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Legacies of human land use have the potential to impact demographic responses to climate. However, few studies have investigated the interactive effects of land use legacies and climate change on tree demography. The demographic performance of rear edge populations in particular is an important determinant of a species' long-term persistence. In this study, we investigated whether human land use legacies affect demographic responses to climate and population isolation in rear edge European beech populations (Fagus sylvatica L.) at the temperate-Mediterranean transition zone in the NE Iberian Peninsula. We utilised data from the Spanish Forest Inventory and generalised linear mixed models to compare the potential interactions across four different demographic rates (tree growth, survival probability, new adult recruitment and sapling recruitment). We found that the demographic rates were affected by the combination of land use legacies, climate and population isolation in different ways, which could potentially lead to complex shifts in future population dynamics under climate change. We identified that intense historic management either magnified negative relationships between tree demography and climate or population isolation, or reduced demographic performance in favourable climates to levels observed in unfavourable climates. Through either form of interaction, we found that intense historic forest management had a negative impact on tree demography, which has the potential to compromise future carbon stocks and long-term population viability. Overall, we show that disentangling human and environmental factors can enable us to better understand heterogeneous demographic performance across the rear edge of species distributions., (© 2024 The Author(s). Global Change Biology published by John Wiley & Sons Ltd.)

Countries within the tropics face ongoing challenges in completing or updating their national forest inventories (NFIs), critical for estimating aboveground biomass (AGB) and for forest-related greenhouse gas (GHG) accounting. While previous studies have explored the integration of map information with local reference data to fill in data gaps, limited attention has been given to the specific challenges presented by the clustered plot designs frequently employed by NFIs when combined with remote sensing-based biomass map units. This research addresses these complexities by conducting four country case-studies, encompassing a variety of NFI characteristics within a range of AGB densities. Examining four country case-studies (Peru, Guyana, Tanzania, Mozambique), we assess the potential of European Space Agency's Climate Change Initiative (CCI) global biomass maps to increase precision in (sub)national AGB estimates. We compare a baseline approach using NFI field-based data with a model-assisted scenario incorporating a locally calibrated CCI biomass map as auxiliary information. The original CCI biomass maps systematically underestimate AGB in three of the four countries at both the country and stratum level, with particularly weak agreement at finer map resolution. However, after calibration with country-specific NFI data, stratum and country-level AGB estimates from the model-assisted scenario align well with those obtained solely from field-based data and official country reports. Introducing maps as a source of auxiliary information fairly increased the precision of stratum and country-wise AGB estimates, offering greater confidence in estimating AGB for GHG reporting purposes. Considering the challenges tropical countries face with implementing their NFIs, it is sensible to explore the potential benefits of biomass maps for climate change reporting mechanisms across biomes. While country-specific NFI design assumptions guided our model-assisted inference strategies, this study also uncovers transferable insights from the application of global biomass maps with NFI data, providing valuable lessons for climate research and policy communities., 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., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an 'extreme' niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning., 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., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β-diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species-neutral dissimilarity (Bray-Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large-scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s). Ecology and Evolution published by John Wiley & Sons Ltd.)

