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Article

Benthic Mollusk Biodiversity Correlates with Polluted Sediment Conditions in a Shallow Subtropical Estuary

by
Rachael H. Stark
1,* and
Kevin B. Johnson
2,*
1
Department of Ocean Engineering and Marine Science, College of Engineering and Science, Florida Institute of Technology, Melbourne, FL 32901, USA
2
Department of Biological Sciences, College of Science and Mathematics, Tarleton State University, Stephenville, TX 76401, USA
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(1), 13; https://doi.org/10.3390/jmse13010013
Submission received: 14 November 2024 / Revised: 21 December 2024 / Accepted: 22 December 2024 / Published: 26 December 2024
(This article belongs to the Special Issue Estuarine and Coastal Ecosystems: Anthropogenic Degradation and the Impacts of Restoration Efforts)
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Abstract

:
To quantify the ecological impacts of organic sediments and environmental dredging, benthic mollusks were chosen as bioindicators of environmental change, measured as sediment organic content and associated parameters. Data on species richness, ecological diversity (which was measured as biodiversity), and abundances were collected alongside sediment and near-bottom water quality data before, during, and after environmental dredging. Organic sediment content was found to have an inverse logarithmic relationship with benthic mollusk biodiversity, species richness, and abundance. Post hoc analyses found that percent dissolved oxygen, which correlates with sediment organic content, was responsible for 29.31–34.12% of the benthic mollusk community variation. Sediments with lower organic content had higher biodiversity (organism densities up to 1 organism m−2), abundance (over 2.0 × 105 organisms m−2), and species richness (organism densities up to 4 organisms m−2). In comparison, sediments with higher organic content had low biodiversity (organism densities 0–1 organisms m−2), abundance (as low as 0 organisms m−2), and species richness (organism densities as low as 0 organisms m−2).

1. Introduction

Eutrophication is a major anthropogenic stressor afflicting global estuaries [1]. While some nutrients are in estuaries naturally due to geological weathering and upwelling, extreme nutrient loading is likely due to a combination of anthropogenic influences. Issues related to eutrophication are an increase in harmful algal blooms (HABs) and turbidity. These issues can lead to hypoxic conditions, fish kills, declining submerged aquatic vegetation, shifting to pelagic-dominated productivity, and biodiversity loss. These ecosystem effects can have significant economic impacts (e.g., tourism, fisheries, recreation, and real estate) [1]. An estuary facing these issues is the Indian River Lagoon (IRL, Florida), a shallow, polluted subtropical estuary with a low flushing rate [2]. Bloom events and anthropogenic influences in this estuary have led to the accumulation of fine-grained, organic-rich sediments on the bottom of the estuary, colloquially known as muck.
Muck is a primary repository for toxic metals, organic substances, and anthropogenic nutrients [3]. IRL muck comprises 10–30% organic matter, 60% silt and clay, and a high water content (about 75% by weight) [3]. It covers around 10% of the IRL, ranging from a few centimeters to several meters in thickness [4]. Muck aggravates the IRL’s habitat degradation by contributing additional nutrients for HABs, blocking sunlight, depleting oxygen, and smothering benthic organisms [3,5,6]. Turbidity, HABs, and hypoxia can lead to the loss of seagrass meadows and other important benthic habitats [7]. Habitat loss decreases biodiversity, species richness, and benthic population abundances. Hypoxic conditions alter community composition and may selectively eliminate sensitive species, thereby promoting the growth and recruitment of those with a higher tolerance for less ideal conditions [8].
Restoration projects for polluted and impaired estuaries may include flow redirection, point-source pollution reduction, or dredging to remove legacy loads of pollutants [9]. Environmental dredging is a remedial process attempting to remove contaminated sediments, improve water quality, and restore benthic habitats [10]. In the complex Indian River Lagoon System, environmental dredging for restoration has been carried out in several locations, including Crane Creek, the Eau Gallie River, Sykes Creek, and the Mims Boat Ramp canal.
Mollusks are diverse in estuaries and coastal habitats, second only to arthropods in metazoan taxonomic diversity [11,12,13], and arguably more reliably identified to lower taxonomic levels than other benthic groups [8]. Many benthic mollusks are relatively sessile, and even errant species tend to stay within a localized region. They have longer life cycles and are sensitive to environmental changes [13]. These characteristics, along with their known tolerance to low oxygen and sediment organic, may allow them to serve as effective bioindicators of environmental change, sediment health, and water quality [14,15,16,17,18]. In IRL benthic habitats, bivalves and gastropods are especially abundant [11]. Some common species of gastropods include Acteocina canaliculata (Say, 1826), Astyris lunata (Say, 1826), Haminoea succinea (Conrad, 1846), and Phrontis vibex (Say, 1822) [19]. Some common species of bivalves include Amygdalum papyrium (Conrad, 1846), Mulinia lateralis (Say, 1822), Parastarte triquetra (Conrad, 1846), and Mercenaria mercenaria (Linnaeus, 1758) [19].
Many mollusks have lower metabolic and oxygen consumption rates than other benthic invertebrates [20,21,22,23], helping them persist in polluted organic sediments. Bayne and Newell [20] compared mollusk oxygen consumption rates with those of other taxa based on trophic roles and found that mollusk rates were lower. This included grazers like the periwinkle snail Littorina littorea (Linnaeus, 1758) (oxygen consumption rate 2.052 mL hr−1); the suspension-feeding oyster Crassostrea virginica (Gmelin, 1791) (oxygen consumption rate 0.496 mL hr−1); and the predatory snail Nassarius reticulatus, which has since been reclassified as Tritia reticulata (Linnaeus, 1758), (oxygen consumption rate 1.63 mL hr−1). In contrast, Fox and Simmonds [21] found arthropod consumption rates were much higher, including the amphipod Gammarus marinus, which has since been reclassified as Marinogammarus marinus (Leach, 1816) (oxygen consumption rate 191–367 mL g−1 hr−1); the amphipod Gammarus locusta (Linnaeus, 1758) (oxygen consumption rate 188–207 mL g−1 hr−1); and the isopod Idotea neglecta (G. O. Sars, 1897) (oxygen consumption rate 125–321 mL g−1 hr−1). Salvato et al. [23] compared the effects of several environmental conditions on oxygen consumption by the decapod crustaceans Palaemon serratus (Pennant, 1777) (mean oxygen consumption rate 0.35 mL O2 g−1 wet tissue hr−1) and Panaeus monodon (Fabricius, 1798) (mean oxygen consumption rate 0.53 mL O2 g−1 wet tissue hr−1), and the prosobranch gastropods Trunculariopsis trunculus, which has since been reclassified as Hexaplex trunculus (Linnaeus, 1758) (mean oxygen consumption rate 0.04 mL O2 g−1 wet tissue hr−1), and Nassarius mutabilis, which has since been reclassified as Tritia mutabilis (Linnaeus, 1758) (mean oxygen consumption rate 0.07 mL O2 g−1 wet tissue hr−1). Pamatmat [22] also compared bivalves’ aerobic and anaerobic metabolic rates to those of benthic polychaetes and found that those of bivalves were lower. Lower mollusk oxygen consumption rates help them survive in more polluted conditions relative to other benthic taxa. Some mollusks can also withstand physiological stress caused by toxic H2S, often associated with muck sediments [14,24]. Theede et al. [25] found that Mytilus edulis can survive 25 days in water containing H2S and 35 days in water with 0.15 mL O2 L−1. To survive in hypoxic and anoxic environments, H2S tolerance is essential for benthic organisms [24,25].
Despite being otherwise well suited to polluted estuarine conditions [9,14,18], finely ciliated gills and other structures may be clogged by silt and clay particles, potentially suffocating benthic mollusks [23,26]. Pearson [26] found that benthic communities exposed to anthropogenically polluted effluents typically exhibit low diversity, are poorly organized, and are composed of small r-selected species. Both Pearson, and Rhoads and Young [26,27] suggest that, in some systems, this could be due to trophic amensalism, where deposit feeders exclude filter-feeding neighbors by disturbing and resuspending sediments, clogging the filter-feeding mechanisms of less tolerant competitors. Since muck is easily resuspended [28], smothering or suffocation significantly threatens benthic mollusks in degraded habitats. Some mollusks in the IRL persist in polluted conditions yet may remain stressed in organic sediments [28]. Moraitis et al. [18] stated that effective bioindicators, like the presence of benthic estuarine mollusks, are easily monitored and display the effects of environmental change.
This study addresses the following hypotheses:
(1)
There is an inverse relationship between ecological diversity (measured as biodiversity via the Shannon–Weiner Index), species richness, abundance of mollusks, and sediment percent organic content.
(2)
Removal of sediments with high concentrations of fine-grained organic matter by dredging will increase the biodiversity, species richness, and abundances of benthic mollusks.

