Sustainability

BackgroundThe relationship between aesthetic appreciation and environmental values remains a critical yet under-researched area in environmental psychology. Although the Two-Major Environmental Values (2-MEV) model--encompassing preservation and utilization dimensions--serves as a standard framework for assessing environmental attitudes, the integration of aesthetic perception within this structure has largely remained unexplored. This study investigates the conceptual linkages across a diverse international sample to determine whether aesthetic appreciation functions independently of a traditional environmental value framework. Methods and FindingsWe conducted a large-scale, cross-sectional survey involving 11,800 pre- and in-service teachers across 34 countries. Participants environmental values were evaluated using the 2-MEV scale, while their aesthetic appreciation of nature and the built environment was assessed using Osgoods semantic differential technique. Employing principal component analysis, hierarchical exploratory factor analysis (EFA), analysis of variance (ANOVA), and within-class analysis (WCA), we accounted for cross-national variations and evaluated response consistency. The results demonstrate that aesthetic appreciation comprises two distinct dimensions-- focusing separately on nature and the built environment--that operate independently of traditional preservation and utilization values, showing only weak correlations. Furthermore, while the overarching psychological structure remains consistent globally, our findings reveal significant cross-national variations in respondent scores, particularly concerning utilization-related values. ConclusionsThese findings establish that aesthetic appreciation constitutes a distinct psychological construct separate from conventional environmental value frameworks. The observed cross-cultural variability underscores the necessity of accounting for national and cultural contexts when designing environmental education programs. By leveraging a robust, comprehensive global dataset, this study provides a vital empirical foundation for integrating aesthetic and value-based dimensions into future environmental research and educational policy.

Investigating Changes Detection in Land Use and Land Cover Analysis of Opencast Singareni Coal Mine Area Using Remote Sensing and GIS Techniques was the focus of the current investigation. Change Detection Analysis for Land Use Land Cover over a decade frequency (2005-2015) of cultivated soils surrounding OpenCast Coal Mine areas namely Ramakrishnapur, Srirampur and Medipalli of Telangana State.AWiFS data of IRS Resoursesat Satellite images of 2005 and 2015 were taken for the detection of temporal variation in Land use/ Land cover due to open cast coal mining.The ability of remote sensing techniques to produce precise spatiotemporal statistics of LULC and its changes in the typical coal mining region has been demonstrated, in open cast coal mining per year was the highest at Ramakrishnapur (@ 300 ha / year) followed by Srirampur (@ 150 ha / year) and Medipalli (@ 100 ha / year).At Ramakrishnapur with the onset of mining there was considerable decrease in agricultural land, shallow water bodies and deciduous forests i.e., conversion of these land uses to mining was evident. Whereas, at other two mining sites (Srirampur and Medipalli) conversion of scrubland followed by shallow water bodies, agriculture lands and deciduous forests into mining area was detected. However, the built-up land at all three-mining site was from cultivated lands and at Medipalli and Srirampur it was also contributed from scrubland. On the whole impact of open cast mining on deep water bodies was not detected. If the mining and surrounding regions continued and appropriate use of the land and water resources at hand, careful long-term planning is of utmost importance.

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use of global modeling techniques to predict cancer at the county level that overlooks important spatial differences. This study aims to develop geographically weighted machine learning models to predict cancer prevalence at the census tract level in the United States and identify local determinants of cancer burden. First, a scoping review was conducted to find a list of measurable drivers of cancer in the United States. Using this list, the data of these variables for 84415 census tracts were obtained from the Center for Disease Control and Prevention PLACES dataset and other publicly accessible resources. Then, several predictive models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), as well as Random Forest, XGBoost, and Deep Neural Network and their geographically weighted counterparts, were developed and compared using the Coefficient of Determination, Root Mean Square Error, and Absolute Error. Results presented that geographically weighted models outperformed other methods, and geographically weighted XGBoost achieved the strongest and most consistent overall performance with pseudo-R2 ranging between 0.89 and 0.98. Feature importance analysis of this model illustrated that most important cancer drivers changed location by location. Aged people, racial composition, preventative behaviors, and metabolic conditions such as diabetes, hypertension, and high cholesterol were determined as influential predictors, although their relative importance varied across regions. These findings revealed the value of localized models at a small geographic scale to identify regional cancer risk patterns and help the allocation of proper resources to hotspot areas. Keywords: Cancer prevalence, Census tracts, geographically weighted machine learning models, Deep neural network, XGBoost, Random Forest, Ordinary Least Squares, risk factor, determinant