Carbon sequestration via afforestation and forest growth is effective for mitigating global warming. Accurate and robust information on forest growth characteristics by tree species, region, and large-scale land-use change is vital and future prediction of forest carbon stocks based on this information is of great significance. These predictions allow exploring forestry practices that maximize carbon sequestration by forests, including wood production. Forest inventories based on field measurements are considered the most accurate method for estimating forest carbon stocks. Japan's national forest inventories (NFIs) provide stand volumes for all Japanese forests, and estimates from direct field observations (m-NFIs) are the most reliable. Therefore, using the m-NFI from 2009 to 2013, we selected four major forest plantation species in Japan: Cryptomeria japonica, Chamaecyparis obtusa, Pinus spp., and Larix kaempferi and presented their forest age-carbon density function. We then estimated changes in forest carbon stocks from the past to the present using the functions. Next, we investigated the differences in the carbon sequestration potential of forests, including wood production, between five forestry practice scenarios with varying harvesting and afforestation rates, until 2061. Our results indicate that, for all four forest types, the estimates of growth rates and past forest carbon stocks in this study were higher than those considered until now. The predicted carbon sequestration from 2011 to 2061, assuming that 100 % of harvested carbon is retained for a long time, twice the rate of harvesting compared to the current rate, and a 100 % afforestation rate in harvested area, was three to four times higher than that in a scenario with no harvesting or replanting. Our results suggest that planted Japanese forests can exhibit a high carbon sequestration potential under the premise of active management, harvesting, afforestation, and prolonging the residence time of stored carbon in wood products with technology development., Competing Interests: Declaration of competing interest The authors have no conflicts of interest associated with this work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In forests, the amount and diversity of structural features with high value for biodiversity, such as large trees and dead wood, are affected by productivity, stand age, and forest management. For efficient conservation of forest biodiversity, it is essential to understand the combined effects of these drivers. We used data from the Swedish National Forest Inventory to study the combined effects of productivity, stand age, and management for wood production on structures with high value for biodiversity: tree species richness, large living trees, dead wood volume, and specific dead wood types. Forest management changed the relationship between productivity and amount or diversity of some of the structures. Most structures increased with productivity and stand age, but decreased due to management. The negative effect of management was greatest for structures occurring mainly in high-productivity forests, such as deciduous dead wood. Thus, biodiversity conservation should target high-productivity forests to preserve these structures., (© 2024. The Author(s).)

The link between biodiversity and ecosystem function can depend on environmental conditions. This contingency can impede our ability to predict how biodiversity-ecosystem function (BEF) relationships will respond to future environmental change, causing a clear need to explore the processes underlying shifts in BEF relationships across large spatial scales and broad environmental gradients. We compiled a dataset on five functional traits (maximum height, wood density, specific leaf area [SLA], seed size, and xylem vulnerability to embolism [P 50 ]), covering 78%-90% of the tree species in the National Forest Inventory from Italy, to test (i) how a water limitation gradient shapes the functional composition and diversity of forests, (ii) how functional composition and diversity of trees relate to forest annual increment via mass ratio and complementarity effects, and (iii) how the relationship between functional diversity and annual increment varies between Mediterranean and temperate climate regions. Functional composition varied with water limitation; tree communities tended to have more conservative traits in sites with higher levels of water limitation. The response of functional diversity differed among traits and climatic regions but among temperate forest plots, we found a consistent increase of functional diversity with water limitation. Tree diversity was positively associated with annual increment of Italian forests through a combination of mass ratio and niche complementarity effects, but the relative importance of these effects depended on the trait and range of climate considered. Specifically, niche complementarity effects were more strongly associated with annual increment in the Mediterranean compared to temperate forests. Synthesis : Overall, our results suggest that biodiversity mediates forest annual increment under water-limited conditions by promoting beneficial interactions between species and complementarity in resource use. Our work highlights the importance of conserving functional diversity for future forest management to maintain forest annual increment under the expected increase in intensity and frequency of drought., Competing Interests: The corresponding author confirms on behalf of all authors that there have been no involvements that might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Non-native trees may have significant impacts on the carbon sink capacity of forested lands. However, large-scale patterns of the relative capacity of native and non-native forests to uptake and store carbon remain poorly described in the literature, and this information is urgently needed to support management decisions. In this study, we analyzed 17,065 plots from the Spanish Forest Inventory (covering c. 30 years) to quantify carbon storage and sequestration of natural forests and plantations of native and non-native trees under contrasting climate types, while controlling for the effects of environmental factors (forest structure, climate, soil, topography, and management). We found that forest origin (non-native vs. native) highly influenced carbon storage and sequestration, but such effect was dependent on climate. Carbon storage was greater in non-native than in native forests in both wet and dry climates. Non-native forests also had greater carbon sequestration than native ones in the wet climate, due to higher carbon gains by tree growth. However, in the dry climate, native forests had greater carbon gains by tree ingrowth and lower carbon loss by tree mortality than non-native ones. Furthermore, forest type (classified by the dominant species) and natural forests versus tree plantations were important determinants of carbon storage and sequestration. Native and non-native Pinus spp. forests had low carbon storage, whereas non-native Eucalyptus spp. forests and native Quercus spp., Fagus sylvatica, and Eurosiberian mixed forests (especially not planted ones) had high carbon storage. Carbon sequestration was greatest in Eucalyptus globulus, Quercus ilex, and Pinus pinaster forests. Overall, our findings suggest that the relative capacity of native and non-native forests to uptake and store carbon depends on climate, and that the superiority of non-native forests over native ones in terms of carbon sequestration declines as the abiotic filters become stronger (i.e., lower water availability and higher climate seasonality)., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, that is, the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short time series from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities., Competing Interests: No competing interests., (© 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

