Full View Normal View

data

SIPPER plankton and marine snow abundance and distribution data for the northeastern Gulf of Mexico: June 2012 – August 2014

Center for Integrated Modeling and Analysis of Gulf Ecosystems (C-IMAGE)

DOI:
10.7266/N76T0JKS
 
UDI:
R1.x135.120:0017
Last Update:
Mar 17 2016 18:25 UTC
 
Dataset Author(s):
Daly, Kendra
Point of Contact:
Daly, Kendra
University of South Florida / College of Marine Science
140 7th Ave South
St. Petersburg, FL  33701
USA
kdaly@mail.usf.edu
Funding Source:
RFP-I
Data Collection Period:
2012-06-29 to 2014-08-12

Identified Submitted Metadata Available
3 3 3 3

Abstract:

This dataset contains seasonal and interannual marine snow, phytoplankton, zooplankton, and larval fish abundance and distributions collected from the northeastern Gulf of Mexico using the Shadowed Image Particle Profiling Evaluation Recorder (SIPPER). The purpose of the dataset is to assess the seasonal and interannual zooplankton abundance and distribution after the Deepwater Horizon oil spill.

Purpose:

To assess the seasonal and interannual zooplankton abundance and distribution after the Deepwater Horizon oil spill.

Theme Keywords:

zooplankton, phytoplankton, plankton, suspended particles, detritus, marine snow, larval fish, abundance, density, distribution, lower trophic food web

File Format:

xlsx

SIPPER plankton and marine snow abundance and distribution data for the northeastern Gulf of Mexico: June 2012 – August 2014



Identification Information
Distribution Information
Metadata Maintenance Information

Metadata: 
  File identifier: 
      R1.x135.120-0017-metadata.xml
  Language: 
      eng; USA
  Character set: 
    Character set code: 
      utf8
  Hierarchy level: 
    Scope code: 
      dataset
  Metadata author: 
    Responsible party: 
      Individual name: 
          Richard McKenzie
      Organisation name: 
          University of South Florida
      Position name: 
          GIS Analyst
      Contact info: 
        Contact: 
          Phone: 
            Telephone: 
              Voice: 
                  8139742852
              Facsimile: 
                  8139742852
          Address: 
            Address: 
              Delivery point: 
                  4202 E. Fowler Ave.
                  LIB122
              City: 
                  Tampa
              Administrative area: 
                  Florida
              Postal code: 
                  33620
              Country: 
                  USA
              Electronic mail address: 
                  rwmcken2@usf.edu
      Role: 
        Role code: 
          pointOfContact
  Date stamp: 
      2016-12-15T22:10:30+00:00
  Metadata standard name: 
      ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
  Metadata standard version: 
      ISO 19115-2:2009(E)
  Dataset URI: 
      https://data.gulfresearchinitiative.org/metadata/R1.x135.120:0017
Return To Index

