With a little help from my colleagues: A social embeddedness approach to perceived organizational support

We examined the role of social embeddedness in creating positive perceptions of organizational support among managerial and staff employees (n = 72) of a large manufacturing firm. We operationalized social embeddedness as the size, density, and quality of employees' networks of multiplex, reciprocated exchange relationships with colleagues. After controlling for support from supervisors and upper management, we found all three aspects of social embeddedness to be associated with perceived organizational support (POS). This research suggests that in addition to the top‐down influence of the organizational hierarchy, POS results from the organizational community within which employees are embedded through their social network in the workplace. Copyright © 2011 John Wiley & Sons, Ltd.     

Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China

In this study, we applied Bayesian networks to prioritize the factors that influence hazardous material (Hazmat) transportation accidents. The Bayesian network structure was built based on expert knowledge using Dempster-Shafer evidence theory, and the structure was modified based on a test for conditional independence. We collected and analyzed 94 cases of Chinese Hazmat transportation accidents to compute the posterior probability of each factor using the expectation-maximization learning algorithm. We found that the three most influential factors in Hazmat transportation accidents were human factors, the transport vehicle and facilities, and packing and loading of the Hazmat. These findings provide an empirically supported theoretical basis for Hazmat transportation corporations to take corrective and preventative measures to reduce the risk of accidents.     

基于区间活性数据的定量结构-活性关系

利用贝叶斯统计方法构建了基于区间活性数据的取代苯胺和苯酚类化合物对大型溞(Daphniamagna)24 h急性毒性的定量结构-活性关系模型,并与基于平均数和中位数的点估计活性数据的定量结构-活性关系模型进行了比较.结果表明,前者可以充分利用化合物的活性数据信息,模型具有更好的拟合效果与预测能力以及较宽的应用范围.基于区间活性数据的定量结构-活性关系模型可为生态风险评价等提供更加可靠的预测数据.     

On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called _R0$R_0$) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) _R0$R_0$ cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations.     

Association networks in spider monkeys (<Emphasis Type="Italic">Ateles geoffroyi</Emphasis>)

We use two novel techniques to analyze association patterns in a group of wild spider monkeys (Ateles geoffroyi) studied continuously for 8 years. Permutation tests identified association rates higher or lower than chance expectation, indicating active processes of companionship and avoidance as opposed to passive aggregation. Network graphs represented individual adults as nodes and their association rates as weighted edges. Strength and eigenvector centrality (a measure of how strongly linked an individual is to other strongly linked individuals) were used to quantify the particular role of individuals in determining the network's structure. Female–female dyads showed higher association rates than any other type of dyad, but permutation tests revealed that these associations cannot be distinguished from random aggregation. Females formed tightly linked clusters that were stable over time, with the exception of immigrant females who showed little association with any adult in the group. Eigenvector centrality was higher for females than for males. Adult males were associated mostly among them, and although their strength of association with others was lower than that of females, their association rates revealed a process of active companionship. Female–male bonds were weaker than those between same-sex pairs, with the exception of those involving young male adults, who by virtue of their strong connections both with female and male adults, appear as temporary brokers between the female and male clusters of the network. This analytical framework can serve to develop a more complete explanation of social structure in species with high levels of fission–fusion dynamics. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James)     

Potential banana skins in animal social network analysis

Social network analysis is an increasingly popular tool for the study of the fine-scale and global social structure of animals. It has attracted particular attention by those attempting to unravel social structure in fission–fusion populations. It is clear that the social network approach offers some exciting opportunities for gaining new insights into social systems. However, some of the practices which are currently being used in the animal social networks literature are at worst questionable and at best over-enthusiastic. We highlight some of the areas of method, analysis and interpretation in which greater care may be needed in order to ensure that the biology we extract from our networks is robust. In particular, we suggest that more attention should be given to whether relational data are representative, the potential effect of observational errors and the choice and use of statistical tests. The importance of replication and manipulation must not be forgotten, and the interpretation of results requires care. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

Quantifying Eradication Success: the Removal of Feral Pigs from Santa Cruz Island, California

