A decade of sampling in the Bay of Biscay: What are the zooplankton time series telling us?

Abstract

The project “Studies on time series of oceanographic data” was established as a pilot project by the Instituto Español de Oceanografía (I.E.O.) in 1991. After more than a decade, the project has grown to encompass a network of 19 sampling stations in five different transects along the North and Northwest coast of Spain: Vigo, Coruña, Cudillero, Gijón and Santander beginning in 1987, 1988, 1993, 2001, 1991, respectively. At each location a coastal-ocean gradient is sampled monthly for hydrography, nutrients and planktonic communities. We have used these data to set with statistical significance the range of variability of several environmental variables and biological communities and determined the rates and trends of warming due to climate change as well as to describe some direct and indirect effects of the increase in water temperature on the pelagic ecology. The project had substantially contributed to get a deeper knowledge on planktonic communities and species and to produce baselines, climatologies and reference levels for the North coast of Spain, which allow us to do accurate evaluations on the effects of environmental perturbations on the ecosystem and forecast the expected recovery time. These changes both in the physical structure of the water column and in the trophic level that drives ecosystem production and functioning are likely to also modify the structure, production and organization of higher trophic levels like zooplankton. Our analysis shows that only through sustained and repeated time series sampling it is feasible to detect these changes. Indeed, the annual cycle of zooplankton biomass seems to be restricted in time, with the annual decrease in zooplankton biomass matching the onset of stratification. The observed patterns in the seasonal occurrence of incoming species like Temora stylifera are related to those observed in the water column stratification, which is reinforced by the warming trend.