Skip to main content
Log in

Mapping the Information Landscape: Discerning Peaks and Valleys for Ecological Monitoring

  • Research Paper
  • Published:
Journal of Biological Physics Aims and scope Submit manuscript

Abstract

We investigate previously unreported phenomena that have a potentially significant impact on the design of surveillance monitoring programs for ecological systems. Ecological monitoring practitioners have long recognized that different species are differentially informative of a system’s dynamics, as codified in the well-known concepts of indicator or keystone species. Using a novel combination of analysis techniques from nonlinear dynamics, we describe marked variation among spatial sites in information content with respect to system dynamics in the entire region. We first observed these phenomena in a spatially extended predator–prey model, but we observed strikingly similar features in verified water-level data from a NOAA/NOS Great Lakes monitoring program. We suggest that these features may be widespread and the design of surveillance monitoring programs should reflect knowledge of their existence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yoccoz, N., Nichols, J., Boulinier, T.: Monitoring of biological diversity in space and time. Trends Ecol. Evol. 16, 446–453 (2001)

    Article  Google Scholar 

  2. Thompson, S.: Sampling. Wiley, New York (2002)

    MATH  Google Scholar 

  3. Nichols, J., Moniz, L., Nichols, J., Pecora, L., Cooch, E.: Assessing spatial coupling in complex population dynamics using mutual prediction and continuity statistics. Theor. Popul. Biol. 67, 9–21 (2005)

    Article  MATH  Google Scholar 

  4. Jonzen, N., Rhodes, J.B., Possingham, H.P.: Trend detection in source-sink systems: when should sink habitats be monitored? Ecol. Appl. 15(1), 326–334 (2005)

    Article  Google Scholar 

  5. Schreiber, T.: Measuring information transfer. Phys. Rev. Lett. 85, 462–464 (2000)

    Article  ADS  Google Scholar 

  6. Fisher, R.: Theory of statistical estimation. Trans. Camb. Philos. Soc. 22, 700–725 (1925)

    Article  MATH  Google Scholar 

  7. Kullback, S.: Information Theory and Statistics. Dover, Mineola, New York (1997)

    MATH  Google Scholar 

  8. Hartley, R.: Transmission of information. Bell Syst. Tech. J. 7, 535–563 (1928)

    Google Scholar 

  9. Shannon, C., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Chicago, IL (1949)

    MATH  Google Scholar 

  10. Fraser, A., Swinney, H.: Independent coordinates for strange attractors from mutual information. Phys. Rev., A. 33, 1134–1140 (1986)

    Article  ADS  MathSciNet  Google Scholar 

  11. Pecora, L., Carroll, T., Heagy, J.: Statistics for mathematical properties of maps between time series embeddings. Phys. Rev., E 52(4), 3420–3439 (1995)

    Article  ADS  Google Scholar 

  12. Silverman, B.: Density estimation for Statistics and Data Analysis, Monographs on Statistics and Applied Probability. Chapman and Hall, London. Available online at http://www.doc.ic.ac.uk/~xh1/Reference/density-estimation/density-extimation-for-statistics-and-data-analysis.pdf (1986)

  13. Kaiser, A., Schreiber, T.: Information transfer in continuous processes. Physica, D 166, 43–62 (2002)

    Article  MATH  ADS  MathSciNet  Google Scholar 

  14. Marschinski, R., Kantz, H.: Analysing the information flow between financial time series: an improved estimator for transfer entropy. Eur. Phys. J., B 30, 275–281 (2002)

    Article  ADS  MathSciNet  Google Scholar 

  15. Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: Engenhoffer, M.J., Herring J.R. (eds.) Proceedings of the 4th Symposium on Large Spatial Databases, vol. 951 of Lecture Notes in Computer Science, pp. 83–95. Springer, Berlin (1995)

    Google Scholar 

  16. Hunt, B., Ott, E., Yorke, J.A.: Generalized differentiable synchronization of chaos. Phys. Rev., E. 44, 4029–4034 (1997)

    Article  ADS  MathSciNet  Google Scholar 

  17. Pascual, M.: Diffusion-induced chaos in a spatial predator–prey system. Proc. R. Soc. B 25, 1–7 (1993)

    Article  ADS  Google Scholar 

  18. NOAA/NOS: Verified Lake Level Data for the Great Lakes. http://www.glerl.noaa.gov/data/now/wlevels/levels.html (2005)

  19. Bendat, J., Piersol, A.: Random Data: Analysis and Measurement Procedures. Wiley, New York (1986)

    MATH  Google Scholar 

  20. Pecora, L., Moniz, L., Nichols, J., Carroll, T.: A unified approach to attractor reconstruction. Chaos 17, 013110 (2007)

    Article  ADS  MathSciNet  Google Scholar 

  21. Sauer, T., Yorke, J.A., Casdagli, M.: Embedology. J. Stat. Phys. 65(3/4), 579–616 (1991)

    Article  MATH  ADS  MathSciNet  Google Scholar 

  22. Noon, B.: Conceptual issues in monitoring ecological resources. In: Busch, D., Trexler, J. (eds.) Monitoring Ecosystems, pp. 27–71. Island Press, Washington, DC (2003)

    Google Scholar 

  23. Bjornstad, O., Ims, R., Lambin, X.: Spatial population dynamics: analyzing patterms and processes of population synchrony. Trends Ecol. 14, 427–432 (1999)

    Article  Google Scholar 

  24. Koenig, W.: Spatial autocorrelation of ecological phenomena. Trends Ecol. 14, 427–432 (1999)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to L. J. Moniz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Moniz, L.J., Nichols, J.D. & Nichols, J.M. Mapping the Information Landscape: Discerning Peaks and Valleys for Ecological Monitoring. J Biol Phys 33, 171–181 (2007). https://doi.org/10.1007/s10867-007-9047-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10867-007-9047-y

Keywords

Navigation