# Global biogeography of prokaryotes in mangrove sediments: Spatial patterns and ecological insights from 16S rDNA metabarcoding

Emma Jamon-Haon, Philippe Cuny, Alix Rossi, Léa Sylvi, Maud Fiard, Cécile Militon

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Emma Jamon-Haon, Philippe Cuny, Alix Rossi, Léa Sylvi, Maud Fiard, et al.. Global biogeography of prokaryotes in mangrove sediments: Spatial patterns and ecological insights from 16S rDNA metabarcoding. Estuarine, Coastal and Shelf Science, 2025, 325, pp.109485. 10.1016/j.ecss.2025.109485. hal-05233803

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# Global biogeography of prokaryotes in mangrove sediments: Spatial patterns and ecological insights from 16S rDNA metabarcoding

Emma Jamon-Haon* ID, Philippe Cuny, Alix Rossi ID, Léa Sylvi, Maud Fiard, Cécile Militon

Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France

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

## ARTICLE INFO

**Keywords:**

- Mangroves
- Prokaryotes
- Biogeography
- 16S rDNA metabarcoding
- Sediments

## ABSTRACT

The global asymmetry in mangrove floristic diversity is well-documented, yet the biogeography of sediment microbial communities remains overlooked. This meta-analysis of 16S rDNA datasets from 12 countries investigates the diversity, composition, and distribution of prokaryotic communities across mangrove bioregions. We found no significant correlation between tree richness and microbial diversity, but identified a microbial hotspot in the Indo-West Pacific. Microbiota associated with different mangroves displayed opposite latitudinal diversity gradients and beta-dispersions. Random forest models highlighted key microbial taxa, while shared OTUs across bioregions suggested a core mangrove microbiota. Importantly, substantial heterogeneity in microbiota composition was strongly influenced by methodological factors such as molecular protocols used in the different studies. While these variations limit direct dataset comparisons, they underscore the urgent need for more consistent and harmonized sampling and sequencing approaches in future research. Despite these challenges, the observed distance-decay relationship supports the role of dispersal barriers in shaping mangrove sediment microbiota. This study provides insights into the potential influence of environmental factors, beyond plant diversity alone, in structuring prokaryotic communities in mangrove sediments. By acknowledging methodological biases and proposing standardized frameworks for future research, we aim to refine our understanding of mangrove microbiome dynamics and their ecological implications.

## 1. Introduction

Mangroves are intertidal forests found along relatively sheltered coastal margins of intertropical regions, covering approximately 75% of these areas (Giri et al., 2011). The tree and shrub species primarily composing these forests are considered as ecosystem engineers as they trap sediments and support diverse life forms. Fossil records and molecular markers indicate that Indo-West-Pacific (IWP) and Atlantic-East-Pacific (AEP) mangroves diverged around 10 to 15 Mya, coinciding with the closure of the Tethys Seaway and the mid-Miocene cooling (Takayama et al., 2021; Harzhauser and Piller, 2007). Consequently, the AEP and IWP are separated by the African continent and the Pacific Ocean, with no shared tree species between them (Tomlinson, 2016). Further disjunctions within these regions have formed subregions, including Eastern Pacific, Western Atlantic, Caribbean, West Africa, East Africa, Indo-Malaysia, and Australasia (Hogarth, 1999). Excluding hybrids, the IWP region hosts 54 true mangrove species, with a biodiversity hotspot in the Indo-Malaysian bioregion, whereas the AEP

region is home to only 17 species, with a hotspot in the Caribbean (Duke, 1992, 2017). This creates a significant asymmetry in the global distribution of floristic diversity in mangrove assemblages. Previous studies have shown that factors such as environmental conditions, biotic interactions, ocean currents and geographic barriers play a crucial role in the distribution of mangrove species (Duke et al., 1998). However, beyond the diversity of mangrove flora, these ecosystems also harbor rich sediment microbial communities that play essential roles in mangrove functioning, including organic matter decomposition and nutrient cycling (Lai et al., 2022). Despite their critical roles, sediment microbial communities are often overlooked in discussions of mangrove biogeography.

Compared to other biomes, mangrove sediment prokaryotic communities are particularly rich and complex, shaped by extreme environmental gradients like salinity, oxygen, and temperature, as well as high rates of bioturbation (Zhang et al., 2019; Booth et al., 2023). Additionally, climate and precipitation patterns have been shown to play key roles in shaping microbial communities (Zhang et al., 2019).

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: emma.jamon-haon@univ-amu.fr (E. Jamon-Haon), philippe.cuny@univ-amu.fr (P. Cuny), maud.fiard@mio.osupytheas.fr (M. Fiard), cecile.militon@univ-amu.fr (C. Militon).</footnote>
Dominated by groups like Gammaproteobacteria, Deltaproteobacteria, Alphaproteobacteria, Chloroflexi, Firmicutes, Euryarchaeota and Crenarchaeota, these communities are highly sensitive to environmental heterogeneity (Lai et al., 2022). Studies from different bioregions have emphasized that tree species significantly affect mangrove sediment microbiota composition through selective processes (Gomes and Cleary, 2013; Wu et al., 2016; Muwawa et al., 2020; Mai et al., 2021; Sui et al., 2023), with the combined actions of root exudates, litter deposition, and associated endofauna (Twilley, 2019; Zhuang et al., 2020). These relationships raise the question whether global patterns in microbial community distribution parallel the known global biogeographic patterns of mangrove trees.

The global distribution of microbes is shaped by mechanisms such as dispersal, environmental filtering, adaptation, diversification, and local extinction (Xu et al., 2020). The longstanding hypothesis that “everything is everywhere, but the environment selects” (Baas Becking, 1934) posits that microbial distribution is determined primarily by environmental selection due to their small size and high densities, as well as their ability to enter dormancy and disperse. However, for host-associated microbes, particularly those of sessile plants, geographic structuring along environmental gradients is likely, potentially resulting in patterns like distance-decay relationships or latitudinal diversity gradients (Härer and Rennison, 2023). To date, these hypotheses regarding microbial biogeography have rarely been tested in mangrove sediments, despite these ecosystems being ideal candidates for such studies.

