scispace - formally typeset
Search or ask a question
Institution

Santa Fe Institute

NonprofitSanta Fe, New Mexico, United States
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.


Papers
More filters
Journal ArticleDOI
Anne D. Bjorkman1, Anne D. Bjorkman2, Isla H. Myers-Smith1, Sarah C. Elmendorf3, Sarah C. Elmendorf4, Sarah C. Elmendorf5, Signe Normand2, Nadja Rüger6, Pieter S. A. Beck, Anne Blach-Overgaard2, Daan Blok7, J. Hans C. Cornelissen8, Bruce C. Forbes9, Damien Georges1, Scott J. Goetz10, Kevin C. Guay11, Gregory H. R. Henry12, Janneke HilleRisLambers13, Robert D. Hollister14, Dirk Nikolaus Karger15, Jens Kattge16, Peter Manning, Janet S. Prevéy, Christian Rixen, Gabriela Schaepman-Strub17, Haydn J.D. Thomas1, Mark Vellend18, Martin Wilmking19, Sonja Wipf, Michele Carbognani20, Luise Hermanutz21, Esther Lévesque22, Ulf Molau23, Alessandro Petraglia20, Nadejda A. Soudzilovskaia24, Marko J. Spasojevic25, Marcello Tomaselli20, Tage Vowles23, Juha M. Alatalo26, Heather D. Alexander27, Alba Anadon-Rosell28, Alba Anadon-Rosell19, Sandra Angers-Blondin1, Mariska te Beest29, Mariska te Beest30, Logan T. Berner10, Robert G. Björk23, Agata Buchwal31, Agata Buchwal32, Allan Buras33, Katherine S. Christie34, Elisabeth J. Cooper35, Stefan Dullinger36, Bo Elberling37, Anu Eskelinen38, Anu Eskelinen39, Esther R. Frei15, Esther R. Frei12, Oriol Grau40, Paul Grogan41, Martin Hallinger, Karen A. Harper42, Monique M. P. D. Heijmans33, James I. Hudson, Karl Hülber36, Maitane Iturrate-Garcia17, Colleen M. Iversen43, Francesca Jaroszynska44, Jill F. Johnstone45, Rasmus Halfdan Jørgensen37, Elina Kaarlejärvi46, Elina Kaarlejärvi30, Rebecca A Klady12, Sara Kuleza45, Aino Kulonen, Laurent J. Lamarque22, Trevor C. Lantz47, Chelsea J. Little48, Chelsea J. Little17, James D. M. Speed49, Anders Michelsen37, Ann Milbau50, Jacob Nabe-Nielsen2, Sigrid Schøler Nielsen2, Josep M. Ninot28, Steven F. Oberbauer51, Johan Olofsson30, Vladimir G. Onipchenko52, Sabine B. Rumpf36, Philipp R. Semenchuk36, Philipp R. Semenchuk35, Rohan Shetti19, Laura Siegwart Collier21, Lorna E. Street1, Katharine N. Suding5, Ken D. Tape53, Andrew J. Trant21, Andrew J. Trant54, Urs A. Treier2, Jean-Pierre Tremblay55, Maxime Tremblay22, Susanna Venn56, Stef Weijers57, Tara Zamin41, Noémie Boulanger-Lapointe12, William A. Gould58, David S. Hik59, Annika Hofgaard, Ingibjörg S. Jónsdóttir60, Ingibjörg S. Jónsdóttir61, Janet C. Jorgenson62, Julia A. Klein63, Borgthor Magnusson, Craig E. Tweedie64, Philip A. Wookey65, Michael Bahn66, Benjamin Blonder67, Benjamin Blonder68, Peter M. van Bodegom24, Benjamin Bond-Lamberty69, Giandiego Campetella70, Bruno Enrico Leone Cerabolini71, F. Stuart Chapin53, William K. Cornwell72, Joseph M. Craine, Matteo Dainese, Franciska T. de Vries73, Sandra Díaz74, Brian J. Enquist75, Brian J. Enquist76, Walton A. Green77, Rubén Milla78, Ülo Niinemets79, Yusuke Onoda80, Jenny C. Ordoñez81, Wim A. Ozinga33, Wim A. Ozinga82, Josep Peñuelas40, Hendrik Poorter83, Hendrik Poorter84, Peter Poschlod85, Peter B. Reich86, Peter B. Reich87, Brody Sandel88, Brandon S. Schamp89, Serge N. Sheremetev90, Evan Weiher91 
University of Edinburgh1, Aarhus University2, National Ecological Observatory Network3, Institute of Arctic and Alpine Research4, University of Colorado Boulder5, Smithsonian Institution6, Lund University7, VU University Amsterdam8, University of Lapland9, Northern Arizona University10, Bigelow Laboratory For Ocean Sciences11, University of British Columbia12, University of Washington13, Grand Valley State University14, Swiss Federal Institute for Forest, Snow and Landscape Research15, Max Planck Society16, University of Zurich17, Université de Sherbrooke18, University of Greifswald19, University of Parma20, Memorial University of Newfoundland21, Université du Québec à Trois-Rivières22, University of Gothenburg23, Leiden University24, University of California, Riverside25, Qatar University26, Mississippi State University27, University of Barcelona28, Utrecht University29, Umeå University30, Adam Mickiewicz University in Poznań31, University of Alaska Anchorage32, Wageningen University and Research Centre33, Alaska Department of Fish and Game34, University of Tromsø35, University of Vienna36, University of Copenhagen37, Helmholtz Centre for Environmental Research - UFZ38, University of Oulu39, Spanish National Research Council40, Queen's University41, Saint Mary's University42, Oak Ridge National Laboratory43, University of Aberdeen44, University of Saskatchewan45, Vrije Universiteit Brussel46, University of Victoria47, Swiss Federal Institute of Aquatic Science and Technology48, Norwegian University of Science and Technology49, Research Institute for Nature and Forest50, Florida International University51, Moscow State University52, University of Alaska Fairbanks53, University of Waterloo54, Laval University55, Deakin University56, University of Bonn57, United States Forest Service58, Simon Fraser University59, University of Iceland60, University Centre in Svalbard61, United States Fish and Wildlife Service62, Colorado State University63, University of Texas at El Paso64, University of Stirling65, University of Innsbruck66, University of Oxford67, Rocky Mountain Biological Laboratory68, Pacific Northwest National Laboratory69, University of Camerino70, University of Insubria71, University of New South Wales72, University of Manchester73, National University of Cordoba74, Santa Fe Institute75, University of Arizona76, Harvard University77, King Juan Carlos University78, Estonian University of Life Sciences79, Kyoto University80, World Agroforestry Centre81, Radboud University Nijmegen82, Forschungszentrum Jülich83, Macquarie University84, University of Regensburg85, University of Minnesota86, University of Sydney87, Santa Clara University88, Algoma University89, Komarov Botanical Institute90, University of Wisconsin–Eau Claire91
04 Oct 2018-Nature
TL;DR: Biome-wide relationships between temperature, moisture and seven key plant functional traits across the tundra and over time show that community height increased with warming across all sites, whereas other traits lagged behind predicted rates of change.
Abstract: The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.

