A multilevel model of emotional skills,communication performance,and task performance in teams

There is increasing research regarding the influence of emotions on teamwork. In this study, we use a multilevel approach to examine how team members' use of emotion‐related skills affects team task performance and communication performance within the team. We measured individual self‐reported emotional skills prior to team formation and then collected peer‐rated individual communication performance and independently rated team task performance eight weeks later. Although there was no influence at the individual level between emotional skills and performance, team‐level emotional skills positively predicted team task performance scores. At the cross level, team‐level emotional skills predicted individual‐level communication performance. These findings emphasize the importance of distinct team emotional skills in shaping both team performance and individual team member performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Effectiveness of Panama as an intercontinental land bridge for large mammals

Habitat fragmentation is a primary driver of wildlife loss, and establishment of biological corridors is a common strategy to mitigate this problem. A flagship example is the Mesoamerican Biological Corridor (MBC), which aims to connect protected forest areas between Mexico and Panama to allow dispersal and gene flow of forest organisms. Because forests across Central America have continued to degrade, the functioning of the MBC has been questioned, but reliable estimates of species occurrence were unavailable. Large mammals are suitable indicators of forest functioning, so we assessed their conservation status across the Isthmus of Panama, the narrowest section of the MBC. We used large-scale camera-trap surveys and hierarchical multispecies occupancy models in a Bayesian framework to estimate the occupancy of 9 medium to large mammals and developed an occupancy-weighted connectivity metric to evaluate species-specific functional connectivity. White-lipped peccary (Tayassu pecari), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), white-tailed deer (Odocoileus virginianus), and tapir (Tapirus bairdii) had low expected occupancy along the MBC in Panama. Puma (Puma concolor), red brocket deer (Mazama temama), ocelot (Leopardus pardalis), and collared peccary (Pecari tajacu), which are more adaptable, had higher occupancy, even in areas with low forest cover near infrastructure. However, the majority of species were subject to ≥1 gap that was larger than their known dispersal distances, suggesting poor connectivity along the MBC in Panama. Based on our results, forests in Darien, Donoso–Santa Fe, and La Amistad International Park are critical for survival of large terrestrial mammals in Panama and 2 areas need restoration.     

Applying the zoo model to conservation of threatened exceptional plant species

Maintaining a living plant collection is the most common method of ex situ conservation for plant species that cannot be seed banked (i.e., exceptional species). Viability of living collections, and their value for future conservation efforts, can be limited without coordinated efforts to track and manage individuals across institutions. Using a pedigree-focused approach, the zoological community has established an inter-institutional infrastructure to support long-term viability of captive animal populations. We assessed the ability of this coordinated metacollection infrastructure to support the conservation of 4 plant species curated in living collections at multiple botanic gardens around the world. Limitations in current practices include the inability to compile, share, and analyze plant collections data at the individual level, as well as difficulty in tracking original provenance of ex situ material. The coordinated metacollection framework used by zoos can be adopted by the botanical community to improve conservation outcomes by minimizing the loss of genetic diversity in collections. We suggest actions to improve ex situ conservation of exceptional plant species, including developing a central database to aggregate data and track unique individuals of priority threatened species among institutions and adapting a pedigree-based population management tool that incorporates life-history aspects unique to plants. If approached collaboratively across regional, national, and global scales, these actions could transform ex situ conservation of threatened plant species.     

LANDSAT MID-INFRARED DATA AND GIS IN REGIONAL SURFACE SOIL-MOISTURE ASSESSMENT1

ABSTRACT: Landsat satellite Thematic Mapper (TM) data were used to assess regional soil moisture conditions. The mid-infrared (MIR) data of TM band 7 were overlain onto four principal land-use categories (Agricultural/Irrigated, Urban/Clearings, Forest/ Wetlands, Water) using a geographic information system (GIS). M data were used to assess four qualitative surface soil-moisture conditions (water/very wet, wet, moist, and dry) within each land-use category of a 208,354 ha southwestern Florida study area. The MIR response was inversely related to the qualitative surface soil-moisture content. Integration of Landsat TM MIR data with land use through GIS appears to be a useful technique for high-resolution regional soil moisture assessment, and further research to reline this technique is recommended.     

ATRAZINE IN SPRING RUNOFF AS RELATED TO ENVIRONMENTAL SETTING EBRASKA, 19921

ABSTRACT: A synoptic sampling of five surface-water sites in central Nebraska was conducted by the U.S. Geological Survey as part of its National Water-Quality Assessment Program during storm runoff in May 1992 to relate transport, yields, and concentrations of atrazine to environmental setting. Atrazine was the most extensively applied pesticide in the study unit. Atrazine transport was related to the size of contributing drainage area, quantity of atrazine applied, amount of precipitation, and volume of stream-flow. Estimated yields and mean concentrations of atrazine were related to the percentage of cropland in a drainage area. The largest estimated yields and mean concentrations of atrazine in surface water were associated from drainage areas with the highest percentage of cropland, and the smallest was associated with the smallest amount of cropland. Atrazine concentrations increased as streamflow increased but decreased at or near the time of peak streamflows, perhaps due to dilution. Atrazine concentrations then increased and remained elevated far into the stream recession. Atrazine is a regulated contaminant in finished public-water supplies. Large concentrations of atrazine could affect the management of public-water supplies because atrazine remains in solution in contrast to many other pesticides that are more easily removed.     

Experimental investigation of hydrogen–air deflagrations and detonations in semi-confined flat layers

This paper presents results of an experimental investigation on the deflagration and deflagration-to-detonation transition (DDT) in an obstructed (blockage ratio BR = 50%), semi-confined flat layer filled with uniform hydrogen–air mixtures. The effect of mixture reactivity depending on flat layer thickness and its width is studied to evaluate the critical conditions for sonic flame propagation and the possibility for detonation onset. The experiments were performed in a transparent, rectangular channel with a length of 2.5 m. The flat layer thickness was varied from 0.06 to 0.24 m and the experiments were performed for different channel widths of 0.3, 0.6 and 0.9 m. The experimental results show flame velocity vs. hydrogen concentration for different thicknesses and widths of the semi-confined flat layer. Three different flame propagation regimes were observed: slow subsonic flame (M << 1), sonic deflagration (M ～ 1) and detonation (M >> 1). It is shown that flame acceleration (FA) to sonic speed is independent of the width of the flat layer. The critical expansion ratio for effective flame acceleration to sonic speed was found to be linearly dependent on the reciprocal layer thickness.     

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.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Challenges and Opportunities for Creating Intelligent Hazard Alerts: The “FloodHippo” Prototype

Disasters evolving from hazards are a persistent and deadly occurrence in the United States. Despite this, hazard alerts have remained spatially vague, temporally imprecise, and lack actionable information. These deficiencies indicate a divide between the status quo and what is possible given modern environmental models, geographic information systems (GIS), and smartphone capabilities. This work describes an alternative, prototype system, “FloodHippo,” which integrates operational model outputs, cloud‐based GIS, and expanded communication channels to provide personal and interactive disaster alerts for floods. The precepts and methods underpinning FloodHippo apply equally to other disasters that evolve over space and time, presenting the opportunity for a more intelligent disaster response system. The development of such a system would not only minimize current shortcomings in disaster alerts but also improve resilience through individual action, along with community, academic, and federal cooperation.