gov.noaa.nmfs.inport:39623 eng UTF8 dataset National Centers for Coastal Ocean Science resourceProvider NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov pointOfContact 2026-03-04T19:17:21 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data ISO 19115-2:2009(E) 2 row 420 column 416 area 0 A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples zooplankton_krig 2006-12 publication NOAA/NMFS/EDM 39623 https://www.fisheries.noaa.gov/inport/item/39623 WWW:LINK-1.0-http--link Full Metadata Record View the complete metadata record on InPort for more information about this dataset. information Clark, Randy 228-688-3732 1021 Balch Blvd Stennis Space Center MS 39529 randy.clark@noaa.gov principalInvestigator https://ccma.nos.noaa.gov/ecosystems/sanctuaries/stellwagen_nms/ WWW:LINK-1.0-http--link Citation URL Online Resource download Zooplankton communities have been well studied in the northeast Atlantic (Sherman et al., 1983) and on Georges Bank within the Gulf of Maine (Bigelow, 1927; Davis, 1984; Backus, 1987; Kane, 1993; Pershing et al., 2004). Few studies have examined zooplankton spatial patterns within the Gulf of Maine. Twelve years (1977-1988) of zooplankton data from the National Marine Fisheries Service Northeast Fisheries Science Center (NEFSC) Marine Resources Monitoring Assessment and Prediction Program (MARMAP) were obtained to examine spatial and temporal patterns. A subset of the entire database was selected to include all zooplankton surveys in the Gulf of Maine during this time period (Figure 1.7.4). Overall, 6,864 samples were collected within this area; sampling methodology is described in Sibunka and Silverman (1989). To examine the spatial distribution of zooplankton in the Gulf of Maine and, in particular, Stellwagen Bank National Marine Sanctuary NOAA/NOS/CCMA/NCCOS/Biogeography Program completed NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov pointOfContact NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov custodian asNeeded Environmental Monitoring theme NOS Data Explorer Topic Category distribution krig zooplankton theme Gulf of Maine Stellwagen Bank National Marine Sanctuary place DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce dataCentre Global Change Master Directory (GCMD) Data Center Keywords 2017-04-24 publication 8.5 Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA) https://forum.earthdata.nasa.gov/app.php/tag/GCMD%2BKeywords GCMD Keyword Forum Page Global Change Master Directory (GCMD). 2025. GCMD Keywords, Version 22. Greenbelt, MD: Earth Science Data and Information System, Earth Science Projects Division, Goddard Space Flight Center (GSFC), National Aeronautics and Space Administration (NASA). URL (GCMD Keyword Forum Page): https://forum.earthdata.nasa.gov/app.php/tag/GCMD+Keywords information custodian SBNMS_Assess_Fish_and_Invertebrates project InPort otherRestrictions Cite As: National Centers for Coastal Ocean Science, [Date of Access]: A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary - Kriged Predictive Map of Zooplankton Samples [Data Date Range], https://www.fisheries.noaa.gov/inport/item/39623. NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose. otherRestrictions Access Constraints: none otherRestrictions Use Constraints: none otherRestrictions Distribution Liability: These data were prepared by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. Any views and opinions expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Although all data have been used by NOAA, no warranty, expressed or implied, is made by NOAA as to the accuracy of the data and/or related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by NOAA in the use of these data or related materials.NOAA makes no warranty regarding these data, expressed or implied, nor does the fact of distribution constitute such a warranty. NOAA can not assume liability for and damages caused by any errors or omissions in these data, nor as a result of the failure of these data to function on a particular system. unclassified NOAA Data Management Plan (DMP) NOAA/NMFS/EDM 39623 https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/nccos/dmp/pdf/39623.pdf WWW:LINK-1.0-http--link NOAA Data Management Plan (DMP) NOAA Data Management Plan for this record on InPort. information crossReference grid eng; US biota oceans -74.8846 -62.2017 37.9393 46.307 | Currentness: Ground Condition 2006-09-01 false eng false kriged predictive map of zooplankton samples NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov distributor https://ccma.nos.noaa.gov/ecosystems/sanctuaries/stellwagen_nms/ WWW:LINK-1.0-http--link https://ccma.nos.noaa.gov/ecosystems/sanctuaries/stellwagen_nms/ Online Resource download dataset Completeness Report Unknown Conceptual Consistency Unknown To examine the spatial and temporal patterns of zooplankton abundance and distribution the data were divided into three-year seasonal bins. Yearly bins included 1977-79, 1980-82, 1983-85, 1986-88 and seasons include: spring-March, April, May; summer-June, July, August; fall-September, October, November; and, winter-December, January, February. These binned data were separately mapped in a GIS and interpolated to create a predictive surface of zooplankton abundance throughout the Gulf of Maine. Prior to interpolation, all data were tested for spatial autocorrelation. Spatial autocorrelation is frequently encountered in ecological data, and many ecological theories and models assume an underlying spatial pattern in the distributions of organisms and their environment (Legendre and Fortin, 1989). Typically, species abundance is positively autocorrelated, such that nearby points have similar values than distant points. Moran's I and Geary's C statistics were calculated for all the data to identify significant autocorrelation (Levine, 2002). Detrending was done to standardize estimates across the analysis extent, and is a prerequisite for the interpolation used here. Empirical variograms show the decrease in relatedness between pairs of points as a function of distance. Chapter 1 page 38A Biogeographic Assessment of the Stellwagen Bank National Marine Sanctuary AnalysisSample sizeLag SizeNumber of LagsCross Validation Prediction Map -r2Cross Validation Probability Map -r2Neighbors (total, minimum)spring 77-7936420120.020.035, 2spring 80-8235220120.020.025, 2spring 83-8539120120.580.265, 2spring 86-8832020120.270.265, 2spring all1,22320120.140.145, 2summer 77-7923120120.360.325, 2summer 80-8229820120.300.255, 2summer 83-8536720120.370.395, 2summer 86-8834630120.580.535, 2summer all1,02930120.360.325, 2fall 77-7944620120.360.235, 2fall 80-8232020120.390.315, 2fall 83-8540220120.580.505, 2fall 86-8846320120.550.475, 2fall all1,35220120.470.395, 2winter 77-7917530120.240.195, 2winter 80-8215220120.360.355, 2winter 83-8514720120.540.455, 2winter 86-8822530120.580.555, 2winter all44520120.450.405, 2Table 1.7.1. Summary statistics for NEFSC MARMAP zooplankton ordinary and indicator kriging. In order to calculate the empirical variogram, pairs of points must be binned by distance and an average value calculated for all pairs in a given bin. The size of the bin is called the lag size. A variogram model is fit to the empirical variogram and its parameters are later used in the interpolation. Empirical variograms were calculated using the default lag size and number of lags, as well as for 10, 15, 20 and 30 km lag sizes. The appropriate lag size and number of lags were chosen to optimize variogram coherence. The interpolation method used is termed ordinary kriging. Kriging is a linear interpretation method that allow predictions of unknown values of a random function from observations at known locations (Kaluzny et al., 1998). Ordinary kriging is the method generally used for interpolation of a single continuous variable of unknown mean. Kriging is a preferred method because weights are based on the data's spatial structure (the variogram) and has been shown to outperform other interpolation techniques, such as inverse distance weighting and triangulated irregular networking (Guan et al., 1999). (continued) 2005-12-01T00:00:00 (continued from above) Trend analysis was conducted using JMP statistical software (SAS Institute), while detrending, variogram modeling, and kriging were conducted using ArcMap Geostatistical Analyst Extension (ESRI, Inc.).In addition to creating predictive maps, probability maps were developed using indicator kriging. Indicator kriging is a technique used to identify areas or values that exceed a certain threshold (Isaaks and Srivastava, 1989). Through indicator kriging, data values are transformed into binary indicator values (1 or 0), values which exceed a chosen threshold are coded 1, those below coded 0. These indicators are then analyzed to determine their spatial direction variability with a series of variograms. By inspection of these variograms, orientations of greatest and least spatial continuity are determined. Variogram models are fit to the experimental variograms corresponding to the directional continuity. Then the indicator data are kriged using the variogram models to determine the probability of exceeding the threshold value in a spatial extent. For this analysis, the spatial mean of the zooplankton data was used as the threshold to compare predicted estimates and to also identify areas of high zooplankton abundance.The kriging neighborhood was set to the nearest 5 neighbors with a minimum of 2 to capture small scale variability(NCCOS, 2002). Cross validation was conducted to assess model accuracy by regressing observed versus predicted values (See modeling statistics Table 1.7.1). Maps of the kriging standard error were also generated and used to exclude poorly interpolated areas within the analysis extent. The lowest 50% of standard error was clipped from the interpolated map to depict the areas of strongest interpolation. (end continuation) 2005-12-01T00:00:00 otherRestrictions otherRestrictions Access Constraints: none | Use Constraints: none