Sediment texture influences extracellular enzyme activity and stoichiometry across vegetated and non-vegetated coastal ecosystems

doi:10.21203/rs.3.rs-2367660/v1

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Sediment texture influences extracellular enzyme activity and stoichiometry across vegetated and non-vegetated coastal ecosystems

https://doi.org/10.21203/rs.3.rs-2367660/v1

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The conversion of organic matter by extracellular enzymes can reveal important insights into carbon processing and nutrient cycling. The activity and stoichiometry of hydrolytic extracellular enzymes were investigated to assess the effects of sediment texture on microbially-mediated decomposition in coastal ecosystems. Enzyme activity was quantified across transects from vegetated (mangrove) to non-vegetated (tidal flat) habitats in two New Zealand coastal ecosystems that vary in sediment texture (sandy: Hobson Bay, muddy: Snells Beach). The activity of five key hydrolyzing enzymes involved in organic matter processing and nutrient cycling were determined: 1) β-glucosidase (hydrolysis of cellulose to glucose); 2) β-N-acetylglucosaminidase (catalyzes the terminal reaction in chitin degradation); 3) alkaline phosphatase (releases soluble inorganic phosphate groups from organophosphates); 4) β-D-cellobiohydrolase (hydrolyzes cellulose to generate cellobiose); and 5) β-xylosidase (catalyzes hemicellulose degradation). All enzymes had higher activity at the muddy site but enzyme activities in these coastal habitats were generally lower than has been reported for terrestrial, freshwater, and other estuarine ecosystems. Extracellular enzyme activities (EEA) did not differ between habitats at the sandy site, whereas EEA was lower in the non-vegetated habitats for some enzymes at the muddy site. Enzyme stoichiometric ratios showed that most habitats at both muddy and sandy sites were predominately C and P limited. These results can be used to advance our understanding of the biogeochemical processes underpinning the response of coastal ecosystems to land-derived nutrient and sediment inputs.

coastal wetlands

enzyme stoichiometry

hydrolytic enzyme activities

mangroves

mud content

nutrient limitation

tidal flat

Coastal ecosystems (e.g., estuaries, mangroves, tidal wetlands) located at the interface between the land and ocean are biologically diverse. These ecosystems transform, store and transport material (Ward et al. 2021). Although the area covered by coastal ecosystems covers only a small fraction of the Earth’s surface, these ecosystems provide important ecosystem services such as carbon (C) and nutrient cycling, contaminant removal, and flood control (Barbier 2017). Vegetated and non-vegetated coastal ecosystems play an important role in storing C and nutrients as anaerobic conditions constrain microbial activities, and low oxygen concentrations limit biogeochemical processes (Kristensen et al. 2008; Helton et al. 2015; Marchand 2017). While many studies have been conducted across vegetated coastal habitats in the tropics, subtropics, and temperature zone, non-vegetated coastal ecosystems (e.g., tidal flats) are less well studied. Tidal flats, located at the interface of the ocean and vegetated-coastal ecosystems form a buffer zone protecting intertidal habitats by decreasing wave power (Chmura et al. 2003). Further, these transition zones fulfill critical functions in the exchange of C and nutrients as tidal exchange can both deposit marine-derived matter onto vegetated areas and export land-derived material to the sea and sediments originating from terrestrial systems are deposited on tidal flats (Ward et al. 2021; Chen and Lee 2022). Given the large range of potential organic matter (OM) sources it is critical to determine C- and nutrient-related processes in vegetated and non-vegetated coastal ecosystems.

Microorganisms play a vital role in organic matter decomposition and nutrient cycling. Of particular importance in heterotrophic microbial cycling of C, nitrogen (N), and phosphorus (P) are extracellular enzymes (Sinsabaugh et al. 2009; Burns et al. 2013). Triggered by the need for nutrients, microbes can produce and release enzymes into their immediate environments. These extracellular enzymes hydrolyze complex organic macromolecules into smaller compounds that are consumed by enzyme-producing organisms as a source of C, energy, and nutrients (Sinsabaugh et al. 2009). Important enzymes involved in OM processing and nutrient cycling are β-glucosidase (BG, hydrolysis of cellulose to glucose), N-acetylglucosaminidase (NAG, catalyzes the terminal reaction in chitin degradation), and alkaline phosphatase (ALP, releases soluble inorganic phosphate groups from organophosphates) (Hoppe et al. 2002; Arnosti et al. 2014) (Table 1). The simultaneous measurement of enzymes related to C, N, and P cycling significantly enhances the mechanistic understanding of decomposition (Penton and Newman 2007). Extracellular enzyme activity (EEA) is usually the limiting step in the process of soil OM decomposition (Conant et al. 2011; Wallenstein and Burns 2011). Therefore, changes in EEA indicate modifications in OM decomposition and microbial nutrient requirements (Shao et al. 2015).

The activity and stoichiometry of C-, N-, and P-degrading enzymes in soils and sediments is controlled by biological (microbial biomass, microbial community composition, OM quantity and quality) and environmental (temperature, moisture) and soil biogeochemical (pH, texture, salinity, nutrient content) factors (Burns et al. 2013; Arnosti et al. 2014). Organic matter quality and quantity affect bacterial abundance and enzyme activity with higher quality OM increasing EEA (Hoppe et al. 2002). For example, EEA related to N acquisition decreased in the presence of mangrove detritus (Crawshaw et al. 2019). Previous studies have also shown that enzyme activity rates are highest in surface or near-surface soils and decrease with depth (Minick et al. 2022) likely due to the influx of fresh OM that stimulates microbial activity in sediment surfaces (Naylor et al. 2020). A recent global meta-analysis showed that hydrolytic C-acquiring enzyme activity is negatively correlated with salinity (Yang et al. 2022). Given that processing and protection takes place in different micro-sites within the sediment matrix, grain size distribution is an important factor (Kravchenko et al. 2019). In a temperature coastal system, EEA has been found to be higher in muddy than sandy sediment (Thomas 2020). In contrast, the highest activities of BG and NAG were found in the sand fraction in intertidal and mangrove sediments (Luo and Gu 2015). In terrestrial soil, pH has also been found to be an important factor influencing EEA, but the evidence in marine sediments is limited or conflicting (Arnosti et al. 2014). Changing water regime (tidal cycle) can also affect microbial community composition, enzyme synthesis and activities (Ferronato et al. 2019).

