Adaptive Mechanism Research Articles

Local adaptation, recombination, and the fate of neopolyploids.

Polyploidy is widely recognized as an important speciation mechanism because it isolates tetraploids from their diploid progenitors. Polyploidy also provides new genetic material that may facilitate adaptive evolution. However, new mutations are more likely to arise after a neopolyploid already has successfully invaded a population. Thus, the role of adaptive forces in establishing a polyploid remains unclear. One solution to this apparent paradox may lie in the capacity of polyploids to suppress recombination among preexisting locally adapted alleles. The local adaptation mechanism requires that spatially heterogeneous selection acts on multiple loci and that gene flow introduces maladapted alleles to the population where the polyploid forms. The mechanism requires neither strong genetic drift nor any intrinsic benefit of genome doubling and can accommodate any mode of gene action. A unique prediction of the mechanism is that adaptive alleles should predate polyploidization, a pattern consistent with observations from a few well-studied polyploids. The mechanism is also consistent with the coexistence of both diploid and tetraploid cytotypes, fitness heterogeneity among independently derived polyploids, and the prevalence of outcrossing among older polyploids. The local adaptation mechanism also makes novel predictions about circumstances favoring polyploid invasions that can be tested using molecular genetic or comparative approaches.

Development of a wheeled wall‐climbing robot with an internal corner wall adaptive magnetic adhesion mechanism

AbstractThe wall‐climbing robot is a growing trend for robotized intelligent manufacturing of large and complex components in shipbuilding, petrochemical, and other industries, while several challenges remain to be solved, namely, low payload‐to‐weight ratio, poor surface adaptability, and ineffective traversal maneuverability, especially on noncontinuous surfaces with internal corners (non‐CSIC). This paper designs a high payload‐to‐weight ratio wheeled wall‐climbing robot which can travel non‐CSIC effectively with a payload capacity of up to 75 kg, and it can carry a maximum load of 141.5 kg on a vertical wall. By introducing a semi‐enclosed magnetic adhesion mechanism, the robot preserves a redundant magnetic adsorption ability despite the occurrence of significant gaps between localized body components and the wall surface. In addition, by ingeniously engineering a passive adaptive module into the robot, both the surface adaptability and crossability are enhanced without increasing the gap between the body and the wall, thereby ensuring the optimization of the adsorption force. Considering the payload capacity and diversity when climbing on vertical walls, inclined walls, ceilings, and internal corner transitions, control equations for internal corner transitions and comprehensive simulations of magnetic adsorption forces are performed using FEA tools. Finally, a functional prototype was developed for rigorous experimental testing, with the results confirming that the robot successfully meets the desired functionality and performance benchmarks.

Multi-kernel support vector regression with improved moth-flame optimization algorithm for software effort estimation

In this paper, a novel Moth-Flame Optimization (MFO) algorithm, namely MFO algorithm enhanced by Multiple Improvement Strategies (MISMFO) is proposed for solving parameter optimization in Multi-Kernel Support Vector Regressor (MKSVR), and the MISMFO-MKSVR model is further employed to deal with the software effort estimation problems. In MISMFO, the logistic chaotic mapping is applied to increase initial population diversity, while the mutation and flame number phased reduction mechanisms are carried out to improve the search efficiency, as well the adaptive weight adjustment mechanism is used to accelerate convergence and balance exploration and exploitation. The MISMFO model is verified on fifteen benchmark functions and CEC 2020 test set. The results show that the MISMFO has advantages over other meta-heuristic algorithms and MFO variants in terms of convergence speed and accuracy. Additionally, the MISMFO-MKSVR model is tested by simulations on five software effort datasets and the results demonstrate that the proposed model has better performance in software effort estimation problem. The Matlab code of MISMFO can be found at https://github.com/loadstar1997/MISMFO.

