# Dynamics of diazotroph particle colonization in the Arctic Ocean

Arthur Coët, Cécile Carpaneto Bastos, Mathias Lechelon, Ruth Hawley, Oliver Flanagan, Maeve C Lohan, Pierre Ronceray, Joanne E Hopkins, Claire Mahaffey, Mar Benavides

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Arthur Coët, Cécile Carpaneto Bastos, Mathias Lechelon, Ruth Hawley, Oliver Flanagan, et al. Dynamics of diazotroph particle colonization in the Arctic Ocean. The International Society of Microbiological Ecology Journal, 2025, 10.1093/ismejo/wraf098. hal-05101951

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# Dynamics of diazotroph particle colonization in the Arctic Ocean

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Arthur Coët¹,², Cécile Carpaneto Bastos¹,², Mathias Lechelon³, Ruth Hawley⁴, Oliver Flanagan⁴,

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Maeve C. Lohan⁴, Pierre Ronceray², Joanne E. Hopkins⁵, Claire Mahaffey⁴, Mar Benavides¹,⁶

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(1) Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO UM 110, Marseille, France

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(2) Aix Marseille Univ, CNRS, CINAM, Turing Center for Living Systems, Marseille, France

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(3) Aix Marseille Univ, CNRS, INSERM, MEP Centuri, Turing Center for Living Systems,

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Marseille, France

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(4) Department of Earth, Ocean, and Ecological Sciences, University of Liverpool, Liverpool, UK

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(5) National Oceanography Centre, Liverpool, L3 5DA, UK

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(6) National Oceanography Centre, European Way, Southampton, SO14 3ZH, UK

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16 *Corresponding author : Mar Benavides, National Oceanography Centre, European Way,
17 Southampton, SO14 3ZH, UK

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19 Running title : Arctic particle-attached diazotrophs

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21 Keywords : Nitrogen fixation, Organic matter, Chemotaxis, Barents Sea

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24 arthur.coet@mio.osupytheas.fr, cecile.carpaneto-bastos@mio.osupytheas.fr, mathias.LECHELON@univ-amu.fr, rh3g22@soton.ac.uk, M.Lohan@soton.ac.uk, pierre.RONCERAY@univ-amu.fr,
25 jeh200@noc.ac.uk, mahaffey@liverpool.ac.uk, mar.benavides@noc.ac.uk

26
27 Abstract

28
29 Global warming is causing sea ice retreat and intensifying algal blooms in the Arctic Ocean, in
30 turn increasing nitrogen limitation in surface waters. Dinitrogen fixation by diazotrophic
31 microorganisms, usually favored in low reactive nitrogen systems, may become an increasingly
32 important source of nitrogen in the Arctic. Previous studies have shown that non-cyanobacterial
33 diazotrophs are dominant in the Arctic Ocean. Lacking a photosynthetic apparatus, non-
34 cyanobacterial diazotrophs may utilize organic particles as carbon- and energy-rich niches.
35 However, cyanobacterial diazotrophs may also form particles by aggregation. To further
36 understand diazotroph-particle associations, here we study the chemotactic behavior and
37 colonization dynamics of diazotrophs on model organic particles using a modified chemotaxis
38 assay. Artificial organic particles (agarose, alginate) were incubated with surface seawater
39 communities from four contrasted stations in the Barents Sea, and their DNA was sequenced
40 targeting nifH and 16S rRNA genes after 2, 36, and 72 h of incubation. Our results show that
41 diazotroph groups have selective colonization behaviors, with *Gammaproteobacteria* members
42 preferentially colonizing alginate particles derived from brown algae, a form of organic matter
43 becoming more common in the Arctic as it warms up. We also observe niche partitioning among
44 microbial groups, with diazotrophs colonizing nitrogen-poor, carbon-rich particles earlier than
45 non-diazotrophic prokaryotes. As Arctic warming proceeds, increased algal blooms may expand
46 the niches for particle-associated diazotrophs, whose dinitrogen fixation supports phytoplankton
47 growth and primary productivity.
48 **Introduction**

49
50 Global warming is driving significant transformations in the Arctic Ocean (1, 2) triggering
51 profound shifts in its biogeochemistry, nutrient cycles, and primary production through coastal
52 morphology changes and diminishing ice cover (3, 4). The increasingly early seasonal ice
53 retreat (5), potentially leading to ice-free summers by 2050 (6), catalyzes a cascade of
54 biogeochemical alterations (7). This dramatic transformation unleashes nutrients previously
55 sequestered beneath the ice (8) allowing deeper light penetration, ultimately stimulating algal
56 and phytoplankton proliferation (9). These blooms deplete bioavailable nitrogen, leading to
57 surface nitrogen limitation (10-11). In other nitrogen-limited systems such as subtropical gyres,
58 dinitrogen (N₂) fixation represents a major source of bioavailable nitrogen (12). As the Arctic
59 becomes increasingly nitrogen-limited, N₂ fixation is expected to become a key source of
60 bioavailable nitrogen in this region as well (13). These changes in nitrogen availability, coupled
61 with increasing organic carbon input from algal bloom and ice melt, are reshaping microbial
62 community dynamics and biogeochemical cycles in the Arctic Ocean (14).

