Building Greater Sustainability in Supply Chains

Challenges companies face and tools they use to identify and reduce their environmental footprints across their supply chains     

The effect of octopamine on behavioral responses of free-foraging bumblebees to a change in food source profitability

The invertebrate neuromodulator octopamine is known to be involved in bees' associative learning, enhancing the responsiveness of a bee to a conditioned stimulus. In this study, we tested the effect of octopamine on the choice behavior of free-flying bumblebees using a two-phase experiment in an array of artificial flowers. During the first phase of the experiment, the bee was allowed to collect octopamine-laden sugar water from two types of equally rewarding flowers (yellow versus blue). In the second phase, one type of flower was set to be unrewarding. The behavior of the bee (proportion of visits to the unrewarding flowers) over the two phases was fitted to a sigmoid regression model. Our results show that octopamine had no significant effect on the bees' equilibrium choice or on the overall rate of the behavioral change in response to the change in reward. Rather, octopamine significantly affected the time interval between the change in reward status and the initiation of behavioral change in the bee.     

The green dividend of urban biking? Evidence of improved community and sustainable development

As the cost of car ownership has skyrocketed, urban biking has experienced the largest share increase of any transportation mode, rising by 40% between the years 2000 and 2014. Growing attention is being paid to the potential local economic development impacts of urban neighbourhoods becoming more bike-friendly. It is now a green economic development strategy in cities as diverse as Chicago, New York City, Portland, and San Francisco to increase bicycling as a transportation mode. This paper reports the results of a survey of 2032 responses from faculty, staff, and students of a car-dependent, downtown university. We use a mixed methods approach, including data from the American Community Survey, to support our arguments and to inform potential savings and economic benefit calculations that can be achieved from bicycle infrastructure investments and anticipated redistributed spending patterns. We argue that urban biking results in a green dividend that promotes local community development and more importantly results in zero carbon emissions.     

Managing the nitrogen cycle to reduce greenhouse gas emissions from crop production and biofuel expansion

Public policies are promoting biofuels as an alternative to fossil fuel consumption in order to mitigate greenhouse gas (GHG) emissions. However, the mitigation benefit can be at least partially compromised by emissions occurring during feedstock production. One of the key sources of GHG emissions from biofuel feedstock production, as well as conventional crops, is soil nitrous oxide (N₂O), which is largely driven by nitrogen (N) management. Our objective was to determine how much GHG emissions could be reduced by encouraging alternative N management practices through application of nitrification inhibitors and a cap on N fertilization. We used the US Renewable Fuel Standards (RFS2) as the basis for a case study to evaluate technical and economic drivers influencing the N management mitigation strategies. We estimated soil N₂O emissions using the DayCent ecosystem model and applied the US Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG) to project GHG emissions for the agricultural sector, as influenced by biofuel scenarios and N management options. Relative to the current RSF2 policy with no N management interventions, results show decreases in N₂O emissions ranging from 3 to 4 % for the agricultural sector (5.5–6.5 million metric tonnes CO₂?eq.?year^?1; 1 million metric tonnes is equivalent to a Teragram) in response to a cap that reduces N fertilizer application and even larger reductions with application of nitrification inhibitors, ranging from 9 to 10 % (15.5–16.6 million tonnes CO₂?eq.?year^?1). The results demonstrate that climate and energy policies promoting biofuel production could consider options to manage the N cycle with alternative fertilization practices for the agricultural sector and likely enhance the mitigation of GHG emissions associated with biofuels.     

