Metacommunity structure of benthic foraminifera in Rio de Janeiro coastal lagoons

Metacommunity structure of benthic foraminifera in Rio de Janeiro coastal lagoons | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metacommunity structure of benthic foraminifera in Rio de Janeiro coastal lagoons Pierre Belart, Maria Lucia Lorini, Marcos Souza Lima Figueiredo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872884/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Oct, 2024 Read the published version in Estuaries and Coasts → Version 1 posted 5 You are reading this latest preprint version Abstract Metacommunity theory addresses local interactions and regional processes, offering a powerful framework to comprehend the species composition of a region and the factors that shape its structure along environmental gradients. By incorporating spatial dynamics, the metacommunity analysis explores the relationships that govern the ecological communities at different spatial scales. The objective of this work is to describe the structure of a metacommunity of living foraminifera, to relate it to physical and chemical variables of water and sediment, and to identify the environmental characteristics associated to the assemblages. A total of 534,416 living foraminifera, belonging to 65 species, were collected at 106 stations across five tropical urban coastal lagoons along the coast of the Rio de Janeiro State (Brazil), subjected to a strong salinity gradient. The results of the Elements of Metacommunity Structure (EMS) analysis identified four distinct assemblages of living foraminifera across the lagoonal systems. These metacommunities fitted a quasi-nested pattern, with the total variation explained by a shared influence of environmental factors (primarily hydrological drivers associated with marine influence, such as salinity, pH, and temperature) and spatial predictors. Organic enrichment descriptors (TOC, TS, CHO, PTN, LIP) played a secondary role in the ordination of the sites. The findings of this work demonstrate the potential of the EMS approach as a valuable tool for establishing a baseline in environmental monitoring plans. environmental gradient community structure marine bioindicators Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The debate about changing in the communities along environmental gradients has permeated the Ecology of Communities since its origin (Barone et al. 2008 ). That is represented by the conflict between the point of view of Clements ( 1916 ) and Gleason ( 1926 ) on how species respond to environmental gradients and their consequences for community structure. Nowadays, new forms of community structuring have been described (Diamond 1975 ; Patterson & Atmar 1986 ), each related to different potentially involved ecological processes (Leibold and Mikkelson 2002 , Presley et al. 2010 ), which represent different relationships of communities with the characteristics of the environments in which they operate. The concept of metacommunities was developed from a perspective of regional communities, and it is defined as the “set of local communities linked by the dispersal of multiple potentially interactive species” (Leibold et al. 2004 , Holyoak et al. 2005 ). Based on this concept, we seek to understand the distribution of organisms along existing environmental gradients in order to know how communities that make up a metacommunity are structured in space (Presley et al. 2010 ). Presley et al. ( 2010 ) proposed an analytical framework to distinguish between different types of metacommunity structures by analyzing three key properties (Elements of Metacommunity Structure – EMS): coherence, species turnover and boundary clumping, as defined by Leibold & Mikkelson ( 2002 ). This approach has been increasingly used to investigate the spatial structuring of terrestrial vertebrate communities (Presley et al. 2009 , Presley et al. 2011 , López-González et al. 2012 , de la Sancha et al. 2014 , Cisneros et al. 2015 ), freshwater and estuarine organisms (Henriques-Silva et al. 2013 , Fernandes et al. 2014 , Heino et al. 2015 a, 2015 b, Castillo-Escrivà et al. 2017 , Alves et al. 2020 ) and plants (Barone et al. 2008 , Shevtsov et al. 2013 ) along environmental gradients, being able to detect discontinuities and structures caused by environmental heterogeneity as in transitional environments. Coastal lagoons are among the most biologically productive environments of our planet and harbour a rich and unique biodiversity (Esteves et al. 2008 ; Whitfield et al. 2008). Tropical coastal lagoons are one the most threatened ecosystems on Earth due to multiple disturbances, including habitat alterations (Bulleri & Chapman 2010), climate change (Anthony et al. 2009 ; Clausen & Clausen 2014), and various anthropogenic perturbations (McKinney et al. 2010). Therefore, for evaluating the ecological status of those coastal environments, it is necessary to continuously assess the quality of the sediments and the ecological responses of living benthic organisms to the presence of pollutants that cause high environmental stress (Borja et al., 2012 ). Biological monitoring enables the detection of unforeseen impacts that are more directly related to ecosystem health than the actual geochemical data. In this context, benthic foraminifera (BF) are often used to monitor the health of marine and coastal ecosystems because they are abundant in the sediment, have a short life cycle and respond sensitively to environmental changes (Murray, 1991 ). Recent studies have focused on the effects of anthropogenic disturbance of BF communities in urban areas in Brazil (Belart et al., 2019 ; Laut et al., 2021; Raposo et al., 2022 ; Nunes et al., 2023 ) and as well as in a range of other coastal environments globally (such as Alves Martins et al., 2015; Hohenegger et al., 2018; Armynot du Châtelet et al., 2018; Bouchet et al., 2021). Over time, natural or human-induced factors can significantly impact the ecological gradient and, subsequently, the structure of benthic assemblages. These impacts compromise the ecological integrity of coastal ecosystems, leading to changes in species diversity, abundance, and functional roles. Therefore, understanding the BF communities and the key driver of this structure becomes increasingly important in the face of a changing environment. This study aims to: 1) elucidate the spatial distribution of living benthic foraminifera along pronounced salinity gradients established in the coastal lagoons of Rio de Janeiro; and 2) evaluate the underlying processes that shape this metacommunity structure. By doing so, it seeks to provide valuable insights into how salinity-tolerant species respond to environmental changes. Material and Methods Study Area The Rio de Janeiro state presents a unique set of lagoon systems, most of them along the east coast (Fig. 1 a). These systems are confined to a narrow coastal plain approximately 120 km long and 10 km wide (Ekau and Knoppers 1999 ). The lagoons are delimited on the continent by a continuous mountainous relief (Serra do Mar) and protected from the sea by a sandy bar that gives them a similar typology. The essential differences are related to the climate of the region, which varies from humid tropical to semi-arid, and the intensity of anthropogenic impacts on their catchment basins (Knoppers and Kjerve 1999). For this study were selected the data of BF from five Rio de Janeiro lagoons that represent a latitudinal and climate gradient. • Itaipu Lagoon – located between latitudes 22°57ʹ S to 22°58ʹ S and longitudes 043°01ʹW to 043°03ʹW in Niterói city, Rio de Janeiro state. (Fig. 1 ). The lagoon covers an area of 1.2 km 2 and has a water depth between 0.2 and 2.0 m. The climate is warm and humid with a rainy season in summer (December to March), dry season in winter (June to September) and an average rainfall between 1,000 and 1,500 mm/year (Barbiére and Coe-Neto 1999 ). Itaipu Lagoon is connected to the Atlantic by the Tibau Channel which is an artificial channel that provides an open tidal inlet (Salvador et al. 2002 ). The lagoon is affected by a microtide effect that has an average height of 0.71 m and the width can increase up to 10 m during the tide of syzygy (Fig. 1 E). • Maricá-Guarapina Lagoon System (MGLS) – located between latitudes 22°52’ to 23°00’ S and longitudes 43°00’ to 42°45’ W) in Maricá city, Rio de Janeiro. The MGLS has a circular shape and is inserted into a kind of deformed rocky amphitheatre with its boundaries determined by the Atlantic Ocean and the Itacoatiara and Negra hills (Perrin, 1984 ). It is composed by four lagoons comprising a total area of 43.2 km 2 , which are currently distributed in: Maricá (20.5 km 2 ), Barra (11.7 km 2 ), Padre (1.6 km 2 ) and Guarapina (9.4 km 2 ) which is connected with the ocean by Ponta Negra Channel (Oliveira et al., 1955). The drainage basin consists of three main sub-basins: Vigário River Basin; Ubatiba River, which mouth is located in Maricá Lagoon and Caranguejo River Basin which mouth is located in Guarapina Lagoon (Comitê Gestor da Região Hidrográfica da Baía de Guanabara e dos Sistemas Lagunares de Maricá e Jacarepaguá, 2006). Barbiere (1985) described the bathymetry of the MGLS: Maricá Lagoon has a maximum depth of 2.0 m and a mean depth of 1.0 m; Barra and Padre lagoons represent the shallower part of this system, since 70% of their areas have depths of ≈ 0.5 m; Guarapina is the deepest lagoon of this system, with depths ranging from 2.0 to 5.0 m. The climate of the MGLS region is tropical humid to semi humid, with tropical air masses from oceanic and continental origins Barroso-Vanacôr et al., 1994 ). It is characterized by the mean annual temperature of ≈ 23°C and the precipitation between ≈ 1100 and ≈ 1500 mm/year (Fig. 1 D). • Saquarema Lagoon System (SLS) - Is located between latitudes 22°55’ to 22°56’S and longitudes 42°35’ to 42°29’W in Saquarema city, Rio de Janeiro. It has an area of 21.2 km 2 , which extends approximately 11.8 km along the coast with an average depth of less than 2.0 m (Belart et al., 2018 ; Dias et al., 2017 ). This system is composed of four large connected lagoons (Fig. 1 ): Urussanga (12.6 km 2 ), Jardim (2 km 2 ), Boqueirão (0.6 km 2 ) and Saquarema (6 km 2 ). The climate in SLS is sub–humid, with prolonged periods of drought, and high temperatures (Carmouze & Vasconcelos, 1992 ). The climate in SLS is also highly influenced by Mato Grosso Hills with sub-humid conditions and prolonged periods of drought (Belart et al. 2017). The Mato Grosso Hills effects primarily the rivers that discharge into the Urussanga Lagoon (Mato Grosso, Tingui, and the Jundiá rivers) (Carmouze 1994 ). This complex ecosystem is connected to the Atlantic Ocean through an artificial channel called Barra Franca channel located in Saquarema lagoon (Fig. 1 C). • Araruama Lagoon – located between longitudes 42°00′W to 42°25′W and latitudes 22°49′ to 22°57′S delimited by five cities, Araruama, Arraial do Cabo, Cabo Frio, São Pedro d’Aldeia and Iguaba Grande in Rio de Janeiro (Fig. 1 A). This lagoon comprises approximately 200 km 2 (André et al., 1981 ) and presents salinity ranges from 35 to 56% throughout the year, making this the largest permanent hypersaline lagoon in the world (Coutinho et al., 1999). The local climate is classified as an equatorial desert, according to Kottek et al. ( 2006 ). The area displays annual temperatures around 25°C and a rainfall rate of about 1500 mm/year, reaching the highest values between November and March. The average annual rainfall is less than the