Hasil untuk "Plant ecology"

N. Terry, G. Bañuelos

David U Hooper, D. Bignell, V. Brown et al.

Simon Woodruff

This paper summarizes and consolidates fusion power-plant costing work performed in support of ARPA-E from 2017 through 2024, and documents the evolution of the associated analysis framework from early capital-cost-focused studies to a standards-aligned, auditable costing capability. Early efforts applied ARIES-style cost-scaling relations to generate Nth-of-a-kind (NOAK) estimates and were calibrated through a pilot study with Bechtel and Decysive Systems to benchmark balance-of-plant (BOP) costs and validate plant-level reasonableness from an engineering, procurement, and construction (EPC) perspective. Subsequent work, informed by Lucid Catalyst studies of nuclear cost drivers, expanded the methodology to treat indirect costs explicitly and to evaluate cost-reduction pathways for non-fusion-island systems through design-for-cost practices, modularization, centralized manufacturing, and learning. As ARPA-E's fusion portfolio expanded, these methods were applied across BETHE and GAMOW concepts (and select ALPHA revisits), including enhanced treatment of tritium handling and plant integration supported by Princeton/PPPL expertise. In 2023 the capability was refactored to align with the IAEA-GEN-IV EMWG-EPRI code-of-accounts lineage, while key ARIES-derived scaling relations were replaced by bottom-up subsystem models for dominant fusion cost drivers (e.g., magnets, lasers, power supplies, and power-core components) coupled to physics-informed power balances and engineering-constrained radial builds. These developments were implemented in the spreadsheet-based Fusion Economics code (FECONs) and released as an open-source Python framework (pyFECONs), providing a transparent mapping from subsystem estimates to standardized accounts and a consistent computation of LCOE.

Abdullah Al Mamun, Miaohua Zhang, David Ahmedt-Aristizabal et al.

Self-supervised learning (SSL) is attractive for plant disease detection as it can exploit large collections of unlabeled leaf images, yet most existing SSL methods are built on CNNs or vision transformers that are poorly matched to agricultural imagery. CNN-based SSL struggles to capture disease patterns that evolve continuously along leaf structures, while transformer-based SSL introduces quadratic attention cost from high-resolution patches. To address these limitations, we propose StateSpace-SSL, a linear-time SSL framework that employs a Vision Mamba state-space encoder to model long-range lesion continuity through directional scanning across the leaf surface. A prototype-driven teacher-student objective aligns representations across multiple views, encouraging stable and lesion-aware features from labelled data. Experiments on three publicly available plant disease datasets show that StateSpace-SSL consistently outperforms the CNN- and transformer-based SSL baselines in various evaluation metrics. Qualitative analyses further confirm that it learns compact, lesion-focused feature maps, highlighting the advantage of linear state-space modelling for self-supervised plant disease representation learning.

Herve Goeau, Pierre Bonnet, Alexis Joly

Automated identification of plants has improved considerably thanks to the recent progress in deep learning and the availability of training data. However, this profusion of data only concerns a few tens of thousands of species, while the planet has nearly 369K. The LifeCLEF 2019 Plant Identification challenge (or "PlantCLEF 2019") was designed to evaluate automated identification on the flora of data deficient regions. It is based on a dataset of 10K species mainly focused on the Guiana shield and the Northern Amazon rainforest, an area known to have one of the greatest diversity of plants and animals in the world. As in the previous edition, a comparison of the performance of the systems evaluated with the best tropical flora experts was carried out. This paper presents 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.

Iván Felipe Benavides-Martínez, Cristiam Victoriano Portilla-Cabrera, Katherine E. Mills et al.

