Experimental and Applied Acarology

, Volume 68, Issue 2, pp 155–171 | Cite as

Characterization of spatial distribution of Tetranychus urticae in peppermint in California and implication for improving sampling plan

  • Jhalendra P. Rijal
  • Rob Wilson
  • Larry D. Godfrey


Twospotted spider mite, Tetranychus urticae Koch, is an important pest of peppermint in California, USA. Spider mite feeding on peppermint leaves causes physiological changes in the plant, which coupling with the favorable environmental condition can lead to increased mite infestations. Significant yield loss can occur in absence of pest monitoring and timely management. Understating the within-field spatial distribution of T. urticae is critical for the development of reliable sampling plan. The study reported here aims to characterize the spatial distribution of mite infestation in four commercial peppermint fields in northern California using spatial techniques, variogram and Spatial Analysis by Distance IndicEs (SADIE). Variogram analysis revealed that there was a strong evidence for spatially dependent (aggregated) mite population in 13 of 17 sampling dates and the physical distance of the aggregation reached maximum to 7 m in peppermint fields. Using SADIE, 11 of 17 sampling dates showed aggregated distribution pattern of mite infestation. Combining results from variogram and SADIE analysis, the spatial aggregation of T. urticae was evident in all four fields for all 17 sampling dates evaluated. Comparing spatial association using SADIE, ca. 62 % of the total sampling pairs showed a positive association of mite spatial distribution patterns between two consecutive sampling dates, which indicates a strong spatial and temporal stability of mite infestation in peppermint fields. These results are discussed in relation to behavior of spider mite distribution within field, and its implications for improving sampling guidelines that are essential for effective pest monitoring and management.


Tetranychus urticae Peppermint Sampling Variogram SADIE Clustering indices 



We would like to thank the California Department of Food and Agriculture-Specialty Crop Block Grant Program for funding support. We also like to thank two unknown reviewers who have provided constructive comments in this manuscript.