With climate change, natural disturbances such as storm or fire are reshuffled, inducing pervasive shifts in forest dynamics. To predict how it will impact forest structure and composition, it is crucial to understand how tree species differ in their sensitivity to disturbances. In this study, we investigated how functional traits and species mean climate affect their sensitivity to disturbances while controlling for tree size and stand structure. With data on 130,594 trees located on 7617 plots that were disturbed by storm, fire, snow, biotic or other disturbances from the French, Spanish, and Finnish National Forest Inventory, we modeled annual mortality probability for 40 European tree species as a function of tree size, dominance status, disturbance type, and intensity. We tested the correlation of our estimated species probability of disturbance mortality with their traits and their mean climate niches. We found that different trait combinations controlled species sensitivity to disturbances. Storm-sensitive species had a high height-dbh ratio, low wood density and high maximum growth, while fire-sensitive species had low bark thickness and high P50. Species from warmer and drier climates, where fires are more frequent, were more resistant to fire. The ranking in disturbance sensitivity between species was overall consistent across disturbance types. Productive conifer species were the most disturbance sensitive, while Mediterranean oaks were the least disturbance sensitive. Our study identified key relations between species functional traits and disturbance sensitivity, that allows more reliable predictions of how changing climate and disturbance regimes will impact future forest structure and species composition at large spatial scales., (© 2023 John Wiley & Sons Ltd.)

Standing deadwood is an important structural component of forest ecosystems. Its occurrence and dynamics influence both carbon fluxes and the availability of habitats for many species. However, deadwood is greatly reduced in managed, and even in many currently unmanaged temperate forests in Europe. To date, few studies have examined how environmental factors, forest management and changing climate affect the availability of standing deadwood and its dynamics.Data from five periods of the Austrian National Forest Inventory (1981-2009) were used to (I) analyse standing deadwood volume in relation to living volume stock, elevation, eco-region, forest type, ownership and management intensity, (II) investigate the influence of forest ownership and management intensity on snag persistence and (III) define drivers of standing deadwood volume loss for seven tree genera ( Abies , Alnus , Fagus , Larix , Picea , Pinus and Quercus ) using tree-related, site-related and climate-related variables, and predict volume loss under two climate change scenarios.Standing deadwood volume was mainly determined by living volume stock and elevation, resulting in different distributions between eco-regions. While forest type and management intensity influenced standing deadwood volume only slightly, the latter exhibited a significant effect on persistence. Snag persistence was shorter in intensively managed forests than in extensively managed forests and shorter in private than in public forests.Standing deadwood volume loss was driven by a combination of diameter at breast height, elevation, as well as temperature, precipitation and relative humidity. Volume loss under climate change predictions revealed constant rates for moderate climate change (RCP2.6) by the end of the 21st century. Under severe climate change conditions (RCP8.5), volume loss increased for most tree genera, with Quercus , Alnus and Picea showing different predictions depending on the model used as the baseline scenario. We observed trends towards faster volume loss at higher temperatures and lower elevations and slower volume loss at high precipitation levels. The tree genera most susceptible to climate change were Pinus and Fagus , while Abies was least susceptible. Synthesis and applications. We recommend to protect standing dead trees from regular harvesting to ensure the full decomposition process. The consequences for decomposition-dependent species must be taken into account to evaluate the influences of management and climate change on standing deadwood dynamics., Competing Interests: 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., (© 2023 The Authors. Journal of Applied Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

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.)

