https://orcid.org/0000-0001-7023-3916
Figures
Abstract
Mangrove forests are fundamental coastal ecosystems for the variety of services they provide, including green-house gas regulation, coastal protection and home to a great biodiversity. Mexico is the fourth country with the largest extension of mangroves of which 60% occurs in the Yucatan Peninsula. Understanding the microbial component of mangrove forests is necessary for their critical roles in biogeochemical cycles, ecosystem health, function and restoration initiatives. Here we study the relation between the microbial community from sediments and the restoration process of mangrove forests, comparing conserved, degraded and restored mangroves along the northern coast of the Yucatan peninsula. Results showed that although each sampling site had a differentiated microbial composition, the taxa belonged predominantly to Proteobacteria (13.2–23.6%), Desulfobacterota (7.6–8.3%) and Chloroflexi (9–15.7%) phyla, and these were similar between rainy and dry seasons. Conserved mangroves showed significantly higher diversity than degraded ones, and restored mangroves recovered their microbial diversity from the degraded state (Dunn test p-value Benjamini-Hochberg adjusted = 0.0034 and 0.0071 respectively). The structure of sediment microbial β-diversity responded significantly to the mangrove conservation status and physicochemical parameters (organic carbon content, redox potential, and salinity). Taxa within Chloroflexota, Desulfobacterota and Thermoplasmatota showed significantly higher abundance in degraded mangrove samples compared to conserved ones. This study can help set a baseline that includes the microbial component in health assessment and restoration strategies of mangrove forests.
Citation: Esguerra-Rodríguez D, De León-Lorenzana A, Teutli C, Prieto-Davó A, García-Maldonado JQ, Herrera-Silveira J, et al. (2024) Do restoration strategies in mangroves recover microbial diversity? A case study in the Yucatan peninsula. PLoS ONE 19(8): e0307929. https://doi.org/10.1371/journal.pone.0307929
Editor: Jiang-Shiou Hwang, National Taiwan Ocean University, TAIWAN
Received: January 24, 2024; Accepted: July 15, 2024; Published: August 16, 2024
Copyright: © 2024 Esguerra-Rodríguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Sequence reads are available in the NCBI Sequence Read Archive under Bioproject number PRJNA1039151.
Funding: DER received a national graduate studies scholarship from CONAHCYT Mexico, No. 788670 as part of his Doctoral degree research in the Posgrado en Ciencias del Mar y Limnologia, UNAM. AdLL received a postgraduate scholarship “Estancias Postdoctorales por Mexico” from CONAHCYT. LIF received funding from UNAM-PAPIIT IN204224.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Mangroves are tropical and sub-tropical coastal forests that, tolerate salinity, desiccation, flooding, high temperatures, among other challenging conditions associated with the tidal and hydrological regime [1–3]. These forests contribute to the lives and livelihoods of millions of people through ecosystem services that include fishery resources, nurseries, coastal protection and stabilization, acting as carbon sinks [4–6].
Mangrove forests have become one of the most threatened ecosystems due to coastal infrastructure development, industrial activity and urban expansion, factors that cause deforestation and disturbance [7,8]. The increase in intensity and frequency of extreme weather events puts the functionality and conservation of mangrove ecosystems at risk [9,10].
Particularly, microorganisms in mangrove sediments play crucial roles in the biogeochemical cycles, ecosystem health and function [6,11–16]. Bacteria, Archaea and fungi are the main biomass components of mangrove sediments and are key in maintaining mangrove ecosystem services (e.g water quality, carbon uptake) [16,17]. Nevertheless, the vegetation structure and abiotic parameters are currently the main elements considered to evaluate the condition and health of mangrove ecosystems [18]. Furthermore, there is little understanding of the response and specific roles of microorganisms in maintaining conserved mangroves, supporting restoration processes and enduring degradation since most studies aim to understand local particular processes that influence microbial patterns [11,19–25]. There are several microbial metabolic routes and interactions that are potentially important for the mangrove’s health, that is why it is important to understand the ecological dynamics associated with these microorganisms as a conservation resource [26–29]. The environmental fluctuation, complex forest structure and high productivity, provide unique settings that harbor great biodiversity, which in turn models their microbial component diversity and structure [1,30,31].
In Mexico, mangroves are a significant reservoir of organic carbon (OC) and particularly in the Yucatan Peninsula (YP) they account for >50% of blue carbon storage in sediments. The YP is a karstic platform, with a great diversity of coastal lagoons and heterogeneous environmental conditions that harbor mangroves, and at the same time multiple niches for the microorganisms that inhabit them [32]. The mangroves in the YP have been degraded because of land use changes, infrastructure, urban expansion, pollution, and extreme climatic events. Many of these impacts have been identified and successful rehabilitation programs have been designed for this region [33,34]. Although knowledge of microbial component variation associated to different conditions of mangrove forests in the YP has only started to be incorporated in local projects.
Work in the Red Sea [8] and in Colombia [35] suggest differences in microbial composition in mangroves with higher anthropic impact than in pristine ecosystems, generally showing higher abundance of microbial taxa that potentially respond to disturbance in degraded areas. Further, studies in China highlight that mangrove restoration stimulates microbial diversity recovery in sediments, nutrient availability and extracellular polymeric substance production [36–38]. A recent study of the sediment microbial component in Celestun (northwest YP), reported that the red mangrove, Rhizophora mangle, in different ecological types had a more cosmopolitan and heterogenous composition than the less abundant black mangrove, Avicennia germinans, that hosts a specific microbiome, suggesting the relevance of incorporating microbial ecology studies to better define restoration strategies of mangrove forests [32]. Hence, the aim of this study was to make a description of the composition of bacteria and archaea from mangrove sediments along the northern YP coast incorporating mangroves in different status: conserved, degraded and restored. We aimed to understand if conserved mangroves shared a particular microbial composition, identify if specific groups of microbes are lost when mangroves become degraded, and which are recovered during the process of restoration. This study sets a baseline of the microbial component found in sediments from mangroves with different conservation status and will impact local and global mangrove restoration initiatives, which are fundamental in tropical regions subject to environmental challenges.
Materials and methods
Study site and sample collection
Mangrove ecosystems included in this study are located at the north coast of the YP, Mexico. The study included four locations in the state of Yucatan, from west to east: Sisal, Progreso, Dzilam and Ria Lagartos (Fig 1). Together they cover an area of influence of approximately 430 ha of degraded (without vegetation), restored and conserved mangroves. Hydrological restoration was carried out in all locations hence no reforestation efforts were implemented, and mangrove composition in restored plots are product of natural succession processes. Every sampling site was associated with coastal lagoon basin mangrove ecosystems, where conserved plots did not share dominant mangrove species with restored plots (Tables 1 and 2). Within each location, plots with different conservation status were sampled (conserved, degraded, restored). Conservation status of the different plots was categorized based on continuous ecosystem monitoring of the forest ecology parameters (diameter at breast height, basal area, density, height, structural complexity index, regeneration potential, productivity) made by “Laboratorio de Produccion Primaria–CINVESTAV” [39], where conserved mangroves had a higher complexity index and density, whereas degraded plots had no vegetation, restored mangroves were those in the area hydrologically restored. Characterization of the sampling sites are shown in Tables 1 and 2 and S1 Table.
