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Highlights • In a span of seven years Texas experienced three major disturbance events. • National forest inventory data are useful for assessing disturbance effects. • Masking of trends was observed in dead tree estimates using the full set of data. • Estimates using specific subsets of data were helpful in assessing effects. • Adding new estimation capabilities to online analytical tools is recommended. Abstract Understanding the impacts of large-scale disturbances on forest conditions is necessary to support forest management, planning, and policy decision making. National forest inventories (NFIs) are an important information source that provide consistent data encompassing large areas, covering all ownerships, and extending through time. Here we compare how temporal aggregation approaches with NFI data affects estimates of standing dead trees as these respond to extreme disturbance events. East Texas was selected for this study owing to the occurrence of three significant disturbance events in a short span: Hurricane Rita in 2005, Hurricane Ike in 2008, and a historic drought in 2011. Wide-spread tree damage and mortality were reported after each disturbance and estimates of standing dead trees were used as the inventory variable for assessment. In the NFI of the US, the plot network is systematically divided into panels and one panel is measured each year. A measurement cycle is completed when all panels have been measured, which varies between 5 and 10 years depending on the region. Using the standard estimation approach of the US NFI, we computed population estimates using data from the full set of panels (FSP), multiple sets of panels (MSP), and single set of panels (SSP). For estimation, a single plot observation is computed from the most recent measurement of the plot that does not exceed the estimate year. Because one panel is measured per year, FSP and MSP estimates will necessarily consist of plot observations whose measurements were collected over a number of years. The SSP estimate is computed from one panel and thus all the plot observations are based on measurements collected over one year. We found that interpretations of disturbance event impacts varied depending on which sets of estimates were considered. All sets of estimates suggested a large and significant drought impact. However, differences existed among the estimates in the timing and magnitude of the impacts. The FSP estimates showed clear lag bias and smoothing of trends relative to the SSP estimates. MSP estimates were intermediate between FSP and SSP estimates. Differences in Hurricane Rita impacts were also observed between sets of estimates. Evidence of a net impact on standing dead trees following Hurricane Ike was weak among all sets of estimates. Given the potential for lag bias and smoothing, we recommend that SSP and MSP estimates be considered along with FSP estimates in assessments of large-scale disturbance impacts on forest conditions. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is the largest terrestrial carbon (C) sink on Earth; this pool plays a critical role in ecosystem processes and climate change. Given the cost and time required to measure SOC, and particularly changes in SOC, many signatory nations to the United Nations Framework Convention on Climate Change report estimates of SOC stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, SOC in forests is monitored by the national forest inventory (NFI) conducted by the Forest Inventory and Analysis (FIA) program within the U.S. Department of Agriculture, Forest Service. The FIA program has been consistently measuring soil attributes as part of the NFI since 2001 and has amassed an extensive inventory of SOC in forest land in the conterminous United States and southeast and southcentral coastal Alaska. That said, the FIA program has been using country-specific predictions of SOC based, in part, upon a model using SOC estimates from the State Soil Geographic (STATSGO) database compiled by the Natural Resources Conservation Service. Estimates obtained from the STATSGO database are averages over large map units and are not expected to provide accurate estimates for specific locations, e.g., NFI plots. To improve the accuracy of SOC estimates in U.S. forests, NFI SOC observations were used for the first time to predict SOC density to a depth of 100 cm for all forested NFI plots. Incorporating soil-forming factors along with observations of SOC into a new estimation framework resulted in a 75% (48 ± 0.78 Mg/ha) increase in SOC densities nationally. This substantially increases the contribution of the SOC pool, from approximately 44% (17 Pg) of the total forest ecosystem C stocks to 56% (28 Pg), in the forest C budget of the United States. [ABSTRACT FROM AUTHOR]

Aim Gymnosperms do not follow a latitudinal diversity gradient across the Northern Hemisphere but are influenced by geography at continental scales. Tolerance to physiological aridity is thought to be the main driver of this distribution, yet through evolutionary time conifers have also faced conditions of frost, shade and fire. We tested four predictions to evaluate how environmental stressors and geographical and evolutionary patterns of traits influence conifer distributions: (1) environmental variables related to aridity are most important in explaining geographical patterns of traits; (2) traits responsible for survival in stressful conditions have evolved under a niche conservatism constraint; (3) phylogenetic correlations among traits as the result of complex evolutionary responses to multiple abiotic stressors are widespread; (4) there are parallelisms between spatial trait associations and correlated trait evolution. Location The conterminous United States. Methods We combined conifer occurrences with 10 traits related to drought, freezing, shade and fire. The spatial distribution of traits was mapped and the relationship between environment and the geographical patterns of traits was explored. Niche conservatism was assessed comparing patterns of trait evolution against Brownian motion. We computed geographical and phylogenetic correlations among traits to determine the correspondence between spatial and evolutionary trade-offs. Results (1) Maximum temperature followed by precipitation were the environmental variables that best described the geographical distributions of traits. (2) Most traits contain a phylogenetic signal consistent with niche conservatism: major exceptions being fire-related traits and frost tolerance. (3) Drought and shade tolerances show one of the strongest negative phylogenetic correlations. (4) The drought-shade tolerance trade-off is mirrored at the biogeographical scale. Main conclusions Unlike in angiosperms, cold does not seem to have been a major driver in the evolutionary history of temperate conifers. A strong tradeoff between drought and shade tolerance is the simplest explanation for understanding the current distribution of conifers in North America. [ABSTRACT FROM AUTHOR]