Tree-ring data has been widely used to inform about tree growth responses to drought at the individual scale, but less is known about how tree growth sensitivity to drought scales up driving changes in forest dynamics. Here, we related tree-ring growth chronologies and stand-level forest changes in basal area from two independent data sets to test if tree-ring responses to drought match stand forest dynamics (stand basal area growth, ingrowth, and mortality). We assessed if tree growth and changes in forest basal area covary as a function of spatial scale and tree taxa (gymnosperm or angiosperm). To this end, we compared a tree-ring network with stand data from the Spanish National Forest Inventory. We focused on the cumulative impact of drought on tree growth and demography in the period 1981-2005. Drought years were identified by the Standardized Precipitation Evapotranspiration Index, and their impacts on tree growth by quantifying tree-ring width reductions. We hypothesized that forests with greater drought impacts on tree growth will also show reduced stand basal area growth and ingrowth and enhanced mortality. This is expected to occur in forests dominated by gymnosperms on drought-prone regions. Cumulative growth reductions during dry years were higher in forests dominated by gymnosperms and presented a greater magnitude and spatial autocorrelation than for angiosperms. Cumulative drought-induced tree growth reductions and changes in forest basal area were related, but initial stand density and basal area were the main factors driving changes in basal area. In drought-prone gymnosperm forests, we observed that sites with greater growth reductions had lower stand basal area growth and greater mortality. Consequently, stand basal area, forest growth, and ingrowth in regions with large drought impacts was significantly lower than in regions less impacted by drought. Tree growth sensitivity to drought can be used as a predictor of gymnosperm demographic rates in terms of stand basal area growth and ingrowth at regional scales, but further studies may try to disentangle how initial stand density modulates such relationships. Drought-induced growth reductions and their cumulative impacts have strong potential to be used as early-warning indicators of regional forest vulnerability., (© 2022 The Ecological Society of America.)

To understand the state and trends in biodiversity beyond the scope of monitoring programs, biodiversity indicators must be comparable across inventories. Species richness (SR) is one of the most widely used biodiversity indicators. However, as SR increases with the size of the area sampled, inventories using different plot sizes are hardly comparable. This study aims at producing a methodological framework that enables SR comparisons across plot-based inventories with differing plot sizes. We used National Forest Inventory (NFI) data from Norway, Slovakia, Spain, and Switzerland to build sample-based rarefaction curves by randomly incrementally aggregating plots, representing the relationship between SR and sampled area. As aggregated plots can be far apart and subject to different environmental conditions, we estimated the amount of environmental heterogeneity (EH) introduced in the aggregation process. By correcting for this EH, we produced adjusted rarefaction curves mimicking the sampling of environmentally homogeneous forest stands, thus reducing the effect of plot size and enabling reliable SR comparisons between inventories. Models were built using the Conway-Maxell-Poisson distribution to account for the underdispersed SR data. Our method successfully corrected for the EH introduced during the aggregation process in all countries, with better performances in Norway and Switzerland. We further found that SR comparisons across countries based on the country-specific NFI plot sizes are misleading, and that our approach offers an opportunity to harmonize pan-European SR monitoring. Our method provides reliable and comparable SR estimates for inventories that use different plot sizes. Our approach can be applied to any plot-based inventory and count data other than SR, thus allowing a more comprehensive assessment of biodiversity across various scales and ecosystems., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Efficient forest operations are required for the provision of biodiversity and numerous ecosystem services, such as wood production, carbon sequestration, protection against natural hazards and recreation. In numerous countries, under difficult terrain conditions, the costs of forest management and harvesting are not covered by timber revenue. One possible option to increase the cost-effectiveness of the forestry sector is the application of state-of-the-art harvesting and extraction techniques, so-called best suitable harvesting methods. We present a case study from Switzerland, where a lack of competitiveness in the forestry sector is of particular interest, with the aim of quantifying the efficiency gains if estimated best suitable harvesting methods were to be rigorously applied instead of the currently applied harvesting methods. For this purpose, we developed a spatial decision support system to allocate estimated best suitable harvesting methods to plots, while concurrently considering hauling route limitations, extraction route properties and stand characteristics. Our approach was based on productivity models and supported with expert-defined decision trees. The evaluation of the estimated best suitable harvesting methods and the comparison with the currently applied harvesting methods were completed for all 6500 National Forest Inventory (NFI) plots in Switzerland. We draw the following three major conclusions from our study: First, our modeling approach is an effective method to allocate estimated best suitable harvesting methods to NFI plots. Second, applying estimated best suitable harvesting methods would lead to cost reductions, in particular in the regions that include steep terrain and where harvesting mainly relies on cable- and air based extraction methods. Third, assuming an average timber price of 75 CHF m -3 , 64 % instead of 52 % of the forest area could be harvested economically over the whole country if estimated best suitable methods were applied. This advantage would mainly be caused by a shift towards more mechanized harvesting methods. Improving the cost-effectiveness of the forestry sector is of high global relevance, as the increased use of domestic timber resources is a cost-efficient way to reduce atmospheric carbon emissions. The methodological framework described here was developed for Switzerland in particular, but it could be applied to Central Europe and other parts of Europe with a large amount of mountain forests., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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.)

