Quality-Aware Selective Fusion Network for V-D-T Salient Object Detection.

Depth images and thermal images contain the spatial geometry information and surface temperature information, which can act as complementary information for the RGB modality. However, the quality of the depth and thermal images is often unreliable in some challenging scenarios, which will result in the performance degradation of the two-modal based salient object detection (SOD). Meanwhile, some researchers pay attention to the triple-modal SOD task, namely the visible-depth-thermal (VDT) SOD, where they attempt to explore the complementarity of the RGB image, the depth image, and the thermal image. However, existing triple-modal SOD methods fail to perceive the quality of depth maps and thermal images, which leads to performance degradation when dealing with scenes with low-quality depth and thermal images. Therefore, in this paper, we propose a quality-aware selective fusion network (QSF-Net) to conduct VDT salient object detection, which contains three subnets including the initial feature extraction subnet, the quality-aware region selection subnet, and the region-guided selective fusion subnet. Firstly, except for extracting features, the initial feature extraction subnet can generate a preliminary prediction map from each modality via a shrinkage pyramid architecture, which is equipped with the multi-scale fusion (MSF) module. Then, we design the weakly-supervised quality-aware region selection subnet to generate the quality-aware maps. Concretely, we first find the high-quality and low-quality regions by using the preliminary predictions, which further constitute the pseudo label that can be used to train this subnet. Finally, the region-guided selective fusion subnet purifies the initial features under the guidance of the quality-aware maps, and then fuses the triple-modal features and refines the edge details of prediction maps through the intra-modality and inter-modality attention (IIA) module and the edge refinement (ER) module, respectively. Extensive experiments are performed on VDT-2048 dataset, and the results show that our saliency model consistently outperforms 13 state-of-the-art methods with a large margin. Our code and results are available at https://github.com/Lx-Bao/QSFNet.

Global change progressively increases foliar nitrogen-phosphorus ratios in China's subtropical forests.

Globally increased nitrogen (N) to phosphorus (P) ratios (N/P) affect the structure and functioning of terrestrial ecosystems, but few studies have addressed the variation of foliar N/P over time in subtropical forests. Foliar N/P indicates N versus P limitation in terrestrial ecosystems. Quantifying long-term dynamics of foliar N/P and their potential drivers is crucial for predicting nutrient status and functioning in forest ecosystems under global change. We detected temporal trends of foliar N/P, quantitatively estimated their potential drivers and their interaction between plant types (evergreen vs. deciduous and trees vs. shrubs), using 1811 herbarium specimens of 12 widely distributed species collected during 1920-2010 across China's subtropical forests. We found significant decreases in foliar P concentrations (23.1%) and increases in foliar N/P (21.2%). Foliar N/P increased more in evergreen species (22.9%) than in deciduous species (16.9%). Changes in atmospheric CO2 concentrations ( P CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ ), atmospheric N deposition and mean annual temperature (MAT) dominantly contributed to the increased foliar N/P of evergreen species, while P CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ , MAT, and vapor pressure deficit, to that of deciduous species. Under future Shared Socioeconomic Pathway (SSP) scenarios, increasing MAT and P CO 2 $$ {\mathrm{P}}_{{\mathrm{CO}}_2} $$ would continuously increase more foliar N/P in deciduous species than in evergreen species, with more 12.9%, 17.7%, and 19.4% versus 6.1%, 7.9%, and 8.9% of magnitudes under the scenarios of SSP1-2.6, SSP3-7.0, and SSP5-8.5, respectively. The results suggest that global change has intensified and will progressively aggravate N-P imbalance, further altering community composition and ecosystem functioning of subtropical forests.

Reformulating Graph Kernels for Self-Supervised Space-Time Correspondence Learning.

