Mark C. Urban

Why intraspecific trait variation matters in community ecology

Natural populations consist of phenotypically diverse individuals that exhibit variation in their demographic parameters and intra- and inter-specific interactions. Recent experimental work indicates that such variation can have significant ecological effects. However, ecological models typically disregard this variation and focus instead on trait means and total population density. Under what situations is this simplification appropriate? Why might intraspecific variation alter ecological dynamics? In this review we synthesize recent theory and identify six general mechanisms by which trait variation changes the outcome of ecological interactions. These mechanisms include several direct effects of trait variation per se and indirect effects arising from the role of genetic variation in trait evolution.

A framework for community interactions under climate change.

Predicting the impacts of climate change on species is one of the biggest challenges that ecologists face. Predictions routinely focus on the direct effects of climate change on individual species, yet interactions between species can strongly influence how climate change affects organisms at every scale by altering their individual fitness, geographic ranges and the structure and dynamics of their community. Failure to incorporate these interactions limits the ability to predict responses of species to climate change. We propose a framework based on ideas from global-change biology, community ecology, and invasion biology that uses community modules to assess how species interactions shape responses to climate change.

Accelerating extinction risk from climate change

Predicting extinction in a changing world There is great interest in understanding how species might respond to our changing climate, but predictions have varied greatly. Urban looked at over 130 studies to identify the level of risk that climate change poses to species and the specific traits and characteristics that contribute to risk (see the Perspective by Hille Ris Lambers). If climate changes proceed as expected, one in six species could face extinction. Several regions, including South America, Australia, and New Zealand, face the greatest risk. Understanding these patterns will help us to prepare for, and hopefully prevent, climate-related loss of biodiversity. Science, this issue p. 571; see also p. 501 A meta-analysis details how current climate trends could result in increases in extinction. [Also see Perspective by Hille Ris Lambers] Current predictions of extinction risks from climate change vary widely depending on the specific assumptions and geographic and taxonomic focus of each study. I synthesized published studies in order to estimate a global mean extinction rate and determine which factors contribute the greatest uncertainty to climate change–induced extinction risks. Results suggest that extinction risks will accelerate with future global temperatures, threatening up to one in six species under current policies. Extinction risks were highest in South America, Australia, and New Zealand, and risks did not vary by taxonomic group. Realistic assumptions about extinction debt and dispersal capacity substantially increased extinction risks. We urgently need to adopt strategies that limit further climate change if we are to avoid an acceleration of global extinctions.

Can mechanism inform species’ distribution models?

Two major approaches address the need to predict species distributions in response to environmental changes. Correlative models estimate parameters phenomenologically by relating current distributions to environmental conditions. By contrast, mechanistic models incorporate explicit relationships between environmental conditions and organismal performance, estimated independently of current distributions. Mechanistic approaches include models that translate environmental conditions into biologically relevant metrics (e.g. potential duration of activity), models that capture environmental sensitivities of survivorship and fecundity, and models that use energetics to link environmental conditions and demography. We compared how two correlative and three mechanistic models predicted the ranges of two species: a skipper butterfly (Atalopedes campestris) and a fence lizard (Sceloporus undulatus). Correlative and mechanistic models performed similarly in predicting current distributions, but mechanistic models predicted larger range shifts in response to climate change. Although mechanistic models theoretically should provide more accurate distribution predictions, there is much potential for improving their flexibility and performance.

On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change

Most climate change predictions omit species interactions and interspecific variation in dispersal. Here, we develop a model of multiple competing species along a warming climatic gradient that includes temperature-dependent competition, differences in niche breadth and interspecific differences in dispersal ability. Competition and dispersal differences decreased diversity and produced so-called ‘no-analogue’ communities, defined as a novel combination of species that does not currently co-occur. Climate change altered community richness the most when species had narrow niches, when mean community-wide dispersal rates were low and when species differed in dispersal abilities. With high interspecific dispersal variance, the best dispersers tracked climate change, out-competed slower dispersers and caused their extinction. Overall, competition slowed the advance of colonists into newly suitable habitats, creating lags in climate tracking. We predict that climate change will most threaten communities of species that have narrow niches (e.g. tropics), vary in dispersal (most communities) and compete strongly. Current forecasts probably underestimate climate change impacts on biodiversity by neglecting competition and dispersal differences.

The cane toad's (Chaunus [Bufo] marinus) increasing ability to invade Australia is revealed by a dynamically updated range model

Invasive species threaten biological diversity throughout the world. Understanding the dynamics of their spread is critical to mitigating this threat. In Australia, efforts are underway to control the invasive cane toad (Chaunus [Bufo] marinus). Range models based on their native bioclimatic envelope suggest that the cane toad is nearing the end of its invasion phase. However, such models assume a conserved niche between native and invaded regions and the absence of evolution to novel habitats. Here, we develop a dynamically updated statistical model to predict the growing extent of cane toad range based on their current distribution in Australia. Results demonstrate that Australian cane toads may already have the ability to spread across an area that almost doubles their current range and that triples projections based on their native distribution. Most of the expansion in suitable habitat area has occurred in the last decade and in regions characterized by high temperatures. Increasing use of extreme habitats may indicate that novel ecological conditions have facilitated a broader realized niche or that toad populations at the invasion front have evolved greater tolerance to extreme abiotic conditions. Rapid evolution to novel habitats combined with ecological release from native enemies may explain why some species become highly successful global invaders. Predicting species ranges following invasion or climate change may often require dynamically updated range models that incorporate a broader realization of niches in the absence of natural enemies and evolution in response to novel habitats.

The evolutionary ecology of metacommunities.

