Abstract Accurately predicting forest growth under changing climate conditions is essential for sustainable forest management and carbon accounting. Yet, many empirical growth models still lack climate sensitivity, limiting their relevance in a warming world. In this study, we leveraged a large network of permanent sample plots across Quebec, Canada, to develop climate-sensitive models of diameter increment for 29 tree species. Starting from a general model based on tree- and site-level variables, we incorporated tree-level random effects and tested three climate-related hypotheses: (H1) longer growing periods increase growth, (H2) increased atmospheric dryness reduces growth, and (H3) limited soil water availability reduces growth. For 21 species, models including climate variables significantly outperformed general and mixed-effects models. Hypothesis H1 was most frequently supported, followed by hypotheses H3 and H2. Only one species showed support for all three hypotheses simultaneously. Our findings highlight the value of integrating climate variables into empirical growth models, while also revealing limitations related to the spatial and temporal resolution of climate data. Future improvements may come from refining climate metrics and combining tree-ring and inventory data to better capture climate-growth relationships.

When an even-aged forest stand reaches maturity, it can be renewed within a limited period to maintain the even-aged structure or gradually transformed to an uneven-aged stand. However, there is still debate as to which silvicultural approach is more profitable, conducive to carbon storage, favourable to biodiversity or resilient. In this study, we simulated the evolution of fifteen stands representative of the Walloon forest, whose initial structure was even-aged. The stands were managed according to two contrasting silvicultural approaches (continuation of the even-aged system vs transformation to an uneven-aged one), and the simulations were run with the SSP3-7.0 climate projections produced by five global circulation models. Our simulations indicate that even-aged and uneven-aged silviculture yield similar outcomes in terms of carbon storage. Financial indicators were likewise largely unaffected, except in the oak-beech mixture, where uneven-aged silviculture increased profitability through the substitution of oak by beech. This shift reduced tree species diversity in uneven-aged oak-beech stands. Tree microhabitats, except in beech stands, were more abundant under uneven-aged silviculture. While mean values of forest ecosystem functioning indicators are largely comparable between the two approaches, uneven-aged stands exhibit higher temporal stability. Uneven-aged silviculture produces stands that are more wind-resistant and avoid periods of extreme vulnerability. Our study shows that the most appropriate silviculture may vary depending on the aspect considered. Uneven-aged silviculture has a definite advantage in terms of stability, risk management, and tree microhabitats. However, maintaining even-aged patches at the landscape scale remains important to facilitate the regeneration of shade-intolerant species.

Background tree mortality can be defined as the death of trees that naturally occurs as stands develop, in the absence of major or sudden stand disturbances. The phenomenon is often linked to ontogeny and competition and generally affects individual trees, unlike catastrophic mortality, which affects most trees in the stand. To forecast stand characteristics and to estimate how stand development could change in response to changing climate, it is necessary to quantify background mortality and to identify the most important factors involved. Using data from 10 045 permanent sample plots, we modeled background tree mortality for the nine most abundant tree species of the eastern Canadian boreal forest. We used explanatory variables related to stand and tree ontogeny, competition, site characteristics, and climate to calibrate the models. We found that an increase in age, competition, and the presence of partial cut increased the mortality risk. However, the effect of DBH and site-related variables varied among species. We also found that higher temperatures, less precipitation, and higher aridity index values increased background tree mortality. According to mortality simulations under different future climate scenarios, background tree mortality could increase in the next decades for six of the nine tree species studied.

