The present study was conducted to assess the diversity, distribution, and morphometric characteristics of freshwater crabs inhabiting the tributaries of the Godavari River system across Nanded, Hingoli, and Parbhani districts in Maharashtra. Extensive field surveys were carried out between June 2023 and June 2025 in various freshwater habitats, including rivers, tributaries, streams, lakes, farming areas and ponds. Some areas yet to be remain study and surveys. Specimens were collected manually and identified based on their external morphological features. A total of two crab species belonging to the genus Barytelphusa were recorded such as Barytelphusa guerini and Barytelphusa cunicularis. Among them, B. guerini was the most widely distributed, occurring in several tributaries such as the Aasna, Manyad, and Kayadhu rivers, whereas B. cunicularis was restricted to deeper and stable water bodies. Distinct variations in body coloration, size, and chela structure were noted between species. Morphometric analysis showed clear sexual dimorphism, with males exhibiting larger carapace dimensions, heavier body weight, and more robust chelae, while females possessed broader abdomens adapted for egg carrying. The findings highlight the Ecological richness of the Godavari-river basin and the adaptive diversity of its freshwater crab Fauna. Despite moderate anthropogenic pressure, these tributaries continue to support stable crab populations. The data generated in this study serve as a baseline for future ecological monitoring, biodiversity assessment, and conservation planning of freshwater ecosystems in the Marathwada region.

In this study we used environmental DNA metabarcoding and field-based microcosms to assess three classes of aquatic contaminants (metals (copper), pesticides (diuron), and pharmaceuticals (venlafaxine)) and their impact on the structure of freshwater wetland sedimentary bacterial communities. Our results showed that copper had the most influence on bacterial community structure, followed by venlafaxine, then diuron. We also saw that the addition of copper facilitated the release of other sediment-bound metals (barium, zinc, and vanadium), also having significant impacts on community structure. The bacterial communities were mostly dominated by Proteobacteria, Cyanobacteria, Bacteroidota, and Verrucomicrobiota, which were key drivers of variation among treatments, along with Actinobacteriota. Our findings indicate that the ideal taxonomic level for the assessment and identification of bacterial bioindicators is family, with constraints at lower taxonomic levels. We identified five phyla, 13 families, and three species which show strong potential to be either diagnostic bioindicators of one or more of the chemicals assessed or broad bioindicators of common urban contaminants, eight of which are novel bioindicators. Our study highlights the effectiveness of eDNA metabarcoding to efficiently characterise sedimentary bacterial community assemblages and emphasises its value in aquatic ecosystem assessments, particularly for the prediction of contaminants driving ecosystem change.

The future of Global Environmental Assessments: 20 years after the Millennium Ecosystem Assessment

ABSTRACT Holothurian populations in the Mediterranean are relatively understudied, with limited knowledge of their spatial distribution, habitat preferences, and ecological dynamics, making their monitoring a key challenge for ecosystem assessment and sustainable management. However, species distribution modeling is often complicated by the presence‐only nature of the data and heterogeneous sampling designs. This study develops a spatio‐temporal framework based on Log‐Gaussian Cox Processes to analyze Holothurians' positions collected across nine survey campaigns conducted from 2022 to 2024 near Giglio Island, Italy. The surveys combined high‐resolution photogrammetry with diver‐based visual censuses, leading to varying detection probabilities across habitats, especially within Posidonia oceanica meadows. We adopt a model with a shared spatial Gaussian process component to accommodate this complexity, accounting for habitat structure, environmental covariates, and temporal variability. Model estimation is performed using Integrated Nested Laplace Approximation. We evaluate the predictive performances of alternative model specifications through a novel k‐fold cross‐validation strategy for point processes, using the Continuous Ranked Probability Score. Results highlight the influence of habitat‐type covariates, strong variability across campaigns, and a locally structured spatial field capturing residual spatial heterogeneity. Our approach provides a flexible and computationally efficient framework for integrating heterogeneous presence‐only data in marine ecology and comparing the predictive ability of alternative models.

