1 Microbial photosynthesis mitigates carbon loss from northern

2 peatlands under warming

3 Samuel Hamard¹, Sophie Planchenault¹,², Romain Walcker¹, Anna Sytiuk¹, Marie Le Geay¹, Martin

4 Küttim³, Ellen Dorrepaal⁴, Mariusz Lamentowicz⁵, Owen Petchey⁶, Bjorn Robroek⁷,⁸, Eeva-Stiina

5 Tuittila⁹, Maialen Barret¹, Régis Céréghino¹, Frédéric Delarue¹⁰, Jessica Ferriol¹, Tristan Lafont

6 Rapnouil¹, Joséphine Leflaive¹, Gaël Le Roux¹, Vincent E.J. Jassey¹,*

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8 ¹ Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse,
9 CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3), Toulouse, France

10 ²Institute of Environmental Sciences (CML), Leiden University, Leiden, the Netherlands

11 ³Institute of Ecology, School of Natural Sciences and Health, Tallinn University, Uus-Sadama 5,
12 Tallinn, Estonia

13 ⁴ Climate Impacts Research Centre, Department of Ecology and Environmental Science, Umeå
14 University, SE-981 07, Abisko, Sweden

15 ⁵Climate Change Ecology Research Unit, Adam Mickiewicz University, Poznań, Poland

16 ⁶Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich,
17 Switzerland

18 ⁷Department of Ecology, Radboud Institute for Biological and Environmental Sciences, Radboud
19 University, Nijmegen, the Netherlands

20 ⁸School of Biological Sciences, University of Southampton, Southampton, United Kingdom

21 ⁹ School of Forest Sciences, Joensuu campus, University of Eastern Finland, FI-80100 Joensuu,
22 Finland

23 ¹⁰CNRS, Sorbonne Université, EPHE, UMR 7619 METIS, 4 place Jussieu, 75005 Paris, France

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27 *Author for correspondence:

28 Dr Vincent E.J. Jassey, Centre de Recherche sur la Biodiversité et l'Environnement (CRBE),
29 Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 – Paul Sabatier (UT3),
30 Toulouse, France; vincent.jassey@univ-tlse3.fr

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**Abstract**

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The future of the northern peatland carbon (C) sink is uncertain as the effects of warming on microbial
metabolisms are unclear. While increased microbial CO₂ emissions are expected under warming,
the response of microbial photosynthesis remains unknown, complicating predictions of net microbial
effects on peatland carbon emissions. Here, using a continental-scale experimental study, we show
that warming amplifies microbial photosynthesis by 3.4 mgC m⁻² h⁻¹ per 1°C increase. By 2100, this
increase translates to a gain of 51.1 Tg of carbon per year from the northern peatland area under
the pessimistic (SSP5-8.5) climatic change scenario, offsetting ~14% of projected heterotrophic CO₂
emissions in northern peatlands. By linking field and microcosm experiments, we further show that
enhanced microbial photosynthesis accelerates peatland CO₂ uptake as photosynthetic microbial-C
subsidies stimulate nutrient mineralisation. These results underscore the importance of
photosynthetic microbes for mitigating carbon emissions and supporting long-term carbon storage
in peatlands.
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## Main

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Northern peatlands are one of the most important carbon (C) hotspots in the world¹, providing globally significant climate-regulating services². Whether they continue to exert a net cooling effect on future climate depends on their sensitivity to climate change²,³. Their C storing capacity hinges on the delicate balance of C uptake and loss. C loss is strongly hampered by waterlogged conditions, poor nutrient availability, and cold temperatures that slow down microbial respiration and decomposition processes, favouring the accumulation of peat⁴. The size of the peatland organic C pool thus strongly relies on microbial growth and the magnitude of microbially-driven C fluxes, making the overall warming response highly uncertain³. Indeed, recent findings show the potential for peatlands to lose C through increased decomposition rates⁵,⁶ and enhanced microbial CO₂⁷,⁸ and CH₄⁹ emissions with warming, weakening the C sink strength of peatlands. However, understanding the net impacts of climate change on microbial processes requires untangling the diverse microbial life strategies involved in C dynamics¹⁰,¹¹. In particular, photoautotrophic microbes that fix atmospheric CO₂ through photosynthesis have been recently recognized to be important players in peatland C dynamics¹²,¹³.

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Peatland photoautotrophic microbes are incredibly diverse¹², with Cyanobacteria, Chlorophyta, and Ochrophyta as the predominant phyla¹²,¹⁴. They comprise a variety of lifestyles, from strict photoautotrophs to mixotrophs (a combination of photosynthetic and predatory-based nutrition)¹⁵. Photoautotrophs make up c. 21% of total microbial biomass in the surface peat¹⁴⁻¹⁶ and may account for c. 10% of the peatland CO₂ fixation¹²,¹³. Despite the significant contribution of photoautotrophs to peatland C uptake, their sensitivity to climate warming remains uncertain¹⁵,¹⁷, and ultimately, the consequences for peatland C processes and dynamics remain unclear. Given the considerable effect of temperature on photosynthesis¹⁸⁻²⁰, we hypothesise that warming will increase photoautotrophic CO₂ fixation, elevate peatland C uptake, and hence mitigate future global warming.

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Simultaneously, accelerated photoautotrophic photosynthesis has the potential to mediate important feedback within the microbial food web and influence heterotrophic processes such as
72 CO₂ respiration. Photoautotrophs release 20–40% of their photosynthetic products into their
73 surrounding environment²¹. These exudates often comprise carbohydrates such as polysaccharides
74 and amino acids²²,²³, providing energy to heterotrophic organisms²¹. Photoautotrophs can also
75 support other microbial trophic levels with their living (e.g. consumers) and dead biomass (e.g.
76 bacteria), mediating changes in peatland C dynamic through trophic interactions²⁴. Therefore, we
77 hypothesise that increased microbial photosynthesis will accelerate microbial CO₂ emissions by
78 providing energy subsidies to heterotrophic microorganisms in the form of fresh C biomass, C
79 exudates, and necromass. Elucidating whether the possible elevation of photoautotrophic CO₂
80 fixation caused by warming will outweigh the rise of photoautotrophic-induced heterotrophic CO₂
81 emissions will be essential for predicting the strength of CO₂ feedback in global peatlands under
82 climate warming.

83 To test the above-mentioned hypotheses, we combined a manipulative field experiment with
84 spatial modelling to quantify the magnitude and variability of the response of photoautotrophic CO₂
85 fixation rates to warming and generate a quantitative assessment of the possible gain of C at the
86 surface of northern peatlands. Using microcosm experiments, we further explored the utilisation of
87 photoautotrophic-C subsidies by heterotrophic microbes (decomposers and consumers), and the
88 possible allocation of this C pool to CO₂ respiration, to understand how warming-induced changes
89 in photoautotrophic activity feedback on peatland CO₂ dynamics. Consequently, we provide an
90 integrated assessment of the warming-induced effects of photoautotrophs on peatland CO₂ fluxes
91 for the coming decades.
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Warming increases photoautotrophic CO₂ fixation rates

94 A reciprocal transplant experiment within five European peatlands (n = 150; Extended Fig. 1) (ref.²⁵)
95 evaluated the response of microbial photosynthesis to climate warming and identified its main biotic
96 and abiotic drivers. This fully reciprocal transplant of peat mesocosms across a gradient of nearly
97 10°C air temperature and 200 mm precipitation (Extended Fig. 1, Table S1) both mimicked the
98 expected changes in climate at northern latitudes by 2100 (ref.²⁶) and assessed the response of the
CO₂ fixation rate of five photoautotrophic communities while disentangling the effects of climate from
geography and microbial legacy²⁷.

Microbial photosynthesis varied across seasons (*P* < 0.001, 25.9% variance explained by season, Fig. 1a, d) and microbial communities (*P* < 0.001, 18.4% variance explained by microbial community, Fig. 1b, d), with rates ranging from 0.68 to 78.4 mgC m⁻² h⁻¹. In addition, there were widespread changes in microbial photosynthetic rates along the transplant latitudinal gradient (*P* < 0.001; 19.6% variance explained by transplantation, Fig. 1c, d). On average, microbial photosynthesis increased by 3.4 ± 0.6 mgC m⁻² h⁻¹ per 1°C increase in mean air temperature (*P* < 0.001, Fig. 1e). However, it remained stable with total precipitation change (*P* = 0.69, Supplementary Fig. 1). Hence, warming plays a dominant and important role in driving rates of microbial photosynthesis. Therefore, we hereafter primarily focus on the effects of warming.

