Climate factors affect forest biomass allocation by altering soil nutrient availability and leaf traits

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a significant challenge. This study employed machine learning (ML) techniques to model heavy metal pollution in soils within this critical ecosystem. A total of 91 standardized soil samples were analyzed to predict the accumulation of eight heavy metals using four distinct ML algorithms. Among them, random forest model outer performed in predicting As (0.79), Cd (0.89), Cr (0.63), Ni (0.56), Cu (0.60), and Zn (0.52), achieving notable R squared values. The feature attribute analysis identified As-K, Pb-K, Cd-S, Zn-Fe2O3, Cr- Fe2O3, Ni-Al2O3, Cu-P, and Mn- Fe2O3 relationships resembled with correlation coefficients among them. The developed models revealed that the contamination factor for metals in soils indicated extremely high levels of Pb contamination (CF ≥ 6). In conclusion, this research offers a robust framework for predicting heavy metal pollution in coal mining soils, highlighting critical areas that require immediate conservation efforts. These findings emphasize the necessity for targeted environmental management and mitigation to reduce heavy metal pollution in mining sites.

The pivotal role of arbuscular mycorrhizal fungi in enhancing plant biomass and nutrient availability under drought stress conditions: A global meta-analysis

Drought is a serious threat to crop productivity and global food security. About 40 % of the worldwide land is considered arid dryland soil because of a lack of rainfall, high solar radiation, and temperature fluctuations. Though rhizobacteria, particularly mycorrhizal fungi (MF), assist plants in coping with drought stress, an intensive quantitative assessment of their effects on plant growth and nutrient availability is still limited. We systematically carried out a global meta-analysis using 122 peer-reviewed publications comprising 3534 observations to investigate the effects of MF on plant biomass (PB) and nutrient availability (nitrogen: N and phosphorus: P) under drought-stress conditions. The results show that the MF inoculation significantly increased mycorrhizal colonization (MC), N and P uptakes, and plant biomass (PB) at a C:N ratio > 15 by 2171.44 %, 23.74 %, 135.61 %, and 220.91 %, respectively. The MF species Claroideoglomus etunicatum and Glomus significantly influenced the MC, N, and PB concentrations by 2541.68 %, 40.35 %, and 110.85 %, respectively. Moreover, the concentrations of MC, N, and PB were considerably affected by the soil texture categories, with the maximum levels of 4940.04 %, 127.05 %, and 84.04 % found in sandy, clay, and clay loam soils, respectively. In addition, soil pH, crop types, soil textural class, and MF species were identified as vital pedologic factors affecting plant growth and nutrient availability during fungal inoculation. Overall, this meta-analysis addresses gaps in understanding the effects of MF inoculation on plant development and nutrient availability under drought stress.

Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities

Leaf functional traits (LFTs) have become a popular topic in ecological research in recent years. Here, we measured eight LFTs, namely leaf area (LA), specific leaf area (SLA), leaf thickness (LT), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC), in six typical vegetation communities (sclerophyllous evergreen broad-leaved forests, temperate evergreen coniferous forests, cold-temperate evergreen coniferous forests, alpine deciduous broad-leaved shrubs, alpine meadows, and alpine scree sparse vegetation) in the Chayu River Basin, southeastern Qinghai-Tibet Plateau. Our aim was to explore their relationships with evolutionary history and environmental factors by combining the RLQ and the fourth-corner method, and the method of testing phylogenetic signal. The results showed that (i) there were significant differences in the eight LFTs among the six vegetation communities; (ii) the K values of the eight LFTs were less than 1; and (iii) except for LCC, all other LFTs were more sensitive to environmental changes. Among these traits, LA was the most affected by the environmental factors, followed by LNC. It showed that the LFTs in the study were minimally influenced by phylogenetic development but significantly by environmental changes. This study further verified the ecological adaptability of plants to changes in environmental factors and provides a scientific basis for predicting the distribution and diffusion direction of plants under global change conditions.

Leaf nutrient traits of planted forests demonstrate a heightened sensitivity to environmental changes compared to natural forests

Leaf nutrient content (nitrogen, phosphorus) and their stoichiometric ratio (N/P) as key functional traits can reflect plant survival strategies and predict ecosystem productivity responses to environmental changes. Previous research on leaf nutrient traits has primarily focused on the species level with limited spatial scale, making it challenging to quantify the variability and influencing factors of forest leaf nutrient traits on a macro scale. This study, based on field surveys and literature collected from 2005 to 2020 on 384 planted forests and 541 natural forests in China, investigates the differences in leaf nutrient traits between forest types (planted forests, natural forests) and their driving factors. Results show that leaf nutrient traits (leaf nitrogen content (LN), leaf phosphorus content (LP), and leaf N/P ratio) of planted forests are significantly higher than those of natural forests (P< 0.05). The impact of climatic and soil factors on the variability of leaf nutrient traits in planted forests is greater than that in natural forests. With increasing forest age, natural forests significantly increase in leaf nitrogen and phosphorus content, with a significant decrease in N/P ratio (P< 0.05). Climatic factors are key environmental factors dominating the spatial variability of leaf nutrient traits. They not only directly affect leaf nutrient traits of planted and natural forest communities but also indirectly through regulation of soil nutrients and stand factors, with their direct effects being more significant than their indirect effects.

