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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., Journal of Applied Ecology, 60 (4), ISSN:0021-8901, ISSN:1365-2664

20 Pág., Key message: This paper proposes a methodology that could be considered as a base for a harmonized protocol for stem-quality reporting in Europe while conducting National Forest Inventories, in order to cost-efficiently obtain a visual wood quality proxy. The importance of the variables selected, the limitations identified, and some improvements to the methodology are suggested. Forest areas with better wood quality, which in turn it would be useful for breeding programs, can be easily detected. Context: The establishment of harmonized standards or indicators that allow us to determine the quality of the wood present in a forest prior to its exploitation has long been demanded by the European forestry sector, although agreed methodologies for the evaluation of wood quality in standing trees, which is one of the sector’s most urgent requirements, have not, as yet, been implemented. Aims: To develop a protocol that visually characterizes wood quality on standing trees in a cost-effective way for the National Forest Inventory (NFI). After some improvements, it can be considered as a base for a European harmonized protocol. Methods: In this article, we analyze the implementation, in the NFI, of a visual wood-quality assessment methodology in forests of Central Spain based on the different European standards as well as on research papers addressing this issue. Results: The silvicultural practices employed are of the utmost importance to obtain the best wood quality, regardless of the species. Several areas with higher wood quality were identified as well as areas most affected by specific pests in the studied region. The impact of the variables measured (e.g., branchiness, crookedness, maximum branch diameter) is discussed. Conclusion: It is feasible to estimate a proxy for wood quality on standing trees in the NFI. Furthermore, after studying the inventory data provided, several enhancements are proposed, not only to improve wood-quality estimates but also to optimize fieldwork costs. Harmonizing NFIs to assess and map European standing wood quality can be achieved., This work was supported by the Spanish Ministry of Agriculture, Fishing and Food (Encomienda de Gestión. EG17-042 “Soporte científico a la generación de información Forestal”).

When conducting a forest inventory, sometimes portions of plots cannot be measured due to inaccessibility. Two primary methods have been presented to account for partial nonresponse in the estimation phase: (i) use a ratio-to-size estimator or (ii) apply an adjustment factor to all plot observations in proportion to the missing area. Both approaches provide identical estimates of the population mean, but the estimates of variance differ when partial nonresponse is present. The performance of variance estimators was examined for a range of population forest area and partial nonresponse proportions in the sample. The ratio-to-size variance estimator performed unbiasedly with respect to simulation results, but the adjustment factor variance estimates were biased, with magnitude and direction dependent upon the forest area proportion and amount of partial nonresponse. The bias is relatively small when the partial nonresponse is small, which is often the case; however, the ratio-to-size method is preferred to ensure accurate variance estimation for a wide range of circumstances.

Key message Authors have analyzed the possible correlation between measurements/indicators of forest structure and species richness of many taxonomic or functional groups over three regions of Germany. Results show the potential to use structural attributes as a surrogate for species richness of most of the analyzed taxonomic and functional groups. This information can be transferred to large-scale forest inventories to support biodiversity monitoring. Context We are currently facing a dramatic loss in biodiversity worldwide and this initiated many monitoring programs aiming at documenting further trends. However, monitoring species diversity directly is very resource demanding, in particular in highly diverse forest ecosystems. Aims We investigated whether variables applied in an index of stand structural diversity, which was developed based on forest attributes assessed in the German National Forest Inventory, can be calibrated against richness of forest-dwelling species within a wide range of taxonomic and functional groups. Methods We used information on forest structure and species richness that has been comprehensively assessed on 150 forest plots of the German biodiversity exploratories project, comprising a large range of management intensities in three regions. We tested, whether the forest structure index calculated for these forest plots well correlate with the number of species across 29 taxonomic and functional groups, assuming that the structural attributes applied in the index represent their habitat requirements. Results The strength of correlations between the structural variables applied in the index and number of species within taxonomic or functional groups was highly variable. For some groups such as Aves, Formicidae or vascular plants, structural variables had a high explanatory power for species richness across forest types. Species richness in other taxonomic and functional groups (e.g., soil and root-associated fungi) was not explained by individual structural attributes of the index. Results indicate that some taxonomic and functional groups depend on a high structural diversity, whereas others seem to be insensitive to it or even prefer structurally poor stands. Conclusion Therefore, combinations of forest stands with different degrees of structural diversity most likely optimize taxonomic diversity at the landscape level. Our results can support biodiversity monitoring through quantification of forest structure in large-scale forest inventories. Changes in structural variables over inventory periods can indicate changes in habitat quality for individual taxonomic groups and thus points towards national forest inventories being an effective tool to detect unintended effects of changes in forest management on biodiversity., Annals of Forest Science, 80 (1), ISSN:1286-4560, ISSN:1297-966X