Consistent with previous reports in aquatic ecosystems²⁸⁻³⁰, warming elevated microbial photosynthesis by promoting photoautotrophic abundance and chlorophyll biomass through changes in photoautotrophic community composition (Extended Figs. 2 and 3). Using regression coefficients (β) from linear mixed-effects models (LMMs), which represent the direction and magnitude of the warming effect, we found that warming increased the relative abundance of Chrysophyceae (32.4% of total relative abundance; β = 0.18, *P* = 0.08; Extended Fig. 3, Supplementary Fig. 2), likely due to their preference for warmer conditions¹⁷,³¹. Warming also decreased the relative abundance of Dinophyceae (1.7% of total relative abundance; β = -0.23, *P* = 0.03; Extended Fig. 3, Supplementary Fig. 2). This can be explained by the mixotrophic nature of many Dinophyceae³², which makes them particularly sensitive to warming¹⁵,³³ and less competitive than strict photoautotrophs as temperatures rise³⁴. Of the 132 photoautotrophic OTUs, only ten positively responded to warming (*P* < 0.05), even though many OTUs tended to increase with warming (Extended Fig. 3). Such results suggest that, although diverse photoautotrophic bacteria and protists perform photosynthesis¹², their temperature sensitivity seems to be dominated by a few taxa that grow better under high temperatures¹⁷,³⁵.
Similar responses of photosynthesis to temperature have been found in plants³⁶,³⁷ and phytoplankton²⁸,³⁸, underlining its universal thermal sensitivity across phylogenetic groups and ecosystem types. Our LMMs showed that the response of microbial photosynthesis to warming was consistent among microbial communities (warming x community, *P* = 0.08, Fig. 1e). However, the LMM regression coefficients (β) revealed that the strength of the response to warming varied across communities (Fig. 1e, f). In particular, we found that the photosynthetic rate of photoautotrophic communities with historically wider temperature ranges was more sensitive to warming (Fig. 1f). This finding suggests that with progressing climate change, increased photoautotrophic CO₂ fixation will be particularly accentuated at higher latitudes, as these communities are disproportionately exposed to a wider seasonal temperature range and faster climatic change than lower latitudes³⁹.

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## Soil moisture modulates increased microbial photosynthesis

We found that the response of microbial photosynthesis to warming differed across seasons (warming x season, *P* < 0.001, LMM). It changed from strong positive effects in spring (β = 0.38, +4.2 ± 3.1 mgC.m⁻².h⁻¹.°C⁻¹ across all microbial communities) to moderate (β = 0.05, +0.5 ± 1.3 mgC.m⁻².h⁻¹.°C⁻¹) and null (β = 0.02, +0.08 ± 3.6 mgC.m⁻².h⁻¹.°C⁻¹) effects in summer and autumn, respectively (Fig. 1g, Extended Fig. 4, Supplementary Fig. 3). This agrees with recent findings on peatland net CO₂ exchanges⁴⁰, and highlights that accounting for seasonally varying response of microbial CO₂ uptake to warming is crucial to accurately estimate future change in peatland primary productivity.

Many factors, such as nutrients, light, and altered soil moisture could have influenced warming effects on microbial CO₂ fixation rates over seasons¹⁹,²⁴,²⁸. Our data showed that light availability did not vary across sites over seasons (LMMs, *P* = 0.98; Table S1), indicating that similar amounts of light were available for microbial photosynthesis in each site across seasons. C and N content in the pore water, as well as soil water content (SWC), all significantly varied across sites and seasons (Table S2 and S3). However, among these factors, only SWC significantly interacted with warming to explain variability in photoautotrophic photosynthesis (warming x SWC, *P* < 0.001,

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LMM; Table S4). In particular, when peat was dry (20th-40th percentile, typically in summer) or extremely moist (80th-100th percentile, typically in autumn), warming effects on microbial photosynthesis were minor or null, but became positive when peat was moist (60th-80th percentile, typically in spring; Fig. 2). Such patterns are explained by the sensitivity of photoautotrophic growth and photosynthetic efficiency to soil moisture extremes (Supplementary Fig. 4a, b). Thus, fluctuating soil moisture can reduce the benefits of warming on microbial photosynthesis over the growing season, especially during periods of drought or intense rainfalls, which aligns with recent findings for plant photosynthesis¹⁹ and soil respiration⁴¹ in boreal forests.

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## Prospects of carbon gains at the northern peatland scale

The consistent response of photoautotrophic CO₂ fixation to warming across microbial communities enabled us to extend the relationship across the global northern peatland area (>45°N) and generate estimates of potential gains in photoautotrophic CO₂ fixation for the next century. Using the LMM models, which predict the response of photoautotrophic photosynthesis to warming for the five microbial communities of origin (Fig. 1e), we projected annual gains in photoautotrophic CO₂ fixation under future climatic conditions (shared socio-economic pathways, SSP1-2.6, SSP2-4.5, and SSP5-8.5) for the near (2021-2040), medium (2041-2060) and long-term (2081-2100). Predicted versus observed plots revealed that our five predictive models tended to overrepresent the observed rates at low CO₂ fixation rates and underrepresent CO₂ fixation at higher observed rates (Supplementary Fig. 5). Nevertheless, sensitivity analyses revealed that all models made predictions with a small margin error (Methods and Supplementary Fig. 5), with nearly 89% of observations falling within the prediction intervals (Supplementary Fig. 6). This indicates that our models provide predictions falling within the full range of observed data, and shows that increased photoautotrophic CO₂ fixation caused by warming can be reasonably projected while providing predictions with confidence intervals.

The resulting maps reveal variability in predicted increased of photoautotrophic CO₂ fixation across northern peatlands, ranging from 0.1 to 30 MgC km⁻² yr⁻¹ (Fig. 3). Specifically, in the near
term, these increases are concentrated in five hotspots (> 5 MgC km⁻² yr⁻¹): northern Ontario, Manitoba and the Northwest Territories in Canada, Central Siberia, north of Scandinavia, and Scotland (Fig. 3a, d, g). These regions are notable for their high density of peatlands (Extended Fig. 1a), explaining their heightened response. However, the relative importance of these hotspots will reduce over time as new regions with values exceeding 5 MgC km⁻² yr⁻¹ will emerge under more intense climate warming (SSP2-4.6 and SSP5-8.5; Fig. 3e,f,h,i). This implies that while current hotspots may experience early gains, other areas will become more prominent in the longer term (Extended Fig. 5). High variability among predictive models (Extended Fig. 6), however, highlights an urgent need to address community variation in follow-up studies. Nevertheless, our model projections can still provide an approximation of how much more C would be fixed by photoautotrophs under future warming.

Across the study area, we estimate that warming induces additional photoautotrophic CO₂ fixation of 12.3 TgC yr⁻¹ (10-90% confidence interval (CI): 10.5-14.1 TgC yr⁻¹) for the near term (2021-2040, all SSP scenarios together), of 15.5 TgC yr⁻¹ (CI 13.7-17.3 TgC yr⁻¹) for the medium term (2041-2060), and of 28.6 TgC yr⁻¹ (CI 27.1-30.1 TgC yr⁻¹) for the long-term (2081-2100; Table 1). By comparison, this amount of carbon gained through microbial photosynthesis is equivalent to roughly 11% (SSP1 2.6) to 26% (SSP5 8.5) of the present-day net primary production of all northern peatlands (estimated at 110 TgC yr⁻¹)³. In terms of mitigation potential impacts, we found that increased microbial photosynthesis would compensate the projected rise in heterotrophic CO₂ emissions⁴² from northern peatlands by 14.1% (10-90% confidence interval (CI): 12.2-15.9%; all scenarios together) under 2021-2040 conditions, and by 6.0% (CI 2.5-8.6%; SSP2-4.5) and 13.7% (CI 11.5-15.9%; SSP5-8.5) under the 2100 conditions, respectively (Fig. 4). This finding is generally consistent with the expectation that increased photosynthesis could offset predicted raises in CO₂ emissions, maintaining the C sink potential of peatlands for the next century³,⁴³.

