Forest Inventory And Analysis Research Articles

Analysis of location, feedstock availability, and economic impacts of potential mass timber processing facilities in Michigan

Mass timber, an engineered wood product, offers exceptional strength and versatility as a building material, is lightweight, which makes it easy to transport and assemble, is aesthetically appealing, and offers the potential to reduce GHG emissions. This study attempts to identify the potential locations for mass timber production in Michigan, estimate the available feedstock, and evaluate the economic impacts of mass timber production on the state's economy. Procurement zones for all softwood lumber producers in Michigan were identified and overlaid to create competition hotspots for the softwood sawtimber industry. Two potential locations were identified for mass timber production within the state. Forest Inventory and Analysis (FIA) data was used to estimate the annual sustainable availability of softwood (ASAW) for mass timber production. Finally, IMPLAN was used to assess the economic impacts associated with mass timber production in Michigan. We identified two sawmills for mass timber production and concluded that there is sufficient ASAW for mass timber production in these facilities. Producing 12,429 m3 of mass timber supports at least 93 total jobs (35 direct, 29 indirect, and 29 induced) with a total output of $12.52 million in Michigan's economy. Similarly, upgrading the capacity of existing sawmills to supply lumber for mass timber production will result in 34 additional jobs with a total output of $5.63 million.

Structural diversity is better associated with forest productivity than species or functional diversity.

Understanding the relationship between biodiversity and productivity can be advanced by improving metrics used to quantify biodiversity. Structural diversity, that is, variation of size and form of plant organs, is an emerging biodiversity metric. However, compared with the other biodiversity metrics, its relative importance in specific components of forest productivity, for example, recruitment of new individuals, biomass net change after accounting for mortality, is largely unknown, particularly across a large spatial scale with multiple influential gradients. To address the knowledge gap, we used USDA Forest Service Forest Inventory and Analysis (FIA) data across the southcentral USA from 2008 to 2017. We calculated forest biomass increments due to recruitment and growth and net change in biomass. Then, we quantified the effects of a range of abiotic and biotic variables on the biomass increments and net change. Our results showed that (1) Structural diversity was negatively associated with the two biomass increments and net change in biomass. The negative effects were supported by increased occurrences of insects and diseases with greater structural diversity. (2) Compared with species and functional diversity, structural diversity showed a better association with biomass increments and net change, suggested by its larger absolute values of standardized coefficients, and the effects of structural diversity were negative in contrast to species diversity. (3) The effects of structural diversity, stand age, and elevation differed between natural and planted forests that may stem from the differences in stand development and species composition between the two forest types. Together, structural diversity may represent an important dimension of biodiversity impacts on plant productivity, which could be related to the exacerbated disturbances with greater structural diversity.

Human risk to tick encounters in the southeastern United States estimated with spatial distribution modeling.

Expanding geographic distribution and increased populations of ticks has resulted in an upsurge of human-tick encounters in the United States (US), leading to an increase in tickborne disease reporting. Limited knowledge of the broadscale spatial range of tick species is heightened by a rapidly changing environment. Therefore, we partnered with the Forest Inventory and Analysis (FIA) program of the Forest Service, U.S. Department of Agriculture and used passive tick surveillance to better understand spatiotemporal variables associated with foresters encountering three tick species (Amblyomma americanum L., Dermacentor variabilis Say, and Ixodes scapularis L.) in the southeastern US. Eight years (2014-2021) of tick encounter data were used to fit environmental niche and generalized linear models to predict where and when ticks are likely to be encountered. Our results indicate temporal and environmental partitioning of the three species. Ixodes scapularis were more likely to be encountered in the autumn and winter seasons and associated with soil organic matter, vegetation indices, evapotranspiration, temperature, and gross primary productivity. By contrast, A. americanum and D. variabilis were more likely to be encountered in spring and summer seasons and associated with elevation, landcover, temperature, dead belowground biomass, vapor pressure, and precipitation. Regions in the southeast least suitable for encountering ticks included the Blue Ridge, Mississippi Alluvial Plain, and the Southern Florida Coastal Plain, whereas suitable regions included the Interior Plateau, Central Appalachians, Ozark Highlands, Boston Mountains, and the Ouachita Mountains. Spatial and temporal patterns of different tick species can inform outdoorsmen and the public on tick avoidance measures, reduce tick populations by managing suitable tick habitats, and monitoring areas with unsuitable tick habitat for potential missed encounters.

