Climatic Change

, Volume 87, Supplement 1, pp 167–192

Climate change effects on poikilotherm tritrophic interactions

  • Andrew Paul Gutierrez
  • Luigi Ponti
  • Thibaud d’Oultremont
  • C. K. Ellis
Article

DOI: 10.1007/s10584-007-9379-4

Cite this article as:
Gutierrez, A.P., Ponti, L., d’Oultremont, T. et al. Climatic Change (2008) 87: 167. doi:10.1007/s10584-007-9379-4

Abstract

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.

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Andrew Paul Gutierrez
    • 1
    • 2
    • 3
  • 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