The relative abundance of enzymes involved in C, N, and P cycling reflects the biogeochemical equilibrium between microbial biomass stoichiometry, the elemental composition of OM and the efficiencies of microbial nutrient assimilation and growth (Sinsabaugh et al. 2008; Sinsabaugh et al. 2012). Due to the conservative production of enzymes by the microbe community, sediment enzymes can help determine which nutrients are the limiting factors and their activities have been suggested as important determinant of soil quality (Sinsabaugh et al. 2008). The BG:NAG and BG:ALP ratios have been used as indicators of microbial demand for C relative to nutrients, and NAG:ALP ratios as an indicator of microbial demand for N relative to P (Sinsabaugh et al. 2009). A ratio equal to one indicates a balance of nutrient demand, whereas unbalanced ratios indicate either C or N limitation (Hill et al. 2014; Kanté et al. 2021). To gain insight into the effects of nutrient availability on enzyme-mediated decomposition processes in coastal ecosystems, understanding of the enzymatic stoichiometry ratio of C, N or P-acquiring enzymes is necessary (Penton and Newman 2007; Keuskamp et al. 2015).

Several studies on EEA and enzyme stoichiometry have been conducted in saltmarsh, mangroves, and tidal flats (Keuskamp et al. 2015; Shao et al. 2015). However, few studies have investigated EEA along transects covering vegetated and non-vegetated habitats (Luglia et al. 2014; Luo and Gu 2018). Further, there is a paucity of studies on how anthropogenic stressors (e.g., sediment input, nutrient enrichment) may alter physical and biological factors and its interactions in coastal ecosystems (Arnosti 2011; Carrier-Belleau et al. 2021). These modifications can result in changed in environmental parameters and shifts in diversity and abundance in microbial communities Snelgrove et al. 2014; Aguirre et al. 2017). Understanding how enzyme activity and stoichiometry in estuarine sediments may change in response to land-derived nutrient and sediment inputs will advance our understanding of the mechanisms underlying biogeochemical processes.

In this study, we quantified the activity of EEA along a habitat gradient from vegetated (mangrove) to non-vegetated (tidal flat) in two New Zealand coastal ecosystems that vary in sediment texture (sandy versus muddy). The activity of five key hydrolyzing enzymes, related to C, N, and P cycling, was determined. Relationships between EEA and biotic and abiotic sediment characteristics were identified. Models of microbial metabolic limitations were established based on the stoichiometric variance.

Study area

The study was conducted in two estuaries in the Auckland region, New Zealand (Fig. 1). The Auckland region is characterised by warm humid summers and mild damp winters. The mean annual temperature in the Auckland region is between 14°C and 16°C. Rainfall across the Auckland region averages 1210 mm per year, with around 32% of the annual rainfall in austral winter (June to August) and around 20% of the rain in austral summer (December to February).

Mangroves are found throughout estuaries in the northern part of the North Island, New Zealand (Morrisey et al. 2010). Only one species of mangrove (Avicennia marina subsp. australasica) is found in New Zealand. Typical mangrove heights range between 0.5–4 m, however adult trees can exceed 6 m (Bulmer et al. 2016; Morrisey et al. 2010). Increased sediment input has resulted in mangrove expansion in New Zealand (Horstmann et al. 2018). In the Auckland region, expansion rates vary between estuaries and mangrove stature (tall, 1.1%; dwarf, 20.7% per year) (Suyadi et al. 2019). High land-based sediment erosion and transport have also resulted in an increase in non-vegetated tidal flats (Suyadi et al. 2019).

Snells Beach (174°42'58"E − 174°43'25"E, 36°25'59"S − 36°26'14"S) is situated within an enclosed river embayment on the eastern coast of the Mahurangi Peninsula (Fig. 1). Tidal amplitude ranges from 0.4 m to 3.6 m. Mud content is 29 to 46%. Mangroves cover 28% of the estuary (Wei et al. in review). Hobson Bay (174°47'29.1"E − 174°47'46.98"E, 36°51'38.16"S − 36°51'45"S) is a small, drowned valley estuary in the Waitemata Harbour (Hume and Herdendorf, 1988). The mean tidal amplitude is 0.5 m − 3.7 m (LINZ 2014). The mud content is 8 to 11%. The mangrove area is 5% of the estuary (Wei et al. in review).

Sediment Sampling And Enzyme Sample Preparation

Three (Snells Beach) to four (Hobson Bay) transects extending from the mangroves to the tidal flat were set up (Fig. 1C, D). Along each transect one sediment core was collected within the mangroves, one at the edge and two to three tidal flat (distanced 10 to 15 apart) locations. Sediment cores were collected at low tide by pushing a PVC tube (20 cm long, 10 cm in diameter) into the sediment, sealed with a cap and extracted. After extraction the bottom piece was also closed with a cap. The sediment cores were transferred to the lab and refrigerated (4 ℃) until sample preparation and further analysis.

Sediment cores were cut into sections (0–2 cm, 2–5 cm, 5–10 cm, 10–15 cm, 15–20 cm), homogenised and separated into four subsamples. One subsample was oven dried at 40°C for physical and chemical analyses, the second subsamples was oven dried at 100°C to estimate the moisture content and bulk density, the third subsample was kept refrigerated (4 ℃) for cold and hot water extraction and the fourth subsamples was frozen (-2 ℃) for enzyme analysis.

Physical and chemical sediment analyses

Sediment:distilled water solutions (ratio 1:2.5) were prepared for pH and conductivity measurements. pH and conductivity were measured in the supernatant (Sension + PH 3, Hach, USA). Salinity was calculated based on conductivity. For particle size analysis, sediment (10 g) was mixed with 30 ml Calgon and then measured using a laser diffraction analyser (Malvern 3000E, Malvern Panalytical, Malvern, UK). Mud content (%) was calculated as the sum of all particle size fractions under 63 µm.

Sediment total C and N concentration was determined using an elemental analyser (vario El Cube, Elementar, Langenselbold, Germany) after grinding the samples into powder. A subset of samples (30%) were acidified to determine the organic C concentration (Wei et al. in review). Organic C for the samples which were not acidified was estimated using a linear regression (r² = 0.986, p < 0.001).

