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  • PANGAEA  (7)
  • Libertas Academica
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  • 1
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    Catchment characteristics and soil and water chemistry of the San Francisco basin and subbasins, Ecuador (2012)
    PANGAEA
    In:  Supplement to: Crespo, Patricio; Bücker, Amelie; Feyen, Jan; Vaché, Kellie; Frede, Hans-Georg; Breuer, Lutz (2012): Preliminary evaluation of the runoff processes in a remote montane cloud forest basin using Mixing Model Analysis and Mean Transit Time. Hydrological Processes, 26(25), 3896-3910, https://doi.org/10.1002/hyp.8382
    Details
    Publication Date: 2023-11-23
    Description: In this study, the Mean Transit Time and Mixing Model Analysis methods are combined to unravel the runoff generation process of the San Francisco River basin (73.5 km**2) situated on the Amazonian side of the Cordillera Real in the southernmost Andes of Ecuador. The montane basin is covered with cloud forest, sub-páramo, pasture and ferns. Nested sampling was applied for the collection of streamwater samples and discharge measurements in the main tributaries and outlet of the basin, and for the collection of soil and rock water samples. Weekly to biweekly water grab samples were taken at all stations in the period April 2007-November 2008. Hydrometric data, Mean Transit Time and Mixing Model Analysis allowed preliminary evaluation of the processes controlling the runoff in the San Francisco River basin. Results suggest that flow during dry conditions mainly consists of lateral flow through the C-horizon and cracks in the top weathered bedrock layer, and that all subcatchments have an important contribution of this deep water to runoff, no matter whether pristine or deforested. During normal to low precipitation intensities, when antecedent soil moisture conditions favour water infiltration, vertical flow paths to deeper soil horizons with subsequent lateral subsurface flow contribute most to streamflow. Under wet conditions in forested catchments, streamflow is controlled by near surface lateral flow through the organic horizon. Exceptionally, saturation excess overland flow occurs. By absence of the litter layer in pasture, streamflow under wet conditions originates from the A horizon, and overland flow.
    Keywords: Ecuador; Human Dimensions; Lakes & Rivers; Land Surface; Rio_SanFrancisco; RIVER; Sampling river
    Type: Dataset
    Format: application/zip, 4 datasets
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    DATA PANGAEA
  • 2
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    (Figure 2) Mean discharge per day (total and baseflow part) of the San Francisco River and daily precipitation at station ECPL (Planta), in 2007 (2010)
    PANGAEA
    In:  Supplement to: Bücker, Amelie; Crespo, Patricio; Frede, Hans-Georg; Vaché, Kellie; Cisneros, Felipe; Breuer, Lutz (2010): Identifying controls on water chemistry of tropical cloud forest catchments: Combining descriptive approaches and multivariate analysis. Aquatic Geochemistry, 16(1), 127-149, https://doi.org/10.1007/s10498-009-9073-4
    Details
    Publication Date: 2023-11-23
    Description: We investigated controls on the water chemistry of a South Ecuadorian cloud forest catchment which is partly pristine, and partly converted to extensive pasture. From April 2007 to May 2008 water samples were taken weekly to biweekly at nine different subcatchments, and were screened for differences in electric conductivity, pH, anion, as well as element composition. A principal component analysis was conducted to reduce dimensionality of the data set and define major factors explaining variation in the data. Three main factors were isolated by a subset of 10 elements (Ca2+, Ce, Gd, K+, Mg2+, Na+, Nd, Rb, Sr, Y), explaining around 90% of the data variation. Land-use was the major factor controlling and changing water chemistry of the subcatchments. A second factor was associated with the concentration of rare earth elements in water, presumably highlighting other anthropogenic influences such as gravel excavation or road construction. Around 12% of the variation was explained by the third component, which was defined by the occurrence of Rb and K and represents the influence of vegetation dynamics on element accumulation and wash-out. Comparison of base- and fast flow concentrations led to the assumption that a significant portion of soil water from around 30 cm depth contributes to storm flow, as revealed by increased rare earth element concentrations in fast flow samples. Our findings demonstrate the utility of multi-tracer principal component analysis to study tropical headwater streams, and emphasize the need for effective land management in cloud forest catchments.
