Landscape Ecology

, Volume 32, Issue 3, pp 501–514 | Cite as

A multi-scale analysis of western spruce budworm outbreak dynamics

  • Cornelius SenfEmail author
  • Elizabeth M. Campbell
  • Dirk Pflugmacher
  • Michael A. Wulder
  • Patrick Hostert
Research Article



Forest insect outbreaks are influenced by ecological processes operating at multiple spatial scales, including host-insect interactions within stands and across landscapes that are modified by regional-scale variations in climate. These drivers of outbreak dynamics are not well understood for the western spruce budworm, a defoliator that is native to forests of western North America.


Our aim was to assess how processes across multiple spatial scales drive western spruce budworm outbreak dynamics. Our objective was to assess the relative importance and influence of a set of factors covering the stand, landscape, and regional scales for explaining spatiotemporal outbreak patterns in British Columbia, Canada.


We used generalized linear mixed effect models within a multi-model interference framework to relate annual budworm infestation mapped from Landsat time series (1996–2012) to sets of stand-, landscape-, and regional-scale factors derived from forest inventory data, GIS analyses, and climate models.


Outbreak patterns were explained well by our model (R 2 = 93%). The most important predictors of infestation probability were the proximity to infestations in the previous year, landscape-scale host abundance, and dry autumn conditions. While stand characteristics were overall less important predictors, we did find infestations were more likely amongst pure Douglas-fir stands with low site indices and high crown closure.


Our findings add to growing empirical evidence that insect outbreak dynamics are driven by multi-scaled processes. Forest management planning to mitigate the impacts of budworm outbreaks should thus consider landscape- and regional-scale factors in addition to stand-scale factors.


Disturbance Budworm (Choristoneura ssp.) Western spruce budworm (Choristoneura freemani Razowski = Choristoneura occidentalis Freeman) Defoliation Landsat British Columbia 



Cornelius Senf gratefully acknowledges financial support from the Elsa Neumann Scholarship of the Federal State of Berlin. The research presented here contributes to the Landsat Science Team (