Research on the interactions between evolutionary and ecological dynamics has largely focused on local spatial scales and on relatively simple ecological communities. However, recent work demonstrates that dispersal can drastically alter the interplay between ecological and evolutionary dynamics, often in unexpected ways. We argue that a dispersal-centered synthesis of metacommunity ecology and evolution is necessary to make further progress in this important area of research. We demonstrate that such an approach generates several novel outcomes and substantially enhances understanding of both ecological and evolutionary phenomena in three core research areas at the interface of ecology and evolution.

Microgeographic adaptation and the spatial scale of evolution

Local adaptation has been a major focus of evolutionary ecologists working across diverse systems for decades. However, little of this research has explored variation at microgeographic scales because it has often been assumed that high rates of gene flow will prevent adaptive divergence at fine spatial scales. Here, we establish a quantitative definition of microgeographic adaptation based on Wrights dispersal neighborhood that standardizes dispersal abilities, enabling this measure to be compared across species. We use this definition to evaluate growing evidence of evolutionary divergence at fine spatial scales. We identify the main mechanisms known to facilitate this adaptation and highlight illustrative examples of microgeographic evolution in nature. Collectively, this evidence requires that we revisit our understanding of the spatial scale of adaptation and consider how microgeographic adaptation and its driving mechanisms can fundamentally alter ecological and evolutionary dynamics in nature.

DISTURBANCE HETEROGENEITY DETERMINES FRESHWATER METACOMMUNITY STRUCTURE

Metacommunity theories, which consider communities as interacting species assemblages connected by dispersal, differ in their assumptions about the importance of interspecific adaptations and environmental heterogeneity as controls of assemblage composition. I assess the relative importance of regional (dispersal) and local (abiotic and biotic environmental variation) processes in explaining the structure of a freshwater pond metacommunity. Results did not support the hypothesis that dispersal was limited by interpatch distance. Instead, community diversity, composition, and trophic structure were best explained by local environmental variation associated with pond permanence. Many taxa were restricted either to temporary or semipermanent ponds, an outcome that suggests species trade off adaptations to disturbance with those to biotic interactions (species-sorting model) and that refutes the neutral model of interspecific equivalence. However, evidence for high dispersal rates, low-fitness habitats, and high temporal environmental variability indicated that interpatch dispersal also may influence local dynamics through mass effects. These results suggest that integrating the species-sorting and mass-effect niche assembly frameworks will provide a necessary step in the successful application of metacommunity theory.

Improving the forecast for biodiversity under climate change

BACKGROUND As global climate change accelerates, one of the most urgent tasks for the coming decades is to develop accurate predictions about biological responses to guide the effective protection of biodiversity. Predictive models in biology provide a means for scientists to project changes to species and ecosystems in response to disturbances such as climate change. Most current predictive models, however, exclude important biological mechanisms such as demography, dispersal, evolution, and species interactions. These biological mechanisms have been shown to be important in mediating past and present responses to climate change. Thus, current modeling efforts do not provide sufficiently accurate predictions. Despite the many complexities involved, biologists are rapidly developing tools that include the key biological processes needed to improve predictive accuracy. The biggest obstacle to applying these more realistic models is that the data needed to inform them are almost always missing. We suggest ways to fill this growing gap between model sophistication and information to predict and prevent the most damaging aspects of climate change for life on Earth. ADVANCES On the basis of empirical and theoretical evidence, we identify six biological mechanisms that commonly shape responses to climate change yet are too often missing from current predictive models: physiology; demography, life history, and phenology; species interactions; evolutionary potential and population differentiation; dispersal, colonization, and range dynamics; and responses to environmental variation. We prioritize the types of information needed to inform each of these mechanisms and suggest proxies for data that are missing or difficult to collect. We show that even for well-studied species, we often lack critical information that would be necessary to apply more realistic, mechanistic models. Consequently, data limitations likely override the potential gains in accuracy of more realistic models. Given the enormous challenge of collecting this detailed information on millions of species around the world, we highlight practical methods that promote the greatest gains in predictive accuracy. Trait-based approaches leverage sparse data to make more general inferences about unstudied species. Targeting species with high climate sensitivity and disproportionate ecological impact can yield important insights about future ecosystem change. Adaptive modeling schemes provide a means to target the most important data while simultaneously improving predictive accuracy. OUTLOOK Strategic collections of essential biological information will allow us to build generalizable insights that inform our broader ability to anticipate species’ responses to climate change and other human-caused disturbances. By increasing accuracy and making uncertainties explicit, scientists can deliver improved projections for biodiversity under climate change together with characterizations of uncertainty to support more informed decisions by policymakers and land managers. Toward this end, a globally coordinated effort to fill data gaps in advance of the growing climate-fueled biodiversity crisis offers substantial advantages in efficiency, coverage, and accuracy. Biologists can take advantage of the lessons learned from the Intergovernmental Panel on Climate Change’s development, coordination, and integration of climate change projections. Climate and weather projections were greatly improved by incorporating important mechanisms and testing predictions against global weather station data. Biology can do the same. We need to adopt this meteorological approach to predicting biological responses to climate change to enhance our ability to mitigate future changes to global biodiversity and the services it provides to humans. Emerging models are beginning to incorporate six key biological mechanisms that can improve predictions of biological responses to climate change. Models that include biological mechanisms have been used to project (clockwise from top) the evolution of disease-harboring mosquitoes, future environments and land use, physiological responses of invasive species such as cane toads, demographic responses of penguins to future climates, climate-dependent dispersal behavior in butterflies, and mismatched interactions between butterflies and their host plants. Despite these modeling advances, we seldom have the detailed data needed to build these models, necessitating new efforts to collect the relevant data to parameterize more biologically realistic predictive models. New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species’ responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.