The enigma of terrestrial primary productivity: measurements, models, scales and the diversity-productivity relationship
Abstract
Net primary productivity (NPP) is a variable of primary interest to ecologists, as it is related both to resource availability, potentially affecting biological diversity, and to the dynamics of the carbon cycle. However, there are alarming discrepancies in NPP estimates as well as in the reported form of the relationship between NPP and species richness. Such discrepancies could be due to the different and often simplified assumptions of various global NPP models and the heterogeneity of field NPP measurements that comprise a mix of natural vegetation and plantations. Here we review different global models of NPP and available original sources of NPP field measurements in order to examine how their geographic patterns are affected by various assumptions and data selection, respectively. Then we review studies dealing with diversity-productivity relationships in view of different NPP estimates. We show that although NPP does generally decrease with increasing latitude, geographic NPP patterns considerably differ between individual models as well as between the models and field NPP data. Such inconsistencies might be partially responsible for discrepancies in productivity-richness relationships, although these are also driven by other factors that covary with productivity and affect diversity patterns. To reconcile the discrepancies between various NPP measures, it is necessary to
1) standardize field NPP data,
2) develop scaling techniques that bridge the gap between the scale of field NPP measurements and NPP models, and
3) build global NPP models that account for nutrient limitation (especially concerning phosphorus in the tropics) and are parameterized by field measurements. Also,
4) a better theory needs to be developed to distinguish the effect of productivity from the effects of other environmental variables on diversity patterns. Improving our ability to estimate NPP will help us predict future NPP changes and understand the drivers of species richness patterns.