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Annual Review of Entomology

Volume 62, 2017

Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise

Abstract

Ecoinformatics, as defined in this review, is the use of preexisting data sets to address questions in ecology. We provide the first review of ecoinformatics methods in agricultural entomology. Ecoinformatics methods have been used to address the full range of questions studied by agricultural entomologists, enabled by the special opportunities associated with data sets, nearly all of which have been observational, that are larger and more diverse and that embrace larger spatial and temporal scales than most experimental studies do. We argue that ecoinformatics research methods and traditional, experimental research methods have strengths and weaknesses that are largely complementary. We address the important interpretational challenges associated with observational data sets, highlight common pitfalls, and propose some best practices for researchers using these methods. Ecoinformatics methods hold great promise as a vehicle for capitalizing on the explosion of data emanating from farmers, researchers, and the public, as novel sampling and sensing techniques are developed and digital data sharing becomes more widespread.

Keyword(s): data analysis, large scale, observational studies, spurious correlations
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2017-01-31
2024-04-19
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Literature Cited

  1. Aizen MA, Garibaldi LA, Cunningham SA, Klein AM. 1.  2009. How much does agriculture depend on pollinators? Lessons from long-term trends in crop production. Ann. Bot. 103:91579–88 [Google Scholar]
  2. Allstadt AJ, Haynes KJ, Liebhold AM, Johnson DM. 2.  2013. Long-term shifts in the cyclicity of outbreaks of a forest-defoliating insect. Oecologia 172:1141–51 [Google Scholar]
  3. Anderson C. 3.  2008. The end of theory: The data deluge makes the scientific method obsolete. Wired June 23. https://www.wired.com/2008/06/pb-theory/
  4. Andert S, Bürger J, Gerowitt B. 4.  2015. On-farm pesticide use in four Northern German regions as influenced by farm and production conditions. Crop Prot 75:1–10 [Google Scholar]
  5. Bacon SJ, Bacher S, Aebi A. 5.  2012. Gaps in border controls are related to quarantine alien insect invasions in Europe. PLOS ONE 7:10e47689 [Google Scholar]
  6. Bahlai CA, Colunga-Garcia M, Gage SH, Landis DA. 6.  2014. The role of exotic ladybeetles in the decline of native ladybeetle populations: evidence from long-term monitoring. Biol. Invasions 17:41005–24 [Google Scholar]
  7. Bahlai CA, van der Werf W, O'Neal M, Hemerik L, Landis DA. 7.  2015. Shifts in dynamic regime of an invasive lady beetle are linked to the invasion and insecticidal management of its prey. Ecol. Appl. 25:71807–18 [Google Scholar]
  8. Bartomeus I, Ascher JS, Gibbs J, Danforth BN, Wagner DL. 8.  et al. 2013. Historical changes in northeastern US bee pollinators related to shared ecological traits. PNAS 110:124656–60 [Google Scholar]
  9. Beale CM, Lennon JJ, Yearsley JM, Brewer MJ, Elston DA. 9.  2010. Regression analysis of spatial data. Ecol. Lett. 13:2246–64 [Google Scholar]
  10. Bebber DP, Ramotowski M, Gurr SJ. 10.  2013. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3:11985–88Large, decadal, global database used to examine geographic range expansion by insect and fungal pests. [Google Scholar]
  11. Behmann J, Mahlein A-K, Rumpf T, Römer C, Plümer L. 11.  2014. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis. Agric. 16:3239–60 [Google Scholar]
  12. Bekker RM, van der Maarel E, Bruelheide H, Woods K. 12.  2007. Long-term datasets: from descriptive to predictive data using ecoinformatics. J. Veg. Sci. 18:4458–62 [Google Scholar]
  13. Bell JR, Alderson L, Izera D, Kruger T, Parker S. 13.  et al. 2015. Long-term phenological trends, species accumulation rates, aphid traits and climate: five decades of change in migrating aphids. J. Anim. Ecol. 84:121–34 [Google Scholar]
  14. Benton T, Bryant D, Cole L, Crick H. 14.  2002. Linking agricultural practice to insect and bird populations: a historical study over three decades. J. Appl. Ecol. 39:4673–87 [Google Scholar]
  15. Bisanzio D, Mutuku F, LaBeaud AD, Mungai PL, Muinde J. 15.  et al. 2015. Use of prospective hospital surveillance data to define spatiotemporal heterogeneity of malaria risk in coastal Kenya. Malar. J. 14:1482 [Google Scholar]
  16. Bohan DA, Caron-Lormier G, Muggleton S, Raybould A, Tamaddoni-Nezhad A. 16.  2011. Automated discovery of food webs from ecological data using logic-based machine learning. PLOS ONE 6:12e29028 [Google Scholar]
