Hasil untuk "Plant ecology"

M. Kéry, J. Andrew Royle

Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. * Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection* Presents models and methods for identifying unmarked individuals and species* Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses* Includes companion website containing data sets, code, solutions to exercises, and further information

M. Lesser

F. Chapin, A. Bloom, C. Field et al.

Luke Meyers, Anirudh Potlapally, Yuyan Chen et al.

Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu'u Maka'ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.

Torsten Lindström

May (1974,1976) opened the debate on whether biological populations might exhibit nonlinear dynamics and chaos. However, it has in general been difficult to verify nonlinear dynamics in biological populations. There are many reports concerning problems with this issue and some of them can be traced back to Hassell, Lawton, and May (1976) and Morris (1990). Our objective is not a discussion of the presence of nonlinear dynamics in biological populations. Instead, we analyze whether ecological census data can be used for validating nonlinearities at all. We choose our models and our situation so that as much as possible can be done rigorously with by hand computations. We consider a clearly nonlinear chemostat based model that is isolated. Some noise must be considered, and we choose a minimal approach: Only noise originating from the fact that ecological populations remain finite is considered, cf. Bailey (1964). In ecology, exceptionally long and famous time series are those collected by Nicholson (1954) and Utida (1957). Our judgement is that ecological time series data containing a few hundred data points is exceptionally long.

Asish Bera, Subhajit Roy, Sudiptendu Banerjee

Convolutional neural networks have remarkably progressed the performance of distinguishing plant diseases, severity grading, and nutrition deficiency prediction using leaf images. However, these tasks become more challenging in a realistic in-situ field condition. Often, a traditional machine learning model may fail to capture and interpret discriminative characteristics of plant health, growth and diseases due to subtle variations within leaf subcategories. A few deep learning methods have used additional preprocessing stages or network modules to address the problem, whereas several other methods have utilized pre-trained backbone CNNs, most of which are computationally intensive. Therefore, to address the challenge, we propose a lightweight autoencoder using separable convolutional layers in its encoder decoder blocks. A sigmoid gating is applied for refining the prowess of the encoders feature discriminability, which is improved further by the decoder. Finally, the feature maps of the encoder decoder are combined for rich feature representation before classification. The proposed Convolutional Lightweight Autoencoder for Plant disease classification, called CLAP, has been experimented on three public plant datasets consisting of cassava, tomato, maize, groundnut, grapes, etc. for determining plant health conditions. The CLAP has attained improved or competitive accuracies on the Integrated Plant Disease, Groundnut, and CCMT datasets balancing a tradeoff between the performance, and little computational cost requiring 5 million parameters. The training time is 20 milliseconds and inference time is 1 ms per image.

Ajay Kumar, R. Umamaheswari, C. Vasugi

The present experiment was conducted in the Division of Crop Protection at the ICAR-Indian Institute of Horticultural Research, Bengaluru (Karnataka), India during 2022–2023 for nine months to investigate the effect of root-knot nematodes (M. incognita and M. enterolobii) on the biomass of resistant wild parent P. cattleianum, common guava; P. guajava and the interspecific hybrid progenies. Results revealed significant variations in the growth of shoot and root parameters among the parents and hybrid progenies. Reduction in shoot and root weight was recorded in all the susceptible plants whereas, the resistant species recorded increased growth parameters of both shoot and root. In the susceptible plants inoculated with M. enterolobii a drastic reduction in shoot weight, root weight and root length were observed. However, in the resistant wild species (P. cattleianum var. cattleianum and P. cattleianum var, lucidum) the growth of shoot and root was increased as normal. In contrast, it was noticed that the root length was decreased in susceptible plants inoculated with M. enterolobii due to decomposition and deterioration of roots over three to nine months whereas, resistant wild species did not express these kinds of symptoms. Based on the shoot and root growth parameters, this study confirms that M. enterolobii is a more dangerous, devastating and virulent species compared to M. incognita. This indicates that these resistant species have the potential to exploit their resistance in guava breeding, especially the development of resistant varieties.

Stefanie Simpson, Lindsey S. Smart, Lindsey S. Smart et al.