In each location red (conserved), green (degraded) and blue (restored) points show the conservation status, light blue layer shows water bodies (INEGI, 2010 [40]) and light green layer shows mangrove coverage (CONABIO, 2021 [41]). The vectorial data of Mexico administrative boundaries are from the following source: https://www.geoboundaries.org. The vectorial data of Yucatan hydrography are from the following source: INEGI, 2010 [40]. The vectorial data of Yucatan’s mangrove coverage are from the following source: CONABIO [41]. The pipeline to construct the map with R libraries: ggplot2, ggspatial, sf and ggpubr is available in Github (https://doi.org/10.5281/zenodo.11269158). The terms of use of vectorial information can be consulted in https://www.geoboundaries.org/index.html#usage, https://en.www.inegi.org.mx/inegi/terminos.html and http://geoportal.conabio.gob.mx/metadatos/doc/html/mx_man20gw.html respectively [42].
Between 8 and 10 samples of superficial sediment (first 10 cm) adjacent to mangrove roots were taken in each plot, during the two main climatic seasons (rainy and dry). Samples were taken with a core sampler (diameter of 5.5 cm), and physical-chemical parameters (salinity, temperature, pH, Redox potential) were measured in interstitial water with portable multiparametric probes (ATAGO CO. Ltd. hand refractometer and Myron L® Ultrameter II™ 6PFCE). Sediments sampled were stored in sterile tubes, refrigerated, and immediately transported to the laboratory where they were stored for posterior biogeochemical and molecular analysis. All samples were obtained under national environmental agency permit No. 04857 "Direccion General de Vida Silvestre–Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT)”.
Sample processing
Sediment microbial composition was characterized from environmental DNA, which was extracted (0.25 g per sample) following the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) protocol. The DNA was amplified for the 16SrRNA hypervariable V4 region using the 515F-806R primer set, following the protocol described in Gómez-Acata et al., [32]. The amplicon products were purified with magnetic beads (Agencourt AMPure XP, Beckman Coulter) and 20 ng of each sample were sequenced with an Illumina MiSeq (Illumina, San Diego, CA, USA) at the Yale Kegg Center for Genome Analysis. Sequence reads are available in the NCBI Sequence Read Archive under Bioproject number PRJNA1039151.
To determine total carbon, total nitrogen and organic carbon, samples were ground, homogenized and sieved through 250 μm. From each sample 2g were taken to remove inorganic carbon with acidification [43,44]. Treated and untreated samples were subsequently weighed (1g) in silver and tin capsules respectively, and were analyzed with an automatic elemental analyzer (model ECS 4010, Costech Analytica Technologies Inc.) [45]. The parameters for this analysis were: mode CHNS, left reactor between 1020–1050 °C, right reactor at 650 °C, 2 m chromatography column at 60–80 °C, pneumatic autosampler calibrated for 5 min runs. Results were reported in terms of percentage.
Data analysis
To analyze the sequence reads we used Qiime2 software [46]; briefly, demultiplexed paired sequences were denoised using the DADA2 algorithm [47] with the function qiime dada2 denoise-paired. Based on quality plots, sequences were trimmed at 14 and 234 pair bases for the dry season, and at 14 and 220 for the rainy season. Sequences were clustered in amplicon sequence variants (ASVs) and taxonomy was assigned to variants using the qiime2 function qiime feature-classifier classify-sklearn and SILVA 138-99-nb classifier [48,49].
Phylogeny was constructed based on a mafft alignment [50] and FastTree2 [51] using the qiime2 function qiime phylogeny align-to-tree-mafft-fasttree. Phylogenetic tree, taxonomy table and ASV counts were imported for further analysis in R phyloseq package [52]. Taxa with very low prevalence within the samples were omitted (taxa with less than 2 reads in less than 1% of the total number of samples), synthetic samples, unassigned ASVs at phylum level and Eukarya assignments were also removed.
The filtered dataset was used to estimate alpha diversity as inverse Simpson index (1/D) using the phyloseq’s function estimate_richness; beta diversity analysis was estimated using vegan package function ordinate [53] from weighted UniFrac distance [54] and visualized in a PCoA [55] with ggplot2 package [56]. A PERMANOVA using the same distance metrics with the adonis function from vegan was used to test for differences between conservation status. An analysis of the most determinant taxa to differentiate sediment microbial composition (differential expression analysis) was done with DESeq2 [57]. For this analysis the factor (conservation condition) size effect was estimated using geometric means of the counts, and the dispersions were calculated on locally fitted data to negative binomial distribution with maximum likelihood estimates, with its results expressed as log fold change. The Approximate Posterior Estimation for generalized linear model was used to moderate the logarithmic fold change estimated as the differential expression [58]. The physical-chemical parameters and organic carbon estimations were incorporated along the conservation status into a canonical correspondence analysis (CCA) [55] to describe possible predictors to explain differences in composition between samples. The pipeline to the raw sequence analysis in Qiime2 and for the exploration and further statistical analysis in R is available in Github (https://doi.org/10.5281/zenodo.11122245).
Results
Sediment microbial diversity and composition
The number of sequences (single reads) obtained for the dry and rainy seasons were 12,865,108 and 14,099,658 respectively. After alignment and denoising 47,570 and 55,098 representative sequences were recovered for dry and rainy seasons correspondingly. A total of 210 samples and 76,369 ASVs were recovered for analysis, corresponding to four locations in the YP, including three mangrove conservation conditions, during rainy and dry seasons.
The alpha diversity and taxonomic composition analyses suggested that sediment microbial composition of mangroves differs between conservation status. The samples with the highest diversity (1/D) corresponded to conserved and restored conditions (Fig 2) (Kruskall—Wallis test: p-value = 0.004, chi-squared = 10.865). The highest differences between mangrove conditions were found between degraded and restored (Dunn test: Z = -2.59, p-value = 0.007) and degraded versus conserved mangroves (Dunn test: Z = 3.05, p-value = 0.003). Sisal mangroves were an exception since degraded and conserved conditions showed higher microbial diversity than restored plots (Fig 2). In Sisal, the degraded site receives marine water influx.
Microbial genetic diversity (inverted Simpson index) in sediments along the northern coast of Yucatan.
Overall, the most abundant phyla were Proteobacteria (13.2–23.6%), Desulfobacterota (7.6–8.3%), Chloroflexi (9–15.7%), Bacteroidota (8.3–15.1%), Planctomycetota (5.9–8%), Gemmatimonadota (3.3–5.7%), Actinobacteriota (2–6.6%), Crenarchaeota (1.1–2.1%), Acidobacteriota (2.6–4.9%), Spirochaetota (1.9–4.7%), Nanoarchaeota (1–2.1%), Cyanobacteria (0.6–1.8%), Myxococcota (1.5–1.9%), Verrucomicrobiota (1–1.9%) and Thermoplasmatota (1–4,4%) (S1 Fig). At lower taxonomic levels, orders including Rhizobiales (2.3%) and Actinomarinales (5.1%) showed higher (two times or more) average abundance in conserved than the other conservation status. Other taxa including SBR1031 (6.1%) and “Unassigned Order Thermoplasmata Class” (2.1%) showed greater (two times or more) average abundance in degraded than conserved samples (Fig 3). Mangrove sediments in Dzilam and Sisal showed Desulfobacterales, Desulfatiglandales and Bacteroidales in greater proportions in degraded mangroves (S2 Fig).