63
64 N₂ fixation is facilitated by nitrogenase, an oxygen-sensitive enzyme found in a group of
65 microorganisms called "diazotrophs" (12). Cyanobacteria have traditionally been regarded as
66 the primary diazotrophs in marine environments (15). However, growing evidence shows that
67 non-cyanobacterial diazotrophs (NCDs) dominate not only in low latitudes but also in coastal,
68 deep, and polar oceans (13,16,17). Despite their widespread distribution, there are uncertainties
69 about the N₂ fixation potential of NCDs (18,19). In contrast to cyanobacteria, NCDs are unable
70 to do photosynthesis and are thought to rely on particulate organic matter to obtain carbon and
71 energy (20–22). However, diazotrophic cyanobacteria may also rely on dissolved organic matter
72 under nitrogen limitation or environmental stress (23,24), suggesting that cyanobacterial
73 diazotrophs might increase its organic matter dependence in the changing Arctic (25)
74 Marine particles, commonly known as "marine snow", are aggregates of organic and inorganic material such as phytoplankton, algae, zooplankton excretion, cellular debris, and sediment 75 (26). These aggregates play a pivotal role in the marine carbon cycle by transporting captured 76 carbon dioxide from the atmosphere to the deep ocean, reducing atmospheric CO₂ levels (the 77 "biological carbon pump"; (27,28)). Additionally, marine particles serve as hotspots of microbial 78 activity where carbon and nitrogen cycles are tightly coupled through diverse microbial 79 metabolisms (22,29). Particles are considered favorable loci for NCDs, provided by their labile 80 carbon content and low oxygen microniches (22,30,31). Although *nifH* gene sequences 81 (encoding for a subunit of the nitrogenase enzyme) are frequently detected in particle- 82 associated fractions (e.g., >3 µm; (32–34), the specific mechanisms governing these 83 associations remain poorly understood (22). With increasing nitrogen limitation and changing 84 particle flux in the Arctic (35), understanding NCDs interactions with particles is crucial for 85 predicting reactive nitrogen supply in future conditions. 86

87
88 Particles in the Arctic Ocean originate from diverse sources, including phytoplankton, algae, and glacial runoff (14,36,37). Reduced ice cover increases the input of fresh organic matter, thereby altering the composition and behavior of the resulting particulate matter and organic aggregates (38). Moreover, recent studies have shown that melting Arctic sea ice significantly influences carbon export through the ballasting effect of cryogenic gypsum and terrigenous materials (39). These particle dynamics are distinctive of the Arctic environment, making it an ideal location for investigating their complex interactions with diazotrophs. 93 94

95
96 Planktonic microorganisms use chemotaxis to colonize particles, moving along chemical gradients (40). This process helps microorganisms locate optimal environments, especially for 97 carbon sources, and shapes microbial community assembly on marine particles, a behavior 98 observed in various bacterial groups including marine diazotrophs (41–43). However, the 99
100 interplay between carbon availability and nitrogen metabolism in driving particle colonization is
101 poorly understood. Here we hypothesized that the composition of particles (e.g., derived from
102 brown vs. red algae) may favor different diazotroph groups due to their carbon and energy
103 requirements to fuel N₂ fixation. We further propose that diazotroph colonization strategies
104 might differ from non-diazotrophs, as NCDs can meet nitrogen needs via N₂ fixation, potentially
105 giving them an advantage in colonizing nitrogen-poor, carbon-rich particles. Following the major
106 phytoplankton bloom detected by TOPAZ5-ECOSMO satellite data in June 2023 (Fig. S1), our
107 study explores how diazotrophs interact with marine particles amid Arctic ecosystem changes.
108 Using an adapted chemotaxis assay (44) deployed across four contrasting sampling stations
109 and *nifH*/16S rRNA gene sequencing, we assessed whether diazotroph groups show distinct
110 colonization preferences on different polysaccharide particles and how environmental conditions
111 affect these interactions. Our findings shed light on the interactions between diazotrophs and
112 particles, which may be increasingly relevant as ice retreats, expanding diazotroph niches and
113 enriching the particle loading of the Arctic pelagic system.
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## Materials and methods

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### *Chemotaxis assay*

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Four distinct sampling stations were selected for diazotroph chemotaxis experiments using a custom made modified version of the in situ chemotaxis assay (ISCA; Fig 1). Based on water mass definitions (45), surface waters were predominantly characterized as warm Polar Water, except for station N01 located in Atlantic Water in the deep Norwegian Basin (70.59°N, 10.59°E). Station N12 was situated on the shallow, well-mixed Spitzbergen Bank (75.30°N, 22.29°E), influenced by a mixture of Polar and Atlantic waters. Station N08 was characterized
125 by seasonal ice-melt water east of Svalbard (79.20°N, 33.58°E), and station N16 was influenced
126 by glacial melt at the northern end of Storfjorden (78.50°N, 19.26°E) (Fig. 2, Fig. S2). Surface
127 waters showed distinct physical properties, with temperatures ranging from 2.5 to 11°C and
128 salinities from 32.5 to 35 g.kg⁻¹ (Fig. S2).

129
130 The ISCA (Fig. 1a, S6) contains multiple bars (Fig. 1b) with five 3D-printed wells each. Wells
131 hold agarose, alginate, glass beads, or remain non-inoculated. Each well (Fig. 1c, S6) has a lid
132 with a 600 µm hole allowing chemical diffusion without cross-contamination. The design (Fig.
133 1d) enables chemical cues from artificial particles to attract and facilitate microbial colonization.
134 Additional technical details appear in Supplementary Methods.

135
136 Artificial particles included inorganic controls (glass) and organic particles (agarose and
137 alginate) (Supplementary Methods). Alginate and agarose were selected for their prevalence in
138 marine ecosystems (brown and red algae, respectively) (46,47), and their ecological relevance
139 in Arctic coastal waters as bacterial substrates (47,48). Before incubations, artificial particles
140 were rinsed with filtered seawater and measured with a Dino-Lite digital microscope (200 X
141 magnification, 640 x 480 pixels; Hsinchu, Taiwan). Six lanes of five wells containing artificial
142 organic and inorganic particles with empty controls for free-living organisms (Fig.1) were fixed to
143 the ISCA incubator base. A funnel was used to add 4 L of 10 µm-filtered seawater, preventing
144 air bubbles. The incubations were conducted in triplicate for each artificial particle with inorganic
145 and free-living controls added in duplicate (Fig. 1). The incubation of the ISCA was carried out in
146 an incubator set to the temperature and light conditions present in the surface water (5 m) at
147 each station (Fig. 2). Particles and free-living samples were collected at 2, 36, and 72 h and
148 preserved in 50% ethanol-filtered seawater solution (49). The samples were then stored at -
149 80°C until further analysis. DNA was extracted from individual particles and particle-free wells at
150 each sampling time point and sequenced for *nifH* and 16S rRNA genes (Supplementary methods).