Tree-ring time series provide long-term, annually resolved information on the growth of trees. When sampled in a systematic context, tree-ring data can be scaled to estimate the forest carbon capture and storage of landscapes, biomes, and-ultimately-the globe. A systematic effort to sample tree rings in national forest inventories would yield unprecedented temporal and spatial resolution of forest carbon dynamics and help resolve key scientific uncertainties, which we highlight in terms of evidence for forest greening (enhanced growth) versus browning (reduced growth, increased mortality). We describe jump-starting a tree-ring collection across the continent of North America, given the commitments of Canada, the United States, and Mexico to visit forest inventory plots, along with existing legacy collections. Failing to do so would be a missed opportunity to help chart an evidence-based path toward meeting national commitments to reduce net greenhouse gas emissions, urgently needed for climate stabilization and repair., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Institute of Biological Sciences.)

Plant functional traits are highly plastic to changes in climatic factors and nutrient availability. However, the intraspecific plant response to abiotic factors and the overall effect on tree growth and productivity is still under debate. We studied forest productivity for 30 Quercus ilex subsp. ballota forests in Spain along a broad climatic gradient of aridity (mean annual precipitation from 321 to 858 mm). We used linear mixed models to quantify the effect of climatic and edaphic (soil nutrients, topography, and texture) factors on tree functional traits (leaf and branch traits), and subsequently, the effect of such functional traits and abiotic factors on the relative growth rate (RGR) of adult trees. We used piecewise structural equation models (SEMs) to determine the causal effect of intrinsic and extrinsic factors on forest productivity. Our results showed that tree functional traits were mainly explained by climatic and edaphic factors. Functional traits and tree biomass explained forest biomass and RGR, respectively, which ultimately explained forest productivity. In conclusion, intraspecific variability of functional traits has a significant effect on plant biomass and growth, which ultimately may explain forest productivity in Quercus ilex forests., 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., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate-related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross-validated (50%:50% - training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS-weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak-stand locations as a training dataset; the national forest inventory (NFI) "published regional reports" of oak woodland area; and an "NFI map" of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from "NFI survey" sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% ( p  = .53) to 83% coincidence success rate ( p  < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice., Competing Interests: None declared., (© 2021 Crown copyright. Ecology and Evolution published by John Wiley & Sons Ltd. This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.)

Identifying the signs and symptoms of pathogens, insects, and other biotic and abiotic agents provides valuable information about the absolute and relative impacts of different types of damage across the forest landscape. In the USA, damage collection protocols have been included in various forms since the initiation of state-level forest surveys in the early twentieth century; however, changes in the protocols over time have made it difficult for the data to be used to its full potential. This article outlines differences in protocols across inventory regions, changes in protocols over time, and limitations and utility of the data so that those interested in using the US national forest inventory database will better understand what data are available and how they have been and can be used.