MedT2T: An adaptive pointer constrain generating method for a new medical text-to-table task

Medical information extraction is a crucial task in the governance of healthcare data within medical information systems in the medical internet network, aimed at extracting vital information from existing content. However, structuring this key information into a table is currently a challenge, hindering the development of AI-driven smart health. In this study, we study the medical text-to-table task based on a new generative perspective. To address the challenges of ineffective numerical embedding, flexible table formats, and dense medical terminology and numerical entities in an end-to-end manner, we present the innovative medical text-to-table model called MedT2T. This model, built on the BART backbone, operates in an end-to-end manner and comprises three essential modules: Encoder, Decoder, and Adapter. The Encoder utilizes an innovative adaptive medical numerical constraint to facilitate precise embedding and generation of medical numerical data. The generated output of the Decoder adheres to relational constraints and table formats, ensuring the desired structure and organization. Additionally, the Adapter incorporates an adaptive pointer generation mechanism, allowing for dynamic referencing of medical terminology and numerical information either from the source text or generated through the vocabulary distribution of the Decoder. Our method outperforms existing baselines in terms of exact match, character level match, and BERTScore. We also proved that MedT2T can serve as an essential table extraction tool to bring informative gains for medical downstream classifiers and predictors. This study not only achieved accurate entity generation for tables from lengthy medical texts to improve physician efficiency in accessing critical information for decision-making, but also provided large-scale structured training table data for downstream tasks such as AI-driven smart healthcare.

Editorial Expression of Concern: Generation of TRAIL-resistant cell line models reveals distinct adaptive mechanisms for acquired resistance and re-sensitization.

Editorial Expression of Concern: Generation of TRAIL-resistant cell line models reveals distinct adaptive mechanisms for acquired resistance and re-sensitization.

Leaf Trait Variations and Ecological Adaptation Mechanisms of Populus euphratica at Different Developmental Stages and Canopy Heights

Leaf Trait Variations and Ecological Adaptation Mechanisms of Populus euphratica at Different Developmental Stages and Canopy Heights

Molecular mechanism of a coastal cyanobacterium Synechococcus sp. PCC 7002 adapting to changing phosphate concentrations

AbstractPhosphorus concentration on the surface of seawater varies greatly with different environments, especially in coastal. The molecular mechanism by which cyanobacteria adapt to fluctuating phosphorus bioavailability is still unclear. In this study, transcriptomes and gene knockouts were used to investigate the adaptive molecular mechanism of a model coastal cyanobacterium Synechococcus sp. PCC 7002 during periods of phosphorus starvation and phosphorus recovery (adding sufficient phosphorus after phosphorus starvation). The findings indicated that phosphorus deficiency affected the photosynthesis, ribosome synthesis, and bacterial motility pathways, which recommenced after phosphorus was resupplied. Even more, most of the metabolic pathways of cyanobacteria were enhanced after phosphorus recovery compared to the control which was kept in continuous phosphorus replete conditions. Based on transcriptome, 54 genes potentially related to phosphorus-deficiency adaptation were selected and knocked out individually or in combination. It was found that five mutants showed weak growth phenotype under phosphorus deficiency, indicating the importance of the genes (A0076, A0549-50, A1094, A1320, A1895) in the adaptation of phosphorus deficiency. Three mutants were found to grow better than the wild type under phosphorus deficiency, suggesting that the products of these genes (A0079, A0340, A2284–86) might influence the adaptation to phosphorus deficiency. Bioinformatics analysis revealed that cyanobacteria exposed to highly fluctuating phosphorus concentrations have more sophisticated phosphorus acquisition strategies. These results elucidated that Synechococcus sp. PCC 7002 have variable phosphorus response mechanisms to adapt to fluctuating phosphorus concentration, providing a novel perspective of how cyanobacteria may respond to the complex and dynamic environments.

