This study analyses some hydrological driving forces and their interrelation with surface-flow initiation in a semiarid Caatinga basin (12km(2)), Northeastern Brazil. During the analysis period (2005 - 2014), 118 events with precipitation higher than 10mm were monitored, providing 45 events with runoff, 25 with negligible runoff and 49 without runoff. To verify the dominant processes, 179 on-site measurements of saturated hydraulic conductivity (Ksat) were conducted. The results showed that annual runoff coefficient lay below 0.5% and discharge at the outlet has only occurred four days per annum on average, providing an insight to the surface-water scarcity of the Caatinga biome. The most relevant variables to explain runoff initiation were total precipitation and maximum 60-min rainfall intensity (I-60). Runoff always occurred when rainfall surpassed 31mm, but it never occurred for rainfall below 14mm or for I-60 below 12mmh(-1). The fact that the duration of the critical intensity is similar to the basin concentration time (65min) and that the infiltration threshold value approaches the river-bank saturated hydraulic conductivity support the assumption that Hortonian runoff prevails. However, none of the analysed variables (total or precedent precipitation, soil moisture content, rainfall intensities or rainfall duration) has been able to explain the runoff initiation in all monitored events: the best criteria, e.g. failed to explain 27% of the events. It is possible that surface-flow initiation in the Caatinga biome is strongly influenced by the root-system dynamics, which changes macro-porosity status and, therefore, initial abstraction. Copyright (c) 2016 John Wiley & Sons, Ltd.
Processing of reward is the basis of adaptive behavior of the human being. Neural correlates of reward processing seem to be influenced by developmental changes from adolescence to late adulthood. The aim of this study is to uncover these neural correlates during a slot machine gambling task across the lifespan. Therefore, we used functional magnetic resonance imaging to investigate 102 volunteers in three different age groups: 34 adolescents, 34 younger adults, and 34 older adults. We focused on the core reward areas ventral striatum (VS) and ventromedial prefrontal cortex (VMPFC), the valence processing associated areas, anterior cingulate cortex (ACC) and insula, as well as information integration associated areas, dorsolateral prefrontal cortex (DLPFC), and inferior parietal lobule (IPL). Results showed that VS and VMPFC were characterized by a hyperactivation in adolescents compared with younger adults. Furthermore, the ACC and insula were characterized by a U-shape pattern (hypoactivation in younger adults compared with adolescents and older adults), whereas the DLPFC and IPL were characterized by a J-shaped form (hyperactivation in older adults compared with younger groups). Furthermore, a functional connectivity analysis revealed an elevated negative functional coupling between the inhibition-related area rIFG and VS in younger adults compared with adolescents. Results indicate that lifespan-related changes during reward anticipation are characterized by different trajectories in different reward network modules and support the hypothesis of an imbalance in maturation of striatal and prefrontal cortex in adolescents. Furthermore, these results suggest compensatory age-specific effects in fronto-parietal regions. Hum Brain Mapp 35:5153-5165, 2014. (c) 2014 Wiley Periodicals, Inc.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.
RangeShiftR
(2021)
Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species' distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses to environmental change. We present RangeShiftR, an R implementation of a flexible individual-based modelling platform which simulates eco-evolutionary dynamics in a spatially explicit way. The package provides flexible and fast simulations by making the software RangeShifter available for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package's functionality, describe the underlying model structure with its main components and present a short example. RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with elaborate tutorials and comprehensive documentation to facilitate learning the software and provide help at all levels. As the core code is implemented in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible. The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.