Tree growing conditions are changing rapidly in the face of climate change. Capturing tree-growth response to such changes across environmental contexts and tree species calls for a continuous forest monitoring over space. Based on >10,000 tree-ring measurements sampled across the systematic grid of the continuous French national forest inventory (NFI) over the 2006-2016 period, we evaluated the radial growth trends of eight conifer tree species prevalent in European forests across their native and introduced ranges and various bioclimatic contexts (n = 16 forest systems). For each forest system, radial increments were filtered out from tree, plot, soil and climatic normal influences to isolate environment-driven growth signals and quantify residual time-series. Associated growth trends across forest systems were then confronted against environmental variables (e.g. short-term averages and trends in seasonal climate). Trends for a given species were systematically more positive in cooler contexts (higher elevations or northern distribution margins) than in warmer contexts (plains). Decreases and increases in precipitation regimes were found to be associated with negative and positive tree growth trends, respectively. Remarkably, positive growth trends were mainly observed for native forest systems (7/9) and negative trends for introduced systems (5/7). Native forests showed a more heterogeneous forest structure as compared to introduced forests that, in line with observed positive dependence of tree growth trends onto both water availability and forest heterogeneity, appears to modulate the competitive pressure on water resource with ongoing summer maximum temperature increase. Over a short annually-resolved study period, we were able to capture tree growth responses coherent with climate change across diverse forest ecosystems. With ongoing accumulation of data, the continuous French NFI hence arises as powerful support to monitoring climate change effects on forests., Competing Interests: Declaration of competing interest The authors declare no competing interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The quantification of forests available for wood supply (FAWS) is essential for decision-making with regard to the maintenance and enhancement of forest resources and their contribution to the global carbon cycle. The provision of harmonized forest statistics is necessary for the development of forest associated policies and to support decision-making. Based on the National Forest Inventory (NFI) data from 13 European countries, we quantify and compare the areas and aboveground dry biomass (AGB) of FAWS and forest not available for wood supply (FNAWS) according to national and reference definitions by determining the restrictions and associated thresholds considered at country level to classify forests as FAWS or FNAWS. FAWS represent between 75 and 95 % of forest area and AGB for most of the countries in this study. Economic restrictions are the main factor limiting the availability of forests for wood supply, accounting for 67 % of the total FNAWS area and 56 % of the total FNAWS AGB, followed by environmental restrictions. Profitability, slope and accessibility as economic restrictions, and protected areas as environmental restrictions are the factors most frequently considered to distinguish between FAWS and FNAWS. With respect to the area of FNAWS associated with each type of restriction, an overlap among the restrictions of 13.7 % was identified. For most countries, the differences in the FNAWS areas and AGB estimates between national and reference definitions ranged from 0 to 5 %. These results highlight the applicability and reliability of a FAWS reference definition for most of the European countries studied, thereby facilitating a consistent approach to assess forests available for supply for the purpose of international reporting., (© 2019 The Authors.)

Forest ecosystems contribute substantially to carbon (C) storage. The dynamics of litter decomposition, translocation and stabilization into soil layers are essential processes in the functioning of forest ecosystems, as these processes control the cycling of soil organic matter and the accumulation and release of C to the atmosphere. Therefore, the spatial distribution of litter and soil C stocks are important in greenhouse gas estimation and reporting and inform land management decisions, policy, and climate change mitigation strategies. Here we explored the effects of spatial aggregation of climatic, biotic, topographic and soil variables on national estimates of litter and soil C stocks and characterized the spatial distribution of litter and soil C stocks in the conterminous United States (CONUS). Litter and soil variables were measured on permanent sample plots (n = 3303) from the National Forest Inventory (NFI) within the United States from 2000 to 2011. These data were used with vegetation phenology data estimated from LANDSAT imagery (30 m) and raster data describing environmental variables for the entire CONUS to predict litter and soil C stocks. The total estimated litter C stock was 2.07 ± 0.97 Pg with an average density of 10.45 ± 2.38 Mg ha -1 , and the soil C stock at 0-20 cm depth was 14.68 ± 3.50 Pg with an average density of 62.68 ± 8.98 Mg ha -1 . This study extends NFI data from points to pixels providing spatially explicit and continuous predictions of litter and soil C stocks on forest land in the CONUS. The approaches described illustrate the utility of harmonizing field measurements with remotely sensed data to facilitate modeling and prediction across spatial scales in support of inventory, monitoring, and reporting activities, particularly in countries with ready access to remotely sensed data but with limited observations of litter and soil variables., (Copyright © 2018. Published by Elsevier B.V.)