Effects of Drought Stress on Leaf Functional Traits and Biomass Characteristics of Atriplex canescens

Drought is a critical factor constraining plant growth in arid regions. However, the performance and adaptive mechanism of Atriplex canescens (A. canescens) under drought stress remain unclear. Hence, a three-year experiment with three drought gradients was performed in a common garden, and the leaf functional traits, biomass and biomass partitioning patterns of A. canescens were investigated. The results showed that drought stress had significant effects on A. canescens leaf functional traits. A. canescens maintained the content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), but the peroxidase (POD) and catalase (CAT) activity decreased, and the content of proline (Pro) and soluble sugar (SS) increased only under heavy drought stress. Under drought stress, the leaves became smaller but denser, the specific leaf area (SLA) decreased, but the dry matter content (LDMC) maintained stability. Total biomass decreased 60% to 1758 g under heavy drought stress and the seed and leaf biomass was only 10% and 20% of non-stress group, but there had no significant difference on root biomass. More biomass was allocated to root under drought stress. The root biomass allocation ratio was doubled from 9.62% to 19.81% under heavy drought, and the root/shoot ratio (R/S) increased from 0.11 to 0.25. The MDA was significantly and negatively correlated with biomass, while the SPAD was significantly and positively correlated with total and aboveground organs biomass. The POD, CAT, Pro and SS had significant correlations with root and seed allocation ratio. The leaf morphological traits related to leaf shape and weight had significant correlations with total and aboveground biomass and biomass allocation. Our study demonstrated that under drought stress, A. canescens made tradeoffs between growth potential and drought tolerance and evolved with a conservative strategy. These findings provide more information for an in-depth understanding of the adaption strategies of A. canescens to drought stress and provide potential guidance for planting and sustainable management of A. canescens in arid and semi-arid regions.

LSGDDN-LCD: An appearance-based loop closure detection using local superpixel grid descriptors and incremental dynamic nodes

Loop Closure Detection (LCD) is an essential component of visual Simultaneous Localization and Mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint changes, and dynamic objects in scenes. This paper introduced an online appearance based LCD using Local Superpixel Grids Descriptor (LSGD) and Dynamic Nodes (DN), i.e., LSGDDN-LCD, to find similarities between scenes via handcrafted features extracted from the LSGD. Additionally, we proposed the adaptive mechanism to group similar scenes called DynamicNodes, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision–recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on public and our own datasets, indicating its great potential as a generic LCD framework. Our implementation of the LSGDDN-LCD approach and the datasets were open-sourced on GitHub (https://github.com/BaoshengZhang0/LSGDDN-LCD.git).

A longitudinal examination of appraisal, coping, stress, and mental health in students: A cross-lagged panel network analysis.

In their seminal Transactional Theory of Stress, Lazarus and Folkman described a complex adaptation mechanism that involves appraisal and coping processes that lead to the subjective experience of stress. In this study, we examined the temporal order of all components of this theory using a cross-lagged panel analysis. A sample of 354 students (62.10% female, mean age=20.63y.o.) participated in a 5-week study and completed weekly self-reported measures of prospective appraisal, coping, core burnout, and mental health (i.e. depression, anxiety, and stress). Using cross-lagged panel network analyses, we analyzed how each measured variable is predicted by all variables from the previous measurement moment. Convergent with the transactional stress theory, we found that threat appraisals and negative self-perception predicted future anxiety and depression. Contrary to expectations, core burnout and somatization emerged as significant predictors of poor mental health outcomes, suggesting a self-perpetuating spiral. Furthermore, our study highlighted the importance of recognizing the bidirectional connections between appraisals, coping strategies, and mental well-being.

