We integrated a mechanistic wildfire simulation system with an agent-based landscape change model to investigate the feedbacks among climate change, population growth, development, landowner decision-making, vegetative succession, and wildfire. Our goal was to develop an adaptable simulation platform for anticipating risk-mitigation tradeoffs in a fire-prone wildland–urban interface (WUI) facing conditions outside the bounds of experience. We describe how five social and ecological system (SES) submodels interact over time and space to generate highly variable alternative futures even within the same scenario as stochastic elements in simulated wildfire, succession, and landowner decisions create large sets of unique, path-dependent futures for analysis. We applied the modeling system to an 815 km² study area in western Oregon at a sub-taxlot parcel grain and annual timestep, generating hundreds of alternative futures for 2007–2056 (50 years) to explore how WUI communities facing compound risks from increasing wildfire and expanding periurban development can situate and assess alternative risk management approaches in their localized SES context. The ability to link trends and uncertainties across many futures to processes and events that unfold in individual futures is central to the modeling system. By contrasting selected alternative futures, we illustrate how assessing simulated feedbacks between wildfire and other SES processes can identify tradeoffs and leverage points in fire-prone WUI landscapes. Assessments include a detailed “post-mortem” of a rare, extreme wildfire event, and uncovered, unexpected stabilizing feedbacks from treatment costs that reduced the effectiveness of agent responses to signs of increasing risk.

To develop effective long-term strategies, natural resource managers need to account for the projected effects of climate change as well as the uncertainty inherent in those projections. Vegetation models are one important source of projected climate effects. We explore results and associated uncertainties from the MC2 Dynamic Global Vegetation Model for the Pacific Northwest west of the Cascade crest. We compare model results for vegetation cover and carbon dynamics over the period 1895–2100 assuming: 1) unlimited wildfire ignitions versus stochastic ignitions, 2) no fire, and 3) a moderate CO₂ fertilization effect versus no CO₂ fertilization effect. Carbon stocks decline in all scenarios, except without fire and with a moderate CO₂ fertilization effect. The greatest carbon stock loss, approximately 23% of historical levels, occurs with unlimited ignitions and no CO₂ fertilization effect. With stochastic ignitions and a CO₂ fertilization effect, carbon stocks are more stable than with unlimited ignitions. For all scenarios, the dominant vegetation type shifts from pure conifer to mixed forest, indicating that vegetation cover change is driven solely by climate and that significant mortality and vegetation shifts are likely through the 21^st century regardless of fire regime changes.

Ecosystem services (ES) were conceived to emphasize the role of ecological processes in supporting societal needs and to allow their inclusion in the decision-making process. Currently climate change mitigation is one of the most important services ecosystems can provide to enhance sinks of greenhouse gas emissions as the planet warms and related extreme events take their toll on societies. Because ES cannot always be directly measured and because measurements are often cost prohibitive, process-based models are used to estimate their supply, delivery and/or value. We ran the MC2 dynamic global vegetation model for the conterminous US with/without land use for several future scenarios. We translated results into key ES such as carbon sequestration, which contributes to climate regulation, into a regulatory service or aboveground forest carbon into timber biomass, a provisioning service, and evaluated constraints to maintain them. By comparing projections with/without land use we illustrated differences between managed and natural lands and provided information to help the valuation of societally relevant services.

Conservation Biology Institute (CBI) has been developing web applications to centralize and serve credible and usable information that allows natural resource managers, as well as the general public, to better understand the challenges posed by on-going environmental change. In particular CBI has designed a series of climate consoles that provide natural resource managers the most recent 5th Climate Model Intercomparison Program (CMIP5) climate projections, landscape intactness, and soil sensitivity for a series of reporting units over the western United States. The publically available web sites were refined based on feedback from a variety of users. In this paper, we describe each of the tools developed as open-source applications and provide details of their infrastructure in the hope they can be used and possibly modified by a wider audience. They were designed to be used as stepping-stones towards planning effective climate change adaptation strategies.

Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO₂ levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.