  17. Bollier D. 17.  2010. The Promise and Peril of Big Data Washington, DC: Aspen Inst. Commun. Soc. Progr.
  18. Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T. 18.  et al. 2009. Citizen science: a developing tool for expanding science knowledge and scientific literacy. BioScience 59:11977–84 [Google Scholar]
  19. boyd d, Crawford K. 19.  2012. Critical questions for big data. Inform. Comm. Soc. 15:5662–79 [Google Scholar]
  20. Brooks D, Perry JN, Clark S, Heard M, Firbank L. 20.  et al. 2008. National scale metacommunity dynamics of carabid beetles in UK farmland. J. Anim. Ecol. 77:265–74 [Google Scholar]
  21. Brown M, Pharo H, Hendrikx P, Ribière-Chabert M, Chauzat MP, Marris G. 21.  2014. Epidemiological surveillance and control of bee diseases. Bee Health and Veterinarians W Ritter 193–213 Paris: OIE [Google Scholar]
  22. Cameron SA, Lozier JD, Strange JP, Koch JB, Cordes N. 22.  et al. 2011. Patterns of widespread decline in North American bumble bees. PNAS 108:2662–67 [Google Scholar]
  23. Carpenter JR. 23.  1940. Insect outbreaks in Europe. J. Anim. Ecol. 9:1108–47 [Google Scholar]
  24. Carrière Y, Ellers-Kirk C, Pedersen B, Haller S, Antilla L. 24.  2001. Predicting spring moth emergence in the pink bollworm (Lepidoptera: Gelechiidae): implications for managing resistance to transgenic cotton. J. Econ. Entomol. 94:51012–21 [Google Scholar]
  25. Carrière Y, Ellers-Kirk C, Sisterson M, Antilla L, Whitlow M. 25.  et al. 2003. Long-term regional suppression of pink bollworm by Bacillus thuringiensis cotton. PNAS 100:41519–23 [Google Scholar]
  26. Cattaneo MG, Yafuso C, Schmidt C, Huang C, Rahman M. 26.  et al. 2006. Farm-scale evaluation of the impacts of transgenic cotton on biodiversity, pesticide use, and yield. PNAS 103:207571–76 [Google Scholar]
  27. Chavas J-P, Shi G, Lauer J. 27.  2014. The effects of GM technology on maize yield. Crop Sci 54:41331 [Google Scholar]
  28. Chen Y, Why A, Batista G, Mafra-Neto A, Keogh E. 28.  2014. Flying insect classification with inexpensive sensors. J. Insect Behav. 27:5657–77 [Google Scholar]
  29. Cock J, Oberthür T, Isaacs C, Läderach PR, Palma A. 29.  et al. 2011. Crop management based on field observations: case studies in sugarcane and coffee. Agric. Syst. 104:9755–69 [Google Scholar]
  30. Cohen Y, Cohen A, Hetzroni A, Alchanatis V, Broday D. 30.  et al. 2008. Spatial decision support system for medfly control in citrus. Comput. Electron. Agric. 62:2107–17 [Google Scholar]
  31. Conrad KF, Warren MS, Fox R, Parsons MS, Woiwod IP. 31.  2006. Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biol. Conserv. 132:3279–91 [Google Scholar]
  32. Cooke BJ, Roland J. 32.  2000. Spatial analysis of large-scale patterns of forest tent caterpillar outbreaks. Ecoscience 7:4410–22 [Google Scholar]
  33. Davenport TH, Patil DJ. 33.  2012. Data scientist: the sexiest job of the 21st century. Harv. Bus. Rev. October:70–76 [Google Scholar]
  34. Davey JS, Vaughan IP, King RA, Bell JR, Bohan DA. 34.  et al. 2013. Intraguild predation in winter wheat: prey choice by a common epigeal carabid consuming spiders. J. Appl. Ecol. 50:1271–79 [Google Scholar]
  35. Deguines N, Jono C, Baude M, Henry M, Julliard R, Fontaine C. 35.  2014. Large-scale trade-off between agricultural intensification and crop pollination services. Front. Ecol. Environ. 12:4212–17 [Google Scholar]
  36. de Valpine P, Scranton K, Ohmart CP. 36.  2010. Synchrony of population dynamics of two vineyard arthropods occurs at multiple spatial and temporal scales. Ecol. Appl. 20:71926–35 [Google Scholar]
  37. Dickinson JL, Shirk J, Bonter D, Bonney R, Crain RL. 37.  et al. 2012. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10:6291–97 [Google Scholar]
