Ecological forecasting

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 for 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 affecting human and natural ecosystems.[4]

One major challenge is to predict where, when and with what magnitude changes 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]


  • Palaeobiology modeling: uses fossil and phylogenetic evidence of biodiversity in the past to project the trajectory of biodiversity in the future. Simple plots can be constructed and then adjusted based on the varying quality of the fossil record.[8]
  • Climate envelope modeling: relies on statistical correlations between existing species distributions and environmental variables to define a species' tolerance.[9] 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.[10][11] 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.[9]

Forecasting examples


Using fossil evidence, studies have shown that vertebrate biodiversity has grown exponentially through Earth's history and that biodiversity is entwined with the diversity of Earth's habitats.

"Animals have not yet invaded 2/3 of Earth's habitats, and it could be that without human influence biodiversity will continue to increase in an exponential fashion."

Sahney et al.[8]


External image
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


  1. 1 2 Clark et al. 2001
  2. "Joint science academies' statement: The science of climate change". Royal Society. 2001-05-17. Archived from the original on October 1, 2007. 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. PMID 17943093. Retrieved 2007-11-06.
  4. CCSP 2008
  5. 1 2 Kearney 2006
  6. Gilman et al. 2006
  7. Wethey and Woodin 2008
  8. 1 2 Sahney, S.; Benton, M.J. & Ferry, P.A. (2010). "Links between global taxonomic diversity, ecological diversity and the expansion of vertebrates on land" (PDF). Biology Letters. 6 (4): 544–547. doi:10.1098/rsbl.2009.1024. PMC 2936204. PMID 20106856.
  9. 1 2 Pearson and Dawson 2003
  10. Kearney et al. 2008
  11. Helmuth et al. 2006


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