Landscape Ecology

, Volume 24, Issue 5, pp 657–672 | Cite as

Connecting phenological predictions with population growth rates for mountain pine beetle, an outbreak insect

Research Article


It is expected that a significant impact of global warming will be disruption of phenology as environmental cues become disassociated from their selective impacts. However there are few, if any, models directly connecting phenology with population growth rates. In this paper we discuss connecting a distributional model describing mountain pine beetle phenology with a model of population success measured using annual growth rates derived from aerially detected counts of infested trees. This model bridges the gap between phenology predictions and population viability/growth rates for mountain pine beetle. The model is parameterized and compared with 8 years of data from a recent outbreak in central Idaho, and is driven using measured tree phloem temperatures from north and south bole aspects and cumulative forest area impacted. A model driven by observed south-side phloem temperatures and that includes a correction for forest area previously infested and killed is most predictive and generates realistic parameter values of mountain pine beetle fecundity and population growth. Given that observed phloem temperatures are not always available, we explore a variety of methods for using daily maximum and minimum ambient temperatures in model predictions.


Mountain pine beetle Dendroctonus ponderosae Growth rate prediction Phenology Temperature change Insect outbreak 


  1. Akaike H (1978) On the likelihood of a time series model. Statistician 27:217–235. doi:10.2307/2988185 CrossRefGoogle Scholar
  2. Alfaro R, Campbell R, Vera P, Hawkes B, Shore T (2004) Dendroecological reconstruction of mountain pine beetle outbreaks in the Chilcotin Plateau of British Columbia. In Shore TL, Brooks JE, Stone JE (eds) Mountain pine beetle symposium: challenges and solutions, Natural Resources Canada, Information Report BC-X-399, pp 245–256Google Scholar
  3. Amman GD, Cole WE (1983) Mountain pine beetle dynamics in lodgepole pine forests Part II: population dynamics. USDA For. Serv. Gen. Tech. Rpt. INT-145Google Scholar
  4. Anderson DR, Burnham KP, Thompson WL (2000) Null hypothesis testing: problems, prevalence and an alternative. J Wildl Manag 64:912–923. doi:10.2307/3803199 CrossRefGoogle Scholar
  5. Aukema BH, Carroll AL, Zhu J, Raffa KF, Sickely TA, Taylor SW (2006) Landscape level analysis of mountain pine beetle in British Columbia, Canada: spatiotemporal development and spatial synchrony within the present outbreak. Ecogeography 29:427–441. doi:10.1111/j.2006.0906-7590.04445.x CrossRefGoogle Scholar
  6. Aukema BH, Carroll AL, Zheng Y, Zhu J, Raffa KF, Moore RD, Stahl K, Taylor SW (2008) Movement of outbreak populations of mountain pine beetle: influences of spatiotemporal patterns and climate. Ecogeography 31:348–358. doi:10.1111/j.0906-7590.2007.05453.x CrossRefGoogle Scholar
  7. Bentz BJ (2006) Mountain pine beetle population sampling: inferences from Lindgren pheromone traps and tree emergence cages. Can J For Res 36(2):351–360. doi:10.1139/x05-241 CrossRefGoogle Scholar
  8. Bentz BJ, Mullins DE (1999) Ecology of mountain pine beetle (Coleoptera: Scolytidae) cold hardening in the Intermountain west. Environ Entomol 28(4):577–587Google Scholar
  9. Bentz BJ, Logan JA, Amman GD (1991) Temperature dependent development of the mountain pine beetle (Coleoptera: Scolytidae), and simulation of its phenology. Can Entomol 123:1083–1094Google Scholar
  10. Bentz BJ, Amman GD, Logan JA (1993) A critical assessment of risk classification systems for the mountain pine beetle. For Ecol Manag 61:349–366. doi:10.1016/0378-1127(93)90211-5 CrossRefGoogle Scholar
  11. Berryman AA, Stenseth NC, Wollkind DJ (1984) Metastability of forest ecosystems infested by bark beetles. Res Popul Ecol (Kyoto) 26(1):13–29. doi:10.1007/BF02515505 CrossRefGoogle Scholar
  12. Berryman AA, Raffa KF, Millstein JA, Stenseth NC (1989) Interaction dynamics of bark beetle aggregation and conifer defense rates. Oikos 56:256–263. doi:10.2307/3565345 CrossRefGoogle Scholar
