In 2019, the Pacific Northwest Research Station joined forces with its partners in state governments, universities, and a host of other organizations to combine efforts in the Carbon Research Initiative. The overarching goal of the initiative is to increase understanding of the tradeoffs and synergies among different approaches to forest stewardship. In essence, the Carbon Research Initiative seeks to identify which forest stewardship practices and resource uses are most beneficial for sequestering and storing excess atmospheric carbon emissions that drive global warming.
The 2022 Carbon Research Initiative Business Report summarizes the objectives, progress, and next steps for 8 projects associated with the initiative. The business report also provides an update on the financial contributions by the station and partners and provides links to products such as databases, websites, and publications.
Quantifying above-ground biomass changes, DAGB, is key for understanding carbon dynamics. National Forest Inventories, NFIs, aims at providing precise estimates of DAGB relying on model-assisted estimators that incorporate auxiliary information to reduce uncertainty. Poststratification estimators, PS, are commonly used for this task. Recently proposed endogenous poststratification, EPS, methods have the potential to improve the precision of PS estimates of DAGB. Using the state of Oregon, USA, as a testing area, we developed a formal comparison between three EPS methods, traditional PS estimators used in the region, and the Horvitz-Thompson, HT, estimator. Results showed that gains in performance with respect to the HT estimator were 9.71% to 19.22% larger for EPS than for PS. Furthermore, EPS methods easily accommodated a large number of auxiliary variables, and the inclusion of independent predictions of DAGB as an additional auxiliary variable resulted in further gains in performance.
Long-term, place-based research programs in the National Science Foundation-supported Long Term Ecological Research (LTER) Network have had profound effects on public policies and practices in land use, conservation, and the environment. While less well known than their contributions to fundamental ecological science, LTER programs’ commitment to serving broad public interests has been key to helping achieve their mission to advance basic science that supports society’s need to address major environmental challenges. Several attributes of all LTER programs are critical to these accomplishments: highly credible science, strong site-level leadership, long-term environmental measurements of ecosystem attributes that are relevant to the public and to resource managers, and effective and accessible information that supports sound management practices. Less recognized attributes of three case study LTER sites (Andrews Forest, Harvard Forest, Hubbard Brook) which have contributed to major impacts include strong interdisciplinary research communities with cultures of openness, dispersed leadership within those communities, a commitment to carry science perspectives to society through multiple governance processes, strong public-private partnerships, and communications programs that facilitate the exchange of information and perspectives among science communities, policy-makers, land managers, and the public. Taken together, these attributes of sites drive on-the-ground outcomes. These case studies reveal a virtue of the long-term nature of LTER not anticipated when the program began: that the decades-long engagement of a place-based, science community can have a major impact on environmental policies and practices. These activities, and the cultivation of science communities that can accomplish them, go beyond the initial directives and review criteria for LTER site proposals and programs.
This chapter investigates how the National Science Foundation’s (NSF) Long Term Ecological Research (LTER) Program has changed from 1980 to 2018. The LTER program is designed to balance persistence with response to change in science, society, and ecosystems through renewable 6-year grants subjected to peer review at the midterm and at renewal. The LTER program had an initial period of rapid growth with some terminations (1980s), a middle period of slower growth with no terminations (1990–2010), and a third period of no net growth, with added and terminated sites and an accelerated rate of site probations (2010s). Changes in the character and composition of the LTER program are associated with changes in leadership and research directions within individual LTER sites, as well as changes in the sources of funding for the LTER program within NSF, turnover in NSF program officers, and changes in review criteria used to renew LTER site funding. In the past decade, a focus on conceptual frameworks as a tool for integrating LTER research emerged from the LTER renewal review process. Given the accelerated pace of environmental change, the need for long-term ecological research is even more urgent today than when NSF established the pioneering LTER program. The LTER Program history reveals important lessons for how to structure and manage long-term ecological research.