Definition criteria determine the success of old-growth mapping

Old-growth forests have been widely studied for decades. The extreme diversity of old forest characteristics has inspired an equally diverse set of old-growth definitions, and makes mapping old-growth difficult across large areas and different forest types. While the use of remote sensing in old-growth research is not new, there is a growing need for large scale mapping to improve understanding of old forest processes and to support old-growth conservation. Old-growth mapping requires definitions that are ecologically relevant to old forests while also transferable to remote sensing data. In this paper we develop a conceptual framework to evaluate three dimensions of old-growth—a temporal dimension related to tree ages, a physical dimension related to tree sizes, and a functional dimension related to forest processes. In the first part of our analysis, we classify forests throughout the eastern US as old or not with respect to each old-growth dimension using existing old-growth definitions and data from the US Forest Inventory and Analysis (FIA) program. We estimate the proportion of forest classified as old within a hexagon grid, resulting in a unique map of old forest proportion (OFP) for each dimension. Subsequently, we use spaceborne lidar data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to reproduce each OFP map in a modeling framework designed to 1) assess the extent to which each dimension of forest oldness can be mapped at large spatial scales, and 2) identify biophysical GEDI variables related to each dimension of forest oldness. We estimate that only 2% of forest classified as old in any dimension satisfied the old criteria in all three dimensions. We found substantial spatial variation in the mapped OFP estimates across the three dimensions, highlighting how definition criteria impacts old-growth maps. We also found that physically old forests were more effectively mapped using GEDI data than functionally or temporally old forests, and that physically old forests were more structurally similar to one another than temporally or functionally old forests. Our modeling results indicate that while remote sensing may be best suited to mapping physical old-growth characteristics, definitions that rely solely on physical characteristics do not adequately represent old forests throughout the eastern US. We propose that future efforts to map old-growth with spaceborne remote sensing data may maximize utility through collaboration between western and indigenous old-growth experts to determine broad yet nuanced approaches that are appropriately tailored to the target variable of old forests. These efforts should balance explicit and ecologically relevant old-growth definitions specifically for mapping that can be linked to remotely sensed data, 2) appropriate spatial resolutions, and 3) flexible quantitative frameworks that encompass the complexities and heterogeneity of old forests.

Characterization of Chinese Tallow Invasion in the Southern United States

Chinese tallow is a non-native invasive tree expanding in range and abundance throughout the southern United States. Several biogeographical studies mapping tallow distribution and examining key underlying environmental factors relied on the U.S. Forest Service Forest Inventory and Analysis (FIA) data, representing forestlands at scales of ~2400 ha. However, given that most invasive trees, like tallow, are cosmopolitan and dynamic in nature, FIA data fails to capture the extent and severity of the invasion especially outside areas classified as forestlands. To develop tallow maps that more adequately depict its distribution at finer spatial scales and to capture observations in non-forestlands, we combined verified citizen science observations with FIA data. Further, we described spatiotemporal patterns and compared citizen science to FIA and other previously published distribution maps. From our work, although tallow is prevalent in the south, Louisiana, Texas, and Mississippi were the most invaded states. Tallow was associated with flatwoods and prairie grasslands of the Gulf Coast. Annual extreme minimum temperatures of less than −12.2 °C (10 °F) represented the northern limit of naturalized tallow populations. Tallow’s northward and inland expansion was captured in citizen science and FIA data, indicating a tallow spread rate ranging from 5 to 20 km annually over the last decade. Systematic sampling, such as FIA, and citizen science data both have their own unique pitfalls. However, the use of citizen science data can complement invasive plant distribution mapping, especially when combined with data from established systematic monitoring networks. This approach provides for a more complete understanding of invasive tree extent and spatiotemporal dynamics across large landscapes.

