Estuarine bivalve metabolic response mediated by environmental drivers

Introduction

Estuarine ecosystems are transitional zones where freshwater from rivers meets and mixes with saltwater from the ocean. Because of high nutrient and energy inputs from both endmembers, estuaries are biogeochemical hotspots and they are often noted for their productivity and biodiversity (Thrush et al., 2013; Douglas et al., 2018a; Rosenzweig et al., 2018; Douglas, Lohrer & Pilditch, 2019; Thrush et al., 2021a; Thrush et al., 2021b).

Environmental change driven by anthropogenic pressures is affecting estuarine ecosystems worldwide (IPCC, 2022; Deng, 2024; Prum, Harris & Gardner, 2024). Increases in the frequency and strength of stressors including pollution, habitat fragmentation, coastal development, and nutrient and sediment loads, exacerbated by climate change, are leading to profound alterations of the composition and structure of estuarine communities, with tangible effects on the processes, functions, and services these important ecosystems provide (Lohrer et al., 2010; Ellis et al., 2015; Hewitt, Ellis & Thrush, 2016; Thrush et al., 2017; Carrier-Belleau et al., 2021; Gammal et al., 2022; Douglas et al., 2023). Inputs of fine sediments (silt and clay) and nutrients from land are key stressors for estuaries which can cause shifts towards muddy, organic rich habitats that are prone to eutrophication (Thrush et al., 2003a; Lohrer, Hewitt & Thrush, 2006; Bierschenk, Savage & Matthaei, 2017). Sediment mud content is used as a measure of estuary degradation status since such changes in sedimentary environment are associated with reduced ecosystem functioning, changes in macrobenthic community composition and species loss (Thrush et al., 2004).

Bivalve molluscs are key components of estuarine ecosystems, with contributions to several ecosystem functions and services, including nutrient cycling, food provision, sediment turn-over, water filtration, and habitat provision (Harris et al., 2015; Hewitt, Ellis & Thrush, 2016; Ellis et al., 2017; Douglas et al., 2018a; Douglas, Lohrer & Pilditch, 2019; Rullens et al., 2019; Lam-Gordillo et al., 2024b). Suspension feeding bivalves exert top-down control on phytoplankton populations, affecting rates of nutrient cycling, contribute to benthic-pelagic coupling, and provide an important food resources for fish and birds (Thrush et al., 2003b; Newell, 2004; Thrush et al., 2006; Jones et al., 2011). Yet, increased sediment and nutrient loading to estuaries is challenging the survival and functioning of suspension feeding bivalves by altering primary production, seabed light and oxygen levels, and sediment characteristics such as grain size and organic matter content (Grant & Thorpe, 1991; Thrush et al., 2003b; Norkko, Hewitt & Thrush, 2006; Cummings et al., 2007).

Suspension feeding bivalves are particularly stressed by excessive turbidity and high sediment deposition rates (Thrush et al., 2003b; Norkko, Hewitt & Thrush, 2006; Jones et al., 2011; Lohrer et al., 2012). Their responses can be expressed at different levels, from altered individual feeding behaviour, physiology, and fitness over the short term, to changes in population dynamics due to differential growth, survival, and reproduction over the longer term (Norkko et al., 2002; Lohrer et al., 2006; Norkko, Hewitt & Thrush, 2006; Douglas et al., 2018a).

The bivalve Austrovenus stutchburyi (also known as cockle—hereafter Austrovenus), is a common native suspension feeder in New Zealand intertidal soft sediments (Jones et al., 2011; Ellis et al., 2015; Ellis et al., 2017; Douglas et al., 2023). Austrovenus plays an essential role in intertidal ecosystem functioning as it modifies sediment biogeochemistry (including nutrient regeneration and removal) and pore water solute exchanges through bioturbation and biodeposition (Lohrer et al., 2016; Douglas et al., 2018b; Tricklebank, Grace & Pilditch, 2021). Since large bioturbating fauna accelerate the nutrient removal ecosystem service (i.e., denitrification) (Webb & Eyre, 2004; Stief, 2013), Austrovenus are expected to be important for ecosystem resilience to eutrophication. Austrovenus are also culturally important as a food resources for indigenous Māori, and economically important for recreational and commercial harvesters (Marsden & Adkins, 2010; Fenton et al., 2024; Lam-Gordillo et al., 2024a; Yeoh et al., 2024). However, there is increasing evidence that the populations of Austrovenus are declining in several areas of New Zealand, mainly due to increases in bed sediment muddiness, suspended solids in the water column, increasing air and water temperatures, and more frequent heatwaves (Lohrer et al., 2004; Thrush et al., 2004; Tricklebank, Grace & Pilditch, 2021; Douglas et al., 2023; Lam-Gordillo et al., 2024b).

