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A numerical model study on the spatial and temporal variabilities of dissolved oxygen in Qinzhou Bay of the northern Beibu Gulf
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Gaolei Cheng1, 3, Shiqiu Peng1, 2, 3, *, Bin Yang3, Dongliang Lu3
Acta Oceanologica Sinica | 2024, 43(6) : 49 - 59
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Acta Oceanologica Sinica | 2024, 43(6): 49-59
• Articles •
A numerical model study on the spatial and temporal variabilities of dissolved oxygen in Qinzhou Bay of the northern Beibu Gulf
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Gaolei Cheng1, 3, Shiqiu Peng1, 2, 3, *, Bin Yang3, Dongliang Lu3
Affiliations
  • 1 State Key Laboratory of Tropical Oceanography , South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
  • 2 Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
  • 3 Guangxi Key Laboratory of Marine Environmental Change and Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China
Published: 2024-06-25 doi: 10.1007/s13131-023-2243-1
Outline
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Oxygen facilitates the breakdown of the organic material to provide energy for life. The concentration of dissolved oxygen (DO) in the water must exceed a certain threshold to support the normal metabolism of marine organisms. Located in the northern Beibu Gulf, Qinzhou Bay receives abundant freshwater and nutrients from several rivers which significantly influence the level of the dissolved oxygen. However, the spatial-temporal variations of DO as well as the associated driving mechanisms have been rarely studied through field observations. In this study, a three-dimensional coupled physical-biogeochemical model is used to investigate the spatial and seasonal variations of the DO and the associated driving mechanisms in Qinzhou Bay. The validation against observations indicates that the model can capture the seasonal and inter-annual variability of the DO concentration with the range of 5−10 mg/L. Sensitivity experiments show that the river discharges, winds and tides play crucial roles in the seasonal variability of the DO by changing the vertical mixing and stratification of the water column and the circulation pattern. In winter, the tide and wind forces have strong effects on the DO distribution by enhancing the vertical mixing, especially near the bay mouth. In summer, the river discharges play a dominant role in the DO distribution by inhibiting the vertical water exchange and delivering more nutrients to the Bay, which increases the DO depletion and results in lower DO on the bottom of the estuary salt wedge. These findings can contribute to the preservation and management of the coastal environment in the northern Beibu Gulf.

river plume  /  dissolved oxygen  /  stratification  /  physical-biological model
Gaolei Cheng, Shiqiu Peng, Bin Yang, Dongliang Lu. A numerical model study on the spatial and temporal variabilities of dissolved oxygen in Qinzhou Bay of the northern Beibu Gulf[J]. Acta Oceanologica Sinica, 2024 , 43 (6) : 49 -59 . DOI: 10.1007/s13131-023-2243-1
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1 Introduction
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With the fast development of urban areas, the population growth rapidly, resulting in large emissions of various pollutants in estuaries (Bennett et al., 2001; Boyer and Howarth, 2008). The nutrient inputs such as nitrogen and phosphorus caused estuarine eutrophication, bottom water hypoxia (dissolved oxygen concentration (DO) < 2 mg/L), degradation of water quality, and harmful algal blooms (Diaz and Rosenberg, 2008; Visser et al., 2005). The habitat degradation and unbalance of ecosystems resulting from human activities have posed a threat to sustainable development in coastal areas. In particular, the DO has been declining in the world’s estuarine and coastal waters for the past few decades, due to the increase in global temperatures and nutrients discharged (Breitburg et al., 2018; Diaz and Rosenberg, 2008). It is thus important to understand the temporal and spatial variability of DO in estuaries as well as the associated dynamical mechanisms (Yu et al., 2015b).
The distribution of DO is modulated by both variable physical processes and biological responses over different time scales, such as on the Baltic Sea, the Zhujiang (Pearl) River Estuary and the Changjiang River Estuary (Carstensen et al., 2014; Li et al., 2021; Zhang et al., 2018). Li et al. (2020) investigated that tide-induced mixing plays a critical role in the DO budget due to the biophysical responses to the spring-neap tide cycle. The seasonal variability of DO is usually related to the development of hypoxia in the coastal ocean, where water ventilation is not able to resupply the amount of oxygen consumed by organic matter (Capet et al., 2013). Previous studies have shown that the hypoxic extent increased because of rising anthropogenic nutrient inputs from the watershed (Obenour et al., 2013). The statistical regression models showed that the nitrogen load could explain 24% of the variability in the observed hypoxic areas over the Texas-Louisiana continental shelf (Forrest et al., 2011). Bianchi et al. (2010) suggested that the hypoxic area had a similar correlation with either nutrient loading or river flow because of the high correlation between nutrient loading and river discharge. Therefore, it is very important to evaluate the role of river discharge on the variability of dissolved oxygen.