Knowledge of habitat requirements of saproxylic insects and their response to habitat changes is critical for assessing the ecological impacts of forest management. Several studies have demonstrated a positive relationship of tree-species richness, deadwood volume, or structural diversity with saproxylic species diversity, while the relationship with the abundance of potential pest species have often been negative. A better understanding of which factors drive saproxylic insects’ occurrence is therefore essential for deriving urgently needed thresholds for key habitat conditions. We tested a rapid assessment method applicable at large scale based on recorded feeding galleries and boreholes assessed during the Austrian National Forest Inventory to investigate the drivers and habitat thresholds of different saproxylic insect families; i.e. Buprestidae, Cerambycidae, Curculionidae, Siricidae, at multiple spatial scales; i.e. at the object, forest stand and landscape level. We modelled the relative abundance of all insects and these families considering nineteen explanatory variables using ordinal logistic regression models. Key habitat characteristics were identified using recursive partitioning. Our results revealed complex interactions among influencing factors at different spatial scales. We showed that deadwood volume was of surprisingly little importance. Instead, individual tree characteristics were of major importance, demonstrating the value of resource quality and variability. The abundance of all saproxylic insect families increased with advancing decomposition, on trees taller than 18 m, and above a living stand volume of 41 m3ha−1. Aiming to guide forest management, not only forest type-specific, but tree species-specific deadwood management is needed, taking into account site-specific conditions, including temperature and precipitation. For assessing temporal trends in insect colonization and habitat dynamics as well as the effects of forest management, we propose a continuous monitoring of insect traces, including living but weakened trees. This will allow for further thresholds that are urgently needed for maintaining biological diversity in forest ecosystems in the face of climate change., Ecological Indicators, 145, ISSN:1470-160X, ISSN:1872-7034

Post-stratification is often used to increase the precision of estimates arising from large-area forest inventories with plots established at permanent locations. Remotely sensed data and associated spatial products are often used for developing the post-stratification, which offers a mechanism to increase precision for less cost than increasing the sample size. While important variance reductions have been shown from post-stratification, it remains unknown where observed gains lie along the continuum of possible gains. This information is needed to determine whether efforts to further improve post-stratification outcomes are warranted. In this study, two types of optimal post-stratification were compared with typical production-based post-stratifications to estimate the magnitude of remaining gains possible. Although the optimal post-stratifications were derived using methods inappropriate for operational usage, the results indicated that substantial further increases in precision for estimates of both forest area and total tree biomass could be obtained with better post-stratifications. The potential gains differed by the attribute being estimated, the population being studied, and the number of strata. Practitioners seeking to optimize post-stratification face challenges, such as evaluation of numerous auxiliary data sources, temporal misalignment between plot observations and remotely sensed data acquisition, and spatial misalignment between plot locations and remotely sensed data due to positional errors in both data types.

