Submitted to: Global Ecology and Biogeographical Letters
Publication Type: Peer reviewed journal
Publication Acceptance Date: 2/20/2001
Publication Date: 1/22/2002
Citation: Gill, R.A., Kelly, R.H., Parton, W.J., Day, K.A., Jackson, R.B., Morgan, J.A., Scurlock, J.M., Tieszen, L.L., Castle, J.V. 2002. Using simple environmental variables to estimate belowground productivity in grasslands. Global Ecology and Biogeographical Letters 11(1):79-86. Interpretive Summary: Ecologists have long been interested in being able to quantify the net primary production (NPP) of ecosystems. NPP is the cumulative amount of increased plant production that occurs in a year, and while it may seem like an easy attribute to measure, it is anything but. It is important because it is one of the most important measures of an ecosystems functioning, and also because it provides information about the capability of an ecosystem to absorb CO2 from the atmosphere, thereby mitigating the current problem of rising levels of atmospheric CO2 from the burning of fossil fuels. Simply measuring the amount of plants biomass you see at the end of a growing season doesn't provide an accurate answer of NPP since 1) in some ecosystems, most production occurs in roots and organs belowground, and these are hard to measure, 2) by the end of a growing season, considerable plant material has already been lost by leaf drop and other mechanisms, and therefore, is unaccountable, and 3) much of the production which occurs in a year is lost back to the atmosphere in respiration, the same process by which animals and humans lose CO2 through their normal metabolism. Herein we describe a computer simulation model we developed which utilizes easily remembered traits like peak seasonal aboveground biomass and mean annual temperature to estimate NPP in world grasslands.
Technical Abstract: In many grasslands, aboveground net primary productivity (NPP) can be estimated by measuring peak aboveground biomass. Estimates of belowground net primary productivity, and consequently, total net primary productivity, are more difficult. We addressed one of the three main objectives of the Global Primary Productivity Data Initiative for grassland systems - to develop simple models or algorithms to estimate missing components of total system NPP. Any estimate of belowground NPP requires an accounting of total root biomass, the percentage of living biomass, and annual turnover of live roots. We derived a relationship using aboveground peak biomass and mean annual temperature as predictors of belowground biomass (r2=0.54; P less than or equal to 0.01). The percentage of live material was 0.6, based on published values. We used three different functions to describe root turnover: constant, a direct function of aboveground biomass, or as a positive exponential relationship with mean annual temperature. We tested the various models against a large database of global grassland NPP and the constant turnover and direct function models were approximately equally descriptive (r2=0.31 and 0.37), while the exponential function had a stronger correlation with the measured values (r2=0.40) and had a better fit than the other two models at the productive end of the BNPP gradient. When applied to extensive data we assembled from two grassland sites with reliable estimates of total NPP, the direct function was most effective, especially at lower productivity sites. We provide some caveats for its use in systems that lie at the extremes of the grassland gradient and stress that htere are large uncertainities associated with measured and modeled estimates of BNPP.