Beyond demographic identities and motivation to learn: The effect of organizational identification on diversity training outcomes

There is a pressing need for better explanations of diversity training effectiveness so that organizations can administer training programs that facilitate positive intergroup interactions. In this paper, we consider the unique predictive effect of organizational identification on diversity training outcomes beyond the effects of the traditional predictors of demographic-based identities and motivation to learn across two samples of employees involved in diversity-related training at their employing organizations. Organizational identification predicted unique variance in voluntary participation in diversity training, diversity training-related knowledge application, motivation to transfer diversity training, and diversity training-related organizational citizenship behavior intentions. Research and practitioner implications are discussed based on our findings.     

Using environmental and geographic data to optimize ex situ collections and preserve evolutionary potential

Maintenance of biodiversity through seed banks and botanical gardens, where the wealth of species’ genetic variation may be preserved ex situ, is a major goal of conservation. However, challenges can persist in optimizing ex situ collections if trade-offs exist among cost, effort, and conserving species evolutionary potential, particularly when genetic data are not available. We evaluated the genetic consequences of population preservation informed by geographic (isolation by distance [IBD]) and environmental (isolation by environment [IBE]) distance for ex situ collections for which population provenance is available. We used 19 genetic and genomic data sets from 15 plant species to assess the proportion of population genetic differentiation explained by geographic and environmental factors and to simulate ex situ collections prioritizing source populations based on pairwise geographic distance, environmental distance, or both. Specifically, we tested the impact prioritizing sampling based on these distances may have on the capture of neutral, functional, or putatively adaptive genetic diversity and differentiation. Individually, IBD and IBE explained limited population genetic differences across all 3 genetic marker classes (IBD, 10–16%; IBE, 1–5.5%). Together, they explained a substantial proportion of population genetic differences for functional (45%) and adaptive (71%) variation. Simulated ex situ collections revealed that inclusion of IBD, IBE, or both increased allelic diversity and genetic differentiation captured among populations, particularly for loci that may be important for adaptation. Thus, prioritizing population collections based on environmental and geographic distance data can optimize genetic variation captured ex situ. For the vast majority of plant species for which there is no genetic information, these data are invaluable to conservation because they can guide preservation of genetic variation needed to maintain evolutionary potential within collections.     

Disentangling horizontal pay dispersion: Experimental evidence

Although horizontal pay dispersion has been explored extensively using cross‐sectional field methods, it has received little attention using the control available through experimental designs. Many of the questions relevant to pay dispersion research can be addressed by taking an individual‐level experimental approach because this allows for clean separation of pay policies and individual effects. In this paper, we hypothesize both the motivation‐based and affect‐based effects of pay dispersion policies and test our hypotheses with a sample of over 400 participants in a real‐pay, real‐effort experiment. Results of the experiment provide support that two pay dispersion‐related pay policies, performance‐based pay dispersion and allocation criteria, have unique effects. Whereas motivation and performance effects are direct, pay satisfaction and interest in continuing work effects are the result of interactions, incorporating the performance level of workers. In follow‐up analyses, we find evidence that the temporal nature of responses to pay dispersion should be incorporated into future studies of pay dispersion.     

Gender and career success: A typology and analysis of dual paradigms

Despite women's advancements in the workplace, gender inequality persists. We classify and test two frameworks used to explain gender differences in career success: unequal attributes and unequal effects. The unequal attributes framework suggests that gender is related to other attributes, which result in unequal career outcomes for men and women (i.e., a mediated effect). The unequal effects framework suggests that even when men and women share the same attribute or circumstances, they are rewarded differently, such that individual attributes have unequal effects on career outcomes for men and women (i.e., a moderated effect). We collected survey data from a gender‐balanced sample of 394 business school alumni. Using structural equation modeling to test the unequal attributes framework, we found that work hours, career orientation, having a nonemployed spouse, and working in a predominantly female job were unequal attributes that explained gender differences in career success. Using multigroup path analysis to examine unequal effects, we found that being agentic, married, having children at home, and working in a predominantly female workplace had unequal effects in relation to career success for men and women. We find support for both models across three categories of career success antecedents (i.e., personal, family, and job attributes).     

Restoration of rivers used for timber floating: effects on riparian plant diversity.