Sediment samples were extracted using cold and hot water (Ghani et al. 2003). Field moist sediment samples (equivalent of 3 g dry weight) were mixed with 30 ml distilled water. The supernatant was filtered with 0.45 µm polyamide membrane filter (Sartorius Stedim Biotech GmbH, Goettingen, Germany). For the hot water extracts 30 ml distilled water was added to the material remaining in the tube, shaken, and then placed in a hot water bath (80 ℃) for 16 h followed centrifugation and filtration. Concentrations of C (determined by non-purgeable organic carbon analysis) and total dissolved N from cold and hot water extracts were determined by high temperature combustion using a Total Carbon and Nitrogen analyser with a high salt sample combustion tube (TOC-L, TNM-L, Shimadzu Corporation, Kyoto, Japan).

Enzyme Assay

We measured potential activities of five extracellular hydrolytic enzymes related to C, N, and P cycling (Table 1) using fluorescent substrates labelled with methylumbelliferone (MUB) ( German et al. 2011; Dunn et al. 2014). The MUB substrate solutions (200 µmol L^− 1) were prepared as follows: BG: 6.77 mg 100 mL^− 1 DI H₂O; CB: 10 mg 100 mL^− 1 DI H₂O; XYL: 6.17 mg 100 mL^− 1 DI H₂O; NAG: 7.59 mg 100 mL^− 1 DI H₂O; ALP: 5.12 mg 100 mL^− 1 DI H₂O. Standard stock solutions were diluted from 200 µmol L^− 1 to 100, 50, 25, 10, 5, 2.5 µmol L^− 1 for each enzyme.

After thawing, 0.9 g of sediment was weighed into a centrifuge tube and 30 mL acetate buffer was added. Acetate buffer (50 mM) was prepared by dissolving 2.05 g sodium acetate in 500 mL deionized water (DI) water. The buffer was adjusted to the sediment pH (Snells Beach ≈ 7, Hobson ≈ 7.5) with 1 M NaOH. Centrifuge tubes were capped, the sediment slurry was shaken vigorously with a Vortex for 1 min, and then homogenized on a stir plate. 200 µL of the sediment slurry was pipetted into each well of a black 96-well microplate. Fifty µL of the enzyme specific substrate solution was added to each well and the microplates (sample and standard plates) were incubated in the dark for 3 h. The enzyme assays were performed in 3 replicates and one control (substrate added after incubation) for each sample. Fluorescence was measured at room temperature (20 ℃) using a microplate reader (EnSpire Multimode Plater Reader, PerkinElmer) at an excitation wavelength of 365 nm and emission wavelength of 450 nm. Each microplate was read three times and plates were kept in the dark between readings.

Enzyme activity expressed per sediment dry weight provides an absolute metric of potential enzyme activity and was calculated following as follows (Bell et al. 2013; German et al. 2011).

$$\text{A}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y} \left(\text{n}\text{m}\text{o}\text{l} {\text{h}}^{-1}{\text{g}}^{-1}\right)=\left(\frac{\text{E}\text{n}\text{z}\text{y}\text{m}\text{e} \text{c}\text{o}\text{n}\text{c}\text{e}\text{n}\text{t}\text{r}\text{a}\text{t}\text{i}\text{o}\text{n} (\times \text{B}\text{u}\text{f}\text{f}\text{e}\text{r} \left(\text{m}\text{l}\right)}{\text{S}\text{e}\text{d}\text{i}\text{m}\text{e}\text{n}\text{t} \text{s}\text{l}\text{u}\text{r}\text{r}\text{y} \text{v}\text{o}\text{l}\text{u}\text{m}\text{e} \left(\text{m}\text{l}\right)\times \text{T}\text{i}\text{m}\text{e} \left(\text{h}\right)\times \text{S}\text{e}\text{d}\text{i}\text{m}\text{e}\text{n}\text{t} \text{m}\text{a}\text{s}\text{s} \left(\text{g}\right)}\right)\times 1000$$

Where enzyme concentration was calculated using the slope and intercept derived from plotting the fluorescence raw read over the standard concentration (µmol) of a given enzyme; separate regression curves were established for low and high concentrations (0–25 µmol L^− 1 and 10–100 µmol L^− 1, respectively; buffer volume is 30 ml, sediment slurry volume is 0.2 ml, incubation time is 3 hours, and sediment mass is 0.9 g.

We used the ratios of measured enzyme activity (enzyme stoichiometry) to assess which nutrients are limiting as the ratios reflect the relative allocation of microbial resources to the acquisition of C, N and P (Sinsabaugh et al. 2009). Element limitations were identified by drawing the ratio of NAG:ALP on the x-axis and BG:NAG ratio on the y-axis. The horizontal and vertical coordinates of 1 were the baselines.

Statistical analysis

Parameters were checked for normality using the Shapiro-Wilks test. As most parameters were not normally distributed, nonparametric analyses (Kruskal Wallis followed by post-hoc test) were applied to test for differences between depths and habitats. Spearman rank correlations were performed to determine the relationship between physico-chemical sediment characteristics and extracellular enzyme activities. The significance level for the statistical analysis was assessed at p < 0.05. Data analysis was conducted using SPSS (IBM SPSS Statistics 2020) and JMP (SAS 15.0).

