Investing in projects that support environmental benefits, such as tree harvesting, has the potential to reduce air pollution levels in the atmosphere in the future. However, this kind of investment may increase the current level of emissions. Therefore, it is necessary to estimate how much the policy affects the current level of CO2 emissions. This makes sure the policy doesn’t increase the level of CO2 emissions. This study aims to analyze the effect of the One Billion Trees program on CO2 emissions in New Zealand by employing the 2020 input–output table analysis. This investigation examines the direct and indirect effects of policy on both the demand and supply sides across six regions of New Zealand. The results of this study for the first year of plantation suggest that the policy increases the level of CO2 emissions in all regions, especially in the Waikato region. The direct and indirect impact of the policy leads to 64 kt of CO2 emissions on the demand side and 270 kt of CO2 emissions on the supply side. These lead to 0.19 and 0.74% of total CO2 emissions being attributed to investment shocks. Continuing the policy is recommended, as it has a low effect on CO2 emissions. However, it is crucial to prioritize the use of low-carbon machinery that uses fossil fuels during the plantation process.

Forest fires are natural disasters that can occur suddenly and can be very damaging, burning thousands of square kilometers. Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model, the geographical weighted logistic regression model, the Lasso regression model, the random forest model, and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province. The models, along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area. Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models. The accuracies of the random forest model, the support vector machine model, geographical weighted logistic regression model, the Lasso regression model, and logistic model were 88.7%, 87.7%, 86.0%, 85.0% and 84.6%, respectively. Weather is the main factor affecting forest fires, while the impacts of topography factors, human and social-economic factors on fire occurrence were similar.

Invasive pests and pathogens cause immense damage globally, costing an estimated US$ 248 billion to the agricultural industry alone. Vehicles, such as farming and timber harvesting machinery and transportation trucks, can facilitate the rapid spread of biological invaders over distances far greater and more quickly than their natural dispersal ability. Understanding how frequent trips by these vehicles increase the spread of invasive agricultural and forestry pests can help inform effective biosecurity procedures before, during, or after an incursion. We used a case study of timber transport trucks in Aotearoa New Zealand to examine whether and how vehicles facilitate the spread of soil-borne pathogens between commercial forest plantations. Our results show that long-distance dispersal associated with truck movement facilitated the introduction of oomycete-like pathogens in 97% of forest sites within only one year, with pathogen loads within infected sites predicted at 84% of the sites’ carrying capacity. Implementing preventative management strategies to reduce the transportation of infected soil by logging trucks, however, can reduce the spread by up to 50% after one year and reduce the pathogen load within infested sites by more than three times. Mitigating other human-assisted dispersal pathways can also help reduce spread. Reducing movement of forest visitors not involved in forestry activities, for instance, by closing forest sites to the public, can help to further reduce spread in addition to management related to harvesting activities. These results highlight the benefits of preventative management strategies in reducing the spread rate of novel soil pathogens through a high-intensity commercial forestry network but show that pest spread is still likely even with significant investment.

Climate change disrupts the distribution of species and restructures their richness patterns. The genus of Asian bamboo, Phyllostachys, possesses significant ecological and economic values, and represents the most species-rich genus in the Bambusoideae subfamily. Based on the distribution data of 46 species and 20 environmental variables, we used the MaxEnt model combined with ArcGIS calculations to simulate current and future potential richness distributions under three distinct CO2 emission scenarios. The results showed that the MaxEnt model had a good predictive ability, with a mean area under the working characteristic curve (AUC value) of 0.91 for all species. The main environmental variables that impacted the future distribution of most Phyllostachys species were elevation, variations of seasonal precipitation, and mean diurnal range. Phyllostachys species are currently concentrated in southeastern China. Under future climate projections, 18 species exhibited significant habitat contraction across three or more future climate scenarios, but suitable habitats for other species will expand. This enhancement is most pronounced under the extreme climate scenario (2090s-SSP585), primarily driven by high species gains contributing to elevated turnover values across scenarios. The center of maximum richness will progressively shift southwestward over time. Predictive modeling of Phyllostachys richness distribution dynamics under climate change enhances our understanding of its biogeography and informs strategic introduction programs to bamboo management and augments China's carbon sequestration capacity.

Soil organic carbon in forest affects nutrient availability, microbial processes, and organic matter inputs. Dominant tree species have increasingly shifted from ectomycorrhizal to arbuscular mycorrhizal associations in subtropical forests. However, the consequences of this shift for soil organic carbon is poorly understood. To address this, a field study was conducted across a natural gradient of arbuscular tree associations to investigate how different mycorrhizal associations affect soil organic carbon quantity, composition, chemical stability, and related soil properties. Soil organic carbon fractions, functional groups, microbial enzyme activities were analyzed. Results showed that increasing arbuscular mycorrhizal dominance was associated with declines in total soil organic carbon, particularly in recalcitrant and aromatic carbon forms. Ectomycorrhizal-dominated forests exhibited higher nitrogen availability and elevated nitrogen-hydrolyzing enzyme activity, suggesting enhanced nitrogen acquisition strategies that suppress soil organic carbon decomposition and promote carbon retention. These findings indicate that mycorrhizal-mediated shifts in tree composition may significantly alter soil carbon sequestration potential. Incorporating mycorrhizal functional traits into forest management and carbon modeling could improve predictions of soil organic carbon responses under future environmental change. Graphical abstract

Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology. This study evaluated the consistency between alternative climate datasets (including station and gridded data) and actual climate data (fixed-point observations near the sampling sites), in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index. The results were: (1) Gridded temperature data, as well as precipitation and relative humidity data from the Huailai meteorological station, was more consistent with the actual climate data; in contrast, gridded soil moisture content data showed significant discrepancies. (2) Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences. (3) Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices. In some growing season months, correlation coefficients, both in magnitude and sign, differed significantly from those based on actual data. The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change, which is detrimental to the management of forest ecosystems in harsh environments. Therefore, the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.

Allometric equations are fundamental tools in ecological research and forestry management, widely used for estimating above-ground biomass and production, serving as the core foundations of dynamic vegetation models. Using global datasets from Tallo (a tree allometry and crown architecture database encompassing thousands of species) and TRY (a plant traits database), we fit Bayesian hierarchical models with three alternative functional forms (power-law, generalized Michaelis–Menten (gMM), and Weibull) to characterize how diameter at breast height (DBH), tree height (H), and crown radius (CR) scale with and without wood density as a species-level predictor. Our analysis revealed that the saturating Weibull function best captured the relationship between tree height and DBH in both functional groups, whereas the CR–DBH relationship was best predicted by a power-law function in angiosperms and by the gMM function in gymnosperms. Although including wood density did not significantly improve predictive performance, it revealed important ecological trade-offs: lighter-wood angiosperms achieve taller mature heights more rapidly, and denser wood promotes wider crown expansion across clades. We also found that accurately estimating DBH required considering both height and crown size, highlighting how these variables together distinguish trees of similar height but differing trunk diameters. Our results emphasize the importance of applying saturating functions for large trees to improve forest biomass estimates and show that wood density, though not always predictive at broad scales, helps illuminate the biomechanical and ecological constraints underlying diverse tree architectures. These findings offer practical pathways for integrating height- and crown-based metrics into existing carbon monitoring programs worldwide.

The northeastern permafrost region of China is one of the most vulnerable areas to climate warming in mid-latitude areas. Despite this, the specific pathways of water vapor circulation and transport i...