Adaptive Value Research Articles

The Effect of Prenatal Stress on the Level of NO-Metabolites in the Central Nervous System

Nitric oxide performs a number of essential functions in the central nervous system. This neurotransmitter regulates apoptotic processes, differentiation and proliferation of neurons, synaptic activity, plasticity. Prenatal stress may be a factor affecting the NO level in different parts of the central nervous system (CNS). The aim of the work was to study the level of NO metabolites in phylogenetically different parts of the central nervous system in prenatally stressed mature male and female rats, depending on the stage of the estrous cycle. Pregnant female rats (n = 12) were subjected to stress from the 16th to the 19th days of pregnancy for 3 hours in the morning. The NO level was assessed in adult (4-month-old) offspring of both sexes. In males, there was a decrease in the level of NO metabolites in the cerebellum and hypothalamus and an increase in the spinal cord. The level of NO metabolites within the studied parts of the CNS of females in the control was higher, after undergoing prenatal stress it changed less compared to males: significant changes were noted in the spinal cord regardless of the estrus cycle stage and in the cerebellum at the stage of estrus. Thus, regardless of gender, the phylogenetically younger structure, the cerebral cortex, turned out to be the most resistant to prenatal stress; the most pronounced changes were noted in the phylogenetically ancient part of the CNS, the spinal cord. Given the importance of NO in the CNS as a key signaling molecule, any changes in its level under the influence of prenatal stress can both have a significant adaptive value and have negative consequences for the functional state of the tissue.

Abstract 4136290: Toward Quantitative Assessment of Cerebrovascular Autoregulation in Neonates using Ultrafast Ultrasound Power Doppler

Introduction: Newborns with congenital heart diseases requiring cardiopulmonary bypass are at risk of neurodevelopmental impairment. The impact of deep hypothermia during cardiopulmonary bypass (DH-CPB) on cerebrovascular autoregulation (CAR) that controls brain perfusion in the presence of variable blood pressure is not well understood. Recently, Ultrafast Power Doppler (UPD) showed potential to study CAR in neonates based on the measurement of cerebral blood volume (CBV). However, since CAR relies mainly on the arterial vasoconstriction/vasodilation, the interpretation of brain perfusion based on CBV requires further separation of arterial CBV from total CBV. This study aims to use UPD to monitor CAR during DH-CPB in neonates. Methods: An ultrasonic probe (5.7 MHz) was placed on the anterior fontanel of 6 newborns before, during and after DH-CPB. Using ultrafast sequences generated by a Verasonic Vantage research system, mid-coronal plane images were acquired (PRF of 9 kHz, 5 compounded diverging waves). An adaptive spatiotemporal singular value decomposition was used to separate the blood signal from tissue signal and obtain UPD. CBV was computed in the deep gray matter (DGM). This region was chosen due to its higher susceptibility to hypoxic-ischemic injury. Arterial CBV was isolated from total CBV based on the upward arterial flow obtained using signed-power Doppler (See Fig A). Pearson correlation ® was computed between mean arterial blood pressure (MAP) and arterial CBV to study CAR. Results: Before and after DH-CPB, negative correlations were found between arterial CBV and MAP: -0.30 and -0.43 respectively. This indicates that an increase in MAP is associated with a decrease in cerebral arterial blood volume. This decrease in arterial CBV likely arises from arterial vasoconstriction, suggesting an active CAR response. Conversely, during DH-CPB, no correlation was found (r = -0.02). This means that despite the increase in MAP, arterial CBV remains unchanged. This lack of variation of arterial CBV is likely due to absent arterial vasoconstriction, suggesting an impaired CAR response. Conclusion: Our findings highlight the potential of UPD for continuous monitoring of CAR, paving the way for improving neurovascular management strategies during DH- CPB.

