Climatic Change

, Volume 87, Supplement 1, pp 167–192 | Cite as

Climate change effects on poikilotherm tritrophic interactions

  • Andrew Paul GutierrezEmail author
  • Luigi Ponti
  • Thibaud d’Oultremont
  • C. K. Ellis


Species of plants and animals have characteristic climatic requirements for growth, survival and reproduction that limit their geographic distribution, abundance and interactions with other species. To analyze this complexity requires the development of models that include not only the effects of biotic factors on species dynamics and interactions, but also the effects of abiotic factors including weather. The need for such capacity has appreciably increased as we face the threat of global climate change. In this paper, bi- and tri-trophic physiologically based demographic models of alfalfa, cotton, grape, olive and the noxious weed yellow starthistle systems are used to explore some of the potential effects of climate change. A general model that applies to all species in all trophic levels (including the economic one) is used to simulate the effects of observed and projected weather on system dynamics. Observed daily weather and that of climate model scenarios were used as forcing variables in our studies. Geographic information system (GRASS GIS) is used to map the predicted effects on species across the varied ecological zones of California. The predictions of the geographic distribution and abundance of the various species examined accords well with field observations. Furthermore, the models predict how the geographic range and abundance of the some species would be affected by climate change. Among the findings are: (1) The geographic range of tree species such as olive that require chilling to break dormancy (i.e. vernalization) may be limited in some areas due to climate warming, but their range may expand in others. For example, olive phenology and yield will be affected in the southern part of California due to high temperature, but may expand in northern areas until limited by low winter temperatures. Pest distribution and abundance will also be affected. For example, climate warming would allow the cold intolerant pink bollworm in cotton to expand its range into formerly inhospitable heavy frost areas of the San Joaquin Valley, and damage rates will increase throughout its current range. The distribution and abundance of other cold intolerant pests such as olive fly, the Mediterranean fruit fly and others could be similarly affected. In addition, species dominance and existence in food webs could change (e.g. in alfalfa), and the biological control of invasive species might be adversely affected (e.g. vine mealybug in grape). The distribution and abundance of invasive weeds such as yellow starthistle will be altered, and its control by extant and new biological control agents will be difficult to predict because climate change will differentially affects each. (2) Marginal analysis of multiple regression models of the simulation data provides a useful way of analyzing the efficacy of biological control agents. Models could be useful as guides in future biological control efforts on extant and new exotic pest species. (3) Major deficiencies in our capacity to predict the effects of climate change on biological interactions were identified: (1) There is need to improve existing models to better forecast the effects of climate change on crop system components; (2) The current system for collecting daily weather data consists of a patchwork of station of varying reliability that often record different variables and in different units. Especially vexing, is the dearth of solar radiation data at many locations. This was an unexpected finding as solar energy is an important driving variable in biological systems.


