Hasil untuk "Plant ecology"

D. Pearsall

C. McSweeney, B. Palmer, D. McNeill et al.

J. Lund, G. E. Fogg

C. Gaudet, P. Keddy

Anna Rice, P. Šmarda, M. Novosolov et al.

Wei Wu, Wenhua Chen, Shiyu Liu et al.

Plants benefit extensively from endophytic bacteria, which live in host plant tissues exerting no harmful effects. Bacterial endophytes promote the growth of host plants and enhance their resistance toward various pathogens and environmental stresses. They can also regulate the synthesis of secondary metabolites with significant medicinal properties and produce various biological effects. This review summarizes recent studies on the relationships between bacterial endophytes and medicinal plants. Endophytic bacteria have numerous applications in agriculture, medicine, and other industries: improving plant growth, promoting resistance toward both biotic and abiotic stresses, and producing metabolites with medicinal potential. Their distribution and population structure are affected by their host plant’s genetic characteristics and health and by the ecology of the surrounding environment. Understanding bacterial endophytes can help us use them more effectively and apply them to medicinal plants to improve yield and quality.

J. Silvertown

M. Peters, A. Hemp, T. Appelhans et al.

The factors determining gradients of biodiversity are a fundamental yet unresolved topic in ecology. While diversity gradients have been analysed for numerous single taxa, progress towards general explanatory models has been hampered by limitations in the phylogenetic coverage of past studies. By parallel sampling of 25 major plant and animal taxa along a 3.7 km elevational gradient on Mt. Kilimanjaro, we quantify cross-taxon consensus in diversity gradients and evaluate predictors of diversity from single taxa to a multi-taxa community level. While single taxa show complex distribution patterns and respond to different environmental factors, scaling up diversity to the community level leads to an unambiguous support for temperature as the main predictor of species richness in both plants and animals. Our findings illuminate the influence of taxonomic coverage for models of diversity gradients and point to the importance of temperature for diversification and species coexistence in plant and animal communities. Explaining species richness patterns is a key question in ecology. Peterset al. sample diverse plant and animal groups across elevation on Mt. Kilimanjaro to show that, while disparate factors drive distributions of individual taxa, diversity overall decreases with elevation, mostly driven by effects of temperature.

Fudong Zhou, Yilin Lu, Jianping Liu et al.

ABSTRACT Brood parasitism negatively affects the reproductive success of hosts, leading many hosts to evolve defense strategies to recognize and reject parasitized eggs. While studies have shown that hosts may adjust their defensive behavior according to parasitism risk, whether different geographical populations of the azure‐winged magpie (Cyanopica cyanus), which are parasitized by multiple cuckoo species and face varying parasitism risks, exhibit geographical variation in egg rejection behavior is unclear. As studies have shown that the color of model eggs can influence the egg rejection behavior of hosts, we also tested whether red or blue model eggs would affect the egg rejection behavior of the azure‐winged magpies. From April to June in 2023, we investigated the egg recognition ability of azure‐winged magpie populations in Fusong County, Jilin Province; Huangpi District, Hubei Province; and Luqu County, Gansu Province, China. The results showed that the azure‐winged magpie populations in all three regions rejected approximately 100% of the model eggs, with no significant difference in rejection rates between red and blue model eggs. This study revealed that Chinese populations of azure‐winged magpies exhibited high egg recognition ability. There was no geographical variation in their egg rejection behavior when presented with non‐mimetic blue or red model eggs. This study provided basic data for further research on the anti‐parasitic strategies of the azure‐winged magpie.

Miguel Moreno, Miguel Jacome-Flores, Pedro Jordano et al.

