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Inferences About Coupling from Ecological Surveillance Monitoring: Approaches Based on Nonlinear Dynamics and Information Theory

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Towards an Information Theory of Complex Networks

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Abstract

Some monitoring programs for ecological resources are developed as components of larger science or management programs and are thus guided by a priori hypotheses. More commonly, ecological monitoring programs are initiated for the purpose of surveillance with no a priori hypotheses in mind. No conceptual framework currently exists to guide the development of surveillance monitoring programs, resulting in substantial debate about program design. We view surveillance monitoring programs as providing information about system dynamics and focus on methods for extracting such information from time series of monitoring data. We briefly describe methods from the general field of nonlinear dynamics that we believe may be useful in extracting information about system dynamics. In looking at the system as a network of locations or components, we emphasize methods for assessing coupling between system components for use in understanding system dynamics and interactions and in detecting changes in system dynamics. More specifically, these methods hold promise for such ecological problems as identifying indicator species, developing informative spatial monitoring designs, detecting ecosystem change and damage, and investigating such topics as population synchrony, species interactions, and environmental drivers. We believe that these ideas and methods provide a useful conceptual framework for surveillance monitoring and can be used with model systems to draw inferences about the design of surveillance monitoring programs. In addition, some of the current methods should be useful with some actual ecological monitoring data, and methodological extensions and modifications should increase the applicability of these approaches to additional sources of actual ecological data.

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Acknowledgments

We acknowledge the support of Paul Dresler and US Geological Survey inventory and monitoring program for research on these topics.

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Authors and Affiliations

  1. Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD, 20723, USA

    L. J. Moniz

  2. Patuxent Wildlife Research Center, U.S. Geological Survey, Laurel, MD, 20708, USA

    J. D. Nichols

  3. Optical Sciences Division, Naval Research Laboratory, Code 5673, Washington, DC, 20375, USA

    J. M. Nichols

  4. Department of Natural Resources, Cornell University, Ithaca, NY, 14853, USA

    E. G. Cooch

  5. Naval Research Laboratory, Code 6362, Washington, DC, 20375, USA

    L. M. Pecora

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  1. Medizinische Informatik und Technik, Institute for Bioinformatics and Transla, UMIT-Private Universität für Gesundheits, Eduard Wallnöfer-Zentrum 1, Hall in Tirol, A-6060, Austria

    Matthias Dehmer

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    Alexander Mehler

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Moniz, L.J., Nichols, J.D., Nichols, J.M., Cooch, E.G., Pecora, L.M. (2011). Inferences About Coupling from Ecological Surveillance Monitoring: Approaches Based on Nonlinear Dynamics and Information Theory. In: Dehmer, M., Emmert-Streib, F., Mehler, A. (eds) Towards an Information Theory of Complex Networks. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-0-8176-4904-3_8

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