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

Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro- vided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test- statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.

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A new method for non-parametric multivariate analysis of variance

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

The metacommunity concept is an important way to think about linkages between different spatial scales in ecology. Here we review current understanding about this concept. We first investigate issues related to its definition as a set of local communities that are linked by dispersal of multiple potentially interacting species. We then identify four paradigms for metacommunities: the patch-dynamic view, the species-sorting view, the mass effects view and the neutral view, that each emphasizes different processes of potential importance in metacommunities. These have somewhat distinct intellectual histories and we discuss elements related to their potential future synthesis. We then use this framework to discuss why the concept is useful in modifying existing ecological thinking and illustrate this with a number of both theoretical and empirical examples. As ecologists strive to understand increasingly complex mechanisms and strive to work across multiple scales of spatio-temporal organization, concepts like the metacommunity can provide important insights that frequently contrast with those that would be obtained with more conventional approaches based on local communities alone.

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The metacommunity concept: a framework for multi-scale community ecology

Body size is manifestly one of the most important attributes of an organism from an ecological and evolutionary point of view. Size has a predominant influence on an animal's energetic requirements, its potential for resource exploitation, and its susceptibility to natural enemies. A large literature now exists on how physiological, life history, and population parameters scale with body dimensions (24, 131). The ecological literature on species interactions and the structure of animal communities also stresses the importance of body size. Differences in body size are a major means by which species avoid direct overlap in resource use (153), and size-selective predation can be a primary organizing force in some communities (20, 70). Size thus imposes important constraints on the manner in which an organism interacts with its environment and influences the strength, type, and symmetry of interactions with other species (152, 207). Paradoxically, ecologists have virtually ignored the implications of these observations for interactions among species that exhibit size-distributed populations. For instance, it has been often suggested that competing species

https://www.jstor.org/stable/pdf/2096954.pdf

The ontogenetic niche and species interactions in size-structured populations

Predation, Competition, and Prey Communities: A Review of Field Experiments

The advent of fast, relatively inexpensive (thus, widely available) microcomputers is transforming the way we analyze data in ecological and evolutionary research. Even more profound, however, are the associated changes in questions asked, empirical methods used, studies conducted, and interpretations offered. Now that an array of computation-intensive statistical methods is newly available for general use, it seems particularly important to assess their advantages and limitations, to note how they are currently being used, and then to consider implications for the future. I focus in this review on four related techniques known in the statistical and biological literature as randomization (or permutation) tests, Monte Carlo methods, bootstrapping, and the jackknife. I refer to them collectively as resampling methods, because each involves taking several-to-many samples from the original data set (randomization, bootstrap, jackknife) or from a stochastic process like the one believed to have generated the data set (Monte Carlo). Each of these methods is actually an extensive family of techniques and specific applications that cannot be thoroughly examined here; instead, I briefly characterize the focal methods and then survey the recent literature in ecology and evolution to identify the issues most frequently associated with these techniques. It emerges that resampling methods are well represented in

Resampling methods for computation-intensive data analysis in ecology and evolution

In a laboratory study of the dragonfly Tetragoneuria cynosura (Say) (Odonata: Corduliidae), we measured the feeding rates of second-year-class larvae (Tc2) as a function of Tc2 density and of the density of their first-year-class conspecific prey (Tc1). The experiments were conducted for 24 hr in small, structurally simple, cylindrical plastic aquaria within a controlled environment chamber under a 14L:10D photoperiod. The resulting functional response to prey density followed the decelerated curve (type 2) typical of many predators. Strong feeding interference among Tc2 larvae was indicated by an inverse relationship between feeding rate and predator density. Since we detected no effects of Tc1 or Tc2 densities on movement by larvae within aquaria, density-specific differences in movement probably cannot account for the observed interference. Both a distraction model (in which prey and predators "compete" for the predator's attention) and a pre-emption model (in which interference takes precedence over f...

Functional Responses and Interference within and between Year Classes of a Dragonfly Population

We describe and analyze a computer-simulation model of mate choice, featuring two different quality groups (based on offspring per mating) in each sex. Mating between quality groups results from two-dimensional random encounter and mutual assent, where assent reflects an attempt to maximize expected lifetime reproductive success, E(LRS). Premating predation (via random encounter with predators) and other mortality also influence E(LRS). Given potentially conflicting optimal choices, the model finds the evolutionarily stable patterns of choosiness for the four quality groups. When there are multiple mating episodes by individuals through the season, the resulting dynamic game is solved to obtain a seasonal pattern of mate choice and reproduction. The model generates seven different mating patterns among quality groups. These patterns imply different opportunities for selection, as indicated by the variance components of normalized lifetime reproductive success, var(LRS). The changes in E(LRS), var(LRS), an...

Mate Density, Predation Risk, and the Seasonal Sequence of Mate Choices: A Dynamic Game

A predator-prey interaction between spatially dispersed populations can be considered as many local interactions connected by dispersal. Even when the local subsystems (cells) quickly become extinct in isolation, an ensemble of interconnected cells can, under certain conditions, persist much longer. These conditions include: (1) sufficiently low exchange rates among cells; or (2) a sufficiently high level of environmental stochasticity (noise), either of which can prevent local populations from becoming synchronized; or (3) a large number of cells within the system. Computer simulations based on a broadly applicable predator-prey model demonstrate the efficacy of the conditions 1-3; neither spatial structure (texture) nor density-dependent dispersal are additionally required to obtain long-term persistence. The critical problem of avoiding synchrony in a spatially contiguous, locally unstable system can apparently be solved by size alone. If the system is large enough, it should tend to fragment into two ...

Philip H. Crowley

Papers

Predation, Competition, and Prey Communities: A Review of Field Experiments

Resampling methods for computation-intensive data analysis in ecology and evolution

Functional Responses and Interference within and between Year Classes of a Dragonfly Population

Mate Density, Predation Risk, and the Seasonal Sequence of Mate Choices: A Dynamic Game

Dispersal and the Stability of Predator-Prey Interactions