Pollination success is influenced by factors such as density and distance from neighbouring conspecifics. However, the pure neighbourhood effects of spatial patterns of interaction on pollination success remains poorly understood. In this study, we used techniques of spatial point pattern analysis (SPPA) to investigate the relationship between the spatial distribution of a specialist pollinator, the weevil Derelomus chamaeropis, and the fruiting success of its host plant, the dwarf palm Chamaerops humilis, within a nursery pollination system. We georeferenced a dwarf palm population in a 22-hectare plot (96 individuals: 41 females/ 55 males), located at the Doñana National Park (SW Spain). We quantified the abundance and spatial pattern of adult weevil occupancy, and their correlation with the spatial distribution of dwarf palms. Additionally, we analysed the spatial pattern of fruiting success and how palm traits (number of inflorescences and flowers, and sex) influenced adult weevil abundance and fruiting success. Our findings revealed that presence/abundance of D. chamaeropis depended on plant sex, with female inflorescences showing significantly higher adult weevil abundances. We found a significant, negative density-dependent response, where higher neighbourhood density of palms led to reduced local weevil abundance. In contrast, we observed positive density and distance dependence for fruiting success at small spatial scales (2-5 m and 5-15 m), suggesting enhanced pollination success in dense patches. Our results indicate that weevil distribution is shaped by local resource availability and competitive interactions rather than broader palm arrangement. Additionally, pollination efficiency and fruit set benefit from positive distance-density dependence in dense patches.

Oksana Buzhdygan, Selina Baldauf, Dariia Borovyk et al.

ABSTRACT Understanding the factors governing grassland biodiversity across different spatial scales is crucial for effective conservation and management. However, most studies focus on single grain sizes, leaving the scale‐dependent mechanisms of biodiversity drivers unclear. We investigated how climate, soil properties, abiotic disturbance, and land use influence plant diversity across two fine spatial scales in various grassland types in Ukraine. Using spatially explicit data on plant species presence and their cover, collected at smaller (10 m2) and larger (100 m2) grain sizes, we assessed spatial β‐diversity—the variability of biodiversity between scales. We analyzed whether the effects of ecological drivers on β‐diversity are mediated by changes in species evenness, density (total cover), and intraspecific aggregation in plant community. In our study, the most influential factors of local plant diversity at both grain sizes were climate variables, followed by soil humus content, litter cover, and soil pH. Soil and litter effects were primarily driven by the response of locally rare species, while climate and grazing effects were driven by locally common species. The strength of most of these effects varied between spatial scales, affecting β‐diversity. Soil properties influenced β‐diversity through changes in total plant community cover, while the effects of climate and litter operated via changes in species evenness and aggregation. Our findings highlight that biodiversity responses to climate, soil factors, and litter depend on the size of the sampled area and reveal the role of total plant cover, evenness, and aggregation in driving fine‐scale β‐diversity in grasslands across different habitat types.

Aisha Zulfiqar, Ebroul Izquiedro

Plant species exhibit significant intra-class variation and minimal inter-class variation. To enhance classification accuracy, it is essential to reduce intra-class variation while maximizing inter-class variation. This paper addresses plant species classification using a limited number of labelled samples and introduces a novel Local Foreground Selection(LFS) attention mechanism. LFS is a straightforward module designed to generate discriminative support and query feature maps. It operates by integrating two types of attention: local attention, which captures local spatial details to enhance feature discrimination and increase inter-class differentiation, and foreground selection attention, which emphasizes the foreground plant object while mitigating background interference. By focusing on the foreground, the query and support features selectively highlight relevant feature sequences and disregard less significant background sequences, thereby reducing intra-class differences. Experimental results from three plant species datasets demonstrate the effectiveness of the proposed LFS attention mechanism and its complementary advantages over previous feature reconstruction methods.

Stephen Ampleman, Himanshu Sharma, Sayak Mukherjee et al.

Hybrid power plants (HPPs) combine multiple power generators (conventional/variable) and energy storage capabilities to support generation inadequacy and grid demands. This paper introduces a modeling and control design framework for hybrid power plants (HPPs) consisting of a wind farm, solar plant, and battery storage. Specifically, this work adapts established modeling paradigms for wind farms, solar plants and battery models into a control affine form suitable for control design at the supervisory level. In the case of wind and battery models, generator torque and cell current control laws are developed using nonlinear control and control barrier function techniques to track a command from a supervisory control law while maintaining safe and stable operation. The utility of this modeling and control framework is illustrated through a test case using a utility demand signal for tracking, time varying wind and irradiance data, and a rule-based supervisory control law.

Yang Yang, Risa Shinoda, Hiroaki Santo et al.

