- Publications
- Projects
- Tools/Techniques
- Software
Below is a list of publications from members of the laboratory. If you do not find a link to the paper and cannot access it at your library, please email us and we will send you a PDF copy.
2009 :: 2007 :: 2006 :: 2005 :: 2003 :: 2002 :: 2001
2009
Borrett, S.R., S.J. Whipple, B.C. Patten. in press. Rapid development of indirect effects in ecosystem networks. Oikos (preprint)
Kaufman, A., S.R. Borrett. in review. Ecosystem network analysis indicators are generally robust to parameter uncertainty in a phosphorus model of Lake Sidney Lanier, USA. Ecological Modelling.
Billman, D., W. Bridewell, S.R. Borrett. in revision. Model revision in science: Human-computer partnership viewed through Prometheus. Cognitive Science
Bridewell, W., S.R. Borrett, P. Langley. (2009) Innovative construction of explanatory scientific models. In: A.B. Markman and K.L. Wood (Eds.) Tools for Innovation. Oxford University Press, NY.
2008
2007
Borrett, S.R., W. Bridewell, P. Langley, K.R. Arrigo. A method for representing and developing process models. Ecological Complexity 4: 1–12. doi:10.1016/j.ecocom.2007.02.017
Bridewell, W., Borrett, S.R., & Todorovski, L. 2007. Extracting constraints for process modeling. Proceedings of the Fourth International Conference on Knowledge Capture (pp. 87-94). Whistler, BC. [pdf]
Schramski, J.R., D.K. Gattie, B.C. Patten, S.R. Borrett, B.D. Fath, S.J. Whipple. 2007. Indirect effects and distributed control in ecosystems: Distributed control in the environ networks of a seven-compartment model of nitrogen flow in the
Whipple, S.J., S.R. Borrett, B.C. Patten, D.K. Gattie, J.R. Schramski, S.A. Bata. 2007. Indirect effects and distributed control in ecosystems: Comparative network environ analysis of a seven-compartment model of nitrogen flow in the
Whipple, S.J., B.C. Patten, P.G. Verity, M.E. Frischer, J.D. Long, J.C. Nejstgaard, J.T. Anderson, A. Jacobsen, A. Larsen, J. Martinez-Martinez, and S.R. Borrett. 2007 Gaining integrated understanding of Phaeocystis spp. through model-driven laboratory and mesocosm studies. Biogeochemistry 83:293–309. doi:10.1007/s10533-007-9089-z
Borrett, S.R., B.D. Fath, B.C. Patten. 2007. Functional integration of ecological networks through pathway proliferation. Journal of Theoretical Biology 245: 98-111. doi:10.1016/j.jtbi.2006.09.024 arXiv
Borrett, S.R. and O.O. Osidele. 2007. Environ indicator sensitivity to flux uncertainty in a phosphorus model of
2006
Bridewell, W., Langley P., Racunas, S., and Borrett, S.R. 2006. Learning process models with missing data. Proceedings of the Seventeenth European Conference on Machine Learning (pp. 557-565).
Nejstgaard, J.C., M.E. Frischer, P.G. Verity, J.T. Anderson, A. Jacobsen, M.J. Zirbel, A. Larson, J. Martínez-Martínez, A.F. Sazhin, T. Walters, D.A. Bronk, S.J. Whipple, S.R. Borrett, B.C. Patten, and J.D. Long. 2006. Plankton development and trophic transfer in sea water enclosures with added nutrients and Phaeocystis pouchetii. Marine Ecology Progress Series 321:99-121.
Gattie, D.K., J.R. Schramski, S.R. Borrett, B.C. Patten, S.A. Bata, S.J. Whipple. 2006. Indirect effects and distributed control in ecosystems: Network environ analysis of a seven-compartment model of nitrogen flow in the
Borrett, S.R., S.J. Whipple, B.C. Patten, R.R. Christian. 2006. Indirect effects and distributed control in ecosystems: Temporal variation of indirect effects in a seven-compartment model of nitrogen flow in the
Schramski, J.R., D.K. Gattie, B.C. Patten, S.R. Borrett, B.D. Fath, C.R. Thomas, S.J. Whipple. 2006. Indirect effects and distributed control in ecosystems: Distributed control in the environ networks of a seven-compartment model of nitrogen flow in the
Fath, B.D. and S.R. Borrett. 2006. A MATLAB® function for network environ analysis. Environmental Modelling & Software 21:375-405. doi:10.1016/j.envsoft.2004.11.007 [pdf] The matlab software is available from here.