Natural Enemy Geophysical Fluid Dynamic Laboratory Pink Bollworm Parallel Climate Model Environ Entomol 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Andrewartha HG, Birch LC (1954) The distribution and abundance of animals. The University of Chicago Press, ChicagoGoogle Scholar
  2. Bartlett BR (1974) Introduction into California USA of cold tolerant biotypes of the mealybug predator Cryptolaemus montrouzieri and laboratory procedures for testing natural enemies for cold hardiness. Environ Entomol 3:553–556Google Scholar
  3. Carey JR (1996) The incipient Mediterranean fruit fly population in California: implications for invasion biology. Ecology 77:1690–1697CrossRefGoogle Scholar
  4. Clark JS, Carpenter SR, Barber M, Collins S, Dobson A, Foley JA, Lodge DM, Pascual M, Pielke R Jr, Pizer W, Pringle C, Reid WV, Rose KA, Sala O, Schlesinger WH, Wall DH, Wear D (2001) Ecological forecasts: an emerging imperative. Science 293:657–660CrossRefGoogle Scholar
  5. Coakley S, Scherm H, Chakraborty S (1999) Climate change and plant disease management. Annu Rev Phytopathol 37:399–426CrossRefGoogle Scholar
  6. Daane KM, Malakar-Kuenen R, Guillén M, Bentley WJ, Bianchi M, Gonzalez D (2003) Abiotic and biotic refuges hamper biological control of mealybug pests in California vineyards. In: van Driesch R (ed) Proceedings of the 1st International Symposium on Biological Control of Arthropods. USDA Forest Service, Morgantown, West Virginia, pp 389–398Google Scholar
  7. Dalla Marta A, Orlandini S, Sacchetti P, Belcari A (2004) Olea europaea: integration of GIS and simulation modelling to define a map of “dacic attack risk” in Tuscany. Adv Hort Sci 18:168–172Google Scholar
  8. Davis AJ, Jenkinson LS, Lawton JH, Shorrocks B, Wood S (1998) Making mistakes when predicting shifts in species range in response to global warming. Nature 391:783–786CrossRefGoogle Scholar
  9. DeBach P (1964) Biological control of insect pests and weeds. Chapman and Hall, LondonGoogle Scholar
  10. DeBach P, Sundby RA (1963) Competitive displacement between ecological homologues. Hilgardia 34:105–166Google Scholar
  11. De Melo-Abreu JP, Barranco D, Cordeiro AM, Tous J, Rogado BM, Villalobos FJ (2004) Modelling olive flowering date using chilling for dormancy release and thermal time. Agric For Meteorol 125:117–127CrossRefGoogle Scholar
  12. De Michele DW, Curry GL, Sharpe PJH, Barfield CS (1976) Cotton bud drying: a theoretical model. Environ Entomol 5:1011–1016Google Scholar
  13. Denney JO, McEachern GR, Griffiths JF (1985) Modeling the thermal adaptability of the olive (Olea europaea L.) in Texas. Agric For Meteorol 35:309CrossRefGoogle Scholar
  14. de Wit CT, Goudriaan J (1978) Simulation of ecological processes. PUDOC, The NetherlandsGoogle Scholar
  15. Di Cola G, Gilioli G, Baumgärtner J (1999) Mathematical models for age-structured population dynamics. In: Huffaker CB, Gutierrez AP (eds) Ecological entomology. Wiley, New YorkGoogle Scholar
  16. Fitzpatrick EA, Nix HA (1968) The climatic factor in Australian grasslands ecology. In: Moore RM (ed) Australian Grasslands. Australian National University PressGoogle Scholar
  17. Gilbert N, Gutierrez AP, Frazer BD, Jones RE (1976) Ecological relationships. Freeman, New YorkGoogle Scholar
  18. Gutierrez AP (1992) The physiological basis of ratio dependent theory. Ecology 73:1552–1563CrossRefGoogle Scholar
  19. Gutierrez AP (1996) Applied population ecology: a supply-demand approach. Wiley, New York, USAGoogle Scholar
  20. Gutierrez AP (2000) Climate change: effects on pest dynamics. In: Reddy KR, Hodges HF (eds) Climate change and global crop productivity. CABI, Wallingford, UKGoogle Scholar