Self-supervised space-time correspondence learning utilizing unlabeled videos holds great potential in computer vision. Most existing methods rely on contrastive learning with mining negative samples or adapting reconstruction from the image domain, which requires dense affinity across multiple frames or optical flow constraints. Moreover, video correspondence prediction models need to uncover more inherent properties of the video, such as structural information. In this work, we propose HiGraph+, a sophisticated space-time correspondence framework based on learnable graph kernels. By treating videos as a spatial-temporal graph, the learning objective of HiGraph+ is issued in a self-supervised manner, predicting the unobserved hidden graph via graph kernel methods. First, we learn the structural consistency of sub-graphs in graph-level correspondence learning. Furthermore, we introduce a spatio-temporal hidden graph loss through contrastive learning that facilitates learning temporal coherence across frames of sub-graphs and spatial diversity within the same frame. Therefore, we can predict long-term correspondences and drive the hidden graph to acquire distinct local structural representations. Then, we learn a refined representation across frames on the node-level via a dense graph kernel. The structural and temporal consistency of the graph forms the self-supervision of model training. HiGraph+ achieves excellent performance and demonstrates robustness in benchmark tests involving object, semantic part, keypoint, and instance labeling propagation tasks. Our algorithm implementations have been made publicly available at https://github.com/zyqin19/HiGraph.

Nitrogen addition and mowing had only weak interactive effects on macronutrients in plant-soil systems of a typical steppe in Inner Mongolia.

Effective management of macronutrients is pivotal in the optimization and provisioning of ecosystem services in grassland areas, particularly in degraded grasslands. In such instances where mowing and nitrogen (N) fertilization have emerged as predominant management strategies, nutrient management is especially important. However, the precise effects of these concurrent practices on the distribution of macronutrients in plant-soil systems remain unclear. Here we evaluated the effects of 12 years of N addition (2, 10, and 50 g N m-2 year-1) and mowing on the concentrations and pools of six macronutrients (i.e., N; phosphorus P; sulfur S, calcium Ca, magnesium Mg, and potassium K) in three plant components (aboveground plants, litter, and belowground roots) at the community level and in the soil in a typical steppe in Inner Mongolia. Our results revealed that N addition generally raised the N concentration in the entire plant-soil system, regardless of whether plots were mowed. Higher N addition (10 and 50 g N m-2 year-1) also led to higher concentrations of P (+22%, averaging two N addition rates), S (+16%), K (+22%), Ca (+22%), and Mg (+24%) in plants but lower concentrations of these nutrients in the litter. Similar decreases in K (-9%), Ca (-46%), and Mg (-8%) were observed in the roots. In light of the observed increases in vegetation biomass and the lack of pronounced changes in soil bulk density, we found that the ecosystem N enrichment resulted in increased pools of all measured macronutrients in plants, litter, and roots (with the exception of Ca in the roots) while concurrently decreased the pools of P (-20%, averaging two higher N addition rates), S (-12%), K (-10%), Ca (-37%), and Mg (-19%) in the soil, with no obvious effect of the mowing practice. Overall, mowing exhibited a very limited capacity to alleviate the effects of long-term N addition on macronutrients in the plant-soil system. These findings highlight the importance of considering the distribution of macronutrients across distinct plant organs and the dynamic nutrient interplay between plants and soil, particularly in the context of long-term fertilization and mowing practices, when formulating effective grassland management strategies.

Missingness-Pattern-Adaptive Learning With Incomplete Data.

Many real-world problems deal with collections of data with missing values, e.g., RNA sequential analytics, image completion, video processing, etc. Usually, such missing data is a serious impediment to a good learning achievement. Existing methods tend to use a universal model for all incomplete data, resulting in a suboptimal model for each missingness pattern. In this paper, we present a general model for learning with incomplete data. The proposed model can be appropriately adjusted with different missingness patterns, alleviating competitions between data. Our model is based on observable features only, so it does not incur errors from data imputation. We further introduce a low-rank constraint to promote the generalization ability of our model. Analysis of the generalization error justifies our idea theoretically. In additional, a subgradient method is proposed to optimize our model with a proven convergence rate. Experiments on different types of data show that our method compares favorably with typical imputation strategies and other state-of-the-art models for incomplete data. More importantly, our method can be seamlessly incorporated into the neural networks with the best results achieved. The source code is released at https://github.com/YS-GONG/missingness-patterns.

