Hasil untuk "Plant ecology"

B. Mcrae, P. Beier

G. Lövei, Keith D. Sunderland

S. Chapman, R. W. Pearcy, J. Ehleringer et al.

Ilaria Cazzaniga, Ana I. Dogliotti, Susanne Kratzer et al.

The use of high-resolution data in aquatic applications increased significantly in the last decade with the launch of decametre-scale optical sensors. More recently, commercial very-high resolution (VHR) sensors, offering finer spatial and temporal resolutions, have shown the potential of complementing data from high-resolution missions. Planet SuperDove (SD), with a band-setting similar to the Copernicus Sentinel-2 MultiSpectral Instrument (S2-MSI), a 3-m spatial resolution and quasi-daily revisiting time, show the potential for widening water monitoring applications to smaller water basins, and finer-scale phenomena. However, the uncertainties in SD products need to be quantified, to assess their fitness-for-purpose for these applications. This work aims to provide uncertainty estimates for SD-derived aquatic remote sensing reflectance (RRS) in different water types, benefitting from the radiometric measurements of the AERONET-OC network. RRS was derived from both Surface Reflectance (SR) products, distributed by Planet, or from data processed with ACOLITE. The comparability between SD and S2-MSI products was also assessed comparing RRS and Rayleigh-corrected reflectance (RRC) from S2-MSI and SD. The results indicate generally low performance across all bands for both SD RRS products, except in the most turbid waters, and highlight the lack of a publicly available robust atmospheric correction processor for SD data for most optical water types. The comparison to S2-MSI shows promising results only when comparing RRC values, but differences still suggest issues associated with calibration and radiometry of the SD sensors. The results also highlight the need for a harmonization strategy to ensure consistent integration of these datasets within multi-source monitoring systems.

P. S. Nobel

Aniruddha Acharya, Kaitlin Hopkins, Tatum Simms

Silicon has striking similarity with carbon and is found in plant cells. However, there is no specific role that has been assigned to silicon in the life cycle of plants. The amount of silicon in plant cells is species specific and can reach levels comparable to macronutrients. Silicon is the central element for artificial intelligence, nanotechnology and digital revolution thus can act as an informational molecule like nucleic acids while the diverse bonding potential of silicon with different chemical species is analogous to carbon and thus can serve as a structural candidate such as proteins. The discovery of large amounts of silicon on Mars and the moon along with the recent developments of enzyme that can incorporate silicon into organic molecules has propelled the theory of creating silicon-based life. More recently, bacterial cytochrome has been modified through directed evolution such that it could cleave silicon-carbon bonds in organo-silicon compounds thus consolidating on the idea of utilizing silicon in biomolecules. In this article the potential of silicon-based life forms has been hypothesized along with the reasoning that autotrophic virus-like particles can be a lucrative candidate to investigate such potential. Such investigations in the field of synthetic biology and astrobiology will have corollary benefit on Earth in the areas of medicine, sustainable agriculture and environmental sustainability. Bibliometric analysis indicates an increasing interest in synthetic biology. Germany leads in research related to plant synthetic biology, while Biotechnology and Biological Sciences Research Council (BBSRC) at UK has highest financial commitments and Chinese Academy of Sciences generates the highest number of publications in the field.

Andreas Gilson, Lukas Meyer, Oliver Scholz et al.

Accurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping. Existing solutions are designed problem-specific with a focus on certain plant species or specified sensor-modalities for data acquisition. Furthermore, it is common to use extensive pre-processing and down-sample the plant point clouds to meet hardware or neural network input size requirements. We propose a simple, yet effective algorithm KDSS for sub-sampling of biological point clouds that is agnostic to sensor data and plant species. The main benefit of this approach is that we do not need to down-sample our input data and thus, enable segmentation of the full-resolution point cloud. Combining KD-SS with current state-of-the-art segmentation models shows satisfying results evaluated on different modalities such as photogrammetry, laser triangulation and LiDAR for various plant species. We propose KD-SS as lightweight resolution-retaining alternative to intensive pre-processing and down-sampling methods for plant organ segmentation regardless of used species and sensor modality.

Arif Ahmed, Ritvik Agarwal, Gaurav Srikar et al.

Strawberry farming demands intensive labor for monitoring and maintaining plant health. To address this, Team SARAL develops an autonomous robot for the 2024 ASABE Student Robotics Challenge, capable of navigation, unhealthy leaf detection, and removal. The system addresses labor shortages, reduces costs, and supports sustainable farming through vision-based plant assessment. This work demonstrates the potential of robotics to modernize strawberry cultivation and enable scalable, intelligent agricultural solutions.