Benthic habitat is challenging due to the environmental complexity of the seafloor, technological limitations, and elevated operational costs, especially in under-explored regions. This generates knowledge gaps for the sustainable management of hydrobiological resources and their nexus with society. We developed ECOSAIC (Ecological Compression via Orthogonal Specialized Autoencoders for Interpretable Classification), an Artificial Intelligence framework for automatic classification of benthic habitats through interpretable latent representations using a customizable autoencoder. ECOSAIC compresses n-dimensional feature space by optimizing specialization and orthogonality between domain-informed features. We employed two domain-informed categories: biogeochemical and hydrogeomorphological, that together integrate biological, physicochemical, hydrological and geomorphological, features, whose constraints on habitats have been recognized in ecology for a century. We applied the model to the Colombian Pacific Ocean and the results revealed 16 benthic habitats, expanding from mangroves to deep rocky areas up to 1000 m depth. The candidate habitats exhibited a strong correspondence between their environmental constraints, represented in latent space, and their expected species composition. This correspondence reflected meaningful ecological associations rather than purely statistical correlations, where the habitat's environmental offerings align semantically with the species' requirements. This approach could improve the management and conservation of benthic habitats, facilitating the development of functional maps that support marine planning, biodiversity conservation and fish stock assessment. We also hope it provides new insights into how ecological principles can inform AI frameworks, particularly given the substantial data limitations that characterize ecological research.

Adam J Riesselman, Evan M Cofer, Therese LaRue et al.

Quantifying organism-level phenotypes, such as growth dynamics and biomass accumulation, is fundamental to understanding agronomic traits and optimizing crop production. However, quality growing data of plants at scale is difficult to generate. Here we use a mobile robotic platform to capture high-resolution environmental sensing and phenotyping measurements of a large-scale hydroponic leafy greens system. We describe a self-supervised modeling approach to build a map from observed growing data to the entire plant growth trajectory. We demonstrate our approach by forecasting future plant height and harvest mass of crops in this system. This approach represents a significant advance in combining robotic automation and machine learning, as well as providing actionable insights for agronomic research and operational efficiency.

Sayak Mukherjee, Himanshu Sharma, Wenceslao Shaw Cortez et al.

This research investigates designing a supervisory feedback controller for a hybrid power plant that coordinates the wind, solar, and battery energy storage plants to meet the desired power demands. We have explored an online feedback control design that does not require detailed knowledge about the models, known as feedback optimization. The control inputs are updated using the gradient information of the cost and the outputs with respect to the input control commands. This enables us to adjust the active power references of wind, solar, and storage plants to meet the power generation requirements set by grid operators. The methodology also ensures robust control performance in the presence of uncertainties in the weather. In this paper, we focus on describing the supervisory feedback optimization formulation and control-oriented modeling for individual renewable and storage components of the hybrid power plant. The proposed supervisory control has been integrated with the hybrid plant co-simulation engine, Hercules, demonstrating its effectiveness in more realistic simulation scenarios.

S. Taugourdeau, S. Taugourdeau, M. Machdoud et al.

This position article advocates for the creation of a participatory pastoral observatory, leveraging accessible technologies, including smartphones, to monitor rangeland ecosystems. Rather than reiterating the already accepted need for monitoring, we focus on how technological progress, ranging from ground-based field plots to satellite imagery, UAVs, and smartphones using Structure from Motion (SfM) methods, has transformed rangeland observation. We argue that an imagery-based community monitoring system can provide accurate, relevant, and timely data while empowering local stakeholders and informing policy decisions. We detail the operational steps of smartphone-based observatory, highlight its capacity to reduce labor-intensive biomass sampling, and discuss its feasibility when applied by pastoralists. We also draw lessons from related participatory approaches in Mongolia, Ireland, and East Africa. By integrating traditional ecological knowledge with scientific approaches, this initiative can strengthen the resilience of pastoral systems, support sustainable management practices, and contribute to evidence-based policymaking. The proposed observation framework builds on existing research and technological innovations to promote a decentralized and inclusive monitoring system. We imagine such a type of observatory could be useful in Sahel Region or in Northern Africa, could describe practical challenges (smartphone penetration, network coverage, training for low-literacy users), and outline next steps for implementation.

Susanne Werle, Anna Preußner, Kenneth Kuba et al.