Active teaching methodologies have been widely used to promote meaningful learning and student autonomy. In this context, quantitative approaches can help assess how students organize and integrate knowledge throughout the learning process. Among these approaches, semantic co-occurrence networks stand out, as they are capable of identifying relationships between words and revealing the conceptual structure of textual productions. The objective of this study was to investigate whether semantic network analyses can characterize differences in students conceptual organization in Microbiology during their participation in the active teaching methodology "Adopt a Bacterium." To this end, a case study was conducted in the Bacteriology course at the Institute of Biomedical Sciences of the University of Sao Paulo, analyzing the textual productions of two groups of students in the years 2024 and 2025 during their study of the bacterial genus Bacillus. The texts were evaluated using semantic co-occurrence networks, taking into account metrics of structure and conceptual integration. The results showed that both groups covered the microbiological content outlined in the course, though with different thematic focuses and approaches to integrating the concepts. Although both years featured modular structures (a statistical mode of 9 subgraphs), in 2025 the network exhibited greater discursive robustness (2 to 4 times more words with high Betweenness centrality) than in 2024. It is concluded that semantic network analysis allows for the characterization of differences in conceptual organization among students using active learning methodologies, serving as a complementary tool for assessing meaningful learning in Microbiology.

Background: Stress research in nursing education has largely focused on distress, stressors, and negative outcomes, although challenging experiences may also support motivation, confidence, learning, and growth when appraised positively. Objective: To develop and evaluate the psychometric properties of the Nursing Student Positive Stress Scale (NSPSS). Design: A methodological instrument development and psychometric evaluation study. Methods: The NSPSS was developed using a deductive, theory-driven approach informed by the transactional theory of stress and coping and positive psychology perspectives. Content validity was assessed by an international nursing expert panel. Psychometric evaluation used national survey data from nursing students in New Zealand. Of 539 responses, 507 were analysed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted using separate subsamples. Internal consistency was assessed using Cronbach's alpha and McDonald's omega, and convergent validity through correlation with Perceived Stress Scale-10 scores. Results: Content validity was strong (I-CVI = .88-1.00; S-CVI/Ave = .975; S-CVI/UA = .800). EFA identified a dominant factor explaining 41.38% of variance (loadings = .528-.735). CFA supported a two-context Academic and Clinical Positive Stress model with correlated residuals between five parallel item pairs, chi-square(29) = 60.49, CFI = .970, TLI = .954, RMSEA = .063, SRMR = .065. Internal consistency was good (alpha = .839; omega = .843). NSPSS scores correlated negatively with PSS-10 scores (r = -.298, p < .001). Conclusion: The NSPSS demonstrated strong content validity, preliminary evidence of structural and convergent validity, and good internal consistency reliability for assessing positive stress appraisal among nursing students. Further validation in independent samples is warranted.

