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.

The origin of nest complexity in social insects

Abstract: Guy Theraulaz is a CNRS (Centre Nationalde la Recherche Scientifique) fellow andis currently at the Ethology and AnimalPsychology Laboratory, Paul Sabatier Uni-versity in Toulouse, France. Eric Bonabeauis the Interval Research Fellow at the SantaFe Institute, Santa Fe, New Mexico. Jean-Louis Deneubourg is a Lecturer at theUniversite Libre de Bruxelles and a fellowof the Belgian FNRS. He is currently run-ning the Theoretical Behavioral Ecologyunit at the Non-Linear Phenomena andComplex Systems Study Center.

The megabiota are disproportionately important for biosphere functioning.

Abstract: A prominent signal of the Anthropocene is the extinction and population reduction of the megabiota-the largest animals and plants on the planet. However, we lack a predictive framework for the sensitivity of megabiota during times of rapid global change and how they impact the functioning of ecosystems and the biosphere. Here, we extend metabolic scaling theory and use global simulation models to demonstrate that (i) megabiota are more prone to extinction due to human land use, hunting, and climate change; (ii) loss of megabiota has a negative impact on ecosystem metabolism and functioning; and (iii) their reduction has and will continue to significantly decrease biosphere functioning. Global simulations show that continued loss of large animals alone could lead to a 44%, 18% and 92% reduction in terrestrial heterotrophic biomass, metabolism, and fertility respectively. Our findings suggest that policies that emphasize the promotion of large trees and animals will have disproportionate impact on biodiversity, ecosystem processes, and climate mitigation.

Nonparametric sparsification of complex multiscale networks.

Abstract: Many real-world networks tend to be very dense. Particular examples of interest arise in the construction of networks that represent pairwise similarities between objects. In these cases, the networks under consideration are weighted, generally with positive weights between any two nodes. Visualization and analysis of such networks, especially when the number of nodes is large, can pose significant challenges which are often met by reducing the edge set. Any effective “sparsification” must retain and reflect the important structure in the network. A common method is to simply apply a hard threshold, keeping only those edges whose weight exceeds some predetermined value. A more principled approach is to extract the multiscale “backbone” of a network by retaining statistically significant edges through hypothesis testing on a specific null model, or by appropriately transforming the original weight matrix before applying some sort of threshold. Unfortunately, approaches such as these can fail to capture multiscale structure in which there can be small but locally statistically significant similarity between nodes. In this paper, we introduce a new method for backbone extraction that does not rely on any particular null model, but instead uses the empirical distribution of similarity weight to determine and then retain statistically significant edges. We show that our method adapts to the heterogeneity of local edge weight distributions in several paradigmatic real world networks, and in doing so retains their multiscale structure with relatively insignificant additional computational costs. We anticipate that this simple approach will be of great use in the analysis of massive, highly connected weighted networks.

Evolutionary rescue in populations of Pseudomonas fluorescens across an antibiotic gradient.

Abstract: Environmental change represents a major threat to species persistence. When change is rapid, a population's only means of persisting may be to evolve resistance. Understanding such ‘evolutionary rescues’ is important for conservation in the face of global change, but also in the agricultural and medical sciences, where the objective is rather population control or eradication. Theory predicts that evolutionary rescue is fostered by large populations and genetic variation, but this has yet to be tested. We replicated hundreds of populations of the bacterium Pseudomonas fluorescens SBW25 submitted to a range of doses of the antibiotic streptomycin. Consistent with theory, population size, and initial genetic diversity influenced population persistence and the evolution of antibiotic resistance. Although all treated populations suffered initial declines, those experiencing the smallest decreases were most likely to be evolutionarily rescued. Our results contribute to our understanding of how evolution may or may not save populations and species from extinction.

GENERATING NONTRIVIAL LONG-RANGE CORRELATIONS AND 1/f SPECTRA BY REPLICATION AND MUTATION

Abstract: This paper aims at understanding the statistical features of nucleic acid sequences from the knowledge of the dynamical process that produces them. Two studies are carried out: first, mutual information function of the limiting sequences generated by simple sequence manipulation dynamics with replications and mutations are calculated numerically (sometimes analytically). It is shown that elongation and replication can easily produce long-range correlations. These long range correlations could be destroyed in various degrees by mutation in different sequence manipulation models. Second, mutual information functions for several human nucleic acids sequences are determined. It is observed that intron sequences (noncoding sequences) tend to have longer correlation lengths than exon sequences (protein-coding sequences).