The most abundant microbial orders found along the northern coast of Yucatan. Taxa within the “Other” category have abundances lower than 0.5%.
Mangrove forest conservation status and microbial structure
Mangrove conservation status and location are the main factors that explain the microbial composition of sediments (R2 condition = 0.0769, F condition = 11.075, R2 site = 0.2145, F site = 20.592, p-values = < 0.001). The differences between sediment from degraded and conserved mangroves are more evident in Sisal and Dzilam locations, while degraded and restored samples are closely aggregated (Fig 4). Ria Lagartos sediments show the smallest dispersion and distances between the different conservation status. Samples from degraded and restored mangroves from Progreso are grouped apart from the rest (Fig 4). Additionally, pairwise comparisons performed between samples according to their conservation status for each location determined significant differences in all cases (S2 Table).
Dispersion of sediment microbial composition along the northern Yucatan coast. Distances were measured with Weighted UniFrac metrics. Ellipses account for 85% of the samples within each group. Mangrove conservation status (colors) and study sites (shapes).
Canonical correspondence analysis (CCA) for each location confirms that sediments from different conservation status ordinate separately, in this analysis differences between conservation status seem more contrasting in Sisal and Progreso. The ordination of sediment microbial diversity also responded significantly to organic carbon content, redox potential, and salinity (Fig 5 and S3 Table).
Ordination from weighted UniFrac distances. Conservation status: red circles (conserved), green triangles (degraded), blue squares (restored). Vectors correspond to physicochemical variables: temperature (T°), salinity (Sal), organic carbon (OC), Redox potential and pH.
Microbial differential abundance
Sediment microbial composition from mangrove forests along the YP coast have common patterns despite their conservation status, and prevalent taxa that may constitute a core microbiome (S3 Fig, S4 Table). Nonetheless, there are taxa that show significant differences in abundances between conserved and degraded mangroves as shown in an analysis of differential expression that identified 951 ASVs (lowest Benjamini-Hochberg adjusted p-values < 0.01) (Fig 6). When comparing conserved and restored samples, 213 ASVs showed significant differential abundances (not shown), while 99 ASVs showed significant differential abundances when degraded and restored samples were compared (Fig 7).
Orders (vertical axis) and corresponding phyla (colors) along the northern YP coast are shown. Positive values show more abundant ASVs in degraded samples, while negative values show less abundant ASVs in degraded samples.
Orders (vertical axis) and corresponding phyla (colors) along the northern YP are shown. Positive values show more abundant ASVs in degraded samples, while negative values show less abundant ASVs in degraded samples.
Differences displayed between conservation status were in many cases related to the orders and phyla previously mentioned as the most abundant throughout the study. There are taxa with the potential to be indicators of degraded mangrove sediments, including anaerobic groups like Chloroflexota orders: SBR1031 and Anaerolineales. Desulfobacterota orders like Desulfobacterales and Desulfatiglandales had higher abundances in degraded samples, also halophilic and thermophilic archaea including Thermoplasmatota and Halobacterota groups significantly differentiated sediments from conserved mangroves compared to degraded mangroves across the different study sites (S5 Table).
Each site harbored differential abundances between conservation status. Ria Lagartos had 147 ASVs that were significantly different between conserved and degraded plots, including Desulfobulbales, Desulfatiglandales, Lachnospirales, OPB41, Chlorobiales, WCHB1-81, Desulfobacterales and Steroidobacterales (S4A Fig and S6 Table). While Nitrosococcales, Cytophagales, Rhodobacterales, and several orders of Desulfobacterota, among others, showed higher abundances in restored than in degraded mangroves (S4B Fig and S7 Table).
In the case of Dzilam, 600 ASVs had significant differences in abundance between conserved and degraded status including PAUC43f, Ardenicatenales, Euzebyales, Polyangiales, Subgroup 26 (Acidobacteriota), Thermoanaerobaculales, Pirellulales, Actinomarinales, Balneolales, Vicinamibacterales, BD2–11 terrestrial group, NB1–j, Subgroup 17 (Acidobacteriota), Terrybacterales, Rhodobacterales, Subgroup 21 (Acidobacteriota), Rhodothermales, Microtrichales and Nitrososphaerales (S5A Fig and S8 Table). Restored mangrove sediments were significantly more abundant than degraded sediments in Actinomarinales, Nitrosococcales, Nitrosopumilales, Balneolales, Cytophagales, Rhodothermales, Rhizobiales, as well as other groups of Proteobacteria, Bacteroidota, Acidobacteriota and Gemmatimonadota (S5B Fig and S9 Table).
The mangroves of Progreso had 950 ASVs representing differential abundances when comparing conserved and degraded mangroves. Conserved mangroves had more abundant taxa in the orders Actinomarinales, Subgroup 17 (Acidobacteriota), Latescibacterales, Sphingomonadales, Syntrophobacterales, Rhodothermales, Desulfobulbales, PAUC43f, Steroidobacterales, Bathyarchaeia, Subgroup 21 (Acidobacteriota), Cellvibrionales and Vicinamibacterales (S6A Fig and S10 Table). Restored mangroves were more abundant than degraded ones in Spirochaetales, Kiloniellales, MBG-D_and_DHVEG-1, Nitrosopumilales, Phycisphaerales, Latescibacterales, Aminicenantales, Sphingobacteriales, and groups in the classes Bathyarchaeia and Lokiarchaeia (S6B Fig and S11 Table).
In Sisal, 1562 ASVs with differential abundance between conserved and degraded conditions were identified, where conserved mangrove sediments were significantly more abundant in Nitrospirales, Nitrosococcales, Tistrellales and Balneolales (S7A Fig and S12 Table). Restored mangroves sediment samples were significantly more abundant than degraded samples in Ignavibaterilaes, Rhizobiales, Synechococcales, Rhodobacterales, LCP-89, Nannocystales, Desulfomonilales, Silvanigrellales, Cyanobacteriales, Flavobacteriales, Aminicenantales, groups within the classes Odinarchaeia and Lokiarchaeia, and groups within the phylum Thermoplasmatota (S7B Fig and S13 Table).
Discussion
Mangrove forests in the YP have an extension of over 500,000 ha constituting 60% of mangroves in Mexico [59]. The most abundant mangrove species in the YP are Rhizophora mangle (red mangrove), Avicennia germinans (black mangrove), Laguncularia racemosa (white mangrove)) and Conocarpus erectus (buttonwood) [59]. Previous studies in recently restored mangrove forests in the YP suggest that L. racemosa is a pioneer species since it has the highest growth rates [60]. Here, three of the four study areas had dominance of L. racemosa in the restored plots except in Progreso where A. germinans dominated. Conserved mangroves had dominance of A. germinans except again in Progreso where R. mangle was dominant. All sites analyzed had a differential mangrove composition between conserved and restored plots, yet mangrove species was not a variable that grouped sediment microbial composition along different study sites. Although, the results of this study strongly suggest that microbial composition responds to mangrove conservation status, further research that explores specific mangrove species relation with microbial composition is still needed. In this regard, a study in mangroves from the northwest YP [32] highlights that R. mangle associated microbial diversity is more generalist compared to A. germinans.