Optimal adaptive coordination of overcurrent relays in power systems protection using a new hybrid metaheuristic algorithm

AbstractAdvent of distributed generation and progression towards an intelligent grid infrastructure within the domain of contemporary electrical power systems have created dynamic load profiles. Accompanying these developments, protective relays are faced with an evolving electrical load landscape and variable fault current conditions, resulting in disparate operational timings throughout the diurnal cycle. In light of these challenges, this paper delineates the formulation and simulation of a novel adaptive protection strategy for overcurrent relays, meticulously tailored to accommodate the fluctuations in electrical load. To construct a robust framework for this adaptive mechanism, a series of hypothetical fault current scenarios are meticulously crafted to activate the relays within the briefest time interval feasible. Further innovating within this sphere, this paper introduces a new hybrid algorithm, deftly amalgamating the strengths of three preeminent metaheuristic models: Improved Harmony Search, Particle Swarm Optimization, and Differential Evolution. Simulations and analyses substantiate the efficacy of the algorithm in optimizing the coordination among overcurrent relays aiming to uphold the overarching protective imperatives of the grid. For the IEEE 6‐bus system, the mean value of the objective function during 24 h in Monte Carlo is 292.6607 and very close to 272.0758 in the simulation of eight stochastic scenarios, which contributes to the validity of the approach in practical settings. Also, in the IEEE 30‐bus system, the results of the mean relay operation time set for the hours with the lowest and highest consumption load are 17.1297 and 14.8049 s, which reveals the increase in the operation speed of the relays.

Building a programme theory of a specialist paediatric palliative and hospice care programme: development process and methodological reflection

BackgroundPaediatric palliative and hospice care aims to improve the quality of life of children with life-limiting and life-threatening conditions and their families. The number of these patients has risen significantly in recent years, resulting in an increased need for palliative care for this population. Although the need for paediatric palliative and hospice care is growing, meaningful outcome evaluation to demonstrate its effectiveness as a complex healthcare intervention is in its early stages. For complex interventions (programmes), theory-based evaluations have grown in prominence in recent years. They seek to understand how and why an intervention works by uncovering its underlying mechanisms by means of programme theory. To support both outcome evaluation in paediatric palliative care and a reflective practice of programme theorizing, we aimed to describe the construction of a programme theory for a specialist paediatric palliative and hospice care programme in Austria and to offer a reflective account of its development process.MethodsWe drew on a combination of theory-based evaluation frameworks to construct a programme theory consisting of an action and a change component. Through multiple iterations, incorporating different stakeholders’ perspectives and drawing on different sources of knowledge and theory, we theorized how and why the programme likely achieves its intended outcomes.ResultsThe programme theory outlines the proposed chains of events, causal mechanisms and outcomes of a specialist paediatric palliative and hospice care programme for children and families in several areas corresponding to its main conceptual tenets. Through a range of activities and interventions, the programme triggers coping and adaptation mechanisms that ultimately contribute to family and child wellbeing in physical, psychological, social, and spiritual dimensions. Established trust and partnership between children/families and healthcare professionals as well as a person-centered and family-centered approach were identified as enabling factors.ConclusionsOur findings provide insights into how a specialized paediatric palliative and hospice care programme works to achieve its intended outcomes for children and families. This helps demonstrate its impact, contributing to meaningful outcome evaluation and service improvement.

Dynamic variable analysis guided adaptive evolutionary multi-objective scheduling for large-scale workflows in cloud computing

Energy consumption and makespan of workflow execution are two core performance indicators in operating cloud platforms. But, simultaneously optimizing these two indicators encounters various challenges, such as elastic resources, large-scale decision variables, and sophisticated workflow structures. To handle these challenges, we design an adaptive evolutionary scheduling algorithm, namely AESA, with three innovative strategies. First, a heuristic population initialization strategy is devised to gather workflow tasks onto limited potential resources, thereby alleviating the negative impact of redundant cloud resources on evolutionary search efficiency. Then, a variable analysis strategy is designed to dynamically measure the contribution of each decision variable in pushing the population towards Pareto-optimal fronts. Moreover, AESA embraces an adaptive strategy to reward more evolutionary opportunities for decision variables with higher contributions to handle large-scale decision variables in a targeted manner, further improving the efficiency of evolutionary search. Finally, extensive experiments are performed based on real-world cloud platforms and workflow traces to verify the effectiveness of the proposed AESA. The comparison results validate its superior performance by significantly outperforming five representative baselines in optimizing makespan and energy consumption. Also, the results of ablation experiments demonstrate that all three components contribute to AESA’s overall performance, with the adaptive reward mechanism being the most significant.

