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.

Trait variation and integration across scales: is the leaf economic spectrum present at local scales?

Abstract: Trait-based approaches have taken an increasingly dominant role in community ecology. Although trait-based strategy dimensions such as the leaf economic spectrum (LES) have been identified primarily at global-scales, trait variation at the community scale is often interpreted in this context. Here we argue from several lines of evidence that a research priority should be to determine whether global-scale trait relationships hold at more local scales. We review recent literature assessing trait variation at smaller scales, and then present a case study exploring the relationship between the correlation strength of leaf traits and their similarity in variation structure across ecological scales. We find that the correlation strength between pairs of leaf traits does not predict whether the traits respond similarly to different drivers of variation. Instead, correlation strength only sets an upper bound to the dissimilarity in trait variation structure. With moderate correlation strengths, LES traits largely retain the ability to respond independently to different drivers of phenotypic variation at different scales. Recent literature and our results suggest that LES relationships may not hold at local scales. Clarifying under what conditions and at which scales the LES is consistently expressed is necessary for us to make the most of the emerging trait toolbox.

Role of design complexity in technology improvement

Abstract: We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d - 1 other components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: the more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity depends on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.

Mappings and metaphors in auditory displays: An experimental assessment

Abstract: Auditory displays are becoming more and more common, but there are still no general guidelines for mapping data dimensions (e.g., temperature) onto display dimensions (e.g., pitch). This paper presents experimental research on different mappings and metaphors, in a generic process-control task environment, with reaction time and accuracy as dependent measures. It is hoped that this area of investigation will lead to the development of mapping guidelines applicable to auditory displays in a wide range of task domains.

The Evolution and Distribution of Species Body Size

Abstract: The distribution of species body size within taxonomic groups exhibits a heavy right tail extending over many orders of magnitude, where most species are much larger than the smallest species. We provide a simple model of cladogenetic diffusion over evolutionary time that omits explicit mechanisms for interspecific competition and other microevolutionary processes, yet fully explains the shape of this distribution. We estimate the model's parameters from fossil data and find that it robustly reproduces the distribution of 4002 mammal species from the late Quaternary. The observed fit suggests that the asymmetric distribution arises from a fundamental trade-off between the short-term selective advantages (Cope's rule) and long-term selective risks of increased species body size in the presence of a taxon-specific lower limit on body size.