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Despite the potential of secondary tropical forests to store and sequester substantial amounts of carbon, little is known about their above-ground carbon (AGC) stocks and the factors affecting them, especially in West Africa. This information is of key importance if the countries in this region want to achieve their forest restoration and climate mitigation commitments. To fill in this gap, we investigated how environmental and local management (e.g. remnant trees) factors influenced AGC and tree species richness in secondary forests at seven sites across Guinea, Sierra Leone and Liberia. We established 140 plots (20 x 50 m) in fallows <15 years (20 plots per site) and sampled all trees ≥10 cm diameter following standardised protocols. We found that AGC stocks and tree species richness increased with fallow age, but were highly variable across sites driven by both climatic and local management practices. While drought stress negatively affected AGC, remnant trees had a positive effect. AGC recovery rates ranged between 0.72 Mg C ha−1 y−1 (second driest site) and 13.76 Mg C ha−1 y−1 (wettest site). Given its low cost, our findings highlight the potential of passive restoration in secondary forests for carbon sequestration, particularly in wetter landscapes and areas with remnant trees from prior land use. • Above-ground carbon and tree species diversity increased with fallow age. • Above-ground carbon recovered slower in drier compared to wetter sites. • Remnant trees had a positive effect on above-ground carbon and species recovery. • Previous estimates from Ivory Coast are not applicable to the countries we studied. [ABSTRACT FROM AUTHOR]

Overcoming the climate crisis and achieving the 1.5 °C target requires the exploration of climate-resilient development pathways (CRDPs), as emphasized in the intergovernmental panel on climate change (IPCC) AR6 report. Republic of Korea has aligned itself with the international context by setting nationally determined contributions (NDC) and long-term low greenhouse gas emission development strategies (LEDS) goals. In addition, the country has announced plans to enhance carbon sink in the forestry sector. This study explored the CRDP in the forestry sector using an advanced Korean forest dynamic growth model (AKO-G-Dynamic model) with refined management algorithms. We utilized this model and applied various options for forest management based on the available detailed data, including climate change scenarios and policies reflecting possible CRDPs in the Republic of Korea. As a result, CO 2 sequestration in the 2050s was predicted to be 23.08 million tCO 2 year−1 if climate change SSP 5–8.5 and the current forest management level are maintained and 28.49 million tCO 2 year−1 if climate change SSP 1–2.6 and resilient level of forest management are applied. Furthermore, from the perspective of the age class of the forest, the proportion of over-matured forests decreased, leading to an improvement in the imbalance of age classes as climate change mitigation and sustainable forest management were implemented. Therefore, this study demonstrated realizable CRDPs and their implementation in decision-making concerning the NDC and LEDS. This comprehensive analysis of climate change and forest management, exploring the CRDP from various perspectives, can contribute to the development of forest management policies for climate adaptation strategies and carbon sink enhancement, thereby influencing the allocation of the carbon budget. • Explored climate-resilient development pathways in South Korea's forestry sector using the AKO-G-Dynamic model. • Predicted CO 2 sequestration for the 2050s to be 23.08 million tCO 2 year−1 under SSP5–85 and 28.49 million tCO 2 year−1 under SSP1–26 with resilient management. • Reduced over-matured forests and improved age class balance through sustainable forest management. • Provided insights for developing forest management policies to enhance climate adaptation and carbon sinks [ABSTRACT FROM AUTHOR]

Phenolic compounds (PC) are important secondary metabolites in plants, playing a crucial role in plant defense mechanisms against pathogens and other plants. Monitoring PC levels is important for understanding tree stress and implementing effective breeding programs. However, traditional methods for monitoring PC are time-consuming, prone to altering the phenolic composition, and mostly applicable only on a small scale. In this study, we evaluated the performance of Unoccupied Aerial Vehicles (UAV) multispectral imaging in estimating the canopy phenolic content in slash pine over an 11-month period in 2021 and a seven-month period in 2022. Three machine learning models including Partial least squares regression (PLSR), Random forest (RF) and Support Vector Machine (SVM) were compared to determine the optimal predictive model for canopy PC. The RF model provided the best predictive results, with R2 values of 0.82 for the validation set and 0.94 for the calibration set. Additionally, the study assesses the heritable variation in canopy PC over time, with the monthly heritability (h 2 ) of PC ranging from 0 to 0.26 in 2021 and from 0 to 0.35 in 2022; The highest h 2 levels were observed in July and September 2021and July 2022. The findings demonstrate significant genetic control over the variation of PC. Furthermore, we observed higher breeding values and genetic gains in July and November, which further supports the strong correlation between PC and environmental factors such as temperature and light intensity. To the best of our knowledge, this is the first study to employ time-series UAV multispectral imaging to predict secondary metabolites in pine trees and estimate their genetic variation over time. As a proof of concept, these findings provide more reliable information for tree breeding programs, ultimately enhancing their overall performance. • UAV Innovation: First-time use of UAV multispectral imaging for phenolics in slash pine. • ML Models: Random Forest model showed highest accuracy in predicting canopy phenolics. • Genetic Insights: Detailed analysis of heritable variation in canopy phenolics over time. • Seasonal Analysis: Conducted comprehensive statistical analysis of phenolic content variations. [ABSTRACT FROM AUTHOR]

• The spatial and temporal patterns of China's carbon changes at the pixel scale were provided. • China's aboveground biomass carbon (AGC) stock showed an increase in recent years. • The carbon sink rate of China's forests peaked in middle-aged forests and then decreased with increasing forest age. • The Grain for Green Program contributed most to the increase of aboveground carbon sinks in China. China's terrestrial ecosystems are pivotal in upholding the global carbon balance, with their recognized capacity for carbon sequestration holding significant importance on a global scale, but there are still challenges in accurately monitoring its spatial-temporal patterns. SMOSMAP-IB L - VOD, a recently developed low-frequency L - band Vegetation Optical Depth (L-VOD) product, was employed to monitor the fluctuations in aboveground vegetation carbon stock over China between 2015 and 2021, and to investigate the influence of forest age and ecological restoration projects on China's carbon sink capacity. The carbon sink of above-ground vegetation in China was + 169. 98 + 135.59 + 179.44 Tg C yr-1 during 2015–2021. Southwest and northeast China's forests exhibit higher carbon sink capacity, contrasting with lower values per unit area are mainly found in northwestern China. The forest served as the foremost contributor to the carbon sink of above-ground vegetation in China, although their carbon density peaked in the middle-aged forests and then gradually decreased as the age of the forest grew. Furthermore, carbon sink areas were observed in most regions where ecological restoration projects were implemented, indicating that the execution of afforestation and ecological restoration initiatives has proven effective in carbon sequestration, creating a sink in these areas. Our results provide an improved foundation for formulating new afforestation policies in the future. [ABSTRACT FROM AUTHOR]

Until now, top-down estimation using the areal weighting interpolation method was applied to estimate the woody biomass energy potential for each of the 1741 municipalities in Japan. However, it was difficult to utilize the uncertain results in policy making. In contrast, bottom-up estimation can reflect the regional characteristics and provide novel benefits to policymakers. In this study, bottom-up estimation using the method of aggregation approach was carried out from the geospatial data for artificial forests, excluding protected forest, and considering forest growth. The data was collected from both national and each prefecture government. The forest growth of each forest division was adjusted by curve fitting and compared with statistical values to verify the estimation results. The woody biomass energy potential was defined as the amount of unused wood generated from harvesting to produce materials. In Japan, the total potential was 0.26–0.74 [EJ/year]. Comparing with the top-down estimation, these results were 34 % overestimated for the maximum value and 54 % underestimated for the minimum value. The detail results of geospatial distribution were statistically analyzed. Moran's I statistic was 0.68, and a hierarchical clustering with proportion resulted in the largest distribution with the majority of Japanese Ceder. [ABSTRACT FROM AUTHOR]

While there is consensus that fertilization with nitrogen (N) is a cost-effective way of increasing both forest biomass yield and timber harvest profitability, the strength and longevity of legacy effects are debated. To quantify legacy effects of past fertilization, we analysed 21 mixed Pinus sylvesteris and Picea abies stands. The stands, on average 23 years old at the time of this study, were either unfertilized (n=7), fertilized with 150 kg N ha−1 once 36 years ago (n=7), or twice, 45 and 36 years ago, respectively (n=7), during the previous stand rotation. We performed measurements on soil N mineralisation and N availability, forest growth, ground vegetation community composition, soil and vegetation C/N ratios and soil C and N stocks, many of which responded to legacy N fertilization earlier in stand development. Our results show that the legacy effects of fertilization during the previous stand rotation have diminished through time, indicating an eventual convergence of stand properties. Specifically, all significant effects present in the previous measurement period (over a decade ago), were weaker or completely absent in the current study (i.e. 36 years after fertilization and 23 years after initiation of the new stands). None-the-less, this indicates a longer legacy effect of N fertilization than what is normally considered and suggests that care should be taken to mitigate unwanted, long-term effects when utilizing N addition to promote tree growth in boreal forests. • Legacy effects of N applied during the previous stand rotation were analyzed. • No significant effects were present in soil, vegetation composition or forest growth. • The absence of responses contrasts previous analysis that occurred 10 years ago. • Diminishing effects indicates a convergence of stand properties after ca. 45 years. [ABSTRACT FROM AUTHOR]

