- Publications
- Projects
- Tools/Techniques
- Software & Data/Models
Below is a list of publications from members of the laboratory. If you do not find a link to the paper or cannot access it at your library, please email Dr. Borrett (borretts [at] uncw [dot] edu) and he will send you a copy.
2021
Network construction, evaluation, and documentation: A guideline
Scharler, U.M., Borrett, S.R.
Environmental Modelling & Software https://doi.org/10.1038/s41559-020-1123-8
2020
Biodiversity increases multitrophic energy use efficiency, flow and storage in grasslands
Buzhdygan, OY, Meyer, ST, Weisser, WW, Eisenhauer, N, Ebeling, A, Borrett SR, De Deyn, GB, Hines, J, Mommer, L, Petermann, JS.
Nature Ecology and Evolution https://doi.org/10.1038/s41559-020-1123-8
Shifting levels of ecological network’s analysis reveals different system properties
Niquil, N., Haraldsson, M., Sime-Ngando, T., Huneman, P., Borrett, S.R.
Philosophical Transactions of the Royal Society B - Biological Sciences 375:20190326 https://doi.org/10.1098/rstb.2019.0326
2019
Borrett, S.R., Sharler, U.
Ecological Indicators 106:105451 doi: 10.1016/j.ecolind.2019.105451
Bennett, Alison; Preedy, Katharine; Golubski, Antonio; Umbanhowar, James; Borrett, Stuart R.; Byrne, Loren; Apostol, Kent; Bever, James; Biederman, Lori; Classen, Aimee; Cuddington, Kim; de Graaff, Marie-Anne; Garrett, Karen; Gross, Lou; Hastings, Alan; Hrynkiv, Volodymyr; Karst, Justine; Kummel, Miroslav; Lee, Charlotte; Liang, Chao; Liao, Wei; Mack, Keenan; Miller, Laura; Ownley, Bonnie; Rojas, Claudia; Simms, Ellen; Walsh, Vonda; Warren, Matthew; Zhu, Jun
Ecosphere 10(7): e02799 doi: 10.1002/ecs2.2799
Bentley, J.W., Hines, D.E., Borrett, S.R., Serpetti, N., Hernandez-Milian, G., Fox, C., Heymans, J.J., Reid, D.G.
ICES Journal of Marine Science 76(7): 2218-2234 https://doi.org/10.1093/icesjms/fsz121
Fath, BD, Asmus, H, Asmus, R, Baird, D., Borrett, SR, de Jonge, VN, Ludovisi, A, Niquil, N, Scharler, UM, Schueckel, U, Wolff, M.
Ocean & Coastal Management 174:1-14 doi: 10.1016/j.ocecoaman.2019.03.007
Diet uncertainty analysis strengthens model-derived indicators of food web structure and function
Bentley, J.W., Hines, D., Borrett, S.R., Serpetti, N., Fox, C., Reid, D.G., Heymans, J.J.
Ecological Indicators 98:239–250 doi: 10.1016/j.ecolind.2018.11.008
2018
Bibliometric review of Ecological Network Analysis: 2010-2016
Borrett, S.R., Sheble, L., Moody, J., Anway, E.
Ecological Modelling 382:63–82 doi: 10.1016/j.ecolmodel.2018.04.020
Seasonal dynamics and ecosystem functioning of the Sylt-Romo Bight, Northern Wadden Sea
de la Vega, C. Horne, S., Baird, D., Hines, D., Borrett, S.R. Jensen, L., Schwemmer, P, Asmus, R., Siebert, U., Asmus, H.
Estuarine, Coastal and Shelf Science 203: 100-118
Uncertainty analysis for Ecological Network Analysis enable stronger inferences
Hines, D.E., Ray, S., Borrett, S.R.