2. Materials and Methods

2.1. Location and Seasons

To determine the impacts of organic sediments and dredging restoration on benthic mollusk communities, benthic grabs, sediment samples, and water quality data were collected before, during, and after dredging at muck sites-to-be-dredged (“Environmental Dredging Treatment” or EDT), as well as undredged control muck sites (“Environmental Dredging Control” or EDC). Seagrass sites adjacent to muck dredging and muck control sites were also sampled for comparison. Seagrass sites adjacent to EDT (dredged) sites are identified as “Environmental Dredging Treatment—Seagrass” (EDTS). Seagrass sites adjacent to EDC (undredged) muck sites are identified as “Environmental Dredging Control—Seagrass” (EDCS) (Figure 1). Data were collected quarterly from March 2017 until October 2020 from 14 stations in Mims, FL (Figure 1). All 14 stations are located within the Indian River Lagoon (IRL) proper (Figure 1). All stations were sampled every quarter (season) from 2017 to 2020, except the control stations in the winter of 2017 and summer of 2019 and all stations in the winter of 2020.

2.2. Sample Collection

Water quality was recorded at each sampling station and included temperature, salinity, percent saturation of dissolved oxygen (DO), mg L−1 DO, and turbidity. The Secchi depth (0.5–1 m deep) and total water column depth (0.5–5 m deep) were also determined for each sampling station. A Yellow Springs Instrument (YSI) Multimeter was calibrated before sampling against factory standards and used to measure water quality at the Secchi depth and near the bottom.
On a given date, four sediment grabs were collected at each station (Figure 1B) with a Petite Ponar grab (benthic sampling area of 225 cm2), with one grab for sediment analysis and three grabs for the faunal survey [2,29,30]. Total sediment volumes were recorded to calculate organism densities and penetration depth of the Petite Ponar Grab. Benthic fauna grabs were sieved through a 0.5 mm sieve and placed in labeled plastic bags. One unsieved grab from each station was bagged for sediment characterization (% water content, % organic content, and % silt/clay content). Samples were then transported to the laboratory in a cooler. Fauna samples were frozen pending microscope sorting, and sediment samples were refrigerated while they awaited processing.

2.3. Laboratory Analysis

Fauna samples were thawed and sorted via light stereomicroscopy (8–35× magnification). Higher volume samples were split 1–4 times to reduce the amount of time required to process a sample [2,29,30] and so that the data may be compared with other studies in the literature. It is common in studies of benthic infauna to process lesser aliquots or subsamples and then extrapolate the numbers to densities per square meter [31,32,33,34,35,36]. This is an accepted estimate of the densities of most found organisms but may fail to document or overestimate the abundance of uncommon species [32,33,34] and does not address whether organisms were aggregated on scales < 1 square meter [34,35,36].
Fauna were identified to the lowest possible taxonomic level using taxonomic identification guides, and both researchers corroborated faunal identifications. Only whole organisms, apparently alive at collection, were counted. After processing, each sample was preserved in a 4% formaldehyde solution for long-term storage. Counts were extrapolated to whole grabs and field densities to reduce the amount of time required to process faunal samples [2,29,30].
Ten grams of sample was used to determine the silt/clay content of the sediments with visibly higher muck content [37]. For sandier samples, more sediment was required for accuracy, and 30 g of sediment was used to determine silt/clay content. Samples were then sieved through 63-micron mesh to separate silt/clay from larger sediment particles. Both smaller sample components were dried by baking at 105 degrees Celsius for 24 h and then re-weighed, with the relative proportions yielding the % silt/clay content [37]. In a similar process, the % water content of the sediments was determined by weighing un-sieved sediments before and after baking them at 105 degrees Celsius for 24 h [37]. Sediment organic content was determined with the mass loss-on-ignition (LOI) method, where sediment is dried, ground, and weighed before and after baking at 550 °C for 4 h [38,39].

