Forest Gradient Research Articles

Novel Ensemble Learning Algorithm for Early Detection of Lower Back Pain Using Spinal Anomalies

Lower back pain (LBP) is a musculoskeletal condition that affects millions of people worldwide and significantly limits their mobility and daily activities. Appropriate ergonomics and exercise are crucial preventive measures that play a vital role in managing and reducing the risk of LBP. Individuals with LBP often exhibit spinal anomalies, which can serve as valuable indicators for early diagnosis. We propose an advanced machine learning methodology for LBP detection that incorporates data balancing and bootstrapping techniques. Leveraging the features associated with spinal anomalies, our method offers a promising approach for the early detection of LBP. Our study utilizes a standard dataset comprising 310 patient records, including spinal anomaly features. We propose an ensemble method called the random forest gradient boosting XGBoost Ensemble (RGXE), which integrates the combined power of the random forest, gradient boosting, and XGBoost methods for LBP detection. Experimental results demonstrate that the proposed ensemble method, RGXE Voting, outperforms state-of-the-art methods, achieving a high accuracy of 0.99. We fine-tuned each method and validated its performance using k-fold cross-validation in addition to determining the computational complexity of the methods. This innovative research holds significant potential to revolutionize the early detection of LBP, thereby improving the quality of life.

Ecological transcriptomics reveals stress response pathways of a ground-herb species in a waterlogging gradient of Amazonian riparian forests.

Environmental stress is a fundamental facet of life and a significant driver of natural selection in the wild. Gene expression diversity may facilitate adaptation to environmental changes, without necessary genetic change, but its role in adaptive divergence remains largely understudied in Neotropical systems. In Amazonian riparian forests, species distribution is predominantly influenced by species' waterlogging tolerance. The flooding gradient delineates distinct wetland forest types, shaping habitats and species characteristics. Here we investigated the molecular basis of environmental stress response in a tropical ground-herb species (Ischnosiphon puberulus) to environmental variation in Amazonian riparian forests. We compared environmental variables and gene expression profiles from individuals collected in two forest types: Igapó and Terra firme in the Amazonian riparian forests. Predictable seasonal flooding poses a significant challenge in Igapó compared to Terra firme environments, with the former presenting higher water column height and longer flooding duration. Our findings suggest that contrasting environmental conditions related to flooding regimes are important drivers of population genetic differentiation and differential gene expression in I. puberulus. Enriched gene ontology terms highlight associations with environmental stresses, such as defence response, water transport, phosphorylation, root development, response to auxin, salicylic acid and oxidative stress. By uncovering key environmental stress response pathways conserved across populations, I. puberulus offers novel genetic insights into the molecular basis of plant reactions to environmental constraints found in flooded areas of this highly biodiverse neotropical ecosystem.

Fungi and deadwood diversity: A test of the area‐heterogeneity trade‐off hypothesis

Abstract Environmental heterogeneity is one of the most fundamental drivers of species diversity. For decades, ecologists have suggested that heterogeneity–diversity relationships are generally positive. But today, a greater variety of heterogeneity–diversity relationships is discussed. In this study, we contrasted two hypotheses for wood‐inhabiting fungi: The classical heterogeneity–diversity hypothesis, which predicts positive relationships due to an increase in niche dimensionality with increasing heterogeneity. And the more recently stated area‐heterogeneity trade‐off hypothesis, that predicts a unimodal pattern due to an inherent trade‐off between the number of occupied niches and the effective area per species. It allows positive and negative relationships only as special cases. We sampled 3715 deadwood objects on 135 plots along a forest structure gradient in the Black Forest, Germany, and recorded 284 wood‐inhabiting fungal species. To assess heterogeneity of deadwood structures, we calculated two multidimensional structural diversity indices: Structural richness was used as a measure of available niche space, and structural divergence as a measure of multivariate variance within that niche space. Those indices were then related to species richness estimates for rare, common and dominant species using the framework of Hill numbers. We found a linear, positive effect of structural richness and a unimodal effect for structural divergence on estimated species diversity. Structural richness, but not structural divergence, was strongly correlated with the number of sampled deadwood objects. No clear differences between the responses of rare, common and dominant species to the two heterogeneity gradients were found. We also estimated the mean abundance as a proxy for mean population size, which decreased significantly with structural richness but was non‐significantly related to structural divergence. Synthesis. In general, the results of this study suggest a unimodal heterogeneity–diversity relationship for deadwood‐inhabiting fungi and are thereby in line with the area‐heterogeneity trade‐off hypothesis. Thus, the negative effect of heterogeneity should lead to lower species richness and a higher risk of stochastic extinctions at high levels of heterogeneity. However, as deadwood amount and deadwood diversity are often strongly correlated, we argue that the positive effect of resource availability on species richness may mask the negative effect of structural heterogeneity in some cases.