Table 1

Extracellular enzymes included in this study
Enzyme	EC*	Abbrev.	Substrate	Function
β-glucosidase	EC 3.2.1.21	BG	4-MUB-β-D-glucoside	Cellulose degradation: hydrolyzes cellobiose into glucose
β-D-cellobiohydrolase	EC 3.2.1.91	CB	4-MUB-β-D-cellobioside	Cellulose degradation: hydrolyzes cellulose to generate cellobiose
β-xylosidase	EC 3.2.1.37	XYL	4-MUB-β-D-xylopyranoside	Hemicellulose degradation: hydrolyzes hemicellulose to xylose
β-N-acetylglucosaminidase	EC 3.2.1.14	NAG	4-MUB-ß-N-D-acetylglucosaminide	Chitin degradation: hydrolyzes glucosamine from chitobiose
Alkaline phosphatase	EC 3.1.3.1	ALP	4-MUB-phosphate	Phosphate degradation: hydrolyzes phosphoric acid monoesters into a phosphate ion
*Enzyme Commission number; MUB = methylumbelliferyl

Sediment characteristics

Mud content across habitats at Snells Beach (29–46%) was up to seven times higher than Hobson Bay (6–11%) (Table 2). Mud content at both sites varied considerably within habitats but mud content tended to be higher in the mangroves than tidal flat (Table 2). Salinity was two times higher at Snells Beach (19–23%) than Hobson Bay (9–13%) (Table 2). The pH values at Snells Beach (6.3–6.9) were lower than Hobson Bay (6.9–7.9) but at both sites sediment pH under mangroves tended to be lower than tidal flat (Table 2). Bulk and hot water extractable carbon and nitrogen at Snells Beach was up to 10 times higher than Hobson Bay (Table 2). Bulk and water extractable carbon and nitrogen concentrations at both sites tended to be lower in the tidal flat than vegetated habitats (mangroves, edge) (Table 2). In summary, Snells Beach was characterised by high mud content, salinity, and OM content while Hobson Bay was associated with a sand dominated texture, low salinity, and OM content.

Table 2

Physico-chemical sediment characteristics at Snells Beach and Hobson Bay; values presented as mean ± standard deviation; cweC, cweN = cold water extractable carbon and nitrogen; hweC, hweN = hot water extractable carbon and nitrogen.
	Mud (%)	Salinity (ppt)	pH	Carbon (%)	Nitrogen (%)	C:N ratio	cweC (µg g^− 1)	cweN (µg g^− 1)	hweC (µg g^− 1)	hweN (µg g^− 1)
Snells Beach
Mangrove	45.90 ± 14.93	23.38 ± 3.47	6.34 ± 0.30	2.58 ± 0.38	0.22 ± 0.05	12.2 ± 1.9	89.06 ± 39.45	9.28 ± 2.92	1604.78 ± 447.24	145.11 ± 54.31
Edge	44.01 ± 13.40	23.06 ± 5.82	6.48 ± 0.34	1.76 ± 0.23	0.17 ± 0.03	10.3 ± 1.3	49.26 ± 19.52	7.45 ± 2.08	1103.33 ± 434.60	120.60 ± 53.13
Tidal flat	28.74 ± 8.94	19.34 ± 3.87	6.92 ± 0.21	0.89 ± 0.19	0.09 ± 0.02	9.7 ± 0.9	39.98 ± 17.46	7.17 ± 3.14	535.00 ± 167.42	56.98 ± 19.52
Hobson Bay
Mangrove	10.45 ± 5.76	12.48 ± 3.58	6.93 ± 0.58	0.48 ± 0.32	0.06 ± 0.04	8.8 ± 1.5	69.72 ± 49.15	11.28 ± 10.17	365.44 ± 260.75	41.35 ± 31.85
Edge	6.36 ± 4.04	10.34 ± 2.53	7.59 ± 0.45	0.23 ± 0.11	0.02 ± 0.01	8.8 ± 3.1	21.27 ± 16.05	3.16 ± 3.20	102.28 ± 28.06	12.37 ± 5.63
Tidal flat	7.74 ± 4.13	9.24 ± 1.39	7.83 ± 0.40	0.24 ± 0.12	0.03 ± 0.01	8.8 ± 3.5	18.66 ± 13.57	3.05 ± 2.77	129.04 ± 57.90	15.02 ± 8.79

Enzymes activities

Enzyme activity varied between enzymes in the order ALP (2–320 nmol h^− 1 g^− 1) > BG (1–150 nmol h^− 1 g^− 1) ≈ NAG (0.4–120 nmol h^− 1 g^− 1) > XYL (0.1–50 nmol h^− 1 g^− 1) ≈ CB (0.1–35 nmol h^− 1 g^− 1) (Fig. 2).

Significant positive correlations were observed among the five hydrolytic enzymes (Table 3). At Snells Beach, the correlations were strongest between enzymes involved in C and N acquisition (rho > 0.8, p < 0.05). The relationship between ALP and C and N-related enzymes was less strong with correlation coefficients ranging between 0.673 and 0.825 (Table 3). At Hobson Bay the correlation between enzymes was less strong (but significant) in particular between ALP and C and N related enzymes (rho: 0.506–0.758, p < 0.05) (Table 3).

Table 3

Correlation between the extracellular enzyme activities (expressed as nmol h^− 1 g^− 1 sediment) at Snells Beach and Hobson Bay. Values are spearman rank correlation coefficients. Bold values are significant at p < 0.05.
Snells Beach					Hobson Bay
	CB	XYL	NAG	ALP	CB	XYL	NAG	ALP
BG	0.922	0.872	0.916	0.825	0.716	0.667	0.809	0.758
CB		0.816	0.856	0.804		0.843	0.795	0.570
XYL			0.928	0.673			0.865	0.506
NAG				0.777				0.667

Independent of the enzyme, activities varied considerably between transects at a given depth as shown by the large error bars, in particular in the vegetated habitats. At both sites (Snells Beach and Hobson Bay), no significant differences in enzyme activity were found between tidal flat locations (n = 2–3 locations per transect). Therefore, we grouped the tidal flat locations into one group and compared this group with the mangrove and edge habitat. Given the difference in mud content (Table 2), we present the results for Snells Beach and Hobson Bay separately.

Carbon degrading enzymes

All enzymes involved in C acquisition (BG, CB and XYL) had up to 10-fold higher activities at Snells Beach (Fig. 2a,c,e) than Hobson Bay (Fig. 2b,d,f). A significant effect of depth on BG, CB and XYL enzyme activity was found at the tidal flat at Snells Beach (Fig. 2a,c,e) and on BG activity at the edge at Hobson Bay (Fig. 2b). A habitat effect was found for BG and XYL at Snells Beach with enzyme activities being significantly lower in the tidal flat than in mangroves (Fig. 2a,e). At Hobson Bay, the enzyme activity of BG, CB and XYL did not differ significantly between mangrove, edge and tidal flat (Fig. 2b,d,f).