Forest ecosystems are the largest terrestrial carbon sink on earth, with more than half of their net primary production moving to the soil via the decomposition of litter biomass. Therefore, changes in the litter carbon (C) pool have important implications for global carbon budgets and carbon emissions reduction targets and negotiations. Litter accounts for an estimated 5% of all forest ecosystem carbon stocks worldwide. Given the cost and time required to measure litter attributes, many of the signatory nations to the United Nations Framework Convention on Climate Change report estimates of litter carbon stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, the country-specific model used to predict litter C stocks is sensitive to attributes on each plot in the national forest inventory, but these predictions are not associated with the litter samples collected over the last decade in the national forest inventory. Here we present, for the first time, estimates of litter carbon obtained using more than 5000 field measurements from the national forest inventory of the United States. The field-based estimates mark a 44% reduction (2081 ± 77 Tg) in litter carbon stocks nationally when compared to country-specific model predictions reported in previous United Framework Convention on Climate Change submissions. Our work suggests that Intergovernmental Panel on Climate Change defaults and country-specific models used to estimate litter carbon in temperate forest ecosystems may grossly overestimate the contribution of this pool in national carbon budgets. [ABSTRACT FROM AUTHOR]

Estimating tree leaf biomass can be challenging in applications where predictions for multiple tree species is required. This is especially evident where there is limited or no data available for some of the species of interest. Here we use an extensive national database of observations (61 species, 3628 trees) and formulate models of varying complexity, ranging from a simple model with diameter at breast height (DBH) as the only predictor to more complex models with up to 8 predictors (DBH, leaf longevity, live crown ratio, wood specific gravity, shade tolerance, mean annual temperature, and mean annual precipitation), to estimate tree leaf biomass for any species across the continental United States. The most complex with all eight predictors was the best and explained 74%-86% of the variation in leaf mass. Consideration was given to the difficulty of measuring all of these predictor variables for model application, but many are easily obtained or already widely collected. Because most of the model variables are independent of species and key species-level variables are available from published values, our results show that leaf biomass can be estimated for new species not included in the data used to fit the model. The latter assertion was evaluated using a novel "leave-one-species-out" cross-validation approach, which showed that our chosen model performs similarly for species used to calibrate the model, as well as those not used to develop it. The models exhibited a strong bias toward overestimation for a relatively small subset of the trees. Despite these limitations, the models presented here can provide leaf biomass estimates for multiple species over large spatial scales and can be applied to new species or species with limited leaf biomass data available., (© 2022 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

The relationship between number of species and area observed has been described using numerous approaches and has been discussed for more than a century. The general objectives of our study were fourfold: (1) to evaluate the behaviour of species-area curves across geographic scales, (2) to determine sample sizes necessary to produce acceptably precise estimates of tree species diversity, (3) to evaluate relationships among estimates of tree species diversity for local to large geographic scales, and (4) to determine whether the proportion of native tree species may be precisely estimated using sample sizes smaller than those necessary to estimate total tree species diversity. Such investigations are necessary to improve biodiversity monitoring at large scales. The analytical approaches included Monte Carlo bootstrap simulations and two geospatial methods and relied on a database populated with data for more than 12,000 national forest inventory plots (NFI) from 16 regional units, 13 European countries and three ecoprovinces of the United States of America (USA). Four primary results were obtained. First, tree species diversity may be precisely estimated using observations for a random subsample of 2,000-4,000 NFI plots. Second, large sample sizes are necessary to estimate tree species diversity for regional units or forest categories, except possibly for boreal forests for which the number of tree species is small. Third, estimates of the proportion of native tree species may be precisely estimated using tree species information for a random sample of approximately 30 NFI plots. Finally, our estimated species-area curves show that the curve shapes and relationships among estimates of tree species diversity at large scales clearly depend on the relative geographic location of the anchor regional unit (a European country or ecoprovince of the USA) relative to the other regional units. None of the well-known models for species-area curves adequately describes our results. The conclusion was that the uncertainties of the estimates reflect the unfavourable state of global biodiversity monitoring of species groups. The total numbers of tree species in our regional units, which cannot be adequately estimated, are small relative to other tree species-rich regions. Consequently, monitoring of tree species diversity is currently a highly uncertain enterprise at large scales. [ABSTRACT FROM AUTHOR]