  38. Dormann CF, McPherson J, Araújo M, Bivand R, Bolliger J. 38.  et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:5609–28 [Google Scholar]
  39. Ekbia H, Mattioli M, Kouper I, Arave G, Ghazinejad A. 39.  et al. 2015. Big data, bigger dilemmas: a critical review. J. Assoc. Inf. Sci. Technol. 66:81523–45Excellent overview of epistemological, social, and scientific challenges in Big Data. [Google Scholar]
  40. Ewald JA, Wheatley CJ, Aebischer NJ, Moreby SJ, Duffield SJ. 40.  et al. 2015. Influences of extreme weather, climate and pesticide use on invertebrates in cereal fields over 42 years. Glob. Change Biol. 21:113931–50 [Google Scholar]
  41. Fieberg J, Johnson DH. 41.  2015. MMI: multimodel inference or models with management implications?. J. Wildl. Manag. 79:5708–18 [Google Scholar]
  42. Flockhart DTT, Wassenaar LI, Martin TG, Hobson KA, Wunder MB, Norris DR. 42.  2013. Tracking multi-generational colonization of the breeding grounds by monarch butterflies in eastern North America. Proc. R. Soc. B 280:176820131087 [Google Scholar]
  43. Fountas S, Carli G, Sorensen CG, Tsiropoulos Z, Cavalaris C. 43.  et al. 2015. Farm management information systems: current situation and future perspectives. Comput. Electron. Agric. 115:40–50 [Google Scholar]
  44. Fox R, Randle Z, Hill L, Anders S, Wiffen L, Parsons MS. 44.  2011. Moths count: recording moths for conservation in the UK. J. Insect Conserv. 15:155–68 [Google Scholar]
  45. Frost KE, Esker PD, Van Haren R, Kotolski L, Groves RL. 45.  2013. Factors influencing aster leafhopper (Hemiptera: Cicadellidae) abundance and aster yellows phytoplasma infectivity in Wisconsin carrot fields. Environ. Entomol. 42:3477–90 [Google Scholar]
  46. Gaines-Day HR, Gratton C. 46.  2016. Crop yield is correlated with honey bee hive density but not in high-woodland landscapes. Agric. Ecosyst. Environ. 218:53–57 [Google Scholar]
  47. Gardiner MM, Allee LL, Brown PM, Losey JE, Roy HE, Smyth RR. 47.  2012. Lessons from lady beetles: accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10:9471–76 [Google Scholar]
  48. Garibaldi LA, Aizen MA, Klein AM, Cunningham SA, Harder LD. 48.  2011. Global growth and stability of agricultural yield decrease with pollinator dependence. PNAS 108:145909–14 [Google Scholar]
  49. Gluskin RT, Johansson MA, Santillana M, Brownstein JS. 49.  2014. Evaluation of Internet-based dengue query data: Google Dengue Trends. PLOS Negl. Trop. Dis. 8:2e2713 [Google Scholar]
  50. Glymour C, Scheines R, Spirtes P. 50.  2014. Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling New York: Academic
  51. Gross K, Rosenheim JA. 51.  2011. Quantifying secondary pest outbreaks in cotton and their monetary cost with causal-inference statistics. Ecol. Appl. 21:72770–80 [Google Scholar]
  52. Haack RA. 52.  2006. Exotic bark-and wood-boring Coleoptera in the United States: recent establishments and interceptions. Can. J. For. Res. 36:2269–88 [Google Scholar]
  53. Hampton SE, Strasser CA, Tewksbury JJ, Gram WK, Budden AE. 53.  et al. 2013. Big data and the future of ecology. Front. Ecol. Environ. 11:3156–62 [Google Scholar]
  54. Hanewinkel M, Breidenbach J, Neeff T, Kublin E. 54.  2008. Seventy-seven years of natural disturbances in a mountain forest area—the influence of storm, snow, and insect damage analysed with a long-term time series. Can. J. For. Res. 38:82249–61Long-term forest inventory study uses temporal analyses to understand extent and drivers of insect damage. [Google Scholar]
  55. Harford T. 55.  2014. Big data: a big mistake?. Significance 11:514–19 [Google Scholar]
