Back

Trade-off between competition ability and invulnerability to predation in marine microbes; protist grazing versus viral lysis effects

Yang, J. W.; Chang, F.-H.; Yeh, Y.-C.; Tsai, A.-Y.; Chiang, K.-P.; Shiah, F.-K.; Gong, G.-C.; Hsieh, C.-h.

2021-10-09 ecology
10.1101/2021.10.09.463787 bioRxiv
Show abstract

Trade-offs between competition ability and invulnerability to predation are important mechanisms explaining how predation promotes bacterial diversity. However, existence of these trade-offs has apparently not been investigated in natural marine bacterial communities. Here, we address this question with growth-based measurements for each marine bacterial taxon by conducting on-board dilution experiments to manipulate predation pressure and using high-throughput sequencing to assess the response of bacterial communities. We determined that bacterial taxa with a higher predation-free growth rate were accompanied with higher predation-caused mortality, supporting existence of competitiveness-invulnerability trade-off. This trade-off was stronger and more consistent under viral lysis than protist grazing. In addition, predation generally flattened out the rank-abundance distribution and increased the evenness and richness of the bacterial community. These findings supported the "Kill-the-Winner" hypothesis. All experiments supported a significant competitiveness-invulnerability trade-off, but there was substantial variation among bacterial communities in response to predation across experiments conducted in various sites and seasons. Therefore, we inferred that the Kill-the-Winner hypothesis is important but likely not the only deterministic mechanism explaining how predation shapes bacterial assemblages in natural marine systems.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.