425 citations

Journal ArticleDOI
TL;DR: In this article, the authors examine the effect of agglomeration in the context of regional economics and show that the presence of increasing returns implies that one product or one technology, out of several possible must come to dominate a market.

424 citations

Journal ArticleDOI
TL;DR: The authors hypothesize that the losses were initiated by the rapid unwinding of one or more sizable quantitative equity market-neutral portfolios, possibly due to margin calls or a risk reduction, and that the main driver of the losses in August 2007 was the resale liquidation of similar portfolios that happened to be quantitatively constructed.
Abstract: During the week of August 6, 2007, a number of high-prole and highly successful quantitative long/short equity hedge funds experienced unprecedented losses. Based on empirical results from TASS hedge-fund data as well as the simulated performance of a specic long/short equity strategy, we hypothesize that the losses were initiated by the rapid unwinding of one or more sizable quantitative equity market-neutral portfolios. Given the speed and price impact with which this occurred, it was likely the result of a sudden liquidation by a multi-strategy fund or proprietary-trading desk, possibly due to margin calls or a risk reduction. These initial losses then put pressure on a broader set of long/short and long-only equity portfolios, causing further losses on August 9th by triggering stop-loss and de-leveraging policies. A signican t rebound of these strategies occurred on August 10th, which is also consistent with the sudden liquidation hypothesis. This hypothesis suggests that the quantitative nature of the losing strategies was incidental, and the main driver of the losses in August 2007 was the resale liquidation of similar portfolios that happened to be quantitatively constructed. The fact that the source of dislocation in long/short equity portfolios seems to lie elsewhere|apparently in a completely unrelated set of markets and instruments|suggests that systemic risk in the hedge-fund industry may have increased in recent years.