Environmental Modelling & Software 101: 117–127 doi: 10.1016/j.envsoft.2017.12.011
2017
Ecological network metrics: opportunities for synthesis
Matthew K Lau, Stuart R Borrett, Benjamin Baiser, Nicholas Gotelli, Aaron M Ellison
EcoSphere 8(8): e01900
bioRxiv 125781; doi: https://doi.org/10.1101/125781
Estimating the impact of oyster restoration scenarios on transient fish production
McCoy, E., Borrett, S.R., LaPeyre, M.K., Peterson, B.J.
Restoration Ecology. 10.1111/rec.12498
Rakshit, N, Banerjee, A, Mukherjee, J, Chakrabarty, M, Borrett, S.R., Ray, S.
Ecological Modelling. 356: 25–37
enaR: Ecological Network Analysis with R version 2.10
Lau, M., Singh, P, Borrett, S.R.
R package Vignette
2016
Weighting and indirect effects identify keystone species in food webs
Zhao, L., Zhang, H., O'Gorman, E.J., Tian, W., Ma, A., Moore, J.C., Borrett, S.R., Woodward, G.
Ecology Letters 19(9): 1032-1040
Lau, M.K., Keith, A.R., Borrett, S.R., Shuster, S.M., and Whitham, T.G.
Ecology 97:733-742.
Six general ecosystem properties are more intense in biogeochemical cycling networks than food webs
Borrett, S.R, M. Carter, D.E. Hines
Journal of Complex Networks. 4:575-603 Preprint: http://arxiv.org/abs/1507.05050v1
Hines, D.E, Singh, P., Borrett, S.R.
Ecological Engineering 89:70-79
2015
Spatial heterogeneity in soil microbes alters establishment success of an introduced plant
Abbott, K.C., J. Karst, L. Biederman, S.R. Borrett, A. Hastings, J.D. Bever, V. Walsh, L. Miller.
PLoS ONE 10(5): e0125788.
Hines, D.E., J.A. Lisa, B. Song, C.R. Tobias, S.R. Borrett.
Marine Ecology Progress Series 524: 137-154 Preprint: arXiv:1311.1171 [q-bio.QM].
View of the oligohaline study site in the Cape Fear River. Also visible in this image is the Duke Sutton power plant and a portion of the Brunswick River.
2014
enaR: An R package for Ecological Network Analysis
Borrett, S.R. and M. Lau
Methods in Ecology and Evolution 5: 1206-1213
~Software is available from our GitHub site or CRAN
The rise of Network Ecology: Maps of the topic diversity and scientific collaboration
Borrett, S.R., J. Moody, A. Edelmann.
Ecological Modelling 293: 111–127. Preprint: arXiv:1311.1785 [q-bio.QM]
Introduction to the special issue Systems Ecology: A Network Perspective and Retrospective
Borrett, S.R., Fath, B.D., Whipple, S.J.
Ecological Modelling 293:1–3
Hines, D.E., S.R. Borrett
Ecological Modelling 293:210–220
Whipple, S.J., B.C. Patten, S.R.Borrett.
Ecological Modelling 293:210–220
2013
Brasso, R., B. Drummond, S.R. Borrett, A. Chiaradia, M. Polito, A. Raya-Rey.
Environmental Toxicology & Chemistry 32:2331-2334
Throughflow centrality is a global indicator of the functional importance of species in ecosystems
Borrett, S.R.
Ecological Indicators 32:182-196 Preprint: arXiv:1209.0725v1 [q-bio.QM]
2012
Network Ecology (Revised)
Borrett, S.R., R.R. Christian, R., R.E. Ulanowicz.
In: A.H. El-Shaarawi and W.H. Piegorsch (Eds.). Encyclopedia of Environmetrics (2nd edition). John Wiley and Sons: Chinchester, pp. 1767-1772. [PDF]
enaR: Tools for ecological network analysis. R package version 1.01
Lau, M.K., S.R. Borrett and D.E. Hines.
Hines, D.E., J.A. Lisa, B. Song, C.R. Tobias, S.R. Borrett.
Estuarine, Coastal and Shelf Science 20:45-57
Fann, S.L. and S.R. Borrett.
