Parsimonious spatial representation of tropical soils within dynamic rainfall-runoff models

Introduction Models are used increasingly to simulate hydrological processes within tropical regions. There is now a wealth of publications addressing evaporation modelling (particularly wet-canopy evaporation) of local areas of tropical forest in, for example, Niger (Gash et al., 1997), Guyana (Jetten, 1996), Puerto Rico (Schellekens et al., 1999), Columbia (Marin et al., 2000) and Indonesia (Asdak et al., 1999; van Dijk and Bruijnzeel, 2001). Elsewhere in this volume, Roberts et al. provide an overview of evaporation processes and modelling. Other modelling studies have addressed the impact of such tropical evaporation on regional climates and global circulation (e.g. Polcher and Laval, 1994; Zeng, 1999; Zeng and Neelin, 1999; Zheng et al., 2001). New studies using time-series models are highlighting the effects of cycles in the rainfall, such as the El Nino Southern Oscillation (ENSO) on tropical evaporation, riverflow and water quality (e.g. Zeng, 1999; Chappell et al., 2001; Krishnaswamy et al., 2001; Whitaker et al., 2001; Chappell, Tych et al., this volume). Similarly, models that simulate the generation of riverflow from the rainfall received by a tropical catchment are also beginning to be applied more frequently. These models include: Metric-conceptual models of waterflow, based upon transfer functions. For example, application of the DBM modelling approach to a nested catchment system in Malaysian Borneo (Chappell et al., 1999a) and the application of IHACRES to a large Thai basin (Scoccimarro et al., 1999). Conceptual models of waterflow based upon stores and pre-determined empirical relationships. For example, application of the Nash model to Kenyan catchments (Onyando and Sharma, 1995), the Modhydrolog model to a tropical catchment (Chiew et al., 1996), the Reservoir-Water-Balance-Simulation model to Namibian catchments (Hughes and Metzler, 1998), and the HBV-96 model (discussed in Barnes and Bonell, this volume) to catchments in Zimbabwe, Tanzania and Bolivia (Liden and Harlin, 2000).[&]. © UNESCO 2005 and Cambridge University Press, 2009

Review Section : Nature/Nurture Revisited I

Biologically oriented approaches to the study of human conflict have thus far been limited largely to the study of aggression. A sample of the literature on this topic is reviewed, drawing upon four major approaches: comparative psychology, ethology (including some popularized accounts), evolutionary-based theories, and several areas of human physiology. More sophisticated relationships between so-called "innate" and "acquired" determinants of behavior are discussed, along with the proper relevance of animal behavior studies for human behavior. Unless contained in a comprehensive theory which includes social and psychological variables, biolog ically oriented theories (although often valid within their domain) offer at best severely limited and at worst highly misleading explanations of complex social conflicts. The review concludes with a list of several positive contributions of these biological approaches and suggests that social scientists must become more knowledgeable about them.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68270/2/10.1177_002200277401800206.pd

The genetic architecture of the human cerebral cortex

INTRODUCTION The cerebral cortex underlies our complex cognitive capabilities. Variations in human cortical surface area and thickness are associated with neurological, psychological, and behavioral traits and can be measured in vivo by magnetic resonance imaging (MRI). Studies in model organisms have identified genes that influence cortical structure, but little is known about common genetic variants that affect human cortical structure. RATIONALE To identify genetic variants associated with human cortical structure at both global and regional levels, we conducted a genome-wide association meta-analysis of brain MRI data from 51,665 individuals across 60 cohorts. We analyzed the surface area and average thickness of the whole cortex and 34 cortical regions with known functional specializations. RESULTS We identified 306 nominally genome-wide significant loci (P < 5 × 10−8) associated with cortical structure in a discovery sample of 33,992 participants of European ancestry. Of the 299 loci for which replication data were available, 241 loci influencing surface area and 14 influencing thickness remained significant after replication, with 199 loci passing multiple testing correction (P < 8.3 × 10−10; 187 influencing surface area and 12 influencing thickness). Common genetic variants explained 34% (SE = 3%) of the variation in total surface area and 26% (SE = 2%) in average thickness; surface area and thickness showed a negative genetic correlation (rG = −0.32, SE = 0.05, P = 6.5 × 10−12), which suggests that genetic influences have opposing effects on surface area and thickness. Bioinformatic analyses showed that total surface area is influenced by genetic variants that alter gene regulatory activity in neural progenitor cells during fetal development. By contrast, average thickness is influenced by active regulatory elements in adult brain samples, which may reflect processes that occur after mid-fetal development, such as myelination, branching, or pruning. When considered together, these results support the radial unit hypothesis that different developmental mechanisms promote surface area expansion and increases in thickness. To identify specific genetic influences on individual cortical regions, we controlled for global measures (total surface area or average thickness) in the regional analyses. After multiple testing correction, we identified 175 loci that influence regional surface area and 10 that influence regional thickness. Loci that affect regional surface area cluster near genes involved in the Wnt signaling pathway, which is known to influence areal identity. We observed significant positive genetic correlations and evidence of bidirectional causation of total surface area with both general cognitive functioning and educational attainment. We found additional positive genetic correlations between total surface area and Parkinson’s disease but did not find evidence of causation. Negative genetic correlations were evident between total surface area and insomnia, attention deficit hyperactivity disorder, depressive symptoms, major depressive disorder, and neuroticism. CONCLUSION This large-scale collaborative work enhances our understanding of the genetic architecture of the human cerebral cortex and its regional patterning. The highly polygenic architecture of the cortex suggests that distinct genes are involved in the development of specific cortical areas. Moreover, we find evidence that brain structure is a key phenotype along the causal pathway that leads from genetic variation to differences in general cognitive function