The USDA Forest Service’s Forest Inventory and Analysis Database and the National Register of Champion Trees — A Potentially Symbiotic Relationship

Abstract This article shows how probability sampling and citizen science efforts can complement each other, using the USDA Forest Service’s Forest Inventory and Analysis (FIA) program and the ongoing search by the National Register of Champion Trees (NRCT) for the largest specimen of each naturally occurring tree species in the United States as an example. We develop a ratio statistic (Zs) that uses the difference in size of the largest tree of a species from each database to order the tree species according to the assumed ease with which a larger specimen than the current national champion might be found. Our results show ninety-two candidate species that have been recorded by FIA for which there is no national champion and sixty-five species for which a new champion should be easy to find. In a supplemental table, we show ninety-four species listed as observable by FIA in the NRCT but not recorded in the FIA sample. Study Implications: An interest in forests and forestry is always accompanied by an interest in trees, especially very big trees. Two very different ways of learning about trees are analyzed concurrently in a way that reveals their complementarity. The two efforts are the probability sample, conducted by the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, and the citizen science effort known as the National Register of Champion Trees (NRCT). We develop a statistic that will help tree sleuths find champion trees and provide FIA practitioners with a quality control measure and an indication of which species would benefit from an increase in sample intensity.

Profit-efficiency analysis of forest ecosystem services in the southeastern US

Technical, allocative, and profit efficiency of longleaf pine (Pinus palustris. Mill) forests in the southeastern United States, producing ecosystem services such as timber, tree biodiversity, water, and carbon sequestration, were estimated. This study employed a non-parametric two-stage approach involving data envelopment analysis (DEA) and robust linear regression. Utilizing data from the Forest Inventory and Analysis (FIA) program spanning 1977–2015 and covering 2,282 forest plots, most longleaf pine forest plots were technically and profit inefficient in ecosystem service production. The inefficiency in profit appeared more attributable to allocative rather than technical inefficiency. Furthermore, the impact of various exogenous variables on inefficiency scores was assessed through robust linear regression. The findings suggested that forest disturbances under private ownership could reduce technical inefficiency. Surprisingly, contrary to stochastic frontier analysis (SFA) results, the robust regression model, considering geographical factors, disturbance, ownership, management, and time in the presence of outliers/influential observations, indicated that disturbances often increased technical inefficiency. Therefore, forest management strategies aiming to mimic or replicate the effects of forest disturbances might compromise the efficiency of ecosystem service provision.

Forest-associated habitat variables influence human-tick encounters in the southeastern United States.

Due to the increased frequency of human-tick encounters and expanding ranges of ticks in the United States, there is a critical need to identify environmental conditions associated with tick populations and their likelihood to contact human hosts. In a passive tick surveillance partnership with the US Department of Agriculture Forest Inventory and Analysis (FIA) program, we identified environmental variables associated with tick encounters by forestry personnel. Ticks were identified by species and life stage, and site-specific variables were associated with each tick using FIA forest inventory datasets and generalized linear models with negative binomial distributions. Of the 55 FIA variables available, we identified biotic and abiotic environmental variables associated with Amblyomma americanum L. (carbon in litter material and standing dead tree aboveground dry biomass), Dermacentor variabilis Say (seedling species unevenness and elevation), and Ixodes scapularis L. (carbon in dead woody material and seedling species unevenness). We propose conducting future treatment-control studies using these forestry-related environmental variables to test their ability to alter tick abundance at sites. Land management decisions not only affect common flora and fauna, but changes to these habitats can also alter the way ticks parasitize hosts and use vegetation to find those hosts. These results can be used with land management decisions to prevent future human-tick encounters and highlight risk areas.

Sustainability of White Oak (Quercus alba) Timber Supply in Kentucky

Abstract White oak is a major commercial tree species and an important timber resource in Kentucky. However, current forest inventory trends from the Central Hardwood Forest Region (CHFR) reveal a sustainability threat from declining white oak regeneration and recruitment leading to a disproportionate inventory structure. Using Forest Inventory and Analysis (FIA) data together with the Forest Vegetation Simulator (FVS), we performed a base run analysis of projected inventory levels of white oak sawlogs to better understand whether the inventory level is sustainable to support the current harvest level of white oak in the future. The projections were further examined by tree grades to provide a general outlook on the quality of white oak timber growing in Kentucky’s forests in posterity. By doing so, we generated results that indicate that projected inventory levels of white oak sawlogs cannot be considered sustainable to support current harvest levels from 2058 onwards. In addition, the long-term trends in inventory levels of high-quality white oak sawlogs would be continuously declining whereas that of low-quality sawlogs would be steadily increasing. On the brink of these significant inventory shifts, our study calls for proactive forest management approaches to stabilize the white oak timber resource supply in Kentucky and beyond.

