Erin Fleming Jones

Brigham Young University | Graduate student

Subject Areas: Water quality, aquatic microbial ecology

 Recent Activity

ABSTRACT:

This dataset includes stream solute concentrations measured in the Utah Lake Watershed. Because synoptic sampling requires rapid data collection in a short period of time, we used a citizen science approach to collect over 200 water samples in a single day. Samples were collected over three citizen science synoptic water sampling events which were conducted in March (Spring), July (Summer), and October (Fall) 2018. Volunteers were trained in person on the day of sampling and were provided with site coordinates, detailed written instructions, and sampling materials. Upon completion of sampling, participants returned their sampling kits, samples were manually inspected for quality control and any samples with incomplete data or other irregularities were discarded. Samples were filtered in the field with DI water-rinsed 0.45 µm cellulose acetate filters (Millipore Millex-GV) and immediately frozen or refrigerated and analyzed within 2 weeks of sampling. Anions (NO^3-, NO^2-, SO[4] ^2-, Cl-, and PO[4] ^3-) and cations (NH^4+) were quantified by ion chromatography (Dionex Thermofisher HPIC). Soluble reactive phosphorus was quantified colorimetrically using the ascorbic acid method (ref). We assumed phosphorus concentrations to be equal to the average of both the values determined by ion chromatography and ascorbic acid methods. Dissolved organic carbon (DOC) and total dissolved nitrogen (TN) were quantified using a C/N auto-analyzer (Elementar, Ronkonkoma, NY). Dissolved inorganic nitrogen (DIN) was calculated as the sum of N species (i.e. NO^3-, NO^2-, and NH^4+) from the ion chromatography analysis. We used the application USGS StreamStats to delineate watersheds and calculate watershed area (km2). The application also calculates percent land cover from the National Land Cover Database (NLCD) 1992 and 2011, classified as forested, developed, impervious surface, or herbaceous upland for each site.

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ABSTRACT:

This dataset includes R code, specifically the package WaterML, to download water quality data from iUTAH GAMUT station sensors installed to look at water quality/quantity along three montane-to-urban watersheds: Logan River, Red Butte Creek, and Provo River. An explanation of the GAMUT sensor network can be found at gamut.iutahepscor.org. The code requires installation of packages 'plyr' and 'WaterML'. Instructions for modifying code to extract sensor data for your timepoint of interest are included in the README file. The code has the option to write sensor data to .csv files in your working directory.

Additional code available at https://github.com/erinfjones/GAMUTdownload

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ABSTRACT:

This data was collected as part of a study to understand the connectivity and diversity of stream bacteria communities in streams flowing from high to low elevation through different types of urbanization and in different seasons. The dataset contains OTU (operational taxonomic units, the bacterial surrogate for species) abundance for bacteria at iUTAH aquatic GAMUT sites (http://data.iutahepscor.org/mdf/Data/Gamut_Network/) in three watersheds at 3 time points (November 2014, February 2015, and May 2015). Briefly, we filtered water column samples onto 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits (alternate low protocol yield using phenol chloroform). We sequenced bacterial DNA (PCR-amplified using V4 region specific primers) using Illumina Hi-Seq. We cleaned, clustered (using 97% OTU similarity cut-off) and aligned sequences using the Kozich et al. 2013 protocol, and classified OTUs to taxonomy based on the SILVA bacteria database. Code used for processing is available at https://github.com/erinfjones/mothurcode.

There are three files; site and sample metadata (e.g. date sampled) is included in the file stream_design.txt, observed OTU counts by sample are in the .shared file, and the taxonomic classification of OTUs is in the .taxonomy file.

Also included are zipped folders containing raw fastq files.

Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. (2013): Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and Environmental Microbiology. 79(17):5112-20.

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ABSTRACT:

This dataset contains a .biom file of OTU (operational taxonomic units) classifications for eukaryotes at iUTAH GAMUT sites in three watersheds in May of 2015. Briefly, water column samples were filtered on 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits. We sequenced 18S region amplicons using Illumina Hi-Seq and then a modified MOTHUR protocol (Silva v. 132 reference file, command codes in mothureukaryotes.txt) to generate OTU classifications. Sample metadata (including site information and sample chemistry from grab samples or sensor data) is in design.csv.

