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Ecological Network Metrics: Opportunities For Synthesis, Matthew K. Lau, Stuart R. Borrett, Benjamin Baiser, Nicholas J. Gotelli, Aaron M. Ellison
Ecological Network Metrics: Opportunities For Synthesis, Matthew K. Lau, Stuart R. Borrett, Benjamin Baiser, Nicholas J. Gotelli, Aaron M. Ellison
College of Arts and Sciences Faculty Publications
Network ecology provides a systems basis for approaching ecological questions, such as factors that influence biological diversity, the role of particular species or particular traits in structuring ecosystems, and long-term ecological dynamics (e.g., stability). Whereas the introduction of network theory has enabled ecologists to quantify not only the degree, but also the architecture of ecological complexity, these advances have come at the cost of introducing new challenges, including new theoretical concepts and metrics, and increased data complexity and computational intensity. Synthesizing recent developments in the network ecology literature, we point to several potential solutions to these issues: integrating network metrics …
Coupling Self-Organizing Maps With A Naïve Bayesian Classifier: Stream Classification Studies Using Multiple Assessment Data, Nikolaos Fytilis, Donna M. Rizzo
Coupling Self-Organizing Maps With A Naïve Bayesian Classifier: Stream Classification Studies Using Multiple Assessment Data, Nikolaos Fytilis, Donna M. Rizzo
College of Engineering and Mathematical Sciences Faculty Publications
Organizing or clustering data into natural groups is one of the most fundamental aspects of understanding and mining information. The recent explosion in sensor networks and data storage associated with hydrological monitoring has created a huge potential for automating data analysis and classification of large, high-dimensional data sets. In this work, we develop a new classification tool that couples a Naïve Bayesian classifier with a neural network clustering algorithm (i.e., Kohonen Self-Organizing Map (SOM)). The combined Bayesian-SOM algorithm reduces classification error by leveraging the Bayesian's ability to accommodate parameter uncertainty with the SOM's ability to reduce high-dimensional data to lower …
Heating Up The Forest: Open-Top Chamber Warming Manipulation Of Arthropod Communities At Harvard And Duke Forests, Shannon L. Pelini, Francis P. Bowles, Aaron M. Ellison, Nicholas J. Gotelli, Nathan J. Sanders, Robert R. Dunn
Heating Up The Forest: Open-Top Chamber Warming Manipulation Of Arthropod Communities At Harvard And Duke Forests, Shannon L. Pelini, Francis P. Bowles, Aaron M. Ellison, Nicholas J. Gotelli, Nathan J. Sanders, Robert R. Dunn
College of Arts and Sciences Faculty Publications
1.Recent observations indicate that climatic change is altering biodiversity, and models suggest that the consequences of climate change will differ across latitude. However, long-term experimental field manipulations that directly test the predictions about organisms' responses to climate change across latitude are lacking. Such experiments could provide a more mechanistic understanding of the consequences of climate change on ecological communities and subsequent changes in ecosystem processes, facilitating better predictions of the effects of future climate change. 2.This field experiment uses octagonal, 5-m-diameter (c.22m 3) open-top chambers to simulate warming at northern (Harvard Forest, Massachusetts) and southern (Duke Forest, North Carolina) hardwood …