Abstract:  A major challenge facing pest-eradication efforts is determining when eradication has been achieved. When the pest can no longer be detected, managers have to decide whether the pest has actually been eliminated and hence to decide when to terminate the eradication program. For most eradication programs, this decision entails considerable risk and is the largest single issue facing managers of such programs. We addressed this issue for an eradication program of feral pigs ( Sus scrofa ) from Santa Cruz Island, California. Using a Bayesian approach, we estimated the degree of confidence in the success of the eradication program at the point when monitoring failed to detect any more pigs. Catch-effort modeling of the hunting effort required to dispatch pigs during the eradication program was used to determine the relationship between detection probability and searching effort for different hunting methods. We then used these relationships to estimate the amount of monitoring effort required to declare eradication successful with criteria that either set a threshold for the probability that pigs remained undetected (type I error) or minimized the net expected costs of the eradication program (cost of type I and II errors). For aerial and ground-based monitoring techniques, the amount of search effort required to declare eradication successful on the basis of either criterion was highly dependent on the prior belief in the success of the program unless monitoring intensities exceeded 30 km of searching effort per square kilometer of search area for aerial monitoring and, equivalently, 38 km for ground monitoring. Calculation of these criteria to gauge the success of eradication should form an essential component of any eradication program as it allows for a transparent assessment of the risks inherent in the decision to terminate the program.     

Comparing model averaging with other model selection strategies for benchmark dose estimation

Model averaging (MA) has been proposed as a method of accommodating model uncertainty when estimating risk. Although the use of MA is inherently appealing, little is known about its performance using general modeling conditions. We investigate the use of MA for estimating excess risk using a Monte Carlo simulation. Dichotomous response data are simulated under various assumed underlying dose–response curves, and nine dose–response models (from the USEPA Benchmark dose model suite) are fit to obtain both model specific and MA risk estimates. The benchmark dose estimates (BMDs) from the MA method, as well as estimates from other commonly selected models, e.g., best fitting model or the model resulting in the smallest BMD, are compared to the true benchmark dose value to better understand both bias and coverage behavior in the estimation procedure. The MA method has a small bias when estimating the BMD that is similar to the bias of BMD estimates derived from the assumed model. Further, when a broader range of models are included in the family of models considered in the MA process, the lower bound estimate provided coverage close to the nominal level, which is superior to the other strategies considered. This approach provides an alternative method for risk managers to estimate risk while incorporating model uncertainty.

Bayesian Decision‐Network Modeling of Multiple Stakeholders for Reef Ecosystem Restoration in the Coral Triangle

Proposals for marine conservation measures have proliferated in the last 2 decades due to increased reports of fishery declines and interest in conservation. Fishers and fisheries managers have often disagreed strongly when discussing controls on fisheries. In such situations, ecosystem‐based models and fisheries‐stock assessment models can help resolve disagreements by highlighting the trade‐offs that would be made under alternative management scenarios. We extended the analytical framework for modeling such trade‐offs by including additional stakeholders whose livelihoods and the value they place on conservation depend on the condition of the marine ecosystem. To do so, we used Bayesian decision‐network models (BDNs) in a case study of an Indonesian coral reef fishery. Our model included interests of the fishers and fishery managers; individuals in the tourism industry; conservation interests of the state, nongovernmental organizations, and the local public; and uncertainties in ecosystem status, projections of fisheries revenues, tourism growth, and levels of interest in conservation. We calculated the total utility (i.e., value) of a range of restoration scenarios. Restricting net fisheries and live‐fish fisheries appeared to be the best compromise solutions under several combinations of settings of modeled variables. Results of our case study highlight the implications of alternate formulations for coral reef stakeholder utility functions and discount rates for the calculation of the net benefits of alternative fisheries management options. This case study may also serve as a useful example for other decision analyses with multiple stakeholders. Modelo de Red de Decisión Bayesiana de Múltiples Actores Interesados en la Restauración de Ecosistemas de Arrecife en el Triángulo de Coral     

Multi-hazard failure assessment of atmospheric storage tanks during hurricanes

Hurricane as one of the most destructive natural hazards can make a devastating impact on the industrial equipment, especially atmospheric storage tanks, leading to the release of stored chemicals and disastrous safety and environmental issues. These catastrophic consequences are caused not only by strong winds but also by the torrential rainfall and inundating floods. The objective of this study is to present a risk-based methodology for assessing and reducing the vulnerability of atmospheric storage tanks to hurricanes. Considering the shell buckling, flotation, sliding, and roof sinking as dominant failure modes of atmospheric storage tanks during hurricanes, Bayesian network (BN) has been employed to combine the failure modes while considering their conditional dependencies. The probability updating feature of the developed BN was employed to indicate that the flood is the most critical hazard during hurricanes while the impact of wind and rainfall cannot be neglected. Extending the developed BN to an influence diagram, the cost-benefit filling of storage tanks with water prior to the advent of hurricanes was shown as a viable measure for reducing the damage probability. The results show that the proposed methodology can be used as an effective decision support tool for assessing and reducing the vulnerability of atmospheric storage tanks to natural hazards.