Operational assessment tool for forest carbon dynamics for the United States: a new spatially explicit approach linking the LUCAS and CBM-CFS3 models.

BACKGROUND: Quantifying the carbon balance of forested ecosystems has been the subject of intense study involving the development of numerous methodological approaches. Forest inventories, processes-based biogeochemical models, and inversion methods have all been used to estimate the contribution of U.S. forests to the global terrestrial carbon sink. However, estimates have ranged widely, largely based on the approach used, and no single system is appropriate for operational carbon quantification and forecasting. We present estimates obtained using a new spatially explicit modeling framework utilizing a "gain-loss" approach, by linking the LUCAS model of land-use and land-cover change with the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3). RESULTS: We estimated forest ecosystems in the conterminous United States stored 52.0 Pg C across all pools. Between 2001 and 2020, carbon storage increased by 2.4 Pg C at an annualized rate of 126 Tg C year-1. Our results broadly agree with other studies using a variety of other methods to estimate the forest carbon sink. Climate variability and change was the primary driver of annual variability in the size of the net carbon sink, while land-use and land-cover change and disturbance were the primary drivers of the magnitude, reducing annual sink strength by 39%. Projections of carbon change under climate scenarios for the western U.S. find diverging estimates of carbon balance depending on the scenario. Under a moderate emissions scenario we estimated a 38% increase in the net sink of carbon, while under a high emissions scenario we estimated a reversal from a net sink to net source. CONCLUSIONS: The new approach provides a fully coupled modeling framework capable of producing spatially explicit estimates of carbon stocks and fluxes under a range of historical and/or future socioeconomic, climate, and land management futures.

Assessing ecological uncertainty and simulation model sensitivity to evaluate an invasive plant species' potential impacts to the landscape.

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass (Cenchrus ciliaris) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.

Great Basin land management planning using ecological modeling.

This report describes a land management modeling effort that analyzed potential impacts of proposed actions under an updated Bureau of Land Management Resource Management Plan that will guide management for 20 years on 4.6 million hectares in the Great Basin ecoregion of the United States. State-and-transition models that included vegetation data, fire histories, and many parameters (i.e., rates of succession, fire return intervals, outcomes of management actions, and invasion rates of native and nonnative invasive species) were developed through workshops with scientific experts and range management specialists. Alternative restoration scenarios included continuation of current management, full fire suppression, wildfire use in designated fire use zones, wildfire use in resilient vegetation types only, restoration with a tenfold budget increase, no restoration treatments, and no livestock grazing. Under all the scenarios, cover of vegetation states with native perennial understory declined and was replaced by tree-invaded and weed-dominated states. The greatest differences among alternative management scenarios resulted from the use of fire as a tool to maintain native understory. Among restoration scenarios, only the scenario assuming a tenfold budget increase had a more desirable outcome than the current management scenario. Removal of livestock alone had little effect on vegetation resilience. Rather, active restoration was required. The predictive power of the model was limited by current understanding of Great Basin vegetation dynamics and data needs including statistically valid monitoring of restoration treatments, invasiveness and invasibility, and fire histories. The authors suggest that such computer models can be useful tools for systematic analysis of potential impacts in land use planning. However, for a modeling effort to be productive, the management situation must be conducive to open communication among land management agencies and partner entities, including nonprofit organizations.