Incorporating molecular-based functional and co-occurrence network properties into benthic marine impact assessments

Abstract

Taxonomic and functional community structures may respond differently to anthropogenic stressors. Used in combination they can provide an estimate of functional redundancy, a key component of ecosystem resilience. In this study, the utility of incorporating functional community structure and co-occurrence network properties into impact assessments of offshore oil and gas (O&G) operations on benthic bacterial communities was investigated. Sediment samples and physico-chemical data were collected along a transect at increasing distances from one exploratory drilling (ED), and one gas production and drilling (GPD) field. Bacterial community composition was determined by 16S rRNA metabarcoding. A hidden-state prediction method (PAPRICA) was used to characterize bacterial metabolic community functions. At both sites, diversity differed significantly between near-field (impacted) and far-field (non-impacted) stations, with both taxonomic and functional alpha-diversity positively affected in near-field stations at the GPD site. The opposite pattern was observed in the near-field samples of ED with lower and higher values respectively. Overall, impacted stations displayed a distinct network signature, with a lower ratio of positive interactions and signs of higher community cohesion. Community profiles from metabolic inference and co-occurrence network topology provided complementary information to taxonomic indices, which may assist with assessing the effects of O&G activities on benthic communities.

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