Distribution of denitrifiers predicted by correlative niche modeling of changing environmental conditions and future climatic scenarios across the Baltic Sea

• Denitrifying microbial communities provide an important ecosystem function in aquatic systems. Yet, knowledge on predictive and modeling of these complex and changing communities is limited. The emergently challenging question of how the geographical distribution of denitrifiers responds to ongoing and future environmental change is not yet fully understood. In our study we use metadata-based correlative niche modeling to analyze the geographical distribution of selected putative denitrifiers in the genus Sphingomonas, Mycoplana, Shewanella, and Alteromonas at different predicted environmental conditions and future climatic scenarios across the Baltic Sea. Using the predictive power of an ensemble modeling approach and eight different machine-learning algorithms, habitat suitability and the distribution of the selected denitrifiers were evaluated using geophysical and bioclimatic variables, benthic conditions, and four Representative Concentration Pathway (RCP) trajectories of future global warming scenarios. All algorithms provided successful prediction capabilities both for variable importance, and for habitat suitability with Area Under the Curve (AUC) values between 0.89 and 1.00. Model findings revealed that salinity and nitrate concentrations significantly explained the variation in distribution of the selected denitrifiers. Rising temperatures of 0.8 to 1.8 °C at future RCP60–2050 trajectories are predicted to diminish or eliminate the bioclimatic suitable habitats for denitrifier distributions across the Baltic Sea. Multi-collated terrestrial and marine environmental variables contributed to the successful prediction of denitrifier distributions within the study area. The correlative niche modeling approach with high AUC values presented in the study allowed for accurate projections of the future distributions of the selected denitrifiers. The modeling approach can be used to improve our understanding of how ongoing and predicted future environmental changes may affect habitat suitability for organisms with denitrification capacity across the Baltic Sea.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Miljövetenskap (hsv//swe)

NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Environmental Sciences (hsv//eng)

Keyword

Climate change

Benthic conditions

Ensemble modeling

Habitat suitability

Machine-learning

Multi-collated variables

Environmental Studies

Miljövetenskapliga studier

Östersjö- och Östeuropaforskning

Baltic and East European studies

Publication and Content Type

ref (subject category)

art (subject category)