Recent studies have barely begun to explore microbial biogeography in mangroves. For instance, Du et al. (2023) observed country-scaled distance-decay relationships in prokaryotic communities between China and South America. Tavares et al. (2021) further demonstrated that local environmental factors in Brazil better explained microbial community differences than geographic distance, even amidst genetic divergences among mangrove trees. Similarly, Thomson et al. (2022a) found that microbial communities in Australian and Saudi Arabian mangroves, differed primarily in structure rather than composition. Building on these studies, we hypothesized that mangrove sediment prokaryotic communities are shaped by deterministic processes and may reflect the global biogeographic patterns of their host plants.

In this study, we aimed to investigate the diversity, composition, and distribution of prokaryotic communities across different mangrove bioregions on a global scale, a novel approach that has not been previously tested for these ecosystems. To achieve this, we leveraged the extensive pool of 16S rDNA metabarcoding data generated in recent decades to investigate the composition of the prokaryotic community in mangrove sediments on a global scale (Lai et al., 2022). Metabarcoding is one of the most widely used methods for assessing the environmental biodiversity of microbes, organisms invisible to the naked eye and still mostly uncultured. This interest has been accompanied by the diversification of molecular techniques to study their biodiversity (e.g. extraction methods, primer choice) and bioinformatical pipelines for sequence processing (Compson et al., 2020). Recognizing the variation in molecular and bioinformatic methodologies across the studies, we developed a Python algorithm to standardize the analysis by targeting the V4 region of the 16S rDNA gene (540 samples from 17 studies). Using community ecology analyses and random forest models (Cutler et al., 2007), we identified key microbial taxa and their potential ecological drivers, contributing to our understanding of microbial biogeography in mangrove ecosystems.

## 2. Methods

### 2.1. 16S rDNA datasets collection

A search was conducted on the NCBI Sequence Reads Archive (SRA) on April 18, 2023, using the query « mangrove* AND sediment* » (see PRISMA diagram in Supplementary Fig. 1 (Haddaway et al., 2022)). A

total of 5285 SRA runs (i.e. files related to one sequenced sample in SRA format) from 110 bioprojects (i.e. collections of biological data related to a research effort) were extracted, meeting two main criteria: i) they were in fastq format and ii) the samples were sequenced using Illumina platforms. After extracting the runs from SRA, they were filtered based on SRA metadata and supplemented with additional searches in related publications (when available). Briefly, we excluded runs from samples that were not assigned to the correct bioproject, did not target the 16S rRNA gene, were not from sediments, lacked a related published paper, did not have available geographical coordinates, or involved anthropogenically altered mangroves, cultivation approaches, or forced bio-turbation. We retained only the runs targeting a portion of the 16S rRNA gene containing the V4 region and collected between 0 cm and 20 cm, as these dominated the dataset. At this stage, 904 runs from 33 related studies with a corresponding DOI remained. These runs were not deposited in a consistent format across the different bioprojects (e.g. missing primers, absent R1 or R2 files, pre-merged sequences, or no corresponding sample names). A standardization effort was made to recover raw sequencing files or obtain further information about the runs by contacting the authors. At this process, runs from 24 studies were ready for preprocessing. Metadata were compiled based on SRA metadata, literature searches, or directly correspondence with authors, and included only qualitative variables such as bioregion, mangrove geomorphological type, country, related study, or primers used.

### 2.2. Processing of the reads and OTU clustering

To obtain comparable data, we developed a Python script using a regular expression to identify all sequences matching a consensus region with a one-base mismatch tolerance, corresponding to the universal primers 515F (in the R1 fastq files) and 806R (in the R2 fastq files) (5'-GTGYCAGCMGCCGCGGT-A-3' and 5'-GACTACNVGGGTWTCTAAT-3', respectively). The script then generates a new dataset in fastq format, containing only the bases downstream of the *in silico* primers. If the primer patterns were not found in a sequence, that sequence was excluded from the new file, resulting in orphaned sequences lacking complementary sequences in the other file, which were also discarded. The reads were then processed per study following the standard pipeline of 'dada2' 1.24.0 (Callahan et al., 2016). The 'truncLen' argument was adjusted based on the length of the reads (250 or 300 b) and the observed diminishing quality scores in each study. Amplicon Sequence Variants (ASVs) were taxonomically assigned with SILVA 138.1 database (Quast et al., 2012), and the sequences corresponding to chloroplasts and mitochondria were removed. We then clustered ASVs into Operational Taxonomic Units (OTUs) using a 99 % similarity threshold (Edgar, 2018) to minimize variation arising from the different runs of sequencing with the function 'asv2otu' from 'MiscMetabar' 0.40. At this stage, some samples were removed due to data corruption (e.g. could not be read by the 'filterAndTrim' function for unknown reason) or insufficient sequencing depth. Additionally, studies with less than five samples were removed. Ultimately, we retained 17 studies, resulting in 540 samples.

### 2.3. Taxonomy and statistical analyses

Most abundant taxa were displayed using the 'fantaxtic' package (Teunisse, 2022). Alpha-diversity was computed on rarefied samples to account for uneven sequencing depths across samples and studies, which involved randomly drawing 3000 sequences from each sample. The computed taxonomy indices included OTU richness (S) and Shannon-Weaver diversity (H'). Significant differences were tested between qualitative variables. Normality was assessed using Shapiro's tests, and homoscedasticity was evaluated with Levene's tests. Since both conditions were not met and the number of samples per tested group was quite heterogeneous, significant differences between categories that involved two groups (e.g. AEP versus IWP) were tested
non-parametrically using Mann-Whitney test, while Dunn's pairwise
multiple comparisons were conducted for parameters involving more
than two groups (e.g. bioregion, mangrove type, study). The skewness of
the distributions was assessed using the 'datawizard' package (Patil
et al., 2022). We calculated Spearman's correlations to evaluate the
effects of latitude and longitude on alpha-diversity using the cor.test
function from the 'stats' package, which implements algorithm AS 89.
The distance-decay relationship was tested with a linear regression, with
the data from Thomson et al. (2022a) excluded due to the compositional
similarity between the Saudi Arabian and Australian samples, which was
influenced by methodological factors (see the results).