  56. Higbee B, Siegel J. 56.  2009. New navel orangeworm sanitation standards could reduce almond damage. Calif. Agric. 63:124–28 [Google Scholar]
  57. Hill A, Guralnick R, Smith A, Sallans A, Gillespie R. 57.  et al. 2012. The notes from nature tool for unlocking biodiversity records from museum records through citizen science. ZooKeys 209:219–33 [Google Scholar]
  58. Howard E, Davis AK. 58.  2015. Investigating long-term changes in the spring migration of monarch butterflies (Lepidoptera: Nymphalidae) using 18 years of data from Journey North, a citizen science program. Ann. Entomol. Soc. Am. 108:5664–69 [Google Scholar]
  59. Howe D, Costanzo M, Fey P, Gojobori T, Hannick L. 59.  et al. 2008. Big data: the future of biocuration. Nature 455:720947–50 [Google Scholar]
  60. Huang J, Hu R, Pray C, Qiao F, Rozelle S. 60.  2003. Biotechnology as an alternative to chemical pesticides: a case study of Bt cotton in China. Agric. Econ. 29:155–67 [Google Scholar]
  61. Huang J, Hu R, Rozelle S, Pray C. 61.  2005. Insect-resistant GM rice in farmers’ fields: assessing productivity and health effects in China. Science 308:5722688–90 [Google Scholar]
  62. Hutchison WD, Burkness EC, Mitchell PD, Moon RD, Leslie TW. 62.  et al. 2010. Areawide suppression of European corn borer with Bt maize reaps savings to non-Bt maize growers. Science 330:6001222–25Elegant example of a long-term and large-scale ecoinformatics data set to study pest suppression. [Google Scholar]
  63. Isaac NJB, van Strien AJ, August TA, de Zeeuw MP, Roy DB. 63.  2014. Statistics for citizen science: extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5:101052–60 [Google Scholar]
  64. James G, Witten D, Hastie T, Tibshirani R. 64.  2013. An Introduction to Statistical Learning New York: Springer
  65. Jiang J-A, Tseng C-L, Lu F-M, Yang E-C, Wu Z-S. 65.  et al. 2008. A GSM-based remote wireless automatic monitoring system for field information: a case study for ecological monitoring of the oriental fruit fly, Bactrocera dorsalis (Hendel). Comput. Electron. Agric. 62:2243–59 [Google Scholar]
  66. Jiménez D, Cock J, Jarvis A, Garcia J, Satizábal HF. 66.  et al. 2011. Interpretation of commercial production information: a case study of lulo (Solanum quitoense), an under-researched Andean fruit. Agric. Syst. 104:3258–70 [Google Scholar]
  67. Jiménez D, Dorado H, Cock J, Prager SD, Delerce S. 67.  et al. 2016. From observation to information: data-driven understanding of on farm yield variation. PLOS ONE 11:3e0150015 [Google Scholar]
  68. Kampen H, Medlock JM, Vaux AGC, Koenraadt CJM, van Vliet AJ. 68.  et al. 2015. Approaches to passive mosquito surveillance in the EU. Parasites Vectors 8:19 [Google Scholar]
  69. Kao RH, Gibson CM, Gallery RE, Meier CL, Barnett DT. 69.  et al. 2012. NEON terrestrial field observations: designing continental-scale, standardized sampling. Ecosphere 3:121–17 [Google Scholar]
  70. Kaskey J. 70.  2013. Monsanto buying Climate Corp. to add big data for farmers. Bloomberg October 3. http://www.bloomberg.com/news/articles/2013-10-02/monsanto-to-buy-climate-corp-profit-forecast-trails-estimates
  71. Kathage J, Qaim M. 71.  2012. Economic impacts and impact dynamics of Bt (Bacillus thuringiensis) cotton in India. PNAS 109:2911652–56Carefully assesses how biases can intrude into ecoinformatics analyses and describes effective statistical solutions. [Google Scholar]
  72. Kelling S, Hochachka WM, Fink D, Riedewald M, Caruana R. 72.  et al. 2009. Data-intensive science: a new paradigm for biodiversity studies. BioScience 59:7613–20 [Google Scholar]
  73. Kitchin R. 73.  2014. Big data, new epistemologies and paradigm shifts. Big Data Soc 1:11–12Explores ways in which Big Data is changing the way we do science. [Google Scholar]
