Anticipating plausible future ecosystem states is necessary for effective ecosystem management. We use climate analog-based impact models and a co-production process with land managers to project future vegetation changes for the state of Oregon, United States, (2041–2070, RCP 8.5) at a management-relevant spatial resolution (270-m). We explore multiple analog-based methodologies, evaluate analog model performance with contemporary validation, and leverage climate analogs to assess projection uncertainty by quantifying areas where multiple vegetation trajectories are plausible under a single climate scenario. We find that analog-based models performed well at reproducing landscape-level vegetation composition, and moderately well at reproducing vegetation at the pixel level. Our results suggest that 64% of the study area will experience future climate conditions that support different potential natural vegetation types and 59% will experience climates corresponding with different potential plant physiognomic types, compared to reference-period conditions. We project a 60% reduction of mesic conifer-dominated forests with transitions to mixed evergreen forest types. We also project losses to dry forests, cold forests and parklands, with commensurate expansions of shrublands, grasslands, and geographic redistribution of dry forest types. We find that in many areas, several vegetation trajectories are plausible under a single climate scenario. Finally, we provide guidance for using future vegetation projections and uncertainty outputs in management decisions using the Resist-Accept-Direct (RAD) adaptation framework.

We investigated recent forest mortality dynamics by revisiting a network of 1617 trees in unlogged roadless areas in the Blue Mountains of eastern Oregon for which we have precise age and growth data. There was extensive mortality of the oldest trees in stands-a quarter of trees >= 300 years of age died over a ten-year period. Trees with below average basal area increment and older trees were more likely to die in stands that have not experienced wildfire in the last 130 years. Smaller trees were more likely to die in stands that experienced wildfire during the ten-year observation period. Douglas-fir and western juniper experienced a lower rate of mortality than ponderosa pine, western larch, or grand fir in sites that have not experienced wildfire. There was little evidence for a difference in mortality between species in sites that experienced wildfire. We describe a novel simulation model which demonstrated that succession of young trees is unlikely to replace recent losses of old trees. The confluence of a rare insect defoliator outbreak, drought, and past management decisions that increased competitive stress and fuel connectivity is the most likely explanation for the elevated mortality we observed. The protected areas where we collected data are not achieving old tree conservation objectives. Active management to reduce forest competition is the most realistic pathway for old tree conservation in dry forests.

This study of late-seral Douglas-fir forests in coastal Washington examined scales from 0.1 to 65-80 ha to quantify persistence of the Douglas-fir overstory and patterns of aboveground biomass (henceforth biomass) distribution. Plot-based biomass estimates were scaled from 0.1-1 ha via tree-level allometry in four forests with Douglas-fir 180-600 yr old. At the 0.1 ha scale, biomass ranged from 69-2905 Mg ha(-1) live and 12-792 Mg ha(-1) dead with magnitude and spatial variability increasing with dominant tree age. Some 0.1 ha areas had > 2000 Mg ha(-1) live Douglas-fir alone. Our 1-3 ha plots held 1100-1700 Mg ha(-1) biomass with Douglas-fir always accounting for > 70 % of live biomass and > 46 % of crown volume despite having only 23 trees ha(-1) when trees exceeded 500 yr old. Dead biomass in plots ranged from 10-25 % of the total with 50-85 % being Douglas-fir. A segmented canopy height model from airborne laser scanning (i.e., ALS segments) was mapped to 191 measured trees to create an allometric equation for predicting live biomass. This equation was applied to all ALS segments across 288 ha of forest with dominant trees ranging from 340-620 yr old. Within plot boundaries, ALS-predicted live tree biomass errors ranged from 10-23 %. At 65-80 ha spatial scales, patterns of biomass revealed a breakup of high-biomass forest into aggregations of < 1-ha patches. The largest trees (> 50 Mg) were associated with high-biomass areas when Douglas-fir were younger, but beyond 500 yr, such elite trees increasingly occurred in relatively low-biomass areas. Estimated live tree biomass ranged from 112-1600 Mg ha(-1) with median values decreasing with Douglas-fir age from similar to 900 Mg ha(-1) at 350 yr to similar to 600 Mg ha(-1) at 600 yr. In Douglas-fir-dominated rainforests, restoration management can promote long-lived high-biomass forests and arboreal biodiversity through actions that enhance development of elite trees across all overstory age cohorts, emulating patterns of biomass distribution observed here.

