Janneke HilleRisLambers

SEED DISPERSAL NEAR AND FAR: PATTERNS ACROSS TEMPERATE AND TROPICAL FORESTS

Dispersal affects community dynamics and vegetation response to global change. Understanding these effects requires descriptions of dispersal at local and regional scales and statistical models that permit estimation. Classical models of dispersal describe local or long-distance dispersal, but not both. The lack of statistical methods means that models have rarely been fitted to seed dispersal in closed forests. We present a mixture model of dispersal that assumes a range of disperal patterns, both local and long distance. The bivariate Student’s t or “2Dt” follows from an assumption that the distance parameter in a Gaussian model varies randomly, thus having a density of its own. We use an inverse approach to “compete” our mixture model against classical alternatives, using seed rain databases from temperate broadleaf, temperate mixed-conifer, and tropical floodplain forests. For most species, the 2Dt model fits dispersal data better than do classical models. The superior fit results from the potential f...

Interpreting recruitment limitation in forests

Studies of tree recruitment are many, but they provide few general insights into the role of recruitment limitation for population dynamics. That role depends on the vital rates (transitions) from seed production to sapling stages and on overall population growth. To determine the state of our understanding of recruitment limitation we examined how well we can estimate parameters corresponding to these vital rates. Our two-part analysis consists of (1) a survey of published literature to determine the spatial and temporal scale of sampling that is basis for parameter estimates, and (2) an analysis of extensive data sets to evaluate sampling intensity found in the literature. We find that published studies focus on fine spatial scales, emphasizing large numbers of small samples within a single stand, and tend not to sample multiple stands or variability across landscapes. Where multiple stands are sampled, sampling is often inconsistent. Sampling of seed rain, seed banks, and seedlings typically span <1 yr and rarely last 5 yr. Most studies of seeding establishment and growth consider effects of a single variable and a single life history stage. By examining how parameter estimates are affected by the spatial and temporal extent of sampling we find that few published studies are sufficiently extensive to capture the variability in recruitment stages. Early recruitment stages are especially variable and require samples across multiple years and multiple stands. Ironically, the longest duration data sets are used to estimate mortality rates, which are less variable (in time) than are early life history stages. Because variables that affect recruitment rates interact, studies of these interactions are needed to assess their full impacts. We conclude that greater attention to spatially extensive and longer duration sampling for early life history stages is needed to assess the role of recruitment limitation in forests.

The importance of niches for the maintenance of species diversity.

Ecological communities characteristically contain a wide diversity of species with important functional, economic and aesthetic value. Ecologists have long questioned how this diversity is maintained. Classic theory shows that stable coexistence requires competitors to differ in their niches; this has motivated numerous investigations of ecological differences presumed to maintain diversity. That niche differences are key to coexistence, however, has recently been challenged by the neutral theory of biodiversity, which explains coexistence with the equivalence of competitors. The ensuing controversy has motivated calls for a better understanding of the collective importance of niche differences for the diversity observed in ecological communities. Here we integrate theory and experimentation to show that niche differences collectively stabilize the dynamics of experimental communities of serpentine annual plants. We used field-parameterized population models to develop a null expectation for community dynamics without the stabilizing effects of niche differences. The population growth rates predicted by this null model varied by several orders of magnitude between species, which is sufficient for rapid competitive exclusion. Moreover, after two generations of community change in the field, Shannon diversity was over 50 per cent greater in communities stabilized by niche differences relative to those exhibiting dynamics predicted by the null model. Finally, in an experiment manipulating species’ relative abundances, population growth rates increased when species became rare—the demographic signature of niche differences. Our work thus provides strong evidence that species differences have a critical role in stabilizing species diversity.