Climate change has driven forest growth modellers to develop different climate sensitivity implementations (CSIs) for their models. Among others, a model can rely on annual climate variables or average climate variables, such as 30-year normals. The novelty of this study was to develop a framework based on lifetime analysis to enable annual or average CSI in empirical models of tree mortality. Using this framework, we compared models of individual tree mortality based on an annual CSI with similar models relying on two average CSIs, one using interval-averaged climate variables, and the other, 30-year normals. We fitted these models to permanent-plot data of eight species in Ontario and tested the effects of summer and winter temperature as well as spring and summer precipitation in the models. Our results showed that the annual CSI was not superior to the average CSIs, but could be a valid alternative for some species. Warmer winter temperature was detrimental to the survival of Betula papyrifera, Picea glauca, and Pinus strobus, whereas greater spring and summer precipitation resulted in greater mortality occurrence for Picea mariana, Pinus banksiana, and Populus tremuloides. In most cases, the effects of climate variables were contrary to our initial hypotheses. We conclude that the effects of climate on tree mortality occurrence interact with other factors such as species distribution and ecophysiology.

In Canada, satellite-derived data are available as National Terrestrial Ecosystem Monitoring System (NTEMS) products, which can be used as auxiliary information to improve sample-based National Forest Inventory (NFI) estimates. This study explored statistical approaches of using these satellite-derived data to improve vegetated tree (VT) cover estimate in the Atlantic Maritime Ecozone. First, a model-assisted regression estimator with beta regression (MA beta ) was evaluated using simulated population datasets. Then, the efficiency of the MA beta estimator was compared to a design-based ratio estimator (DB) in two scenarios: one with temporally matched survey and auxiliary data, and another with temporally unmatched survey and auxiliary data. The assisting model was also used to generate model-based bootstrap aggregate annual estimates (Mb BAE ) of VT cover proportion to explore temporal trends and assess sensitivity to disturbances during 2007–2017. Results showed that the MA beta estimator provided nearly unbiased estimates with a coverage rate of about 95% (when n ≥ 100). The MA beta estimates were more precise than the DB estimates, especially when survey and auxiliary data were temporally matched (RE = 3.04 with g-weights, RE = 3.25 without g-weights). Moreover, the MB BAE -based annual estimates of VT cover proportion indicated that stand-replacing disturbances drive VT cover dynamics in the ecozone. The results suggest that incorporating remote sensing based, independently derived, wall-to-wall data products can improve the accuracy and precision of Canada's NFI, aiding in monitoring forest resources at various scales.

The market of wood products is the primary system used to assess the value of timber. Understanding its dynamics could help in forecasting future trends and guiding decision-makers on what and when to harvest, enabling better market anticipation. Previous research has mainly studied wood products prices through general indices, which does not allow differentiation of wood products based on their dimensions, key information that can be linked with the structure of forest stands. We examined how macroeconomic variables, the coronavirus disease of 2019 (COVID-19) pandemic, and climate-related disasters costs affected the prices of eight wood products (seven softwood lumber products and one oriented strand board) in North America. We fitted first-order autoregressive models with a variance function, on time series of prices recorded between 1990 and 2023. We found that, contrary to common assumptions, price changes cannot be attributed to the same variables, nor do these variables impact prices with the same magnitude across different products. Moreover, disruptive events, such as the COVID-19 pandemic, mainly contributed to uncertainty without entirely determining prices. Such findings can help link the value of standing trees, based on the products they can generate, and therefore inform on optimal silviculture and harvesting strategies.

Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.

