Individual Selection Research Articles

A competitive variety of winter wheat Zhila with high-quality grain

Winter wheat is Russia's leading strategic crop. It ensures sustainable grain production, largely determines food security, and political and economic stability of the country. The aim of the research is to create adaptive, stress-resistant genotypes of wheat of a universal type with a complex of basic economic characteristics for cultivation in the conditions of the North Caucasus, Lower Volga and Central Chernozem regions of Russia. The creation of new wheat varieties is carried out using modern methods of classical, haploid and marker-assisted breeding. The basis of these methods is a complex, step-by-step hybridization and purposeful, continuous individual selection in all parts of the breeding process, starting with the third generation of hybrids. The selection of adaptive, competitive wheat genotypes is carried out according to the characteristics of the model of universal varieties of different intensity developed by us. The varieties were studied according to the type of competitive tests, the method of placing variants was systematic, the repetition was 4-fold. At the last stage of crosses, the Solokha wheat variety was used as the maternal form, the Smuglyanka variety was the paternal form. The Zhila variety belongs to the erythrospermum variety. This is a high-yielding variety, on average, over the years of study, the grain yield excess over the standard Grom variety was high, reliable and amounted to 1.62 t/ha. The variety is quickly-ripening; its growing season is 4 days shorter compared to the mid-ripening Grom variety. It has a high resistance to lodging and belongs to the intensive-type varieties. In terms of the main structural elements of yield (the number of grains per ear, the weight of the grain and the weight of 1000 grains) significantly exceeds the Grom variety. The Zhila variety is characterized by high drought resistance and heat resistance. According to flour-baking indicators, the new variety corresponds to strong wheat. It is characterized by high field resistance to a complex of diseases (powdery mildew, brown, yellow and stem rust, loose smut, tan spot, fusarial wilt, septoriose).

TIBA: A web application for the visual analysis of temporal occurrences, interactions, and transitions of animal behavior.

Data in behavioral research is often quantified with event-logging software, generating large data sets containing detailed information about subjects, recipients, and the duration of behaviors. Exploring and analyzing such large data sets can be challenging without tools to visualize behavioral interactions between individuals or transitions between behavioral states, yet software that can adequately visualize complex behavioral data sets is rare. TIBA (The Interactive Behavior Analyzer) is a web application for behavioral data visualization, which provides a series of interactive visualizations, including the temporal occurrences of behavioral events, the number and direction of interactions between individuals, the behavioral transitions and their respective transitional frequencies, as well as the visual and algorithmic comparison of the latter across data sets. It can therefore be applied to visualize behavior across individuals, species, or contexts. Several filtering options (selection of behaviors and individuals) together with options to set node and edge properties (in the network drawings) allow for interactive customization of the output drawings, which can also be downloaded afterwards. TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. The web application and usage instructions are available at tiba.inf.uni-konstanz.de. The source code is publicly available on GitHub: github.com/LSI-UniKonstanz/tiba.

In silico drug repurposing approach to predict most effective HAART for HIV drug resistance variants prevalent in the Indian HIV-positive population.

HIV epidemics still exist as a major global public health burden, especially in middle- and low-income countries. Given the lack of approved vaccines, antiretroviral therapy (ART) remains the primary approach to reduce the mortality and morbidity linked to this disease. Effective treatment for HIV-1 requires the simultaneous administration of multiple drugs. However, the virus can show resistance to antiretroviral drugs, resulting in treatment failure. Therefore, this study focused on assessing the prevalence of mutations within the Indian HIV-positive population. After assessing the data, we intended to identify the most effective highly active ART (HAART) regimens for individuals with drug-resistant variants. Furthermore, our analysis revealed a spectrum of HIV mutations, with varying effects on protein stability. The significance of this analysis lies in its potential to optimize HAART selection for HIV-positive individuals by accounting for both prevalence and stability-altering mutations. By considering mutation effects on protein stability, we can modify treatment regimens, increasing the likelihood of therapy success and diminishing the risk of resistance. Moreover, this study contributes to the broader field of drug repurposing, offering insights into the rational design of antiretroviral therapies.

