Abstract
The article reviews main principles of running geo-computations in ecology, as illustrated with case studies from the EcoGRID and FlySafe projects, and emphasizes the advantages of using R computing environment as the most attractive programming/scripting environment. Three case studies (including R code) of interest to ecological applications are described: (a) analysis of GPS trajectory data for two gull-birds species; (b) species distribution mapping in space and time for a bird species (sedge warbler; EcoGRID project); and (c) change detection using time-series of maps. The case studies demonstrate that R, together with its numerous packages for spatial and geostatistical analysis, is a well-suited tool to produce quality outputs (maps, statistical models) of interest in Geo-Ecology. Moreover, due to the recent implementation of the maptools and sp packages, such outputs can be easily exported to popular geographical browsers such as Google Earth and similar. The key computational challenges for Computational Geo-Ecology recognized were: (1) solving the problem of input data quality (filtering techniques), (2) solving the problem of computing with large data sets, (3) improving the over-simplistic statistical models, and (4) producing outputs of increasingly higher level of detail.
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References
Shamoun, J.Z., Sierdsema, H., van Loon, E.E., van Gasteren, H., Bouten, W., Sluiter, F.: Linking Horizontal and Vertical Models to Predict 3D + time Distributions of Bird Densities. In: International Bird Strike Committee, Athens, p. 12 (2005)
Van Belle, J., Bouten, W., Shamoun-Baranes, J., van Loon, E.E.: An operational model predicting autumn bird migration intensities for flight safety. Journal of Applied Ecology 11, 864–874 (2007)
Bivand, R.: Implementing Spatial Data Analysis Software Tools in R. Geographical Analysis 38, 23–40 (2006)
Pebesma, E.J.: Multivariable geostatistics in S: the gstat package. Computers & Geosciences 30(7), 683–691 (2004)
Pebesma, E.J., Bivand, R.S.: Classes and methods for spatial data in R. R News 5(2), 9–13 (2005)
Bivand, R.S.: Interfacing GRASS 6 and R. Status and development directions. GRASS Newsletter 3, 11–16 (2005)
Baddeley, A., Turner, R.: Spatstat: an R package for analyzing spatial point patterns. Journal of Statistical Software 12(6), 1–42 (2005)
Calenge, C.: The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling 197(3–4), 516–519 (2006)
Waller, L.A., Gotway, C.A.: Applied Spatial Statistics for Public Health Data, p. 520. Wiley, Hobokone (2004)
Bivand, R., Pebesma, E., Rubio, V.: Applied Spatial Data Analysis with R. Use R Series, p. 400. Springer, Heidelberg (2008)
Hengl, T.: A Practical Guide to Geostatistical Mapping of Environmental Variables. In: EUR 22904 EN. Office for Official Publications of the European Communities, Luxembourg, p. 143 (2007)
Rossiter, D.G.: Introduction to the R Project for Statistical Computing for use at ITC. In: International Institute for Geo-information Science & Earth Observation (ITC), Enschede, Netherlands, p. 136 (2007)
Rowlingson, B., Diggle, P.: Splancs: spatial point pattern analysis code in S-Plus. Computers & Geosciences 19, 627–655 (1993)
Guth, P.L.: Slope and aspect calculations on gridded digital elevation models: Examples from a geomorphometric toolbox for personal computers. Zeitschrift für Geomorphologie 101, 31–52 (1995)
Scott, J.M., Heglund, P.J., Morrison, M.L.: Predicting Species Occurrences: Issues Of Accuracy And Scale. Habitat (Ecology), p. 840. Island Press, Washington, DC (2002)
Compieta, P., Di Martino, S., Bertolotto, M., Ferrucci, F., Kechadi, T.: Exploratory spatio-temporal data mining and visualization. Journal of Visual Languages and Computing 18(3), 255–279 (2007)
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Hengl, T., van Loon, E., Sierdsema, H., Bouten, W. (2008). Advancing Spatio-temporal Analysis of Ecological Data: Examples in R . In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2008. ICCSA 2008. Lecture Notes in Computer Science, vol 5072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69839-5_51
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DOI: https://doi.org/10.1007/978-3-540-69839-5_51
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