Blue carbon ecosystems, such as mangroves, tidal marshes, and seagrasses, are important for climate mitigation. As carbon sinks, they often exhibit higher per hectare carbon storage capacity and sequestration rates than terrestrial systems. These ecosystems provide additional benefits, including enhancing water quality, sustaining biodiversity, and maintaining coastal resilience to climate change impacts. The widespread loss of blue carbon ecosystems due to anthropogenic activities can contribute to increasing carbon emissions globally. Monetizing blue carbon through carbon credits offers an avenue to generate revenue and incentivize conservation and restoration efforts. However, limited data on project costs and carbon benefits make prioritization of blue carbon projects challenging. To address these challenges, we have developed, in collaboration with blue carbon experts, the Blue Carbon Cost Tool. This is a user-friendly interface enabling comparison of three core market project components – 1) carbon credit estimation, 2) project cost estimation, and 3) a qualitative, non-economic feasibility assessment – to assess and compare potential for blue carbon projects. Tool simulations with data available from nine countries demonstrate (a) how factors such as country, ecosystem type and project scale drive variability, (b) the need for local or project-specific data to enhance accuracy and reduce uncertainty, particularly in tidal marsh and seagrass systems, and (c) that higher price tolerance or upfront capital is needed to bridge implementation and maintenance cost gaps. The Blue Carbon Cost Tool can aid project developers and investors to better understand market opportunity and the resources needed to develop high quality blue carbon market projects.

André Geremia Parise, Vinicius Henrique De Oliveira, Mark Tibbett et al.

Mycorrhizal fungi are known to support their host plants by facilitating nutrient acquisition and enhancing resistance to biotic and abiotic stress. However, the possibility that they also convey structural information about the soil has not yet been tested. Here, we attempted to investigate whether ectomycorrhizal hyphae could guide root growth in response to physical obstacles by using Scots pine (Pinus sylvestris) and Suillus granulatus in a microcosm experiment fitted with U-shaped silicone mazes. Despite initial success in achieving ectomycorrhizal colonisation (88% of the inoculated seedlings), the fungi failed to produce the expected hyphal networks. Extensive and unexpected root growth rendered the system unsuitable for testing our hypothesis. Furthermore, structural issues with the microcosms compromised substrate integrity, possibly inhibiting fungal development. While our results were inconclusive, this report highlights challenges associated with replicating classical ectomycorrhizal experiments, underscoring the need for methodological refinement. We provide detailed recommendations and methodological clarifications that may aid future research. Although our initial hypothesis could not be tested, we argue that traditional microcosm experiments retain potential for advancing our understanding of mycorrhizal ecology, provided they are critically revisited and technically improved. Negative results, when well contextualised, are valuable contributions toward more robust and reproducible experimental frameworks.

Guozhe Zhang, Yu Zhao, Zhiqiang Wu et al.

Lagerstroemia indica is popular for its bright flower colors and long bloom period. However, although L. indica has a long flowering period, the flowering time of a single flower is short, lasting only 1−2 d. Petal expansion is a key process that affects the length and ornamental quality of the flowering period. However, the molecular mechanism of petal expansion in L. indica remains unclear. The molecular mechanisms underlying flower opening in L. indica were investigated through transcriptome sequencing of flower buds and blooms at four developmental stages. Analysis of differentially expressed genes (DEGs) indicated enrichment in cellular processes, metabolic regulation, and biological signaling pathways. KEGG pathway analysis revealed significant roles for carbohydrate, lipid, and amino acid metabolism in the flowering process. Additional pathway analysis identified key genes and processes related to carbohydrate utilization, hormone signaling, water transport, and cell wall expansion that contribute to petal opening regulation. A comprehensive examination of the expansin gene family proteins, known for promoting cell wall loosening and extension, identified 27 expansin genes in L. indica, which were categorized into four subfamilies with conserved structures and motifs. Of these, LiEXPA10, LiEXPA19, LiEXLA1, and LiEXLA2 showed heightened expression in the later stages of flowering (S3−S4), suggesting a central role in petal expansion. Functional validation in Arabidopsis thaliana demonstrated that LiEXLA1 and LiEXLA2 promote accelerated flowering and enhanced petal expansion in transgenic lines. These findings offer new insights into the genetic and molecular basis of flower opening in L. indica and provide a foundation for breeding strategies aimed at improving ornamental traits.

Prakash Kolanchi, Murugan Marimuthu, Balasubramani Venkatasamy et al.

