Browsing by Author "Wan, Haibo"

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    Development and evaluation of methodologies for the classification of ecological communities.
    (2008-12) Wan, Haibo
    I developed and evaluated methodologies for studying ecological communities. The first component of my work, Chapter 2, develops a scientific framework, referred to as the nutshell philosophy, for classifying aquatic habitats in the St. Croix National Scenic Riverway. The "nutshell" is a compact philosophy for practitioners, including two parts: (1) a classification framework using a small set of environmental predictor variables; and (2) test of the classification using biological data. My classification resulted in two units: segments and reaches. Segments have a dimension of 15+ km and are delineated by major tributary outlets and channel slopes. Reaches are nested within the segments, with a dimension of 2+ km and are delineated by changes in stream substrate. A data set of mussel communities was clustered to test the classification. The clusters were consistent with the segments. The "nutshell" philosophy was validated in this case. The second component of my work, Chapter 3, introduces a method investigating stream substrate with underwater videos. With it a desired large area can be covered, and the resulting videos can be archived and used for quantitative analysis. The third component of my dissertation, Chapters 4 and 5, relate to clustering methods. Chapter 4 investigates impacts of selected cluster number on the accuracy of clustering algorithms. I clustered data sets of known structure with three different methods, varying the cluster number; clustering accuracy was evaluated using the Rand statistic. The Elbow phenomenon was typically found for the response of the Rand statistic with the cluster number. I described the Rand curves with an analytical relationship and proposed a threshold slope of 0.001 to locate the optimal cluster number. Chapter 5 evaluates performance of an emerging clustering method, Self-Organizing Map (SOM), with the more traditional methods of K-mean and Unweighted-Pair-Group-Method-using-Arithmetic-Averages (UPGMA). The SOM method was similar to the K-means in performance, and it was the best clustering method overall because of its additional and exceptional visualization feature. Although the UPGMA method also has visualization feature and worked well with data of low complexity, its performance decreases substantially with data of medium or high complexity.
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    Efficient Algorithms for Geographic Watershed Analysis
    (2012-07-03) Barnes, Richard; Lehman, Clarence; Mulla, David; Galzki, Jacob; Wan, Haibo; Nelson, Joel
    This project is to analyze where wetlands and other vegetated buffers can be placed on the landscape to intercept drain waters and help purify them before they reach the natural watershed. The computational problem comes because new LIDAR images have expanded the resolution of geographic digital elevation models (DEMs) up to a thousandfold or more. This in turn has taxed the ability of existing algorithms to process the expanded datasets. Here we explain the project and present new efficient algorithms for parallel and scalar processing that reduce run-times from days on ordinary computers to minutes or second using the new algorithms in a parallel supercomputing environment.
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    The impact of rare taxa on a fish index of biotic integrity
    (2010) Wan, Haibo; Chizinski, Christopher, J.; Dolph, Christine, L.; Vondracek, Bruce; Wilson, Bruce, N.
    The index of biotic integrity (IBI) is a commonly used bioassessment tool that integrates abundance and richness measures to assess water quality. In developing IBIs that are both responsive to human disturbance and resistant to natural variability and sampling error, water managersmust decide how to weigh information about rare and abundant taxa, which in turn requires an understanding of the sensitivity of indices to rare taxa. Herein, we investigated the influence of rare fish taxa (within the lower 5% of rank abundance curves) on IBI metric and total scores for stream sites in two of Minnesota’smajor river basins, the St. Croix (n = 293 site visits) and Upper Mississippi (n = 210 site visits). We artificially removed rare taxa from biological samples by (1) separately excluding each individual taxon that fell within the lower 5% of rank abundance curves; (2) simultaneously excluding all taxa that had an abundance of one (singletons) or two (doubletons); and (3) simultaneously excluding all taxa that fell within the lower 5% of rank abundance curves. We then compared IBI metric and total scores before and after removal of rare taxa using the normalized root mean square error (nRMSE) and regression analysis. The difference in IBI metric and total scores increased as more taxa were removed. Moreover, when multiple rare taxa were removed, the nRMSE was related to sample abundance and to total taxa richness, with greater nRMSE observed in samples with a larger number of taxa or sample abundance. Metrics based on relative abundance of fish taxa were less sensitive to the loss of rare taxa, whereas those based on taxa richness were more sensitive, because taxa richness metrics give more weight to rare taxa compared to the relative abundance metrics.