Nitrogen and phosphorus degrading enzymes

Similar to C-related enzymes, N (NAG) and P (ALP) associated enzyme activities were higher at Snells Beach (Fig. 2g,i) compared to Hobson Bay (Fig. 2h,j). A significant decrease in NAG and ALP activity with depth was found at the tidal flat at Snells Beach (Fig. 2g,i). At Hobson Bay, NAG activity decreased with depth at the edge (Fig. 2h) whereas ALP activity decreased at both vegetated habitats (mangrove, edge) (Fig. 2j). A habitat effect was found for NAG and ALP at Snells Beach with enzyme activities being significantly higher in the mangroves than tidal flat (Fig. 2g,i). At Hobson Bay, NAG and ALP did not differ significantly between habitats (Fig. 2h,j).

Relationships Between Enzyme Activities And Physico-chemical Sediment Characteristics

We found significant positive correlations between mud content and enzyme activity of all five hydrolytic enzymes at Snells Beach (rho > 0.381; p < 0.05; Table 4). In contrast, at Hobson Bay, mud content and enzyme activity was negatively correlated (Table 4). At Snells Beach, enzyme activity increased with increasing salinity (rho > 0.397; p < 0.05) whereas no significant relationship was found between salinity and enzyme activity at Hobson Bay (Table 4). Enzyme activity was negatively correlated with pH at Snells Beach (Table 4). At Snells Beach, the enzyme activity of all five hydraulic enzymes increased with increasing sediment bulk C and N concentration and hot water extractable carbon and nitrogen (rho > 0.481, p < 0.05) (Table 4). At Hobson Bay, significant negative correlations were found between sediment C concentration and XYL and NAG enzyme activity. The activity of all enzymes (except for BG) at Hobson Bay decreased with increasing C:N ratio (Table 4).

Table 4

Correlations between enzyme activities (nmol h^− 1 g^− 1) and physico-chemical sediment characteristics. cweC, cweN = cold water extractable carbon and nitrogen; hweC, hweN = hot water extractable carbon and nitrogen. Values are spearman rank correlation coefficients. Negative correlations are shown in *italics*. * indicates correlation is significant at p < 0.05
	Snells Beach					Hobson Bay
	BG	CB	XYL	NAG	ALP	BG	CB	XYL	NAG	ALP
Mud	0.448*	0.387*	0.574*	0.389*	0.381*	-0.190	-0.330*	-0.425*	-0.410*	-0.261*
Salinity	0.575*	0.400*	0.690*	0.640*	0.397*	-0.074	-0.155	-0.194	-0.184	-0.126
pH	-0.670*	-0.592*	-0.754*	-0.719*	-0.341*	-0.099	-0.058	0.070	-0.041	-0.060
Carbon	0.675*	0.513*	0.809*	0.731*	0.481*	-0.034	-0.215	-0.290*	-0.253*	-0.184
Nitrogen	0.721*	0.572*	0.828*	0.777*	0.612*	0.123	-0.024	-0.172	-0.091	0.042
C:N ratio	-0.008	-0.000	0.089	-0.026	-0.337*	-0.200	-0.358*	-0.302*	-0.374*	-0.392*
cweC	0.204	0.140	0.364*	0.281*	0.069	0.081	0.141	-0.012	0.056	0.143
cweN	0.036	0.046	0.179	0.184	0.110	0.182	0.241*	0.110	0.205	0.356*
hweC	0.701*	0.550*	0.765*	0.729*	0.551*	0.184	0.100	-0.010	0.097	0.226*
hweN	0.717*	0.584*	0.744*	0.718*	0.630*	0.272*	0.214	0.104	0.253*	0.383*

Enzyme stoichiometry

The BG:NAG (0.7–2.5) ratios at Snells Beach (Fig. 3a) tended to be lower than Hobson Bay (3.2–11.5) (Fig. 3b). At Snells Beach, the BG:NAG ratios did not differ significantly between depth at the mangrove and edge but decreased with depth at the tidal flat (Fig. 3a). Habitat did not affect the BG:NAG ratio at Snells Beach (Fig. 3a). At Hobson Bay, the BG:NAG ratios were highly variable, and no effect of depth and habitat was found (Fig. 3b).

The BG:ALP ratios at Snells Beach (0.6–1.8, Fig. 3c) tended to be slightly lower compared to Hobson Bay (0.4–3.7, Fig. 3d). The BG:ALP ratios tended to increase with depth in the vegetated habitats, but a significant depth effect was only observed at the edge at Snells Beach (Fig. 3c). A habitat effect was found for BG:ALP ratio in deeper layers at Snells Beach with BG:ALP ratios in the mangroves being higher than the edge and tidal flat (Fig. 3c). At Hobson Bay, BG:ALP ratios did not change with depth nor habitat (Fig. 3d).

The NAG:ALP ratios tended to be lower (0.05–1.4) than the BG:NAG and BG:ALP ratios and were similar at Snells Beach and Hobson Bay (Fig. 4e,f). No depth effect on the BG:ALP ratio was found at Snells Beach and Hobson Bay. At Snells Beach the BG:ALP ratio at deeper layers was significantly in the mangroves than edge and tidal flat (Fig. 4e).

The BG:NAG ratio at Snells Beach was significantly negatively related with cweN (rho = 0.327, p < 0.01). At Hobson Bay a positive correlation was found between the BG:NAG ratio and sediment C (rho = 0.278, p < 0.05), sediment N (rho = 0.243, p < 0.05) and mud content (rho = 0.230, p < 0.05). At Snells Beach, BG:ALP ratio was positively correlated with sediment C content (rho = 0.244, p < 0.05), sediment C:N ratio (0.507, p < 0.01) and salinity (rho = 0.282, p < 0.05) and negatively correlated with pH (rho = -0.599, p < 0.01). At Hobson Bay, the BG:ALP ratio was positive correlated with the sediment C:N ratio (rho = 0.272, p < 0.05) and negatively correlated with cweN (rho = -0.239, p < 0.05).

Scatter plots of BG:NAG ratios versus NAG:ALP ratios showed that most locations at Snells Beach and Hobson Bay were predominately C and P limited (Fig. 4a, b). At Snells Beach, several locations in the tidal flat were also P limited and some mangrove locations were C and N or N limited (Fig. 4a).