Site Index Models for Loblolly Pine Forests in the Southern United States Developed with Forest Inventory and Analysis Data

Abstract Accurate productivity estimates are essential to assess the overall sustainability of forest resources. Site index (SI) models for loblolly pine (Pinus taeda L.) in plantation and natural forests of the southeastern United States were developed using the Forest Inventory and Analysis (FIA) database. We extracted short (~20 years), unbalanced panel data from the FIA database. Ten different nonlinear models derived from the base models using the algebraic difference approach (ADA) or the generalized algebraic difference approach (GADA) were fitted to the extracted data. The performance of the models was ranked based on a variety of fit and evaluation statistics. The results showed that all top three models were derived using the GADA approach. The best model for loblolly pine plantation and natural forest stands was derived from the Hossfeld model and the Chapman–Richards model, respectively. The best-fitted models for planted forests were also compared with previously developed models. This study demonstrated that base-age invariant and polymorphic SI models could be developed using short panel data extracted from FIA data. The SI models presented here can be used as a height growth model component in forest growth and yield model systems. Study Implications: Improved site index equations for assessing the site quality of loblolly pine plantation and natural stands are now available to stakeholders at the policy, management, and operational levels. Activities such as forest management, restoration, and wildlife management, which require site quality data, will benefit from the new models. Furthermore, the approach of deriving panel data based on Forest Inventory and Analysis data offers information on developing and updating models for other species. Finally, the approach of this study, to use permanent plot measurement data in developing growth and yield models, is cost-effective and time-efficient.

Where are the large trees? A census of Sierra Nevada large trees to determine their frequency and spatial distribution across three large landscapes

Large trees (≥76.2 cm/≥30″ DBH) and especially very large trees (≥101.6 cm/≥40″ DBH) are key structures of Sierra Nevada forests for their ecological function, habitat, and carbon storage. Many of these trees have been lost to historic harvest and more recently to drought and wildfires. Understanding the current frequency and distribution of these large trees is essential to understanding their ecological contribution and management needs. We used airborne lidar to census large trees across three Sierra Nevada landscapes (cumulatively 396 K ha) in lower (dominated by ponderosa pine and mixed conifer) and upper (dominated by red fir) montane forest zones. We used data from a network of Forest Inventory and Analysis (FIA) plots to interpret our lidar-based results for large tree frequency, species, and ages. The lidar data identified > 8 M large and > 2.7 M very large trees, and their mean densities were similar to those from FIA data. Large portions of our study areas had either no or low densities (<20) of large trees per hectare. We found that large and very large tree concentrations were spatially aggregated with most in denser patches containing 20 to 50 + large trees per hectare. Depending on the study area, these often sizable (>1000 ha) patches of dense large trees can cover 20% to 40% of the landscape. (Patches of denser very large trees cover less of the landscape, typically 5% to 10%). However, these large patches are rarely simple blocks. Instead, they typically form complex amorphous matrices interspersed with patches of forests containing shorter trees or non-forest cover. Crucially, almost all large trees were in stands with high canopy cover, suggesting horizontal fuel continuity and low resilience to future wildfires. For lower montane large trees, canopy cover versus large tree density showed almost a unimodal response with canopy cover of 60% to 80% for locations with > 20 large trees per ha. For upper montane large trees, canopy cover versus large tree density showed a more linear relationship for all three study areas. High levels of canopy cover, especially for lower montane forests, suggest settings in which infilling following decades of fire suppression have created overly dense stands with lower resilience to drought and wildfire. Other studies have documented substantial recent losses of these large trees to both factors. The high canopy cover within which almost all large trees exist emphasizes the need for treatment almost everywhere that large trees are present for lower montane forests. This likely will require treatments both within the stands that contain large trees and across the landscapes in which they are found.

