Feedbacks between vegetation pattern and resource loss dramatically decrease ecosystem resilience and restoration potential in a simple dryland model

Conceptual frameworks of dryland degradation commonly include ecohydrological feedbacks between landscape spatial organization and resource loss, so that decreasing cover and size of vegetation patches result in higher water and soil losses, which lead to further vegetation loss. However, the impacts of these feedbacks on dryland dynamics in response to external stress have barely been tested. Using a spatially-explicit model, we represented feedbacks between vegetation pattern and landscape resource loss by establishing a negative dependence of plant establishment on the connectivity of runoff-source areas (e.g., bare soils). We assessed the impact of various feedback strengths on the response of dryland ecosystems to changing external conditions. In general, for a given external pressure, these connectivity-mediated feedbacks decrease vegetation cover at equilibrium, which indicates a decrease in ecosystem resistance. Along a gradient of gradual increase of environmental pressure (e.g., aridity), the connectivity-mediated feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state (ecosystem resilience). If environmental conditions improve, these feedbacks increase the pressure release needed to achieve the ecosystem recovery (restoration potential). The impact of these feedbacks on dryland response to external stress is markedly non-linear, which relies on the non-linear negative relationship between bare-soil connectivity and vegetation cover. Modelling studies on dryland vegetation dynamics not accounting for the connectivity-mediated feedbacks studied here may overestimate the resistance, resilience and restoration potential of drylands in response to environmental and human pressures. Our results also suggest that changes in vegetation pattern and associated hydrological connectivity may be more informative early-warning indicators of dryland degradation than changes in vegetation cover.

On Modeling an Innovative Monitoring Network for Protecting and Managing Cultural Heritage from Risk Events

In this paper the model of an Innovative Monitoring Network involving properly connected nodes to develop an Information and Communication Technology (ICT) solution for preventive maintenance of historical centres from early warnings is proposed. It is well known that the protection of historical centres generally goes from a large-scale monitoring to a local one and it could be supported by a unique ICT solution. More in detail, the models of a virtually organized monitoring system could enable the implementation of automated analyses by presenting various alert levels. An adequate ICT solution tool would allow to define a monitoring network for a shared processing of data and results. Thus, a possible retrofit solution could be planned for pilot cases shared among the nodes of the network on the basis of a suitable procedure utilizing a retrofit catalogue. The final objective would consist in providing a model of an innovative tool to identify hazards, damages and possible retrofit solutions for historical centres, assuring an easy early warning support for stakeholders. The action could proactively target the needs and requirements of users, such as decision makers responsible for damage mitigation and safeguarding of cultural heritage assets.

Perceived emotional intelligence, general intelligence and early professional success: predictive and incremental validity

Although the study of factors affecting career success has shown connections between biographical and other aspects related to ability, knowledge and personality, few studies have examined the relationship be-tween emotional intelligence and professional success at the initial career stage. When these studies were carried out, the results showed significant relationships between the dimensions of emotional intelligence (emotional self-awareness, self-regulation, social awareness or social skills) and the level of professional competence. In this paper, we analyze the relationship between perceived emotional intelligence, measured by the Trait Meta-Mood Scale (TMMS-24) questionnaire, general intelligence assessed by the Cattell factor "g" test, scale 3, and extrinsic indicators of career success, in a sample of 130 graduates at the beginning of their careers. Results from hierarchical regression analysis indicate that emotional intelligence makes a specific contribution to the prediction of salary, after controlling the general intelligence effect. The perceived emotional intelligence dimensions of TMMS repair, TMMS attention and sex show a higher correlation and make a greater contribution to professional success than general intelligence. The implications of these results for the development of socio-emotional skills among University graduates are discussed.

General mental ability as moderator of personality traits as predictors of early career success

In this paper, we examine the effects of general mental ability (GMA) and the personality traits defined in the big five model on extrinsic and intrinsic indicators of career success, in a sample of 130 graduates who were in the early stages of their careers. Results from hierarchical regression analyses indicated that GMA does not predict any of the success indicators. In contrast, the combination of GMA and three of the Big Five Personality traits, conscientiousness, neuroticism, and openness, is significantly associated with greater early career success and has incremental predictive validity.

Does Trait Emotional Intelligence Predict Unique Variance in Early Career Success Beyond IQ and Personality?

In order to determine the contribution of emotional intelligence (EI) to career success, in this study, we analyzed the relationship between trait EI (TEI), general mental ability (GMA), the big five personality traits, and career success indicators, in a sample of 130 graduates who were in the early stages of their careers. Results from hierarchical regression analyses indicated that TEI, and especially its dimension “repair,” has incremental validity in predicting one of the career success indicators (salary) after controlling for GMA and personality. These findings provide support for the use of TEI measures as predictors of career success in the early stage.