average annual evaporation, which promoted high salinity values (Dias and Kjerfve, 2009 ). The lagoon has only one main fluvial channel, while some intermittent tributaries are observed during the rainy season (Barroso, 1987; Kjerfve et al., 1996). The average depth of the lagoon is 2.5 m, but shallow areas ranging from 0.5 to 1.5 m and depth up to 17 m are also present (Souza et al., 2003). The Itajuru Channel is the lagoon's only communication with the ocean, located in the eastern portion of Araruama Lagoon and crossing the urban perimeter of Cabo Frio and São Pedro d’Aldeia. This channel is very narrow (5.5 km length x 180 m width x 3 m depth) and does not allow for efficient water exchanges with the ocean, leading to a long Araruama Lagoon water residence time of around 84 days (Primo and Bizerril, 2002 ; Valentini et al., 2002 ). • Vermelha Lagoon - located in the Massambaba Environmental Protection Area (22˚55’S to 42˚25’W) in Araruama city, Rio de Janeiro. It has 4.3 km in length, 10.88 km perimeter, 0.75 km maximum width and covers an area of ca. 2.5 km 2 with the maximum depth of 2 m. The region presents relatively low rainfall (854 mm/year), high annual evaporation rates (between 1,200 and 1,400 mm), average temperature of 23˚C. The climate is classified as an equatorial savanna (Aw—precipitation less than 60 mm) in accordance with Kottek et al. ( 2006 ) with annual temperatures around 25˚C. Vermelha Lagoon is not receiving freshwater directly from rivers and streams. This ecosystem is connected to the Araruama Lagoon through an artificial channel inside the salt pan area (Laut et al. 2017 ). Vermelha Lagoon is the most hypersaline coastal lagoon in Brazil (recording salinities of about 60 on average; maximum 120) and its name is related to the huge proliferation of microbial mats and the purplish bacteria that give a reddish appearance to bottom sediment (Silva e Silva et al., 2004 ) (Fig. 1 B). Sediment and Water Sampling Method The present study used the database of the Laboratório de Micropaleontologia da UNIRIO – LABMICRO from which 116 samples were selected. They were collected through the Rio de Janeiro coastal lagoons and originally studied by Raposo et al. ( 2018 ) in Itaipu lagoon, Laut et al. ( 2022 ) in Marica-Guarapina Lagoonal System, Belart et al. ( 2018 ) in Saquarema lagoon, Laut et al. (2019) in Vermelha lagoon and Laut et al. (2024) in Araruama lagoon (Fig. 1 ). To investigate the factors that drive the distribution of benthic foraminifera metacommunities, both physicochemical data (salinity (Sal), temperature (T), dissolved oxygen (DO), and pH) and sedimentary data (total organic carbon (TOC), total sulphur (TS), carbohydrates (CHO), lipids (LIP), and proteins (PTN)) were selected for analysis. Foraminiferal Analysis The sampling, processing and screening methodology followed a standardized methodology proposed by Schönfeld et al. ( 2012 ) and it happened as follows: The first upper centimetre of sediment (50 ml) was recovered in triplicate and stained with Rose Bengal plus alcohol solution to stain the living specimens and washed over sieves with mesh openings of 500 and 63µm. The residual fraction in each sieve was dried at 50°C, and the foraminiferal specimens were concentrated by flotation in trichloroethylene (Martins et al. 2015a). All living foraminiferal tests were picked, identified and counted under stereoscopic microscope at 80x magnification. The number of specimens found in the three replicates was then averaged. The generic taxonomical classification of Loeblich and Tappan ( 1987 ), and specific concepts of Boltovskoy et al. ( 1980 ) and Raposo et al. ( 2018 ) and Belart et al. ( 2018 ) were followed. After identification, the names of species were checked using the platform available online, WoRMS (World Register of Marine Species; Hayward et al. 2016 ). Metacommunity Analysis Metacommunity structure To identify the response of the species to unmeasured environmental characteristics (= latent environmental gradient) affecting community structuring, we applied the elements of metacommunity structure (EMS) approach (Leibold and Mikkelson, 2002 ). This approach uses a Correspondence Analysis (CA) to ordinate the communities by reciprocal averaging, maximizing the proximity of species with similar distributions, as well as the proximity of sites with similar species compositions. To identify these proximities, we considered the abundances of the species on the EMS analyses, discarding those species represented by singletons (one individual on the entire sample), as these species don't provide much information regarding metacommunity structure (Presley et al., 2011 ). We applied the EMS framework (Presley et al., 2010 ) with 1,000 simulated null matrices generated by maintaining the species richness of the sites constant and filling species ranges based on their marginal probabilities (“r1” method of null model randomization). This protocol allowed us to discriminate among the metacommunity structures presented by Leibold & Mikkelson ( 2002 ), and we investigated the metacommunity structure existent on the latent environmental gradients represented by only the first axis of the CA. Three interrelated metacommunity metrics were used to verify the structure: coherence, turnover, and boundary clumping. Their values and statistical significance determine the metacommunity's type within the 14 categories defined by the EMS framework (Presley et al. 2010 ). Different combinations of metric values indicate different underlying processes shaping the metacommunity structure. Predictor variables We created spatial variables to represent spatially structured processes operating in the lagoons (Peres-Neto and Legendre, 2010 ), in broader scales (e.g., unmeasured environmental factors or the different hydrodynamic regimes of the Lagoon’ compartments). To obtain these variables we built Moran’s eigenvector maps (MEMs) from a Principal Coordinates of Neighbour Matrices (PCNM) based on a network connecting all the sampling stations following the Gabriel graph criterion (Dray et al., 2006). Only those eigenvectors which represented significant spatial structure (Moran’s I > 0.1) were selected to be used in posterior analyses (Diniz-Filho et al., 2012), thus resulting in 25 MEMs. To summarize the environmental variation existing in the lagoons and reduce the number of predictors we performed a Principal Component Analysis (PCA) (Legendre and Legendre, 1998 ) in R environment (R Core Team, 2017) using the four hydrological parameters (SAL, T, DO and pH) and five sedimentological descriptors of organic enrichment (TOC, TS CHO, LIP, PTN). Whenever necessary, we log-transformed environmental variables to closer them to a normal distribution and/or to improve linearity in relationships. We applied the Kaiser-Guttman criterion (Legendre and Legendre, 1998 ) to select the number of PCA axes which would be used in further statistical analyses. Data analysis We applied a two-stage approach to identify the effects of spatial structure, hydrological and organic matter determinants over foraminifera community structuring at Rio de Janeiro lagoons. First, we calculated Pearson correlation coefficients between the scores obtained for the two axes of the EMS analysis and each of the predictor variables (the spatial structure represented by the MEMs and the environmental gradients represented by the axes of the PCA) to identify the variables that could explain the latent environmental gradients. Only the positive MEMs were used in this analysis as we were interested in the broad-scale spatial structure affecting the metacommunity. On the second stage, we used variation partitioning (Borcard et al., 1992) to quantify the relative contribution of spatial, hydrological and organic matter components on the BF metacommunity. We applied a partial Redundancy Analysis (RDA) (Legendre and Legendre, 1998 ) to decompose the variation in the relative abundance and distribution of foraminifera explained by single and shared fractions of three components: spatial, hydrological, and organic matter. Before executing this analysis, we log-transformed the abundance of foraminifera species and some environmental variables. In order to avoid model overfitting due to the high number of variables, we selected the environmental and spatial variables applying a forward selection procedure (Blanchet et al., 2008 ). A variable would be selected if it explained variance was significant (p < 0.05) and the adjusted coefficient of determination (R²) accumulated by the selected variables did not exceed the adjusted R² calculated using all explanatory variables. We executed all analyses on R version 3.4.3 (R Core Team, 2017). The package ‘metacom’ (Dallas, 2015) was used to perform the EMS analysis and to obtain the scores of the first two axes of the CA, which represents the latent environmental gradients. The packages ‘spdep’ (Bivand et al., 2015 ) and ‘adespatial’ (Dray et al., 2018) were used to obtain MEMs. Principal component and variation partitioning analyses (PCA and RDA) were performed with the package ‘vegan’ (Oksanen et al., 2020; see https://cran.r-project.org/package=vegan ) The species represented by singletons (that is, those that only appear once) were discarded from analyses. Results A total of 537,416 specimens belonging to 65 species were selected for analyses. The species Ammonia rolshauseni , Bulimina marginata , Eponides repandus , Globocassidulina subglobosa , Nonionella auris , Pyrgo comata , Quinqueloculina polygona , Rosalina floridana , Rosalina williamsoni and Valvulineria candeiana represented singletons, and were discarded from analyses. The absolute abundance ranged from more than 1,000 individuals ( Ammonia parkinsoniana , Ammonia tepida , Bolivina variabilis , Elphidium excavatum , Miliolinella subrotunda , Quinqueloculina laevigata , Quinqueloculina miletti , Quinqueloculina poeyana , Quinqueloculina seminulum , Quinqueloculina vulgaris , Rosalina bradyi and Triloculina oblonga ) to less than twenty individuals ( Adelosina longirostra , Alliatinella differens , Ammobaculites dilatatus , Ammobaculites exiguus , Asterotrochammina camposi , Bolivina doniezi , Bulimina patagonica , Cibicidoides variabilis , Cornuspira involvens , Cornuspira involvens , Cribroelphidium poeyanum , Entizia macrescens , Fursenkoina pontoni , Hanzawaia concentrica , Haplophragmoides wilberti , Leptohalysis scotti , Miliolinella fichteliana , Pseudononion japonicum , Nonionella opima , Pyrgo oblonga, Rosalina globularis , Rosalina rugosa , Textularia earlandi , Trochammina salsa and Warrenita palustris ). Ammonia tepida , Q. seminulum and M. subrotunda were the most constant species along the lagoons, being detected in 92 (79%), 71 (61%) and 47 (40%) stations, respectively. The first two PCA-axes explained a large fraction (61.19%) of the environmental variability found on the lagoons, with the first axis representing more than half of this percentage (Table 1 ). PCA-axis 1 was related to sediment variables such as lower values of TS, PTN and COT and lower values of pH. PCA-axis 2 was negatively related to salinity and positively correlated with temperature, pH and DO. Marica Lagoon had the highest pH and temperature values and the lowest salinity values. Araruama, Itaipu, and Vermelha Lagoons exhibited the opposite pattern along this axis. Saquarema Lagoon's distribution was more closely associated with higher concentrations of biopolymer (PTN, CHO, and LIP), TOC, and TS values. PCA revealed that Marica Lagoon had the highest values of