Brian N. Bailey

This work presents a new framework for procedural generation of dynamic 3D plant model geometries, which has been implemented in the Helios modeling system. Key goals of this work were to develop a model that 1) has a generalized set of parameters that are conserved across species, which are botanically-consistent and readily measurable; 2) significantly reduces the time and effort needed to create photorealistic, dynamically evolving plant models; 3) allows for encoding of the entire plant structure into a character-based representation that can integrated with machine learning models, and 4) includes realistic and computationally efficient collision physics. A model framework that satisfies these specifications is presented in this report. The model was implemented in the Helios C++ and PyHelios Python frameworks, which are open-source libraries that can be used to generate 3D plant geometries based on this model.

Alexandria N. Igwe, Karlisa A. Callwood, Delia S. Shelton

Humans shape the world through policies, practices, and behavior that create environmental heterogeneity. Political and critical ecology offer frameworks for understanding how societies have historically and currently used power, policies, and practices to shape environmental landscapes and conditions, ultimately influencing the ecology and evolution of biodiversity. We suggest that integrating political and critical ecology can enhance our understanding of anthropogenic influences, such as luxury effects and legacy effects, including redlining—a form of structural racism implemented in the United States. Here, we review the consequences of legacy and luxury effects on urban ecosystems, with a focus on their impact on the fauna and flora. We propose that legacy and luxury effects can have independent and interdependent influences on ecological diversity, abundance, biological invasions, and pollution exposure. Although these effects can persist, environmental remediation may provide a pathway to restorative justice. We also discuss Plantago, herbaceous plants with the potential to mitigate the impacts of cadmium, a notorious environmental contaminant whose disposition parallels redlining patterns. Phytoremediation can contribute to biofuels, biofoundries, and the green economy, offering solutions to restore affected communities. By applying political and critical ecology lenses, we can identify socio-ecological mechanisms that affect humans and the environment. These insights can inform the development of green infrastructure to help remediate adverse effects. Ideally, these approaches provide pathways to address historical injustices, enhance equity, and restore ecological landscapes.

Mostafa Moghadami Rad, Vahid Rizvandi

Extended Abstract Background: In recent decades, the escalating impacts of climate change, coupled with increasing anthropogenic pressures particularly in forest ecosystems—have raised significant concerns regarding the degradation of vital natural resources, such as soil and water. Among these pressures, logging operations, especially timber extraction via skid trails, result in substantial physical disturbances to the soil structure and surface hydrological behavior. These activities, create conditions conducive to enhanced surface erosion and runoff generation through soil compaction, reduction of vegetative cover, and increased effective slope. Hydrological and geomorphological processes, such as the transformation of rainfall into runoff and sediment transport, are strongly influenced by soil properties, slope gradient, rainfall intensity, and land-use practices. Consequently, examining both the individual and interactive effects of these factors, particularly in ecologically sensitive forested regions, is essential for formulating sustainable land management strategies. This study aims to quantitatively and qualitatively assess the influence of soil properties and slope gradients on runoff generation, sediment yield, and rill erosion along skid trails in Compartments 106 and 107 of the Loohe Forest Management Plan in northern Iran. The research endeavors to enhance our understanding of the environmental implications of timber extraction and provide a scientific foundation for the sustainable management of soil and water resources. Methods: To meet the study objectives, the skid trails were categorized into five slope classes: less than 5%, 5–10%, 10–15%, 15–20%, and 20–25%. Three treatment types: (1) wheel track (machinery path), (2) trail centerline, and (3) undisturbed control area (natural forest without anthropogenic interference) were identified within each slope class. Soil samples were collected at three depths (0–10 cm, 10–20 cm, and 20–30 cm) from each treatment using steel cylinders. Physical soil parameters, including texture, bulk density, porosity, and gravimetric moisture content, as well as chemical properties (such as organic matter content and electrical conductivity), were analyzed in this research. To simulate hydrological processes, a rainfall simulator was employed to deliver precipitation at an intensity of 65 mm/h for 30 min, reflecting a 10-year return interval for the region. Hydrological and erosional variables, including runoff volume, time to runoff initiation, runoff coefficient, sediment concentration and yield, and rill dimensions (depth and width), were measured and recorded after simulated rainfall application. Statistical analysis of the data was conducted using the Analysis of Variance (ANOVA) and mean comparison tests at a 5% significance level to evaluate both main and interaction effects. Results: The results indicated statistically significant differences (p < 0.05) among treatments (wheel track, trail center, and control) and slope classes for most of the measured variables, including runoff volume, sediment yield and concentration, runoff coefficient, runoff initiation time, and rill erosion intensity. The highest values of runoff and sediment yield were recorded in the wheel track treatment, identified as the most compacted and disturbed area due to repeated machinery traffic, particularly on slopes exceeding 20%. In contrast, the control plots, characterized by natural vegetation and the absence of mechanical disturbance, exhibited the lowest values across all variables. Soil compaction in the wheel tracks, evidenced by increased bulk density and reduced porosity, resulted in a marked decrease in infiltration capacity, thereby promoting increased surface runoff. The interaction between soil properties and slope gradient significantly influenced the hydrological and erosional responses; steeper slopes amplified the negative effects of soil compaction on runoff and sediment production. Moreover, runoff volume demonstrated greater responsiveness to environmental changes compared to sediment yield, reacting more rapidly and directly to alterations in physical conditions. This suggests that runoff may serve as a reliable early indicator for identifying areas at risk of erosion in disturbed forest environments. Conclusion: The findings of this study underscore that timber extraction via skid trails significantly alters soil physical characteristics due to mechanical compaction and, when combined with steep slopes, exacerbates runoff, sediment generation, and rill erosion processes. The wheel track treatment emerged as the most vulnerable area hydrologically and erosively due to its elevated soil compaction. These results highlight the urgent need to reevaluate the planning and implementation of skid trails, advocating for protective measures, such as revegetation, mechanical soil stabilization, slope limitation, and designated routes for machinery movement. Ultimately, this research provides a scientific basis for the development of technical guidelines aimed at promoting sustainable forest management and conserving natural resources in mountainous regions. The outcomes may serve as a valuable resource for forest managers, natural resource engineers, and policymakers.