The indicator parameters that address the ecosystem condition are those related to the vegetation, sediments, and water [61]. Within this framework, conserved mangroves of the YP have been typically described with high foliage coverage and productivity, higher establishment of seedlings, and high environmental heterogeneity that facilitate resources and hydroperiod gradients. The inverse pattern is depicted for degraded areas, in which no mangrove trees are established. Restored mangroves can show intermediate conditions between these two, depending on the success and stage of the restoration process [62–64]. Restoration strategies included in this study are hydrological, hence there is no reforestation and mangrove forests are naturally recovering their coverage [39]. There are complex processes that lead to mangrove forest recovery, and in this study, patterns associated to conservation status were found to be site-specific. The response of microbes to degradation, restoration or conserved environments may help discern the ecological functioning of different mangroves and give hints to better address the managing of these ecosystems and improve restoration policies, especially in mangroves with high potential for climate change mitigation such as those in the YP [65].
Microbial response to mangrove conservation status
Mangrove forests have sediment microbial communities that play crucial roles in the biogeochemical cycles of the ecosystem. In this study, bacterial and archaeal diversity from mangrove sediments show changes depending on the conservation status of the mangrove forest and therefore may be indicators of ecosystem health. Mangrove sediments along the northern YP show phyla abundance similarities with other mangrove ecosystems [16,66–76]. Also, differences between the microbial communities in mangroves with contrasting conservation status are associated to local sediment environmental conditions including salinity, redox potential, and organic carbon content. Specific taxa are commonly found in mangroves with the same conservation status along the YP coast, for example: Chloroflexi, Bacteroidota, Proteobacteria and Actinobacteria phyla were always found in conserved mangroves. Furthermore, no significant differences were observed between restored and conserved mangrove microbial diversity, leading us to think that restoration efforts are working towards recovering sediment microbial functionality, however this hypothesis needs to be further tested using other approaches like comparative metagenomics [77].
Ria Lagartos is a Natural Biosphere Reserve and as such, subject to management and protection [78]. The results from this site show the relevance of ecosystem management, where although sediment microbial diversity is significantly different among conservation status there is a smaller dispersion, which suggests a more resilient ecosystem that shows more homogeneous conditions and microbial composition. In general, conserved mangroves had higher values of alpha diversity, except those from Sisal where degraded plots were the most diverse. These plots are located near the mouth of the swamp where hydrodynamics favors the flux with the sea, and at these small-scale physical characteristics can also promote the constant migration of microbial pools [79,80]. Richness is known to be an important factor to ecosystems functionality [81,82] and in this study, high microbial diversity in sediments was an indicator in most of the conserved mangroves, highlighting the importance of these microbial communities in maintaining ecosystem functions in conserved regions. However, high richness has also been associated with polluted or anthropogenically impacted systems [83], where input of organic and inorganic compounds has been hypothesized to give a variety of substrates for many opportunistic microbial species that can use them [76]. The above might be the case in Sisal, where higher alpha diversity values were associated with the degraded mangroves and organic carbon content, presumably linked to historically reported aquaculture and urban discharges [84].
The YP is characterized for being a karstic platform whose geomorphology depends on the dissolution of carbonate minerals. A characteristic formation in the region are sinkholes (cenotes, from the mayan dzonot), which are part of a complex underground freshwater system [85]. Dzilam, Sisal and Ria Lagartos are located in the area of influence of the ring of cenotes, where greater permeability and underground freshwater discharge depict conditions with lower salinity and small temperature fluctuations in interstitial water [85]. The environmental stability of these locations (especially in Ria Lagartos and Dzilam) may allow the mangroves to compromise less in homeostatic maintenance and have faster growth rates, more resources allocated in tissue formation, hence more organic matter input to the sediments [86]. The microbial communities of the sediments in these locations are more similar even if they were linked to mangrove plots with a different conservation status. In these mangroves, the microbial diversity and its functional redundancy may help maintain the biogeochemical functions of the sediment and confer resilience to the ecosystem [87,88]. Studies in the northern Yucatan coast evidence that restoration actions result in a faster recovery of mangroves’ health, particularly in locations like Ria Lagartos and Dzilam [39,89].
Progreso is an example of environments in the northern coast of the YP with more arid conditions, due to its weathering processes this site has lower permeability than other subregions [85]. Furthermore, this location has had higher anthropogenic intervention resulting in hydrologic modifications, which in turn have changed mangrove coverage and structure [39]. There is a significant difference in abundance between degraded and conserved mangroves in taxa within Gemmatimonadota (BD2-11 terrestrial group, PAUC43F marine benthic group and MD2902-B12), which have been reported in high relative abundance in arid and marine conditions [90–92].
The restored mangroves depend in their early stages mainly on allochthonous sources of carbon and nutrients [63], where mangroves with longer times in the restoration process should therefore have microbial communities that are more similar to those in the conserved reference mangrove. Nonetheless, in the Yucatan karstic system the input coming from underground freshwater could also bring high concentrations of compounds that can be used as substrate or electron acceptors by diverse microbes, helping restore microbial geochemical functionality in sediments faster than in locations with mostly autochthonous sources of nutrients and carbon. Although no significant differences were found between climatic seasons and sediment microbial composition, the effect of hydroperiod variability and underground water inputs should be further studied in different time scales. Furthermore, urban expansion will probably increase, along with pollution from sewage coming through the underground freshwater system, and if not managed correctly it might affect the dynamics of mangroves of northern YP and its associated microbes [35].
Potential of microbes to monitor restoration
In this study, physicochemical parameters and organic carbon associated significantly to sediment microbial diversity. Particular microbial groups can create a resilient environment, maintaining a basal biogeochemical functionality that helps the survival of the vegetation and facilitates seedling recruitment [93–95]. Furthermore, microbial taxa can serve as an indicator of degradation processes. In this regard the presence of taxa like Sphingobacteriales, BD2-11-terrestrial-group and SBR1031 that in this study are differentially abundant in contrasting conservation status is remarkable, since they have been reported in maintaining a priming effect when allochthonous organic carbon sources are available in different terrestrial soils [96]. Taxa that represented differences between conservation status in this study, ASVs within Bacteroidota and Gemmatimonadota, have been reported associated with high salinities in China coastal estuarine wetlands [80]. Other taxa, within Acidobacteriota and Chloroflexi were related to low salinities in the same study, and showed ASVs that were more abundant in conserved mangroves from Ria Lagartos, Dzilam and Progreso. Taxa within Acidobacteriota and Chloroflexi could be further explored as indicators of specific biogeochemical activity in mangrove ecosystems. Nonetheless, proposed assemblages would not necessarily be indicative of the same processes or conditions in other ecological types of mangroves (e.g., fringe mangroves with direct marine influence) or in mangrove ecosystems composed by other mangrove species, since microbial communities’ composition has been suggested to be species-specific [32,97].
Conserved and restored mangrove sediments are dominated by the rhizobiome of the dominant vegetal species which in this study included L. racemosa, A. germinans and R. mangle. L. racemosa was the dominant species in hydrologically restored plots. Potentially, the microbial component associated to the rhizobiome could facilitated the succession process to more stable conditions where A. germinans dominates (e.g. Ria Lagartos, Dzilam, Sisal). Prevalence of ASVs that could be associated to specific mangrove species biogeochemical requirements, included taxa within Acidobacteriota, Rhizobiales, Nitrosopumilales and Nitrosococcales, that were enriched in conserved and restored conditions. Representatives of these and other groups were prevalent through this study and have been related with nitrification and other biogeochemical processes important for mangroves, including mineralization of carbon and sulfate reduction [98–101]. These microbial taxa should be further studied to understand their functional role in mangroves subject to hydrologic restoration [102]. Therefore, this study contributes to understanding the delicate and complex processes of hydrological restoration in mangrove forests, where knowledge of the microbial component is important to create well-constructed baselines and promote ecosystem resilience.