2.4. Data Analysis

Ecological diversity was measured as biodiversity and was calculated using the Shannon–Weiner Index, as it takes into account the relative abundances of species in the system. However, for a broader understanding of basic biodiversity in this ecosystem, we have also presented richness values, where the numbers represent simply how many species are present. Ecological dominance was calculated using the Berger–Parker Index. Abundances, species richness, and biodiversity are used to evaluate mollusk community responses to dredging. All calculations and specific values from the calculations can be found in the open access drive link. Diversity was calculated because it considers when a species is disproportionately abundant, a weakness of solely using species richness. Generalized linear models were utilized to compare how dredged conditions, seasons, time since dredging, sediment organic content, silt/clay content, and water content affect mollusk species richness, biodiversity, and abundance. ANOVA was employed to check for parametric relationships when comparing between multiple means for data that meet parametric requirements. GLM was employed for data which did not follow a normal distribution and for which we expected binary outcomes (similar/dissimilar). ANOSIM was used to attach statistical significance to NMDS graphics, which were employed to provide the reader with visualization of the relationships. The use of GLM, NMDS, and ANOSIM are attempts to present the data in the context of models that better reflect the relationships than the regressions yielding very low R2 values. All statistical tests, nMDS plots, and figures were carried out and created in R and Microsoft Excel.

3. Results

3.1. Presence and Absence of Species

Twenty-seven mollusk species were identified at the treatment and control sites during this study. These represented two classes in Phylum Mollusca: Gastropoda and Bivalvia. These mollusk species and their respective appearances are summarized by season in Table 1 below.
Benthic species richness and diversity are expressed per meter squared, as is common practice in benthic ecology [31,32,33,34,35,36]. Environmental Dredging Treatment (EDT) stations had an average ecological diversity (measured as biodiversity via the Shannon–Weiner Index) of 0 organisms m−2, an average species richness of 1 m−2, and an average abundance of 211 organisms m−2 before dredging. After dredging, these values were 0 organisms m−2, 1 organisms m−2, and 182 organisms m−2, respectively. Control stations (EDC) had an average biodiversity of 0 organisms m−2, an average species richness of 1 organisms m−2, and an average abundance of 531 organisms m−2 before dredging. After dredging, these values were 0 organisms m−2, 1 organisms m−2, and 767 organisms m−2, respectively. Environmental Dredging Treatment—Seagrass (EDTS) sites, adjacent to EDT stations, had an average biodiversity of 0.6 organisms m−2, an average species richness of 2 organisms m−2, and an average abundance of 759 organisms m−2 before dredging. After dredging, these values were 1 organisms m−2, 3 organisms m−2, and 4.5 × 103 organisms m−2, respectively. Control seagrass stations, adjacent to EDC stations, had an average biodiversity of 1 organism m−2, an average species richness of 2 organisms m−2, and an average abundance of 919 organisms m−2 before dredging. After dredging, these values were 1 organism m−2, 3 organisms m−2, and 2.4 × 104 organisms m−2, respectively.
While only 30% of the identified species were bivalves, the two species with the highest observed abundances were the clams Parastarte triquetra and Mulinia lateralis. These species were particularly abundant in sandier stations, such as EDTS and EDCS (Figure 1). The highest observed abundances of P. triquetra and M. lateralis were 1.7 × 105 and 3.7 × 105 organisms m−2 at EDCS in 2020 and 2019, respectively (Figure 1). The adjacent seagrass stations (EDTS and EDCS) were also found to be the stations with the overall highest species richness (the peak richness of six organisms m−2 was found at EDCS in the winter of 2019). All 27 species were found to average 5.4 × 103 organisms m−2 or greater with an average species richness of two organisms m−2 throughout this study.
In order to assess if one species was potentially having a greater influence over the others, the Berger–Parker Index was used to calculate ecological dominance for each site during each season each year. The results of the index have been summarized in Table 2 below. Individual results and calculations can be found in the open access Google Drive.
Two-way ANOVAs were conducted on EDT stations, EDC stations, and the adjacent seagrass stations for each of the 27 individual species identified over the course of this study, as well as on a categorical breakdown of gastropods and bivalves at each of the EDT and EDC stations. The results of these ANOVAs have been summarized in Table 3 below.

3.2. Relationship Between Mollusk Abundance, Species Richness, and Biodiversity and Sediment Contents

The organic content of the sediments ranged from 0.7% to 36%. The higher organic content sediments (0.8% to 36%) were found in the Environmental Dredging Treatment (EDT) and Control (EDC) stations (Figure 1: EDC 1, 2, and 3 and EDT 1, 2, 3, and 4). Accordingly, these organic-rich stations consistently had the lowest overall ecological diversity (measured as biodiversity via the Shannon–Weiner Index) (0–1 organisms m−2), species richness (0–3 organisms m−2), and abundances (0–3 × 103 organisms m−2). Sediments lower in percent organic content (0.7% to 6.1%) were located near the adjacent seagrass beds. Seagrass-adjacent sediments with higher organic content generally had low biodiversity (less than 1 organisms m−2). Seagrass-adjacent sediments with lower organic content generally had higher biodiversity (closer to 1 organisms m−2). Generalized linear models (Figure 2, Figure 3 and Figure 4) found that sediment percent organic content had an inverse logarithmic correlation with overall mollusk biodiversity, species richness, and abundance at the EDT and EDC stations. Sediment percent organic content also had an inverse relationship with benthic mollusk abundance at the adjacent seagrass stations. However, all R2 values were weak except for the control in Figure 2C (R2 = 0.56).
With taxonomic family-separated data, gastropod biodiversity ranged between 0 and 0.5 organisms m−2 with higher sediment organic content and between 0 and 1 organisms m−2 with lower sediment organic content. Bivalve biodiversity ranged between 0 and 0.6 organisms m−2 with higher sediment organic content and between 0 and 0.67 organisms m−2 with lower sediment organic content.
The water content of the sediments ranged from 21% to 87%. Sediments generally higher in water content, ranging from 26% to 87%, were found in the treatment and control muck (EDT and EDC) stations, whereas sediments generally lower in water content, ranging 21% to 49% water content, were located near the adjacent seagrass stations.
The sediment % silt–clay content of the sediments ranged from 0.2% to 99.7%. Sediments generally higher in silt–clay content, ranging from 1% to 99.7%, were found in the treatment and control muck (EDT and EDC) stations, whereas sediments generally lower in silt–clay content, ranging from 0.2% to 74% were found in the adjacent seagrass stations. Generalized linear models found an inverse relationship between sediment percent silt–clay content and benthic mollusk biodiversity and abundance.
The percent dissolved oxygen in the water ranged from 0.01 to 141%. The treatment and control muck (EDT and EDC) stations typically had the lowest percent dissolved oxygen (0.01–114%). The adjacent seagrass stations typically had the highest percent of dissolved oxygen (19–141%).