Consistent knowledge about the increment in European forests gained amplified importance in European policies and decision processes related to forest-based bioeconomy, carbon sequestration, sustainable forest management and environmental changes. Until now, large-area increment information from European countries was lacking international comparability. In this study we present a harmonisation framework in accordance with the principles and the approach established for the harmonisation of National Forest Inventories (NFIs) in Europe. 11 European NFIs, representing a broad range of increment measurement and estimation methods, developed unified reference definitions and methods that were subsequently implemented to provide harmonised increment estimates by NUTS regions (Nomenclature of territorial units for statistics of the European Union), main forest types and tree species groups, and to rate the impact of harmonisation measures. The main emphasis was on gross annual increment (GAI), however, also annual natural losses (ANL) and net annual increment (NAI) were estimated. The data from the latest available NFI cycles were processed. The participating countries represent a forest area of about 130 million ha, and 82% of the European Unions' (EU) forest area, respectively. The increments were estimated in terms of volume (m³ year−1, m³ ha−1 year−1) and above-ground biomass (t year−1, t ha−1 year−1). The harmonised GAI volume estimates deviate in a range of +12.3% to −26.5% from the estimates according to the national definitions and estimation methods. Within the study area, the harmonised estimates show a considerable range over the NUTS regions for GAI, from 0.6 to 12.3 m³ ha−1 year−1, and 0.8–6.4 t ha−1 year−1, of volume and above-ground biomass, respectively. The largest increment estimates are found in Central Europe and gradually decrease towards the North, South, West and East. In most countries coniferous forests show larger increment estimates per hectare than broadleaved forests while mixed forests are at an intermediate level. However, in some instances, the differences were small or mixed forests revealed the largest increment estimates. The most important tree species groups in the study area are Pinus spp. and Picea spp., contributing 29% and 26% of the estimated total GAI volume, respectively. The shares of the prevalent broadleaved species are smaller with contributions of 9%, 7% and 6% by Quercus spp., Fagus sylvatica and Betula spp. The results underline the importance of harmonisation in international forest statistics. Looking ahead, harmonised large-area increment estimation is pivotal for accurate monitoring and evidence-based policy decisions in the changing context of future forest ecosystems dynamics, management strategies and wood availability. [Display omitted] • Increment estimation approaches differ among European NFIs. • Harmonisation framework developed for gross and net annual increment. • Common increment estimation method implemented by 10 European countries. • Harmonised increments estimated for NUTS regions, forest types and tree species. • Harmonised increments improve the comparability of international forest statistics. [ABSTRACT FROM AUTHOR]

The Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. It is also used by landowners, companies and researchers, and is therefore regularly applied at local to continental scales. FullCAM is a stand level model with daily, monthly or annual temporal resolutions, depending on the users' preference. FullCAM has been developed over the past 20 years, however, no studies have reviewed and described the current structure, parameters, calibration, and uses of FullCAM, which was the objective of this study. We also discuss the potential for further developments that could improve FullCAM's ability to model responses to disturbances and climate, while also increasing its local accuracy. • FullCAM simulates carbon follows between live biomass, standing dead, debris and soil. • FullCAM is used at local, regional and continental scales, and daily, monthly or annual steps. • FullCAM is used for native forests, woodlands and savannas, as well as plantations. [ABSTRACT FROM AUTHOR]

Continuous cover forestry (CCF) is forest management based on ecological principles and this management type is currently re-visited in many countries. CCF woodlands are known for their structural diversity in terms of tree size and species and forest planning in CCF needs to make room for multiple forest development pathways as opposed to only one management target. As forest management diversifies and management types such as CCF become more common, models used for projecting forest development need to have a generic and flexible bottom-up design. They also need to be able to handle uncertainty to a larger extent and more comprehensively than is necessary with single, traditional forest management types. In this study, a spatial tree model was designed for analyzing a data set involving 18 plots from CCF stands in Southern Finland. The tree model has specific ingrowth, growth and mortality model components, each including a spatially explicit competition effect involving neighboring trees. Approximations were presented that allow inference of the model components operating in annual steps based on time-series measurements from several years. We employed Bayesian methodology and posterior predictive distributions to simulate forest development for short- and long-term projections. The Bayesian approach allowed us to incorporate uncertainties related to model parameters in the projections, and we analyzed these uncertainties based on three scenarios: (1) known plot and tree level random effects, (2) known plot level random effects but unknown tree level random effects, and (3) unknown random effects. Our simulations revealed that uncertainties related to plot effects can be rather high, particularly when accumulated across many years whilst the length of the simulation step only had a minor effect. As the plot and tree effects are not known when tree models are applied in practice, in such cases, it may be possible to significantly improve model projections for a single plot by taking one-off individual-tree growth measurements from the plot and using them for calibrating the model. Random plot effects as used in our tree model are also a way of describing environmental conditions in CCF stands where other traditional descriptors based on stand height and stand age fail to be suitable any more. [Display omitted] • Spatially-explicit tree models can be used to project continuous cover forestry (CCF). • Bayesian methodology was employed to model uncertainties associated with projections. • Uncertainties were high if plot or stand effects were unknown. • Random plot effects can describe environmental conditions in CCF stands. • The length of simulation steps had a minor effect on the simulation results. [ABSTRACT FROM AUTHOR]

• A comprehensive sensitivity analysis was completed for the FullCAM model. • Carbon stock outputs were most sensitive to background mortality rates, age, site conditions, decomposition/turnover parameters and stand structure. • Model applications showed the importance of background mortality in relation to fire and harvesting. • Sensitivity of FullCAM outputs is very context dependent in relation to age and site conditions. • Implications for further developing FullCAM are discussed. In Australia, the Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. Consequently, over the last 20 years, it has been routinely applied at continental, regional and local scales, and has been subject to on-going development to improve its accuracy and representativeness. Given its importance, a sensitivity analysis could facilitate our understanding of model behaviour and aid the planning of future work and data collection. In particular, the sensitivity of a given model parameter is often context dependent such that it depends on the level of other parameters or variables. Key FullCAM parameters have generally been calibrated using data from empirical studies, with differing levels of confidence based on the sample size and data quality. The objective of this study was to apply a sensitivity analysis to examine (i) the sensitivity of FullCAM carbon stock outputs to its parameters and inputs, and how the sensitivity indices compare with the sample sizes used to calculate the respective parameters, (ii) the context dependency in terms of how the sensitivity varies with age, potential maximum biomass (a key FullCAM parameter), and disturbance severity or type, and (iii) to identify the implications for further development of FullCAM for woody vegetation systems. Of the 67 parameters tested, FullCAM carbon stock outputs were most sensitive to background mortality rates, age, potential maximum biomass, climate variability, age of maximum growth, decomposition or turnover parameters, and stand structure (regenerating or mature). The context dependency of the sensitivity analysis followed a consistent pattern depending on two main conditions, such that the sensitivity was higher when (i) the carbon stock was large and (ii) the parameter had a strong influence on that carbon stock. Several strong context dependencies occurred because the size of carbon stocks often vary through space and time within an ecosystem, and different processes (i.e. parameters) are more important at different times and locations. There was a strong context dependency in relation to age and potential maximum biomass, and FullCAM simulations indicated an interaction between fire disturbances and vegetation types. Lastly, background mortality was one of the least available inputs, but one to which FullCAM outputs were very sensitive. FullCAM was applied to show that the long-term contributions of low rates of background mortality to standing dead and debris C stocks were often as large as those from frequent (every 30 years) and intense harvesting and fire events because of the regular (annual) contribution of mortality and its accumulating influence of C stocks. [ABSTRACT FROM AUTHOR]