The representational geometry for naturalistic textures in macaque V1 and V2.

Our understanding of visual cortical processing has relied primarily on studying the selectivity of individual neurons in different areas. A complementary approach is to study how the representational geometry of neuronal populations differs across areas. Though the geometry is derived from individual neuronal selectivity, it can reveal encoding strategies difficult to infer from single neuron responses. In addition, recent theoretical work has begun to relate distinct functional objectives to different representational geometries. To understand how the representational geometry changes across stages of processing, we measured neuronal population responses in primary visual cortex (V1) and area V2 of macaque monkeys to an ensemble of synthetic, naturalistic textures. Responses were lower dimensional in V2 than V1, and there was a better alignment of V2 population responses to different textures. The representational geometry in V2 afforded better discriminability between out-of-sample textures. We performed complementary analyses of standard convolutional network models, which did not replicate the representational geometry of cortex. We conclude that there is a shift in the representational geometry between V1 and V2, with the V2 representation exhibiting features of a low-dimensional, systematic encoding of different textures and of different instantiations of each texture. Our results suggest that comparisons of representational geometry can reveal important transformations that occur across successive stages of visual processing.

Adaptive Bi-Level Variable Selection for Quantile Regression Models with a Diverging Number of Covariates

The paper develops an innovatively adaptive bi-level variable selection methodology for quantile regression models with a diverging number of covariates. Traditional variable selection techniques in quantile regression, such as the lasso and group lasso techniques, offer solutions predominantly for either individual variable selection or group-level selection, but not for both simultaneously. To address this limitation, we introduce an adaptive group bridge approach for quantile regression, to simultaneously select variables at both the group and within-group variable levels. The proposed method offers several notable advantages. Firstly, it adeptly handles the heterogeneous and/or skewed data inherent to quantile regression. Secondly, it is capable of handling quantile regression models where the number of parameters grows with the sample size. Thirdly, via employing an ingeniously designed penalty function, our method surpasses traditional group bridge estimation techniques in identifying important within-group variables with high precision. Fourthly, it exhibits the oracle group selection property, implying that the relevant variables at both the group and within-group levels can be identified with a probability converging to one. Several numerical studies corroborated our theoretical results.

The effect of sowing dates on the yield of soybean (Glycine max (L.) Merr.) accessions differing in maturity schedules under the climate conditions of Irkutsk Province

Background. Soybean is the main supplier of complete vegetable protein and oil. Prior to the emergence of new early-ripening cultivars, this crop was not cultivated in Irkutsk Province. Its introduction into the area requires a detailed study of agricultural practices, primarily the choice of optimal sowing dates. Materials and methods. Soybean cv. ‘Barguzin’, late-ripening under the conditions of Irkutsk Province, and early-ripening accession 15 served as the research objects. Field studies were carried out in 2019–2021. There were four sowing dates, from the first ten-day period of May through the first ten-day period of June. Results and conclusion. Cv. ‘Barguzin’ appeared highly productive under the climate of Irkutsk Province (its average seed yield in 2019–2021 was 2.7 t/ha), but its productivity was significantly influenced by weather conditions and sowing dates (seed yields varied from 1.4 to 3.6 t/ha). The optimal sowing times for this cultivar are the first and second ten-day periods of May. Early-ripening accession 15 was characterized by more stable yield values in the studied years (average productivity was 2.4 t/ha, minimum 1.9 t/ha, and maximum 3.2 t/ha). The preferred sowing times for this accession are the second and third ten-day periods of May. The case study of two soybean genotypes differing in maturation schedules revealed the need for individual selection of sowing dates. It was shown that the early-ripening soybean genotype imposed less stringent requirements on the choice of sowing dates.