  1. Blom PE, Fleischer SJ (2001) Dynamics in the spatial structure of Leptinotarsa decemlineata (Coleoptera: Chrysomelidae). Environ Entomol 30:350–364CrossRefGoogle Scholar
  2. Blom PE, Fleischer SJ, Smilowitz Z (2002) Spatial and temporal dynamics of Colorado potato beetle (Coleoptera: Chrysomelidae) in fields with perimeter and spatially targeted insecticides. Environ Entomol 31:149–159CrossRefGoogle Scholar
  3. Bohling G (2005) Introduction to geostatistics and variogram analysis. Kansas Geological Survey. University of Kansas, Manhattan, Kansas
  4. CIMIS (2014) California irrigation and management information system, California Department of Water Resources. Accessed 2 Feb 2015
  5. Davis PM (1994) Statistics for describing populations. In: Pedigo LP, Buntin GD (eds) Handbook of sampling methods for arthropods in agriculture. CRC, Boca Raton, pp 34–54Google Scholar
  6. DeAngelis JD, Larson KC, Berry RE, Krantz GW (1982) Effects of spider mite injury on transpiration and leaf water status in peppermint. Environ Entomol 11:975–978CrossRefGoogle Scholar
  7. DeAngelis JD, Larson KC, Berry RE, Krantz GW (1983a) Evidence for spider mite (Acari: Tetranychidae) injury-induced leaf water deficits and osmotic adjustment in peppermint. Environ Entomol 12:336–339CrossRefGoogle Scholar
  8. DeAngelis JD, Larson KC, Berry RE, Krantz GW (1983b) Photosynthesis, leaf conductance, and leaf chlorophyll content in spider mite (Acari: Tetranychidae)-injured peppermint leaves. Environ Entomol 12:345–348CrossRefGoogle Scholar
  9. DeAngelis JD, Marin AB, Berry RE, Krantz GW (1983c) Effects of spider mite (Acari: Tetranychidae) injury on essential oil metabolism in peppermint. Environ Entomol 12:522–527CrossRefGoogle Scholar
  10. Farias PRS, Barbosa JC, Vieira SR, Sanchez-Vila X, Ferraz LCCB (2002) Geostatistical analysis of spatial distribution on Rotylenchulus reniformis cotton cultivated under crop rotation. Russ J Nematol 10:1–9Google Scholar
  11. Farias PRS, Roberto SR, Lopes JRS, Perecin D (2003) Geostatistical characterization of the spatial distribution of Xylella fastidiosa sharpshooter vectors on citrus. Neotrop Entomol 33:13–20CrossRefGoogle Scholar
  12. Fleischer SJ, Blom PE, Weisz R (1999) Sampling in precision IPM: when the objective is a map. Phytopathology 89:1112–1118PubMedCrossRefGoogle Scholar
  13. Fortin MJ, Dale MRT (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, CambridgeGoogle Scholar
  14. Frank DL, Brewster CC, Leskey TC, Bergh JC (2011) Factors influencing the temporal and spatial patterns of dogwood borer (Lepidoptera: Sesiidae) infestations in newly planted apple orchards. Environ Entomol 40:173–183CrossRefGoogle Scholar
  15. Frick KE (1961) Control of insects and mites attacking mint in central Washington. J Econ Entomol 54:644–649CrossRefGoogle Scholar
  16. Fuchs SJ, Hirnyck RE (2000) Crop profile for mint in Idaho. University of Idaho-Boise Center, BoiseGoogle Scholar
  17. Gamma Design Software (2008) GS Ver. 9.0.11. Gamma Design Software LLC, PlainwellGoogle Scholar
  18. Gershenzon J, McConkey ME, Croteau RB (2000) Regulation of monoterpene accumulation in leaves of peppermint. Plant Physiol 122:205–213PubMedPubMedCentralCrossRefGoogle Scholar
  19. Gerson U (1985) Webbing. In: Helle W, Sabelis MW (eds) Spider mites: their biology, natural enemies and control, vol 1A. Elsevier, New York, pp 223–232Google Scholar
  20. Hollingsworth CS (1980) Twospotted spider mite, Tetranychus urticae Koch, in Oregon peppermint, Mentha piperita L.: sampling, population dynamics, and economic injury. Dissertation, Oregon State UniversityGoogle Scholar
  21. Hollingsworth CS, Berry RE (1982a) Regression sampling plan for twospotted spider mite (Acari: Tetranychidae) in Oregon peppermint. J Econ Entomol 75:497–500CrossRefGoogle Scholar
  22. Hollingsworth CS, Berry RE (1982b) Twospotted spider mite (Acari: Tetranychidae) in peppermint: population dynamics and influence of cultural practices. Environ Enlomol 11:1280–1284CrossRefGoogle Scholar
  23. Isaaks EH, Srivastava RM (1989) Applied geostatistics. Oxford, New YorkGoogle Scholar
  24. Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic, New YorkGoogle Scholar
