Effects of wind direction on coarse and fine particulate matter concentrations in southeast Kansas

Field data for coarse particulate matter ([PM] PM10) and fine particulate matter (PM2.5) were collected at selected sites in Southeast Kansas from March 1999 to October 2000, using portable MiniVol particulate samplers. The purpose was to assess the influence on air quality of four industrial facilities that burn hazardous waste in the area located in the communities of Chanute, Independence, Fredonia, and Coffeyville. Both spatial and temporal variation were observed in the data. Variation because of sampling site was found to be statistically significant for PM10 but not for PM2.5. PM10 concentrations were typically slightly higher at sites located within the four study communities than at background sites. Sampling sites were located north and south of the four targeted sources to provide upwind and downwind monitoring pairs. No statistically significant differences were found between upwind and downwind samples for either PM10 or PM2.5, indicating that the targeted sources did not contribute significantly to PM concentrations. Wind direction can frequently contribute to temporal variation in air pollutant concentrations and was investigated in this study. Sampling days were divided into four classifications: predominantly south winds, predominantly north winds, calm/variable winds, and winds from other directions. The effect of wind direction was found to be statistically significant for both PM10 and PM2.5. For both size ranges, PM concentrations were typically highest on days with predominantly south winds; days with calm/variable winds generally produced higher concentrations than did those with predominantly north winds or those with winds from "other" directions. The significant effect of wind direction suggests that regional sources may exert a large influence on PM concentrations in the area.     

Comparing different environmental variables in predictive models of bird distribution

The response variable (often the presence of a species) in predictive habitat models relies on a set of environmental predictors. Among all known environmental predictors, vegetation has the most effect on species abundance and on their habitat preferences, due to the wide range of necessary resources that it provides for the survival of bird species. However, other predictors, in turn, affect bird distribution, and some-times they play a more important role in habitat selection, depending on the natural history and ecological needs of the bird species. In this regard, different analyses have been conducted to predict the distribution, and define habitat suitability (such as discriminant function analysis, General Linear Models, and ANOVA). In this study, all three analytical designs were used to investigate the relationship of seven bird species to the major environmental gradients in the study area, to find out the significance of each of these factors on habitat selection. GIS has been used to prepare spatial distributional data, and to overlay and calculate different aspects of the environmental factors. The results suggest that potential individual habitat patches play a small role compared to the landscape (entire corresponding habitat patches), when considering vegetation. The influence of built-up areas is significant for all the species, and the proximity to the sea shore is very significant for at least one of the species, however, it is not neutral for all other species.     

A critical number of workers in a honeybee colony triggers investment in reproduction

Social insect colonies, like individual organisms, must decide as they develop how to allocate optimally their resources among survival, growth, and reproduction. Only when colonies reach a certain state do they switch from investing purely in survival and growth to investing also in reproduction. But how do worker bees within a colony detect that their colony has reached the state where it is adaptive to begin investing in reproduction? Previous work has shown that larger honeybee colonies invest more in reproduction (i.e., the production of drones and queens), however, the term ‘larger’ encompasses multiple colony parameters including number of adult workers, size of the nest, amount of brood, and size of the honey stores. These colony parameters were independently increased in this study to test which one(s) would increase a colony’s investment in reproduction via males. This was assayed by measuring the construction of drone comb, the special type of comb in which drones are reared. Only an increase in the number of workers stimulated construction of drone comb. Colonies with over 4,000 workers began building drone comb, independent of the other colony parameters. These results show that attaining a critical number of workers is the key parameter for honeybee colonies to start to shift resources towards reproduction. These findings are relevant to other social systems in which a group’s members must adjust their behavior as a function of the group’s size.     