Learnable self-supervised support vector machine based individual selection strategy for multimodal multi-objective optimization

Multimodal multi-objective optimization problem (MMOP) is a frontier research problem, which can provide decision makers with more choices without making trade-offs. Many multimodal multi-objective evolutionary algorithms (MMOEAs) have been proposed to solve MMOP. However, most MMOEAs tend to prioritize the objective dominance of individuals in the process of individual selection, and only individuals with the same objective dominance will be considered the diversity, which leads to the loss of many promising solutions. To solve the above problem, this paper proposes a learnable self-supervised support vector machine (SVM) based multimodal multi-objective optimization algorithm (SVMEA). Support vector machine can learn the knowledge about distinguishing the advantages and disadvantages of individuals from the data in the existing training set and select individuals, in which the objective dominance of individuals is as important as diversity. Moreover, a crowding distance calculation method based on Manhattan distance is designed. Compared with the traditional method using Euclidean distance to calculate crowding distance, it can better evaluate the diversity of individuals in the decision space and assist the selection of elite solutions. Experimental results show that the proposed SVMEA is competitive with seven other advanced MMOEAs on 34 benchmark problems and a practical application problem.

Analysis of Qualitative Indicators of Norway Spruce and Silver Fir Seed Stands in the Federation of Bosnia and Herzegovina

The research aims to determine the quantity and quality of Norway spruce and silver fir seed stands in the Federation of Bosnia and Herzegovina and to recommend measures for their improvement to produce the highest quality reproduction material and achieve genetic gain in newly planted forests. For this research, four measured and 19 observed phenotypic traits were analyzed in 11 Norway spruce and 14 silver fir seed stands. The average age of the trees in Norway spruce seed stands was 70 years, and in silver fir stands 85 years. The average diameter of trees in Norway spruce seed stands was 49 cm, and 46 cm for silver fir. The average height of trees for both species was 26 m. In all seed stands of both species, there was a sufficient number of favorably shaped and straight trees. There was a small incidence of forkness and a high incidence of thin and medium-thick branches. The number of branches in the whorl was high in the seed stands of both species. There was a large number of trees growing poorly and trees with poor trunk clearance from dead branches. A small number of damaged trees in seed stands were also registered, and a low percentage of trees with moderate twisting. The appearance of mistletoe was registered in silver fir seed stands. Some of the analyzed seed stands have shown very good quality, and individual selection should be made in them. In some of the analyzed stands, activities of arrangement and removal of part of the trees are necessary. Considering the great ecological-vegetation diversity of Bosnia and Herzegovina, with four ecological-vegetation regions, 14 areas and 20 districts, most of which are also represented in the Federation of BiH, a higher number of seed stands is needed. When selecting new seed stands for spruce and fir, the focus should be on small stands that grow in extreme conditions.

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression.

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

Adaptability of hybrid populations of Linum usitatissimum L. in conditions of the Northern Transural region

Relevance. The selection of genotypes with high adaptive properties for the conditions of the Tyumen region allows us to contribute to the development of a regional strategy for breeding and genetic work, selection and cultivation of varieties in connection with changing weather and climatic conditions.Material and methodology. The research was carried out over a three-year period (2020-2022) in the northern forest-steppe zone of the Tyumen region. Intervarietal hybridization (4x4 scheme), evaluation of the obtained material according to morphological (4 pcs.), biological (1 pc.), adaptive indicators (2 pcs.) was carried out using generally accepted methods. The objects of study were 12 combinations of fiber flax, first obtained in the region.Results. Significant differences (p&lt;0.05*) were determined between hybrid populations of fiber flax in the influence of genotype, environment, and genotype-environment interaction on the degree of implementation of the studied traits. Correlation relationships have been identified that determine the fiber content in the stem (r=0.79-0.91*, growing season, plant height, inflorescence length, softness, camber), the number and weight of seeds per plant (r=0.79-0 ,91*, inflorescence length, number, size and crackability of the capsule). Based on the results of individual selection in the third hybrid generation (F3), early ripening (4 pieces), tall (n=4), with the maximum number of bolls (n=4) and the number of seeds in them (n=7), fiber content (n =4) in the stem of the combination.Conclusions. Hybrid combinations of fiber flax with high levels of stable properties (G1, G3, G9, G10, G11), productivity criteria (G1, G2, G4, G7, G8, G9, G11, G12) can be valuable in breeding work.