Holocene Neolithic human activity shaped ecosystem functions through the altering of vegetation traits in Zhejiang, eastern China

Since the Holocene, long–term human activities have altered the composition, structure, and function of ecosystems, yet uncertainty remains regarding the underlying mechanism of a variety of vegetation responses to both natural and anthropogenic forces. In this study, we collected and categorized 37 fossil pollen records from Zhejiang, a region characterized by continuous Neolithic Cultures in eastern China, into natural and human–impacted sites. The aim was to reconstruct past long–term changes in plant traits and to investigate variations in ecosystem functioning under the pressure of natural climate shifts and anthropogenic disturbances. Our findings reveal that, during the Holocene, plant functional traits at natural sites gradually adapted to warmer and wetter conditions, with the natural functional dispersion (FDis) showing a steady decrease between 9.0 and 4.0 cal ka BP. At human–impacted sites, intensified human activities gradually transformed the landscape from forest to grassland and shrubland, allowing light–demanding vegetation to thrive due to extended canopy gaps. Pioneer plants flourished in the secondary succession, leading communities to exhibit more aggressive resource acquisition strategies compared to a natural community. With the progression of agriculture towards intensive practices, ancestors might have expanded their original ecological niches by shaping a landscape encompassing a gradient of grasslands, shrublands, and forests, leading to an increase in plant diversity and a reversal in the trend of decreasing FDis. Higher functional diversity is considered a potential factor in maintaining ecosystem stability. Neolithic human activities, by modifying plant composition, community structure, and shaping landscape gradients, probably contributed to enhancing the stability of past terrestrial ecosystems.

Tropical forest succession increases tree taxonomic and functional richness but decreases evenness

AbstractAimSuccessional changes in functional diversity provide insights into community assembly by indicating how species are filtered into local communities based on their traits. Here, we assess successional changes in taxonomic and functional richness, evenness and redundancy along gradients of climate, soil pH and forest cover.LocationNeotropics.Time periodLast 0–100 years.Major taxa studiedTrees.MethodsWe used 22 forest chronosequence studies and 676 plots across the Neotropics to analyse successional changes in Hill's taxonomic and functional diversity of trees, and how these successional changes vary with continental‐scale gradients in precipitation, soil pH and surrounding forest cover.ResultsTaxonomic and functional richness and functional redundancy increased, while taxonomic and functional evenness decreased over time. Functional richness and evenness changed strongly when not accounting for taxonomic richness, but changed more weakly after statistically accounting for taxonomic richness, indicating that changes in functional diversity are largely driven by taxonomic richness. Nevertheless, the successional increases in functional richness when correcting for taxonomic richness may indicate that environmental heterogeneity and limiting similarity increase during succession. The taxonomically‐independent successional decreases in functional evenness may indicate that stronger filtering and competition select for dominant species with similar trait values, while many rare species and traits are added to the community. Such filtering and competition may also lead to increased functional redundancy. The changes in taxonomically‐independent functional diversity varied with resource availability and were stronger in harsh, resource‐poor environments, but weak in benign, productive environments. Hence, in resource‐poor environments, environmental filtering and facilitation are important, whereas in productive environments, weaker abiotic filtering allows for high initial functional diversity and weak successional changes.Main conclusionWe found that taxonomic and functional richness and functional redundancy increased and taxonomic and functional evenness decreased during succession, mainly caused by the increasing number of rare species and traits due to the arrival of new species and due to changing (a)biotic filters.

