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Climate Constrains the Enhancement of CO\u3csub\u3e2\u3c/sub\u3e Fertilization on Forest Gross Primary Productivity
Get PDFForest gross primary production (GPP) is influenced by the interplay between climate conditions and atmospheric CO2 levels, which interact in complex ways, generating both compensating and amplifying effects. In this study, eddy covariance flux measurements from 50 forest ecosystems were integrated with simulations from 14 terrestrial biosphere models to investigate how climate conditions and atmospheric CO2 concentrations regulate forest GPP. This approach bridges site-level observations with biome-scale model estimates to develop a global understanding. Our findings suggest that in boreal and cold temperate regions, temperature primarily constrains the enhancement of the CO2 fertilization on forest GPP; however, warming and higher atmospheric CO2 levels are projected to alleviate these limitations. In tropical forests, CO2 fertilization strongly enhances GPP, but this benefit will be counterbalanced by the adverse impacts of projected climate warming. Consequently, the interplay between climate and atmospheric CO2 in affecting forest GPP is dynamic and subject to continual change
Childhood Prosocial Behavior and Body Mass Index: Longitudinal Findings in the Millennium Cohort Study
Get PDFObjective: Childhood obesity affects millions worldwide but is challenging to treat. Prosocial or helping behavior may help mitigate childhood obesity. We investigated whether prosocial behavior at ages 5–11 was associated with body mass index (BMI) and reduced obesity risk through age 17. Method: Data were from 8,894 participants in the Millennium Cohort Study. Parents reported children’s prosocial behavior using the Strengths and Difficulties Questionnaire at ages 5, 7, and 11. BMI scores (kg/m2) were calculated from children’s height and weight at ages 5, 7, 11, 14, and 17, which were then standardized by sex (M = 0, SD = 1) to examine BMI patterns in relation to the sample mean. Obesity risk was defined by established cut points from the International Obesity Task Force. Linear mixed models evaluated associations between age 5, 7, or 11 prosocial behavior and BMI Z scores (BMIz) through age 17, adjusted for relevant covariates, including baseline BMI. Associations with obesity risk were also examined at each follow-up assessment using logistic regression. Results: Obesity prevalence doubled from 5% to 10% between ages 5 and 17 years old. Prosocial behaviors at age 5 were not substantively associated with BMIz profiles or obesity risk. Similarly, associations between prosocial behavior at other developmental stages (ages 7 and 11, respectively) were largely unrelated to both subsequent BMIz patterns and obesity risk. Conclusions: Associations between childhood prosocial behaviors and BMIz through age 17 were mostly null. No relationships were observed with obesity or with prosocial behaviors assessed later in childhood or adolescence
Probabilities with Values in Scaled Hyperbolic Numbers
Get PDFIn this paper, we introduce a notion of a probabilistic measure which takes values in t-scaled hyperbolic numbers for t ∈ R, with a system of axioms generalizing directly Kolmogorov’s axioms. i.e., we establish a suitable measure theory in the set Dt of all t-scaled hyperbolic numbers for arbitrarily fixed t ∈ R
FISH-SPEC: Fast Identification System for Handheld Spectroscopy and Species Classification
No full textAccurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, while specialized dispute models for commonly misclassified species improved performance to 90 % ± 6.1 %. Individual models for thawed and frozen fillets achieved 90 ± 6.0 % and 90 ± 5.4 %, respectively, with dispute models in the thawed dataset increasing accuracy to 93 ± 4.3 %. These results demonstrate that portable multi-mode spectroscopy, combined with machine learning, provides a fast and reliable tool for on-site fish species identification
Harnessing Hyperspectral Imaging and Deep Learning for Terrestrial Habitat Mapping in Arid Landscapes: A Case Study in Saudi Arabia
No full textArid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 encoder backbones, resulting in four model configurations for comparative evaluation. Among the tested models, U-Net with a VGG19 backbone achieved the highest performance, attaining an F1 score of 0.90, demonstrating superior capability for habitat mapping in arid environments. The resulting map segmented the dendritic wadi network, Rawdat depressions, and sand-plateau contacts with sharp boundaries. Aligned with the IUCN habitat scheme, Rawdat, seasonal vegetation-bearing desert carbonate sinkholes, are introduced as a new IUCN habitat class. The map produces verifiable indicators relevant to Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), supporting protection, restoration targeting, and monitoring. Therefore, the proposed deep learning hyperspectral framework can be applied to other arid and semi-arid regions worldwide to upgrade terrestrial ecosystem mapping and conservation planning
Dramatic Biases in Terrestrial Nitrogen Fixation in Earth System Models Revealed by Natural Isotope Signatures
No full textBiological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ15N) in plants (δP) and soil (δS). Together with pairwise δP and δS measurements from 18 forest sites covering diverse climates and thousands of δP and δS observations worldwide, we show that the spatial distribution of the fraction of symbiotic BNF relative to the total external N acquisition by plants (fBNFs) is primarily controlled by temperature (29%) and mycorrhizal fungi (14%), with colder climate and higher ectomycorrhizal fungi abundance leading to a lower fBNFs. We find a large discrepancy between the spatial distributions of isotope-based BNF and those simulated by using Earth System Models (ESMs) in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Moreover, we constrain the global total BNF from natural terrestrial ecosystems as 78.2–89.8 Tg N yr−1, suggesting a ≥18% underestimation of the global BNF in CMIP6 models. In addition to the temperature dependence found in previous laboratory studies, our isotope-based study suggests a competitive relationship between BNF and mycorrhizal N uptake as another important control mechanism. This complex interplay remains unresolved in ESMs and has the potential to improve BNF simulations in the next phase of CMIP