Evaluation of germplasm resources and selection of elite individuals of Gardenia jasminoides

Evaluation of germplasm resources and selection of elite individuals of Gardenia jasminoides

Genetic analysis for reproductive and longevity traits in Landlly sows

The rate of reproduction and lifetime productivity of the animals determine the success of a herd. As these features significantly impact sow productivity, welfare, and profitability, there is considerable interest in global swine breeding programmes to genetically select better sows for these attributes. In this scenario, reliable heritability estimates and genetic correlations between traits are essential for the success of genetic selection for such traits. The goals of the current study were to examine the reproductive and longevity traits and to estimate the genetic and phenotypic parameters for these traits in Landlly sows kept at a swine production farm, Izatnagar, Uttar Pradesh, India. The effect of non-genetic factors on different reproductive traits was estimated using SPSS version 16.0 and Bayesian approach which were utilized to estimate the genetic parameters for reproductive and longevity traits. The current study’s finding that the year and season of birth significantly affect reproductive traits generally suggests that there is potential for boosting productivity by optimising managemental practices. The heritability estimates were low for the reproductive and longevity traits, indicating a very little additive genetic variance in these traits, and individual selection will not be helpful for improving them. As some traits, such as LSB_FF &amp; LSW_FF and LSB _SF &amp; LSW_SF, have a moderate genetic correlation, indirect selection can be used to improve these two pairs of features.

Sex-specific expression of circadian rhythms enables allochronic speciation

Abstract Noctuid moths provide prime examples of species in various stages of allochronic speciation, where reproductive barriers are mediated by genetic divergence in daily or seasonal timing. Theory indicates that allochronic divergence might be one of the most plausible mechanisms of adaptive speciation, especially when timing is subject to divergent ecological selection. Here, we show that the validity of this theoretical expectation is entirely contingent on species characteristics of the mating system. Our analysis focuses on the moth Spodoptera frugiperda (Lepidoptera, Noctuidae), which occurs as two strains that differ in circadian reproductive activity. Unlike in generic models of assortative mating, where chronotypes diverge under mild assumptions, individual-based evolutionary simulations of the mating system and life cycle of S. frugiperda fail to recover allochronic diversification, even under conditions highly conducive to speciation. Instead, we observe that both chronotypes advance their activity schedule toward the early night, resulting in a rapid loss of allochronic variation. This outcome is caused by the fact that mating in S. frugiperda takes considerable time and potential mates are encountered sequentially, so that early males enjoy a systematic advantage. The undermining effect of male mate competition can be overcome when circadian genes evolve sex-specific expression, enabling early and late chronotypes to be maintained or even to diversify in sympatry. These results give new significance to sex differences in biological rhythms and suggest that species characteristics of the mating system and genetic architecture are key to understanding the scope for allochronic speciation across diverse species exhibiting variation in timing.

Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy.

Social comparison selection in individuals with depression and the role of self-assessment and self-enhancement motives: An experimental investigation