Tree functional diversity can increase forest productivity by enhancing species interactions and providing greater growth stability. However, very few studies have examined the influence of tree community trait structure on survivor growth, recruitment and mortality simultaneously, which are the main drivers of forest population dynamics. Here, we explore the interactions among functional diversity, productivity and climate to investigate the role of the trait structure of communities on forest productivity and to determine under what circumstances functional diversity should be promoted to ensure forest adaptive capacity under future climate. Using random-forest modelling and a network of permanent sample plots covering a broad gradient of climatic conditions, we isolated the effects of functional diversity-described as the distribution of trait values in a community-and climate variables on net forest productivity (NFP), survivor growth, recruitment and mortality. Based on our findings, community-level trait structure affects forest productivity in different ways. NFP was influenced by three traits from three different plant strategy dimensions, whereas survivor growth and recruitment were strongly correlated with leaf and resource acquisition traits, and tree mortality with a mix of traits reflecting various plant strategies. We also observed climate interactions with the functional trait structure of tree communities. For instance, we observed an interaction between drought tolerance and mean annual temperature: At low temperatures, NFP biomass accumulation increased with the value of the drought tolerance trait; however, at higher temperatures, the opposite pattern was observed. However, we found contrasting patterns of population response to climate variability, depending on their functional diversity. Greater functional diversity does not necessarily increase biomass accumulation under different climatic conditions. Synthesis. As all components of forest productivity contribute to NFP, studies on forest productivity should consider not only survivor growth but also recruitment and mortality. Each component responds differently in terms of biomass changes in climatic variation, according to the trait structure of tree communities. This study provides a framework to identify the trait structure that should be targeted under different climate scenarios to anticipate change and help strengthen forest response capacity to climate change. La diversit & eacute; fonctionnelle des arbres peut accro & icirc;tre la productivit & eacute; des for & ecirc;ts en am & eacute;liorant les interactions entre les esp & egrave;ces, en plus d'assurer une plus grande stabilit & eacute; pour la croissance. Cependant, tr & egrave;s peu d'& eacute;tudes ont examin & eacute; l'influence de la diversit & eacute; fonctionnelle des arbres sur, & agrave; la fois, la croissance, le recrutement et la mortalit & eacute;. Cette & eacute;tude explore les interactions diversit & eacute;-productivit & eacute;-climat afin d'& eacute;tudier le r & ocirc;le des traits pour la productivit & eacute; foresti & egrave;re et d & eacute;terminer dans quelles circonstances ils doivent & ecirc;tre promus pour garantir la capacit & eacute; d'adaptation des for & ecirc;ts dans le contexte du climat futur. En utilisant des mod & egrave;les random-forest et un r & eacute;seau de placettes & eacute;chantillons permanentes couvrant une vari & eacute;t & eacute; de conditions climatiques, les effets des traits et des variables climatiques sur la productivit & eacute; foresti & egrave;re nette (PFN), la croissance, le recrutement et la mortalit & eacute; ont & eacute;t & eacute; isol & eacute;s. Selon nos r & eacute;sultats, la structure des traits affecte diff & eacute;remment la productivit & eacute;. La PFN est influenc & eacute;e par des traits appartenant & agrave; trois strat & eacute;gies v & eacute;g & eacute;tales. La croissance et le recrutement ont & eacute;t & eacute; li & eacute;s aux traits foliaires et & agrave; l'acquisition des ressources, tandis que la mortalit & eacute; a & eacute;t & eacute; li & eacute;e & agrave; un m & eacute;lange de traits refl & eacute;tant diverses strat & eacute;gies. Les communaut & eacute;s d'arbres ont g & eacute;n & eacute;ralement r & eacute;agi de mani & egrave;re positive & agrave; l'augmentation des temp & eacute;ratures et pr & eacute;cipitations. Par exemple, une interaction entre la tol & eacute;rance & agrave; la s & eacute;cheresse et la temp & eacute;rature a & eacute;t & eacute; observ & eacute;e: & agrave; basse temp & eacute;rature, la biomasse li & eacute;e & agrave; la PFN augmentait avec la valeur de ce trait, alors qu'& agrave; des temp & eacute;ratures & eacute;lev & eacute;es, l'inverse & eacute;tait observ & eacute;. Cependant, selon les traits, les communaut & eacute;s d'arbres ont montr & eacute; des r & eacute;ponses contrast & eacute;es aux variations climatiques. Synth & egrave;se. Toutes les composantes de la productivit & eacute; foresti & egrave;re contribuent & agrave; la PFN, il est alors imp & eacute;ratif de non seulement consid & eacute;rer la croissance, mais & eacute;galement le recrutement et la mortalit & eacute;. En fonction des traits, chaque composante de la PFN a r & eacute;agi diff & eacute;remment en termes d'accumulation de biomasse face aux variations climatiques. Cette & eacute;tude fournit un cadre utile pour identifier la structure de trait qui devrait & ecirc;tre privil & eacute;gi & eacute;e en fonction du climat afin d'anticiper les changements et de contribuer & agrave; renforcer la capacit & eacute; d'adaptation des for & ecirc;ts. As all components of forest productivity contribute to net forest productivity, studies on forest productivity should consider not only survivor growth but also recruitment and mortality. Each component responds differently in terms of biomass changes in climatic variation, according to the trait structure of tree communities. This study provides a framework to identify the trait structure that should be targeted under different climate scenarios to anticipate change and help strengthen forest response capacity to climate change.image