We present a method for jointly recovering the appearance and internal structure of botanical plants from multi-view images based on 3D Gaussian Splatting (3DGS). While 3DGS exhibits robust reconstruction of scene appearance for novel-view synthesis, it lacks structural representations underlying those appearances (e.g., branching patterns of plants), which limits its applicability to tasks such as plant phenotyping. To achieve both high-fidelity appearance and structural reconstruction, we introduce GaussianPlant, a hierarchical 3DGS representation, which disentangles structure and appearance. Specifically, we employ structure primitives (StPs) to explicitly represent branch and leaf geometry, and appearance primitives (ApPs) to the plants' appearance using 3D Gaussians. StPs represent a simplified structure of the plant, i.e., modeling branches as cylinders and leaves as disks. To accurately distinguish the branches and leaves, StP's attributes (i.e., branches or leaves) are optimized in a self-organized manner. ApPs are bound to each StP to represent the appearance of branches or leaves as in conventional 3DGS. StPs and ApPs are jointly optimized using a re-rendering loss on the input multi-view images, as well as the gradient flow from ApP to StP using the binding correspondence information. We conduct experiments to qualitatively evaluate the reconstruction accuracy of both appearance and structure, as well as real-world experiments to qualitatively validate the practical performance. Experiments show that the GaussianPlant achieves both high-fidelity appearance reconstruction via ApPs and accurate structural reconstruction via StPs, enabling the extraction of branch structure and leaf instances.

Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang et al.

Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection. Raman spectroscopy provides a powerful, non-invasive means to quantify these biomolecules through their molecular vibrational signatures. However, traditional Raman analysis relies on customized data-processing workflows that require fluorescence background removal and prior identification of Raman peaks of interest-introducing potential biases and inconsistencies. Here, we introduce DIVA (Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis), a fully automated workflow based on a variational autoencoder. Unlike conventional approaches, DIVA processes native Raman spectra-including fluorescence backgrounds-without manual preprocessing, identifying and quantifying significant spectral features in an unbiased manner. We applied DIVA to detect a range of plant stresses, including abiotic (shading, high light intensity, high temperature) and biotic stressors (bacterial infections). By integrating deep learning with vibrational spectroscopy, DIVA paves the way for AI-driven plant health assessment, fostering more resilient and sustainable agricultural practices.

Fang-Ming Cui, Hui Dong

Nuclear power plants are prominent examples of heat-to-work conversion systems, and optimizing their thermodynamic performance offers significant potential for enhancing energy efficiency. With a development history of less than a century, optimization trends in nuclear power plants indicate that classical thermodynamics alone may be insufficient, particularly when maximizing output power rather than efficiency becomes the primary focus. This paper re-examines nuclear power plant thermodynamic cycles through the lens of finite-time thermodynamics, an approach specifically developed to address the practical requirement of enhancing power output. Beginning with the simpler Brayton cycle without phase transitions, we obtain the famous Curzon-Ahlborn formula for efficiency at maximum power. Subsequently we analyze the more complex Rankine cycle, which incorporates phase transitions. By explicitly considering the working fluid undergoing phase transitions within the cycle, we uncover the inherent trade-off between output power and efficiency. Additionally, we demonstrate that both the maximum attainable power and efficiency increase as latent heat rises. These findings shall provide insights and methodologies for future thermodynamic optimization of nuclear power plants and other Rankine-type cycle systems.

Musa Sadeghi, Majeed Askari seyahooei, Majid Fallahzadeh et al.

Using pheromone traps is one of the standard methods to control the mango fruit fly, Bactrocera zonata (Saunders) (Diptera: Tephritidae). The lower efficiency of traps has made it inevitable to consider more efficient methods of controlling this pest. This study investigated the effect of integrated control treatments. The experiment was a randomized block design with split plots, including four main treatments, four sub-treatments, and five replications to control this pest. Using the pheromone traps alone (two types of traps: McPhail and Delta with two different dispensers: chipboard sheets (neopan chipboard sheets) and glass tubes), the combination of traps and shade-spraying, the combination of traps, and trunk spraying by poisonous bait (a mixture of insecticide and hydrolyzed protein), and finally the combination of traps, shade-spraying, and trunk spraying were compared. The results indicated that applying traps alone was the least effective, and the combination of shade-spraying, trunk spraying, and pheromone traps was the most effective treatment for controlling the pest. Concerning the pheromone traps and dispensers, the McPhail trap was more effective than the delta trap, and the chipboard dispenser was more effective than the glass tubes regarding the number of pest individuals captured and, consequently, the rate of damage reduced. According to the present study’s findings, combining different control measures with pheromone traps, replacing delta traps with McPhail ones, and chipboard dispensers with glass tubes would lead to appropriate control of B. zonata.

