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Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecosystems will change in the future in response to environmental factors such as climate change. The ultimate goal of the approach is to provide people such as resource managers and designers of marine reserves with information that they can then use to respond, in advance, to future changes,[1] a form of adaptation to global warming.

One of the most important environmental factors impacting organisms today is global warming. Most physiological processes are affected by temperature, and so even small changes in weather and climate can lead to large changes in the growth, reproduction and survival of animals and plants. The scientific consensus[2][3] is that the increase in atmospheric greenhouse gases due to human activity caused most of the warming observed since the start of the industrial era. These changes are in turn impacting both humans and natural ecosystems.[4]

One major challenge is to predict where, when and with what magnitude impacts are likely to occur so that we can mitigate or at least prepare for them.[1] Ecological forecasting applies existing knowledge of how animals and plants interact with their physical environment[5] to ask how changes in environmental factors might result in changes to the ecosystems as a whole.[6][7]

Contents

Approaches

  • Climate envelope modeling: relies on statistical correlations between existing species distributions and environmental variables to define a species' tolerance.[8] Envelopes of tolerance are then drawn around existing ranges. By predicting future levels of factors such as temperature, rainfall, and salinity, new range boundaries are then predicted. These methods are good for examining large numbers of species, but are likely not a good means of predicting effects at fine scales.
  • Niche level modeling: is a newer method which links physiological information about a species to models of animal and plant body temperature.[9] [10] In contrast to “climate envelope” approaches, environmental variables are predicted at the level of the niche and are therefore much more exact.[5] However, the approach is also usually more time consuming.[8]

Forecasting example

External images
Intertidal temperature forecasting
University of South Carolina

Forecasts of temperature, shown in the diagram at the right as colored dots, along the North Island of New Zealand in the austral summer of 2007. As per the temperature scale shown at the bottom, intertidal temperatures were forecast to exceed 30°C at some locations on February 19; surveys later showed that these sites corresponded to large die-offs in burrowing sea urchins.

See also

Notes

  1. ^ a b Clark et al. 2001
  2. ^ "Joint science academies' statement: The science of climate change" (ASP). Royal Society. 2001-05-17. http://www.royalsoc.ac.uk/displaypagedoc.asp?id=13619. Retrieved 2007-04-01. "The work of the Intergovernmental Panel on Climate Change (IPCC) represents the consensus of the international scientific community on climate change science" 
  3. ^ "Rising to the climate challenge". Nature 449 (7164): 755. 2007-10-18. doi:10.1038/449755a. http://www.nature.com/nature/journal/v449/n7164/full/449755a.html. Retrieved 2007-11-06. 
  4. ^ CCSP 2008
  5. ^ a b Kearney 2006
  6. ^ Gilman et al. 2006
  7. ^ Wethey and Woodin 2008
  8. ^ a b Pearson and Dawson 2003
  9. ^ Kearney et al. 2008
  10. ^ Helmuth et al. 2006

References

  • CCSP, 2008. The effects of climate change on agriculture, land resources, water resources, and biodiversity. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research., P. Backlund, A. Janetos, D. Schimel, J. Hatfield, K. Boote, P. Fay, L. Hahn, C. Izaurralde, B.A. Kimball, T. Mader, J. Morgan, D. Ort, W. Polley, A. Thomson, D. Wolfe, M. Ryan, S. Archer, R. Birdsey, C. Dahm, L. Heath, J. Hicke, D. Hollinger, T. Huxman, G. Okin, R. Oren, J. Randerson, W. Schlesinger, D. Lettenmaier, D. Major, L. Poff, S. Running, L. Hansen, D. Inouye, B.P. Kelly, L Meyerson, B. Peterson, R. Shaw. U.S. Environmental Protection Agency, Washington, D.C. 362 pp. Available online at [1]
  • Clark, J.S. et al. 2001. Ecological forecasts: an emerging imperative. Science 293: 657-660.
  • Gilman, S.E., D.S. Wethey and B. Helmuth 2006. Variation in the sensitivity of organismal body temperature to climate change over local and geographic scales. Proceedings of the National Academy of Sciences, 103 (25): 9560-9565.
  • Helmuth, B., N. Mieszkowska, P. Moore and S.J. Hawkins. 2006. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annual Review of Ecolology Evolution and Systematics 37: 373-404. [2]
  • Kearney, M. 2006. Habitat, environment and niche: what are we modelling? Oikos 115: 186-191.
  • Kearney, M, B.L. Phillips, C.R. Tracy, K.A. Christian, G. Betts, and W.P. Porter, 2008. Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates. Ecography 31 (4): 423-434.
  • Oreskes, N., 2004. The scientific consensus on climate change. Science 306, 1686.
  • Pearson, R. G. and T. P. Dawson, 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography 12, 361-371.
  • Wethey, D.S,. and S.A. Woodin. 2008. Ecological hindcasting of biogeographic responses to climate change in the European intertidal zone. Hydrobiologia 606:139-151. [3]

External links

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