3.3. Multivariate Analysis

ANOSIM was performed on these data, which found that treatment, year, sediment percent organic content, and sediment percent water content were statistically significant factors (p < 0.05). With family-separated data at the adjacent seagrass stations (Figure 1), it was found that year and treatment were statistically significant (p < 0.05) for both gastropods and bivalves. Salinity and water temperature were statistically significant for bivalves at the treatment (EDT) stations.
An R-value closer to 0 than 1 determines an even distribution between groups. High and low ranks are evenly distributed for comparisons within and between groups (Table 4). Higher R-values indicate dissimilarity of groups. The ANOSIMs were paired with non-metric multi-dimensional scaling (nMDS) models to represent similarities and dissimilarities between groups visually. Overall, there was a compelling trend in grouping between stations. At all of the treatment and control muck (EDT and EDC) stations, there was a large overlap with all groups, with some separation of the Treatment Before Dredging (Pre-EDT) group. There was also considerable overlap at adjacent seagrass stations, with some separation of the Treatment Post Dredging (Post-EDT) group. An example of the aforementioned separation pattern is included below (Figure 5).
The abundance spread of each of the 27 identified species was also analyzed as a community. These results also reflect the aforementioned pattern (Figure 6).

4. Discussion

4.1. Relationship Between Mollusk Biodiversity, Species Richness, and Abundance and Sediment Organic Content

This study aimed to determine the effects of environmental dredging on benthic mollusk communities in a shallow and diverse estuary and examine the taxonomic differences in dredging responses. The results show an inverse logarithmic relationship between benthic mollusk biodiversity, species richness, abundance, and sediment % organic content.
Sediment percent organic content and silt–clay content have inverse relationships with benthic mollusk ecological diversity (measured as biodiversity via the Shannon–Weiner Index), species richness, and abundances (Figure 2, Figure 3 and Figure 4), supporting the first hypothesis of this study that biological parameters will have an inverse relationship with sediment organic content. With population measurements plummeting as muck organic content increases, this inverse relationship arises from increased stress stemming from the many impacts of living in polluted sediments.
While the aforementioned inverse relationships were found with significance (p < 0.05) (Figure 2, Figure 3 and Figure 4), the R2 values were weak, except for the control locations in Figure 2C. An R2 value can predict the percentage of variability driven by a specific factor. In the control case in Figure 2C, 56% of the variability of benthic mollusk abundance is driven by sediment percent silt–clay content. The remaining 44% of the variability is driven by some unknown factor not measured during this study. This means that, under certain circumstances, a portion of the variability of benthic mollusk abundances (0.1–56%) (Figure 2), biodiversity (6–24%) (Figure 3), and species richness (15–21%) (Figure 4) is determined by variability in sediment percent organic content or sediment percent silt–clay content.
In this study, treatment and control muck (EDT and EDC) stations (Figure 1) had high sediment percent organic content, sometimes nearing 40% (Figure 2, Figure 3 and Figure 4). Those muck stations also had low percent dissolved oxygen in the boundary bottom water (47.55% ± 29.99%) and, accordingly, the lowest overall biodiversity (0.11 ± 0.23 m−2), species richness (0.7 ± 0.8 m−2), and abundances (406 ± 1.1 × 103 organisms m−2). Sediment organic content can potentially represent benthic community health because it substantially and directly influences benthic biochemistry [26]. As fine-grained benthic organic matter (a.k.a. muck) accumulates, it binds to silt and clay particulates [40,41]. Benthic bacteria then break down and digest organic matter, depleting dissolved oxygen and increasing H2S [42]. This recycling process is a natural phenomenon in aquatic and marine ecosystems. However, with excessive concentrations of organic matter, this process exposes benthic life to hypoxia and toxic hydrogen sulfide. These conditions can alter community composition by excluding sensitive species and restricting sediment bioturbation [43,44,45]. A lack of bioturbation can perpetuate hypoxic conditions in sediments. This creates a positive feedback loop, further increasing inhospitable conditions [46,47].
Negative impacts of degraded sediments on benthic mollusk communities have been observed and modeled in other systems. The Pearson and Rosenberg model [48] showcases a pattern of faunal richness and abundance increasing with lower levels of sediment organic content and then decreasing in response to higher concentrations. Other studies have also found that species richness and biodiversity increase with organic matter concentration, up to about 3% in some cases [49], but then rapidly plummet as organic content increases [26,49,50]. These corroborating studies all show sediment relationships similar to those in this study. However, the Pearson and Rosenberg model may need to be modified for coastal environments due to system energy disparities since systems with less wave energy have more sedimentation and lower dissolved oxygen [51]. Abundant oxygen and low toxicity generally foster healthier, more diverse, and abundant benthic communities. As adverse conditions increase, some taxa are better adapted than others to withstand the impacts [52]. This phenomenon can be seen in Table 2, where either P. triquetra or M. lateralis have been the dominant species in most seasons at EDTS, EDCS, and EDC.

