Search Results (223)

Contact unmodified antisense DNA biotechnology (CUADb), developed in 2008, employs short antisense DNA oligonucleotides (oligos) as a novel approach to insect pest control. These oligonucleotide-based insecticides target pest mature rRNAs and/or pre-rRNAs and have demonstrated high insecticidal efficacy, particularly against sap-feeding insect pests, which are key vectors of plant DNA viruses and among the most economically damaging herbivorous insects. To further explore the potential of CUADb, this study evaluated the insecticidal efficacy of short 11-mer antisense DNA oligos against Coccus hesperidum, in comparison with long 56-mer single-stranded and double-stranded DNA sequences. The short oligos exhibited higher insecticidal activity. By day 9, the highest mortality rate (97.66 ± 4.04%) was recorded in the Coccus-11 group, while the most effective long sequence was the double-stranded DNA in the dsCoccus-56 group (77.09 ± 6.24%). This study also describes the architecture of the DNA containment (DNAc) mechanism, highlighting the intricate interactions between rRNAs and various types of DNA oligos. During DNAc, the Coccus-11 treatment induced enhanced ribosome biogenesis and ATP production through a metabolic shift from carbohydrates to lipid-based energy synthesis. However, this ultimately led to a ‘kinase disaster’ due to widespread kinase downregulation resulting from insufficient ATP levels. All DNA oligos with high or moderate complementarity to target rRNA initiated hypercompensation, but subsequent substantial rRNA degradation and insect mortality occurred only when the oligo sequence perfectly matched the rRNA. Both short and long oligonucleotide insecticide treatments led to a 3.75–4.25-fold decrease in rRNA levels following hypercompensation, which was likely mediated by a DNA-guided rRNase, such as RNase H1, while crucial enzymes of RNAi (DICER1, Argonaute 2, and DROSHA) were downregulated, indicating fundamental difference in molecular mechanisms of DNAc and RNAi. Consistently, significant upregulation of RNase H1 was detected in the Coccus-11 treatment group. In contrast, treatment with random DNA oligos resulted in only a 2–3-fold rRNA decrease, consistent with the normal rRNA half-life maintained by general ribonucleases. These findings reveal a fundamental new mechanism of rRNA regulation via complementary binding between exogenous unmodified antisense DNA and cellular rRNA. From a practical perspective, this minimalist approach, applying short antisense DNA dissolved in water, offers an effective, eco-friendly and innovative solution for managing sternorrhynchans and other insect pests. The results introduce a promising new concept in crop protection: DNA-programmable insect pest control. Full article

Osteoarthritis (OA) remains a major contributor to pain and disability; however, the current management is largely reactive, focusing on symptoms rather than preventing irreversible cartilage loss. This review first examines the mechanistic foundations for pharmacological chondroprotection—illustrating how conventional agents, such as glucosamine sulfate and chondroitin sulfate, can potentially restore extracellular matrix (ECM) components, may attenuate catabolic enzyme activity, and might enhance joint lubrication—and explores the delivery challenges posed by avascular cartilage and synovial diffusion barriers. Subsequently, a practical “What–How–When” framework is introduced to guide community pharmacists in risk screening, DMOAD selection, chronotherapeutic dosing, safety monitoring, and lifestyle integration, as exemplified by the CHONDROMOVING infographic brochure designed for diverse health literacy levels. Building on these strategies, the P4–4P Chondroprotection Framework is proposed, integrating predictive risk profiling (physicians), preventive pharmacokinetic and chronotherapy optimization (pharmacists), personalized biomechanical interventions (physiotherapists), and participatory self-management (patients) into a unified, feedback-driven OA care model. To translate this framework into routine practice, I recommend the development of DMOAD-specific clinical guidelines, incorporation of chondroprotective chronotherapy and interprofessional collaboration into health-professional curricula, and establishment of multidisciplinary OA management pathways—supported by appropriate reimbursement structures, to support preventive, team-based management, and prioritization of large-scale randomized trials and real-world evidence studies to validate the long-term structural, functional, and quality of life benefits of synchronized DMOAD and exercise-timed interventions. This comprehensive, precision-driven paradigm aims to shift OA care from reactive palliation to true disease modification, preserving cartilage integrity and improving the quality of life for millions worldwide. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