Modeling global oceanic nitrogen deposition from food systems and its mitigation potential by reducing overuse of fertilizers.

Growing population and consumption pose unprecedented demands on food production. However, ammonia emissions mainly from food systems increase oceanic nitrogen deposition contributing to eutrophication. Here, we developed a long-term oceanic nitrogen deposition dataset (1970 to 2018) with updated ammonia emissions from food systems, evaluated the impact of ammonia emissions on oceanic nitrogen deposition patterns, and discussed the potential impact of nitrogen fertilizer overuse. Based on the chemical transport modeling approach, oceanic ammonia-related nitrogen deposition increased by 89% globally between 1970 and 2018, and now, it exceeds oxidized nitrogen deposition by over 20% in coastal regions including China Sea, India Coastal, and Northeastern Atlantic Shelves. Approximately 38% of agricultural nitrogen fertilizer was excessive, which corresponds to 15% of global oceanic ammonia-related nitrogen deposition. Policymakers and water quality managers need to pay increasingly more attention to ammonia associated with food production if the goal of reducing coastal nitrogen pollution is to be achieved for Sustainable Development Goals.

Long-term phosphorus addition alleviates CO2 and N2O emissions via altering soil microbial functions in secondary rather primary tropical forests.

Tropical forests, where the soils are nitrogen (N) rich but phosphorus (P) poor, have a disproportionate influence on global carbon (C) and N cycling. While N deposition substantially alters soil C and N retention in tropical forests, whether P input can alleviate these N-induced effects by regulating soil microbial functions remains unclear. We investigated soil microbial taxonomy and functional traits in response to 10-year independent and interactive effects of N and P additions in a primary and a secondary tropical forest in Hainan Island. In the primary forest, N addition boosted oligotrophic bacteria and phosphatase and enriched genes responsible for C-, P-mineralization, nitrification and denitrification, suggesting aggravated P limitation while N excess. This might stimulate P excavation via organic matter mineralization, and enhance N losses, thereby increasing soil CO2 and N2O emissions by 86% and 110%, respectively. Phosphorus and NP additions elevated C-mining enzymes activity mainly due to intensified C limitation, causing 82% increase in CO2 emission. In secondary forest, P and NP additions reduced phosphatase activity, enriched fungal copiotrophs and increased microbial biomass, suggesting removal of nutrient deficiencies and stimulation of fungal growth. Meanwhile, soil CO2 emission decreased by 25% and N2O emission declined by 52-82% due to alleviated P acquisition from organic matter decomposition and increased microbial C and N immobilization. Overall, N addition accelerates most microbial processes for C and N release in tropical forests. Long-term P addition increases C and N retention via reducing soil CO2 and N2O emissions in the secondary but not primary forest because of strong C limitation to microbial N immobilization. Further, the seasonal and annual variations in CO2 and N2O emissions should be considered in future studies to test the generalization of these findings and predict and model dynamics in greenhouse gas emissions and C and N cycling.

Distinct Responses of Abundant and Rare Soil Bacteria to Nitrogen Addition in Tropical Forest Soils.

Soil microbial responses to anthropogenic nitrogen (N) enrichment at the overall community level has been extensively studied. However, the responses of community dynamics and assembly processes of the abundant versus rare bacterial taxa to N enrichment have rarely been assessed. Here, we present a study in which the effects of short- (2 years) and long-term (13 years) N additions to two nearby tropical forest sites on abundant and rare soil bacterial community composition and assembly were documented. The N addition, particularly in the long-term experiment, significantly decreased the bacterial α-diversity and shifted the community composition toward copiotrophic and N-sensitive species. The α-diversity and community composition of the rare taxa were more affected, and they were more closely clustered phylogenetically under N addition compared to the abundant taxa, suggesting the community assembly of the rare taxa was more governed by deterministic processes (e.g., environmental filtering). In contrast, the abundant taxa exhibited higher community abundance, broader environmental thresholds, and stronger phylogenetic signals under environmental changes than the rare taxa. Overall, these findings illustrate that the abundant and rare bacterial taxa respond distinctly to N addition in tropical forests, with higher sensitivity of the rare taxa, but potentially broader environmental acclimation of the abundant taxa. IMPORTANCE Atmospheric nitrogen (N) deposition is a worldwide environmental problem and threatens biodiversity and ecosystem functioning. Understanding the responses of community dynamics and assembly processes of abundant and rare soil bacterial taxa to anthropogenic N enrichment is vital for the management of N-polluted forest soils. Our sequence-based data revealed distinct responses in bacterial diversity, community composition, environmental acclimation, and assembly processes between abundant and rare taxa under N-addition soils in tropical forests. These findings provide new insight into the formation and maintenance of bacterial diversity and offer a way to better predict bacterial responses to the ongoing atmospheric N deposition in tropical forests.

