Empirical models predicting primary productivity from chlorophyll a and water temperature for stream periphyton and lake and ocean phytoplankton

Published data on stream periphyton, lake phytoplankton, and ocean phytoplankton were analyzed to 1) quantify regression models relating daily gross primary production (GPP) to chlorophyll a (chl a) standing stock and water temperature, 2) compare regressions across assemblages, and 3) compare the precision of regression estimates of daily primary production and of production integrated over time to those obtained by measurements using radioisotopes. Regression models predicting daily GPP explained between 29% and 86% of the variance in Log GPP with chi a accounting for 28 to 85% of the explained variance. Regression models differed significantly across assemblages. Chlorophyll-specific production, corrected for the effect of temperature, declined with increasing chi a standing stock presumably because of increased self shading, and was lower in stream periphyton than in lake or marine phytoplankton presumably because of reduced nutrient diffusion in algal mats. Gross primary production was more intensely related to water temperature in stream periphyton (Q(10) = 2.5) than in either ocean phytoplankton (Q(10) = 1.2) or lake phytoplankton (Q(10) = 1.4). Precision measured as the error factor (EF) by which means have to be multiplied or divided to obtain the limits of a 95% confidence interval, was lower for regression estimates of daily production (EF = 3.4-6.7) and for production integrated over time (EF = 3.4) than for measurements of daily production (EF = 1.2-2). Considering the reduced effort required to obtain estimates of primary production using these regression models, we argue that they could be useful when coarse production estimates are sufficient or when adequate resources are not available to make direct measurements.