PurposeDiscussions surrounding the biomedical faculty job market often focus on applicant competitiveness and external metrics such as number of publications and funding records. Consequently, there is typically less discussion about applicant readiness, the point at which applicants perceive themselves as prepared to enter the market. Since 2018 our group, the Faculty Job Market Collaboration (FJMC), has conducted annual end-of-cycle surveys of biomedical faculty job applicants, producing the largest longitudinal dataset on this process to date. MethodsWe employed a mixed-methods design examining faculty applicants in biological science fields in North America. Regression analyses were conducted on a longitudinal dataset of 729 respondents across multiple hiring cycles. To determine how applicants evaluated their own preparation, qualitative interviews were conducted with a separate cohort of biomedical postdoctoral applicants during the 2024-2026 job cycles. ResultsOur findings demonstrate that rather than depending on a single quantitative threshold, readiness is a multifaceted construct shaped by actionable and interpersonal drivers. Key factors influencing an applicants perceived readiness include taking agency to submit applications, receiving explicit support from a mentor, incorporating strategic use of artificial intelligence tools into application preparation, and their career stage. ConclusionBy distinguishing individual readiness from systemic assumptions of market competitiveness, this study highlights a blind spot in academic workforce development. Our results suggest that applicants can achieve readiness and successful outcomes through different combinations of support, strategy, and timing rather than a uniform metric profile. By integrating quantitative and qualitative data, our study provides an evidence-based framework for understanding applicant readiness and offers practical guidance to help trainees navigate the increasingly competitive academic job market. Teaser TextOur mixed-model analysis of the biomedical faculty job market is designed to help prospective faculty candidates assess their readiness to enter the job market. By integrating multiple indicators of academic productivity, funding success, and professional experience, our study provides evidence-based benchmarks that can guide applicants in evaluating their competitiveness and identifying areas for further development before pursuing faculty positions.

The global market concentrates extractive pressure on lands held by Indigenous peoples, including peoples living in isolation, for whom free, prior and informed consent cannot be obtained and protection must therefore rest on territorial instruments. Halmahera, Indonesia, holds some of the worlds largest lateritic nickel reserves beneath a lowland rainforest inhabited by the Hongana Manyawa, yet the trajectory of land use and cover change across the island has not been quantified. We characterised land use and cover change over the 17,437 km2 island between 2014 and 2024 using MapBiomas time series, and projected a business-as-usual scenario to 2054 with a stochastic cellular-automata model implemented in Dinamica EGO, calibrated with weights of evidence on eight variables describing mining and logging concessions, transport infrastructure, settlements and previous clearing. Forest covered 83.0% of the island in 2014 and 82.1% in 2024; under unchanged policy it falls to 73.7% by 2054, a net loss of 162 thousand ha, or 11.2% of the 2014 baseline, at gross rates of 47,000-51,000 ha per decade. Deforestation probability is highest within 500 m of previous clearing and declines with distance from settlements, cities and mining sites, while proximity to national parks carries a negative weight of evidence. The frontier is self-propagating and spatially predictable, and legally designated territory retains forest within it. Protecting the Hongana Manyawa consequently depends on excluding extractive licensing from the interior forest ahead of the frontier rather than behind it.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

Businesses are increasingly encouraged to disclose their nature-related dependencies, impacts, risks and opportunities. A common component of sustainability reporting is screening operational sites for ecologically sensitivity to identify locations for further evaluation and action. However, with 600+ biodiversity metrics available, selecting and interpreting appropriate metrics remains challenging for business. We developed a simple screening framework informed by the Taskforce for Nature-Related Financial Disclosures guidance, grouping eleven widely used global biodiversity metrics into four complementary [&prime]baskets[&prime], representing different aspects of biodiversity. We created hypothetical but realistic mining, onshore wind energy and agricultural companies, to assess how metric choice, buffer size, scoring approach and sensitivity thresholds influence screening outcomes. Our basket framework consistently identified similar high-priority sites across metric combinations, but site rankings varied with methodological choices. We recommend clearer guidance on metric selection and application, alongside greater transparency from business regarding assumptions, methods and limitations when screening sites for ecological sensitivity.

