Explore the vast range of titles available.

Pearly mussels (Unionoidea) live in lakes, rivers, and streams around the world. These bivalves play important roles in freshwater ecosystems and were once both culturally and economically valuable as sources of food, pearls, and mother-of-pearl. Today, however, hundreds of species of these mussels are extinct or endangered. David L. Strayer provides a critical synthesis of the factors that control the distribution and abundance of pearly mussels. Using empirical analyses and models, he assesses the effects of dispersal, habitat quality, availability of fish hosts, adequate food, predators, and parasites. He also addresses conservation issues that apply to other inhabitants of fresh waters around the globe and other pressing issues in contemporary ecology.

Studies conducted in forests have resulted in much of the ecological theory we build upon today. However, our basic understanding of forest ecology comes almost exclusively from the study of trees, even though they represent only a small fraction of the plant diversity present in forests. In recent decades there has been an increasing number of studies of forest herbs, broadening our understanding of plant community ecology in forest ecosystems. Here we highlight ten recent studies examining patterns and drivers of, as well as threats to, herbaceous plant diversity in forests. We first examine local, regional, and global patterns of herbaceous diversity in forests and how such patterns differ for woody versus herbaceous species. We then focus on ecological mechanisms that contribute to forest herb diversity, including the role of abiotic and biotic interactions. We end by discussing some major anthropogenic impacts on forest herb diversity,identifying where herbs are particularly susceptible or particularly resilient to current and predicted changes in comparison to trees. The studies we feature demonstrate that patterns and drivers of diversity often differ between woody and herbaceous plant communities. To facilitate cross-site comparisons, there is great need for more standardized survey methods for herbaceous plants, for simultaneous measurements of multiple plant growth forms, and for incorporating herbs into long-term forest monitoring networks. In addition, the selected studies reveal how land-use history, overabundant herbivores, invasive species, and climate change are all impacting forest herb communities. Some common characteristics of herbaceous plants, such as limited dispersal and small stature, may make forest herb communities more susceptible to these anthropogenic impacts, while others (e.g., resprouting ability, clonal reproduction) may make them more resilient compared to forest trees. More research is needed from both plant ecologists and applied forest practitioners to predict how herbaceous forest diversity will change in the future.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.

Plant mortality is a complex process influenced by both biotic and abiotic factors. In recent decades, widespread mortality events have been attributed to increasing drought severity, which has motivated research to examine the physiological mechanisms of drought-induced mortality, particularly hydraulic failure. Drought-based mortality mechanisms are further influenced by plant interactions with biota such as neighboring plants, insect pests, and microbes. In this review, we highlight some of the most influential papers addressing these biotic interactions and their influence on plant mortality. Plant–plant interactions can be positive (facilitation), neutral, or negative (competition), depending on drought intensity and neighbor identity. For example, standscale mortality likely increases with basal area (an index of competition). However, the diversity of forest stands matters, as more diverse forests suffer less mortality from drought than species-poor forests. Dense forest stands also increase bark beetle attack frequency, which can exacerbate drought stress and mortality, particularly for fast-growing species with lower defense allocation. In some cases, however, drought stress can alleviate biotic attack, depending on feedbacks between plant and pest physiology. Finally, plant interactions with beneficial microorganisms can increase drought tolerance, reduce the likelihood of mortality, and even extend plant distributions into drier habitats. Our review suggests more work is needed in natural herbaceous plant communities as well as dry tropical ecosystems where mortality mechanisms are less understood. Overall, relatively few studies directly link biotic interactions with the physiological mechanisms of mortality. Simultaneous manipulations of biotic interactions and measurements of physiological thresholds (e.g., xylem cavitation) are needed to fully represent biotic interactions in predictive models of plant mortality.