Context: Over the last decade, the forestry sector has undergone substantial changes, evolving from a post-2008 financial crisis landscape to incorporating policies favoring sustainable and green alternatives, especially after the 2015 Paris agreement. This evolution was drastically disrupted with the advent of the COVID-19 pandemic in 2020, causing unprecedented interruptions in supply chains, product markets, and data collection. Grasping the aftermath of the COVID-19, regional instances of the forest supply chain sector need synthetic pictures of their present state and future opportunities for emerging wood products and better regional-scale carbon balance. But given the impact of COVID-19 lock-down on data collection, the production of such synthetic pictures has become more complex, yet essential. This was the case for the regional supply chain of the Grand-Est region in France that we studied. Aims: For this study, our aim was to demonstrate that an integrated methodology could provide such synthetic picture even though we sued heterogenous sources of data and different analytical objectives: i.e. (1) retrospectively evaluate the aftermath of COVID-19 pandemic on the supply chain outcomes within the forestry sector; and then (2) retrospectively explore possible options of structural change of regional supply chain that would be required to simultaneously recover from COVID-19 and transit to new objectives in line with the extraction of new bio-molecules from wood biomass, and with the reduction of the regional scale carbon footprint (in line with the IPCC Paris Agreement) Methods: To achieve this, our methodological approach was decomposed into three steps. We first used a Material Flow Analysis (MFA) recently conducted on the forestry sector in the Grand Est region to establish a Sankey diagram (i.e. a schematic representation of industrial sectors and biomass flows along the supply chain) for the pre-Covid-19 period (2014-2018). Then we compared pre-Covid-19 Sankey diagram to the only source of data we could access from the post-Covid-19 period (2020-2021) in order to estimate the impact of Covid-19. Finally, we used as input the reconciled supply chain model into a consequential Wood Product Model (WPMs), called CAT (carbon accounting tool) in order to compare three prospective scenarios: (1) a scenario that projected 2020-2021 Covid-19 conditions and assumed pre-Covid-19 business as usual practices, (2) a scenario illustrating the consequence or rerouting some of the biomass to satisfy the expected increase in pulp and paper production to satisfy the needs of the industry after Covid-19, and (3) a scenario that explored new opportunities in term of extraction of novel bio-molecules by the emerging biochemical wood industry. For every scenario we also evaluated the regional carbon gains and losses that these changes implied. Results: Our study conducted a detailed analysis of the impacts of the COVID-19 pandemic on the forestry sector's supply chain in the Grand Est region, using a dynamic and integrated Wood Product Model. We found significant disruptions during the pandemic period, with notable declines in industrial wood chips and timber hardwood production by 41.8 Conversely, there were substantial increases in fuelwood, timber sawdust, and timber softwood, rising by 14.15 fluctuations underscore the resilience and vulnerabilities within the regional wood supply chain. Our findings also emphasize the potential for strategic rerouting of biomass flows to meet changing industry demands, which could play a crucial role in supporting the sector's recovery and adaptation to post-pandemic conditions. Discussion and conclusion: In addition, our study recognizes the limitations of the current approach combining MFA and WPM and suggests potential areas of enhancement. Ultimately, our findings shed light on the need to develop more integrated analytical methods to provide useful synthetic pictures of regional scale supply chains, when there is a need to adapt it to evolving situations and complex data landscapes.