Beta-diversity was visualized by computing non-metric multidimensional scaling (NMDS) on the Jaccard distance matrix with ‘metamDS’ from ‘vegan’ v2.6-4. The effects of qualitative variables on beta-diversity were tested using Permutational Analysis of Variance (PERMANOVA) using ‘adonis2’ (1000 permutations) on the Jaccard distance matrix. Given that community dissimilarities were significantly influenced by the methodological parameters and varied according to the original study, we employed a random forest (RF) model approach to uncover the importance of bioregions (variables) and taxa (features) (Cutler et al., 2007). To reduce the high dimensionality of the OTU table and mitigate the dominance of zeros, we selected only the most abundant OTUs (>1 % in at least one sample). Proportional abundances of OTUs were retained for classification (ranging between 0 and 1). RF models were computed using ‘scikit-learn’ v1.5.0 in Python (Pedregosa et al., 2011). A classifier model was constructed for each parameter based on a training set (80 %) and tested on a separate set (20 %). To avoid overfitting, each model was built with 15 estimators, a maximum depth of 8, and the Gini coefficient as decision criterion. Model scores were computed using a 10-fold cross-validation. OTU importances, based on the mean accumulation of impurity decrease within each tree, were retrieved and ranked for each model of the cross-validation. OTUs with non-zero importance in at least 8 out of 10 models were considered the most important OTUs for the corresponding class.

3. Results

A total of 540 samples from 12 countries and territories were
analyzed in the study: 130 from the Caribbean bioregion, 40 from
Southeast America, 169 from East Africa, 154 from Indo-Malaysia and
48 from Australasia (Fig. 1). The number of mangrove tree species

reported in the studies varied between bioregions, with 3 species from 3
genera in AEP and 12 species from 7 genera in IWP. The sampling pe-
riods spanned from 2014 to 2020, with most samples collected outside of
the rainy seasons (Table 1). Coastal and estuarine mangroves constituted
over 72 % of the samples.

**3.1. Diversity patterns**

A total of 128,196 OTUs were initially identified, of which 84,293 remained after rarefaction. Observed OTU richness (S) differed significantly across both global areas and bioregions (Fig. 2A). The IWP bioregion generally exhibited higher richness compared to the AEP region (mean S: 1372 OTUs versus 1289 OTUs). The Indo-Malaysian bioregion showed the highest richness and variability (max S: 2024 OTUs), while Southeast America and Australasia exhibited the lowest richness (min S: 217 OTUs). Shannon's diversity (H'), with an overall mean of 6.7 ± 0.5 (from 3 to 7.4), followed a similar pattern to richness S (Fig. 2A). However, East African samples did not differ significantly in diversity from those in Indo-Malaysian samples (mean H': 6.8 ± 0.3 and 6.7 ± 0.5 respectively). Both AEP and IWP exhibited negative skewness in richness, indicating left-skewed distributions, indicating that lower richness was less common (Skewness: -1.2 ± 0.2 and -0.24 ± 0.2, respectively). The skewness of IWP's richness was closer to 0, suggesting a more uniformly distributed richness. Shannon's diversity distributions were similarly skewed, particularly for AEP (AEP Skewness: -2.7 ± 0.2; IWP Skewness: -1.4 ± 0.2). Overall, alpha-diversity distributions suggested that AEP samples were poorer and less evenly distributed than those from IWP, with less heterogeneity but more outliers.

There was no significant linear relationship between geographical coordinates and overall alpha-diversity. However, absolute latitude showed a weak but significant negative correlation with Shannon's diversity (Spearman; H' p < 0.0001, corr = -0.15; Fig. 2B), while longitude was positively correlated with richness and diversity (Spearman; S p < 0.01, corr = 0.12; H' p < 0.0001, corr = 0.14). Similarly, no clear relationship was found between microbial richness (S, H') and tree richness (Supplementary Fig. 2).

Focusing specifically on coastal mangroves, a significant linear
relationship was observed between richness and latitude (LM; R² = 0.13,
p < 0.001 for S, and LM; R² = 0.09, p < 0.001 for H'), indicating lower
OTU richness and diversity further from the equator (Fig. 2B). Estuarine
mangroves showed the opposite trend, with higher richness and

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** (A) Map of sampling locations of data retrieved on SRA that covered 16S regions from mangrove sediments (black dots), of the samples included for the meta-analysis (grey dots) and relative proportions of sample related mangrove tree species across the bioregions. Mangrove cover data (greyish green) was uploaded from Giri et al. (2011). Bar plots indicate (B) the number of samples per country and (C) for each mangrove geomorphological type. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
<caption>**Table 1**</caption>

Studies included in the meta-analysis and their related production information. Letters indicate institutes of provenance. See supplementary table for more details about the metadata and the primer sequences.