The objective of this study was to evaluate the effect of two limestone granulometries (0.568 and 1.943 mm) and different levels of vitamin D3 (12.5 g and 25 g) on the thermoregulatory and behavioral responses of replacement pullets. Lohman brown lineage pullets (270 birds), with an average weight of 639.60 g ± 6.05 and an initial age of eight weeks, were used in this study. The experimental design adopted was completely randomized in a 2 × 2 × 2 + 1 factorial arrangement (2 limestone granulometries × 2 levels of vitamin D3 × 2 shifts). The respiratory rate (RR), cloaca temperature (CT), feather surface temperature (FST) and featherless surface temperature (FLST) were higher in the afternoon (p < 0.05), while the thermal gradient (TG) was higher in the morning (p < 0.05). Birds supplemented with different limestone granulometries and different levels of vitamin D3 showed similar thermoregulatory and behavioral responses. The “eating” activity was more frequent in the morning, while in the afternoon, the birds remained seated for longer (p < 0.05). The dietary supplementation with different limestone granulometries and vitamin levels did not impair thermoregulation even at higher temperatures. Regardless of the level of vitamin D3, they showed a better expression of welfare-related behavioral activities in the morning in the semiarid region. Full article

The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha⁻¹) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha⁻¹) and Manipur the lowest (102.8 Mg ha⁻¹). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha⁻¹). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R² = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article

Artificial reservoirs in arid regions provide unique ecological environments for studying the spatial and functional dynamics of plankton communities under the combined stressors of climate change and anthropogenic activities. This study conducted a systematic investigation of the phytoplankton community structure and its environmental drivers in 17 artificial reservoirs in the Ili region of Xinjiang in August and October 2024. The Ili region is located in the temperate continental arid zone of northwestern China. A total of 209 phytoplankton species were identified, with Bacillariophyta, Chlorophyta, and Cyanobacteria comprising over 92% of the community, indicating an oligarchic dominance pattern. The decoupling between numerical dominance (diatoms) and biomass dominance (cyanobacteria) revealed functional differentiation and ecological complementarity among major taxa. Through multivariate analyses, including Mantel tests, principal component analysis (PCA), and redundancy analysis (RDA), we found that phytoplankton community structures at different ecological levels responded distinctly to environmental gradients. Oxidation-reduction potential (ORP), dissolved oxygen (DO), and mineralization parameters (EC, TDS) were key drivers of morphological operational taxonomic unit (MOTU). In contrast, dominant species (SP) were more responsive to salinity and pH. A seasonal analysis demonstrated significant shifts in correlation structures between summer and autumn, reflecting the regulatory influence of the climate on redox conditions and nutrient solubility. Machine learning using the random forest model effectively identified core taxa (e.g., MOTU1 and SP1) with strong discriminatory power, confirming their potential as bioindicators for water quality assessments and the early warning of ecological shifts. These core taxa exhibited wide spatial distribution and stable dominance, while localized dominant species showed high sensitivity to site-specific environmental conditions. Our findings underscore the need to integrate taxonomic resolution with functional and spatial analyses to reveal ecological response mechanisms in arid-zone reservoirs. This study provides a scientific foundation for environmental monitoring, water resource management, and resilience assessments in climate-sensitive freshwater ecosystems. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