Aquatic ecosystems face mounting threats from climate warming and persistent pollutants like per- and polyfluoroalkyl substances (PFAS). Conventional risk assessments based on single-chemical exposure and species sensitivity distributions (SSDs) overlook ecological complexity. Here, we present a network-based framework to assess ecosystem health under PFAS (10 μg/L, an SSD-defined safe level) and warming (+3.0 °C) using mesocosm experiments. PFAS altered carbon source use in top consumers and reduced energy transfer to higher trophic levels, while warming narrowed niche space and intensified competition. Combined stressors partially offset PFAS effects but disrupted overall trophic structure. All treatments induced an unstable "inverted triangle" trophic configuration in plankton communities. We identify keystone taxa (e.g., Nicholsicypris normalis) and sensitive species (e.g., ciliates) that regulate stability via trophic cascades, using biomass loss, niche contraction, energy flow shifts, and network connectivity. Based on these, we develop a novel Ecological Health Quality Index (EHQI), integrating trophic topology and interaction strength shaped by sensitive species. EHQI declined from 41.83 (healthy) to 22.51 under PFAS and to 21.51 under combined stress. Our approach advances ecosystem assessment by emphasizing sensitive species and trophic stability, enabling early warning detection in aquatic ecosystems under complex stressors.

Here, we explore the determinants and territorial heterogeneity of regional innovation development across Russian regions, employing the Russian Regional Innovation Index (RRII) and indicators of Gross Regional Product (GRP). The empirical database comprises 1363 small innovation enterprises (SMEs) spun-off from budgetary and research organizations and universities, specifically 34 flagship universities, 28 innovation clusters, 156 technology parks, and 15 science and technology innovation centers, along with indicators of the infrastructure–institutional environment, innovation–investment activity, scientific–educational potential, and human–social characteristics. Regression analysis enabled the identification of major factor groups that strongly effect regional innovation development, with infrastructure–institutional and innovation–investment indicators being the most significant. Cluster analysis of RRII and GRP delineated three groups of regions, (1) leaders with high innovation activity and substantial economic potential, (2) intermediate regions with moderate innovation activity and varying economic capacity, and (3) regions with high economic capacity but low innovation activity, exhibiting structural disparities between the economy and innovation. By combining regression and cluster analyses, we provide a comprehensive assessment of regional innovation ecosystems, reveal spatial imbalances, and identify priority areas for regional innovation policy. The study contributes to the theory of regional innovation systems and offers practical recommendations for strategic planning and optimizing the allocation of resources among key elements of innovation infrastructure.

Water security — defined as reliable access to sufficient, safe, and affordable water for human well-being, economic development, and ecosystem health — is India's most urgent national challenge (NITI Aayog, 2019). Home to over 1.4 billion people and ranked 120th out of 122 countries in the EIU's Water Quality Index, India confronts a deepening freshwater crisis wherein 600 million citizens face high to extreme water stress annually. At the heart of this crisis lies a fundamental, often overlooked truth: the systematic destruction of India's natural water security infrastructure — its wetlands. India's wetlands — encompassing vast floodplains, coastal mangroves, highland lakes, seasonal ponds (talaabs), sacred groves (kunds), urban lakes, and trans-Himalayan marshes — are not peripheral ecological features. They are the functional backbone of the country's water security system (Millennium Ecosystem Assessment, 2005). They store and regulate water flows, recharge aquifers upon which 85% of rural India depends for drinking water, agricultural needs, mitigate monsoonal floods that kill over 1,600 people annually, purify drinking water naturally, and sustain livelihoods for over 200 million people. India has forfeited an estimated 30–40% of its wetland area since independence (SAC-ISRO, 2011), with losses exceeding 70% in states like Punjab and Haryana — directly translating into aquifer depletion, increased flooding, deteriorating water quality, and shrinking water availability.

Despite the ecological and socio-economic significance, the limnology and fish biodiversity of Chhattisgarh have not received commensurate scientific attention. The state's aquatic ecosystems support remarkable biodiversity that remains systematically under-studied relative to its ecological and socio-economic significance. This review synthesises published information on the physicochemical properties, biological communities, and ichthyofaunal diversity of Chhattisgarh's water bodies, integrating findings from regional, national, and international scholarly sources. The physicochemical regime of the state's rivers and reservoirs exhibits pronounced seasonal variability, governed predominantly by the South-West monsoon, and is increasingly modified by anthropogenic perturbations including industrial effluents from coal-mining operations, agrochemical runoff, and urban wastewater discharges. The recorded fish fauna encompasses species from at least 15 families and spans multiple trophic guilds, with the Cyprinidae, Bagridae, and Channidae constituting the most species-rich groups. Biologically significant taxa — including large- bodied mahseers (Tor spp.) and the clown knifefish (Chitala chitala) — have undergone documented population contractions owing to habitat degradation and overexploitation. Plankton communities, macrophyte assemblages, and benthic invertebrate fauna reflect the trophic gradient of individual water bodies, ranging from oligotrophic montane streams of the Bastar plateau to hypereutrophic periurban water bodies near Raipur and Korba. Critical lacunae persist in long-term limnological monitoring, molecular-level phylogenetic assessment, and quantitative fisheries surveys. This review identifies priority research areas and proposes integrated, landscape-scale conservation strategies to safeguard the aquatic heritage of Chhattisgarh within the broader context of India's freshwater biodiversity crisis. An integrated, basin-scale approach to aquatic resource management, grounded in ecological flows, freshwater protected areas, community co-management, and pollution control, offers the most promising pathway to securing the long-term viability of Chhattisgarh's aquatic biodiversity.