pH and temperature and lowest values of salinity. Araruama, Itaipu and Vermelha exhibited opposite pattern Saquarema Lagoon's distribution was more closely with higher concentrations of biopolimers (PTN, CHO and LIP), TOC and TS. Table 1 Principal Correspondence Analysis results. PTN – Protein, CHO – Carbohydrates, LIP – Lipids, DO – Dissolved oxygen, Sal – Salinity, TOC – total organic carbon, TS – total sulfur. PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PTN -0.3871 -0.0545 -0.2943 0.0841 -0.5838 0.5929 -0.1275 0.1892 -0.0951 CHO -0.3056 -0.3139 0.0703 -0.6018 0.5017 0.2574 -0.1083 0.1927 -0.2724 LIP -0.2924 -0.3064 -0.1890 0.6757 0.4269 -0.0356 0.2211 0.3089 0.0231 COT -0.3813 -0.3142 0.3495 0.1724 -0.2204 -0.2856 -0.0672 -0.4797 -0.4892 TS -0.4606 -0.1491 0.3727 -0.1719 -0.1683 -0.1576 0.1245 0.0919 0.7211 DO 0.0350 0.3571 0.7214 0.2858 0.1584 0.4781 -0.0586 0.0867 -0.0680 pH -0.3549 0.3566 -0.2934 0.0230 0.3337 0.2313 0.0098 -0.6698 0.2162 Temp -0.3219 0.4350 -0.0700 0.0575 0.0663 -0.3969 -0.6652 0.3015 -0.0606 Sal 0.2880 -0.4897 0.0185 0.1717 0.0862 0.1870 -0.6761 -0.2148 0.3191 % Variance 0.3636 0.2483 0.1094 0.0788 0.0687 0.0592 0.0302 0.0286 0.0131 Cum % 0.3636 0.6119 0.7213 0.8002 0.8689 0.9281 0.9583 0.9869 1.0000 We observed that the first axis on the EMS analysis presented a positive and significant coherence (coh value and p), a negative non-significative turnover (tur value and p), and a significant boundary clumping > 1 (index and p). This way, EMS-axis indicates that this metacommunity has a quasi-nested clumped structure. Based on the results of the EMS analysis (Fig. 3 ), the sites were grouped in four distinct metacommunities assemblages, which were organized depending on the relationship between the species that composes each assemblage. Group 1 was dominated by agglutinated foraminifera such as Arenoparrella mexicana and Paratrochammina guaratibaensis . This group was restricted to Maricá Lagoon and in the inner sector of the Saquarema Lagoon (Fig. 3 ). The Group 2 was considered the open lagoonal assemblage and was dominated by calcareous foraminifera, mostly hyaline ( Ammonia tepida and Elphidium excavatum ) and some porcelaneous species. This group occurred in Itaipu Lagoon and most parts of the Saquarema Lagoon (Fig. 3 ). Group 3 was dominated by both porcelaneous and hyaline species and was almost exclusively found in the Araruama Lagoon. The Group 4 can be considered as a subset of the Group 3 and was composed by a few species, mostly porcelaneous foraminifera such as Quinqueloculina seminulum and Miliolinella subrotunda , and total absence of agglutinated species. This group was observed only in Vermelha lagoon (Fig. 3 ). Based on a correlation analysis (CA), it was possible to see that the community was structured according to a strong positive relationship with PTN, pH, temperature and TS and a negative relationship with salinity (Table 2 ). Parameters like CHO, LIP, DO and TOC did not show significant correlation in this analysis. Table 2 Results of Correlation Analysis between EMS and PCA-axis 1. PTN – Protein, CHO – Carbohydrates, LIP – Lipids, DO – Dissolved oxygen, Sal – Salinity, TOC – total organic carbon, TS – total sulfur. PTN CHO LIP DO pH T Sal TOC TS r 0.4416 0.0392 0.0601 -0.0894 0.5019 0.3261 -0.5059 0.0736 0.2733 p 5.324E-06 7.02E-01 5.57E-01 3.81E-01 1.41E-07 1.05E-03 1.08E-07 4.71E-01 6.48E-03 RDA analysis showed that variation in community structure could be explained by the ensemble of variables used in the present study, as it constrained 61% of the variance on foraminifera abundance data (Fig. 4 ). Variance partitioning indicated that a major part of the constrained variation was due to the pure space fraction (28.7%) and the shared fraction between space and environment components (22.4%), followed by the shared fraction between space, hydrological and OM components (7.5%). The variance constrained at all other fractions, whether single or shared, was negligible (Fig. 4 ). Discussion We used EMS combined with a variation partitioning technique to identify the relationships between environmental, spatial and organic matter predictors structuring benthic foraminifera metacommunities in coastal lagoon systems. We found Q-nested species composition with clumped species loss as the EMS pattern in this system. We found a higher importance of the shared fraction between environment and space influencing benthic foraminifera metacommunity. Therefore, in this system, benthic communities along coastal lagoons were generally subsets of a large pool of species (i.e., nested or Q-nested) with space and salinity most strongly associated with this pattern. According to Presley et al ( 2010 ) the metacommunity approach aims to identify significant patterns of coherence of species substitution and/or clustering of distribution boundaries. In the present study, the distribution pattern is directly related to water renewal standards. In this way, the EMS used the first axis of the PCA to classify the metacommunities according to a positive relation with PTN, pH, temperature and TS and strong negative relation with salinity values, organizing the sites and assemblages based on this gradient. Vermelha lagoon stations were all grouped together with the highest values of salinity and lowest values of TS associated with the dominance of porcelaneous foraminifera (miliolids). Laut et al. ( 2022 ) report that the dominance of porcelaneous species (mainly Quinqueloculina seminulum ) in the Vermelha lagoon is related to hypersalinity and high primary productivity in the region. The species distribution along Maricá-Guarapina Lagoon System was influenced by the saline gradient, being the most oligohaline environment among those studied with greater dominance of agglutinated tests such as Trochammina inflata . Araruama, Saquarema and Itaipu lagoons were characterized as eurialine environments, although they have some hypersaline zones, most of them have conditions close to those found in the ocean (Laut et al. 2021; Belart et al. 2019 ; Raposo et al. 2018 ). Ammonia spp. and Elphidium excavatum were commonly recognised in transitional environments and are tolerant to a wider range of environmental parameters (Martins et al. 2016 ; Laut et al. 2016; Alves Martins et al. 2015). These association of species were also found in the strongly confined areas, as for instance in Italian lagoons: Orbetello lagoon (Tuscany, coast of Tyrrhenian Sea), Lake Varano (Southern Italy), and the Santa Gilla lagoon (Cagliari) (Frontalini et al. 2011 ), Among the studied lagoons, this association of species was dominant in Araruama, Itaipu and Saquarema. The occurrence of these species in coastal environments is favoured by reduced competition under high levels of environmental stress (Murray 1991 ). They can be characterised as eurytopic with a wide geographical distribution and/or adapt to all environmental conditions. The results obtained through the EMS approach corroborate the interpretations of previous studies and represent an advance in the knowledge about the foraminifera of transitional environments. They allowed us to verify a distribution pattern of the communities with ecological succession responding primarily to salinity and pH (marine influence gradient). That is, brackish environments with a higher representation of agglutinated species (Group 1 of EMS), open lagoon environments dominated by hyaline calcareous (Group 2 of EMS) and hypersaline environments totally dominated by milliolids, mainly of the genus Quinqueloculina (Groups 3 and 4). The recognition of these patterns had fundamental importance to reduce the ecological shortfalls that this group of organisms still has. It was possible to define that the main variables acting on the structure of communities were PTN, pH, temperature, salinity and TS, that is, the influence of parameters of the water and sediment. The biopolymers content in marine and estuarine environments is used for the characterization and interpretation of the origin of organic matter accumulated in sediments (Silva et al., 2011 ; Cotano and Villate, 2006 ; Fabiano and Danovaro, 1994 ; Danovaro et al., 1993 ). Belart et al. ( 2018 ), Raposo et al. ( 2018 ) and Laut et al. (2021) were the first to relate foraminifera with biopolymers in Brazil, and generally had a consensus that porcelaneous foraminifera (miliolids) preferred protein-enriched environments. While regions rich in LIP and CHO had the lowest species richness with dominance of Ammonia tepida and Elphidium excavatum . For instance, Cotano & Villate ( 2006 ) identify that the organic matter provided by domestic liquid effluents and other anthropogenic activities could have high concentrations of PTN and LIP, whereas organic matter from a phytoplanktonic origin and vegetal detritus may have high CHO content. This pattern was the same described by Belart et al. ( 2018 ) in Saquarema Lagoon System, where the highest values of CHO were related to a peat bog area and the highest values of PTN and LIP to the most urbanized region of lagoon. Raposo et al. ( 2018 ) and Laut et al. (2021) founded the same in Itaipu lagoon and Maricá-Guarapina lagoon, where the highest values of PTN were found in a region heavily impacted by domestic sewage. The RDA revealed that, despite a very strong spatial correlation, the patterns of species distribution also showed a correlation with environmental variables, mainly salinity. This result aligns with those of Armynot et al. (2018), who found that foraminiferal communities exhibit similar responses in coastal environments from France. Based on RDA, the water parameters were more important than the organic matter availability for the foraminifera distribution in Rio de Janeiro lagoons. This result is different from all those published in Rio de Janeiro lagoons, where the organic matter compounds were the main factors for the benthic foraminifera distribution (Laut et al. 2021, Belart et al. 2019 , Raposo et al. 2018 ). The results showed that organic matter values are high in all studied lagoon environments, regardless of whether the source is natural or anthropogenic. It seems that once the basic requirements for life are met, the organic matter compounds play a secondary role in the distribution of species among the studied lagoons. This contrast with the pattern founded by Bouchet et al. (2021) in the Mediterranean environments with much lower amounts of organic matter. In that case, they realized that when the amount of organic matter exceeds the species tolerance limit, sensitive species naturally decline providing perfect conditions for the establishment of tolerant and opportunistic ones, this pattern was also observed by Elliott and Quintino ( 2007 ) and Munari and Mistri ( 2008 ). The lack of information about the real tolerance limit of foraminifera species towards the levels of organic matter in tropical environments highlights an ecological problem defined as Hutchinsonian shortfall (Cardoso et al 2011 ). In our study area, the EMS analysis revealed that TS had a greater influence than TOC on the site ordination. This suggests that under specific local conditions, such as high temperatures and longer residence time of lagoon waters, TS becomes a more significant limiting factor for the abundance and richness of species. Studies such as those by Belart et al. ( 2019 ) in Saquarema lagoon, Laut et al. ( 2022 ) in Vermelha lagoon and Raposo et al. ( 2022 ) in Cachoeira river estuary has been filling these gaps, but in a coast with more than 7,491 km and hundreds of transitional environments with the potential to harbor BF communities, the solution to define the species tolerance limit for Brazilian environments should perhaps be to use cultivation techniques in laboratory, such as the experiments by Frontalini et al. (2012 and 2018). In recent years progress has been also made in the development of biological indices which relate benthic foraminifera to diversity indices (Alve et al., 2009 ; Bouchet et al., 2012 ; 2013 ) or the amount and sensitivity of species to organic pollution (Barras et al., 2014 ; Dimiza et al., 2016 ; Alve et al., 2016 ; Jorissen et al., 2018 ). However, our findings, suggest that the structuring of the BF community of the studied lagoons is primarily driven by salinity, pH, temperature, PTN and TS, with TOC playing a lesser role. Therefore, we recommend that further studies be carried out to assess the relationship between these communities and organic matter compounds, applying indices that take more parameters into account. Additionally, investigating the tolerance of species to TS, which has proved to be decisive in structuring the proposed metacommunities, warrants further investigation. Conclusion The present study is pioneer in using EMS applied to foraminifera and was able to describe the patterns and factors that structure the composition and distribution of the species assemblages in the Rio de Janeiro lagoon systems. Our results indicated that benthic foraminifera communities follow Q-nested structure and are influenced by the shared influence of spatial and hydrological drivers. The BF metacommunities respond primarily to site-specific characteristics, which makes each studied lagoonal system unique. 