Abundance Effects in Classification


Book Description

The general discussions of the roles of photometric and spectroscopic classification at Cordoba in 1971 (lAU Symposium No. 50), and of the calibration of classification indices at Geneva in 1972 (IAU Symposium No. 54), revealed clearly the steadily in creasing importance of abundance parameters. The multipliCity of these, however, raised so many new problems that it was logical that the 1975 meeting at Lausanne should be concerned with ways in which differences in abundance affect both spectral types and photometric indices. Commissions 29 and 36 joined with Commission 45 in sponsoring this Symposium. Since the date of the meeting came shortly after the formal retirement of Professor William W. Morgan from the University of Chicago, it was quickly agreed that this meeting should be dedicated to him in recognition of his unique contributions to spectral classification. In the opening paper of the Symposium Dr. Bengt Stromgren has summarized these. To his remarks we should add only that it was about 1940 that Morgan first distinguished the group of G- and K-type stars with weak CN bands and metallic lines - stars which have since been recognized as having the abundance of all metals relative to hydrogen much lower than in stars of the solar population. Spectra of two of these, HD 81192 (Boss 2527) and 8 Lep, were later shown as examples of the group in the Yerkes Atlas of 1943.




Literature 1976, Part 2


Book Description

Astronomy and Astrophysics Abstracts, which has appeared in semi-annual volumes since 1969, is de voted to the recording, summarizing and indexing of astronomical publications throughout the world. It is prepared under the auspices of the International Astronomical Union (according to a resolution adopted at the 14th General Assembly in 1970). Astronomy and Astrophysics Abstracts aims to present a comprehensive documentation of literature in all fields of astronomy and astrophysics. Every effort will be made to ensure that the average time interval between the date of receipt of the original literature and publication of the abstracts will not exceed eight months. This time interval is near to that achieved by monthly abstracting journals, com pared to which our system of accumulating abstracts for about six months offers the advantage of greater convenience for the user. Volume 18 contains literature published in 1976 and received before March 1, 1977; some older liter ature which was received late and which is not recorded in earlier volumes is also included.




Machine Learning for Ecology and Sustainable Natural Resource Management


Book Description

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.




Ecology


Book Description

This best-selling majors ecology book continues to present ecology as a series of problems for readers to critically analyze. No other text presents analytical, quantitative, and statistical ecological information in an equally accessible style. Reflecting the way ecologists actually practice, the book emphasizes the role of experiments in testing ecological ideas and discusses many contemporary and controversial problems related to distribution and abundance. Throughout the book, Krebs thoroughly explains the application of mathematical concepts in ecology while reinforcing these concepts with research references, examples, and interesting end-of-chapter review questions. Thoroughly updated with new examples and references, the book now features a new full-color design and is accompanied by an art CD-ROM for instructors. The field package also includes The Ecology Action Guide, a guide that encourages readers to be environmentally responsible citizens, and a subscription to The Ecology Place (www.ecologyplace.com), a web site and CD-ROM that enables users to become virtual field ecologists by performing experiments such as estimating the number of mice on an imaginary island or restoring prairie land in Iowa. For college instructors and students.




Relative Deprivation and Social Comparison


Book Description

First published in 1986. This volume presents papers from the fourth Ontario Symposium on Personality and Social Psychology, held at the University o f Western Ontario, October 15- 16, 1983. The contributors are active researchers in the areas of relative deprivation and social com parison, whose chapters document the continuing vitality of these topics. One of the purposes of this volume is to provide an accurate picture of our current knowledge about relative deprivation and social comparison processes.




Measuring Biological Diversity


Book Description

This accessible and timely book provides a comprehensive overview of how to measure biodiversity. The book highlights new developments, including innovative approaches to measuring taxonomic distinctness and estimating species richness, and evaluates these alongside traditional methods such as species abundance distributions, and diversity and evenness statistics. Helps the reader quantify and interpret patterns of ecological diversity, focusing on the measurement and estimation of species richness and abundance. Explores the concept of ecological diversity, bringing new perspectives to a field beset by contradictory views and advice. Discussion spans issues such as the meaning of community in the context of ecological diversity, scales of diversity and distribution of diversity among taxa Highlights advances in measurement paying particular attention to new techniques such as species richness estimation, application of measures of diversity to conservation and environmental management and addressing sampling issues Includes worked examples of key methods in helping people to understand the techniques and use available computer packages more effectively













INIS Atomindex


Book Description