Seagrass beds are critical coastal ecosystems that play a fundamental role in carbon sequestration, sediment stabilization, and marine biodiversity contributing directly to UN Sustainable Development Goal 14 (SDG 14), particularly target 14.5 which aims to conserve at least 10% of coastal and marine areas by 2030 (UN, 2015). Along the West African coast, and particularly in Senegal, their spatial distribution remains poorly documented. To address this gap, this study, based on 2022 satellite data and conducted under the SEDAD (Ecosystem solutions Ecosystem for sustainable adaptation) project framework. It proposes an integrated approach combining remote sensing, environmental modeling, and participatory knowledge. Satellite data (Sentinel-2 and Landsat 9) were processed in Google Earth Engine to extract spectral indices used as proxies of environmental gradients, including the Normalized Difference Turbidity Index (NDTI), the Normalized Difference Chlorophyll Index (NDCI), and the Normalized Difference Salinity Index (NDSI), as well as sea surface temperature derived from Landsat thermal data. These indices were integrated into a binary logistic regression model to predict the probability of seagrass presence. Additionally, field and participatory surveys were used to guide data interpretation. Spectral indices such as NDVI, EVI, MSAVI2, and NDWI were computed to enhance underwater vegetation detection. The logistic regression model achieved a strong predictive performance, with 81.1% overall accuracy and 76.0% recall. Model robustness was confirmed through a 5-fold cross-validation, ensuring the stability of predictive performance. Among the predictors, NDTI and surface temperature emerged as the most influential variables (38% and 34% relative importance, respectively), while NDCI and NDSI played a secondary role. These findings underscore the value of combining satellite-derived spectral indices with environmental spectral indices to map seagrass meadows in shallow coastal waters. The resulting probability map provides an operational basis for monitoring and managing priority habitats in Senegal’s Marine Protected Areas, and represents a replicable framework for coastal ecosystem assessment across West Africa.

OVERVIEW: The fourth-generation university (4GU) represents a fundamental shift in how universities engage with innovation ecosystems. While entrepreneurial universities emphasize commercialization and direct economic engagement, 4GUs explicitly organize their teaching, research, and valorization activities around societal transformation missions while orchestrating regional innovation ecosystems. For R&D managers, this transition creates new strategic opportunities: Small and medium-sized enterprises (SMEs) gain access to research infrastructure and collaborative networks otherwise beyond their reach, while large corporations can externalize exploratory research and participate in system-level solutions to grand challenges. We develop a working definition of the 4GU and demonstrate its practical implications through Eindhoven University of Technology’s evolution within the Brainport ecosystem, showing how this model creates value for R&D managers through ecosystem participation rather than bilateral knowledge transfer. We provide a staged implementation framework that guides firms from ecosystem assessment to co-orchestration, supported by multitier performance metrics that balance traditional innovation outputs with ecosystem development indicators. This article contributes to innovation management practice by reframing university–industry collaboration as ecosystem development—essential for addressing the complex, interdependent challenges that define contemporary innovation.

The proliferation of digitalization, along with advanced computational techniques, in the healthcare ecosystem has expedited the process of patient care, treatment, and disease diagnosis globally. Medical research, especially involving computational techniques, is heavily dependent on the availability of high-quality datasets generated at the point of care for effective translational research. Our study aims to understand the state of the digital ecosystem (i.e., digitalization, usage of electronic health records (EHRs), and medical data) for the purpose of improving healthcare services and research in hospitals. We conducted a questionnaire-based survey at 16 upper-primary health care centers and public hospitals in the district of Bikaner, Rajasthan, India, to understand the current practices of medical data digitalization and data repository development. The survey results have been analyzed using Principal Component Factor Analysis (PCFA) and statistical tests, including Cronbach’s Alpha, the Kaiser-Meyer-Olkin (KMO) measure, and Bartlett’s test of sampling adequacy, which indicate that the state of digitalization is in its initial phase. Among technical professionals, 35.6% agreed that digitalization has been implemented, while 12.3% remained neutral and 52.1% disagreed. For the same, 41.4% agreed, 13.0% remained neutral, and 45.6% disagreed among non-technical professionals. These highlight that almost half of the groups recognize slow progress in this area, implying that digitalization is still in its initial phase. Our study also indicates that the lack of access to structured and semi-structured medical datasets is a key barrier to applying Artificial Intelligence (AI) and Machine Learning (ML) in Indian healthcare research, where these technologies could play a crucial role in improving healthcare diagnostics, outcome prediction, enhancing clinical decision-making, etc., for better healthcare services, esp. in resource-constrained settings.