Although the estimated gains in photoautotrophic CO₂ fixation are robust with respect to model uncertainties, these estimates pertain solely to temperature sensitivity and do account neither for increasing CO₂ levels in the atmosphere nor future changes in peatland water levels. Interactions
between elevated CO₂ and warming can cause diverging responses within peatland photoautotrophic communities¹⁷. However, the consistent increase in Chrysophyceae abundance under warming and/or elevated CO₂ suggests that microbial photosynthesis may remain enhanced under these conditions, as we found that elevated Chrysophyceae drove increased microbial photosynthesis under warming (Fig. 2). At the same time, climate model simulations project a decrease in surface soil moisture across the northern hemisphere for the next century²⁶, which may reduce the positive effect of warming on microbial photosynthesis. Nevertheless, we found that soil moisture would need to drop substantially (>50% decrease, Fig. 2) to negate warming effects on microbial photosynthesis, a threshold that remains highly uncertain³,⁴². Furthermore, photoautotrophs may have overreacted to warming in the short term, leading to overestimations of long term C gains as increased photoautotrophic CO₂ fixation may stagnate or even decrease after a certain temperature threshold. However, while warming effects on microbial processes can lead to a less extreme long-term response, they are not lost over periods of years⁴⁴, suggesting that increased photoautotrophic CO₂ fixation will remain significant in the future. Finally, our predictions were made with constant peatland area and photoautotrophic CO₂ fixation season length (see Methods). Future changes in the surface area of northern peatlands and other factors such as decreasing snow cover will likely affect our predictions. For instance, Earth Surface Models predict that northern peatlands will expand⁴², especially in high latitudes where new areas that have been too cold or too dry will be suitable for peatland growth. Although the magnitude of these potential changes is still uncertain, more carbon will likely be accumulated by photoautotrophs in these areas.

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## Photoautotrophic-C subsidies raise ecosystem CO₂ uptake

As a result of warming, photoautotrophic growth may prime heterotrophic activity and ultimately promote CO₂ emissions, as suggested by the strong correlation between heterotrophic bacterial and photoautotrophic absolute densities in the transplant experiment (Supplementary Fig. 7). We addressed this question by conducting two complementary microcosm experiments. We first explored whether more photoautotrophic biomass boosts heterotrophic CO₂ emissions by releasing
more C exudates and producing more necromass (Extended Fig. 7a). Second, we tested whether an increase in photoautotrophic biomass elevates heterotrophic CO₂ emissions by supporting higher-level consumers (Extended Fig. 7b). In both experiments, we used microcosms containing axenic *Sphagnum* moss to which we added a standardised microbial food web and either photoautotrophic-C exudates and necromass (experiment 1) or living photoautotrophic biomass (experiment 2, see Methods).

The addition of photoautotrophic-C exudates and necromass increased CO₂ respiration in microcosms with a microbial food web (+4.4 ± 2.1 mg CO₂ m⁻² h⁻¹; Fig. 5a), likely driven by increased bacterial abundance (Supplementary Fig. 8a). However, we found that the addition of photoautotrophic-C exudates and necromass further enhanced CO₂ fixation rates in microcosms with a microbial food web (+21.3 ± 11.7 mg CO₂ m⁻² h⁻¹; Fig. 5a). As such, we suggest that the addition of photoautotrophic-C subsidies primed bacterial activity²¹, releasing nutrients²² embedded in photoautotrophic exudates and necromass such as nitrogen (Supplementary Fig. 8b). This nitrogen was most probably used in turn by photoautotrophs and *Sphagnum* moss for photosynthesis, for instance to synthesize the nitrogen-rich enzyme Rubisco⁴⁵.

In the second microcosm experiment, the addition of living photoautotrophs increased CO₂ fixation rates (+6.1 ± 0.7 mg CO₂ m⁻² h⁻¹; Fig. 5b). This effect was, however, particularly prominent when a microbial food web was added to the microcosms (+7.1 ± 2.1 mg CO₂ m⁻² h⁻¹; Fig. 5b, Supplementary Fig. 9). The addition of living photoautotrophs also increased respiration rates in microcosms, but to a lesser extent and only when a microbial food web was also added (+2.7 ± 1.9 mg CO₂ m⁻² h⁻¹; Fig. 5b). These results indicate that photoautotrophic-based trophic cascades mediate changes in CO₂ uptake that surpass CO₂ loss by heterotrophs. Photoautotrophs may have adapted to predation with the 'flight or fight' response, which demands increased mobilization of energy and results in increased metabolic rate⁴⁶, thus explaining the prominent increase of photoautotrophic photosynthesis in microcosms with predators.

Collectively, the findings of these two microcosm experiments demonstrate a clear potential for photoautotrophs to modify the magnitude and direction of feedback from peatlands to future
climate warming. The extent to which this will occur depends on whether adaptation to predation keeps pace allowing photoautotrophs to continue building living and dead biomass⁴⁷, and whether other factors associated with warming, such as vascular plant encroachment, become dominant selective forces of the microbiome. We caution that we quantified ecosystem CO₂ exchange in microcosms containing axenic *Sphagnum* moss. As such, future studies are needed to test the generality of the priming effects detected here and quantify their importance at the global peatland scale. Nevertheless, we observed no effect of sterilization on the chlorophyll a:b ratio in *Sphagnum* compared to samples from each donor site of the transplant experiment (Supplementary Fig. 10). As this ratio reflects plant photosynthetic efficiency and tolerance to nutrient limitation⁴⁸, the similarity between axenic *Sphagnum* and *Sphagnum* from our field-experiment conditions suggests comparable physiological capacities for photosynthesis. This raises the possibility that the enhanced ecosystem CO₂ uptake driven by photoautotrophs in microcosm experiments will also occur under field conditions.

In summary, our results highlight the overriding role of photoautotrophs in enhancing peatland CO₂ uptake under climate warming. We find that the elevation of microbial photosynthesis caused by warming may raise future northern peatland C uptake by 28.6 million tonnes yearly by 2100 (all SSP scenarios together), mitigating nearly 14% of the predicted increase in heterotrophic CO₂ emissions. We are witnessing global interest in peatland conservation and restoration as a nature-based solution for mitigating global C emissions². However, peatland conservation and restoration can be an effective climate mitigation strategy only if their net CO₂ sink function can be maintained in the future²,³,⁴³. Microbiota are rarely considered in conservation and restoration planning⁴⁹. Although our estimates will undoubtedly be refined, for instance, by including microbial dark CO₂ fixation⁵⁰, integrating photoautotrophs into peatland conservation and restoration efforts will certainly improve their outcomes by slowing down future peatland CO₂ emissions.
284 References

285 1. Nichols, J. E. & Peteet, D. M. Rapid expansion of northern peatlands and doubled estimate of carbon storage. *Nat. Geosci.* **12**, 917–921 (2019).

287 2. Dise, N. B. Environmental science. Peatland response to global change. *Science* **326**, 810–811 (2009).

289 3. Qiu, C. et al. A strong mitigation scenario maintains climate neutrality of northern peatlands. *One Earth* **5**, 1–12 (2022).

291 4. van Breemen, N. How Sphagnum bogs down other plants. *Trends Ecol. Evol.* **10**, 270–275 (1995).

293 5. Gao, S. et al. Effects of temperature increase and nitrogen addition on the early litter decomposition in permafrost peatlands. *CATENA* **209**, 105801 (2022).

295 6. Bell, M. C. et al. Sensitivity of peatland litter decomposition to changes in temperature and rainfall. *Geoderma* **331**, 29–37 (2018).

297 7. Dorrepaal, E. et al. Carbon respiration from subsurface peat accelerated by climate warming in the subarctic. *Nature* **460**, 616–619 (2009).

299 8. Gavazov, K. et al. Vascular plant-mediated controls on atmospheric carbon assimilation and peat carbon decomposition under climate change. *Glob. Chang. Biol.* **24**, 3911–3921 (2018).

302 9. Hopple, A. M. et al. Massive peatland carbon banks vulnerable to rising temperatures. *Nat. Commun.* **11**, 2373 (2020).

304 10. Worden, A. Z. et al. Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes. *Science.* **347**, 1257594 (2015).

306 11. Bay, S. K. et al. Trace gas oxidizers are widespread and active members of soil microbial communities. *Nat. Microbiol.* **6**, 246–256 (2021).