Understanding species’ elevational distributions in mountain ecosystems is needed under climate change, but remote biodiverse mountain areas may be poorly documented. National Forest Inventories (NFIs) offer a potential source of data. We used NFI records from Bhutan to ask three questions about elevational richness patterns of Himalayan woody plant species. First, does the mean elevation for all species differ from those species whose entire elevational distribution is recorded in the survey? Second, how does the elevation of maximum richness differ when combining species originating from temperate and tropical regions vs. analyzing them separately? And third, do the highest species turnover rates adjoin elevation zones of maximum species richness? We used 32,198 species records from 1685 forest plots along a 7570 m gradient to map species elevation ranges. Species whose entire range was documented were those whose lowest records are located above 400 m, while bare rock defined all species’ upper limits. We calculated species richness and turnover using 400 m elevation bands. Of 569 species, 79% of temperate and 61% of tropical species’ elevation ranges were fully sampled within the NFI data. Mean elevation of tree and shrub species differed significantly for temperate and tropical species. Maximum combined species richness is from 1300 to 1700 m (277 species), differing significantly from maximum tropical (900–1300 m, 169) and temperate species richness (2500–2900 m, 92). Temperate tree turnover rate was highest in the bands adjoining its maximum species richness (2500–2900 m). But turnover for tropical trees was highest several bands above their maximum species richness, where turnover and decrease in richness interact. Shrub species turnover patterns are similar, but rates were generally higher than for trees. Bhutan’s NFI records show that woody plant species are arrayed on the Himalaya in part according to floristic origins, and that combining temperate- and tropical-originating floras for gradient-based studies such as species richness and turnover obscures actual elevational patterns. In addition, species whose ranges extend below the Himalayan elevation gradient should be accounted for in future studies that correlate climate and environment factors with elevational species richness patterns.

National Forest Inventories are important primary data sources for large-scale forest resource surveys, in which volume estimates of sampled trees are essential for quantitative analysis. Volume prediction models in natural forests are scarce in Brazil due to legal restrictions for cutting trees, especially in the Atlantic Forest. This study aimed to fit volume models for the main forest types and timber species of the Atlantic Forest in Rio de Janeiro state, considering two hypotheses: (I) generic volume models provide greater generalizability of estimates; however, (II) they may reduce the accuracy of forest type- and species-specific predictions. Four linear models with logarithmic transformation of variables were evaluated to fit volume models for generic and specific datasets, which correspond to the main forest types and timber species. Goodness-of-fit statistics were calculated to compare the accuracy and efficiency of the models, and selected models were validated through leave-one-out cross-validation procedures. The estimates obtained by generic and specific models were compared by non-parametric hypothesis tests. Generic models showed similar predictions to the specific models for forest types and timber species, with similar potential for stem and total volume predictions. Therefore, generic models can be used for Atlantic Forests in Rio de Janeiro state, while specific models are recommended to obtain more detailed local estimates.

Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Background The aim of this study was to construct a nationwide stand age model by using National Forest Inventory (NFI) data and nationwide airborne laser scanning (ALS) data. In plantation forestry, age is usually known. While this is not the case in boreal managed forests, age is still seldom predicted in forest management inventories. Measuring age accurately in situ is also very laborious. On the other hand, tree age is one of the accurately measured sample tree attributes in NFI field data. Many countries also have a nationwide coverage of airborne laser scanning (ALS) data. In this study, we merged these data sources and constructed a nationwide, area-based model for stand age. Results While constructing the model, we omitted old forests from the data, since the correlation between ALS height metrics and stand age diminished at stands with age > 100 years. Additionally, the effect of growth conditions was considerable, so we also utilized different geographical and NFI variables such as site fertility and soil type in the modeling. The resultant nationwide model for the stand age of managed forests yielded a root mean square error (RMSE) of about 14 years. The model could be improved further by additional forest structure variables, but such information may not be available in practice. Conclusions The results showed that the prediction of stand age by ALS, geographical and NFI information was challenging, but still possible with moderate success. This study is an example of the joint use of NFI and nationwide ALS data and re-use of NFI data in research.