Multi-technique radiometric assessment of a desert-marine ecosystem facilitating radiological-environmental safety in hyper-arid regions

Dry-season precipitation reshapes functional gene composition and modulates ecosystem functioning in the tropical eutrophic Mirongo river.

ABSTRACTFreshwaters face increasing pressures from chemical, hydrological and climatic changes, yet tools for assessing their condition remain limited. River biofilms, composed of diverse microbial communities, integrate environmental signals over space and time, making them sensitive indicators of river health. Using 16S rRNA gene sequencing of more than 1600 biofilms collected across a national river network, we quantified bacterial diversity and community composition and applied network analysis to identify ecologically cohesive sub‐communities with keystone taxa underpinning community stability. Alkalinity, dissolved oxygen, nitrate‐nitrogen and temperature were among the principal gradients shaping community composition. Threshold indicator analyses identified taxa with breakpoints along these gradients, revealing interpretable ecological thresholds. Our results demonstrate the potential for microbiome‐based monitoring frameworks that could complement existing biotic indices, enabling early detection of ecological changes and supporting the integration of genomic indicators into routine ecosystem assessment. This scalable approach offers a powerful strategy for managing freshwaters under accelerating anthropogenic pressures.

We present a ship-borne, LiDAR-enabled marine ecosystem assessment and prediction system that overcomes sparse coverage, shallow sensing, and daylight dependence of conventional platforms. A dual-band configuration (905 nm surface LiDAR; 532 nm fluorescence/Raman channels) is coupled with deep learning: a CNN-LSTM for wave-height forecasting, a Raman-normalized 1D-CNN for chlorophyll-a inversion, and an adaptive subsurface phytoplankton layer detection method (IASPLDM). The pipeline spans data acquisition, GPU-accelerated preprocessing, and Bayesian-optimized training. Under moderate sea states, the system profiles phytoplankton beyond 50 m and suppresses biological noise by >60% relative to acoustics; wave forecasts reach correlation >0.998; chlorophyll-a inversion error is ≤0.5 μg/L; and layer detection attains ≤5% miss and ≤3% false-alarm rates. These capabilities enable early warning of harmful algal blooms, fishery habitat management, and blue-carbon assessment with high spatiotemporal resolution, robustness in low-light or turbid waters, and readiness for scalable operations.

The increasing exposure of coastal ecosystems to pollution, eutrophication, ocean acidification, hypoxia and accelerating climate change has highlighted the need for molecular tools capable of detecting sublethal and early biological responses before ecological deterioration becomes evident. In this context, epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNA expression provide sensitive and mechanistically informative indicators of organismal responses to environmental stress. This manuscript synthesizes current knowledge on how these epigenetic pathways respond to key anthropogenic and climate-driven stressors across marine taxa, emphasizing their roles in mediating plasticity, acclimatization and potential adaptive trajectories. We review methodological advances in environmental epigenomics, including high-throughput DNA methylation and chromatin-mapping techniques, and discussed the challenges posed by non-model marine species, including the scarcity of reference genomes. We also evaluate the practical application of epigenetic biomarkers as part of marine biomonitoring frameworks, with particular attention to their potential integration into effect-based assessment tools within the European Union Water Framework Directive. By connecting mechanistic insights with applied management perspectives, this manuscript highlights how epigenetic markers can improve early-warning capabilities, guide conservation planning and enhance the predictive power of coastal ecosystem assessments in the face of rapid environmental change.

Internships are widely promoted as a response to workforce skill gaps, yet they are often examined as isolated educational experiences rather than as components of regional workforce systems. This study addresses this gap by examining how internships can function as ecosystem-level mechanisms for workforce development. Drawing on a comprehensive literature review, the research proposes the Regional Internship Ecosystem Assessment Framework (RIEAF), which integrates institutional capacity, experiential design, and strategic outcomes through a continuous feedback loop. The framework is applied to a qualitative case study of Passage to ASEAN (P2A), using documentary analysis and 18 semi-structured interviews. The findings indicate that P2A supports professional identity formation, technological adaptability, and cross-border employability, demonstrating the potential of regional internship networks to generate meaningful workforce outcomes. However, the transition from student mobility to coordinated workforce deployment remains largely informal in the absence of coordinated institutional mechanisms. The study, therefore, highlights the need for system-level mechanisms that capture and validate internship project portfolios and graduate competencies, enabling coordinated talent deployment and long-term workforce sustainability.