Secondary forests recovering after disturbances currently comprise about half of the world′s tropical forests. A better understanding of the recovery of species composition, stand structure, and biomass stocks of these secondary forests is critical for the sustainable management of these ecosystems. The main objective of this research was to develop the successional trends in forest biomass, tree diversity, and species composition recovery. We monitored the changes in tree species composition, stand structural characteristics, and biomass growth in a chronosequence of tropical secondary forests in the Yucatan Peninsula of Southeastern Mexico. We used both linear and non-linear regressions to develop biomass growth models during forest succession. We calculated tree species diversity and similitude indices between forest successional groups (young, medium-aged, and old-growth forests). The aboveground biomass stocks ranged from 5.2 to 121 Mg C ha-1, which increased gradually with forest age. We observed a divergent trend of biomass accumulation between soft- and densewood species during succession. The contribution of densewood species on tree biomass stock increased (from 32 % to 81 %) with the increasing forest ages. However, the tree diversity indices did not change significantly with forest age and were a poor predictor of aboveground biomass growth during succession. Secondary forests (SFs) of advanced age were more similar to old-growth forests (OF) in their species composition compared to young ones, as evidenced by the Chao-Jaccard index. However, it may take more than a century for these secondary forests to recover to OF-level species composition. Furthermore, the structural equation model allowed us to infer the relative importance of forest age, tree diversity, soil properties, and stand structural attributes, including wood density on biomass growth along the forest chronosequence. Forest age-mediated effect of species composition shift and stand structural changes was a better predictor of biomass accumulation than the changes in tree diversity or soil properties during succession. However, the soil properties as a construct (latent variable), had a significant positive covariance with stand structure, indirectly influencing biomass growth. These biomass and species composition recovery models could be useful in forest management for biodiversity conservation, landscape restoration, and ecosystem services, including carbon sequestration. • Divergent trends of biomass accumulation between soft- and densewood species during succession. • Secondary forests take more than 100 years to restore their original species composition and biomass stocks. • The tree diversity index was a poor predictor of aboveground biomass storage during succession. • Wood traits and stand structural properties predicted the change in biomass stocks. • Soil properties covaried positively with forest structure and composition, indirectly influencing biomass change. [ABSTRACT FROM AUTHOR]

Abstract When considering options for adapting forests under climate change, climate is treated as the dominant driver of forest growth, while soil properties are often ignored mainly due to shortage of accurate data. The effects of climate and soil on forest growth may vary due to local adaptation to both climate and soil, and these local adaptations might need to be considered when transferring seed provenances under climate change. Data from 29 provenance trials of Norway spruce (Picea abies (L.) Karst.) across a wide gradient of planting conditions in Austria was used to develop Structural Equation Models (SEMs) to quantified the role of climatic and soil drivers and their interactions on juvenile growth performance and to test if provenance origin affects the relative importance of these drivers. Climate and soil of the planting site location were found to have similar direct effects on juvenile tree growth, however, climate was found to be more important because of additional indirect effects via interactions with soil parameters. Notably, the relative effects of climate and soil vary among different provenance groups. Climate constraints are dominant for seed sources originating from colder and/or high altitude locations, while test site climate and soil are equally important contributors of growth for provenances originating from warmer origin and lower elevation sites. Together with the better growth performance of the latter provenance group their plasticity allows them to utilize a wide range of soil conditions. Graphical abstract Unlabelled Image Highlights • We disentangle the relative roles of climate and soil of planting locations and provenance origins on juvenile tree growth. • Climate and soil of the planting location are dominant drivers of growth wheras provenance origin play minor role. • The relative effects of climate and soil vary among different provenance groups • Climatic constraints are dominant, if materials from colder origin and higher altitude are planted. • Soil and climate conditions are equally important if provenances originating from warm sites planted. [ABSTRACT FROM AUTHOR]

Abstract C:N:P stoichiometry in the plants and soils is an important indicator of biogeochemical cycles and functioning in ecosystems; however, the response of plant growth to ecological stoichiometry following afforestation remains unclear. To illustrate the C:N:P stoichiometry connections between the plants and soils and their effects on the growth of plants, soil and plant samples were collected from one farmland site and four Robinia pseudoacacia (RP) forests that were planted for 10, 15, 25, and 35 years. Plant community characteristics, growth of overstory trees and understory plants, physical and chemical properties of the soil, and C:N:P stoichiometry in plants and soil were measured. The results revealed that soil organic C, total N, total P, dissolved organic N, C:P and N:P ratios, and water content significantly increased, whereas soil bulk density, available P, and pH decreased with afforestation age. Leaf C:P and N:P ratios in RP were significantly higher than that in understory biomass because P concentrations gradually decreased in RP leaves and increased in understory biomass. The C, N, and P contents and stoichiometry in the soils and plants were significantly correlated, particularly for N:P ratio that can be used to reveal close coupling between plant and soil nutrients. The N:P ratio in RP leaf and understory biomass increased with afforestation age and varied from 14.44 to 20.81 and 7.59 to 10.96, respectively, suggesting that P limitation gradually increased in RP, and N limitation gradually declined in understory biomass. Furthermore, the responses of overstory trees and understory plant growth to N:P ratios were contradictory, because of their different nutrient acquisition patterns and nutrient-use efficiencies. Therefore, this finding provides evidence that the strong correlation between the plants and soils were tightly coupled to N and P concentrations and N:P ratios, and therefore, have the potential to influence the growth of forests on the Loess Plateau. Graphical abstract Unlabelled Image Highlights • Robinia pseudoacacia afforestation improved soil and plant community characteristics. • Leaf C:P and N:P ratios were higher for Robinia pseudoacacia than for understory plants. • N:P ratios indicate a tight coupling between soil and plant. • P limitation increased in Robinia pseudoacacia and N limitation declined in understory biomass. • N:P ratios negatively correlated with Robinia pseudoacacia growth, but positively correlated with understory biomass. [ABSTRACT FROM AUTHOR]

• Long-term increase in forest growth in Finland mainly due to improved forest structure. • Environmental factors induced a significant share (20 – 31 %) of the growth increase. • The recent growth reduction mainly caused by environmental factors. After a rising trend for 1971 – 2013, during which the annual volume growth of the forests of Finland increased by more than 70 %, a recent reduction has been observed. We analyzed the development of annual growth in the forest of Finland, focusing on the component not explainable by changes in growing stock. The data originate from nine consecutive Finnish National Forest Inventories. In the data, diameter increments were measured from increment cores and tree height increments from standing sample trees in the field. We developed models predicting periodic (5 years) annual volume increment per hectare with properties of the trees and stands as predictor variables. Deviations from model-predicted values were interpreted to be induced by environmental variation. The development was analyzed for all tree species combined and separately for three species groups: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and broadleaves. We observed a rising growth trend not solely explainable by increased growing stock. The species groups produced rather a similar pattern in different parts of Finland: from the 1960s to the mid-1990s, the observed volume increment was mainly below the model-predicted level, thereafter above it. During the current century, the difference between observed and predicted annual volume increment has shown a downward trend for Scots pine. For Norway spruce, the difference has continued to increase in southern Finland, but shows little change in the north. For broadleaved species, the difference between measured and predicted increment shows a recent increase as well, though not as large as for Norway spruce. The geographical pattern of the environment-induced increment component was described in more detail via maps using a 75 km × 75 km grid. The changing environment has enhanced forest productivity in Finland over a period of nearly six decades, but recent years have not been favorable for Scots pine, which represents 50 % of the growing stock volume of the forests of Finland. [ABSTRACT FROM AUTHOR]

• We fitted growth models suitable for both continuous cover and rotation forestry stands. • Sensitivity to climate was restricted by using only temperature sum. • Species-specific symmetric and asymmetric competition affected tree growth. Forest growth models employed in Fennoscandia have been generally targeted at rotation forestry (RF) stands, relying on age as a key predictor. Uneven aged, irregular stands, such as the ones managed with continuous cover forestry, are becoming of increasingly common. New models suited for all kind of management approaches (i.e., age-independent) have been developed in Fennoscandia. Although the ongoing climate change is projected to strongly affect tree growth in boreal regions, climatic variables included in current models are usually restricted to temperature sum averages with simple links. The objectives of our research were: 1) fitting a new age independent empirical tree basal area increment model (B2023) with inclusion of additional climatic variables for the main Nordic tree species (Norway spruce, Scots pine, and birches); 2) using independent data to validate both the new model and other two age-independent published empirical models (P2013, P2021); and 3) investigating the sensitivity of growth predictions of all the empirical models to climate change. Our results showed that the new model B2023 was as accurate as P2013 when independently validated. Both models performed well in different forest structures and management alternatives (namely rotation forestry, continuous cover forestry, two-storied stands, and old-growth natural forests), although with few differences, and on average slightly better than P2021. At plot level, the new model B2023 showed slight underprediction for the overstorey pine layer in continuous cover forestry and two-storied stands. The predicted climate change scenarios increased simulated growth in all models, although P2021 showed very high values for spruce. We failed to include additional climatic variables than temperature sum in B2023, thus not improving much its accuracy under historical data, nor its sensitivity to future climate. Concluding, the individual tree models here presented can be applied to a wide range of forest structures and managements in Fennoscandia. For long-term simulation scenarios, different approaches to improve the climate sensitivity of empirical, individual tree model should be explored. [ABSTRACT FROM AUTHOR]