308 12. Hamard, S. et al. Contribution of microbial photosynthesis to peatland carbon uptake along a latitudinal gradient. *J. Ecol.* **109**, 3424–3441 (2021).

310 13. Wyatt, K. H. et al. Contributions of algae to GPP and DOC production in an Alaskan fen: effects of historical water table manipulations on ecosystem responses to a natural flood. *Oecologia* **169**, 821–832 (2011).

313 14. Gilbert, D., Amblard, C., Bourdier, G. & Francez, A. The Microbial Loop at the Surface of a Peatland: Structure, Function, and Impact of Nutrient Input. *Microb. Ecol.* **35**, 83–93 (1998).

315 15. Jassey, V. E. J. et al. An unexpected role for mixotrophs in the response of peatland carbon cycling to climate warming. *Sci. Rep.* **5**, 16910–16931 (2015).

317 16. Mitchell, E. A. D. et al. Structure of microbial communities in Sphagnum peatlands and effect of atmospheric carbon dioxide enrichment. **46**, 187–199 (2003).

319 17. Kilner, C. L. et al. Temperature and CO2 interactively drive shifts in the compositional and functional structure of peatland protist communities. *Glob. Chang. Biol.* **30**, e17203 (2024).

321 18. Rokitta, S. D. et al. Future warming stimulates growth and photosynthesis in an Arctic microalga more strongly than changes in light intensity or pCO2. *Limnol. Oceanogr.* **68**, 2789–2799 (2023).

324 19. Reich, P. B. et al. Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture. *Nature* **562**, 263–267 (2018).

326 20. Kumarathunge, D. P. et al. Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. *New Phytol.* **222**, 768–784 (2019).

328 21. Wyatt, K. H. & Turetsky, M. R. Algae alleviate carbon limitation of heterotrophic bacteria in a boreal peatland. *J. Ecol.* **103**, 1165–1171 (2015).

330 22. Moran, M. A. et al. Microbial metabolites in the marine carbon cycle. *Nat. Microbiol.* **7**, 508–523 (2022).

332 23. Bligh, M., Nguyen, N., Buck-Wiese, H., Vidal-Melgosa, S. & Hehemann, J. H. Structures and functions of algal glycans shape their capacity to sequester carbon in the ocean. *Curr. Opin. Chem. Biol.* **71**, 102204 (2022).

335 24. Wyatt, K. H., McCann, K. S., Rober, A. R. & Turetsky, M. R. Trophic interactions regulate peatland carbon cycling. *Ecol. Lett.* **24**, 781–790 (2021).
337 25. Sytiuk, A. et al. Linkages between *Sphagnum* metabolites and peatland CO₂ uptake are sensitive to seasonality in warming trends. *New Phytol.* **237**, 1164–1178 (2023).

339 26. IPCC. *Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change*. (2021).

341 27. Glassman, S. I. et al. Decomposition responses to climate depend on microbial community composition. *Proc. Natl. Acad. Sci. U. S. A.* **25**, 201811269 (2018).

343 28. Yvon-Durocher, G. et al. Five Years of Experimental Warming Increases the Biodiversity and Productivity of Phytoplankton. *PLOS Biol.* **13**, e1002324 (2015).

345 29. Yvon-Durocher, G., Jones, J. I., Trimmer, M., Woodward, G. & Montoya, J. M. Warming alters the metabolic balance of ecosystems. *Philos. Trans. R. Soc. B Biol. Sci.* **365**, 2117–2126 (2010).

348 30. Regaudie-De-Gioux, A. & Duarte, C. M. Temperature dependence of planktonic metabolism in the ocean. *Global Biogeochem. Cycles* **26**, (2012).

350 31. Bock, C., Olefeld, J. L., Vogt, J. C., Albach, D. C. & Boenigk, J. Phylogenetic and functional diversity of Chrysophyceae in inland waters. *Org. Divers. Evol.* **1**, 3 (2022).

352 32. Jeong, H. J. et al. Growth, feeding and ecological roles of the mixotrophic and heterotrophic dinoflagellates in marine planktonic food webs. *Ocean Sci. J.* **2010** **452** **45**, 65–91 (2010).

354 33. Wilken, S., Huisman, J., Naus-Wiezer, S. & Van Donk, E. Mixotrophic organisms become more heterotrophic with rising temperature. *Ecol. Lett.* **16**, 225–233 (2012).

356 34. Chu, T., Moeller, H. V. & Archibald, K. M. Competition between phytoplankton and mixotrophs leads to metabolic character displacement. *Ecol. Modell.* **481**, 110331 (2023).

358 35. Rasconi, S., Winter, K. & Kainz, M. J. Temperature increase and fluctuation induce phytoplankton biodiversity loss – Evidence from a multi-seasonal mesocosm experiment. *Ecol. Evol.* **7**, 2936 (2017).

361 36. Ma, S. et al. Thermal acclimation of plant photosynthesis and autotrophic respiration in a northern peatland. *Environ. Res. Clim.* **2**, 025003 (2023).

363 37. Reich, P. B. et al. Effects of climate warming on photosynthesis in boreal tree species depend on soil moisture. *Nat. 2018* **562** **726** **562**, 263–267 (2018).

365 38. Barton, S. et al. Evolutionary temperature compensation of carbon fixation in marine phytoplankton. *Ecol. Lett.* **23**, 722–733 (2020).

367 39. Antão, L. H. et al. Climate change reshuffles northern species within their niches. *Nat. Clim. Chang.* **12**, 587–592 (2022).

369 40. Helbig, M. et al. Warming response of peatland CO₂ sink is sensitive to seasonality in warming trends. *Nat. Clim. Chang.* **12**, 743–749 (2022).

371 41. Liang, G. et al. Soil respiration response to decade-long warming modulated by soil moisture in a boreal forest. *Nat. Geosci.* **17**, 905–911 (2024).

373 42. Qiu, C., Zhu, D., Ciais, P., Guenet, B. & Peng, S. The role of northern peatlands in the global carbon cycle for the 21st century. *Glob. Ecol. Biogeogr.* **29**, 956–973 (2020).

375 43. Gallego-Sala, A. V et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. *Nat. Clim. Chang.* **1–8** (2018).

377 44. Walker, T. W. N. et al. A systemic overreaction to years versus decades of warming in a subarctic grassland ecosystem. *Nat. Ecol. Evol.* **1–13** (2019).

379 45. Andersson, I. & Backlund, A. Structure and function of Rubisco. *Plant Physiol. Biochem.* **46**, 275–291 (2008).

381 46. Grzesiuk, M., Pietrzak, B., Wacker, A. & Pijanowska, J. Photosynthetic activity in both algae and cyanobacteria changes in response to cues of predation. *Front. Plant Sci.* **13**, 907174 (2022).

384 47. Osmond, M. M., Otto, S. P. & Klausmeier, C. A. When predators help prey adapt and persist in a changing environment. *Am. Nat.* **190**, 83–98 (2017).

386 48. Kitajima, K. & Hogan, K. P. Increases of chlorophyll a/b ratios during acclimation of tropical woody seedlings to nitrogen limitation and high light. *Plant. Cell Environ.* **26**, 857–865 (2003).

389 49. Averill, C. et al. Defending Earth’s terrestrial microbiome. *Nat. Microbiol.* **2022** **7**, 1–9 (2022).

390 50. Spohn, M., Müller, K., Höschen, C., Mueller, C. W. & Marhan, S. Dark microbial CO₂ fixation
391 in temperate forest soils increases with CO₂ concentration. *Glob. Chang. Biol.* **26**, 1926–

392 1935 (2020).

393
396 All details on the experimental design are given in Supplementary Methods. Briefly, we conducted a
397 reciprocal transplant experiment across five European peatlands spanning a latitudinal gradient from
398 Sweden to France (Extended Fig. 1). Across the five sites, mean air temperature ranged from 5.7—
399 14.2°C and total precipitation from 357 to 693 mm throughout the experiment (Supplementary Table
400 1; Supplementary Fig. 11). These sites thus represent a nearly 10°C temperature and 200 mm
401 precipitation gradients, which are comparable to climatic changes predicted at northern latitudes
402 through the SSP5-8.5 scenario from IPCC²⁶.

