Solar cell research, phase 2 Semiannual report

Radiation effects on properties of lithium solar cell

Preferences in Information Processing, Marginalized Identity, and Non-Monogamy: Understanding Factors in Suicide-Related Behavior among Members of the Alternative Sexuality Community

Suicide-related behavior (SRB) is a mental health disparity experienced by the alternative sexuality community. We assessed mental health, relationship orientation, marginalized identities (i.e., sexual orientation minority, gender minority, racial minority, ethnic minority, and lower education), and preferences in information processing (PIP) as factors differentiating lifetime SRB groups. An online cross-sectional survey study was conducted in 2018. Members of the National Coalition for Sexual Freedom (NCSF; n = 334) took part. Bivariate analyses identified the following SRB risk factors: female and transgender/gender non-binary identity, sexual orientation minority identity, lower education, suicide attempt/death exposure, Need for Affect (NFA) Avoidance, depression, and anxiety. Monogamous relationship orientation was a protective factor. Multi-nomial regression revealed the following: (1) monogamous relationship orientation was a protective factor for suicidal ideation and attempt; (2) lower education was a risk factor for suicide attempt; (3) anxiety was a risk factor for suicide attempt; and (4) depression was a risk factor for suicidal ideation. A two-way interaction showed that elevated NFA Approach buffered the negative impacts of depression. Relationship orientation, several marginalized identities (i.e., based on gender, sexual orientation, and educational level), and PIP all contributed uniquely to SRB. Further study is necessary to understand the role of relationship orientation with suicide. Health education and suicide prevention efforts with NCSF should be tailored to account for marginalized identity, mental health, and NFA factors

Sexual orientation and the integrated motivational-volitional model of suicidal behavior : results from a cross-sectional study of young adults in the United Kingdom

Sexual orientation minority (i.e., lesbian, gay, bisexual, queer and other; LGBQ+) persons represent a vulnerable population with respect to suicide-related behavior. An emerging theory of suicide, the Integrated Motivational-Volitional Model of Suicide (IMV; O’Connor, 2011; O’Connor & Kirtley, 2018), is utilized in the present study to examine sexual orientation, as well as a number of other IMV-defined pre-motivational factors (i.e., demographics, psychological distress and personality), as they impact the IMV motivational factors of defeat, entrapment, and suicidal ideation/intent. The present investigation featured a cross-sectional online survey of young adults (ages 18 to 34; n = 418; 27% identified as LGBTQ+) across the United Kingdom. The key findings included: (1) high rates of 12-month suicidal ideation prevalence (54.5%) and willingness to enact a future suicide attempt (60.8%); (2) bisexual and other (e.g., pansexual)-identifying sexual minority persons reported higher levels of IMV-related outcomes (e.g., internal entrapment, defeat); (3) sexual orientation accounted for significant variance in predicting motivational constructs controlling for a number of other pre-motivational factors; (4) other sexual minority status, compared to heterosexual identity, predicted all motivational outcomes, and; (5) extraversion, agreeableness, and emotional stability emerged as pre-motivational protective factors for varying motivational outcomes. Findings are discussed with respect to the suicide and sexual minority theories, as well as tailored suicide prevention efforts and future research

Habitat Associations of Fish Species of Greatest Conservation Need at Multiple Spatial Scales in Wadeable Iowa Streams

Fish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaike’s information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohen’s kappa (κ = 0.59) were greatest for multiple-scale models (i.e., those including both large-scale and small-scale variables), intermediate for small-scale models (CCR = 75%; κ = 0.49), and lowest for large-scale models (CCR = 73%; κ = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale

Biodiversity increases the resistance of ecosystem productivity to climate extremes