[Display omitted] • Low soil moisture reduced stem growth of Norway spruce in southern Sweden. • High temperature sums and nitrogen deposition tended to increase growth. • Ozone exposure and start of growing season could not be shown to influence growth. Associations between the annual stem basal area increment growth and soil moisture, nitrogen deposition, ground level ozone exposure, air temperatures and the timing of the start of the growing season have been investigated for a twenty four-year period, 1990–2013, based on tree-ring width measurements from seventeen monitoring sites with Norway spruce (Picea abies) forests in southern Sweden. The stem growth-environment associations were analyzed using a fixed effect regression model, with annual stem basal area increment (BAI) as the dependent variable and annual values for a soil moisture index, ozone exposure estimated as AOT30, bulk deposition of nitrogen, summed air temperatures above a threshold and the timing of the start of the growing season as explanatory variables. The statistical analysis was made with and without taking clustering of the sampled trees into account, i.e. that several different tree observations were made at the same monitoring site. The annual number of days with soil moisture below a threshold was the only explanatory variable that could be demonstrated to be negatively associated with changes in BAI, regardless of statistical approach. Positive associations between temperature sums as well as nitrogen deposition with changes in BAI were indicated by low p values using standard p-values, but not when clustering was taken into consideration. Associations between ozone exposure as well as the start date of the growing season with changes in BAI could not be demonstrated since the estimated p values were high regardless of statistical approach. The results show that soil water deficit may considerably limit forest growth in northern European forests. [ABSTRACT FROM AUTHOR]

Abstract We present an open-source computational tool for the 2D simulation of the Diffusive-Logistic Growth (DLG) model. The r.recovery module offers a complete environment for the simulation of forestry regeneration in conservation areas and includes a built-in tool for calibration and validation of the model parameters through the use of standard and freely available satellite imagery. It was implemented as an add-on to the GRASS software, a largely applied open-source Geographic Information System (GIS). To illustrate its application, we present a complete case study of forest regeneration carried out in the Espigão Alto State Park (EASP), Brazil, from which we assess typical values of forest diffusion and growth rate parameters, along with the prognostics of forest density status for the coming decades. We observe that the r.recovery tool can be advantageously applied by forestry managers and policy-makers as a form of acquiring technical and scientifically-based information for strategy development and decision-making. Highlights • This research makes a state-of-the-art forestry modeling framework available for a large public. • The module can be coupled to hydrological models in long-term simulations. • The module is user-friendly and can enhance the efficiency and response time of forestry management processes. • A complete walkthrough is presented to illustrate the application of the module. • The module is open-source and freely available for download at http://modelagemambientaluffs.blogspot.com.br/. [ABSTRACT FROM AUTHOR]

Highlights • Forest structure explains evapotranspiration in seasonal streamflow model. • "Top-down" model's unconstrained store accounts for catchment memory and thresholds. • Very good performance for large forested catchments affected by fire and logging. • Regional consistency of parameters allows model use in ungauged catchments. • Rainfall estimation procedure for remote regions with high relief. Abstract Forest growth dynamics affect streamflow with changes in actual evapotranspiration (AET) during forest regeneration. Using a "top-down" model approach, we present a seasonal forest hydrology model that estimates streamflow in forested catchments containing sparse soil and climate data. Across a 1423 km2 remote study area, a rainfall interpolation procedure using available rainfall data and information on terrain was integrated into a new seasonal streamflow model, called ABCF, which uses catchment sapwood area (SA) as the emergent property that equilibrates with potential evapotranspiration (PET), and a soil storage threshold that reduces AET below PET when soil water is limiting. We produce seasonal estimates of streamflow with Nash Sutcliffe efficiencies of 0.85, 0.87, and 0.91 for three major catchments within the study area. A fundamental feature of the "top-down" model approach is the use of LiDAR data and forest inventory data to model forest structural properties that relate strongly with SA. Building on our previous work with this modelling framework, our representation of eco-hydrological properties of the forest has been refined with a more accurate procedure for estimating stand mean sapwood thickness, and hence SA , and a remotely sensed tree stocking density (N) of old-growth forests to correct the temporal evolution of N as a means to improve SA estimates. Regional consistency of model parameters shows that the "top-down" modelling framework may be used to estimate streamflow in ungauged catchments using a forest growth model. The seasonal model generalised for both water-limited and water-unlimited forest conditions has significant potential for application in water supply planning and drought security. [ABSTRACT FROM AUTHOR]

Highlights • Biomass growth in control treated plots was 12.4 and 14.8 Mg ha−1 yr−1 • Light use in control and ttreated plots was 1344 and 1406 MJ m−2 yr−1respectively, or 4.6% increase. • The increase in LUE was 20%. Control and treated plots had 0.9 g DM/MJ and 1.10 g DM/MJ f. • Stem biomass growth was 13.4, 15.1, and 17.3 Mg ha−1 yr−1 for control, weed control, and fertilization + weed control treatments. Abstract Increasing forest site productivity is a need. Land use conflicts, high land prices, or owners' objectives, have motivated forest managers to apply more intensive silvicultural treatments to increase forest site productivity. Understanding how intensive silvicultural practices such as weed control and fertilization + weed control affect productivity will permit managers to select the best treatment to increase it. Our objectives were: (1) to estimate the gap between current and attainable productivity of Eucalyptus plantations and (2) to determine the effect of treatment on light use and light use efficiency. To estimate the gap between current and attainable productivity, we established 53 pairs of plots, which were measured for two years. Each pair consisted of a control plot, which received the management regime that is regularly applied to the stands, and a treated plot, which received intensive silvicultural treatment (fertilization + weed control) in addition to the operational management applied to the control plots. At 25% of the sites, a third plot (weed control only) was established. Stem biomass growth in the control and treated plots was 12.4 and 14.8 Mg ha−1 yr−1, respectively. We found significant differences in light use between the control and treated plots, i.e., averages of 1344 MJ m−2 yr−1 and 1406 MJ m−2 yr−1, respectively, representing a 4.6% increase. The increase in light use efficiency (LUE) was higher and reached 20%. On average, the control plots had a LUE of 0.9 g of dry mass (DM)/MJ compared with 1.10 g DM/MJ for the treated plots. There is a considerable opportunity to increase forest productivity through fertilization, and it may be possible to obtain greater gains than those observed in this study. This information is important to estimate the expected responses to intensive silviculture and will help to decide where silvicultural treatments should be applied to maximize the gains obtained from the investment. [ABSTRACT FROM AUTHOR]

Abstract The demand for large-scale and long-term information on tree growth is increasing rapidly as environmental change research strives to quantify and forecast the impacts of continued warming on forest ecosystems. This demand, combined with the now quasi-global availability of tree-ring observations, has inspired researchers to compile large tree-ring networks to address continental or even global-scale research questions. However, these emergent spatial objectives contrast with paleo-oriented research ideas that have guided the development of many existing records. A series of challenges related to how, where, and when samples have been collected is complicating the transition of tree rings from a local to a global resource on the question of tree growth. Herein, we review possibilities to scale tree-ring data (A) from the sample to the whole tree, (B) from the tree to the site, and (C) from the site to larger spatial domains. Representative tree-ring sampling supported by creative statistical approaches is thereby key to robustly capture the heterogeneity of climate-growth responses across forested landscapes. We highlight the benefits of combining the temporal information embedded in tree rings with the spatial information offered by forest inventories and earth observations to quantify tree growth and its drivers. In addition, we show how the continued development of mechanistic tree-ring models can help address some of the non-linearities and feedbacks that complicate making inference from tree-ring data. By embracing scaling issues, the discipline of dendrochronology will greatly increase its contributions to assessing climate impacts on forests and support the development of adaptation strategies. Highlights • Scaling issues complicate the growing use of tree rings in large-spatial analyses. • New tree-ring networks should represent and quantify forest growth across scales. • Forest plot and remote sensing data help account for non-climatic drivers of growth. • Hind- and forecasting tree growth requires broadly applicable mechanistic models. • Dendrochronology needs new quality criteria for the collection and scaling of data. [ABSTRACT FROM AUTHOR]

Mountainous terrain creates variability in microclimate, including nocturnal cold air drainage and resultant temperature inversions. Driven by the elevational temperature gradient, vapor pressure deficit (VPD) also varies with elevation. Soil depth and moisture availability often increase from ridgetop to valley bottom. These variations complicate predictions of forest productivity and other biological responses. We analyzed spatiotemporal air temperature (T) and VPD variations in a forested, 27-km 2 catchment that varied from 1000 to 1650 m in elevation. Temperature inversions occurred on 76% of mornings in the growing season. The inversion had a clear upper boundary at midslope (∼1370 m a.s.l.). Vapor pressure was relatively constant across elevations, therefore VPD was mainly controlled by T in the watershed. We assessed the impact of microclimate and soil moisture on tree height, forest productivity, and carbon stable isotopes (δ 13 C) using a physiological forest growth model (3-PG). Simulated productivity and tree height were tested against observations derived from lidar data. The effects on photosynthetic gas-exchange of dramatic elevational variations in T and VPD largely cancelled as higher temperature (increasing productivity) accompanies higher VPD (reducing productivity). Although it was not measured, the simulations suggested that realistic elevational variations in soil moisture predicted the observed decline in productivity with elevation. Therefore, in this watershed, the model parameterization should have emphasized soil moisture rather than precise descriptions of temperature inversions. [ABSTRACT FROM AUTHOR]

