Continuous planting under a high density enhances the competition for nutrients among young Cunninghamia lanceolata saplings

International audienceAbstractKey messageA high-density plantation inhibited growth and biomass accumulation of Cunninghamia lanceolata(Lamb.) Hook. saplings, as well as their photosynthesis. This inhibition was enhanced in a soil that had been previously planted with the same species. The main factors limiting photosynthesis and growth were leaf-level irradiance and nutrient availability, mainly of P and Mg.ContextThe planting density and continuous planting greatly affect the photosynthesis and productivity of Chinese fir plantations. The effects of high density and of continuous plantations over several revolutions need be disentangled.AimsIn this study, the responses of C. lanceolata seedlings to a high planting density were tested. Two soils were compared: a soil from a secondary forest and one from a continuous Chinese fir plantation. The study focused on growth and the potential processes involved in deduced photosynthesis.MethodsC. lanceolata seedlings were planted in wooden boxes (100 × 100 × 50 cm) with high and low planting densities (16 vs 1 plant m−2) in two types of soil.ResultsUnder the high planting density, C. lanceolata showed less growth and biomass accumulation at the individual level and lower photosynthetic rate and instantaneous photosynthetic nutrient use efficiency (PNUE and PPUE) at the leaf level. These negative effects were larger in soils that have been continuously planted with Chinese fir. The low photosynthesis was related to low phosphorus and magnesium contents in the leaves, changes in the foliar N/P and chlorophyll a/b ratios, and the limitation of the mesophyll conductance.ConclusionsThe study showed that a high planting density induced enhanced competition for nutrients (particularly for P and Mg) and that this competition is enhanced in soils from continuous plantations compared to soils from natural forests

Asymmetric pruning reveals how organ connectivity alters the functional balance between leaves and roots of Chinese fir

Following asymmetric pruning of leaves and/or roots, the functional balance of distribution of carbon, but not of nitrogen, in Cunninghamia lanceolata is more readily achieved for the roots and leaves on the same side of the pruning compared with those on the opposite side. Abstract The functional balance between leaves and roots is believed to be mediated by the specific location of shoots and roots, i.e. differences in transport distances and degrees of organ connectivity. However, it remains unknown whether the adaptive responses of trees to biomass removal depend on the relative orientation of leaf and root pruning. Here, we applied five pruning treatments to saplings of Cunninghamia lanceolata (Chinese fir) under field and glasshouse conditions, namely no pruning (control), half of lateral branches pruned, half of lateral roots pruned, half of the branches and roots pruned on the same side of the plant, and half of the branches and roots pruned on opposite sides of the plant. The effects of pruning on the growth, carbon storage and allocation, and physiology of leaves and fine roots on the same and opposite sides of the plant were investigated. Compared with the effect of root-pruning on leaves, fine roots were more limited by carbon availability and their physiological activity was more strongly reduced by shoot pruning, especially when branches on the same side of the plant were removed. Pruning of branches and roots on the opposite side of the plant resulted in the lowest carbon assimilation rates and growth among all treatments. The results of a stable-isotope labeling indicated that less C was distributed to fine roots from the leaves on the opposite side of the plant compared to those on the same side, but N allocation from roots to leaves depended less on the relative root and leaf orientation. The results collectively indicate that the functional responses of C. lanceolata to pruning are not only determined by the source-sink balance model but are also related to interactions between leaves and fine roots. We argue that the connectivity among lateral branches and roots depends on their relative orientation, which is therefore critical for the functional balance between leaves and fine roots.Peer reviewe

FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection

3D object detection with multi-sensors is essential for an accurate and reliable perception system of autonomous driving and robotics. Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm which merely relies on LiDAR point clouds for 3D proposal refinement. Though impressive, the sparsity of point clouds, especially for the points far away, making it difficult for the LiDAR-only refinement module to accurately recognize and locate objects.To address this problem, we propose a novel multi-modality two-stage approach named FusionRCNN, which effectively and efficiently fuses point clouds and camera images in the Regions of Interest(RoI). FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from camera in a unified attention mechanism. Specifically, it first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step; then leverages an intra-modality self-attention to enhance the domain-specific features, following by a well-designed cross-attention to fuse the information from two modalities.FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors.Remarkably, FusionRCNN significantly improves the strong SECOND baseline by 6.14% mAP on Waymo, and outperforms competing two-stage approaches. Code will be released soon at https://github.com/xxlbigbrother/Fusion-RCNN.Comment: 7 pages, 3 figure

Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions

Robust 3D perception under corruption has become an essential task for the realm of 3D vision. While current data augmentation techniques usually perform random transformations on all point cloud objects in an offline way and ignore the structure of the samples, resulting in over-or-under enhancement. In this work, we propose an alternative to make sample-adaptive transformations based on the structure of the sample to cope with potential corruption via an auto-augmentation framework, named as AdaptPoint. Specially, we leverage a imitator, consisting of a Deformation Controller and a Mask Controller, respectively in charge of predicting deformation parameters and producing a per-point mask, based on the intrinsic structural information of the input point cloud, and then conduct corruption simulations on top. Then a discriminator is utilized to prevent the generation of excessive corruption that deviates from the original data distribution. In addition, a perception-guidance feedback mechanism is incorporated to guide the generation of samples with appropriate difficulty level. Furthermore, to address the paucity of real-world corrupted point cloud, we also introduce a new dataset ScanObjectNN-C, that exhibits greater similarity to actual data in real-world environments, especially when contrasted with preceding CAD datasets. Experiments show that our method achieves state-of-the-art results on multiple corruption benchmarks, including ModelNet-C, our ScanObjectNN-C, and ShapeNet-C.Comment: Accepted by ICCV2023; code: https://github.com/Roywangj/AdaptPoin