152
153 *Statistics*
154
155 Statistical analyses were performed using R version 4.4.1. ASV counts for free-living communities (110 µL) were normalized to match particle sample volumes (0.5 µL), calculated based on the average diameter (1 mm) of the artificial particles. Relative abundance of each taxonomic class was calculated as percentage of total ASVs per sample. To handle the high variability in abundance between taxa and focus on relative changes between treatments, data were log-transformed. Following transformation, data normality was assessed using Shapiro-Wilk (SW) test. Due to persistent non-normal distribution, Mann-Whitney U-tests (MW) were conducted for each sampling station and for each taxonomic class of interest. The model compared relative abundances between carbon sources (agarose vs alginate). *P* values were adjusted using the Benjamini-Hochberg correction.

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166 For colonization dynamics, temporal patterns were further standardized by normalizing the maximum relative abundance observed across the three time points (2, 36, and 72 h) to 100% for each microbial class, station, and carbon source. The relative abundances at all other incubation times were then expressed as a percentage of this maximum value. Bootstrap confidence intervals (n=1000, α=0.05) assessed abundance measurement uncertainty. Kruskal-Wallis tests followed by Bonferroni-corrected pairwise Wilcoxon rank sum tests evaluated temporal colonization pattern variations. This approach enabled the quantification of temporal changes in colonization patterns for each microbial class, station, and artificial particle type.
174 Fold changes were calculated using the initial time point as the reference for both *nifH* and 16S
175 rRNA genes datasets. To compare colonization patterns between particle types, Fligner-
176 Killeen's test was used to assess variance homogeneity, followed by Kruskal-Wallis, and
177 pairwise Mann-Whitney U-tests to determine significant differences in fold changes at each time
178 point. This approach was applied to both diazotrophic and total bacterial communities to assess
179 their respective colonization dynamics.

UNCORRECTED MANUSCRIPT
180 **Results**

181

182 *Diazotroph and bulk prokaryotic community composition*

183

184 We assigned 16S rRNA and *nifH* gene amplicon sequences to the class level to compare the diversity of bulk and diazotrophic microbial communities. From the 120 samples collected, only 185 94 and 52 yielded sufficient amplification of the 16S rRNA and *nifH* gene, respectively (Table 186 S3, S4, S7, S8). The low concentration of extracted DNA (< 2 ng µL⁻¹) may have contributed to the reduced number of successful amplifications. A total of 1,462,209 reads were generated 187 from the 16S rRNA gene sequencing, resulting in 1,119 ASVs. The taxonomic breakdown 188 included 19 phyla, 34 classes, 80 orders, 134 families, and 179 genera (Table S5). In contrast, 189 *nifH* sequencing yielded 273,993 reads and 999 ASVs, encompassing five phyla, eight classes, 190 15 orders, 14 families, and 14 genera (Table S6). The free-living fraction served as a reference 191 for normalization and represented the available seed bank community (Figure S7), allowing 192 identification of active colonizers beyond their initial presence in surrounding water. *nifH* 193 sequences were predominantly assigned to families within *Alphaproteobacteria*, 194 “*Betaproteobacetria*”, and *Gammaproteobacteria* classes, as well as *Cyanophyceae*, including 195 *Alcaligenaceae*, and orders like *Hyphomicrobiales*. In contrast, the 16S rRNA gene dataset 196 revealed a more diverse bacterial community (Tables S5, S6). We used 16S rRNA gene 197 sequence abundance to represent non-diazotrophs, as diazotrophs typically comprise less than 198 1% of the bacterial community in the small size fraction (<10 µm) (50). Class-level taxonomy 199 was used for all analyses to directly compare 16S rRNA gene and *nifH* datasets, as genus-level 200 analysis of 16S rRNA gene showed few substrate preferences differences and *nifH* lacked 201 sufficient depth for genus-level resolution. Family level analyses were also performed but 202 revealed higher variability between replicates and taxonomic discordance between *nifH* and 203 204

204 revealed higher variability between replicates and taxonomic discordance between *nifH* and 16S
205 rRNA genes datasets, potentially masking ecological signals (Figure S8). Although the term

206 "*Betaproteobacteria*" is no longer formally recognized in current bacterial taxonomy, we retain

207 its use here for consistency with the *nifH* database classification.

<figure><img src="image_2.png" /></figure>
208 *Environmental settings*

209
210 Environmental conditions varied among stations (N01, N08, N12, N16), reflecting Barents Sea
211 heterogeneity (Fig. 2). Sea surface temperature showed a latitudinal gradient from 9.92°C (N01)
212 to 1.79°C (northernmost N08) (Fig. 2d; Fig. S2). Phosphate and nitrate + nitrite (PO4 and NN;
213 Fig 2b and 2e, respectively) had a comparable pattern across stations, aligning with the
214 distribution of SST and Chl a (Fig. 2b,c). Atlantic-influenced stations (N01, N16) had highest
215 nutrients (NN: 0.63, 0.45 µM; PO4: 0.12, 0.04 µM), elevated temperatures (9.92°C, 6.56°C), and
216 Chl a concentrations (1.13, 1.74 µg L⁻¹), indicating productive waters. In contrast, station N08
217 had the lowest nutrient (NN: 0.13 µM, PO4: 0.02 µM) and Chl a concentrations (0.22 µg L⁻¹),
218 indicative of more relatively oligotrophic conditions. The higher nutrient concentrations at station
219 N16 situated in proximity to the Storfjorden glacier are likely attributable to glacial meltwater
220 inputs (Fig. S2), supporting high Chl a concentrations with low photosynthetic active radiation
221 (PAR : 62.71 W m⁻² ; Fig. 1f). This suggests that phytoplankton biomass reduced light
222 penetration deeper into the water column at this station. In contrast with the observed trends for
223 macronutrients, the highest measurable concentrations of dissolved iron (dFe ; Fig. 1g) were
224 observed at stations N12 and N16 (4.18 and 2.74 nM, respectively), with potentially higher
225 concentrations at nearby stations. In contrast, dFe concentrations at station N01 (0.10 nM),
226 influenced by Atlantic waters, were approximately 40 times lower than at station N12. Distinct
227 nutrient and iron gradients across stations provided an ideal setting to examine how
228 environmental factors shape microbial communities and particle colonization dynamics.

