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S. Kathleen Lyons

Bio: S. Kathleen Lyons is an academic researcher from University of Nebraska–Lincoln. The author has contributed to research in topics: Megafauna & Macroecology. The author has an hindex of 32, co-authored 68 publications receiving 4141 citations. Previous affiliations of S. Kathleen Lyons include National Museum of Natural History & Old Dominion University.


Papers
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Journal ArticleDOI
TL;DR: This work examines several well-known ecogeographical rules, especially those pertaining to body size in contemporary, historical and fossil taxa, and reviews the evidence showing that rules of geographical variation in response to variation in the local environment can also apply to morphological changes through time in Response to climate change.
Abstract: Patterns of ecotypic variation constitute some of the few ‘rules’ known to modern biology. Here, we examine several well-known ecogeographical rules, especially those pertaining to body size in contemporary, historical and fossil taxa. We review the evidence showing that rules of geographical variation in response to variation in the local environment can also apply to morphological changes through time in response to climate change. These rules hold at various time scales, ranging from contemporary to geological time scales. Patterns of body size variation in response to climate change at the individual species level may also be detected at the community level. The patterns underlying ecotypic variation are complex and highly context-dependent, reducing the ‘predictive-power’ of ecogeographical rules. This is especially true when considering the increasing impact of human activities on the environment. Nonetheless, ecogeographical rules may help interpret the likely influences of anthropogenic climate change on ecosystems. Global climate change has already influenced the body size of several contemporary species, and will likely have an even greater impact on animal communities in the future. For this reason, we highlight and emphasise the importance of museum specimens and the continued need for documenting the earth's biological diversity.

486 citations

Journal ArticleDOI
01 Dec 2003-Ecology
TL;DR: The purpose of this data set was to compile body mass information for all mammals on Earth so that it could investigate the patterns of body mass seen across geographic and taxonomic space and evolutionary time.
Abstract: The purpose of this data set was to compile body mass information for all mammals on Earth so that we could investigate the patterns of body mass seen across geographic and taxonomic space and evolutionary time. We were interested in the heritability of body size across taxonomic groups (How conserved is body mass within a genus, family, and order?), in the overall pattern of body mass across continents (Do the moments and other descriptive statistics remain the same across geographic space?), and over evolutionary time (How quickly did body mass patterns iterate on the patterns seen today? Were the Pleistocene extinctions size specific on each continent, and did these events coincide with the arrival of man?). These data are also part of a larger project that seeks to integrate body mass patterns across very diverse taxa (NCEAS Working Group on Body Size in Ecology and Paleoecology: linking pattern and process across space, time, and taxonomic scales). We began with the updated version of D. E. Wilson an...

452 citations

Journal ArticleDOI
TL;DR: Computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and three of the 'control knobs' for a general simulation model that specify simple rules for dispersal, evolutionary origins and environmental gradients are described.
Abstract: Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve-fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non-linearity. However, curve-fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve-fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the 'control knobs' for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.

294 citations

Journal ArticleDOI
26 Nov 2010-Science
TL;DR: Analysis suggests that although the primary driver for the evolution of giant mammals was diversification to fill ecological niches, environmental temperature and land area may have ultimately constrained the maximum size achieved.
Abstract: The extinction of dinosaurs at the Cretaceous/Paleogene (K/Pg) boundary was the seminal event that opened the door for the subsequent diversification of terrestrial mammals. Our compilation of maximum body size at the ordinal level by sub-epoch shows a near-exponential increase after the K/Pg. On each continent, the maximum size of mammals leveled off after 40 million years ago and thereafter remained approximately constant. There was remarkable congruence in the rate, trajectory, and upper limit across continents, orders, and trophic guilds, despite differences in geological and climatic history, turnover of lineages, and ecological variation. Our analysis suggests that although the primary driver for the evolution of giant mammals was diversification to fill ecological niches, environmental temperature and land area may have ultimately constrained the maximum size achieved.

