Biodiversity and global change
Research in the Clark lab focuses on biodiversity of forests, including how species coexist and how they are influenced by changing climate and natural and human disturbance. Studies range in scale from field plots to continental data sets. Large, long-term experiments are central to the approach. There is emphasis on modeling innovation to synthesize evidence from many sources.
Summaries of some current projects are below:
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. 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 collabor
ation 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 re
productive 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.
A few widely cited examples:
JS Clark, M Silman, R Kern, E Macklin, J HilleRisLambers. Ecology 80 (5), 1475-1494
The capacity for replacement through reproduction is one of the first requirements for persistence of any species. The variables that control maturation, fecundity, and seed dispersal must all be inferred together from highly indirect data. This paper is cited as the framework for accurate inference and prediction. It has been implemented by labs all over the globe. Software is available here.
JS Clark, SR Carpenter, M Barber, S Collins, A Dobson, JA Foley, …. Science 293, 657-660
This paper laid the foundation for a new initiative in ecology, focused on using information to anticipate, and potentially mitigate, responses to global change. It is cited as the motivation for many new efforts to provide predictive understanding of change.
JS Clark. Science 327, 1129-1132
This paper can resolve a decades-old question in ecology, that of explaining why so many competing species could coexist on a small number of limiting resources. Species-level differences are not apparent from species-level data, masked by averaging over individuals in species-level data. Species differences masked by the phenomenon known as Simpson’s Paradox or the ‘ecological fallacy’ become apparent when analyzed at the individual scale: individuals respond more like others of the same species, thus concentrating competition within the species.
K Zhu, CW Woodall, JS Clark. Global Change Biology 18, 1042-1052
Changing geographic distributions have been the focus of climate change studies—evidence that populations can move to sites that become favorable. More concerning is the possibility that they cannot. From a large analysis of inventory data across North America, results showed that tree species lack the capacity to track rapid contemporary climate change.
JS Clark, D Nemergut, B Seyednasrollah, PJ Turner, S Zhang. Ecological Monographs, 87, 34–56.
Synthesis of biodiversity data has not had a valid basis for inference and prediction. Species are observed on different scales, and most are absent from most observations. They respond to one another at the same time that each is responding in its own way a changing environment. Generalized joint attribute modeling (GJAM) provides a first advance towards integration, providing excellent parameter recovery and precise prediction of entire ecological communities. More on GJAM software here.