also known to transmit stunted viral disease; the insect alone or in combination with a virus causes the break­down of rice vascular system, leading to economic losses in commercial rice production. Despite its immense economic importance, information on its potential distribution and factors governing the present and future distribution patterns is limited. Thus, in the present study we used maximum entropy modelling with bioclimatic variables to predict the present and future potential distribution of N. lugens in India as an indicator of risk. The predictions were mapped for spatio-temporal variation and area was analysed under suitability ranges. Jackknife analysis indicated that N. lugens geographic distribution was mostly influenced by temperature-based variables that explain up to 68.7% of the distribution, with precipitation factors explaining the rest. Among individual factors, the most important for distribution of N. lugens was annual mean temperature followed by precipitation of coldest quarter and precipitation seasonality. Our results highlight that the highly suitable areas under current climate conditions are 7.3%, whereas all projections show an increase under changing climatic conditions with time up to 2090, and with emission scenarios and a corresponding decrease in low-risk areas. We conclude that climate change increa­ses the risk of N. lugens with increased temperature as it is likely to spread to the previously unsuitable areas in India, demanding adaptation strategies.

Keywords


Climate Change, Maximum Entropy Modeling, Nilaparvata lugens, Potential Distribution, Rice.

References





DOI: https://doi.org/10.18520/cs%2Fv121%2Fi12%2F1600-1609