Forest Growth Research Articles

Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach

Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry.

Recent changes in forest structure and growth at the alpine‐treeline ecotone in the Rocky and Columbia Mountains of central British Columbia, Canada

AbstractAimGlobally, forests at the alpine‐treeline ecotone (ATE) are considered sensitive to warming temperatures; however, responses to recent climate change show high variability and many underlying processes remain unclear. This study aims to provide further insight into possible ATE forest responses to climate change by examining spatiotemporal patterns in recent tree regeneration and growth responses to climate across treeline forms.LocationThis study is situated at the ATE in the Rocky Mountain and Columbia Mountain ranges in central British Columbia, Canada.TaxonGymnosperms – subalpine fir (Abies lasiocarpa Hooker (Nutall)).MethodsWe collected tree and stand data from 48 plots across five study sites. Plots were distributed across three treeline stand types: (i) islands; (ii) abrupt; and (iii) fringes of regeneration adjacent to tree islands. We used a dendrochronological approach to analyse the ages of recently established trees in fringe stand types, detect long‐term trends in annual tree growth and quantify climate‐growth relationships.ResultsSeedling recruitment adjacent to tree islands occurred over a period of approximately 40 years (1960–2000), with two regeneration “pulses” in the late 1970s and 1980s. Abrupt and fringe trees showed a similar age structure and annual radial growth has increased in most trees over the past 30 years. Across the study region and stand types, summer temperature has the strongest influence on radial growth. Over the past 70 years, growth in tree islands has become increasingly correlated with growing season temperature variables.Main ConclusionsForest growth and structure have changed in coherent spatial and temporal patterns over recent decades at the ATE in central BC. Projections for sustained warming in this region will likely result in increased tree growth and potential continued expansion of forests into untreed areas below the treeline. These changes will have implications for hydrological regimes, wildlife habitat and carbon sequestration.

A climate-spatial matrix growth model for major tree species in Lesser Khingan Mountains and responses of forest dynamics change to different representative concentration path scenarios

IntroductionClimate change affects forest distribution, species composition, structure, and yield due to the sensitivity of forests to temperature, precipitation, and CO2. Therefore, for forest management decisions regarding climate change, it is crucial to explore the response of forest growth, mortality, and recruitment to future climate. We aimed to establish tree species’ responses by introducing variables such as climate, stand spatial structure parameters, and diversity indices.MethodsWe produced fixed parameter transition matrix model (FM), climate-sensitive matrix growth model (CM) and climate-spatial matrix growth model (SCM) using data from 786 plots collected during the 7th (2010), and 8th (2015), Chinese National Forest Inventories in Heilongjiang Province, and long-term predictive performance of CM, SCM, and FM were compared using same data. The models were compared using tenfold cross-validation and long-term predictive performance analysis. To predict the response of major tree species in the Lesser Khingan Mountains to three future climate change scenarios (RCP2.6, RCP4.5, RCP8.5).Results and discussionThe cross-validation results show small significant differences among the three models for short-term prediction (5 years), with the FM performing slightly better than the CM and the SCM. In contrast, for long-term projections (85 years), SCM outperformed FM and CM under three different RCPs, and SCM and CM under three representative concentration paths (RCPs), i.e., RCP2.6, RCP4.5, and RCP8.5, suggesting that rather different dynamics are more reliable, since climatic factors are taken into account which may significantly affect forest dynamics, while changes in stand spatial structure also affect the sensitivity of trees to climate, especially in long-term prediction interval, the results of this paper may provide a theoretical basis for optimizing forest management strategies under climate change.

What happens to trees and soils during five decades of experimental nitrogen loading?