Unravelling the impact of soil data quality on species distribution models of temperate forest woody plants

Soil properties influence plant physiology and growth, playing a fundamental role in shaping species niches in temperate forest ecosystems. Here, we investigated the impact of soil data quality on the performance of species distribution models (SDMs) of 41 woody plant species in Swiss forests. We compared models based on measured soil properties with those based on digitally mapped soil properties on regional (Swiss Forest Soil Maps) and global scales (SoilGrids). We first calibrated topo-climatic SDMs with measured soil data and plant species presences and absences from mature temperate forest stand plots. We developed further models using the same soil predictors, but with values extracted from digital soil maps at the nearest neighbouring plots of the Swiss National Forestry Inventory. The predictive power of SDMs without soil information compared to those with soil information, as well as measured soil information vs digitally mapped, was evaluated with metrics of model performance and variable contribution. On average, models with measured and digitally mapped soil properties performed significantly better than those without soil information. SDMs based on measured and Swiss Forest Soil Maps showed higher performance, especially for species with an ‘extreme’ niche position (e.g., preference for high or low pH), compared to those using SoilGrids. Nevertheless, if no regional soil maps are available, SoilGrids should be tested for their potential to improve SDMs. Moreover, among the tested soil predictors, pH, and clay content of the topsoil layers most improved the predictive power of SDMs for forest woody plants. In conclusion, we demonstrate the value of regional soil maps for predicting the distribution of woody species across strong environmental gradients in temperate forests. The improved accuracy of SDMs and insights into drivers of distribution may support forest managers in strategies supporting e.g. biodiversity conservation, or climate adaptation planning.

Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.

Dispersal limitation and environmental filtering effects: The taxonomic and functional beta diversity of ground beetles along the altitudinal gradient in Chinese warm-temperature forests.

Beta diversity patterns along environmental gradients and underlying mechanisms constitute key research inquiries in biogeography. However, ecological processes often also influence the functional traits of biological communities, making the assessment of functional β-diversity crucial. Ground beetles (Coleoptera: Carabidae) are one of the most species-rich groups in the insect community, displaying strong habitat specificity and morphological differences. In this study, we explored the patterns of taxonomic and functional beta diversity in ground beetle communities along the altitudinal gradient of warm-temperature forests. By partitioning beta diversity into turnover and nestedness components, we evaluated their relationship with spatial distance. Our findings indicate a decline in species and functional trait similarity with increasing elevation and geographic distance. Further analysis attributed both types of beta diversity in carabids to a combination of dispersal limitation and environmental filtering, with elevation and geographic distance emerging as significant factors. Interestingly, forest-type variations were found to have no impact on the beta diversity of these communities. Our study reveals the impact of environmental filtering and dispersal limitation on both taxonomic and functional beta-diversity, shedding light on carabid community assembly in localized warm-temperature forest areas in eastern China.

Variation in fern species richness, assemblages and morphology along a tropical riparian humidity gradient: an example from Borneo

ABSTRACT Riparian vegetation experiences a little spatial distance humidity gradient, especially in the hyper-humid tropics. In Borneo, with almost no seasons and rather constant local climatic conditions, the riparian plant species show a wide range of morphological and physiological adaptations. This study assesses the adaptation strategies of ferns along a riparian humidity gradient in the submontane forest of Sarawak, Malaysia (Borneo). The species richness and cover, morphological traits, leaf area, leaf perimeter, leaf dissection, leaf elongation ratio, and stomatal density of 63 fern species were surveyed in six study areas along six stream transects. Significant trends in species richness and species cover were identified along the six transects, and changes in morphological traits such as leaf area, leaf perimeter, and leaf dissection were also discovered. With increasing distance from the streams (i.e. decreasing humidity), ferns tended to show longer leaves and a lower stomatal density. We conclude that the morphological traits of fronds change significantly within a short tropical riparian humidity gradient. This reveals variations in the energy investment and adaptation efficiency of ferns within a tropical forest microsite.