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30 m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

Classifying mature federal forests in the United States: The forest inventory growth stage system

President Biden’s executive order, ‘Strengthening the Nation's Forests, Communities, and Local Economies,’ (EO#14072, April 22, 2022) acknowledges the interest in mature and old-growth (MOG) forests by directing U.S. Federal agencies to define and inventory these resources on United States Forest Service (USFS) and Bureau of Land Management (BLM) lands. We propose using an effective and enduring mature forest classification system that could be adaptable to social paradigms, monitoring data streams, scientific information, and global change factors. We accommodate these design aspects by defining mature forests as a growth stage prior to the onset of old-growth attributes within a proposed Forest Inventory Growth Stage System (FIGSS). FIGSS uses the long-established USFS old-growth assessments often conducted in concert with public dialogues to identify key structural indicators of older forests. The system informs inverse modeling of the prior “mature” stage’s structural thresholds enabling initial population estimates of mature forest extent using the USFS’ nationally consistent Forest Inventory and Analysis (FIA) program data. With this approach, we estimate that approximately 45 percent of all USFS/BLM forest is mature. The FIGSS system is based on a variety of components that could be used to account for cultural values ascribed to forest conditions which could be refined across future versions such as assumptions about the relative length of growth stages, incorporating data from emerging monitoring technologies along with old-growth/mature field sampling campaigns, accommodating spectrums of site-limited and/or disturbance-driven stand development, refined variable selection processes such as machine-learning, and consideration of traditional ecological knowledge.

Comparing harmonic regression and GLAD Phenology metrics for estimation of forest community types and aboveground live biomass within forest inventory and analysis plots

The Landsat multispectral time series is a valuable source of moderate spatial resolution data to support forest mapping and monitoring tasks. Using United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) plots within the states of Michigan, Oregon, and West Virginia, two methods to summarize time series observations, harmonic regression coefficients and Global Land Analysis & Discovery (GLAD) Phenology metrics, are compared for predicting forest community type, total aboveground live biomass (AGLBM), and species-specific AGLBM. Harmonic regression coefficients, which provided mean overall accuracies (OAs) between 62.8% and 73.1% and map image classification efficacies (MICEs) varying between 0.455 and 0.566 for the three studied states and calculated using multiple model replicates, generally provided better predictive performance for differentiating forest community types in comparison to GLAD phenology metrics. However, differences were not always statistically significant. DTM-derived terrain variables improved classification performance in some landscapes when using machine learning, random forest classification (for example, the highest obtained mean OA of 78.5% (MICE = 0.557) was obtained for West Virginia when using the combined harmonic regression and terrain variables). For the regression-based estimation of total and species-specific AGLBM, the spectral data used and the incorporation of terrain variables had less of an impact on model performance. Further, the regression models in Oregon provided larger mean R-squared values (0.452 to 0.490) in comparison to those in Michigan and West Virginia, where all R-squared values were lower than 0.200, suggesting that AGLBM prediction may be more or less challenging in different landscapes depending on forest characteristics, terrain, management practices, and disturbance histories.

Structural and species diversity explain aboveground carbon storage in forests across the United States: Evidence from GEDI and forest inventory data

Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions.

Precise and unbiased biomass estimation from GEDI data and the US Forest Inventory

Atmospheric CO2 concentrations are dependent on land-atmosphere carbon fluxes resultant from forest dynamics and land-use changes. These fluxes are not well-constrained, in part because reliable baseline estimates of forest carbon stocks and the associated uncertainties are lacking. NASA's Global Ecosystem Dynamics Investigation (GEDI) produces estimates of aboveground biomass density (AGBD) that are unique because GEDI's hybrid estimation framework enables formal uncertainty calculations that accompany the biomass estimates. However, GEDI's estimates are not without issue; a recent validation using design-based AGBD estimates from the US Forest Inventory and Analysis (FIA) program revealed systematic differences between GEDI and FIA estimates within a hexagon tessellation of the continental United States. Here, we explored these differences and identified two issues impacting GEDI's estimation process: incomplete filtering of low quality GEDI observations and regional biases in GEDI's footprint-level biomass models. We developed a solution to each, in the form of improved data filtering and GEDI-FIA fusion AGBD models, developed in a scale-invariant small area estimation framework, that were compatible with hybrid estimation. We calibrated 10 regional Fay-Herriot models at the hexagon scale for application at the unit scale of GEDI footprints, for which we provide a mathematical justification and empirical testing of the models' scale-invariance. These models predicted realistic distributions of unit level AGBD, with equal or improved performance relative to GEDI's L4A models for all regions. We then produced GEDI-FIA fusion estimates that were more precise than the FIA estimates and resulted in a bias reduction of 86.7% relative to the original GEDI estimates: 19.3% due to improved data filtering and 67.5% due to the new AGBD models. Our findings indicate that (1) small area estimation models trained in a scale-invariant framework can produce realistic predictions of AGBD, and (2) there is substantial spatial variability in the relationship between GEDI forest structure metrics and AGBD. This work is a step toward achieving reliable baseline forest carbon stocks, provides a viable methodology for training remote sensing biomass models, and may serve as a reference for other investigations of GEDI AGBD estimates.