• Soil organic carbon distribution in forests responded to external environmental factors. • Forest soil organic carbon distribution exhibited surface aggregation at multiple scales. • Forest growth and soil enzyme activity were important factors affecting soil organic carbon distribution. • The interaction of various environmental factors on soil organic carbon distribution showed a weakening trend. To investigate the effects of stand age and soil layer on the temporal and spatial distribution of soil organic carbon (SOC) in Cunninghamia lanceolata plantations, five experimental plots and 15 soil sampling sites were established in five stands of different ages (3, 6, 9, 15, and 25 years) in Huitong, Hunan Province. Tree diameter at breast height, tree height, canopy density and elevation in sample plots, and soil organic carbon concentrations (SOCC), soil enzyme activity, soil pH, and soil bulk density at 0–15, 15–30, 30–45 cm soil layers were measured during stand development. The change in SOC distribution with stand age and soil layer, and soil enzyme activity were investigated using ANOVA, LSD test, and inter-subject effect test and simulation analysis. The results showed that the SOC distribution in Cunninghamia lanceolata plantations had spatiotemporal heterogeneity and surface aggregation. The SOC increased with increasing stand age and decreased with increasing soil depth. Furthermore, SOCC and SOC density (SOCD) were strongly correlated with environmental factors; however, the interaction between forest growth and soil layer reduced the variation of SOC distribution. Forest growth and soil enzyme activity were important factors that affect the temporal and spatial distribution of SOC. Therefore, the management measures to promote the growth of the plantation and improve soil enzyme activity can enhance the SOC storage capacity from the angle of balancing ecological and economic benefits, and contribute to the achievement of the goal of "carbon neutrality". [ABSTRACT FROM AUTHOR]

Biomass and leaf area equations are often required to assess or model forest productivity, carbon stocks and other ecosystem services. These factors are influenced by climate, age and stand structural attributes including stand density and tree species diversity or species composition. However, such covariates are rarely included in biomass and leaf area equations. We reviewed the literature and built a database of biomass and leaf area equations for 24 European tree species and 3 introduced species. The final dataset contained 973 equations. Most of the equations were site-specific and therefore restricted to the edaphic, climatic and stand structural conditions of the given site. To overcome this limitation, the database was used to develop regional species-specific equations that can be used in a wide range of stands and to quantify the effects of climate, age and stand structure on biomass or leaf area. The analysis showed considerable inter- and intra-specific variability in biomass relationships. The intra-specific variability was related to climate, age or stand characteristics, while the inter-specific variability was correlated with traits such as wood density, specific leaf area and shade tolerance. The analysis also showed that foliage mass is more variable than stem or total aboveground biomass, both within and between species, and these biomass components have contrasting responses to age and changes in stand structure. Despite the large number of published equations, many species are still not well represented. Therefore, generic equations were developed that include species-specific wood density instead of species identity. Further improvements may be possible if future studies quantify the stand structure of individual tree neighbourhoods instead of using the stand means for all trees sampled with the given stand. [ABSTRACT FROM AUTHOR]

The annual growth of the Finnish forests has doubled in less than a century but the reasons for the increase have been a subject of much debate. Site index aggregates the mid- to long-term impacts of variable edaphic and climatic factors on site productivity and is an important characteristic with regard to the ongoing environmental changes. We quantified changes of site index in Finland over time by applying a new approach developed in our previous study in Germany. A large data base for Norway spruce ( Picea abies (L.) Karst.), Scots pine ( Pinus sylvestris L.) and larch ( Larix spp.) was available from the network of long-term growth and yield experiments in southern and central Finland. For all tree species, site indices fluctuated over time, i.e. they slightly decreased in the 1970s, followed by an increasing trend in the 1980s and a decline again in the 1990s. Apparently, the only significant change was a change point indicated for Scots pine around the year 1944; however, indication was weak due to notable limitations of the database during the respective period. Interestingly, the result of this study in Finland differs from central Europe, where Norway spruce site productivity has displayed a considerable increase since the 1950s. Potentially, the low nitrogen deposition in Finland may be of major importance accounting for the divergent changes in site productivity between Finland and central Europe. Thus, the results indicate that a large share of the growth increase in the Finnish forests might actually be due to altered management. [ABSTRACT FROM AUTHOR]

This study compared the densest available database of tree-ring growth with the longest Normalized Difference Vegetation Index (NDVI) information available at the global scale to quantify the relationship between annual forest growth and the NDVI across different forest types and regions and to characterize the patterns of response of forest growth to NDVI values at different temporal scales. We found a general positive relationship between the inter-annual NDVI variability and the annual tree growth in most of the analyzed forests. Nevertheless, there were strong differences in the tree growth responses to NDVI, given that the annual tree-ring records in each forest responded in a different way to the magnitude, seasonality and accumulation period of the NDVI values. Thus, we found eight main patterns of tree-ring response to the NDVI, which were related to the forest type and climate conditions of each corresponding site. The identified patterns may be useful for determining early-warning signals of changes in forest growth over large areas based on remote sensing information. [ABSTRACT FROM AUTHOR]

Concerns about the sustainability of tropical forestry motivated this study on post-logging timber and carbon dynamics over a 20-year period in Paragominas, Pará, Brazil. Previously unlogged forest was subjected to conventional logging (CL), reduced-impact logging (RIL), or was set aside as an unlogged control. All trees ⩾25 cm DBH and all trees of commercial species ⩾10 cm DBH were monitored in a 24.5 ha plot in each treatment, with a 5.25 ha subplot in each for monitoring all trees ⩾10 cm DBH. Above-ground biomass and bole volumes of merchantable species were tracked based on 10 inventories made between 1993 and 2014. Pre-logging biomass and bole volumes of commercial species were estimated as 237, 231, and 211 Mg ha −1 , and 78, 80, and 70 m 3 ha −1 , in the RIL, CL, and unlogged plots, respectively. One year after logging, biomass was reduced 14% by RIL and 24% by CL with corresponding merchantable species volume reductions of 21% and 31%. By 2014, biomass and bole volumes of commercial species had recovered 95% and 98% of their pre-logging stocks in the RIL plot but only 76% and 72% in the CL plot, respectively; timber volumes from large trees (⩾50 cm DBH) were only recovered to 81% in the RIL plot and to 53% in the CL plot. Over the first twenty years after logging, average volume increments from commercial species were substantially higher in the RIL plot (0.72 m 3 ha −1 year −1 ) than in the CL plot (0.08 m 3 ha −1 year −1 ). Recovery of both biomass and timber volumes were temporarily reversed between 2009 and 2014 due to a 4-fold increase in annual mortality rates in the RIL plot and a 5.5-fold increase in the CL plot (as well as a 3-fold increase in the control plot), all presumably related to the extreme drought of 2010. Our findings support the claim that use of RIL techniques accelerates rates of biomass and timber stock recovery after selective logging. [ABSTRACT FROM AUTHOR]

Estimating the change in the magnitude and distribution of forest biomass is important for monitoring carbon dynamics and understanding the implications on the terrestrial carbon cycle. In this study, we assessed the capacity of multi-temporal airborne Light Detection and Ranging (LiDAR) data to estimate forest biomass dynamics at a mixed subtropical forest of southeast China, and analyzed the magnitude and spatial patterns of the above ground biomass (AGB) change over a 6-year period. To do so, we evaluated different approaches to estimate change in AGB, specifically direct (i.e., predicting biomass change using the differences in LiDAR metrics) and indirect (i.e., modeling biomass for each point in time and predicting the change as their differences) approaches. Once models were developed, the change in AGB was mapped across the study site and examined in relation to forest type and age. Our results demonstrated that the direct approach (CV- R 2 = 0.63, CV-rRMSE = 25.64%) produced more accurate estimates in change of AGB than the indirect approach (CV- R 2 = 0.59, CV-rRMSE = 28.35%). Canopy height metrics of delta mean height (∆ h mean ) and a number of upper percentile heights (i.e., ∆ h 75 and ∆ h 95 ) , calculated from the LiDAR first returns, were found to be most sensitive to changes in biomass, whereas metrics based on maximum canopy height or canopy density had less predictive power. Furthermore, differences in point cloud densities did not significantly impact the estimations of AGB and change of AGB at the plot-scale (30 × 30 m). Spatial extrapolation of the AGB change indicated that, in general, most of the forest area had an overall gain in biomass. The middle-aged stands (typically dominated by Quercus acutissima between 41 and 60 years old) show marked growth of biomass (median ∆ AGB = 2.29 Mg ha − 1 year − 1 ), followed by mature (typically dominated by Pinus massoniana between 31 and 60 years old) (median ∆ AGB = 1.72 Mg ha − 1 year − 1 ) and young stands (typically consisting of regenerated broadleaved trees younger than 40 years old) (median ∆ AGB = 1.48 Mg ha − 1 year − 1 ), whereas over-mature stands (typically dominated by Cunninghamia lanceolata older than 35 years) show the lowest growth (median ∆ AGB = 0.76 Mg ha − 1 year − 1 ). This study demonstrates benefits of using multi-temporal LiDAR data to help identify specific areas for management interventions, to enhance forest productivity, maintain biomass stocks and optimize carbon sequestration. [ABSTRACT FROM AUTHOR]

