Minireviews provides an opportunity to summarize existing knowledge of selected ecological areas, with special emphasis on current topics where rapid and significant advances are occurring. Reviews should be concise and not too wide-ranging. All key references should be cited. A summary is required.

By integrating a wide range of experimental, comparative, and theoretical approaches, ecologists are starting to gain a detailed understanding of the long-term dynamics of vegetation. We explore how patterns of variation in demographic traits among species have provided insight into the processes that structure plant communities. We find a common set of mechanisms, derived from ecological and evolutionary principles, that underlie the main forces shaping systems as diverse as annual plant communities and tropical forests. Trait variation between species maintains diversity and has important implications for ecosystem processes. Hence, greater understanding of how Earth's vegetation functions will likely require integration of ecosystem science with ideas from plant evolutionary, population, and community ecology.The past decade has seen the emergence of a new synthesis in plant ecology that draws together a variety of once disparate approaches in studies of vegetation dynamics. Questions about the determinants of plant life histories, species composition, diversity, productivity, and stability-previously considered separate areas of inquiry-have become increasingly closely integrated. Findings from long-term experimental and observational studies, combined with comparative and theoretical work, have helped synthesize the questions and approaches of evolutionary ecology, population ecology, and ecosystem ecology. The link has come from the realization that many of the same environmental constraints and organismal tradeoffs that shape the evolution of plant morphologies, life histories, and physiologies also influence the dynamics of interspecific interactions and the mechanisms of coexistence that control community and ecosystem functioning (1-3). We provide a brief tour of the developments in vegetation science, highlighting areas where known patterns of variation in demographic rates between species have provided insights into the structure, dynamics, and functioning of plant communities.

Summary1. Plant growth is a fundamental ecological process, integrating across scales from physiology to community dynamics and ecosystem properties. Recent improvements in plant growth modelling have allowed deeper understanding and more accurate predictions for a wide range of ecological issues, including competition among plants, plant-herbivore interactions and ecosystem functioning. 2. One challenge in modelling plant growth is that, for a variety of reasons, relative growth rate (RGR) almost universally decreases with increasing size, although traditional calculations assume that RGR is constant. Nonlinear growth models are flexible enough to account for varying growth rates. 3. We demonstrate a variety of nonlinear models that are appropriate for modelling plant growth and, for each, show how to calculate function-derived growth rates, which allow unbiased comparisons among species at a common time or size. We show how to propagate uncertainty in estimated parameters to express uncertainty in growth rates. Fitting nonlinear models can be challenging, so we present extensive worked examples and practical recommendations, all implemented in R. 4. The use of nonlinear models coupled with function-derived growth rates can facilitate the testing of novel hypotheses in population and community ecology. For example, the use of such techniques has allowed better understanding of the components of RGR, the costs of rapid growth and the linkage between host and parasite growth rates. We hope this contribution will demystify nonlinear modelling and persuade more ecologists to use these techniques.

Summary1 A seed-addition experiment using seven co-occurring annual plant species with a range of seed masses was carried out in a limestone grassland in South Wales. 2 If seedlings compete for establishment sites, then large seed size may confer enhanced competitive ability. However, the simple reciprocal relationship found between seed mass and per capita seed output showed that species producing larger seeds suer reduced fecundity. Seed size may therefore act as a surrogate in a competition/colonization trade-o. 3 Equal numbers of seeds of all species were sown in a mixture over a range of densities. As sowing density increases, all species should reach a higher proportion of the available microsites. If large-seeded species are the best competitors they are expected to win all the sites they reach, and hence to occupy an increasing proportion of sites as sowing density increases. 4 The three species with the largest seeds made up 49% of individuals at low-density sown plots but 83% of individuals in high-density sown plots. In addition, seed mass and plant density were not correlated in unsown plots, but were strongly correlated in high-density sown plots. However, all small-seeded species maintained a presence in sown plots. 5 Although species were sown at random with respect to one another, individuals were up to ®ve times more likely than expected to have a conspeci®c as a nearest neighbour. This could be caused by interspeci®c competition and/or by environmental heterogeneity that favours dierent species in dierent patches. 6 The results suggest that seedlings do compete for establishment sites and that large-seeded species generally win when in direct competition. In unsown areas small-seeded species win many sites by forfeit (because large-seeded species are strongly recruitment limited) but there may be a restricted subset of potential sites for which they are the best competitors and which they can win outright.

