HR: 14:30h
AN: B53B-05    [Abstracts]
TI: A Clustering Algorithm for Ecological Stream Segment Identification from Spatially Extensive Digital Databases
AU: * Brenden, T O
EM: tbrenden@umich.edu
AF: University of Michigan, 212 Museums Annex, Ann Arbor, MI 48109 United States
AU: Clark, R D
EM: rdclark@umich.edu
AF: University of Michigan, 212 Museums Annex, Ann Arbor, MI 48109 United States
AU: Wiley, M J
EM: mjwiley@umich.edu
AF: University of Michigan, 212 Museums Annex, Ann Arbor, MI 48109 United States
AU: Seelbach, P W
EM: seelbachp@michigan.gov
AF: Michigan Department of Natural Resources, 212 Museums Annex, Ann Arbor, MI 48109 United States
AU: Wang, L
EM: wangl@michigan.gov
AF: Michigan Department of Natural Resources, 212 Museums Annex, Ann Arbor, MI 48109 United States
AB: Remote sensing and geographic information systems have made it possible to attribute variables for streams at increasingly detailed resolutions (e.g., individual river reaches). Nevertheless, management decisions still must be made at large scales because land and stream managers typically lack sufficient resources to manage on an individual reach basis. Managers thus require a method for identifying stream management units that are ecologically similar and that can be expected to respond similarly to management decisions. We have developed a spatially-constrained clustering algorithm that can merge neighboring river reaches with similar ecological characteristics into larger management units. The clustering algorithm is based on the Cluster Affinity Search Technique (CAST), which was developed for clustering gene expression data. Inputs to the clustering algorithm are the neighbor relationships of the reaches that comprise the digital river network, the ecological attributes of the reaches, and an affinity value, which identifies the minimum similarity for merging river reaches. In this presentation, we describe the clustering algorithm in greater detail and contrast its use with other methods (expert opinion, classification approach, regular clustering) for identifying management units using several Michigan watersheds as a backdrop.
DE: 1803 Anthropogenic effects
DE: 1848 Networks
DE: 1894 Instruments and techniques
SC: Biogeosciences [B]
MN: 2005 Joint Assembly