A shift in transitional forests of the North American boreal will persist through 2100

High northern latitude changes with Arctic amplification across a latitudinal forest gradient suggest a shift towards an increased presence of trees and shrubs. The persistence of change may depend on the future scenarios of climate and on the current state, and site history, of forest structure. Here, we explore the persistence of a gradient-based shift in the boreal by connecting current forest patterns to recent tree cover trends and future modeled estimates of canopy height through 2100. Results show variation in the predicted potential height changes across the structural gradient from the boreal forest through the taiga-tundra ecotone. Positive potential changes in height are concentrated in transitional forests, where recent positive changes in cover prevail, while potential change in boreal forest is highly variable. Results are consistent across climate scenarios, revealing a persistent biome shift through 2100 in North America concentrated in transitional landscapes regardless of climate scenario.

Trees adjust nutrient acquisition strategies across tropical forest secondary succession.

Nutrient limitation may constrain the ability of recovering and mature tropical forests to serve as a carbon sink. However, it is unclear to what extent trees can utilize nutrient acquisition strategies - especially root phosphatase enzymes and mycorrhizal symbioses - to overcome low nutrient availability across secondary succession. Using a large-scale, full factorial nitrogen and phosphorus fertilization experiment of 76 plots along a secondary successional gradient in lowland wet tropical forests of Panama, we tested the extent to which root phosphatase enzyme activity and mycorrhizal colonization are flexible, and if investment shifts over succession, reflective of changing nutrient limitation. We also conducted a meta-analysis to test how tropical trees adjust these strategies in response to nutrient additions and across succession. We find that tropical trees are dynamic, adjusting investment in strategies - particularly root phosphatase - in response to changing nutrient conditions through succession. These changes reflect a shift from strong nitrogen to weak phosphorus limitation over succession. Our meta-analysis findings were consistent with our field study; we found more predictable responses of root phosphatase than mycorrhizal colonization to nutrient availability. Our findings suggest that nutrient acquisition strategies respond to nutrient availability and demand in tropical forests, likely critical for alleviating nutrient limitation.

Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis

AbstractImaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

AbstractEffective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

Climate-related naturally occurring epimutation and their roles in plant adaptation in A. thaliana.

DNA methylation has been proposed to be an important mechanism that allows plants to respond to their environments sometimes entirely uncoupled from genetic variation. To understand the genetic basis, biological functions and climatic relationships of DNA methylation at a population scale in Arabidopsis thaliana, we performed a genome-wide association analysis with high-quality single nucleotide polymorphisms (SNPs), and found that ~56% on average, especially in the CHH sequence context (71%), of the differentially methylated regions (DMRs) are not tagged by SNPs. Among them, a total of 3235 DMRs are significantly associated with gene expressions and potentially heritable. 655 of the 3235 DMRs are associated with climatic variables, and we experimentally verified one of them, HEI10 (HUMAN ENHANCER OF CELL INVASION NO.10). Such epigenetic loci could be subjected to natural selection thereby affecting plant adaptation, and would be expected to be an indicator of accessions at risk. We therefore incorporated these climate-related DMRs into a gradient forest model, and found that the natural A. thaliana accessions in Southern Europe that may be most at risk under future climate change. Our findings highlight the importance of integrating DNA methylation that is independent of genetic variations, and climatic data to predict plants' vulnerability to future climate change.

Effects of increasing soil moisture on Antarctic desert microbial ecosystems.