We quantified the potential activity of common hydrolytic enzymes associated with C (BG, CB, XY), N (NAG) and P (ALP) degradation. The EEA in this study were at the lower end of EEA reported across selected terrestrial, freshwater, and estuarine ecosystems (Table 5). Differences in potential enzyme activity between studies are likely explained by differences in biological and physico-chemical site conditions. Investigating freshwater wetlands across the United States, Hill et al. (2018) found that EEA were strongly correlated with sediment C, N and P, suggesting site-specific controls which partly explains the considerable differences in EEA between Snells Beach and Hobson Bay.

In this study, ALP activities were higher than the activity of C and N associated enzymes in both estuaries (Fig. 2). Lower activities of BG and NAG compared to ALP have also been reported in estuarine mudflats in French Guiana (Luglia et al. 2019) and mangrove habitats (Saraswati et al. 2016) (Table 5). This finding suggests deficiencies of inorganic N and P. Under P limitation, microbial communities will spend energy to produce C- and N-acquiring enzymes related to the P metabolisms (Chen et al. 2018). This is supported by the correlation between ALP, BG and NAG activities (Table 3). Higher ALP have also been related to sediment OM quantity and quality (Waldrop et al. 2004). Enrichment of C and/or could result in changes of soil C:N:P ratios and greater deficiencies of N and/or P, resulting in increased activities of N and P-cycling extracellular enzymes (Hoppe et al. 2002; Criquet and Braud 2008). The positive correlation between ALP activity and sediment C and N content at Snells Beach supports the existence of such a link.

Table 5

Hydrolytic enzyme activities (min - max) of soils and sediments across selected ecosystems/studies
Ecosystems	Location	BG	CB	XYL	NAG	ALP	Reference
Ecosystems	Location	(nmol h^− 1 g^− 1)
Terrestrial soils	Global	176–9200	33-2700	nd	42-4820	983- 15800	(Sinsabaugh et al. 2008)
Freshwater wetlands	Global	nd	857–4008	nd	1480–13278	322–3150	(Kang and Freeman 2009)
Mangrove peat (fertilised)	Belize	30–375	10–240	nd	20–300	10–750	(Keuskamp et al. 2015)
Mangroves	Florida, USA	6-300	nd	30–210	nd	10–900	(Saraswati et al. 2016)
Mangroves	South Andamans, India	170–260	nd	nd	nd	460–600	(Dinesh et al. 1998)
Estuarine mudflats	French Guiana	90–540	nd	nd	nd	240–2280	(Luglia et al. 2019)
Coastal ecosystems	Hongkong	150–4000	nd	nd	200–2100	1000–13000	(Luo et al. 2018)
Tidal flat/salt marsh	Hangzhou Bay, China	2870–4170	nd	nd	nd	16200–21400	(Shao et al. 2015)
Estuarine sediment	Whangateau, New Zealand	320–710	nd	nd	nd	350–550	(Crawshaw et al. 2019)
Tidal flat	Snells Beach, New Zealand	10–65	2–16	1–9	5–51	19–156	This study
Edge		30–127	4–25	8–24	25–88	33–189	This study
Mangroves		34–152	2–36	8–49	29–117	32–320	This study
Tidal flat	Hobson Bay, New Zealand	3–48	0.1-3	0.1-4	1–8	2–84	This study
Edge		1–39	0.1-6	0.1-2	1–7	2–91	This study
Mangroves		2–34	0.1-4	0.1–14	1–26	3–52	This study
nd = not determined

Sediment depth and habitat effects

Enzyme activities varied considerable between transects especially in the upper sediment layer (Fig. 2a-j). A high variability in enzyme activities between locations has also been reported by others (Arnosti et al. 2014) and is partly explained the variation in microbial communities, environmental conditions, and sediment characteristics. For example, bioturbation by burrowing macrofauna results in small-scale differences in EEA (Luo and Gu 2018). These differences have been related to changes in sediment characteristics such as C and N content and quality of burrow walls compared to the surrounding sediment (Luo and Gu 2018). Varying distances of the transects to tidal creeks and thus differences in inundation regimes may contribute to the large-scale differences in EEA as enzyme activity has been shown to respond quickly to inputs of OM from sedimentation events (Meyer-Reil 1987).

Despite the spatial variation in EEA, we observed a sediment depth and habitat effect (Fig. 2i-j). Extracellular enzyme activity (per g sediment) of C, N and P associated enzymes tended to decrease with depth across habitats (Fig. 2a-j). A decrease in enzymatic activity with soil and sediment depth has been reported terrestrial and coastal ecosystems (Freeman et al. 2001; Luo and Gu 2018; Chowdhury et al. 2019). A major factor explaining the decrease in microbial and enzyme activity is the decline in OM, root biomass, available nutrients, and microbial activity with depth (Eilers et al. 2012; Stone et al. 2014; Li et al. 2015; Liu et al. 2021). This is supported by our findings showing strong relationships between sediment and OM related sediment characteristics (sediment C and N) and indicators of microbial activity (hot water extractable organic matter) and enzyme activities (Table 4). Higher activity of hydrolytic enzymes in the upper sediment layer may also be driven by the higher availability of oxygen in this layer enhancing microbial activity (McKee and Seneca, 1982; Freeman et al. 2001).

Besides depth, habitat had an effect on the enzyme activity (per g sediment) at the muddy site (Snells Beach), where higher EEA was observed in sediment under mangroves compared to tidal flat habitats (Fig. 2a,c,e,g,i). Consistently higher EEA in the sediment under saltmarsh communities compared to non-vegetated tidal flats were found in tidal wetlands in China (Shao et al. 2015; Liu et al. 2021). Further, a gradual decline in EEA with decrease in mangrove forest coverage across the Indian Sundarbans was reported by Chowdary et al. (2019). Higher EEA in vegetated habitats have been related to higher above and below ground biomass influencing microbial community composition and activity and in turn EEA (Sinsabaugh et al. 2008; Kang and Freeman 2009; Shao et al. 2015; Liu et al. 2021; Tang et al. 2021). Further, root exudates have been reported to stimulate microbial activity through the provision of high-quality sources of C (Reboreda and Caçador 2008; Oliveira et al. 2010; Luo and Gu 2018). It has also been reported that root biomass can influence enzymatic activities directly, through the exudation of phosphatases or low molecular weight organic molecules, enhancing enzymatic activity (Nannipieri et al. 2011).

