Make A Graph

Dataset Title:	Path analysis, run in Stata v. 11.1, for direct/indirect effects of upwelling on seabirds; data were collected at Dassen and Robben Islands, Malgas Island and in Lamberts Bay, South Africa
Institution:	BCO-DMO (Dataset ID: bcodmo_dataset_679946)
Information:	Summary \| License \| ISO 19115 \| Metadata \| Background \| Data Access Form \| Files

To work correctly, this web page requires that JavaScript be enabled in your browser. Please:
1) Enable JavaScript in your browser:
      • Chrome: "Settings : Show advanced settings : Privacy / Content settings : JavaScript"
      • Firefox: (it should be always on!)"
      • Internet Explorer: "Tools : Internet Options : Security : Internet : Custom level :
          Sripting/ Active Scripting : Enable : OK : OK"
      • Opera: "Settings : Websites : JavaScript"
      • Safari: "Safari : Preferences : Security : Enable JavaScript"
2) Reload this web page.

Graph Type:

X Axis:

Y Axis:

Color:

Constraints	Optional Constraint #1		Optional Constraint #2

Server-side Functions

distinct()

Hover here to see a list of options. Click on an option to select it.

Graph Settings

Marker Type:

Size:

Color:

Color Bar:

Continuity:

Scale:

Minimum:

Maximum:

N Sections:

Y Axis Minimum:

Maximum:

(Please be patient. It may take a while to get the data.)

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

or view the URL:
(Documentation / Bypass this form

)

[The graph you specified. Please be patient.]