229
230
231 *Dynamics of diazotroph particle colonization*

232

233 Different diazotroph groups exhibited distinct colonization patterns across particle types.

234 Although inorganic particles showed minimal variance changes typical of passive colonization

235 (Fig. S3d), both organic substrates demonstrated evidence of active colonization processes,
236 although through different patterns. Agarose particles showed consistent colonization patterns

237 with progressively decreasing variance over time, suggesting a convergent community

238 assembly process (Supplementary Results). In contrast, alginate particles exhibited a more
239 dramatic temporal heterogeneity (mean fold changes: 12.1→3.73→12.3) with heterogeneous

240 variances (0.247→2.50→0.610; Fligner-Killeen test: χ² = 10.226, *P* < 0.01), indicating highly
241 dynamic colonization processes (Supplementary Results).

242 *Gammaproteobacteria* members colonized agarose particles rapidly at stations N01 and N12,
243 reaching 100% relative abundance within 2 h (Fig. 3a, e) (KW, *P* < 0.05). This was followed by a

244 systematic succession of other groups, suggesting an organized colonization process driven by
245 substrate-specific interactions. "*Betaproteobacteria*" representatives showed sequential

246 colonization peaks at stations N08, N12, and N16, reaching 100% abundance at 36 h or later
247 (Fig. 3c-e, g-h) with significant variations at N08 and N12 (KW, *P* < 0.05), while

248 *Alphaproteobacteria* members increased steadily at stations N08 (KW, *P* < 0.05) and N12 (KW,
249 *P* < 0.05) reaching 100% by 72 h (Fig. 3c, e). The colonization dynamics for

250 *Alphaproteobacteria* taxa at stations N01 and N16 could not be assessed due to lack of *nifH*
251 amplification at several time points (Table S3).

252

253 The colonization patterns on alginate particles were more variable, characterized by a swift

254 initial colonization of *Gammaproteobacteria* at stations N01, N08, and N16 (KW, *P* < 0.05) (Fig.
3b, d, h), followed by substantial temporal fluctuations in community composition. Other diazotroph groups showed less predictable patterns of colonization, particularly at station N12 where *Alpha*- and "*Betaproteobacteria*" declined abruptly after 2 h (KW, *P* < 0.05) (Fig. 3f). The absence of *Alphaproteobacteria* on alginate particles at stations N01 and N16, coupled with their variable colonization patterns, indicates substrate-specific selection processes.

260
261 *Carbon source preferences*

262 To investigate carbon source preferences among taxa, we analyzed the relative abundance of
263 taxa at the class level on alginate and agarose artificial particles, examining diazotrophic (*nifH*)
264 and total prokaryotic communities (16S RNA gene) separately (Tables S1, S2). Within
265 diazotrophic communities *Gammaproteobacteria* members demonstrated a clear preference for
266 alginate over agarose at most stations, except for station N16. At station N01, the relative
267 abundance of diazotrophic *Gammaproteobacteria* taxa on alginate particles reached 48%,
268 significantly higher than on agarose (MW, *P* < 0.05; Fig. 4). Higher preferences for alginate
269 compared to agarose were observed too at station N08 (34% vs. 32%; MW, *P* < 0.05) and
270 station N12 (26% vs. 19%; MW, *P* < 0.05). However, at station N16 members of diazotrophic
271 *Gammaproteobacteria* exhibited a reversed preference showing significantly higher abundance
272 on agarose (52%) as compared to alginate (18.5%; MW, *P* < 0.05; Fig. 4). Diazotrophic
273 *Cyanophyceae* representatives exhibited varying substrate associations across stations. At
274 stations N08 and N16, they showed a significant preference for alginate over agarose (42.5%
275 vs. 26.2%, MW, *P* < 0.001; 56.8% vs. 37.1%, MW, *P* < 0.001 respectively; Fig. 4, Table S1),
276 while showing no significant substrate preference at stations N01 and N12. Diazotrophic
277 *Alphaproteobacteria* taxa were either undetected on alginate particles at stations N01 and N16
278 or showed no significant carbon source preferences at stations N08 and N12 (Table S1, Fig.
279 3d,f). Diazotrophic "*Betaproteobacteria*" members showed no significant substrate preference
280 across stations (MW, *P* > 0.05), with abundances ranging from 8.31-23.9% on agarose and
281 8.90-24.8% on alginate.

282
283 Analysis of the bulk prokaryotic community revealed high variability across substrates and
284 stations, with no taxonomic groups showing no significant preference for either carbon source
285 (Table S2; Fig. 3). All major taxonomic groups displayed high variability in their relative abundances both between stations and between replicates, with differences ranging from 1% to over 70% for some groups. For example, *Alphaproteobacteria* abundances varied from 14.4% to 68.9% on alginate and from 27.3% to 43.9% on agarose across stations, whereas *Gammaproteobacteria* showed similarly wide ranges (18.8-72.4% on alginate, 31.8-43.9% on agarose). Although substantial variations in relative abundances were observed, these differences did not reach statistical significance (all *P* > 0.05). Unlike the diazotrophic community, where *Gammaproteobacteria* and *Cyanophyceae* showed significant substrate preferences across multiple stations, their non-diazotrophic counterparts displayed no such selectivity.