Overgeneralization and a lack of baseline data for microorganisms in high-latitude environments have restricted the understanding of the microbial response to climate change, which is needed to establish Antarctic conservation frameworks. To bridge this gap, we examined over 17,000 sequence variants of bacteria and microeukarya across the hyperarid Vestfold Hills and Windmill Islands regions of eastern Antarctica. Using an extended gradient forest model, we quantified multispecies response to variations along 79 edaphic gradients to explore the effects of change and wind-driven dispersal on community dynamics under projected warming trends. We also analyzed a second set of soil community data from the Windmill Islands to test our predictions of major environmental tipping points. Soil moisture was the most robust predictor for shaping the regional soil microbiome; the highest rates of compositional turnover occurred at 10-12% soil moisture threshold for photoautotrophs, such as Cyanobacteria, Chlorophyta, and Ochrophyta. Dust profiles revealed a high dispersal propensity for Chlamydomonas, a microalga, and higher biomass was detected at trafficked research stations. This could signal the potential for algal blooms and increased nonendemic species dispersal as human activities increase in the region. Predicted increases in moisture availability on the Windmill Islands were accompanied by high photoautotroph abundances. Abundances of rare oligotrophic taxa, such as Eremiobacterota and Candidatus Dormibacterota, which play a crucial role in atmospheric chemosynthesis, declined over time. That photosynthetic taxa increased as soil moisture increased under a warming scenario suggests the potential for competition between primary production strategies and thus a more biotically driven ecosystem should the climate become milder. Better understanding of environmental triggers will aid conservation efforts, and it is crucial that long-term monitoring of our study sites be established for the protection of Antarctic desert ecosystems. Furthermore, the successful implementation of an improved gradient forest model presents an exciting opportunity to broaden its use on microbial systems globally.

Designing high-efficiency organic semi-conductors for organic photodetectors assisted by machine learning and property prediction

Quantum chemical calculations have been widely utilized to design and explore organic semi-conductors for various photovoltaics applications. Photovoltaic devices such as photodetectors work on the principle of photo-electric-effect and widely used for optical communications. In recent years, machine learning has become famous in many research fields. In present study, we have predicted reorganization energy of organic semi-conductors with the aid of machine learning. Statistical analysis is used to select best molecular descriptors (features). About 10 machine learning models are tested for selecting the best one. Among various tested models, gradient forest regressor and random forest regressor are the top models. By using our new designing approach, easy to synthesize building blocks are connected to design new organic semiconductors. Their reorganization energies are predicted using fast machine learning method. For this purpose, already trained and hyper parameter optimized gradient boosting regressor is used. It is generally accepted that to obtain higher charge mobility molecules should have lower reorganization energy. Ten organic semi-conductors with lower reorganization energy are selected for density functional calculations and their energy levels are calculated. The chemical similarity analysis using clustering and heatmap is also done for selected organic semi-conductors. Synthetic accessibility score is calculated and majority of semi-conductors are easy to synthesize. These molecules are potential candidates for device fabrication. The proposed python-based pipeline has ability to select the molecules for organic photodetectors in fast and cheaper way.

Carbon stock of shatto forest along altitudinal gradients in masha district, southwest ethiopia: a contribution to climate change mitigation

Forests play a significant role in climate change mitigation through carbon sequestration from the atmosphere and retaining carbon in above and belowground biomass of trees, litter biomass and soil. However, there is little empirical data on the potential carbon stock of Ethiopia’s various forests. Therefore, this study was carried out to estimate the carbon stock potential of Shatto Forest along altitudinal gradients and its contribution to climate change mitigation in Masha district, southwest Ethiopia. Using a systematic sampling design, a total of 30 plots measuring 20 x 20 m were established at 200 m intervals along seven transect lines in order to inventory the woody species. Within the main plot, a 1 m x 1 m sub-plot was used to collect soil samples and litter biomass. The biomass carbon stock was estimated using allometric equations. Results revealed that the mean total carbon stock of the study forest was 714.4 ton/ha, of which 337.38ton/ha, 67.48 ton/ha, 0.79 ton/ha and 308.75 ton/ha were stored in the aboveground carbon, belowground carbon, leaf litter carbon and soil organic carbon, respectively. The mean total CO2 equivalent of the study forest was also 2621.85 ton/ha. Along the altitudinal gradients of the study forest, the total carbon stocks contributed by the forest varied significantly at (p < 0.05), with the largest stock found at lower elevation and the lowest stock found at higher elevation. Based on the study findings, it is concluded that the forest under investigation possessed a substantial carbon stock capacity, allowing it to sequester a significant amount of CO2 and mitigate climate change while offering other ecosystem services. Thus, in order to optimize the forest’s capacity to store carbon and obtain the financial benefits of CO2 mitigation, reduced emission from deforestation and degradation (REDD+) program in the area should be integrated with carbon sequestration.