<table><thead><tr><th>Region</th><th>SRA bioproject</th><th>Number of samples</th><th>Sampling period</th><th>Sampling depths</th><th>Extraction kit</th><th>Targetted region</th><th>Forward primer</th><th>Reverse primer</th><th>Taq polymerase</th></tr></thead><tbody><tr><td colspan="10"><strong>Atlantic-East-Pacific (AEP)</strong></td></tr><tr><td colspan="10"><strong>Caribbean</strong></td></tr><tr><td>Mexico</td><td>PRJNA550111<sup>a</sup> (Gómez-Acata et al., 2023)</td><td>53</td><td>October 2018</td><td>0-2 cm</td><td>DNeasy PowerSoil</td><td>V4V5</td><td>515F<br>Caporaso</td><td>806R<br>Caporaso</td><td>Takara<br>ExTaq</td></tr><tr><td>Guadeloupe</td><td>PRJNA1118708<sup>b</sup></td><td>42</td><td>June 2019</td><td>0-2 cm,<br>2-10 cm,<br>10-20 cm</td><td>DNeasy PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Pfu Promega</td></tr><tr><td>Martinique</td><td>PRJNA874590<sup>b</sup> (Fiard et al., 2024)</td><td>35</td><td>June 2018</td><td>0-2 cm,<br>2-10 cm,<br>10-20 cm</td><td>DNeasy PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Pfu Promega</td></tr><tr><td colspan="10"><strong>Southeast America</strong></td></tr><tr><td>French<br>Guiana</td><td>PRJNA735070<sup>b</sup> (Fiard et al., 2022)</td><td>26</td><td>November<br>2017</td><td>0-2 cm,<br>2-10 cm,<br>10-20 cm</td><td>DNeasy PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Pfu Promega</td></tr><tr><td>Brazil</td><td>PRJNA608697<sup>c</sup> (De<br>Santana et al., 2021)</td><td>9</td><td>July 2018</td><td>0-10 cm</td><td>MO BIO PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>806R<br>Caporaso</td><td>KAPA Hifi</td></tr><tr><td>Brazil</td><td>PRJNA817610<sup>d</sup> (Costa et al., 2023)</td><td>5</td><td>January 2018</td><td>0-20 cm</td><td>MO BIO PowerSoil</td><td>V3V4</td><td>Bakt_341F<br>Herlemann</td><td>Bakt_805R<br>Herlemann</td><td>KAPA HiFi</td></tr><tr><td colspan="10"><strong>Indo-West Pacific (IWP)</strong></td></tr><tr><td colspan="10"><strong>East Africa</strong></td></tr><tr><td>Mayotte</td><td>PRJNA1118920<sup>b</sup></td><td>42</td><td>October 2018</td><td>0-2 cm,<br>2-10 cm,<br>10-20 cm</td><td>DNeasy PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Pfu Promega</td></tr><tr><td>Kenya</td><td>PRJNA644929<sup>e</sup> (Muwawa et al., 2020)</td><td>20</td><td>May 2018</td><td>1-5 cm,<br>10-15 cm</td><td>DNeasy PowerSoil</td><td>V3V4</td><td>Bakt_341F<br>Herlemann</td><td>Bakt_805R<br>Herlemann</td><td>High Fidelity<br>DNA Q5</td></tr><tr><td>Saudi<br>Arabia</td><td>PRJNA720541<sup>f</sup> (Thomson et al., 2022a)</td><td>62</td><td>October 2018</td><td>0-2 cm,<br>5-10 cm</td><td>DNeasy PowerSoil</td><td>V3V4</td><td>Pro341F<br>Takahashi</td><td>Pro805R<br>Takahashi</td><td>–</td></tr><tr><td colspan="10"><strong>Indo-Malaysia</strong></td></tr><tr><td>China</td><td>PRJNA692026<sup>g</sup> (Huang et al., 2021)</td><td>5</td><td>June 2021</td><td>0-5 cm</td><td>Spin FastDNA</td><td>V3V4</td><td>338F Huws</td><td>806R<br>Caporaso</td><td>FastPfu</td></tr><tr><td>China</td><td>PRJNA732523<sup>h</sup> (Liu et al., 2022)</td><td>54</td><td>April 2020</td><td>5-15 cm</td><td>HiPure sediment<br>DNA Kit B</td><td>V3V4</td><td>Bakt_341F<br>Herlemann</td><td>Bakt_805R<br>Herlemann</td><td>–</td></tr><tr><td>China</td><td>PRJNA475455<sup>i</sup> (Zhang et al., 2019)</td><td>36</td><td>September<br>2017</td><td>0-10 cm,<br>10-20 cm</td><td>DNeasy PowerSoil</td><td>V4</td><td>515F<br>Caporaso</td><td>806R<br>Caporaso</td><td>Takara<br>ExTaq</td></tr><tr><td>China</td><td>PRJNA797991<sup>j</sup> (Zhang et al., 2022)</td><td>6</td><td>November<br>2020</td><td>0-20 cm</td><td>Spin FastDNA</td><td>V4</td><td>515FY<br>Parada</td><td>806R Apprill</td><td>Phusion Hifi</td></tr><tr><td>China</td><td>PRJNA404001<sup>k</sup> (Zhu et al., 2018)</td><td>20</td><td>June 2014</td><td>0-5 cm</td><td>Spin FastDNA</td><td>V4</td><td>515F<br>Caporaso</td><td>806R<br>Caporaso</td><td>Phusion Hifi</td></tr><tr><td>China</td><td>PRJNA779243<sup>l</sup> (Zhu et al., 2022)</td><td>18</td><td>? 2020</td><td>0-15 cm</td><td>DNA extraction<br>TiangenBiotechCo<br>Soil DNA Kit EZNA</td><td>V3V4</td><td>338F Huws</td><td>806R<br>Caporaso</td><td>Takara Taq</td></tr><tr><td>Malaysia</td><td>PRJNA756333<sup>m</sup> (Mai et al., 2021)</td><td>15</td><td>December<br>2019</td><td>0-5 cm</td><td>MO BIO PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Taq<br>Invitrogen</td></tr><tr><td colspan="10"><strong>Australasia</strong></td></tr><tr><td>New<br>Caledonia</td><td>PRJEB25548<sup>n</sup> (Luis et al., 2019)</td><td>12</td><td>March and<br>November<br>2016</td><td>0-10 cm</td><td>MO BIO PowerSoil</td><td>V4V5</td><td>515FY<br>Parada</td><td>909R<br>WangQian</td><td>Taq<br>Invitrogen</td></tr><tr><td>Australia</td><td>PRJNA720541<sup>f</sup> (Thomson et al., 2022a)</td><td>36</td><td>June 2018</td><td>0-2 cm,<br>5-10 cm</td><td>DNeasy PowerSoil</td><td>V3V4</td><td>Pro341F<br>Takahashi</td><td>Pro805R<br>Takahashi</td><td>–</td></tr></tbody></table>

diversity away from the equator (LM; R² = 0.26, p < 0.001 for S, and LM; R² = 0.11, p < 0.001 for H'). The distance-decay relationship across all mangrove samples was weak (LM; R² = 0.12, p < 0.001 Fig. 2C), though

this relationship was stronger when considering only coastal mangroves (LM; R² = 0.34, p < 0.001). Estuarine mangroves could not be evaluated due to uneven geographic distribution.

<footnote>a Instituto de Ecología UNAM.</footnote>

<footnote>b Mediterranean Institute of Oceanography (MIO).</footnote>

<footnote>c New York University.</footnote>

<footnote>d Universidade Federal do Pará.</footnote>

<footnote>e Pwani University.</footnote>

<footnote>f King Abdullah University of Science and Technology (KAUST).</footnote>

<footnote>g College of Oceanology and Food Science, Quanzhou Normal University.</footnote>

<footnote>h Sun-Yat-Sen University.</footnote>

<footnote>i Shenzhen University.</footnote>

<footnote>j Xiamen University Tan Kah Kee College.</footnote>

<footnote>k Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences.</footnote>

<footnote>l Guangdong Ocean University.</footnote>

<footnote>m South China Sea of Oceanography.</footnote>

<footnote>n UMR CNRS 5557 Ecologie Microbienne</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** (A) Violin diagrams of OTU richness (left) and Shannon's Diversity (right) in each bioregion. Letters indicate the significances of the Dunn's pairwise comparisons (W: \*p < 0.05; \*\*p < 0.01). (B) Linear regressions of OTU Richness (top) and Shannon's Diversity (bottom) against the absolute latitude (distance from the Equator), analyzed by mangrove geomorphological types. (C) Distance-decay relationships without Thomson et al., 2022 data, shown for all samples (top) and coastal data only (bottom).</figcaption></figure>

