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 Theory of Island Biogeography

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.).

Machine learning

https://www.demogr.mpg.de/publications/files/3444_1264435180_1_Christensen%20et%20al%20Lancet%20374%209696%202009.pdf

Ageing populations: the challenges ahead

Is human life expectancy approaching its limit? Many--including individuals planning their retirement and officials responsible for health and social policy--believe it is, but the evidence presented in the
 Policy Forum
 suggests otherwise. For 160 years, best-performance life expectancy has steadily increased by a quarter of a year per year, an extraordinary constancy of human achievement. Mortality experts have repeatedly asserted that life expectancy is close to an ultimate ceiling; these experts have repeatedly been proven wrong. The apparent leveling off of life expectancy in various countries is an artifact of laggards catching up and leaders falling behind.

/pdf/broken-limits-to-life-expectancy-3fna6yf8to.pdf

Broken Limits to Life Expectancy

The Visual Display of Quantitative Information

Old-age survival has increased substantially since 1950 Death rates decelerate with age for insects, worms, and yeast, as well as humans This evidence of extended postreproductive survival is puzzling Three biodemographic insights—concerning the correlation of death rates across age, individual differences in survival chances, and induced alterations in age patterns of fertility and mortality—offer clues and suggest research on the failure of complicated systems, on new demographic equations for evolutionary theory, and on fertility-longevity interactions Nongenetic changes account for increases in human life-spans to date Explication of these causes and the genetic license for extended survival, as well as discovery of genes and other survival attributes affecting longevity, will lead to even longer lives

https://science.sciencemag.org/content/sci/280/5365/855.full.pdf

Biodemographic Trajectories of Longevity

Invasions of nonindigenous species threaten native biodiversity, ecosystem functioning, animal and plant health, and human economies. The best solution is to prevent the introduction of exotic organisms but, once introduced, eradication might be feasible. The potential ecological and social ramifications of eradication projects make them controversial; however, these programs provide unique opportunities for experimental ecological studies. Deciding whether to attempt eradication is not simple and alternative approaches might be preferable in some situations.

https://www.cell.com/trends/ecology-evolution/pdf/S0169-5347(00)01914-5.pdf

Eradication revisited: dealing with exotic species.

1. Introduction 1.1 Formalization 1.2 Elementary Characteristics of Populations 2. Life Tables 2.1 General Concepts 2.2 The Single Decrement Life Tables 2.3 The Abridged Life Table 2.4 The Multiple Decrement Life Table 2.5 Selected Properties of Model Life Tables 3. Reproduction 3.1 General Background 3.2 Per Capita Reproductive Rates 3.3 Reproductive Heterogeneity 3.4 Generalizations 4. Population I. Basic Concepts and Models 4.1 Background 4.2 The Stable Population Model 4.3 Population Projection 4.4 Fundamental Properties of Populations 5. Population II. Extensions of Stable Theory 5.1 Two-Sex Models 5.2 Stochastic Demography 5.3 Multiregional Demography 5.4 Demographic Theory of Social Insects: The Honeybee 5.5 The Unity of Demographic Population Models 6. Demographic Applications 6.1 Estimation 6.2 Curve Fitting 6.3 Mass Rearing: Basic Harvesting Concepts 6.4 Parasitoid Mass Rearing 6.5 Pros and Cons of Demographic Modeling

Applied Demography for Biologists: with Special Emphasis on Insects

It is generally assumed for most species that mortality rates increase monotonically at advanced ages. Mortality rates were found to level off and decrease at older ages in a population of 1.2 million medflies maintained in cages of 7,200 and in a group of approximately 48,000 adults maintained in solitary confinement. Thus, life expectancy in older individuals increased rather than decreased with age. These results cast doubt on several central concepts in gerontology and the biology of aging: (i) that senescence can be characterized by an increase in age-specific mortality, (ii) that the basic pattern of mortality in nearly all species follows the same unitary pattern at older ages, and (iii) that species have absolute life-span limits.

https://science.sciencemag.org/content/sci/258/5081/457.full.pdf

Slowing of mortality rates at older ages in large medfly cohorts.

Mite populations grew more rapidly on new growth of cotton seedlings that had never been exposed to mites than on new growth of plants whose cotyledons had been previously exposed to them. Experiments in which a second mite introduction on the exposed plants involved a different mite species produced this same result. The substance or substances responsible for the response are transported systemically among leaves of cotton seedlings.

James R. Carey

Papers

Biodemographic Trajectories of Longevity

Eradication revisited: dealing with exotic species.

Applied Demography for Biologists: with Special Emphasis on Insects

Slowing of mortality rates at older ages in large medfly cohorts.

Induced resistance of cotton seedlings to mites.