Background: The gut microbiota is increasingly recognized as a key modulator in obesity management, influencing host energy balance, lipid metabolism, and inflammatory pathways. With obesity prevalence continuing to rise globally, dietary interventions that promote beneficial microbial shifts are essential for enhancing weight loss outcomes and long-term health. Objective: This study investigated the effects of the multicomponent Weight Loss Maintenance 3 Phases Program (WLM3P), which integrates caloric restriction, a high-protein low-carbohydrate diet, time-restricted eating (10h TRE), dietary supplementation (prebiotics and phytochemicals), and digital app-based support on gut microbiota composition compared to a standard low-carbohydrate diet (LCD) in adults with obesity. The analysis focused exclusively on the 6-month weight loss period corresponding to Phases 1 and 2 of the WLM3P intervention. Methods: In this sub-analysis of a randomized controlled trial (ClinicalTrials.gov Identifier: NCT04192357), 58 adults with obesity (BMI 30.0–39.9 kg/m²) were randomized to the WLM3P (n = 29) or LCD (n = 29) groups. Stool samples were collected at baseline and 6 months for 16S rRNA sequencing. Alpha and beta diversity were assessed, and genus-level differential abundance was determined using EdgeR and LEfSe. Associations between microbial taxa and clinical outcomes were evaluated using regression models. Results: After 6-month, the WLM3P group showed a significant increase in alpha diversity (p = 0.03) and a significant change in beta diversity (p < 0.01), while no significant changes were observed in the LCD group. Differential abundance analysis revealed specific microbial signatures in WLM3P participants, including increased levels of Faecalibacterium. Notably, higher Faecalibacterium abundance was associated with greater reductions in fat mass (kg, %) and visceral adiposity (cm²) in the WLM3P group compared to LCD (p < 0.01). Conclusions: These findings suggest a potential microbiota-mediated mechanism in weight loss, where Faecalibacterium may enhance fat reduction effectiveness in the context of the WLM3P intervention. Full article

Burnout syndrome is characterized mainly by three criteria (emotional exhaustion, depersonalization, and low personal accomplishment), and further exacerbated by night shift work, with profound implications for individual and societal well-being. The Maslach Burnout Inventory survey applied to 97 medical care professionals (with day and night work) revealed different scores for these criteria. Blood metabolic profiles were obtained by UHPLC-QTOF-ESI⁺-MS untargeted metabolomics and multivariate statistics using the Metaboanalyst 6.0 platform. The Partial Least Squares Discrimination scores and VIP values, Random Forest graphs, and Heatmaps, based on 99 identified metabolites, were complemented with Biomarker Analysis (AUC ranking) and Pathway Analysis of metabolic networks. The data obtained reflected the biochemical implications of night shift work and correlated with each criterion’s burnout scores. Four main metabolic pathways with important consequences in burnout were affected, namely lipid metabolism, especially steroid hormone synthesis and cortisol, the energetic mitochondrial metabolism involving acylated carnitines, fatty acids, and phospholipids as well polar metabolites’ metabolism, e.g., catecholamines (noradrenaline, acetyl serotonin), and some amino acids (tryptophan, tyrosine, aspartate, arginine, valine, lysine). These metabolic profiles suggest potential strategies for managing burnout levels in healthcare professionals, based on validated criteria, including night shift work management. Full article

Rapid urbanization worldwide has led to ecological challenges, undermining eco-environmental resilience (EER). Understanding the coupling coordination between new-type urbanization (NTU) and EER is critical for achieving sustainable urban development. This study investigates the Chengdu–Chongqing Economic Circle using the coupling coordination degree (CCD) model to evaluate NTU-EER coordination levels and their spatiotemporal evolution. A random forest (RF) model, interpreted with Shapley Additive exPlanations (SHAP) and Partial Dependence Plot (PDP) algorithms, explores nonlinear driving mechanisms, while Geographically and Temporally Weighted Regression (GTWR) assesses drivers’ spatiotemporal heterogeneity. The results reveal the following: (1) NTU and EER levels steadily improved from 2004 to 2022, although coordination between cities still requires enhancement; (2) CCD exhibited a temporal pattern of “progressive escalation and continuous optimization,” and a spatial pattern of “dual-core leadership and regional diffusion,” with most cities shifting from NTU-lagged to synchronized development; (3) environmental regulations (MAR) and fixed asset investment (FIX) emerged as the most influential CCD drivers, and significant nonlinear interactions were observed, particularly those involving population size (HUM); (4) CCD drivers exhibited complex spatiotemporal heterogeneity, characterized by “stage dominance—marginal variation—spatial mismatch.” These findings enrich existing research and offer policy insights to enhance coordinated development in the Chengdu–Chongqing Economic Circle. Full article