Drought and low temperatures are major abiotic factors affecting key physiological and biochemical processes and limiting the yields of soybean (Glycine max L. Merr.). To in-crease soybean production in Europe, different agricultural strategies are applied to re-duce abiotic stress, including biostimulants. Therefore, studies on the effectiveness of local strains isolated in Europe are becoming increasingly relevant. In this study two bacterial strains Arthrobacter pascens (AP) and Bradyrhizobium japonicum (BJ) along with plant-derived protein hydrolysate (PH) were analysed with soybean plans under abiotic stress conditions in plant growth chambers. Six treatments (control; AP; BJ; PH; BJ+AP; BJ+AP+PH) were tested to evaluate biostimulation effect before stress induction (VC stage) and to determine stress reduction effect on soybeans after plants recovery period (V3 stage). Biostimulants application has positive effect on soyabean biometric parameters in early plant development stage and post stress periods. More stable long-term effect was found on structural plant development parameters, than on pigment accumulation. The best results on plant biometric parameters were found where (AP) and (BJ+AP+PH) com-bination was inoculated. (BJ+AP+PH) combination was the only effective treatment, which showed significantly different results in pigments indices, compared to the control, after stress period. Author summaryYasha Jamil: Conceptualization, Data curation, Formal analysis, Writing- original draft, Giuseppe Colla: Formal analysis, Writing- original draft, Writing- review & editing, Justina Kaziuniene: Data curation, Formal analysis, Sarune Ramoskaite :Writing- review & editing. Monika Toleikien[e]: Conceptualization, Data curation, Formal analysis, Writing- original draft, Funding acquisition, Supervision, Writing- review & editing.

Background Floods are among the most devastating natural disasters worldwide and are increasingly associated with adverse mental health outcomes, particularly Post-Traumatic Stress Disorder (PTSD). In December 2023, Hanang District in northern Tanzania experienced catastrophic mud floods that resulted in extensive loss of life, destruction of property, displacement of households, and disruption of livelihoods. While emergency humanitarian responses focused primarily on physical needs, limited evidence exists regarding the long-term psychological consequences among survivors. Therefore, this study aimed to determine the patterns of PTSD manifestations and assess cognitive factors associated with PTSD symptoms among flood victims in Hanang District, Tanzania. Methods A community-based cross-sectional study was conducted among 360 flood victims one year after the disaster. PTSD symptoms were assessed using the PTSD Checklist for DSM-5 (PCL-5). Descriptive statistics summarized PTSD severity, while chi-square tests and regression analyses examined associations between socio-demographic characteristics and PTSD manifestations. Cognitive factors were assessed based on participants' exposure to traumatic experiences and perceptions of traumatic events. Results The mean PCL-5 score was 39.2 (SD = 20.6), indicating a high burden of psychological distress. Approximately 45% of respondents had severe PTSD symptoms (PCL-5 [&ge;]45), while another substantial proportion demonstrated moderate symptom severity. PTSD manifestations varied significantly by geographical location (p < 0.001), household income (p = 0.011), and marital status (p = 0.002). Age positively predicted PTSD severity ({beta} = 0.019, p = 0.001), whereas household income negatively predicted symptom severity ({beta} = -0.297, p = 0.001). Exposure to natural disasters constituted the predominant cognitive factor, with 45% directly experiencing the flood and 38.3% witnessing the event. Exposure to secondary traumatic experiences through witnessing or learning about violent events was also common. Cognitive trauma exposure demonstrated a significant association with PTSD symptoms ({chi}2, p < 0.001). Conclusion PTSD remains highly prevalent among flood survivors in Hanang district. Both direct and indirect trauma exposure significantly contributed to PTSD manifestations. Comprehensive disaster recovery programmes should integrate trauma-focused psychological services, cognitive behavioural interventions, routine PTSD screening, and community-based psychosocial support alongside socioeconomic recovery initiatives.

