Hasil untuk "Plant ecology"

Vipul Bundake, Veena Khilnani, Archana Kale et al.

A pot experiment of maize was carried during summer seasons of March–July, 2023 and 2024 at the experimental field of Rashtriya Chemicals and Fertilizers, Mumbai, India, to assess the impact of multi nano micronutrients formulation (NM) on maize growth. The experiment was structured using a Completely Randomized Block Design with 12 treatments, including control with only water, Recommended Dose of Fertilizer (RDF), and different concentrations of NM having zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron (B) ranging from 20 mg to 0.15 mg 15 kg-1 of soil, as well as commercial micronutrients and micronutrient salts. Results revealed that application of 100% RDF+0.312 mg (T9) and 0.156 mg (T10) of nano micronutrients with drenching recorded better results of nutrient uptake (NU), apparent recovery (ANR) and agronomic efficiency (ARE). The NU (kg ha-1) of nitrogen (120.368), potassium (101.422), Cu (0.114), Fe (1.235), Mn (0.107) and Zn (6.069) was higher in T9 when compared to 100% RDF. The ANR was 9154.19% higher in T10 and 158.28% higher for Nitrogen(N), Phosphorus (P), and Potassium compared to 100% RDF. The protein and chlorophyll content were better in T9 and T10 of nano micronutrients respectively. The applications of T9 and T10 was found to be most effective in NU, ARE, ANR, protein content and chlorophyll content. Higher nutrient content in soil was found in treatment with lower concentrations. Overall, lower concentration of nano micronutrients appeared to be more effective for all traits.

Gerda Claeskens, Céline Cunen, Nils Lid Hjort

Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale.

Sunusi Ibrahim Muhammad, Ismail Ismail Tijjani, Saadatu Yusuf Jumare et al.

This paper presents the Sesame Plant Segmentation Dataset, an open source annotated image dataset designed to support the development of artificial intelligence models for agricultural applications, with a specific focus on sesame plants. The dataset comprises 206 training images, 43 validation images, and 43 test images in YOLO compatible segmentation format, capturing sesame plants at early growth stages under varying environmental conditions. Data were collected using a high resolution mobile camera from farms in Jirdede, Daura Local Government Area, Katsina State, Nigeria, and annotated using the Segment Anything Model version 2 with farmer supervision. Unlike conventional bounding box datasets, this dataset employs pixel level segmentation to enable more precise detection and analysis of sesame plants in real world farm settings. Model evaluation using the Ultralytics YOLOv8 framework demonstrated strong performance for both detection and segmentation tasks. For bounding box detection, the model achieved a recall of 79 percent, precision of 79 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 58 percent. For segmentation, it achieved a recall of 82 percent, precision of 77 percent, mean average precision at IoU 0.50 of 84 percent, and mean average precision from 0.50 to 0.95 of 52 percent. The dataset represents a novel contribution to sesame focused agricultural vision datasets in Nigeria and supports applications such as plant monitoring, yield estimation, and agricultural research.

Tian Jiang, Lei Zhao, Xu Zhang et al.

Species of <i>Craterellus</i> are interesting and important due to their mycorrhizal properties, medicinal value, and edibility. Despite extensive research on <i>Craterellus</i> in China, its taxonomy remains inadequately understood. This study presents three newly described species of <i>Craterellus</i>, namely <i>C. albimarginatus</i>, <i>C. involutus</i>, and <i>C. longitipes</i>, identified through morphological and phylogenetic analyses, with the goal of refining the taxonomy of Chinese <i>Craterellus</i>.

Jinli Liu, Haimin Liao, Shasha Chen et al.

Salt stress limits plant growth and yield. Though nitrogen fertilizer can alleviate salt damage, the effects of salt and nitrate on the stress resistance gene OsAAI1 are unclear. This study examined the Salt stress sensitivity of OsAAI1 transgenic lines and nitrate's role. Results showed OsAAI1 expression decreased with Salt and increased with nitrate. Under salt stress, the mutant OsAAI1 (osaai1) had significantly higher plant height, root length and number, and lower ROS accumulation than ZH 11, while OsAAI1 overexpression (OE 19) showed opposite trends. OE 19 also had lower antioxidant enzyme activities and higher MDA content. Analyses of topology, biomass distribution and connectivity of root scans after 30 and 50 days of salt stress treatment showed that osaai1 was able to sustain root growth and development under salt stress conditions, whereas OE 19 was more damaged. Exogenous salt stress tests confirmed these findings. Notably, nitrate application enhanced OsAAI1 is salt tolerance, improving root growth and increasing ROS scavenging enzyme activities. Under KNO₃ induction, high-concentration KNO₃ restores the root phenotype in OE 19. In conclusion, overexpression of OsAAI1 was more sensitive to salt, and OsAAI1 regulated ROS homeostasis through the nitrate pathway to enhance its tolerance to salt stress.

