Carbon science synthesis project

Project Description

Team members will search existing online databases to gather available literature and data on carbon dynamics. That knowledge will then be synthesized within the geographic bounds of Alaska, British Columbia, Washington, Oregon, California, and Hawai’i to determine what is currently known about carbon dynamics within the PNW plus geographic region.

Purpose and Scope

The primary goal of this synthesis is to (1) help land managers and other interested parties understand what is and is not known about managing forests, rangelands, and associated aquatic resources for carbon and (2) identify critical gaps in knowledge about land management for carbon. Identifying these critical gaps will help in developing a cohesive research agenda for Forest Service researchers and partners. Our team has engaged individuals from agencies and organizations in west coast states, National Forest System personnel, and university faculty to identify what carbon dynamics (interactions between ecosystem carbon pools and fluxes) are most critical to understand.

Methods

This study focuses on carbon dynamics in the PNW plus geographic region. We are consolidating existing literature and data into easily accessed databases to serve as resources for synthesis products. These databases will be the foundation for a carbon research repository that can be updated as needed. Topic experts reviewed the collection of literature and data to ensure that major research topics are well represented in the collection and that foundational literature is accounted for.

The initial literature collection was gathered by downloading from TreeSearch all Pacific Northwest Research Station publications with the keyword carbon. This initial set of papers can be used to determine where the research station is well represented in carbon dynamics research, and where gaps exist. Additional published articles were added from an existing review paper (McKinely et al. 2011), and a forward search from this paper captured more recent publications on carbon dynamics within the PNW plus geographic region. Additional literature searches were performed using online search engines WebOfKnowledge and Google Scholar to ensure that important research not contained in the previous searches is included in the collection. This literature collection is available in an online reference manager, searchable with keyword tags for quick sorting of papers into specific topic categories.

Harvesting data from existing online databases, as well as from the literature collected in our literature database, creates an opportunity to evaluate carbon dynamics across different regions and helps determine underlying relationships between carbon pools, fluxes, disturbances, and management. This data collection effort is led by the core research station team members with assistance from carbon research initiative participants. All data collected will be georeferenced when possible and the final database will be analyzed for relationships to climates and geography. By combining the data in one database, trends in carbon dynamic response to time, disturbance, and management should be easier to determine across the large geographic range of the study.

The literature review and analysis on harvested data will lead to the creation of two synthesis papers targeted to peer-reviewed journals. The initial synthesis paper will cover key ecoregions within the PNW plus geographic region, combining ecoregions with similar trends in carbon dynamics to determine where the largest pools and fluxes are, what form they generally take, how management impacts carbon dynamics, and what critical gaps exist in the research.

The second paper will focus on analyzing all the data we extracted along with available online databases for a more detailed view of ecosystem carbon processes resulting in detailed maps of carbon pool and flux estimates for the PNW plus geographic region.

Project Deliverables

Peer-reviewed journal articles, a shared bibliographic database, and shared input and output datasets.