ABSTRACT Animal pollination, the transfer of pollen by animal agents, is essential for plant reproduction. Methods like microscopy and DNA metabarcoding have been used to investigate pollen transport and plant–pollinator interactions. DNA metabarcoding, in particular, is a reliable method to identify the origins of mixed pollen samples. Although it has mainly been used to study pollinators' dietary patterns, it does not provide insights from the plant's perspective, such as the type of viable pollen received. We aimed to explore the potential of DNA metabarcoding to analyse heterospecific pollen transfer to plants in semi‐natural and agricultural landscapes along a land‐use intensity gradient. We collected stigmas of three closely related Ranunculus species (R. acris, R. bulbosus and R. repens) from 20 grassland plots in Germany with varying land‐use intensities and flowering plant diversity and subjected them to internal transcribed spacer 2 (ITS2) metabarcoding. Our results revealed a nonlinear relationship between flowering plant species richness and heterospecific pollen richness on Ranunculus stigmas. The lowest heterospecific pollen diversity occurred in landscapes with intermediate plant species richness, whereas plots with low or high richness showed greater heterospecific pollen diversity. Reduced plant species richness, found mostly on intermediate and high LUI plots, forces pollinators to visit multiple plant species and thus increases heterospecific pollen transfer. Plots with intermediate plant species richness, on the contrary, likely provide a balanced mix of resources for pollinators, visiting multiple plant species within a foraging round and thus decreasing the amount of heterospecific pollen. Increased heterospecific pollen at high‐richness plots may result from competition in pollinator‐rich communities. Our results show that DNA metabarcoding is a powerful tool for assessing heterospecific pollen diversity, revealing that pollen transfer is heavily influenced by plant community composition. This approach provides novel insights into pollinator fidelity and potential pollination outcomes across diverse environments.

Zhenyu Fang, Yuyong Li, Jianqiang Huang et al.

ABSTRACT The fungal pathogens exhibit diverse genome architecture, which facilitates the host adaptation. Although increasing high-quality genomic data enable insights into the genome architecture of many fungal pathogens during the last decades, genomic features of many fungal species are still not fully characterized. Here, we identified a Pyriculariaceae family fungal strain Xenopyricularia zizaniicola JB-1 causing the leaf spot disease on Zizania latifolia and revealed its distinct genome compartment features. The fungal strain JB-1 was identified as X. zizaniicola based on the Koch’s postulate, conidial morphology, and phylogenetic analysis. Using 2.51 Gb PacBio HiFi sequencing data, the JB-1 genome was assembled into nine contigs, five of which contain telomeric repeats at both ends. The genome size is 40,888,459 bp with an N50 of 6,431,016 bp, and a total of 9,894 protein-coding genes were predicted. BUSCO assessment demonstrated high completeness, with 754 (99.47%) of the 758 BUSCO orthologs identified as complete. The absence of both repeat-rich regions at chromosome ends and preferential residing of pathogenicity-associated genes (PAGs) in the repeat-rich regions indicated a genome compartment dissimilar to the “two-speed genome” commonly observed in Pyricularia oryzae, indicating a distinct evolution drive of the PAGs in X. zizaniicola strain JB-1. Additionally, the JB-1 genome encodes fewer PAGs compared to other members of family Pyriculariaceae. These findings provide valuable genomic resources of family Pyriculariaceae and will facilitate future studies on host-pathogen interactions and the development of effective disease management strategies for X. zizaniicola.IMPORTANCEThe family Pyriculariaceae includes notorious pathogens that annually result in significant agricultural losses. The genome architecture of plant fungal pathogens reflects their evolutionary adaptation to host-pathogen interactions. However, limited knowledge exists regarding the genomic features of other species within family Pyriculariaceae, particularly those associated with the economically important crop Zizania latifolia. In this study, we assembled the first high-quality genome of Xenopyricularia zizaniicola strain JB-1, which infects Z. latifolia, and revealed its distinct genome architecture. We provide evidence that the distribution pattern of pathogenicity-associated genes in X. zizaniicola strain JB-1 closely resembles the “one-speed genome” structure, which contrasts with Pyricularia oryzae. Our findings provide valuable resources for genomic studies within family Pyriculariaceae and contribute to our understanding of the adaptive evolution of pathogens to their hosts.