Fluvial processes such as flooding and sediment deposition play a crucial role in structuring riparian plant communities. In rivers throughout the world, these processes have been altered by channelization and other anthropogenic stresses. Yet despite increasing awareness of the need to restore natural flow regimes for the preservation of riparian biodiversity, few studies have examined the effects of river restoration on riparian ecosystems. In this study, we examined the effects of restoration in the Ume River system, northern Sweden, where tributaries were channelized to facilitate timber floating in the 19th and early 20th centuries. Restoration at these sites involved the use of heavy machinery to replace instream boulders and remove floatway structures that had previously lined stream banks and cut off secondary channels. We compared riparian plant communities along channelized stream reaches with those along reaches that had been restored 3-10 years prior to observation. Species richness and evenness were significantly increased at restored sites, as were floodplain inundation frequencies. These findings demonstrate how river restoration and associated changes in fluvial disturbance regimes can enhance riparian biodiversity. Given that riparian ecosystems tend to support a disproportionate share of regional species pools, these findings have potentially broad implications for biodiversity conservation at regional or landscape scales.     

Photochemical reduction of hexavalent chromium in glycerol-containing solutions

Hexavalent chromium [Cr(VI)] in the form of potassium dichromate was photochemically reduced to trivalent chromium [Cr(III)] in aqueous solutions containing glycerol. This reaction occurred rapidly during irradiation with either unfiltered sunlight or a UVA-emitting light source. Photochemical reduction of Cr(VI) was pH-dependent and did not occur in dilute solutions of sodium hydroxide. In acidified solutions, the reduction occurred at elevated rates and at lower concentrations of glycerol. This reaction was found to be dependent on the unsubstituted alcohol groups of glycerol since alpha-phosphoglycerol and beta-phosphoglycerol did not support the photochemical reduction of Cr(VI). These findings suggest that glycerol or related polyols can be used for the remediation of hexavalent (toxic) chromium at contaminated environmental sites.     

The distinctive isotopic signature of plant-derived chloromethane: possible application in constraining the atmospheric chloromethane budget

Chloromethane (CH(3)Cl) is the most abundant halocarbon in the atmosphere. Although largely of natural origin it is responsible for around 17% of chlorine-catalysed ozone destruction. Sources identified to date include biomass burning, oceanic emissions, wood-rotting fungi, higher plants and most recently tropical ferns. Current estimates reveal a shortfall of around 2 million ty(-1) in sources versus sinks for the halocarbon. It is possible that emissions from green plants have been substantially underestimated. A potentially valuable tool for validating emission flux estimates is comparison of the delta13C value of atmospheric CH(3)Cl with those of CH(3)Cl from the various sources. Here we report delta13C values for CH(3)Cl released by two species of tropical ferns and show that the isotopic signature of CH(3)Cl from pteridophytes like that of CH(3)Cl from higher plants is quite different from that of CH(3)Cl produced by biomass burning, fungi and industry. delta13C values for CH(3)Cl produced by Cyathea smithii and Angiopteris evecta were respectively -72.7 per thousand and -69.3 per thousand representing depletions relative to plant biomass of 42.3 per thousand and 43.4 per thousand. The characteristic isotopic signature of CH(3)Cl released by green plants should help constrain their contribution to the atmospheric burden when reliable delta13C values for all other major sources of CH(3)Cl are obtained and a globally averaged delta13C value for atmospheric CH(3)Cl is available.     

A qualitative study of college students' perceptions of risky driving and social influences

Objectives: Young adults and teens are documented as the riskiest drivers on the road, and newer issues such as texting and driving are a growing concern. This study sought to determine the risk perceptions of young adults regarding various driving behaviors, their past experiences, how their social circles are structured, and how this might affect their driving. Methods: This study conducted qualitative research with 25 college undergraduate students to determine their peer and social influences regarding distracted driving. Data were analyzed and related to the health belief model and past research on social influence. Results: Though most participants felt that their behaviors were set after learning to drive, they were, in fact, quite susceptible to the influence of those in their social circles (e.g., fear of judgment and accountability) and, more broadly, to social norms. Texting and driving was the largest and most topical distracted driving issue and was also identified as very difficult to stop due to perceived barriers and the idea that intervening is rude. Participants identified low perceived susceptibility and severity (perceived threat) for a number of risky driving behaviors, including texting and driving. Conclusions: Training is needed to encourage people to intervene and speak up regarding behaviors other than drinking and driving, and cues to action and campaigns should target intervention to increase self-efficacy, as well as norms, susceptibility, and common rationalizations for risky behavior.     

Near-term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.