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Benthic foraminiferal and organic matter compounds as proxies of environmental quality in a tropical coastal lagoon: the Itaipu lagoon (Brazil). Marine Pollution Bulletin 129: 114–125. https://doi.org/10.1016/j.marpolbul.2018.02.018 . Raposo, D., F. Frontalini, and I. Clemente et al. 2022. Benthic Foraminiferal Response to Trace Elements in a Tropical Mesotidal Brazilian Estuary. Estuaries and Coasts . https://doi.org/10.1007/s12237-022-01095-5 . Salvador, M. V. S., M. A. M. And, and Silva. 2002. Morphology and sedimentology of Itaipu embayament – Niterói/RJ. Anais da Academia Brasileira de Ciências, 74 (1) (2002), pp. 127–134. Schönfeld, J., E. Alve, E. Geslin, F. Jorissen, S. Korsun, and S. Spezzaferri. 2012. The FOBIMO (FOraminiferal BIo-MOnitoring) initiative—Towards a standardised protocol for soft-bottom benthic foraminiferal monitoring studies. Marine Micropaleontology 94: 1–13. Shevtsov, J., K. Wickings, and B. C. Patten. 2013. Evaluating the role of biotic interactions in structuring communities using a gradient analysis of multiple interacting guilds. Oikos 122(11): 1594–1605. 10.1111/j.1600-0706.2013.00267.x . Silva, F. S., J. A. P. Bitencourt, F. Savergnini, L. V. Guerra, J. A. Baptista-Neto, and M. A. C. Crapez. 2011. Bioavailability of organic matter in the superficial sediment of Guanabara Bay, Rio de Janeiro, Brazil. Anuário do Instituto de Geociências-UFRJ 34 (1): 52–63. Silva e Silva, L. H., M. C. E. Senra, T. C. L. M. Faruolo, S. B. V. Carvalhal, S. A. Alves, and C. M. Damazio et al. 2004. Composição paleobiológica e tipos morfológicos das construções estromatolíticas da lagoa Vermelha, RJ, Brasil. Revista Brasileira Paleontologia. ; 7(2): 193–198. Valentini, E. M. S., P. C. C. Rosman, V. N. Oliveira, and A. C. B. Cunha. 2002. Modelagem da Lagoa de Araruama, RJ. Relatório/Projeto PEC- 1984 (Fundação COPPETEC-COPPE/UFRJ). Cite Share Download PDF Status: Published Journal Publication published 26 Oct, 2024 Read the published version in Estuaries and Coasts → Version 1 posted Reviewers agreed at journal 26 Jan, 2024 Reviewers invited by journal 25 Jan, 2024 Editor invited by journal 23 Jan, 2024 Editor assigned by journal 17 Jan, 2024 First submitted to journal 17 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3872884","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269486356,"identity":"eb8e3e2f-bc7b-4654-9743-4010f8267592","order_by":0,"name":"Pierre Belart","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYDACCQbGA0BKDsQ+8IBILQxALQbGYC0JpGhJbABxiNJiLt184MDPPX/S54cdfgi0xU5Ot4GAFss5xxIO9jwzyN14O80AqCXZ2OwAAS0GN3IMDvAcAGqZnQDSciBxG2Et+R8O/jlgkG44O/0DsVpyGA4DbUmQl84h0hagXwwOyxwwNtwgnVNwIMGACL8AQ+zhwzcH5OTlZ6dv/vChwk6OsPfhjAMoXGK0yDcQoXoUjIJRMApGJgAAIKRMR2pU7hsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1508-5564","institution":"Universidade Federal do Estado do Rio de Janeiro","correspondingAuthor":true,"prefix":"","firstName":"Pierre","middleName":"","lastName":"Belart","suffix":""},{"id":269486357,"identity":"fd1395f6-5124-4871-824f-16b5d0d49a7f","order_by":1,"name":"Maria Lucia Lorini","email":"","orcid":"","institution":"Universidade Federal do Estado do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Lucia","lastName":"Lorini","suffix":""},{"id":269486358,"identity":"02d3b107-c808-49b1-ba6d-8c60a50385c4","order_by":2,"name":"Marcos Souza Lima Figueiredo","email":"","orcid":"","institution":"Universidade Federal do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Marcos","middleName":"Souza Lima","lastName":"Figueiredo","suffix":""},{"id":269486359,"identity":"ca22a1fa-0e05-4d98-ba80-b831923247eb","order_by":3,"name":"Carla Bonetti","email":"","orcid":"","institution":"Universidade Federal de Santa Catarina - Campus Florianópolis: Universidade Federal de Santa Catarina","correspondingAuthor":false,"prefix":"","firstName":"Carla","middleName":"","lastName":"Bonetti","suffix":""},{"id":269486360,"identity":"6e98f02a-6733-4505-810a-df69eddee4d3","order_by":4,"name":"Lazaro Laut","email":"","orcid":"","institution":"Universidade Federal do Estado do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Lazaro","middleName":"","lastName":"Laut","suffix":""}],"badges":[],"createdAt":"2024-01-17 12:24:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872884/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872884/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12237-024-01451-7","type":"published","date":"2024-10-26T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50362420,"identity":"b3c61cc4-fbb4-4167-bcfe-bbdf94e131c7","added_by":"auto","created_at":"2024-01-30 10:36:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":190083,"visible":true,"origin":"","legend":"\u003cp\u003eStudied lagoons.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3872884/v1/d08aca096e4286cc4973cdf6.jpeg"},{"id":50362417,"identity":"8f8c399d-3304-40dc-88bd-fc8a2e4a66ae","added_by":"auto","created_at":"2024-01-30 10:36:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19709,"visible":true,"origin":"","legend":"\u003cp\u003eResults of PCA-axis 1 and PCA-axis 2. PTN – Protein, CHO – Carbohydrates, LIP – Lipids, DO – Dissolved oxygen, Sal – Salinity, TOC – total organic carbon, TS – total sulfur\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3872884/v1/e3a3ebdade483368e54aae93.png"},{"id":50362872,"identity":"fbeca861-27e8-40cb-9248-3ad1126e8206","added_by":"auto","created_at":"2024-01-30 10:44:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":180674,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies versus sites after ordination according to results of Elements of Metacommunity Structure.Gray scale squares indicates the abundance of species along the studied lagoons.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3872884/v1/a1abafb9cd0e6647311ea181.png"},{"id":50362418,"identity":"817bcf48-59ce-4ab5-8812-8b87ad1f13de","added_by":"auto","created_at":"2024-01-30 10:36:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54720,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram summarising the results of Redundancy Analysis (RDA) for the three components: spatial patterns (Space), hydrological filters (Environment) and organic matter filters (OM). The numbers denote the explained variation in each partition (total explained variance = 0.61)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3872884/v1/cb7f2fd03e2ec07c32af4da1.png"},{"id":67681882,"identity":"1b957f89-92b5-4b49-b77a-187fdcfa72fc","added_by":"auto","created_at":"2024-10-28 16:10:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":954053,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872884/v1/8b6aac11-9ea7-4ac6-abfb-4380b5eeea09.pdf"}],"financialInterests":"","formattedTitle":"Metacommunity structure of benthic foraminifera in Rio de Janeiro coastal lagoons","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe debate about changing in the communities along environmental gradients has permeated the Ecology of Communities since its origin (Barone et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). That is represented by the conflict between the point of view of Clements (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1916\u003c/span\u003e) and Gleason (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1926\u003c/span\u003e) on how species respond to environmental gradients and their consequences for community structure. Nowadays, new forms of community structuring have been described (Diamond \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Patterson \u0026amp; Atmar \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), each related to different potentially involved ecological processes (Leibold and Mikkelson \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Presley et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which represent different relationships of communities with the characteristics of the environments in which they operate.\u003c/p\u003e \u003cp\u003eThe concept of metacommunities was developed from a perspective of regional communities, and it is defined as the \u0026ldquo;set of local communities linked by the dispersal of multiple potentially interactive species\u0026rdquo; (Leibold et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Holyoak et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Based on this concept, we seek to understand the distribution of organisms along existing environmental gradients in order to know how communities that make up a metacommunity are structured in space (Presley et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePresley et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) proposed an analytical framework to distinguish between different types of metacommunity structures by analyzing three key properties (Elements of Metacommunity Structure \u0026ndash; EMS): coherence, species turnover and boundary clumping, as defined by Leibold \u0026amp; Mikkelson (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This approach has been increasingly used to investigate the spatial structuring of terrestrial vertebrate communities (Presley et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Presley et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, L\u0026oacute;pez-Gonz\u0026aacute;lez et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, de la Sancha et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Cisneros et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), freshwater and estuarine organisms (Henriques-Silva et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Fernandes et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Heino et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003ea, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003eb, Castillo-Escriv\u0026agrave; et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Alves et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and plants (Barone et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Shevtsov et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) along environmental gradients, being able to detect discontinuities and structures caused by environmental heterogeneity as in transitional environments.