Chunmei Yu, Min Wang, Long Li et al.

Wheat (Triticum aestivum L.) is the widest cultivated crop in the world. Abiotic stress, such as drought and high salinity, dramatically impacts the growth and development of wheat and leads to remarkable yield loss. Understanding the underlying mechanisms of abiotic stress tolerance is of great importance to develop high yield varieties with wide adaptability. Ubiquitination is a major type of post-translational modification in eukaryotes. The plant U-Box (PUB) protein is the smallest family in the E3 ligase superfamily, and involved in the responses to various environmental stimuli. Currently, TaPUB57 has been cloned from wheat. It was induced by multiple abiotic stresses and phytohormone. Its ectopic expression increased grain size and drought tolerance, but caused hypersensitive to salt stress in rice. TaPUB57 interacted with and ubiquitinated TaEXPB3. Constitutive expression of TaEXPB3 resulted in small grain size and remarkably enhanced salt tolerance. Moreover, TaPUB57/TaEXPB3 co-expressing rice plants exhibited phenotypes of salt sensitivity and larger grain size relative to TaEXPB3 transgenic lines. Therefore, it is speculated that TaPUB57 acts on grain size and the salt tolerance by ubiquitinating TaEXPB3.

T. Makhalanyane, A. Valverde, Eoin Gunnigle et al.

R. Salguero‐Gómez, O. Jones, C. R. Archer et al.

Haiyang Shi

Due to the heterogeneity of the global distribution of ecological and hydrological ground-truth observations, machine learning models can have limited adaptability when applied to unknown locations, which is referred to as weak extrapolability. Domain adaptation techniques have been widely used in machine learning domains such as image classification, which can improve the model generalization ability by adjusting the difference or inconsistency of the domain distribution between the training and test sets. However, this approach has rarely been used explicitly in machine learning models in ecology and hydrology at the global scale, although these models have often been questioned due to geographic extrapolability issues. This paper briefly describes the shortcomings of current machine learning models of ecology and hydrology in terms of the global representativeness of the distribution of observations and the resulting limitations of the lack of extrapolability and suggests that future related modelling efforts should consider the use of domain adaptation techniques to improve extrapolability.

Eugene Koh, Rohan Shawn Sunil, Hilbert Yuen In Lam et al.