Norway spruce ( Picea abies (L.) Karst.) stands in certain areas of Central Europe have experienced substantial dieback since the 1970s. Understanding the reasons for this decline and reexamining the response of forests to acid deposition reduction remains challenging because of a lack of long and well-replicated tree-ring width chronologies. Here, spruce from a subalpine area heavily affected by acid deposition (from both sulfur and nitrogen compounds) is evaluated. Tree-ring width measurements from 98 trees between 1000 and 1350 m above sea level (a.s.l.) reflected significant May–July temperature signals. Since the 1970s, acid deposition has reduced the growth–climate relationship. Efficient pollution control together with a warmer but not drier climate most likely caused the increased growth of spruce stands in this region, the so-called “Black Triangle,” in the 1990s. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop, test and evaluate a software prototype capable of modeling forest growth in consideration of winter storm disturbance and to simulate storm damage in forests under different forest management regimes. The results of a test application showed that simulated storm damage was more strongly influenced by the input data (e.g. tree species and tree height) than by the different forest management regimes. However, early, intense thinnings as well as reducing target diameters by 10% led to reduced storm damage, with decreases as large as 50% of the damage in certain forest stands. The coupled modeling framework was able to simulate interactions between forest growth, storm damage and forest management regimes. Further testing of the prototype appears necessary to investigate a wider range of tree species, soil and site conditions. Also, the use of computational system resources needs improvement. [ABSTRACT FROM AUTHOR]

Summary The Coalburn research catchment (1.5 km 2 ) in Kielder Forest, Northern England, is a long-term project to study the effect of upland afforestation on hydrology. There is now a unique 45-year record; making it Britain’s longest running forest hydrology research catchment. The site was instrumented in 1967, ploughed and planted in 1972/73 and the trees have now reached maturity. Hourly meteorological data have been measured since 1993 and these have enabled hydrological simulations to be carried out using the Shetran model for the period 1993–2011. The results from this work show that after ploughing there was an increase of around 50–100 mm in annual streamflow compared with the original upland grassland vegetation. However, the mature trees now show a decrease of around 250–300 mm in the annual streamflow compared with the original vegetation and a decrease of around 350 mm in the annual streamflow compared with when the site was ploughed. The simulation results show very clearly the non-stationary nature of the catchment during 1993–2011 with an annual increase in intercepted evaporation and a decrease in discharge as the trees grow. Simulation results also show that peak discharges are higher for a cover of smaller trees compared with taller trees. However, the results suggest that the bigger the event the smaller is the difference, i.e. there is absolute convergence for the two different tree scenarios at higher discharges. The study shows how modelling can compensate for data deficiencies, to maximise outcomes. As a rare example of long-term analysis of non-stationary catchment behaviour it also provides real evidence of change that would otherwise have had to be inferred theoretically. [ABSTRACT FROM AUTHOR]

Climate change is an important factor affecting forest growth. Therefore, approaching the impacts of climate change on forest growth is of great significance to ameliorate this degraded land and push up forestry development. This paper initially probes the impacts of climate change on tree growth in Yellow River Delta region and responds of different tree species on the change. In this study, five species of 22-year-old trees were selected, and the tree biomass was measured by standard site methods and tree ring sampling to pursue the impacts of climate change on forest growth. Besides, growth models of the different tree species were established and verified using Robinia pseudoacacia as an example. The results showed: (1) In the Yellow River Delta, the most adapted tree species are Fraxinus chinensis and R. pseudoacacia. (2) Precipitation is the main meteorological factor affecting tree growth, while temperature and air pressure are also significantly correlated with tree growth. (3) Linear and power function models can simulate tree growth well. From the verification results, the modified R. pseudoacacia biomass is 294.54 t/ha, and the simulated biomass of the linear function model is close to the value. It is expected that the research not only provides a theoretical basis for forestry development in saline lands, but also helps to rehabilitate saline-alkali lands and cope with climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • In asynchronous climates, the forest growth response to rainfall is not well known. • Lagged relationship of forest growth to antecedent rainfall was explored. • Vegetation in the asynchronous climate have a long memory to the rainfall. • The memory of vegetation to rain could indicate soil/rooting depth across the landscape. The out-of-phase rainfall and temperature and deep root system make the sequential connection between past rainfall events, soil water storage, and forest growth response complicated and temporally extended in asynchronous climates with Mediterranean-type settings. Unfortunately, these location-specific deep-soil water stores are rarely measured due to logistic and financial constraints, especially in the forest. Therefore, at a large spatio-temporal scale, forest growth relationship to growth drivers is still unknown in these ecosystems, limiting our knowledge to understand the functioning of these forests and their links with hydrological processes. Although process-based water balance models can analyze vegetation growth response to the input climate forcing, they rely upon some significant assumptions regarding plants access to deep soil water storage. Thus, this study aims to understand how the out-of-phase rainfall events affect the current ecosystem growth response, represented by the observed Normalized Difference Vegetation Index (NDVI), across the landscape. We have used an empirical approach on long term observed data without any assumption on access to deep-soil water stores. We estimated time lags between forest growth and rainfall events using a lagged correlation analysis applied to monthly anomalies of NDVI and rainfall against their climatological averages over 2002–2018. The study found that the forests in asynchronous climates exhibit unexpectedly long (10–25 months) memory to rain, and this memory has a systematic pattern across the landscape, which we contend has highlighted three things: 1) the forest in the middle aridity (∼3–4.5) range are relatively more sensitive to changes in the short-term rainfall than the forest in lower (<3) and higher (>5) aridity regions, could be due to rapid depletion of relatively small soil water storage in between the storms, 2) the variable memory of forest to rain across the landscape can be an indicator of soil depth/rooting depth, and 3) the variable sized location specific antecedent rainfall windows can explain significant variability in forest growth status in asynchronous climates, thus these rainfall windows can be employed to forecast forest growth with a lead time (>4 months). [ABSTRACT FROM AUTHOR]

Abstract: Growing evidence suggests environmental change to be most severe across the semi-arid subtropics, with past, present and projected drying of the Mediterranean Basin posing a key multidisciplinary challenge. Consideration of a single climatic factor, however, often fails to explain spatiotemporal growth dynamics of drought-prone ecosystems. Here, we present annually resolved and absolutely dated ring width measurements of 871 Scots pines (Pinus sylvestris) from 18 individual plot sites in the Central Spanish Pinar Grande forest reserve. Although comprising tree ages from 6 to 175years, this network correlates surprisingly well with the inverse May–July diurnal temperature range (r =0.84; p <0.00011956–2011). Ring width extremes were triggered by pressure anomalies of the North Atlantic Oscillation, and the long-term growth decline coincided with Iberian-wide drying since the mid-1970s. Climate model simulations not only confirm this negative trend over the last decades but also project drought to continuously increase over the 21st century. Associated ecological effects and socio-economic consequences should be considered to improve adaptation strategies of agricultural and forest management, as well as biodiversity conservation and ecosystem service. [Copyright &y& Elsevier]

Abstract: Although they were originally introduced for different purposes, forest models are often used today for scenario development, which includes forest production as one aspect of forest development. However, studies using an independent data set to compare different simulators are rarely found. In this study a subset of National Forest Inventory data for the whole of Austria was compared to simulations of the biogeochemistry model Biome-BGC, the hybrid gap model PICUS and a climate sensitive version of the growth and yield model PrognAus. The models were used to simulate the development of approximately 700 forest inventory sample plots over a period of 15 years. The study focussed on the models'' sensitivity to varying environmental conditions; thus, the comparison was based on the mean current annual volume increment per hectare. All models showed a significant average deviation from the inventory (over- or under-estimation). The estimated year-to-year variation was best reproduced by PICUS. However, the 15 year growth trend was also shown by Biome-BGC and PrognAus. Potential model users interested in relating mean current annual volume increment to climate data will need to weigh accuracy against applicability when choosing among these models. [Copyright &y& Elsevier]