\u003c/p\u003e \u003cp\u003eCoastal lagoons are among the most biologically productive environments of our planet and harbour a rich and unique biodiversity (Esteves et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Whitfield et al. 2008). Tropical coastal lagoons are one the most threatened ecosystems on Earth due to multiple disturbances, including habitat alterations (Bulleri \u0026amp; Chapman 2010), climate change (Anthony et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Clausen \u0026amp; Clausen 2014), and various anthropogenic perturbations (McKinney et al. 2010). Therefore, for evaluating the ecological status of those coastal environments, it is necessary to continuously assess the quality of the sediments and the ecological responses of living benthic organisms to the presence of pollutants that cause high environmental stress (Borja et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Biological monitoring enables the detection of unforeseen impacts that are more directly related to ecosystem health than the actual geochemical data.\u003c/p\u003e \u003cp\u003eIn this context, benthic foraminifera (BF) are often used to monitor the health of marine and coastal ecosystems because they are abundant in the sediment, have a short life cycle and respond sensitively to environmental changes (Murray, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Recent studies have focused on the effects of anthropogenic disturbance of BF communities in urban areas in Brazil (Belart et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Laut et al., 2021; Raposo et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nunes et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and as well as in a range of other coastal environments globally (such as Alves Martins et al., 2015; Hohenegger et al., 2018; Armynot du Ch\u0026acirc;telet et al., 2018; Bouchet et al., 2021). Over time, natural or human-induced factors can significantly impact the ecological gradient and, subsequently, the structure of benthic assemblages. These impacts compromise the ecological integrity of coastal ecosystems, leading to changes in species diversity, abundance, and functional roles. Therefore, understanding the BF communities and the key driver of this structure becomes increasingly important in the face of a changing environment.\u003c/p\u003e \u003cp\u003eThis study aims to: 1) elucidate the spatial distribution of living benthic foraminifera along pronounced salinity gradients established in the coastal lagoons of Rio de Janeiro; and 2) evaluate the underlying processes that shape this metacommunity structure. By doing so, it seeks to provide valuable insights into how salinity-tolerant species respond to environmental changes.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003eStudy Area\u003c/p\u003e\n\u003cp\u003eThe Rio de Janeiro state presents a unique set of lagoon systems, most of them along the east coast (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). These systems are confined to a narrow coastal plain approximately 120 km long and 10 km wide (Ekau and Knoppers \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). The lagoons are delimited on the continent by a continuous mountainous relief (Serra do Mar) and protected from the sea by a sandy bar that gives them a similar typology. The essential differences are related to the climate of the region, which varies from humid tropical to semi-arid, and the intensity of anthropogenic impacts on their catchment basins (Knoppers and Kjerve 1999). For this study were selected the data of BF from five Rio de Janeiro lagoons that represent a latitudinal and climate gradient.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Itaipu Lagoon \u0026ndash; located between latitudes 22\u0026deg;57ʹ S to 22\u0026deg;58ʹ S and longitudes 043\u0026deg;01ʹW to 043\u0026deg;03ʹW in Niter\u0026oacute;i city, Rio de Janeiro state. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The lagoon covers an area of 1.2 km\u003csup\u003e2\u003c/sup\u003e and has a water depth between 0.2 and 2.0 m. The climate is warm and humid with a rainy season in summer (December to March), dry season in winter (June to September) and an average rainfall between 1,000 and 1,500 mm/year (Barbi\u0026eacute;re and Coe-Neto \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). Itaipu Lagoon is connected to the Atlantic by the Tibau Channel which is an artificial channel that provides an open tidal inlet (Salvador et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). The lagoon is affected by a microtide effect that has an average height of 0.71 m and the width can increase up to 10 m during the tide of syzygy (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003e\u0026bull; Maric\u0026aacute;-Guarapina Lagoon System (MGLS) \u0026ndash; located between latitudes 22\u0026deg;52\u0026rsquo; to 23\u0026deg;00\u0026rsquo; S and longitudes 43\u0026deg;00\u0026rsquo; to 42\u0026deg;45\u0026rsquo; W) in Maric\u0026aacute; city, Rio de Janeiro. The MGLS has a circular shape and is inserted into a kind of deformed rocky amphitheatre with its boundaries determined by the Atlantic Ocean and the Itacoatiara and Negra hills (Perrin, \u003cspan class=\"CitationRef\"\u003e1984\u003c/span\u003e). It is composed by four lagoons comprising a total area of 43.2 km\u003csup\u003e2\u003c/sup\u003e, which are currently distributed in: Maric\u0026aacute; (20.5 km\u003csup\u003e2\u003c/sup\u003e), Barra (11.7 km\u003csup\u003e2\u003c/sup\u003e), Padre (1.6 km\u003csup\u003e2\u003c/sup\u003e) and Guarapina (9.4 km\u003csup\u003e2\u003c/sup\u003e) which is connected with the ocean by Ponta Negra Channel (Oliveira et al., 1955). The drainage basin consists of three main sub-basins: Vig\u0026aacute;rio River Basin; Ubatiba River, which mouth is located in Maric\u0026aacute; Lagoon and Caranguejo River Basin which mouth is located in Guarapina Lagoon (Comit\u0026ecirc; Gestor da Regi\u0026atilde;o Hidrogr\u0026aacute;fica da Ba\u0026iacute;a de Guanabara e dos Sistemas Lagunares de Maric\u0026aacute; e Jacarepagu\u0026aacute;, 2006). Barbiere (1985) described the bathymetry of the MGLS: Maric\u0026aacute; Lagoon has a maximum depth of 2.0 m and a mean depth of 1.0 m; Barra and Padre lagoons represent the shallower part of this system, since 70% of their areas have depths of \u0026asymp;\u0026thinsp;0.5 m; Guarapina is the deepest lagoon of this system, with depths ranging from 2.0 to 5.0 m. The climate of the MGLS region is tropical humid to semi humid, with tropical air masses from oceanic and continental origins Barroso-Vanac\u0026ocirc;r et al., \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). It is characterized by the mean annual temperature of \u0026asymp;\u0026thinsp;23\u0026deg;C and the precipitation between \u0026asymp;\u0026thinsp;1100 and \u0026asymp;\u0026thinsp;1500 mm/year (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n\u003cp\u003e\u0026bull; Saquarema Lagoon System (SLS) - Is located between latitudes 22\u0026deg;55\u0026rsquo; to 22\u0026deg;56\u0026rsquo;S and longitudes 42\u0026deg;35\u0026rsquo; to 42\u0026deg;29\u0026rsquo;W in Saquarema city, Rio de Janeiro. It has an area of 21.2 km\u003csup\u003e2\u003c/sup\u003e, which extends approximately 11.8 km along the coast with an average depth of less than 2.0 m (Belart et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dias et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). This system is composed of four large connected lagoons (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e): Urussanga (12.6 km\u003csup\u003e2\u003c/sup\u003e), Jardim (2 km\u003csup\u003e2\u003c/sup\u003e), Boqueir\u0026atilde;o (0.6 km\u003csup\u003e2\u003c/sup\u003e) and Saquarema (6 km\u003csup\u003e2\u003c/sup\u003e). The climate in SLS is sub\u0026ndash;humid, with prolonged periods of drought, and high temperatures (Carmouze \u0026amp; Vasconcelos, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). The climate in SLS is also highly influenced by Mato Grosso Hills with sub-humid conditions and prolonged periods of drought (Belart et al. 2017). The Mato Grosso Hills effects primarily the rivers that discharge into the Urussanga Lagoon (Mato Grosso, Tingui, and the Jundi\u0026aacute; rivers) (Carmouze \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). This complex ecosystem is connected to the Atlantic Ocean through an artificial channel called Barra Franca channel located in Saquarema lagoon (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003e\u0026bull; Araruama Lagoon \u0026ndash; located between longitudes 42\u0026deg;00\u0026prime;W to 42\u0026deg;25\u0026prime;W and latitudes 22\u0026deg;49\u0026prime; to 22\u0026deg;57\u0026prime;S delimited by five cities, Araruama, Arraial do Cabo, Cabo Frio, S\u0026atilde;o Pedro d\u0026rsquo;Aldeia and Iguaba Grande in Rio de Janeiro (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). This lagoon comprises approximately 200 km\u003csup\u003e2\u003c/sup\u003e (Andr\u0026eacute; et al., \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e) and presents salinity ranges from 35 to 56% throughout the year, making this the largest permanent hypersaline lagoon in the world (Coutinho et al., 1999). The local climate is classified as an equatorial desert, according to Kottek et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The area displays annual temperatures around 25\u0026deg;C and a rainfall rate of about 1500 mm/year, reaching the highest values between November and March. The average annual rainfall is less than the average annual evaporation, which promoted high salinity values (Dias and Kjerfve, \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The lagoon has only one main fluvial channel, while some intermittent tributaries are observed during the rainy season (Barroso, 1987; Kjerfve et al., 1996). The average depth of the lagoon is 2.5 m, but shallow areas ranging from 0.5 to 1.5 m and depth up to 17 m are also present (Souza et al., 2003). The Itajuru Channel is the lagoon\u0026apos;s only communication with the ocean, located in the eastern portion of Araruama Lagoon and crossing the urban perimeter of Cabo Frio and S\u0026atilde;o Pedro d\u0026rsquo;Aldeia. This channel is very narrow (5.5 km length x 180 m width x 3 m depth) and does not allow for efficient water exchanges with the ocean, leading to a long Araruama Lagoon water residence time of around 84 days (Primo and Bizerril, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Valentini et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026bull; Vermelha Lagoon - located in the Massambaba Environmental Protection Area (22˚55\u0026rsquo;S to 42˚25\u0026rsquo;W) in Araruama city, Rio de Janeiro. It has 4.3 km in length, 10.88 km perimeter, 0.75 km maximum width and covers an area of ca. 2.5 km\u003csup\u003e2\u003c/sup\u003e with the maximum depth of 2 m. The region presents relatively low rainfall (854 mm/year), high annual evaporation rates (between 1,200 and 1,400 mm), average temperature of 23˚C. The climate is classified as an equatorial savanna (Aw\u0026mdash;precipitation less than 60 mm) in accordance with Kottek et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) with annual temperatures around 25˚C. Vermelha Lagoon is not receiving freshwater directly from rivers and streams. This ecosystem is connected to the Araruama Lagoon through an artificial channel inside the salt pan area (Laut et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Vermelha Lagoon is the most hypersaline coastal lagoon in Brazil (recording salinities of about 60 on average; maximum 120) and its name is related to the huge proliferation of microbial mats and the purplish bacteria that give a reddish appearance to bottom sediment (Silva e Silva et al., \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eSediment and Water Sampling Method\u003c/p\u003e\n\u003cp\u003eThe present study used the database of the Laborat\u0026oacute;rio de Micropaleontologia da UNIRIO \u0026ndash; LABMICRO from which 116 samples were selected. They were collected through the Rio de Janeiro coastal lagoons and originally studied by Raposo et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Itaipu lagoon, Laut et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Marica-Guarapina Lagoonal System, Belart et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Saquarema lagoon, Laut et al. (2019) in Vermelha lagoon and Laut et al. (2024) in Araruama lagoon (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To investigate the factors that drive the distribution of benthic foraminifera metacommunities, both physicochemical data (salinity (Sal), temperature (T), dissolved oxygen (DO), and pH) and sedimentary data (total organic carbon (TOC), total sulphur (TS), carbohydrates (CHO), lipids (LIP), and proteins (PTN)) were selected for analysis.