Four new minor monosulfated triterpene penta- and hexaosides, cladolosides S (1), S₁ (2), T (3), and T₁ (4), were isolated from the Vietnamese sea cucumber Cladolabes schmeltzii (Sclerodactylidae, Dendrochirotida). The structures of the compounds were established based on extensive analysis of 1D and 2D NMR spectra as well as HR-ESI-MS data. Cladodosides S (1), S₁ (2) and T (3), T₁ (4) are two pairs of dehydrogenated/hydrogenated compounds that share identical carbohydrate chains. The oligosaccharide chain of cladolosides of the group S is new for the sea cucumber glycosides due to the presence of xylose residue attached to C-4 Xyl1 in combination with a sulfate group at C-6 MeGlc4. The oligosaccharide moiety of cladolosides of the group T is unique because of the position of the sulfate group at C-3 of the terminal sugar residue instead of the 3-O-Me group. This suggests that the enzymatic processes of sulfation and O-methylation that occur during the biosynthesis of glycosides can compete with each other. This can presumably occur due to the high level of expression or activity of the enzymes that biosynthesize glycosides. The mosaicism of glycoside biosynthesis (time shifting or dropping out of some biosynthetic stages) may indicate a lack of compartmentalization inside the cells of organism producers, leading to a certain degree of randomness in enzymatic reactions; however, this also offers the advantage of providing chemical diversity of the glycosides. Analysis of the hemolytic activity of a series of 26 glycosides from C. schmeltzii revealed some patterns of structure–activity relationships: the presence or absence of 3-O-methyl groups has no significant impact, hexaosides, which are the final products of biosynthesis and predominant compounds of the glycosidic fraction of C. schmeltzii, are more active than their precursors, pentaosides, and the minor tetraosides, cladolosides of the group A, are weak membranolytics and therefore are not synthesized in large quantities. Two glycosides from C. schmeltzii, cladolosides D (18) and H₁ (26), display selectivity of cytotoxic action toward triple-negative breast cancer cells MDA-MB-231, while remaining non-toxic in relation to normal mammary cells MCF-10A. Quantitative structure–activity relationships (QSAR) were calculated based on the correlational analysis of the physicochemical properties and structural features of the glycosides and their hemolytic and cytotoxic activities against healthy MCF-10A cells and cancer MCF-7 and MDA-MB-231 cell lines. QSAR highlighted the complexity of the relationships as the cumulative effect of many minor contributions from individual descriptors can have a significant impact. Furthermore, many structural elements were found to have different effects on the activity of the glycosides against different cell lines. The opposing effects were especially pronounced in relation to hormone-dependent breast cancer cells MCF-7 and triple-negative MDA-MB-231 cells. Full article

Satellite image-based farmland classification plays an essential role in agricultural monitoring. However, typical tiling-based classification approaches, which extract patches at fixed offsets within each image during training, often suffer from structural issues such as patch duplication, limiting training diversity. Additionally, farmland classification frequently exhibits class imbalance due to uneven cultivation areas, resulting in biased training toward majority classes and poorer performance on minority classes. To overcome these issues, we propose Class-Balanced Random Patch Training, which combines Random Patch Extraction (RPE) and Class-Balanced Sampling (CBS). This method improves patch-level diversity and ensures balanced class representation during training. We evaluated our method on the FarmMap dataset, using a validation set from the same region and year as the training data, and a test set from a different year and region to simulate domain shifts. Our approach improved the F1 scores of minority classes and overall performance. Furthermore, our analysis across varying levels of class difficulty showed that the method consistently outperformed other configurations, regardless of minority-class difficulty. These results demonstrate that the proposed method offers a practical and generalizable solution for addressing class imbalance in tiling-based remote sensing classification, particularly under real-world conditions with spatial and temporal variability. Full article

