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      Topographic and Bioclimatic Determinants of the Occurrence of Forest and Grassland in Tropical Montane Forest-Grassland Mosaics of the Western Ghats, India

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          Abstract

          The objective of this analysis was to identify topographic and bioclimatic factors that predict occurrence of forest and grassland patches within tropical montane forest-grassland mosaics. We further investigated whether interactions between topography and bioclimate are important in determining vegetation pattern, and assessed the role of spatial scale in determining the relative importance of specific topographic features. Finally, we assessed the role of elevation in determining the relative importance of diverse explanatory factors. The study area consists of the central and southern regions of the Western Ghats of Southern India, a global biodiversity hotspot. Random forests were used to assess prediction accuracy and predictor importance. Conditional inference classification trees were used to interpret predictor effects and examine potential interactions between predictors. GLMs were used to confirm predictor importance and assess the strength of interaction terms. Overall, topographic and bioclimatic predictors classified vegetation pattern with approximately 70% accuracy. Prediction accuracy was higher for grassland than forest, and for mosaics at higher elevations. Elevation was the most important predictor, with mosaics above 2000m dominated largely by grassland. Relative topographic position measured at a local scale (within a 300m neighbourhood) was another important predictor of vegetation pattern. In high elevation mosaics, northness and concave land surface curvature were important predictors of forest occurrence. Important bioclimatic predictors were: dry quarter precipitation, annual temperature range and the interaction between the two. The results indicate complex interactions between topography and bioclimate and among topographic variables. Elevation and topography have a strong influence on vegetation pattern in these mosaics. There were marked regional differences in the roles of various topographic and bioclimatic predictors across the range of study mosaics, indicating that the same pattern of grass and forest seems to be generated by different sets of mechanisms across the region, depending on spatial scale and elevation.

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          Random forests for classification in ecology.

          Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
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            Comparing sequences without using alignments: application to HIV/SIV subtyping

            Background In general, the construction of trees is based on sequence alignments. This procedure, however, leads to loss of informationwhen parts of sequence alignments (for instance ambiguous regions) are deleted before tree building. To overcome this difficulty, one of us previously introduced a new and rapid algorithm that calculates dissimilarity matrices between sequences without preliminary alignment. Results In this paper, HIV (Human Immunodeficiency Virus) and SIV (Simian Immunodeficiency Virus) sequence data are used to evaluate this method. The program produces tree topologies that are identical to those obtained by a combination of standard methods detailed in the HIV Sequence Compendium. Manual alignment editing is not necessary at any stage. Furthermore, only one user-specified parameter is needed for constructing trees. Conclusion The extensive tests on HIV/SIV subtyping showed that the virus classifications produced by our method are in good agreement with our best taxonomic knowledge, even in non-coding LTR (Long Terminal Repeat) regions that are not tractable by regular alignment methods due to frequent duplications/insertions/deletions. Our method, however, is not limited to the HIV/SIV subtyping. It provides an alternative tree construction without a time-consuming aligning procedure.
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              Author and article information

              Contributors
              Role: Editor
              Journal
              PLoS One
              PLoS ONE
              plos
              plosone
              PLoS ONE
              Public Library of Science (San Francisco, CA USA )
              1932-6203
              29 June 2015
              2015
              : 10
              : 6
              Affiliations
              [1 ]Ashoka Trust for Research in Ecology and the Environment (ATREE), Bangalore, Karnataka, India
              [2 ]Manipal University, Madhav Nagar, Manipal, Karnataka, India
              [3 ]School of Development, Azim Premji University, Bangalore, Karnataka, India
              [4 ]School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada
              University of New England, AUSTRALIA
              Author notes

              Competing Interests: The authors have declared that no competing interests exist. Though the support to the first author (AD) was in the form of a PhD stipend from Tata Social Welfare Trust, this support was provided as an institutional grant to the Ashoka Trust for Research in Ecology and the Environment and was not paid to the first author (AD) directly by TWST. Also TWST had no role to play in the disbursement of the grant. The first author (AD) is not, nor has ever been directly employed or hired as a consultant, nor involved in any patents, products in development, marketed products, etc. for the Tata Group. This stipend support does not alter the authors adherence to PLOS ONE policies on sharing data and materials.

              Conceived and designed the experiments: AD HN. Performed the experiments: AD. Analyzed the data: AD HN. Wrote the paper: AD HN MA MB. Conceived the work, acquired and analyzed data: AD HN. Interpreted results: AD HN MA MB.

              Article
              PONE-D-15-00964
              10.1371/journal.pone.0130566
              4488301
              26121353

              This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

              Page count
              Figures: 4, Tables: 3, Pages: 19
              Product
              Funding
              The lead author was supported by the Tata Social Welfare Trust of India in the form of a PhD student stipend during the conception, data gathering, analysis and writing of the work presented here. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
              Categories
              Research Article
              Custom metadata
              Relevant data are available at Figshare.com and the DOI for accessing the dataset is as follows: http://dx.doi.org/10.6084/m9.figshare.1444439.

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