High deposition of nitrogen was postulated to drive losses of NO3- and nutrient base cations, causing soil acidification, nutrient deficiencies reducing tree growth and ultimately tree mortality. We tested these predictions in a uniquely long-term study involving three NH4NO3 addition treatments (N1-N3) in a boreal Pinus sylvestris forest. The lowest level (N1), 30 kg N ha−1 yr−1 was applied during 50 years. Twice this rate (N2) was added 38 years, followed by 12 years of recovery, while thrice this rate (N3) was added 20 years followed by 30 years of recovery. We compared tree growth, changes in foliar and soil chemistry among treatments including control plots without N additions. As predicted, the N treatments lowered soil pH and reduced soil base saturation by around 50 %. They also lowered foliar levels of Ca, Mg, K, P and B initially, but after 50 years only Ca and Mg remained lower than in the control. Lack of B motivated a single addition of 2.5 kg ha−1 after ten years of N treatment. Tree stem growth became and then remained higher in N1 than in the other treatments through the 50 years of treatments. In N2 and N3, foliar δ15N increased during the N-loading phase, but declined during the recovery phase, indicating a return of ectomycorrhizal fungi and their role in tightening the N cycle in N-limited forests. In the terminated, initially highest N treatments, N2 and N3, the trees even show signs of returning to N-limitation. In these treatments, the soil base saturation remains lower, while the pH was only lower at 0–10 depth in the mineral soil, but not in the 10–20 cm depth horizon or in the superficial organic mor-layer. Accurately documenting the effect of N additions on forest growth required a long-term approach, where reasonable rates of application could be compared with extreme rates. Such long-term experiments are necessary to support forest management in achieving goals for developing future forests as they shift in response to major, global-scale changes.

Evaluation of the management potential of timber resources in clearwater floodplain forests in the Amazon using growth models

The Amazonian clearwater igapós are poorly studied floodplain ecosystems that are mainly covered by forests and are undergoing massive threats due to changes in land use and climate. Their hydrochemical characteristics and edaphic conditions fall between those of the eutrophic várzea floodplains on whitewater rivers and those of the oligotrophic igapós on blackwater rivers. Previous studies have indicated the management potential of timber species in the highly dynamic várzea floodplains due to the fast tree growth and high forest productivity. Timber resource management, however, is not recommended for the blackwater ecosystem because of its slow dynamics and high vulnerability to disturbances. For clearwater igapós, information on the potential for sustainable management of timber resources is lacking. In this study, we modeled the growth in diameter, height, and volume to derive species-specific minimum logging diameters (MLD) and felling cycles (FC) for eight merchantable species in the clearwater igapós of the Branco and Tapajós rivers in the northern and southern Amazon Basin, respectively. Diameter growth was modeled by analyzing the tree rings that are annually formed in the Amazonian floodplains as a consequence of the regular and predicable long-term flooding. Growth modeling followed the guidelines of the Growth-Oriented Logging (GOL) concept, with the adjustment of diameter growth improved by applying nonlinear mixed-effects regression. MLDs varied from 36 to 90 cm and FCs ranged from 6 to 21 years, which diverges from the standards of Brazilian logging regulations (MLD: 50 cm; FC: 25–35 years). This indicates the potential for timber resource management, which should be tested and introduced at small scales, integrated in protected areas to stepwise promote the sustainable management of these natural resources by traditional communities to increase their income and the conservation of this ecosystem.

Soil phosphorus cycling across a 100-year deforestation chronosequence in the Amazon rainforest.

Deforestation of tropical rainforests is a major land use change that alters terrestrial biogeochemical cycling at local to global scales. Deforestation and subsequent reforestation are likely to impact soil phosphorus (P) cycling, which in P-limited ecosystems such as the Amazon basin has implications for long-term productivity. We used a 100-year replicated observational chronosequence of primary forest conversion to pasture, as well as a 13-year-old secondary forest, to test land use change and duration effects on soil P dynamics in the Amazon basin. By combining sequential extraction and P K-edge X-ray absorption near edge structure (XANES) spectroscopy with soil phosphatase activity assays, we assessed pools and process rates of P cycling in surface soils (0-10 cm depth). Deforestation caused increases in total P (135-398 mg kg-1 ), total organic P (Po ) (19-168 mg kg-1 ), and total inorganic P (Pi ) (30-113 mg kg-1 ) fractions in surface soils with pasture age, with concomitant increases in Pi fractions corroborated by sequential fractionation and XANES spectroscopy. Soil non-labile Po (10-148 mg kg-1 ) increased disproportionately compared to labile Po (from 4-5 to 7-13 mg kg-1 ). Soil phosphomonoesterase and phosphodiesterase binding affinity (Km ) decreased while the specificity constant (Ka ) increased by 83%-159% in 39-100y pastures. Soil P pools and process rates reverted to magnitudes similar to primary forests within 13 years of pasture abandonment. However, the relatively short but representative pre-abandonment pasture duration of our secondary forest may not have entailed significant deforestation effects on soil P cycling, highlighting the need to consider both pasture duration and reforestation age in evaluations of Amazon land use legacies. Although the space-for-time substitution design can entail variation in the initial soil P pools due to atmospheric P deposition, soil properties, and/or primary forest growth, the trend of P pools and process rates with pasture age still provides valuable insights.