    Keywords: DATE/TIME; ECPL; Ecuador; Monitoring station; MONS; Planta; Precipitation, daily total; River discharge, daily mean
    Type: Dataset
    Format: text/tab-separated-values, 816 data points
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    DATA PANGAEA
  • 3
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    (Table 2) Soil properties and soil chemistry under forest, sub-páramo and pasture in the San Francisco basin, Ecuador (2012)
    PANGAEA
    Details
    Publication Date: 2023-11-23
    Keywords: Aluminium; Calcium; Carbon, total; Conductivity, hydraulic, field-saturated; Ecuador; Human Dimensions; Iron; Lakes & Rivers; Land Surface; Land use; Layer thickness; Magnesium; Manganese; pH, soil; Potassium; Rio_SanFrancisco; RIVER; Sample type; Sampling river; Sodium; Soil horizon; Time coverage
    Type: Dataset
    Format: text/tab-separated-values, 399 data points
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    DATA PANGAEA
  • 4
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    (Tables 1-3) Water chemistry of cloud forest streams at baseflow conditions, Rio San Francisco, Ecuador (2010)
    PANGAEA
    In:  Supplement to: Bücker, Amelie; Crespo, Patricio; Frede, Hans-Georg; Vaché, Kellie; Cisneros, Felipe; Breuer, Lutz (2010): Identifying controls on water chemistry of tropical cloud forest catchments: Combining descriptive approaches and multivariate analysis. Aquatic Geochemistry, 16(1), 127-149, https://doi.org/10.1007/s10498-009-9073-4
    Details
    Publication Date: 2023-11-23
    Description: We investigated controls on the water chemistry of a South Ecuadorian cloud forest catchment which is partly pristine, and partly converted to extensive pasture. From April 2007 to May 2008 water samples were taken weekly to biweekly at nine different subcatchments, and were screened for differences in electric conductivity, pH, anion, as well as element composition. A principal component analysis was conducted to reduce dimensionality of the data set and define major factors explaining variation in the data. Three main factors were isolated by a subset of 10 elements (Ca2+, Ce, Gd, K+, Mg2+, Na+, Nd, Rb, Sr, Y), explaining around 90% of the data variation. Land-use was the major factor controlling and changing water chemistry of the subcatchments. A second factor was associated with the concentration of rare earth elements in water, presumably highlighting other anthropogenic influences such as gravel excavation or road construction. Around 12% of the variation was explained by the third component, which was defined by the occurrence of Rb and K and represents the influence of vegetation dynamics on element accumulation and wash-out. Comparison of base- and fast flow concentrations led to the assumption that a significant portion of soil water from around 30 cm depth contributes to storm flow, as revealed by increased rare earth element concentrations in fast flow samples. Our findings demonstrate the utility of multi-tracer principal component analysis to study tropical headwater streams, and emphasize the need for effective land management in cloud forest catchments.
    Keywords: Aluminium; Aluminium, standard deviation; Area; Arsenic; Arsenic, standard deviation; Barium, standard deviation; Barium 2+; Calcium; Calcium, standard deviation; Calculated; Cerium; Cerium, standard deviation; Chloride; Chloride, standard deviation; Chromium; Chromium, standard deviation; Conductivity, electrical; Conductivity, standard deviation; Conductivity and pH meter, pH/Cond 340i (WTW, Weilheim); Copper; Copper, standard deviation; Dysprosium; Dysprosium, standard deviation; Ecuador; Erbium; Erbium, standard deviation; Gadolinium; Gadolinium, standard deviation; Height above sea level; Human Dimensions; ICP-MS, Agilent 7500c; Ion chromatograph, Dionex Corporation, DX-120; Iron; Iron, standard deviation; Lakes & Rivers; Land Surface; Land use; Lanthanum; Lanthanum, standard deviation; LATITUDE; Lead; Lead, standard deviation; Lithium; Lithium, standard deviation; LONGITUDE; Magnesium; Magnesium, standard deviation; Manganese, standard deviation; Manganese 2+; Neodymium; Neodymium, standard deviation; Nickel; Nickel, standard deviation; Nitrate; Nitrate, standard deviation; pH; pH, standard deviation; Potassium; Potassium, standard deviation; Praseodymium; Praseodymium, standard deviation; Rio_SanFrancisco; River; RIVER; Rubidium; Rubidium, standard deviation; Samarium; Samarium, standard deviation; Sample code/label; Sampling river; Sodium; Sodium, standard deviation; Strontium, standard deviation; Strontium 2+; Sulfate; Sulfate, standard deviation; Uranium; Uranium, standard deviation; Vanadium; Vanadium, standard deviation; Ytterbium; Ytterbium, standard deviation; Yttrium; Yttrium, standard deviation; Zinc; Zinc, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 730 data points
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    DATA PANGAEA
  • 5
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    (Table 3) Chemical characteristics of rainfall, soil water, rock water and stream water in the San Francisco basin and subbasins, Ecuador (2012)
    PANGAEA
    Details
    Publication Date: 2023-11-23