A systematic analysis of GASA family members in the Salix matsudana genome: Characterization, expression profile, and putative function in antioxidation

The gibberellic acid stimulated Arabidopsis (GASA) gene family, specific to plants, plays a pivotal role in growth, development, and response to diverse environmental stresses. Despite their importance, GASA genes in willow trees have been little studied. Here, 35 SmGASAs were identified from the Salix matsudana genome, which exhibited similar physical and chemical proprieties to GASAs in other species. A phylogenetic analysis revealed that GASAs from Populus trichocarpa, Vitis vinifera, Nicotiana tabacum, Oryza sativa, Arabidopsis thaliana, and S. matsudana are categorized into three distinct subfamilies. It appeared that polyploidization and segmental duplication have contributed to the expansion and diversification of the SmGASA family. These genes were significantly enriched in response to GA and oxygen-containing compounds. The diverse expression patterns observed across willow varieties suggested functional variation of SmGASAs throughout their evolutionary history. The expression profiles of SmGASAs were further analyzed in response to different treatments (i.e., salt, submergence, and polyethylene glycol (PEG)), indicating a potential role for SmGASAs in adapting to these stressors in willows. Remarkably, SmGASAs were shown to detoxify hydrogen peroxide (H2O2), highlighting their potent antioxidant properties. Overall, the comprehensive genomic analysis of willow SmGASAs shed light on their role in stress responses, offering valuable insights for future research and supporting the hypothesis of their involvement in stress adaptation mechanisms.

Parallel Dual Adaptive Genetic Algorithm: A Method for Satellite Constellation Task Assignment in Time-Sensitive Target Tracking

The evolution of satellite surveillance technology, bolstered by advanced onboard intelligent systems and enhanced attitude maneuver capabilities, has thrust mission scheduling and execution into the spotlight as a prominent and dynamic research field in recent years. As the demand intensifies for mission scheduling and execution to transition from static ground targets to time-sensitive moving targets, conventional scheduling methods often fall short of delivering satisfactory results for continuously tracking these dynamic targets with constellation. The paper introduces a rapid yet efficacious satellite constellation task assignment method, termed the Parallel Dual Adaptive Genetic Algorithm (PDA-GA), for the task assignment of multiple moving target tracking. Specifically, the dual adaptive mechanism isolates the genetic algorithm’s sensitivity to parameters, while the parallel mechanism increases the evolutionary process’s computation speed by deploying complex computations to the GPU. Based on the meticulous analysis of the relevant factors that need to be considered in real tracking scenarios, the proposed PDA-GA can improve the search quality and efficiency of the task assignment solution. We conduct an extensive array of contrast and ablation experiments to showcase the performance and efficiency of PDA-GA in conjunction with autonomous attitude control algorithms across five simulated tracking scenarios. Furthermore, to enable high-fidelity simulation of tracking scenarios, we introduce the Constellation Target Tracking Environment (CTTE), which is equipped with a physics engine and algorithms for multi-satellite task assignment and single-satellite attitude control. This endeavor lays a foundation for future research endeavors focused on autonomous tracking of multiple time-sensitive moving targets within large-scale constellation.

Effects of α-olefin sulfonate (AOS) on Tubifex tubifex: toxicodynamic-toxicokinetic inferences from the general unified threshold (GUTS) model, biomarker responses and molecular docking predictions.

