Hasil untuk "Plant ecology"

L. Götzenberger, Francesco de Bello, K. Bråthen et al.

N. S. Talekar, A. Shelton

D. Richardson, P. Pyšek

J. Silvertown

D. Zak, W. Holmes, D. White et al.

M. Willson

R. Hobbs, S. Humphries

Hans Lambers, F. Stuart Chapin, T. Pons

J. Andrews, Robin F. Harris

Shuo Sun, Huiyan Gu, Xiuwei Wang

Calibrating sap flow sensors enhances the accurate estimation of whole-tree transpiration is crucial for understanding forest water use and managing water resources, however calibration approaches that rely on different directional forces to move water through tree stems (push force vs transpirational pull) can result in different calibration coefficients. It remains unclear whether coefficients differ between approaches that use the same directional force. In this study, we compared transpiration estimates obtained from the balance and potometric sap flow calibration approaches using thermal dissipation (TD) sap flow sensors deployed in the same five white birch (Betula platyphylla) trees. We first conducted balance calibration using five intact potted trees, followed by cutting the materials to perform potometric calibration with rootless whole plants. We found that coefficients generated from one calibration approach serve as reliable predictors of reference transpiration when applied to TD measurements obtained from the other approach. Moreover, when applied to a growing season of TD sap flow measurements, balance coefficients yielded transpiration estimates 30 % higher (P < 0.05) than potometric calibrations. The results indicate that the potometric calibration is effective at predicting transpiration at low flow rates; however, as the proportion of high sap flow rates increases, it tends to underestimate transpiration estimates. Future research should focus on enhancing the accuracy of potometric calibration to improve its application in TD measurement studies. This enhancement will facilitate the precise estimation of whole-tree transpiration in the context of climate change, thereby elevating the quality of research in forestry science and promoting the sustainable management of water resources.

Hao Lan, Maohua Dai, Xiugui Chen et al.

Alkaline stress causes significant adverse effects that slows down the growth of plants and lowers the yield of crops; hence, it is a major challenge in cotton farming. Spermidine (Spd), a vital polyamine, plays a significant role in enhancing plant resistance to stress caused by various abiotic factors. The molecular mechanism of Spd biosynthesis and especially the role of spermidine synthase (SPDS) in tolerance of alkaline stress in cotton is, however, little known. In this study, a systematic comparative analysis of SPDS-associated genes was performed across four representative cotton cultivars (Gossypium spp.), followed by preliminary functional characterization through promoter cis-acting element profiling. Virus-induced gene silencing (VIGS) was utilized to disrupt GhSPDS11-mediated Spd biosynthesis. Under alkaline stress, GhSPDS11-silenced seedlings exhibited 29.14% and 11.12% reductions in superoxide dismutase (SOD) and catalase (CAT) activities, 31.57% and 15.16% decreases in soluble sugar and proline (Pro) content, along with 42.38% and 38.66% increases in malondialdehyde (MDA) and hydrogen peroxide (H₂O₂) compared to controls. Concurrently, silenced plants showed 44.87% fewer open stomata and significant declines in Spd content, relative water content, and biomass. These results indicate the key importance of Spd, which is composed of GhSPDS11, in improving alkali tolerance in cotton. This research gives good information regarding the molecular processes that take part in the tolerance of cotton to the saline-alkaline soils, and that GhSPDS11 could be a good genetic target in cotton enhancement in this tough agro-climatic condition.

Guangzhen Zhou, Jing Liang, Ying Li et al.

Hevea brasiliensis, the primary source of natural rubber (NR), faces significant production challenges owing to high disease susceptibility and inefficient conventional breeding methods. The absence of a stable and efficient genetic transformation system has hindered advances in molecular breeding and functional gene research in rubber trees. In this study, we established an optimized Agrobacterium tumefaciens-mediated transformation system using single anther–induced embryogenic callus as the transformation receptor, integrated with a temporary immersion system (TIS). We identified optimal explants—Group ii buds with longitudinal diameters of 2.81–3.20 mm by systematically correlating pollen developmental stages with floral bud morphology which resulted in a 60.6 % increase in embryogenic callus induction through single-anther inoculation. Compared to traditional solid culture, TIS significantly improved both somatic embryogenesis and regeneration efficiency. The optimized transformation protocol included a 15-day preculture of selected embryogenic calli on calcium-free medium, infection with a bacterial suspension (OD600 = 1.0) for 20 min, followed by dark co-cultivation. Transgene integration (GUS, GFP, HbCERK1) was confirmed via histochemical staining, fluorescence microscopy, and RT-PCR analysis. To address the challenge of root regeneration in transformed plants, we developed a grafting method using 4-week-old rootstocks, which established functional vascular connections within 7 days and yielded viable transgenic plants. This integrated approach—combining optimized explant selection, TIS-enhanced embryogenesis, transformation, and grafting—provided a robust platform for functional genomics and genome editing in rubber trees.