  74. Kitchin R, Lauriault TP. 74.  2014. Small data in the era of big data. GeoJournal 80:4463–75 [Google Scholar]
  75. Kouser S, Qaim M. 75.  2013. Valuing financial, health, and environmental benefits of Bt cotton in Pakistan. Agric. Econ. 44:3323–35 [Google Scholar]
  76. Krapivin VF, Varotsos CA, Soldatov VY. 76.  2015. New Ecoinformatics Tools in Environmental Science: Applications and Decision-Making Berlin: Springer
  77. Kremen C, Ullman KS, Thorp RW. 77.  2011. Evaluating the quality of citizen-scientist data on pollinator communities. Conserv. Biol. 25:3607–17 [Google Scholar]
  78. Larsen AE. 78.  2013. Agricultural landscape simplification does not consistently drive insecticide use. PNAS 110:3815330–35 [Google Scholar]
  79. Larsen AE, Gaines SD, Deschênes O. 79.  2015. Spatiotemporal variation in the relationship between landscape simplification and insecticide use. Ecol. Appl. 25:71976–83 [Google Scholar]
  80. Lawes RA, Lawn RJ. 80.  2005. Applications of industry information in sugarcane production systems. Field Crops Res 92:2–3353–63 [Google Scholar]
  81. Liebhold AM, Work TT, McCullough DG, Cavey JF. 81.  2006. Airline baggage as a pathway for alien insect species invading the United States. Am. Entomol. 52:48–54 [Google Scholar]
  82. Little RJ, Rubin DB. 82.  2000. Causal effects in clinical and epidemiological studies via potential outcomes: concepts and analytical approaches. Annu. Rev. Public Health 21:121–45 [Google Scholar]
  83. Liu EM, Huang J. 83.  2013. Risk preferences and pesticide use by cotton farmers in China. J. Dev. Econ. 103:202–15 [Google Scholar]
  84. Lu Y, Wu K, Jiang Y, Guo Y, Desneux N. 84.  2012. Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services. Nature 487:7407362–65Ideal integration of large ecoinformatics data set with smaller, researcher-collected observational and experimental data sets. [Google Scholar]
  85. Lu Y, Wu K, Jiang Y, Xia B, Li P. 85.  et al. 2010. Mirid bug outbreaks in multiple crops correlated with wide-scale adoption of Bt cotton in China. Science 328:59821151–54 [Google Scholar]
  86. Lye GC, Osborne JL, Park KJ, Goulson D. 86.  2012. Using citizen science to monitor Bombus populations in the UK: nesting ecology and relative abundance in the urban environment. J. Insect Conserv. 16:5697–707 [Google Scholar]
  87. Mayer-Schonberger V, Cukier K. 87.  2013. Big Data: A Revolution That Will Change How We Live London: John Murray
  88. McAfee A, Brynjolfsson E. 88.  2012. Big data: the management revolution. Harv. Bus. Rev. 90:1060–68 [Google Scholar]
  89. Meehan TD, Gratton C. 89.  2015. A consistent positive association between landscape simplification and insecticide use across the Midwestern US from 1997 through 2012. Environ. Res. Lett. 10:11114001Demonstrates that uncorrected spatial autocorrelation in data sets can create spurious correlations that distort central results. [Google Scholar]
  90. Meehan TD, Werling BP, Landis DA, Gratton C. 90.  2011. Agricultural landscape simplification and insecticide use in the Midwestern United States. PNAS 108:2811500–505 [Google Scholar]
  91. Meisner MH, Rosenheim JA. 91.  2014. Ecoinformatics reveals effects of crop rotational histories on cotton yield. PLOS ONE 9:1e85710 [Google Scholar]
  92. Meisner MH, Zaviezo T, Rosenheim JA. 92.  2016. Landscape crop composition effects on cotton yield, Lygus hesperus densities and pesticide use. Pest Manag. Sci. In press. https://doi.org/10.1002/ps.4290
  93. Michener WK, Jones MB. 93.  2012. Ecoinformatics: supporting ecology as a data-intensive science. Trends Ecol. Evol. 27:285–93 [Google Scholar]