\u003c/p\u003e\n\u003cp\u003eForaminiferal Analysis\u003c/p\u003e\n\u003cp\u003eThe sampling, processing and screening methodology followed a standardized methodology proposed by Sch\u0026ouml;nfeld et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) and it happened as follows: The first upper centimetre of sediment (50 ml) was recovered in triplicate and stained with Rose Bengal plus alcohol solution to stain the living specimens and washed over sieves with mesh openings of 500 and 63\u0026micro;m. The residual fraction in each sieve was dried at 50\u0026deg;C, and the foraminiferal specimens were concentrated by flotation in trichloroethylene (Martins et al. 2015a). All living foraminiferal tests were picked, identified and counted under stereoscopic microscope at 80x magnification. The number of specimens found in the three replicates was then averaged. The generic taxonomical classification of Loeblich and Tappan (\u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e), and specific concepts of Boltovskoy et al. (\u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Raposo et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Belart et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) were followed. After identification, the names of species were checked using the platform available online, WoRMS (World Register of Marine Species; Hayward et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMetacommunity Analysis\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eMetacommunity structure\u003c/h2\u003e\n \u003cp\u003eTo identify the response of the species to unmeasured environmental characteristics (=\u0026thinsp;latent environmental gradient) affecting community structuring, we applied the elements of metacommunity structure (EMS) approach (Leibold and Mikkelson, \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). This approach uses a Correspondence Analysis (CA) to ordinate the communities by reciprocal averaging, maximizing the proximity of species with similar distributions, as well as the proximity of sites with similar species compositions. To identify these proximities, we considered the abundances of the species on the EMS analyses, discarding those species represented by singletons (one individual on the entire sample), as these species don\u0026apos;t provide much information regarding metacommunity structure (Presley et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). We applied the EMS framework (Presley et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) with 1,000 simulated null matrices generated by maintaining the species richness of the sites constant and filling species ranges based on their marginal probabilities (\u0026ldquo;r1\u0026rdquo; method of null model randomization). This protocol allowed us to discriminate among the metacommunity structures presented by Leibold \u0026amp; Mikkelson (\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), and we investigated the metacommunity structure existent on the latent environmental gradients represented by only the first axis of the CA.\u003c/p\u003e\n \u003cp\u003eThree interrelated metacommunity metrics were used to verify the structure: coherence, turnover, and boundary clumping. Their values and statistical significance determine the metacommunity\u0026apos;s type within the 14 categories defined by the EMS framework (Presley et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Different combinations of metric values indicate different underlying processes shaping the metacommunity structure.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003ePredictor variables\u003c/h2\u003e\n \u003cp\u003eWe created spatial variables to represent spatially structured processes operating in the lagoons (Peres-Neto and Legendre, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), in broader scales (e.g., unmeasured environmental factors or the different hydrodynamic regimes of the Lagoon\u0026rsquo; compartments). To obtain these variables we built Moran\u0026rsquo;s eigenvector maps (MEMs) from a Principal Coordinates of Neighbour Matrices (PCNM) based on a network connecting all the sampling stations following the Gabriel graph criterion (Dray et al., 2006). Only those eigenvectors which represented significant spatial structure (Moran\u0026rsquo;s I\u0026thinsp;\u0026gt;\u0026thinsp;0.1) were selected to be used in posterior analyses (Diniz-Filho et al., 2012), thus resulting in 25 MEMs.\u003c/p\u003e\n \u003cp\u003eTo summarize the environmental variation existing in the lagoons and reduce the number of predictors we performed a Principal Component Analysis (PCA) (Legendre and Legendre, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) in R environment (R Core Team, 2017) using the four hydrological parameters (SAL, T, DO and pH) and five sedimentological descriptors of organic enrichment (TOC, TS CHO, LIP, PTN). Whenever necessary, we log-transformed environmental variables to closer them to a normal distribution and/or to improve linearity in relationships. We applied the Kaiser-Guttman criterion (Legendre and Legendre, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) to select the number of PCA axes which would be used in further statistical analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eWe applied a two-stage approach to identify the effects of spatial structure, hydrological and organic matter determinants over foraminifera community structuring at Rio de Janeiro lagoons. First, we calculated Pearson correlation coefficients between the scores obtained for the two axes of the EMS analysis and each of the predictor variables (the spatial structure represented by the MEMs and the environmental gradients represented by the axes of the PCA) to identify the variables that could explain the latent environmental gradients. Only the positive MEMs were used in this analysis as we were interested in the broad-scale spatial structure affecting the metacommunity.\u003c/p\u003e\n \u003cp\u003eOn the second stage, we used variation partitioning (Borcard et al., 1992) to quantify the relative contribution of spatial, hydrological and organic matter components on the BF metacommunity. We applied a partial Redundancy Analysis (RDA) (Legendre and Legendre, \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e) to decompose the variation in the relative abundance and distribution of foraminifera explained by single and shared fractions of three components: spatial, hydrological, and organic matter. Before executing this analysis, we log-transformed the abundance of foraminifera species and some environmental variables. In order to avoid model overfitting due to the high number of variables, we selected the environmental and spatial variables applying a forward selection procedure (Blanchet et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). A variable would be selected if it explained variance was significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and the adjusted coefficient of determination (R\u0026sup2;) accumulated by the selected variables did not exceed the adjusted R\u0026sup2; calculated using all explanatory variables.\u003c/p\u003e\n \u003cp\u003eWe executed all analyses on R version 3.4.3 (R Core Team, 2017). The package \u0026lsquo;metacom\u0026rsquo; (Dallas, 2015) was used to perform the EMS analysis and to obtain the scores of the first two axes of the CA, which represents the latent environmental gradients. The packages \u0026lsquo;spdep\u0026rsquo; (Bivand et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) and \u0026lsquo;adespatial\u0026rsquo; (Dray et al., 2018) were used to obtain MEMs. Principal component and variation partitioning analyses (PCA and RDA) were performed with the package \u0026lsquo;vegan\u0026rsquo; (Oksanen et al., 2020; see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/package=vegan\u003c/span\u003e\u003c/span\u003e) The species represented by singletons (that is, those that only appear once) were discarded from analyses.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 537,416 specimens belonging to 65 species were selected for analyses. The species \u003cem\u003eAmmonia rolshauseni\u003c/em\u003e, \u003cem\u003eBulimina marginata\u003c/em\u003e, \u003cem\u003eEponides repandus\u003c/em\u003e, \u003cem\u003eGlobocassidulina subglobosa\u003c/em\u003e, \u003cem\u003eNonionella auris\u003c/em\u003e, \u003cem\u003ePyrgo comata\u003c/em\u003e, \u003cem\u003eQuinqueloculina polygona\u003c/em\u003e, \u003cem\u003eRosalina floridana\u003c/em\u003e, \u003cem\u003eRosalina williamsoni\u003c/em\u003e and \u003cem\u003eValvulineria candeiana\u003c/em\u003e represented singletons, and were discarded from analyses. The absolute abundance ranged from more than 1,000 individuals (\u003cem\u003eAmmonia parkinsoniana\u003c/em\u003e, \u003cem\u003eAmmonia tepida\u003c/em\u003e, \u003cem\u003eBolivina variabilis\u003c/em\u003e, \u003cem\u003eElphidium excavatum\u003c/em\u003e, \u003cem\u003eMiliolinella subrotunda\u003c/em\u003e, \u003cem\u003eQuinqueloculina laevigata\u003c/em\u003e, \u003cem\u003eQuinqueloculina miletti\u003c/em\u003e, \u003cem\u003eQuinqueloculina poeyana\u003c/em\u003e, \u003cem\u003eQuinqueloculina seminulum\u003c/em\u003e, \u003cem\u003eQuinqueloculina vulgaris\u003c/em\u003e, \u003cem\u003eRosalina bradyi\u003c/em\u003e and \u003cem\u003eTriloculina oblonga\u003c/em\u003e) to less than twenty individuals (\u003cem\u003eAdelosina longirostra\u003c/em\u003e, \u003cem\u003eAlliatinella differens\u003c/em\u003e, \u003cem\u003eAmmobaculites dilatatus\u003c/em\u003e, \u003cem\u003eAmmobaculites exiguus\u003c/em\u003e, \u003cem\u003eAsterotrochammina camposi\u003c/em\u003e, \u003cem\u003eBolivina doniezi\u003c/em\u003e, \u003cem\u003eBulimina patagonica\u003c/em\u003e, \u003cem\u003eCibicidoides variabilis\u003c/em\u003e, \u003cem\u003eCornuspira involvens\u003c/em\u003e, \u003cem\u003eCornuspira involvens\u003c/em\u003e, \u003cem\u003eCribroelphidium poeyanum\u003c/em\u003e, \u003cem\u003eEntizia macrescens\u003c/em\u003e, \u003cem\u003eFursenkoina pontoni\u003c/em\u003e, \u003cem\u003eHanzawaia concentrica\u003c/em\u003e, \u003cem\u003eHaplophragmoides wilberti\u003c/em\u003e, \u003cem\u003eLeptohalysis scotti\u003c/em\u003e, \u003cem\u003eMiliolinella fichteliana\u003c/em\u003e, \u003cem\u003ePseudononion japonicum\u003c/em\u003e, \u003cem\u003eNonionella opima\u003c/em\u003e, \u003cem\u003ePyrgo oblonga, Rosalina globularis\u003c/em\u003e, \u003cem\u003eRosalina rugosa\u003c/em\u003e, \u003cem\u003eTextularia earlandi\u003c/em\u003e, \u003cem\u003eTrochammina salsa\u003c/em\u003e and \u003cem\u003eWarrenita palustris\u003c/em\u003e). \u003cem\u003eAmmonia tepida\u003c/em\u003e, \u003cem\u003eQ. seminulum\u003c/em\u003e and \u003cem\u003eM. subrotunda\u003c/em\u003e were the most constant species along the lagoons, being detected in 92 (79%), 71 (61%) and 47 (40%) stations, respectively.