Social comparison plays an important role in depression. However, the process of social comparison selection among depressed individuals remains understudied. This study investigated the choices made by individuals with elevated depression scores (assessed with the Patient Health Questionnaire-8) when exposed to vignettes depicting individuals with varying depression severity and coping ability. We experimentally activated either self-assessment or self-enhancement motives and had participants choose from four vignettes: (1) Mild Depression/Coping Well; (2) Mild Depression/Coping Poorly; (3) Severe Depression/Coping Well; and (4) Severe Depression/Coping Poorly. Our first hypothesis was that most participants would select the Mild Depression/Coping Well vignette, presenting a potential upward social comparison standard (H1). Additionally, we expected higher depression scores to correlate with selecting vignettes featuring greater severity (H2). We further hypothesized that those in the self-enhancement condition would prefer coping poorly vignettes more than those in the self-assessment condition (H3). We conducted a between-subject online experiment ( N = 449). Confirming H1, most participants favored the Mild Depression/Coping Well vignette, while the Severe Depression/Coping Poorly vignette was least selected. Supporting H2, higher depression levels were linked to opting for alternative comparison standards over the Mild Depression/Coping Well vignette. Contrary to H3, participants’ choices were not influenced by self-motives.

Community Trait Variation Drives Selection on Species Diversity Through Feedback With Predator Density.

Identifying the processes underlying community assembly and dynamics remains a central goal in ecology. Although much research has been devoted to analyzing how environments affect species diversity, fewer studies have resolved the link between the fundamental process of ecological selection and species diversity. It has been suggested that identifying ecological selection by estimating changes in community-weighted variance (CWV) and mean (CWM) of functional traits may help to identify more general rules of community assembly. Here, we asked whether and how selection by predation and competition affect species diversity, and how this is determined by the initial CWV and CWM for traits governing species interactions, as in our case: Competitiveness and defense against a predator. We tracked experimental five-species phytoplankton communities in the presence and absence of a rotifer predator over time. We manipulated the initial community composition so that communities shared at least three of the five species but differed in CWV and CWM for defense against predation. We found that species diversity was highest with higher initial trait distributions and that temporal changes in diversity correlated with trait selection. The initial distributions determined the form of selection over time, with directional selection for defense and competitiveness, followed by reduced selection and an increase in niche availability when the initial trait distribution was low or high. For intermediate initial trait distributions, we observed directional selection in only one trait, followed by stabilizing selection. Differences and changes in selection for defense, competitiveness, and species diversity correlated with the changes in predator density over time. This suggests that the initial trait distribution determined species diversity through a feedback loop with changes in selection on traits and predator density. Overall, our study shows that identifying ecological selection on functional traits can provide a mechanistic understanding of community assembly.

Linking resource selection to population performance spatially to identify species' habitat across broad scales: An example of greater sage-grouse in a distinct population segment.

Management decisions often focus on the habitat selection of marked individuals without considering the contribution to demographic performance in selected habitats. Because habitat selection is not always adaptive, understanding the spatial relationship between habitat selection and demographic performance is critical to management decisions. Mapping both habitat selection and demographic performance for species of conservation concern can help guide population-scale conservation efforts. We demonstrate a quantitative approach to differentiate areas supporting selection and survival at large spatial extents. As a case study, we applied this approach to greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), an indicator species for sagebrush ecosystems. We evaluated both habitat selection and survival across multiple reproductive life stages (nesting, brood-rearing) in the Bi-State Distinct Population Segment, a genetically distinct and geographically isolated population of sage-grouse on the southwestern edge of the species' range. Our approach allowed us to identify both mismatches between selection and survival and trade-offs between reproductive life stages. These findings suggest resource demands vary across time, with predation risk being a dominant driver of habitat selection during nesting and early brood-rearing periods when chicks are smaller and flightless, whereas access to forage resources becomes more important during late brood rearing when resources become increasingly limited. Moving beyond identifying and managing habitat solely based on species occupancy or use by incorporating demographic measures allows managersto tailor actions to their specific goals; for example, protections of areas that support high selection and high survival and restoration actions focused on increasing survival in areas of high selection and low survival.

Dune Hares: Population Indices, Home Range Size, and Habitat Selection of the European Hare on a Danish Island.