  25. Kamdem C, Fouet C, Etouna J, Etoa F-X, Simard F, Besansky NJ, Costantini C (2012) Spatially explicit analyses of anopheline mosquitoes indoor resting density: implications for malaria control. PLoS One 7:e31843PubMedPubMedCentralCrossRefGoogle Scholar
  26. Kennedy GG, Smitley DR (1985) Dispersal. In: Helle W, Sabelis MW (eds) Spider mites: their biology, natural enemies and control, vol 1A. Elsevier, New York, pp 233–242Google Scholar
  27. Kumar B, Shukla AK, Samad A (2014) Development and characterization of the menthofuran-rich inter-specific hybrid peppermint variety CIMAP-Patra. Mol Breeding 34:717–724CrossRefGoogle Scholar
  28. Kuno E (1991) Sampling and analysis of insect populations. Annu Rev Entomol 36:285–304CrossRefGoogle Scholar
  29. Liebhold AM, Zhang XU, Hohn ME, Elkinton JS, Ticehurst M, Benzon GL, Campbell RW (1991) Geostatistical analysis of gypsy moth (Lepidoptera: Lymantriidae) egg mass populations. Environ Entomol 20:1407–1417CrossRefGoogle Scholar
  30. Liebhold AM, Rossi RE, Kemp WP (1993) Geostatistics and geographic information systems in applied insect ecology. Annu Rev Entomol 38:303–327CrossRefGoogle Scholar
  31. Madden LV, Hughes G (1995) Plant disease incidence: distributions, heterogeneity, and temporal analysis. Annu Rev Phytopathol 33:529–564PubMedCrossRefGoogle Scholar
  32. Mahmoud SS, Croteau RB (2003) Menthofuran regulates essential oil biosynthesis in peppermint by controlling a downstream monoterpene reductase. Proc Natl Acad Sci USA 100:14481–14486PubMedPubMedCentralCrossRefGoogle Scholar
  33. Marcum DB, Hanson BR (2006) Effect of irrigation and harvest timing on peppermint oil yield in California. Agric Water Manag 82:118–128CrossRefGoogle Scholar
  34. Margolies DC, Kennedy GG (1985) Movement of the twospotted spider mite, Tetranychus urticae, among hosts in a corn-peanut agroecosystem. Entomol Exp Appl 37:55–61CrossRefGoogle Scholar
  35. McMurtry JA, Huffaker CB, van de Vrie M (1970) Ecology of tetranychid mites and their natural control: a review. I. Tetranychid enemies: their biological characters and the impact of spray practices. Hilgardia 40:331–390CrossRefGoogle Scholar
  36. Midgarden DG, Youngman RR, Fleischer SJ (1993) Spatial analysis of counts of western com rootworm (Coleoptera: Chrysomelidae) adults on yellow sticky traps in corn: geostatistics and dispersion indices. Environ Entomol 22:1124–1133CrossRefGoogle Scholar
  37. Morris MA, Berry RE, Croft BA (1999) Phytoseiid mites on peppermint and effectiveness of Neoseiulus fallacis to control Tetranychus urticae (Acari: Phytoseiidae, Tetranychidae) in arid growing regions. J Econ Entomol 92:1072–1078PubMedCrossRefGoogle Scholar
  38. Park YL, Tollefson JJ (2005) Characterization of the spatial dispersion of corn root injury by corn rootworms (Coleoptera: Chrysomelidae). J Econ Entomol 98:378–383PubMedCrossRefGoogle Scholar
  39. Perry JN (1995) Spatial analysis by distance indices. J Anim Ecol 64:303–314CrossRefGoogle Scholar
  40. Perry JN (1998) Measures of spatial pattern and spatial association for counts of insects. In: Baumgartner J, Brandmayr P, Manly BFJ (eds) Population and community ecology for insect management and conservation, proceedings of the ecology and population dynamics, 20th international congress of entomology, 25–31 August 1996, Florence, Italy, pp 21–33Google Scholar
  41. Perry JN, Dixon PM (2002) A new method to measure spatial association for ecological count data. Ecoscience 9:133–141Google Scholar
  42. Perry JN, Winder L, Holland JM, Alston RD (1999) Red–blue plots for detecting clusters in count data. Ecol Lett 2:106–113CrossRefGoogle Scholar
  43. Perry JN, Liebhold AM, Rosenberg MS, Dungan J, Miriti M, Jakomulska A, Citron-Pousty S (2002) Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography 25:578–600CrossRefGoogle Scholar
  44. Queiroz JW, Dias GH, Nobre ML, Dias MCDS, Araújo SF, Barbosa JD, Trindade-Neto PB, Blackwell JM, Jeronimo SMB (2010) Geographic information systems and applied spatial statistics are efficient tools to study Hansen’s disease (Leprosy) and to determine areas of greater risk of disease. Am J Trop Med Hyg 82:306–314PubMedPubMedCentralCrossRefGoogle Scholar