Biomass conversion and expansion factors (BCEFs) are important parameters for estimating carbon storage in forest biomass. Clarifying the source of differences in estimating BCEFs could reduce uncertainties in forest biomass carbon estimation. The decision tree models of ensemble learning can be used to properly figure out the source of differences in estimating BCEFs. However, the comparison of different decision tree models for analyzing differences in estimating BCEFs has never been reported. In this study, three models [the boosted regression trees (BRT), random forest(RF), and Cubist] and data of 331 masson pine plots from the 8 th Chinese National Forest Inventory for Fujian Province were used to analyze the differences in estimating BCEFs (including above- and below-ground). The results showed that BCEFs were following right-skewed distribution, with the mean, minimum and maximum value being 0.69 t·m -3 , 0.67 t·m -3 and 0.71 t·m -3 , respectively. All three models performed well in BCEFs prediction and fitting, and could explain more than 92.8% variations of BCEFs. All three models showed that average DBH and volume were the top two highest relative importance predictors. BCEFs decreased with the increases of average DBH and volume. Stand characteristics factors, such as average DBH, volume, average age and average height, had great influence on BCEFs. Both soil factors and topographic factors had little influence on BCEFs. Using a few variables (such as average DBH, volume, average age and avera-ge height) which contained more BCEFs prediction information could have preferable forecasting precision when building BCEFs models. Moreover, widely representative samples with different average tree ages, average DBH and volume should be chosen to calculate BCEFs when applying constant BCEFs.

Accurate carbon-balance accounting in forest soils is necessary for the development of climate change policy. However, changes in soil organic carbon (SOC) occur slowly and these changes may not be captured through repeated soil inventories. Simulation models may be used as alternatives to SOC measurement. The Yasso07 model presents a suitable alternative because most of the data required for the application are readily available in countries with common forest surveys. In this study, we test the suitability of Yasso07 for simulating SOC stocks and stock changes in a variety of European forests affected by different climatic, land use and forest management conditions and we address country-specific cases with differing resources and data availability. The simulated SOC stocks differed only slightly from measured data, providing realistic, reasonable mean SOC estimations per region or forest type. The change in the soil carbon pool over time, which is the target parameter for SOC reporting, was generally found to be plausible although not in the case of Mediterranean forest soils. As expected under stable forest management conditions, both land cover and climate play major roles in determining the SOC stock in forest soils. Greater mean SOC stocks were observed in northern latitudes (or at higher altitude) than in southern latitudes (or plains) and conifer forests were found to store a notably higher amount of SOC than broadleaf forests. Furthermore, as regards change in SOC, an inter-annual sink effect was identified for most of the European forest types studied. Our findings corroborate the suitability of Yasso07 to assess the impact of forest management and land use change on the SOC balance of forests soils, as well as to accurately simulate SOC in dead organic matter (DOM) and mineral soil pools separately. The obstacles encountered when applying the Yasso07 model reflect a lack of available input data. Future research should focus on improving our knowledge of C inputs from compartments such as shrubs, herbs, coarse woody debris and fine roots. This should include turnover rates and quality of the litter in all forest compartments from a wider variety of tree species and sites. Despite the limitations identified, the SOC balance estimations provided by the Yasso07 model are sufficiently complete, accurate and transparent to make it suitable for reporting purposes such as those required under the UNFCCC (United Nations Framework Convention on Climate Change) and KP (Kyoto Protocol) for a wide range of forest conditions in Europe., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Intense droughts combined with increased temperatures are one of the major threats to forest persistence in the 21st century. Despite the direct impact of climate change on forest growth and shifts in species abundance, the effect of altered demography on changes in the composition of functional traits is not well known. We sought to (1) quantify the recent changes in functional composition of European forests; (2) identify the relative importance of climate change, mean climate and forest development for changes in functional composition; and (3) analyse the roles of tree mortality and growth underlying any functional changes in different forest types. We quantified changes in functional composition from the 1980s to the 2000s across Europe by two dimensions of functional trait variation: the first dimension was mainly related to changes in leaf mass per area and wood density (partially related to the trait differences between angiosperms and gymnosperms), and the second dimension was related to changes in maximum tree height. Our results indicate that climate change and mean climatic effects strongly interacted with forest development and it was not possible to completely disentangle their effects. Where recent climate change was not too extreme, the patterns of functional change generally followed the expected patterns under secondary succession (e.g. towards late-successional short-statured hardwoods in Mediterranean forests and taller gymnosperms in boreal forests) and latitudinal gradients (e.g. larger proportion of gymnosperm-like strategies at low water availability in forests formerly dominated by broad-leaved deciduous species). Recent climate change generally favoured the dominance of angiosperm-like related traits under increased temperature and intense droughts. Our results show functional composition changes over relatively short time scales in European forests. These changes are largely determined by tree mortality, which should be further investigated and modelled to adequately predict the impacts of climate change on forest function., (© 2017 John Wiley & Sons Ltd.)