  94. Mooney SJ, Westreich DJ, El-Sayed AM. 94.  2015. Commentary: epidemiology in the era of big data. Epidemiology 26:3390–94 [Google Scholar]
  95. Moritz RFA, Erler S. 95.  2016. Lost colonies found in a data mine: global honey trade but not pests or pesticides as a major cause of regional honeybee colony declines. Agric. Ecosyst. Environ. 216:44–50 [Google Scholar]
  96. O'Brien JM, Thorne JH, Rosenzweig ML, Shapiro AM. 96.  2011. Once-yearly sampling for the detection of trends in biodiversity: the case of Willow Slough, California. Biol. Conserv. 144:72012–19 [Google Scholar]
  97. Papadopoulos NT, Plant RE, Carey JR. 97.  2013. From trickle to flood: the large-scale, cryptic invasion of California by tropical fruit flies. Proc. R. Soc. B 280:176820131466 [Google Scholar]
  98. Parsa S, Ccanto R, Olivera E, Scurrah M, Alcázar J, Rosenheim JA. 98.  2012. Explaining Andean potato weevils in relation to local and landscape features: a facilitated ecoinformatics approach. PLOS ONE 7:5e36533 [Google Scholar]
  99. Parsa S, Ccanto R, Rosenheim JA. 99.  2011. Resource concentration dilutes a key pest in indigenous potato agriculture. Ecol. Appl. 21:2539–46 [Google Scholar]
  100. Parsa S, Medina C, Rodríguez V. 100.  2015. Sources of pest resistance in cassava. Crop Prot 68:79–84 [Google Scholar]
  101. Peters DPC, Havstad KM, Cushing J, Tweedie C, Fuentes O, Villanueva-Rosales N. 101.  2014. Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere 5:667 [Google Scholar]
  102. Philimis P, Psimolophitis E, Hadjiyiannis S, Giusti A, Perelló J. 102.  et al. 2013. A centralised remote data collection system using automated traps for managing and controlling the population of the Mediterranean (Ceratitis capitata) and olive (Dacus oleae) fruit flies. Proc. SPIE 8795: First Int. Conf. Remote Sens. Geoinform. Environ (RSCy2013) 87950X
  103. Poole C. 103.  2001. Low p-values or narrow confidence intervals: Which are more durable?. Epidemiology 12:3291–94 [Google Scholar]
  104. Pray CE, Huang J, Hu R, Rozelle S. 104.  2002. Five years of Bt cotton in China – the benefits continue. Plant J 31:4423–30 [Google Scholar]
  105. Preisler HK, Hicke JA, Ager AA, Hayes JL. 105.  2012. Climate and weather influences on spatial temporal patterns of mountain pine beetle populations in Washington and Oregon. Ecology 93:112421–34 [Google Scholar]
  106. Qaim M, Zilberman D. 106.  2003. Yield effects of genetically modified crops in developing countries. Science 299:5608900–2 [Google Scholar]
  107. Qiao F. 107.  2015. Fifteen years of Bt cotton in China: the economic impact and its dynamics. World Dev 70:177–85 [Google Scholar]
  108. Ries L, Oberhauser K. 108.  2015. A citizen army for science: quantifying the contributions of citizen scientists to our understanding of monarch butterfly biology. BioScience 65:4419–30 [Google Scholar]
  109. Rochester WA, Zalucki MP, Ward A, Miles M, Murray DAH. 109.  2002. Testing insect movement theory: empirical analysis of pest data routinely collected from agricultural crops. Comput. Electron. Agric. 35:2–3139–49 [Google Scholar]
  110. Rosenheim JA. 110.  2013. Costs of Lygus herbivory on cotton associated with farmer decision-making: an ecoinformatics approach. J. Econ. Entomol. 106:31286–93 [Google Scholar]
  111. Rosenheim JA, Meisner MH. 111.  2013. Ecoinformatics can reveal yield gaps associated with crop-pest interactions: a proof-of-concept. PLOS ONE 8:11e80518 [Google Scholar]
  112. Rosenheim JA, Parsa S, Forbes AA, Krimmel WA, Law YH. 112.  et al. 2011. Ecoinformatics for integrated pest management: expanding the applied insect ecologist's tool-kit. J. Econ. Entomol. 104:2331–42 [Google Scholar]