Over millions of years, the coevolution between plants and insect herbivores has led to the development of diverse defense mechanisms in response to herbivory. In response to herbivory, plants mount a defense response by the onset of specific metabolic mechanisms and hormonal pathways, including salicylic acid (SA), jasmonic acid (JA), ethylene (ET), cytokinin (CT), abscisic acid (ABA), auxin (AUX), and gibberellic acids (GAs). Indeed, plants employ antixenosis and antibiosis as defense mechanisms, triggering various morphological, biochemical, and behavioral responses that include the increase of trichomes, sclerophylly, callose deposition, and the production of defensive secondary metabolites to combat herbivory. This intricate network of phytohormonal signaling and their molecular interplay in response to herbivory damages, immune priming against herbivory induced by microbes and abiotic stress, systemic signaling to prime distant tissues for pre-emptive immune responses, and the transmission of immune memory to subsequent generations by plants are all adaptive mechanism, plants follow which are all essential areas covered in this comprehensive review. Additionally, the recognition of damage/herbivory-associated molecular patterns (D/HAMPs) and pathogen/microbe-associated molecular patterns (P/MAMPs) by pattern recognition receptors (PRRs) triggers intracellular signaling events that lead to pattern-triggered immunity (PTI). This occurs through pre-signaling events such as plasma transmembrane potential depolarization, ion efflux/influx, cytosolic calcium ([Ca2+] cyt) elevation, molecular pattern recognition, and defensive effector identification. These processes are crucial for the plant to mount a rapid and appropriate response to herbivory, which is discussed in detail. Furthermore, understanding these complex natural defense mechanisms that may navigate through new avenues is essential for developing sustainable crop production practices with minimal external inputs for an eco-friendly environment.

Giulio Martellucci, Herve Goeau, Pierre Bonnet et al.

Quadrat images are essential for ecological studies, as they enable standardized sampling, the assessment of plant biodiversity, long-term monitoring, and large-scale field campaigns. These images typically cover an area of fifty centimetres or one square meter, and botanists carefully identify all the species present. Integrating AI could help specialists accelerate their inventories and expand the spatial coverage of ecological studies. To assess progress in this area, the PlantCLEF 2025 challenge relies on a new test set of 2,105 high-resolution multi-label images annotated by experts and covering around 400 species. It also provides a large training set of 1.4 million individual plant images, along with vision transformer models pre-trained on this data. The task is formulated as a (weakly labelled) multi-label classification problem, where the goal is to predict all species present in a quadrat image using single-label training data. This paper provides a detailed description of the data, the evaluation methodology, the methods and models used by participants, and the results achieved.

Jing Li, Giulia Forghieri, Danny Geelen et al.

Climate change is a major threat to crop potential and is characterized by both long-term shifts in temperature and precipitation patterns as well as increased occurrence of extreme weather events, these extreme weather events are the most immediate and intractable threat to agriculture. Crop resilience in the face of stress depends upon the speed and effectiveness with which plants and cropping systems sense and respond to that stress. A variety of agronomic practices including breeding, exogenous inputs (nutrients, water, biostimulants and others) and shifts in cultivation practice have been used to influence plant stress response to achieve the goal of increased plant and cropping system resilience. Traditional breeding is a powerful tool that has resulted in stable and long-term cultivar improvements but is often too slow and complex to meet the diverse, complex and unpredictable challenges of climate induced stresses. Increased inputs (water, nutrients, pesticides etc.) and management strategies (cropping system choice, soil management etc.) can alleviate stress but are often constrained by cost and availability of inputs. Exogenous biostimulants, microbials and plant hormones have shown great promise as mechanisms to optimize natural plant resilience resulting in immediate but non-permanent improvements in plant responses to climate induced stresses. The failure to modernize regulatory frameworks for the use of biostimulants in agriculture will constrain the development of safe effective tools and deprive growers of means to respond to the vagaries of climate change. Here we discuss the scientific rationale for eliminating the regulatory barriers that constrain the potential for biostimulants or products that modulate plant regulatory networks to address climate change challenges and propose a framework for enabling legislation to strengthen cropping system resilience.

Md. Kamuruzzaman, Robert M. Rees, Md. Torikul Islam et al.

Achieving high-yielding crops while also improving nitrogen use efficiency is a significant challenge for agricultural production in Bangladesh. We investigated the impacts of applying nitrogen (N) using different management options in wetland rice on a calcareous dark gray soil over three seasons. These included (1) the recommended dose of available N as prilled urea, (2) the recommended N dose plus 25% extra of available N as prilled urea, (3) 25% less than the recommended dose of available N as prilled urea, (4) the recommended dose of prilled urea in 2 t ha⁻¹ cow dung, (5) the recommended dose as urea super granules (USGs) by deep placement, (6) 4 t ha⁻¹ biochar with the recommended dose of prilled urea, and (7) Zero N. It was found that the growth, yield, and N use efficiency (NUE) were significantly different from the results obtained for prilled urea in all the alternative fertilizer options. The deep placement of USG consistently increased plant height, total number of tillers per plant, effective tillers per plant, chlorophyll content, panicle length, grains per panicle, and 1000-grain weight. The yield increases over recommended prilled urea were 5.22% for USG followed by biochar with the recommended dose. Similarly, using the deep placement of USG gave the highest yield and harvest index. In addition, compared to the recommended dose of prilled urea, the deep placement of USG increased NUE by 13%, agronomic N efficiency by 20%, and recovery N use efficiency by 19%. This suggests the rate of N application could be reduced by up to 8% without impacting yield by using deep placement of USG instead of prilled urea. The cost–benefit ratio was higher for the deep placement of USG than all other treatments. Biochar with the recommended dose of prilled urea also showed good results in terms of growth, yield, and NUE (41.8, 43.0, and 41.7, respectively, during three sequential years), but the extra cost of the biochar reduced the cost–benefit ratio. These findings suggest that the deep placement of USG is the best option for improving the yield of rice while also improving N use efficiency.

