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Process‐based proxy of oxygen stress surpasses indirect ones in predicting vegetation characteristics
Author(s) -
Bartholomeus Ruud P.,
Witte JanPhilip M.,
Bodegom Peter M.,
Dam Jos C.,
Becker Piet,
Aerts Rien
Publication year - 2012
Publication title -
ecohydrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.982
H-Index - 54
eISSN - 1936-0592
pISSN - 1936-0584
DOI - 10.1002/eco.261
Subject(s) - environmental science , proxy (statistics) , vegetation (pathology) , water content , hydrology (agriculture) , moisture , climate change , soil science , ecology , statistics , mathematics , meteorology , geography , geology , medicine , geotechnical engineering , pathology , biology
Robust relationships among soil, water, atmosphere and plants are needed to reliably forecast the plant species composition. In this paper, we show the need for, and the application of, a process‐based relationship between soil moisture conditions and vegetation characteristics. We considered 366 groundwater‐dependent sites, where oxygen stress, caused by a surplus of soil moisture, codetermines plant performance. We compared two existing indirect proxies for the soil oxygen status – namely mean spring groundwater level ( MSL ) and sum exceedence value ( SEV ) – with our newly developed process‐based proxy, viz . root respiration stress ( RS ). The two indirect proxies and the process‐based proxy for oxygen stress performed equally well in describing vegetation characteristics for the Netherlands under the current climate. However, relationships based on MSL and SEV appeared to produce systematic prediction errors when applied outside their calibration range, in contrast to the relationship based on RS . Hence, the two indirect proxies cannot be used in projections, such as in predicting effects of climate change on vegetation composition, all the more because they – unlike RS – do not account for essential parameters that determine oxygen stress (e.g. temperature and extreme rainfall events in the growing season). We advocate using RS for estimating vegetation impacts in climate projections to increase the reliability and effectiveness of adaptive strategies. Copyright © 2011 John Wiley & Sons, Ltd.