403 In July 2018, 125 intact peat blocks (60 x 40 x 20 cm) were transplanted across sites, 404 encased in sterilized plastic boxes with nylon mesh to allow nutrient and water flows but minimize 405 microbial immigration²⁵. Five blocks per site included peat from each location, creating mesocosms 406 that experienced warmer, colder, wetter, or drier climates, depending on origin and transplant 407 location. Untouched control plots (n=25) were also established in each site, one in each block 408 (Extended Fig. 1a). Environmental variables, including air/soil temperature, photosynthetic active 409 radiation (PAR), and precipitation, were continuously monitored (Extended Fig. 1b). pH and peat 410 chemistry (dissolved organic carbon [DOC], total nitrogen [TN]) and soil water content (SWC) were 411 also monitored. DOC and TN were measured by combustion on a Shimadzu TOC-L. SWC was 412 quantified by weighing fresh and oven-dried *Sphagnum* shoots, and the water content was calculated 413 as grams of water per gram of *Sphagnum* dry weight (gH₂O.g⁻¹ DW).

414 Sampling campaigns occurred seasonally in 2019 (spring, summer, and autumn) to capture 415 different climatic conditions (Extended Fig. 1c). Peat samples (0–5 cm depth) were collected from 416 three marked spots per mesocosm or control plot, homogenized into composite samples, and 417 processed for analyses. The top 5 cm was chosen as the most favourable zone for photoautotrophic 418 growth¹²,⁵¹,⁵². Swedish plots could not be sampled in autumn due to ice cover, resulting in 420 total 419 samples collected. To ensure that encasing peat into plastic box did not alter the functioning of 420 microbial communities as well as their micro-environment, we tested the box effect on the different
421 environmental and biotic variables and did not find any encasing effect (see Supplementary Methods
422 and Supplementary Figures 12, 13, 14 and 15, Table S5).

423

424 Abundance of bacteria and photoautotrophs

425 Bacterial and photoautotrophic densities were quantified using flow cytometry⁵³. Immediately after
426 sampling, about 3 grams of the composite sample was preserved in 20 ml formaldehyde (4% final
427 concentration) and stored at 4°C. Back in the laboratory, we extracted microorganisms from the peat.
428 Each sample was shaken 1 min on a vortex and then pressed to extract microorganisms¹². We used
429 1 ml aliquots to measure the concentrations of photoautotrophs and bacteria with flow cytometry.
430 The aliquot was filtered at 40 µm to prevent clogging in the capillary of the flow cytometer, stained
431 with SYBR Green I (0.1x final concentration) and incubated in the dark for 15 minutes. Then it was
432 run in the flow cytometer (Guava® easyCyte) at a speed of 2 µL s⁻¹ and at a count rate not exceeding
433 1000 events s⁻¹. Photoautotrophs were distinguished from bacteria using their autofluorescence
434 (Supplementary Fig. 16). Abundances of bacteria and photoautotrophs were expressed as the
435 number of individuals per gram of dry peat (ind. g⁻¹ dw), then converted per surface unit (ind. m⁻²)
436 using the mass of peat per squared centimetres measured in each plot (see details in ref.⁵⁴).

437

438 Photoautotrophic CO₂ fixation rates

439 Photoautotrophic CO₂ fixation rates were quantified and calculated based on pulse amplitude
440 modulation (PAM) fluorometry to assess the quantum yield of photosystem II (ΦPSII) of the
441 photoautotrophic community, and using Chlorophyll a (Chl a) concentration and photoautotrophic
442 abundance¹²,³³,⁵⁵, as detailed in Supplementary Methods. Photoautotrophic CO₂ fixation rates,
443 ΦPSII, abundance and Chl a biomass quantifications were all made at the community level, thus
444 comprising all photosynthetic microbes from strict photoautotrophs to mixotrophs.

445

446

447
448 **Photoautotrophic communities**

449 The richness and composition of photoautotrophic bacteria and protists were measured via amplicon
450 sequencing using the Illumina MiSeq platform (2 x 250 pb for 16S and 18S rRNA amplicons). Peat
451 DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer's
452 instructions. The richness (that is the number of OTUs) of photoautotrophic bacteria and protists was
453 determined from rarefied OTU abundance tables (Supplementary Fig. 17 and 18). The V3-V4
454 hypervariable region of 16S rRNA gene was amplified using the PCR1_515F (5'-
455 GTGYCAGCMGCCGCGGTA-3')/ PCR1_909R (5'- CCCCGYCAATTCMTTTRAGT-3') primers⁵⁶ to
456 assess photoautotrophic bacteria. The V9 hypervariable region of the 18S rRNA gene was amplified
457 using the TAReuk454FWD1(5'-CCAGCASCYGCGGTAATTCC-3')/TAReukREV3 (5'-
458 ACTTTCGTTCTTGATYRA-3') primers⁵⁷ to assess the photoautotrophic protists. Bioinformatic
459 processing was performed using FROGS⁵⁸, VSEARCH⁵⁹, and SWARM⁶⁰ pipeline on the Galaxy web
460 platform⁶¹. We used the Silva 138 representative set of sequences⁶² for prokaryotic assignations and
461 the PR2 sequence database⁶³ for micro-eukaryotic assignations.

462 Microbial photoautotrophs, spanning bacterial and protistan domains, were analysed by pre-
463 selecting photoautotrophic OTUs from 16S and 18S rRNA reads. Bacterial photoautotrophs were
464 identified using PICRUSt2 to predict functional genes from 16S sequences, linking KEGG orthologs
465 to metabolic pathways (Table S6). Through this process, we identified 51 photoautotrophic bacterial
466 OTUs (Supplementary Fig. 19) for community analyses. Protistan photoautotrophs were filtered from
467 18S rRNA sequences using a 23S rRNA reference table (Table S7), focusing on photosynthetic taxa
468 like Chlorophyta and including mixotrophs. Using these two approaches, we are certain we did not
469 exclude relevant and important photoautotrophic taxa from our community analyses, as shown in
470 Supplementary Fig. 2 and Table S8. More details can be found in Supplementary Methods.

471 To enhance our community composition analyses with meaningful diversity indices and
472 photoautotrophic taxa, we initially pre-selected biodiversity attributes. These attributes included the
473 richness and phylogenetic diversity of photoautotrophic bacteria and protists, as well as individual
taxa positively or negatively associated with warming (see refs.⁶⁴,⁶⁵ for similar approaches in different context).

476

477 Microcosm experiments

Experimental designs. We performed the two microcosm experiments with a common moss in peatlands, *Sphagnum cuspidatum* Ehrh. Ex Hoffm. In these experiments, we manipulated the microbial food web structure and the abundance of photoautotrophs or the quantity of their derived photosynthetically fixed C (exudates and necromass). The first experiment consisted of the following treatments: sterile *Sphagnum* (S, control consisting of sterilised *Sphagnum*), S + photoautotrophic exudates and necromass (S+EN), S + microbial food web (S+FW), S+ microbial food web + photoautotrophic exudates and necromass (S+FW+EN). Each treatment was repeated four times; in total, 4 treatments x 4 replicates = 16 microcosms. The second experiment involved the following treatment combinations: S, S + microbial food web (S+FW), S + a mixture of photoautotrophs (S+P), and S + mixture of photoautotrophs + microbial food web (S+P+FW). Each treatment was replicated five times (in total, 4 treatments x 5 replicates = 20 microcosms). Both microcosms experiments were maintained in a POL-EKO climatic chamber (POL-EKO Aparatura) for 4 weeks: 14°C air temperature, 12h/12h, day/night. Microcosms were assigned in the growth chamber into blocks (one block per growth chamber floor), the blocks were disposed following a Latin square design within the chamber to consider any potential bias related to chamber side effects.

Each microcosm consisted of a cylindrical glass test tube (160 x 25 mm) filled with axenic *Sphagnum* (see details in Supplementary Methods, Table S9, S10). For each microcosm experiment, we used a mixture of standardized cultures of photoautotrophs commonly observed in peatlands⁵¹,⁶⁶⁻⁶⁸. In the first experiment, photoautotrophic necromass was created by drying and grinding the photoautotroph mixture. It was then diluted into sterile water (0.05 g/100 mL). Photoautotrophic C exudates were retrieved by filtering 100 mL of the photoautotrophic mixture solution at 0.45 µm. Necromass (1 mL) was added weekly, and exudates (200 µL) daily. The second experiment involved spraying the photoautotrophic mixture on axenic *Sphagnum* mosses, matching
warming-induced abundance increases recorded in the field experiment (1.4 × 10⁴ ± 5.8 × 10³ cells per ml). The microbial food web, extracted from natural *Sphagnum* mosses, was inoculated with bacteria, predators, and photoautotrophs in amounts comparable to natural peatlands. CO₂ fluxes in microcosms were measured weekly using wireless sensors. We calculated net exchange (NEE) and respiration (ER) under light and dark conditions, respectively. CO₂ flux rates were determined using linear regression, corrected for volume and surface area, with positive values indicating release and negative values uptake. More details can be found in Supplementary Methods.

