G. Volpe's research while affiliated with Virginia Institute of Marine Science and other places

... This hypothesis is supported by our observation of spectral signature shifts that coincide with known nutrient dynamics and light conditions, which are critical drivers of phytoplankton community structure [22,23]. Interestingly, the variation in signatures across different depths and months, particularly the observed depth-wise attenuation of signature enrichment in DW sensor data, underscores the stratification effects on light availability and nutrient distribution, aligning with observations that certain cyanobacteria and smaller phytoplankton are adapted to low-nutrient, high-light conditions in stratified waters [24]. The consistent patterns seen in certain signatures during specific months could also reflect the physiological adaptations of phytoplankton to seasonal light variations, potentially impacting their backscattering properties as suggested by the taxonomic variability in particle backscattering (BBP)-to-phytoplankton carbon (Cphyto) scaling [25]. ...

... We obtained temperature data from the EU Copernicus Marine Service platform (https://marine.copernicus.eu/), which provides accessible, regular, and authoritative global and regional information on the physical and biogeochemical aspects of our oceans (Le Traon et al., 2019). Specifically, we acquired daily vertical profile data (0-40 m) of temperature from the Mediterranean Sea Physics Reanalyses (Escudier et al., 2021) from the med-cmcc-tem-rean-d dataset of the MEDSEA_-MULTIYEAR_PHY_006_004 product (https://doi.org/10.25423/CMCC/ ...

... The advent of underwater optical sensors has increased our understanding of the spatial and temporal characteristics of phytoplankton photosynthesis in the ocean and its role in carbon cycling [8]. However, the increased knowledge of these temporal and spatial characteristics also led to the identification of complexities in certain variables, such as changes in the chlorophyll index due to the physiological variability of phytoplankton [4,[9][10][11][12] and null or negative correlations between the concentration of chlorophyll a (Chla) and the particulate backscattering coefficient (b bp (λ)) due to photoacclimation processes or changes in the relative abundance of non-algal particles (NAPs) in subtropical regimes [5][6][7]13,14]. These complexities lead to low correlations between b bp (λ) values and Chla in ultra-oligotrophic oceanic areas and prevent precise estimation of relevant parameters in oligotrophic regions, such as subtropical gyres [13]. ...

... To complement the short (wind-forced) waves spectrum estimated from the drone footage, we used long-wave (or swell) wave estimates from the CMEMS (Copernicus Marine Environment Monitoring Service) Mediterranean Sea waves reanalysis [36]. The reanalysis consists of a numerical simulation of sea surface wave physics, forced by best estimates of wind conditions, and constrained by daily observations of sea waves. ...

... We applied a temperature correction 79 using the coefficients provided by the manufacturer and the water temperature from the CTD onboard the Thetis profiler to minimize the effects of optical misalignments and drift in AC-S readings. The M2 model from Pitarch et al. 80 was used to correct for scattering errors. Furthermore, depth profiles of absorption and attenuation spectra were investigated systematically to identify and reanalyze the errors in the form of missing values, noise, or non-physical values due to instrumental artifacts (e.g., random electronic noise) or poor sample baselines. ...

... Indeed, several factors can affect the relationship between Chla and b bp (λ) over space and time. These include the ratio of non-algal to phytoplankton biomass, the carbon/Chla ratio of phytoplankton [26], the species composition and diversity of phytoplankton (which vary in size and shape), and the characteristics of non-algal components [4,11,13,27]. Further study of the variability of Chla and b bp (λ) -especially in ultra-oligotrophic oceanic areas -is needed to improve our understanding of their major determinants and improve our estimates of phytoplankton carbon cycling derived from remote sensing data [26,28]. ...

... It is a direct indicator of phytoplankton biomass (Sabatés et al., 2007;Santos et al., 2007) and is widely used to understand the evolution of the functioning of marine ecosystems (Rykaczewski et al., 2008;Xu et al., 2015;Falcini et al., 2015;Colella et al., 2016). Ac-cording to several studies, the concentration of Chl-a in the Mediterranean Sea basin decreases from west to east, and a clear separation between the two sub-basins can be shown in the Strait of Sicily (Volpe et al., 2012;Colella et al., 2016). High concentrations of Chl-a have been observed in coastal areas, particularly near river mouths, that provide significant continental nutrient loads (Colella et al., 2016). ...

... The Aarhus Bay Chl a concentrations are, in comparison, higher than the average in central Kattegat of 1.94 mg Chl a m − 3 , based on a timeseries from 1989-1999 (Hasager et al., 2003), but the average Chl a is comparable to concentrations around 3.0 mg Chl a m − 3 from the central Baltic Sea (Pitarch et al., 2016). Wasmund and Uhlig (2003) observed a decrease in Chl a in the Kattegat and Belt Sea although their period of observations partly differed from the present, which actually showed an increase between 1993 and 2000 (Fig. 2c). ...

... Alkaline phosphatase activity in the ocean ranges from below the detection limit (denoted by 0, e.g. <=0.002 nmol L −1 h −1 in Yamaguichi et al. 68 ) to very high rates as much as 6583 nmol L −1 h −1 for MUF-P, which is largely controlled by ambient DIP concentration and DOP availability 9,36,44 . Therefore, APA rates are not normally distributed and show a positively skewed distribution with long tails of high values. ...

... At present, a number of global or regional comparison of international operational forecast systems based on the IV-TT Class4 standard are available, and the results show that no single system performs optimally in all forecasting parameters [9][10][11][12]. This paper is an evaluation of the NMEFC-NEMO based on the GOV-IVTT class 4 metrics for the first time. ...