Back

Niche-based deterministic assembly processes strengthen the effects of β-diversity on metacommunity functioning of marine bacterioplankton

Chang, F.-H.; Yang, J. W.; Liu, A. C.-H.; Lu, H.-P.; Gong, G.-C.; Shiah, F.-K.; Hsieh, C.-h.

2022-03-11 ecology
10.1101/2022.03.09.483723 bioRxiv
Show abstract

Studies at a local community () level have shown that biodiversity positively affects ecosystem functioning due to niche-based deterministic processes like resource partitioning. Extending to a metacommunity ({beta}) level, we hypothesize that {beta} diversity also positively affects metacommunity functioning. We further hypothesize that the {beta} diversity effect is stronger (more positive) when {beta} diversity is increased by deterministic/non-random assembly processes. To test the hypotheses, we collected bacterioplankton along a transect of 6 stations in the southern East China Sea in 14 cruises. All 6 stations within a cruise were regarded as a metacommunity. For any pairs of the 6 stations, the Bray-Curtis index and summed bacterial biomass were calculated to represent {beta} diversity and metacommunity functioning, respectively. We also calculated deviation of observed mean pairwise phylogenetic similarities among species from random to represent the deterministic influences of assembly processes. We found that bacterial {beta} diversity generally positively affects metacommunity functioning; however, the {beta} diversity effect varied among cruises. Cross-cruise comparison indicates that the {beta} diversity effect increased with the deterministic assembly processes selecting for phylogenetically dissimilar species. This study extends the biodiversity-ecosystem functioning research to a metacommunity level, complementing the current framework by considering community assembly in natural environments.

Matching journals

The top 6 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.