In linear regression, log transforming the response variable is the usual workaround regarding departures from the assumption of normality. However, the response variable is often subject to natural constraints, which can result in a truncated distribution of the residual errors on the log scale. In forestry, allometric relationships and tree growth are two typical examples a natural constraint; the response variable cannot be negative. Traditional least squares estimators do not account for constrained response variables. For this study, a modified maximum likelihood (MML) estimator that takes natural constraints into account was developed. This estimator was tested through a simulation study and showcased with black spruce tree diameter increment data. Results show that the ordinary least squares estimator underestimated large conditional expectations of the response variable on the original scale. In contrast, the MML estimator showed no evidence of bias for large sample sizes. Departures from distributional assumptions cannot be overlooked when the model is used for predictive purposes. Both Monte Carlo error propagation and prediction intervals rely on these assumptions. In this context, the MML estimator developed for this study can be used to properly propagate the errors and produce reliable prediction intervals.

This article aims to compare different forest adaptation strategies based on stand diversification from an economic perspective in order to reduce extreme drought- and windstorm-induced risks of dieback. We tested the efficiency of the strategies individually and then combined through a simulation study in which we evaluated the financial loss and the reduction of the carbon sequestration capacity. We used a stochastic forest growth model to simulate forest growth and carbon sequestration and developed a forest economic approach based on the land expectation value (LEV) to account for the stochastic simulations. Results showed that diversification increased timber production (from +4 to +81%) and LEV (from +27 to +398%), but reduced carbon storage (from −9 to −49%). Trade-offs between the financial balance and the carbon balance (adaptation vs. mitigation) are achievable. The valorisation of carbon services in addition to timber ones increases the forest value and changes the strategy that provides the highest economic return for a carbon price of 110 EUR/tC. Our study presents a new approach for the economic valuation of multiple risks in forest management, highlighting the importance of integrating several risks in a common analysis rather than investigating one risk at a time (traditional economic approach).

Diversification of forests, both in terms of structure and species, has been identified as one of the main strategies for adapting forests to climate change. Among the different options available for managers to promote diversification, regeneration cuttings are a suitable option to meet these objectives in adult stands close to rotation ages. The regeneration of maritime pine (Pinus pinaster Ait.) stands is a major issue throughout its distribution area, with summer survival being the main bottleneck for seedling establishment and development. Group selection cutting system may be the best option to promote uneven-aged and mixed structures in shade intolerant species, such as maritime pine. Gaps generate different regeneration niches for species of contrasting shade tolerance while creating uneven-aged stands, which are potentially more resilient to the impacts of climate change. In our experiment, gaps of two different sizes (1.5 and 2.5 times the dominant height of the stand, both sizes being smaller than those proposed in literature) were opened up to test the effects on natural regeneration success in a planted 53-year-old maritime pine stand in Central Spain. There were nine gaps of each size, along with nine control plots, containing 1-m radius subplots distributed within them to record natural regeneration of maritime pine and other species, in addition to several ecological factors and seedling characteristics. A survival model was fitted to highlight the main factors driving seedling survival. Gaps were found to have a significant positive effect on seedling survival, as well as mid-shade positions within the gaps and the age of the seedlings. Summer was found to have a negative effect on seedling survival. No effect of inter-species competition (scrub or herbs), litter coverage or geomorphological characteristics (slope, aspect or altitude) was found. Our results indicate, therefore, that group selection system cuttings, even with small gap sizes (1.5 and 2.5 times the dominant height), would provide a suitable method for the regeneration of Mediterranean maritime pine plantations.