Sphagnum capillifolium holobiont from a subarctic palsa bog aggravates the potential of nitrous oxide emissions.

Melting permafrost mounds in subarctic palsa mires are thawing under climate warming and have become a substantial source of N2O emissions. However, mechanistic insights into the permafrost thaw-induced N2O emissions in these unique habitats remain elusive. We demonstrated that N2O emission potential in palsa bogs was driven by the bacterial residents of two dominant Sphagnum mosses especially of Sphagnum capillifolium (SC) in the subarctic palsa bog, which responded to endogenous and exogenous Sphagnum factors such as secondary metabolites, nitrogen and carbon sources, temperature, and pH. SC's high N2O emission activity was linked with two classes of distinctive hyperactive N2O emitters, including Pseudomonas sp. and Enterobacteriaceae bacteria, whose hyperactive N2O emitting capability was characterized to be dominantly pH-responsive. As the nosZ gene-harboring emitter, Pseudomonas sp. SC-H2 reached a high level of N2O emissions that increased significantly with increasing pH. For emitters lacking the nosZ gene, an Enterobacteriaceae bacterium SC-L1 was more adaptive to natural acidic conditions, and N2O emissions also increased with pH. Our study revealed previously unknown hyperactive N2O emitters in Sphagnum capillifolium found in melting palsa mound environments, and provided novel insights into SC-associated N2O emissions.

Drivers of foliar 15 N trends in southern China over the last century.

Foliar stable nitrogen (N) isotopes (δ15 N) generally reflect N availability to plants and have been used to infer about changes thereof. However, previous studies of temporal trends in foliar δ15 N have ignored the influence of confounding factors, leading to uncertainties on its indication to N availability. In this study, we measured foliar δ15 N of 1811 herbarium specimens from 12 plant species collected in southern China forests from 1920 to 2010. We explored how changes in atmospheric CO2 , N deposition and global warming have affected foliar δ15 N and N concentrations ([N]) and identified whether N availability decreased in southern China. Across all species, foliar δ15 N significantly decreased by 0.82‰ over the study period. However, foliar [N] did not decrease significantly, implying N homeostasis in forest trees in the region. The spatiotemporal patterns of foliar δ15 N were explained by mean annual temperature (MAT), atmospheric CO2 ( P CO 2 ), atmospheric N deposition, and foliar [N]. The spatiotemporal trends of foliar [N] were explained by MAT, temperature seasonality, P CO 2 , and N deposition. N deposition within the rates from 5.3 to 12.6 kg N ha-1  year-1 substantially contributed to the temporal decline in foliar δ15 N. The decline in foliar δ15 N was not accompanied by changes in foliar [N] and therefore does not necessarily reflect a decline in N availability. This is important to understand changes in N availability, which is essential to validate and parameterize biogeochemical cycles of N.

Exploring global changes in agricultural ammonia emissions and their contribution to nitrogen deposition since 1980.