The sampling intensity of a national forest inventory is usually low. Forest dynamics models can be used to update plots from past inventory campaigns to enhance the precision of the estimate on smaller areas. By doing this, however, the inference relies not only on the sampling design, but also on the model. In this study, the contribution of model predictions to the variance of enhanced small-area estimates was assessed through a case study. The French national forest inventory provided different annual campaigns for a particular region and department of France. Three past campaigns were updated using a forest dynamics model, and estimates of the standing volumes were obtained through two methods: a modified multiple imputation and the Bayesian method. The update greatly increased the precision of the estimate, and the gain was similar between the two methods. The sampling-related variance represented the largest share of the total variance in all cases. This study suggests that plot updating provides more precise estimates as long as (i) the forest dynamics model exhibits no systematic lack of fit and was fitted to a large data set and (ii) the sampling-related variance clearly outweighs the model-related variance.

The forest in India have a long history of human induced modifications through intensive forest management that began in the colonial period but continued afterwards to meet the wood-based demand until the forestry goals shifted towards conservation efforts. All this greatly influenced the plant community structure. We attempt to describe the abundance and distribution pattern of trees in recorded forestland of Western Himalaya using the National Forest Inventory database. A total of 3549 sample plots laid across different forest types were analyzed to reveal the abundance, frequency and distribution pattern of 226 tree species/genus using various indices i.e. abundance by frequency ratio, Morisita index and variance mean ratio. Twelve tree species and Eucalyptus genus were found to be most abundantly found tree species in recorded forest area of Western Himalaya. Ten tree species were found to have regular distribution pattern, one hundred ninety-two were contagiously distributed while twenty-five were randomly distributed. Results derived from huge datasets helps in establishing firm statements with quoted significance value. Myrica esculenta and Aegle marmelos were found among the most abundantly occurring tree species whereas Myrica esculenta, Syzygium cumini and Pyrus pashia, were among the most relatively frequently occurring wild fruit yielding tree species. These species (apart from fodder tree species) can be considered in social forest and joint forest management programs in forest fringe villages/areas to enhance and sustain the provisional ecosystem services in Western Himalaya.

National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.

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. USDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agriculture Published version USDA Forest Service Forest Inventory and Analysis Program; National Institute of Food and Agriculture Public domain – authored by a U.S. government employee

Federally designated wilderness areas in the US are mandated to fulfill social and ecological goals. Long‐term monitoring is key to this mandate, yet is seldom consistent across boundaries. Our purpose is to compile literature and data to examine the utility of a strategic forest inventory for meeting wilderness management objectives and for enabling coordinated interagency responses to boundary‐crossing challenges (e.g., pollution, wildfires, plant invasions, global changes). We provide examples from a national forest inventory (NFI), which provides nationally consistent information that helps stakeholders comply with protected‐areas policy, reduce risks to public safety, define economic opportunities, guide conservation efforts and forecast key ecological processes. For example, the NFI has helped detect long‐distance air‐pollution, document high‐elevation whitebark pine declines and evaluate the risk of wilderness fires spreading to adjacent lands and private property. We identify opportunities to improve monitoring by expanding existing measurements of nonforest ecosystems and linking supplemental monitoring efforts to the existing framework. Though the NFI alone cannot fulfill all monitoring functions, strategic monitoring enables effective wilderness management by revealing socioecological feedbacks between human communities and the wildlands on which they depend.

Effective modelling of forest susceptibility to defoliating insect outbreaks requires a better understanding of outbreak dynamics, which includes detailed knowledge of the pre- and post-out...

The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities, durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7.272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.

International audience; National Forest Inventories (NFIs) perform systematic forest surveys across space and time. They are hence powerful tools to understand climate controls on forest growth at wide geographical scales and account for the effects of local abiotic and biotic interactions. To investigate the effects of climate change upon growth dynamics of four major European conifer species along elevation and continentality gradients, we herein provide an original harmonization of the French and Austrian NFI datasets. The growth of Norway spruce, Scots pine, silver fir and European larch over the 1996-2016 period was studied in pure and evenaged plots across different ecological regions. We derived climate-driven growth trends from > 65, 000 radial increment series filtered-out from major biotic and abiotic influences using statistical modeling. We further identified primary environmental drivers of conifer growth by regressing growth trends against regionally-aggregated biotic and abiotic forest attributes. Negative growth trends were observed in continental regions undergoing the most rapid warming and thermal amplitude contraction over the study period. Negative trends were also associated with lower forest structural heterogeneity and, surprisingly, with greater available water capacity. Remarkably, we observed these associations both at the inter-and intraspecies levels, suggesting the universality of these primary growth determinants. Our study shows that harmonized NFI data at the transnational level provide reliable information on climate-growth interactions. Here, greater forest structural complexity and greater water resource limitation were highlighted as drivers of greater forest resilience to climate change at large-scale. This result forms crucial bases to implementing climate-smart forest management.

