Seasonal variability of planktic foraminiferal downward fluxes induced by environmental sensitivity of foraminiferal species from observation to prediction

Planktic foraminifera are among the most valuable signal-carriers in paleoceanography. However, due to different ecological preferences there is often a distinct seasonality in planktic foraminiferal fluxes, which is crucial for paleoceanographic interpretations of foraminifera-based proxies. Hence, this study focuses on the temporal and spatial variability of planktic foraminiferal fluxes and on the underlying environmental sensitivity of foraminiferal species.:p:Planktic foraminiferal fluxes, analyzed in the equatorial upwelling region of the eastern Atlantic Ocean, clearly reflect the pattern of small-scale regional and seasonal oceanographic variability in surface waters. Short-term flux variability in many foraminiferal species is probably the result of synchronized reproduction triggered by the lunar cycle.:p:A newly compiled data set of foraminiferal fluxes derived from globally distributed sediment traps was used to reassess preferences of selected species to key environmental parameters on a global scale. Sea-surface temperature (SST) markedly affects fluxes and relative abundances of most investigated species. The influence of export production (PEX) on species fluxes and abundances is not as pronounced, and more obvious within the relative abundances.:p:Finally, the compiled global data set of foraminiferal fluxes together with data on SST, PEX and mixed-layer depth is used to calibrate an empirical model that calculates monthly foraminiferal fluxes at species level. The model is then forced with a global data set of hydrographic and productivity data to globally predict monthly foraminiferal fluxes. The model is able to reproduce many general distribution patterns of foraminiferal assemblages observed in the world´s oceans. However, absolute foraminiferal fluxes are underestimated in most cases, while seasonal variations can be reproduced for some species. The limited predictive skills of the model suggest that additional parameters should be considered.