Background: Neighborhood disadvantage indices used in public health research and policy include multiple economic, social, and housing items. However, research has failed to question whether it is necessary to include a multitude of economic, social, and housing variables in a single index. The purpose of this work was to examine three different neighborhood indices: a multidimensional disadvantage index, a unidimensional disadvantage index, and a unidimensional affluence index, and examine their performance with respect to distinguishing between healthy and unhealthy census tract neighborhoods in the United States. Methods: The 2022 disadvantage and affluence indices came from the National Neighborhood Data Archive, which are derived from census tract data from the American Community Survey 5-year estimates (2018-2022). The multidimensional disadvantage index included seven economic, social (e.g., single parent households), and housing items; the unidimensional disadvantage index included three poverty and income items; the unidimensional affluence index included 3 items capturing greater social and economic resources. Data on neighborhood health status (census tract prevalence of obesity, diabetes, and coronary heart disease) was obtained from the Population Level Analysis and Community EStimates database for 2022 and linked to the disadvantage and affluence indices for 83,522 census tracts. Contingency tables examined the degree of correspondence in quintiles across the three different indices and the corresponding disease prevalence in each cell. Generalized linear mixed models regressed the disease prevalence variables on index quintiles to determine the predicted prevalence of disease across the disadvantage gradient for each index. Results: Compared to the unidimensional disadvantage and affluence indices, the multidimensional disadvantage index underestimated disease burden in the most disadvantaged census tracts, and overestimated disease burden in the least disadvantaged tracts. Conclusions: Using a disadvantage or affluence index with a more parsimonious set of items would have greater precision in identifying communities at risk for poor health.

Coastal forests are increasingly threatened by saturated soil and elevated salinity levels resulting from sea level rise, saltwater intrusion, and storm surges. In response to rising salinization and flooding, healthy coastal forests that rely on freshwater (both wetland forests and low-elevation upland forests) are transitioning into landscapes dominated by dead or dying trees, known as ghost forests. Situated among salt-tolerant shrubs and grasses, ghost forests eventually become marshes or open water. Here, our main objective was to quantify the dynamics and pathways of these forest landscape conversions, as well as the factors contributing to the changes, which is vital for understanding the progression of coastal ecosystem degradation and forecasting future changes. We focused first on identifying the best method to track forest landscape change by exploring the role of multiple remote sensing indices (i.e., multispectral, bi-seasonal, topographical, and phenological metrics) in enhancing the performance of deep learning models (convolutional neural networks, CNNs) for land cover classification in the coastal plain of North Carolina using surface reflectance of Landsat 8 and Sentinel-2 images. Then, we used the best available data (Landsat 8) to understand long-term change and identify patterns of land cover change from 1985 to 2021. Our study reveals that incorporating phenology and topographical indices enhances the separability of the ghost forests class from all other vegetation classes. In our assessment, the higher-resolution Sentinel-2 data (F1 Score = 96.3) outperformed Landsat images (F1 score = 93.4) for the 2021 co-available year. However, Landsat remains an important tool used due to its long-term data record. Therefore, we used Landsat to determine that 21% of forests were lost between 1985 and 2021, and that the rate of loss is increasing. Between 2010 and 2021, 23,876 ha of forest were converted to marsh, ghost forest, and shrub, which is 1.5 times higher than the 16,968 ha lost between 1985 and 2010. These conversions from forest to ghost forest and marshes were driven primarily by proximity to the channel, salinity, and the increasing rate of relative sea level rise (RSLR), which are the key environmental drivers of observed changes. By quantifying these changes, we highlight regions most vulnerable to environmental stressors, providing a basis for targeted conservation strategies.

