Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Scalable detection of statistically significant communities and hierarchies, using message passing for modularity

Abstract: Modularity is a popular measure of community structure. However, maximizing the modularity can lead to many competing partitions, with almost the same modularity, that are poorly correlated with each other. It can also produce illusory ‘‘communities’’ in random graphs where none exist. We address this problem by using the modularity as a Hamiltonian at finite temperature and using an efficient belief propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it. We show analytically and numerically that the proposed algorithm works all of the way down to the detectability transition in networks generated by the stochastic block model. It also performs well on real-world networks, revealing large communities in some networks where previous work has claimed no communities exist. Finally we show that by applying our algorithm recursively, subdividing communities until no statistically significant subcommunities can be found, we can detect hierarchical structure in real-world networks more efficiently than previous methods.

Energy Uptake and Allocation During Ontogeny

Abstract: All organisms face the problem of how to fuel ontogenetic growth. We present a model, empirically grounded in data from birds and mammals, that correctly predicts how growing animals allocate food energy between synthesis of new biomass and maintenance of existing biomass. Previous energy budget models have typically had their bases in rates of either food consumption or metabolic energy expenditure. Our model provides a framework that reconciles these two approaches and highlights the fundamental principles that determine rates of food assimilation and rates of energy allocation to maintenance, biosynthesis, activity, and storage. The model predicts that growth and assimilation rates for all animals should cluster closely around two universal curves. Data for mammals and birds of diverse body sizes and taxa support these predictions.

Strong Reciprocity and the Roots of Human Morality

Abstract: Human morality is a key evolutionary adaptation on which human social behavior has been based since the Pleistocene era. Ethical behavior is constitutive of human nature, we argue, and human morality is as important an adaptation as human cognition and speech. Ethical behavior, we assert, need not be a means toward personal gain. Because of our nature as moral beings, humans take pleasure in acting ethically and are pained when acting unethically. From an evolutionary viewpoint, we argue that ethical behavior was fitness-enhancing in the years marking the emergence of Homo sapiens because human groups with many altruists fared better than groups of selfish individuals, and the fitness losses sustained by altruists were more than compensated by the superior performance of the groups in which they congregated.

Dynamics of North American breeding bird populations

Abstract: Population biologists have long been interested in the variability of natural populations1,2,3,4,5,6. One approach to dealing with ecological complexity is to reduce the system to one or a few species, for which meaningful equations can be solved. Here we explore an alternative approach7,8 by studying the statistical properties of a data set containing over 600 species, namely the North American breeding bird survey9. The survey has recorded annual species abundances over a 31-year period along more than 3,000 observation routes10. We now analyse the dynamics of population variability using this data set, and find scaling features in common with inanimate systems composed of strongly interacting subunits11. Specifically, we find that the distribution of changes in population abundance over a one-year interval is remarkably symmetrical, with long tails extending over six orders of magnitude. The variance of the population over a time series increases as a power-law with increasing time lag, indicating long-range correlation in population size fluctuations12. We also find that the distribution of species lifetimes (the time between colonization and local extinction) within local patches is a power-law with an exponential cutoff imposed by the finite length of the time series. Our results provide a quantitative basis for modelling the dynamics of large species assemblages.

New approaches for delineating n-dimensional hypervolumes

Abstract: 1.Hutchinson's n-dimensional hypervolume concept underlies many applications in contemporary ecology and evolutionary biology. Estimating hypervolumes from sampled data has been an ongoing challenge due to conceptual and computational issues. 2.We present new algorithms for delineating the boundaries and probability density within n-dimensional hypervolumes. The methods produce smooth boundaries that can fit data either more loosely (Gaussian kernel density estimation) or more tightly (one-classification via support vector machine). Further, the algorithms can accept abundance-weighted data, and the resulting hypervolumes can be given a probabilistic interpretation and projected into geographic space. 3.We demonstrate the properties of these methods on a large dataset that characterizes the functional traits and geographic distribution of thousands of plants. The methods are available in version ≥2.0.6 of the hypervolume R package. 4.These new algorithms provide: (i) a more robust approach for delineating the shape and density of n-dimensional hypervolumes; (ii) more efficient performance on large and high-dimensional datasets; and (iii) improved measures of functional diversity and environmental niche breadth. This article is protected by copyright. All rights reserved.