Abstract: Large scale forest productivity estimates are of increasing interest as more demands are made on forest resources. In principle three different methods are currently available: (i) forest growth data from forest research plots and/or forest inventory sampling points based on repeated tree observations, (ii) flux tower observations which record the gas exchange of the plant atmosphere interactions for a given vegetation type, and (iii) remotely sensed data for a continuous cover of net primary productivity estimates. In this paper we focus on the conceptual challenge in comparing “space based” moderate resolution imaging spectroradiometer (MODIS) satellite driven net primary production (NPP) vs. terrestrial “ground based” productivity estimates using forest increment data from 151 long term research plots with a well documented management history. The Austrian biomass functions are applied to derive ground based NPP estimates based on repeated tree observations from the plots. In addition we use BIOME-BGC as a diagnostic tool for exploring conceptual constraints among the two methods. The results of the study can be summarized as follows: (i) MODIS satellite driven annual NPP estimates provide a continuous productivity estimate across Austria and no significant differences between different daily climate input data sets were evident. (ii) MODIS NPP predictions provide forest productivity estimates of fully stocked forests with a complete crown cover. This is confirmed by the results of spin-up runs of the BIOME-BGC model. (iii) Terrestrial driven NPP predictions using the Austrian biomass functions compared well with MODIS driven estimates after addressing stand density effects of the forest plot data. The influence of stand density were known to be an integral component in reconciling “space based” satellite vs. “ground based” derived forest productivity estimates. After addressing stand density, computed forest productivity estimates compared well with MODIS-based estimates. This suggests that combining both methods will enhance our ability to generate forest productivity assessments across large forest areas. [Copyright &y& Elsevier]

Abstract: We analyzed the impact of drought measured on different time-scales on radial growth of eight tree species during the period 1950–2005 growing across a wide climatic gradient encompassing semiarid Mediterranean woodlands and wet mountain forests in north-eastern Spain. A drought index (standardized precipitation index, SPI) at different time scales (1–48 months) was correlated with chronologies of ring width to determine the significant time scale at which drought affected most tree growth. The findings indicated that the impact of drought on growth varied noticeably among species and sites. Two distinct patterns were clearly observed considering spatial and temporal differences in the response of species to drought. Species growing in xeric sites (Pinus and Quercus species and Juniperus thurifera) showed the highest responses to SPI time-scales of 9–11 months while those located in mesic sites (Abies alba, Pinus sylvestris) did respond more to SPI time scales shorter than 5 months. The SPI-growth correlations were significant, although weak, up to 30 months in xeric sites while no consistent association was observed at higher time scales. Important seasonal differences were noticed in the SPI-growth associations. Species growing in xeric areas responded to spring-summer SPI while those distributed in mesic sites responded more to summer SPI. Our findings should be useful to understand forest responses to climate change, including an increasing frequency of severe droughts, and to adapt appropriate management strategies to mitigate the impact of drought on tree growth. [Copyright &y& Elsevier]

Abstract: A predicted rise in anthropogenic greenhouse gas emissions and associated effects on the Earth’s climate system likely imply more frequent and severe weather extremes with alternations in hydroclimatic parameters expected to be most critical for ecosystem functioning, agricultural yield, and human health. Evaluating the return period and amplitude of modern climatic extremes in light of pre-industrial natural changes is, however, limited by generally too short instrumental meteorological observations. Here we introduce and analyze 11,873 annually resolved and absolutely dated ring width measurement series from living and historical fir (Abies alba Mill.) trees sampled across France, Switzerland, Germany, and the Czech Republic, which continuously span the AD 962–2007 period. Even though a dominant climatic driver of European fir growth was not found, ring width extremes were evidently triggered by anomalous variations in Central European April–June precipitation. Wet conditions were associated with dynamic low-pressure cells, whereas continental-scale droughts coincided with persistent high-pressure between 35 and 55°N. Documentary evidence independently confirms many of the dendro signals over the past millennium, and further provides insight on causes and consequences of ambient weather conditions related to the reconstructed extremes. A fairly uniform distribution of hydroclimatic extremes throughout the Medieval Climate Anomaly, Little Ice Age and Recent Global Warming may question the common believe that frequency and severity of such events closely relates to climate mean stages. This joint dendro-documentary approach not only allows extreme climate conditions of the industrial era to be placed against the backdrop of natural variations, but also probably helps to constrain climate model simulations over exceptional long timescales. [Copyright &y& Elsevier]

Abstract: Stand-level tree diameter growth patterns were explored for evergreen moist forests in the southern Cape, South Africa. Results of standard multiple regression analyses, involving 934 permanent sample plots with data spanning a 10-year interval, revealed that stand-level increment of canopy species in the canopy layer (>30cm dbh) was significantly determined by inherent species-specific growth capacities (species composition of the stand), water availability, forest matrix crowding and tree condition impairment (age-related manifestations of reduced vitality indicated by signs of crown die-back, damage and stem rot). In contrast, stand-level increment of trees of canopy species in the subcanopy layer (10–20cm dbh) was prominently shaped by light availability, as mainly determined by the degree of canopy-level disturbance (mortality rate of trees >30cm dbh), crowding (canopy-level overhead and forest matrix crowding) and proximity to conspecific adults (within 6–8m). In addition to species-inherent and resource factors, considerable variation in stand-level growth resulted from site–climate interactions. For 507 of the permanent sample plots, increment data was available for two consecutive 10-year intervals; permitting the analysis of spatiotemporal interactions of growth patterns (repeated measures ANOVA). In the Knysna forests higher canopy-level increment rates were associated with the moister southerly facing slope sites in comparison with the drier northerly facing and ridge sites during the first increment period. During the second increment period, increment rates on the drier, but better illuminated sites had increased disproportionately. In contrast, in the Tsitsikamma forests, higher increment rates during the second increment period were encountered on moister flat bottomland sites (with extended periods of subsoil wetness) than on the comparatively drier southerly facing slope sites (increment period×site-based water availability×forests interaction). In both forests relatively higher growth performance of subcanopy-level trees during the second increment period was associated with stands experiencing conditions of enhanced light availability. Atmospheric temperatures were higher during the second increment period (mean periodic T max: +0.64°C). The detected spatiotemporal interactions were interpreted as site×climate interactions where site-related conditions of favourable light or water availability resulted in enhanced temperature-linked growth responses during the second increment period. A metabolic performance trade-off model provided a framework for the interpretation of these complex site–climate interactions by placing the patterns of forest growth into an ecophysiological explanatory context. [Copyright &y& Elsevier]

Abstract: Availability of free, high quality Landsat data portends a new era in remote sensing change detection. Using dense (~annual) Landsat time series (LTS), we can now characterize vegetation change over large areas at an annual time step and at the spatial grain of anthropogenic disturbance. Additionally, we expect more accurate detection of subtle disturbances and improved characterization in terms of both timing and intensity. For Landsat change detection in this new era of dense LTS, new detection algorithms are required, and new approaches are needed to calibrate those algorithms and to examine the veracity of their output. This paper addresses that need by presenting a new tool called TimeSync for syncing algorithm and human interpretations of LTS. The tool consists of four components: (1) a chip window within which an area of user-defined size around an area of interest (i.e., plot) is displayed as a time series of image chips which are viewed simultaneously, (2) a trajectory window within which the plot spectral properties are displayed as a trajectory of Landsat band reflectance or index through time in any band or index desired, (3) a Google Earth window where a recent high-resolution image of the plot and its neighborhood can be viewed for context, and (4) an Access database where observations about the LTS for the plot of interest are entered. In this paper, we describe how to use TimeSync to collect data over forested plots in Oregon and Washington, USA, examine the data collected with it, and then compare those data with the output from a new LTS algorithm, LandTrendr, described in a companion paper (Kennedy et al., 2010). For any given plot, both TimeSync and LandTrendr partitioned its spectral trajectory into linear sequential segments. Depending on the direction of spectral change associated with any given segment in a trajectory, the segment was assigned a label of disturbance, recovery, or stable. Each segment was associated with a start and end vertex which describe its duration. We explore a variety of ways to summarize the trajectory data and compare those summaries derived from both TimeSync and LandTrendr. One comparison, involving start vertex date and segment label, provides a direct linkage to existing change detection validation approaches that rely on contingency (error) matrices and kappa statistics. All other comparisons are unique to this study, and provide a rich set of means by which to examine algorithm veracity. One of the strengths of TimeSync is its flexibility with respect to sample design, particularly the ability to sample an area of interest with statistical validity through space and time. This is in comparison to the use of existing reference data (e.g., field or airphoto data), which, at best, exist for only parts of the area of interest, for only specific time periods, or are restricted thematically. The extant data, even though biased in their representation, can be used to ascertain the veracity of TimeSync interpretation of change. We demonstrate that process here, learning that what we cannot see with TimeSync are those changes that are not expressed in the forest canopy (e.g., pre-commercial harvest or understory burning) and that these extant reference datasets have numerous omissions that render them less than desirable for representing truth. [Copyright &y& Elsevier]