  1. Alfaro RI, Sickle GAV, Thomson AJ, Wegwitz E (1982) Tree mortality and radial growth losses caused by the western spruce budworm in a Douglas-fir stand in British Columbia. Can J For Res 12:780–787CrossRefGoogle Scholar
  2. Alfaro RI, Taylor S, Brown RG, Clowater JS (2001) Susceptibility of northern British Columbia forests to spruce budworm defoliation. For Ecol Manag 145:181–190CrossRefGoogle Scholar
  3. Alfaro RI, Thomson AJ, Sickle GAV (1985) Quantification of Douglas-fir growth losses caused by western spruce budworm defoliation using stem analysis. Can J For Res 15:5–9CrossRefGoogle Scholar
  4. Anderson DP, Sturtevant BR (2011) Pattern analysis of eastern spruce budworm Choristoneura fumiferana dispersal. Ecography 34:488–497CrossRefGoogle Scholar
  5. Bartoń K (2009) MuMIn: multi-model inference. R-Package. Accessed 11 Nov 2016
  6. Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48CrossRefGoogle Scholar
  7. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MH, White JS (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecology and Evolution 24:127–135CrossRefPubMedGoogle Scholar
  8. Bouchard M, Auger I (2013) Influence of environmental factors and spatio-temporal covariates during the initial development of a spruce budworm outbreak. Landscape Ecol 29:111–126CrossRefGoogle Scholar
  9. Brookes MH, Campbell RW, Colbert JJ, Mitchell RG, Stark RW (1987) Western Spruce Budworm. USDA Forest Service Technical Bulletin No. 1694. Washington, DC. Accessed: 11 Nov 2016
  10. Brookes MH, Colbert JJ, Mitchell RG, Stark RW (1985) Managing Trees and Stands Susceptible to Western Spruce Budworm. USDA Forest Service Technical Bulletin No. 1695. Washington, DC. Accessed: 11 Nov 2016
  11. Burnham KP, Anderson DR, Huyvaert KP (2010) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav Ecol Sociobiol 65:23–35CrossRefGoogle Scholar
  12. Campbell EM, MacLean DA, Bergeron Y (2008) The Severity of Budworm-Caused Growth Reductions in Balsam Fir/Spruce Stands Varies with the Hardwood Content of Surrounding Forest Landscapes. For Sci 54:195–205Google Scholar
  13. Cappuccino N, Lavertu D, Bergeron Y, Régnière J (1998) Spruce budworm impact, abundance and parasitism rate in a patchy landscape. Oecologia 114:236–242CrossRefGoogle Scholar
  14. Carlson CE, Wulf NW (1989) Silvicultural strategies to reduce stand and forest susceptibility to the western spruce budworm. In: USDA Forest Service Agricultural handbook No. 676. Washington, DC. Accessed 11 Nov 2016
  15. Daly C, Gibson WP, Taylor GH, Johnson GL, Pasteris P (2002) A knowledge-based approach to the statistical mapping of climate. Clim Res 22:99–113CrossRefGoogle Scholar
  16. Dymond CC, Neilson ET, Stinson G, Porter K, MacLean DA, Gray DR, Campagna M, Kurz WA (2010) Future Spruce Budworm Outbreak May Create a Carbon Source in Eastern Canadian Forests. Ecosystems 13:917–931CrossRefGoogle Scholar
  17. Flower A, Gavin DG, Heyerdahl EK, Parsons RA, Cohn GM (2014) Drought-triggered western spruce budworm outbreaks in the interior Pacific Northwest: a multi-century dendrochronological record. For Ecol Manag 324:16–27CrossRefGoogle Scholar
  18. Foster JR, Townsend PA, Mladenoff DJ (2013) Spatial dynamics of a gypsy moth defoliation outbreak and dependence on habitat characteristics. Landscape Ecol 28:1307–1320CrossRefGoogle Scholar
  19. Gray DR, MacKinnon WE (2006) Outbreak patterns of the spruce budworm and their impacts in Canada. For Chronicle 82:550–561CrossRefGoogle Scholar
  20. Grueber CE, Nakagawa S, Laws RJ, Jamieson IG (2011) Multimodel inference in ecology and evolution: challenges and solutions. J Evol Biol 24:699–711CrossRefPubMedGoogle Scholar
  21. Hadley KS, Veblen TT (1993) Stand response to western spruce budworm and Douglas-fir bark beetle outbreaks, Colorado Front Range. Can J For Res 23:479–491CrossRefGoogle Scholar
  22. Heppner D, Turner J (2006) Spruce weevil and western spruce budworm forest health stand establishment decision aids. J Ecosyst Manag 7:45–49Google Scholar
  23. Hicke JA, Allen CD, Desai AR, Dietze MC, Hall RJ, Ted Hogg EH, Kashian DM, Moore D, Raffa KF, Sturrock RN, Vogelmann J (2012) Effects of biotic disturbances on forest carbon cycling in the United States and Canada. Glob Change Biol 18:7–34CrossRefGoogle Scholar