508

## 509 Statistical analyses

All statistical analyses were performed in R v4.1.2 (R Core Team, 2023) using specific packages, as indicated afterwards.

512

*Treatment, seasonal and warming effects by LMMs.* Due to the block design and repeated measurements over seasons, the experimental data are not completely independent. Therefore, LMMs were used to assess the treatment effects on environmental variables and photoautotrophic features (Chl *a* biomass, photosynthesis, and abundance). The *nlme* (v3.1-166) R package was used to implement LMMs⁶⁹. In the LMMs, site (donor site, qualitative variable), transplantation (receptor site, qualitative variable), and season (qualitative variable), and their interactions, were the fixed effects, while block nested in season was termed as random intercept effect. Photoautotrophic photosynthesis, Chl *a* biomass and abundance data were log-transformed to improve normality.

Similar models were computed to test for the effects of changing temperature and precipitation using mean air temperature and total precipitation, as well as the interaction between warming and SWC percentiles (fixed effects) on photoautotrophic features. *F*-tests were used to calculate the *P*-values.

Effect sizes of temperature and precipitation effects on photoautotrophic variables were represented by using the regression coefficients ($\beta$) from the LMMs. $\beta$ coefficients provide the direction and magnitude of the temperature (or precipitation) effect. Positive coefficients indicate increased

525

526
microbial photosynthesis along with increasing temperature, while negative coefficients indicate the
opposite. The normality and homoscedasticity of linear models residuals were visually assessed.

*Variance partitioning modelling.* We used variation partitioning modelling<sup>70</sup> to quantify the relative importance of treatments (donor site, receptor site, and season) or photoautotrophic properties (Chl *a* biomass and abundance, diversity, taxa positively associated with warming) and warming in explaining microbial photosynthesis variation. Using this method, we identified the unique and shared portions of the variation in the distribution of the response of microbial photosynthesis to the treatments and its potential biotic drivers. The *partR2* (v0.9.2) function from the R package *partR2* was used<sup>71</sup>. This function partitions the variance explained in linear mixed models (LMMs) into variations unique to and shared among predictors.

*Alpha-diversity and community analyses.* OTU Richness was calculated using the *vegan* R package<sup>72</sup>. Faith's index was used to measure the phylogenetic <i>&infin;</i>-diversity of photoautotrophic bacteria and protists using the *picante* (v1.8.2) R package<sup>72</sup>. The effects of transplantation and shifts in temperatures were assessed independently on richness and phylogenetic diversity indices using linear-mixed effects models, with either treatments (donor site, receptor site, both qualitative variables) or mean air temperature and total precipitation with donor site as fixed effects. Block was termed as random intercept effect. To assess the effect of warming on the relative abundance of photoautotrophic classes or individual OTUs, we used LMMs as described above. Effect sizes of temperature were represented by the *β* coefficients in the LMMs. The normality and homoscedasticity of linear models residuals was visually assessed.

*Treatment effects in microcosm experiments.* To test the effects of the different treatments on CO<sub>2</sub> fluxes and photoautotrophic ETR and abundance, we also used LMMs as our data were not completely independent because of the growth chamber and repeated measurements over time. In LMMs, microbiome (yes or no, categorical variable) and photoautotrophic treatment (either the
addition of living biomass or C-related products addition, categorical variables), and their
interactions, were the fixed effects, while block nested in week were termed as random intercept
effect. CO₂ flux data were log-transformed to improve normality. F-tests were used to calculate the
P-values. The normality and homoscedasticity of linear model residuals were visually assessed.

Structural equation modelling. To analyse the direct and indirect effects of warming on photoautotrophic photosynthesis in the reciprocal transplant experiment, structural equation modelling (SEM) was used<sup>73</sup>. An a priori path diagram (Supplementary Fig. 20a) outlined hypothesized causal relationships between environmental and biotic predictors. As photoautotrophic community structure was assessed only in summer, we used SEM with a composite variable<sup>74</sup> integrating the effect of temperature on the relative abundance of photoautotrophic bacteria and protists OTUs that positively or negatively responded to warming. A second SEM model examined the effect of microbial trophic interactions and increased photoautotrophic photosynthesis on microcosms' CO<sub>2</sub> uptake (Supplementary Fig. 20b). Both models were fitted using the lavaan<sup>75</sup> (v0.6-19) R package. χ<sup>2</sup> tests and AICs were used to judge variable and/or path inclusion based on the a priori models, as well as model adequacy. Model fit indices such as low root-mean-square error of approximation index (RMSEA ≤ 0.1), low standardized root-mean-square residual index (SRMR ≤ 0.1) and high comparative fit index (CFI ≥ 0.95) were further used for additional diagnostic evaluations.

573

**Predicting changes in photoautotrophic CO₂ fixation rate in response to warming**

We implemented LMMs to predict changes in microbial photosynthesis caused by future warming.
Five LMMs were applied, each based on microbial communities from the reciprocal transplant
experiment. Each model included mean microbial photosynthetic rate and mean temperature across
seasons as fixed effects (n = 25 for each), with block as a random intercept effect. To avoid
overfitting, we used a series of targeted sensitive analyses testing the robustness of our predictions,
580 with particular attention given to the distribution of data, cross-validation strategy, and confidence
581 prediction intervals⁷⁶:

582 1) Inspecting the distribution of photoautotrophic productivity values and identifying outliers,
583 though no data were removed.

584 2) Using a k-fold cross-validation strategy, we randomly split the dataset into 100 test sets (k =
585 100) breaking any structure of potential spatial and community dependences in the data.
586 Then, we used the 100 test sets to test the predictive performance of each LMM model. For
587 each test set, we generated cross validation statistics, namely the squared Pearson's
588 correlation between observed and predicted values (noted R²) and the slope coefficient of
589 the predictions.

590 3) We calculated prediction intervals to quantify uncertainty, assessing how far predictions
591 might differ from actual values⁷⁷. For each independent test set, we calculated prediction
592 intervals and classified whether data points from the test data set fell within or outside LMM
593 prediction intervals.

594

595 Annual flux of photoautotrophic CO₂ uptake

596 Photoautotrophic photosynthesis does not occur every day of the year or all day long. To obtain the
597 best estimates of total increased photoautotrophic photosynthesis annually, we based our
598 calculations on physiological and climatic data. First, we determined the minimum light used by
599 photoautotrophs to fix CO₂ through photosynthesis. We performed light-response curves of ΦPSII in
600 each plot (from 0 to 400 µmol m⁻² s⁻¹) using the Phyto-PAM and calculated ETR as described above.
601 Our data showed that ETR reached stable values at a PAR of 64 µmol m⁻² s⁻¹ (Supplementary Fig.
602 21a). We thus used this PAR value as a threshold to determine the period where photoautotrophs
603 were potentially active during the day (Supplementary Fig. 21b). Our data also showed that
604 photoautotrophic photosynthesis was minimal below a temperature of 5°C (Supplementary Fig. 21c).
605 We thus used this threshold to estimate the number of days with a mean PAR > 64 µmol m⁻² s⁻¹
606 within a year where photoautotrophs are potentially photosynthetically active (Supplementary Fig.
21d). Therefore, we multiplied our increased photoautotrophic photosynthesis values (in mgC.m⁻².h⁻¹) by the total amount of number of hours per year with a PAR>64 µmol m⁻² s⁻¹ and a mean temperature >5°C to obtain estimates yearly (see below).