Downed woody material (DWM) is a unique part of the forest carbon cycle serving as a pool between living biomass and subsequent atmospheric emission or transference to other forest pools. Thus, DWM is an individually defined pool in national greenhouse gas inventories. The diversity of DWM carbon drivers (e.g., decay, tree mortality, or wildfire) and associated high spatial variability make this a difficult-to-predict component of forest ecosystems. Using the now fully established nationwide inventory of DWM across the United States (US), we developed models, which substantially improved predictions of stand-level DWM carbon density relative to the current national-reporting model ('previous' model, here). The previous model was developed from published DWM carbon densities prior to the NFI DWM inventory. Those predictions were tested using NFI DWM carbon densities resulting in a poor fit to the data (coefficient of determination, or R 2  = 0.03). We present new random forest (RF) and stochastic gradient boosted (SGB) regression models to prediction DWM carbon density on all NFI plots and spatially on all forest land pixels. We evaluated various biotic and abiotic regression predictors, and the most important were standing dead trees, long-term annual precipitation, and long-term maximum summer temperature. A RF model scored best for expanding predictions to NFI plots (R 2  = 0.31), while an SGB model was identified for DWM carbon predictions based on purely spatial data (i.e., NFI-plot-independent, with R 2  = 0.23). The new RF model predicts conterminous US DWM carbon stocks to be 15% lower than the previous model and 2% higher than NFI data expanded according to inventory design-based inference. The new NFI data-driven models not only improve the predictions of DWM carbon density on all plots, they also provide flexibility in extending these predictions beyond the NFI to make spatially explicit and spatially continuous estimates of DWM carbon on all forest land in the US., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but requires capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service's (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA's experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs. [ABSTRACT FROM AUTHOR]

Land use change (LUC), disturbances, and their interactions play an important role in regional forest carbon (C) dynamics. Here we quantified how these activities and events may influence future aboveground biomass (AGB) dynamics in forests using national forest inventory (NFI) and Landsat time series data in the Northern United States (US). Total forest AGB predictions were based on simulations of diameter growth, mortality, and recruitment using matrix growth models under varying levels of LUC and disturbance severity (low (L), medium (M), and high (H)) every five years from 2018 to 2098. Land use change included the integrated effects of deforestation and reforestation/afforestation (forest [F]→agriculture [A], settlements [S, urbanization/other], and A&S→F), specifically, conversion from F→A, F→S, F→A&S, A→F, S→F, and A&S→F. Disturbances included natural and anthropogenic disturbances such as wildfire, weather, insects and disease, and forest harvesting. Results revealed that, when simultaneously considering both medium LUC and disturbances, total forest AGB predictions of LUC + fire, LUC + weather, LUC + insect & disease, and LUC + harvest indicated substantial increases in regional C stocks (± standard deviation) from 1.88 (±0.13) to 3.29 (±0.28), 3.10 (±0.24), 2.91 (±0.19), and 2.68 (±0.17) Pg C, respectively, from 2018 to 2098. An uncertainty analysis with fuzzy sets suggested that medium LUC under disturbances would lead to greater forest AGB C uptake than undisturbed forest C uptake with high certainty, except for LUC + harvest. The matrix models in this study were parameterized using NFI and Landsat data from the past few decades. Thus, our results imply that if recent trends persist, LUC will remain an important driver of forest C uptake, while disturbances may result in C emissions rather than undisturbed forest C uptake by 2098. The combined effects of LUC and disturbances may serve as an important driver of C uptake and emissions in the Northern US well into the 21st century. [ABSTRACT FROM AUTHOR]

Forest land in the United States offsets more than 11% of total domestic greenhouse gas emissions each year through growth of live woody biomass and accumulation of carbon in trees, dead organic matter, and harvested wood products. Forest lands owned and managed by various agencies of the U.S. government cover 77 million hectares, which is 29% of U.S. forest land and an estimated 33%, or 17.2 Pg C, of forest carbon stocks. Here, we summarize forest inventory‐based estimates of forest carbon stocks and indications of carbon stock change on forest lands managed by agencies within the U.S. federal government. Within the conterminous USA, the proportion of forest land that is federally owned is higher in the West representing two‐thirds of forest carbon stocks; in the East, federal lands represent 9% of forest carbon. The majority of federal forests and forest carbon are managed by the U.S. Forest Service (13.8 Pg C), but 20% of federal forest carbon stocks, or 3.5 Pg C, are managed by other federal agencies (e.g., National Park Service, Bureau of Land Management). We also briefly review some broad characteristics of the forest inventory that affect forest carbon reported for the USA as included in greenhouse gas inventories such as for United Nations Framework Convention on Climate Change reporting. [ABSTRACT FROM AUTHOR]