We investigated the potential ecological risks and harm to aquatic organisms posed by anionic surfactants such as α-olefin sulfonate (AOS), which are commonly found in industrial and consumer products, including detergents. This study assessed acute (96-h) and subchronic (14-day) responses using antioxidant activity, protein levels, and histopathological changes in Tubifex tubifex exposed to different AOS concentrations (10% of the LC50, 20% of the LC50, and a control). Molecular docking was used to investigate the potential interactions between the key stress biomarker enzymes (superoxide dismutase, catalase, and cytochrome c oxidase) of Tubifex tubifex. Acute AOS exposure showed a concentration-dependent decrease in survival, and the general unified threshold (GUTS) model revealed that survivorship is linked to individual response patterns rather than random (stochastic) fluctuations. The GUTS model also revealed dose-dependent toxicity patterns in Tubifex tubifex exposed to α-olefin sulfonate (AOS), with adaptive mechanisms at lower concentrations but significant increases in mortality beyond a certain threshold, emphasizing the role of the AOS concentration in shaping its toxicological impact. Exposure to AOS disrupted antioxidant activity, inducing oxidative stress, with GST and GPx showing positive associations with surfactant concentration and increased lipid peroxidation (elevated MDA levels); moreover, AOS exposure decreased protein concentration, signifying disturbances in vital cellular processes. Histopathological examinations revealed various tissue-level alterations, including cellular vacuolation, cytoplasmic swelling, inflammation, necrosis, and apoptosis. Molecular docking analysis demonstrated interactions between AOS and enzymes (-catalase, superoxide dismutase, and cytochrome c oxidase) in Tubifex tubifex, including hydrophobic and hydrogen bond interactions, with the potential to disrupt enzyme structures and activities, leading to cellular process disruptions, oxidative stress, and tissue damage. According to the species sensitivity distribution (SSD), the difference in toxicity between Tilapia melanopleura (higher sensitivity) and Daphnia magna (low sensitivity) to AOS suggests distinct toxicokinetic and toxicodynamic mechanisms attributable to more complex physiology in Tilapia and efficient detoxification in Daphnia due to its smaller size.

Membrane vectorial lipidomic features of coral host cells’ plasma membrane and lipid profiles of their endosymbionts Cladocopium

The symbiotic relationships between coral animal host and autotrophic dinoflagellates are based on the mutual exchange and tight control of nutritional inputs supporting successful growth. The corals Sinularia heterospiculata and Acropora aspera were cultivated using a flow-through circulation system supplying seawater during cold and warm seasons of the year, then sorted into host cells and symbionts and subjected to phylogenetic, morphological, and advanced lipid analyses. Here we show, that the lipidomes of the dinoflagellates Cladocopium C1/C3 and acroporide-specific Cladocopium hosted by the corals, are determined by lipidomic features of different thermosensitivity and unique betaine- and phospholipid molecular species. Phosphatidylserines and ceramiaminoethylphosphonates are not detected in the symbionts and predominantly localized on the inner leaflet of the S. heterospiculata host plasma membrane. The transmembrane distribution of phosphatidylethanolamines of S. heterospiculata host changes during different seasons of the year, possibly contributing to mutualistic nutritional exchange across this membrane complex to provide the host with a secure adaptive mechanism and ecological benefits.

Insights into the genomic homogeneity of Moroccan indigenous sheep breeds though the lens of runs of homozygosity