Higher enzyme activity at Snells Beach between the mangrove and edge habitat (Fig. 2) might be related to changes in the aboveground-belowground linkages. Mangrove habitats were characterised by higher aboveground biomass compared to the edge habitat (Wei et al. in review). A higher activity of BG in older mangrove ecosystem than pioneer stages in French Guiana was explained by an increase in OM decomposition of aboveground litter (Luglia et al. 2014).

Extracellular Enzyme Activity And Physico-chemical Sediment Characteristics

Enzyme activities of all five hydraulic enzymes differed considerably between the muddy and sandy estuary with EEA at the muddy estuary being up to 10-fold higher than the sandy estuary (Fig. 2). Further, the relationships between EEA and physico-chemical sediment characteristics were markedly different between the two sites (Table 4). We hypothesise that sediment texture (mud content) was the main underlying factor governing EEA in this study. Snells Beach was characterised by high mud content, salinity, more acidic pH, and relatively high OM content. In contrast, Hobson Bay was sand dominated, had a lower salinity and OM content.

We found a strong positive correlation between mud content and EEA at muddy Snells Beach (Table 4). An increase of enzyme activity with increasing mud content have been reported in previous studies and has been explained by the higher surface area of mud rich sediments providing a larger area for bacteria to live ( Oliveira et al. 2010; Kravchenko et al. 2019). Similarly, clay particles may adsorb phosphatase and thus contribute to high ALP activities found in estuaries in French Guinea during the wet season (Luglia et al. 2019). In contrast, larger pore sizes and lower aggregation in the sandy Hobson Bay may have resulted in the loss of OM (Kravchenko et al. 2019). The negative correlation between mud content and enzyme activity at Hobson Bay (Table 4) is in line with findings reported by Luo and Gu (2015). Luo and Gu (2015) measured enzyme activity in bulk sediment and different particle size fractions (clay, silt, sand) collected from non-vegetated intertidal and mangrove habitats and found that C and N associated enzymes had a higher activity in the sand fraction. The findings were explained by a smaller C:N ratio in sandy soils suggesting a higher supplier of N (Luo and Gu 2015). Enzyme activity at Hobson Bay was significantly negatively correlated with sediment C:N ratio which implies that quality of OM may have been an important factor driving EEA at Hobson Bay (see below). Further, coarser sand fractions (> 200 µm) have been found to contain a large amount of polymeric material which may partly contribute to higher enzyme activity (Marx et al. 2005).

We found positive correlations between salinity and EEA at muddy Snells Beach (Table 4). While EEA increased with salinity in some studies (Neubauer et al. 2013; Bai et al. 2021), a decrease (Morrissey et al. 2014; Servais et al. 2019; Yang et al. 2022) or no correlation was reported in other studies (Chambers et al. 2013). Increasing salinity in a subtropical tidal wetland stimulated root growth and activity which in turn enhanced the production of C-degrading enzymes (Bai et al. 2021).

Enzyme activities at muddy Snells Beach were negatively correlated with sediment pH (Table 4). Negative (but not significant) relationships between enzyme activities and soil pH were reported in mangroves in the South Andamans (Dinesh et al. 1998). Sediment pH at Snells Beach was outside the optimal pH range of C, N, and P associated enzymes (Sinsabaugh et al. 2009) which may have hampered enzyme production. Sediment pH reflects the plant community composition at a given site and has been shown to influence OM quality and microbial community composition through changes in nutrient availability (Sinsabaugh et al. 2009). The relationships between enzymes and soil pH differs across studies depending on the pH range of sampled soils and enzyme assay (e.g. has the buffer pH been adjusted to match the enzyme pH optima) (Uwituze et al. 2022).

High mud content at Snells Beach was associated with higher sediment C and N content and water extractable carbon and nitrogen (data not shown). In this estuary, we also found strong positive correlations between sediment C and N and the EEA all five hydrologic enzymes (Table 4). This is in line with other studies and a global metanalysis reporting close relationships between soil OM content and C, N, and P related enzymes (Dinesh et al. 1998; Sinsabaugh et al. 2008; Luo and Gu 2018). This suggests that hydrolytic enzymes are sensitive to resource availability across vegetated and non-vegetated habitats with higher OM stimulating microbial activity and thus enzyme activity (Sinsabaugh et al. 2010; Luo and Gu 2018; Luglia et al. 2019). The strong relationship between enzyme activity and hot water extractable carbon (Table 4), which is an indicator of microbial biomass (Ghani et al. 2003), supports the assumption of microbial activity driving EEA. Higher soil organic C content may also result in the build-up of enzymes in the soil matrix as soil OM can form stable complexes with free enzymes (Naidja et al. 2000).

We found a decrease in ALP (Snells Beach) and CB, XYL, NAG, and ALP (Hobson Bay) with increasing sediment C:N ratio (Table 4). The C:N ratio is used as an indicator of source of OM (terrestrial versus marine) (Emerson and Hedges 1988; Meyers 1994) and a measure of OM quality (Swift et al. 1979). The C:N ratios found at Snells Beach (10–12) show that OM is of mixed origin with both marine and terrestrial input. The catchments around Snells Beach are dominated by pasture and plantation forestry and sediment erosion rates are high (Temple and Parsonson 2014) which may partly explain the higher C:N ratio. The slightly lower sediment C:N ratio at Hobson Bay (9) suggest a higher proportion of marine input enriched in proteinaceous material (C:N ratio of 7, Meyer-Reil 1987). Phosphatase activity was negatively correlated with sediment C:N ratio in both estuaries (Table 4) which may indicate the role of ALP as an important enzyme regulating microbial responses to substrate quality.

Relationships between cold water extractable organic matter and EEA varied between enzymes and estuaries (Table 4). The inconstant pattern may be explained by the fact that cold water extractable organic matter contains both substrates and end products of enzymatic reactions ( Herbert and Bertsch 1995; Bonnett et al. 2006). Based on previous studies, higher cweC and cweN concentration increases substrate supply for microbial activity and stimulates the synthesis of new enzymes (Song et al. 2011). However, the tidal cycles accelerate the diffusion and dilution of enzymes and soluble substrates, reducing contact probability (Burns et al. 2013). Given the comparatively low sediment C and N concentration at sandy Hobson Bay, substrate-limitation may explain low EEA (Burns et al. 2013). Some of our findings suggest that enzyme activity at Hobson Bay is mainly driven by the quality of the OM.