  21. Gutierrez AP, Baumgärtner JU (1984) Multitrophic level models of predator-prey energetics: I. Age-specific energetics models - pea aphid Acyrthosiphon pisum (Homoptera: Aphididae) as an example. Can Entomol 116:924–932Google Scholar
  22. Gutierrez AP, Baumgärtner JU (2007) Modeling the dynamics of tritrophic population interactions. In: Kogan M, Jepson P (eds) Perspectives in ecological theory and integrated pest management. Cambridge University PressGoogle Scholar
  23. Gutierrez AP, Pizzamiglio MA (2007) A regional analysis of weather mediated competition between a parasitoid and a coccinellid predator of oleander scale. Neotrop Entomol 36:70–83CrossRefGoogle Scholar
  24. Gutierrez AP, Regev U (2005) The bioeconomics of tritrophic systems: applications to invasive species. Ecol Econ 52:383Google Scholar
  25. Gutierrez AP, Wang YH (1977) Applied population ecology: models for crop production and pest management. In: Norton GA, Holling CS (eds) Pest management. Proc. Ser.. Pergamon, OxfordGoogle Scholar
  26. Gutierrez AP, Yaninek JS (1983) Responses to weather of eight aphid species commonly found in pastures in southeastern Australia. Can Entomol 115:1359–1364CrossRefGoogle Scholar
  27. Gutierrez AP, Nix HA, Havenstein DE, Moore PA (1974) The ecology of Aphis craccivora Koch and Subterranean Clover Stunt Virus in south-east Australia. III. A regional perspective of the phenology and migration of the cowpea aphid. J Appl Ecol 11:21–35CrossRefGoogle Scholar
  28. Gutierrez AP, Falcon LA, Loew W, Leipzig PA, van-den Bosch R (1975) An analysis of cotton production in California: a model for acala cotton and the effects of defoliators on its yields. Environ Entomol 4:125–136Google Scholar
  29. Gutierrez AP, Butler GD Jr, Wang Y, Westphal D (1977) The interaction of pink bollworm (Lepidoptera: Gelichiidae), cotton, and weather: a detailed model. Can Entomol 109:1457–1468Google Scholar
  30. Gutierrez AP, Baumgärtner JU, Summers CG (1984) Multitrophic level models of predator–prey energetics: III. A case study of an alfalfa ecosystem. Can Entomol 116:950–963Google Scholar
  31. Gutierrez AP, Wermelinger B, Schulthess F, Baumgärtner JU, Herren HR, Ellis CK, Yaninek JS (1988) Analysis of biological control of cassava pests in Africa. I. Simulation of carbon, nitrogen and water dynamics in cassava. J Appl Ecol 25:901–920CrossRefGoogle Scholar
  32. Gutierrez AP, Mills NJ, Schreiber SJ, Ellis CK (1994) A physiologically based tritrophic perspective on bottom up-top down regulation of populations. Ecology 75:2227–2242CrossRefGoogle Scholar
  33. Gutierrez AP, Pitcairn MJ, Ellis CK, Carruthers N, Ghezelbash R (2005) Evaluating biological control of yellow starthistle (Centaurea solstitialis) in California: A GIS based supply-demand demographic model. Biol Control 34:115CrossRefGoogle Scholar
  34. Gutierrez AP, d’Oultremont T, Ellis CK, Ponti L (2006) Climatic limits of pink bollworm in Arizona and California: effects of climate warming. Acta Oecol 30:353–364CrossRefGoogle Scholar
  35. Gutierrez AP, Daane KM, Ponti L, Walton VM, Ellis CK (2007) Prospective evaluation of the biological control of vine mealybug: refuge effects and climate. J Appl Ecol (in press): DOI  10.1111/j.1365-2664.2007.01356.x
  36. Gutierrez AP, Ponti L, Cossu QA (submitted) Effects of climate warming on the distribution of olive and olive fly (Bactrocera oleae) (Gmelin): Arizona, California and Italy. J Appl EcolGoogle Scholar
  37. Hamilton JG, Dermody O, Aldea M, Zangerl AR, Rogers A, Berenbaum MR, Delucia EH (2005) Anthropogenic changes in tropospheric composition increase susceptibility of soybean to insect herbivory. Environ Entomol 34:479–455Google Scholar
  38. Hartmann HT, Opitz KW (1980) Olive production in California. Leaflet 2474 University of California Div. Agric. Sci.. Davis, USAGoogle Scholar