Conclusions
The mangroves of the northern coast of the YP are complex ecosystems where different mangrove species dominate depending on multiple factors. L. racemosa has been suggested to be a pioneer species after hydrological restoration, and in this study, dominated three of four restored sites. A. germinans was the dominant species in Progreso after restoration and in the rest of the sites in conserved plots. Previous studies have suggested that A. germinans has a specific rhizobiome and here, it was evident that this composition is also associated to each mangrove location, shedding new light to the complex patterns of rhizobiome-mangrove associations. Degraded plots harbor a microbial composition that allows biogeochemical functionality which when enhanced by hydrological restoration practices, modifies the conditions to promote the establishment of mangroves. Further understanding of the complexity of the dynamics that lead to the establishment of the rhizobiome is needed for each mangrove species and presumably, for each location. These processes help assure the survival of the vegetation and endure stress, facilitating seedling recruitment. This study shows that sediment microbial communities have great potential to be an integral part in the evaluation and management of mangrove ecosystems and should be explored further specially to understand their functional role in mangrove forest health and biogeochemical cycling.
Supporting information
S1 Fig. Bar plot of the most abundant phyla in the four study sites.
Relative abundance (y-axis). Bottom labels are the samples and their corresponding climatic season.
https://doi.org/10.1371/journal.pone.0307929.s001
(TIF)
S2 Fig. Heatmap of most abundant bacterial/archaeal orders.
Relative abundances arranged by mangrove conservation condition (horizontal axis upper label) and the four study sites (horizontal axis lower label).
https://doi.org/10.1371/journal.pone.0307929.s002
(TIF)
S3 Fig. Venn diagram.
Shows the core microbiome (50% prevalence in the group pictured, reads with abundances above 0.1%) along the conservation status. The values inside the ellipses are the number of core ASVs and in parentheses the average relative abundance.
https://doi.org/10.1371/journal.pone.0307929.s003
(TIF)
S4 Fig. The one hundred most significantly different ASVs (orders) between the conservation status in Ria Lagartos.
Log2 fold change comparing differentially abundant taxa (positive values are ASVs that have significantly higher abundance than degraded samples, while negative values are ASVs that are significantly less abundant than degraded samples). (A) Compares Conserved vs Degraded. (B) Compares Restored vs Degraded.
https://doi.org/10.1371/journal.pone.0307929.s004
(TIF)
S5 Fig. The one hundred most significantly different ASVs (orders) between the conservation status in Dzilam.
Log2 fold change comparing differentially abundant taxa (positive values are ASVs that have significantly higher abundance than degraded samples, while negative values are ASVs significantly less abundant than degraded samples). (A) Compares Conserved vs Degraded. (B) Compares Restored vs Degraded.
https://doi.org/10.1371/journal.pone.0307929.s005
(TIF)
S6 Fig. The one hundred most significantly different ASVs (orders) between the conservation status in Progreso.
Log2 fold change comparing differentially abundant taxa (positive values are ASVs that have significantly higher abundance than degraded samples, while negative values are ASVs significantly less abundant than degraded samples). (A) Compares Conserved vs Degraded. (B) Compares Restored vs Degraded.
https://doi.org/10.1371/journal.pone.0307929.s006
(TIF)
S7 Fig. The one hundred most significantly different ASV’s (orders) between the conservation status in Sisal.
Log2 fold change comparing differentially abundant taxa (positive values are ASVs that have significantly higher abundance than degraded samples, while negative values are ASVs significantly less abundant than degraded samples). (A) Compares Conserved vs Degraded. (B) Compares Restored vs Degraded.
https://doi.org/10.1371/journal.pone.0307929.s007
(TIF)
S1 Table. Raw physicochemical information associated to the sediment samples.
https://doi.org/10.1371/journal.pone.0307929.s008
(TXT)
S2 Table. PERMANOVA pairwise comparisons between conservation status, for each study site.
https://doi.org/10.1371/journal.pone.0307929.s009
(PDF)
S3 Table. PERMANOVA results of the model used in the CCA.
Shows the values of the multiple physical-chemical parameters that cause the divergence between the conservation status.
https://doi.org/10.1371/journal.pone.0307929.s010
(PDF)
S5 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status along all the study sites.
The reference point of comparison is the conserved status. The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s012
(TXT)
S6 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status in Ria Lagartos.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s013
(TXT)
S7 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and restored status in Ria Lagartos.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s014
(TXT)
S8 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status in Dzilam.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s015
(TXT)
S9 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and restored status in Dzilam.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s016
(TXT)
S10 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status in Progreso.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s017
(TXT)
S11 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and restored status in Progreso.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s018
(TXT)
S12 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status in Sisal.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s019
(TXT)
S13 Table. ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and restored status in Sisal.
The column “baseMean” are the normalized mean abundance of the ASV, “log2FoldChange” is the logarithmic abundance difference between the compared status, “lfcSE” is the standard error on the logarithmic scale, “padj” is the Benjamini-Hochberg adjusted p-value.
https://doi.org/10.1371/journal.pone.0307929.s020
(TXT)
Acknowledgments
DER is grateful to Posgrado en Ciencias del Mar y Limnología, UNAM, and to CONAHCyT for a graduate studies scholarship. The authors are grateful to Dr. Osiris Gaona (IE, UNAM), MSc. Korynthia Lopez (FQ, UNAM), Dr. Joanna Ortiz (FC, UNAM) and Jessica Olivares (FQ, UNAM) for their technical assistance.
References
- 1.
Robertson AI (Alistar I), Alongi DM (Daniel M). Tropical mangrove ecosystems. American Geophysical Union; 1992.
- 2. Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen M. Mangroves among the most carbon-rich forests in the tropics. Nat Geosci. 2011;4: 293–297.
- 3. McDonald KO, Webber DF, Webber MK. Mangrove forest structure under varying envrionmental conditions. Bull Mar Sci. 2003;73: 491–505.
- 4. Dahdouh-Guebas F, Hugé J, Abuchahla GMO, Cannicci S, Jayatissa LP, Kairo JG, et al. Reconciling nature, people and policy in the mangrove social-ecological system through the adaptive cycle heuristic. Estuar Coast Shelf Sci. 2021;248: 1–29.
- 5. Barbier EB, Hacker SD, Kennedy C, Koch EW, Stier AC, Silliman BR. The value of estuarine and coastal ecosystem services. Ecol Monogr. 2011;81(2): 169–193.
- 6. Kathiresan K, Bingham BL. Biology of mangroves and mangrove ecosystems. Advances in Marine Biology. 2001.