Population indices, such as transect counts of animals, can provide important information concerning population changes over time. Moreover, data concerning the home range size and habitat selection of individuals can provide valuable insight into spatial requirements of animals and how they can adapt to variable environments. Here, we describe the population development of European hares (Lepus europaeus) and investigated home range sizes and habitat selection of six radio-tagged individuals on the small (80 ha) Danish Wadden Sea island Langli. The average minimum hare population density from 1983 to 1997 was 64 ± 36 (mean ± SD) hares/km2, with hare numbers varying among years and seasons. The average home range size was 23.3 (CI: 18.9-28.7) ha, which is comparable to agricultural areas of high structural diversity. Moreover, hare habitat selection was generally bimodal, with hares moving over larger areas and selecting marsh habitat for foraging during nighttime, and dune and grassland habitat for resting during daytime, especially during winter. Combined, our results indicate that hare abundance and space use in the dunal landscape of Langli Island were similar to agricultural areas of comparatively high habitat quality. Thus, dunal marsh landscapes offer high-quality habitat for hares and might be of importance as population strongholds at a time when hare populations are declining in many agricultural areas across Europe.

Impact of intraspecific genetic variation on interspecific competition: a theoretical case study of forage binary mixtures

IntroductionIncreasing intraspecific genetic variation (IV) has been identified as a potential factor to improve productivity and stabilise botanical composition in plant communities. In grasslands systems, this could offer a lever to manage uncertainties of production and variability in the harvested species balance. However, little is known about the conditions to favour IV impact and the mechanisms at play.MethodsThe dependency of IV impact on traits holding it and environmental stressors were analysed using a spatially-explicit individual-based model (IBM) of grassland communities. Sixty-three binary mixtures were defined to reflect a gradient of functional divergence between species regarding light and nitrogen (N) acquisition. The growth and dynamics of these communities were simulated for one year with three possible IV levels under two environments contrasting in terms of soil N fertility.Results and discussionThe model predicted a positive impact of moderate and high IV levels on maintaining the species balance over time, but no marked effects on mixture productivity. This stabilising effect increased at higher IV levels and under low soil N fertility. It also tended to be more pronounced in communities with intermediate functional divergence offering a significant overlap between light and N acquisition parameter values of both species. The major traits involved in the plant response to neighbours differed depending on the most contested resource, as indicated by the within-population selection of individuals with favourable N-related parameters under low N and light-related parameters under high N environments. The hypothesis that IV favours a complementarity of resource use between species was not supported. Rather, a greater spatial heterogeneity in competitive interactions was demonstrated, leading to a higher probability of growth and survival for individuals within the subordinate species. These results highlight the potential usefulness of IV to design forage mixtures with improved stability and resilience.

Morphophysiological Parameters of Growth and Development of Seedlings of Gleditsia triacanthos L. in the Early Stages of Ontogenesis under Conditions of Changing Lighting Intensity

Background: Gleditsia triacanthos L. is a valuable taxon for agroforestry. This species is promising for the creation of protective plantations and has a wide range of economically valuable traits, in particular, drought tolerance. In this regard, the aim of the work was to evaluate morphophysiological parameters of growth and development of G. triacanthos seedlings at early stages of ontogenesis under conditions of changing light intensity. Methods: To achieve this goal, seeds of G. triacanthos were sown with subsequent selection of individuals for the study possessing homogeneity of genetic structure. Analysis of close relatedness of individuals of G. triacanthos showed low intraspecific diversity by Nei (0.27 to 0.62). To evaluate the development of seedlings of G. triacanthos at early stages of ontogenesis, morphophysiological methods were used. Result: Under unfavorable conditions due to low light, the growth and development of the embryonic root and seedling formation was slow. However, all subphases of G. triacanthos seed organogenesis were passed in 98% of individuals, thus forming seedlings under standard criteria. Accumulation of total chlorophyll content in unfavorable conditions was 3 times less compared to individuals in favorable conditions. At the same time, there was a tendency to increase the sum of chlorophylls in the leaves of G. triacanthos in each experimental group at 30 days (p less than 0.05). As a result, it was revealed that light intensity is a limiting parameter for accelerated growth and development of G. triacanthos seedlings.