2005
Cowie, G.M. and S.R. Borrett. 2005.Institutional perspectives on participation and information in water management. Environmental Modeling & Software 20: 469-483. doi:10.1016/j.envsoft.2004.02.006 [pdf]
Borrett, S.R. 2005. Ecosystem Organization and Transformation: The Role of Network Architecture in the Development of Indirect Effects. Ph.D. Dissertation, University of Georgia, Athens, GA. [pdf]
2003
Borrett, S.R. and B.C. Patten. 2003. Structure of pathways in ecological networks: Relationship between length and number. Ecological Modelling 170: 173-184. doi:10.1016/S0304-3800(03)00224-2 [pdf]
2002
Beck, M.B., B.D. Fath, A. K. Parker, O.O.
Patten, B.C., B.D. Fath , J.S. Choi, S. Bastianoni, S.R. Borrett, S. Brandt-Williams, M. Debeljak, J. Fonseca, W.E. Grant, D. Karnawati, J.C. Marques, A. Moser, F. Müller, C. Pahl-Wostl, R. Seppelt, W.H. Seinborn, Y.M. Svirezhev. 2002. Complex adaptive hierarchical systems. In: R. Costanza and S.E. Jørgensen (Eds.). Understanding and Solving Environmental Problems in the 21st Century: Toward a New, Integrated Hard Problem Science. Elsevier Science, Ltd,
2001
Cowie, G.M., S.R. Borrett et al. 2001. Foresight for Lanier: A workshop. Summary of Results.
We have a number of projects on going in laboratory. They all center on our mission to understand ecosystem organization and transformation and involve developing quantitative informatic and analytical tools and techniques.
Dominance of Indirect Effects ValidationPatten and colleagues (Borrett and Osidele, 2007; Borrett et al., 2006; Fath, 2004; Higashi and Patten, 1986, 1989; Patten, 1983, 1991, in prep.) have argued that indirect effects are dominant components of interactions in ecosystems. This conclusion is based on their work with Network Environ Analysis (NEA), which is an environmental extension of economic Input–Output analysis (Leontief, 1966). While there are good theoretical reasons why this should be true and the hypothesis holds in ecosystem models built from community assembly rules, the hypotheses have not yet been tested across a range of empirically-based ecosysetm models. Thus, we are building a database of empirically-based ecosystem models, which we will then analyse with NEA to test the dominance of indirect effects hypothesis. |
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Computational Induction of Scientific Process ModelsWe have an ongoing collaboration with the Computational Learning Laboratory at Stanford University to develop and evaluate inductive process modeling. You can learn more about this project on the CLL web page here. Our work continues on three related subprojects: (1) Inductive process modeling to explain the phytoplankton dynamics in the Ross Sea, Antarctica; (2) Process based sensitivity analysis of simulation models; (3) Value of data type, quantity, and quality in constraining process model discovery; and (4) Model discrimination.
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Eigen Analysis of Ecological NetworksMatrix algebra and graph theory lie at the heart of most network analyses of complex systems. Many of the ecosystem properties that have been described in flow networks appear to be related to the eigenvalues and eigenvectors of the underlying matrices. We are exploring previous applications of eigen analysis to network models of complex systems and interpreting the elements for ecosystems. For example, Borrett, Fath and Patten 2007 showed that, given certain conditions, the dominant eigenvalue of the adjacency matrix is the rate of pathway proliferation in the extended pathway network.
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We use a variety of software, tools, and techniques for computational ecology and ecoinformatics. We briefly describe many of these below and provide links to the software and tutorials we have found useful.
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Apple Macintosh Computers: Our laboratory is largely built on the Macintosh operating system. This provides us with a fairly user friendly environment as well as many UNIX tools. Switching to the Mac, Shortcuts, Terminal, Mac OS X for Oceanographers and Atmospheric Scientists, Mac Research, High Performance Computing on Mac, ssh. |
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Emacs: A good text editor is essential for quantitative ecology, computational work, and programming tasks. Debates rage as to which editor is superior (Emacs, vi, WinEdt, TextWrangler, etc.). I (SRB) prefere Emacs because it works across platforms, across network connections, and fairly easily performs multiple types of text editing tasks. The draw back of course is that it has a steep learing curve. To get started: Manual, Carbon Emacs, tutorial. |
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Matlab/Octave: Matlab is a powerful mathematical tool and programming environment built on matrix algebra. Octave is an open source implementation. To get started: Mathworks, GNU Octave, tutorial, Dynamic Models in Biology. |
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R: This is a free and open source statistical programming environment. Like Matlab, it has powerful data visualization capabilities. This has become a popular environment for many kinds of ecological data analyses and modeling in part because it is so flexible. For example, R can also be used to solve ordinary differential equaions. To get started: R Project, R programming, Simple R, R Wiki, wikipedia. R in Ecology: Ecological Models and Data in R, Dynamic Models in Biology. |
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LaTeX: This is a tool for generating and publishing papers. With a bit of effort, it generates nice looking documents, and it has a number of useful features and makes typesetting mathematics much simplier. To get started: LaTeX project, Using Endnote with LaTeX and BibTeX, CSLI info, LaTeX Intro |