  39. Hayhoe K, Cayan D, Field C, Frumhoff P, Maurer E, Miller N, Moser S, Schneider S, Cahill K, Cleland E, Dale L, Drapek R, Hanemann RM, Kalkstein L, Lenihan J, Lunch C, Neilson R, Sheridan S, Verville J (2004) Emissions pathways, climate change, and impacts on California. Proc Natl Acad Sci USA 101:12422–12427CrossRefGoogle Scholar
  40. Huffaker CB (1971) Biological control. Plenum, New YorkGoogle Scholar
  41. Huffaker CB, Kennett CE (1966) Studies of the olive scale, Parlatoria oleae (Colvee) IV Biological control of Parlatoria oleae (Colvee) through the compensatory action of two introduced parasites. Hilgardia 37:283–334Google Scholar
  42. Hughes RD, Maywald GW (1990) Forecasting the favorableness of the Australian environment for the Russian wheat aphid, Diuraphis noxia (Homoptera: Aphididae), and its potential impact on Australian wheat yields. Bull Entomol Res 80:165–175Google Scholar
  43. Janssen JAM (1993) African armyworm outbreaks: why do they occur after drought. PhD Thesis, Wageningen Agricultural University, The NetherlandsGoogle Scholar
  44. Mancuso S, Pasquali G, Fiorino P (2002) Phenology modelling and forecasting in olive (Olea europaea L.) using artificial neural networks. Adv Hort Sci 16:155–164Google Scholar
  45. Maurer EP (2007) Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California, under two emissions scenarios. Clim Change 82:309–325CrossRefGoogle Scholar
  46. Maurer EP, Duffy PB (2005) Uncertainty in projections of stream flow changes due to climate change in California. Geophys Res Let 32:L03704 DOI  03710.01029/02004GL021462 CrossRefGoogle Scholar
  47. Messenger PS (1964) Use of life-tables in a bioclimatic study of an experimental aphid-braconid wasp host-parasite system. Ecology 45:119–131CrossRefGoogle Scholar
  48. Messenger PS (1968) Bioclimatic studies of the aphid parasite Praon exsoletum. 1. effects of temperature on the functional response of females to varying host densities. Can Entomol 100:728–741Google Scholar
  49. Messenger PS, Flitters NE (1954) Bioclimatic studies of three species of fruit flies in Hawaii. J Econ Entomol 47:756–765Google Scholar
  50. Nechols JR, Tauber MJ, Tauber CA, Masaki S (1999) Adaptations to hazardous seasonal conditions: dormancy, migration, and polyphenism. In: Huffaker CB, Gutierrez AP (eds) Ecological entomology. Wiley, New YorkGoogle Scholar
  51. Neuenschwander P, Hammond WNO, Gutierrez AP, Cudjoe AR, Adjakloe R, Baumgartner JU, Regev U (1989) Impact assessment of the biological control of the cassava mealybug, Phenacoccus manihoti Matile-Ferrero (Hemiptera: Pseudococcidae), by the introduced parasitoid Epidinocarsis lopezi (De Santis) (Hymenoptera: Encyrtidae). Bull Entomol Res 79:579–594Google Scholar
  52. Noyes JS, Hayat M (1994) Oriental mealybug parasitoids of the Anagyrini (Hymenoptera: Encyrtidae). CAB International, Natural History Museum, London. University Press, CambridgeGoogle Scholar
  53. Petrusewicz K, MacFayden A (1970) Productivity of terrestial animals: principles and methods. Blackwell, OxfordGoogle Scholar
  54. Pickering J, Gutierrez AP (1991) Differential impact of the pathogen Pandora neoaphidis (R.&H.) Humber (Zygomycetes: Entomophthorales) on the species composition of Acyrthosiphon aphids in alfalfa. Can Entomol 123:315–320Google Scholar
  55. Pimentel D, Lach L, Zuniga R, Morrison D (2000) Environmental and economic costs of nonindigenous species in the United States. BioScience 50:53–65CrossRefGoogle Scholar
  56. Quezada JR, DeBach P (1973) Bioecological and population studies of the cottony scale, Icerya purchasi Mask., and its natural enemies, Rodolia cardinalis Mul. and Cryptochaetum iceryae Wil., in southern California. Hilgardia 41:631–688Google Scholar
  57. Reddy KR, Hodges HF (eds) (2000) Climate change and global crop productivity. CABI International, Wallingford, UKGoogle Scholar
