Microbiomes respond predictably to built habitats on the seafloor

Abstract

The seafloor contains complex ecosystems where habitat heterogeneity influences biodiversity. Natural biological and geological features including vents, seeps and reefs create habitats that select for distinct populations of micro‐ and macrofauna. While largely studied for macrobiological diversity, built habitats may also select distinct microbiomes. Built habitat density on the seafloor is increasing with ocean sprawl expanding in the continental shelf and slope, potentially having widespread effects on benthic communities. This study addresses one type of built habitat, shipwrecks, on microbiomes in surrounding sediment. Using deep‐sea sediment samples (762 total) from the Gulf of Mexico, we report elevated diversity and a predictable core microbiome around nine shipwrecks. We show the sphere of influence of built habitats extends up to 300 m onto the seafloor. Supervised learning made predictions of sample proximity to structures based on frequency of taxa. Strongest predictions occurred in sediments nearest and furthest from sites for archaea and mid‐transect for bacteria. The response of archaea to built habitats was consistent across sites, while bacteria showed greater between site variability. The archaeal core shipwreck microbiome was enriched in taxa (e.g., Bathyarchaeia, Lokiarchaeia, Thermoplasmata) not present in the surrounding seafloor. Shipwrecks shaped microbiomes in expected ways, providing insight on how built habitats impact microbiome biodiversity in the Anthropocene.

Insights into Ecological & Evolutionary Processes via community metabarcoding

“…This study paves the way for applying high throughput data to measure attributes of biodiversity, and its resilience to anthropogenic modification. The paper by Hampel et al (2022) showed that the presence of undersea "built habitats" (shipwrecks) causes increased microbial biodiversity and a predictable core microbiome in their surrounding deep-sea sediments (extending up to 300 m from the wrecks). Specific archaeal groups showed enrichment around shipwrecks, suggesting metabolic shifts towards chemolithoautotrophy in these proximate sediments.…”

“…(2022). Another supervised learning method was used to make predictions of sediment sample proximity to shipwrecks based on frequency of microbial taxa (Hampel et al., 2022). Machine learning inference methods paired with eco‐evolutionary simulation models can additionally identify the ecological and evolutionary processes that interact to generate biodiversity patterns, as demonstrated by Overcast et al.…”

“…For example, machine learning visual processing approaches may be effectively applied to image recognition analysis to study arthropod biodiversity as in Emerson et al (2022). Another supervised learning method was used to make predictions of sediment sample proximity to shipwrecks based on frequency of microbial taxa (Hampel et al, 2022).…”

Insights into Ecological & Evolutionary Processes via Community Metabarcoding

“…Metabarcoding approaches can clearly identify the effect of anthropogenic habitat modification on species assemblages, including key taxa that are associated with modified environments, as well as the overall homogenizing effects of invasions. For example, Hampel et al (Hampel, Moseley, & Hamdan, 2022) show that the presence of undersea "built habitats" (shipwrecks) causes increased microbial biodiversity and a predictable core microbiome in their surrounding deep-sea sediments (extending up to 300m from the wrecks). Specific archaeal groups showed enrichment around shipwrecks, suggesting metabolic shifts towards chemolithoautotrophy in these proximate sediments.…”

“…Incorporation of Deep Learning / Artificial Intelligence approaches applied to image analysis to study arthropod biodiversity . Supervised learning was used to make predictions of sediment sample proximity to shipwrecks based on frequency of microbial taxa (Hampel et al, 2022).…”