  113. Rothman KJ, Greenland S. 113.  2005. Causation and causal inference in epidemiology. Am. J. Public Health 95:S1S144–50 [Google Scholar]
  114. Sagarin R, Pauchard A. 114.  2009. Observational approaches in ecology open new ground in a changing world. Front. Ecol. Environ. 8:7379–86 [Google Scholar]
  115. Scheper J, Reemer M, van Kats R, Ozinga WA, van der Linden GTJ. 115.  et al. 2014. Museum specimens reveal loss of pollen host plants as key factor driving wild bee decline in The Netherlands. PNAS 111:4917552–57 [Google Scholar]
  116. Schmidt NP, O'Neal ME, Anderson PF, Lagos D, Voegtlin D. 116.  et al. 2012. Spatial distribution of Aphis glycines (Hemiptera: Aphididae): a summary of the suction trap network. J. Econ. Entomol. 105:1259–71 [Google Scholar]
  117. Schmitz OJ, Hambäck Beckerman AP. 117.  2000. Trophic cascades in terrestrial systems: a review of the effects of carnivore removals on plants. Am. Nat. 155:2141–53 [Google Scholar]
  118. Shi G, Chavas J-P, Lauer J. 118.  2013. Commercialized transgenic traits, maize productivity and yield risk. Nat. Biotechnol. 31:2111–14 [Google Scholar]
  119. Siontis GC, Ioannidis JP. 119.  2011. Risk factors and interventions with statistically significant tiny effects. Int. J. Epidemiol. 40:51292–1307 [Google Scholar]
  120. Sivakoff FS, Rosenheim JA, Dutilleul P, Carrière Y. 120.  2013. Influence of the surrounding landscape on crop colonization by a polyphagous insect pest. Entomol. Exp. Appl. 149:111–21 [Google Scholar]
  121. Smyth RR, Allee LL, Losey JE. 121.  2013. The status of Coccinella undecimpunctata (L.) (Coleoptera: Coccinellidae) in North America: an updated distribution from citizen science data. Coleopt. Bull. 67:4532–35 [Google Scholar]
  122. Soranno PA, Schimel DS. 122.  2014. Macrosystems ecology: big data, big ecology. Front. Ecol. Environ. 12:13 [Google Scholar]
  123. Stack-Whitney K, Meehan TD, Kucharik CJ, Zhu J, Townsend P. 123.  et al. 2016. Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance. Ecol. Appl. In press. doi: 10.1002/eap.1418
  124. Stafford R, Hart AG, Collins L, Kirkhope CL, Williams RL. 124.  et al. 2010. Eu-social science: the role of internet social networks in the collection of bee biodiversity data. PLOS ONE 5:12e14381 [Google Scholar]
  125. Steel EA, Kennedy MC, Cunningham PG, Stanovick JS. 125.  2013. Applied statistics in ecology: common pitfalls and simple solutions. Ecosphere 4:9115 [Google Scholar]
  126. Steinger T, Gilliand H, Hebeisen T. 126.  2014. Epidemiological analysis of risk factors for the spread of potato viruses in Switzerland. Ann. Appl. Biol. 164:2200–7 [Google Scholar]
  127. Steinger T, Goy G, Gilliand H, Hebeisen T, Derron J. 127.  2015. Forecasting virus disease in seed potatoes using flight activity data of aphid vectors. Ann. Appl. Biol. 166:3410–19Multiyear data set that links aphid activity from suction traps with prevalence of plant disease. [Google Scholar]
  128. Steinmann KP, Zhang M, Grant JA. 128.  2011. Does use of pesticides known to harm natural enemies of spider mites (Acari: Tetranychidae) result in increased number of miticide applications? An examination of California walnut orchards. J. Econ. Entomol. 104:51496–501 [Google Scholar]
  129. Storkey J, Bohan DA, Haughton AJ, Champion GT, Perry JN. 129.  et al. 2008. Providing the evidence base for environmental risk assessments of novel farm management practices. Environ. Sci. Policy 11:7579–87 [Google Scholar]
  130. Suarez AV, Holway DA, Ward PS. 130.  2005. The role of opportunity in the unintentional introduction of nonnative ants. PNAS 102:4717032–35 [Google Scholar]