Juliane Mailly, Louise Riotte-Lambert, Mathieu Lihoreau

Accurate prediction of pollination processes is a key challenge for sustainable food production and the conservation of natural ecosystems. For many plants, pollen dispersal is mediated by the foraging movements of nectarivore animals. While most current models of pollination ecology assume random pollen movements, studies in animal behaviour show how pollinating insects, birds and bats rely on sensory cues, learning and memory to visit flowers, thereby producing complex movement patterns. Building upon a brief review of pollination and movement models, we argue that we need to better consider pollinators’ cognition to improve predictions of animal-mediated pollination across all spatial scales, from individual flowers, to plants, habitat patches and landscapes. We propose a practical roadmap for the integration of behavioural models into pollination models and discuss how this synthesis can refine predictions regarding plant mating patterns and fitness. Such crosstalk between animal behaviour and plant ecology research will provide powerful mechanistic tools to predict and act on pollination services in the context of a looming crisis.

Robert Fontan, Christopher M. Perez, Ashish Adhikari et al.

1. Expert knowledge elicitation provides information to characterize ecological systems and management options. Linking expert knowledge elicitation with a curated question catalog supports a community of practice for ongoing improvement of question quality. 2. The MetaQuestion web app we introduce here draws on the PlantQuest catalog of questions addressing applied plant ecology and management options, making the catalog available in a flexible form for organizers of expert knowledge elicitation. Organizers can select among questions in the catalog, modify them as needed, and generate an instrument customized to their elicitation project. MetaQuestion makes available PlantQuest questions specialized for the study of invasive species such as pathogens and arthropod pests, such as geographic analyses of prevalence and network analysis of the movement of plant materials. 3. Experts answer questions in the customized instrument and their responses are compiled. For settings where internet access may be sporadic, there are options to download the instrument for experts' work and then upload responses later. MetaQuestion provides the resulting dataset in a CSV file for analysis in users' choice of software 4. Development of the PlantQuest catalog and the MetaQuestion app is ongoing, incorporating lessons learned from applications of the app. The MetaQuestion app could also be adapted to address questions from other subject areas.

Catarina Leal, Ales Eichmeier, Kateřina Štůsková et al.

With a reduction in available chemical treatments, there is an increased interest in biological control of grapevine trunk diseases. Few studies have investigated the impact of introducing beneficial microorganisms in the rhizosphere on the existing indigenous soil microbiome. In this study, we explored the effect of two biocontrol agents (BCAs), Trichoderma atroviride SC1 (Ta SC1) (Vintec; Certis Belchim) and Bacillus subtilis PTA-271 (Bs PTA-271), on the grapevine rhizosphere bacterial and fungal microbiome as well as plant defense expression using high-throughput amplicon sequencing and quantitative real-time polymerase chain reaction (PCR), respectively. Additionally, we quantified both Ta SC1 and Bs PTA-271 in the rhizosphere over time using droplet digital PCR. The fungal microbiome was more affected by factors such as soil type, BCA treatment, and sampling time compared with the bacterial microbiome. Specifically, Ta SC1 application produced negative impacts on fungal diversity, whereas application of BCAs did not affect bacterial diversity. Interestingly, the survival and establishment of both BCAs showed opposite trends depending on the soil type, indicating that the physicochemical properties of soils have a role in BCA establishment. Fungal co-occurrence networks were less complex than bacterial networks but highly impacted by Ta SC1 application. Soils treated with Ta SC1 presented more complex and stable co-occurrence networks, with a higher number of positive correlations. Induced grapevine defenses also differed according to the soil, being more affected by BCA inoculation on sandy soil. The findings of this research emphasize the complex relationships among microorganisms in the rhizosphere and highlight the significance of taking into account various factors, such as soil type, sampling time, and BCA treatment, and their influence on the structure and dynamics of microbial communities.

Tae Hyeon Heo, Hyeon Park, Nam-Wook Kim et al.