## 3.2. Taxonomic composition and core microbiota

Among the 97 bacterial phyla identified, Proteobacteria was the most abundant, representing 24 % of total reads analyzed, dominated by *Gammaproteobacteria* (15.6 %) and *Alphaproteobacteria* (7.9 %) (Fig. 3A, Supplementary Table 4). Chloroflexi (11 %) followed, with *Anaerolineae* (9 %) and *Dehalococcoidia* (1.8 %) as the major classes. Other notable phyla included Desulfobacterota (7 %), with *Desulfobacteria* (3.9 %) and *Desulfobulbia* (2.1 %) as the dominant classes, Bacteroidota (6 %), Planctomycetota (4 %), Acidobacteriota (3 %), Actinobacteriota (2 %), Firmicutes (2 %), Verrucomicrobiota (1 %), Cyanobacteria (1 %),

Myxococcota (1 %), and Calditrichota (1 %). Among the 16 archaeal phyla identified, Crenarchaeota was the dominant phylum (2 %), represented by *Bathyarchaeia* (1.4 %) and *Nitrososphaeria* (0.3 %), while Thermoplasmatota contributed 1 % of the total reads, mainly from the class *Thermoplasmata* (0.7 %).

A total of 1351 OTUs (1.6 %), associated with 32 phyla (Fig. 3C), were shared across all five bioregions (Fig. 3B), representing the OTU-based core microbiota in mangrove sediments. This core microbiota primarily comprised Proteobacteria, Chloroflexi, and Desulfobacterota (Fig. 3C–Supplementary Table 4). Within Proteobacteria, notable genera included *Woeseia* (10.6 % of total abundance), *Thiogranum* (3.8 %),
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** A) Taxonomy of top 20 Phyla and top 4 related Families observed in pooled samples from the meta-analysis of mangrove sediment microbiota. B) Venn Diagram illustrating the distribution of OTUs number across mangrove bioregions, highlighting 1744 core OTUs common to all five bioregions. C) Taxonomy of core OTUs, showing the top genera identified for each Phylum, with relative abundances in the original samples.</figcaption></figure>

*Microbulbifer* (1.4 %), *Thiodiazotropha* (0.12 %) and *Vibrio* (0.12 %). For Chloroflexi, *Litorilinea* (3.5 %) and *SCGC-AB-539-J10* (3.19 %) were dominant. Desulfobacterota was represented by *Desulfatiglans* (2.4 %), *Sva0081* sediment group (2.2 %), *Desulfobulbus* (0.91 %), and *Desulfobacca* (0.49 %). Additional genera included *Mangrovimonas* (1.1 %), *Labilibacter* (0.78 %) and *Lewinella* (0.72 %) for Bacteroidota, and *Thermoanaerobaculaceae* Subgroup 23 (2.6 %) for Acidobacteriota. Other

significant genera were *Mycobacterium* (1.5 %) from Actinobacteria and the *Pir4* lineage (2.6 %) from Planctomycetota. Among archaea, only a few core OTUs were identified, mostly within Crenarcheaota, though none could be assigned at the genus level.

Surprisingly, a large proportion of the OTUs remained unassigned at broad taxonomic levels: 0.9 % at the kingdom, 23 % at the phylum, 30 % at the class, 49 % at the order, 64 % at the family, and up to 83 % at the
genus, with nearly 100 % of species remaining unassigned. The pro-
portions of unassigned OTUs were similar between bacterial and
archaeal kingdoms and consistent across all the studies from which the
data in this study were derived (Supplementary Table 3).

3.3. *Bioregional microbiota specificities*

While the main taxa were consistently shared at the phylum level
across the globe, OTUs specificity varied significantly among bioregions,
with only 1.6 % of OTUs shared across all bioregions (Fig. 3C). Between
the AEP and IWP, only 21 % of OTUs were shared, with AEP-specific
OTUs accounting for 25 % of the total and IWP-specific OTUs consti-
tuting 54 %. The Indo-Malaysia bioregion contained the highest number
of unshared OTUs (27 %), followed by the Caribbean (21 %) while
Southeast America had the fewest unique OTUs (3.4 %).

Non-metric multidimensional scaling (NMDS) analysis accompanied by PERMANOVA tests (Fig. 4, Supplementary Table 2) indicated weak but significant effects of bioregion (R²: 8.4%, Pseudo-F: 12.2, p < 0.001) and mangrove type (R²: 6.8%, Pseudo-F: 13.1, p < 0.001) on microbial community structure (Fig. 4A and B). However, it is important to note that due to the high dimensionality and the large number of zeros in the OTU table (96.2 %), the NMDS stress was quite high (>0.2). Patchiness in microbial composition was observed at the study level, confirmed by PERMANOVA (R²: 26 %, Pseudo-F: 11.7, p < 0.001) (Fig. 4C).

RF models, based on 664 OTUs (filtered to include those >1 % in at
least one sample), achieved a bioregion classification accuracy of 90.3
%. A total of 150 computed trees are presented in Supplementary Fig. 4.
Among the 76 most important OTUs retrieved for this model, 30
belonged to the core OTUs (Fig. 5). Notably, 35 were shared with the
classification model for the study class, which achieved a classification
accuracy of 98 %. For the mangrove type classification model, 66 OTUs
were identified as being most important, resulting in a classification
accuracy of 96 %. Of these, 24 OTUs were shared with the bioregion
model, and 33 OTUs were common with the study class model
(Supplementary Table 5).

Overall, OTUs most representative of the Caribbean bioregion pre-
dominantly belonged to the classes *Anaerolineae* and *Calditrichaceae*.
Southeast America was also represented by *Anaerolineae*; however, a
notable distinction was observed in Brazil, where a *Bhargavaea* OTU
stood out with a relative abundance of nearly 4 %. This OTU, alongside
members of the *Bacillaceae* family, were the only Firmicutes identified
within the bioregion model results.

East Africa had fewer prominent OTUs compared to other bioregions,
except for a *Xenococcaceae* OTU from the Cyanobacteria phylum.
Nevertheless, this region displayed a fairly uniform representation of
core OTUs. In contrast, almost all identified OTUs were present in the
Indo-Malaysian bioregion, making it a highly representative area.

Australasia had the fewest distinctive OTUs, suggesting that this bioregion was the least influential in classification decision-making.