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ABSTRACT:

This data was collected as part of a study to understand the connectivity and diversity of stream bacteria communities in streams flowing from high to low elevation through different types of urbanization and in different seasons. The dataset contains OTU (operational taxonomic units, the bacterial surrogate for species) abundance for bacteria at iUTAH aquatic GAMUT sites (http://data.iutahepscor.org/mdf/Data/Gamut_Network/) in three watersheds in September 2014. Briefly, water column samples were filtered onto 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits. We sequenced bacterial DNA (PCR-amplified using V4-region specific primers) using 454-pyrosequencing (an older sequencing method with more limitations). We cleaned, clustered (using 97% OTU similarity cut-off) and aligned sequences using the Schloss 2011 protocol, and classified OTUs to taxonomy based on the SILVA bacteria database. Code used for analysis is available at https://github.com/erinfjones/mothurcode.
There are three files; site and sample metadata (e.g. date sampled) is included in the file stream_design.txt, observed OTU counts by sample are in the .shared file, and the taxonomic classification of OTUs is in the .taxonomy file.

Schloss PD, Gevers D, Westcott SL. (2011). Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PloS ONE. 6:e27310.

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Wasatch Range Metropolitan Area E. coli densities
Created: Sept. 10, 2015, 8:40 p.m.
Authors: Erin Jones

ABSTRACT:

E. coli densities measured in 3 Wasatch Range Metropolitan Area watersheds: Logan, Red Butte and Provo for 1 year.

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Resource Resource

ABSTRACT:

This dataset includes sub-watershed delineations created for ~66 stream sites across the three iUTAH GAMUT watersheds, Logan River, Red Butte Creek, and Provo River (including GAMUT Aquatic stations: gamut.iutahepscor.org ), where monthly water quality samples were collected 2013-2014. Sub-watershed delineations will allow for analysis of the landscape contributing to these sites, which can then be used in models of water quality for the stream sites. This will contribute to the iUTAH goal of modeling the impact of land-use changes on in-stream water quality.

Delineations were generated from 10 m DEMs (Utah GIS portal) using ArcMap tools: mosiac, fill, flow direction, flow accumulation, snap pour point, watershed and raster to polygon. Default settings were used for all tools.

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Resource Resource
Aquatic GAMUT Bacterial Community Target Metagenomics-454 Pyro
Created: July 27, 2016, 12:36 a.m.
Authors: Erin Jones · Zach Aanderud

ABSTRACT:

This data was collected as part of a study to understand the connectivity and diversity of stream bacteria communities in streams flowing from high to low elevation through different types of urbanization and in different seasons. The dataset contains OTU (operational taxonomic units, the bacterial surrogate for species) abundance for bacteria at iUTAH aquatic GAMUT sites (http://data.iutahepscor.org/mdf/Data/Gamut_Network/) in three watersheds in September 2014. Briefly, water column samples were filtered onto 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits. We sequenced bacterial DNA (PCR-amplified using V4-region specific primers) using 454-pyrosequencing (an older sequencing method with more limitations). We cleaned, clustered (using 97% OTU similarity cut-off) and aligned sequences using the Schloss 2011 protocol, and classified OTUs to taxonomy based on the SILVA bacteria database. Code used for analysis is available at https://github.com/erinfjones/mothurcode.
There are three files; site and sample metadata (e.g. date sampled) is included in the file stream_design.txt, observed OTU counts by sample are in the .shared file, and the taxonomic classification of OTUs is in the .taxonomy file.

Schloss PD, Gevers D, Westcott SL. (2011). Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PloS ONE. 6:e27310.

Show More
Resource Resource
Aquatic GAMUT Eukaryote Metagenomics
Created: July 31, 2017, 6:10 p.m.
Authors: Erin Jones · Zach Aanderud

ABSTRACT:

This dataset contains a .biom file of OTU (operational taxonomic units) classifications for eukaryotes at iUTAH GAMUT sites in three watersheds in May of 2015. Briefly, water column samples were filtered on 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits. We sequenced 18S region amplicons using Illumina Hi-Seq and then a modified MOTHUR protocol (Silva v. 132 reference file, command codes in mothureukaryotes.txt) to generate OTU classifications. Sample metadata (including site information and sample chemistry from grab samples or sensor data) is in design.csv.

