Paleoecological Patterns, Ecological Processes, Modeled Scenarios: Crossing Scales to Understand an Uncertain Future
Wednesday, August 12, 2015: 1:30 PM-5:00 PM
314, Baltimore Convention Center
Simon J Goring, University of Wisconsin
John W. (Jack) Williams, University of Wisconsin, Madison;
Andrea Dawson, University of California - Berkeley;
Michael Dietze, Boston University; and
Amy E. Hessl, West Virginia University
John W. (Jack) Williams, University of Wisconsin, Madison
Ecological models have become increasingly complex making it imperative to constrain them with long term ecological data. The fusion of modern data, long-term ecological records, and statistical modeling presents researchers with novel frameworks to improve our understanding of the past, the present and the future, but achieving these advances also presents significant challenges.
Extending ecological data-model fusion across temporal scales requires that we accept the limitations of each data set. Modern datasets are often highly resolved spatially and taxonomically, but often lack the temporal extent to capture ‘slow’ processes such as succession. Historical datasets are often incomplete snapshots - a glimpse into past states of ecosystems - but were often collected for purposes other than scientific inquiry. Paleoecological datasets are able to capture processes that operate on long time-scales (succession, migration ,extinction) or occur infrequently (volcanic events, rapid climate change, and mega-droughts), but paleoecological datasets offer lower taxonomic resolution, higher temporal uncertainty, and uneven spatial coverage.
Researchers are beginning to use long term datasets to help model future carbon dynamics and understand ecosystem changes under scenarios of global change in a more integrated fashion. Advances in computational and statistical theory present us with new methods to integrate long term ecological data into ecosystem models. These new methods are rapidly being taken up by the ecological community. Data assimilation methods and broader Bayesian approaches are commonly taught in both workshops and as part of the core curriculum for graduate students in ecology programs worldwide. These methods help us constrain uncertainties in modeled results, but themselves require an understanding of the uncertainties inherent in the ecological data. Thus these approaches often require intense collaboration between ecologists specializing in modelling and data collection, statisticians, and, increasingly, computer scientists.
Combining paleoecological data with modern datasets presents challenges, but recent conceptual developments leave us poised to integrate paleoecological data into modern ecological analysis in a direct manner, by coupling paleoecological pattern to modern ecological process. Extending the temporal scale of our models and datasets leaves us poised on the edge of an exciting frontier; ecological change as both a temporal and disciplinary continuum, rather than discrete units, binned across sub-disciplinary boundaries defined by time. The 2015 ESA meeting sees the society on the cusp of its second century, the perfect opportunity to look forward with the past.