Effects of experimental nitrogen additions on plant diversity in an old-growth tropical forest

Response of plant biodiversity to increased availability of nitrogen (N) has been investigated in temperate and boreal forests, which are typically N-limited, but little is known in tropical forests. We examined the effects of artificial N additions on plant diversity (species richness, density and cover) of the understory layer in an N saturated old-growth tropical forest in southern China to test the following hypothesis: N additions decrease plant diversity in N saturated tropical forests primarily from N-mediated changes in soil properties. Experimental additions of N were administered at the following levels from July 2003 to July 2008: no addition (Control); 50 kg N ha−1 yr−1 (Low-N); 100 kg N ha−1 yr−1 (Medium-N), and 150 kg N ha−1 yr−1 (High-N). Results showed that no understory species exhibited positive growth response to any level of N addition during the study period. Although low-to-medium levels of N addition (≤100 kg N ha−1 yr−1) generally did not alter plant diversity through time, high levels of N addition significantly reduced species diversity. This decrease was most closely related to declines within tree seedling and fern functional groups, as well as to significant increases in soil acidity and Al mobility, and decreases in Ca availability and fine-root biomass. This mechanism for loss of biodiversity provides sharp contrast to competition-based mechanisms suggested in studies of understory communities in other forests. Our results suggest that high-N additions can decrease plant diversity in tropical forests, but that this response may vary with rate of N addition

Teacher-Students Knowledge Distillation for Siamese Trackers

In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. However, state-of-the-art Siamese trackers suffer from high memory cost which restricts their applicability in mobile applications having strict constraints on memory budget. To address this issue, we propose a novel distilled Siamese tracking framework to learn small, fast yet 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 a one-teacher vs multi-students learning mechanism, which is the most usual teaching method in the school. In particular, it contains a single teacher-student distillation model and a student-student knowledge sharing mechanism. The first one is designed by a tracking-specific distillation strategy to transfer knowledge from the teacher to students. The later is utilized for mutual learning between students to enable an in-depth knowledge understanding. To the best of our knowledge, we are the first to investigate knowledge distillation for Siamese trackers and propose a distilled Siamese tracking framework. We demonstrate the generality and effectiveness of our framework by conducting a theoretical analysis and extensive empirical evaluations on several popular Siamese trackers. The results on five tracking benchmarks clearly show that the proposed distilled trackers achieve compression rates up to 18$\times$ and frame-rates of $265$ FPS with speedups of 3$\times$, while obtaining similar or even slightly improved tracking accuracy

Nitrogen input ¹⁵N signatures are reflected in plant ¹⁵N natural abundances in subtropical forests in China

Natural abundance of ¹⁵N (<i>δ</i>¹⁵N) in plants and soils can provide time-integrated information related to nitrogen (N) cycling within ecosystems, but it has not been well tested in warm and humid subtropical forests. In this study, we used ecosystem <i>δ</i>¹⁵N to assess effects of increased N deposition on N cycling in an old-growth broad-leaved forest and a secondary pine forest in a high-N-deposition area in southern China. We measured <i>δ</i>¹⁵N of inorganic N in input and output fluxes under ambient N deposition, and we measured N concentration (%N) and <i>δ</i>¹⁵N of major ecosystem compartments under ambient deposition and after decadal N addition at 50 kg N ha⁻¹yr⁻¹, which has a <i>δ</i>¹⁵N of −0.7 ‰. Our results showed that the total inorganic N in deposition was ¹⁵N-depleted (−10 ‰) mainly due to high input of strongly ¹⁵N-depleted NH₄⁺-N. Plant leaves in both forests were also ¹⁵N-depleted (−4 to −6 ‰). The broad-leaved forest had higher plant and soil %N and was more ¹⁵N-enriched in most ecosystem compartments relative to the pine forest. Nitrogen addition did not significantly affect %N in the broad-leaved forest, indicating that the ecosystem pools are already N-rich. However, %N was marginally increased in pine leaves and significantly increased in understory vegetation in the pine forest. Soil <i>δ</i>¹⁵N was not changed significantly by the N addition in either forest. However, the N addition significantly increased the <i>δ</i>¹⁵N of plants toward the ¹⁵N signature of the added N, indicating incorporation of added N into plants. Thus, plant <i>δ</i>¹⁵N was more sensitive to ecosystem N input manipulation than %N in these subtropical forests. We interpret the depleted <i>δ</i>¹⁵N of plants as an imprint from the high and ¹⁵N-depleted N deposition that may dominate the effects of fractionation that are observed in most warm and humid forests. Fractionation during the steps of N cycling could explain the difference between negative <i>δ</i>¹⁵N in plants and positive <i>δ</i>¹⁵N in soils, and the increase in soil <i>δ</i>¹⁵N with depths. Nevertheless, interpretation of ecosystem <i>δ</i>¹⁵N from high-N-deposition regions needs to include data on the deposition ¹⁵N signal

Attentional Prototype Inference for Few-Shot Segmentation

This paper aims to address few-shot segmentation. While existing prototype-based methods have achieved considerable success, they suffer from uncertainty and ambiguity caused by limited labeled examples. In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. The probabilistic modeling of the prototype enhances the model's generalization ability by handling the inherent uncertainty caused by limited data and intra-class variations of objects. To further enhance the model, we introduce a local latent variable to represent the attention map of each query image, which enables the model to attend to foreground objects while suppressing the background. The optimization of the proposed model is formulated as a variational Bayesian inference problem, which is established by amortized inference networks. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods. We also provide comprehensive analyses and ablation studies to gain insight into the effectiveness of our method for few-shot segmentation.Comment: Pattern Recognition Journa

Nutrient limitation of woody debris decomposition in a tropical forest:contrasting effects of N and P addition

1.Tropical forests represent a major terrestrial store of carbon (C), a large proportion of which is contained in the soil and decaying organic matter. Woody debris plays a key role in forest C dynamics because it contains a sizeable proportion of total forest C. Understanding the factors controlling the decomposition of organic matter in general, and woody debris in particular, is hence critical to assessing changes in tropical C storage. 2.We conducted a factorial fertilization experiment in a tropical forest in South China to investigate the influence of nitrogen (N) and phosphorus (P) availability onwoody debris decomposition using branch segments (5-cm diameter) of four species (Acacia auriculaeformis, Aphanamixis polystachya, Schefflera octophylla, Carallia brachiata) in plots fertilized with +N, +P, or +NP, and controls. 3.Fertilization with +P and +NP increased decomposition rates by 5-53% and the magnitude was species-specific. Contrary to expectations, we observed no negative effect of +N addition on decay rates or mass loss of woody debris in any of the four study species. Decomposition rates of woody debris were higher in species with lower C:P ratios regardless of treatment. 4.We observed significant accumulation of P in the woody debris of all species in plots fertilized with +P and +NP during the early stages of decomposition. N-release from woody debris of Acacia (N-fixing) was greater in the +P plots towards the end of the study, whereas fertilization with +N had no impact on the patterns of nutrient release during decomposition. 5.Synthesis: Our results indicate that decomposition of woody debris is primarily constrained by P availability in this tropical forest. However, contrary to expectations, +N addition did not exacerbate P-limitation. It is conceivable that decay rates of woody debris in tropical forests can be predicted by C:P or lignin:P ratios but additional work with more tree species is needed to determine whether the patterns we observed are more generally applicable