Soil microbes drive the classic plant diversity-productivity pattern

Ecosystem productivity commonly increases asymptotically with plant species diversity, and determining the mechanisms responsible for this well-known pattern is essential to predict potential changes in ecosystem productivity with ongoing species loss. Previous studies attributed the asymptotic diversity-productivity pattern to plant competition and differential resource use (e.g., niche complementarity). Using an analytical model and a series of experiments, we demonstrate theoretically and empirically that host-specific soil microbes can be major determinants of the diversity-productivity relationship in grasslands. In the presence of soil microbes, plant disease decreased with increasing diversity, and productivity increased nearly 500%, primarily because of the strong effect of density-dependent disease on productivity at low diversity. Correspondingly, disease was higher in plants grown in conspecific-trained soils than heterospecific-trained soils (demonstrating host-specificity), and productivity increased and host-specific disease decreased with increasing community diversity, suggesting that disease was the primary cause of reduced productivity in species-poor treatments. In sterilized, microbe-free soils, the increase in productivity with increasing plant species number was markedly lower than the increase measured in the presence of soil microbes, suggesting that niche complementarity was a weaker determinant of the diversity-productivity relationship. Our results demonstrate that soil microbes play an integral role as determinants of the diversity-productivity relationship.

Increased structure and active learning reduce the achievement gap in introductory biology.

A focus on problem-solving skills reduced achievement gaps in university classes. Science, technology, engineering, and mathematics instructors have been charged with improving the performance and retention of students from diverse backgrounds. To date, programs that close the achievement gap between students from disadvantaged versus nondisadvantaged educational backgrounds have required extensive extramural funding. We show that a highly structured course design, based on daily and weekly practice with problem-solving, data analysis, and other higher-order cognitive skills, improved the performance of all students in a college-level introductory biology class and reduced the achievement gap between disadvantaged and nondisadvantaged students—without increased expenditures. These results support the Carnegie Hall hypothesis: Intensive practice, via active-learning exercises, has a disproportionate benefit for capable but poorly prepared students.

ESTIMATING POPULATION SPREAD: WHAT CAN WE FORECAST AND HOW WELL?

Recent literature on plant population spread advocates quantification of long- distance dispersal (LDD). These estimates could provide insights into rates of migration in response to climate change and rates of alien invasions. LDD information is not available for parameterization of current models because it is hard to obtain. We combine a new stochastic model with a flexible framework that permits assimilation of evidence that might be derived from a range of sources. Results are consistent with the prediction of traditional diffusion that population spread has a finite asymptotic velocity. Unlike traditional diffusion, spread is not well described by the mean; it is erratic. In contrast with deterministic models, our results show that inherent uncertainty, rather than parameter sensitivity, thwarts informative forecasts of spread velocity. Analysis shows that, because LDD is inherently unpredictable, even full knowledge of LDD parameters might not provide informative estimates of velocity for populations characterized by LDD. Although predictive distributions are too broad to provide precise estimates of spread rate, they are valuable for comparing spread potential among species and for identifying potential for invasion. Using combinations of dispersal data and the estimates provided by dispersal biologists that derive from multiple sources, the model predicts spread rates that are much slower than those from traditional (deterministic) fat-tailed models and from simulation models of spread, but for different reasons. Deterministic fat-tailed models overestimate spread rate, because they assume that fractions of individuals can rapidly occupy distant sites. Stochastic models recognize that distant colonization is limited to discrete individuals. Stochastic simulations of plant migration overestimate migration of trees, because they typically assume values of R 0 that are too large.

Climate variability has a stabilizing effect on the coexistence of prairie grasses

How expected increases in climate variability will affect species diversity depends on the role of such variability in regulating the coexistence of competing species. Despite theory linking temporal environmental fluctuations with the maintenance of diversity, the importance of climate variability for stabilizing coexistence remains unknown because of a lack of appropriate long-term observations. Here, we analyze three decades of demographic data from a Kansas prairie to demonstrate that interannual climate variability promotes the coexistence of three common grass species. Specifically, we show that (i) the dynamics of the three species satisfy all requirements of “storage effect” theory based on recruitment variability with overlapping generations, (ii) climate variables are correlated with interannual variation in species performance, and (iii) temporal variability increases low-density growth rates, buffering these species against competitive exclusion. Given that environmental fluctuations are ubiquitous in natural systems, our results suggest that coexistence based on the storage effect may be underappreciated and could provide an important alternative to recent neutral theories of diversity. Field evidence for positive effects of variability on coexistence also emphasizes the need to consider changes in both climate means and variances when forecasting the effects of global change on species diversity.