Climate change and the compounding effects of drought and wildfire are catalyzing rapid ecosystem changes throughout the world. Relatively dry, trailing edge (TE) forests are especially vulnerable to ecological transformation when tree regeneration is moisture-limited following high-severity fire. Here, we illustrate the potential landscape-scale impacts of changing disturbance regimes by focusing on TE forests in the eastern Cascades of Washington, USA. Our specific objectives were to: (1) map TE forests based on climatic water deficit and forest cover; (2) characterize the composition, structure, and ownership of TE and non-TE forests; (3) quantify recent fire activity in TE and non-TE forests; (4) identify locations of potential forest loss where recent fires have burned severely in TE forests. Across the study area, TE forests encompassed 387 000 ha, representing a substantial portion (21 per cent) of the total forested landscape. TE forests generally were characterized by dry, mixed-conifer forest types with more open structure and less biomass than non-TE forests. The structural and compositional conditions within TE forests make them ideal locations for management strategies designed to enhance landscape resilience and sustain fire-resistant trees. TE forestland ownership is diverse (35 per cent federal, 19 per cent Tribal, 16 per cent Washington State, 14 per cent private non-industrial and 13 per cent private industrial), indicating that successful land management will require collaboration among numerous partners. Recent wildfires (1984-2020) cumulatively covered 84 300 ha (22 per cent) of TE forests and 363 500 ha (25 per cent) of non-TE forests. TE forests experienced less high-severity fire than non-TE forests (39 per cent vs. 46 per cent, respectively). Recent high-severity fire effects in TE forests occurred primarily in the northern portion of the study region, reflecting the distribution of individual large fires. By quantifying the variability of TE forests and their recent fire activity, this study supports adaptive management strategies for landscape restoration, post-disturbance reforestation and climate adaptation.

Contemporary reference sites in California's Sierra Nevada represent areas where a frequent, low-intensity fire regime – an integral ecological process in temperate dry forests – has been reintroduced after several decades of fire suppression. Produced by an intact fire regime, forest structural patterns in these sites are likely more resilient to future disturbances and climate, and thus can provide reference conditions to guide management and ecological research. In this paper, we present a set of 119 delineated contemporary reference sites in the Sierra Nevada yellow pine and mixed-conifer zone along with a suite of key remote sensing-derived forest structure metrics representing conditions within these sites. We also provide a set of summary figures for individual reference sites and sites grouped by dominant climate class. We identified restored frequent-fire landscapes using a combination of fire history, burn severity, management history, and forest type datasets and we delineated individual polygons using catchment basins, fire perimeters, and imagery. Reference sites ranged in size from 101-966 ha with a mean size of 240 ha. Where available (for 59 sites), we used airborne lidar datasets to characterize a suite of key forest structure metrics within reference sites. Across all 119 sites, we provide a set of forest structure metrics produced by the California Forest Observatory. Reference sites were categorized based on their dominant climate class to assist users in identifying the most climatically relevant reference conditions for their project or study area. We encourage the use of the reference sites and associated forest structure datasets for guiding ecologically focused forest management and research in the Sierra Nevada.

Geospatial polygons representing fire-suppressed control sites against which fire-intact reference sites were compared in Chamberlain et al. (2023). Control sites represent areas with 1) no record of fire history, 2) no record of late 20th century or early 21st century timber management, and 3) no "Fast Change" detected by the Landscape Change Monitoring System dataset. All sites are predominantly within the yellow pine and mixed-conifer zone of California's Sierra Nevada, USA. Polygon boundaries were defined using the NHDPlusV2 catchments, and were manually reshaped using aerial imagery to ensure that polygons were > 100 ha, represented primarily forested areas, and excluded major roads, infrastructure, and major rock outcrops. Detailed description of the methods used to produce this dataset provided in: Chamberlain, C.P., Cova, G.R., Cansler, C.A., North, M.P., Meyer, M.D., Jeronimo, S.M.A., Kane, V.R., 2023. Consistently heterogeneous structures observed at multiple spatial scales across fire-intact reference sites. Forest Ecology and Management.

