welcome

Welcome to the Systems Ecology and Ecoinformatics Laboratory at the UNCW. The overarching goals of our work are

  1. to understand the lawful processes that create, constrain, and sustain ecosystems, and
  2. to develop a formal science of environment that we can use to comprehend the causes and consequences of both local and global environmental changes.

We use a variety of quantitative, computational and informatic methods to address questions like:

  • What role do indirect effects play in ecological interactions? How rapidly do they develop?
  • What processes are required to explain phytoplankton dynamics in the Ross Sea?
  • What makes an ecosystem sustainable?
  • How do we quantitatively predict environmental impacts?
  • What is environment?

If you are interested in learning more, joining us, or collaborating with us, please contact Dr. Stuart Borrett at borretts _at_ uncw _dot_ edu.

news

Our paper "Rise of Network Ecology: Maps of the topic diversity and scientific collaboration" is now available in Ecological Modelling (doi: 10.1016/j.ecolmodel.2014.02.019).

Emily Oxe, David Hines, and Dr. Borrett pesented new research at the 2014 Benthic Ecology Meeting in Jacksonville, FL.

Dr. Borrett presented new research at the International Conference on Environmental Biology and Ecological Modelling, Feb 24-26 held at Visva-Bharati University in India

UNCW Spring 2014 classes began Monday Jan 13. Dr. Borrett is teaching Ecology Lecture and coordinating the Ecology Laboratories.

We've released version 2.5 of our enaR package for Ecological Network Analysis. It is now available from CRAN and this vignette shows how to use it. Matt Lau introduced this software at the Ecological Society of America meeting in August. This package now includes a library of 100 ecosystem models.

Our new article titled "Throughflow centrality is a global indicator of the functional importance of species in ecosystems" is now in print at Ecological Indicators. doi:10.1016/j.ecolind.2013.03.014.

We've learned new tricks to visualize our network models in R. See here and click on software & data (models) tab to learn more.