- 7. Goldberg L, Lagomasino D, Thomas N, Fatoyinbo T. Global declines in human-driven mangrove loss. Glob Chang Biol. 2020;26: 5844–5855. pmid:32654309
- 8. Ullah R, Yasir M, Khan I, Bibi F, Sohrab SS, Al-Ansari A, et al. Comparative bacterial community analysis in relatively pristine and anthropogenically influenced mangrove ecosystems on the Red Sea. Can J Microbiol. 2017;63: 649–660. pmid:28376307
- 9. Murakami H, Delworth TL, Cooke WF, Zhao M, Xiang B, Hsu PC. Detected climatic change in global distribution of tropical cyclones. Proc Natl Acad Sci U S A. 2020;117: 10706–10714. pmid:32366651
- 10. Barbier EB. The protective service of mangrove ecosystems: A review of valuation methods. Mar Pollut Bull. 2016;109: 676–681. pmid:26851868
- 11. Holguin G, Vazquez P, Bashan Y. The role of sediment microorganisms in the productivity, conservation, and rehabilitation of mangrove ecosystems: An overview. Biol Fertil Soils. 2001;33: 265–278.
- 12. Alongi DM, Christoffersen P, Tirendi F. The influence of forest type on microbial-nutrient relationships in tropical mangrove sediments. J Exp Mar Biol Ecol. 1993;171: 201–223.
- 13. Cao H, Li M, Hong Y, Gu JD. Diversity and abundance of ammonia-oxidizing archaea and bacteria in polluted mangrove sediment. Syst Appl Microbiol. 2011;34: 513–523. pmid:21665398
- 14. Li M, Hong YG, Cao HL, Gu JD. Mangrove trees affect the community structure and distribution of anammox bacteria at an anthropogenic-polluted mangrove in the Pearl River Delta reflected by 16S rRNA and hydrazine oxidoreductase (HZO) encoding gene analyses. Ecotoxicology. 2011;20: 1780–1790. pmid:21735127
- 15. De Santana CO, Spealman P, Melo VMMI, Gresham D, De Jesus TB, Chinalia FA. Effects of tidal influence on the structure and function of prokaryotic communities in the sediments of a pristine Brazilian mangrove. Biogeosciences. 2021;18: 2259–2273.
- 16. Lin X, Hetharua B, Lin L, Xu H, Zheng T, He Z, et al. Mangrove Sediment Microbiome: Adaptive Microbial Assemblages and Their Routed Biogeochemical Processes in Yunxiao Mangrove National Nature Reserve, China. Microb Ecol. 2019;78: 57–69. pmid:30284602
- 17. Banerjee S, van der Heijden MGA. Soil microbiomes and one health. Nat Rev Microbiol. 2022;19. pmid:35999468
- 18. Boesch DF, Paul JF. An overview of coastal environmental health indicators. Hum Ecol Risk Assess. 2001;7: 1409–1417.
- 19. Sahoo K, Dhal NK. Potential microbial diversity in mangrove ecosystems:A review. Indian J Mar Sci. 2009;38: 249–256.
- 20. Xie C, Ma X, Zhao Y, Dai T, Song W, Qi Q, et al. Nitrogen addition and warming rapidly alter microbial community compositions in the mangrove sediment. Science of the Total Environment. 2022;850. pmid:35970461
- 21. Erazo NG, Bowman JS. Sensitivity of the mangrove-estuarine microbial community to aquaculture effluent. iScience. 2021;24: 102204. pmid:33786421
- 22. Cotta SR, Cadete LL, van Elsas JD, Andreote FD, Dias ACF. Exploring bacterial functionality in mangrove sediments and its capability to overcome anthropogenic activity. Mar Pollut Bull. 2019;141: 586–594. pmid:30955771
- 23. Gomes NCM, Cleary DFR, Pinto FN, Egas C, Almeida A, Cunha A, et al. Taking root: Enduring effect of rhizosphere bacterial colonization in mangroves. PLoS One. 2010;5: 1–10. pmid:21124923
- 24. Jeyanny V, Norlia B, Getha K, Nur-Nabilah A, Lee SL, Rozita A, et al. Bacterial communities in a newly regenerated mangrove forest of sungai haji dorani mangroves in the west coast of Selangor, Malaysia. Journal of Tropical Forest Science. 2020;32: 268–282.
- 25. Ma J, Zhou T, Xu C, Shen D, Xu S, Lin C. Spatial and temporal variation in microbial diversity and community structure in a contaminated Mangrove Wetland. Applied Sciences (Switzerland). 2020;10.
- 26. Li Y, Zheng L, Zhang Y, Liu H, Jing H. Comparative metagenomics study reveals pollution induced changes of microbial genes in mangrove sediments. Sci Rep. 2019;9: 1–11. pmid:30952929
- 27. Imchen M, Kumavath R, Barh D, Avezedo V, Ghosh P, Viana M, et al. Searching for signatures across microbial communities: Metagenomic analysis of soil samples from mangrove and other ecosystems. Sci Rep. 2017;7: 1–13. pmid:28821820
- 28. Priya G, Lau NS, Furusawa G, Dinesh B, Foong SY, Amirul AAA. Metagenomic insights into the phylogenetic and functional profiles of soil microbiome from a managed mangrove in Malaysia. Agri Gene. 2018;9: 5–15.
- 29. Cabral L, Noronha MF, de Sousa STP, Lacerda-Júnior GV, Richter L, Fostier AH, et al. The metagenomic landscape of xenobiotics biodegradation in mangrove sediments. Ecotoxicol Environ Saf. 2019;179: 232–240. pmid:31051396
- 30. Bishop MJ, Byers JE, Marcek BJ, Gribben PE. Density-dependent facilitation cascades determine epifaunal community structure in temperate Australian mangroves. Ecology. 2012;93: 1388–1401. pmid:22834379
- 31. Delfan N, Shojaei MG, Naderloo R. Patterns of structural and functional diversity of macrofaunal communities in a subtropical mangrove ecosystem. Estuar Coast Shelf Sci. 2021;252: 107288.
- 32. Gómez-Acata ES, Teutli C, Falcón LI, García-Maldonado JQ, Prieto-Davó A, Yanez-Montalvo A, et al. Sediment microbial community structure associated to different ecological types of mangroves in Celestún, a coastal lagoon in the Yucatan Peninsula, Mexico. PeerJ. 2023;11: e14587. pmid:36785710
- 33. Batllori-Sampedro E, Febles-Patrón J, Diaz-Sosa J. Landscape Change in Yucatan’s Northwest Coastal Wetlands (1948–1991). Human Ecology Review. 1999;6: 8–20.
- 34. Teutli-Hernández C, Herrera-Silveira JA. The success of hydrological rehabilitation in mangrove wetlands using box culverts across coastal roads in northern yucatán (SE, méxico). Coastal Research Library. 2018;25: 607–619.
- 35. Torres GG, Figueroa-Galvis I, Muñoz-García A, Polanía J, Vanegas J. Potential bacterial bioindicators of urban pollution in mangroves. Environmental Pollution. 2019;255. pmid:31563776
- 36. Wu P, Xiong X, Xu Z, Lu C, Cheng H, Lyu X, et al. Bacterial Communities in the Rhizospheres of Three Mangrove Tree Species from Beilun Estuary, China. PLoS One. 2016;11: e0164082. pmid:27695084
- 37. Ma X-X, Jiang Z-Y, Wu P, Wang Y-F, Cheng H, Wang Y-S, et al. Effect of mangrove restoration on sediment properties and bacterial community. Ecotoxicology. 2021;30: 1672–1679. pmid:33864552
- 38. Mai Z, Zeng X, Wei X, Sun C, Niu J, Yan W, et al. Mangrove restoration promotes the anti-scouribility of the sediments by modifying inherent microbial community and extracellular polymeric substance. Science of The Total Environment. 2022;811: 152369. pmid:34919933
- 39.
