Adaptive radiation is defined as the evolution of ecological and phenotypic diversity within a rapidly multiplying lineage. When it occurs, adaptive radiation typically follows the colonization of a new environment or the establishment of a ''key innovation,'' which opens new ecological niches and͞or new paths for evolution. Here, we take advantage of recent developments in speciation theory and modern computing power to build and explore a large-scale, stochastic, spatially explicit, individual-based model of adaptive radiation driven by adaptation to multidimensional ecological niches. We are able to model evolutionary dynamics of populations with hundreds of thousands of sexual diploid individuals over a time span of 100,000 generations assuming realistic mutation rates and allowing for genetic variation in a large number of both selected and neutral loci. Our results provide theoretical support and explanation for a number of empirical patterns including ''area effect,'' ''overshooting effect,'' and ''least action effect,'' as well as for the idea of a ''porous genome.'' Our findings suggest that the genetic architecture of traits involved in the most spectacular radiations might be rather simple. We show that a great majority of speciation events are concentrated early in the phylogeny. Our results emphasize the importance of ecological opportunity and genetic constraints in controlling the dynamics of adaptive radiation.ecological ͉ modeling ͉ speciation ͉ diversification ͉ parapatric

A recent study of a pair of sympatric species of cichlids in Lake Apoyo in Nicaragua is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here, we describe and study a stochastic, individual-based, explicit genetic model tailored for this cichlid system. Our results show that relatively rapid (< < < < 20 000 generations) colonization of a new ecological niche and (sympatric or parapatric) speciation via local adaptation and divergence in habitat and mating preferences are theoretically plausible if: (i) the number of loci underlying the traits controlling local adaptation, and habitat and mating preferences is small; (ii) the strength of selection for local adaptation is intermediate; (iii) the carrying capacity of the population is intermediate; and (iv) the effects of the loci influencing nonrandom mating are strong. We discuss patterns and timescales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.

A recent study of a pair of sympatric species of palms on the Lord Howe Island is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here we describe and study a stochastic, individual-based, explicit genetic model tailored for this palms system. Overall, our results show that relatively rapid (<50,000 generations) colonization of a new ecological niche, and sympatric or parapatric speciation via local adaptation and divergence in flowering periods are theoretically plausible if (i) the number of loci controlling the ecological and flowering period traits is small; (ii) the strength of selection for local adaptation is intermediate; and (iii) an acceleration of flowering by a direct environmental effect associated with the new ecological niche is present. We discuss patterns and time-scales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically in order to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.

The exact nature of the relationship among species range sizes, speciation, and extinction events is not well understood. The factors that promote larger ranges, such as broad niche widths and high dispersal abilities, could increase the likelihood of encountering new habitats but also prevent local adaptation due to high gene flow. Similarly, low dispersal abilities or narrower niche widths could cause populations to be isolated, but such populations may lack advantageous mutations due to low population sizes. Here we present a large-scale, spatially explicit, individual-based model addressing the relationships between species ranges, speciation, and extinction. We followed the evolutionary dynamics of hundreds of thousands of diploid individuals for 200,000 generations. Individuals adapted to multiple resources and formed ecological species in a multidimensional trait space. These species varied in niche widths, and we observed the coexistence of generalists and specialists on a few resources. Our model shows that species ranges correlate with dispersal abilities but do not change with the strength of fitness trade-offs; however, high dispersal abilities and low resource utilization costs, which favored broad niche widths, have a strong negative effect on speciation rates. An unexpected result of our model is the strong effect of underlying resource distributions on speciation: in highly fragmented landscapes, speciation rates are reduced.

The ''Machiavellian intelligence'' hypothesis (or the ''social brain'' hypothesis) posits that large brains and distinctive cognitive abilities of humans have evolved via intense social competition in which social competitors developed increasingly sophisticated ''Machiavellian'' strategies as a means to achieve higher social and reproductive success. Here we build a mathematical model aiming to explore this hypothesis. In the model, genes control brains which invent and learn strategies (memes) which are used by males to gain advantage in competition for mates. We show that the dynamics of intelligence has three distinct phases. During the dormant phase only newly invented memes are present in the population. During the cognitive explosion phase the population's meme count and the learning ability, cerebral capacity (controlling the number of different memes that the brain can learn and use), and Machiavellian fitness of individuals increase in a runaway fashion. During the saturation phase natural selection resulting from the costs of having large brains checks further increases in cognitive abilities. Overall, our results suggest that the mechanisms underlying the ''Machiavellian intelligence'' hypothesis can indeed result in the evolution of significant cognitive abilities on the time scale of 10 to 20 thousand generations. We show that cerebral capacity evolves faster and to a larger degree than learning ability. Our model suggests that there may be a tendency toward a reduction in cognitive abilities (driven by the costs of having a large brain) as the reproductive advantage of having a large brain decreases and the exposure to memes increases in modern societies.

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We present a scalable cross-platform hybrid MPI / OpenMP / OpenACC implementation of the Divide-Expand-Consolidate (DEC) formalism with portable performance on heterogeneous HPC architectures. The Divide-Expand-Consolidate formalism is designed to reduce the steep computational scaling of conventional many-body methods employed in electronic structure theory to linear scaling, while providing a simple mechanism for controlling the error introduced by this approximation. Our massively parallel implementation of this general scheme has three levels of parallelism, being a hybrid of the loosely coupled task-based parallelization approach and the conventional MPI+X programming model, where X is either OpenMP or OpenACC. We demonstrate strong and weak scalability of this implementation on heterogeneous HPC systems, namely on the GPU-based Cray XK7 Titan supercomputer at the Oak Ridge National Laboratory. Using the "resolution of the identity second-order Møller-Plesset perturbation theory" (RI-MP2) as the physical model for simulating correlated electron motion, the linear-scaling DEC implementation is applied to 1-aza-adamantane-trione (AAT) supramolecular wires containing up to 40 monomers (2440 atoms, 6800 correlated electrons, 24440 basis functions and 91280 auxiliary functions). This represents the largest molecular system treated at the MP2 level of theory, demonstrating an efficient removal of the scaling wall pertinent to conventional quantum many-body methods.

The porting of a key kernel in the tracer advection routines of the Community Atmosphere Model-Spectral Element (CAM-SE) to use Graphics Processing Units (GPUs) using Ope-nACC is considered in comparison to an existing CUDA FORTRAN port. The development of the OpenACC kernel for GPUs was substantially simpler than that of the CUDA port. Also, OpenACC performance was about 1.5x slower than the optimized CUDA version. Particular focus is given to compiler maturity regarding OpenACC implementation for modern fortran, and it is found that the Cray implementation is currently more mature than the PGI implementation. Still, for the case that ran successfully on PGI, the PGI OpenACC runtime was slightly faster than Cray. The results show encouraging performance for OpenACC implementation compared to CUDA while also exposing some issues that may be necessary before the implementations are suitable for porting all of CAM-SE. Most notable are that GPU shared memory should be used by future OpenACC implementations and that derived type support should be expanded.