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Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

Abstract The forest soil methane (CH 4 ) flux exhibits high spatiotemporal variability. Understanding these variations and their driving factors is crucial for accurately assessing the forest CH 4 budget. In this study, we monitored the diurnal and seasonal variations in soil CH 4 fluxes in two poplar ( Populus spp.) plantations (Sihong and Dongtai) with different soil textures using the static chamber-based method. The results showed that the annual average soil CH 4 flux in the Sihong and Dongtai poplar plantations was 4.27 ± 1.37 kg CH 4 -C ha –1  yr –1 and 1.92 ± 1.07 kg CH 4 -C ha –1  yr –1 , respectively. Both plantations exhibited net CH 4 emissions during the growing season, with only weak CH 4 absorption (–0.01 to –0.007 mg m –2  h –1 ) during the non-growing season. Notably, there was a significant difference in soil CH 4 flux between the clay loam of the Sihong poplar plantation and the sandy loam of the Dongtai poplar plantation. From August to December 2019 and from July to August and November 2020, the soil CH 4 flux in the Sihong poplar plantation was significantly higher than in the Dongtai poplar plantation. Moreover, the soil CH 4 flux significantly increased with rising soil temperature and soil water content. Diurnally, the soil CH 4 flux followed a unimodal variation pattern at different growing stages of poplars, with peaks occurring at noon and in the afternoon. However, the soil CH 4 flux did not exhibit a consistent seasonal pattern across different years, likely due to substantial variations in precipitation and soil water content. Overall, our study emphasizes the need for a comprehensive understanding of the spatiotemporal variations in forest soil CH 4 flux with different soil textures. This understanding is vital for developing reasonable forest management strategies and reducing uncertainties in the global CH 4 budget.

Abstract Environmental and socioeconomic drivers would alter landscapes, bringing various effects with different directions and magnitudes. Demonstrating these driving effects is key to relieving the conflicts between territorial vegetation restoration and regional economic growth. However, the relationship between ecological protection and economic development due to landscape dynamics has not been systematically demonstrated as environment is difficult to quantify by the monetary value. In this article, we explored the changes in gross ecosystem product (GEP) in the Three Gorges (TG) reservoir area and constructed a conceptual framework to explicate its driving mechanism. Our results suggested that topographic, soil, and climatic factors positively impact on GEP through their important effects on vegetation structure, distribution, and succession. Additionally, reforestation policies promote the conversion of farmland and grassland to forestland in the TG reservoir region, which was the main contributor to enhancing GEP. Conversely, socioeconomic factors negatively impact GEP, of which effects were mainly manifested by changes in the proportion of ecological land. Therefore, it is essential to maintain a suitable land use proportion in this region to optimize GEP, and we proposed a landscape restoration program to enhance four ecosystem productions. This article provides a reference for land resource allocation for environmental protection and sustainable development in ecologically fragile areas.

Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM 2.5 ) due to their low cost, wide coverage, and all-weather capability. Estimation of PM 2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM 2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM 2.5 monitoring stations and historical PM 2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

Machine learning, an important branch of artificial intelligence, is increasingly being applied in sciences such as forest ecology. Here, we review and discuss three commonly used methods of machine learning (ML) including decision-tree learning, artificial neural network, and support vector machine and their applications in four different aspects of forest ecology over the last decade. These applications include: (i) species distribution models, (ii) carbon cycles, (iii) hazard assessment and prediction, and (iv) other applications in forest management. Although ML approaches are useful for classification, modeling, and prediction in forest ecology research, further expansion of ML technologies is limited by the lack of suitable data and the relatively “higher threshold” of applications. However, the combined use of multiple algorithms and improved communication and cooperation between ecological researchers and ML developers still present major challenges and tasks for the betterment of future ecological research. We suggest that future applications of ML in ecology will become an increasingly attractive tool for ecologists in the face of “big data” and that ecologists will gain access to more types of data such as sound and video in the near future, possibly opening new avenues of research in forest ecology.

Abstract Canada's boreal forests, which occupy approximately 30% of boreal forests worldwide, play an important role in the global carbon budget. However, there is little quantitative information available regarding the spatiotemporal changes in the drought‐induced tree mortality of Canada's boreal forests overall and their associated impacts on biomass carbon dynamics. Here, we develop spatiotemporally explicit estimates of drought‐induced tree mortality and corresponding biomass carbon sink capacity changes in Canada's boreal forests from 1970 to 2020. We show that the average annual tree mortality rate is approximately 2.7%. Approximately 43% of Canada's boreal forests have experienced significantly increasing tree mortality trends (71% of which are located in the western region of the country), and these trends have accelerated since 2002. This increase in tree mortality has resulted in significant biomass carbon losses at an approximate rate of 1.51 ± 0.29 MgC ha −1  year −1 (95% confidence interval) with an approximate total loss of 0.46 ± 0.09 PgC year −1 (95% confidence interval). Under the drought condition increases predicted for this century, the capacity of Canada's boreal forests to act as a carbon sink will be further reduced, potentially leading to a significant positive climate feedback effect.

CENTURY 4.0, a simulation model of carbon and nitrogen dynamics of terrestrial ecosystems based on the relationships between climate, soil texture, plant productivity, decomposition and human management, was tested against observed data along the boreal forest transect case study (BFTCS) in central Canada. The results show that the simulated average aboveground biomass and net N mineralization were consistent with observed data. The modeled estimates for soil carbon were consistent with those from regional‐scale empirical regression models. High correlation ( R 2 = 0.92) with data was obtained for the simulation of soil carbon dynamics of the boreal forest, but the model overestimated soil carbon (O–20 cm) by 2–8% for fine‐textured soil and underestimated soil carbon by 5–18% for sandy soil. The effects of climatic variation on temporal changes in biomass and soil carbon storage over the past century were found to be very different for southern and northern sites but relatively insensitive to site‐specific soil texture. The main discrepancies between observed data and CENTURY 4.0 results are associated with the effects of soil texture and an inadequate representation of fire disturbance on C dynamics of boreal forests. Further improvements, particularly in the representation of disturbance regimes and in the simulation of slow pool C dynamics, are suggested to enhance its predictive capability.

Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2  = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2  = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome

The spruce budworm (SBW) defoliates and kills conifer trees, consequently affecting carbon (C) exchanges between the land and atmosphere. Here, we developed a new TRIPLEX-Insect sub-model to quantify the impacts of insect outbreaks on forest C fluxes. We modeled annual defoliation (AD), cumulative defoliation (CD), and tree mortality. The model was validated against observed and published data at the stand level in the North Shore region of Québec and Cape Breton Island in Nova Scotia, Canada. The results suggest that TRIPLEX-Insect performs very well in capturing tree mortality following SBW outbreaks and slightly underestimates current annual volume increment (CAI). In both mature and immature forests, the simulation model suggests a larger reduction in gross primary productivity (GPP) than in autotrophic respiration (Ra) at the same defoliation level when tree mortality was low. After an SBW outbreak, the growth release of surviving trees contributes to the recovery of annual net ecosystem productivity (NEP) based on forest age if mortality is not excessive. Overall, the TRIPLEX-Insect model is capable of simulating C dynamics of balsam fir following SBW disturbances and can be used as an efficient tool in forest insect management.