4.2. Impacts of Dredging on Benthic Mollusk Community Composition

There was no discernable difference in benthic mollusk community composition between dredged (EDT) and undredged (EDC) sediments. During the study, removing sediments with high concentrations of fine-grained organic matter by dredging did not increase the ecological diversity (measured as biodiversity via the Shannon–Weiner Index), species richness, or overall community abundance of benthic mollusks. Since the decay of organic matter creates stressful hypoxic or anoxic conditions, low dissolved oxygen is a likely limiting factor for some benthic infauna [26,48,53,54]. However, this did not translate to substantial distinctiveness between communities in this study (Figure 5 and Figure 6). This could be due to mollusks’ lower metabolic and oxygen consumption rates, which allow them to persist in polluted sediments where oxygen is more limiting [20,21,22,23]. When oxygen is not a limiting factor, more tolerant species will gain an advantage in polluted/organic-rich sediments over the more sensitive competitors [26,45,47]. This study shows high abundances of Parastarte triquetra and Ameritella versicolor (De Kay, 1843). P. triquetra was the most abundant mollusk in this study, reaching counts of 8.6 × 103 organisms m−2 at the control muck stations and up to 2.6 × 105 organisms m−2 at the control seagrass stations. In this study, A. versicolor was recorded at only three station sampling events, reaching counts of 15 organisms m−2 at the control muck stations and 356 organisms m−2 at the control seagrass stations. Lower metabolic rates in mollusks may prevent oxygen levels from rapidly shaping benthic communities. However, variability was high, and no significant pre- vs. post-dredging differences were observed in the community (Figure 5 and Figure 6). One possible explanation is that dredging could remove many benthic organisms along with the organic-rich sediments. Another possible explanation is that dredging does not remove all of the muck overlaying the prime sediments, and organic sediment conditions persist after environmental dredging is complete.
Anoxia resulting from the mass decomposition of organic matter fosters the occurrence of sulfur-reducing bacteria [53,54]. These bacteria release H2S that can saturate the benthic boundary layer and interstitial water, rendering hypoxic habitats even more hostile to fauna and flora [42]. Fauna with H2S resistance survive better in hypoxic environments [24,25]. Gray et al. [46] observed that crustaceans and echinoderms are most sensitive to polluted fine-grained organic-rich sediments. Anelids are more tolerant than these two groups, and mollusks are the most tolerant of organic sediments. This is consistent with their documented ability to withstand the physiological stressors of hypoxia and H2S [24].
Competition is a significant factor in shaping community structures in many ecosystems [55]. However, Thistle [56] found that colonizing species’ succession patterns may be driven by the nature of the resource base rather than competition. Additionally, Virnstein [57] observed that Mulinia lateralis persisted at high densities in the absence of predation and could exclude other species. In this study, M. lateralis was one of the most abundant species identified, reaching densities of 4.3 × 103 organisms m−2 at the control seagrass stations and 3.8 × 103 organisms m−2 at the control muck stations. However, Virnstein [57] postulated that the potential for M. lateralis to reach these densities and exclude other species could have been due to predation on settling larvae or another amensalistic interaction, as some species are poor “predation avoiders”. Other corroborating predator exclusion studies have also reported an absence of competitive exclusion, even where benthic fauna occurred at higher densities [58,59]. Peterson [60] postulated that this absence of competitive exclusion in soft sediments could be due to reduced opportunities for interference competition and, instead, increased opportunities for amensalistic interactions. Since the introduction of the trophic amensalism hypothesis [32], it has been used to explain the distribution of deposit feeders and suspension feeders in numerous polluted and other soft-sediment benthic environments [55,60,61,62,63]. Rhoades and Young’s trophic amensalism hypothesis proposes that deposit feeders exclude filter-feeding neighbors by disturbing and resuspending fine-grained sediments that can clog their finely constructed gills and feeding structures [27]. However, the results of this study did not see a discrepancy in the abundances of suspension-feeding vs. deposit-feeding mollusks before or after dredging, despite the polluted sediment conditions.
Many benthic macroinvertebrates are used as bioindicators for environmental change because they are easy to monitor, have limited escape mechanisms to avoid disturbances, and provide a good indication of change over time [64,65]. Numerous studies have shown that the benthos responds rapidly to natural and anthropogenic stressors [66]. Benthic mollusks are typically abundant, relatively sessile, and have longer life cycles [14,66]. Some studies on macrofaunal succession found that mollusks dominate less polluted habitats and that degraded habitats replace mollusks with unshelled organisms (e.g., polychaetes) [65]. However, our study found that mollusks can tolerate very degraded conditions, reaching densities as high as 9.0 × 103 organisms m−2 and 1.7 × 103 organisms m−2 at the control and treatment (EDC and EDT) sites. In one study conducted by Pelletiera et al. (2010) [64], it was found that the snail Acteocina sp. is a “pollution-sensitive species”, making its absence a potential bioindicator of polluted sediments. Two species of Acteocina were observed in this study: Acteocina atrata (P. S. Mikkelsen & P. M. Mikkelsen, 1984 [11]) and Acteocina canaliculata. A. atrata was only recorded at 13 station sampling events over the course of this study, 12 of which were at seagrass stations. A. atrata reached counts of 830 organisms m−2 at the control seagrass-adjacent stations and 59 organisms m−2 at the one control muck station. A. canaliculata occurred more frequently and was far denser than A. atrata. A. canaliculata was recorded at 79 station sampling events over the course of this study, reaching counts of 1.8 × 103 organisms m−2 at the control seagrass stations and 2.7 × 102 organisms m−2 at the control muck stations. Two-way ANOVAs were conducted on all 27 species identified throughout this study. The ANOVAs conducted on both A. atrata and A. canaliculata found statistically significant differences in the abundances of both species between the seagrass stations and the muck stations. These findings and the documented sensitivity to pollution could make A. atrata and A. canaliculata potential indicator species for future studies on benthic restoration. An index of ecological integrity could also be adopted into future studies, as they can indicate the quality/health of an environment to aid in forming management decisions regarding environmental conditions by integrating physical habitat structure with benthic communities [67,68]. Borjaa et al. (2007) [68] found that if the same ecological basis was used, different indices could produce a level of agreement. Different indices produce comparable results when used in the same environment.
No shift in the benthic mollusk community was found due to dredging, possibly due to mollusks’ tolerance of degraded sediment conditions [20,21,22,23]. This study focused on the benthic mollusk communities at the bottom of the dredge line. Significant changes could be found in adjacent communities, or muck could be removed more thoroughly by environmental dredging. Much is still to be learned about the causal relationships relating dredging processes to potential impacts [67,69]. One study highlighted the “4 Rs of Environmental Dredging”: Resuspension, Release, Residuals, and Risk [69]. Resuspension of polluted sediments can cause them to spread and contaminate neighboring areas. Removing surface sediments and resuspending sediments (Figure 7) can release deeply sequestered muck contaminants into the water column [67]. One of the top objectives of environmental dredging is to dredge with enough accuracy that deeper, clean sediments are not impacted or removed [68,70]. However, this approach inevitably leaves a residual layer of contaminated/polluted sediment. Additionally, muck from the top of the dredge slope may slide down and join the residual layer (Figure 7), potentially re-establishing high organic content and preventing significant shifts in benthic faunal communities. The sloughing of the organic-rich sediments to the bottom of the dredge pit could create a scenario in which the downslope environment experiences little to no change following dredging. Dredging deeper and completely removing the residual layer, on the other hand, could result in cleaner, less organic sediments that remain longer after dredging. This could support a more abundant and diverse benthic community, including sensitive filter- and deposit-feeders. The latter would increase sediment bioturbation, potentially breaking up the positive feedback loop of anoxic, organic-rich sediment accumulation.