Located in the northwestern Beibu Gulf, Qinzhou Bay is an important estuary with extraordinary ecological and economic values by providing a habitat for the Chinese white dolphin, shellfish, and mangroves (Jiang et al., 2020; Lu et al., 2020; Pan et al., 2021a). Like many other estuaries in the world, the ecosystem of Qinzhou Bay has also been influenced by natural and human activities (Wang et al., 2021a; Zhang et al., 2019a). Field investigations on nutrients, chlorophyll, and DO show that eutrophication is a growing problem in Qinzhou Bay in recent decades (Lao et al., 2020; Lu et al., 2020; Yang et al., 2015, 2019; Zhang et al., 2019a). The water is in a phosphorus-limited state in Qinzhou Bay because of a higher nitrogen-to-phosphorus ratio than the Redfield value in most years (Yang et al., 2015). In addition, previous studies suggested that meteorological factors play an important role in the seasonal variability of nutrients in Qinzhou Bay (Zhang et al., 2019a). The annual runoff and aquaculture result in the decreasing distribution of nutrient concentration from the inner bay to the outer bay (Lan, 2011; Yang et al., 2015). Numerical models have been used to study variations of pollutants and nutrients in Qinzhou Bay (Chen et al., 2017; Wang et al., 2014, 2021a). More recently, Pan et al. (2021b) analyzed the spatial and temporal distributions of ecological variables and the nutrient budget in the Beibu Gulf using a coupled hydrodynamic-biological model. However, the spatial-temporal variability of DO and how it is determined by the interaction of winds, runoff and tidal mixing has never been explored in Qinzhou Bay.
Utilizing a coupled hydrodynamic and ecosystem model Regional Ocean Modeling System (ROMS), this study aims to investigate spatial and temporal variability of chlorophyll, nutrients and DO in Qinzhou Bay from 2010 to 2019. The rest of this paper is organized as follows. The observational data and model setup are described in Section 2. Validation of the model and analysis are presented in Section 3, followed by a discussion in Section 4. Conclusions are given in Section 5.
2 Materials and methods
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2.1 Field observations and satellite data
In situ hydrodynamics, nutrients, and satellite surface chlorophyll data are utilized to validate the model. Tidal elevation data (Fig. 1b) are from a tide gauge located at Qinzhougang Port. The spring-neap tidal currents at the C1 station (Fig. 1b) are from two stationary vessels (Yang et al., 2019). Monthly nutrient data are from field cruises between May 2017 and April 2018 in the inner bay (Zhang et al., 2019a). The monthly mean surface chlorophyll concentrations from 2010−2019 are extracted from MODIS (https://oceancolor.gsfc.nasa.gov/l3/order/). The climatological monthly chlorophyll is used to describe chlorophyll variation in the study area. In addition, the temperature-salinity and DO were collected from 2011−2018 at twenty-four stations (Zhang, 2020), and the mean values in the inner and the outer bay are used to assess the model’s performance in capturing the physical and biological processes (Fig. 1b).
2.2 Numerical model
A coupled physical-biological model is used to study the relative contribution of river discharge and oceanic dynamics to the seasonal variability of dissolved oxygen in Qinzhou Bay and the adjacent sea areas. The physical model is based on the Regional Ocean Modeling System (ROMS; Haidvogel et al., 2000; Shchepetkin and McWilliams, 2005), which has been set up for the sediments simulation of the Beibu Gulf by Cheng et al. (2017). Our model domain covers the northern Beibu Gulf and the entire Qinzhou Bay (Fig.1a). An orthogonal curvilinear coordinate system is designed to follow the coastline. The model domain is set up with 583 × 532 horizontal grid cells with a resolution of 0.1−2 km in the horizon and 10 vertical terrain-following s-levels, with high resolution near the surface and bottom to accurately resolve the surface and bottom boundary layers. The tidal water level and depth-averaged tidal velocity for the model are obtained from the Oregon State Tidal Prediction Software TPXO8, including 8 tidal constituents of P1, Q1, O1, K1, M2, S2, N2, and K2. The open boundary conditions for the temperature, salinity and baroclinic current are radiation conditions. The temperature and salinity are derived from the monthly Hybrid Coordinate Ocean Model (HYCOM) model outputs (http://hycom.org/hycom). Atmospheric pressure and wind forcing are derived from monthly data provided by the ERA5 (Fifth Generation ECMWF Atmospheric Reanalysis). The climatological monthly freshwater discharges from the Maoling, Qinjiang, Dafeng and Nanliu rivers are used the same as those of Gao et al. (2013). We initially assume that the statistical distribution of the river force is adequate for our domain because the seasonal cycle of DO is more sensitive to the variations in river discharge at longer time scales (Scully, 2013). Then, the climatological monthly discharges during 2012−2017 are multiplied by some factors (Table 1), which are the annual runoff ratios between time series and climatological data (Chen and Lin, 2020).
