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The knowledge of tropical palaeoclimates is crucial for understanding global climate change, because it is a test bench for general circulation models that are ultimately used to predict future global warming. A longstanding issue concerning the last glacial maximum in the tropics is the discrepancy between the decrease in sea-surface temperatures reconstructed from marine proxies and the high-elevation decrease in land temperatures estimated from indicators of treeline elevation. In this study, an improved inverse vegetation modeling approach is used to quantitatively reconstruct palaeoclimate and to estimate the effects of different factors (temperature, precipitation, and atmospheric CO 2 concentration) on changes in treeline elevation based on a set of pollen data covering an altitudinal range from 100 to 3,140 m above sea level in Africa. We show that lowering of the African treeline during the last glacial maximum was primarily triggered by regional drying, especially at upper elevations, and was amplified by decreases in atmospheric CO 2 concentration and perhaps temperature. This contrasts with scenarios for the Holocene and future climates, in which the increase in treeline elevation will be dominated by temperature. Our results suggest that previous temperature changes inferred from tropical treeline shifts may have been overestimated for low-CO 2 glacial periods, because the limiting factors that control changes in treeline elevation differ between glacial and interglacial periods.

ABSTRACT Aim  To document the patterns of fish species richness and their possible causes in China's lakes at regional and national scales. Location  Lakes across China. Methods  We compiled data of fish species richness, limnological characteristics and climatic variables for 109 lakes across five regions of China: East region, Northeast region, Southwest region, North‐Northwest region, and the Tibetan Plateau. Correlation analyses, regression models and a general linear model were used to explore the patterns of fish species richness. Results  At the national scale, lake altitude, energy availability (potential evapotranspiration, PET) and lake area explained 79.6% of the total variation of the lake fish species richness. The determinants of the fish richness pattern varied among physiographic regions. Lake area was the strongest predictor of fish species richness in the East and Southwest lakes, accounting for 22.2% and 82.9% of the variation, respectively. Annual PET explained 68.7% of the variation of fish richness in the Northeast lakes. Maximum depth, mineralization degree, and lake area explained 45.5% of the fish variation in the lakes of the North‐Northwest region. On the Tibetan Plateau, lake altitude was the first predictor variable, interpreting 32.2% of the variation. Main conclusions  Lake altitude was the most important factor explaining the variation of fish species richness across China's lakes, and accounted for 74.5% of the variation. This may stem in part from the fact that the lakes investigated in our study span the largest altitudinal range anywhere in the world. The effects of the lake altitude on fish species richness can be separated into direct and indirect aspects due to its collinearity with PET. We also found that the fish diversity and its determinants were scale‐dependent. Fish species richness was probably energy‐determined in the cold region, while it was best predicted by the lake area in the relatively geologically old region. The independent variables we used only explained a small fraction of the variations in the lake fish species richness in East China and the Tibetan Plateau, which may be due to the effects of human activity and historical events, respectively.

Abstract This study investigated seasonal patterns in stoichiometric ratios, nutrient resorption characteristics, and nutrient use strategies of dominant tree species at three successional stages in subtropical China, which have not been fully understood. Fresh leaf and leaf litterfall samples were collected in growing and nongrowing seasons for determining the concentrations of carbon (C), nitrogen (N), and phosphorus (P). Then, stoichiometric ratios (i.e., C:N, C:P, N:P, and C:N:P) and resorption parameters were calculated. Our results found that there was no consistent variation in leaf C:N and C:P ratios among different species. However, leaf N:P ratios in late‐successional species became significantly higher, indicating that P limitation increases during successional development. Due to the P limitation in this study area, P resorption efficiency and proficiency were higher than corresponding N resorption parameters. Dominant tree species at early‐successional stage adopted “conservative consumption” nutrient use strategy, whereas the species at late‐successional stage inclined to adopt “resource spending” strategy.

Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Abstract Forest insects are major disturbances that induce tree mortality in eastern coniferous (or fir‐spruce) forests in eastern North America. The spruce budworm ( SBW ) ( Choristoneura fumiferana [Clemens]) is the most devastating insect causing tree mortality. However, the relative importance of insect‐caused mortality versus tree mortality caused by other agents and how this relationship will change with climate change is not known. Based on permanent sample plots across eastern Canada, we combined a logistic model with a negative model to estimate tree mortality. The results showed that tree mortality increased mainly due to forest insects. The mean difference in annual tree mortality between plots disturbed by insects and those without insect disturbance was 0.0680 per year ( P  <   0.0001, T ‐test), and the carbon sink loss was about 2.87t C ha −1  year −1 larger than in natural forests. We also found that annual tree mortality increased significantly with the annual climate moisture index ( CMI ) and decreased significantly with annual minimum temperature ( T min ), annual mean temperature ( T mean ) and the number of degree days below 0°C ( DD 0), which was inconsistent with previous studies (Adams et al. ; van Mantgem et al. ; Allen et al. ). Furthermore, the results for the trends in the magnitude of forest insect outbreaks were consistent with those of climate factors for annual tree mortality. Our results demonstrate that forest insects are the dominant cause of the tree mortality in eastern Canada but that tree mortality induced by insect outbreaks will decrease in eastern Canada under warming climate.