Waxy maize is mainly cultivated in South Korea for the production of food and snacks, and colored maize with increased anthocyanin content is used in the production of functional foods and medicinal products. Association mapping analysis (AMA) is supported as the preferred method for identifying genetic markers associated with complex traits. Our study aimed to identify molecular markers associated with two anthocyanin content and six seed coat color traits in near-isogenic lines (NILs) of colored waxy maize assessed through AMA. We performed AMA for 285 SSR loci and two anthocyanin content and six seed coat color traits in 10 NILs of colored waxy maize. In the analysis of population structure and cluster formation, the two parental lines (HW3, HW9) of “Mibaek 2ho” variety waxy maize and the 10 NILs were clearly divided into two groups, with each group containing one of the two parental inbred lines. In the AMA, 62 SSR markers were associated with two seed anthocyanin content and six seed coat color traits in the 10 NILs. All the anthocyanin content and seed coat color traits were associated with SSR markers, ranging from 2 to 12 SSR markers per characteristic. The 12 SSR markers were together associated with both of the two anthocyanin content (kuromanin and peonidin) traits. Our current results demonstrate the effectiveness of SSR analysis for the examination of genetic diversity, relationships, and population structure and AMA in 10 NILs of colored waxy maize and the two parental lines of the “Mibaek 2ho” variety waxy maize.

Riko Wahyudi, Wahyu Marjaka, Christian Silangen et al.

The jurisdictional REDD+ (JREDD+) mechanism, aimed at reducing emissions from deforestation and forest degradation, has been crucial in global climate change mitigation efforts. However, designing effective, efficient, and equitable benefit-distribution policy at the site level remains a challenge. This research assesses three benefit distribution mechanisms in Indonesia for JREDD+ initiatives, facilitated by the Indonesian Environment Fund (IEF). They include: (1) distribution through the provincial revenue and expenditure budget (APBD), (2) distribution through intermediary institutions (LEMTARA), and (3) direct distribution or transfer to beneficiaries. Each mechanism is evaluated on effectiveness, efficiency, and equity, considering bureaucratic processes and stakeholder capacities. The study utilizes public deliberation by involving relevant stakeholders at the national and Jambi province levels and expert judgment by purposively selecting based on certain criteria to help determine the optimal mechanism as the reference for achieving Indonesia's climate mitigation goals and the administrative intricacies involved. The findings suggest that direct distribution to beneficiaries is the most efficient and equitable, although using LEMTARA is deemed slightly more effective for targeted fund allocation. The study provides recommendations for policy makers on enhancing institutional capacities and integrating flexible inclusive mechanisms to optimize JREDD+ benefit distribution at the sub-national level.

Afaq Jebir, Shaker Ajeel, Talib Khalaf

Zooplankton is the important component of aquatic ecosystems. These organisms are important biological indicator of water quality of aquatic ecosystem due to their response to the environmental changes. In this study, we investigated distribution of zooplankton biomass in the Shatt Al-Basra Canal and Shatt Al-Arab River. Zooplankton samples were collected from two stations in the Shatt Al-Basra Canal, before (S1) and after (S2) the dam, and two stations in the Shatt Al-Arab River, Al-Siba (S3) and Al-Faw (S4). The biomass of zooplankton in the Shatt Al-Basra Canal varied between 23.102 - 520.875 mg/m3 in terms of wet weight and 3.787 - 102.132 mg/m3 in terms of dry weight at two stations (before the dam and after the dam) during the period of January and May, respectively. The displacement volume and standing crops also showed variations of the biomass of zooplankton. In the Shatt Al-Basra Canal, the range was from 0.06 ml/m3 and 3.9 mgC/m3 during January at S1 to 1.083 ml/m3 and 70.395 mgC/m3 during May at S2. While in the Shatt Al-Arab River, the biomass of zooplankton in terms of wet weight ranged from 10.671 - 655.78 mg/m3 during December at S3 (Al-Siba) and may at S4 (Al-Faw) respectively. In terms of dry weight, the biomass ranged from 1.423 to 168.149 mg/m3 in S3 during the December and in S4 during May respectively. In terms of displacement volume and standing crops, they ranged from 0.03 ml/m3 to 1.95 mgC/m3 during December at S3 to 1.819 ml/m3 and 118.235 mgC/m3 during February at S4.

Muhammad Arbab Arshad, Talukder Jubery, James Afful et al.