Aim: Tree mortality is increasing world-wide, leading to changes in forest composi-tion and altering global biodiversity. Nonetheless, owing to the multifaceted stochas-tic nature of tree mortality, large-scale spatial patterns of mortality across species ranges and their underlying drivers remain difficult to understand. Our main goal was to describe the geographical patterns and drivers of the occurrence of mortality (presence of a mortality event) and the intensity of tree mortality (amount of mortal-ity related to that mortality event) in Europe. We hypothesized that the occurrence of mortality represents background mortality and is higher in the margin than in core populations, whereas the intensity of mortality could have a more even distribution according to the spatial and temporal stochasticity of die-off events.Location: Europe (Spain, France, Germany, Belgium, Sweden and Finland).Major taxa studied: More than 1.5 million trees belonging to 20 major forest tree species.Methods: We developed binomial and truncated negative binomial models to tease apart the occurrence and intensity of tree mortality in National Forest Inventory plots at the range-wide scale. The occurrence of mortality indicated that at least one tree had died in the plot, whereas the intensity of mortality referred to the number of dead trees per plot.Results: The highest occurrence of mortality was found in peripheral regions and the climatic trailing edge linked with drought, whereas the intensity of mortality was driven by competition, drought and high temperatures and was scattered uniformly across species ranges.Main conclusions: We show that tree background mortality, but not die-off, is gener-ally higher in the trailing-edge populations. It remains to be explored whether other demographic traits, such as growth, reproduction and regeneration, also decrease at the trailing edge of European tree populations., Université de Bordeaux

4 Pág. Centro de Investigación Forestal (CIFOR)., In 2019, 100 years had elapsed since the first National Forest Inventory (NFI) was established in Norway. Motivated by a fear of over-exploitation of timber resources, NFIs today enable informed policy making by providing data vital to decision support at international, national, regional, and local scales. This Collection of articles celebrates the 100th anniversary of NFIs with a description of past, present, and future research aiming at improving the monitoring of forest and other terrestrial ecosystems., The Beijing Forestry University waived the Open Access fees of all studies published in this Collection.

Key message Large-scale forest resource maps based on national forest inventory (NFI) data and airborne laser scanning may facilitate synergies between NFIs and forest management inventories (FMIs). A comparison of models used in such a NFI-based map and a FMI indicate that NFI-based maps can directly be used in FMIs to estimate timber volume of mature spruce forests. Context Traditionally, FMIs and NFIs have been separate activities. The increasing availability of detailed NFI-based forest resource maps provides the possibility to eliminate or reduce the need of field sample plot measurements in FMIs if their accuracy is similar. Aims We aim to (1) compare a timber volume model used in a NFI-based map and models used in a FMI, and (2) evaluate utilizing additional local sample plots in the model of the NFI-based map. Methods Accuracies of timber volume estimates using models from an existing NFI-based map and a FMI were compared at plot and stand level. Results Estimates from the NFI-based map were similar to or more accurate than the FMI. The addition of local plots to the modeling data did not clearly improve the model of the NFI-based map. Conclusion The comparison indicates that NFI-based maps can directly be used in FMIs for timber volume estimation in mature spruce stands, leading to potentially large cost savings.