Plant domestication can increase propagule pressure and alter functional traits that may contribute to invasion risk, such as high productivity. Domesticated biotypes (i.e., any cultivar, variety, or other infraspecific taxa that have been domesticated) have a unique evolutionary history that often involves enhanced propagule pressure, selection of traits for human use, and cultural practices to increase establishment success. These elements can alter the invasion process from that of non-domesticated non-native species. Understanding which traits are consistently selected for, how strongly those traits are altered by selection, and in which cases they influence invasion risk would facilitate actions that reduce impacts of domesticated biotypes on natural and agricultural systems. We identified commonalities in trait selection and management intensity among groups of domesticated biotypes selected for particular purposes (e.g., food, ornamental, and biomass production) that indicate predictable invasion risk and management approaches for biotypes from each group. Broadly, food crops that rely on high intensity management tend to have relatively low invasion risk, while biotypes developed for ornamental purposes are subject to more variable management intensity and may have greater risk of establishment and spread in natural areas without cultural practices (e.g., weeding, fertilizer). Biomass-producing biotypes are usually bred for high productivity under low management intensity and have a high-risk of becoming invasive. For groups of domesticated plants bred for different purposes, and across all management levels, efforts to reduce risk of producing invasive biotypes include selection of parent taxa with low invasion risk, development of domesticated biotypes focused on specific low-risk traits and dependence on cultural practices, and selection of durable low-risk traits over consecutive generations.

Advances in computing power in the past 20 years have led to a proliferation of spatially explicit, individual-based models of population and ecosystem dynamics. In forest ecosystems, the individual-based models encapsulate an emerging theory of \"neighborhood\" dynamics, in which fine-scale spatial interactions regulate the demography of component tree species. The spatial distribution of component species, in turn, regulates spatial variation in a whole host of community and ecosystem properties, with subsequent feedbacks on component species. The development of these models has been facilitated by development of new methods of analysis of field data, in which critical demographic rates and ecosystem processes are analyzed in terms of the spatial distributions of neighboring trees and physical environmental factors. The analyses are based on likelihood methods and information theory, and they allow a tight linkage between the models and explicit parameterization of the models from field data. Maximum likelihood methods have a long history of use for point and interval estimation in statistics. In contrast, likelihood principles have only more gradually emerged in ecology as the foundation for an alternative to traditional hypothesis testing. The alternative framework stresses the process of identifying and selecting among competing models, or in the simplest case, among competing point estimates of a parameter of a model. There are four general steps involved in a likelihood analysis: (1) model specification, (2) parameter estimation using maximum likelihood methods, (3) model comparison, and (4) model evaluation. Our goal in this paper is to review recent developments in the use of likelihood methods and modeling for the analysis of neighborhood processes in forest ecosystems. We will focus on a single class of processes, seed dispersal and seedling dispersion, because recent papers provide compelling evidence of the potential power of the approach, and illustrate some of the statistical challenges in applying the methods.

We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.

Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministic subprocesses that characterize processes such as birth, survival, maturation, and movement. Observations of the population can be modeled as functions of true abundance with realistic probability distributions to describe observation or estimation error. Computer-intensive procedures, such as sequential Monte Carlo methods or Markov chain Monte Carlo, condition on the observed data to yield estimates of both the underlying true population abundances and the unknown population dynamics parameters. Formulation and fitting of a hidden process model are demonstrated for Sacramento River winter-run chinook salmon (Oncorhynchus tshawytsha).

Here we give an introduction to the growing number of statistical techniques for analyzing data that are not independent realizations of the same sampling process--in other words, correlated data. We focus on regression problems, in which the value of a given variable depends linearly on the value of another variable. To illustrate different types of processes leading to correlated data, we analyze four simulated examples representing diverse problems arising in ecological studies. The first example is a comparison among species to determine the relationship between home-range area and body size; because species are phylogenetically related, they do not represent independent samples. The second example addresses spatial variation in net primary production and how this might be affected by soil nitrogen; because nearby locations are likely to have similar net primary productivity for reasons other than soil nitrogen, spatial correlation is likely. In the third example, we consider a time-series model to ask whether the decrease in density of a butterfly species is the result of decreases in its host-plant density; because the population density of a species in one generation is likely to affect the density in the following generation, time-series data are often correlated. The fourth example combines both spatial and temporal correlation in an experiment in which prey densities are manipulated to determine the response of predators to their food supply. For each of these examples, we use a different statistical approach for analyzing models of correlated data. Our goal is to give an overview of conceptual issues surrounding correlated data, rather than a detailed tutorial in how to apply different statistical techniques. By dispelling some of the mystery behind correlated data, we hope to encourage ecologists to learn about statistics that could be useful in their own work. Although at first encounter these techniques might seem complicated, they have the power to simplify ecological research by making more types of data and experimental designs open to statistical evaluation.