Additional AM Fungi Inoculation Increase Populus cathayana Intersexual Competition

Sex-specific responses to mycorrhiza have been reported in dioecious plant species, but little attention has been paid to the influence of arbuscular mycorrhizal (AM) fungi on competitive ability under intersexual competition. To further address whether this competition is affected by an additional AM fungi supply, Populus cathayana saplings were chosen and subjected to two mycorrhizal treatments [inoculated and non-inoculated (control) with an additional AM fungi Funneliformis mosseae] while growing with the opposite sex for 3 months. Compared with the control, the additional AM fungi inoculation induced P. cathayana saplings to exhibit significant sexual differences in root structure and nutrient uptake (e.g., cortical layer, cross-section area, radius of root tips, and N, K, and Mg content), and enlarged sexual differences in morphology and biomass accumulation (e.g., leaf number increment, shoot height increment, total leaf area, total specific root length, stem dry mass, leaf dry mass, and total dry mass). Meanwhile, inoculated females presented higher values in most of these traits mentioned above than males under intersexual competition. Therefore, we conclude that the intersexual competition can be increased by an additional AM fungi supply, with females gaining more symbiosis-mediated benefits than males

Do tree cavity density and characteristics vary across topographical habitats in the tropics? A case study from Xishuangbanna, southwest China

Despite the influence of cavities on the survival and distribution of cavity-dependent fauna, the variation in the density and characteristics of tree cavities across different habitat types in tropical forests is unknown. In this study, we surveyed 26 312 living trees from 376 species and compared cavity density and characteristics (height, size, type, and orientation) across five habitat types (valley, low-slope, high-slope, high-gully, and high-plateau) in a 20-hectare tropical rainforest in southwest China. From a total of 2047 cavities, we found that cavity density was mainly driven by habitat rather than tree species richness or diameter at breast height (DBH), and the characteristics of cavities were not uniformly distributed across habitats. Cavities were significantly more abundant in high- and low-slope than high-plateau habitats. Compared with other habitats, more “butt hollow” cavity types were found in high-slope habitat and they occurred at a lower tree height, whereas more “crack” cavities were found in low-slope habitat and they had a narrower entrance diameter. Although the mean orientation of cavities faced towards the northeast, cavity orientation varied significantly across habitat types. Our results indicate that certain types of cavities are concentrated in specific habitat types, which can provide avenues for forest management and biodiversity conservation. We highlight the importance of habitat heterogeneity in providing resources for cavity nesters

Sexual differences in growth and defence of Populus yunnanensis under drought stress

Dioecious woody species often exhibit male biased sex ratios under drought habitat, which may result in vulnerability in drying soils. However, whether biased sex ratios are associated with sex-specific responses in growth and defence against insect herbivory under drought stress conditions is unknown. We investigated the sexual responses in terms of growth and defence in Populus yunnanensis in response to two water treatments (40% and 100% field capacity). Drought stress reduced the plant growth (e.g., height increment, gas exchange, photosynthetic nitrogen use efficiency, water potential) of both sexes and reduced the leaf water content and defensive performance (increased damaged area by leaf-chewing generalists and decreased leaf contents of total phenolics, condensed tannins and flavonoids) of the females but not the males. Moreover, female defensive performance was lower than that of the males under drought conditions. Additionally, the plant mortality rate increased for both sexes, and it was higher for females than for males during drought stress in response to all leaf defoliation treatments. Our results suggest that sexual responses in terms of growth and defence to drought are significantly different and that P. yunnanensis females have a lower defence capability than males do, which may explain the male-biased sex ratio that exists in the natural population. Hence, this study provides new insight into forecasting population dynamics for dioecious plant species in drying habitats.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

FusionRCNN: LiDAR-Camera Fusion for Two-Stage 3D Object Detection

Accurate and reliable perception systems are essential for autonomous driving and robotics. To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors have significantly improved accuracy by adopting a two-stage paradigm that relies solely on LiDAR point clouds for 3D proposal refinement. However, the sparsity of point clouds, particularly for faraway points, makes it difficult for the LiDAR-only refinement module to recognize and locate objects accurately. To address this issue, we propose a novel multi-modality two-stage approach called FusionRCNN. This approach effectively and efficiently fuses point clouds and camera images in the Regions of Interest (RoI). The FusionRCNN adaptively integrates both sparse geometry information from LiDAR and dense texture information from the camera in a unified attention mechanism. Specifically, FusionRCNN first utilizes RoIPooling to obtain an image set with a unified size and gets the point set by sampling raw points within proposals in the RoI extraction step. Then, it leverages an intra-modality self-attention to enhance the domain-specific features, followed by a well-designed cross-attention to fuse the information from two modalities. FusionRCNN is fundamentally plug-and-play and supports different one-stage methods with almost no architectural changes. Extensive experiments on KITTI and Waymo benchmarks demonstrate that our method significantly boosts the performances of popular detectors. Remarkably, FusionRCNN improves the strong SECOND baseline by 6.14% mAP on Waymo and outperforms competing two-stage approaches