Journal of Theoretical Biology 294: 74-86. Preprint arXiv:1110.5385v1 [q-bio.PE]
2011
Borrett, S.R., M.A. Freeze, A.K. Salas.
Ecological Modelling 222: 2142-2148. Preprint: arXiv:1103.6276v1 [q-bio.QM]
~Download the models analyzed.
Reconnecting environs to their environment
Borrett, S.R. and M.A. Freeze.
Ecological Modelling. 222: 2393-2403.
~Download the models analyzed.
Evidence for dominance of indirect effects in 50 trophic ecosystem network
Salas, A.K. and S.R. Borrett.
Ecological Modelling 222: 1192--1204. preprint arXiv:1009.1841v1 [q-bio.PE]
~Download the models analyzed.
2010
Rapid development of indirect effects in ecosystem networks
Borrett, S.R., S.J. Whipple, B.C. Patten
Oikos 119: 1136-1148 preprint
Evidence for resource homogenization in 50 trophic ecosystem networks
Borrett, S.R., A.K. Salas
Ecological Modelling 221: 1710-1716
~Download the models analyzed.
Kaufman, A., S.R. Borrett
Ecological Modelling 221: 1230-1238
2009
Innovative construction of explanatory scientific models
Bridewell, W., S.R. Borrett, P. Langley. In: A.B. Markman and K.L. Wood (Eds.) Tools for Innovation. Oxford University Press, NY.
2007
A method for representing and developing process models
Borrett, S.R., W. Bridewell, P. Langley, K.R. Arrigo.
Ecological Complexity 4: 1–12
Bata
Ecological Modelling. 206: 400–406
Extracting constraints for process modeling
Bridewell, W., Borrett, S.R., & Todorovski, L.
Proceedings of the Fourth International Conference on Knowledge Capture (pp. 87-94). Whistler, BC.
Schramski, J.R., D.K. Gattie, B.C. Patten, S.R. Borrett, B.D. Fath, S.J. Whipple
Ecological Modelling 206: 18-30
Whipple, S.J., S.R. Borrett, B.C. Patten, D.K. Gattie, J.R. Schramski, S.A. Bata.
Ecological Modelling 206: 1-17.
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
Biogeochemistry 83:293–309
Functional integration of ecological networks through pathway proliferation
Borrett, S.R., B.D. Fath, B.C. Patten.
Journal of Theoretical Biology 245: 98-111. Preprint arXiv
Environ indicator sensitivity to flux uncertainty in a phosphorus model of Lake Sidney Lanier, USA
Borrett, S.R. and O.O. Osidele
Ecological Modelling, 200: 371-383
Learning process models with missing data
Bridewell, W., Langley P., Racunas, S., and Borrett, S.R.
Proceedings of the Seventeenth European Conference on Machine Learning (pp. 557-565).
2006
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.
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.
Ecological Modelling 194: 162–177
Borrett, S.R., S.J. Whipple, B.C. Patten, R.R. Christian
Ecological Modelling 194: 178–188
Indirect effects and distributed controls in ecosystems: Distributed control in the environ network sof a seven-compartment model of nitrogen flow in the Neuse River Estuary, USA — Steady-state analysis
Schramski, J.R., D.K. Gattie, B.C. Patten, S.R. Borrett, B.D. Fath, C.R. Thomas, S.J. Whipple.
Ecological Modelling 194: 189–201
A MATLAB® function for network environ analysis
Fath, B.D. and S.R. Borrett
Environmental Modelling & Software 21:375-405
~The matlab software is available from our GitHub site
2005
Institutional perspectives on participation and information in water management
Cowie, G.M. and S.R. Borrett.
Environmental Modeling & Software 20: 469-483
Borrett, S.R.
Ph.D. Dissertation, University of Georgia, Athens, GA
2003
Structure of pathways in ecological networks: Relationship between length and number
Borrett, S.R. and B.C. Patten.