Understanding the adaptations that allow species to live in temporally variable environments is essential for predicting how they may respond to future environmental change. Variation at the intergenerational scale can allow the evolution of bet-hedging strategies: a novel genotype may be favoured over an alternative with higher arithmetic mean fitness if the new genotype experiences a sufficiently large reduction in temporal fitness variation; the successful genotype is said to have traded off its mean and variance in fitness in order to 'hedge its evolutionary bets'. We review the evidence for bet-hedging in a range of simple plant systems that have proved particularly tractable for studying bet-hedging under natural conditions. We begin by outlining the essential theory, reiterating the important distinction between conservative and diversified bet-hedging strategies. We then examine the theory and empirical evidence for the canonical example of bet-hedging: diversification via dormant seeds in annual plants. We discuss the complications that arise when moving beyond this simple case to consider more complex life-history traits, such as flowering size in semelparous perennial plants. Finally, we outline a framework for accommodating these complications, emphasizing the central role that model-based approaches can play.

Summary1. Integral projection models (IPMs) use information on how an individual's state influences its vital rates -survival, growth and reproduction -to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. 2. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species' geographic distributions and life-history strategies. 3. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.

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Matrix projection models occupy a central role in population and conservation biology. Matrix models divide a population into discrete classes, even if the structuring trait exhibits continuous variation (e.g., body size). The integral projection model (IPM) avoids discrete classes and potential artifacts from arbitrary class divisions, facilitates parsimonious modeling based on smooth relationships between individual state and demographic performance, and can be implemented with standard matrix software. Here, we extend the IPM to species with complex demographic attributes, including dormant and active life stages, cross-classification by several attributes (e.g., size, age, and condition), and changes between discrete and continuous structure over the life cycle. We present a general model encompassing these cases, numerical methods, and theoretical results, including stable population growth and sensitivity/ elasticity analysis for density-independent models, local stability analysis in density-dependent models, and optimal/evolutionarily stable strategy life-history analysis. Our presentation centers on an IPM for the thistle Onopordum illyricum based on a 6-year field study. Flowering and death probabilities are size and age dependent, and individuals also vary in a latent attribute affecting survival, but a predictively accurate IPM is completely parameterized by fitting a few regression equations. The online edition of the American Naturalist includes a zip archive of R scripts illustrating our suggested methods.Keywords: structured populations, integral model, matrix model, sensitivity analysis, latent variability, thistle.* Corresponding author; e-mail: spe2@cornell.edu. † E-mail: m.rees@sheffield.ac.uk.Am. Nat. 2006. Vol. 167, pp. 410- Matrix projection models are probably the most commonly used approach for modeling structured biological populations (Caswell 2001) and play a central role in population and conservation biology (e.g., Morris and Doak 2002). The popularity of matrix models is easy to understand. They are conceptually the simplest way to represent population structure, can be parameterized directly from observational data on the fate and reproductive output of individuals, and yield a great deal of useful information. The dominant eigenvalue l of the projection matrix gives the population's projected long-term growth rate; the dominant right and left eigenvectors are, respectively, the stable stage distribution w and relative reproductive value v; and the eigenvectors determine the effect on l of changes in individual matrix entries, which are often the key quantities for management applications. These and other metrics can be used as response variables to summarize population responses to changes in environmental conditions (Caswell 2001, chap. 10). Density dependence, stochasticity, and spatial structure can all be incorporated, and there is a growing body of theory for these situations (e.g., Tuljapurkar 1990;Cushing 1998;Caswell 2001;Tuljapurkar et al. 2003;Doak et al. 2005...