SignificanceAgricultural systems are already major forces of ammonia pollution and environmental degradation. How agricultural ammonia emissions affect the spatio-temporal patterns of nitrogen deposition and where to target future mitigation efforts, remains poorly understood. We develop a substantially complete and coherent agricultural ammonia emissions dataset in nearly recent four decades, and evaluate the relative role of reduced nitrogen in total nitrogen deposition in a spatially explicit way. Global reduced nitrogen deposition has grown rapidly, and will occupy a greater dominant position in total nitrogen deposition without future ammonia regulations. Recognition of agricultural ammonia emissions on nitrogen deposition is critical to formulate effective policies to address ammonia related environmental challenges and protect ecosystems from excessive nitrogen inputs.

Divergent responses of soil microbial functional groups to long-term high nitrogen presence in the tropical forests.

A massive rise in atmospheric nitrogen deposition (ND) has threatened ecosystem health through accelerating soil nitrogen (N) cycling rates. While soil microbes serve a crucial function in soil N transformation, it remains poorly understood on how excess ND affects microbial functional populations regulating soil N transformation in tropical forests. To address this gap, we conducted 13-year N (as NH4NO3) addition experiments in one N-rich tropical primary forest (PF) and two N-poor tropical reforested forests (rehabilitated and disturbed) in South China. Based on our data, 13-year N introduction markedly enhanced soil N2O generation in all forests, regardless of soil N status, but microbial functional groups showed divergent responses to excess N addition among the studied forests. In the PF, long-term N introduction markedly decreased presence of bacterial 16S rRNA gene, nitrifier (amoA) and denitrifier genes (nirK, nirS and nosZ) and bacteria/fungi ratio, which could be attributed to the decreases in soil pH, dissolved organic carbon to N ratio and understory plant richness. In the two reforested forests, however, long-term N introduction generally did neither alter soil properties nor the abundance of most microbial groups. We further found that the elevated N2O generation was related to the increased soil N availability and decreased nosZ abundance, and the PF has the highest N2O generation than the other two forests. Overall, our data indicates that the baseline soil N status may dominate response of microbial functional groups to ND in tropical forests, and N-rich forests are more responsive to excess N inputs, compared to those with low-N status. Forests with high soil N status can produce more N2O than those with low-N status. With the spread of elevated ND from temperate to tropical zones, tropical forests should merit more attention because ecosystem N saturation may be common and high N2O emission will occur.

Zero-Shot Video Object Segmentation With Co-Attention Siamese Networks.

We introduce a novel network, called CO-attention siamese network (COSNet), to address the zero-shot video object segmentation task in a holistic fashion. We exploit the inherent correlation among video frames and incorporate a global co-attention mechanism to further improve the state-of-the-art deep learning based solutions that primarily focus on learning discriminative foreground representations over appearance and motion in short-term temporal segments. The co-attention layers in COSNet provide efficient and competent stages for capturing global correlations and scene context by jointly computing and appending co-attention responses into a joint feature space. COSNet is a unified and end-to-end trainable framework where different co-attention variants can be derived for capturing diverse properties of the learned joint feature space. We train COSNet with pairs (or groups) of video frames, and this naturally augments training data and allows increased learning capacity. During the segmentation stage, the co-attention model encodes useful information by processing multiple reference frames together, which is leveraged to infer the frequently reappearing and salient foreground objects better. Our extensive experiments over three large benchmarks demonstrate that COSNet outperforms the current alternatives by a large margin. Our implementations are available at https://github.com/carrierlxk/COSNet.

Deep Object Tracking With Shrinkage Loss.

In this paper, we address the issue of data imbalance in learning deep models for visual object tracking. Although it is well known that data distribution plays a crucial role in learning and inference models, considerably less attention has been paid to data imbalance in visual tracking. For the deep regression trackers that directly learn a dense mapping from input images of target objects to soft response maps, we identify their performance is limited by the extremely imbalanced pixel-to-pixel differences when computing regression loss. This prevents existing end-to-end learnable deep regression trackers from performing as well as discriminative correlation filters (DCFs) trackers. For the deep classification trackers that draw positive and negative samples to learn discriminative classifiers, there exists heavy class imbalance due to a limited number of positive samples when compared to the number of negative samples. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data mostly coming from the background, which facilitates both deep regression and classification trackers to better distinguish target objects from the background. We extensively validate the proposed shrinkage loss function on six benchmark datasets, including the OTB-2013, OTB-2015, UAV-123, VOT-2016, VOT-2018 and LaSOT. Equipped with our shrinkage loss, the proposed one-stage deep regression tracker achieves favorable results against state-of-the-art methods, especially in comparison with DCFs trackers. Meanwhile, our shrinkage loss generalizes well to deep classification trackers. When replacing the original binary cross entropy loss with our shrinkage loss, three representative baseline trackers achieve large performance gains, even setting new state-of-the-art results.