Ana Cano-Ortiz, José Carlos Piñar Fuentes, Carmelo Maria Musarella et al.

We propose a method consisting of four steps for phytosociological research and education on flora and vegetation diversity. We demonstrate the application of this method using as an example a territory of special interest due to its high index of endemism (<i>Dominican Republic Island</i>), which is a biodiversity hotspot and hosts several protected areas, such as the Jaragua-Bahoruco-Enriquillo Biosphere Reserve. Nonetheless, this model, based on teaching the phytosociological method, can be extrapolated to any location worldwide. As an example, we analyzed the dry and humid forests. Through the four research phases, this study revealed a greater number of endemic species in the dry forest compared to the humid forest, with more endemics found in districts A12 and A16. The sequenced teaching of the research phases allows for the training of university students, future managers, and educators. The model enables learning sampling techniques, developing analysis and interpretation skills, and assessing the need for conservation of habitats rich in endemic species. The teaching outcomes of this study provide optimal training for the management and dissemination of ecological values, which allow broader society to learn to respect the environment.

Natalia Cuberos, Ignacio Sanz-Benito, Tatek Dejene et al.

The Mediterranean region is renowned for its natural susceptibility to wildfires. In recent years, this risk has intensified due to various factors, including climate change and rural abandonment. Castilla y Leon stands out as one of the most severely impacted areas grappling with rural exodus. This evolving scenario accentuates the urgency of implementing forest management strategies to mitigate the escalating threat of wildfires, with a primary focus on fuel reduction. Although prescribed fires represent an efficient and cost-effective tool for wildfire prevention, they remain a contentious subject in Europe. Fungi not only contribute significantly to rural economies but also play a pivotal role in maintaining the equilibrium of forest ecosystems. The main objective of this study was to examine the effects of prescribed burning on Pinus sylvestris soils, with a specific focus on the recovery of fungal populations after such fires. To assess the short-term effects of prescribed fires on soil fungal communities, we collected soil samples from both burned and unburned plots 12 months post-burning to perform genomic DNA analyses. Our findings indicate that prescribed burning does not significantly alter fungal diversity or composition, with only litter saprotrophs showing significantly higher levels of abundance in burned areas than in unburned areas. Valuable edible fungi persisted post-burning, suggesting that prescribed burning could be used to reduce wildfire fuel loads while preserving fungal biodiversity and valuable edible fungi. These results advocate for the use of prescribed burning as a viable, myco-friendly forest management practice, offering a balance between fire prevention and ecological conservation.

Jean Bosco Nkurikiye, Valens Uwizeyimana, Kato Van Ruymbeke et al.

Agroforestry is recognized as an effective land restoration strategy in Rwanda. To effectively implement agroforestry practices, it is crucial to examine and address the preferences of farmers. However, there is limited knowledge concerning farmers’ preferences for different agroforestry characteristics and their level of willingness to engage in such systems. This paper examines farmers’ preferences for adopting agroforestry in the Eastern Province of Rwanda using a discrete choice experiment (DCE) conducted among 248 households. The analysis conducted at plot level included a total of eight attributes: number of fruit trees, number of non-fruit trees, root system and canopy, change in maize yield resulting from tree planting, extension assistance, additional labor, distance to the tree nursery, and the cost of tree seedlings. Results indicate that farmers are generally willing to adopt agroforestry practices. They prefer agroforestry systems that incorporate non-fruit and particularly fruit trees, trees with smaller canopies, agroforestry systems with potential to increase maize yields, and regular extension assistance. However, farmers dislike increases in the cost of tree seedlings. Furthermore, findings reveal two distinct latent classes of plots, encompassing 34.5 % (Class 1) and 65.5 % (Class 2) of the plots. Farmers in Class 1 possess plots that are farther away from their homesteads and are indifferent towards the number of trees to be planted but prefer trees that increase maize yields. Farmers in Class 2 exhibit a strong inclination towards planting a greater number of trees that increase maize yields and have a small canopy. The proximity to tree nurseries and the availability of low-cost tree seedlings contribute to the likelihood of tree planting in this class. This study recommends aligning agroforestry-related policies and interventions with the preferences and needs of the farmers, taking into account plot characteristics.