Diameter at breast height (DBH) distributions offer valuable information for operational and strategic forest management decisions. We predicted DBH distributions using Norwegian national forest inventory and airborne laser scanning data and compared the predictive performances of linear mixed-effects (PPM), generalized linear-mixed (GLM) and k nearest neighbor (NN) models. While GLM resulted in smaller prediction errors than PPM, both were clearly outperformed by NN. We therefore studied the ability of the NN model to improve the precision of stem frequency estimates by DBH classes in the 8.7 Mha study area using a model-assisted (MA) estimator suitable for systematic sampling. MA estimates yielded greater than or approximately equal efficiencies as direct estimates using field data only. The relative efficiencies (REs) associated with the MA estimates ranged between 0.95-1.47 and 0.96-1.67 for 2 and 6 cm DBH class widths, respectively, when dominant tree species were assumed to be known. The use of a predicted tree species map, instead of the observed information, decreased the REs by up to 10%., Accepted preprint; Canadian Journal of Forest Research

Since the 1990s the invasive fungus Hymenoscyphus fraxineus has been threatening European ash (Fraxinus excelsior), a tree species with high ecological and economic importance. This pathogen is causing severe crown dieback, leading to high mortality rates across Europe and is present in Switzerland since 2008. In this study, we provide a comprehensive overview of the temporal evolution of crown damage and mortality rates in Switzerland over the 2009–2019 period. Harnessing the power of the annualized design of the Swiss national forest inventory (NFI), we show that annual mortality rates (AMRs) of ash increased significantly since the arrival of the fungus, with stronger effects in small trees [, Frontiers in Forests and Global Change, 4, ISSN:2624-893X

International audience; Stem biomass ratio (SBR, kg m(-3)) is a forest state variable that converts forest volume of growing stock into biomass. However, huge intraspecific variation in wood density (WD) driven by biotic and abiotic environments of tree growth remains ignored in C budgets. The aims of this study were (i) to identify variations in SBR along water, soil nutrition and elevation gradients, (ii) to test for differences between broadleaved and conifer tree species in SBR variations, and (iii) to weight the contributions of interspecific and intraspecific diversity in SBR variations. Analyses were based on massive X-ray WD measurements performed on 54,700 tree cores collected in 2016 and 2017 on the spatially systematic plot sampling design of the French national forest inventory (NFI) programme. Stem biomass ratio (SBR) variations along the three gradients were found to be significant, with differences between botanical classes found on the water gradient only. SBR hence decreased by 73 kg m(-3) (conifers) and 126 kg m(-3) (broadleaves) along a 180 mm gradient of soil water holding capacity (SWHC), increased by 153 kg m(-3) along a full gradient of soil basicity index (SBI), and decreased by 155 kg m(-3) from 200 to 2000 m of elevation asl. Species distribution, not intraspecific variation in WD, was found to be the main cause of SBR variation along these gradients. SBR was found to be efficient for analysing large scale vegetation changes along environmental gradients, with interspecific variability being well explained by the three gradients. Mean WD species values were found to suffice to assess these SBR variations at the scale of the French forest resources.

The changing forest disturbance regimes emphasize the need for improved damage risk information. Here, our aim was to (1) improve the current understanding of snow damage risks by assessing the importance of abiotic factors, particularly the modelled snow load on trees, versus forest properties in predicting the probability of snow damage, (2) produce a snow damage probability map for Finland. We also compared the results for winters with typical snow load conditions and a winter with exceptionally heavy snow loads. To do this, we used damage observations from the Finnish national forest inventory (NFI) to create a statistical snow damage occurrence model, spatial data layers from different sources to use the model to predict the damage probability for the whole country in 16 x 16 m resolution. Snow damage reports from forest owners were used for testing the final map. Our results showed that best results were obtained when both abiotic and forest variables were included in the model. However, in the case of the high snow load winter, the model with only abiotic predictors performed nearly as well as the full model and the ability of the models to identify the snow damaged stands was higher than in other years. The results showed patterns of forest adaptation to high snow loads, as spruce stands in the north were less susceptible to damage than in southern areas and long-term snow load reduced the damage probability. The model and the derived wall-to-wall map were able to discriminate damage from no-damage cases on a good level. The damage probability mapping approach identifies the drivers of snow disturbances across forest landscapes and can be used to spatially estimate the current and future disturbance risks in forests, informing practical forestry and decision-making and supporting the adaptation to the changing disturbance regimes.

