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Biology subjects

Larsson, A. I.

Publications and source records attributed to Larsson, A. I..

2 recordsLinked to original sources

High variability and limited host specificity characterise the microbiome of Desmophyllum pertusum across a cold-water coral reef

Cold-water corals (CWC), like their tropical counterparts, are ecosystem engineers that create complex habitats supporting high biodiversity. Despite their broad distribution and increasing exposure to climate change and anthropogenic stressors, CWC remain comparatively understudied, particularly with respect to their associated microbiomes. Unlike tropical corals, CWC lack photosynthetic algal symbionts, suggesting that microbial associates may play a particularly important role in nutrient acquisition, biogeochemical cycling, and host resilience. Here, we investigated microbial community composition across the reef ecosystem formed by the cosmopolitan CWC Desmophyllum pertusum at Tisler Reef (NE Skagerrak, Kosterfjord, Norway). Using Nanopore sequencing of the full-length 16S rRNA gene, we characterised microbial communities associated with coral, seawater, substrate, and coral rubble to (i) assess reef-scale patterns, (ii) characterise variation in microbial assemblages between coral colonies and between branch and mucus compartments, as well as according to branch position, and (iii) identify putative core microbiome members and indicator taxa associated with D. pertusum relative to its surrounding environment. At the reef scale, microbial community structure differed among habitat compartments. Within D. pertusum, both colony identity and compartment (branch vs. mucus) contributed to structuring microbial assemblages, while branch position also explained an additional but limited proportion of the variation. The coral-associated microbiome contained a set of recurrent taxa, but many of these were also widespread in the surrounding environment, and few were consistently enriched in coral samples. Together, these findings indicate that the D. pertusum microbiome at Tisler Reef comprises recurrent microbial associates embedded within a highly variable assemblage, rather than a large and abundant core microbiome exclusive to the coral habitat. These results contribute to establishing a baseline understanding of D. pertusum microbiomes and provide insights into microbial community structure across cold-water coral reef ecosystems.

microbiology↗

New Interactive Machine Learning Tool for Marine Image Analysis

Due to advances in imaging technologies, the rate of marine video and image data collection is drastically increasing. Often these datasets are not analysed to their full potential as extracting information for multiple species, such as their presence and surface area, is incredibly time-consuming. This study demonstrates the potential of a new open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The tool was initially developed to measure plant roots, but here was tested on its ability to measure the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, in two types of underwater image data: 18,346 time-lapse images and 1,420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter, such as dice score and species area error, allow for the objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate Mycale lingua models were created using RootPainter, as indicated by their average dice score of 0.94 {+/-} 0.06. Model transfer and optimisation aided in the production of two of these models, increasing analysis efficiency from 6 to 16 times faster than manual annotation in Photoshop, for underwater observatory images. Sponge and surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. This study demonstrates that interactive machine learning tools and model sharing have the potential to dramatically increase image analysis speeds, collaborative research, and our collective knowledge on spatiotemporal patterns in biodiversity.

ecology↗