  58. Regev U, Gutierrez AP, Schreiber SJ, Zilberman D (1998) Biological and economic foundations of renewable resource exploitation. Ecol Econ 26:227–242CrossRefGoogle Scholar
  59. Ritchie JT (1972) Models for predicting evapotranspiration from a crop with incomplete cover. Water Resour Res 8:1204–1213CrossRefGoogle Scholar
  60. Rochat J, Gutierrez AP (2001) Weather-mediated regulation of olive scale by two parasitoids. J Anim Ecol 70:476–490CrossRefGoogle Scholar
  61. Roffey J, Popov G (1968) Environmental and behavioural processes in Desert locust outbreaks. Nature 219:446–450CrossRefGoogle Scholar
  62. Rogers DJ, Randolph SE (2000) The global spread of malaria in a future, warmer world. Science 289:1763–1766CrossRefGoogle Scholar
  63. Schreiber SJ, Gutierrez AP (1998) A supply/demand perspective of species invasions and coexistence: applications to biological control. Ecol Model 106:27–45CrossRefGoogle Scholar
  64. Shelford VE (1931) Some concepts of bioecology. Ecology 12:455–467CrossRefGoogle Scholar
  65. Sutherst RW (2004) Global change and human vulnerability to vector-borne diseases. Clin Microbiol Rev 17:136–173CrossRefGoogle Scholar
  66. Sutherst RW, Maywald GF, Bottomly W (1991) From CLIMEX to PESKY, a generic expert system for risk assessment. EPPO Bulletin 21:595–608CrossRefGoogle Scholar
  67. Terblanche JS, Klok CJ, Krafsur ES, Chown SL (2006) Phenotypic plasticity and geographic variation in thermal tolerance and water loss of the tsetse Glossina pallidipes (Diptera: Glossinidae): implications for distribution modelling. Am J Trop Med Hyg 74:786–794Google Scholar
  68. Vansickle J (1977) Attrition in distributed delay models. IEEE T Syst Man Cyb 7:635–638Google Scholar
  69. Venette RC, Naranjo SE, Hutchison WD (2000) Implications of larval mortality at low temperatures and high soil moistures for establishment of pink bollworm (Lepidoptera: Gelechiidae) in Southeastern United States cotton. Environ Entomol 29:1018–1026CrossRefGoogle Scholar
  70. von Liebig J (1840) Chemistry and its applications to agriculture and physiology. Taylor and Walton, LondonGoogle Scholar
  71. Walther GR (2002) Ecological responses to recent climate change. Nature 416:389–395CrossRefGoogle Scholar
  72. Watson RT (2002) The future of the intergovernmental panel on climate change. Climate Policy 2(4):269–271CrossRefGoogle Scholar
  73. Wermelinger B, Baumgärtner J, Gutierrez AP (1991) A demographic model of assimilation and allocation of carbon and nitrogen in grapevines. Ecol Model 53:1–26CrossRefGoogle Scholar
  74. White TCR (1984) The abundance of invertebrate herbivores in relation to the availability of nitrogen in stressed food plants. Oecologia 63:90–105CrossRefGoogle Scholar
  75. Williams DW, Liebhold AM (2002) Climate change and the outbreak ranges of two North American bark beetles. Agric Forest Entomol 4:87–99CrossRefGoogle Scholar
  76. Ziska LH (2003) Evaluation of growth response of six invasive species to past, present, and future atmospheric CO2. J Exp Bot 54:395–406CrossRefGoogle Scholar
  77. Ziska LH, Teasdale JR, Bunce JA (1999) Future atmospheric CO2 may increase tolerance to glyphosate. Weed Sci 47:608–615Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Andrew Paul Gutierrez
    • 1
    • 2
    • 3
    Email author
  • Luigi Ponti
    • 1
    • 2
  • Thibaud d’Oultremont
    • 1
    • 2
  • C. K. Ellis
    • 1
  1. 1.Division of Ecosystem Science, Department of Environmental Science, Policy and ManagementUniversity of CaliforniaBerkeleyUSA
  2. 2.Center for the Analysis of Sustainable Agricultural Systems (CASAS)KensingtonUSA
  3. 3.Department of Environmental Science, Policy and ManagementUniversity of CaliforniaBerkeleyUSA

Personalised recommendations