The accelerated loss of biodiversity in sub-Saharan Africa threatens the functioning of tropical ecosystems. In Cote dIvoire, the Comoe National Park (PNCOMOE), a Sudano-Guinean savannah, and the Tai National Park (PNTAI), a dense rainforest, both UNESCO World Heritage Sites, are home to fauna assemblages of global importance, whose long-term resilience remains insufficiently quantified. This study assesses and compares, over a decade (2014-2025), the functional stability of vertebrate communities in these two contrasting ecosystems, using nine metrics covering resistance, invariance, persistence, interspecific synchrony, Tilmans stability, Jacobian resilience and a Composite Stability Index (CSI). Abundance data for 107 vertebrate species were collected via foot transects at PNTAI and aerial surveys at PNCOMOE. The stability metrics were calculated using the R package estar, integrated with an alpha diversity analysis (Shannon H', species richness S, Pielous evenness J') and a Jacobian spectral analysis within a multidimensional ecological assessment. PNTAI (0.708) exhibits significantly higher alpha diversity (H' = 2.82; S = 55.7 taxa) and community resilience 4.6 times higher than in the PNCOMOE (0.153). Its interspecific asynchrony index (0.504) reveals a strong portfolio effect, absent in the PNCOMOE (0.232). In contrast, PNCOMOE exhibits higher temporal invariance (0.382 versus 0.116) and Tilman stability (0.276 versus 0.152), reflecting more predictable dynamics. The overall ICS favours the PNTAI (0.484) and (0.370). The Jacobian analysis detects local instability in both parks (Re({lambda}max) = 5.58 at the PNTAI; 3.73 at the PNCOMOE). The two parks exhibit distinct yet complementary stability architectures: PNTAI relies on dynamic stability based on resilience and interspecific compensation, whilst PNCOMOE demonstrates conservative stability through temporal regularity. The absence of calculable resilience at PNCOMOE suggests a potential crossing of a functional degradation threshold, arguing for urgent restoration interventions and differentiated conservation strategies, tailored to the resilience mechanisms specific to each ecosystem.

Indiana still faces significant health challenges, ranking among the least healthy U.S. states due to high obesity rates, mental health issues, and other chronic conditions. These disparities are closely linked to inequities in healthcare access, which are largely shaped by social determinants of health. Using data from the Social Vulnerability Index and County Health Rankings and Roadmaps, this study analyzes trends in obesity, mental health, and premature death across Indiana counties before, during, and after the COVID-19 pandemic. Descriptive statistics, correlation analyses, and Negative Binomial regression models were used to evaluate county-level disparities. In 2018, higher rates of uninsured, obese, and physically inactive populations were associated with increased premature death. In 2020, diabetes, smoking, and alcohol consumption were significant factors. By 2022, unemployment, education, obesity, insurance, exercise access, and mental health provider availability were associated with premature death. Findings indicate that socially vulnerable counties experienced amplified health impacts, with obesity rising most sharply where exercise infrastructure was limited and poor mental health days increasing across all counties. These results highlight persistent service gaps and the critical need for targeted investments in recreational infrastructure and mental healthcare. Future research should examine policy influences and causal relationships to inform equity-focused interventions.

Deadwood is a fundamental component of forest ecosystem, supporting biodiversity and driving multiple ecological processes. However, structures that may develop on downed coarse woody debris (CWD) create additional microhabitats that are used by numerous organisms and contribute to small-scale habitat heterogeneity. To date, quality of CWD is commonly characterized by its volume, diameter, tree species or stage of decomposition, while fine-scale structures occurring there remain not surveyed and their role in ecosystem is rarely quantified. Here, we introduce the novel concept of microhabitats on CWD. i.e. Deadwood-related Microhabitats (DreMs), defined as distinct features occurring on CWD that may provide shelter, breeding or foraging sites for forest-dwelling organisms. Using an original catalogue comprising 14 groups and 30 types, we inventoried DreMs on 6,003 CWD across 423 study plots located in best-preserved old-growth forests in Poland. We quantified the frequency and richness of DreMs and assessed the link between CWD characteristics and DreM richness in spruce, beech, willow-poplar, fir-beech and oak-lime- hornbeam forests. All inventoried DreMs occurred on both deciduous and coniferous taxa. The most frequent DreM included bryophyte mats, loose bark patches, insect galleries, fungal fruiting bodies and polypores. DreM richness increased with CWD diameter, more complex architecture and the presence of multiple decay classes within single debris. DreM richness peaked at intermediate decay classes and was higher on deciduous than on coniferous taxa. Our study is the first large-scale qualitative and quantitative assessment of DreMs in temperate forests. By focusing on forests characterized by ecological continuity and minimal human-related disturbance, the results provide a reference for downed deadwood-associated structures. By complementing inventory of tree-related microhabitats, DreMs extend potential monitoring schemes of habitat quality and contribute to biodiversity-oriented forest management. Highlights* Downed coarse woody debris (CWD) hosts Deadwood-related microhabitats (DreMs) * Higher DreM richness is associated with CWD diameter * DreM richness peak at intermediate stages of wood decay * CWD of deciduous taxa support more DreMs than coniferous * Diversified CWD and DreMs increase habitat heterogeneity for forest-dwelling taxa