  131. Tamaddoni-Nezhad A, Milani GA, Raybould A, Muggleton S, Bohan DA. 131.  2013. Construction and validation of food webs using logic-based machine learning and text mining. Adv. Ecol. Res. 49:225–89Innovative application of machine learning to automate the construction of hypothesized food web linkages. [Google Scholar]
  132. Teacher AGF, Griffiths DJ, Hodgson DJ, Inger R. 132.  2013. Smartphones in ecology and evolution: a guide for the app-rehensive. Ecol. Evol. 3:165268–78 [Google Scholar]
  133. Tobin PC, Kean JM, Suckling DM, McCullough DG, Herms DA, Stringer LD. 133.  2014. Determinants of successful arthropod eradication programs. Biol. Invasions 16:2401–14 [Google Scholar]
  134. Vandenbroucke JP. 134.  2004. When are observational studies as credible as randomised trials?. Lancet 363:94221728–31 [Google Scholar]
  135. van der Voet H, Goedhart PW. 135.  2015. The power of statistical tests using field trial count data of nontarget organisms in environmental risk assessment of genetically modified plants: power analysis for field trial count data. Agric. For. Entomol. 17:2164–72 [Google Scholar]
  136. vanEngelsdorp D, Hayes JH Jr.. 136.  Underwood RM, Pettis J. 2008. A survey of honey bee colony losses in the U.S., fall 2007 to spring 2008. PLOS ONE 3:12e4071 [Google Scholar]
  137. Waloff ZV. 137.  1940. The distribution and migrations of Locusta in Europe. Bull. Entomol. Res. 31:3211–46 [Google Scholar]
  138. Wang L, Hui C, Sandhu HS, Li Z, Zhao Z. 138.  2015. Population dynamics and associated factors of cereal aphids and armyworms under global change. Sci. Rep. 5:18801 [Google Scholar]
  139. Wang Z-J, Lin H, Huang J-K, Hu R-F, Rozelle S, Pray C. 139.  2009. Bt cotton in China: Are secondary insect infestations offsetting the benefits in farmer fields?. Agric. Sci. China 8:183–90 [Google Scholar]
  140. Wan P, Huang Y, Tabashnik BE, Huang M, Wu K. 140.  2012. The halo effect: suppression of pink bollworm on non-Bt cotton by Bt cotton in China. PLOS ONE 7:7e42004 [Google Scholar]
  141. Welch JR, Vincent JR, Auffhammer M, Moya PF, Dobermann A, Dawe D. 141.  2010. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. PNAS 107:3314562–67 [Google Scholar]
  142. Widenfalk LA, Ahrné K, Berggren Å. 142.  2014. Using citizen-reported data to predict distributions of two non-native insect species in Sweden. Ecosphere 5:12156 [Google Scholar]
  143. Williams DW, Liebhold AM. 143.  1995. Herbivorous insects and global change: potential changes in the spatial distribution of forest defoliator outbreaks. J. Biogeogr. 22:4/5665–71 [Google Scholar]
  144. Williams DW, Liebhold AM. 144.  2000. Spatial synchrony of spruce budworm outbreaks in eastern North America. Ecology 81:102753–66 [Google Scholar]
  145. Wu K-M, Lu Y-H, Feng H-Q, Jiang Y-Y, Zhao J-Z. 145.  2008. Suppression of cotton bollworm in multiple crops in China in areas with Bt toxin–containing cotton. Science 321:58961676–78 [Google Scholar]
  146. Yang LH, Karban R. 146.  2009. Long‐term habitat selection and chronic root herbivory: explaining the relationship between periodical cicada density and tree growth. Am. Nat. 173:1105–12 [Google Scholar]
  147. Yang W-R, Grieneisen M, Chen H, Zhang M. 147.  2015. Reduction of crop diversity does not drive insecticide use. J. Agric. Sci. 7:101 [Google Scholar]
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Ecoinformatics (Big Data) for Agricultural Entomology: Pitfalls, Progress, and Promise
Annual Review of Entomology 62, 399 (2017); https://doi.org/10.1146/annurev-ento-031616-035444
/content/journals/10.1146/annurev-ento-031616-035444
/content/journals/10.1146/annurev-ento-031616-035444
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