Forest Age Drives the Resource Utilization Indicators of Trees in Planted and Natural Forests in China

Specific leaf area (SLA) and leaf dry matter content (LDMC) are key leaf functional traits commonly used to reflect tree resource utilization strategies and predict forest ecosystem responses to environmental changes. Previous research on tree resource utilization strategies (SLA and LDMC) primarily focused on the species level within limited spatial scales, making it crucial to quantify the spatial variability and driving factors of these strategies. Whether there are discrepancies in resource utilization strategies between trees in planted and natural forests, and the dominant factors and mechanisms influencing them, remain unclear. This study, based on field surveys and the literature from 2008 to 2020 covering 263 planted and 434 natural forests in China, using generalized additive models (GAMs) and structural equation models (SEMs), analyzes the spatial differences and dominant factors in tree resource utilization strategies between planted and natural forests. The results show that the SLA of planted forests is significantly higher than that of natural forests (p < 0.01), and LDMC is significantly lower (p < 0.0001), indicating a "faster investment-return" resource utilization strategy. As the mean annual high temperature (MAHT) and mean annual precipitation (MAP) steadily rise, trees have adapted their resource utilization strategies, transitioning from a "conservative" survival tactic to a "rapid investment-return" model. Compared to natural forests, planted forest trees exhibit stronger environmental plasticity and greater variability with forest age in their resource utilization strategies. Overall, forest age is the dominant factor influencing resource utilization strategies in both planted and natural forests, having a far greater direct impact than climatic factors (temperature, precipitation, and sunlight) and soil nutrient factors. Additionally, as forest age increases, both planted and natural forests show an increase in SLA and a decrease in LDMC, indicating a gradual shift towards more efficient resource utilization strategies.

Climate Factors Influence Above- and Belowground Biomass Allocations in Alpine Meadows and Desert Steppes through Alterations in Soil Nutrient Availability

Biomass is a direct reflection of community productivity, and the allocation of aboveground and belowground biomass is a survival strategy formed by the long-term adaptation of plants to environmental changes. However, under global changes, the patterns of aboveground-belowground biomass allocations and their controlling factors in different types of grasslands are still unclear. Based on the biomass data of 182 grasslands, including 17 alpine meadows (AMs) and 21 desert steppes (DSs), this study investigates the spatial distribution of the belowground biomass allocation proportion (BGBP) in different types of grasslands and their main controlling factors. The research results show that the BGBP of AMs is significantly higher than that of DSs (p < 0.05). The BGBP of AMs significantly decreases with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) (p < 0.05), while it significantly increases with increasing soil nitrogen content (N), soil phosphorus content (P), and soil pH (p < 0.05). The BGBP of DSs significantly decreases with increasing MAP (p < 0.05), while it significantly increases with increasing soil phosphorus content (P) and soil pH (p < 0.05). The random forest model indicates that soil pH is the most important factor affecting the BGBP of both AMs and DSs. Climate-related factors were identified as key drivers shaping the spatial distribution patterns of BGBP by exerting an influence on soil nutrient availability. Climate and soil factors exert influences not only on grassland biomass allocation directly, but also indirectly by impacting the availability of soil nutrients.

Precipitation Dominates the Allocation Strategy of Above- and Belowground Biomass in Plants on Macro Scales

The allocation of biomass reflects a plant's resource utilization strategy and is significantly influenced by climatic factors. However, it remains unclear how climate factors affect the aboveground and belowground biomass allocation patterns on macro scales. To address this, a study was conducted using aboveground and belowground biomass data for 486 species across 294 sites in China, investigating the effects of climate change on biomass allocation patterns. The results show that the proportion of belowground biomass in the total biomass (BGBP) or root-to-shoot ratio (R/S) in the northwest region of China is significantly higher than that in the southeast region. Significant differences (p < 0.05) were found in BGBP or R/S among different types of plants (trees, shrubs, and herbs plants), with values for herb plants being significantly higher than shrubs and tree species. On macro scales, precipitation and soil nutrient factors (i.e., soil nitrogen and phosphorus content) are positively correlated with BGBP or R/S, while temperature and functional traits are negatively correlated. Climate factors contribute more to driving plant biomass allocation strategies than soil and functional trait factors. Climate factors determine BGBP by changing other functional traits of plants. However, climate factors influence R/S mainly by affecting the availability of soil nutrients. The results quantify the productivity and carbon sequestration capacity of terrestrial ecosystems and provide important theoretical guidance for the management of forests, shrubs, and herbaceous plants.