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

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

Human biochemical genetics

Physiological and ecological constraints play key roles in the evolution of plant growth patterns, especially in relation to defenses against herbivores. Phenotypic and life history theories are unified within the growth-differentiation balance (GDB) framework, forming an integrated system of theories explaining and predicting patterns of plant defense and competitive interactions in ecological and evolutionary time. Plant activity at the cellular level can be classified as growth (cell division and enlargement) of differentiation (chemical and morphological changes leading to cell maturation and specialization). The GDB hypothesis of plant defense is premised upon a physiological trade-off between growth and differentiation processes. The trade-off between growth and defense exists because secondary metabolism and structural reinforcement are physiologically constrained in dividing and enlarging cells, and because they divert resources from the production of new leaf area. Hence the dilemma of plants: Th...

https://www.jstor.org/stable/pdfplus/2830650.pdf

The dilemma of plants: To grow or defend.

https://deim.urv.cat/~alexandre.arenas/publicacions/pdf/review.pdf

Synchronization in complex networks

Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

Because of their small size, great abundance and easy dispersal, it is often assumed that marine planktonic microorganisms have a ubiquitous distribution that prevents any structured assembly into local communities. To challenge this view, marine bacterioplankton communities from coastal waters at nine locations distributed world-wide were examined through the use of comprehensive clone libraries of 16S ribosomal RNA genes, used as operational taxonomic units (OTU). Our survey and analyses show that there were marked differences in the composition and richness of OTUs between locations. Remarkably, the global marine bacterioplankton community showed a high degree of endemism, and conversely included few cosmopolitan OTUs. Our data were consistent with a latitudinal gradient of OTU richness. We observed a positive relationship between the relative OTU abundances and their range of occupation, i.e. cosmopolitans had the largest population sizes. Although OTU richness differed among locations, the distributions of the major taxonomic groups represented in the communities were analogous, and all local communities were similarly structured and dominated by a few OTUs showing variable taxonomic affiliations. The observed patterns of OTU richness indicate that similar evolutionary and ecological processes structured the communities. We conclude that marine bacterioplankton share many of the biogeographical and macroecological features of macroscopic organisms. The general processes behind those patterns are likely to be comparable across taxa and major global biomes.

Global patterns of diversity and community structure in marine bacterioplankton

In conservation biology it is necessary to make management decisions for endangered and threatened species under severe uncertainty. Failure to acknowledge and treat uncertainty can lead to poor decisions. To illustrate the importance of considering uncertainty, we reanalyze a decision problem for the Sumatran rhino, Dicerorhinus sumatrensis, using information-gap theory to propagate uncertainties and to rank management options. Rather than requiring information about the extent of parameter uncertainty at the outset, information-gap theory addresses the question of how much uncertainty can be tolerated before our decision would change. It assesses the robustness of decisions in the face of severe uncertainty. We show that different management decisions may result when uncertainty in utilities and probabilities are considered in decision-making problems. We highlight the importance of a full assessment of uncertainty in conservation management decisions to avoid, as much as possible, undesirable outcomes.

/pdf/robust-decision-making-under-severe-uncertainty-for-40liaqn6mg.pdf

Robust decision‐making under severe uncertainty for conservation management

Evidence from wildlife and human populations indicates that conditions during early development can have marked effects on the subsequent performance of individuals and cohorts. Likewise, the effects of maternal and, more generally, parental environments can be transferred among individuals between generations. These delayed life-history effects are found consistently and suggestions have been made that they can be one source of both variability and of delayed density dependence in population dynamics. Assessments of several different time series indicate that population variability and delayed density dependence are common and that understanding the mechanisms giving rise to them is crucial for the interpretation and application of such models to basic and applied research. Therefore, it is necessary to assess the different ways in which history in the life history might give rise to variability and delayed density dependence in population dynamics. Here, we build on recent appraisals of the pervasive influence of past environmental conditions on current and future fitness and link the details of these life-history studies to classic features of population dynamics.