Identification info: Data identification: Citation: Citation: Title: SIPPER plankton and marine snow abundance and distribution data for the northeastern Gulf of Mexico: June 2012 – August 2014 Alternate title: SIPPER plankton data Date: Date: Date: 2014-08-12 Date type: Date type code: creation Abstract: This dataset contains seasonal and interannual marine snow, phytoplankton, zooplankton, and larval fish abundance and distributions collected from the northeastern Gulf of Mexico using the Shadowed Image Particle Profiling Evaluation Recorder (SIPPER). The purpose of the dataset is to assess the seasonal and interannual zooplankton abundance and distribution after the Deepwater Horizon oil spill. Purpose: To assess the seasonal and interannual zooplankton abundance and distribution after the Deepwater Horizon oil spill. Status: Progress code: completed Point of contact: Responsible party: Individual name: Kendra Daly Organisation name: University of South Florida Position name: Professor Contact info: Contact: Phone: Telephone: Voice: 1 727-553-1041 Facsimile: 1 727-553-1189 Address: Address: Delivery point: 140 7th Ave S City: St. Petersburg Administrative area: Florida Postal code: 33701 Country: USA Electronic mail address: kdaly@mail.usf.edu Role: Role code: principalInvestigator Descriptive keywords: Keywords: Keyword: zooplankton Keyword: phytoplankton Keyword: plankton Keyword: suspended particles Keyword: detritus Keyword: marine snow Keyword: larval fish Keyword: abundance Keyword: density Keyword: distribution Keyword: lower trophic food web Type: Keyword type code: theme Descriptive keywords: Keywords: Keyword: Northeastern Gulf of Mexico Keyword: West Florida Shelf Type: Keyword type code: place Language: eng;USA Topic category: Topic category code: biota Topic category: Topic category code: oceans Extent: Extent: Geographic element: Geographic bounding box: West bound longitude: -88.89545 East bound longitude: -84.73283 South bound latitude: 26.72992 North bound latitude: 29.52156 Geographic element: BoundingPolygon: Polygon: gml:MultiCurve: gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.74479 -84.81097 26.7523 -84.80829 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.73412 -84.8539 26.73265 -84.84802 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.72992 -84.89192 26.73028 -84.88523 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.77091 -84.86034 26.7692 -84.85355 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.79457 -84.79402 26.79128 -84.78893 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.79872 -84.87987 26.79549 -84.87526 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.94016 -85.02151 26.93232 -85.00267 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.98089 -84.94717 26.97257 -84.9333 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.80623 -84.73701 26.80429 -84.73283 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.85485 -84.74103 26.85235 -84.73609 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.8292 -84.81033 26.82392 -84.80525 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.98392 -84.78461 26.98351 -84.77682 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.93529 -84.86751 26.93686 -84.86158 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 26.91037 -84.92253 26.91197 -84.91659 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.13991 -85.95427 29.15105 -85.95318 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.14138 -86.12817 29.15299 -86.12475 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.14483 -86.28308 29.15441 -86.27609 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.30362 -86.33888 29.30249 -86.34528 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.30771 -86.4969 29.30724 -86.50282 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.30439 -86.66895 29.30958 -86.67 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.1398 -86.4621 29.14175 -86.47558 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.14103 -86.58791 29.14615 -86.60457 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.13983 -86.70857 29.15728 -86.71369 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.13385 -86.7976 29.13911 -86.79508 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.13628 -86.9924 29.13981 -87.00735 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.15709 -87.10433 29.16153 -87.10879 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.04523 -87.27683 29.04755 -87.25103 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.00025 -87.50449 28.9978 -87.46477 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.18937 -87.42865 29.19236 -87.42335 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.28892 -87.34255 29.2935 -87.33803 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.43328 -87.29511 29.43922 -87.29147 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.49247 -87.10919 29.48649 -87.09372 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.37246 -87.07003 29.36485 -87.04964 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.29103 -86.99806 29.27288 -86.97818 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.44922 -86.95079 29.44429 -86.94666 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.46559 -86.88882 29.4609 -86.88372 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.52156 -86.78489 29.51763 -86.78111 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.04157 -87.28136 29.01401 -87.29673 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.99225 -87.51044 28.97596 -87.54514 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.89594 -87.62451 28.85817 -87.61549 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.97455 -87.86671 28.94226 -87.85887 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.12247 -87.86873 29.10335 -87.86198 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.29401 -87.79128 29.28279 -87.79471 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.25563 -87.7254 29.23825 -87.72385 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.72005 -88.10591 28.68619 -88.10161 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.92836 -88.06851 28.89541 -88.07471 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.93025 -88.50889 28.91098 -88.52127 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.99936 -88.55106 28.99432 -88.56862 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.07185 -88.62577 29.06943 -88.63789 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 29.06965 -88.63912 28.90491 -88.8826 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.84266 -88.88385 28.83285 -88.89545 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.8165 -88.82959 28.82063 -88.85405 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.74537 -88.5708 28.74723 -88.60174 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.64591 -88.62218 28.64775 -88.65179 gml:curveMember: gml:Curve: gml:segments: gml:LineStringSegment: gml:posList: 28.1536 -86.74904 28.13997 -86.79895 Temporal element: Temporal extent: Extent: Time period: Description: ground condition Begin date: 2012-06-29 End date: 2014-08-12 Supplemental Information: 1. Date (local and GMT), time (local and GMT), Latitude (N), Longitude (W), station, depth of tow (m), volume filtered (m3), marine snow, phytoplankton, and zooplankton abundance of species and taxa (number/m3) are provided. 