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 {
 s {
  year {
    Int16 _FillValue 32767;
    Int16 actual_range 1978, 2015;
    String bcodmo_name "year";
    String description "4-digit year of sampling";
    String long_name "Year";
    String nerc_identifier "https://vocab.nerc.ac.uk/collection/P01/current/YEARXXXX/";
    String units "unitless";
  }
  anc_tot_biomass {
    Float64 _FillValue NaN;
    Float64 actual_range 162048.4866, 6720286.963;
    String bcodmo_name "biomass";
    String description "Total anchovy biomass for the southwestern Benguela Current Ecosystem along the west coast of South Africa southeast through Cape Agulhas. Sampling season: November.";
    String long_name "Anc Tot Biomass";
    String units "tons";
  }
  sar_tot_biomass {
    Float32 _FillValue NaN;
    Float32 actual_range 0.045, 4.206;
    String bcodmo_name "biomass";
    String description "Total sardine biomass for the southwestern Benguela Current Ecosystem along the west coast of South Africa southeast through Cape Agulhas. Sampling season: November.";
    String long_name "Sar Tot Biomass";
    String units "millions of tons";
  }
  anc_recruitment {
    Float32 _FillValue NaN;
    Float32 actual_range 25.771, 627.2;
    String bcodmo_name "unknown";
    String description "Total anchovy recruitment for the southwestern Benguela Current Ecosystem along the west coast of South Africa southeast through Cape Infanta. Sampling season: May.";
    String long_name "Anc Recruitment";
    String units "billions of fish";
  }
  sardine_recruitment {
    Float32 _FillValue NaN;
    Float32 actual_range 0.439, 60.065;
    String bcodmo_name "unknown";
    String description "Total sardine recruitment for the southwestern Benguela Current Ecosystem along the west coast of South Africa southeast through Cape Infanta. Sampling season: May.";
    String long_name "Sardine Recruitment";
    String units "billions of fish";
  }
  ap_di_anc_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 24.9, 98.8;
    String bcodmo_name "mass";
    String description "Percent of the Dassen Island African penguin diet mass that was anchovy";
    String long_name "Ap Di Anc Pcnt Diet";
    String units "percent (%)";
  }
  ap_di_sar_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 32.2;
    String bcodmo_name "mass";
    String description "Percent of the Dassen Island African penguin diet mass that was sardine";
    String long_name "Ap Di Sar Pcnt Diet";
    String units "percent (%)";
  }
  ap_di_bs {
    Float32 _FillValue NaN;
    Float32 actual_range 0.651, 1.376;
    String bcodmo_name "unknown";
    String description "Dassen Island African penguin breeding success";
    String long_name "Ap Di Bs";
    String units "number of chicks fledged/pair";
  }
  ap_di_surv {
    Float32 _FillValue NaN;
    Float32 actual_range 0.43, 0.824;
    String bcodmo_name "unknown";
    String description "Dassen Island African penguin adult survival (fraction surviving)";
    String long_name "Ap Di Surv";
    String units "unitless (fraction)";
  }
  ap_ri_anc_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 33.1, 98.3;
    String bcodmo_name "mass";
    String description "Percent of the Robben Island African penguin diet mass that was anchovy";
    String long_name "Ap Ri Anc Pcnt Diet";
    String units "percent (%)";
  }
  ap_ri_sar_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 12.1;
    String bcodmo_name "mass";
    String description "Percent of the Robben Island African penguin diet mass that was sardine";
    String long_name "Ap Ri Sar Pcnt Diet";
    String units "percent (%)";
  }
  ap_ri_bs {
    Float32 _FillValue NaN;
    Float32 actual_range 0.319, 0.968;
    String bcodmo_name "unknown";
    String description "Robben Island African penguin breeding success";
    String long_name "Ap Ri Bs";
    String units "number of chicks fledged/pair";
  }
  ap_ri_surv {
    Float32 _FillValue NaN;
    Float32 actual_range 0.464, 0.998;
    String bcodmo_name "unknown";
    String description "Robben Island African penguin adult survival (fraction surviving)";
    String long_name "Ap Ri Surv";
    String units "unitless (fraction)";
  }
  cg_lb_anc_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 1.5, 83.1;
    String bcodmo_name "mass";
    String description "Percent of the Lamberts Bay Cape gannett diet mass that was anchovy";
    String long_name "Cg Lb Anc Pcnt Diet";
    String units "percent (%)";
  }
  cg_lb_sar_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 2.5, 66.5;
    String bcodmo_name "mass";
    String description "Percent of the Lamberts Bay Cape gannett diet mass that was sardine";
    String long_name "Cg Lb Sar Pcnt Diet";
    String units "percent (%)";
  }
  cg_lb_bs {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.982;
    String bcodmo_name "unknown";
    String description "Lamberts Bay Cape gannett breeding success";
    String long_name "CG LB BS";
    String units "number of chicks fledged/pair";
  }
  cg_lb_surv {
    Float32 _FillValue NaN;
    Float32 actual_range 0.768, 0.901;
    String bcodmo_name "unknown";
    String description "Lamberts Bay Cape gannett adult survival (fraction surviving)";
    String long_name "Cg Lb Surv";
    String units "unitless (fraction)";
  }
  cg_mi_anc_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 0.8, 46.5;
    String bcodmo_name "mass";
    String description "Percent of the Malgas Island Cape gannett diet mass that was anchovy";
    String long_name "Cg Mi Anc Pcnt Diet";
    String units "percent (%)";
  }
  cg_mi_sar_pcnt_diet {
    Float32 _FillValue NaN;
    Float32 actual_range 1.0, 64.1;
    String bcodmo_name "mass";
    String description "Percent of the Malgas Island Cape gannett diet mass that was sardine";
    String long_name "Cg Mi Sar Pcnt Diet";
    String units "percent (%)";
  }
  cg_mi_bs {
    Float32 _FillValue NaN;
    Float32 actual_range 0.0, 0.819;
    String bcodmo_name "unknown";
    String description "Malgas Island Cape gannett breeding success";
    String long_name "Cg Mi Bs";
    String units "number of chicks fledged/pair";
  }
  cg_mi_surv {
    Float32 _FillValue NaN;
    Float32 actual_range 0.761, 0.947;
    String bcodmo_name "unknown";
    String description "Malgas Island Cape gannett adult survival (fraction surviving)";
    String long_name "Cg Mi Surv";
    String units "unitless (fraction)";
  }
 }
  NC_GLOBAL {
    String access_formats ".htmlTable,.csv,.json,.mat,.nc,.tsv";
    String acquisition_description 
"These data include total anchovy and sardine biomass west and southeast of
Cape Agulhas (sampled in November), and anchovy and sardine recruitment west
of Cape Infanta (sampled in May). Seabird variables included % of the diet
comprised of anchovy, % of the diet comprised of sardine, breeding success,
and survival. African penguin data were collected at Dassen and Robben
Islands, South Africa. Cape gannets data were collected at colonies on Malgas
Island and in Lamberts Bay, South Africa.
 