  13. Borden JH (1974) Aggregation pheromones in the Scolytidae. In: Birch MC (ed) Pheromones. North-Holland Publishing Co., Amsterdam, pp 135–160Google Scholar
  14. Brody AK (1997) Effects of pollinators, herbivores, and seed predators on flowering phenology. Ecology 78:1624–1631Google Scholar
  15. Burnham KP, Anderson DR (2002) Model selection and multi-model inference: a practical information-theoretic approach, 2nd edn. Springer, New York 488 ppGoogle Scholar
  16. Calabrese JM, Fagan WF (2004) Lost in time, lonely and single: reproductive asynchrony and the Allee effect. Am Nat 164:25–37. doi:10.1086/421443 PubMedCrossRefGoogle Scholar
  17. Carroll A, Taylor S, Regniere J, Safranyik L (2004) Effects of climate change on range expansion by the mountain pine beetle in British Columbia. In TL Shore, JE Brooks, JE Stone (eds) Mountain Pine Beetle Symposium: Challenges and Solutions, Natural Resources Canada, Information Report BC-X-399, pp. 223–232Google Scholar
  18. Crookston NL, Stark RW, Adams DL (1977) Outbreaks of mountain pine beetle in northwestern lodgepole pine forests—1945 to 1975. Forest, Wildlife and Range Experiment Station Bulletin No. 22. University of Idaho, Moscow, 7 ppGoogle Scholar
  19. Danks HV (1987) Insect dormancy: an ecological perspective. Monograph Series No. 1. Biological Survey of Canada (Terrestrial Arthropods), OttowaGoogle Scholar
  20. Friedenberg NA, Powell JA, Ayres MP (2007) Synchrony’s double edge: transient dynamics and the Allee effect in stage structured populations. Ecol Lett 10:564–573. doi:10.1111/j.1461-0248.2007.01048.x PubMedCrossRefGoogle Scholar
  21. Gilbert E, Powell JA, Logan JA, Bentz BJ (2004) Comparison of three models predicting developmental milestones given environmental and individual variation. Bull Math Biol 66:1821–1850. doi:10.1016/j.bulm.2004.04.003 PubMedCrossRefGoogle Scholar
  22. Heavilin J, Powell J (2008) A novel method of fitting spatio-temporal models to data, with applications to dynamics of mountain pine beetles. Nat Resour Model 21:489–524Google Scholar
  23. Hicke JA, Logan JA, Powell J, Ojima DS (2006) Changing temperatures influence suitability for modeled mountain pine beetle outbreaks in the western United States. J Geophys Res 11:GO2019. doi:10.1029/2005JG000101 Google Scholar
  24. Hunter AF, Elkinton JS (2000) Effects of synchrony with host plant on populations of a spring-feeding lepidopteran. Ecology 81:1248–1261CrossRefGoogle Scholar
  25. IPCC (2007) Climate change 2007: the scientific basis. Contribution of working group 1 to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  26. Lieutier F (2002) Mechanisms of resistance in conifers and bark beetle attack strategies. In: Wagner MR, Clancy KM, Lieutier F (eds) Mechanisms and deployment of resistance in trees to insects. Kluwer Academic Publishers, Boston, pp 31–78CrossRefGoogle Scholar
  27. Logan JA, Bentz BJ (1999) Model analysis of mountain pine beetle (Coleoptera: Scolytidae) seasonality. Environ Entomol 28(6):924–934Google Scholar
  28. Logan JA, Powell JA (2001) Ghost forests, global warming, and the mountain pine beetle (Coleoptera: Scolytidae). Am Entomol 47(3):160–173Google Scholar
  29. Logan JA, White P, Bentz BJ, Powell JA (1998) Model analysis of spatial patterns in mountain pine beetle outbreaks. Theor Popul Biol 53:235–255. doi:10.1006/tpbi.1997.1350 Google Scholar
  30. Logan JA, Regniere J, Powell JA (2003) Assessing the impact of global warming on forest pest dynamics. Front Ecol Environ 1(3):130–137Google Scholar
  31. Logan JD, Wolesensky W, Joern A (2006) Temperature-dependent phenology and predation in arthropod systems. Ecol Model 196:471–482. doi:10.1016/j.ecolmodel.2006.02.034 CrossRefGoogle Scholar
  32. McGregor MD (1978) Status of mountain pine beetle Glacier National Park and Glacier View Ranger District, Flathead National Forest, MT, 1977. Forest insect and disease management report No. 78-6, MissoulaGoogle Scholar
  33. Nelson WA, Lewis MA (2008) Connecting host physiology to host resistance in the conifer-bark beetle system. Theor Ecol 1:163–177CrossRefGoogle Scholar