Abstract Vegetation restoration has been proposed as an effective measure for rehabilitating degraded land and slowing desertification in arid regions. However, the spatial variation in soil quality and plant diversity following vegetation restoration remains unclear. This study was designed to explore soil nutrient dynamics and how soil nutrients affect plant diversity and spatial heterogeneity after shrub restoration. We assessed the effect of Haloxylon ammodendron (C.A.Mey.) Bunge (which has been planted over 30 years) on the soil nutrients and plant diversity in a desert–oasis ecotone in Minqin County, Gansu, China, using geostatistics, beta diversity and rarefaction analyses, and Hill number extrapolation. Soil nutrients, including soil organic matter, total nitrogen, and alkali nitrogen, increased significantly after H. ammodendron planting. Species richness gradually increased from 1–5 years to 10–20 years after H. ammodendron was planted but then decreased at 20–30 years. The largest differences in plant composition were observed at 15 and 20 years. Plant diversity increased in the whole 30 years after shrub planting, increasing in the first 25 years and then decreasing at 26–30 year stage. The maximum coefficient of determination for the spatial heterogeneity model fit was 0.84 (25 years). The spatial heterogeneity in vegetation decreased with increasing soil available K content at 1–10 years. Our results suggest that planting shrubs can improve soil conditions and plant species diversity in desert–oasis ecotones and soil nutrients have a strong influence on plant diversity patterns and spatial heterogeneity following vegetation restoration.

Abstract Forest soils play an important role in controlling global warming by reducing atmospheric methane (CH 4 ) concentrations. However, little attention has been paid to how nitrogen (N) deposition may alter microorganism communities that are related to the CH 4 cycle or CH 4 oxidation in subtropical forest soils. We investigated the effects of N addition (0, 30, 60, or 90 kg N ha −1  yr −1 ) on soil CH 4 flux and methanotroph and methanogen abundance, diversity, and community structure in a Moso bamboo ( Phyllostachys edulis ) forest in subtropical China. N addition significantly increased methanogen abundance but reduced both methanotroph and methanogen diversity. Methanotroph and methanogen community structures under the N deposition treatments were significantly different from those of the control. In N deposition treatments, the relative abundance of Methanoculleus was significantly lower than that in the control. Soil pH was the key factor regulating the changes in methanotroph and methanogen diversity and community structure. The CH 4 emission rate increased with N addition and was negatively correlated with both methanotroph and methanogen diversity but positively correlated with methanogen abundance. Overall, our results suggested that N deposition can suppress CH 4 uptake by altering methanotroph and methanogen abundance, diversity, and community structure in subtropical Moso bamboo forest soils.

Abstract Background The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary. Methods This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks. Results The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error (5.1), mean error (− 0.85), and mean square prediction error (29). The accuracy rate of the combined k -nearest neighbors ( k -NN) local support vector machines model (i.e. k -nearest neighbors -support vector machine (KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network (0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%. Conclusions Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum , results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.

Abstract Biomass has been promoted as a promising energy resource to mitigate global climate change. To evaluate the contribution of biomass utilization to climate change mitigation under the “Grain for Green” program in Northern Shaanxi, China, a soil carbon dynamic model and a life cycle assessment model were integrated to examine the benefits of using Caragana korshinskii Kom. as an energy crop. We found that the annual dry biomass output is maintained at 0.7 Tg during the simulation period (2020–2097). Due to the compensatory effect of biomass regrowth, the global warming potential of biomass‐derived CO 2 emissions is approximately 0.045; therefore, the total annual biogenic CO 2 emission is 57,211 ± 6,168 Mg CO 2 eq. The total annual life cycle CO 2 emissions approach 867,072 Mg CO 2 eq yr −1 . Under the scenario of no biomass removal, final carbon storage ranges from 15.7 to 19.3 TgC, and the highest carbon sequestration rate is 0.47 TgC yr −1 . In comparison with the no biomass removal scenario, the carbon sequestration rate (close to 0 MgC yr −1 ) in the biomass utilization scenario indicates a carbon loss; however, a portion of the carbon loss (31.39–62.09%) can be offset by carbon emission reductions from the substitution of fossil fuels.

The study was to investigate the change patterns of soil organic carbon (SOC), total nitrogen (TN), and soil C/N (C/N) in each soil sublayer along vegetation restoration in subtropical China. We collected soil samples in four typical plant communities along a restoration chronosequence. The soil physicochemical properties, fine root, and litter biomass were measured. Our results showed the proportion of SOC stocks (Cs) and TN stocks (Ns) in 20–30 and 30–40 cm soil layers increased, whereas that in 0–10 and 10–20 cm soil layers decreased. Different but well-constrained C/N was found among four restoration stages in each soil sublayer. The effect of soil factors was greater on the deep soil than the surface soil, while the effect of vegetation factors was just the opposite. Our study indicated that vegetation restoration promoted the uniform distribution of SOC and TN on the soil profile. The C/N was relatively stable along vegetation restoration in each soil layer. The accumulation of SOC and TN in the surface soil layer was controlled more by vegetation factors, while that in the lower layer was controlled by both vegetation factors and soil factors.

Abstract The fate of soil organic carbon (SOC) under warming is poorly understood, particularly across large extents and in the whole‐soil profile. Using a data‐model integration approach applied across the globe, we find that downward movement of SOC along the soil profile reduces SOC loss under warming. We predict that global SOC stocks (down to 2 m) will decline by 4% (~80 Pg) on average when SOC reaches the steady state under 2°C warming, assuming no changes in net primary productivity (NPP). To compensate such decline (i.e. maintain current SOC stocks), a 3% increase of NPP is required. Without the downward SOC movement, global SOC declines by 15%, while a 20% increase in NPP is needed to compensate that loss. This vital role of downward SOC movement in controlling whole‐soil profile SOC dynamics in response to warming is due to the protection afforded to downward‐moving SOC by depth, indicated by much longer residence times of SOC in deeper layers. Additionally, we find that this protection could not be counteracted by promoted decomposition due to the priming of downward‐moving new SOC from upper layers on native old SOC in deeper layers. This study provides the first estimation of whole‐soil SOC changes under warming and additional NPP required to compensate such changes across the globe, and reveals the vital role of downward movement of SOC in reducing SOC loss under global warming.