Tree recruitment is affected by numerous biotic and abiotic factors, including climate. However, the relative importance of climate variables in empirical models of tree recruitment remains to be evaluated. We fitted models of tree recruitment to 26 species in the province of Quebec, Canada. For a better understanding of the recruitment process, we used a two-part model to distinguish recruitment occurrence from abundance. The relative importance of the different variables was assessed using Akaike weights. Our main hypothesis was that climate is one of the major drivers of tree recruitment. Our results showed that growing degree-days counted among the major drivers of recruitment occurrence but not of recruitment abundance. Stand variables, such as the presence and abundance of adult trees of the species, and broadleaved and coniferous basal areas were found to be relatively more important than all the climate variables for both recruitment occurrence and abundance. Species occupancy within a 10-km radius also had a significant effect on recruitment occurrence for two-thirds of the species, but it was less important than growing degree-days and other stand variables. Climate change is expected to improve the suitability of habitats located at the northern edge of species distributions. However, our model predictions point to a low probability of colonization in newly suitable habitats in the short term.

Large-area growth estimates can be obtained by coupling growth model predictions with wall-to-wall remotely sensed aux-iliary variables through a generalized hierarchical model-based (GHMB) inferential framework. So far, most GHMB variance estimators do not account for the residual errors of the submodels and their spatial correlations. This likely induces an under-estimation of the true variance of the point estimator. In this study, we provide an example of large-area growth estimation obtained through the GHMB framework. To do this, we developed a new variance estimator that accounts for residual errors as well as potential spatial correlations among them. We tested this variance estimator through a simulation study and then used it to estimate the annual volume increment for a forest management unit in Quebec, Canada. Our results show that, contrary to our expectation, neglecting the residual errors of the different submodels leads to overestimating the true variance of the point estimator. We observed increases in the overestimation with small populations and spatially correlated residual errors. Our developed variance estimator corrected this overestimation and made it possible to derive reliable confidence intervals for annual volume increments at the population level.

Landscape-level studies such as those on forest management planning and carbon accounting rely on large-area growth projections provided by forest growth models. Nowadays, most of these models are individual tree-based models. The detailed input they require and their complexity are a challenge for the integration into a landscape-level study. A possible alternative consists of approximating the complex model through a meta-model. A meta-model mimics the behaviour of the original model, while being simpler in terms of input and computation. In this study, we developed a Bayesian meta-modelling approach that can be used to obtain a simplified growth model from an individual tree-based model. The approach was exemplified through a real-world case study, namely a forest management unit in the province of Quebec, Canada. Using a Markov chain Monte Carlo method, we managed to fit meta-models based on the Chapman-Richards equation or its derivative for the main potential vegetation types. This meta-modelling approach has the advantages of (i) being an effective method of upscaling, (ii) providing simple meta-models suitable for landscape-level studies, and (iii) ensuring a proper error propagation from the original individual tree-based model into the meta-model.

Climate is an essential component of environmental models. Over the last two decades, many weather generators have been presented in the literature. Although their implementation into software has been of great help to environmental modellers, their lack of integration into modelling frameworks still represents a challenge for end users. In many cases, end users have to retrieve the climate variables by themselves in order to use an environmental model. In some other cases, the weather generator software is embedded into the modelling framework, but this increases the maintenance effort.In this paper, we present a different approach: the deployment of a weather generator as a Web API. A few application examples are provided to illustrate the benefits of this implementation. In summary, a Web API facilitates the integration into modelling frameworks, decreases the maintenance effort and avoid interoperability issues due to different programming languages.

Extreme or recurrent drought events are the principal source of stress on forests, impairing their overall health. They result in financial losses for forest owners and ecosystem service losses for society. Most of the forested area in the Grand-Est region, France, is covered by European beech, which is projected to decline in the future due to repeated drought events driven by climate change. Diversification is a management option that can reduce the drought-induced risk of dieback. Two types of diversification were separately and jointly analyzed: a mixture of beech species with oak species and a mixture of different tree diameter classes. Two types of losses were also considered: financial and in terms of carbon storage under different occurrences of drought events derived from climate change scenarios. We combined an individual-based model of forest growth with a forest economic approach (i.e., land expectation value or LEV), which we adapted to the stochastic context by developing a doubly-weighted LEV. The maximization of the LEV made it possible to identify the most effective adaptation strategies in terms of timber revenue and carbon storage by means of three different carbon values (i.e., market value, shadow price, and social cost). The results showed that diversification increases timber returns and reduces the loss in timber volume due to the drought-induced risk of forest dieback. However, diversification negatively affects carbon storage. Integrating the value of carbon storage increases the value of the forest stand, but only a high carbon value has a significant economic impact.