  24. Hope GD, Mitchell WR, Lloyd DA, Erickson WR, Harper WL, Wikeen BM (1991) Interior Douglas-fir Zone. In: Meidinger D, Pojar J (eds) Ecosystems of British Columbia. British Columbia Ministry of Forests. Victoria, British Columbia, Canada, pp 153–166Google Scholar
  25. Kennedy RE, Andréfouët S, Cohen WB, Gómez C, Griffiths P, Hais M, Healey SP, Helmer EH, Hostert P, Lyons MB, Meigs GW, Pflugmacher D, Phinn SR, Powell SL, Scarth P, Sen S, Schroeder TA, Schneider A, Sonnenschein R, Vogelmann JE, Wulder MA, Zhu Z (2014) Bringing an ecological view of change to Landsat-based remote sensing. Front Ecol Environ 12:339–346CrossRefGoogle Scholar
  26. King G, Zeng L (2001) Logistic regression in rare events data. Polit Anal 9:137–163CrossRefGoogle Scholar
  27. Kurz WA, Stinson G, Rampley GJ, Dymond CC, Neilson ET (2008) Risk of natural disturbances makes future contribution of Canada’s forests to the global carbon cycle highly uncertain. Proc Natl Acad Sci USA 105:1551–1555CrossRefPubMedPubMedCentralGoogle Scholar
  28. Leckie DG, Gillis MD (1995) Forest inventory in Canada with emphasis on map production. For Chronicle 71:74–88CrossRefGoogle Scholar
  29. Link WA, Barker RJ (2006) Model weights and the foundations of multimodel inference. Ecology 87:2626–2635CrossRefPubMedGoogle Scholar
  30. Long JN (2009) Emulating natural disturbance regimes as a basis for forest management: a North American view. For Ecol Manag 257:1868–1873CrossRefGoogle Scholar
  31. Maclauchlan LE, Brooks JE, Hodge JC (2006) Analysis of historic western spruce budworm defoliation in south central British Columbia. For Ecol Manag 226:351–356CrossRefGoogle Scholar
  32. MacLean DA (1980) Vulnerability of fir/spruce stands during uncontrolled spruce budworm outbreaks: a review and discussion. For Chronicle 56(213):221Google Scholar
  33. MacLean DA, Erdle TA, MacKinnon WE, Porter KB, Beaton KP, Cormier G, Morehouse S, Budd M (2001) The Spruce Budworm Decision Support System: forest protection planning to sustain long-term wood supply. Can J For Res 31:1742–1757CrossRefGoogle Scholar
  34. Meigs GW, Kennedy RE, Gray AN, Gregory MJ (2015) Spatiotemporal dynamics of recent mountain pine beetle and western spruce budworm outbreaks across the Pacific Northwest Region, USA. For Ecol Manag 339:71–86CrossRefGoogle Scholar
  35. Mildrexler D, Yang Z, Cohen WB, Bell DM (2016) A forest vulnerability index based on drought and high temperatures. Remote Sens Environ 173:314–325CrossRefGoogle Scholar
  36. Millar CI, Stephenson NL, Stephens SL (2007) Climate change and forests of the future: managing in the face of uncertainty. Ecol Appl 17:2145–2151CrossRefPubMedGoogle Scholar
  37. Murdock TQ, Taylor SW, Flower A, Mehlenbacher A, Montenegro A, Zwiers FW, Alfaro R, Spittlehouse DL (2013) Pest outbreak distribution and forest management impacts in a changing climate in British Columbia. Environ Sci Policy 26:75–89CrossRefGoogle Scholar
  38. Nakagawa S, Schielzeth H, O’Hara RB (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4:133–142CrossRefGoogle Scholar
  39. Nealis V (2008) Spruce budworms, Choristoneura Lederer (Lepidoptera: Tortricidae). In: Capinera J (ed) Encyclopedia of entomology. Springer, New York, pp 3524–3531Google Scholar
  40. Nealis VG (2012) The phenological window for western spruce budworm: seasonal decline in resource quality. Agric For Entomol 14:340–347CrossRefGoogle Scholar
  41. Nealis VG, Noseworthy MK, Turnquist R, Waring VR (2009) Balancing risks of disturbance from mountain pine beetle and western spruce budworm. Can J For Res 39:839–848CrossRefGoogle Scholar
  42. Nealis VG, Régnière J (2009) Risk of dispersal in western spruce budworm. Agric For Entomol 11:213–223CrossRefGoogle Scholar
  43. Nealis VG, Regniere J (2014) An individual-based phenology model for western spruce budworm (Lepidoptera: Tortricidae). Can Entomol 146:306–320CrossRefGoogle Scholar
  44. R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Accessed 11 Nov 2016
  45. Radeloff VC, Mladenoff DJ, Boyce MS (2000) The changing relation of landscape patterns and jack pine budworm populations during an outbreak. Oikos 90:417–430CrossRefGoogle Scholar
  46. Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, Romme WH (2008) Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience 58:501CrossRefGoogle Scholar
