4.7 Article

Unraveling the predictive role of temperature in the gut microbiota of the sea urchin Echinometra sp. EZ across spatial and temporal gradients

期刊

MOLECULAR ECOLOGY
卷 30, 期 15, 页码 3869-3881

出版社

WILEY
DOI: 10.1111/mec.15990

关键词

Echinometra; gut microbiota; microbial ecology; Persian; Arabian Gulf; sea urchin; thermal gradient

资金

  1. ICRS Fellowship
  2. NSF [1924498]
  3. NYUAD Water Research Center [CG007]
  4. NSF GRF
  5. Division Of Ocean Sciences
  6. Directorate For Geosciences [1924498] Funding Source: National Science Foundation

向作者/读者索取更多资源

This study explores how microbial communities change under different temperature conditions, identifying microbial taxa that are closely correlated with the thermal environment.
Shifts in microbial communities represent a rapid response mechanism for host organisms to respond to changes in environmental conditions. Therefore, they are likely to be important in assisting the acclimatization of hosts to seasonal temperature changes as well as to variation in temperatures across a species' range. The Persian/Arabian Gulf is the world's warmest sea, with large seasonal fluctuations in temperature (20celcius - 37celcius) and is connected to the Gulf of Oman which experiences more typical oceanic conditions (<32celcius in the summer). This system is an informative model for understanding how symbiotic microbial assemblages respond to thermal variation across temporal and spatial scales. Here, we elucidate the role of temperature on the microbial gut community of the sea urchin Echinometra sp. EZ and identify microbial taxa that are tightly correlated with the thermal environment. We generated two independent datasets with a high degree of geographic and temporal resolution. The results show that microbial communities vary across thermally variable habitats, display temporal shifts that correlate with temperature, and can become more disperse as temperatures rise. The relative abundances of several ASVs significantly correlate with temperature in both independent datasets despite the >300 km distance between the furthest sites and the extreme seasonal variations. Notably, over 50% of the temperature predictive ASVs identified from the two datasets belonged to the family Vibrionaceae. Together, our results identify temperature as a robust predictor of community-level variation and highlight specific microbial taxa putatively involved in the response to thermal environment.

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