In Marine environmental research
Marine heterotrophic prokaryotes degrade, transform, and utilize half of the organic matter (OM) produced by photosynthesis, either in dissolved or particulate form. Microbial metabolic rates are affected by a plethora of different factors, spanning from environmental variables to OM composition. To tease apart the environmental drivers underlying the observed organic matter utilization rates, we analysed a 21 year-long time series from the Gulf of Trieste (NE Adriatic Sea). Heterotrophic carbon production (HCP) time series analysis highlighted a long-term structure made up by three periods of coherent observations (1999-2007; 2008-2011; 2012-2019), shared also by OM concentration time series. Temporal patterns of HCP drivers, extracted with a random forest approach, demonstrated that a period of high salinity anomalies (2002-2008) was the main driver of this structure. The reduced river runoff and the consequent depletion of river-borne inorganic nutrients induced a long-term Chl a decline (2006-2009), followed by a steady increase until 2014. HCP driving features over the three periods substantially changed in their seasonal patterns, suggesting that the years following the draught period represented a transition between two long-term regimes. Overall, temperature and particulate organic carbon concentration were the main factors driving HCP rates. The emergence of these variables highlighted the strong control exerted by the temperature-substrate co-limitation on microbial growth. Further exploration revealed that HCP rates did not follow the Arrhenius' linear response to temperature between 2008 and 2011, demonstrating that microbial growth was substrate-limited following the draught event. By teasing apart the environmental drivers of microbial growth on a long-term perspective, we demonstrated that a substantial change happened in the biogeochemistry of one of the most productive areas of the Mediterranean Sea. As planktonic microbes are the foundation of marine ecosystems, understanding their past dynamics may help to explain present and future changes.
Manna Vincenzo, De Vittor Cinzia, Giani Michele, Del Negro Paola, Celussi Mauro
Adriatic Sea, Carbon cycle, Heterotrophic carbon production, Machine learning, Mediterranean Sea, Organic matter, Prokaryotic metabolism, River runoff, Time series