Herrera-Silveira JA, Teutli-Hernández C, Cinco-Castro S, Ramirez-Ramirez J, Carrillo Baeza L, Pech Poot E, et al. Red Multi-institucional. Programa Regional para la Caracterización y el Monitoreo de Ecosistemas de Manglar del Golfo de México y Caribe Mexicano: Península de Yucatán. Ciudad de México; 2018.
- 40.
Instituto Nacional de Estadística y Geografía (INEGI). Red hidrográfica edición 2.0. Cuenca Yucatan. Red Hidrográfica escala 1:50 000—Cuencas edición 2.0. https://www.inegi.org.mx/app/biblioteca/ficha.html?upc=702825006949; 2010.
- 41.
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). Distribución de los manglares en México en 2020, escala: 1:50000. Sistema de Monitoreo de los Manglares de México (SMMM). http://www.conabio.gob.mx/informacion/gis/maps/geo/mx_man20gw.zip; 2021.
- 42. Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. geoBoundaries: A global database of political administrative boundaries. PLoS One. 2020;15: e0231866. pmid:32330167
- 43. Froelich PN. Analysis of organic carbon in marine sediments1. Limnol Oceanogr. 1980;25: 564–572.
- 44. Harris D, Horwáth WR, van Kessel C. Acid fumigation of soils to remove carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Science Society of America Journal. 2001;65: 1853–1856.
- 45. Verardo DJ, Froelich PN, McIntyre A. Determination of organic carbon and nitrogen in marine sediments using the Carlo Erba NA-1500 analyzer. Deep Sea Research Part A Oceanographic Research Papers. 1990;37: 157–165.
- 46. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37: 852–857. pmid:31341288
- 47. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13: 581–583. pmid:27214047
- 48. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41: D590–D596. pmid:23193283
- 49. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6: 90. pmid:29773078
- 50.
Katoh K, Asimenos G, Toh H. Multiple Alignment of DNA Sequences with MAFFT. 2009. pp. 39–64.
- 51. Price MN, Dehal PS, Arkin AP. FastTree 2 –Approximately Maximum-Likelihood Trees for Large Alignments. PLoS One. 2010;5: e9490. pmid:20224823
- 52. McMurdie PJ, Holmes S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS One. 2013;8. pmid:23630581
- 53.
J Oksanen, FG Blanchet, R Kindt PL. Vegan: community ecology package Vegan: community ecology package, 2006. 2020. https://cran.r-project.org/package=vegan
- 54. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5: 169–172. pmid:20827291
- 55.
Legendre P, Legendre L. Numerical Ecology. Elsevier; 2012.
- 56.
Wickham H. ggplot2. Cham: Springer International Publishing; 2016.
- 57. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15: 550. pmid:25516281
- 58. Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics. 2019;35: 2084–2092. pmid:30395178
- 59.
Velazquez-Salazar S, Rodriguez-Zuñiga MT, Alcantara-Maya JA, Villeda-Chavez E, Valderrama-Landeros L, Troche-Souza C, et al. Manglares de Mexico. Actualizacion y analisis de los datos 2020. CDMX, Mexico: Comision Nacional para el Conocimiento y Uso de la Biodiversidad; 2021.
- 60.
Teutli C. Una aproximación a la integración de escalas ecológicas para la restauración de ecosistemas de manglar. Universitat de Barcelona. 2017.
- 61. Twilley RR, Rivera-Monroy VH. Developing performance measures of mangrove wetlands using simulation models of hydrology, nutrient biogeochemistry, and community dynamics. J Coast Res. 2005;21: 79–93.
- 62. Herrera-Silveira JA, Lara-Domínguez AL, Day JW, Yáñez-Arancibia A, Ojeda SM, Hernández CT, et al. Ecosystem Functioning and Sustainable Management in Coastal Systems With High Freshwater Input in the Southern Gulf of Mexico and Yucatan Peninsula. Coasts and Estuaries. Elsevier; 2019. pp. 377–397.
- 63. Soria-Barreto M, Pérez-Ceballos R, Zaldívar-Jiménez A, Gelabert Fernández R. Assessment of aquatic food web and trophic niche as a measurement of recovery function in restored mangroves in the Southern Gulf of Mexico. PeerJ. 2023;11: e15422. pmid:37304885
- 64.
Herrera-Silveira JA, Comin FA, Capurro-Filograsso L, Day JW, Yáñez Arancibia A. Landscape, land-use, and management in the Coastal Zone of Yucatán Peninsula. Gulf of Mexico: origin, waters, and biota. 2013. pp. 225–242.
- 65. Herrera-Silveira JA, Pech-Cardenas MA, Morales-Ojeda SM, Cinco-Castro S, Camacho-Rico A, Caamal Sosa JP, et al. Blue carbon of Mexico, carbon stocks and fluxes: a systematic review. PeerJ. 2020;8: e8790. pmid:32292646
- 66. Basak P, Majumder NS, Nag S, Bhattacharyya A, Roy D, Chakraborty A, et al. Spatiotemporal Analysis of Bacterial Diversity in Sediments of Sundarbans Using Parallel 16S rRNA Gene Tag Sequencing. Microb Ecol. 2015;69: 500–511. pmid:25256302
- 67. Gong B, Cao H, Peng C, Perčulija V, Tong G, Fang H, et al. High-throughput sequencing and analysis of microbial communities in the mangrove swamps along the coast of Beibu Gulf in Guangxi, China. Sci Rep. 2019;9: 1–10. pmid:31253826
- 68. Becker CC, Weber L, Suca JJ, Llopiz JK, Mooney TA, Apprill A. Microbial and nutrient dynamics in mangrove, reef, and seagrass waters over tidal and diurnal time scales. Aquatic Microbial Ecology. 2020;85: 101–119.
- 69. Purahong W, Sadubsarn D, Tanunchai B, Wahdan SFM, Sansupa C, Noll M, et al. First insights into the microbiome of a mangrove tree reveal significant differences in taxonomic and functional composition among plant and soil compartments. Microorganisms. 2019;7. pmid:31756976
- 70. Liu M, Huang H, Bao S, Tong Y. Microbial community structure of soils in Bamenwan mangrove wetland. Sci Rep. 2019;9: 1–11. pmid:31182804
- 71. Li P, Wu S, Yin H, Wu H, Peng Q, Zhang W, et al. Bacterial community diversity and dynamics of dongzhai harbor mangrove soils in China. Can J Microbiol. 2019;65: 703–712. pmid:31158318
- 72. Liao S, Wang Y, Liu H, Fan G, Sahu SK, Jin T, et al. Deciphering the Microbial Taxonomy and Functionality of Two Diverse Mangrove Ecosystems and Their Potential Abilities To Produce Bioactive Compounds. mSystems. 2020;5. pmid:33109752
- 73. Zhou Z, Meng H, Liu Y, Gu JD, Li M. Stratified bacterial and archaeal community in mangrove and intertidal wetland mudflats revealed by high throughput 16S rRNA gene sequencing. Front Microbiol. 2017;8: 1–19. pmid:29163432
- 74. Barreto CR, Morrissey EM, Wykoff DD, Chapman SK. Co-occurring Mangroves and Salt Marshes Differ in Microbial Community Composition. Wetlands. 2018;38: 497–508.