The small yellow croaker (Larimichthys polyactis), a migratory estuarine-demersal fish critical to East Asian fisheries, has faced severe population declines because of anthropogenic pressures (e.g., overfishing and anthropogenic habitat modification) and shifting environmental conditions. This study investigates its spatio-temporal habitat dynamics in Hangzhou Bay (2017–2023) using fisheries surveys and species distribution models (SDMs), with insights applicable to Pacific Coast migratory fish conservation. We evaluated the performance of eleven modeling algorithms to identify the most accurate model for predicting small yellow croaker distributions. Our results showed that the random forest algorithm outperformed other models, with a high sensitivity (95.238) and specificity (99.49), demonstrating its ability to capture complex non-linear relationships between environmental factors and species distribution. Depth emerged as the most influential factor, accounting for 30% of the importance in the model, with small yellow croakers preferring deeper waters around 60 m. Salinity was the second most important factor, with higher occurrence probabilities in areas where salinity exceeded 25 PSU. Other environmental factors, such as temperature and dissolved oxygen, had relatively smaller impacts on distribution. Spatially, small yellow croakers were predominantly distributed in offshore regions east of 122.5° E, where deeper waters and higher salinity levels provided suitable habitat conditions. This study underscores the need for targeted management measures, such as habitat restoration, to ensure the sustainable management of small-bodied yellow croaker populations. Full article

Ozone (O₃) and fine particulate matter (PM_2.5) are critical atmospheric pollutants whose complex chemical coupling presents significant challenges for multi-pollutant control strategies. This study investigated the spatiotemporal variations and driving mechanisms of O₃ and PM_2.5 in Jiaxing, China, during different COVID-19 lockdown periods from November 2019 to January 2024. Using high-resolution monitoring data, random forest modeling, and HYSPLIT backward trajectory analysis, we quantified the relative contributions of anthropogenic emissions, meteorological conditions, and regional transport to the formation and variation of O₃ and PM_2.5 concentrations. The results revealed a distinct inverse relationship between O₃ and PM_2.5, with meteorologically normalized PM_2.5 decreasing significantly (−5.0 μg/m³ compared to the pre-lockdown baseline of 0.6 μg/m³), while O₃ increased substantially (15.2 μg/m³ compared to the baseline of 5.3 μg/m³). Partial dependency analysis revealed that PM_2.5-O₃ relationships evolved from linear to non-linear patterns across lockdown periods, while NO₂-O₃ interactions indicated shifts from VOC-limited to NO_x-limited regimes. Regional transport patterns exhibited significant temporal variations, with source regions shifting from predominantly northern areas pre-lockdown to more diverse directional contributions afterward. Notably, the partial lockdown period demonstrated the most balanced pollution control outcomes, maintaining reduced PM_2.5 levels while avoiding O₃ increases. These findings provide critical insights for developing targeted multi-pollutant control strategies in the Yangtze River Delta region and similar urban environments. Full article

Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance of PSC distributions, especially in the face of climate change, long-term studies on PSC variability and its driving factors are lacking. This study aimed to identify the key environmental drivers affecting summer PSC variability in the northern East China Sea (NECS) by analyzing 27 years (1998–2024) of satellite-derived data. Statistical analyses using random forest and multiple linear regression models revealed that euphotic depth (Z_eu) and suspended particulate matter (SPM) were the primary factors influencing PSC variation; deeper Z_eu values favored smaller picophytoplankton, whereas higher SPM concentrations supported larger PSCs. Long-term trend analysis showed a clear shift toward increasing picophytoplankton contributions (+2.4% per year), with corresponding declines in nano- and microphytoplankton levels (2.2% and 0.4% annually, respectively). These long-term changes are hypothesized to result from a persistent decline in SPM concentrations, which modulate light attenuation and nutrient dynamics in the euphotic zone. Marine heat waves intensify these shifts by promoting picophytoplankton dominance through enhanced stratification and reduced nutrient availability. These findings underscore the need for continuous monitoring to inform ecosystem management and predict the impacts of climate change in the NECS. Full article