295
296 *Bulk prokaryotic community particle colonization dynamics*
297
298 The bulk prokaryotic community exhibited colonization dynamics distinct from those of diazotrophs. Although maintaining similar substrate preferences, with more complex interactions on organic particles compared to passive accumulation on inorganic ones (Fig. S4, Supplementary results), the temporal patterns and taxonomic succession differed. Indeed, both agarose and alginate particles exhibited active colonization signatures, suggesting substrate-specific selection processes (Supplementary Results). Contrary to the members of diazotrophic *Gammaproteobacteria*, the bulk prokaryotic *Gammaproteobacteria* ASVs were not the fastest particle colonizers, the faster group being *Alphaproteobacteria* taxa (Fig. 5a, c, g, h). These results indicate that although diazotrophic *Gammaproteobacteria* members initially dominate in both particle types, they do not maintain this predominance over time according to the 16S rRNA gene amplicon data. It appears that differences in colonization dynamics are slowing the overall colonization process, delaying the establishment of certain classes as other classes
310 begin to emerge. For instance, the two-fold decrease in *Gammaproteobacteria* members on
311 agarose at Station N01 between 36 and 72 h (Fig. 5a) coincided with a substantial increase in
312 the relative abundance of *Alphaproteobacteria* representatives over the same period. This
313 pattern was reversed on alginate particles at the same station (Fig. 5b). Certain classes
314 appeared to coexist on the same carbon source with similar colonization dynamics. The
315 colonization patterns of *Gammaproteobacteria* and *Cyanophyceae* taxa at station N08 provide
316 an illustrative example of this (Fig. 5c). In contrast, on alginate particles at the same station,
317 these two classes seemed to engage in competitive interactions. The rapid decline of
318 *Cyanophyceae* ASVs between 2 and 36 h (from 100% to 0%) coincided with a sharp increase in
319 *Gammaproteobacteria* ASVs (from 2 to 100%) during the same period (Fig. 5d). In line with
320 these observations both Gamma- and *Alphaproteobacteria* members demonstrated similar
321 colonization dynamics on alginate particles, albeit at different stations. At station N08 these two
322 classes showed analogous patterns of colonization (Fig. 5d), suggesting a potential overlap in
323 their ecological roles despite being observed at separate locations. At station N12,
324 *Alphaproteobacteria* and *Gammaproteobacteria* taxa competed on alginate:
325 *Alphaproteobacteria* ASVs declined from 100% to 14.9% while *Gammaproteobacteria* ASVs
326 reached 100% between 2-36 h (Fig. 5f).

327
328 **Discussion**
329
330 *Niche partitioning drives diazotroph succession on particles*
331
332 Particle-associated diazotrophic communities showed clear succession patterns, following
333 classical ecological succession theory where early colonizers modify the environment for
334 subsequent colonizers (51). Transitions from rapid colonizers to secondary resource specialists
335 align with the tolerance model, where later colonizers exploit altered conditions independently
336 (52). Across different stations and carbon sources we observed an initial phase dominated by
337 rapid colonizers followed by a gradual shift towards taxa better adapted to exploit the
338 byproducts of earlier colonizers, demonstrating both niche partitioning and facilitation
339 mechanisms (53). Sequential colonization indicates that the environmental biogeochemical
340 conditions and species competition shape particle communities, affecting organic matter cycling
341 and N₂ fixation by particle-associated diazotrophs.

342
343 *Gammaproteobacteria* representatives colonized agarose particles first, rapidly occupying
344 niches before declining from resource depletion or competition. Their decline created
345 opportunities for members of *Cyanophyceae* and *Alphaproteobacteria* (which tend to exploit
346 more labile resources) to establish themselves in the following phase of succession, particularly
347 at stations N01 and N08 (Fig. 3 c, e). This pattern aligns with niche differentiation theory
348 (53,54), where different taxa occupy distinct ecological roles over time, effectively partitioning
349 the available resources. The shift from *Gammaproteobacteria* to *Alphaproteobacteria* and
350 *Cyanophyceae* representatives follows a secondary succession pattern, where species with
351 higher competitive abilities replace pioneer taxa once the initial conditions are modified (52).

352 *Alphaproteobacteria* members were the most persistent group in our study, being present throughout
353 the entire incubation period at certain stations and suggesting an adaptive advantage in oligotrophic
354 conditions. Their sustained presence at station N08, where they colonized both alginate and
355 agarose particles, highlights their resilience in nutrient-poor environments, likely due to efficient
356 nutrient uptake systems (55). This persistence contrasts with a decline in *Gammaproteobacteria*
357 ASVs, highlighting a competition-colonization trade-off (56). Similarly, "*Betaproteobacteria*"
358 members exhibited delayed but notable increases in abundance later in the incubation period,
359 suggesting a more opportunistic strategy. At stations N08, N12, and N16, "*Betaproteobacteria*"
360 peaked around 36 h, exemplifying secondary succession dynamics where organisms colonize
361 pre-conditioned substrates (57) (Fig. 3c, d, e, g, h). This delayed colonization strategy allows
362 "Betaproteobacteria" members to avoid competition with faster colonizers and simultaneously exploit environments modified by earlier colonizers, demonstrating temporal niche partitioning (58). However, members of "Betaproteobacteria" are expected to face competition from other non-diazotrophic bacteria, which could also be competing for available resources. Finally, 365 Cyanophyceae representatives demonstrated variable colonization dynamics influenced by abiotic factors like light intensity. In the Arctic summer, high and continuous light exposure can 366 induce oxidative stress in cyanobacteria, potentially leading to a shift towards mixotrophy to 367 meet energy needs (23,24,59,60). This adaptation provides Cyanophyceae members with a 368 competitive advantage in environments where other non-phototrophic diazotrophs are less 369 suited to stable light and nutrient conditions. 370

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372
373 The colonization patterns of Gammaproteobacteria, Alphaproteobacteria, "Betaproteobacteria", 374 and Cyanophyceae members reveal how biotic and abiotic factors shape diazotrophic 375 communities. This complexity reflects the diverse adaptive strategies that microbial taxa employ 376 to thrive in nitrogen-poor, carbon-rich, and environmentally dynamic marine ecosystems. 377