Soil Environments Regulate Dominant Soil Fungal Communities along an Elevational Gradient in Subtropical Forests

Soil fungal communities play a critical role in the promotion of nutrient cycling and the stabilization of ecosystem functions in subtropical forests. Yet, clarifying the relationships between soil fungal diversity and microclimate variability along an elevational gradient, as well as understanding the driving mechanisms of their variations in subtropical forests, remains insufficiently understood. In this study, we recorded the composition and soil fungal diversity along an elevational gradient in Daiyun Mountain of China, aiming to elucidate the primary factors influencing the structure of the dominant soil fungal along an elevational gradient in subtropical forests. The results showed that (1) the dominant phylum of soil fungi at different elevations were Basidiomycota, Ascomycota (relative abundance &gt; 10%) and Zygomycota (relative abundance &gt; 1%). The Simpson index of soil fungi showed a clear upward trend along the elevational gradient, while no significant difference was observed in the other indices, and both overall reached their maximum value at the elevation of 1200 m. (2) The mean annual soil temperature and moisture, soil pH and available phosphorus were the main factors driving the dominant soil fungal along the elevational gradient. (3) Co–occurrence network analyses revealed a distinct modular structure of dominant soil fungal communities at different elevations, with Ascomycetes identified as the key taxa in fungi network relationships. Our research holds ecological significance in understanding the pivotal role of soil environmental factors in shaping the complex composition and interactions within soil fungal communities.

Seedling potential of trees species along the elevational gradient in temperate hill forest of central Nepal

Seedling potential of trees species along the elevational gradient in temperate hill forest of central Nepal

Analyzing fishery data and exploring the resource status of Antarctic krill based on its environmental dependence

AbstractAntarctic krill Euphausia superba is a key species in the Southern Ocean ecosystem and has important ecological value. It is also one of the main fishery resources in the Southern Ocean with high economic value. In recent years, with the development of krill fishery and rapid climate change, the conservation and management of krill resources have faced unprecedented challenges. The krill resource is different from the general fishery resources. Due to the krill schooling characteristics, fishery data has some advantages over scientific survey data. Reviewing and analyzing the krill fishery data is necessary. Therefore, in this study, four fishery resource indices (FRIs) were calculated based on Antarctic krill fishery data, and based on the environmental dependence of Antarctic krill, the FRI with the strongest environmental dependence was screened by using the Gradient Forest Model, and the step changes of the FRI with strongest environmental dependence was examined by using the sequential t‐test analyses of regime shifts (STARS). The generalized additive model (GAM) to analyze the correlation between the FRI with the strongest environmental dependence and the main environmental factors, and then explore the resource status of Antarctic krill in the study area. The model results showed that the “FRI_hual” had the strongest environmental dependence and was a better indicator of the status of the Antarctic krill in the study area than the others. In the 2009–2010 year, “FRI_hual” had an obvious regime shift. The interaction of environmental factors “ice_area_2” and “SSH_1” has a great impact on “FRI_hual”. Methods used and results obtained in the study can provide new ideas and a scientific reference for future work, including integrated stock assessment of krill and krill resource monitoring, conservation, and management based on the ecosystem.