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PreprintCC BYbioRxiv

Warming responses of microbial respiration emerge from spatially structured species interactions

  1. 1KU Leuven/BioTeC+

Abstract

Microbial communities are the main drivers of global carbon cycling. Yet, predicting their respiratory responses to warming is challenging because the underlying mechanisms remain elusive. Here, we developed a spatially explicit model, integrating single-cell metabolism with metabolic exchange and endogenous heat production, to disentangle the mechanisms driving community-level temperature responses. We reveal that mutualism amplifies respiratory temperature sensitivity compared to isolated taxa, as exchanged metabolites sustain a secondary growth phase with higher respiratory demand, and this phase creates disproportionately large CO2; emissions under warming (apparent activation energy Ea; increases from 0.65 to 0.77 eV). Spatial structure amplifies this further through threshold effects, whereby diffusion limitation shifts from constraining to enabling mutualistic benefits as metabolic rates increase with temperature (Ea; increases to 1.18 {+/-} 0.09 eV). Endogenous heat production drives additional amplification through positive feedback between local temperature rises and resource turnover rates (Ea; further increasing to 1.78 {+/-} 0.14 eV). Collectively, our results demonstrate that community-level temperature responses are not determined by enzyme kinetics alone but emerge from the interplay of ecological, physical, and metabolic interactions. Our model provides a general framework for understanding the combined effects of the underlying mechanisms on microbial temperature responses. Future climate carbon models should incorporate these considerations to improve predictions.

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BibTeX
@misc{impe2026warming,
  title = {{Warming responses of microbial respiration emerge from spatially structured species interactions}},
  author = {J. VAN IMPE and J. Wang and I. Hashem and K. Zhang and S. Bhonsale},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.09.757985},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.09.757985v1},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Warming responses of microbial respiration emerge from spatially structured species interactions
AU  - J. VAN IMPE
AU  - J. Wang
AU  - I. Hashem
AU  - K. Zhang
AU  - S. Bhonsale
PY  - 2026
DA  - 2026/10/09
PB  - bioRxiv
DO  - 10.64898/2026.10.09.757985
UR  - https://www.biorxiv.org/content/10.64898/2026.10.09.757985v1
AB  - Microbial communities are the main drivers of global carbon cycling. Yet, predicting their respiratory responses to warming is challenging because the underlying mechanisms remain elusive. Here, we developed a spatially explicit model, integrating single-cell metabolism with metabolic exchange and endogenous heat production, to disentangle the mechanisms driving community-level temperature responses. We reveal that mutualism amplifies respiratory temperature sensitivity compared to isolated taxa, as exchanged metabolites sustain a secondary growth phase with higher respiratory demand, and this phase creates disproportionately large CO2; emissions under warming (apparent activation energy Ea; increases from 0.65 to 0.77 eV). Spatial structure amplifies this further through threshold effects, whereby diffusion limitation shifts from constraining to enabling mutualistic benefits as metabolic rates increase with temperature (Ea; increases to 1.18 {+/-} 0.09 eV). Endogenous heat production drives additional amplification through positive feedback between local temperature rises and resource turnover rates (Ea; further increasing to 1.78 {+/-} 0.14 eV). Collectively, our results demonstrate that community-level temperature responses are not determined by enzyme kinetics alone but emerge from the interplay of ecological, physical, and metabolic interactions. Our model provides a general framework for understanding the combined effects of the underlying mechanisms on microbial temperature responses. Future climate carbon models should incorporate these considerations to improve predictions.
ER  -