Yasna Tapia, Osvaldo Salazar, Oscar Seguel et al.

Copper mining drives economic growth, with the global demand expected to reach 120 million metric tons annually by 2050. However, mining produces tailings containing heavy metals (HMs), which poses environmental risks. This study investigated the efficacy of phytoremediation (Phy) combined with electrokinetic treatment (EKT) to increase metal uptake in <i>Carpobrotus aequilaterus</i> grown in tailings from the Metropolitan Region of Chile. The plants were exposed to varying voltages and treatment durations. In the control (no EKT), the root metal contents were Fe (1008.41 mg/kg) > Cu (176.38 mg/kg) > Mn (103.73 mg/kg) > Zn (30.26 mg/kg), whereas in the shoots, the order was Mn (48.69 mg/kg) > Cu (21.14 mg/kg) > Zn (17.67 mg/kg) > Fe (27.32 mg/kg). The optimal EKT (15 V for 8 h) significantly increased metal uptake, with roots accumulating Fe (5997.24 mg kg⁻¹) > Mn (672 mg kg⁻¹) > Cu (547.68 mg kg⁻¹) > Zn (90.99 mg kg⁻¹), whereas shoots contained Fe (1717.95 mg kg⁻¹) > Mn (930 mg kg⁻¹) > Cu (219.47 mg kg⁻¹) > Zn (58.48 mg kg⁻¹). Although EKT enhanced plant growth and biomass, higher voltages stressed the plants. Longer treatments were more effective, suggesting that EK–Phy is a promising method for remediating metal-contaminated tailings.

LIU Rong, TAN Haotian, ZHENG Si et al.

In order to explore the effects of nonphosphorus washing powder on different hydrophilous plants, rice (<i>Oryza sativa</i> L.), water lily (<i>Nelumbo noucifera</i> Gaertn) and <i>Pontederia cordata</i> L. were selected as experimental materials to study the effects of nonphosphorus washing powder on the growth and seed germination of hydrophilous plants. Five treatments were set up in the experiment, namely, clean water control, two low concentration solution (mass fraction 0.02%, 0.05%) and two high concentration solution (mass fraction 0.10%, 0.30%) of nonphosphorus washing powder. The results showed that:(1) With the increase of nonphosphorus washing powder concentration, the germination rate of water lily and rice seeds decreased, the bud length shortened, the root growth stagnated and shrank, the root tip turned black, and the development characteristics became worse; (2) With the increase of the concentration of phosphate-free washing powder solution, the height of the plant became shorter, the leaves turned yellow, the health of the plant declined, the growth was inhibited, and the plant showed signs of wilting. Two genes related to the growth and development of rice roots, <i>OsDREB2B</i> and <i>OsARF12</i>, were selected. It was found that the expression levels of these two genes in the roots of rice seedlings treated with nonphosphate washing powder solution were significantly lower than those in the clean water group. The results show that nonphosphorus washing powder can inhibit the growth and seed germination of water-loving plants. Nonphosphorus washing powder is not a perfect substitute for phosphorus-containing washing powder, but still has side effects on the ecology. So it is necessary to further develop environmentally friendly alternative products.

Vladimir Mahalec

This paper is an invitation to the process systems engineering community to change the paradigm for process plants. The goal is to achieve much easier convergence while retaining accuracy on par with the rigorous models. Accurate plant models of existing plants can be linear or much less nonlinear if they are based on mass component flows and stream properties per unit mass properties instead of molar flows and mole fractions. Accurate stream properties per unit mass can be calculated at stream specific conditions by linear approximations which in many instances eliminates mole fraction-based flash calculations. Hybrid data-driven node models fit naturally in this paradigm, since they used measured data, which is either in mass or in volumetric units, but never in moles. Instantiation of models at all levels of abstraction (planning, scheduling, optimization, and control models) from the same plant topology representation will ensure inheritance of solutions from mass-only to mass-and-energy to mass-and-energy-and-stream-properties, thereby ensuring consistency of solutions between these models. None of the existing software provides inheritance between different levels of plant abstraction (i.e. inheritance between models for different business applications) or different levels of abstractions per plant sections or per time periods, which motivates this exposition.