Transformational leadership and creativity: A meta‐analytic review and identification of an integrated model

We conduct a meta‐analytic review that yields important insights about the existing research on transformational leadership and creativity. Additionally, we propose and test an integrated model using meta‐analytic structural equation modeling (MASEM) and full information MASEM (FIMASEM) techniques to better understand the intervening mechanism through which transformational leadership acts on creativity. The results of the meta‐analysis of 127 studies show that most of the bivariate relationships among transformational leadership, employee creativity, and pre‐identified mediators are significant; further, geographic base of studies significantly moderates some of the relationships. The MASEM results indicate that several mediators intervene in the relationship between transformational leadership and creativity. Although the total effect of transformational leadership on creativity is positive, its direct effect is negative when mediators are included. Additionally, there are significant relationships among the mediators that can be theoretically supported, but have not been investigated in prior transformational leadership and creativity studies. On the basis of these findings, we provide conclusions and directions for future studies.     

成都平原蔬菜生产中灌溉水对农药渗漏的影响研究

针对成都平原蔬菜生产中大量施用农药、大量灌水的特点,首次在国内应用欧洲官方成熟的农药评估模型——PEARL模型,研究评估了成都平原当前蔬菜生产中的灌溉方式对两种不同特性农药的渗漏的影响。杀毒矾(土壤吸附力(KOM)为0 L/kg,半衰期(DT50)为80 d)在没有灌溉条件下,其渗出土体时的最大质量浓度是190μg.L-1,而有灌溉条件下其渗出质量浓度则可达523μg.L-1,是没有灌溉条件下的2.75倍。三唑磷(KOM为200 L.kg-1,DT50为60 d)在没有灌溉条件下,其渗出土体时的最大质量浓度是0.025μg.L-1,而有灌溉条件下其渗出质量浓度为0.13μg.L-1,比没有灌溉时提高了4.2倍。不管有没有灌溉三唑磷在该地区对地下水的污染风险都很小,而杀毒矾的风险则很高。因此,农药的化学特性是影响农药渗漏的最重要的因子。在蔬菜生产中应尽量选用被土壤吸附力强、半衰期短的农药,例如:三唑磷;农药在土壤中的移动载体是土壤水,不合理的灌溉会大大地加大农药淋溶,应当多使用微喷、滴灌等节水灌溉措施,减少使用漫灌等耗水多的原始灌溉方式。     

Using Modeling to Improve Monitoring of Structured Populations: Are We Collecting the Right Data?

Abstract:  Population monitoring is central to most demographic studies and conservation efforts, but it may not always be directed at the most appropriate life stage. We used stochastic simulation modeling to evaluate the effectiveness of a monitoring program for a well-studied population of Eastern Imperial Eagles ( Aquila heliaca ) in Kazakhstan. Specifically, we asked whether the most appropriate data were being collected to understand system state and population dynamics. Our models were parameterized with data collected over the course of 25 years of study of this population. We used the models to conduct simulation experiments to evaluate relationships between monitored or potentially monitored parameters and the demographic variables of interest—population size ( N ) and population growth (λ). Static analyses showed that traditional territory-based monitoring was a poor indicator of eagle population size and growth and that monitoring survivorship would provide more information about these parameters. Nevertheless, these same traditionally monitored territory-based parameters had greater power to detect long-term changes in population size than did survivorship or population structure. Regardless of the taxa considered, threats can have immediate impacts on population size and growth or longer-term impacts on population dynamics. Prudently designed monitoring programs for any species will detect the demographic effects of both types of threats.     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

Delayed dispersal as a potential route to cooperative breeding in ambrosia beetles

Xyleborini are a species-rich tribe of ambrosia beetles, which are haplodiploid and typically mate among siblings within their natal brood chamber. Several characteristics of this tribe would predict the evolution of higher levels of sociality: high genetic relatedness within galleries due to inbreeding, high costs of dispersal and the potential benefit of cooperation in brood care within the natal gallery (e.g. by fungus gardening, gallery extension, offspring feeding and cleaning). However, information on the social system of these beetles is very limited. We examined the potential for cooperative breeding in Xyleborinus saxeseni by monitoring dispersal in relation to brood size and composition. Results show that adult female offspring delay dispersal despite dispersal opportunities, and apparently some females never disperse. The females’ decision to stay seems to depend on the presence of eggs and dependent siblings. We found no indication that female offspring reproduce in their natal gallery, as colonies with many mature daughters do not contain more eggs than those with few or no daughters. There is a significant positive relationship between the number of females present and the number of dependent siblings (but not eggs), which suggests that cooperative brood care of female offspring raises colony productivity by improving survival rates of immatures. Our results suggest that cooperative breeding is likely to occur in X. saxeseni and possibly other xyleborine species. We argue that a closer look at sociality within this tribe may yield important information on the factors determining the evolution of cooperative breeding and advanced social organization.     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

Using the expert model PERPEST to translate measured and predicted pesticide exposure data into ecological risks

An important topic in the registration of pesticides and the interpretation of monitoring data is the estimation of the consequences of a certain concentration of a pesticide for the ecology of aquatic ecosystems. Solving these problems requires predictions of the expected response of the ecosystem to chemical stress. Up until now, a dominant approach to come up with such a prediction is the use of simulation models or safety factors. The disadvantage of the use of safety factors is a crude method that does not provide any insight into the concentration–response relationships at the ecosystem level. On the other hand, simulation models also have serious drawbacks like that they are often very complex, lack transparency, their implementation is expensive and there may be a compilation of errors, due to uncertainties in parameters and processes. In this paper, we present the expert model prediction of the ecological risks of pesticides (PERPEST) that overcomes these problems. It predicts the effects of a given concentration of a pesticide based on the outcome of already performed experiments using experimental ecosystems. This has the great advantage that the outcome is more realistic. The paper especially discusses how this model can be used to translate measured and predicted concentrations of pesticides into ecological risks, by taking data on measured and predicted concentrations of atrazine as an example. It is argued that this model can be of great use to evaluate the outcome of chemical monitoring programmes (e.g. performed in the light of the Water Framework Directive) and can even be used to evaluate the effects of mixtures.     

Survival and productivity benefits to social nesting in the sweat bee <Emphasis Type="Italic">Megalopta genalis</Emphasis> (Hymenoptera: Halictidae)

Facultatively solitary and eusocial species allow for direct tests of the benefits of group living. We used the facultatively social sweat bee Megalopta genalis to test several benefits of group living. We surveyed natural nests modified for observation in the field weekly for 5 weeks in 2003. First, we demonstrate that social and solitary nesting are alternative behaviors, rather than different points on one developmental trajectory. Next, we show that solitary nests suffered significantly higher rates of nest failure than did social nests. Nest failure apparently resulted from solitary foundress mortality and subsequent brood orphanage. Social nests had significantly higher productivity, measured as new brood cells provisioned during the study, than did solitary nests. After accounting for nest failures, per capita productivity did not change with group size. Our results support key predictions of Assured Fitness Return models, suggesting such indirect fitness benefits favor eusocial nesting in M. genalis. We compared field collections of natural nests to our observation nest data to show that without accounting for nest failures, M. genalis appear to suffer a per capita productivity decrease with increasing group size. Calculating per capita productivity from collected nests without accounting for the differential probabilities of survival across group sizes leads to an overestimate of solitary nest productivity.