Enzyme Stoichiometry

In addition to quantifying enzyme activity, we calculated enzyme activity ratios to assess the relative recalcitrance of sediment (i.e., BG:ALP ratio) and the acquisition of N (BG:NAG ratio) and P (BG:ALP ratio) relative to C. The mean BG:NAG ratio at Snells Beach was 1.25 which is lower than the global average of 1.81 (Sinsabaugh et al. 2009). This suggests that the sediment at Snells Beach might require more N for microbial growth and cell maintenance compared to Hobson Bay (BG:NAG ratio: 5.13). Mean BG:ALP ratios (Snells Beach: 0.66; Hobson Bay: 0.77) and NAG:ALP ratios (Snells Beach: 0.55; Hobson Bay: 0.19) were lower than the global average of 1.64 and 0.79, respectively (Sinsabaugh et al. 2012; Moorhead et al. 2013; Waring et al. 2014). Further, the ratio of BG:NAG versus BG:ALP ratio in most samples in this study plotted above the 1:1 line. These findings suggest that the sediment across all habitats at Snells Beach and Hobson Bay were P and P & C limited (Fig. 4). This is in line with previous findings reporting P limitation in mangrove sediments (Luo and Gu 2018) and tidal wetlands (Zhai et al. 2022).

Although mangroves are often rich in OM several studies reported that mangrove sediments are often deficient in nutrients, in particular N and P (Middelburg et al. 1996; Reef et al. 2010). At Snells Beach, the high mud content may contribute to a physical and/or chemical protection of C, resulting in a microbial C limitation (Lehmann and Kleber 2015). In contrast, nutrient limitation at Hobson Bay may be driven by the low OM content. In addition, cyanobacteria in tidal flats and mangroves are able to fix both atmospheric C and N (Alvarenga et al. 2015; Vogt et al. 2019), which in turn may reinforce P limitation. Further, P limitations can occur, particularly when N loads to estuarine or coastal waters are high relative to P (Li et al. 2015). Both estuaries are potentially subject to increased N influx from urban (Hobson Bay) and agricultural (Snells Beach) sources. Nitrogen limitation was only observed in a few mangrove samples at Snells Beach (Fig. 4) which supports this explanation.

The common P-limitation for primary productivity in freshwater wetlands (Hill et al. 2014) would induce soil P deficiencies and stronger competitiveness with woody plants (Fenner and Freeman 2020); consequently, P might be more limited than N for microbial decomposers in wetland soils. Higher losses of P due to leaching may partly explain lower P availability and higher levels of P microbial limitation at sandy sites.

Salinity-related reduction in sediment P availability and increase in plant P demand under salt stress accelerating plant microbial competition for P, have been shown to increase microbial P limitation (Zhai et al. 2022). To offset P limitation, microorganisms optimize their energy allocation to produce P-acquiring enzymes (Cui et al. 2020). Moreover, P-containing molecules are physically and chemically shielded by the macromolecular SOM (Chen et al. 2018; Hill et al. 2018). Therefore, if P becomes relatively restricted in soils, microorganisms will simultaneously expend energy to produce C- and N-acquiring enzymes associated with P metabolism (Chen et al. 2018), which supports our finding that microbial P limitation was also related to the activities of BG and NAG.

Activities of all five enzymes were considerably higher in the mud- and OM-rich estuary compared to the sandy estuary. Despite some overall patters such as habitat and sediment depth, relationships between EEA and physico-chemcial characteristics differed between the muddy and sandy estuary. We suggest that mud content was a major underlying factor influencing sediment characterises and in turn EEA. The EEA at muddy sites was positively related to total and hot water extractable carbon and nitrogen indicating the relevance of energy source (sediment C content) and microbial activity (hot water extractable carbon). In contrast cold water extractable C and N showed weak relationships with EEA. Independent of habitat and estuary, most samples were P and P & C limited.

Overall, our findings suggest that EEA is strongly influenced by local conditions including sediment texture and associated factors. Thus, the role of sediment type, vegetation, tidal regime, faunal, and microbial communities warrant further investigation. In particular, the role of benthic communities through their bioturbation activity, would be expected to affect physical sediment properties and OM characteristics and in turn EEA.

Acknowledgements

This study was partly funded through a PhD scholarship to MW by a private donor and the NIWA Coasts and Oceans Programme, Strategic Science Investment Fund (New Zealand Ministry for Business, Innovation and Employment), Project #COME2203 “Biodiversity, Connectivity and Health”. We thank Julia Jakobsson and Melanesia Boseren for their help in the field and Natalia Abrego for technical assistance in the laboratory.

Statements and Declarations

Funding

This study was partly supported through a PhD scholarship to MW by a private donor and the NIWA Coasts and Oceans Programme, Strategic Science Investment Fund (New Zealand Ministry for Business, Innovation and Employment), Project #COME2203 “Biodiversity, Connectivity and Health”.

Competing interest

The authors have no relevant financial or non-financial interests to disclose.

Authorship contributions

MW, CL, and LS designed the study; MW conducted the field work and lab analysis; MW, CL and LS analysed the data; MW prepared the first draft; CL and LS revised and edited the manuscript

Data availability

The datasets generated and/or analysed for this study will be made available via https://auckland.figshare.com/

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Sediment texture influences extracellular enzyme activity and stoichiometry across vegetated and non-vegetated coastal ecosystems

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Abstract

Figures

Introduction

Materials And Methods

Study area

Sediment Sampling And Enzyme Sample Preparation

Physical and chemical sediment analyses

Enzyme Assay

Statistical analysis

Results

Sediment characteristics

Enzymes activities

Relationships Between Enzyme Activities And Physico-chemical Sediment Characteristics

Enzyme stoichiometry

Discussion

Sediment depth and habitat effects

Extracellular Enzyme Activity And Physico-chemical Sediment Characteristics

Enzyme Stoichiometry

Conclusion

Declarations

References

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