Impact of dynamic density decay of growing carbon nanotube forests on electrical resistivity

Vertically aligned carbon nanotube (CNT) forests densely formed on diverse metallic substrates are desirable as large-area electrodes and thermal interface materials. In this study, we achieved high-density CNT forests on stainless steel and Cu substrates by preventing the interaction between the metallic elements and Fe catalysts at high temperatures. We found that coating the substrate surface with a combination of Ta and SiO2 effectively suppressed the diffusion of metallic elements of the substrate to the catalytic layer. The macroscopic electrical resistivity of longer forests was found to increase as the number density of CNTs decreased during the growth process. This increase in resistivity was reproduced by the dynamic density decay model in which CNT density decreases with forest growth. We point out the importance of the relationship between the length and density of CNT forests for applications.

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

Climate-Sensitive Diameter Growth Models for White Spruce and White Pine Plantations

Global change in the climate is affecting tree/forest growth. There have been many studies that analyzed climate effects on tree growth. Results presented in these studies showed that the climate had both positive and negative effects on tree growth. The nature (positive/negative) and magnitude of the effects and the climate variables affecting growth depended on tree species. Climate-sensitive diameter growth models are not available for white pine (Pinus strobus L.) and white spruce (Picea glauca (Moench) Voss) plantations. These models are needed to project forest growth and yield and develop forest management plans. Therefore, diameter growth models were developed for white pine and white spruce plantations by incorporating climate variables. Four hundred white pine and white spruce trees (200 per species) were sampled from 80 (40 per species) even-aged monospecific plantations (five trees per plantation) across Ontario, Canada. Diameter–age pairs were obtained from these trees using stem analysis. A nonlinear mixed-effects modeling approach was used to develop diameter growth models. To make the models climate sensitive, model parameters were expressed in term of climate variables. Inclusion of climate variables significantly improved model fit statistics and predictive accuracy. For evaluation, diameters (inside bark) at breast height were estimated for three geographic locations (east, west, and south) across Ontario for an 80-year growth period (2021–2100) under three climate change (emissions) scenarios (representative concentration pathway or RCP 2.6, 4.5, and 8.5 watts m−2). For both species, the overall climate effects were negative. For white spruce, the maximum pronounced difference in projected diameters after the 80-year growth period was in the west. At this location, compared to the no climate change scenario, projected spruce diameters under RCPs 2.6 and 8.5 were thinner by 4.64 (15.99%) and 3.72 (12.80%) cm, respectively. For white pine, the maximum difference was in the south. Compared to the no climate change scenario, projected pine diameters at age 80 under RCPs 2.6 and 8.5 at this location were narrower by 4.54 (13.99%) and 7.60 (23.43%) cm, respectively. For both species, climate effects on diameter growth were less evident at other locations. If the values of climate variables are unavailable, models fitted without climate variables can be used to estimate these diameters for both species.

Spatiotemporal Evaluation of Regional Land Use Dynamics and Its Potential Ecosystem Impact under Carbon Neutral Pathways in the Guangdong–Hong Kong–Macao Greater Bay Area

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA.

Carbon Sequestration Through Forestry: A Differential Equation Approach

Carbon sequestration through forestry represents a promising approach to partially counteract anthropogenic greenhouse gas emissions driving climate change. Tree growth naturally removes carbon dioxide from the atmosphere, storing it as biomass. Sustainably managed forests can effectively function as carbon sinks. However, determining optimal forestry policies involves balancing complex ecological dynamics with economic constraints. This study develops differential equation models quantitatively capturing forest growth, timber harvesting, and carbon sequestration dynamics. Logistic models are first adapted to simulate biomass accumulation of representative tree species. Lifecycle growth patterns spanning juvenile to mature phases are incorporated, along with climate effects. Biomass levels are proportionally related to carbon dioxide removal rates from the atmosphere. Deforestation impacts are analyzed by incorporating harvesting-induced biomass reductions. Sustainability constraints are implemented to ensure minimum viable tree densities across harvest rotations. Optimization techniques then identify management guidelines maximizing economic returns given ecological stability considerations. The goal is providing quantitative insights into rotation lengths, planting densities, and allowable cuts upholding both climate change mitigation and commercial demands. Findings can inform science-based forestry policies to leverage forests as sustainable natural carbon sinks.