AimHere we aim to disentangle species-specific bioclimatic drivers of forest site productivity and project their future dynamics, providing a spatially explicit basis for anticipating climate-driven shifts in productivity and their implications for forest carbon sequestration. LocationIberian Peninsula. Time period1985-2014 (calibration); 2071-2100 (projected under CMIP6 scenarios). Major taxa studied21 Iberian tree species. MethodsWe used Site Form (SF) maps derived from the Third Spanish National Forest Inventory, spatially interpolating plot-level SF estimates as a continuous productivity index and relating them to 25 bioclimatic variables. Multiple linear regression models were selected via complementary stepwise and subset regression and validated on independent hold-out data (80%/20% split). ResultsValidated [Formula] ranged from 0.46 (Quercus faginea) to 0.97 (Pinus pinaster); 17 of 21 species reached [Formula]. BI013 precipitation of the wettest month), not BI014, was the most frequently retained predictor (15/17); BI014 was retained in only (11/17 models with a near-even sign split. Combining projected changes in mean productivity and habitat extent under SSP5-8.5, fifteen of sixteen applicable species lose total productivity by 2071-2100, six -- including Fagus sylvatica and Betula alba -- collapsing to below 1% of their reference-period value; only Pinus pinaster gains, and only under the lowest-emission pathway (up to 175%) -- under SSP5-8.5 it too loses productivity, albeit less than any other species (35% of its reference-period value retained). Limiting warming to SSP1-2.6 spares Mediterranean pine and oak species but not Euro-Siberian and montane ones. Main conclusionsThese validated, extrapolation-aware models reveal a near-universal, climate-driven collapse in Iberian forest site productivity, with direct implications for the carbon-sink potential currently attributed to these forest types, and provide a route to dynamic, climate-aware carbon-uptake estimates for the region.

This study aimed to examine entomologists' perceptions of equity, inclusion, and exclusion within the discipline, identifying perceived challenges and proposed solutions for advancing equity in the field. The present study surveyed 47 self-identified entomologists living in the United States in 2023. Using a mixed-methods design, the study examined entomologists' perceptions of inclusion and exclusion within the discipline through qualitative and quantitative measures. Respondents highlighted needs for race-conscious funding opportunities, comprehensive inclusion efforts through geographically diverse outreach, and access to role models and mentors with similar identities to future entomologists. Findings are discussed in relation to a smaller 2013 study on recruitment and retention of entomologists of color, which offers preliminary historical context. The 2013 respondents emphasized intrinsic and age-based barriers to recruitment (e.g., lack of interest, limited K-12 outreach), the 2023 findings point toward structural inequities and retention needs (e.g., systemic exclusion). The 2013 comparison is interpreted as exploratory given differences in sample size and scope between the two studies. The 2023 study highlights evolving perceptions of persistent inequities in entomology and identifies opportunities to build a more inclusive and representative discipline.