Interspecific trait differences drive plant community responses on serpentine soils

Abstract Serpentine ecosystems are characterised by multiple environmental stressors: high levels of trace metals such as nickel (Ni), low availability of macronutrients and low water retention. These harsh environmental conditions exert a strong selective force on the vegetation, but their effect on community assembly processes and the functional trait composition remains unknown. In 26 plots on four serpentine sites on Lesbos Island (Greece), we measured six leaf functional traits related to resource acquisition and stress resistance on the 20 most abundant plant species. We quantified the proportion of variance explained by inter‐ and intraspecific trait differences and tested if individual species showed changes in trait values explained by soil Ni content. We investigated the adaptive value and the community level changes for each trait along the natural soil Ni gradient using a mixed model approach and functional diversity analyses. We tested the role of the abundant serpentine endemic and Ni‐hyperaccumulating species Odontarrhena lesbiaca in driving these patterns. Intraspecific variation explained by soil Ni content is smaller than 4%, and most of the variance is explained by interspecific differences in trait values. Most species do not show significant changes in trait values in response to soil Ni. At the community level, low specific leaf areas, small and thick leaves are selected on high Ni soils. Functional diversity analyses suggest a shift towards a stress tolerance syndrome (thick and small leaves with low SLA values) and an increase in functional diversity on Ni‐rich soils. However, these patterns are driven by the increasing abundance of O. lesbiaca. The endemic Ni hyperaccumulator has a stress tolerance strategy with small thick leaves and low SLA, while the community of broadly distributed species show an increase in trait values related to dominance and fast growth. Synthesis. Intraspecific variation in leaf trait responds little to soil metal toxicity. Endemic species harbour unique trait values compared to species with broad distribution which should justify their conservation as a priority.

Generalization Increases the Adaptive Value of Mate Choice Copying When Immediate Copying Is Costly

When assessing potential mates is costly, there will be selection for copying others. Mate choice copying, which is the increased chance of mating with another individual after observing them mating with someone else (i.e., individual-based copying), has been documented in several species, including humans. It is touted as an example of the influence of culture on animal and human mating, but alone it is unlikely to lead the cultural evolution of mating. Rather, it requires mate choice copying with generalization (i.e., trait-based copying), which is the effect of immediate observations on future mating. This sort of mate choice copying has been documented in six species, including humans. Here, I extend an existing game theory model of mate choice copying to include generalization (Extension 1), a cost to immediate copying (Extension 2), and both previous extensions (Extension 3). The results show that Extensions 1 and 2 decrease the adaptive value of mate choice copying when compared to the original model. Extension 3 suggests that adding generalization to mate choice copying with a cost to immediate copying is a more likely evolutionary trajectory than adding it to mate choice copying without this cost. These results have implications for illuminating the emergence of the cultural evolution of mating preferences.

Public health innovation labs framework development: a design study of GGD West-Brabant Netherlands

Abstract Introduction Innovation Labs are spaces that encourage the creation, development and testing of novel ideas within organizations.While there are known frameworks for the organization of such labs in the private sector, current literature does not reflect a framework for developing a public health Innovation Lab (PHI-Lab) specifically.The aim of this study was to develop an evidence-based framework for a PHI-Lab together with the Dutch Municipal Health Service(GGD). Methods Our design thinking study consisted of 4 phases: Discovering, Defining, Creating and Evaluating.An initial framework was developed based on the public and public health sectors in the literature.To create and evaluate the framework,a document analysis of the GGD was performed,and co-creation and testing sessions were organized with 11 internal and 11 external stakeholders of the GGD. Results The final PHI-Lab framework is composed of: rationale, enablers and barriers, environment, approach, outcomes and values. Findings emphasized practical reasons for investing in a PHI-Lab, such as the shortage of health professionals.Stakeholders highlighted that proactive leadership and innovation culture are crucial enablers for the setup of the PHI-Lab.The right space and human resources (environment) and methods including design thinking and cross-collaboration (approach) were highlighted as important strategies for overcoming innovation challenges.Despite barriers such as bureaucracy and resource limitations, tailoring solutions to organizational objectives is key to the PHI-Lab’s success and sustainability, with added values of inclusiveness, adaptability and fun. Conclusions This study highlighted some practical additions to the initial framework that are specific to the public health setting, although mostly an adaptation from the public sector.Findings emphasize that there isn’t a one-size-fits-all solution to develop a PHI-Lab.This framework has the intention of benefiting other public health organizations. Key messages • The framework has the intention of benefiting other public health organizations within established networks and partnerships. • Proactive leadership and innovation culture are crucial enablers for the organizational setup of the innovation lab.