No Archaeal OTUs were among the most important in the model,
except for a Bathyarchaeia OTU. While this OTU was absent from the
core, it remained significant within the study model and was predomi-
nantly found in the AEP.

4. Discussion

4.1. *Microbial diversity hotspots and potential dispersal barriers in global mangrove sediments*

Overall, a high averaged Shannon diversity index was found (6.7 ± 0.5), which aligns with values reported by Lai et al. (2022) for mangrove sediment microbiota (5.7–11.1). This range is comparable to diversity levels found in the Earth Microbiome Project data for marine sediments, soil, and plant rhizosphere, and notably exceeds diversity found in other environments, such as water or animal host-associated microbiota (Bokulich et al., 2020).

Our findings indicate that microbial alpha-diversity is higher and
more heterogeneous in the Indo-West Pacific (IWP) compared to the
Atlantic-East Pacific (AEP), suggesting a potential microbial diversity
hotspot in the Indo-Malaysian subregion, closely followed by East Af-
rica. However, the observed nearly 1.5-fold difference in microbial
alpha-diversity between AEP and IWP is less pronounced than the 3 to 6-
fold disparity in mangrove tree diversity between these regions
(Tomlinson, 2016). Previous studies have documented positive corre-
lations between mangrove tree diversity and associated biota diversity
(e.g. invertebrates and ichtyofauna) (Ellison et al., 1999; Reid et al.,
2010; Lee et al., 2017), but our analysis found no significant relationship
between microbial alpha-diversity and tree richness. On the whole,
while the discrepancy in plant diversity likely arises from factors such as
dispersal barriers, species-specific habitat preferences, and speciation
rates, which have driven vicariance in mangrove flora (Duke, 2017), our
results suggest a different dynamic for their associated microbial
communities.

This absence of correlation between microbial and tree diversity
contrasts with findings in other ecosystems, where microbial diversity
often scales positively with plant diversity, especially in tropical regions
(Liu et al., 2020). In terrestrial systems, this relationship may be driven
by plant productivity, which shapes belowground niches and enhances
microbial diversity (Zak et al., 2003). However, our results could point
that mangrove prokaryotic communities are less subject to such selec-
tion compared to terrestrial tropical forests. Our findings suggest that
mangrove microbiota may be less influenced by plant production dy-
namics than their terrestrial counterparts, indicating that other factors
may govern microbial diversity patterns across mangrove bioregions.

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Non-metric multidimensional scaling (NMDS) plots based on Jaccard distances, colored by (A) bioregion, (B) mangrove geomorphological type, and (C) study.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Relative abundances of taxa in each country, highlighting the 76 most important OTUs identified by the random forest model for the bioregion parameter. (A) OTUs not present in the core and (B) also present in the core. OTUs absent in the most important OTUs for the study ID model are shown in bold text, while OTUs present in the most important OTUs for the mangrove type model are italicized.</figcaption></figure>

Future research should explore how local and regional environmental factors influence these global diversity patterns, particularly interactions between local tree assemblages and microbial communities.

## 4.2. Latitudinal diversity gradients in coastal and estuarine communities

We identified opposing latitudinal diversity gradients (LDGs) between coastal and estuarine mangroves. The negative microbial LDG in coastal mangroves mirrors the negative LDG seen in mangrove trees (Ellison, 2002) and associated ichthyofauna (Lee et al., 2017). This trend likely reflects increasingly unfavorable environmental conditions near 25°S and 25°N, including cooler temperatures and competition with salt marshes.

In contrast, in estuarine mangroves, microbial diversity increases with latitude, an unexpected finding that requires careful interpretation. The interaction between time and regional energy influences the rates of speciation, extinction or dispersal, however those mechanisms are highly challenging to identify precisely (Cerezer et al., 2022; Saupe, 2023). Additionally, certain host-associated microbiomes have already been recognized as exceptions to the LDG theory without clear biotic or abiotic drivers currently explaining these patterns (Amaral-Zettler, 2021; Neu et al., 2021). Estuaries are subject to extent runoff and flooding, driven by the dynamics of climate and upstream watershed conditions. These factors alter the scales and rates of environmental

variation, leading to differences in the drivers shaping microbial communities between estuaries and coasts (Adame et al., 2010).

This inverse microbial LDG in estuarine mangroves is another exception to the LDG theory, warranting further study on the biogeography and diversity of microbial communities in estuarian versus coastal environments.

## 4.3. Distance-decay relationships in Mangrove sediments

A significant, albeit weak, global microbial distance-decay relationship was detected. Distance-decay reflects how microbial compositional similarity declines with geographic distance, providing insights into dispersal barriers or the influence of environmental filtering across scales. Du et al. (2023) observed similar patterns in mangrove sediments within individual regions (e.g., China, South America). The strength of microbial distance-decay relationships can vary with spatial scale and environment type (Clark et al., 2021), with ecological divergences potentially masking biogeographical patterns (Härer and Rennison, 2023). Notably, parallels between microbial and macroorganisms may indicate shared environmental influences (Dickey et al., 2021). Therefore, the weakness of the global distance-decay relationship observed in the present study may reflect the confounding influence of global heterogeneities in mangrove ecosystems, that hampers its significance. Coastal mangroves communities, analyzed separately, exhibited a
stronger distance-decay relationship (twice the slope and thrice the explanatory power). This suggests that shared ecological conditions, such as similar tidal influences or sediment charcateristics, drive consistent responses in both mangrove-associated microbes and their host vegetation. Conversely, the pooled global dataset likely encompasses a broader range of environmental conditions, diluting the distance-decay signal.

Multivariate analysis highlighted that estuarine mangrove communities were less dispersed than those of other mangrove types, possibly due to the more homogeneous conditions in estuaries. Such conditions include substantial freshwater inputs and reduced wave disturbances, which are known to enhance microbial diversity and impose stronger selective pressures on coastal sediment communities (Galand et al., 2016). Similarly in arid mangroves, Thomson et al. (2022b) reported greater homogeneity in fringe mangroves due to water movements, while shrub mangroves displayed higher dispersion driven by the absence of such homogenizing factors.

Another plausible explanation for the weak global relationship observed is the influence of relic or dormant DNA. Relic DNA (extra-cellular or derived from non-intact cells) and dormant DNA (from inactive cells) are not subject to direct environmental selection (Carini et al., 2016; Locey et al., 2020). On average, approximately 33 % of environmental prokaryotic DNA is relic, though this proportion can increase to 80 % depending on the matrix characteristics (Lennon et al., 2018). While surface marine sediments are known to harbor significant quantities of relic DNA (Bairoliya et al., 2022), equivalent estimates for mangrove sediments remain limited.