This chapter assesses the current state of the science regarding the composition, intensity, and drivers of wildland fire emissions in the USA and Canada. Globally and in the USA wildland fires are a major source of gases and aerosols which have significant air quality impacts and climate interactions. Wildland fire smoke can trigger severe pollution episodes with substantial effects on public health. Fire emissions can degrade air quality at considerable distances downwind, hampering efforts by air regulators to meet air standards. Fires are a major global source of aerosols which affect the climate system by absorbing and scattering radiation and by altering optical properties, coverage, and lifetime of clouds. A thorough understanding of fire emissions is essential for effectively addressing societal and climate consequences of wildland fire smoke.
National forest inventories (NFI), such as the one conducted by the United States Forest Service Forest Inventory and Analysis (FIA) program, provide valuable information regarding the status of forests at regional to national scales. However, forest managers often need information at stand to landscape scales. Given various small area estimation (SAE) approaches, including design-based and model-based estimation, it may not be clear which is most appropriate for the user’s application. In this study, our objective was to assess the uncertainty in tree aboveground live carbon (ALC) estimates for differing modes of SAE across multiple scales to provide guidance for appropriate scales of application. We calculated means and variances for ALC with design-based (Horvitz-Thompson), model-assisted (generalized regression), and model-based (k-nearest neighbor synthetic) estimators for estimation units over a range of sizes for 30 subregions in California, United States. For larger areas (10,000-64,800 ha), relative efficiencies greater than one indicated that the generalized regression estimator (GREG) generated estimates with less error than the Horvitz-Thompson estimator (HT), while the bias-adjusted synthetic estimator relative efficiency compared to either the Horvitz- Thompson or model-assisted estimators exceeded one for areas 25,000 ha and smaller. Variance estimates from the unadjusted synthetic estimator underestimated the total error, because the estimator ignores bias and thus only addresses model variance. Across scales (250-64,800 ha, 0-27 plots per area of interest), 93% of the variation in the synthetic estimator’s relative standard error was explained by forest area, forest dominance, and regional variation in forest landscapes. Our results support model-assisted estimation use except for small areas where few plots (< 10 in the current study) are available for generating estimates in spite of biases in estimates. However, users should exercise caution when interpreting model-based estimates of error as they may not account for model mis-specification, and thus induced bias. This research explored multiple scales of application for SAE procedures applied to NFI data regarding carbon pools, potentially supporting a multi-scale approach to forest monitoring. Our results guides users in developing defensible estimates of carbon pools, particularly as it relates to the limits of inference at a variety of spatial scales.
Rapid climate change over the coming century will impact suitable habitat for many tree species. In response to these changes in climate, areas that become unsuitable will see higher mortality and lower growth and recruitment. Therefore, early detection of demographic trends is critical for effective forest management. Recent 10-year remeasurement data from the United States (US) Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) Program’s national annual inventory of forest land provides an ideal data set for analyzing such trends over large areas. However, failure to distinguish between areas of future habitat contraction and expansion or persistence when estimating demographic trends may mask species’ shifts. We used remeasurement data to compare observed tree demographic rates with projected impacts of climate change for five important tree species in the Pacific Northwest. Projected impacts were based on spatial-Bayesian hierarchical models of species distributions, which were used to project areas where habitat would persist (remain climatically suitable), expand (become suitable), or contract (become unsuitable) under four future climate scenarios for the 2080s. We compared estimates of mortality and net-growth between these areas of shifting suitability and a naïve division of habitat based on elevation and latitude. Within these regions, we assessed the sustainability of mortality and determined that observational data suggest that climate change impacts were already being felt in some areas by some species. While there is an extensive literature on bioclimatic species distribution models, this work demonstrates they can be adapted to the practical problem of detecting early climate-related trends using national forest inventory data. Of the species examined, California black oak (Quercus kelloggii) had the most notable instances of observed data suggesting population declines in the core of its current range.