Using principal component analysis among biotic and abiotic variables we found coherent subsets of variables that explain biological processes such as growth, mortality and recruitment in a seagrass (Zostera marina) meadow. The analysis of eigenvalues extracted three significant factors, which together account- ed for 63% of the variability. The first factor was mostly determined by demo- graphic variables (shoot density, growth, and above- and below-ground biomass). The second principal component was represented by abiotic variables such as temperature and solar radiation. The third factor was mainly defined by nutrient concentration (phosphates and nitrates). We found high negative correlation co- efficients between many biotic variables and temperature, which is contrary to some literature reports. This negative correlation coefficient might be due to a compounded lag effect on the meadow of the strong "El Nino" event which occurred during 1998 followed by an equally strong "La Nina event" that took take place in 1999. Variables with correlation less than ± 0.30 were dropped from the analysis. The plots of the abiotic principal components against biotic variables are presented.