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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 - 8 of 8
Journal Article |
Abstract

Thin-layer sediment placement (TLP) is a promising management tool for enhancing tidal marsh resilience to rising seas.

Data |

These data encompass the nearshore fish surveys conducted by the Kachemak Bay Reserve as part of a 2020 catalyst project that expanded research collaborations and completed proof of concept activities to catalyze future research on the mechanisms of paralytic shellfish toxin transfer from forage fish to upper trophic populations.

Journal Article |

This 2021 paper from the University of South Florida discusses how machine learning was used to map aquifers throughout the Kenai Lowlands to locate groundwater discharge, providing a framework to extend this method of modeling groundwater to other reserves.

Journal Article |

This 2021 article which appeared in Ecology and Society describes a research project exploring how alders, peatlands, and groundwater flows were incorporated into a spatial tool that was used in case studies with user groups and in outreach efforts. The paper includes evidence that these efforts to engage with stakeholders are resulting in attitudinal shifts as well as on-the-ground changes in peoples ’ decision-making.

Data |
About this Project

Thin-layer placement (TLP) is an emergent climate adaptation strategy that mimics natural deposition processes in tidal marshes by adding a small amount of sediment on top of marsh in order to maintain elevation relative to sea level rise.

Journal Article |
Abstract

The ocean is inextricably linked to human societies. Climate change and its associated impacts to the aquatic environment pose problems for human communities as well.

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.

Data |

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