Despite these complexities, the clear linear decline in community similarity with increasing spatial distance observed in coastal mangrove sediments underscores the presence of dispersal barriers for mangrove microbial communities. These findings highlight the nuanced interplay between environmental selection, dispersal, and biogeographical structure in mangrove ecosystems on a global scale.

## 4.4. Global mangrove sediment core microbiota and bioregions representative taxa

At the global scale, microbial communities in mangrove sediments were predominantly composed of Gammaproteobacteria, which accounted for nearly one-sixth of all identified sequences. This finding highlights once again their ubiquity and ecological significance in mangrove ecosystems. As highly diverse bacterial class, Gammaproteobacteria are commonly found in marine environments, particularly in mangrove sediments and rhizosphere (Lai et al., 2022). They are involved in carbon, nitrogen and sulfur cycles (Dyksma et al., 2016; Yan et al., 2024), existing both as free-living forms and symbionts (Liang et al., 2007).

Among the Gammaproteobacteria, *Woeseia* was identified as the most abundant genus in the core microbiota, representing almost 11 % of the sequences. A recent study has suggested that *Woeseia* was a key player in nitrogen cycling within the surface sediments of Indo-Malaysian mangroves, where it expresses genes for ammonification, nitrification, and a truncated denitrification pathway (Qian et al., 2024). Additionally, several distinct OTU within *Woeseia* were identified as key bioregion and mangrove type markers through random forest (RF) models. These findings suggest that members of *Woeseia* exhibit biogeographical distribution of distinct ecotypes and niche differentiation, reflecting ecological specialization across bioregions.

The second most abundant phylum was Chloroflexi, primarily represented by family *Anaerolineaceae*, which was also part of the core microbiota. Within this family, *Litorilinea* accounted for 3.5 % of total sequences. Known for their fermentative abilities, *Litorilinea* have been identified as keystone taxa in microbial interactions within mangrove sediments (Lin et al., 2019). Their fermentation products serve as substrates for sulfur-reducing bacteria (SRB), which preferentially utilize low-molecular-weight compounds as electron donors (Kristensen et al.,

2008).

The third most abundant phylum, Desulfobacterota, includes SRB that are critical actors of sulfur cycling within mangrove ecosystems. With high oxygen demand and significant sulfate input from seawater, the sulfur cycle is estimated to account for approximately 50 % of metabolic activity in mangrove sediments (Helfer and Hassenrück, 2021). Among the core microbiota, the genera *Desulfatiglans* and the Sva0081 sediment collectively representing nearly 6 % of the total sequences. RF models identified specific OTU within this genera in the Caribbean, Southeast America, and Indo-Malaysia, aligning with findings from Du et al. (2023), who observed a higher prevalence of Desulfobacterota in South American mangrove sediments. This may be linked to higher organic carbon content, as SRB are known play key roles in organic matter mineralization. Spatial distribution of these OTU could therefore reflect variations in both the quantity and quality of organic carbon across bioregions. Additionnaly, variations in root morphology (e.g., *Avicennia* prop roots versus *Rhizophora* stilt roots) may influence SRB activity, with *Avicennia* promoting oxic zones that could inhibit SRB activity (Balk et al., 2016). Notably, the higher prevalence of *Avicennia* over *Rhizophora* in Australasia compared to the Caribbean for example, may help explain the absence of SRB in Australasia in the RF analysis. Concerning those samples, Luis et al. (2019) observed significantly higher redox potentials under *A. marina* (−136.9 to 74.4 mV) compared to *R. stylosa* (−176.6 to −72.8 mV) in New Caledonia, while Fiard et al. (2024) reported more reduced conditions in Martinique, ranging from −200 mV and −318.3 ± 55.7 mV.

The Desulfobacterota phylum also includes sulfide-oxidizing bacteria (SOB), which are frequently found in anoxic layers with thin aerobic zones, such as the top few millimeters of sediments. These bacteria contribute maintaining sediment homeostasis (SamKamaleson and Gonsalves, 2019). Among SOB, cable bacteria from the *Desulfobulbaceae* family, such as *Desulfobulbus*, were part of the core microbiota. Unique in their ability to transport electrons over centimetre-scale distances, cable bacteria link oxic layers to deeper organic-rich sediments (Pfeffer et al., 2012; Burdorf et al., 2017). Their distribution is strongly influenced by bioturbation, as sediment-disturbing fauna can serve these conductive cables (Malkin et al., 2014). Bioturbation rates, which vary between 20 and 80 % of mangrove sediments depending on the mangrove type and region (Booth et al., 2019), could play a critical role in determining cable bacteria prevalence. However, SOB did not appear in the RF analysis, precluding any clear identification of biogeographical patterns. SOB are also represented within Gammaproteobacteria, with the phototrophic genus *Thiogranum* identified as part of the core microbiota, represented almost 4 % of the sequences.

The archaeal component of the core microbiota was dominated by Crenarchaeota, although one *Bathyarchaeia* OTU appeared in the RF results, mainly in the AEP regions. *Bathyarchaeia* are anaerobes capable of degrading complex organic molecules like lignin, contributing to the breakdown of plant-derived compounds (Yu et al., 2018). Methanogens, represented mainly by *Methanomassiliicoccales* from the *Thermoplasmatota* phylum, were scarce, likely due to the shallow sediment depth (20 cm) analyzed in this study. Methanogens typically thrive in deeper, strictly anoxic sediments where competition with SRB is reduced (Kristensen et al., 2008).

A surprising observation was the disproportionately high abundance of Firmicutes in Brazilian mangroves. An OTU from the Firmicutes phylum, assigned to the *Planococcaceae* family, was identified in the RF analysis, indicating its key biogeographic importance across bioregions. Firmicutes dominance was previously noted in sediments of South American mangroves by Lai et al. (2022). Fernández-Cadena et al. (2020) even reported *Planococcaceae* in their top 50 most abundant OTUs in Ecuadorian mangroves. However, this OTU was also prevalent in Malaysian mangroves (Mai et al., 2021), and comparable patterns have been observed in India (Kutty et al., 2023), suggesting that their distribution cannot be solely explained by dispersal limitations. Instead, environmental factors likely play a significant role. As k-strategists,
Firmicutes are well-adapted to water stress through mechanisms such as thick cell walls and endospores formation, as seen in *Planococcaceae* representatives (Pan et al., 2022; Shivaji et al., 2014). Further research is needed to understand their responses to spatial and temporal stressors in different geomorphological mangroves, particularly across wet and dry periods.

