Biodiversity and global change
From pioneers like G. Evelyn Hutchinson in the 1950s to the present, ecologists have recognized a biodiversity paradox: models predict that only a few competitors for the same resources can coexist, while nature demonstrates dozens to thousands of competitors coexisting in nature, apparently on a small number of resources. Traditional models have not provided guidance on how each species limits its own population growth, as required if species are to coexist with one another. Not nearly enough niche differences emerge from field data that could limit competition and thus explain species coexistence in models. The answer to this paradox came from integration of many types of data with innovation in modern bioinformatics. Recently in Science (reviews in Faculty of 1000 Biology) research in the lab showed that species do indeed compete more within their own species than with others, made possible by the high dimensionality of environmental variation and the many small differences in how species respond to it.
Research in the Clark lab focuses on biodiversity of forests, including how species coexist and how they are influenced by rising CO2, changing climate, and natural and human disturbance. Studies range in scale from field plots to continental scale data sets. Large, long-term experiments are central to the approach. There is emphasis on modeling innovation to synthesize evidence from many sources. Some recent results illustrate
1. How individual scale analysis provides the insight on climate change vulnerability, showing which species are at risk and why, and
2. How the aggregation in regional scale data sets removes the information that would be needed to understand the contribution of species interactions to biodiversity.
3. How tree populations are failing to track changes in climate across eastern North America.
Summaries of some current projects are below:
CDI forest modeling project. Forests respond to climate change at the individual scale, but we care about (and have data on) biodiversity at the regional scale. The inherent mismatch between scales motivates a new approach to understanding the complexity of climate impacts. This research develops a multiscale modeling framework and design algorithms that make environmental models computationally scalable. The approach hinges upon strong interplay of algorithmic and statistical techniques. Statistical inference brings stochastic modeling sophistication in space and time, yielding improved characterization of the process and the possibility of full inference. The collaboration brings researchers from computer science, statistics, and environmental science. New techniques are being applied to the problem of understanding how forests will develop under climate change.
Macrosystem Science: forest dynamics and climate change. Climate change is rapidly transforming forests over much of the globe in ways that are not anticipated by current science. Large-scale forest diebacks, apparently linked to interactions involving drought, warm winters, and other species, are becoming alarmingly frequent. Models of biodiversity and climate have not provided guidance on if/where/when such responses will occur. Instead models often predict potential numbers of extinctions, but these forecasts not are linked in any mechanistic way to the processes that could cause them. Both modeling and field studies rely on aggregate metrics of species presence/absence or relative abundance at regional scales, but climate affects individuals. Aggregation of individual data to the species level, hides or even qualitatively changes climate effects. By sampling and analysis at the individual scale across continental variation in climate, this study can link the individual scale processes to regional responses. This study will exploit existing research sites and the new NEON platform of sites for synthesis of models and data to determine when and where predicting climate impacts on biodiversity is a plausible goal, understand where surprises are likely to occur, and attribute those predictions back to individual tree health and vulnerability to climate risk factors. The collaboration involves six institutions.
Pathogens and forest biodiversity experiment. Fungal pathogens may control biodiversity of forest trees through selective mortality of species that would otherwise threaten less competitive species. Climate warming may increase the amount and severity of disease as these pathogens increasingly survive mild winters, their reproductive rates increase, and plant defenses suffer from drought and temperature stress. We are evaluating the extent to which pathogens regulate tree seedling health, the fungi involved, and their effects on tree growth and survival. We are studying how those interactions are affected by the temperature changes predicted for mid-century. In a warming experiment where tree seedlings are exposed to soil and air temperature increases of 3°C to 5°C in NC and MA, the study allows us to quantify how temperatures affect their hosts when temperatures increase, depending on the competition they simultaneously experience from other tree species.
Forest warming experiment. Climate change is restructuring forests of the United States, although the details of this restructuring are currently uncertain. Rising temperatures of 2 to 8°C and associated changes in soil moisture will shift the competitive balance between species that compete for light and water, changing the abilities to produce seed, germinate, grow, and survive. We are using large scale experiments to determine the effects of warming on the most sensitive stage of species distributions, i.e., recruitment, in mixed deciduous forests in southern New England and in the Piedmont region of North Carolina. We are exposing seedlings to air and soil warming experiments in two eastern deciduous forest sites; one at the Harvard Forest in central Massachusetts, and the other at the Duke Forest in the Piedmont region of North Carolina.