## Upscaling gains in photoautotrophic CO₂ fixation rate

We modelled changes in annual photoautotrophic CO₂ fixation rates across northern peatlands (>45°N) in response to warming using a stack of maps. Peatland cover was obtained from the Peat-ML<sup>78</sup> global raster at 5 arc-minute resolution. Maps of suitable hours for photoautotrophic photosynthesis (air temperature >5°C, PAR >64 µmol.m⁻².s⁻¹, no snow cover) were generated using ERA5 Land dataset<sup>79</sup> data (1990–2020) on Google Earth Engine<sup>80</sup> (Supplementary Fig. 22). Temperature anomaly predictions were retrieved from the IPCC Interactive Atlas<sup>81</sup> for near-term (2021–2040), medium-term (2041–2060), and long-term (2081–2100) periods under SPP1-2.6, SPP2-4.5, and SPP5-8.5 scenarios, based on CMIP6 models. All maps were stacked for model predictions, excluding southern and tropical peatlands (< 45°N), and projected to the North Pole Lambert Azimuthal Equal Area projection with a ground pixel size of 5000 m using the ArcGIS software (ESRI, Redlands, USA). More details are given in Supplementary Methods.

We used an ensemble approach to predict changes in photoautotrophic photosynthetic rate in response to warming and assess the confidence in our estimates at each pixel of the maps<sup>82</sup>. The five independent LMMs were run against IPCC temperature anomalies for each pixel with a peatland cover >0% and according to the selected periods and scenarios. Then, the predicted changes in photoautotrophic CO₂ fixation rate were converted into annual flux by multiplying the predicted value by the average number of hours per year where conditions are favourable to photoautotrophic CO₂ fixation, scaled to the map pixel size (25km²), and weighted by peatland fractional cover. Final estimates provided gains in annual photoautotrophic CO₂ fixation (MgC.km⁻².year⁻¹) and accounted for variability across microbial communities and seasons. Prediction uncertainty was assessed by generating per-pixel coefficient-of-variation values (standard deviation divided by the mean predicted value) as a measure of prediction uncertainty<sup>83</sup>.
635 To estimate the mitigation potential of increased microbial photosynthesis under warming targets, 636 we used heterotrophic respiration projections in northern peatlands (1860–2099) from the 637 ORCHIDEE-PEAT land surface model⁴² under RCP2.6 and RCP6.0 scenarios. Projections were 638 based on IPSL-CM5A-LR and GFDL-ESM2M global climate forcing models from ISIMIP2b106, 639 representing the fastest and slowest temperature increases, respectively. For near-term (2021– 640 2040), medium-term (2041–2060), and long-term (2081–2100) periods, we extracted heterotrophic 641 respiration estimates from ref.⁴² using Web Plot Digitizer (https://automeris.io/ WebPlotDigitizer/), 642 and calculated changes relative to the 1991–2020 baseline (Table S11). Experimental-driven 643 estimations from ref.⁷ supplemented the near-term dataset. We calculated the mitigation potential of 644 altered microbial photosynthetic rates by calculating the percentage contribution of changed 645 microbial photosynthesis to changed heterotrophic respiration rates. RCP6.0 dataset was used for 646 both SSP2-4.5 and SSP5-8.5 scenarios so that our mitigation potential is slightly under- and over- 647 estimated, respectively, for these two scenarios. 648

648

649 **Data availability.** Raw sequence data have been deposited in the Sequence Read Archives with 650 NCBI BioProject accession no. PRJNA1215042. Other raw and processed data associated with the 651 study are available at https://10.6084/m9 figshare.26404867 (ref. ⁸⁴). Temperature anomalies data 652 used for projections can be found in Source Data Fig. 3.

653

654 **Code availability.** The *R* and GEE analysis scripts are provided at 655 https://10.6084/m9 figshare.26404867 (ref. ⁸⁴).

656

657 **Acknowledgements.** This research has been supported by MIXOPEAT (grant no. ANR-17-CE01- 658 0007) and BALANCE (grant no. ANR-23-ERCC-0001-01) projects, both funded by the French 659 National Research Agency to VEJJ. ML was supported by the 2020-2021 Biodiversa+ and Water 660 JPI joint call for research (ERA-NET Cofund, GA no. 101003777) and the National Science Centre
of Poland (2021/41/B/ST10/00060). We are grateful to Dr Christian Raquin for providing axenic
*Sphagnum* mosses. We thank the Plateforme Analyses Physico-Chimiques and Pôle Technique
Observation et Instrumentation from the Centre de Recherche sur la Biodiversité et l'Environnement
(Toulouse, France). We also thank the Swedish Polar Research Secretariat and SITES for the
support of the work done at the Abisko Scientific Research Station. SITES is supported by the
Swedish Research Council's grant 4.3-2021-00164.

**Author contributions.** V.E.J.J. conceptualized, designed and supervised the transplantation study with the help of B.J.M.R., G.L.R., M.L., E-S.T, and E.D. S.H. and V.E.J.J. performed the transplantation of mesocosms with the help of R.C., M.L., M.K., E-S.T, and E.D. S.H., A.S. and V.E.J.J. collected the samples with the help of M.K. S.H. analyzed the samples with the help of A.S., M.B., J.F., J.L. and V.E.J.J. S.P. and V.E.J.J. conceptualized and designed the microcosms studies with the help of O.P and S.H. S.P. and V.E.J.J. performed the microcosms studies with the help of S.H., and J.L. V.E.J.J and R.W. performed the predictive analyses and mapping. V.E.J.J., S.H., and S.P. performed the data analyses with the help of T.L.R. V.E.J.J. wrote the original draft of the manuscript with the help of S.H. and with the contribution of B.J.M.R., O.P., R.W., M.L.G. M.L., F.D and R.C. All authors reviewed and edited the manuscript.

678

679 **Ethics declarations.**

Competing interests. The authors declare no competing interest.
681 Methods-only references

682 51. Reczuga, M. K. et al. Predator–prey mass ratio drives microbial activity under dry conditions in *Sphagnum* peatlands. *Ecol. Evol.* **8**, 5752–5764 (2018).

683 52. Hamard, S., Küttim, M., Céréghino, R. & Jassey, V. E. J. Peatland microhabitat heterogeneity drives phototrophic microbes distribution and photosynthetic activity. *Environ. Microbiol.* **23**, 6811–6827 (2021).

684 53. Sytiuk, A. et al. Predicting the structure and functions of peatland microbial communities from *Sphagnum* phylogeny, anatomical and morphological traits and metabolites. *J. Ecol.* **68**-1365-2745.13728 (2021) doi:10.1111/1365-2745.13728.

685 54. Sytiuk, A. et al. Biochemical traits enhance the trait concept in *Sphagnum* ecology. *Oikos* **122**, e09119 (2022).

686 55. Gorbunov, M. Y. & Falkowski, P. G. Using chlorophyll fluorescence kinetics to determine photosynthesis in aquatic ecosystems. *Limnol. Ocean.* **66**, 1–13 (2020).

687 56. Jassey, V. E. J. et al. Photosynthetic microorganisms effectively contribute to bryophyte CO₂ fixation in boreal and tropical regions. *ISME Commun.* **2022** **21** 2, 1–10 (2022).

688 57. Stoeck, T. et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. *Mol. Ecol.* **19**, 21–31 (2010).

689 58. Escudié, F. et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. *Bioinformatics* **34**, 1287–1294 (2018).

700 59. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. *PeerJ* **2016**, e2584 (2016).

701 60. Mahé, F. et al. Swarm v3: towards tera-scale amplicon clustering. *Bioinformatics* **38**, 267–269 (2021).

702 61. Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. *Nucleic Acids Res.* **50**, W345–W351 (2022).

703 62. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. *Nucleic Acids Res.* **41**, D590–D596 (2013).

704 63. Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. *Nucleic Acids Res.* **41**, 597–604 (2013).

705 64. Delgado Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. *Science* **359**, 320–325 (2018).

706 65. Sáez-Sandino, T. et al. The soil microbiome governs the response of microbial respiration to warming across the globe. *Nat. Clim. Change* **13**, 1382–1387 (2023).

707 66. Jassey, V. E. J., Gilbert, D., Binet, P., Toussaint, M.-L. & Chiapusio, G. Effect of a temperature gradient on *Sphagnum fallax* and its associated living microbial communities: a study under controlled conditions. *Can. J. Microbiol.* **57**, 226–235 (2011).

708 67. Jassey, V. E. J. et al. Above- and belowground linkages in *Sphagnum* peatland: climate warming affects plant-microbial interactions. *Glob. Chang. Biol.* **19**, 811–823 (2013).

709 68. Buttler, A. et al. Ericoid shrub encroachment shifts aboveground–belowground linkages in three peatlands across Europe and Western Siberia. *Glob. Chang. Biol.* **29**, 6772–6793 (2023).