Herve Goeau, Pierre Bonnet, Alexis Joly

The LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually conducted on a set of more than 110K images illustrating 1000 plant species living in West Europe, built through a large-scale participatory sensing platform initiated in 2011 and which now involves tens of thousands of contributors. The main novelty over the previous years is that the identification task was evaluated as an open-set recognition problem, i.e. a problem in which the recognition system has to be robust to unknown and never seen categories. Beyond the brute-force classification across the known classes of the training set, the big challenge was thus to automatically reject the false positive classification hits that are caused by the unknown classes. This overview presents more precisely the resources and assessments of the challenge, summarizes the approaches and systems employed by the participating research groups, and provides an analysis of the main outcomes.

Xiaonan Hu, Xuebing Li, Jinyu Xu et al.

Accurate plant counting provides valuable information for agriculture such as crop yield prediction, plant density assessment, and phenotype quantification. Vision-based approaches are currently the mainstream solution. Prior art typically uses a detection or a regression model to count a specific plant. However, plants have biodiversity, and new cultivars are increasingly bred each year. It is almost impossible to exhaust and build all species-dependent counting models. Inspired by class-agnostic counting (CAC) in computer vision, we argue that it is time to rethink the problem formulation of plant counting, from what plants to count to how to count plants. In contrast to most daily objects with spatial and temporal invariance, plants are dynamic, changing with time and space. Their non-rigid structure often leads to worse performance than counting rigid instances like heads and cars such that current CAC and open-world detection models are suboptimal to count plants. In this work, we inherit the vein of the TasselNet plant counting model and introduce a new extension, TasselNetV4, shifting from species-specific counting to cross-species counting. TasselNetV4 marries the local counting idea of TasselNet with the extract-and-match paradigm in CAC. It builds upon a plain vision transformer and incorporates novel multi-branch box-aware local counters used to enhance cross-scale robustness. Two challenging datasets, PAC-105 and PAC-Somalia, are harvested. Extensive experiments against state-of-the-art CAC models show that TasselNetV4 achieves not only superior counting performance but also high efficiency.Our results indicate that TasselNetV4 emerges to be a vision foundation model for cross-scene, cross-scale, and cross-species plant counting.

Kepeng Lin, Qizhe Zhang, Rui Wang et al.

Understanding the underlying linguistic rules of plant genomes remains a fundamental challenge in computational biology. Recent advances including AgroNT and PDLLMs have made notable progress although, they suffer from excessive parameter size and limited ability to model the bidirectional nature of DNA strands respectively. To address these limitations, we propose PlantBiMoE, a lightweight and expressive plant genome language model that integrates bidirectional Mamba and a Sparse Mixture-of-Experts (SparseMoE) framework. The bidirectional Mamba enables the model to effectively capture structural dependencies across both the forward and reverse DNA strands, while SparseMoE significantly reduces the number of active parameters, improving computational efficiency without sacrificing modeling capacity. We evaluated and tested our model on the Modified Plants Genome Benchmark (MPGB), an enhanced genomic benchmark, which consolidates 31 datasets across 11 representative tasks, with input sequence lengths ranging from 50 to 6,000 bp. Experimental results demonstrate that PlantBiMoE achieves the best performance on 20 out of 31 datasets and the average best when comparing with existing models. In summary, all above results demonstrate that our model can effectively represent plant genomic sequences, serving as a robust computational tool for diverse genomic tasks, while making substantive contributions to plant genomics, gene editing, and synthetic biology. The code is available at: https://github.com/HUST-Keep-Lin/PlantBiMoE

