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Resources

Resources

A repository of data, publications, tools, and other products from project teams, Science Collaborative program, and partners.

Displaying 1 - 10 of 15
Tool |

With funding from the NERRS Science Collaborative, scientists from 12 biogeographically diverse Reserves compared fluorescence measurements taken by the YSI EXO TAL sensor to extracted chlorophyll concentrations processed in the lab.

Tool |

Standardized protocols for sensor-based chlorophyll monitoring are now available for use by staff around the system to implement high frequency chlorophyll monitoring at their reserves.

Report |

Recommendations for the NERRS SWMP, summarizing outputs in an archivable format deemed useful by end users (NERRS research staff).

Project Overview |

This project overview describes a 2018 Catalyst project led by Grand Bay Reserve that developed standardized tools to quality-check, analyze, and visualize Surface Elevation Table data.

Project Overview |

This project overview describes a 2016 Collaborative Research project in which researchers are conducting the first-ever comprehensive blue carbon assessment in Pacific Northwest tidal wetlands.

Project Overview |

This project overview describes a 2018 Catalyst project that demonstrated the feasibility of including carbon finance in funding strategies that support the conservation and restoration of tidal wetlands in the Pacific Northwest.

Project Overview |

This project overview describes a 2018 Catalyst project that created an Olympia oyster restoration network to enhance the success of West Coast restoration efforts.

Report |

This needs assessment of conservation policy stakeholders in the Pacific Northwest identified data needs and barriers for potential blue carbon project partners.

Report |

This national synthesis report analyzes SET data from 15 National Estuarine Research Reserves across the continental United States, summarizing wetland water level trends over a 19-year period.

Tool |

A 2018 catalyst project developed tools for working with SET data including a series of computer codes - R scripts - for processing, quality checking, analyzing and visualizing these complex datasets. The statistical codes re available through GitHub and are explained in a Guide to the SETr Workflow.