\u003c/p\u003e \u003cp\u003eThe first two PCA-axes explained a large fraction (61.19%) of the environmental variability found on the lagoons, with the first axis representing more than half of this percentage (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PCA-axis 1 was related to sediment variables such as lower values of TS, PTN and COT and lower values of pH. PCA-axis 2 was negatively related to salinity and positively correlated with temperature, pH and DO. Marica Lagoon had the highest pH and temperature values and the lowest salinity values. Araruama, Itaipu, and Vermelha Lagoons exhibited the opposite pattern along this axis. Saquarema Lagoon's distribution was more closely associated with higher concentrations of biopolymer (PTN, CHO, and LIP), TOC, and TS values.\u003c/p\u003e \u003cp\u003ePCA revealed that Marica Lagoon had the highest values of pH and temperature and lowest values of salinity. Araruama, Itaipu and Vermelha exhibited opposite pattern Saquarema Lagoon's distribution was more closely with higher concentrations of biopolimers (PTN, CHO and LIP), TOC and TS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrincipal Correspondence Analysis results. PTN \u0026ndash; Protein, CHO \u0026ndash; Carbohydrates, LIP \u0026ndash; Lipids, DO \u0026ndash; Dissolved oxygen, Sal \u0026ndash; Salinity, TOC \u0026ndash; total organic carbon, TS \u0026ndash; total sulfur.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePC1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePC2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePC6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePC7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePC8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePC9\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.5838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.1275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.6018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.1083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.2724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.2924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.0356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.2204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.2856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.4797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.4892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.4606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.1683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.1576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.7211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.4781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.0586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.2313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.6698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.2162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.3219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.3969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.6652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.3015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.0606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.6761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.2148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.3191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCum %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.9869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe observed that the first axis on the EMS analysis presented a positive and significant coherence (coh value and p), a negative non-significative turnover (tur value and p), and a significant boundary clumping\u0026thinsp;\u0026gt;\u0026thinsp;1 (index and p). This way, EMS-axis indicates that this metacommunity has a quasi-nested clumped structure. Based on the results of the EMS analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the sites were grouped in four distinct metacommunities assemblages, which were organized depending on the relationship between the species that composes each assemblage. Group 1 was dominated by agglutinated foraminifera such as \u003cem\u003eArenoparrella mexicana\u003c/em\u003e and \u003cem\u003eParatrochammina guaratibaensis\u003c/em\u003e. This group was restricted to Maric\u0026aacute; Lagoon and in the inner sector of the Saquarema Lagoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Group 2 was considered the open lagoonal assemblage and was dominated by calcareous foraminifera, mostly hyaline (\u003cem\u003eAmmonia tepida and Elphidium excavatum\u003c/em\u003e) and some porcelaneous species. This group occurred in Itaipu Lagoon and most parts of the Saquarema Lagoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Group 3 was dominated by both porcelaneous and hyaline species and was almost exclusively found in the Araruama Lagoon. The Group 4 can be considered as a subset of the Group 3 and was composed by a few species, mostly porcelaneous foraminifera such as \u003cem\u003eQuinqueloculina seminulum\u003c/em\u003e and \u003cem\u003eMiliolinella subrotunda\u003c/em\u003e, and total absence of agglutinated species. This group was observed only in Vermelha lagoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on a correlation analysis (CA), it was possible to see that the community was structured according to a strong positive relationship with PTN, pH, temperature and TS and a negative relationship with salinity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Parameters like CHO, LIP, DO and TOC did not show significant correlation in this analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Correlation Analysis between EMS and PCA-axis 1. PTN \u0026ndash; Protein, CHO \u0026ndash; Carbohydrates, LIP \u0026ndash; Lipids, DO \u0026ndash; Dissolved oxygen, Sal \u0026ndash; Salinity, TOC \u0026ndash; total organic carbon, TS \u0026ndash; total sulfur.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCHO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2733\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.324E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.02E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.57E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.81E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.41E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.08E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.71E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.48E-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRDA analysis showed that variation in community structure could be explained by the ensemble of variables used in the present study, as it constrained 61% of the variance on foraminifera abundance data (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Variance partitioning indicated that a major part of the constrained variation was due to the pure space fraction (28.7%) and the shared fraction between space and environment components (22.4%), followed by the shared fraction between space, hydrological and OM components (7.5%). The variance constrained at all other fractions, whether single or shared, was negligible (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe used EMS combined with a variation partitioning technique to identify the relationships between environmental, spatial and organic matter predictors structuring benthic foraminifera metacommunities in coastal lagoon systems. We found Q-nested species composition with clumped species loss as the EMS pattern in this system. We found a higher importance of the shared fraction between environment and space influencing benthic foraminifera metacommunity. Therefore, in this system, benthic communities along coastal lagoons were generally subsets of a large pool of species (i.e., nested or Q-nested) with space and salinity most strongly associated with this pattern. According to Presley et al (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) the metacommunity approach aims to identify significant patterns of coherence of species substitution and/or clustering of distribution boundaries. In the present study, the distribution pattern is directly related to water renewal standards. In this way, the EMS used the first axis of the PCA to classify the metacommunities according to a positive relation with PTN, pH, temperature and TS and strong negative relation with salinity values, organizing the sites and assemblages based on this gradient.\u003c/p\u003e \u003cp\u003eVermelha lagoon stations were all grouped together with the highest values of salinity and lowest values of TS associated with the dominance of porcelaneous foraminifera (miliolids). Laut et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) report that the dominance of porcelaneous species (mainly \u003cem\u003eQuinqueloculina seminulum\u003c/em\u003e) in the Vermelha lagoon is related to hypersalinity and high primary productivity in the region. The species distribution along Maric\u0026aacute;-Guarapina Lagoon System was influenced by the saline gradient, being the most oligohaline environment among those studied with greater dominance of agglutinated tests such as \u003cem\u003eTrochammina inflata\u003c/em\u003e. Araruama, Saquarema and Itaipu lagoons were characterized as eurialine environments, although they have some hypersaline zones, most of them have conditions close to those found in the ocean (Laut et al. 2021; Belart et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raposo et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). \u003cem\u003eAmmonia\u003c/em\u003e spp. and \u003cem\u003eElphidium excavatum\u003c/em\u003e were commonly recognised in transitional environments and are tolerant to a wider range of environmental parameters (Martins et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Laut et al. 2016; Alves Martins et al. 2015). These association of species were also found in the strongly confined areas, as for instance in Italian lagoons: Orbetello lagoon (Tuscany, coast of Tyrrhenian Sea), Lake Varano (Southern Italy), and the Santa Gilla lagoon (Cagliari) (Frontalini et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Among the studied lagoons, this association of species was dominant in Araruama, Itaipu and Saquarema. The occurrence of these species in coastal environments is favoured by reduced competition under high levels of environmental stress (Murray \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). They can be characterised as eurytopic with a wide geographical distribution and/or adapt to all environmental conditions.