Background: Adolescent and youth substance use is an important public health concern in rapidly urbanizing low- and middle-income countries; however, evidence on how social networks and community substance-use environments jointly shape recent use remains limited, particularly in African megacities. Methods: We conducted a cross-sectional, community-based, convergent mixed-methods study of adolescents and young adults aged 12-24 years in the Yaba Local Council Development Area, Lagos, Nigeria. Quantitative data were collected using a structured questionnaire adapted from established, international survey instruments. The primary outcome was self-reported substance use within the past 30 days. Key exposures included a Social Exposure Score incorporating substance use among friends and family members, membership in a substance-using peer group, and perceived easy community availability of substances. Multivariable logistic regression was used to examine factors associated with past-30-day substance use, followed by an interaction model to assess whether perceived availability modified the association between social exposure and recent use. Open-ended responses on community approaches to reducing substance use were thematically analyzed and integrated with quantitative findings through a joint display. Results: Among 285 participants (mean age 18.9 years; 52.6% male), 66 (23.2%) reported past-30-day substance use and 84 (29.5%) reported lifetime polysubstance use. A Higher Social Exposure Score was associated with increased odds of past-30-day substance use (adjusted odds ratio [aOR]=2.18; 95% CI: 1.59-2.99; p<0.001), while perceived easy community availability was independently associated with recent use (aOR=3.22; 95% CI: 1.42-7.30; p=0.005). The interaction between social exposure and perceived availability was statistically significant (aOR=1.51; 95% CI: 1.01-2.27; p=0.047), indicating that the association between social exposure and recent use varied according to perceived availability. The marginal effect of a one-unit increase in the Social Exposure Score on the predicted probability of past-30-day use was +0.08 when easy availability was not reported and +0.18 when it was reported. Among lifetime substance users, past-30-day use was more common among polysubstance users than single substance users (50.0% vs. 22.0%; chi-square[1]=16.51; p<0.001). Qualitative findings identified supply side law enforcement (43.5%), population awareness campaigns (24.6%), regulatory and legislative control (16.1%), enhanced parental supervision (14.7%), and economic and youth empowerment (13.3%) as prominent community-proposed solutions. Integrated analysis demonstrated complementarity between the quantitatively identified social and environmental correlates and community-proposed intervention priorities. Conclusions: In this urban Nigerian setting, past-30-day substance use was independently associated with social exposure and perceived community availability, with evidence that the association between social exposure and recent use varied according to perceived availability. The findings support multilevel prevention approaches that address environmental access alongside peer, family, community, and broader socioeconomic influences.

Problematic Internet Use (PIU)--marked by compulsive or maladaptive online behavior--is an emerging public health issue among adolescents and is associated with psychological distress, social difficulties, and academic problems. In Finnmark County, Norways northernmost and ethnically diverse region, limited research has examined the underlying mechanisms of PIU among Sami and non-Sami youth, despite increasing levels of digital engagement. This study protocol outlines a population-based cross-sectional survey investigating the associations between social norms (descriptive and injunctive), ethnic identity, and ethnicity-based discrimination in relation to PIU among Sami and non-Sami adolescents in Finnmark. Guided by Social Norm Theory and Ethnic Identity Theory, the study aims to examine risk and resilience factors associated with adolescents digital behavior in a geographically sparsely populated, multiethnic region. A population-based, cross-sectional school survey will include all upper secondary school students in Finnmark County (N {approx} 2,230). A culturally adapted, bilingual questionnaire (Northern Sami - Norwegian) will measure problematic internet use, perceived social norms in family, peer, and school contexts, ethnic identity, ethnicity-based discrimination, positive internet use, and key covariates. Ethnicity will be classified based on indicators of Sami language use and self-identification. Data will be prepared using prespecified quality procedures and analyzed with partial least squares structural equation modeling (PLS-SEM) to examine associations between social norms, ethnic identity, ethnicity-based discrimination, and internet use outcomes, including mediation and moderation. Group differences between Sami and non-Sami adolescents will be assessed using PLS Multi-Group Analysis. The findings may inform the development of culturally appropriate approaches to screening, prevention, and early intervention, and are relevant for mental health services, school-based programs, and public health strategies targeting Indigenous youth in rural and semi-rural regions.