240 citations

Journal ArticleDOI
TL;DR: Temperature-corrected rates of individual-level biomass production show the same body-size dependence across a wide range of aerobic eukaryotes, from unicellular organisms to mammals and vascular plants, as well as other important factors constraining ecological structure and dynamics.
Abstract: Ecosystem properties result in part from the characteristics of individual organisms. How these individual traits scale to impact ecosystem-level processes is currently unclear. Because metabolism is a fundamental process underlying many individual- and population-level variables, it provides a mechanism for linking individual characteristics with large-scale processes. Here we use metabolism and ecosystem thermodynamics to scale from physiology to individual biomass production and population-level energy use. Temperature-corrected rates of individual-level biomass production show the same body-size dependence across a wide range of aerobic eukaryotes, from unicellular organisms to mammals and vascular plants. Population-level energy use for both mammals and plants are strongly influenced by both metabolism and thermodynamic constraints on energy exchange between trophic levels. Our results show that because metabolism is a fundamental trait of organisms, it not only provides a link between individual- and ecosystem-level processes, but can also highlight other important factors constraining ecological structure and dynamics.

235 citations


Cited by
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28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

14,171 citations

Journal Article
TL;DR: For the next few weeks the course is going to be exploring a field that’s actually older than classical population genetics, although the approach it’ll be taking to it involves the use of population genetic machinery.
Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

9,847 citations

Journal ArticleDOI
TL;DR: This work proposes a principled statistical framework for discerning and quantifying power-law behavior in empirical data by combining maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios.
Abstract: Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena. Unfortunately, the detection and characterization of power laws is complicated by the large fluctuations that occur in the tail of the distribution—the part of the distribution representing large but rare events—and by the difficulty of identifying the range over which power-law behavior holds. Commonly used methods for analyzing power-law data, such as least-squares fitting, can produce substantially inaccurate estimates of parameters for power-law distributions, and even in cases where such methods return accurate answers they are still unsatisfactory because they give no indication of whether the data obey a power law at all. Here we present a principled statistical framework for discerning and quantifying power-law behavior in empirical data. Our approach combines maximum-likelihood fitting methods with goodness-of-fit tests based on the Kolmogorov-Smirnov (KS) statistic and likelihood ratios. We evaluate the effectiveness of the approach with tests on synthetic data and give critical comparisons to previous approaches. We also apply the proposed methods to twenty-four real-world data sets from a range of different disciplines, each of which has been conjectured to follow a power-law distribution. In some cases we find these conjectures to be consistent with the data, while in others the power law is ruled out.

8,753 citations

Journal ArticleDOI
01 Jul 2004-Ecology
TL;DR: This work has developed a quantitative theory for how metabolic rate varies with body size and temperature, and predicts how metabolic theory predicts how this rate controls ecological processes at all levels of organization from individuals to the biosphere.
Abstract: Metabolism provides a basis for using first principles of physics, chemistry, and biology to link the biology of individual organisms to the ecology of populations, communities, and ecosystems. Metabolic rate, the rate at which organisms take up, transform, and expend energy and materials, is the most fundamental biological rate. We have developed a quantitative theory for how metabolic rate varies with body size and temperature. Metabolic theory predicts how metabolic rate, by setting the rates of resource uptake from the environment and resource allocation to survival, growth, and reproduction, controls ecological processes at all levels of organization from individuals to the biosphere. Examples include: (1) life history attributes, including devel- opment rate, mortality rate, age at maturity, life span, and population growth rate; (2) population interactions, including carrying capacity, rates of competition and predation, and patterns of species diversity; and (3) ecosystem processes, including rates of biomass production and respiration and patterns of trophic dynamics. Data compiled from the ecological literature strongly support the theoretical predictions. Even- tually, metabolic theory may provide a conceptual foundation for much of ecology, just as genetic theory provides a foundation for much of evolutionary biology.

6,017 citations