Ecological Modelling 170: 173-184
2002
Developing a concept of adaptive community learning: Case study of a rapidly urbanizing watershed
Beck, M.B., B.D. Fath, A. K. Parker, O.O.
Integrated Assessment 3:299-307. [pdf]
Complex adaptive hierarchical systems
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. 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
Foresight for Lanier: A workshop. Summary of Results.
Cowie, G.M., S.R. Borrett et al.
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.
Collaborative Research: MSB: Impact of sea level rise on sedimentary nitrogen removal processes in tidal freshwater ecosystemsPI: BK Song, Co-PI Borrett, collborative with C Tobias. Tidal freshwater ecosystems such as rivers and estuaries are located at the interface between land and sea. These ecosystems provide important natural services including primary production, nutrient distribution and regulation, and waste management - services that are at risk due to climate change. Two aspects of climate change, sea level rise and lower precipitation in some watersheds, will shift the location of freshwater-saltwater mixing zones to upper rivers and continuously increase salinity intrusion into freshwater habitats. Sea level is predicted to rise up to 100 cm by 2100, and by itself can have multiple impacts on freshwater ecosystems, including direct toxic effects, habitat degradation and alteration of nutrient cycles. Salinity increases may also have significant effects on the nitrogen (N) dynamics of freshwater habitats. Nitrification (ammonia conversion to nitrate) and denitrification (nitrate conversion to dinitrogen) are key microbial processes controlling the intensity and duration of eutrophication caused by excess N loading. Salinity intrusion might diminish microbial N removal capacity in freshwater sediments as nitrification and denitrification are reduced due to substrate limitation and physiological inhibition. This could result in prolonged eutrophication and hypoxia at rivers and estuaries. Alternatively, anaerobic ammonium oxidation (anammox; ammonium and nitrite conversion to dinitrogen gas) may become a more important microbial N removal process and help recover N removal capacity in freshwater sediments since anammox bacteria can tolerate salinity increases. This project will study 1) the importance of anammox in tidal freshwater sediments, 2) the effects of salinity on freshwater anammox bacteria and the sedimentary N cycle, and 3) predictions of an alternative N removal pathway in sedimentary communities threatened by sea level rise. The proposed research will integrate molecular microbial techniques, chemical tracer methods and mathematical models to examine sedimentary N removal processes in the Cape Fear River and New River of North Carolina. This proposal will result in the following benefits and outcomes; 1) revealing the structures and activities of microbial communities involved in N removal processes in tidal freshwater sediments, 2) determining differential responses of sediment communities to seawater intrusion, 3) understanding the impacts of sea level rise on sedimentary N removal processes in freshwater ecosystems, 4) predicting a key microbial process for net N removal capacity of freshwater ecosystems experiencing sea level rise. This project will also provide an in-depth understanding of microbial responses in freshwater sediment communities under threat of sea level rise. Interdisciplinary approaches will provide insight into the linkages between community structure and activity as well as salinity effects on riverine sedimentary N cycling. Broader impacts will be manifested through diverse educational components, outreach, and improved research infrastructure at the University of North Carolina Wilmington (UNCW) and the University of Connecticut. Three PIs (microbial ecologist, biogeochemist and ecosystem modeler) will provide interdisciplinary training to at least three graduate students seeking a M.S. or Ph.D. degree. Additional educational impact will be accomplished both in the classrooms and through individual undergraduate research projects at the two institutions. Research experience for high school students will be achieved by including the project within the UNCW "Summer Ocean Ventures" and "Ocean17" programs. The high school students will participate in DNA analysis and mesocosm experiments. The PI will provide a lecture on the microbial nitrogen cycle to the students and design three days of laboratory and field courses to detect and measure denitrification during the Ocean 17 camp. The graduate students recruited for this proposed research will act as teaching assistants for the high school students. The program will culminate with a Summer Ventures Symposium where students will present their work. Outreach will be facilitated by partnering with a local citizens group (NC-Coastal Ocean Federation) to recruit volunteers to participate in sampling cruises and water monitoring program, and by using the existing UNCW "Planet Ocean" lecture series as a public platform to discuss coastal water resources issues and actions. NSF Ecosystems. $500,227 |