710 69. Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. Linear and nonlinear mixed effects models. *R Packag. version* (2007).

711 70. Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R. *Numer. Ecol. with R* (2011) doi:10.1007/978-1-4419-7976-6.

712 71. Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. *Methods Ecol. Evol.* **8**, 1639–1644 (2017).

713 72. Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. *Bioinformatics* **26**, 1463–1464 (2010).

714 73. Grace, J. B., Adler, P. B., Harpole, W. S., Borer, E. T. & Seabloom, E. W. Causal networks clarify productivity–richness interrelations, bivariate plots do not. *Funct. Ecol.* **28**, 787–798 (2014).
735 74. Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. *Nature* **529**, 390–393 (2016).

737 75. Rosseel, Y. lavaan: An R Package for Structural Equation Modeling. *J. Stat. Softw.* **48**, 1–36 (2012).

739 76. Lesmeister, C. *Mastering machine learning with R: advanced machine learning techniques for building smart applications with R 3.5.* (2019).

741 77. Meinshausen, N. Quantile Regression for Left-Truncated Semicompeting Risks Data. *J. Mach. Learn. Res.* **7**, 983–999 (2006).

743 78. Melton, J. R. et al. A map of global peatland extent created using machine learning (Peat-ML). *Geosci. Model Dev.* **15**, 4709–4738 (2022).

745 79. Muñoz-Sabater, J. et al. ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. *Earth Syst. Sci. Data* **13**, 4349–4383 (2021).

747 80. Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. *Remote Sens. Environ.* **202**, 18–27 (2017).

749 81. Iturbide, M. et al. Implementation of FAIR principles in the IPCC: the WGI AR6 Atlas repository. *Sci. Data* **9**, 1–10 (2022).

751 82. van den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. *Nature* **572**, 194–198 (2019).

753 83. Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. *Nat. Ecol. Evol.* **5**, 1110–1122 (2021).

755 84. Jassey, V. E. J. *Microbial photosynthesis mitigates carbon loss from northern peatlands under warming.* (2024) doi:10.6084/m9.fishshare.26404867.

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758
<table><caption>**Table 1.** Future predictions of increased photoautotrophic primary productivity</caption><thead><tr><th rowspan="2">Climatic scenario</th><th colspan="3">Cumulative microbial photosynthesis (TgC yr<sup>-1</sup>)</th></tr><tr><th>Near term (2021-2040)</th><th>Medium term (2041-2060)</th><th>Long term (2081-2100)</th></tr></thead><tbody><tr><td>SSP1 2.6</td><td>12.0 (4.7-31.8)</td><td>13.6 (5.7-33.7)</td><td>14.2 (6.1-34.1)</td></tr><tr><td>SSP2 4.5</td><td>12.1 (4.7-31.8)</td><td>14.9 (6.6-35.2)</td><td>20.6 (10.9-40.6)</td></tr><tr><td>SSP5 8.5</td><td>12.8 (5.2-32.8)</td><td>17.9 (8.8-38.3)</td><td>51.1 (36.5-73.4)</td></tr></tbody></table>

The cumulative microbial CO₂ fixation rate at each pixel is shown (average value among the five LMM models and the minimum and maximum values in brackets).
763 **Figure captions**

764 **Figure 1.** Response of photoautotrophic photosynthesis to warming. Photoautotrophic 765 photosynthesis averaged by season (**a**, *n* = 125 per season), microbial community (or donor site, *n* 766 = 75 per community) (**b**) and receptor site (**c**, *n* = 75 per site). In (**b**), colours represent the microbial 767 communities of origin while the x-axis represents receptor sites. The median and interquartile range 768 with violin distribution are shown. **d**, Variation partitioning modelling evaluating the unique and 769 shared portions of variation in photoautotrophic photosynthesis. Shared refers to the percentage of 770 shared variation explained by all predictors. Receptor indicates the effect of transplantation. **e**, 771 Effects of mean temperature changes across seasons on photoautotrophic photosynthesis averaged 772 for the three seasons. The coloured lines show the regression lines of each microbial community of 773 origin (LMM; *P* < 0.001). The black line shows the global regression line while the grey area 774 represents the standard error among communities. **f**, Magnitude of increased photoautotrophic 775 photosynthesis caused by warming (represented as regression coefficients, *β*) of each communities 776 plotted against the mean temperature range across seasons of each donor site. The black line shows 777 the regression line while the grey area represents the standard error. **g**, Variation of photoautotrophic 778 photosynthesis caused by warming across seasons. Variation rates are calculated for each season 779 and each donor site and are expressed as the variation of microbial photosynthesis (mgC.m⁻².h⁻¹) 780 across temperature gradients (LMM regression coefficients, *β*). Pale dots represent the different 781 replicates, while dark dots present the average. Error bars show the standard error. 782

782 **Figure 2.** Increased photoautotrophic photosynthesis under warming depends on soil water content. **a**, Effects of rising temperatures on microbial photosynthesis according to the water content of *Sphagnum* mats (SWC) during periods that range from dry to wet conditions (SWC 783 percentiles). Periods represent SWC across seasons for each site which are the 20th, 40th, 60th, 784 80th, and 100th wettest percentiles of SWC for each site and treatment. The regression coefficients 785 (*β*) showing the direction and magnitude of the response of microbial photosynthesis to temperature 786 are shown with their variation across every replicate. The median and interquartile and violin 787 distribution are shown. Red dot show the mean. Letters indicate significant differences at *P* < 0.05 788 (LMM). **b**, Histogram of SWC across seasons. 789

792 **Figure 3.** Distribution of the potential gains in annual photoautotrophic CO₂ fixation across the northern peatland region for the near (2021-2040), medium (2041-2060) and long (2081-2100) term and for each scenario (SSP1 2.6, SSP2 4.5, SSP5 8.5). **a–i**, Distribution of the potential gains in photoautotrophic CO₂ fixation (in MgC km⁻² yr⁻¹) as a response to projected temperature anomalies assuming that all photoautotrophic communities in peatlands are susceptible to respond positively to warming. Mean values (*n* = 5) are shown for every scenario. The map of northern peatland extent (>45° N, data from ref. 78) has been used as an envelope for the projections. 793 794 795 796 797 798 799

800 **Figure 4.** Projected mitigation effect driven by increased photoautotrophic photosynthesis. 801 Mitigation effect (in percent) of annual increase of photoautotrophic CO₂ fixation over annual 802 increase in heterotrophic CO₂ respiration for near (2021-2040), medium (2041-2060) and long term 803 (2081-2100) from the original and constrained CMIP6 models under the SSP1-2.6 (blue), SSP2-4.5 804 (orange) and SSP5-8.5 (red) emissions scenarios, respectively. Annual increases in heterotrophic 805 respiration rates are taken from ref.⁷ for experimental data and from ref.⁴² for Earth System Models 806 (ESM) ISPL and GFDL (see Method for details). Median and interquartile range are shown for every 807
scenario, time period and heterotrophic respiration dataset (n = 5 per condition). Symbols show the mean for each particular CMIP6 model or field data set.

**Figure 5.** Photoautotrophic priming effects on peatland CO₂ exchange. **a**, Net Ecosystem Exchange (NEE, mgCO₂.m⁻².h⁻¹) and respiration (ER, mgCO₂.m⁻².h⁻¹) rate in microcosms with and without a microbial food web, and with and without the addition of photoautotrophic exudates and necromass. The median and interquartile range are shown (n = 5 for each condition). **b**, Net Ecosystem Exchange (NEE, mgCO₂.m⁻².h⁻¹) and respiration (ER, mgCO₂.m⁻².h⁻¹) rate in microcosms with and without a microbial food web, and with and without the addition of living photoautotrophs. The median and interquartile range are shown (n = 4 for each condition). Letters indicate significant differences among treatments (*P* < 0.05, LMMs).

819
<figure><img src="image_1.png" /></figure>

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
Absolute change in photoautotrophic photosynthetic rates caused by warming (mgC m⁻² h⁻¹)

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /></figure>
<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /></figure>

s = axenic *Sphagnum*

<figure><img src="image_14.png" /></figure>

fw = microbial food web

<figure><img src="image_15.png" /></figure>

en = photoautotrophic exudates + necromass

<figure><img src="image_16.png" /></figure>

p = living biomass of photoautotrophs