Biological species are widely regarded as fundamental units for generalizing about the natural world. Despite ongoing debates over their conceptual nature, they remain central to research and practice across the life sciences. Species are treated as products of evolution, existing independently of human perception, and discovered through investigation. Their existence is formalized in descriptions and scientific names, with publication in recognized outlets serving as the foundation of identification. The question of how many species exist holds major scientific and political significance. In the context of climate change and biodiversity loss, it is more than academic: species counts are vital indicators for ecosystem assessment and form the basis of conservation policy. Yet this question is not purely empirical. Species numbers are epistemic constructs shaped by historical, methodological, and normative factors. While formal naming under taxonomic codes is treated as discovery, the number of named species is uncertain, and the number of undescribed species remains largely unknown—an urgent issue amid accelerating extinctions. New molecular and AI-based taxonomic methods aim to speed discovery but also challenge traditional notions of knowing a species. Examining species counting—from early catalogues to modern data infrastructures—reveals as much about scientific and cultural frameworks as about biodiversity itself. This paper is a contribution to the Special Issue "Artificial Researchers and ScientificDiscoveries".

While utility-scale solar photovoltaic (PV) farms are being deployed around the world as a climate change mitigation and carbon-neutral energy transition strategy, there is still limited understanding of localized ecological impacts such as microclimate, soils, vegetation, and wildlife, especially in tropical ecosystems. This review synthesizes recent literature and focuses on three thematic areas, namely microclimate modification at PV site, ecophysiological responses (plants and animals) and biomonitoring approach for ecosystem assessment under solar PV farms. This review references peer-reviewed literature accessed via the major scientific databases, with special emphasis on recent studies that reflect the rapid expansion of exploration of the solar system and practical advances in the monitoring of ecological processes. Previous studies demonstrate that photovoltaic (PV) installations may change near surface temperature, radiation balance, wind flow and moisture regimes via panel shading, alterations to surface energy balance or photovoltaic heat island effects. These microclimatic alterations affect soil structure and microbial activity and plant photosynthesis and water relations and the behavior and physiology of birds, insects, mammals, reptiles, and amphibians. Biomonitoring methods such as vegetation indicators, soil biological indicators, insect and bird surveys, and remote sensing methods represent valuable tools for identifying the effects of ecological pressure and facilitating long-term environmental monitoring. Given Liberia’s biodiversity, sensitivity to ecological disruptions, interest in renewable energy development, and lack of monitoring data from which to build comprehensive land-use plans, we examine it as a typical tropical representative case. This review, by combining ecophysiology and biomonitoring views, provides a basis for scaling up ecophysiological studies to inform sustainable solar farm design, environmental monitoring and policy development that will aid the alignment of renewable energy expansion with tropical biodiversity conservation and ecosystem service protection.

Mining activities have expanded rapidly in the Amazon, generating ecological, social, and health concerns. Although the number of publications addressing mining impacts has grown, studies often lack integrative assessments. Here, we provide a systematic synthesis of how the ecological impacts of mining in the Amazon have been framed in the scientific literature over the last 30 years (1995-2025). We analysed 462 peer-reviewed articles and applied Latent Dirichlet Allocation (LDA) to identify the main themes discussed in the literature and the research gaps that structure this field. Twelve topics emerged and were grouped into five categories: Pollution and Toxicology, Land Use, Social-Ecological Systems, Biodiversity and Ecosystem Health, and Monitoring and Assessment. The most prevalent topics were mercury bioaccumulation, human mercury exposure, and post-mining restoration, highlighting persistent concerns with toxicological pathways and ecosystem recovery. In contrast, environmental monitoring & impact indicators, disease hotspots, and landscape changes due to resource extraction were the least represented, revealing limited attention to broader-scale ecological processes and early-warning indicators. Semantic similarity analysis showed close relationships among topics linked to contaminant pathways and human health, as well as between landscape alteration and stream biodiversity. The co-occurrence-based dissimilarity analysis revealed weak connections among several topic pairs and highlighted substantial gaps, particularly the infrequent association between monitoring indicators and either toxicological or biodiversity-focused studies. Together, these findings reveal structural imbalances in the scientific agenda. Bridging ecological, toxicological, and socio-political perspectives is essential to support evidence-based responses and safeguard both biodiversity and human well-being in the Amazon.