Marine environmental monitoring has tended to focus on site-specific methods of investigation. These traditional methods have low spatial and temporal resolution and are relatively labour intensive per unit area/time that they cover. To implement the Marine Strategy Framework Directive (MSFD), European Member States are required to improve marine monitoring and design monitoring networks. This can be achieved by developing and testing innovative and cost-effective monitoring systems, as well as indicators of environmental status. Here, we present several recently developed methodologies and technologies to improve marine biodiversity indicators and monitoring methods. The innovative tools are discussed concerning the technologies presently utilized as well as the advantages and disadvantages of their use in routine monitoring. In particular, the present analysis focuses on: (i) molecular approaches, including microarray, Real Time quantitative PCR (qPCR), and metagenetic (metabarcoding) tools; (ii) optical (remote) sensing and acoustic methods; and (iii) in situ monitoring instruments. We also discuss their applications in marine monitoring within the MSFD through the analysis of case studies in order to evaluate their potential utilization in future routine marine monitoring. We show that these recently-developed technologies can present clear advantages in accuracy, efficiency and cost.

This study relates sea surface temperature (SST) to the upwelling conditions off the southwest coast of Portugal using statistical analyses of publically available data. Optimum Interpolation (OI) of daily SST data were extracted from the United States (US) National Oceanic and Atmospheric Administration (NOAA) and data for wind speed and direction were from the US National Climatic Data Center. Time series were extracted at a daily frequency for a time horizon of 26years. Upwelling indices were estimated using westerly (Qx) and southerly (Qy) Ekman transport components.In the first part of the study, time series were inspected for trend and seasonality over the whole period. The seasonally adjusted time series revealed an increasing slope for SST (0.15°C per decade) and decreasing slopes for Qx (−84.01m3s−1km−1 per decade) and Qy (−25.20m3s−1km−1 per decade), over the time horizon. Structural breaks analysis applied to the time series showed that a statistically significant incremental increase in SST was more pronounced during the last decade.Cross-correlation between upwelling indices and SST revealed a time delay of 5 and 2days between Qx and SST, and between Qy and SST, respectively. A spectral analysis combined with the previous analysis enabled the identification of four oceanographic seasons. Those seasons were later recognised over a restricted time period of 4years, between 2008 and 2012, when there was an extensive sampling programme for the validation of ocean colour remote sensing imagery. The seasons were defined as: summer, with intense and regular events of upwelling; autumn, indicating relaxation of upwelling conditions; and spring and winter, showing high interannual variability in terms of number and intensity of upwelling events

The European Space Agency has acquired 10 years of data on the temporal and spatial distribution of phytoplankton biomass from the MEdium Resolution Imaging Spectrometer (MERIS) sensor for ocean color. The phytoplankton biomass was estimated with the MERIS product Algal Pigment Index 1 (API 1). Seasonal-Trend decomposition of time series based on Loess (STL) identified the temporal variability of the dynamical features in the MERIS products for water leaving reflectance (ρ_w(λ)) and API 1. The advantages of STL is that it can identify seasonal components changing over time, it is responsive to nonlinear trends, and it is robust in the presence of outliers. One of the novelties in this study is the development and the implementation of an automatic procedure, stl.fit(), that searches the best data modeling by varying the values of the smoothing parameters, and by selecting the model with the lowest error measure. This procedure was applied to 10 years of monthly time series from Sagres in the Southwestern Iberian Peninsula at three Stations, 2, 10 and 18 km from the shore. Decomposing the MERIS products into seasonal, trend and irregular components with stl.fit(), the ρ_w(λ) indicated dominance of the seasonal and irregular components while API 1 was mainly dominated by the seasonal component, with an increasing effect from inshore to offshore. A comparison of the seasonal components between the ρ_w(λ) and the API 1 product, showed that the variations decrease along this time period due to the changes in phytoplankton functional types. Furthermore, inter-annual seasonal variation for API 1 showed the influence of upwelling events and in which month of the year these occur at each of the three Sagres stations. The stl.fit() is a good tool for any remote sensing study of time series, particularly those addressing inter-annual variations. This procedure will be made available in R software.