K. Jhansi, P. N. Karanjikar, V. P. Suryavanshi et al.

The field experiment was conducted during kharif, 2023 at Experimental Farm, Department of Agronomy, College of Agriculture, Latur, to study effect of fertilizer levels and row spacings on growth and yield. The experiment was laid out in Factorial Randomized Block Design with two factors and replicated thrice. First factorconsisted of three fertilizer levels viz., fertilizer level-1:75% , fertilizer level-2: 100% and fertilizer level-3: 125% recommended dose of fertilizer, second factor consisted of three-row spacing viz., 2 row spacing-1: 22.5 ×10 cm2, row spacing- 2: 30×10 cm2 and row spacing-3: 45×10 cm2. The results revealed that application of 125 % RDF recorded significantly higher plant height (132.27 cm), number of tillers plant-1 (4.09), number of functional leaves plant-1 (23.84), leaf area plant-1 (2.76 dm2), dry matter plant-1 (20.91 g), LAI (3.50), grain (2833 kg ha-1), straw (8398 kg ha-1) and biological yield (11232 kg ha-1). Among row spacings 22.5×10 cm2 recorded significantly higher plant height (128.95 cm), number of functional leaves plant-1 (23.58), leaf area plant-1 (2.66 dm2), Leaf area index  (3.45), grain (2829 kg ha-1), straw (8300 kg ha-1) biological yield (11130 kg ha-1) and harvest index (25%) which was comparable with 30×10 cm2 and significantly superior over 45×10 cm2. Number of tillers plant-1 (3.90) and dry matter plant-1 (20.44 g) were significantly higher with 45×10 cm2 row spacings, which was comparable with 30×10 cm2 and significantly superior over 22.5×10 cm2.

J. Hierro, R. Callaway

Allelopathy (i.e., chemical interaction among species) was originally conceived as inclusive of positive and negative effects of plants on other plants, and we adopt this view. Most studies of allelopathy have been phenomenological, but we focus on studies that have explored the ecological significance of this interaction. The literature suggests that studies of allelopathy have been particularly important for three foci in ecology: species distribution, conditionality of interactions, and maintenance of species diversity. There is evidence that allelopathy influences local distributions of plant species around the world. Allelopathic conditionality appears to arise through coevolution, and this is a mechanism for plant invasions. Finally, allelopathy promotes species coexistence via intransitive competition, modifications of direct interactions, and (co)evolution. Recent advances additionally suggest that coexistence might be favored through biochemical recognition. The preponderance of phenomenological studies notwithstanding, allelopathy has broad ecological consequences. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sacha Sokoloski, Jure Majnik, Philipp Berens

Animal vision is thought to optimize various objectives from metabolic efficiency to discrimination performance, yet its ultimate objective is to facilitate the survival of the animal within its ecological niche. However, modeling animal behavior in complex environments has been challenging. To study how environments shape and constrain visual processing, we developed a deep reinforcement learning framework in which an agent moves through a 3-d environment that it perceives through a vision model, where its only goal is to survive. Within this framework we developed a foraging task where the agent must gather food that sustains it, and avoid food that harms it. We first established that the complexity of the vision model required for survival on this task scaled with the variety and visual complexity of the food in the environment. Moreover, we showed that a recurrent network architecture was necessary to fully exploit complex vision models on the most visually demanding tasks. Finally, we showed how different network architectures learned distinct representations of the environment and task, and lead the agent to exhibit distinct behavioural strategies. In summary, this paper lays the foundation for a computational approach to visual ecology, provides extensive benchmarks for future work, and demonstrates how representations and behaviour emerge from an agent's drive for survival.

Kosuke Ikeya, Francisco J. Guerrero-Gonzalez, Luca Kiewiet et al.

To establish a self-sustained human presence in space and to explore deeper into the solar system, extensive research has been conducted on In-Situ Resource Utilization (ISRU) systems. Past studies have proposed and researched many technologies to produce oxygen from regolith, such as carbothermal reduction and water extraction from icy regolith, to utilize it for astronauts' life support and as the propellant of space systems. However, determining the most promising technology remains challenging due to uncertainties in the lunar environment and processing methods. To better understand the lunar environment and ISRU operations, it is crucial to gather more information. Motivated by this need for information gathering, this paper proposes a new ISRU plant architecture integrating carbothermal reduction of dry regolith and water extraction from icy regolith. Two different hybrid plant architectures integrating both technologies (1) in parallel and (2) in series are examined. The former involves mining and processing in both a Permanently Shadowed Region (PSR) and a peak of eternal light in parallel, while the latter solely mines in a PSR. In this series hybrid architecture, the dry regolith tailings from water extraction are further processed by carbothermal reduction. This paper conducts a comparative analysis of the landed mass and required power of each plant architecture utilizing subsystem-level models. Furthermore, based on uncertain parameters such as resource content in regolith, the potential performance range of each plant was discovered through Monte Carlo simulations. The result indicates the benefit of the series hybrid architecture in terms of regolith excavation rate, while its mass cost seems the highest among the studied architectures.