5. Conclusions

This study aimed to examine the relationship in benthic mollusk communities with sediment percent organic content and to see if the removal of sediments with high concentrations of fine-grained organic matter by dredging would benefit the benthic mollusk communities.
The findings of this study support that benthic mollusk abundance, ecological diversity (measured as biodiversity via the Shannon–Weiner Index), and species richness have inverse logarithmic relationships with sediment organic content. This could be due to hypoxic and anoxic conditions in boundary bottom water. There were no significant discrepancies between the abundances of suspension-feeding mollusks and deposit-feeding mollusks, so there is likely no relation to trophic amensalism.
However, removing some sediments with high concentrations of fine-grained organic matter by dredging did not significantly increase the biodiversity, species richness, and abundance of benthic mollusks. The high tolerance of many mollusks to stressful environmental conditions may allow mollusks to persist under both types of sediment conditions. Alternatively, as benthic organisms could be removed along with the sediment in the dredging process, the benthic communities may take longer to adjust to new sediment conditions.
As previously stated, the current aim of remedial dredging is not to disturb clean/uncontaminated sediments. The sloughing, fallback, and resettlement of organic-rich sediments to the bottom of the dredge line, where this study occurred, could create a relatively unchanged environment before and after dredging. Future studies could be conducted where remedial dredging goes deeper, completely removing this residual layer.

Author Contributions

Conceptualization, R.H.S. and K.B.J.; methodology, R.H.S. and K.B.J.; validation, K.B.J.; formal analysis, R.H.S.; investigation, R.H.S.; resources, K.B.J.; data curation, R.H.S.; writing—original draft preparation, R.H.S.; writing—review and editing, R.H.S. and K.B.J.; visualization, R.H.S.; supervision, K.B.J.; project administration, K.B.J.; funding acquisition, K.B.J. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Brevard County Natural Resources Management Department (Melbourne, FL, USA) contract number 260070-14-009 and the APC was funded by Tarleton State University (Stephenville, TX, USA).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data, individual results, and appendices can be accessed at this link https://drive.google.com/drive/folders/1B9FAhnWh9RoYjMvBlCBF2FpUkBrrUAi9 (accessed on 21 December 2024).

Acknowledgments

We thank the Brevard County Natural Resources Management Department, and the members of the Johnson Lab at the Florida Institute of Technology for their assistance.