Progress and prospects in the molecular basic research on important traits in sorghum

Sorghum (Sorghum bicolor) is a significant crop serving food, energy, feed, and industrial raw materials, featuring extensive growth adaptability and diverse utility values. Despite the achievements in sorghum breeding in the last decades, conventional breeding methods still confront challenges such as lengthy breeding cycles, low efficiency, and complex genetic backgrounds. With the rapid advancement of molecular biology, genetics, and bioinformatics, molecular breeding has carved new pathways for enhancing sorghum yield and quality. This article reviews the molecular basic research progress in the key agronomic and adaptive traits of sorghum, including grain yield, grain quality, flowering time, plant height, tillering, stress resistance, and male sterility, and discusses future research priorities, offering novel insights and approaches for sorghum breeding.

When sounds come alive: animacy in the auditory sense

Despite the interest in animacy perception, few studies have considered sensory modalities other than vision. However, even everyday experience suggests that the auditory sense can also contribute to the recognition of animate beings, for example through the identification of voice-like sounds or through the perception of sounds that are the by-products of locomotion. Here we review the studies that have investigated the responses of humans and other animals to different acoustic features that may indicate the presence of a living entity, with particular attention to the neurophysiological mechanisms underlying such perception. Specifically, we have identified three different auditory animacy cues in the existing literature, namely voicelikeness, consonance, and acoustic motion. While the first two characteristics are clearly exclusive to the auditory sense and indicate the presence of an animate being capable of producing vocalizations or harmonic sounds—with the adaptive value of consonance also being exploited in musical compositions in which the musician wants to convey certain meanings—acoustic movement is, on the other hand, closely linked to the perception of animacy in the visual sense, in particular to self-propelled and biological motion stimuli. The results presented here support the existence of a multifaceted auditory sense of animacy that is shared by different distantly related species and probably represents an innate predisposition, and also suggest that the mechanisms underlying the perception of living things may all be part of an integrated network involving different sensory modalities.

Major Insect Pests of Sweet Potatoes in Brazil and the United States, with Information on Crop Production and Regulatory Pest Management.

The sweet potato [Ipomoea batatas (L.) Lam] is considered one of the most important crops in the world as food, fodder, and raw material for starch and alcohol production. Sweet potato consumption and demand for its value-added products have increased significantly over the past two decades, leading to new cultivars, expansion in acreage, and increased demand in the United States and its export markets. Due to its health benefits, sweet potato production has multiplied over the past decade in Brazil, promoting food security and economic development in rural areas. Their adaptability and nutritional value make them a food of great importance for Brazil. As pest attacks and disease infection are the main limiting aspects that often cause yield loss and quality degradation in sweet potatoes, there is a great demand to develop effective defense strategies to maintain productivity. There is a critical need for research into non-pesticide control approaches that can provide safe, cost-effective, sustainable, and environmentally friendly pest and disease management techniques. Pests which feed on roots have trade implications worldwide. For example, sweet potato tuber shipments infested with the sweet potato weevil are generally not allowed for trade in North and South America.

Molecular spectral line data preprocessing container load grouping prediction algorithm based on EMD-LSTM

Unbalanced allocation of container resources in cluster environments is an urgent problem to be solved. Aiming at container load prediction and resource allocation strategy, this paper designs an astronomical data processing container load grouping prediction algorithm based on empirical mode decomposition-long short-term memory network, and proposes an adaptive recommendation value generation algorithm based on predicted load information, which can automatically allocate container computing resources according to the degree of load fluctuation. The load prediction accuracy is verified using simulated data and real astronomical observation data. The experimental results show that the algorithm proposed in this paper has higher prediction accuracy than the triple index method and the single long short-term memory network model. In the real-time preprocessing test of astronomical data, the recommendation value generation algorithm proposed in this paper can effectively improve the utilization efficiency of computing resources compared with the default strategy.

A new computational method to estimate adaptation time in Avicennia by using divergence time

Evolutionary studies of plant groups which are distributed in vast geographical regions and face different ecological and environmental conditions are important as they through light on different mechanisms of local adaptation and species divergence through time. The genus Avicennia is one of these plant groups which inspire of few species show interesting geographical distribution with some degree of species-specific geographical isolations. In general, very limited molecular phylogenetic investigations have been carried out in the genus Avicennia, and therefore we conducted the present study with the following aims:To estimate the species divergence time based on different nuclear and chloroplast DNA regions, separately. This will illustrate how different genetic regions evolved in this genus,To identify the sequences with potential adaptive value against geographical variable by latent factor mixed models (LFMM) analysis,To illustrate the phylogenetic signal of these DNA regions and their role in speciation within the genus and,To introducing a new computational strategy for estimating adaptive time for the sequences. The results showed that different genetic regions may produce different species divergent time, both the nuclear ribosomal internal transcribed spacer (ITS) region and chloroplast DNA sequences, contained potentially adaptive single nucleotide polymorphisms (SNPs).We could present a suggestive time for these adaptive sequences for the first time. In conclusion both local adaptation and independent mutations seem to have played role in Avicennia speciation and evolution.