H.G. Skatter, M.L. Charlebois, S. Coats

The federal recovery strategy for woodland caribou identifies wildfires within the last 40 years and anthropogenic disturbance visible at a scale of 1:50,000, including a 500-m buffer, as disturbed. Long-term vegetation recovery on linear features post-fire has not yet been documented. We examined vegetation recovery including stem density and height, hiding cover, and reindeer lichen cover along 40+ year-old legacy linear features in Northern Saskatchewan, in both uplands and lowlands 1–41 years post-fire. We compared these results with burned areas off-lines and unburned lines. On unburned lines in uplands there was minimal recovery, while there was significant recovery of stem count, height and hiding cover on burned lines and burned off-lines. Reindeer lichen cover and thickness remained significantly lower on burned lines and burned off-lines than on unburned lines until the 41-year age group, where there was no longer a significant difference. In lowlands, the stem density and stem height were initially significantly higher on unburned lines than on either burned lines or burned off-lines. After 27–32 years post-fire there was no longer a significant difference in stem densities. Our findings show that fires substantially accelerate natural revegetation and instigate a recovery that is similar on and off disturbance features in both uplands and lowlands. These findings can inform management decisions on restoration planning and calculation of range disturbance metrics. We suggest that the anthropogenic 500-m buffer should be removed post fire, as anthropogenic disturbance is reset, and anthropogenic disturbance should be classified as naturally recovering.

Marylou C. Machingura, Sierra Glover, Alexis Settles et al.

Poly(aspartic acid) (PASP) is an environmentally friendly biopolymer used as a fertilizer synergist and known to increase agricultural yields. The mechanism of PASP enhancement has, however, not been established, although the general hypothesis is that the polymer functions to hold nutrients closer to the root zone. The objective of this study was to determine the physiological and molecular changes that occur when plants are exposed to PASP, with future directions leading to a proposed mode of action. A whole genome transcriptome study was conducted. Arabidopsis thaliana seeds were germinated and grown in sterile plates treated with 250 ppm PASP under continuous light. Total RNA was extracted from whole seedlings and sequenced. The results revealed 462 differentially expressed genes (DEGs), 245 of which were upregulated and 217 downregulated. Gene Ontology, KEGG and MAPman analyses revealed DEGs involved in photosynthesis with 11 light harvesting complexes upregulated (e.g. LHCB1.1, LHCB2.2, LHCA1, LHCB4.2, LHCB2.1, LHCA4, LHCB1.1, LHCB3, LHCA3); the peroxisome pathway had 6 DEGs (CAT1, KAT1 and XDH2) upregulated (CSD1, CSD2 and FSD2) downregulated, the phenylpropanoid biosynthesis pathway had 7 DEGs upregulated. Other key DEGs were associated with the amino acid (e.g. ASN1) and nitrogen metabolism pathways. Physiology assessment results showed significant differences between control and treated plants with a 33 % increase in leaf area, 25 % increase chlorophyll content (p ≤ 0.05) and a 4-fold increase in photosynthetic rate (p ≤ 0.001). This information helps to increase our understanding of the key genes and metabolic pathways associated with plant response to PASP.

Wenbo Zhou, Liujing Zhang, Aleksey Sheshukov et al.

Abstract Ground heat flux (G0) is a key component of the land‐surface energy balance of high‐latitude regions. Despite its crucial role in controlling permafrost degradation due to global warming, G0 is sparsely measured and not well represented in the outputs of global scale model simulation. In this study, an analytical heat transfer model is tested to reconstruct G0 across seasons using soil temperature series from field measurements, Global Climate Model, and climate reanalysis outputs. The probability density functions of ground heat flux and of model parameters are inferred using available G0 data (measured or modeled) for snow‐free period as a reference. When observed G0 is not available, a numerical model is applied using estimates of surface heat flux (dependent on parameters) as the top boundary condition. These estimates (and thus the corresponding parameters) are verified by comparing the distributions of simulated and measured soil temperature at several depths. Aided by state‐of‐the‐art uncertainty quantification methods, the developed G0 reconstruction approach provides novel means for assessing the probabilistic structure of the ground heat flux for regional permafrost change studies.

S. Donn, J. Kirkegaard, Geetha Perera et al.

Shih-Feng Fu, Jyuan-Yu Wei, H. Chen et al.