Biology subjects

Khomich, M.

Publications and source records attributed to Khomich, M..

3 recordsLinked to original sources

bioRxiv2023

What mycologists should talk about when they are talking about the International Code of Nomenclature for algae, fungi, and plants

Fungal metabarcoding of substrates such as soil, wood, and water are uncovering an unprecedented number of fungal species that do not seem to produce tangible morphological structures and that defy our best attempts at cultivation, thus falling outside of the ambit of the International Code of Nomenclature for algae, fungi, and plants. The present study uses the new, ninth release of the species hypotheses of the UNITE database to show that species discovery through environmental sequencing vastly outpaces traditional, Sanger sequencing-based efforts in a strongly increasing trend over the last five years. Our findings challenge the present stance of the mycological community - that "the code" works fine and that these complications will somehow sort themselves out given enough time and a following wind - and suggest that we should be discussing not whether to allow DNA-based descriptions (typifications) of species and by extension higher ranks of fungi, but what the precise requirements for such DNA-based typifications should be. We submit a tentative list of such criteria for further discussion. However, the present authors fear that no waves of change will be lapping the shores of mycology for the foreseeable future, leaving the overwhelming majority of extant fungi without formal names and thus scientific and environmental agency. It is not clear to us who benefits from that, but neither fungi nor mycology are likely to be on the winning side.

scientific communication and education↗

bioRxiv2021

Neural networks and extreme gradient boosting predict multiple thresholds and trajectories of microbial biodiversity responses due to browning

Ecological association studies often assume monotonicity such as between biodiversity and environmental properties although there is growing evidence that non-monotonic relations dominate in nature. Here we apply machine learning algorithms to reveal the non-monotonic association between microbial diversity and an anthropogenic induced large scale change, the browning of freshwaters, along a longitudinal gradient covering 70 boreal lakes in Scandinavia. Measures of bacterial richness and evenness (alpha diversity) showed non-monotonic trends in relation to environmental gradients, peaking at intermediate levels of browning. Depending on the statistical methods, variables indicative for browning could explain 5% of the variance in bacterial community composition (beta diversity) when applying standard methods assuming monotonic relations and up to 45 % with machine learning methods (i.e. extreme gradient boosting and feed-forward neural networks) taking non-monotonicity into account. This non-monotonicity observed at the community level was explained by the complex interchangeable nature of individual taxa responses as shown by a high degree of non-monotonic responses of individual bacterial sequence variants to browning. Furthermore, the non-monotonic models provide the position of thresholds and predict alternative bacterial diversity trajectories in boreal freshwater as a result of ongoing climate and land use changes, which in turn will affect entire ecosystem metabolism and likely greenhouse gas production.

microbiology↗

bioRxiv2020

Evaluating geographic variation within molecular operational taxonomic units (OTUs) using network analyses in Scandinavian lakes

Operational taxonomic units (OTUs) are usually treated as if they are internally uniform in environmental metabarcoding studies of microbial and macrobial eukaryotes, even when the OTUs are being used to infer biogeographic patterns. The OTUs constructed by the program Swarm have underlying network topologies in which nodes represent amplicons and edges represent 1 nucleotide differences between nodes. Such networks can be exploited to search for biogeographic patterns within each OTU. To do this, here we used an available protistan metabarcoding dataset consisting of the hypervariable V4 region of the 18S rRNA locus amplified from 77 lakes collected across Norway and Sweden. The 82 most abundant and wide-spread OTUs constructed by Swarm were evaluated using shortest path, assortativity, and geographical analyses. We found that while pairs of amplicons from the same lake were usually connected directly to each other within the OTUs, these pairs of amplicons from the same lake did not form assortative clusters within the OTUs, and amplicons were not more connected with other amplicons occurring in neighboring lakes than expected by chance. This new approach to looking at within-OTU is applicable to other metabarcoding datasets and we provide code to perform these analyses.

microbiology↗