We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields. Traditional methods usually fail to capture the complex geometric details of plants, which is crucial for phenotyping and breeding studies. We evaluate the reconstruction fidelity of NeRFs in three scenarios with increasing complexity and compare the results with the point cloud obtained using LiDAR as ground truth. In the most realistic field scenario, the NeRF models achieve a 74.6% F1 score after 30 minutes of training on the GPU, highlighting the efficacy of NeRFs for 3D reconstruction in challenging environments. Additionally, we propose an early stopping technique for NeRF training that almost halves the training time while achieving only a reduction of 7.4% in the average F1 score. This optimization process significantly enhances the speed and efficiency of 3D reconstruction using NeRFs. Our findings demonstrate the potential of NeRFs in detailed and realistic 3D plant reconstruction and suggest practical approaches for enhancing the speed and efficiency of NeRFs in the 3D reconstruction process.

Alfreds Lapkovskis, Natalia Nefedova, Ali Beikmohammadi

Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this field by enabling autonomous feature extraction, significantly reducing the dependence on manual expertise. However, conventional DL models often rely solely on single data sources, failing to capture the full biological diversity of plant species comprehensively. Recent research has turned to multimodal learning to overcome this limitation by integrating multiple data types, which enriches the representation of plant characteristics. This shift introduces the challenge of determining the optimal point for modality fusion. In this paper, we introduce a pioneering multimodal DL-based approach for plant classification with automatic modality fusion. Utilizing the multimodal fusion architecture search, our method integrates images from multiple plant organs -- flowers, leaves, fruits, and stems -- into a cohesive model. To address the lack of multimodal datasets, we contributed Multimodal-PlantCLEF, a restructured version of the PlantCLEF2015 dataset tailored for multimodal tasks. Our method achieves 82.61% accuracy on 979 classes of Multimodal-PlantCLEF, surpassing state-of-the-art methods and outperforming late fusion by 10.33%. Through the incorporation of multimodal dropout, our approach demonstrates strong robustness to missing modalities. We validate our model against established benchmarks using standard performance metrics and McNemar's test, further underscoring its superiority.

Florian Hartig, Nerea Abrego, Alex Bush et al.

New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.

Brett R. Lane, Amy E. Kendig, Christopher M. Wojan et al.

Invasive plants, which cause substantial economic and ecological impacts, acquire both pathogens and beneficial microbes in their introduced ranges. Communities of fungal endophytes are known to mediate impacts of pathogens on plant fitness but few studies have examined the temporal dynamics of fungal communities on invasive plants. The annual grass Microstegium vimineum, an invader of forests and riparian areas throughout the eastern United States, experiences annual epidemics of disease caused by Bipolaris pathogens. Our objective was to characterize the dynamics of foliar fungal communities on M. vimineum over a growing season during a foliar disease epidemic. First, we asked how the fungal community in the phyllosphere changed over 2 months that corresponded with increasing disease severity. Second, we experimentally suppressed disease with fungicide in half of the plots and asked how the treatment affected fungal community diversity and composition. We found increasingly diverse foliar fungal communities and substantial changes in community composition between timepoints using high-throughput amplicon sequencing of the internal transcribed spacer 2 region. Monthly fungicide application caused shifts in fungal community composition relative to control samples. Fungicide application increased diversity at the late-season timepoint, suggesting that it suppressed dominant fungicide sensitive taxa and allowed other fungal taxa to flourish. These results raise new questions regarding the roles of putative endophytes found in the phyllosphere given the limited number of pathogens known to cause disease on M. vimineum in its invasive range.

Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert et al.

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Zhiming Feng, Zhiming Feng, Mingyou Li et al.

Rice blast, caused by Magnaporthe oryzae (M. oryzae), is one of the most destructive diseases threatening rice production worldwide. Development of resistant cultivars using broad-spectrum resistance (R) genes with high breeding value is the most effective and economical approach to control this disease. In this study, the breeding potential of Pigm gene in geng/japonica rice breeding practice in Jiangsu province was comprehensively evaluated. Through backcross and marker-assisted selection (MAS), Pigm was introduced into two geng rice cultivars (Wuyungeng 32/WYG32 and Huageng 8/HG8). In each genetic background, five advanced backcross lines with Pigm (ABLs) and the same genotypes as the respective recurrent parent in the other 13 known R gene loci were developed. Compared with the corresponding recurrent parent, all these ABLs exhibited stronger resistance in seedling inoculation assay using 184 isolates collected from rice growing regions of the lower region of the Yangtze River. With respect to panicle blast resistance, all ABLs reached a high resistance level to blast disease in tests conducted in three consecutive years with the inoculation of seven mixed conidial suspensions collected from different regions of Jiangsu province. In natural field nursery assays, the ABLs showed significantly higher resistance than the recurrent parents. No common change on importantly morphological traits and yield-associated components was found among the ABLs, demonstrating the introduction of Pigm had no tightly linked undesirable effect on rice economically important traits and its associated grain weight reduction effect could be probably offset by others grain weight genes or at least in the background of the aforementioned two varieties. Notably, one rice line with Pigm, designated as Yangnonggeng 3091, had been authorized as a new variety in Jiangsu province in 2021, showing excellent performance on both grain yield and quality, as well as the blast resistance. Together, these results suggest that the Pigm gene has a high breeding value in developing rice varieties with durable and broad-spectrum resistance to blast disease.

