Nitrate Mean Concentration

Marine Cadastre Data Registry

URL where the source data were originally accessed.

Provided modeled nitrate data at depth for the US EEZ.

1) Data sets were derived from Bio‐ORACLE (ocean rasters for analysis of climate and environment) global data set.

2) Nitrate concentrations (micromoles per litre) are reported as six statistical measures including Lt. Max, Lt. Min, Min, Max, mean, and range over three depths (surface, mean, maximum). *Lt. (long-term) for average of the minimum and maximum records per year (e.g., temperature of the warmest month, on average). For the Marine Cadastre National Viewer, only the mean surface depth concentration is displayed as an example.

3) The Bio-ORACLE “Present data set” (2000 - 2014) was downloaded as Tiff Raster files (.tif) using Bio-ORACLE’s download manager.

4) This surface and benthic layers, for each of the three nutrients were then entered as a geodatabase into ArcGIS 10.7. Each .tif file was then clipped to the United States Exclusive Economic Zone (EEZ) using the marinecadastre.gov EEZ shapefile.

5) Data were then given a continuous color schema to avoid issues with color-blind audiences (e.g. red to green) over the range of data values for each statistical measure. The lighter shade of a color represents areas with lower nitrate concentrations relative to the darker shades where higher nitrate values occur.

6) Surface, mean, and maximum concentration values were QA/QC’ed for any elements lacking in the data description or attributes, illogical values, spatial accuracy, and overall consistency across the data within the US EEZ.

7) Each data layer was then projected to WGS 1984 Web Mercator Auxiliary Sphere (EPSG: 3857) projection

8) Permission to use this data was received from the authors prior to use in marinecadastre.gov. Comparisons were also made to the ESRI Ecological Marine Unit (EMU) nutrient data where spatial overlap occurred, to ensure likeness in data over geographical space.

9) The Bio-ORACLE data are modeled and have been statistically downscaled and therefore have inherit biases associated with these types of analyses occur. However, methodology implemented here create a more spatially resolved data set for nutrients in US waters than any other known by the authors. However, these data lack high temporal resolution.

10) While the Bio-Oracle Nutrient data may have higher horizontal spatial resolution, the Esri EMU data has higher number of depth levels (102 depth levels). There are pros and cons of both data sets and users are recommended to compare the data sets before deciding which is best for their own analyses.

11) The pros to the ESRI EMU nutrient data is data are present over 102 depth levels, and can be used to statistically differentiate different portions of the water column. The cons lie its course spatial resolution, no temporal component, and no nearshore values (as of 2020). Sayre et al. (2019) (https://www.tandfonline.com/doi/full/10.1080/1755876X.2018.1529714 ) are working towards coastal EMUs, which will increase the spatial coverage of the EMU data. It is recommended that users view the pros and cons of applying the various data sets (Bio-ORACLE vs. EMU) to their own analyses.

More information about methodology used can be found in these resources:

Assis, J., Tyberghein, L., Bosh, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2017). Bio-ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography.

Tyberghein L, Verbruggen H, Pauly K, Troupin C, Mineur F, De Clerck O (2012) Bio-ORACLE: A global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21, 272–281.