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimization-adaptive square root cubature Kalman filter (PSO-ASRCKF) is established to improve the SOC estimation ability and accuracy of LIBs. The filtering algorithm parameters are optimized to achieve high-precision SOC estimation. An improved ASRCKF with the PSO algorithm to optimize the moving estimation window is constructed to obtain the best adaptive window value. Different temperatures and initial SOC values are used to verify the proposed method under dynamic stress test (DST) and other conditions. The results show that the relative error is mainly distributed within 0.5 % when the SOC is stable. In addition, robustness and adaptability are verified with the root mean square error (RMSE) and the mean absolute error (MAE) values of 0.0019 and 0.0017, respectively, under the DST working condition. The experimental results show that the proposed method can achieve accurate SOC estimation under different temperatures, operating conditions, and initial SOC values.

The potential to reduce runoff generation through improving cropping and tillage practices in a sub-humid continental climate

Agricultural sustainability is threatened by both water deficit and water excess, especially at the presence of extreme meteorological events resulting from climate change. However, there has been lack of demonstrations on management options with long-term values for agricultural adaptation to runoff. Using 20 years of monitoring data (1993‒2012) for two experimental fields in the Canadian Prairies as a case study, we quantified the effects of rainfall characteristics, crop type and biomass, and tillage on growing-season runoff generation using regression analyses and thereafter scenario comparisons. With growing-season gross rainfall ranging between 183 and 456 mm, runoff responses varied between 0 and 59 mm. Over the 20-year study period, 70%‒74% of the growing-season runoff was generated by rainfall events > 100 mm. Compared to high-intensity tillage, long-term conservation tillage reduced both overall runoff and runoff in large events likely by improving water infiltration. Under both tillage methods, growing-season runoff significantly increased with increasing rainfall but decreased with increasing biomass (R2 range: 0.40‒0.58; p range: 0.0007‒0.02). At the event level, the rainfall-runoff relationship followed a piecewise regression model (Cd = 0.82; p < 0.0001; “breakpoint” rainfall event = 105 mm), in which runoff increased slowly before reaching the “breakpoint” but rose sharply afterwards. Due to a greater biomass, canola resulted in less runoff than wheat. Scenario analyses showed that increasing crop biomass by 50% under the current average rainfall conditions could reduce runoff by 81‒86% in wheat and 100% in canola. The reduction may be attributed to the combined effects of crop on interception, evapotranspiration, and infiltration. In conclusion, although in a sub-humid continental climate like the Canadian Prairies there are generally low amounts of rainfall runoff, this study demonstrates significant runoff in some years, especially following large rainfall events. Runoff generation can be significantly reduced through improving cropping and tillage practices, and such effects on regional water retention should be further assessed by considering the past and future changes in climate and management.

De-noising magnetotelluric data based on machine learning

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.

Allometric Growth and Scaling of Body Form of the Spadenose Shark (Scoliodon laticaudus).

The versatility of the shark body form is suggested to be one of the key factors underlying their evolutionary success and persistence. Nevertheless, sharks exhibit a huge diversity of body forms and morphological adaptations. More subtly, it is increasingly evident that in many species, morphology varies through ontogeny. Multiple competing hypotheses exist explaining both the function of specific morphological structures and the interspecific distribution of these ontogenetic morphological shifts. However, existing studies are restricted to a small number of mostly large-bodied species. In this study, we report allometric scaling relationships from functionally important morphological structures in the spadenose shark (Scoliodon laticaudus). We find that a mosaic of isometric and allometric growth underlies the scaling trends in this species and that cases of allometry are consistent with an ontogenetic shift in diet. Moreover, our results refute suggestions that small-bodied sharks grow isometrically. Given the small number of existing studies of ontogenetic morphometry in sharks and the life-history/ecological characteristics of S. laticaudus, this study is a valuable contribution to our understanding of the adaptive value of ontogenetic morphological shifts in elasmobranchs.