Distilled Siamese Networks for Visual Tracking.

In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module and a student-student knowledge sharing mechanism. The former is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students. The latter is utilized for mutual learning between students to enable in-depth knowledge understanding. Extensive empirical evaluations on several popular Siamese trackers demonstrate the generality and effectiveness of our framework. Moreover, the results on five tracking benchmarks show that the proposed distilled trackers achieve compression rates of up to 18× and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.

Segmenting Objects From Relational Visual Data.

In this article, we model a set of pixelwise object segmentation tasks - automatic video segmentation (AVS), image co-segmentation (ICS) and few-shot semantic segmentation (FSS) - in a unified view of segmenting objects from relational visual data. To this end, we propose an attentive graph neural network (AGNN) that addresses these tasks in a holistic fashion, by formulating them as a process of iterative information fusion over data graphs. It builds a fully-connected graph to efficiently represent visual data as nodes and relations between data instances as edges. The underlying relations are described by a differentiable attention mechanism, which thoroughly examines fine-grained semantic similarities between all the possible location pairs in two data instances. Through parametric message passing, AGNN is able to capture knowledge from the relational visual data, enabling more accurate object discovery and segmentation. Experiments show that AGNN can automatically highlight primary foreground objects from video sequences (i.e., automatic video segmentation), and extract common objects from noisy collections of semantically related images (i.e., image co-segmentation). AGNN can even generalize segment new categories with little annotated data (i.e., few-shot semantic segmentation). Taken together, our results demonstrate that AGNN provides a powerful tool that is applicable to a wide range of pixel-wise object pattern understanding tasks with relational visual data. Our algorithm implementations have been made publicly available at https://github.com/carrierlxk/AGNN.

Adaptation of Soil Fungal Community Structure and Assembly to Long- Versus Short-Term Nitrogen Addition in a Tropical Forest.

Soil fungi play critical roles in ecosystem processes and are sensitive to global changes. Elevated atmospheric nitrogen (N) deposition has been well documented to impact on fungal diversity and community composition, but how the fungal community assembly responds to the duration effects of experimental N addition remains poorly understood. Here, we aimed to investigate the soil fungal community variations and assembly processes under short- (2 years) versus long-term (13 years) exogenous N addition (∼100 kg N ha-1 yr-1) in a N-rich tropical forest of China. We observed that short-term N addition significantly increased fungal taxonomic and phylogenetic α-diversity and shifted fungal community composition with significant increases in the relative abundance of Ascomycota and decreases in that of Basidiomycota. Short-term N addition also significantly increased the relative abundance of saprotrophic fungi and decreased that of ectomycorrhizal fungi. However, unremarkable effects on these indices were found under long-term N addition. The variations of fungal α-diversity, community composition, and the relative abundance of major phyla, genera, and functional guilds were mainly correlated with soil pH and NO3 --N concentration, and these correlations were much stronger under short-term than long-term N addition. The results of null, neutral community models and the normalized stochasticity ratio (NST) index consistently revealed that stochastic processes played predominant roles in the assembly of soil fungal community in the tropical forest, and the relative contribution of stochastic processes was significantly increased by short-term N addition. These findings highlighted that the responses of fungal community to N addition were duration-dependent, i.e., fungal community structure and assembly would be sensitive to short-term N addition but become adaptive to long-term N enrichment.

Negative effects of long-term phosphorus additions on understory plants in a primary tropical forest.