Divided We Fear: The Politics Behind America\u27s Economic Anxiety
No full textImagine waking up to headlines announcing a market crash. Some picture an end to stability; others see a nonpermanent storm. What separates these reactions is far more than just perspective; it is politics. Using the 2025 Chapman Survey of American Fears, a representative sample of U.S. national adults, I will study the patterns of fears regarding economic collapse across and beyond political lines. Furthermore, I will examine how partisanship shapes perceptions of such collapse, adding layers of analysis with media and personal economic fears. My findings support the hypothesis that Democrats appear to express higher levels of concern about an economic collapse compared to Republicans, suggesting that economic anxiety is deeply united with the Democratic points of view. Additional findings explore related fears such as being unable to pay bills or being unemployed, and the relationship between media consumption (ex. CNN and Fox News), and the fear of an economic collapse. The Chapman Survey of American Fears presents a slight but statistically significant relationship between political parties, media preferences, and personal economic fears with the overall fear of economic collapse. The implication of this research lies in the demonstration that economic concerns are not equally distributed across parties, but rather are filtered through partisanship in matters that shape the debates and discourses around politics and policies that we have today. Understanding these forces is extremely important as the perceptions of a collapse may either create solutions or deepen the present divides, ultimately determining if our society will respond to the fear of crisis with resilience or division
Loneliness and Support for Political Violence Within the United States
No full textAlthough loneliness is a growing public health issue in the United States, its political effects remain understudied. This paper seeks to understand the relationship between loneliness and the support of political violence by utilizing the Chapman Survey of American Fears (CSAF Wave 11). This survey, which is a component of the 2025 Chapman Survey of American Fears, includes the three-item UCLA Loneliness Scale, as well as two separate items specifically measuring political violence. Using a national survey (N=1,015), I address the following questions: (1) Are lonely individuals more likely to support violence on political grounds? (2) Does strongly identifying with a political party correlate with higher or lower support for violence? and (3) Is loneliness more consequential for political violence in younger adults? Contrary to the original hypotheses, logistic regression does not support the idea that loneliness is a predictor of support for political violence. In fact, loneliness decreases the personal willingness to engage in political violence by damaging property. On the other hand, younger adults are more likely to support political violence and display a greater level of approval for violent political actions compared to older adults, reflecting a clear generational difference. Partisan identification also does not appear to be a strong predictor of violence in attitudes for non-White and non-Christian individuals. These findings alter the current stereotypes of a “lonely radical” and suggest that loneliness, aggression, and violence are more tied to the restrictions of social behavior than loss of control. This study further highlights the necessity to better define how psychosocial distress interacts with group identity within a social democracy
“We Needed Something of Our Own:” Latina Students, Belonging, and the Creation of Cultural Space at an Emerging Hispanic Serving Institution
No full textThis qualitative case study examines how Latina undergraduate students at an emerging Hispanic-Serving Institution (HSI) view culturally based sororities as tools for addressing exclusion and fostering belonging. Guided by Latinx Critical Race Theory and Strayhorn’s (2018) sense of belonging framework, the study centers eight first-generation Latinas seeking to establish a Latina-oriented sorority. Findings show students positioned the sorority as a means to challenge exclusionary norms, create inclusive spaces reflecting Latinx diversity, and cultivate leadership, mentorship, and cultural affirmation. This research highlights student agency and the transformative potential of culturally grounded organizations amid political threats to DEI and the rollback of HSI funding
Multi-Crop Systems and Crop-Switching Strategies to Enhance Water Use Efficiency and Climate Resilience in Arid Agricultural Regions
No full textAgriculture in the Lower Colorado River (LCR) region faces mounting challenges from climate change, arid conditions, and water scarcity. This study evaluates water use efficiency (WUEc) and crop-switching strategies under SSP2-4.5 and SSP5-8.5 scenarios for 2025–2049, 2050–2074, and 2075–2099. Using historical data, climatic drivers such as temperature and precipitation were analyzed for their influence on key crops, including durum wheat, winter wheat, and corn. Results show SSP2-4.5 supports water use reductions up to 16%, stable profits (80–90%), and modest calorie increases (up to 15%), while SSP5-8.5 poses severe challenges, with water use reductions of 2–5%, profits dropping to around 20%, and minimal calorie gains (2%). The study highlights the vulnerability of durum wheat and resilience of winter wheat and corn, advocating for crop-switching and resource-efficient practices to sustain agriculture in arid regions like the LCR