Group Social Dynamics in a Seminatural Setup Reflect the Adaptive Value of Aggression in Male Mice

BackgroundMaladaptive aggression in humans is associated with several psychiatric conditions and lacks effective treatment. Nevertheless, tightly regulated aggression is essential for survival throughout the animal kingdom. Studying how social dominance hierarchies regulate aggression and access to resources in an enriched environment (EE) can narrow the translational gap between aggression in animal models and normal and pathological human behavior. MethodsThe social box is a seminatural setup for automatic and prolonged monitoring of mouse group dynamics. We utilized the social box to decipher tradeoffs between aggression, social avoidance, resource allocation, and dominance in 2 mouse models of increased aggression: 1) a model of early exposure to an EE and 2) a model of oxytocin receptor deficiency (OxtR−/−). While an EE increases aggression as an adaptive response to external stimuli, hyperaggression in OxtR−/− mice is accompanied by marked abnormalities in social behavior. ResultsEE groups exhibited significant social avoidance, and an increased proportion of their encounters developed into aggressive interactions, resulting in lower levels of exploratory activity and overall aggression. The hierarchy in EE groups was more stable than in control groups, and dominance was correlated with access to resources. In OxtR−/− groups, mice engaged in excessive social encounters and aggressive chasing, accompanied by increased overall activity. In OxtR−/− groups, dominance hierarchies existed but were not correlated with access to resources. ConclusionsMeasuring aggression and social dominance hierarchies in a seminatural setup reveals the adaptive value of aggression in EE and OxtR−/− mice, respectively. This approach can enhance translational research on pathological aggression.

Construction of English corpus oral instant translation model based on information security and deep learning

In order to improve the quality of translation, avoid translation ambiguity and accurately present the content of the source language, supported by the concept of deep learning and guaranteed by information security, an instant oral translation model is constructed for English corpus. The aim of this study is to enhance the efficiency and accuracy of oral translation systems through the application of deep learning algorithms. Specifically, we employ a sample training mechanism tailored to the unique characteristics of oral translation, allowing for separate training of system interaction and translation data. Furthermore, by redesigning the interaction hardware, this research comprehensively redefines the hardware structure of the translation system, marking a significant step towards improving the usability and performance of such systems. After obtaining and processing effective security sensitive information, language resources are managed by using database management system, which fundamentally improves the level of network information security. The performance of the existing oral automatic translation system (Test Group 1) and the system designed in this paper (Test Group 2) is tested by experiments, and the results are as follows: (1) The translation system designed here has better interactive performance, and it is better than Test Group 1. (2) The adaptive index value of Test Group 1 is 1, and that of Test Group 2 is 0.5, which proves that the adaptive ability of system algorithm of Test Group 2 is better than that of Test Group 1. (3) When comparing the translation speed, the translation time of Test Group 2 is only 70.7 s, while that of Test Group 1 is 130.6 s, so the proposed translation system is obviously superior to that of Test Group 1.

Enhancing floral diversity: A review of mutation breeding techniques in flower crops

Flower crops encompass a wide range of ornamental annuals and perennials that are commercially cultivated for aesthetic appeal of their floral displays. Mutation induction has been used since the early 20th century to increase genetic diversity and develop new flower varieties with improved yield, quality, adaptation and market value. Mutation experiments have successfully created genetic variability and novel phenotypes in diverse floral species. Mutation breeding, which involves the induction of genetic variations via physical and chemical mutagens, has emerged as a vital technique for enhancing ornamental plant traits, such as flower color, shape, disease resistance and stress tolerance. It explores the types and applications of physical mutagens, such as gamma rays and ion beams and chemical mutagens, such as ethyl methane sulfonate (EMS) and sodium azide (SA). This review provides detailed insights into mutation breeding research conducted on major flower crops (e.g., rose, carnation, chrysanthemum and gerbera). This study also highlights achievements in the development of novel flower varieties, highlights the key challenges faced in mutation breeding programs and identifies gaps in research, particularly concerning the comparative efficacy of different mutagens, environmental impacts and genetic stability of mutated varieties. Furthermore, the impact of mutation breeding on the global flower market is discussed, emphasizing its role in expanding trait diversity, catering to niche markets and enhancing the commercial value of flower crops. Mutation breeding offers significant promise in the development of sustainable and climate-resilient ornamental crops that can meet the needs of emerging markets. This review serves as a valuable resource for students, scientists and breeders interested in leveraging mutation breeding for floral crop improvement.