Sustainability is central to forest management. To determine the sustainable annual harvest, practitioners rely on a simulation framework that combines inventory data, growth models, and optimization software. Because this standard simulation framework is based on model predictions aggregated into yield tables, it may not properly capture natural dynamics. In this paper, we designed an alternative simulation framework that does not require aggregated model predictions. However, the growth model must implement a harvest submodel and produce stochastic predictions. To showcase this alternative simulation framework, we used a forest management unit in southwestern Quebec, Canada, and compared our simulation results with those of the standard simulation framework. Our alternative simulation framework showed that the standing volume of most coniferous species would decrease, whereas that of maple species would increase over the 21st century. The annual harvest of one species as determined through the standard simulation framework was found to be unsustainable in the alternative simulation framework. Being much lighter in terms of computation, this alternative simulation framework can be used as a complement to the standard simulation framework, notably for checking if the optimization-based annual harvest is sustainable.

A growing body of research suggests mixed-species stands are generally more productive than pure stands as well as less sensitive to disturbances. However, these effects of mixture depend on species assemblages and environmental conditions. Here, we present the Salem simulator, a tool that can help forest managers assess the potential benefit of shifting from pure to mixed stands from a productivity perspective. Salem predicts the dynamics of pure and mixed even-aged stands and makes it possible to simulate management operations. Its purpose is to be a decision support tool for forest managers and stakeholders as well as for policy makers. It is also designed to conduct virtual experiments and help answer research questions. In Salem, we parameterised the growth in pure stand of 12 common tree species of Europe and we assessed the effect of mixture on species growth for 24 species pairs (made up of the 12 species mentioned above). Thus, Salem makes it possible to compare the productivity of 36 different pure and mixed stands depending on environmental conditions and user-defined management strategies. Salem is essentially based on the analysis of National Forest Inventory data. A major outcome of this analysis is that we found species mixture most often increases species growth, in particular at the poorest sites. Independently from the simulator, foresters and researchers can also consider using the species-specific models that constitute Salem: the growth models including or excluding mixture effect, the bark models, the diameter distribution models, the circumference-height relationship models, as well as the volume equations for the 12 parameterised species. Salem runs on Windows, Linux, or Mac. Its user-friendly graphical user interface makes it easy to use for non-modellers. Finally, it is distributed under a LGPL license and is therefore free and open source.

Peatlands play an important role as carbon pools, storing a third of the world's soil carbon. However, peatlands in Southeast Asia have suffered from depletion due to economic pressure and the demand for natural resources, often caused by land use changes and fires. Usually, land preparation requires drainage and fires, resulting in major greenhouse gas (GHG) emissions into the atmosphere. In this work, we propose a general equation to estimate GHG emissions from fires on peatlands. The contribution of each parameter to the variance of the estimated GHG emissions was also evaluated. We used Monte Carlo simulation, meta‐analyses, and an analytical expression of variance. GHG emissions of a single fire episode were estimated at 842 Mg ha−1 CO2 eq. with a standard deviation of 466 Mg ha−1 CO2 eq. The parameter contributing most to variance was the depth of burn, at 94.2%, followed by bulk density, at 5.5%, and emission factors, at 0.3%. Our estimated GHG emissions were close to the amount estimated from the default values provided by the IPCC, strengthening confidence in the IPCC methodology. When the depth of burn was assessed by remote sensing, the parameter that most contributed to variance became the fire‐damaged area, followed by the depth of burn. The contribution of each parameter to variance, as estimated in this study, made it possible to prioritize the effort in uncertainty reduction. Combining Monte Carlo simulation and an analytical expression of variance could be a promising way of obtaining more reliable confidence intervals.