Functional diversity affects tree vigor, growth, and mortality in mixed-conifer/hardwood forests in California, U.S.A, in the absence of fire

Mixed tree-species forest management can increase forest resilience by reducing the impacts of disturbances that disproportionately affect a single tree species or closely related groups of tree species. Beyond disturbance-risk reduction, tree-species diversification may foster functional-diversity effects (e.g., complementarity or facilitation) that alter the performance of a given tree species in mixed versus pure stands, potentially benefitting carbon sequestration and wildlife habitat. Tree species-mixture effects have been explored to only a limited degree in western US forests and, particularly, in California. Establishing whether vigor, growth, and mortality of common tree species vary with stand composition would help inform restoration and modeling of these forests under climate change. Using data from USDA Forest Service Forest Inventory and Analysis (FIA) plots from California, we examined how individual-tree vigor, as indicated by live crown ratio (LCR), periodic basal area increment (BAI), and mortality odds varied with functional dissimilarity (FDis). We quantified FDis using an index based on 11 traits related to resource acquisition, competition, environmental tolerances, and fire ecology. We classified major tree species into ponderosa pine (Pinus ponderosa), Jeffrey pine (Pinus jeffreyi), incense-cedar (Calocedrus decurrens), true firs (Abies spp.), Douglas-fir (Pseudotsuga menziesii), live oaks (Quercus spp.), and deciduous oaks (Quercus spp.) response groups. We tested for the main effects of FDis on tree responses, as well as for interactions with tree, site, stand, and climate factors. We found that initial tree height modulated the effects of FDis on ponderosa pine, Jeffrey pine, incense-cedar, and true fir LCR, whereas FDis interacted with climate to alter live oak and deciduous oak LCR. FDis decreased BAI in ponderosa pine and increased BAI in live oaks. FDis interacted with tree size to influence BAI for Jeffrey pine, Douglas-fir, and true firs. We found no evidence that climate or site quality modulated FDis effects on BAI for any species group. Tree mortality was not responsive to FDis, except for the true firs, where both initial tree height and competition interacted with FDis to increase and decrease mortality odds, respectively. FDis effects commonly shifted from positive to negative along gradients of stand structure and site quality, indicating that these effects vary with site and stand conditions. Our results have implications for balancing the ecosystem benefits of mixed stands, such as disturbance risk, carbon sequestration, and habitat during forest restoration projects in the region, as well as for more accurate modeling of complex stands.

Wildfires correlate with reductions in aboveground tree carbon stocks and sequestration capacity on forest land in the Western United States

In the United States (US), forest ecosystems are the largest terrestrial carbon sink, offsetting the equivalent of >12 % of economy-wide greenhouse gas (GHG) emissions annually. In the Western US, wildfires have shaped much of the landscape by changing forest structure and composition, increasing tree mortality, impacting forest regeneration, and influencing forest carbon storage and sequestration capacity. Here, we used remeasurements of >25,000 plots from the US Department of Agriculture, Forest Service Forest Inventory and Analysis (FIA) program and auxiliary information (e.g., Monitoring Trends in Burn Severity) to characterize the role of fire along with other natural and anthropogenic drivers on estimates of carbon stocks, stock changes, and sequestration capacity on forest land in the Western US. Several biotic (e.g., tree size, species, and forest structure) and abiotic factors (e.g., warm climate, severe drought, compound disturbances, and anthropogenic interventions) influenced post-fire tree mortality and regeneration and had concomitant impacts on carbon stocks and sequestration capacity. Forest ecosystems in a high severity and low frequency wildfire regime had greater reductions in aboveground biomass carbon stocks and sequestration capacity compared to forests in a low severity and high frequency fire regime. Results from this study can improve our understanding of the role of wildfire along with other biotic and abiotic drivers on carbon dynamics in forest ecosystems in the Western US.

