Ecological forecasting

Ecological forecasting uses knowledge of physics, ecology and physiology to predict how ecological populations, communities, or 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][4] 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.[5]

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[6] to ask how changes in environmental factors might result in changes to the ecosystems as a whole.[7][8]

One of the most complete sources on the topic is the book Ecological Forecasting written by Michael C. Dietze.[9]


Ecological forecasting varies in spatial and temporal extent, as well as in what is being forecast (presence, abundance, diversity, production, etc.).

  • Population models may be used to generate short-term abundance forecasts using knowledge of population dynamics and recent environmental conditions. These models are used especially in fisheries and disease forecasting.
  • Species distribution models (SDMs) may be used to forecast species distribution (presence or abundance) over longer ecological time scales using information about past and projected environmental conditions across the landscape.
    • Correlative SDMs, also known as climate envelope models, rely on statistical correlations between existing species distributions (range boundaries) and environmental variables to outline a range (envelope) of environmental conditions within which a species can exist.[10][11] New range boundaries can then be forecast using future levels of environmental factors such as temperature, rainfall, and salinity from climate model projections. These methods are good for examining large numbers of species, but are likely not a good means of predicting effects at fine scales.
    • Mechanistic SDMs use information about a species' physiological tolerances and constraints, as well as models of organismal body temperature and other biophysical properties, to define the range of environmental conditions within which a species can exist. These tolerances are mapped onto current and projected environmental conditions in the landscape to outline current and forecasted ranges for the species.[12][13] In contrast to "climate envelope" approaches, mechanistic SDMs model the fundamental niche directly, and are therefore much more exact.[6] However, the approach requires more information is also usually more time consuming.[10]
  • Other types of models may be used to forecast (or hindcast) biodiversity over evolutionary time scales. 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.[14]

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.[14]


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. Clark, James S.; et al. (2001-07-27). "Ecological Forecasts: An Emerging Imperative". Science. 293 (5530): 657–660. doi:10.1126/science.293.5530.657. ISSN 0036-8075. PMID 11474103.
  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.
  4. Oreskes, Naomi (2004-12-03). "The Scientific Consensus on Climate Change". Science. 306 (5702): 1686. doi:10.1126/science.1103618. ISSN 0036-8075. PMID 15576594.
  5. "Final Report, CCSP Synthesis and Assessment Product 4.3: The effects of climate change on agriculture, land resources, water resources, and biodiversity". 2008-12-01. Retrieved 2019-02-08.
  6. Kearney, M. (2006). "Habitat, environment and niche: what are we modelling?". Oikos. 115 (1): 186–191. doi:10.1111/j.2006.0030-1299.14908.x. ISSN 1600-0706.
  7. Gilman, Sarah E.; Wethey, David S.; Helmuth, Brian (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. doi:10.1073/pnas.0510992103. ISSN 0027-8424. PMC 1480446. PMID 16763050.
  8. Wethey, David S.; Woodin, Sarah A. (2008). Davenport, John; Burnell, Gavin M.; Cross, Tom; Emmerson, Mark; McAllen, Rob; Ramsay, Ruth; Rogan, Emer (eds.). "Ecological hindcasting of biogeographic responses to climate change in the European intertidal zone". Challenges to Marine Ecosystems. Developments in Hydrobiology. Springer Netherlands. 202: 139–151. doi:10.1007/978-1-4020-8808-7_13. ISBN 9781402088087.
  9. Dietze, M.C. (2017). Ecological Forecasting. Princeton University Press. ISBN 9780691160573.
  10. Pearson, Richard G.; Dawson, Terence P. (2003). "Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?". Global Ecology and Biogeography. 12 (5): 361–371. doi:10.1046/j.1466-822X.2003.00042.x. ISSN 1466-8238.
  11. Elith, Jane; Leathwick, John R. (2009). "Species Distribution Models: Ecological Explanation and Prediction Across Space and Time". Annual Review of Ecology, Evolution, and Systematics. 40 (1): 677–697. doi:10.1146/annurev.ecolsys.110308.120159. ISSN 1543-592X.
  12. Kearney, Michael; Phillips, Ben L.; Tracy, Christopher R.; Christian, Keith A.; Betts, Gregory; Porter, Warren P. (2008). "Modelling species distributions without using species distributions: the cane toad in Australia under current and future climates". Ecography. 31 (4): 423–434. doi:10.1111/j.0906-7590.2008.05457.x. ISSN 1600-0587.
  13. Helmuth, Brian; Mieszkowska, Nova; Moore, Pippa; Hawkins, Stephen J. (2006). "Living on the Edge of Two Changing Worlds: Forecasting the Responses of Rocky Intertidal Ecosystems to Climate Change". Annual Review of Ecology, Evolution, and Systematics. 37 (1): 373–404. doi:10.1146/annurev.ecolsys.37.091305.110149.
  14. Sahney, S.; Benton, M.J. & Ferry, P.A. (2010). "Links between global taxonomic diversity, ecological diversity and the expansion of vertebrates on land". Biology Letters. 6 (4): 544–547. doi:10.1098/rsbl.2009.1024. PMC 2936204. PMID 20106856.
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