We investigated the relative importance of daily fire weather, landscape position, climate, recent forest and fuels management, and fire history to explaining patterns of remotely-sensed burn severity - as measured by the Relativized Burn Ratio - in 150 fires occurring from 2001 to 2019, which burned conifer forests of northeastern Washington State, USA. Daily fire weather, annual precipitation anomalies, and species' fire resistance traits were important predictors of wildfire burn severity. In areas burned within the past two to three decades, prior fire decreased the severity of subsequent burns, particularly for the first 16 postfire years. In areas managed before a wildfire, thinning and prescribed burning treatments lowered burn severity relative to untreated controls. Prescribed burning was the most effective treatment at lowering subsequent burn severity, and prescribed burned areas were usually unburned or burned at low severity in subsequent wildfires. Patches that were harvested and planted <10 years before a wildfire burned with slightly higher severity. In areas managed within 5 years after an initial fire, postfire harvest and planting reduced prevalence of stand-replacing fire in reburns. However, overall, postfire management actions after a first wildfire only weakly influenced the severity of subsequent fires. The importance of fire-fire interactions to moderating burn severity establishes the importance of stabilizing feedbacks in active fire regimes, and our results demonstrate how silvicultural treatments can be combined with prescribed fire and wildfires to maintain resilient landscapes.

Background In explaining and predicting spatial vegetation patterns, ecologists have increasingly favoured the use of climatic water balance variables, including actual evapotranspiration (AET) and climatic water deficit (CWD), for representing the hydrologic and energetic environment experienced by plants. Much of the interest in these variables lies in their hypothesized potential to characterize biologically relevant environmental variation more directly than simple climate variables, such as precipitation and temperature. Practically, obtaining AET and CWD values across space requires hydrologic process models that involve assumptions, including assumptions about vegetation transpiration rates. However, transpiration parameter values are rarely known with precision and can vary several-fold within and among vegetation types. Approach We evaluate the extent to which assumptions about vegetation physiology in water balance models affect (a) relative spatial variation in modelled water balance values and (b) ecological inferences that are derived from analyses using water balance variables. We demonstrate an approach for identifying inferences that are robust to these assumptions. Results Assumptions about vegetation physiology can substantially affect relative spatial variation in modelled water balance values. More importantly, such assumptions can also substantially affect the inferences (e.g., expected vegetation distributions) drawn from ecological analyses that use water balance variables as predictors. Water balance variables are less sensitive to assumptions in environmental settings with abundant water supply, where AET variation is driven primarily by available energy (e.g., temperature and insolation), but they can be highly sensitive to assumptions in drier environments. Conclusion Because of their sensitivity to assumptions, water balance variables are not unambiguously superior to simpler climatic and topographic variables, such as precipitation and temperature. However, they retain some advantages, primarily related to their mechanistic incorporation of interactions between water and energy, which may support their use in applications where sensitivity to hydrologic modelling assumptions is low or of minor concern.

In the western US, wildfires are modifying the structure, composition, and patterns of forested landscapes at rates that far exceed mechanical thinning and prescribed fire treatments. There are conflicting narratives as to whether these wildfires are restoring landscape resilience to future climate and wildfires. To evaluate the landscape-level work of wildfires, we assessed four subwatersheds in eastern Washington, USA that experienced large wildfires in 2014, 2015, or 2017 after more than a century of fire exclusion and extensive timber harvest. We compared preand post-fire landscape conditions to an ecoregion-specific historical (HRV) and future range of variation (FRV) based on empirically established reference conditions derived from a large dataset of historical aerial photo imagery. These four wildfires proved to be a blunt restoration tool, moving some attributes towards more climate-adapted conditions and setting others back. Fires reduced canopy cover and decreased overall tree size and canopy complexity, which moved them into, or slightly outside, the FRV ranges. Moderate- and low-severity fire generally shifted closed-canopy forest structure to open-canopy classes. Patches of high-severity fire shifted patterns of forest, woodland, grassland, and shrubland towards or beyond the HRV ranges and within the FRV ranges by increasing the total area and size of non-forest patches. However, large patches of high-severity fire in dry and moist mixed-conifer forests homogenized landscape patterns beyond FRV ranges towards simplified conditions dominated by non-forest vegetation types. Fires realigned and reconnected landscape patterns with the topo-edaphic template in some cases, but pre-existing fragmentation and spatial mismatches were compounded in many others. Patches of large-tree, closed-canopy forest were reduced by high-severity fire, and the potential to restore more climate-adapted large-tree, open-canopy forest was lost. Re-establishing landscape patterns with desired patch sizes of forest, in particular patches with large trees, will take many decades to centuries and may not occur in drier locations or where seed trees are no longer present. While large wildfires burning during extreme fire weather conditions can move some attributes towards HRV and FRV ranges, intentionally planned mechanical and prescribed-fire treatments that are integrated with strategic wildfire response will better prepare and adapt landscapes for future wildfires and climate.