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Generalization of Environ Properties in Emprically-Based ModelsPatten 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.). Dr. Borrett prefers emacs because it works across platforms, across network connections, and fairly easily performs multiple types of text editing tasks. The draw back 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 equations. 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 |
Git & GitHub: Git is a really handy version control system. This can be used independently on your local machine, but its more powerful when combined with GitHub, which is an online repository. GitHub enables sharing code and collaborative development. The lab has a GitHub account at https://github.com/SEELab. We strive to make the code for our papers and work available on this page. It is also where we host the development of enaR and NEA.m To get started: GitHub for Beginners
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enaR is an R package that we have created to conduct Ecological Network Analysis. The stable version is available from CRAN (enaR). The development of this project is hosted on GitHub here. The package vignette serves as a HOWTO manual for the software. We are also developing tutorial materials that are available on the GitHub enaR wiki, and workshop materials here. Simple enaR Tutorial (illustrated example of use with the Cone Springs model)
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| NEA.m | NEA.m is a matlab function that includes many of the common ecological network analysis algorithms. This is now available on our GitHub site in the NEA project directory.
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The SEE Lab is now using slack for internal communications and to assist with project management. This is very helpful for our often spatially and temporally distribtued team of collaborators. It also has nice integration with GitHub to track our code development. |
We are hosting our code on GitHub at https://github.com/SEELab
Ecosystem Network Models
We collected 50 empirically derived and trophically based ecosystem network models from the literature and analyzed them extensively (Borrett & Salas 2010; Salas & Borrett 2011; Borrett & Freeze 2011; Borrett, Freeze & Salas 2011). We have zipped together these model data here (ecological_networks_50_trophic.tgz). The models are stored in SCOR format becuase of its wide use, rather than the input format required for the NEA.m function below. The enaR pacakge has a read.scor() function to read in this format directly. Notice that some of these models are in the collection by Dr. Ulanowicz on his website as a sample data collection. Thank you to everyone who has contributed models.
The average Lake Lanier phosphorus model (Borrett & Osidele 2007; Kaufman & Borrett 2010 and shown below) is also available in SCOR format here (avgLanier.dat).
Matlab Software for Ecological Network Analysis (NEA.m)
We have developed a couple of Matlab functions to facilitate our work. We make them available here with no guarantee or warranty. The NEA.m function performes Network Environ Analsysis, and oyster.m provides an example of the model input file for the NEA function.
The oyster model is also available as a SCOR formatted file (oyster.dat)
enaR: Software for Ecological Network Analysis
Our enaR package for ecological network analysis is available from CRAN at (http://cran.r-project.org/web/packages/enaR/index.html). If you use the software, you can cite it as follows:
> citation(package="enaR")
To cite package ‘enaR’ in publications use:
M.K. Lau, S.R. Borrett and D.E. Hines (2012). enaR: Tools ecological
network analysis. R package version 1.01.
http://CRAN.R-project.org/package=enaR
A BibTeX entry for LaTeX users is
@Manual{,
title = {enaR: Tools ecological network analysis},
author = {M.K. Lau and S.R. Borrett and D.E. Hines},
year = {2012},
note = {R package version 1.01},
url = {http://CRAN.R-project.org/package=enaR},
}
We are currently working on a manuscript to introduce the package in the literature. In the mean time, please see the vignette distributed with the package. You can open this from R using the following command:
> vignette("enaR")
Model Visualization
The R network and SNA packages are great for general network analysis needs. Here are some of our first attempts using these tools to visualize two of our network models. In these images the node sizes are proportional to the node storage (log10) and the arrows are proportional to the flux magnitude (log10). These first images only include the internal transfers. The Lake Lanier model is the average model of the 122 realizations described in Borrett & Osidele 2007 and Kaufman & Borrett 2010.