\u003c/p\u003e \u003cp\u003eThe results obtained through the EMS approach corroborate the interpretations of previous studies and represent an advance in the knowledge about the foraminifera of transitional environments. They allowed us to verify a distribution pattern of the communities with ecological succession responding primarily to salinity and pH (marine influence gradient). That is, brackish environments with a higher representation of agglutinated species (Group 1 of EMS), open lagoon environments dominated by hyaline calcareous (Group 2 of EMS) and hypersaline environments totally dominated by milliolids, mainly of the genus \u003cem\u003eQuinqueloculina\u003c/em\u003e (Groups 3 and 4). The recognition of these patterns had fundamental importance to reduce the ecological shortfalls that this group of organisms still has.\u003c/p\u003e \u003cp\u003eIt was possible to define that the main variables acting on the structure of communities were PTN, pH, temperature, salinity and TS, that is, the influence of parameters of the water and sediment. The biopolymers content in marine and estuarine environments is used for the characterization and interpretation of the origin of organic matter accumulated in sediments (Silva et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cotano and Villate, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fabiano and Danovaro, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Danovaro et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Belart et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Raposo et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Laut et al. (2021) were the first to relate foraminifera with biopolymers in Brazil, and generally had a consensus that porcelaneous foraminifera (miliolids) preferred protein-enriched environments. While regions rich in LIP and CHO had the lowest species richness with dominance of \u003cem\u003eAmmonia tepida\u003c/em\u003e and \u003cem\u003eElphidium excavatum\u003c/em\u003e. For instance, Cotano \u0026amp; Villate (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) identify that the organic matter provided by domestic liquid effluents and other anthropogenic activities could have high concentrations of PTN and LIP, whereas organic matter from a phytoplanktonic origin and vegetal detritus may have high CHO content. This pattern was the same described by Belart et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in Saquarema Lagoon System, where the highest values of CHO were related to a peat bog area and the highest values of PTN and LIP to the most urbanized region of lagoon. Raposo et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Laut et al. (2021) founded the same in Itaipu lagoon and Maric\u0026aacute;-Guarapina lagoon, where the highest values of PTN were found in a region heavily impacted by domestic sewage.\u003c/p\u003e \u003cp\u003eThe RDA revealed that, despite a very strong spatial correlation, the patterns of species distribution also showed a correlation with environmental variables, mainly salinity. This result aligns with those of Armynot et al. (2018), who found that foraminiferal communities exhibit similar responses in coastal environments from France. Based on RDA, the water parameters were more important than the organic matter availability for the foraminifera distribution in Rio de Janeiro lagoons. This result is different from all those published in Rio de Janeiro lagoons, where the organic matter compounds were the main factors for the benthic foraminifera distribution (Laut et al. 2021, Belart et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Raposo et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results showed that organic matter values are high in all studied lagoon environments, regardless of whether the source is natural or anthropogenic. It seems that once the basic requirements for life are met, the organic matter compounds play a secondary role in the distribution of species among the studied lagoons. This contrast with the pattern founded by Bouchet et al. (2021) in the Mediterranean environments with much lower amounts of organic matter. In that case, they realized that when the amount of organic matter exceeds the species tolerance limit, sensitive species naturally decline providing perfect conditions for the establishment of tolerant and opportunistic ones, this pattern was also observed by Elliott and Quintino (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Munari and Mistri (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The lack of information about the real tolerance limit of foraminifera species towards the levels of organic matter in tropical environments highlights an ecological problem defined as Hutchinsonian shortfall (Cardoso et al \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In our study area, the EMS analysis revealed that TS had a greater influence than TOC on the site ordination. This suggests that under specific local conditions, such as high temperatures and longer residence time of lagoon waters, TS becomes a more significant limiting factor for the abundance and richness of species.\u003c/p\u003e \u003cp\u003eStudies such as those by Belart et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in Saquarema lagoon, Laut et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Vermelha lagoon and Raposo et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in Cachoeira river estuary has been filling these gaps, but in a coast with more than 7,491 km and hundreds of transitional environments with the potential to harbor BF communities, the solution to define the species tolerance limit for Brazilian environments should perhaps be to use cultivation techniques in laboratory, such as the experiments by Frontalini et al. (2012 and 2018).\u003c/p\u003e \u003cp\u003eIn recent years progress has been also made in the development of biological indices which relate benthic foraminifera to diversity indices (Alve et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bouchet et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) or the amount and sensitivity of species to organic pollution (Barras et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dimiza et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alve et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jorissen et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, our findings, suggest that the structuring of the BF community of the studied lagoons is primarily driven by salinity, pH, temperature, PTN and TS, with TOC playing a lesser role. Therefore, we recommend that further studies be carried out to assess the relationship between these communities and organic matter compounds, applying indices that take more parameters into account. Additionally, investigating the tolerance of species to TS, which has proved to be decisive in structuring the proposed metacommunities, warrants further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study is pioneer in using EMS applied to foraminifera and was able to describe the patterns and factors that structure the composition and distribution of the species assemblages in the Rio de Janeiro lagoon systems. Our results indicated that benthic foraminifera communities follow Q-nested structure and are influenced by the shared influence of spatial and hydrological drivers.\u003c/p\u003e \u003cp\u003eThe BF metacommunities respond primarily to site-specific characteristics, which makes each studied lagoonal system unique. This highlights the importance of studies that identify bioindicator communities associated with specific environmental conditions and contribute to their inclusion in further biomonitoring programs customized to the target impact to be monitored.\u003c/p\u003e \u003cp\u003eWhile further studies are warranted, our findings offer a valuable contribution to understanding the ecological structure of benthic foraminifera communities along a pronounced gradient of marine and anthropogenic influence. Additionally, they provide insights into the debate regarding the ecology of metacommunities and its potential to elucidate processes operating at both local and regional scales. 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Relat\u0026oacute;rio/Projeto PEC- 1984 (Funda\u0026ccedil;\u0026atilde;o COPPETEC-COPPE/UFRJ).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"environmental gradient, community structure, marine bioindicators","lastPublishedDoi":"10.21203/rs.3.rs-3872884/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872884/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetacommunity theory addresses local interactions and regional processes, offering a powerful framework to comprehend the species composition of a region and the factors that shape its structure along environmental gradients. By incorporating spatial dynamics, the metacommunity analysis explores the relationships that govern the ecological communities at different spatial scales. The objective of this work is to describe the structure of a metacommunity of living foraminifera, to relate it to physical and chemical variables of water and sediment, and to identify the environmental characteristics associated to the assemblages. A total of 534,416 living foraminifera, belonging to 65 species, were collected at 106 stations across five tropical urban coastal lagoons along the coast of the Rio de Janeiro State (Brazil), subjected to a strong salinity gradient. The results of the Elements of Metacommunity Structure (EMS) analysis identified four distinct assemblages of living foraminifera across the lagoonal systems. These metacommunities fitted a quasi-nested pattern, with the total variation explained by a shared influence of environmental factors (primarily hydrological drivers associated with marine influence, such as salinity, pH, and temperature) and spatial predictors. Organic enrichment descriptors (TOC, TS, CHO, PTN, LIP) played a secondary role in the ordination of the sites. The findings of this work demonstrate the potential of the EMS approach as a valuable tool for establishing a baseline in environmental monitoring plans.\u003c/p\u003e","manuscriptTitle":"Metacommunity structure of benthic foraminifera in Rio de Janeiro coastal lagoons","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 10:36:02","doi":"10.21203/rs.3.rs-3872884/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-26T11:20:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-25T16:06:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2024-01-24T01:22:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-17T13:45:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2024-01-17T07:19:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b06b7da7-5b54-48ea-8fae-d691fa8a458b","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-28T16:02:35+00:00","versionOfRecord":{"articleIdentity":"rs-3872884","link":"https://doi.org/10.1007/s12237-024-01451-7","journal":{"identity":"estuaries-and-coasts","isVorOnly":false,"title":"Estuaries and Coasts"},"publishedOn":"2024-10-26 15:57:44","publishedOnDateReadable":"October 26th, 2024"},"versionCreatedAt":"2024-01-30 10:36:02","video":"","vorDoi":"10.1007/s12237-024-01451-7","vorDoiUrl":"https://doi.org/10.1007/s12237-024-01451-7","workflowStages":[]},"version":"v1","identity":"rs-3872884","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3872884","identity":"rs-3872884","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}