High‐dimensional coexistence based on individual variation: a synthesis of evidence

High biodiversity of forests is not predicted by traditional models, and evidence for trade-offs those models require is limited. High-dimensional regulation (e.g., N factors to regulate N species) has long been recognized as a possible alternative explanation, but it has not be been seriously pursued, because only a few limiting resources are evident for trees, and analysis of multiple interactions is challenging. We develop a hierarchical model that allows us to synthesize data from long-term, experimental, data sets with processes that control growth, maturation, fecundity, and survival. We allow for uncertainty at all stages and variation among 26 000 individuals and over time, including 268 000 tree years, for dozens of tree species. We estimate population-level parameters that apply at the species level and the interactions among latent states, i.e., the demographic rates for each individual, every year. The former show that the traditional trade-offs used to explain diversity are not present. Demographic rates overlap among species, and they do not show trends consistent with maintenance of diversity by simple mechanisms (negative correlations and limiting similarity). However, estimates of latent states at the level of individuals and years demonstrate that species partition environmental variation. Correlations between responses to variation in time are high for individuals of the same species, but not for individuals of different species. We demonstrate that these relationships are pervasive, providing strong evidence that high- dimensional regulation is critical for biodiversity regulation.

How will biotic interactions influence climate change–induced range shifts?

Biotic interactions present a challenge in determining whether species distributions will track climate change. Interactions with competitors, consumers, mutualists, and facilitators can strongly influence local species distributions, but few studies assess how and whether these interactions will impede or accelerate climate change–induced range shifts. In this paper, we explore how ecologists might move forward on this question. We first outline the conditions under which biotic interactions can result in range shifts that proceed faster or slower than climate velocity and result in ecological surprises. Next, we use our own work to demonstrate that experimental studies documenting the strength of biotic interactions across large environmental gradients are a critical first step for understanding whether they will influence climate change–induced range shifts. Further progress could be made by integrating results from these studies into modeling frameworks to predict how or generalize when biotic interactions mediate how changing climates influence range shifts. Finally, we argue that many more case studies like those described here are needed to explore the importance of biotic interactions during climate change–induced range shifts.

DOES METABOLIC THEORY APPLY TO COMMUNITY ECOLOGY? IT'S A MATTER OF SCALE

al. (2004), there were moments of insight, of enthusiastic consensus, and of strongly divergent opinion. We agreed that the empirical relations and scaling theory of Brown et al. (2004) hold great appeal because of their power to abstract and simplify some of the complexity of nature. The earth harbors several million species, each having unique aspects to its morphology, physiology, and life history. A fundamental goal of science is to simplify and explain such complexity. Brown et al. (2004) do just this. They have documented robust patterns relating the body size and temperature of species to their basal metabolic rate; plotted on loglog scales, these empirical functions are well fit by straight lines. Moreover, they have used these scaling relations to make numerous predictions about other patterns and processes, thus greatly extending an approach that already had been shown to have considerable power (e.g., Huxley 1932, McMahon and Bonner 1983, Peters 1983, May 1986). One question that generated considerable debate among us was whether metabolic scaling theory represents a fundamental mechanism that has shaped life on earth, or whether it is a description of correlated patterns of as yet poorly known causes. Brown et al. (2004) hypothesize that scaling relations have a fundamental basis that comes from the universality of metabolic activation energy and of the fractal branching networks that determine resource distribution within