Although there is a wealth of research concerning the effects of environmental conditions on suspension feeding bivalves (e.g., Norkko et al., 2002; Lohrer et al., 2006; Norkko, Hewitt & Thrush, 2006; Douglas et al., 2018a), there is little information about the metabolic response of estuarine suspension feeding bivalves to human-modified environmental drivers. This limits our understanding of how estuarine organisms respond and adapt to different environmental drivers, and our ability to predict community change and the effects on estuarine ecosystem functioning and service provision.

In this study, we surveyed an estuary in New Zealand with the focus of investigating the metabolic response of Austrovenus to site-specific variation in environmental conditions. Specifically, we aimed to evaluate differences in Austrovenus metabolic abundance, diversity, and composition across several monitoring sites within the estuary and investigate the relationship between Austrovenus metabolites and the environmental conditions of the estuary. We hypothesised that higher metabolite abundance and diversity will be recorded at sites with lower levels of degradation/stress (e.g., low mud and organic content in sediment).

Methods

Study area

The investigation was carried out in Waihı¯ Estuary in the Bay of Plenty region, North Island, New Zealand (−37.7695, 176.4870; Fig. 1). Waihı¯ Estuary is a tidal lagoon type estuary that is mostly enclosed by a coastal sandspit but permanently open to the sea at its narrow mouth. It is dominated by intertidal soft-sediment flats and has a relatively high freshwater inflow volume for an estuary of its size (Lam-Gordillo et al., 2024a). Eight sites had been sampled previously for estuarine ecological health assessments (Lam-Gordillo et al., 2024a). We surveyed the eight pre-established monitoring sites, however, Austrovenus was only presented and collected at seven of the eight sites. These seven sites dispersed across the estuary were the focus of the current study. Environmental data and biological data were collected in the same survey and paired at the same locations.

Results

Metabolomic patterns of Austrovenus bivalves

In total, 47 Austrovenus metabolites were identified across the sampling sites (Table S1). Austrovenus metabolite abundance and diversity were different across sampling sites (Fig. 2). The highest mean abundance of metabolites was found at Site 1, followed by Site 4 and Site 3, while the lowest mean metabolite abundance was identified in Site 7 (Fig. 2A). There were significant differences in metabolite abundance between sites (PERMANOVA p < 0.05; Fig. 2A). Pairwise tests revealed that the abundance of metabolites at Site 7 was significantly different to all other sites (p < 0.05; Table 2; Fig. 2A). Metabolite abundance at Site 1 was significantly different to sites 2, 3, and 5. Significant differences between Site 2 and Site 3, and between Site 3 and Site 5, were also recorded (p < 0.05; Table 2; Fig. 2A).

Table 2:

Summary of PERMANOVA pair-wise test.

Comparisons of the abundance of metabolites recorded across sites. Significant differences are shown in bold.

Groups	t	P(perm)	Unique perms
1, 2	5.568	0.008	126
1, 3	3.179	0.031	126
1, 4	0.647	0.564	126
1, 5	4.839	0.008	126
1, 7	10.948	0.006	126
1, 8	2.025	0.070	126
2, 3	4.190	0.008	126
2, 4	2.349	0.051	126
2, 5	0.636	0.492	126
2, 7	6.403	0.008	126
2, 8	1.129	0.300	126
3, 4	0.674	0.527	126
3, 5	3.247	0.007	126
3, 7	10.588	0.009	126
3, 8	0.732	0.538	126
4, 5	1.988	0.083	126
4, 7	6.191	0.010	126
4, 8	1.005	0.364	126
5, 7	6.858	0.009	126
5, 8	0.756	0.409	126
7, 8	5.223	0.007	126

DOI: 10.7717/peerj.20357/table-2

Diversity of metabolites in Austrovenus significantly varied across sites (PERMANOVA p < 0.05; Fig. 2B). The highest mean diversity was identified at Site 7, followed by Site 8, while the lowest diversity of metabolites was identified at Site 4 (Fig. 2B). Pairwise tests revealed that the diversity of metabolites found at Site 7 was significantly different to all other sites except Site 8 (p < 0.05; Table 3; Fig. 2B). Diversity of metabolites at Sites 3 and 4 were also significantly different to Sites 5, 7, and 8, with Site 4 also significantly different than Sites 1 and 2 (p < 0.05; Table 3; Fig. 2B).