Population dynamic consequences of delayed life-history effects

1 Introduction.- 1.1 Objectives.- 1.2 Topics to be Covered.- 1.3 Predator-Prey Dynamics.- 1.4 Competition.- 1.5 Summary.- 2 Diet Selection.- 2.1 Nutrient-Maximizing Diets.- 2.1.1 Experimental Evidence for Optimal Diets.- 2.1.2 Partial Preferences.- 2.1.3 Diet Selection and the Functional Response.- 2.1.4 Experimental Tests of the Functional Response.- 2.1.5 Diet Choice and Population Dynamics.- 2.2 Evolutionary Dynamics of Diet Selection.- 2.3 Balanced Nutrient Diets.- 2.3.1 Experimental Evidence for Balanced Diets.- 2.3.2 Balanced Diets and Population Dynamics.- 2.4 Summary.- 3 Prey Defense.- 3.1 Types of Defenses.- 3.2 Defense Effects and Population Parameters.- 3.3 Parameter Effects on Dynamics.- 3.4 Time Allocation.- 3.4.1 Risk-Sensitive Prey.- 3.4.2 3-Link System.- 3.5 Optimal Defense.- 3.5.1 Perfectly Timed Induced Defense.- 3.5.2 Lagged Induced Defense.- 3.6 Summary.- 4 Habitat Use and Spatial Structure.- 4.1 Habitat Variation.- 4.2 Energy-Maximizing Habitat Use.- 4.2.1 Experimental Evidence for Optimal Habitat Choice.- 4.2.2 Habitat Choice and Predator-Prey Dynamics.- 4.2.3 Habitat Choice and Competitive Dynamics.- 4.3 Evasion of Predators by Prey.- 4.3.1 Experimental Evidence of Predator Evasion.- 4.3.2 Food Chain Dynamics.- 4.4 Spatial Structure.- 4.4.1 Experimental Evidence of Optimal Patch Use.- 4.4.2 Patchy Predator-Prey Dynamics.- 4.4.3 Experimental Evidence of Patchy Predator-Prey Dynamics.- 4.5 Summary.- 5 Size-Selective Predation.- 5.1 Diet Selection Model.- 5.1.1 Self-Thinning.- 5.1.2 Size-Dependent Consumption.- 5.1.3 Size-Selection and Population Dynamics.- 5.2 Partial Predation Model.- 5.2.1 Consumption Model.- 5.2.2 Partial Predation and Population Dynamics.- 5.3 The Size Structure Challenge.- 5.4 Summary.- 6 Interference and Territoriality.- 6.1 Interference.- 6.1.1 Interference and Population Dynamics.- 6.1.2 Social Structure and Interference Levels.- 6.1.3 Spatial Structure and Interference.- 6.2 Territoriality.- 6.2.1 Optimal Territory Size.- 6.2.2 Evidence for Optimal Territory Size.- 6.2.3 Optimal Territory Size and Population Dynamics.- 6.2.4 Systematic Foraging.- 6.2.5 Central-Place Foraging and Prey Spatial Refugia.- 6.2.6 Central-Place Foraging and Population Dynamics.- 6.3 Summary.- 7 Epilogue.- References.

Individual Behavior and Community Dynamics

A recurrent problem in ecology and conservation biology is to estimate the risk of population extinctions. Extinction probabilities are not only imperative for conservation and management, but may also elucidate basic mechanisms of the regulation of natural populations (Burgman et al. 1993; Pimm 1994). The usual way of modelling stochastic influence on population dynamics has been to assume that the external noise is uncorrelated. This means that each and every randomly drawn noise value is totally independent on previous ones. This is what is generally called \`white' noise. However, the noise itself can be temporally autocorrelated. That is, the values of the random numbers used in the noise process will depend on previous ones. Here we show that the autocorrelation, or colour, of the external noise assumed to influence population dynamics strongly modifies estimated extinction probabilities. For positively autocorrelated (\`red') noise, the risk of extinction clearly decreases the stronger the autocorrelation is. Negatively autocorrelated (`blue') noise is more ambiguously related to extinction probability. Thus, the commonly assumed white noise in population modelling will severely bias population extinction risk estimates. Moreover, the extinction probability estimates are also significantly dependent on model structure which calls for a cautious use of traditional discrete-time models.

Per Lundberg

Papers

Global patterns of diversity and community structure in marine bacterioplankton

Robust decision‐making under severe uncertainty for conservation management

Population dynamic consequences of delayed life-history effects

Individual Behavior and Community Dynamics

Noise Colour and the Risk of Population Extinctions