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TraitNet is dedicated to the advancement and integration of trait-based evolutionary and ecological research. Traits are variously defined, but essentially concern species' properties that affect individual fitness and govern species' impacts and responses to their environment. Trait-based research spans an enormous array of ecological and evolutionary disciplines. While each discipline has sought to define traits, apply trait data to test theory, establish protocols for the quantification of traits, and build multi-user databases, little coordination or interaction has occurred across disciplines. TraitNet addresses this extraordinary opportunity to facilitate integration and synthesis across this array of disciplines. TraitNet is structured by five goals (see figure below):

  1. Identify key questions and Core Hypotheses in trait-based research.
  2. Identify data gaps that hinder the advancement of intra- and inter-disciplinary trait-based research.
  3. Coordinate the standardization of collection and curation of trait data.
  4. Build a model database to test explicit Core Hypotheses developed through TraitNet workshops.
  5. Facilitate the development of cross-disciplinary computational tools for merging, disseminating, and sharing trait data.

These activities will be structured around the design of a prototypical universal trait database, here referred to as TraitBank. It is important to note, however, that TraitNet itself, as an RCN, will not undertake to develop TraitBank, but rather lay the groundwork for such a facility.


Open to all individuals interested in trait-based research and/or informatics
Click here to see current list of participants or become a participant.

 

CONTACT >>>

Shahid Naeem :: Principle Coordinator
Daniel Bunker :: Associate Coordinator

TraitNet
Attn: S. Naeem
Columbia University
Department of Ecology, Evolution, and Environmental Biology
1200 Amsterdam Ave MC5557
New York, NY 10027
feedback/suggestion form
email: deb37@columbia.edu
Tel. 212 854-8189
Fax. 212 854-8665

 

NSF SUPPORT

This material is based upon work supported by the National Science Foundation under Grant No. 0639161. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

 

 
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