J. H. Langenheim

Thattantavide Anju, Ajay Kumar

Mullu Kuruman tribes majorly reside in the Wayanad district of Kerala, India. Their dietary practices and food systems are deeply intertwined with wild and underutilised plants, but modern interventions and globalisation have altered them. Therefore, understanding their traditional ecological knowledge regarding the plants used for food and medicine is important for biodiversity conservation and the sustainability of the resources. This study, therefore, explores traditional ecological knowledge of the diversity of wild plant use among 125 respondents from the Mullu Kuruman tribe. Data was collected through semi-structured interviews in Malayalam during household visits and walks in gardens and forests. This study documents 111 plant species across 85 genera and 42 botanical families. Most of these plants were used for food (66.66 %), while 26.13 % were used for medicinal purposes. Bambusa bambos recorded the highest Use Report (281), Cultural Importance Index (2.248), Relative Importance Index (1), Use Value (2.248), and Cultural Value Index (1.327). This study shows the rich diversity of the plants used by the Mullu Kurumans, which is important for their food security and resilience. The agroecological diversity of climate-resilient crops such as Eleusine coracana, Panicum sumatrense, and Sorghum bicolor is suitable for dryland agriculture. Leafy vegetables such as Alternanthera sessilis, and Basella rubra, and fruits such as Artocarpus incisus, Canavalia brasiliensis and Ziziphus oenopolia which are rich in minerals and vitamins can enhance their health and well-being. Using carbohydrate-rich plants such as Dioscorea spp., Amorphophallus paeonifoliius, and Colacasia esculenta contributes to their food security. These insights are crucial for sustainable species use and conservation. This and similar studies from other parts of the world offer new insights into the use of local agro-ecological diversity of plants by the tribal communities to deal with climate change and food security challenges.

Zhiyi Shan, Haiming Chen, Yuan Deng et al.

Thermal pollution from the cooling system of the nuclear power plants greatly changes the environmental and the ecological conditions of the receiving marine water body, but we know little about their impact on the steady-state transition of marine bacterioplankton communities. In this study, we used high-throughput sequencing based on the 16S rRNA gene to investigate the impact of the thermal pollution on the bacterioplankton communities in a subtropical bay (the Daya Bay). We observed that thermal pollution from the cooling system of the nuclear power plant caused a pronounced thermal gradient ranging from 19.6°C to 24.12°C over the whole Daya Bay. A temperature difference of 4.5°C between the northern and southern parts of the bay led to a regime shift in the bacterioplankton community structure. In the three typical scenarios of regime shifts, the steady-state transition of bacterioplankton community structure in response to temperature increasing was more likely consistent with an abrupt regime shift rather than a smooth regime or a discontinuous regime model. Water temperature was a decisive factor on the regime shift of bacterioplankton community structure. High temperature significantly decreased bacterioplankton diversity and shifted its community compositions. Cyanobium and Synechococcus of Cyanobacteria, NS5 marine group of Bacteroidota, and Vibrio of Gammaproteobacteria were found that favored high temperature environments. Furthermore, the increased water temperature significantly altered the community assembly of bacterioplankton in Daya Bay, with a substantial decrease in the proportion of drift and others, and a marked increase in the proportion of homogeneous selection. In summary, we proposed that seawater temperature increasing induced by the thermal pollution resulted in an abrupt regime shift of bacterioplankton community in winter subtropical bay. Our research might broad our understanding of marine microbial ecology under future conditions of global warming.

Hilbert Yuen In Lam, Xing Er Ong, Marek Mutwil

Large Language Models (LLMs), such as ChatGPT, have taken the world by storm and have passed certain forms of the Turing test. However, LLMs are not limited to human language and analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multi-purpose prediction tools able to explain cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.

Zhengle Wang, Ruifeng Wang, Minjuan Wang et al.

Pest and disease classification is a challenging issue in agriculture. The performance of deep learning models is intricately linked to training data diversity and quantity, posing issues for plant pest and disease datasets that remain underdeveloped. This study addresses these challenges by constructing a comprehensive dataset and proposing an advanced network architecture that combines Contrastive Learning and Masked Image Modeling (MIM). The dataset comprises diverse plant species and pest categories, making it one of the largest and most varied in the field. The proposed network architecture demonstrates effectiveness in addressing plant pest and disease recognition tasks, achieving notable detection accuracy. This approach offers a viable solution for rapid, efficient, and cost-effective plant pest and disease detection, thereby reducing agricultural production costs. Our code and dataset will be publicly available to advance research in plant pest and disease recognition the GitHub repository at https://github.com/WASSER2545/GPID-22