Predictability and environmental drivers of chlorophyll fluctuations vary across different time scales and regions of the North Sea

Abstract

Predictability and environmental drivers of chlorophyll fluctuations vary across different time scales and regions of the North Sea Blauw, A.N.; Beninca, E.; Laane, R.W.P.M.; Greenwood, N.; Huisman, J. General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. A B S T R A C TPhytoplankton concentrations display strong temporal variability at different time scales. Recent advances in automated moorings enable detailed investigation of this variability. In this study, we analyzed phytoplankton fluctuations at four automated mooring stations in the North Sea, which measured phytoplankton abundance (chlorophyll) and several environmental variables at a temporal resolution of 12-30 min for two to nine years. The stations differed in tidal range, water depth and freshwater influence. This allowed comparison of the predictability and environmental drivers of phytoplankton variability across different time scales and geographical regions. We analyzed the time series using wavelet analysis, cross correlations and generalized additive models to quantify the response of chlorophyll fluorescence to various environmental variables (tidal and meteorological variables, salinity, suspended particulate matter, nitrate and sea surface temperature). Hour-tohour and day-to-day fluctuations in chlorophyll fluorescence were substantial, and mainly driven by sinking and vertical mixing of phytoplankton cells, horizontal transport of different water masses, and non-photochemical quenching of the fluorescence signal. At the macro-tidal stations, these short-term phytoplankton fluctuations were strongly driven by the tides. Along the Dutch coast, variation in salinity associated with the freshwater influence of the river Rhine played an important role, while in the central North Sea variation in weather conditions was a major determinant of phytoplankton variability. At time scales of weeks to months, solar irradiance, nutrient conditions and thermal stratification were the dominant drivers of changes in chlorophyll concentrations. These results show that the dominant drivers of phytoplankton fluctuations differ across marine environments and time scales. Moreover, our findings show that phytoplankton variability on hourly to daily time scales should not be dismissed as environmental noise, but is...

Short‐term estimates of phytoplankton growth and mortality in a tidal estuary

“…Over the course of sampling (~2 months), total chlorophyll concentration (< 200 μm) and group-specific phytoplankton carbon biomass estimates fluctuated from week to week at each respective tide, supporting previous findings of weekly phytoplankton variation in estuaries, including the Skidaway River Estuary (Verity 2002b;Blauw et al 2012Blauw et al , 2018Bittar et al 2016). Previous long-term sampling efforts in the Skidaway River Estuary have revealed typically higher baseline levels and weekly variability in chlorophyll concentration and phytoplankton abundance (< 20 μm) in summer relative to other seasons (Bittar et al 2016).…”

“…Previous long-term sampling efforts in the Skidaway River Estuary have revealed typically higher baseline levels and weekly variability in chlorophyll concentration and phytoplankton abundance (< 20 μm) in summer relative to other seasons (Bittar et al 2016). Though weekly-biweekly tidal features (i.e., spring-neap tides) are important in some estuaries (Blauw et al 2012(Blauw et al , 2018, we did not find a relationship between biomass or phytoplankton rates and predicted tide height in the estuary and trends in short-term biomass variability between same-day tides were more pronounced.…”

The intrinsic predictability of ecological time series and its potential to guide forecasting

“…This problem is beginning to be resolved with automated measurements of system states, such as chlorophyll a concentrations in aquatic systems (Blauw et al. , Thomas et al. ), assessment of community dynamics in microbiology (Trosvik et al.…”

“…Growth rate, r Time series measured at the appropriate time scales over long periods of time are rare, despite the knowledge that they are among the most effective approaches at resolving long-standing questions regarding environmental drivers (Lindenmayer et al 2012, Giron-Nava et al 2017, Hughes et al 2017). This problem is beginning to be resolved with automated measurements of system states, such as chlorophyll a concentrations in aquatic systems (Blauw et al 2018, Thomas et al 2018, assessment of community dynamics in microbiology (Trosvik et al 2008, Faust et al 2015, Martin-Platero et al 2018, and phenological (Pau et al 2011) and flux measurements (Dietze 2017). Such high-frequency, longterm data are likely to provide a more accurate picture of the range of possible system states, even when systems are non-ergodic and change through time (e.g., Fig.…”

Scaling‐up biodiversity‐ecosystem functioning research

“…Future theory and empirical research could assess three predictors of interspecific synchrony. The first is forcing caused by fluxes in energy which varies considerably across scales on land and in the oceans (Carrara & V azquez 2010;Vogt et al 2011;Acevedo-Trejos et al 2018) and is known to be synchronising when driven by strong periodic cycles (Blauw et al 2018), disturbances (Keitt 2008) and autocorrelated random fluctuations (Petchey et al 1997). This environmental variation engenders different compensatory responses among species or functional groups at different scales, reflecting variation in seasonal and interannual phenology (Thackeray et al 2010;Lasky et al 2016) and asynchronous population fluctuations across trophic levels (Fontaine & Gonzalez 2005;Keitt & Fischer 2006;Vasseur & Gaedke 2007;Loreau & de Mazancourt 2008;Fauchald et al 2011;Vasseur et al 2014;Sheppard et al 2019).…”