Numerous studies have indicated that Morocco’s indigenous sheep breeds are genetically homogenous, posing a risk to their survival in the challenging harsh climate conditions where they predominantly inhabit. To understand the genetic behind genetic homogeneity through the lens of runs of homozygosity (ROH), we analyzed the whole genome sequences of five indigenous sheep breeds (Beni Guil, Ouled Djellal, D’man, Sardi, Timahdite and Admixed).The results from principal component, admixture, Fst, and neighbour joining tree analyses consistently showed a homogenous genetic structure. This structure was characterized by an average length of 1.83 Mb for runs of homozygosity (ROH) segments, with a limited number of long ROH segments (24–48 Mb and > 48 Mb). The most common ROH segments were those ranging from 1–6 Mb. The most significant regions of homozygosity (ROH Islands) were mostly observed in two chromosomes, namely Chr1 and Chr5. Specifically, ROH Islands were exclusively discovered in the Ouled Djellal breed on Chr1, whereas Chr5 exhibited ROH Islands in all breeds. The analysis of ROH Island and iHS technique was employed to detect signatures of selection on Chr1 and Chr5. The results indicate that Chr5 had a high level of homogeneity, with the same genes being discovered across all breeds. In contrast, Chr1 displays some genetic variances between breeds. Genes identified on Chr5 included SLC39A1, IL23A, CAST, IL5, IL13, and IL4 which are responsible for immune response while genes identified on Chr1 include SOD1, SLAMF9, RTP4, CLDN1, and PRKAA2. ROH segment profile and effective population sizes patterns suggests that the genetic uniformity of studied breeds is the outcome of events that transpired between 250 and 300 generations ago. This research not only contributes to the understanding of ROH distribution across breeds but helps design and implement native sheep breeding and conservation strategies in Morocco. Future research, incorporating a broader sample size and utilizing the pangenome for reference, is recommended to further elucidate these breeds’ genomic landscapes and adaptive mechanisms.

Iron (Fe) Nutrient Dynamics in Oil Palm Leaves

Iron (Fe) management is crucial in cultivating oil palm, especially in sandy soils, due to its essential role in supporting photosynthesis and palm metabolism, directly influencing the quality and productivity of oil palms. This study aimed to explore the dynamics of Fe deficiency in oil palm leaves in Central Kalimantan, Indonesia. Using a Split Plot Design, the study compared plant conditions between the control (T0) and three levels of Fe deficiency: low (T1), moderate (T2), and severe (T3). Palm samples were selected using the purposive sampling method. Laboratory analysis of leaf samples indicated a significant decrease in Fe content in deficient palms, with levels of 41.49 µg/g in T1, 42.59 µg/g in T2, and 38.93 µg/g in T3, compared to the control group, which had 67.25 µg/g. The study also revealed that Fe deficiency affects the absorption of other macro and micronutrients. For instance, nitrogen levels increased under moderate Fe deficiency (2.57%), while potassium levels decreased (0.729%) at the same level. Despite the Fe deficiency, the plants adapted by maintaining other nutrient levels within a moderate range. Under severe Fe deficiency conditions, Cu levels reached their highest (5.868 µg/g), while Fe showed a significant decrease. This confirms that oil palm has complex nutrient adaptation and regulation mechanisms to maintain nutrient balance even under deficient conditions. These results emphasize the importance of Fe management in oil palm plantations, especially in sandy soils that are prone to nutrient deficiency.

Adapting to climate change: responses of fine root traits and C exudation in five tree species with different light-use strategy

Trees that are categorised by their light requirements have similarities in their growth strategies and adaptation mechanisms. We aimed to understand the complex responses of elevated air humidity on whole tree fine root carbon (C) exudation (ExC) and respiration rate, morphology, and functional distribution in species with different light requirements. Three light-demanding (LD) species, Populus × wettsteinii, Betula pendula, and Pinus sylvestris, and two shade-tolerant species, Picea abies and Tilia cordata saplings were grown in growth chambers under moderate and elevated air relative humidity (eRH) at two different inorganic nitrogen sources with constant air temperature and light availability. The proportion of assimilated carbon released by ExC, and respiration decreased at eRH; up to about 3 and 27%, respectively. There was an indication of a trade-off between fine root released C and biomass allocation. The elevated air humidity changed the tree biomass allocation and fine root morphology, and the responses were species-specific. The specific fine root area and absorptive root proportion were positively related to canopy net photosynthesis and leaf nitrogen concentration across tree species. The variation in ExC was explained by the trees’ light-use strategy (p < 0.05), showing higher exudation rates in LD species. The LD species had a higher proportion of pioneer root tips, which related to the enhanced ExC. Our findings highlight the significant role of fine root functional distribution and morphological adaptation in determining rhizosphere C fluxes in changing environmental conditions such as the predicted increase of air humidity in higher latitudes.