Conflicts of Interest

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

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Jmse 13 00013 g001
Figure 1. (A) The region in the northern Indian River Lagoon (IRL) in which this study was conducted, including treatment and control sites. (B) Stations were Environmental Dredging Treatment stations (EDT, red), seagrass stations near dredging (EDTS, green), Environmental Dredging Control (undredged) muck stations (EDC, orange), and seagrasses near undredged (control) muck stations (EDCS, blue).
Figure 1. (A) The region in the northern Indian River Lagoon (IRL) in which this study was conducted, including treatment and control sites. (B) Stations were Environmental Dredging Treatment stations (EDT, red), seagrass stations near dredging (EDTS, green), Environmental Dredging Control (undredged) muck stations (EDC, orange), and seagrasses near undredged (control) muck stations (EDCS, blue).
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Figure 2. (A) Mollusk abundance m−2 vs. sediment % organic content at muck stations (EDT = orange; EDC = blue). R2 was 0.1 and 0.01 for EDT and EDC, respectively, and significant for EDT (p = 0.02). (B) Mollusk abundance m−2 vs. sediment % organic content at seagrass sites near muck (EDTS = orange; EDCS = blue). R2 was 0.12 and 0.001 for EDTS and EDCS, respectively, and significant for EDTS (p = 0.028). (C) Benthic mollusk abundance m−2 vs. sediment % silt–clay content at muck station (EDT = orange; EDC = blue). R2 was 6 × 10−3 and 0.56 for EDT and EDC, respectively, and significant in both cases (p = 4.8 × 10−3 and 1 × 10−4, respectively).
Figure 2. (A) Mollusk abundance m−2 vs. sediment % organic content at muck stations (EDT = orange; EDC = blue). R2 was 0.1 and 0.01 for EDT and EDC, respectively, and significant for EDT (p = 0.02). (B) Mollusk abundance m−2 vs. sediment % organic content at seagrass sites near muck (EDTS = orange; EDCS = blue). R2 was 0.12 and 0.001 for EDTS and EDCS, respectively, and significant for EDTS (p = 0.028). (C) Benthic mollusk abundance m−2 vs. sediment % silt–clay content at muck station (EDT = orange; EDC = blue). R2 was 6 × 10−3 and 0.56 for EDT and EDC, respectively, and significant in both cases (p = 4.8 × 10−3 and 1 × 10−4, respectively).
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Figure 3. (A) Benthic mollusk collective biodiversity (m−2) vs. sediment percent organic content at dredging treatment (orange) and control stations (blue). Regression relationships were weak (R2 = 0.15 and 0.2, respectively) but significant in both cases (p = 4 × 10−3 and 9 × 10−3, respectively) (B) Benthic mollusk collective biodiversity (m−2) vs. sediment percent silt–clay content at dredging treatment (orange) and control stations (blue). Regression relationships were moderate to weak (R2 = 0.06 and 0.24, respectively) but significant in the control (p = 7.4 × 10−4).
Figure 3. (A) Benthic mollusk collective biodiversity (m−2) vs. sediment percent organic content at dredging treatment (orange) and control stations (blue). Regression relationships were weak (R2 = 0.15 and 0.2, respectively) but significant in both cases (p = 4 × 10−3 and 9 × 10−3, respectively) (B) Benthic mollusk collective biodiversity (m−2) vs. sediment percent silt–clay content at dredging treatment (orange) and control stations (blue). Regression relationships were moderate to weak (R2 = 0.06 and 0.24, respectively) but significant in the control (p = 7.4 × 10−4).
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Figure 4. Benthic mollusk collective species richness (m−2) vs. sediment percent organic content at dredging treatment (orange) and control stations (blue). Regression relationships were weak (R2 = 0.15 and 0.21, respectively) but significant in the treatment and control (p = 4 × 10−3 and 1 × 10−2, respectively).
Figure 4. Benthic mollusk collective species richness (m−2) vs. sediment percent organic content at dredging treatment (orange) and control stations (blue). Regression relationships were weak (R2 = 0.15 and 0.21, respectively) but significant in the treatment and control (p = 4 × 10−3 and 1 × 10−2, respectively).
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Figure 5. Non-Metric Multidimensional Scaling reveals the similarities and dissimilarities in pooled mollusk species richness at the adjacent seagrass stations in multivariate space. CBD = Control Before Dredging; CPD = Control Post Dredging; TBD = Treatment Before Dredging; TPD = Treatment Post Dredging. Non-overlapping portions of ellipses represent the proportion of group distinctiveness or dissimilarity.
Figure 5. Non-Metric Multidimensional Scaling reveals the similarities and dissimilarities in pooled mollusk species richness at the adjacent seagrass stations in multivariate space. CBD = Control Before Dredging; CPD = Control Post Dredging; TBD = Treatment Before Dredging; TPD = Treatment Post Dredging. Non-overlapping portions of ellipses represent the proportion of group distinctiveness or dissimilarity.
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Figure 6. Non-Metric Multidimensional Scaling reveals the similarities and dissimilarities in the spread of 27 identified mollusk species abundances at all stations in multivariate space. MCM = Mims Control Muck; MCS = Mims Control Seagrass; MDM = Mims Dredged Muck; MDS = Mims Dredged Seagrass. Non-overlapping portions of ellipses represent the proportion of group distinctiveness or dissimilarity.
Figure 6. Non-Metric Multidimensional Scaling reveals the similarities and dissimilarities in the spread of 27 identified mollusk species abundances at all stations in multivariate space. MCM = Mims Control Muck; MCS = Mims Control Seagrass; MDM = Mims Dredged Muck; MDS = Mims Dredged Seagrass. Non-overlapping portions of ellipses represent the proportion of group distinctiveness or dissimilarity.
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Figure 7. Diagram of environmental dredging and its residuals [71]. Arrows indicate movement of sediments. Dredging machinery is used to remove polluted or harmful sediments. It is often not possible, however, to remove 100% of contaminated sediments and adjacent areas may retain thick organic-rich sediments that may slide into the dredge hole after dredging is completed, re-contaminating the sediments.
Figure 7. Diagram of environmental dredging and its residuals [71]. Arrows indicate movement of sediments. Dredging machinery is used to remove polluted or harmful sediments. It is often not possible, however, to remove 100% of contaminated sediments and adjacent areas may retain thick organic-rich sediments that may slide into the dredge hole after dredging is completed, re-contaminating the sediments.
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Table 1. (Top section) Presence/absence table of bivalves at the Environmental Dredging Treatment (EDT) and at the adjacent undredged seagrass (“Environmental Dredging Treatment—Seagrass” or EDTS) stations by season. (Bottom section) Presence/absence table of gastropods at the Environmental Dredging Control (EDC) and adjacent undredged seagrass (“Environmental Dredging Control—Seagrass” or EDCS) stations. Presence is annotated according to the season the species was observed: spring (Sp), summer (Su), fall (F), and winter (W).
Table 1. (Top section) Presence/absence table of bivalves at the Environmental Dredging Treatment (EDT) and at the adjacent undredged seagrass (“Environmental Dredging Treatment—Seagrass” or EDTS) stations by season. (Bottom section) Presence/absence table of gastropods at the Environmental Dredging Control (EDC) and adjacent undredged seagrass (“Environmental Dredging Control—Seagrass” or EDCS) stations. Presence is annotated according to the season the species was observed: spring (Sp), summer (Su), fall (F), and winter (W).
BivalvesPre-EDCPost-EDCPre-EDTPost-EDTPre-EDCSPost-EDCSPre-EDTSPost-EDTS
Amygdalum papyrium WSp F W Su F WF
Angulus versicolor Su FF
Anomalocardia cuneimeris F W Sp Su Su F W
Cyrtopleura costata Su F W