In summary, the biogeographical patterns of mangrove sediment microbiota reflect intricate ecological interactions influenced by environmental heterogeneities, root morphology, and metabolic processes. Further investigations, particularly those integrating metadata such as carbon content and root proximity, are essential to disentangle the complex dynamics shaping microbial distributions across mangrove bioregions.

### 4.5. Challenges and perspectives for global-scale analyses in mangrove sediments

Our study revealed that approximately 23 % of OTUs remained unassigned at the phylum level, consistent with findings from other studies not included in this meta-analysis (Haldar and Nazareth, 2018; Kutty et al., 2023; Yun et al., 2017). This significant proportion of "microbial dark matter" highlights the vast reservoir of uncharacterized microbial diversity in mangrove sediments, a phenomenon common in marine sediments and extreme environments (Schultz et al., 2023). Notably, some of these unassigned sequences were among the most ubiquitous in our dataset, including in the core microbiota, suggesting that they may hold critical insights into mangrove microbial biogeography, as suggested by their significance in the bioregion RF model. Advances in long-read sequencing technologies (Tedersoo et al., 2021) may enhance taxonomic resolution, however database limitations persist due to the difficulty of isolating and identifying microorganisms from the complex, organic-rich environment of mangrove sediments.

Another key observation was the notable inter-study "patchiness", characterized by limited overlap in OTU across studies. This variability was evident in the beta-diversity analyses, where the "study" variable accounted for a significant portion of the observed variation. For instance, East Africa and Australasia exhibited the highest overlap among all bioregions, sharing 6419 OTUs exclusively, with closely intertwined NMDS plots. This overlap was largely attributed to the fact that these samples, derived from arid mangroves, were collected and analyzed within the same study (Thomson et al., 2022a). This "patchiness" likely reflects the high sensitivity of microbial communities to the pronounced heterogeneity of mangrove habitats, shaped by diverse microhabitats like fauna burrows, roots, litter, water channels, and pools (Nagelkerken et al., 2008), as well as ecological and geomorphological types (fringe, riverine, inland, shrub, basin, lagoon, estuary, delta, oceanic island, and coastal mangroves (Twilley, 2019));. Additional drivers of microbial variability include climatic differences, anthropization impacts, mangrove age, or mangrove species composition, all of which influence microbial diversity and activity (Gómez-Acata et al., 2023; Fiard et al., 2022; Muwawa et al., 2020). This inherent complexity makes universal sampling strategies in mangrove studies particularly challenging.

Despite efforts to standardize sampling strategies (e.g., sediment depth, native mangrove ecosystems), unavoidable field-level variability and methodological inconsistencies -such as differences in sample preservation and storage, DNA extraction methods, sequencing platforms, primers, targeted region, and sequencing runs- contributed to inter-study variability (Compson et al., 2020; Salter et al., 2014; Abellan-Schneyder et al., 2021). As noted by Du et al. (2023), using different bioinformatics pipelines for 16S rRNA metabarcoding presents some challenges for comparing mangrove data on a global scale, but it also opens up opportunities for more in-depth future analyses. Consequently, while disentangling ecological factors from methodological biases remains a challenge in microbial ecology, it also presents an opportunity to improve the accuracy of microbial ecology research.

To address these challenges and drawing inspiration from other successful initiatives like the *Earth Microbiome Project* (Shaffer et al., 2022), the *Mangrove Microbiome Initiative* (Allard et al., 2020) could propose harmonized sampling and sequencing methodologies, facilitating more consistent comparisons of mangrove microbial communities worldwide. Such efforts would enhance our ability to resolve global patterns and underlying drivers of microbial communities in mangrove ecosystems.

## 5. Conclusion

This meta-analysis provides novel insights into the global biogeography of mangrove sediment microbiota, uncovering patterns of diversity, dispersal, and community composition that highlight the ecological complexity of these ecosystems. The identification of microbial diversity hotspots in the Indo-West Pacific and the clear differences between coastal and estuarine communities underscore the intricate biogeographical dynamics at play. While our findings confirm the presence of dispersal barriers and distance-decay relationships in coastal mangroves, they also suggest that environmental factors, rather than plant diversity alone, are key determinants for shaping prokaryotic community structures. The challenges posed by inter-study variability, methodological discrepancies, and "microbial dark matter" emphasize the need for standardized methods in future research. Despite these limitations, our findings lay a foundational framework for understanding global microbial biogeography in mangroves, paving the way for further initiatives. Moving forward, coordinated efforts like the *Mangrove Microbiome Initiative* (Allard et al., 2020) could unlock a deeper understanding of the functional roles and ecological processes governing mangrove microbiomes.

### CRediT authorship contribution statement

**Emma Jamon-Haon:** Methodology, Conceptualization, Formal analysis, Visualization, Writing – original draft, Investigation, Data curation, Software. **Philippe Cuny:** Conceptualization, Methodology, Supervision, Writing – original draft, Investigation. **Alix Rossi:** Methodology, Software, Data curation, Investigation, Formal analysis. **Lea Sylvi:** Investigation. **Maud Fiard:** Conceptualization, Investigation. **Cécile Milton:** Investigation, Conceptualization, Methodology, Supervision, Data curation, Writing – original draft.

### Funding

This research was funded by the Office Français de la Biodiversité (OFB). The project leading to this publication has received funding from European FEDER Fund. Emma Jamon-Haon was granted a \MERNT doctoral fellowship (Ministry of Higher Education and Research, France).

### Declaration of competing interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

### Acknowledgements

We thank everyone who responded to our request regarding missing data and kindly provided additional information, including, in no particular order, Luisa Falcón, Meng Li, Zhichao Zhou, Prinpida Sonthiphand, Timothy Thomson, Weidong Chen, Donghui Wen, Jenny Booth, and Benjamin Wainwright. Special thanks to Timothy Thomson, who also graciously agreed to meet virtually for enriching scientific discussions. We also acknowledge the valuable advice of David Nerini
on constructing random forest models. This work is part of the CNRS
Thematic Network "RT Mangroves".

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.ecss.2025.109485.

Data availability

I have shared the link of the data in the data availability statement.

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