508
Views
4
CrossRef citations to date
0
Altmetric
Original Articles

An ArcGIS Tool for Modeling the Climate Envelope with Feed-Forward ANN

, , &

REFERENCES

  • ArcGIS. 2013. ANNDistribution: A tool for modeling the climate envelope with feed-forward artificial neural network. Available at www.arcgis.com/home/item.html?id=2c6a49d147b94503b28ff6342e84b4be ( accessed October 6, 2013).
  • Bede-Fazekas, Á., 2013. Negative impact of climate change on the distribution of some conifers. Hadtudomány 23(Suppl.):234–243.
  • Box, E. O., 1981. Macroclimate and plant forms: An introduction to predictive modelling in phytogeography. The Hague: Dr. W. Junk.
  • Carpenter, G. A., S. Gopal, S. Macomber, S. Martens, C. E. Woodcock, and J. Franklin. 1999. A neural network method for efficient vegetation mapping. Remote Sensing of the Environment 70(3):326–338.
  • Carrer, M., and C. Urbinati. 2006. Long‐term change in the sensitivity of tree‐ring growth to climate forcing in Larix decidua. New Phytologist 170(4):861–872.
  • Cohen, J., 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1):37–46.
  • Czúcz, B., 2010. Modelling the impact of climate change on natural habitats in Hungary (PhD Thesis, Corvinus University of Budapest, Budapest, Hungary).
  • Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, J. R. García Marquéz, B. Gruber, B. Lafourcade, P. J. Leitão, T. Münkemüller, C. McClean, P. E. Osborne, B. Reineking, B. Schröder, A. K. Skidmore, D. Zurell, and S. Lautenbach. 2013. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46.
  • Elith, J., and J. R. Leathwick. 2009. Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40(1):677–697.
  • Elith, J., J. R. Leathwick, and T. Hastie, 2008. A working guide to boosted regression trees. Journal of Animal Ecology 77(4):802–813.
  • Euforgen, 2009. Distribution map of Europaean larch (Larix decidua). Bioversity International, Rome, Italy. www.euforgen.org/distribution_maps.html ( accessed April 1, 2013).
  • Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135(2–3):147–186.
  • Harrison, S., E. I. Damschen, and J. B. Grace. 2010. Ecological contingency in the effects of climatic warming on forest herb communities. Proceedings of the National Academy of Sciences USA. 107(45):19362–19367.
  • Hewitt, C. D., and D. J. Griggs. 2004. Ensembles-based predictions of climate changes and their impacts. Eos 85(52):566.
  • Hijmans, R. J., and C. H. Graham. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12(12):2272–2281.
  • Hilbert, D. W., and B. Ostendorf. 2001. The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling 146(1–3):311–327.
  • Hilbert, D. W., and J. Van Den Muyzenberg. 1999. Using an artificial neural network to characterize the relative suitability of environments for forest types in a complex tropical vegetation mosaic. Diversity and Distributions 5(6):263–274.
  • Ibáñez, I., J. S. Clark, M. C. Dietze, K. Feeley, M. Hersh, S. Ladeau, A. Mcbride, N. E. Welch, and M. S. Wolosin. 2006. Predicting biodiversity change: outside the climate envelope, beyond the species-area curve. Ecology 87(8):1896–1906.
  • Lek, S., M. Delacoste, P. Baran, I. Dimopoulos, J. Lauga, and S. Aulagnier. 1996. Application of neural networks to modelling non linear relationships in ecology. Ecological Modelling 90(1):39–52.
  • Nadezda, M. T., E. R. Gerald, and I. P. Elena. 2006. Impacts of climate change on the distribution of Larix spp. and Pinus sylvestris and their climatypes in Siberia. Mitigation and Adaptation Strategies for Global Change 11(4):861–882.
  • Ogawa-Onishi, Y., P. M. Berry, and N. Tanaka. 2010. Assessing the potential impacts of climate change and their conservation implications in Japan: A case study of conifers. Biological Conservation 143(7):1728–1736.
  • Özesmi, S. L., and U. Özesmi. 1999. An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecological Modelling 116(1):15–31.
  • Özesmi, S. L., C. O. Tan, and U. Özesmi. 2006, Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecological Modelling 195(1–2):83–93.
  • Pearson, R. G., T. P. Dawson, P. M. Berry, and P. A. Harrison. 2002. SPECIES: A spatial evaluation of climate impact on the envelope of species. Ecological Modelling 154(3):289–300.
  • Pickett, S. T. A1989. Space-for-time substitution as an alternative to long-term studies. In Long-term studies in ecology: approaches and alternatives. ed. G. E. Likens, 110–135. New York, NY, USA: Springer.
  • Picton, P. D. 2000. Neural networks. Basingstoke, UK: Palgrave Macmillan.
  • Van Leeuwen, B. 2012. Artificial neural networks and geographic information systems for inland excess water classification (PhD Thesis, University of Szeged, Szeged, Hungary).
  • Van Leeuwen, B., G. Mezősi, Z. Tobak, J. Szatmári, and K. Barta. 2012. Identification of inland excess water floodings using an artificial neural network. Carpathian Journal of Earth and Environmental Sciences 7(4):173–180.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.