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387 Diazotrophs are not the only colonizers of organic particles in the marine environment. To better understand particle colonization and niche differentiation patterns, we compared *nifH* and 16S rRNA gene amplicon datasets to assess the distinct colonization dynamics of diazotrophic and non-diazotrophic microbial communities, respectively. In the Barents Sea, the high and increasing abundance of brown algae (61), particularly kelp, likely plays a key role in structuring these microbial communities and influencing carbon cycling pathways. Recent studies indicate that kelp biomass is not only increasing but is also distributed closer to the surface (62), enhancing its ecological and biogeochemical impact. Kelp biomass can reach up to 20 kg m⁻²
during the summer along the coasts of Svalbard (63), providing a substantial source of organic matter including alginate, a polysaccharide predominant in brown algae. Organic compounds derived from brown algae such as alginate support microbial communities in Arctic regions, significantly contributing to carbon cycling through biodegradation (48,62,64). In contrast, red algae which are also present in this region contribute to the diversity of polysaccharides in seawater. For example agarose, but with much lower biomass (approximately 0.5 kg m⁻²; (63,65)). This disparity in biomass between brown and red algae suggests that alginate is likely a more accessible and influential carbon source in the Barents Sea. The observed preference for alginate across most diazotrophic groups may thus reflect the ecological availability and importance of this compound.

Diazotrophs preferentially colonized alginate over agarose (Fig. 4; Table S1), indicating that alginate-rich brown algae leachates are important substrates for N₂ fixation in this region. In contrast, non-diazotrophs exhibited a greater versatility in their carbon source preferences, colonizing both alginate and agarose with similar effectiveness. Alternatively, non-diazotrophs were more versatile in their carbon source preferences (Figure 4; Table S2). This versatility likely reflects the diversity of taxa captured by 16S rRNA gene amplicon sequencing, which includes bacteria with a broad range of metabolic capabilities. The bulk prokaryotic community harbored diverse metabolic capabilities, accessing both complex polysaccharides and simple carbon substrates beyond diazotrophs' reach (47,66). Additionally, non-diazotrophs are thought to rely on N₂ fixed by diazotrophs reducing their need to invest energy in nitrogen acquisition processes (67). The distinct colonization patterns between diazotrophs and non-diazotrophs demonstrate resource partitioning at both spatial and temporal scales (68). This partitioning follows metacommunity theory principles, where local community assembly is influenced by both environmental filtering and species interactions (69).

413
The competition for resources between diazotrophs and non-diazotrophs is a crucial factor in understanding the cycling of nutrients in the increasingly nitrogen-limited Arctic. Given the substantial energy requirements for N₂ fixation (16 ATP per N₂ molecule; (70)), diazotrophs need to colonize carbon-rich particles to secure a stable energy supply (22). Non-diazotrophs, however, may benefit indirectly from the degradation of complex organic matter by diazotrophs or from the release of freshly fixed N₂ (71) without needing to directly colonize and degrade these particles. Here we observed that NCDs have a propensity to colonize alginate-rich particles faster than non-diazotrophs (Fig. 3; Table S1). In natural marine snow, non-diazotrophic bacteria initially colonize nitrogen-rich particles, depleting the nitrogen, and creating anoxic cores that diazotrophs later colonize to access carbon-rich environments needed for N₂ fixation (31). Furthermore, studies have shown that many microorganisms including non-diazotrophic bacteria exhibit chemotactic responses to microgradients of bioavailable nitrogen compounds such as nitrite, ammonium, and nitrate (68). In our study, however, the artificial particles used did not contain nitrogen (72,73), which likely discouraged an initial colonization by non-diazotrophs. Diazotrophs may have thus directly colonized these nitrogen-poor carbon-rich particles, using the available carbon to meet their energy demands for subsequent N₂ fixation (22). In nitrogen-limited Arctic environments, diazotrophs' early colonization of C-rich particles may contribute to nitrogen cycling when bioavailable nitrogen is scarce.

Even though our artificial particles were nitrogen-free, this composition mirrors late-stage marine particles where preferential nitrogen remineralization leads to higher C:N ratio. As marine snow sinks and ages, its C:N ratio increases from 7.1 to > 8.5 due to microbial nitrogen consumption (74), creating nitrogen-depleted microenvironments that potentially favor N₂-fixing diazotrophs (75). This aligns with a recent study (76), which found that high C:N ratios in marine particles correlate with increased diazotrophic activity, suggesting carbon availability promotes N₂ fixation.
440
441 NCDs prefer colonizing alginate over agarose
442
443 Diazotrophic *Gammaproteobacteria* members were the best competitors for alginate. This class is known for its diverse metabolic capabilities, being able to utilize a wide range of organic substrates including alginate (47). The preference for alginate at all stations except N16 highlights the significant role of *Gammaproteobacteria* representatives in polysaccharide degradation in the Barents Sea. This observation aligns with habitat selection theory (77), where organisms actively choose environments that maximize their fitness. The preference for alginate suggests resource specialization, a key mechanism enabling species coexistence in marine systems (78). In the Arctic, labile organic carbon is often limited outside the brief June-September productivity bloom window (5,79). At station N16, near the Negribreen glacier (Fig. 2a), glacial melt, run-off, and possible localized upwelling could increase organic matter concentrations, particularly algal-derived alginates (80). For diazotrophs, especially heterotrophic ones, colonizing organic-rich particles can be crucial for meeting their metabolic needs in nutrient-poor environments (42), likely explaining their faster colonization of these particles. At station N16, however, *Gammaproteobacteria* members showed a clear preference for agarose instead of alginate. This station had the highest Chl *a* concentration observed during our cruise indicating high organic matter loading in the water column, as also supported by the lower PAR values (Fig. 2f). A redundancy analysis (RDA) shows that the agarose preference of *Gammaproteobacteria* representatives aligns with Chl *a* vector, suggesting Chl *a* concentrations influence this preference (Fig. 6). This observation is consistent with a previous study (78), which demonstrated the rapid and targeted succession of *Gammaproteobacteria* members in response to algal decay, highlighting their opportunistic capacity to exploit algal-derived organic matter.