Human activities have disturbed global phosphorus (P) cycling by introducing substantial amounts of P to natural ecosystems. Although natural P gradients and fertilization studies have found that plant community traits are closely related to P availability, it remains unclear how increased P supply affects plant growth and diversity in P-deficient tropical forests. We used a decadal P-addition experiment (2007-2017) to study the effects of increased P input on plant growth and diversity in understory layer in tropical forests. We monitored the dynamics of seedling growth, survival rate, and diversity of understory plants throughout the fertilization period under control and P addition at 15 g P m-2 yr-1. To identify the drivers of responses, P concentration, photosynthesis rate and nonstructural carbon were analyzed. Results showed that long-term P addition significantly increased P concentrations both in soil pools and plant tissues. However, P addition did not increase the light-saturated photosynthesis rate or growth rate of the understory plants. Furthermore, P addition significantly decreased the survival rate of seedlings and reduced the species richness and density of understory plants. The negative effects of P addition may be attributed to an increased carbon cost due to the tissue maintenance of plants with higher P concentrations. These findings indicate that increased P supply alone is not necessary to benefit the growth of plants in ecosystems with low P availability, and P inputs can inhibit understory plants and may alter community composition. Therefore, we appeal to a need for caution when inputting P to tropical forests ecosystems.

Distinct storage mechanisms of soil organic carbon in coniferous forest and evergreen broadleaf forest in tropical China.

The impact of human activities on soil carbon (C) storage in tropical forests has aroused wide concern during the past decades, because these ecosystems play a key role in ameliorating global climate change. However, there remain uncertainties about how land-use history alters soil organic carbon (SOC) stability and storage in different forests. In this study, we measured the C content and mass distributions of soil aggregates, density fractions, mineral-bound C and microbial biomass C in the organic horizon, 0-10 cm and 10-20 cm soil layers in coniferous forest and evergreen broadleaf forest at Dinghushan Biosphere Reserve in tropical China. The broadleaf forest had larger SOC stocks than the coniferous forest, but the proportion of SOC stored in different density fractions at 0-10 cm soils was similar between forest types, while a greater proportion of SOC was stored in microaggregates in the coniferous forest. Most of the SOC was held as light fraction C in the organic horizon in the coniferous forest, whereas the concentrations of mineral-bound C were higher in the broadleaf forest. These findings indicate clear differences in the protection of SOC between broadleaf and coniferous forests growing on the same soil type. We propose that historic conversion of broadleaf forest to coniferous forest has reduced soil C sequestration capacity by altering the diversity and quality of plant inputs to the soil, which in turn affected macroaggregate formation, soil chemical properties and microbial biomass. Our results thus demonstrate that changes in forest tree species composition could have long-lasting effects on soil structure and carbon storage, providing crucial evidence for policy decisions on forest carbon sink management.

Nitrogen deposition accelerates soil carbon sequestration in tropical forests.

Terrestrial ecosystem carbon (C) sequestration plays an important role in ameliorating global climate change. While tropical forests exert a disproportionately large influence on global C cycling, there remains an open question on changes in below-ground soil C stocks with global increases in nitrogen (N) deposition, because N supply often does not constrain the growth of tropical forests. We quantified soil C sequestration through more than a decade of continuous N addition experiment in an N-rich primary tropical forest. Results showed that long-term N additions increased soil C stocks by 7 to 21%, mainly arising from decreased C output fluxes and physical protection mechanisms without changes in the chemical composition of organic matter. A meta-analysis further verified that soil C sequestration induced by excess N inputs is a general phenomenon in tropical forests. Notably, soil N sequestration can keep pace with soil C, based on consistent C/N ratios under N additions. These findings provide empirical evidence that below-ground C sequestration can be stimulated in mature tropical forests under excess N deposition, which has important implications for predicting future terrestrial sinks for both elevated anthropogenic CO2 and N deposition. We further developed a conceptual model hypothesis depicting how soil C sequestration happens under chronic N deposition in N-limited and N-rich ecosystems, suggesting a direction to incorporate N deposition and N cycling into terrestrial C cycle models to improve the predictability on C sink strength as enhanced N deposition spreads from temperate into tropical systems.