Evaluating the effectiveness of different variable retention harvesting treatments on forest carbon uptake using remote sensing

Variable retention harvesting (VRH) is an ecologically based forest management practice applied to emulate natural post-disturbance residual canopy structure and increase the biodiversity and resilience of a regenerating stand. The pattern and density of canopy retention also influences the productivity, mortality, and carbon (C) sequestration rates of residual trees. In this study, we used high-resolution satellite and drone remote sensing data to evaluate the effect of five different VRH treatments on growth and C uptake of a 90-year-old red pine (Pinus resinosa Ait.) plantation forest in southern Ontario, Canada. The treatments included four different combinations of harvesting intensity residual tree distribution including 33% aggregate retention (33A), 55% aggregate retention (55A), 33% dispersed retention (33D), and 55% dispersed retention (55D) and unharvested control (CN) each replicated four times in 1 ha plots. Satellite- and drone-derived normal difference vegetation index (NDVI) and gross primary productivity (GPP) were estimated for each treatment during the growing seasons from 2010 to 2020. Over this period, observed mean daily NDVI values ranged from 0.25 to 0.86 among treatments, where the 55D treatment consistently showed the highest NDVI values. Overall, the highest mean daily GPP values were observed in the CN treatment, followed by the 55D, 55A, 33D, and 33A treatments. Remote sensing-derived mean annual GPP for the entire 20 ha study site was 1651 ± 89 g C m−2 year−1, with a range of 1407–1864 g C m−2 year−1 from 2010 to 2020. Satellite-derived annual GPP values were linearly related with observed annual GPP (R2 = 0.88, p = 0.032) measured using the eddy covariance technique in an adjacent white pine plantation of similar age and height over the study period. Study results suggested that VRH treatments that create a uniformly dispersed residual canopy retaining more than half of the initial basal area (e.g. 55D) is a viable forest management practice where optimizing forest growth and C uptake is a primary management objective. Our study will help forest managers and researchers to develop methodologies to evaluate the effectiveness of forest management practices, tailor them to achieve climate mitigation and adaptation goals and to develop forest management pathways for nature-based climate solutions.

Increasing ENSO variability synchronizes tree growth in subtropical forests

Rising El Niño–Southern Oscillation (ENSO) variability is expected to influence Earth's forest ecosystems, through changes in how coordinated annual tree growth is across large spatiotemporal scales. However, the mechanisms by which changes in ENSO variability affect tree growth in subtropical forests remains poorly understood. We use a newly built tree ring network collected from 4,028 trees at 144 forest locations across East Asian subtropical forests (EASF), to assess long-term influences of ENSO on the spatiotemporal variability in tree radial growth across China at subcontinental scales (∼2,000 km). Our results demonstrate a west-east dipole pattern of synchronized tree growth in EASF tree populations, with positive growth responses to El Nino in southeastern China, and negative growth responses in southwestern China, likely due to different patterns in moisture limitation. Specifically, the likely contrasting effects of ENSO on drought limitation along a longitudinal gradient resulted in trees that grew more in El Niño years in eastern populations, but less in western populations. Our results also show that increasingly severe El Niño/La Niña years have caused a sharp rise in tree growth coherence over the past 150 years in these moisture-limited populations. Increasing ENSO variability, as is expected with climate change, may potentially further destabilize subtropical forest ecosystems by synchronizing tree growth to an unprecedented level, with unknown consequences for forest stability and resilience.

Influence of crown and canopy structure on light absorption, light use efficiency, and growth in mixed and pure Pseudotsuga menziesii and Fagus sylvatica forests

Mixed-species forests can provide higher levels of ecosystem functions and services and can be more resistant and resilient in the face of global change. While many studies focus on the growth and yield of mixed forests, fewer have examined the underlying processes. Inter- or intra-specific differences and interactions influence tree- and stand-level light absorption by determining the vertical structure of stratified canopies, stand density, leaf area index, and the size or allometry of trees. While canopy light absorption is a very important process, it is difficult to quantify it for individual species within a mixture and is rarely examined. A detailed tree-level model (MAESTRA) was used in combination with measurements of tree sizes and stand structures to examine effects of mixing on absorbed photosynthetically active radiation (APAR) in 41–63-year-old stands of Pseudotsuga menziesii and Fagus sylvatica at three sites in Bavaria, Germany. The effects of initial stand density on APAR were analysed in 46-year-old P. menziesii stands of a spacing experiment at two sites. At the tree level, mixing increased mean height and leaf area, growth (185% higher) and APAR (85% higher) of P. menziesii at all sites. Mean tree heights and crown sizes of F. sylvatica were larger in mixtures, while recent growth rates and APAR were not significantly different to monocultures. Planting density did not influence mean tree variables (e.g. height, leaf area, crown volume), because any initial spacing effects had been gradually removed by thinning across all treatments. At the stand level, there were no differences in growth, basal area or in the annual growth per annual APAR (light use efficiency, LUE) between monocultures and mixtures. The highest APAR values were observed in P. menziesii monocultures, while the lowest APAR values were observed in F. sylvatica monocultures. While mixing these species may not increase stand-level growth during later phases of development, mixing accelerated initial growth of individual trees and reduced the time to reach target diameters, which are both important aspects in adapting forests to global change.