  47. Robert L-E, Kneeshaw D, Sturtevant BR (2012) Effects of forest management legacies on spruce budworm (Choristoneura fumiferana) outbreaks. Can J For Res 42:463–475CrossRefGoogle Scholar
  48. Seidl R, Fernandes PM, Fonseca TF, Gillet F, Jönsson AM, Merganičová K, Netherer S, Arpaci A, Bontemps J-D, Bugmann H, González-Olabarria JR, Lasch P, Meredieu C, Moreira F, Schelhaas M-J, Mohren F (2011) Modelling natural disturbances in forest ecosystems: a review. Ecol Model 222:903–924CrossRefGoogle Scholar
  49. Seidl R, Muller J, Hothorn T, Bassler C, Heurich M, Kautz M (2015) Small beetle, large-scale drivers: how regional and landscape factors affect outbreaks of the European spruce bark beetle. J Appl Ecol 53:530–540CrossRefPubMedPubMedCentralGoogle Scholar
  50. Senf C, Pflugmacher D, Wulder MA, Hostert P (2015) Characterizing spectral–temporal patterns of defoliator and bark beetle disturbances using Landsat time series. Remote Sens Environ 170:166–177CrossRefGoogle Scholar
  51. Senf C, Wulder MA, Campbell EM, Hostert P (2016) Using Landsat to assess the relationship between spatiotemporal patterns of western. Can J Remote Sens. doi:  10.1080/07038992.2016.1220828. Accessed 11 Nov 2016
  52. Shepherd RF (1994) Management strategies for forest insect defoliators in British Columbia. For Ecol Manag 68:303–324CrossRefGoogle Scholar
  53. Simard M, Powell EN, Raffa KF, Turner MG (2012) What explains landscape patterns of tree mortality caused by bark beetle outbreaks in Greater Yellowstone? Glob Ecol Biogeogr 21:556–567CrossRefGoogle Scholar
  54. Sturtevant BR, Gustafson EJ, Li W, He HS (2004) Modeling biological disturbances in LANDIS: a module description and demonstration using spruce budworm. Ecol Model 180:153–174CrossRefGoogle Scholar
  55. Sturtevant B, Cooke B, Kneeshaw D, MacLean D (2015) Modeling insect disturbance across forested landscapes: insights from the spruce budworm. In: Perera AH, Sturtevant BR, Buse LJ (eds) Simulation modeling of forest landscape disturbances. Springer, New York, pp 93–134CrossRefGoogle Scholar
  56. Swetnam TW, Lynch AM (1993) Multicentury, regional-scale patterns of western spruce budworm outbreaks. Ecol Monogr 63:399–424CrossRefGoogle Scholar
  57. Townsend PA, Singh A, Foster JR, Rehberg NJ, Kingdon CC, Eshleman KN, Seagle SW (2012) A general Landsat model to predict canopy defoliation in broadleaf deciduous forests. Remote Sens Environ 119:255–265CrossRefGoogle Scholar
  58. Turner MG (2010) Disturbances and landscape dynamics in a changing world. Ecology 91:2833–2849CrossRefPubMedGoogle Scholar
  59. Turner MG, Gardner RH (2015) Landscape ecology in theory and practice. Springer, New YorkCrossRefGoogle Scholar
  60. Volney WJA, Fleming RA (2007) Spruce budworm (Choristoneura spp.) biotype reactions to forest and climate characteristics. Glob Chang Biol 13:1630–1643CrossRefGoogle Scholar
  61. Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) ClimateWNA—high-resolution spatial climate data for western North America. J Appl Meteorol Climatol 51:16–29CrossRefGoogle Scholar
  62. Wulder MA, Hall RJ, Coops NC, Franklin SE (2004) High spatial resolution remotely sensed data for ecosystem characterization. BioScience 54:511–521CrossRefGoogle Scholar
  63. Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006) Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manag 221:27–41CrossRefGoogle Scholar
  64. Wulder MA, White JC, Grills D, Nelson T, Coops NC, Ebata T (2009) Aerial overview survey of the mountain pine beetle epidemic in British Columbia: communication of impacts. J Ecosyst Manag 10:45–58Google Scholar
  65. Zeileis A (2006) Object-oriented computation of sandwich estimators. J Stat Softw 16:1–16CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Cornelius Senf
    • 1
    Email author
  • Elizabeth M. Campbell
    • 2
  • Dirk Pflugmacher
    • 1
  • Michael A. Wulder
    • 2
  • Patrick Hostert
    • 1
    • 3
  1. 1.Geography DepartmentHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Canadian Forest Service (Pacific Forestry Centre)Natural Resources CanadaVictoriaCanada
  3. 3.Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys)Humboldt-Universität zu BerlinBerlinGermany

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