2. Information on specific image classes and taxa are provided in the methods section. |A. SIPPER Field Deployments The SIPPER camera imaging system (Fig. 1) was developed by the University of South Florida, Center for Ocean Technology and described in Samson et al. (2001) and Remsen et al. (2004). The towed platform carried several environmental sensors (CTD, oxygen, chlorophyll fluorescence, transmissometer: see Supplemental Information- Instruments). SIPPER used a high speed Dalsa Piranha-2 line-scan camera and a pseudo-collimated LED generated light sheet to image the shadows and outlines of resolvable particles that passed through a 100 cm2 field of view. The operational optical resolution of the system is ~65 m. SIPPER was towed at speeds between 2-3 knots in an oblique profile through the water column, spending approximately equal amounts of time at each meter of depth between the surface and 300 m. At stations with a bottom depth shallower than 300 m, SIPPER was towed within approximately 5 m from the seafloor. Imaging and environmental data were stored internally on a Firewire hard drive and processed upon retrieval of the SIPPER instrument from a deployment using a customized software package called the Plankton Image Classification and Extraction Software (PICES). PICES was used to extract images of interest, classify them using user-specified training libraries and to manage the SIPPER images and environmental data collected. Currently, the PICES manages information for over 137 million SIPPER images. B. SIPPER Data Analyses SIPPER data, including concurrently collected environmental instrument data, were offloaded to a desktop PC after every deployment. Using the Plankton Instrument Classification and Extraction Software (PICES) (Kramer et al. 2011), images greater than or equal to 250 pixels in total area (~0.5 mm equivalent spherical diameter or ESD) were extracted from the raw SIPPER data, preliminarily classified and automatically entered into a MYSQL based image database. A custom database management application called PICES Commander was used to manage and classify the resulting dataset. Classification of SIPPER images involved several steps including both automated classification and manual labeling of the images. (1) Image Extraction and Preliminary Classification: During the image extraction step, features for each image are determined and entered into a feature table in the PICES database and an initial classification for each image is predicted using a comprehensive, multi-class feature selection (MFS) support vector machine (SVM) (Kramer et al., 2011). This classifier was initially developed from training images collected in the Gulf of Mexico prior to the Deepwater Horizon oil spill. Training images and new image classes were added throughout the sampling period and slowly replaced the images that were collected before the spill. We utilized a hierarchical naming format for the particle and plankton image classes to reflect the level of similarity between image classes (Fig.1). More closely related image classes shared one or more parts of their name separated by underscores and this naming schema is used in the classification effort. (2) Manual Validation: After image extraction and the first run of the comprehensive classifier, thumbnail images of the resulting classifications were then examined using the image browsing and validation capability of PICES Commander. Image classes that consisted of numerous true positive examples (for example calanoid copepods or detritus) were noted for inclusion in the final classification effort and then ignored while image classes that were less abundant or rare were closely examined. True positive image examples from these rarer image classes were validated, meaning that their image class label could not be relabeled during a subsequent classification. This was done for every predicted image class until all rare images were validated from the first comprehensive classification. Occasionally, a new image class was encountered. When this occurred, that class was added to the comprehensive classifier and any encountered examples would be validated and added to the trai! ning library (3) Run Final Comprehensive MFS-SVM Classification: After all deployments had their images extracted, initially classified, and new image classes encountered, labeled, and added to the training library, the final comprehensive MFS-SVM classifier was rebuilt and rerun on all deployments using all the new training examples that had been uncovered in Step 2. The purpose of this classification cycle was to identify which common classes to include in the ultimate dual classifier, identify deployments where less common image classes might be abundant enough to include in the ultimate dual classifier, and validate any new rare image examples that may have been identified during the second run of the comprehensive classifier. (4) Build Deployment-Specific MFS-SVM Classifiers: For each deployment, after rare and uncommon groups were searched for true positives and validated in Step 3, a MFS_SVM classifier was pared down to the most common classes found in that deployment and rebuilt. New MFS classifiers were, therefore, built for each deployment with classes specific to that deployment. Multiple deployments could share the same final MFS classifier if they shared the same common groups. A final feature selection was then run for all the image classes in the final MFS Classifiers, using Amazon High Performance Computing, to create the most appropriate feature set for the classifiers. (5) Build Deployment-Specific BFS-SVM Classifiers: Another set of SVM classifiers was also built using binary feature selection (BFS) as described in Kramer et al. (2011), where features and parameters are tuned for each class pair combination. These BFS classifiers used the same image classes as the MFS classifiers so that they were specific for the particular conditions encountered during each deployment. By using pair-wise feature selection, overall classification accuracy can be improved, and feature selection and SVM training time can be reduced. (6) Run Dual Classification: Lastly, these two sets of classifiers (MFS-SVM and BFS-SVM) were used to run a modified version of the dual classifier described for the video plankton recorder (VPR) by Hu and Davis (2006). As the name implies, a dual classifier makes use of two classifiers where each classifier makes its own separate prediction and a particle image is only labeled as a specific image class if both classifiers agree, otherwise the particle image is labeled “other”. In the case of the VPR, a SVM classifier using texture based features and a neural net using shaped-based features were used as the two classifiers. For the SIPPER data, a neural net performed poorly and it was determined that a SVM BFS using separate feature sets for each pairwise classification was the best alternative. Along with the SVM MFS classifier, the SVM BFS made up the SIPPER dual classifier. A further modification took advantage of the hierarchical naming structure of SIPPER image cla! sses by allowing for predictions from partial matches between the two classifiers. For example if one classifier predicted that an unknown image was crustacean_copepod_calanoid and the other classifier predicted it was crustacean_copepod_oithona, it was very likely that the image was in fact a copepod. Rather than labeling this disagreement as “other” as the traditional dual classifier would have done, our modified classifier would label the image as crustacean_copepod since both classifiers agreed to that partial match. Only when the two classifiers predictions shared no common root in the SIPPER class names (e.g. one classifier predicts an unknown image is detritus_snow and the other predicts larvacean) would an image be labeled “other”. Dual classifiers are used because they improve specificity (reduce the rate of false positives) and thereby give more accurate abundance estimations, especially in regions of low relative abundance. Dual classifiers were run on ea! ch deployment using the most common image classes in that deployment as determined in Step 3. A total of 32 individual image classes were common enough to be used in at least one deployment and are listed below. Validated images from rare and uncommon classes that were removed from the classifier retained their validated labels in the classifier output. C. SIPPER Data Files SIPPER and environmental data are reported by cruise in excel files. Each deployment is reported in a separate tab in cruise files. Every SIPPER deployment data sheet shows the same set of classes in order to evaluate the spatial and temporal variability of image classes. Zero abundances in any class means that no images were observed for those classes. The SIPPER data are from the downcast, unless otherwise noted. Environmental data are reported to the far right of the SIPPER abundance data in the excel spreadsheet. D. SIPPER Image Class Descriptions There are several types of images classes: (1) dual classifier classes, (2) rare or uncommon classes, and (3) other image classes resulting from the dual classification. Examples of images for these classes are available on http://www.marine.usf.edu/zooplankton/. (1) Dual Classifier Classes. The following training library classes were used in at least one of the final dual classifications. These 25 classes had numerous images in at least one or more deployments. These were the more common classes that made up about 90% of the images. Chaetognath: Chaetognaths of various gen ⋮ sors were calibrated at Seabird and WET Labs and then integrated into the SIPPER towed platform. |||
Return To Index