Yu-Yan Li, Jun-Jie Chen, Meng-Yao Liu et al.

Improved knowledge on the regulation of reproductive diapause in Coccinella septempunctata, an important predator of aphids, is crucial for improving shelf-life and mass production of the ladybeetles. In many insects, the absence of juvenile hormone (JH) is a central regulator of reproductive diapause. JH is principally degraded by JH esterase (JHE) and JH epoxide hydrolase (JHEH). Previous studies have shown that genes encoding these enzymes were upregulated in early diapause of C. septempunctata, but whether increased JH degradation contributes to the reduction of JH levels and facilitates reproductive diapause remains unknown. Here, we investigate the role of JH and JH degradation genes during reproductive diapause in C. septempunctata females. Applying methoprene, a JH analogue, to the diapause preparation females clearly elevated JH signaling and reversed diapause program, suggesting that a lower level of JH is critical for the induction of reproductive diapause in the ladybeetle. Full-length cDNA sequences of JHE and JHEH were cloned and characterized, and their deduced proteins contain all the conserved active domains and typical motifs as identified in other insects. The expressions of JHE and JHEH were both significantly increased in diapause preparation and remained at a high level for a period throughout diapause, and then decreased after the termination of diapause. Knocking down these JH degradation genes clearly increased the expression levels of JH-inducible genes Krüppel-homolog 1 (Kr-h1) and vitellogenin (Vg), indicating an elevated JH level. Simultaneously, silencing JH degradation genes distinctly reduced diapause-related features and promotes reproduction, indicated by accelerated ovary growth, yolk deposition, and suppressed lipid accumulation. These results indicate that the enhanced JH degradation plays a critical role in regulating reproductive diapause of C. septempunctata.

Liyi Luo, Xintong Jiang, Yu Yang et al.

Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge. However, fully supervised deep learning methods require datasets to be point-wise annotated, which is extremely expensive and time-consuming. In our work, we proposed a novel weakly supervised framework, Eff-3DPSeg, for 3D plant shoot segmentation. First, high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system, and the Meshlab-based Plant Annotator was developed for plant point cloud annotation. Second, a weakly-supervised deep learning method was proposed for plant organ segmentation. The method contained: (1) Pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds; (2) Fine-tuning the pre-trained model with about only 0.5% points being annotated to implement plant organ segmentation. After, three phenotypic traits (stem diameter, leaf width, and leaf length) were extracted. To test the generality of the proposed method, the public dataset Pheno4D was included in this study. Experimental results showed that the weakly-supervised network obtained similar segmentation performance compared with the fully-supervised setting. Our method achieved 95.1%, 96.6%, 95.8% and 92.2% in the Precision, Recall, F1-score, and mIoU for stem leaf segmentation and 53%, 62.8% and 70.3% in the AP, AP@25, and AP@50 for leaf instance segmentation. This study provides an effective way for characterizing 3D plant architecture, which will become useful for plant breeders to enhance selection processes.

Katrin Renz, Kashyap Chitta, Otniel-Bogdan Mercea et al.

Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene. While human drivers prioritize important objects and ignore details not relevant to the decision, learning-based planners typically extract features from dense, high-dimensional grid representations containing all vehicle and road context information. In this paper, we propose PlanT, a novel approach for planning in the context of self-driving that uses a standard transformer architecture. PlanT is based on imitation learning with a compact object-level input representation. On the Longest6 benchmark for CARLA, PlanT outperforms all prior methods (matching the driving score of the expert) while being 5.3x faster than equivalent pixel-based planning baselines during inference. Combining PlanT with an off-the-shelf perception module provides a sensor-based driving system that is more than 10 points better in terms of driving score than the existing state of the art. Furthermore, we propose an evaluation protocol to quantify the ability of planners to identify relevant objects, providing insights regarding their decision-making. Our results indicate that PlanT can focus on the most relevant object in the scene, even when this object is geometrically distant.