Examining the transferability of height–diameter model calibration strategies across studies

Abstract Height–diameter (H–D) models are fundamental tools for predicting the relationship between tree H–D at breast height, for numerous applications in forestry. Increasingly, studies develop H–D models that can be calibrated to achieve a high level of precision with only a few observations. Different calibration methods and strategies are employed and compared in these studies, often disregarding the data used to develop the models and the H–D function used. In this study, we examined the transferability of optimal calibration strategies across studies, conducting a literature review and an empirical study. We compared the performance of six H–D functions and different calibration methods when using the same calibration strategies and dataset. Based on our literature review, we found that the most commonly employed calibration strategy is random-effects calibration and that the most common variable used to develop generalized H–D models is dominant height. We observed that different calibration methods can lead to varying results due to their different emphases on various aspects of the data and their individual limitations. Moreover, when the same dataset is used for calibration, different H–D functions may exhibit various performances. However, we found high percentages of agreement for the Curtis, Schumacher, and Wykoff H–D functions across all three calibration methods and low agreement between all functions and the Power H–D function. These observations underscore the need to consider all relevant factors, including the H–D function used, when selecting an H–D function and calibration strategy to ensure optimal transferability of the model. Our study provides insights that can improve the accuracy of H–D models, which are essential for predicting forest growth and structure in the context of changing environmental conditions.

Variation in the physical properties of soil in relation to natural and anthropogenic factors in the hilly loess region of China

Soil particle size distribution (PSD), bulk density (BD), saturated hydraulic conductivity (Ks), soil water content (SWC) and generalized soil structure index (GSSI) are key physical properties for vegetation growth and are easily affected by natural and anthropogenic factors. However, how these soil physical properties respond to land use, slope aspect, slope position, and their interactions are less clearly understood. Here, an experiment with three land uses (cropland, grassland, and black locust [Robinia pseudoacacia L.] forests after 4, 9, 15, and 19 years of restoration), two slope aspects (south and north slope) and two slope positions (up and down slope) were conducted in the hilly loess region of China. The results showed the following: (1) The variation in different physical properties were driven by different factors of land use, slope aspect, slope position and their interactions. (2) The 9, 15 and 19 year old locust forests significantly decreased the BD by 6.75 %–19.32 % and increased the Ks by 69.96 %–195.97 % compared with grassland, while the cropland had higher SWC and GSSI values than the grassland and locust forests. (3) Soil physical properties were higher on the north slope than on the south slope and were larger on the down slope than on the up slope. (4) The BD, Ks, SWC, and GSSI of locust forests displayed different changes with the restoration year. (5) The key factor affecting forest growth and soil quality improvement varied with forest ages. Overall, 19 year old locust was the most effective at improving the BD and Ks. Furthermore, slope aspect, slope position and restoration time should be considered when evaluating the response of physical properties to land uses.

Resource objective wildfire leveraged to restore old growth forest structure while stabilizing carbon stocks in the southwestern United States

Wildfire futures and aboveground carbon (C) dynamics associated with forest restoration programs that integrate resource objective wildfire as part of a larger treatment strategy are not well understood. Using simulation modeling, we examined alternative forest and fuel management strategies on a 237,218-ha study area within a 778,000-ha landscape that is a high priority target for federal restoration programs. We simulated two wildfire management scenarios combined with three levels of conventional forest restoration treatments over 64 years using a detailed landscape disturbance and succession model developed in prior work. We found accelerated forest restoration used in concert with resource objective wildfire was the most effective at returning old growth forest structure, while stabilizing aboveground C stocks and restoring the fire return interval to its historic range of variation. In scenarios without forest restoration, the continued practice of resource objective wildfires during shoulder fire seasons reduced summer emissions in a negative feedback loop. In the short term, scenarios without forest restoration increased live tree C, but also increased the likelihood of C loss during wildfire activity driven by extreme fire weather. We found scenarios most effective at restoring fire-excluded pine forests to their historical old growth conditions came at a short-term cost of lost C, but with the long-term benefit of substantially increasing fire-resistant live tree C. Our results inform how local decision making can best balance competing goals of sequestering C, and stabilizing C stocks in frequent-fire pine forests using the principles of local fire ecology to restore and maintain old growth forest structure.