465
High natural alginate levels from brown algae at station N16 may saturate the chemotactic response of *Gammaproteobacteria* representatives, reducing their attraction to artificial alginate particles. In contrast, the lower environmental concentrations of agarose may have enhanced the sensitivity of these bacteria to agarose particles (81). Bacteria can modify their chemotactic responses based on the prevailing concentrations of chemical cues in their immediate environment (82). High ambient concentrations of a substrate can lead to adaptation and reduced sensitivity, as described by the Weber-Fechner law, and conversely, lower levels may enhance sensitivity (83). This dynamic is expected to help *Gammaproteobacteria* members to disperse and colonize new areas quickly, even when food is abundant locally (84). At station N16, preference for agarose likely reflects chemotactic adaptation, in contrast to other stations where substrate specialization explains the general preference for alginate.

The lack of a clear carbon source preference in *Alphaproteobacteria* representatives may suggest their limited ability to degrade complex polysaccharides like alginate and agarose. Instead, they may rely on simpler organic compounds, reflecting their adaptation to oligotrophic conditions and specialization in low-molecular-weight substrates (85). This aligns with the "Black Queen Hypothesis", which proposes that some microorganisms abandon costly functions and rely on "public goods" produced by others (86), with recent studies suggesting that such metabolic interdependence structures microbial communities and drives their co-evolution (87). Field observations support this: certain *Alphaproteobacteria* taxa lack enzymes to degrade complex organic matter and instead depend on metabolites from other organisms (88). In contrast, "*Betaproteobacteria*" exhibit diverse metabolic capabilities (89), which stands to explain their lack of a pronounced preference for a specific carbon source. This metabolic diversity may stem from genetic variability among populations at different stations. Diazotrophic cyanobacteria showed either a significant preference for alginate particles or no substrate preference across stations (Table S2). At stations N08 and N16, they exhibited higher relative
492 abundances on alginate (42.5% and 56.8% respectively, Fig. 4) compared to agarose particles
493 (26.2% and 37.1% respectively, Fig. 4), yet showed comparable colonization patterns on both
494 substrates at stations N01 and N12 (Fig. 3). This observation suggests that cyanobacteria can
495 effectively establish themselves on both particle types through different ecological strategies.

496 On alginate particles, where they showed significant preference at two out of four stations,
497 diazotrophic cyanobacteria can directly degrade this substrate. However, on agarose particles,
498 they likely rely on cross-feeding relationships with heterotrophic bacteria capable of breaking
499 down the complex polysaccharide (75, 76). Such substrate flexibility is characteristic of marine
500 microbial communities, where metabolic networking enables efficient utilization of diverse
501 organic particles (55, 77). In marine environments, these interactions enable efficient utilization
502 of complex substrates by microbial consortia on organic particles. Additionally, the dFe
503 concentration at the different stations (Fig. 2g) supports cyanobacterial growth and nitrogen
504 fixation, enhancing their ability to utilize degradation products from both particle types (92,93).

505 The temporal dynamics of colonization further illuminate this pattern: whereas
506 *Gammaproteobacteria* members reached full colonization of alginate particles in 72 h,
507 *Cyanophyceae* representatives achieved similar relative abundances on both substrates (Fig.
508 3), suggesting their ability to establish viable populations regardless of substrate composition
509 (53,94). The RDA reveals substrate-specific responses, with iron availability particularly
510 influencing cyanobacterial colonization on alginate particles. In comparison their presence on
511 agarose particles shows weaker correlations with the measured environmental parameters (Fig.
512 6).

513
514 Conclusions

515
516 Our study details diazotroph particle colonization in the Arctic Ocean, highlighting selective
517 substrate preferences and roles in nitrogen cycling. We observed that *Gammaproteobacteria*
518 representatives, a dominant group of NCDs, preferentially colonize alginate particles with
519 abundant polysaccharides from brown algae, becoming more prevalent with Arctic warming.
520 This preference suggests a close ecological connection between these diazotrophs and algal-
521 derived organic matter. The distinct colonization patterns between diazotrophic and non-
522 diazotrophic bacteria indicate niche partitioning within the microbial community. Typically, non-
523 diazotrophs colonize first, attracted by bioavailable nitrogen, which they deplete over time
524 leaving nitrogen-poor particles for diazotrophs that can fix N₂ to meet their needs. In our
525 experiment, nitrogen-free carbon-rich particles may have favored colonization by diazotrophs,
526 which have their own means to obtain nitrogen. Conversely, non-diazotrophs reliant on external
527 nitrogen sources were less inclined to colonize nitrogen-free particles. This delayed colonization
528 by diazotrophs suggests they play a secondary but essential role in nitrogen cycling, particularly
529 after nitrogen depletion. When diazotrophs showed a strong affinity for alginate, likely due to
530 their high energy requirements for N₂ fixation, non-diazotrophs exhibited broader versatility, with
531 no clear preference between alginate and agarose, suggesting different metabolic strategies.

532
533 Arctic warming will increase algal blooms, runoff, and glacial melt, increasing iron and alginate
534 levels, favoring particle-associated diazotrophs and N₂ fixation. This shift is anticipated to
535 significantly impact Arctic biogeochemical cycling, nutrient availability, and primary production in
536 nitrogen-limited waters. Understanding these diazotroph-particle interactions is essential for
537 predicting the Arctic Ocean's response to climate change. Parallel N₂ fixation measurements
538 were conducted during this cruise using ¹⁵N₂ tracer incubations, future studies combining ¹⁵N₂
539 labeled particles with single-cell mass spectrometry analyses (i.e., nanoSIMS) would enable
direct visualization of nitrogen incorporation at the cellular level. Additionally, further research should investigate metabolic interactions between and within diazotroph and non-diazotroph groups, as their selective colonization of algal particles may reshape Arctic nitrogen cycling and ecosystem dynamics.