Life finds a way. For sessile organisms like plants, the need to adapt to changes in the environment is even more poignant. For humanity, the need to develop crops that can grow in diverse environments and feed our growing population is an existential one. The advent of the genomics era enabled the generation of high-throughput data and computational methods that serve as powerful hypothesis-generating tools to understand the genomic and gene functional basis of stress resilience. Today, the proliferation of artificial intelligence (AI) allows scientists to rapidly screen through high-throughput datasets to uncover elusive patterns and correlations, enabling us to create more performant models for prediction and hypothesis generation in plant biology. This review aims to provide an overview of the availability of large-scale data in plant stress research and discuss the application of AI tools on these large-scale datasets in a bid to develop more stress-resilient plants.

L.A. Gorodetskaya, A.Y. Denisova, L.M. Kavelenova et al.

Rare plant species restoration (reintroduction) is one of the main biodiversity conservation activities. Reintroduced plants need constant monitoring in order to study features of their development and control the population state. To reduce the human impact on the natural habitat of plants and simplify the monitoring process, we propose the use of automatic analysis of unmanned aerial vehicles (UAVs) data using the Yolov3 neural network. The article discusses neural network parameters for detecting Paeonia Tenuifolia, reintroduced in the Samara region by ecologists from the Department of Ecology, Botany and Nature Conservation of Samara University. The main issue under research is the possibility of training a neural network from peony images collected in an artificial habitat with a subsequent application to images collected in a natural habitat and the possibilities of using multi-temporal data to improve the network training quality. The experiments have shown that training a neural network exclusively using images collected in the natural habitat makes it possible to achieve a probability of correct detection of peonies of 0.93, while using data obtained at different years allows increasing the probability of correct detection to 0.95.

Michel Génard, Françoise Lescourret

In the spring of 1987, point-count surveys of breeding birds (passerines and picidae) were conducted, resulting in a dataset of 197 counts. The purpose was to analyze the effects of forest fragmentation on bird community composition in a mountain pine forest located in the Néouvielle National Nature Reserve in the central French Pyrenees between 1800 and 2400 metres. The study aimed to differentiate between the impacts of landscape factors (patch area, isolation) and habitat characteristics (altitude, vegetation structure). Additional information was gathered regarding the presence of Common Crossbill (Loxia curvirostra), Great Spotted Woodpecker (Dendrocopos major), Red Squirrel (Sciurus vulgaris), and Capercaillie (Tetrao urogallus) in the forest. The sampling design ensured that the selected patches represented a wide range of sizes and distances to the nearest large pine patch or low-altitude forest stand. Bird sampling utilized the point-count technique [3], focusing on singing passerines and Picidae within a 50-metre radius. The altitude, the percentage of open areas, of stones, boulders and of herbaceous and ligneous plant cover at various heights, the canopy height and number of dead trees, along with landscape variables describing patch size and isolation from large pine stands or low-altitude forests, were assessed for each point count. This dataset offers insight into the breeding bird community and squirrel occurrence in a typical high-altitude mountain pine forest in the Pyrenees in 1987, serving as a baseline for future comparisons to study changes in bird and squirrel populations, the impact of climate change, habitat fragmentation, and conservation priorities. These data aim to inspire further research and enhance our understanding of bird and squirrel ecology in mountain regions.

S. D. Hendrix, P. Price, T. Lewinsohn et al.

Debasmita Pal, Arun Ross

Plant phenology and phenotype prediction using remote sensing data are increasingly gaining attention within the plant science community as a promising approach to enhance agricultural productivity. This work focuses on generating synthetic forestry images that satisfy certain phenotypic attributes, viz. canopy greenness. We harness a Generative Adversarial Network (GAN) to synthesize biologically plausible and phenotypically stable forestry images conditioned on the greenness of vegetation (a continuous attribute) over a specific region of interest, describing a particular vegetation type in a mixed forest. The training data is based on the automated digital camera imagery provided by the National Ecological Observatory Network (NEON) and processed by the PhenoCam Network. Our method helps render the appearance of forest sites specific to a greenness value. The synthetic images are subsequently utilized to predict another phenotypic attribute, viz., redness of plants. The quality of the synthetic images is assessed using the Structural SIMilarity (SSIM) index and Fréchet Inception Distance (FID). Further, the greenness and redness indices of the synthetic images are compared against those of the original images using Root Mean Squared Percentage Error (RMSPE) to evaluate their accuracy and integrity. The generalizability and scalability of our proposed GAN model are established by effectively transforming it to generate synthetic images for other forest sites and vegetation types. From a broader perspective, this approach could be leveraged to visualize forestry based on different phenotypic attributes in the context of various environmental parameters.