Climate change altered the dynamics of stand dominant height in forests during the past century – Analysis of 20 European tree species

Analyzing how climate change has affected forest growth is crucial for predicting future dynamics and adapting forest management to future climate change. In this paper, we investigate how climate change has modified stand dominant height dynamics and site index of 20 European tree species. We used an innovative method based on an annual height increment equation to model stand dominant height as a function of climate back to 1872 and of other stand environmental conditions. We used these models to simulate stand dominant height dynamics and site index under two different climates (prior to climate change and actual recent climate) to analyze the impact of climate change over the past century. To build our models, we combined the recently published FYRE long-term climate database, which provides daily data since 1871, with data from more than 17,000 forest stands of the French National Forest Inventory network. Higher temperature, precipitation and climatic water balance generally favor stand dominant height dynamics when the variables are considered separately. However, the positive effects often saturate at the higher end of the variable distribution. Over the past century, the effect of climate change on the site index has varied widely among species, ranging from a decrease of less than 3% to an increase of more than 5%. The effect of climate change has also varied within species, with more positive effects on initially temperature-limited stands for some species. For the species and environmental conditions considered, our results highlight a positive response of site index to past climate change for most species, albeit with between- and within-species differences. Our results also suggest that this positive response could become negative under continued climate change. These conclusions, as well as the quantitative relationships we provide between climate and stand dominant height dynamics or site index, will help design management strategies to adapt forests to climate change.

A new index integrating forestry and ecology models for quantitatively characterizing forest carbon sequestration potential ability in a subtropical region

Due to the important role of forests in carbon neutrality, it is a big task in accurately calculating and predicting forest carbon storage and carbon sink capacity in recent years. However, considering the factors on the capacity of forest carbon sequestration, ecologists and foresters consider different models to evaluate the forest carbon sink ability at different scales, with foresters focusing more on forest growth models, while ecologists adding more climate and environmental factors, which may result in inconsistent results in carbon storage. Therefore, constructing an integrated model by combining the forestry and ecology models is essential for accurately quantifying and characterizing the forest carbon sink potential at the regional scale. Here, we proposed a new forest carbon sink potential index (FCSPI), which is defined as fractional deficiency of the current forest carbon to its maximum level, based on forest permanent plots, climate, and edaphic data to evaluate the forest sink potential ability from stand level to zonal scale across the northern subtropical zone in Hubei province, China, which coupled the stand growth model and climate-productivity model. The results at stand level showed that the R2 and RMSE of FCSPI were 0.78 and 0.072 respectively, which indicated that the FCSPI is an intuitive, highly practical, straightforward, easy, and rapid to implement methodology for forest carbon sequestration assessment. Moreover, FCSPI can conveniently extended from stand scale to zonal scale based on the site quality index for carbon sink (SIC) and stand age variables, which were derived from the opened climate, edaphic, and topographic data. The results of wall-to-wall FCSPI across the subtropical forest in Hubei province reveal the current and future carbon potential sequestration, which can help managers to focus on forest management for climate-smart actions and planning in forest ecosystem services framework.

Determinants of successful mitigation in coupled social-climate dynamics

Understanding the impact of human behaviour is crucial for successful mitigation of climate change across the globe. To shed light onto this issue, here we couple the forest dieback model with human behaviours. Using evolutionary game theory, we build a time-delay system where forest growth is impacted by both temperature and human mitigation choices, the latter being informed by temperature forecasts. Simulations of the coupled system over 200 years show us the varying outcomes: forest dies out and no one is a mitigator, forest dies out and everyone is a mitigator, or the forest survives and everyone is a mitigator. There exist rare cases where no one is a mitigator and yet the forest survives, but with a low coverage. We also find occasional oscillations where the proportion of mitigators vary between 0 and 1. Our results are based on simple models but have profound insights into determinants of behaviour changes desired in social-climate dynamics.