It remains unclear whether biodiversity buffers ecosystems against climate extremes, which are becoming increasingly frequent worldwide1. Early results suggested that the ecosystem productivity of diverse grassland plant communities was more resistant, changing less during drought, and more resilient, recovering more quickly after drought, than that of depauperate communities2. However, subsequent experimental tests produced mixed results3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13. Here we use data from 46 experiments that manipulated grassland plant diversity to test whether biodiversity provides resistance during and resilience after climate events. We show that biodiversity increased ecosystem resistance for a broad range of climate events, including wet or dry, moderate or extreme, and brief or prolonged events. Across all studies and climate events, the productivity of low-diversity communities with one or two species changed by approximately 50% during climate events, whereas that of high-diversity communities with 16–32 species was more resistant, changing by only approximately 25%. By a year after each climate event, ecosystem productivity had often fully recovered, or overshot, normal levels of productivity in both high- and low-diversity communities, leading to no detectable dependence of ecosystem resilience on biodiversity. Our results suggest that biodiversity mainly stabilizes ecosystem productivity, and productivity-dependent ecosystem services, by increasing resistance to climate events. Anthropogenic environmental changes that drive biodiversity loss thus seem likely to decrease ecosystem stability14, and restoration of biodiversity to increase it, mainly by changing the resistance of ecosystem productivity to climate events

Plant diversity effects on grassland productivity are robust to both nutrient enrichment and drought

Global change drivers are rapidly altering resource availability and biodiversity. While there is consensus that greater biodiversity increases the functioning of ecosystems, the extent to which biodiversity buffers ecosystem productivity in response to changes in resource availability remains unclear. We use data from 16 grassland experiments across North America and Europe that manipulated plant species richness and one of two essential resources—soil nutrients or water—to assess the direction and strength of the interaction between plant diversity and resource alteration on above-ground productivity and net biodiversity, complementarity, and selection effects. Despite strong increases in productivity with nutrient addition and decreases in productivity with drought, we found that resource alterations did not alter biodiversity–ecosystem functioning relationships. Our results suggest that these relationships are largely determined by increases in complementarity effects along plant species richness gradients. Although nutrient addition reduced complementarity effects at high diversity, this appears to be due to high biomass in monocultures under nutrient enrichment. Our results indicate that diversity and the complementarity of species are important regulators of grassland ecosystem productivity, regardless of changes in other drivers of ecosystem function

Multiple Facets of Biodiversity Drive the Diversity-Stability Relationship

A significant body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity–stability relationship remains unclear. Here we used data from 39 biodiversity experiments and structural equation modeling to investigate the roles of species richness, phylogenetic diversity, and both the diversity and community-weighted mean of functional traits representing the ‘fast–slow’ leaf economics spectrum in driving the diversity–stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony. Contrary to our hypothesis, low phylogenetic diversity also enhances ecosystem stability directly, albeit weakly. While the diversity of fast–slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our results demonstrate that biodiversity influences ecosystem stability via a variety of facets, thus highlighting a more multicausal relationship than has been previously acknowledged

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds

Bace1-dependent amyloid processing regulates hypothalamic leptin sensitivity in obese mice

Obesity places an enormous medical and economic burden on society. The principal driver appears to be central leptin resistance with hyperleptinemia. Accordingly, a compound that reverses or prevents leptin resistance should promote weight normalisation and improve glucose homeostasis. The protease Bace1 drives beta amyloid (Aβ) production with obesity elevating hypothalamic Bace1 activity and Aβ₁–₄₂ production. Pharmacological inhibition of Bace1 reduces body weight, improves glucose homeostasis and lowers plasma leptin in diet-induced obese (DIO) mice. These actions are not apparent in ob/ob or db/db mice, indicating the requirement for functional leptin signalling. Decreasing Bace1 activity normalises hypothalamic inflammation, lowers PTP1B and SOCS3 and restores hypothalamic leptin sensitivity and pSTAT3 response in obese mice, but does not affect leptin sensitivity in lean mice. Raising central Aβ₁–₄₂ levels in the early stage of DIO increases hypothalamic basal pSTAT3 and reduces the amplitude of the leptin pSTAT3 signal without increased inflammation. Thus, elevated Aβ₁–₄₂ promotes hypothalamic leptin resistance, which is associated with diminished whole-body sensitivity to exogenous leptin and exacerbated body weight gain in high fat fed mice. These results indicate that Bace1 inhibitors, currently in clinical trials for Alzheimer’s disease, may be useful agents for the treatment of obesity and associated diabetes