Abstract: We introduce and test LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery), a new approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS). The method brings together two themes in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Our strategy is founded on the recognition that change is not simply a contrast between conditions at two points in time, but rather a continual process operating at both fast and slow rates on landscapes. This concept requires both new algorithms to extract change and new interpretation tools to validate those algorithms. The challenge is to resolve salient features of the time series while eliminating noise introduced by ephemeral changes in illumination, phenology, atmospheric condition, and geometric registration. In the LandTrendr approach, we use relative radiometric normalization and simple cloud screening rules to create on-the-fly mosaics of multiple images per year, and extract temporal trajectories of spectral data on a pixel-by-pixel basis. We then apply temporal segmentation strategies with both regression-based and point-to-point fitting of spectral indices as a function of time, allowing capture of both slowly-evolving processes, such as regrowth, and abrupt events, such as forest harvest. Because any temporal trajectory pattern is allowable, we use control parameters and threshold-based filtering to reduce the role of false positive detections. No suitable reference data are available to assess the role of these control parameters or to test overall algorithm performance. Therefore, we also developed a companion interpretation approach founded on the same conceptual framework of capturing both long and short-duration processes, and developed a software tool to apply this concept to expert interpretation and segmentation of spectral trajectories (TimeSync, described in a companion paper by Cohen et al., 2010). These data were used as a truth set against which to evaluate the behavior of the LandTrendr algorithms applied to three spectral indices. We applied the LandTrendr algorithms to several hundred points across western Oregon and Washington (U.S.A.). Because of the diversity of potential outputs from the LTS data, we evaluated algorithm performance against summary metrics for disturbance, recovery, and stability, both for capture of events and longer-duration processes. Despite the apparent complexity of parameters, our results suggest a simple grouping of parameters along a single axis that balances the detection of abrupt events with capture of long-duration trends. Overall algorithm performance was good, capturing a wide range of disturbance and recovery phenomena, even when evaluated against a truth set that contained new targets (recovery and stability) with much subtler thresholds of change than available from prior validation datasets. Temporal segmentation of the archive appears to be a feasible and robust means of increasing information extraction from the Landsat archive. [Copyright &y& Elsevier]

Abstract: In the wet tropics, near the Atlantic Coast of Brazil, drought may reduce plantation yields by as much one-third over a six-to-seven-year rotation. For land owners, annual variation in production cannot be estimated with empirical models. In this paper, we examine whether the process-based growth model, 3-PG is sufficiently sensitive to climatic variation to provide a virtual record of changes in growing stock across 180,000ha eucalypt plantation estate. We first mapped variation in climate and soil properties, and then ran simulations for the current planted forest with ages varying from one to seven years. Model predictions of stand volume and mean tree diameter agreed closely with measurements acquired on 60 reference plots monitored over the test period; the prediction of mean annual increment (MAI) was less reliable. Available soil water (ASW) and leaf area index (LAI) were also measured and compared with the model estimations. Vapour pressure deficit (VPD) and ASW accounted for most of the variation in yields. We conclude that this spatial modelling approach offers a reasonable alternative to extensive ground surveys, particularly when climatic variation extends beyond the historical average for a region. [Copyright &y& Elsevier]

A habitat suitability model for roe deer (Capreolus capreolus) including indices of forest predisposition to browsing damage was tested in an alpine forest region dominated by spruce (Picea abies). A description of the habitat model is presented. Eleven input parameters were weighted and aggregated to the habitat indices hiding cover, thermal cover, lounge index and food supply. These indices are basis for the overall habitat quality and predisposition to browsing damage scores. The sensitivity of the model to the input parameters total ground vegetation, ground vegetation without grass, woody ground vegetation, share of forest regeneration, total crown projection, crown projection of deciduous trees, wildlife ecological stand type, edge density, mesorelief, altitude, and aspect was tested. The model was most sensitive to the parameters (i) abundance of total ground vegetation without grass, (ii) abundance of woody ground vegetation, (iii) wildlife ecological stand type, and (iv) mesorelief. Total abundance of ground vegetation and aspect affected sensitivity least. A trade-off index for habitat quality versus predisposition to browsing damage showed most sensitivity to food supply and least to edge effect. The habitat model is linked with a forest growth model and therefore allows long-term predictions of habitat and browsing predisposition changes in dependence on different silvicultural treatments. Consequences in terms of wildlife and forest management are discussed. [Copyright &y& Elsevier]

Abstract: Approaches combining satellite-based remote sensing data with ecosystem modelling offer potential for the accurate assessment of changes in forest carbon balances, for example, in support of emission credits under the Kyoto Protocol. We investigate the feasibility of two alternative methods of using satellite-derived data to constrain the behaviour of a dynamic ecosystem model, in order to improve the model''s predictions of the net primary production (NPP) of conifer forests in northern Europe (4–30°E, 55–70°N). The ecosystem model incorporates a detailed description of forest stand structure and biogeochemical processes. The satellite product comprises multi-spectral reflectance data from the VEGETATION sensor. The first method combines satellite-based estimates of FPAR, the fraction of incoming photosynthetically active radiation absorbed by vegetation, with the model''s predictions of the efficiency with which trees use the incoming radiation to fix carbon. Results obtained using this method averaged 0.22kgCm−2 yr−1 for the NPP of conifer and mixed forests across the study area, and compared well with forest-inventory-based estimates for Sweden. The second method uses forest stand descriptions derived by application of an inverse radiation transfer scheme to VEGETATION data to prescribe stand structure in the ecosystem model simulations. Predictions obtained by this method averaged 0.31kgCm−2 yr−1, somewhat high compared to forest inventory data for central and northern Sweden. Simulations by the ecosystem model when driven only by climate, CO2 and soils data, but unconstrained by satellite information, yielded an average NPP of 0.41kgCm−2 yr−1, which is likely to be an overestimate. Summed over the study area, the NPP estimates amounted to 0.16–0.23GtCyr−1, around 6–9% of the NPP of all boreal forest globally or 0.3–0.4% of terrestrial NPP globally. The investigated methods of combining process modelling and products derived from remote sensing data offer promise as a step towards the development of operational tools for monitoring forest carbon balances at large scales. [Copyright &y& Elsevier]

Abstract: Over much of south-western Australia there was a rapid decrease in rainfall of around 20% in the mid-1970s. Further declines in rainfall are predicted along with increases in temperature and atmospheric CO2 concentration. This study focused on the implications of such a change for wood production and net ecosystem carbon exchange of pine plantations in that region. The process-based model CenW was evaluated using measurements of basal area and soil moisture in Pinus radiata plantations at seven sites in south-western Australia, encompassing a range of climatic conditions and silvicultural treatments (fertiliser application and thinning). The model predicted basal area very well and soil moisture reasonably well, except for one site where a water table may have provided additional water supply to trees. The model was then applied to assess the effects of the climatic changes that have occurred since 1975 by running the model for whole 30-year rotations, using observed climate data from either the 1945–1975 or 1975–2005 periods. The simulations showed that out of the six sites considered, wood production and net ecosystem carbon exchange were increased in the latter period at three sites, remained almost unchanged at two sites, and decreased at one site. Analysis of the model outputs, aided by additional simulations revealed that the response to the climate shift was determined by soil type (i.e. decreased growth rate was confined to the site with a sandy soil), and by the interaction between rising CO2 levels (positive effect) and changes in rainfall (positive or negative depending on the direction of the change in rainfall). [Copyright &y& Elsevier]

A process-based ecosystem model was applied to analyse the effects of climate change and management on timber yield over a forest management unit in Finland (63° N). The total area of the unit was ca. 1450ha, with 1018 separate tree stands representing the boreal conditions. A representative sample of 42 stands was selected for simulations of Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and silver birch (Betula pendula) dominated stands of various ages and sites. The results of the simulations under the current climate and two climate change scenarios together with six management regimes (five thinning regimes and one unthinned) were extrapolated over the whole unit. Regardless of the climate, the greatest increase in timber yield and percentage of saw logs was found when a thinning regime with high stocking over a hundred-year rotation was used. The gradual increase in temperature and precipitation with a concurrent elevation in CO2 over the simulation period enhanced the growth by an average of 22–26% depending on the climate scenario, resulting in an increase of 12–13% in timber yield. The economic assessment based on net present value (NPV) agreed with these results and also showed the need to adapt management in the future to utilise the increasing growth under the climate change. [Copyright &y& Elsevier]