Timing of the evening emergence from day roosts of the grey-headed flying fox, Pteropus poliocephalus: the effects of predation risk, foraging needs, and social context

This study addresses the functional question of how variation in foraging strategy, predation risk, and social context influence the timing of the evening emergence from day roosts of the grey-headed flying fox, Pteropus poliocephalus. The onset of evening emergence was expected to vary according to the relative costs and benefits of emerging early and should, therefore, reflect an optimal trade-off between predation risks and foraging needs. The onset of the colony-wide emergence was closely correlated with the time of sunset and cloud cover. However, as expected, the onset of the colony-wide emergence was delayed when a diurnal avian predator was present, whereas the onset was advanced during lactation when presumably energetic demands are higher. The trade-off between predation risks and foraging needs was further reflected in the emergence times of individual bats: adult females emerged earlier when they had higher foraging needs as indicated by their body condition; young emerged later when they were smaller and likely to be more at risk from predation due to their less developed flying skills. However, the emergence time of adult males depended on their social status: smaller bachelor males emerged from the colony earlier than larger harem-holding males who guard their harems until the last female had left. Thus, whereas the colony-wide emergence time reflected the outcome of a trade-off between predation risks and general foraging needs, on an individual level, the outcome of this trade-off depended on sex, age, body condition, and structural size and was modified by social context.     

Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO₂ concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO₂ concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO₂ concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.     

Probabilistic movement model with emigration simulates movements of deer in Nebraska, 1990–2006

Movements of deer can affect population dynamics, spatial redistribution, and transmission and spread of diseases. Our goal was to model the movement of deer in Nebraska in an attempt to predict the potential for spread of chronic wasting disease (CWD) into eastern Nebraska. We collared and radio-tracked >600 white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) in Nebraska during 1990–2006. We observed large displacements (>10 km) for both species and sexes of deer, including migrations up to 100 km and dispersals up to 50 km. Average distance traveled between successive daily locations was 166 m for male and 173 for female deer in eastern Nebraska, and 427 m for male and 459 for female deer in western Nebraska. Average daily displacement from initial capture point was 10 m for male and 14 m for female deer in eastern Nebraska, and 27 m for male and 28 m for female deer in western Nebraska. We used these data on naturally occurring movements to create and test 6 individual-based models of movement for white-tailed deer and mule deer in Nebraska, including models that incorporated sampling from empirical distributions of movement lengths and turn angles (DIST), correlated random walks (CRW), home point fidelity (FOCUS), shifting home point (SHIFT), probabilistic movement acceptance (MOVE), and probabilistic movement with emigration (MOVEwEMI). We created models in sequence in an attempt to account for the shortcomings of the previous model(s). We used the Kolmogrov–Smirnov goodness-of-fit test to verify improvement of simulated annual displacement distributions to empirical displacement distributions. The best-fit model (D = 0.07 and 0.08 for eastern and western Nebraska, respectively) included a probabilistic movement chance with emigration (MOVEwEMI) and resulted in an optimal daily movement length of 350 m (maximum daily movement length of 2800 m for emigrators) for eastern Nebraska and 370 m (maximum of 2960 m) for western Nebraska. The proportion of deer that moved as emigrators was 0.10 and 0.13 for eastern and western Nebraska, respectively. We propose that the observed spread of CWD may be driven by large movements of a small proportion of deer that help to establish a low prevalence of the disease in areas east of the current endemic area. Our movement models will be used in a larger individual-based simulation of movement, survival, and transmission of CWD to help determine future surveillance and management actions.     

Trait space of rare plants in a fire‐dependent ecosystem

The causes of species rarity are of critical concern because of the high extinction risk associated with rarity. Studies examining individual rare species have limited generality, whereas trait‐based approaches offer a means to identify functional causes of rarity that can be applied to communities with disparate species pools. Differences in functional traits between rare and common species may be indicative of the functional causes of species rarity and may therefore be useful in crafting species conservation strategies. However, there is a conspicuous lack of studies comparing the functional traits of rare species and co‐occurring common species. We measured 18 important functional traits for 19 rare and 134 common understory plant species from North Carolina's Sandhills region and compared their trait distributions to determine whether there are significant functional differences that may explain species rarity. Flowering, fire, and tissue‐chemistry traits differed significantly between rare and common, co‐occurring species. Differences in specific traits suggest that fire suppression has driven rarity in this system and that changes to the timing and severity of prescribed fire may improve conservation success. Our method provides a useful tool to prioritize conservation efforts in other systems based on the likelihood that rare species are functionally capable of persisting.