## Acknowledgements

The N-ARC project was funded under Natural Environment Research Council (NERC)

Discovery grant Nitrogen fixation in the Arctic Ocean, N-ARC (grant numbers NE/T001240/1 and NE/T000570/1) held at the University of Liverpool (CM) and the National Oceanography Centre Liverpool (JEH). Sample analyses were supported by the projects ANITA "Architecture and dyNamics of marIne parTicle colonizAtion" funded by the Excellence Initiative of Aix-Marseille University A*MIDEX, the project PANDA "PArticles as Niches for non-cyanobacterial DiAzotrophs" funded by INSU-EC2CO, and the project MANIOC "iMpact of pArticle microbial colonisation on Nitrogen cyclIng in the OCean" granted by Aix-Marseille Université Institut Océans to MB. MB was partly funded by the BIOPOLE National Capability Multicentre Round 2 funding from the Natural Environment Research Council (grant no. NE/W004933/1). The project leading to this publication has received funding from France 2030, the French Government program managed by the French National Research Agency (ANR-16-CONV-0001), and from Excellence Initiative of Aix-Marseille University - A*MIDEX. We thank GENXMAP (Marseille, France) for their technical expertise in nifH gene sequencing, 16S rRNA gene library preparation, and sequencing. We gratefully acknowledge Louisa Norman for analyzing and providing nitrate and nitrite nutrient data, Catherine Berridge and Thanh-Ngan Hua for sampling nutrient and chlorophyll a data. The authors warmly thank the captain and crew of the RRS Discovery, as well as NMF staff for assistance with overboard operations.
564 Author contributions

565
566 AC and MB designed and conceptualized the study. ML helped adapt, develop, and optimize
567 the ISCA methodology. AC and MB conducted oceanic sampling during the cruise. AC and CCB
568 performed the laboratory work, including DNA extraction and *nifH* library preparation. AC
569 conducted molecular data analysis, RH, OF, and MCL analyzed the environmental parameters.
570 CM, JEH, and MCL led and coordinated the cruise. JEH did the Temperature-Salinity (T-S)
571 diagram. AC wrote the original manuscript. MB provided substantial feedback on early versions,
572 and all co-authors contributed to manuscript revision and approved the final version.
573 Data availability

574

575 The *nifH* and 16S rRNA gene sequences generated during this study have been deposited in
576 the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1188574 and
577 PRJNA1189317, respectively. All other data generated or analyzed during this study are
578 included in this published article and its supplementary information files. The device
579 specifications and 3D printing files for the In-Situ Chemotaxis Assay are available at:
580 https://github.com/OceanBridges/In-Situ-Chemotaxis-Assay-adapted-version.

581

582 Ethics declarations

583

584 Competing interests: The authors declare no competing interests.

585
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927
<figure><img src="image_3.png" /><figcaption>930
931
932 Figure 1: Overview of the modified ISCA model used on the RSS Discovery. Schematic of the
933 modified ISCA design mounted on the base of a PMMA chamber, facilitating controlled
934 incubations. STEP files used for this 3D design are available in the supplementary materials and
935 parametric FCStd files for device modification are available on the github repository (a). Diagram of
936 the ISCA wells, highlighting the placement of various artificials particles (alginate and agarose in
937 triplicate) and controls (inorganic and free-living in duplicate), along with the assigned incubation
938 durations (b). Detailed close-up of the wells, specifying their dimensions and unique features
939 (c). Simplified illustration of the dynamics of diffusion, chemotaxis, and colonization within the
940 wells during the incubation process (d).
941</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2: Bathymetric map of the sampling stations (N01, N08, N12, N16) in the Barents Sea a), accompanied by bar charts illustrating the concentrations of various environmental parameters measured at the stations. Depths are represented by a color gradient ranging from light blue (0-50 m) to dark blue (4000-6000 m). The bar charts display variations in : b) sea surface temperature (SST), c) nitrates and nitrites (NN), d) phosphate ($PO_4^{3-}$), e) chlorophyll a (Chl a), f) photosynthetically active radiation (PAR), and g) iron (Fe), across the stations. Each station shows distinct gradients, indicating heterogeneity in environmental conditions across the sampling sites. These measurements were conducted without replication.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>Figure 3: Normalized relative abundance of diazotrophs groups over time in agarose (a,c,e,g) and alginate (b, d, f, h) artificial particles at four Barents Sea stations (N01, N08, N12, N16). The diazotroph groups include representatives of the *Gammaproteobacteria*, *Cyanophyceae*, "*Betaproteobacteria*", and *Alphaproteobacteria*. The time points included in the study were 0, 2, 36, and 72 h. Bootstrap confidence intervals (95%) are displayed as semi-transparent shaded areas around each curve. The number of samples included in each data point are provided in Table S7.</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 4: Bar plot showing the relative abundance of microbial classes associated with alginate and agarose artificial particles at stations N01 (a, b), N08 (c, d), N12 (e, f), and N16 (g, h) for both the 16S and *nifH* genes. The bars represent the difference in relative abundance between the two carbon sources, with positive values indicating a preference for alginate and negative values indicating a preference for agarose. The microbial classes shown include *Alphaproteobacteria*, “*Betaproteobacteria*”, *Gammaproteobacteria*, and *Cyanophyceae*. Error bars indicate the standard deviation for each substrate. Asterisks (*) indicate statistically significant differences between substrates (*P* < 0.05). The number of samples included in each data point are provided in Table S7.</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>Figure 5: Normalized relative abundance of bulk microbial groups over time in agarose (a,c,e,g) and alginate (b, d, f, h) artificial particles at four Barents Sea stations (N01, N08, N12, N16). The bulk microbial groups include representatives of the *Gammaproteobacteria*, *Cyanophyceae*, "*Betaproteobacteria*", and *Alphaproteobacteria*. The time points included in the study were 0, 2, 36, and 72 h. Bootstrap confidence intervals (95%) are displayed as semi-transparent shaded areas around each curve. The number of samples included in each data point are provided in Table S8.</figcaption></figure>

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<figure><img src="image_8.png" /><figcaption>Figure 6: Redundancy analysis (RDA) triplot representing the distribution of individual samples as a function of preferences for carbon sources (agarose and alginate) and environmental variables measured at the sampling stations. The dots indicate the samples, with specific colors for each station. The red vectors show the gradients of the environmental variables influencing carbon preferences, their length and direction illustrating the strength and direction of the effect on the groups of microorganisms. The percentage values indicate the inertia explained by the first two axes of the RDA.</figcaption></figure>