Water temperature drives phytoplankton blooms in coastal waters

Abstract

Phytoplankton blooms are an important, widespread phenomenon in open oceans, coastal waters and freshwaters, supporting food webs and essential ecosystem services. Blooms are even more important in exploited coastal waters for maintaining high resource production. However, the environmental factors driving blooms in shallow productive coastal waters are still unclear, making it difficult to assess how environmental fluctuations influence bloom phenology and productivity. To gain insights into bloom phenology, Chl a fluorescence and meteorological and hydrological parameters were monitored at high-frequency (15 min) and nutrient concentrations and phytoplankton abundance and diversity, were monitored weekly in a typical Mediterranean shallow coastal system (Thau Lagoon). This study was carried out from winter to late spring in two successive years with different climatic conditions: 2014/2015 was typical, but the winter of 2015/2016 was the warmest on record. Rising water temperature was the main driver of phytoplankton blooms. However, blooms were sometimes correlated with winds and sometimes correlated with salinity, suggesting nutrients were supplied by water transport via winds, saltier seawater intake, rain and water flow events. This finding indicates the joint role of these factors in determining the success of phytoplankton blooms. Furthermore, interannual variability showed that winter water temperature was higher in 2016 than in 2015, resulting in lower phytoplankton biomass accumulation in the following spring. Moreover, the phytoplankton abundances and diversity also changed: cyanobacteria (< 1 μm), picoeukaryotes (< 1 μm) and nanoeukaryotes (3–6 μm) increased to the detriment of larger phytoplankton such as diatoms. Water temperature is a key factor affecting phytoplankton bloom dynamics in shallow productive coastal waters and could become crucial with future global warming by modifying bloom phenology and changing phytoplankton community structure, in turn affecting the entire food web and ecosystem services.

Marine Microbial Food Web Networks During Phytoplankton Bloom and Non-bloom Periods: Warming Favors Smaller Organism Interactions and Intensifies Trophic Cascade

“…Microbial interaction networks during the bloom and non-bloom periods differ and complexity increases during phytoplankton blooms. Trombetta et al (2019) showed that in the same shallow coastal waters, increase in water temperature during spring triggers phytoplankton bloom. Here, we found that increases in phytoplankton biomass are responsible for profound modifications at the level of microbial interactions.…”

“…Apart from its ecological interest, it is a lagoon of economic relevance, mainly due to oyster farms representing 10% of the French production. Complementary data on hydrological parameters (water temperature, salinity, oxygen concentration, oxygen saturation and turbidity), meteorological parameters (air temperature, wind speed, wind direction, PAR, UVA and UVB) and corrected fluorescence of the chlorophyll a monitored at high frequency are available in open access in Mostajir et al (2018) and Trombetta et al (2019).…”

“…The abundance, diversity and size of large size phytoplankton (6-200 µm) was estimated by microscopy as detailed in Trombetta et al (2019). Duplicates of 100 mL subsamples were taken and fixed with 10 mL formalin solution (about 4% formaldehyde final concentration) and kept cold (4 • C) until analysis.…”

“…Correlation networks were constructed using abundance data of all microbial groups/taxa/species (or nodes) encompassing 1 node for virus, 2 nodes for bacteria, 46 nodes for phytoplankton, 4 nodes for HF, 28 nodes for naked ciliates, and 29 nodes for tintinnids. First, 2015 and 2016 data sets were divided into bloom and non-bloom periods based on the Chl a fluorescence daily mean as an index of phytoplankton biomass as described by Trombetta et al (2019). They defined blooms as periods (1) that started with at least two consecutive days of positive growth rates and (2) where the sum of the net growth rates over at least five consecutive days was positive.…”

“…The survey was performed during the spring in 2015 (two bloom periods) and from winter to spring in 2016 (one non-bloom and one bloom period). The winter of 2016 was the warmest on record, and it displayed abnormally high water temperature, absence of significant water cooling in winter, and slow temperature increase from winter to spring (Trombetta et al, 2019). It modified bloom phenology and composition, offering insights to understand potential changes affecting the bloom and non-bloom microbial network structure under global warming.…”