Table 3:

Summary of PERMANOVA pair-wise test.

Comparisons of the diversity of metabolites recorded across sites. Significant differences are shown in bold.

Groups	t	P(perm)	Unique perms
1, 2	0.558	0.592	126
1, 3	2.314	0.056	126
1, 4	3.189	0.008	125
1, 5	0.342	0.742	126
1, 7	2.812	0.030	126
1, 8	1.555	0.162	126
2, 3	1.471	0.195	126
2, 4	2.535	0.025	126
2, 5	0.422	0.701	126
2, 7	2.970	0.040	126
2, 8	1.824	0.131	125
3, 4	1.439	0.198	126
3, 5	2.570	0.016	126
3, 7	3.979	0.008	126
3, 8	3.285	0.009	126
4, 5	3.332	0.007	126
4, 7	4.544	0.007	126
4, 8	3.938	0.009	126
5, 7	3.117	0.008	126
5, 8	2.038	0.016	126
7, 8	1.658	0.139	126

DOI: 10.7717/peerj.20357/table-3

Significant differences in the structural composition of metabolites within Austrovenus tissues were identified across sites (PERMANOVA p < 0.05; Fig. 3). The bootstrapped nMDS plot showed a clear differentiation between the composition of metabolites in Site 7 and all the other sites (Fig. 3). Pairwise test revealed differences in metabolite composition at some but not all sites, e.g., Site 8 did not differ from Sites 2, 3, and 4, and Site 3 did not differ from Site 4 (Table 4; Fig. 3).

Table 4:

Summary of PERMANOVA pair-wise test.

Comparisons of the structure composition of metabolites recorded across sites. Significant differences are shown in bold

Groups	t	P(perm)	Unique perms
1, 2	3.554	0.008	126
1, 3	1.835	0.025	126
1, 4	1.405	0.121	126
1, 5	4.834	0.008	126
1, 7	6.363	0.010	126
1, 8	1.971	0.030	126
2, 3	2.457	0.008	126
2, 4	2.127	0.040	126
2, 5	3.172	0.009	126
2, 7	4.574	0.008	126
2, 8	1.222	0.209	126
3, 4	0.858	0.551	126
3, 5	4.164	0.009	126
3, 7	5.849	0.007	126
3, 8	1.481	0.063	126
4, 5	3.004	0.008	126
4, 7	5.213	0.008	126
4, 8	1.696	0.062	126
5, 7	4.760	0.007	126
5, 8	2.428	0.009	126
7, 8	4.633	0.007	126

DOI: 10.7717/peerj.20357/table-4

The SIMPER analysis identified six metabolites as the drivers of the significant differences across sites: alanine, aspartic acid, glutamic acid, glycine, proline, and succinic acid. Alanine and glycine were the metabolites that were the most consistently present and abundant across the sites, while glutamic acid and proline were found only in sites 5 and 7 (Fig. 4). Significant differences in abundance of metabolites were identified across all sites (PERMANOVA p < 0.05; Table S2; Fig. 5). The abundance of all metabolites at Site 7 was reduced relative to all other sites (Fig. 5). Alanine was significantly more abundant at Site 4 and significantly lower at Site 7 (p < 0.05; Table S2; Fig. 5A). Glycine showed a similar pattern as alanine: significantly higher at Site 4 and significantly lower at Site 7 (p < 0.05; Table S2; Fig. 5B). Aspartic acid, succinic acid, proline, and glutamic acid were all significantly higher at Site 1 and significantly lower at Site 7 (p < 0.05; Table S2; Figs. 5C and 5F).

The Distance based ReDundancy Analysis (dbRDA) explained a total of 93% of the total variation in metabolites structure (Fig. 6). The analysis indicated strong correlations between Austrovenus metabolite composition and environmental conditions (i.e., coarse, medium, fine, and very fine sand, silt, Mud, and Chl a concentrations). The dbRDA plot also revealed that Austrovenus metabolites at Site 1 were correlated with coarse sand, and with sediment mud content at Site 8, while sites 2, 3, and 4 were less correlated to mud (Fig. 6). Some individual Austrovenus metabolites were correlated with environmental conditions, e.g., alanine and glycine were correlated with coarse sand, and aspartic acid with fine sand, very fine sand, and Chl a (Fig. 6).