424 citations

Journal ArticleDOI
TL;DR: This work uses theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from the method than with gene abundances.
Abstract: The abundance of different SSU rRNA ("16S") gene sequences in environmental samples is widely used in studies of microbial ecology as a measure of microbial community structure and diversity. However, the genomic copy number of the 16S gene varies greatly - from one in many species to up to 15 in some bacteria and to hundreds in some microbial eukaryotes. As a result of this variation the relative abundance of 16S genes in environmental samples can be attributed both to variation in the relative abundance of different organisms, and to variation in genomic 16S copy number among those organisms. Despite this fact, many studies assume that the abundance of 16S gene sequences is a surrogate measure of the relative abundance of the organisms containing those sequences. Here we present a method that uses data on sequences and genomic copy number of 16S genes along with phylogenetic placement and ancestral state estimation to estimate organismal abundances from environmental DNA sequence data. We use theory and simulations to demonstrate that 16S genomic copy number can be accurately estimated from the short reads typically obtained from high-throughput environmental sequencing of the 16S gene, and that organismal abundances in microbial communities are more strongly correlated with estimated abundances obtained from our method than with gene abundances. We re-analyze several published empirical data sets and demonstrate that the use of gene abundance versus estimated organismal abundance can lead to different inferences about community diversity and structure and the identity of the dominant taxa in microbial communities. Our approach will allow microbial ecologists to make more accurate inferences about microbial diversity and abundance based on 16S sequence data.

422 citations

Journal ArticleDOI
TL;DR: This target article sketches the evidence from five domains that bear on the explanatory adequacy of cultural group selection and competing hypotheses to explain human cooperation and presents evidence, including quantitative evidence, that the answer to all of the questions is “yes” and argues that it is not clear that any extant alternative tocultural group selection can be a complete explanation.
Abstract: Human cooperation is highly unusual. We live in large groups composed mostly of non-relatives. Evolutionists have proposed a number of explanations for this pattern, including cultural group selection and extensions of more general processes such as reciprocity, kin selection, and multi-level selection acting on genes. Evolutionary processes are consilient; they affect several different empirical domains, such as patterns of behavior and the proximal drivers of that behavior. In this target article, we sketch the evidence from five domains that bear on the explanatory adequacy of cultural group selection and competing hypotheses to explain human cooperation. Does cultural transmission constitute an inheritance system that can evolve in a Darwinian fashion? Are the norms that underpin institutions among the cultural traits so transmitted? Do we observe sufficient variation at the level of groups of considerable size for group selection to be a plausible process? Do human groups compete, and do success and failure in competition depend upon cultural variation? Do we observe adaptations for cooperation in humans that most plausibly arose by cultural group selection? If the answer to one of these questions is "no," then we must look to other hypotheses. We present evidence, including quantitative evidence, that the answer to all of the questions is "yes" and argue that we must take the cultural group selection hypothesis seriously. If culturally transmitted systems of rules (institutions) that limit individual deviance organize cooperation in human societies, then it is not clear that any extant alternative to cultural group selection can be a complete explanation.

422 citations


Authors

Showing all 606 results

NameH-indexPapersCitations
James Hone127637108193
James H. Brown12542372040
Alan S. Perelson11863266767
Mark Newman117348168598
Bette T. Korber11739249526
Marten Scheffer11135073789
Peter F. Stadler10390156813
Sanjay Jain10388146880
Henrik Jeldtoft Jensen102128648138
Dirk Helbing10164256810
Oliver G. Pybus10044745313
Andrew P. Dobson9832244211
Carel P. van Schaik9432926908
Seth Lloyd9249050159
Andrew W. Lo8537851440
Network Information
Related Institutions (5)
Massachusetts Institute of Technology
268K papers, 18.2M citations

90% related

University of Oxford
258.1K papers, 12.9M citations

90% related

Princeton University
146.7K papers, 9.1M citations

89% related

Max Planck Society
406.2K papers, 19.5M citations

89% related

University of California, Berkeley
265.6K papers, 16.8M citations

89% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202341
202241
2021297
2020309
2019263
2018231