    Keywords: Aluminium; Calcium; Conductivity, electrical; Ecuador; Human Dimensions; Iron; Lakes & Rivers; Land Surface; LATITUDE; LONGITUDE; Magnesium; Manganese; pH; Potassium; Rio_SanFrancisco; RIVER; Sample amount; Sample code/label; Sample type; Sampling river; Sodium; Time coverage
    Type: Dataset
    Format: text/tab-separated-values, 516 data points
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    DATA PANGAEA
  • 6
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    (Table 4) Stable oxygen isotopic ratios of rainfall and stream water in the San Francisco basin and subbasins, Ecuador (2012)
    PANGAEA
    Details
    Publication Date: 2023-11-23
    Keywords: Amplitude; Coefficient of determination; Ecuador; Human Dimensions; Lakes & Rivers; Land Surface; LATITUDE; LONGITUDE; Residence time; Rio_SanFrancisco; RIVER; Sample amount; Sample code/label; Sample type; Sampling river; Time coverage; δ18O, water
    Type: Dataset
    Format: text/tab-separated-values, 108 data points
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    DATA PANGAEA
  • 7
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    (Table 1) Main catchment, land-use and soil characteristics of the San Francisco basin and subbasins, Ecuador (2012)
    PANGAEA
    Details
    Publication Date: 2023-11-23
    Keywords: Area; Average runoff; Coefficient; Coverage; Ecuador; Elevation, maximum; Elevation, minimum; Human Dimensions; Lakes & Rivers; Land Surface; LATITUDE; Lithologic unit/sequence; LONGITUDE; Occurrence; Precipitation, annual mean; Rio_SanFrancisco; River; RIVER; Sample code/label; Sampling river; Slope inclination; Time coverage
    Type: Dataset
    Format: text/tab-separated-values, 190 data points
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    DATA PANGAEA
  • 8
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    Generalized Monotone Incremental Forward Stagewise Method for Modeling Count Data: Application Predicting Micronuclei Frequency (2015)
    Libertas Academica
    Details
    Publication Date: 2015-05-01
    Description: The cytokinesis-block micronucleus (CBMN) assay can be used to quantify micronucleus (MN) formation, the outcome measured being MN frequency. MN frequency has been shown to be both an accurate measure of chromosomal instability/DNA damage and a risk factor for cancer. Similarly, the Agilent 4 × 44k human oligonucleotide microarray can be used to quantify gene expression changes. Despite the existence of accepted methodologies to quantify both MN frequency and gene expression, very little is known about the association between the two. In modeling our count outcome (MN frequency) using gene expression levels from the high-throughput assay as our predictor variables, there are many more variables than observations. Hence, we extended the generalized monotone incremental forward stagewise method for predicting a count outcome for high-dimensional feature settings.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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    PAPER CURRENT
  • 9
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    Identifying Molecular Features Associated with Psychoneurological Symptoms in Women with Breast Cancer Using Multivariate Mixed Models (2015)
    Libertas Academica
    Details
    Publication Date: 2015-05-09
    Description: Breast cancer (BC) is the second most common cancer among women. Research shows many women with BC experience anxiety, depression, and stress (ADS). Epigenetics has recently emerged as a potential mechanism for the development of depression.1 Although there are growing numbers of research studies indicating that epigenetic changes are associated with ADS, there is currently no evidence that this association is present in women with BC. The goal of this study was to identify high-throughput methylation sites (CpG sites) that are associated with three psychoneurological symptoms (ADS) in women with BC. Traditionally, univariate models have been used to examine the relationship between methylation sites and each psychoneurological symptom; nevertheless, ADS can be treated as a cluster of related symptoms and included together in a multivariate linear model. Hence, an overarching goal of this study is to compare and contrast univariate and multivariate models when identifying methylation sites associated with ADS in women with BC. When fitting separate linear regression models for each ADS scale, 3 among 285,173 CpG sites tested were significantly associated with depression. Two significant CpG sites are located on their respective genes FAM101A and FOXJ1, and the third site cannot be mapped to any known gene at this time. In contrast, the multivariate models identified 8,535 ADS-related CpG sites. In conclusion, when analyzing correlated psychoneurological symptom outcomes, multivariate models are more powerful and thus are recommended.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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  • 10
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    Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces (2015)
    Libertas Academica
    Details
    Publication Date: 2015-05-29
    Description: The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.
    Electronic ISSN: 1176-9351
    Topics: Computer Science , Medicine
    Published by Libertas Academica
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