Make A Graph

Dataset Title:	Habitat suitability maps for harbour porpoises in the North Sea-Baltic Sea transition zone from 1997 to 2022
Institution:	Department of Ecoscience, Aarhus University, Denmark (Dataset ID: biology_porpoise_habitat_Baltic_57d1_bf8b_52f6)
Information:	Summary \| License \| FGDC \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

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Graph Type:
X Axis:
Y Axis:
Color:

Dimensions

Start

Stop

Season_years (level)

specify just 1 value →

latitude (degrees_north)

longitude (degrees_east)

Graph Settings
Color Bar:	Continuity:	Scale:
Minimum:	Maximum:	N Sections:
Draw land mask:
Y Axis Minimum:	Maximum:

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Then set the File Type: (File Type information)
and

or view the URL:
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Click on the map to specify a new center point.

Zoom:

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Things You Can Do With Your Graphs

Well, you can do anything you want with your graphs, of course. But some things you might not have considered are:

Web page authors can embed a graph of the latest data in a web page using HTML <img> tags.
Anyone can use ERDDAPs Slide Sorter to build a personal web page that displays graphs with the latest data (or other images or HTML content), each in its own, draggable slide.

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  Season_years {
    Int32 actual_range 1, 6;
    String comment "1- Summers_1997_2004, 2- Winters_1997_2004, 3- Summers_2005_2012, 4- Winters_2005_2012, 5- Summers_2013_2022, 6- Winters_2013_2022";
    String long_name "Season Year Level";
    String units "level";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 53.72108862409777, 60.12196862409777;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range 9.102889276840527, 14.072089276840527;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  habitat_suitability {
    Float32 _FillValue NaN;
    String layer_name_season_period1 "Summers_1997_2004";
    String layer_name_season_period2 "Winters_1997_2004";
    String layer_name_season_period3 "Summers_2005_2012";
    String layer_name_season_period4 "Winters_2005_2012";
    String layer_name_season_period5 "Summers_2013_2022";
    String layer_name_season_period6 "Winters_2013_2022";
    String long_name "Habitat Suitability";
    String units "years";
  }
  NC_GLOBAL {
    String acknowledgement "Funding was provided by the Danish Environmental Protection Agency, Ministry of Environment, Denmark, the DEPONS project, and by the GES4SEAS and OBAMA-NEXT projects funded under the Horizon Europe program (grant agreements 101059877 and 101091642). ";
    String cdm_data_type "Grid";
    String citation "van Beest FM, Carstensen J, Dietz R, Nabe-Nielsen J, Sveegaard S, Teilmann J; Department of Ecoscience, Aarhus University; (2025) Habitat suitability maps for harbour porpoises in the North Sea–Baltic Sea transition zone  from 1997 to 2022";
    String Conventions "CF-1.6, COARDS, ACDD-1.3";
    String creator_email "flbe@ecos.au.dk";
    String creator_institution "Department of Ecoscience, Aarhus University, Denmark";
    String creator_name "Floris M. van Beest";
    String date_created "2025-03-27";
    Float64 Easternmost_Easting 14.072089276840527;
    Float64 geospatial_lat_max 60.12196862409777;
    Float64 geospatial_lat_min 53.72108862409777;
    Float64 geospatial_lat_resolution 0.008990000000000001;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max 14.072089276840527;
    Float64 geospatial_lon_min 9.102889276840527;
    Float64 geospatial_lon_resolution 0.0164;
    String geospatial_lon_units "degrees_east";
    String history 
"2025-07-18T13:55:56Z (local files)
2025-07-18T13:55:56Z https://erddap.emodnet.eu/griddap/biology_porpoise_habitat_Baltic_57d1_bf8b_52f6.das";
    String infoUrl "???";
    String institution "Department of Ecoscience, Aarhus University, Denmark";
    String keywords "aarhus, biodiversity conservation, climate change impacts, data, denmark, department, ecoscience, european, habitat, habitat suitability, habitat_suitability, harbour, latitude, longitude, maps, marine protected areas, marine spatial planning, north, porpoises, sea, sea-baltic, season, Season_years, suitability, transition, union, university, years, zone";
    String license "CC BY 4.0";
    String naming_authority "https://emodnet.ec.europa.eu/en/biology";
    Float64 Northernmost_Northing 60.12196862409777;
    String publisher_institution "Department of Ecoscience, Aarhus University, Denmark";
    String publisher_name "Aarhus University";
    String publisher_type "institution";
    String Seasons "Winter data covers the months Nov–Apr and summer data covers the months May–Oct. This division of months is based on energetic requirements in the reproductive cycle of harbout porpoises";
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 53.72108862409777;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary "The dataset includes 6  layers showing modelled habitat suitability for harbour porpoises (Phocoena phocoena) across the North Sea-Baltic Sea transition zone over a 25-year period (1997-2004; 2005-2012; 2013-2022) during winter (Nov-Apr) and summer (May-Oct). Habitat suitability was estimated using the maximum entropy (MaxEnt) algorithm, based on satellite tag data of 111 individuals and a range of dynamic and static environmental conditions as predictor variables. Habitat suitability models and maps were developed by researchers of the Department of Ecoscience, Aarhus University to quantify potential spatiotemporal changes in the overlap between suitable habitats and an existing network of 62 MPAs dedicated to harbour porpoise protection. The habitat suitability rasters can inform effective design of forthcoming MPAs under the European Union (EU) Biodiversity Strategy for 2030.";
    String title "Habitat suitability maps for harbour porpoises in the North Sea-Baltic Sea transition zone from 1997 to 2022";
    Float64 Westernmost_Easting 9.102889276840527;
  }
}

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link)

Data Access Protocol (DAP) (external link)

and its projection constraints (external link)

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

(easy) You can get data by using the dataset's Data Access Form. It makes the URL for you.
(easy) You can make a graph or map by using the dataset's Make A Graph form. It makes the URL for you.
(not hard) You can bypass the forms and get the data or make a graph or map by generating the URL by hand or with a computer program or script.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.