Pearson correlation showed significant correlations between metabolite abundance and mud and OM, while no significant correlations were found between metabolite diversity and the sedimentary characteristics (Fig. 7). Metabolite abundance was significantly positive correlated to mud, while significatively negative correlate to OM (Fig. 7). The key metabolite identified by the SIMPER analyses also significatively correlated with the sedimentary characteristics, except proline (Fig. 7). Alanine and glycine were significantly positive correlated to mud and significantly negative correlated to OM, aspartic acid and glutamic acid were significantly positive correlated to mud and Chl a, while succinic acid was only significantly positive correlated to Chl a (Fig. 7).

Discussion

Organisms living in estuaries are influenced by a range of human-induced pressures. Changes in environmental conditions are driving alterations of the behaviour, physiology, and fitness of estuarine fauna with implications for ecological functioning at individual, community, and ecosystem levels. Here, we investigated the metabolic expression of a suspension feeding bivalve—Austrovenus stutchburyi across sites that spanned a gradient of environmental degradation and identified relationships between Austrovenus metabolites and sedimentary environmental conditions.

In accordance with our hypothesis, the abundance of Austrovenus metabolites was higher at sites with less signs of stress (i.e., mud and organic content) in Waihı¯ Estuary, yet diversity of Austrovenus metabolites was higher at sites with higher mud content. The metabolic processes of bivalves can be significantly influenced by environmental conditions (e.g., temperature, nutrients, salinity, pollutants, oxygen, sediment), which leads to variations in their metabolic pathways and thus the abundance and diversity of metabolites expressed (Smyth et al., 2018; Steeves et al., 2018; Saulsbury et al., 2019; Jiang et al., 2020; Georgoulis et al., 2022; Venter et al., 2023; Venter et al., 2024). In our study, differences in metabolite abundance and diversity were more evident between sites closer to freshwater inputs and sites in the upper and middle sections of the estuary. Previous studies have shown that macrobenthic communities, including suspension feeding bivalves, are structured by water column characteristics (e.g., food supply, temperature, salinity, and dissolved oxygen) and the sedimentary environment in which they burrow (e.g., mud and organic matter content, which influence permeability and biogeochemistry) (e.g., Lohrer et al., 2004; Norkko, Hewitt & Thrush, 2006; Douglas et al., 2018a; Douglas, Lohrer & Pilditch, 2019; Villnäs et al. 2019). Austrovenus at Site 7, farthest from the mouth of the estuary, showed the lowest abundance of metabolites but the highest metabolite diversity. Sites1, 2, and 3, which were closer to the mouth and more regularly flushed with clean seawater, exhibited the opposite pattern. These findings were aligned with sedimentary conditions: Site 7 with relatively high mud and low Chl a, and Sites 1–3 characterised by less mud and higher Chl a, showing how metabolite expression correlated with site-specific environmental conditions.

Bivalves, especially suspension feeders, are affected by high sediment loads such as increased mud content in sediment and high levels of suspended sediments in water (Lohrer et al., 2004; Lohrer et al., 2006; Norkko, Hewitt & Thrush, 2006; Jones et al., 2011; Lohrer et al., 2016; Douglas et al., 2018a). High content of mud in sediment can impact bivalve metabolism by altering the availability of food and oxygen. Changes in bivalves’ metabolic expression and processes could be influenced by reduced feeding efficiency and depletion of oxygen, potentially increasing the production of anaerobic metabolites due to lower oxygen levels in the sediment (De la Huz, Lastra & López, 2022; Smyth et al., 2018; Steeves et al., 2018; Saulsbury et al., 2019). High levels of suspended sediment in the water column are thought to affect bivalve feeding and respiration by clogging their gills and reducing their ability to filter particles and breathe efficiently (Norkko, Hewitt & Thrush, 2006; Lohrer et al., 2016; Smyth et al., 2018; Steeves et al., 2018; Saulsbury et al., 2019). This could lead to increased production of stress-related metabolites associated with shifts towards anaerobic metabolism (De la Huz, Lastra & López, 2022; Smyth et al., 2018; Steeves et al., 2018; Saulsbury et al., 2019). In low oxygen conditions, some bivalves are able to shift from aerobic to anaerobic metabolic pathways, resulting in the accumulation of anaerobic metabolites and triggering the production of stress-related metabolites that help bivalves survive in such conditions (Alfaro, Nguyen & Mellow, 2019; Jiang et al., 2020; Georgoulis et al., 2022; Muznebin et al., 2022; Hu et al., 2023; Venter et al., 2023). We found evidence of different metabolites across sampling sites, with alanine, aspartic acid, glutamic acid, glycine, proline, and succinic acid as the six most relevant metabolites. Alanine and succinic acid are metabolites related to shifts from aerobic to anaerobic respiration and are also indicative of metabolic stress, signalling a reduced efficiency in energy production (Ivanina et al., 2013; Jiang et al., 2020; Hu et al., 2023; Venter et al., 2023). Aspartic acid and glutamic acid are related to the tricarboxylic acid (TCA) cycle, which supports increased energy demands when the environmental conditions are not optimal (Muznebin et al., 2022; Venter et al., 2023), while metabolites such as glycine and proline respond to heat stress, stabilizing proteins and membranes, and helping cells maintain their structure and function under thermal stress (Muznebin et al., 2022; Hu et al., 2023; Venter et al., 2024; Lam-Gordillo et al., 2025). Interestingly, the six main metabolites, and in general the total abundance of metabolites, were significantly lower in Site 7 compared to any other site, which could be an adaptative response of Austrovenus to the sedimentary conditions at this site. The increased synthesis of these metabolites in response to high mud content in sediment could reflect adaptative mechanisms focused on enhancing cellular protection, shifts in respiration pathways, maintenance of homeostasis, and survival (Alfaro, Nguyen & Mellow, 2019; Jiang et al., 2020; Georgoulis et al., 2022; Muznebin et al., 2022; Hu et al., 2023; Venter et al., 2023; Lam-Gordillo et al., 2025). Decreases of metabolites at Site 7 may indicate increased use of these metabolites to prevent cumulative stress, cellular malfunction, and maintain stability and cell structure (Nguyen et al., 2018; Alfaro, Nguyen & Mellow, 2019; Liu et al., 2023; Venter et al., 2023; Venter et al., 2024; Lam-Gordillo et al., 2025), while increased metabolite abundance at Sites 1, 2, 3, 4, and 8 may indicate a stockpiling of these metabolite at places where environmental conditions are less stressful.