A TRIANGULAR MODEL OF DIMENSIONLESS RUNOFF PRODUCING RAINFALL HYETOGRAPHS IN TEXAS1

ABSTRACT: A synthetic triangular hyetograph for a large data base of Texas rainfall and runoff is needed. A hyetograph represents the temporal distribution of rainfall intensity at a point or over a watershed during a storm. Synthetic hyetographs are estimates of the expected time distribution for a design storm and principally are used in small watershed hydraulic structure design. A data base of more than 1,600 observed cumulative hyetographs that produced runoff from 91 small watersheds (generally less than about 50 km²) was used to provide statistical parameters for a simple triangular shaped hyetograph model. The model provides an estimate of the average hyetograph in dimensionless form for storm durations of 0 to 24 hours and 24 to 72 hours. As a result of this study, the authors concluded that the expected dimensionless cumulative hyetographs of 0 to 12 hour and 12 to 24 hour durations were sufficiently similar to be combined with minimal information loss. The analysis also suggests that dimensionless cumulative hyetographs are independent of the frequency level or return period of total storm depth and thus are readily used for many design applications. The two triangular hyetographs presented are intended to enhance small watershed design practice in applicable parts of Texas.     

Analysis of chromosome 21 copy number in uncultured amniocytes by fluorescence in situ hybridization using a cosmid contig

A comparison of the use of chromosome 21-specific libraries, DOP-PCR 21 paints, yeast artificial chromosome (YAC) clones, single cosmids, and a 21q cosmid contig as probes for the detection of the copy number of chromosome 21 in interphase cells by fluorescence in situ hybridization shows that the cosmid contig is a satisfactory probe for interphase analysis of chromosome 21. The contig cCMP21.a, which is 55 kb in length, is highly chromosome 21-specific and produces intense, compact signals in a high proportion of interphase cells. A retrospective blind analysis of coded uncultured amniotic fluid samples correctly detected four trisomy 21 cases out of 49 samples.     

Governing trade-offs in ecosystem services and disservices to achieve human–wildlife coexistence

Sustaining wildlife populations, which provide both ecosystem services and disservices, represents a worldwide conservation challenge. The ecosystem services and Ostrom's social–ecological systems frameworks have been adopted across natural and social sciences to characterize benefits from nature. Despite their generalizability, individually they do not include explicit tools for addressing the sustainable management of many wildlife populations. For instance, Ostrom's framework does not specifically address competing perspectives on wildlife, whereas the ecosystem services framework provides a limited representation of the social and governance context wherein such competing perspectives are embedded. We developed a unified social–ecological framework of ecosystem disservices and services (SEEDS) that advances both frameworks by explicitly acknowledging the importance of competing wildlife perspectives embedded in the social and governance contexts. The SEEDS framework emulates the hierarchical structure of Ostrom's social–ecological systems, but adds subsystems reflecting heterogeneous stakeholder views and experiences of wildlife-based services and disservices. To facilitate operationalizing SEEDS and further broader analyses across human–wildlife systems, we devised a list of variables to describe SEEDS subsystems, such as types and level of services and disservices, cost and benefit sharing, and social participation of stakeholders. Steps to implement SEEDS involve engaging local communities and stakeholders to define the subsystems, analyze interactions and outcomes, and identify leverage points and actions to remedy unwanted outcomes. These steps connect SEEDS with other existing approaches in social–ecological research and can guide analyses across systems or within individual systems to provide new insights and management options for sustainable human–wildlife coexistence.     

Effects of knowledge and emotion on support for novel synthetic biology applications

There is sometimes an inherent assumption that the logical head will overrule the emotional heart in matters of science and technology. However, the literature on decision making under risk and uncertainty suggests that emotional responses may be more potent. A representative sample of Australians participated in a large, national, online survey (n = 8037), in which we measured the influence of knowledge and emotion in predicting support for possible synthetic biology (synbio) solutions to conservation, environmental, and industrial problems. A hierarchical regression model was used to examine the relative influence of affect- and emotion-related factors beyond the influence of knowledge factors in predicting support for synbio solutions. Subsequently, interaction analyses were conducted to examine the potentially moderating role of emotions in the knowledge–support relationship. There was 64% variance in overall support for synbio solutions (R² = 0.64, p < 0.001). The most influential predictor of support in the model was positive emotion. Feeling hopeful, excited, and curious toward a synbio technology was related to greater overall support for the development of that technology. The second strongest set of predictors was affect-related measures that evaluate the technology as bad or good, harmful or beneficial, and risky or safe. Positive emotion and an assessment that the technology was good significantly moderated the effect of knowledge on support. These findings suggest that, at least initially, people are more likely to be guided by their emotions when considering support for synbio technologies, which has implications for how researchers design and implement engagement and communication strategies more broadly.