Adaptation and mitigation capacity of wildland forests in the northeastern United States

Managing forests to mitigate climate change and increase their capacity to adapt to future climate-related disturbances and conditions typically involves protecting and enhancing forest carbon stocks and sequestration capacity while promoting structural diversity. While the focus has been on comparing active management approaches for meeting these objectives, there are few empirical assessments of passive management. Here we used quasi-experimental methods to compare carbon and structural complexity within “wildlands,” where harvesting and other land uses are prohibited, to environmentally comparable forests without protection from timber harvesting. Using USDA Forest Inventory and Analysis (FIA) plots from the Adirondack-New England region of the Northeastern U.S., we compared aboveground carbon, total forest basal area increment (our proxy for carbon sequestration), and six forest-level structural variables in forests. To help explain observed differences, we examined (1) the recent history of harvesting within unprotected forests, (2) stand age in wildland and unprotected forests, and (3) the carbon and structural attributes of protected and unprotected plots at the initiation of wildlands protection. Aboveground carbon was 20% higher in wildlands overall (P < 0.0001), with differences greatest in wildlands of New York (+32%; P = 0.0001) and in Maine (+34%; P = 0.01) where recent harvesting intensity and differences in stand age between protection categories were highest. Basal area increment did not differ between protected areas at the regional and sub-regional scale, but was 37% higher in wildlands (P = 0.03) than in recently harvested areas. Structural complexity was generally higher in wildlands, with four structural variables – large live (>60 cm DBH) and large dead (>45 cm DBH) tree density, maximum tree height, and diversity of diameter size classes) – greater in wildlands than in unprotected forests. Two variables (adult tree species richness and standard deviation of tree height) did not differ between protection categories. Both carbon and structural differences were amplified by recent harvesting in unprotected plots. For the subset of plots that allowed for comparison, wildlands did not differ in carbon and structural attributes from unprotected plots at the onset of wildlands protection, suggesting that subsequent management rather than initial differences was the driver of carbon and structural differences between protection categories. Our results highlight the adaptation and mitigation benefits of allowing natural processes to predominate in strictly protected areas.

Range-wide population assessments for subalpine fir indicate widespread disturbance-driven decline

Subalpine forests in western North America are threatened by rapid climate change, increased activity by endemic and exotic insects and diseases, and changing wildfire regimes. The interactive effects of these stressors have resulted in pronounced population declines in many subalpine tree species; however, a systematic assessment of the status and trends of subalpine forests is lacking. Subalpine fir (Abies lasiocarpa) is a widespread species across the western United States, with documented population declines in many parts of its distribution. Here we use subalpine fir as an initial leverage point to build a more complete understanding of subalpine forest baseline conditions and responses to environmental change. Specifically, we leverage the USDA Forest Service Forest Inventory and Analysis (FIA) database to (1) ask how subalpine fir populations are changing across the species’ distribution in the western US, (2) assess the drivers of recent subalpine fir population trends, and (3) explore whether those changes imply generalized species-wide and/or system-wide decline. We found that subalpine fir abundance and basal area are declining concurrently across ∼ 62% of the species’ distribution, and increasing across ∼ 19%. Range-wide, we estimated 25.02 ± 2.74 % subalpine fir mortality between 2000 and 2009 and 2010–2019 FIA inventory periods, with higher mortality concentrated in the eastern Oregon Cascades, central Idaho, and parts of southern Colorado. High regeneration density did not predict positive population trajectories, which were instead associated with higher rates of adult recruitment. While the importance of different mortality agents varied substantially between ecoregions, 83.4% of total range-wide mortality was related to fire or biological disturbance. Declining subalpine fir basal area coincided with declines in the basal area of other co-occurring tree species in 39% of subalpine forest area, and with increases in conspecific basal area in 22% of forest area. Fire disturbance was the single largest cause of subalpine fir mortality; however, even where subalpine fir fire mortality was high, mortality among other species was primarily caused by insects. Our results suggest that subalpine fir declines across large portions of the western United States are driven by forest disturbance, and that declines in subalpine fir populations may be indicative of negative change in subalpine forest systems broadly.