{"results":[{"id":"ss_42cedef71a62ad727a39e9f2c793bcca353c4e01","title":"Physiological Plant Ecology I","authors":[{"name":"O. Lange"},{"name":"P. S. Nobel"},{"name":"C. Osmond"},{"name":"H. Ziegler"}],"abstract":"","source":"Semantic Scholar","year":1981,"language":"en","subjects":["Environmental Science"],"doi":"10.1007/978-3-642-68090-8","url":"https://www.semanticscholar.org/paper/42cedef71a62ad727a39e9f2c793bcca353c4e01","is_open_access":true,"citations":2711,"published_at":"","score":80},{"id":"ss_1411eb08b584b6576794c4796ffd52323a316fa0","title":"Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems","authors":[{"name":"S. T. Vo"},{"name":"E. Johnson"}],"abstract":"","source":"Semantic Scholar","year":2001,"language":"en","subjects":["Environmental Science"],"doi":"10.1659/0276-4741(2001)021[0202:APLFPE]2.0.CO;2","url":"https://www.semanticscholar.org/paper/1411eb08b584b6576794c4796ffd52323a316fa0","pdf_url":"https://doi.org/10.1659/0276-4741(2001)021[0202:aplfpe]2.0.co;2","is_open_access":true,"citations":1823,"published_at":"","score":80},{"id":"ss_99ad0c39273c095b6fd477ac4fda3196fbc7168b","title":"Physiological Plant Ecology","authors":[{"name":"W. Larcher"}],"abstract":"","source":"Semantic Scholar","year":1977,"language":"en","subjects":["Environmental Science","Biology"],"doi":"10.2307/2259090","url":"https://www.semanticscholar.org/paper/99ad0c39273c095b6fd477ac4fda3196fbc7168b","is_open_access":true,"citations":3041,"published_at":"","score":80},{"id":"ss_5a808a05a3c6a260f2e00fa6edc8d3e9eef14385","title":"A leaf-height-seed (LHS) plant ecology strategy scheme","authors":[{"name":"M. Westoby"}],"abstract":"","source":"Semantic Scholar","year":1998,"language":"en","subjects":["Mathematics"],"doi":"10.1023/A:1004327224729","url":"https://www.semanticscholar.org/paper/5a808a05a3c6a260f2e00fa6edc8d3e9eef14385","is_open_access":true,"citations":1790,"published_at":"","score":80},{"id":"ss_ec03969741a5d7141b4af022a9334799e9404c49","title":"Physiological Plant Ecology Ecophysiology And Stress Physiology Of Functional Groups","authors":[{"name":"Marcel Abendroth"}],"abstract":"","source":"Semantic Scholar","year":2016,"language":"en","subjects":["Biology"],"url":"https://www.semanticscholar.org/paper/ec03969741a5d7141b4af022a9334799e9404c49","is_open_access":true,"citations":588,"published_at":"","score":77.64},{"id":"ss_d1a9ae92a7d84f9e8e82306d2322143331dca3c8","title":"Alpine Plant Life Functional Plant Ecology Of High Mountain Ecosystems","authors":[{"name":"L. Hoch"}],"abstract":"","source":"Semantic Scholar","year":2016,"language":"en","subjects":["Environmental Science"],"doi":"10.5860/choice.41-4664","url":"https://www.semanticscholar.org/paper/d1a9ae92a7d84f9e8e82306d2322143331dca3c8","pdf_url":"https://bioone.org/journals/mountain-research-and-development/volume-21/issue-2/0276-4741_2001_021_0202_APLFPE_2.0.CO_2/Alpine-Plant-Life--Functional-Plant-Ecology-of-High-Mountain/10.1659/0276-4741(2001)021[0202:APLFPE]2.0.CO;2.pdf","is_open_access":true,"citations":462,"published_at":"","score":73.86},{"id":"ss_218aec7d36ce2c16ef4be959e53080791978a3af","title":"Reinforcing loose foundation stones in trait-based plant ecology","authors":[{"name":"B. Shipley"},{"name":"F. Bello"},{"name":"J. Cornelissen"},{"name":"E. Laliberté"},{"name":"D. Laughlin"},{"name":"P. Reich"},{"name":"P. Reich"}],"abstract":"","source":"Semantic Scholar","year":2016,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1007/s00442-016-3549-x","url":"https://www.semanticscholar.org/paper/218aec7d36ce2c16ef4be959e53080791978a3af","is_open_access":true,"citations":440,"published_at":"","score":73.2},{"id":"ss_01220a8874b159d4670909fb6236b3bbd4bdfc93","title":"A global Fine-Root Ecology Database to address below-ground challenges in plant ecology.","authors":[{"name":"C. Iversen"},{"name":"M. L. McCormack"},{"name":"A. Powell"},{"name":"C. Blackwood"},{"name":"G. T. Freschet"},{"name":"J. Kattge"},{"name":"C. Roumet"},{"name":"D. Stover"},{"name":"Nadejda