Dogs distinguish authentic human emotions without being empathic

Several recent studies have investigated how dogs perceive human emotional expressions. They have measured the reactions of dogs when exposed to stimuli presented in different modalities, such as photographs, audio recordings or odor samples, or to humans simulating various emotional situations. In the current study, dog owners were manipulated to genuinely experience emotions of happiness, sadness, and neutrality. We measured how dogs responded to their owners’ authentic emotions in two different natural situations: induction of the emotion through a video clip and training of a new task. Through a detailed analysis of dog behavior in these naturalistic settings, we investigated whether dogs show behavioral responses to genuine human emotions. We found that dogs behaved differently depending on the owner’s emotional state: they gazed and jumped less at owners when they were sad, and their compliance with the ‘sit’ command was also diminished. When owners were happy, dogs performed better in the trained task. These results are discussed in light of how dogs perceive human emotional expressions and the adaptive value of this skill.

Predicting yolk testosterone allocation using ecological contexts and species-specific traits.

The prenatal transfer of testosterone (T) from mother to offspring is an important source of phenotypic plasticity. In birds, exposure to ecologically relevant stimuli, such as social competition or an attractive mate, can cause females to deposit more T into their egg yolks. Exposure to elevated yolk T can modify the expression of several fitness-related traits in offspring (e.g. growth, immune function, secondary sex traits and behaviour). Despite some of these changes being potentially adaptive, not all studies find that yolk T levels change in response to ecologically relevant stimuli. This heterogeneity is currently unexplained, limiting our ability to predict inter-generational responses to ecological change. Here, we performed a systematic literature search and found 119 observations across 39 wild species that measured inter-female variation in yolk T allocation in response to various stimuli. We used boosted regression trees, a form of machine learning, to examine whether species-specific traits or variation in study-level variables could explain variation in yolk T allocation (i.e. statistically significant vs. non-significant responses). We found that both species-specific traits and study-level variables are important predictors of significant changes in yolk T levels. Geographic range (latitude and longitude), evolutionary distinctiveness, longevity, egg mass relative to female mass, sociality, migration status and time to fledge were among the top 10 most influential predictors of the 48 examined. We also found that studies measuring or manipulating social stimuli (e.g. competition and breeding density) or breeding date were more likely to detect changes in yolk T allocation compared with studies examining other ecological contexts. Overall, these data provide several testable hypotheses concerning yolk T allocation and its adaptive value across species and contexts. Additionally, these findings can help us predict how ecological changes will affect hormonal responses in females that can shape future generations.

Relationship between phosphorus stoichiometric homeostasis and deepwater adaptability of submerged macrophytes in Erhai Lake, China: Insights from allometric plasticity

The state transition theory suggests that the decline of submerged macrophytes in shallow lakes is closely associated with reduced stoichiometric homeostasis, particularly phosphorus homeostasis (HP). The degradation typically progresses from deeper to shallower regions, indicating a potential positive correlation between the deepwater adaptability (DA) and HP values of submerged macrophytes. Here, we investigated the distribution pattern of submerged macrophytes across different water depths of Erhai Lake to test this hypothesis. The results revealed a significant positive correlation between the DA and HP values of submerged macrophytes. Allometric analysis indicated that the morphological plasticity of submerged macrophytes was linked to their HP. Species with higher HP values, like Potamogeton maackianus, had robust plasticity strategies, particularly “real plasticity”, that enabled them to cope with deeper water stress. In contrast, species with lower HP values (Ceratophyllum demersum and Hydrilla verticillata) experienced nutrient declines, which hindered their adaptation. Additionally, species with higher HP values exhibited closer connections within the plant traits-environment network, indicating that their morphological plasticity adjustments allow better adaptation to the environmental changes caused by increasing water depth. These results confirm the relationship between DA and HP in submerged macrophytes and explain the mechanisms underlying the correlation, thus expanding regime shift theory.