  45. Reay-Jones FPF (2012) Spatial analysis of the cereal leaf beetle (Coleoptera: Chrysomelidae) in wheat. Environ Entomol 41:1516–1626PubMedCrossRefGoogle Scholar
  46. Reay-Jones FPF (2014) Spatial distribution of stink bugs (Hemiptera: Pentatomidae) in wheat. J Insect Sci. doi: 10.1093/jis/14.1.98 PubMedCentralGoogle Scholar
  47. Rijal JP, Brewster CC, Bergh JC (2014) Spatial distribution of grape root borer (Lepidoptera: Sesiidae) infestations in Virginia vineyards and implications for sampling. Environ Entomol 43:716–728PubMedCrossRefGoogle Scholar
  48. Robertson GP (2008) GS+: geostatistics for the environmental sciences. Gamma Design Software, PlainwellGoogle Scholar
  49. Robinson TP, Metternicht G (2006) Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electon Agric 50:97–108CrossRefGoogle Scholar
  50. Rossi RE, Mulla DJ, Journne AG, Franz EH (1992) Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol Monogr 62:277–314CrossRefGoogle Scholar
  51. Rothamsted Experimental Station (2008) SADIEShell. Ver. 2. Rothamsted Experimental Station, HarpendenGoogle Scholar
  52. SAS Institute (2010) JMP version 9.0.1. SAS institute Inc., CaryGoogle Scholar
  53. Schotzko DJ, O’Keeffe LE (1989) Geostatistical description of the spatial distribution of Lygus Hesperus (Heteroptera: Miridae) in lentils. J Econ Entomol 82:1277–1288CrossRefGoogle Scholar
  54. Schotzko DJ, O’Keeffe LE (1990) Effect of sample placement on the geostatistical analysis of the spatial distribution of Lygus hesperus (Heteroptera: Miridae) in lentils. J Econ Entomol 83:1888–1900CrossRefGoogle Scholar
  55. Stern VM, Smith RF, van den Bosch R, Hagen KS (1959) The integration of chemical and biological control of the spotted alfalfa aphid (the integrated control concept). Hilgardia 29:81–101CrossRefGoogle Scholar
  56. Taylor LR (1984) Assessing and interpreting the spatial distributions of insect populations. Annu Rev Entomol 29:321–357CrossRefGoogle Scholar
  57. Tollerup KE, Marcum DR, Wilson R, Godfrey LDG (2013) Binomial and enumerative sampling of Tetranychus urticae (Acari: Tetranychidae) on peppermint in California. J Econ Entomol 106:1707–1715PubMedCrossRefGoogle Scholar
  58. Trangmar BB, Yost RS, Uehara G (1986) Application of geostatistics to spatial studies of soil properties. Adv Agron 38:45–94CrossRefGoogle Scholar
  59. van de Vrie M, McMurtry JA, Huffaker CB (1972) Ecology of tetranychid mites and their natural enemies: a review. II. Biology, ecology, pest status and host plant relations of tetranychids. Hilgardia 41:343–432CrossRefGoogle Scholar
  60. Weisz R, Fleischer S, Smilowitz Z (1995) Site-specific integrated pest management for high value crops: sample units for map generation using the Colorado potato beetle (Coleoptera: Chrysomelidae) as a model system. J Econ Entomol 88:1069–1080PubMedCrossRefGoogle Scholar
  61. Weisz R, Fleischer S, Smilowitz Z (1996) Site-specific integrated pest management for high-value crops: impact on potato pest management. J Econ Entomol 89:501–509CrossRefGoogle Scholar
  62. Williams L, Schotzko DJ, McCaffrey JP (1992) Geostatistical description of the spatial distribution of Limonius californicus (Coleoptera: Elateridae) wireworms in the northwestern United States, with comments on sampling. Environ Entomol 21:983–995CrossRefGoogle Scholar
  63. Wright RJ, Devries TA, Young LG, Jarvi KJ, Seymour RC (2002) Geostatistical analysis of the small-scale distribution of European corn borer (Lepidoptera: Crambidae) larvae and damage in whorl stage corn. Environ Entomol 31:160–167CrossRefGoogle Scholar
  64. Young LG, Young JH (1990). A spatial view of negative binomial parameter k when describing insect populations. In: Proceedings of conference on applied statistics in agriculture. Kansas State University, Manhattan, KS, pp 13–17Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jhalendra P. Rijal
    • 1
  • Rob Wilson
    • 2
  • Larry D. Godfrey
    • 3
  1. 1.University of California Cooperative Extension, UC Statewide IPM ProgramModestoUSA
  2. 2.University of California Cooperative Extension, ANR, Intermountain Research and Extension CenterTulelakeUSA
  3. 3.Department of Entomology and NematologyUniversity of California-DavisDavisUSA

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