  34. Perkins DL, Swetnam TW (1996) A dendroecological assessment of whitebark pine in the Sawtooth-Salmon River region, Idaho. Can J For Res 26:2123–2133. doi:10.1139/x26-241 CrossRefGoogle Scholar
  35. Post E, Levin SA, Iwasa Y, Stenseth NC (2001) Reproductive asynchrony increases with environmental disturbance. Evol Int J Org Evol 55:830–834. doi:10.1554/0014-3820(2001)055[0830:RAIWED]2.0.CO;2 Google Scholar
  36. Powell JA, Logan JA (2005) Insect seasonality—circle map analysis of temperature-driven life cycles. Theor Popul Biol 67:161–179. doi:10.1016/j.tpb.2004.10.001 PubMedCrossRefGoogle Scholar
  37. Powell JA, Logan JA, Bentz BJ (1996) Local projections for a global model of mountain pine beetle attacks. J Theor Biol 179:243–260. doi:10.1006/jtbi.1996.0064 CrossRefGoogle Scholar
  38. Powell J, Jenkins J, Logan J, Bentz BJ (2000) Seasonal temperature alone can synchronize life cycles. Bull Math Biol 62:977–998. doi:10.1006/bulm.2000.0192 PubMedCrossRefGoogle Scholar
  39. Raffa KF, Berryman AA (1983) The role of host plant resistance in the colonization behavior and ecology of bark beetles (Coleoptera: Scolytidae). Ecol Monogr 53:27–49. doi:10.2307/1942586 CrossRefGoogle Scholar
  40. Raffa KF, Phillips TW, Salom SM (1993) Strategies and mechanisms of host colonization by bark beetles. In: Schowalter TD, Filip GM (eds) Beetle–pathogen interactions in conifer forests. Academic Press, NY, pp 103–120Google Scholar
  41. 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: dynamics of biome-wide bark beetle eruptions. Bioscience 58(6):501–518. doi:10.1641/B580607 CrossRefGoogle Scholar
  42. Rasmussen LA (1974) Flight and attack behavior of mountain pine beetles in lodgepole pine of northern Utah and southern Idaho. USDA For. Serv. Res. Note INT-180Google Scholar
  43. Régnière J, Bentz B (2007) Modeling cold tolerance in the mountain pine beetle, Dendroctonus ponderosae. J Insect Physiol 53:559–572. doi:10.1016/j.jinsphys.2007.02.007 PubMedCrossRefGoogle Scholar
  44. Reid RW (1962a) Biology of the mountain pine beetle, Dendroctonus monticolae Hopkins, in the east Kootenay region of British Columbia: I. Life cycle, brood development, and flight periods. Can Entomol 94:531–538Google Scholar
  45. Reid RW (1962b) Biology of the mountain pine beetle, Dendroctonus monticolae Hopkins, in the east Kootenay region of British Columbia: II. Behaviour in the host, fecundity, and internal changes in the female. Can Entomol 94:605–613Google Scholar
  46. Safranyik L, Silversides R, McMullen LH, Linton DA (1989) An empirical approach to modeling local dispersal of the mountain pine beetle (Dendroctonus ponderosae) in relation to sources of attraction, wind direction, and speed. J Appl Entomol 108:498–511CrossRefGoogle Scholar
  47. Safrayik L, Srimpton DM, Whitney HS (1975) An interpretation of the interaction between lodgepole pine, the mountain pine beetle and its associated blue stain fungi in western Canada. In Baumgartner DM (ed) Management of Lodgepole pine ecosystems Symp. Proc., Washington State Univ. Cooperative Extension Service, pp. 406–428Google Scholar
  48. Shore TL, Safranyik L (1992) Susceptibility and risk rating systems for the mountain pine beetle in lodgepole pine stands. Inft. Rep. BC-X336, Pacific and Yukon Region. Forestry Canada, Pacific Forestry Centre, Victoria, 12 ppGoogle Scholar
  49. Tauber MJ, Tauber CA, Masaki S (1986) Seasonal adaptations of insects. Oxford University Press, New YorkGoogle Scholar
  50. Taylor F (1981) Ecology and evolution of physiological time in insects. Am Nat 117:1–23. doi:10.1086/283683 CrossRefGoogle Scholar
  51. Xia JY, Rabbinge R, van der Werf W (2003) Multi stage functional responses in a Ladybeetle-Aphid system; scaling up from the laboratory to the field. Environ Entomol 32(1):151–162CrossRefGoogle Scholar
  52. Zaslavski VA (1988) Insect development: photoperiodic and temperature control. Springer, BerlinGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Mathematics and StatisticsUtah State UniversityLoganUSA
  2. 2.USDA Forest Service, Rocky Mountain Research StationLoganUSA

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