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Resource Resource
Aquatic GAMUT Bacterial Community Target Metagenomics-HiSeq
Created: July 31, 2017, 6:30 p.m.
Authors: Erin Jones · Zach Aanderud

ABSTRACT:

This data was collected as part of a study to understand the connectivity and diversity of stream bacteria communities in streams flowing from high to low elevation through different types of urbanization and in different seasons. The dataset contains OTU (operational taxonomic units, the bacterial surrogate for species) abundance for bacteria at iUTAH aquatic GAMUT sites (http://data.iutahepscor.org/mdf/Data/Gamut_Network/) in three watersheds at 3 time points (November 2014, February 2015, and May 2015). Briefly, we filtered water column samples onto 0.2 um membrane filters, which were dissolved and processed with MOBIO Power Soil DNA extraction kits (alternate low protocol yield using phenol chloroform). We sequenced bacterial DNA (PCR-amplified using V4 region specific primers) using Illumina Hi-Seq. We cleaned, clustered (using 97% OTU similarity cut-off) and aligned sequences using the Kozich et al. 2013 protocol, and classified OTUs to taxonomy based on the SILVA bacteria database. Code used for processing is available at https://github.com/erinfjones/mothurcode.

There are three files; site and sample metadata (e.g. date sampled) is included in the file stream_design.txt, observed OTU counts by sample are in the .shared file, and the taxonomic classification of OTUs is in the .taxonomy file.

Also included are zipped folders containing raw fastq files.

Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. (2013): Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Applied and Environmental Microbiology. 79(17):5112-20.

Show More
Resource Resource
Aquatic GAMUT sensor data download (WaterML in R)
Created: Jan. 2, 2018, 8:38 p.m.
Authors: Erin Jones

ABSTRACT:

This dataset includes R code, specifically the package WaterML, to download water quality data from iUTAH GAMUT station sensors installed to look at water quality/quantity along three montane-to-urban watersheds: Logan River, Red Butte Creek, and Provo River. An explanation of the GAMUT sensor network can be found at gamut.iutahepscor.org. The code requires installation of packages 'plyr' and 'WaterML'. Instructions for modifying code to extract sensor data for your timepoint of interest are included in the README file. The code has the option to write sensor data to .csv files in your working directory.

Additional code available at https://github.com/erinfjones/GAMUTdownload

Show More
Resource Resource

ABSTRACT:

This dataset includes stream solute concentrations measured in the Utah Lake Watershed. Because synoptic sampling requires rapid data collection in a short period of time, we used a citizen science approach to collect over 200 water samples in a single day. Samples were collected over three citizen science synoptic water sampling events which were conducted in March (Spring), July (Summer), and October (Fall) 2018. Volunteers were trained in person on the day of sampling and were provided with site coordinates, detailed written instructions, and sampling materials. Upon completion of sampling, participants returned their sampling kits, samples were manually inspected for quality control and any samples with incomplete data or other irregularities were discarded. Samples were filtered in the field with DI water-rinsed 0.45 µm cellulose acetate filters (Millipore Millex-GV) and immediately frozen or refrigerated and analyzed within 2 weeks of sampling. Anions (NO^3-, NO^2-, SO[4] ^2-, Cl-, and PO[4] ^3-) and cations (NH^4+) were quantified by ion chromatography (Dionex Thermofisher HPIC). Soluble reactive phosphorus was quantified colorimetrically using the ascorbic acid method (ref). We assumed phosphorus concentrations to be equal to the average of both the values determined by ion chromatography and ascorbic acid methods. Dissolved organic carbon (DOC) and total dissolved nitrogen (TN) were quantified using a C/N auto-analyzer (Elementar, Ronkonkoma, NY). Dissolved inorganic nitrogen (DIN) was calculated as the sum of N species (i.e. NO^3-, NO^2-, and NH^4+) from the ion chromatography analysis. We used the application USGS StreamStats to delineate watersheds and calculate watershed area (km2). The application also calculates percent land cover from the National Land Cover Database (NLCD) 1992 and 2011, classified as forested, developed, impervious surface, or herbaceous upland for each site.

Show More