A. Soudzilovskaia"},{"name":"O. Valverde‐Barrantes"},{"name":"O. Valverde‐Barrantes"},{"name":"P. Bodegom"},{"name":"C. Violle"}],"abstract":"","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1111/nph.14486","url":"https://www.semanticscholar.org/paper/01220a8874b159d4670909fb6236b3bbd4bdfc93","pdf_url":"https://nph.onlinelibrary.wiley.com/doi/pdfdirect/10.1111/nph.14486","is_open_access":true,"citations":307,"published_at":"","score":70.21000000000001},{"id":"arxiv_2603.16896","title":"Model Selection via Focused Information Criteria for Complex Data in Ecology and Evolution","authors":[{"name":"Gerda Claeskens"},{"name":"Céline Cunen"},{"name":"Nils Lid Hjort"}],"abstract":"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.","source":"arXiv","year":2026,"language":"en","subjects":["stat.AP"],"url":"https://arxiv.org/abs/2603.16896","pdf_url":"https://arxiv.org/pdf/2603.16896","is_open_access":true,"published_at":"2026-03-03T10:07:06Z","score":70},{"id":"arxiv_2601.07970","title":"Sesame Plant Segmentation Dataset: A YOLO Formatted Annotated Dataset","authors":[{"name":"Sunusi Ibrahim Muhammad"},{"name":"Ismail Ismail Tijjani"},{"name":"Saadatu Yusuf Jumare"},{"name":"Fatima Isah Jibrin"}],"abstract":"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.","source":"arXiv","year":2026,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2601.07970","pdf_url":"https://arxiv.org/pdf/2601.07970","is_open_access":true,"published_at":"2026-01-12T20:04:40Z","score":70},{"id":"doaj_10.23910/2/2026.6886a","title":"Effect of Nano-multi Micronutrients on Agronomic Traits, Nutrient Uptake and Soil Fertility in Pot Trial of Maize (Zea mays L.)","authors":[{"name":"Vipul Bundake"},{"name":"Veena Khilnani"},{"name":"Archana Kale"},{"name":"I. L. Pardeshi"}],"abstract":"\nA 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.\n","source":"DOAJ","year":2026,"language":"","subjects":["Agriculture","Plant ecology"],"doi":"10.23910/2/2026.6886a","url":"https://ojs.pphouse.org/index.php/IJEP/article/view/7043","is_open_access":true,"published_at":"","score":70},{"id":"ss_0ea9bcf017e42b2428ec013d43d7caeea2f405c2","title":"Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems","authors":[{"name":"C. Körner"}],"abstract":"","source":"Semantic Scholar","year":2021,"language":"en","subjects":null,"doi":"10.1007/978-3-030-59538-8","url":"https://www.semanticscholar.org/paper/0ea9bcf017e42b2428ec013d43d7caeea2f405c2","is_open_access":true,"citations":142,"published_at":"","score":69.25999999999999},{"id":"ss_9faa32176401a45cd08d7e8e74473f1b004ab79d","title":"Below-ground frontiers in trait-based plant ecology.","authors":[{"name":"E. Laliberté"}],"abstract":"","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Biology","Medicine"],"doi":"10.1111/nph.14247","url":"https://www.semanticscholar.org/paper/9faa32176401a45cd08d7e8e74473f1b004ab79d","pdf_url":"https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/nph.14247","is_open_access":true,"citations":272,"published_at":"","score":69.16},{"id":"arxiv_2509.19393","title":"MetaQuestion: A web application for expert knowledge elicitation addressing plant health and applied plant ecology","authors":[{"name":"Robert Fontan"},{"name":"Christopher M. Perez"},{"name":"Ashish Adhikari"},{"name":"Romaric A. Mouafo-Tchinda"},{"name":"Aaron I. Plex Sulá"},{"name":"Jacobo Robledo"},{"name":"Berea A. Etherton"},{"name":"Manoj Choudhary"},{"name":"Muhammad Aqeel Sarwar"},{"name":"Zunaira Afzal Naveed"},{"name":"Karen A. Garrett"}],"abstract":"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.","source":"arXiv","year":2025,"language":"en","subjects":["q-bio.OT"],"url":"https://arxiv.org/abs/2509.19393","pdf_url":"https://arxiv.org/pdf/2509.19393","is_open_access":true,"published_at":"2025-09-22T22:39:20Z","score":69},{"id":"doaj_10.3390/life15020157","title":"Updated Taxonomy of Chinese \u003ci\u003eCraterellus\u003c/i\u003e (Hydnaceae, Cantharellales) with Three New Species Described","authors":[{"name":"Tian Jiang"},{"name":"Lei Zhao"},{"name":"Xu Zhang"},{"name":"Hua-Zhi Qin"},{"name":"Hui Deng"},{"name":"Xiao-Dong Mu"},{"name":"Nian-Kai Zeng"}],"abstract":"Species of \u003ci\u003eCraterellus\u003c/i\u003e are interesting and important due to their mycorrhizal properties, medicinal value, and edibility. Despite extensive research on \u003ci\u003eCraterellus\u003c/i\u003e in China, its taxonomy remains inadequately understood. This study presents three newly described species of \u003ci\u003eCraterellus\u003c/i\u003e, namely \u003ci\u003eC. albimarginatus\u003c/i\u003e, \u003ci\u003eC. involutus\u003c/i\u003e, and \u003ci\u003eC. longitipes\u003c/i\u003e, identified through morphological and phylogenetic analyses, with the goal of refining the taxonomy of Chinese \u003ci\u003eCraterellus\u003c/i\u003e.","source":"DOAJ","year":2025,"language":"","subjects":["Science"],"doi":"10.3390/life15020157","url":"https://www.mdpi.com/2075-1729/15/2/157","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.1016/j.stress.2025.101091","title":"OsAAI1 is dependent on the nitrate pathway toregulate rice root development in response to high salt stress","authors":[{"name":"Jinli Liu"},{"name":"Haimin Liao"},{"name":"Shasha Chen"},{"name":"Mengxia Wu"},{"name":"Jiaqi Zhang"},{"name":"Qunquan Tian"},{"name":"Rui Luo"},{"name":"Ning Xu"}],"abstract":"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.","source":"DOAJ","year":2025,"language":"","subjects":["Plant ecology"],"doi":"10.1016/j.stress.2025.101091","url":"http://www.sciencedirect.com/science/article/pii/S2667064X25003586","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.3389/fevo.2025.1504480","title":"Integrating pollinators’ movements into pollination models","authors":[{"name":"Juliane Mailly"},{"name":"Louise Riotte-Lambert"},{"name":"Mathieu Lihoreau"}],"abstract":"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.","source":"DOAJ","year":2025,"language":"","subjects":["Evolution","Ecology"],"doi":"10.3389/fevo.2025.1504480","url":"https://www.frontiersin.org/articles/10.3389/fevo.2025.1504480/full","is_open_access":true,"published_at":"","score":69},{"id":"arxiv_2402.10344","title":"Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions","authors":[{"name":"Muhammad Arbab Arshad"},{"name":"Talukder Jubery"},{"name":"James Afful"},{"name":"Anushrut Jignasu"},{"name":"Aditya Balu"},{"name":"Baskar Ganapathysubramanian"},{"name":"Soumik Sarkar"},{"name":"Adarsh Krishnamurthy"}],"abstract":"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.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2402.10344","pdf_url":"https://arxiv.org/pdf/2402.10344","is_open_access":true,"published_at":"2024-02-15T22:17:17Z","score":68},{"id":"arxiv_2406.01455","title":"Automatic Fused Multimodal Deep Learning for Plant Identification","authors":[{"name":"Alfreds Lapkovskis"},{"name":"Natalia Nefedova"},{"name":"Ali Beikmohammadi"}],"abstract":"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.","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV","cs.AI","cs.LG"],"doi":"10.3389/fpls.2025.1616020","url":"https://arxiv.org/abs/2406.01455","pdf_url":"https://arxiv.org/pdf/2406.01455","is_open_access":true,"published_at":"2024-06-03T15:43:29Z","score":68},{"id":"arxiv_2401.10860","title":"Novel community data in ecology -- properties and prospects","authors":[{"name":"Florian Hartig"},{"name":"Nerea Abrego"},{"name":"Alex Bush"},{"name":"Jonathan M. Chase"},{"name":"Gurutzeta Guillera-Arroita"},{"name":"Mathew A. Leibold"},{"name":"Otso Ovaskainen"},{"name":"Loïc Pellissier"},{"name":"Maximilian Pichler"},{"name":"Giovanni Poggiato"},{"name":"Laura Pollock"},{"name":"Sara Si-Moussi"},{"name":"Wilfried Thuiller"},{"name":"Duarte S. Viana"},{"name":"David I. Warton"},{"name":"Damaris Zurell"},{"name":"Douglas W. Yu"}],"abstract":"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.","source":"arXiv","year":2024,"language":"en","subjects":["q-bio.PE"],"doi":"10.1016/j.tree.2023.09.017","url":"https://arxiv.org/abs/2401.10860","pdf_url":"https://arxiv.org/pdf/2401.10860","is_open_access":true,"published_at":"2024-01-19T18:04:01Z","score":68}],"total":6293624,"page":1,"page_size":20,"sources":["arXiv","DOAJ","CrossRef","Semantic Scholar"],"query":"Plant ecology"}