Various tree species contribute differently to total biomass stock, making the development of species-specific stand-level equations critical for better estimation of forest biomass and quantification of carbon stocks. Previously derived dry weight biomass models did not assess the effect of dominant species composition according to stand growing stock. Growing stock definitions and forest species composition differ by country, justifying the need for national stand-level biomass equations. We explored the relationship between growing stock volume and stand biomass density of above- and below-ground components in six common forest categories in Latvia using plot-level data from the National Forest Inventory from 2016 to 2020. Additionally, we explored model dependence on region, forest type, and species composition index. Models that considered growing stock and dominant species composition index performed better than models with growing stock as the only variable, especially for heterogeneous deciduous forests with greater species diversity. The elaborated models are a useful alternative to individual-level assessment for estimating forest biomass stocks in circumstances where individual tree data are not available.

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, The manuscript provides a method to predict a thematic woodland type within broad habitat woodland classes, using oak dominated woodland as a case study. The approach uses different woodland spatial and non‐spatial data with the biomod2 ensemble model. Good results were obtained by the approach, and the resulting oak woodland distribution map will be used to evaluate the interactions between climate, topography and soil conditions under climate change. This will help develop timely adaptation management plans to reduce impacts.

Germany, as an Annex I Party is expected to prepare and submit annual Greenhouse Gas (GHG) Inventories of emissions and removals, including Land Use, Land-Use Change, and Forestry (LULUCF) sector. Uganda, a non-Annex 1 party, is institutionalizing a sustainable national GHG inventory system. The LULUCF sector is a key emission source and plays a vital role in these two countries’ GHG inventories. This research analyzes the differences between applied LULUCF methodologies in Uganda as a developing country and Germany as a developed country with a particular focus on the forestry sector. It further analyzes the root cause factors for the different approaches, existing gaps and gives recommendations for future inventory improvement. The intricate institutional, policy framework, expertise, and applied methodological approaches for carbon change estimations in biomass pools are analyzed. Uncertainty analysis and time-series consistency process is reviewed with regard to how the countries’ quality assurance/control (QA/QC) and verification approaches adhere to the transparency framework. Resource limitations and data collection challenges dictate that Uganda uses the tier 1 methodological approach for emissions inventory. Consolidation and institutionalization of the GHG process will improve inventory accuracy while enhancing adherence to climate commitments. Germany uses higher tiers. Besides, government support for planned improvements using the recently developed country-specific biomass functions for estimating belowground biomass of silver birch, oak, and Scotch pine tree species will be essential for improving inventory quality. Operationalization of the inventory plan (IP) will be critical in driving inventory improvements geared towards time-series consistency, comparability, and transparency.

There is a well-documented opportunity and need to incorporate biodiversity conservation priorities into REDD+ (Reducing Emissions from Deforestation and Forest Degradation) initiatives. This requires thorough monitoring of changes to biodiversity at appropriate temporal and spatial scales. A national forest inventory is one of the essential tools used to monitor carbon stock changes but can also be expanded to include biodiversity indicators. Here we analyse the progress and potential of 70 countries in monitoring primarily non-tree biodiversity using national forest inventories. Progress on national forest inventories among countries participating in REDD+ is variable: 11 countries have not started; 26 have started but do not include non-tree biodiversity indicators; the remaining 33 countries do include non-tree biodiversity indicators but use various methodological approaches, levels of detail and taxonomic groups. Very few of these provide comprehensive and accessible manuals or results, highlighting a need for greater transparency. The capacity of countries to fund ongoing national forest inventories is a constraining factor. Remote sensing technologies can help reduce costs for countries with limited monitoring capacity but the need to understand biodiversity variation at finer scales often limits the utility of such methods.