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CR 10:51-67 (1998)  -  doi:10.3354/cr010051

Predicting the relative sensitivity of forest production in Ireland to site quality and climate change

Christine L. Goodale1,*, John D. Aber1, Edward P. Farrell2

1Complex Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire 03824, USA
2Department of Environmental Resource Management, University College Dublin, Agriculture Building Belfield, Dublin 4, Ireland

Most model-based predictions of climate change effects on forest ecosystems have used either potential or static descriptions of vegetation and site, removing the effects of direct management or land use. In this paper we use a previously developed and validated model of carbon and water balances in forest ecosystems (PnET-II) to assess the relative sensitivity of forest production in Ireland to predicted climate change and to ambient variability in site quality. After validating the model against measured productivity for 2 sets of stands, we ran the model using existing variation in site quality, represented as differences in foliar N concentration, and also for predicted changes in climate and atmospheric CO2. Resulting variations in productivity were compared with those due to potential errors in the specification of input parameters and to variation in current ambient climate across the region. The effects on net primary production (NPP) and wood production of either ambient variation in climate or predicted changes in temperature, precipitation and CO2 are quite small (0 to 30%) relative to the effects of ambient variability in site quality (up to 400%). The range of possible variation in other user-specified physiological parameters resulted in changes of less than 10% in model predictions. We conclude that site-specific conditions and management practices result in a range of forest productivity that is much greater than any likely to be induced by climate change or CO2 enrichment. We also suggest that it is essential to understand and map spatial variability in site quality, as well as to understand how the productive capacity of landscapes will change in response to management and pollution loading, if we are to predict the actual role that climate change will play in altering forest productivity and global biogeochemistry.

Regional modeling · Validation · Foliar nitrogen · Sitka spruce

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