Post-fire tree mortality is a spatially structured process driven by interacting factors across multiple scales. However, empirical models of fire-caused tree mortality are generally not spatially explicit, do not differentiate among scales, and do not differentiate immediate from delayed mortality. We aimed to quantify cross-scale linkages between forest structure—including spatial patterns of trees—and the progression of mortality 1–4 years post-fire in terms of rates, causes, and underlying demography. We used data from a long-term study site in the Sierra Nevada, California to build a post-fire tree mortality model predicted by lidar-measured estimates of structure. We calculated structural metrics at scales from individual trees to 90 × 90 m neighborhoods and combined them with metrics for topography, site water balance, and burn weather to predict immediate and delayed post-fire tree mortality. Mortality rates decreased while average diameter of newly killed trees increased each year post-fire. Burn weather predictors as well as interactive terms across scales improved model fit and parsimony. Including landscape-scale information improved finer-scale predictions but not vice versa. The amount of fuel, fuel configuration, and burning conditions predicted total mortality at broader scales while tree group-scale fuel connectivity, tree species fire tolerance, and local stresses predicted the fine-scale distribution, timing, and agents of mortality. Landscape-scale conditions provide the template upon which finer-scale variation in post-fire tree mortality is arranged. Post-fire forest structure is associated with the etiologies of different mortality agents, and so landscape-level heterogeneity is a key part of ecosystem stability and resilience.

Forest-restoration efforts are increasing in the western United States in response to realized and expected changes in climate and disturbance regimes. Managers are challenged to find practical and defensible targets to shift forest composition, structure, and spatial pattern to a more resistant and resilient state. The Northeast Washington Forest Vision 2020 project on the Colville National Forest presented an opportunity to map and use previously uncaptured mesic stand-level historical reference conditions to a large restoration project. We reconstructed historical forest conditions in 12 plots across a range of plant-association groups and mapped five restoration treatment units after implementation. We evaluated treatment effectiveness both in terms of meeting the prescriptions’ stated objectives and by similarity to observed reference conditions using metrics of density, species composition, clump-size patterns, and open-space patterns. We found that dry plant associations were historically dominated by distributed clumps of large shade-intolerant trees, whereas cold mesic plant associations were structured as a gap-matrix spatial pattern. Treatments were effective at meeting prescribed density and species-composition targets, but generally resulted in stands that were overly uniform or clumped compared to historical reference conditions.

Restoration of fire-dependent forests is often guided by reference conditions from forests with an active fire regime, thought to be resilient to current and future disturbances and stresses. Reference conditions are usually based on historical data or reconstruction, which greatly limits the scale and completeness of data that can be collected. In the Sierra Nevada of California, large areas with reintroduced active fire regimes coupled with extensive lidar data coverage provide the unique opportunity to develop a contemporary regional reference condition dataset across a wide gradient of biophysical conditions. We developed this dataset with a focus on three questions: (1) What is the geographic and environmental distribution of restored active-fire forest areas in the Sierra Nevada mixed-conifer zone? (2) What are the ranges of variation in forest structure and spatial patterns across reference areas? And (3) How do stand density, tree clumping, and canopy opening patterns vary by topography and climate in reference areas? We analyzed fire history and environmental conditions over 10.8 million ha, including 3.9 million ha in the Sierra Nevada mixed-conifer zone, and found 30,377 ha of restored active-fire areas. Although reference areas were distributed throughout the Sierra Nevada they were more abundant on National Park lands (81% of reference areas) than National Forest lands and were associated with higher lightning strike density. Lidar-measured ranges of variation in reference condition structure were broad, with tree densities of 6-320 trees ha(-1) (median 107 trees ha(-1)), basal area of 0.01-113 m(2) ha(-1) (median 21 m(2) ha(-1)), average size of closely associated clumps of trees from > 1 to 207 trees (median 3.1 trees), and average percent of stand area > 6 m from the nearest canopy ranging from 0% to 100% (median 5.1%). These ranges correspond well with past studies reporting density and spatial patterns of contemporary and historical active-fire reference stands in the Sierra Nevada, except this study observed greater total variation due to the much greater spatial extent of sampling. Within the montane forest zone, reference areas at middle elevations had lower density (86 vs. 121 trees ha(-1)), basal area, (13.7 vs. 31 m(2) ha(-1)), and mean clump size (2.7 vs. 4.0 trees) compared to lower- and higher-elevation reference areas, while ridgetops had lower density (101 vs. 115 trees ha(-1)), basal area (19.6 vs. 24.1 m(2) ha(-1)), and mean clump size (3.0 vs. 3.3 trees) and more open space (7.4% vs. 5.1%) than other landforms. Many of the relationships between physiography and reference structure were context-dependent, suggesting that management practices should create heterogeneous forest structure congruent with local climatic and topographic factors influencing stand conditions.