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., (© 2017 by the Ecological Society of America.)

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±77Tg) 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., (Published by Elsevier B.V.)

Background: The German greenhouse gas inventory in the land use change sector strongly depends on national forest inventory data. As these data were collected periodically 1987, 2002, 2008 and 2012, the time series on emissions show several "jumps" due to biomass stock change, especially between 2001 and 2002 and between 2007 and 2008 while within the periods the emissions seem to be constant due to the application of periodical average emission factors. This does not reflect inter-annual variability in the time series, which would be assumed as the drivers for the carbon stock changes fluctuate between the years. Therefore additional data, which is available on annual basis, should be introduced into the calculations of the emissions inventories in order to get more plausible time series., Results: This article explores the possibility of introducing an annual rather than periodical approach to calculating emission factors with the given data and thus smoothing the trajectory of time series for emissions from forest biomass. Two approaches are introduced to estimate annual changes derived from periodic data: the so-called logging factor method and the growth factor method. The logging factor method incorporates annual logging data to project annual values from periodic values. This is less complex to implement than the growth factor method, which additionally adds growth data into the calculations., Conclusion: Calculation of the input variables is based on sound statistical methodologies and periodically collected data that cannot be altered. Thus a discontinuous trajectory of the emissions over time remains, even after the adjustments. It is intended to adopt this approach in the German greenhouse gas reporting in order to meet the request for annually adjusted values.

Aim The fossil record has led to a historical explanation for forest diversity gradients within the cool parts of the Northern Hemisphere, founded on a limited ability of woody angiosperm clades to adapt to mid-Tertiary cooling. We tested four predictions of how this should be manifested in the phylogenetic structure of 91,340 communities: (1) forests to the north should comprise species from younger clades (families) than forests to the south; (2) average cold tolerance at a local site should be associated with the mean family age (MFA) of species; (3) minimum temperature should account for MFA better than alternative environmental variables; and (4) traits associated with survival in cold climates should evolve under a niche conservatism constraint. Location The contiguous United States. Methods We extracted angiosperms from the US Forest Service's Forest Inventory and Analysis database. MFA was calculated by assigning age of the family to which each species belongs and averaging across the species in each community. We developed a phylogeny to identify phylogenetic signal in five traits: realized cold tolerance, seed size, seed dispersal mode, leaf phenology and height. Phylogenetic signal representation curves and phylogenetic generalized least squares were used to compare patterns of trait evolution against Brownian motion. Eleven predictors structured at broad or local scales were generated to explore relationships between environment and MFA using random forest and general linear models. Results Consistent with predictions, (1) southern communities comprise angiosperm species from older families than northern communities, (2) cold tolerance is the trait most strongly associated with local MFA, (3) minimum temperature in the coldest month is the environmental variable that best describes MFA, broad-scale variables being much stronger correlates than local-scale variables, and (4) the phylogenetic structures of cold tolerance and at least one other trait associated with survivorship in cold climates indicate niche conservatism. Main conclusions Tropical niche conservatism in the face of long-term climate change, probably initiated in the Late Cretaceous associated with the rise of the Rocky Mountains, is a strong driver of the phylogenetic structure of the angiosperm component of forest communities across the USA. However, local deterministic and/or stochastic processes account for perhaps a quarter of the variation in the MFA of local communities.