Melongena Corona F
Mercenaria mercenariaFSp F
Mulinia lateralisSp Su F WSp F WSp Su F WSp F WSp Su F WSp F WSp Su F WSp Su F W
Parastarte triquetra Sp Su FSp Su FSp Su F WSp Su F WSp Su F Sp Su F WSp Su F W
Periglypta listeriF FSu F
Gastropods
Acteocina atrataF Sp WSum F WF
Acteocina canaliculataSp Su F WSp F WSp Su WSp F WSp Su F WSp Su FSp Su F WSp Su F W
Astyris lunataSu F W Su F WFSp Su F WSp WSp Su F WSp F W
Crepidula atrasolea F
Eulithidium pterocladicum Su F F
Haminoea succinea Sp WSp SuF WSu F W
Haminoea elegansSuSp Su FSp F WF
Japonactaeon punctostriatusSp FF WSp Su FSu FSp Su F WSu F
Limpet B Su F
Nassarius vibexSu F WSu F WSu F WSp Su F WSp Su F WSp F WSp Su F WSp Su F W
Odostomia laevigata Sp FSp SuSpSu F WSu F W
Prunum apicinum SpSp WSuF W
Epitonium angulatum W
Snail R Su F WSu FSu
Turbonilla sp. SuF Sp Su F WSu F
Table 2. Ecological dominance index at the Environmental Dredging Treatment (EDT) and Environmental Dredging Control (EDC) stations, and the adjacent seagrass stations (EDTS and EDCS). The Berger–Parker Index was used, where values range from 0 to 1. Values close to 0 indicate relatively equal abundances of species, where values close to 1 indicate that one species dominates the sample.
Table 2. Ecological dominance index at the Environmental Dredging Treatment (EDT) and Environmental Dredging Control (EDC) stations, and the adjacent seagrass stations (EDTS and EDCS). The Berger–Parker Index was used, where values range from 0 to 1. Values close to 0 indicate relatively equal abundances of species, where values close to 1 indicate that one species dominates the sample.
EDTBP IndexSDSEDominant Species
Spring-20170.340.430.12Parastarte triquetra
Summer-20170.330.430.14Parastarte triquetra
Fall-20170.370.380.11Parastarte triquetra
Winter-20170.360.480.14Nassarius vibex
Spring-20180.250.450.13Mulinia lateralis
Summer-20180.000.000.00None
Fall-20180.000.000.00None
Winter-20180.000.000.00None
Spring-20190.110.300.09Mulinia lateralis
Summer-20190.080.290.08Nassarius vibex
Fall-20190.620.360.10Japonactaeon punctostriatus
Winter-20190.530.440.13Parastarte triquetra
Spring-20200.000.000.00None
Summer-20200.330.500.17Parastarte triquetra
Fall-20200.220.360.12Japonactaeon punctostriatus
Winter-20200.370.450.15Acteocina canaliculata
EDTS
Spring-20170.590.390.13Mulinia lateralis
Summer-20170.260.280.09Haminoea succinea
Fall-20170.710.370.12Mulinia lateralis
Winter-20170.340.300.10Mulinia lateralis
Spring-20180.550.150.05Mulinia lateralis
Summer-20180.000.000.00None
Fall-20180.740.350.12Parastarte triquetra
Winter-20180.730.420.14Parastarte triquetra
Spring-20190.950.050.02Parastarte triquetra
Summer-20190.530.150.05Parastarte triquetra
Fall-20190.530.160.05Parastarte triquetra
Winter-20190.810.140.05Parastarte triquetra
Spring-20200.000.000.00None
Summer-20200.840.320.11Parastarte triquetra
Fall-20200.860.100.03Parastarte triquetra
Winter-20200.500.260.09Parastarte triquetra
EDC
Spring-20170.000.000.00None
Summer-20170.390.490.16Nassarius vibex
Fall-20170.450.270.09Mulinia lateralis
Winter-20170.520.500.17Acteocina canaliculata
Spring-20180.550.520.17Mulinia lateralis
Summer-20180.330.500.17Mulinia lateralis
Fall-20180.330.500.17Mulinia lateralis
Winter-20180.290.450.15Mulinia lateralis
Spring-20190.560.530.18Mulinia lateralis
Summer-20190.300.450.15Mulinia lateralis
Fall-20190.000.000.00None
Winter-20190.780.440.15Mulinia lateralis
Spring-20200.000.000.00None
Summer-20200.650.490.16Parastarte triquetra
Fall-20200.560.530.18Parastarte triquetra
Winter-20200.590.460.15Mulinia lateralis
EDCS
Spring-20170.000.000.00None
Summer-20170.550.200.06Parastarte triquetra
Fall-20170.460.430.13Mulinia lateralis
Winter-20170.540.320.09Mulinia lateralis
Spring-20180.480.350.10Acteocina canaliculata
Summer-20180.590.310.09Mulinia lateralis
Fall-20180.590.290.08Mulinia lateralis
Winter-20180.810.190.06Parastarte triquetra
Spring-20190.710.290.08Parastarte triquetra
Summer-20190.000.000.00None
Fall-20190.000.000.00None
Winter-20190.700.270.08Parastarte triquetra
Spring-20200.000.000.00None
Summer-20201.000.000.00Parastarte triquetra
Fall-20200.940.010.00Parastarte triquetra
Winter-20200.640.140.05Parastarte triquetra
Table 3. Significant two-way ANOVAs conducted on the biodiversity, species richness, individual species’ abundances, and total pooled abundance of benthic mollusks in the different seasons of the study. Xs indicate statistically significant distinctness of the season for that parameter (p ≤ α = 0.05, 0.05 = α).
Table 3. Significant two-way ANOVAs conducted on the biodiversity, species richness, individual species’ abundances, and total pooled abundance of benthic mollusks in the different seasons of the study. Xs indicate statistically significant distinctness of the season for that parameter (p ≤ α = 0.05, 0.05 = α).
GastropodsSpringSummerFallWinterOverall
Acteocina atrata XX
Acteocina canaliculata X
Astyris lunata
Caecum pulchellum
Crepidula atrasolea X
Eulithidium pterocladicum X
Haminoea succinea X X
Haminoea elegans X
Japonactaeon punctostriatus X X
Limpet B
Phrontis vibex X
Odostomia laevigata
Prunum apicinum X
Epitonium angulatum
Snail R XX X
Turbonilla sp. XX
Bivalves
Amygdalum papyrium
Ameritella versicolor X
Anomalocardia cuneimeris XX X
Cyrtopleura costata
Melongena corona X
Mercenaria mercenaria X
Mulinia lateralis X
Parastarte triquetraX X
Periglypta listeri XX
Overall
Overall BiodiversityXXXXX
Overall Species RichnessXXXXX
Overall AbundanceX X X
Gastropod BiodiversityXXXXX
Gastropod Species RichnessXXXXX
Gastropod AbundanceX XXX
Bivalve Biodiversity X
Bivalve Species RichnessXXXXX
Bivalve AbundanceX X X
Table 4. The ANOSIMs conducted on the biodiversity, species richness, and abundances of the benthic mollusk communities at each of the stations throughout the course of the study. X’s indicate statistical significance (p < 0.05), indicating that there were differences found within that grouping or category.
Table 4. The ANOSIMs conducted on the biodiversity, species richness, and abundances of the benthic mollusk communities at each of the stations throughout the course of the study. X’s indicate statistical significance (p < 0.05), indicating that there were differences found within that grouping or category.
SeasonsTreatmentYearSediment Organic ContentSilt Clay ContentWater ContentPercent Dissolved OxygenWater TemperatureSalinitySeagrass Percent Cover
Overall Biodiversity: Treatment X
Overall Species Richness: Treatment X X
Overall Abundance: Treatment X
Overall Biodiversity: Seagrass X X
Overall Species Richness: Seagrass XX
Overall Abundance: Seagrass XX X
Gastropod Biodiversity: Treatment
Gastropod Species Richness: Treatment
Gastropod Abundance: Treatment
Gastropod Biodiversity: Seagrass X
Gastropod Species Richness: Seagrass XX
Gastropod Abundance: Seagrass X
Bivalve Biodiversity: Treatment
Bivalve Species Richness: Treatment
Bivalve Abundance: Treatment X
Bivalve Biodiversity: Seagrass
Bivalve Species Richness: Seagrass XX
Bivalve Abundance: Seagrass XX X
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Stark, R.H.; Johnson, K.B. Benthic Mollusk Biodiversity Correlates with Polluted Sediment Conditions in a Shallow Subtropical Estuary. J. Mar. Sci. Eng. 2025, 13, 13. https://doi.org/10.3390/jmse13010013

AMA Style

Stark RH, Johnson KB. Benthic Mollusk Biodiversity Correlates with Polluted Sediment Conditions in a Shallow Subtropical Estuary. Journal of Marine Science and Engineering. 2025; 13(1):13. https://doi.org/10.3390/jmse13010013

Chicago/Turabian Style

Stark, Rachael H., and Kevin B. Johnson. 2025. "Benthic Mollusk Biodiversity Correlates with Polluted Sediment Conditions in a Shallow Subtropical Estuary" Journal of Marine Science and Engineering 13, no. 1: 13. https://doi.org/10.3390/jmse13010013

APA Style

Stark, R. H., & Johnson, K. B. (2025). Benthic Mollusk Biodiversity Correlates with Polluted Sediment Conditions in a Shallow Subtropical Estuary. Journal of Marine Science and Engineering, 13(1), 13. https://doi.org/10.3390/jmse13010013

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