Our study revealed differences in Austrovenus metabolites across the seven sampling sites in Waihı¯ Estuary as well as strong correlations between Austrovenus metabolites and sedimentary conditions (i.e., mud and organic matter content in sediment). While sediment characteristics were considered a key explanatory factor, it is possible that other environmental factors and bivalve physiological characteristics contributed to the observed metabolic variation. Previous research has demonstrated that bivalve metabolites are also shaped by a combination of other environmental drivers, including water column properties such as temperature, salinity, and dissolved oxygen, microbial interactions, life history traits, and physiological stressors, which strongly influence amino acid metabolism, energy allocation, and stress response (Viant, 2007; Viant et al., 2009; Ellis et al., 2014; Bayona, De Voogd & Choi, 2022; Lam-Gordillo et al., 2025). Thus, we suggest the implementation of more holistic approaches that integrates multiple variables to disentangle the complex interactions shaping bivalve metabolic variation.

Furthermore, it is important to note that the results presented in this study reflect a narrow temporal window and may not capture the full extent of seasonal variability. Environmental conditions, biological activity, and sedimentary processes can fluctuate significantly throughout the year, potentially influencing the metabolite variation. While our findings offer valuable insights into the system during the sampling period, we acknowledge this limitation and recommend that future research incorporate year-round or long-term sampling to get a more holistic picture of the metabolite dynamics and improve the generalizability of the results.

Conclusion

This study elucidates the metabolic changes of Austrovenus stutchburyi across sites spanning a gradient in degradation status in Waihı¯ Estuary. Abundance and diversity of Austrovenus metabolites varied across sites, showing the greatest differences between sites closer to the freshwater inputs (the most impacted sites) and sites located closer to the estuary mouth (most frequently flushed with cleaner coastal ocean water). In general, metabolite abundance was higher at sites with less mud content and lower in sites with higher mud content in sediment, while metabolite diversity was higher at sites with higher mud content and lower in sites with lower mud content in sediment. We identified six key metabolites Alanine, Aspartic acid, Glutamic acid, Glycine, Proline, and Succinic acid, as the main drivers of differences across sites. Our findings also revealed that the differences in Austrovenus metabolite abundance and composition were correlated with the site-specific environmental conditions, but mainly driven by the muddiness of the sediment and the organic matter content in sediment. We provide evidence showing that changes in metabolite abundance, diversity, and composition responded to processes such as feeding efficiency, oxygen regulation, and energy demands. The observed metabolic patterns highlight the complex interactions between suspension feeding bivalves and their environment, emphasizing the importance of understanding these metabolic responses for assessing the health and resilience of bivalve populations in changing ecosystems. Further research in this area can provide valuable insights into the adaptive mechanisms of bivalves and inform conservation and management strategies for these important aquatic organisms.

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