Peter B. Reich

Bio: Peter B. Reich is an academic researcher from University of Minnesota. The author has contributed to research in topics: Ecosystem & Biodiversity. The author has an hindex of 159, co-authored 790 publications receiving 110377 citations. Previous affiliations of Peter B. Reich include Arizona State University & University of Michigan.

The worldwide leaf economics spectrum

Abstract: Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.

A handbook of protocols for standardised and easy measurement of plant functional traits worldwide

Abstract: There is growing recognition that classifying terrestrial plant species on the basis of their function (into 'functional types') rather than their higher taxonomic identity, is a promising way forward for tackling important ecological questions at the scale of ecosystems, landscapes or biomes. These questions include those on vegetation responses to and vegetation effects on, environmental changes (e.g. changes in climate, atmospheric chemistry, land use or other disturbances). There is also growing consensus about a shortlist of plant traits that should underlie such functional plant classifications, because they have strong predictive power of important ecosystem responses to environmental change and/or they themselves have strong impacts on ecosystem processes. The most favoured traits are those that are also relatively easy and inexpensive to measure for large numbers of plant species. Large international research efforts, promoted by the IGBP–GCTE Programme, are underway to screen predominant plant species in various ecosystems and biomes worldwide for such traits. This paper provides an international methodological protocol aimed at standardising this research effort, based on consensus among a broad group of scientists in this field. It features a practical handbook with step-by-step recipes, with relatively brief information about the ecological context, for 28 functional traits recognised as critical for tackling large-scale ecological questions.

The Influence of Functional Diversity and Composition on Ecosystem Processes

Abstract: Humans are modifying both the identities and numbers of species in ecosystems, but the impacts of such changes on ecosystem processes are controversial. Plant species diversity, functional diversity, and functional composition were experimentally varied in grassland plots. Each factor by itself had significant effects on many ecosystem processes, but functional composition and functional diversity were the principal factors explaining plant productivity, plant percent nitrogen, plant total nitrogen, and light penetration. Thus, habitat modifications and management practices that change functional diversity and functional composition are likely to have large impacts on ecosystem processes.

New handbook for standardised measurement of plant functional traits worldwide

Abstract: Plant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystem-level processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties. We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.

The world‐wide ‘fast–slow’ plant economics spectrum: a traits manifesto

Abstract: Summary 1. The leaf economics spectrum (LES) provides a useful framework for examining species strategies as shaped by their evolutionary history. However, that spectrum, as originally described, involved only two key resources (carbon and nutrients) and one of three economically important plant organs. Herein, I evaluate whether the economics spectrum idea can be broadly extended to water – the third key resource –stems, roots and entire plants and to individual, community and ecosystem scales. My overarching hypothesis is that strong selection along trait trade-off axes, in tandem with biophysical constraints, results in convergence for any taxon on a uniformly fast, medium or slow strategy (i.e. rates of resource acquisition and processing) for all organs and all resources. 2. Evidence for economic trait spectra exists for stems and roots as well as leaves, and for traits related to water as well as carbon and nutrients. These apply generally within and across scales (within and across communities, climate zones, biomes and lineages). 3. There are linkages across organs and coupling among resources, resulting in an integrated whole-plant economics spectrum. Species capable of moving water rapidly have low tissue density, short tissue life span and high rates of resource acquisition and flux at organ and individual scales. The reverse is true for species with the slow strategy. Different traits may be important in different conditions, but as being fast in one respect generally requires being fast in others, being fast or slow is a general feature of species. 4. Economic traits influence performance and fitness consistent with trait-based theory about underlying adaptive mechanisms. Traits help explain differences in growth and survival across resource gradients and thus help explain the distribution of species and the assembly of communities across light, water and nutrient gradients. Traits scale up – fast traits are associated with faster rates of ecosystem processes such as decomposition or primary productivity, and slow traits with slow process rates. 5. Synthesis. Traits matter. A single ‘fast–slow’ plant economics spectrum that integrates across leaves, stems and roots is a key feature of the plant universe and helps to explain individual ecological strategies, community assembly processes and the functioning of ecosystems.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

The Theory of Island Biogeography

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

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Human biochemical genetics

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