Distribution info: Distribution: Distributor: Distributor: Distributor contact: Responsible party: Organisation name: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC) Contact info: Contact: Phone: Telephone: Voice: +1-361-825-3604 Facsimile: +1-361-825-2050 Address: Address: Delivery point: 6300 Ocean Drive Unit 5869 City: Corpus Christi Administrative area: Texas Postal code: 78412-5869 Country: USA Electronic mail address: griidc@gomri.org Online Resource: Online Resource: Linkage: URL: http://data.gulfresearchinitiative.org Protocol: Role: Role code: distributor Distributor format: Format: Name: xlsx Version: inapplicable File decompression technique: zip Distributor transfer options: Digital transfer options: Transfer size: 3.94 Online: Online Resource: Linkage: URL: https://data.gulfresearchinitiative.org/data/R1.x135.120:0017 Protocol: https
Return To Index

Metadata maintenance: Maintenance information: Maintenance and update frequency: Maintenance note: This ISO metadata record was created using the 'Check and Save to File' (with form validation) function of the GRIIDC ISO 19115-2 Metadata Editor on 2015-09-16T18:39:46+00:00 Maintenance note: This ISO metadata record was created using the 'Check and Save to File' (with form validation) function of the GRIIDC ISO 19115-2 Metadata Editor on 2015-09-16T19:34:32+00:00
Return To Index