- 75. Luis P, Saint-Genis G, Vallon L, Bourgeois C, Bruto M, Marchand C, et al. Contrasted ecological niches shape fungal and prokaryotic community structure in mangroves sediments. Environ Microbiol. 2019;21: 1407–1424. pmid:30807675
- 76. Fernandes SO, Kirchman DL, Michotey VD, Bonin PC, LokaBharathi PA. Bacterial diversity in relatively pristine and anthropogenically-influenced mangrove ecosystems (Goa, India). Brazilian Journal of Microbiology. 2014;45: 1161–1171. pmid:25763019
- 77. Maron P-A, Sarr A, Kaisermann A, Lévêque J, Mathieu O, Guigue J, et al. High Microbial Diversity Promotes Soil Ecosystem Functioning. Appl Environ Microbiol. 2018;84. pmid:29453268
- 78. Doyon S, Sabinot C. A New ‘Conservation Space’? Protected Areas, Environmental Economic Activities and Discourses in Two Yucatán Biosphere Reserves in Mexico. Conservation and Society. 2014;12: 133.
- 79. Zhang Y, Naafs BDA, Huang X, Song Q, Xue J, Wang R, et al. Variations in wetland hydrology drive rapid changes in the microbial community, carbon metabolic activity, and greenhouse gas fluxes. Geochim Cosmochim Acta. 2022;317: 269–285.
- 80. Zhang G, Bai J, Tebbe CC, Zhao Q, Jia J, Wang W, et al. Salinity controls soil microbial community structure and function in coastal estuarine wetlands. Environ Microbiol. 2021;23: 1020–1037. pmid:33073448
- 81. Delgado-Baquerizo M, Trivedi P, Trivedi C, Eldridge DJ, Reich PB, Jeffries TC, et al. Microbial richness and composition independently drive soil multifunctionality. Funct Ecol. 2017;31: 2330–2343.
- 82. Wagg C, Bender SF, Widmer F, Van Der Heijden MGA. Soil biodiversity and soil community composition determine ecosystem multifunctionality. Proc Natl Acad Sci U S A. 2014;111: 5266–5270. pmid:24639507
- 83. Palit K, Das S. Community structure, taxonomic diversity and spatio-temporal variation of sediment and water bacteria in Bhitarkanika mangrove ecosystem, India. International Journal of Environmental Science and Technology. 2021;18: 1147–1166.
- 84. Álvarez-Góngora C, Herrera-Silveira JA. Variations of phytoplankton community structure related to water quality trends in a tropical karstic coastal zone. Mar Pollut Bull. 2006;52: 48–60. pmid:16194550
- 85. Perry E, Velazquez-Oliman G, Marin L. The Hydrogeochemistry of the Karst Aquifer System of the Northern Yucatan Peninsula, Mexico. Int Geol Rev. 2002;44: 191–221.
- 86. Kristensen E, Bouillon S, Dittmar T, Marchand C. Organic carbon dynamics in mangrove ecosystems: A review. Aquat Bot. 2008;89: 201–219.
- 87.
Jurburg SD, Salles JF. Functional Redundancy and Ecosystem Function—The Soil Microbiota as a Case Study. Biodiversity in Ecosystems—Linking Structure and Function. InTech; 2015.
- 88. Maron P-A, Sarr A, Kaisermann A, Lévêque J, Mathieu O, Guigue J, et al. High Microbial Diversity Promotes Soil Ecosystem Functioning. Appl Environ Microbiol. 2018;84. pmid:29453268
- 89.
Teutli-Hernández C, Herrera-Silveira J. Estrategias de restauración de manglares de México: el caso Yucatán. In: Ceccon E, Martínez-Garza C, editors. Experiencias mexicanas en la restauración de los ecosistemas. 2016. pp. 459–484.
- 90. Mujakić I, Piwosz K, Koblížek M. Phylum Gemmatimonadota and Its Role in the Environment. Microorganisms. 2022;10: 151. pmid:35056600
- 91. DeBruyn JM, Nixon LT, Fawaz MN, Johnson AM, Radosevich M. Global Biogeography and Quantitative Seasonal Dynamics of Gemmatimonadetes in Soil. Appl Environ Microbiol. 2011;77: 6295–6300. pmid:21764958
- 92. Drees KP, Neilson JW, Betancourt JL, Quade J, Henderson DA, Pryor BM, et al. Bacterial Community Structure in the Hyperarid Core of the Atacama Desert, Chile. Appl Environ Microbiol. 2006;72: 7902–7908. pmid:17028238
- 93. Hausmann B, Knorr K-H, Schreck K, Tringe SG, Glavina del Rio T, Loy A, et al. Consortia of low-abundance bacteria drive sulfate reduction-dependent degradation of fermentation products in peat soil microcosms. ISME J. 2016;10: 2365–2375. pmid:27015005
- 94. Akam SA, Coffin RB, Abdulla HAN, Lyons TW. Dissolved Inorganic Carbon Pump in Methane-Charged Shallow Marine Sediments: State of the Art and New Model Perspectives. Front Mar Sci. 2020;7.
- 95. Mai Z, Ye M, Wang Y, Foong SY, Wang L, Sun F, et al. Characteristics of Microbial Community and Function With the Succession of Mangroves. Front Microbiol. 2021;12. pmid:34950118
- 96. Gaudel G, Xing L, Raseduzzaman M, Poudel M, Dong W, Hu C. Soil microbes, carbon, nitrogen, and the carbon to nitrogen ratio indicate priming effects across terrestrial ecosystems. J Soils Sediments. 2023.
- 97. Ma X-X, Jiang Z-Y, Wu P, Wang Y-F, Cheng H, Wang Y-S, et al. Effect of mangrove restoration on sediment properties and bacterial community. Ecotoxicology. 2021;30: 1672–1679. pmid:33864552
- 98. Palacios MM, Trevathan-Tackett SM, Malerba ME, Macreadie PI. Effects of a nutrient enrichment pulse on blue carbon ecosystems. Mar Pollut Bull. 2021;165: 112024. pmid:33549995
- 99. Zou D, Chen J, Zhang C, Kao S-J, Liu H, Li M. Diversity and salinity adaptations of ammonia oxidizing archaea in three estuaries of China. Appl Microbiol Biotechnol. 2023;107: 6897–6909. pmid:37702790
- 100. Sun D, Huang Y, Wang Z, Tang X, Ye W, Cao H, et al. Soil microbial community structure, function and network along a mangrove forest restoration chronosequence. Science of The Total Environment. 2024;913: 169704. pmid:38163592
- 101. Xu M, Yang X, Shao J, Huang J, Fan W, Yang A, et al. Biogeographic effects shape soil bacterial communities across intertidal zones on island beaches through regulating soil properties. Science of The Total Environment. 2024;930: 172785. pmid:38677414
- 102. Argiroff WA, Zak DR, Lanser CM, Wiley MJ. Microbial Community Functional Potential and Composition Are Shaped by Hydrologic Connectivity in Riverine Floodplain Soils. Microb Ecol. 2017;73: 630–644. pmid:27807645