A century of fire suppression has left fire-dependent forests of the western United States increasingly vulnerable to wildfire, drought, and insects. Forest managers are trying to improve resilience using treatments such as mechanical thinning and prescribed fire; however, operational and resource constraints limit treatments to a fraction of the needed area each year. An alternative is to let wildfires burn under less-than-extreme fire weather where human lives and infrastructure are not at risk. We examined post-fire forest structure using airborne lidar data to determine whether a single wildland fire following an extended fire-free period could produce forest structures resembling fire-resilient historical conditions. We studied forest structures resulting from these "first-entry" fires in a forest with a history of timber management (2008 American River Complex Fires, Tahoe National Forest) and in a wilderness area (2009 Big Meadow Fire, Yosemite National Park). We compared the results of these first-entry fires with nearby reference areas that had experienced 2+ fires that burned predominately at low- and moderate-severity. We identified visible overstory trees from the lidar data and examined their patterns in terms of individuals, tree clumps, and openings. We found that moderate-severity fire effects in these first-entry fires produced similar patterns to the reference areas with area in openings at approximately 40% and trees predominately in small (2 to 4 trees) and medium (5-9 trees) clumps High-severity fire produced mortality likely to lead to large canopy openings that were historically uncharacteristic in these forests. As burn severity increased, the amount of the residual canopy area represented by taller trees ( > 16 m and especially > 32 m) decreased, which could result from fires preferentially killing taller trees or from locations with taller trees more commonly experiencing lower burn severities. Our study suggests that first-entry fires allowed to burn under less-than-extreme conditions can reproduce spatial patterns resembling historical conditions resilient to fires and drought but possibly at the disproportionate expense of larger trees.

Implementing treatments to create structural complexity and spatial heterogeneity within forest stands can be difficult and time consuming. We asked if real-time implementation monitoring with an Android OS tablet application can facilitate successful implementation of such treatments. We compared two tree-marking methods—free selection (FS) and individuals, clumps and openings (ICO)—which were used to implement the same silvicultural prescription. ICO marking guidelines differed from FS in one way: inclusion of targets describing the number of tree clumps of different sizes to be left, with real-time monitoring of progress towards these targets using the tablet app. ICO trials were more successful at producing desired conditions. FS trials resulted in stand densities below the target and lacked large and very large tree clumps. Implementation efficiency (trees marked per person-hour) was similar between the two systems. Real-time implementation monitoring of quantitative targets can increase the likelihood of treatment success.

LiDAR individual tree detection (ITD) is a promising tool for measuring forests at a scale that is meaningful ecologically and useful for forest managers. However, most ITD research evaluates methods over small homogeneous areas, while many forest managers work over large, complex landscapes. We investigated how ITD results varied across diverse structural conditions in California's Sierra Nevada mixed-conifer forests and what this taught us about when and how to apply ITD. Our results suggest that it is advantageous to use ITD when it improves analysis interpretability, when measuring horizontal patterns, or when field data are unavailable. In the latter case, it is best to focus on measures dominated by large trees, like basal area and biomass. Thinking of ITD results as "tree-approximate objects" including one dominant tree and up to a few subordinate tree respects LiDAR's strengths and limitations; we illustrate how this concept keeps analyses consistent across varying structural conditions.