These variables were entered into path analyses, run in Stata v. 11.1
(StataCorp). Path models were designed a priori with upwelling as the base
predictor variable, fish biomass (both species) and anchovy recruitment as
intermediate predictors, and a seabird metric as the response variable. Path
analysis produces beta coefficients for each path segment of the model. To
determine the dominant path of effect, the beta coefficients for each indirect
path were multiplied together, then summed. This sum of the products of all of
the indirect paths was compared to the beta coefficient of the direct path of
effect. The greater value indicated if the dominant path was direct or
indirect. If indirect, the path with the largest beta coefficient product was
considered the dominant path of the model. Note that fish biomass, and anchovy
recruitment data were ln-transformed prior to analysis. Anchovy and sardine
biomass were lagged one year for analyses of penguin responses, to precede the
penguin breeding season when those measurements were collected.";
    String awards_0_award_nid "564664";
    String awards_0_award_number "OCE-1434732";
    String awards_0_data_url "http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1434732";
    String awards_0_funder_name "NSF Division of Ocean Sciences";
    String awards_0_funding_acronym "NSF OCE";
    String awards_0_funding_source_nid "355";
    String awards_0_program_manager "David L. Garrison";
    String awards_0_program_manager_nid "50534";
    String cdm_data_type "Other";
    String comment 
"Benguela Current Ecosystem Path Analysis 
 PI: Bryan Black (Marine Science Institute, UT Austin) 
 Co-PIs: William Sydeman & Marisol Garcia Reyes (Farallon Institute), Steven Bograd (NOAA SWFSC) 
 Version: 01 Feb 2017";
    String Conventions "COARDS, CF-1.6, ACDD-1.3";
    String creator_email "info@bco-dmo.org";
    String creator_name "BCO-DMO";
    String creator_type "institution";
    String creator_url "https://www.bco-dmo.org/";
    String data_source "extract_data_as_tsv version 2.3  19 Dec 2019";
    String date_created "2017-02-01T20:53:45Z";
    String date_modified "2019-08-05T16:35:33Z";
    String defaultDataQuery "&amp;time&lt;now";
    String doi "10.1575/1912/bco-dmo.679946.1";
    String history 
"2024-04-19T14:14:57Z (local files)
2024-04-19T14:14:57Z https://erddap.bco-dmo.org/tabledap/bcodmo_dataset_679946.das";
    String infoUrl "https://www.bco-dmo.org/dataset/679946";
    String institution "BCO-DMO";
    String keywords "anc, anc_recruitment, anc_tot_biomass, ap_di_anc_pcnt_diet, ap_di_bs, ap_di_sar_pcnt_diet, ap_di_surv, ap_ri_anc_pcnt_diet, ap_ri_bs, ap_ri_sar_pcnt_diet, ap_ri_surv, aperture, bco, bco-dmo, biological, biomass, cg_lb_anc_pcnt_diet, cg_lb_bs, cg_lb_sar_pcnt_diet, cg_lb_surv, cg_mi_anc_pcnt_diet, cg_mi_bs, cg_mi_sar_pcnt_diet, cg_mi_surv, chemical, data, dataset, diet, dmo, erddap, management, oceanography, office, pcnt, preliminary, radar, recruitment, sar, sar_tot_biomass, sardine, sardine_recruitment, surv, synthetic, tot, year";
    String license "https://www.bco-dmo.org/dataset/679946/license";
    String metadata_source "https://www.bco-dmo.org/api/dataset/679946";
    String param_mapping "{'679946': {}}";
    String parameter_source "https://www.bco-dmo.org/mapserver/dataset/679946/parameters";
    String people_0_affiliation "University of Texas at Austin";
    String people_0_affiliation_acronym "UT Austin";
    String people_0_person_name "Bryan Black";
    String people_0_person_nid "51409";
    String people_0_role "Principal Investigator";
    String people_0_role_type "originator";
    String people_1_affiliation "National Oceanic and Atmospheric Administration - Southwest Fisheries Science Center";
    String people_1_affiliation_acronym "NOAA SWFSC ERD";
    String people_1_person_name "Steven Bograd";
    String people_1_person_nid "51411";
    String people_1_role "Co-Principal Investigator";
    String people_1_role_type "originator";
    String people_2_affiliation "Farallon Institute for Advanced Ecosystem Research";
    String people_2_person_name "Marisol  Garcia Reyes";
    String people_2_person_nid "674985";
    String people_2_role "Co-Principal Investigator";
    String people_2_role_type "originator";
    String people_3_affiliation "Farallon Institute for Advanced Ecosystem Research";
    String people_3_person_name "William Sydeman";
    String people_3_person_nid "51410";
    String people_3_role "Co-Principal Investigator";
    String people_3_role_type "originator";
    String people_4_affiliation "Farallon Institute for Advanced Ecosystem Research";
    String people_4_person_name "William Sydeman";
    String people_4_person_nid "51410";
    String people_4_role "Contact";
    String people_4_role_type "related";
    String people_5_affiliation "Woods Hole Oceanographic Institution";
    String people_5_affiliation_acronym "WHOI BCO-DMO";
    String people_5_person_name "Shannon Rauch";
    String people_5_person_nid "51498";
    String people_5_role "BCO-DMO Data Manager";
    String people_5_role_type "related";
    String project "CalBenJI";
    String projects_0_acronym "CalBenJI";
    String projects_0_description 
"Desciption from NSF award abstract:
Along the west coasts of North and South America, Africa, and Iberia, alongshore equatorward winds bring nutrient-rich waters to the sunlit surface of the ocean, stimulating phytoplankton blooms that support robust, rich and diverse ecosystems. This process is known as \"upwelling\". Because upwelling is driven by winds, and winds are related to atmospheric conditions, upwelling is highly vulnerable to the effects of climate change. However, the potential impacts of climate change on upwelling and biology remain largely uncertain. In earlier work in the California Current upwelling system, off the west coast of the United States, researchers found that upwelling occurs in distinct winter and summer \"modes\" that have different impacts on biology. In this project, oceanographic and atmospheric data from the Benguela Current system, off South Africa and Namibia, will be analyzed for similar seasonal patterns and relationships with the ecosystem. Comparisons between these two upwelling systems will allow researchers to investigate if previous findings of regional climate impacts on biology are applicable at a global scale and consider how these systems may change in the future. The project will facilitate collaboration between researchers from South Africa, Namibia, and the United States, integrating a team of young and senior scientists from the three countries and providing them with opportunities for broad-scale scientific synthesis early in their careers.
This project will be a comparative analyses of climate forcing and biological responses in the California Current (CCS) and Benguela Current systems (BCS), the two upwelling systems with the most similar time series of atmospheric and oceanographic conditions, seabird demography, and lower (chlorophyll) and mid (forage fish) trophic data. The project will determine whether changes in the ecosystems can be attributed to regional or global climate processes. Growth-increment chronologies from fish in the BCS (deep-water hake) will be developed as indicators of upper-trophic fish growth, and compared to rockfish growth chronologies developed in the CCS. Mid-trophic level fish abundance will be modeled as indices of prey availability for integration between climate and upper-trophic-level parameters. Oceanographic and atmospheric data will be analyzed from global observational and reanalysis data sets, as well as from earth system model projections of climate change. The project will address the following questions:
1) are seasonal upwelling modes (winter and summer) discernible in the BCS as they are in the CCS?
2) are upwelling modes forced by similar or contrasting atmospheric forcing mechanisms?
3) is there evidence of coherence/covariance among mid-trophic fish, upper-trophic fish, and seabirds (and at which lags) within and between the CCS and BCS?
4) will the positioning and amplitude of the atmospheric pressure systems that result in upwelling-favorable winds change coherently between ecosystems under various climate-change scenarios? and
5) what are the fisheries and wildlife management implications for variability in the seasonality and spatial distribution of upwelling in a changing climate?";
    String projects_0_end_date "2016-08";
    String projects_0_geolocation "California Current Ecosystem and Benguela Current Ecosystem";
    String projects_0_name "Climate Change and Upwelling -- Comparative Analysis of Current and Future Responses of the California and Benguela Ecosystems";
    String projects_0_project_nid "564665";
    String projects_0_start_date "2014-09";
    String publisher_name "Biological and Chemical Oceanographic Data Management Office (BCO-DMO)";
    String publisher_type "institution";
    String sourceUrl "(local files)";
    String standard_name_vocabulary "CF Standard Name Table v55";
    String summary "Path analysis, run in Stata v. 11.1, for direct/indirect effects of upwelling on seabirds; data were collected at Dassen and Robben Islands, Malgas Island and in Lamberts Bay, South Africa.";
    String title "Path analysis, run in Stata v. 11.1, for direct/indirect effects of upwelling on seabirds; data were collected at Dassen and Robben Islands, Malgas Island and in Lamberts Bay, South Africa";
    String version "1";
    String xml_source "osprey2erddap.update_xml() v1.3";
  }
}

Using tabledap to Request Data and Graphs from Tabular Datasets

tabledap lets you request a data subset, a graph, or a map from a tabular dataset (for example, buoy data), via a specially formed URL. tabledap uses the OPeNDAP (external link)

Data Access Protocol (DAP) (external link)

and its selection 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 or Subset form. They make 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.

Tabledap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/tabledap/pmelTaoDySst.htmlTable?longitude,latitude,time,station,wmo_platform_code,T_25&time>=2015-05-23T12:00:00Z&time<=2015-05-31T12:00:00Z
Thus, the query is often a comma-separated list of desired variable names, followed by a collection of constraints (e.g., variable<value), each preceded by '&' (which is interpreted as "AND").

For details, see the tabledap Documentation.