Skip to main content

Resources

Resources

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

Displaying 61 - 70 of 99
Data |

This geodatabase contains GIS layers that illustrate the distribution of existing wetlands and identify locations where restoration is likely to have the greatest positive environmental impact in Douglas County, WI.

Multimedia |

This webinar, which originally aired on December 12, 2013, discusses the Tijuana River Reserve's collaborative efforts to develop a vulnerability assessment that informs an adaptation strategy to address sea level rise and riverine flooding.

Report |

Southern California ’s coastal environments are under intense development pressure. In the Tijuana River Valley, this pressure translates into the fragmentation and loss of coastal wetlands that provide invaluable services, such as water quality protection.

Project Overview |

This project overview describes a 2012 Collaborative Research project that developed a decision-making framework and tools to guide coastal wetland recovery and management in Southern California.

Project Overview |

This project overview describes a 2013 Collaborative Research project in which an array of partners created a watershed-scale wetland conservation plan in Wisconsin's Douglas County.

Project Overview |

This project overview describes a 2013 project that created an online portal for scientists and fisheries managers to share and use data on larval fish recruitment and environmental variables.

Report |

This document summarizes a tool developed by the NERRS to evaluate and compare the ability of tidal marshes to thrive as sea level rises.

Journal Article |

This paper, published in Biological Conservation, describes an innovative approach developed by the NERRS to evaluate the ability of tidal marshes to thrive as sea levels rise.

Tool |

This tool is a novel approach to compare the resilience of different marshes to sea level rise.

Data |

This code (R and MATLAB) can be used to analyze NERRS System-Wide Monitoring Program time series data.