The pelagic nitrogen cycle model of Fennel et al. (2006) is coupled with the physical model and has been extended to include phosphate (Laurent et al., 2012). The model contains 13 state variables, including nitrate (${\mathrm{NO}}_3^- $), ammonium (${\mathrm{NH}}_4^+ $), phosphate (${\mathrm{PO}}_4^{3-} $), chlorophyll (Chl), phytoplankton (Phy), zooplankton (Zoo), two forms of small detritus (Sdetritus C and Sdetritus N), two forms of large detritus (Ldetritus C and Ldetritus N), oxygen (Oxyg), total inorganic carbon (TIC), and total alkalinity (TALK). The detailed model feature was described in the Supplement to Laurent et al. (2017). The Fennel model has been successfully applied to study the spatial and temporal variability of nutrients and hypoxia in the Gulf of Mexico and Changjiang River Estuary (Fennel and Laurent, 2018; Zhang et al., 2019b). Most of the parameters used in our study are based on the values in Laurent et al. (2017), with slight adjustments according to previous experimental results (Xu et al., 2020; Zhang et al., 2020). The initial value and boundary conditions for nutrients and dissolved oxygen are derived from the World Ocean Atlas 2013 climatology (WOA13), while those for chlorophyll are from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data using a vertical extrapolation parameterization by Morel and Berthon (1989). The phytoplankton, zooplankton, and detritus are also from the SeaWiFS data, and the ratio of chlorophyll concentration in Liu et al. (2010) is adopted. The seasonal chlorophyll concentration and monthly nutrient concentrations in rivers are obtained from the published data (Lu et al., 2020; Zhang et al., 2019a). The model runs for 11-year simulation from 1 January 2009 to 31 December 2019, and the daily output data from the last 10 years is used in this research.
3 Variability of surface chlorophyll, salinity, temperature, DO and nutrients
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3.1 Model validation against observations
The simulated water level and velocity are compared with the observations (Fig. 2). The simulated water level is highly consistent with the observations between April and May 2011 in Qinzhougang Port (Fig. 2a). The simulated velocity and direction are also very close to the observations (Figs 2b and c). In addition, we calculate the Root Mean Square Error (rmse) and correlation coefficient (r). For water level and velocity, the results of statistics show small rmse and large r values. These results demonstrate our model has good performance in capturing the hydrodynamic characteristics. As a result, using our model to diagnose how multiple physical processes impact the DO field is reliable. The comparison between the simulated monthly chlorophyll and the climatological monthly chlorophyll from MODIS shows a good agreement with each other (Fig. 3a), with the correlation coefficient as high as 0.67 (Fig. 3b). Moreover, the model captures well the mean monthly of surface ${\mathrm{PO}}_4^{3-} $, ${\mathrm{NO}}_3^- $, and ${\mathrm{NH}}_4^+ $ (r = 0.59, 0.77, 0.85) in the inner bay, with higher concentration in spring (Figs 4a-c). For the surface DO (r = 0.85), both model results and observations show a seasonal variation with higher concentration in winter and lower value in summer (Fig. 4d), but the value is underestimated by the model compared to observed data in winter. Furthermore, the observational data in the outer bay (not published) show bottom DO is close to 6 mg/L in summer and 8 mg/L in winter. Similarly, our model results show the bottom DO is between 6 mg/L and 8 mg/L.
3.2 Seasonal and interannual variability of surface chlorophyll, salinity, temperature and DO
We compare the modeled monthly mean surface chlorophyll concentrations with MODIS data from 2010 to 2019 (Fig. 5). The model reproduced reasonably the seasonal and inter-annual variations of the chlorophyll, which is persistently high during warm months and low during cold months. These results show a similarity to the temporal variability of chlorophyll concentration at the shallow water zone in the northern South China Sea, which was affected by the seasonal variations of riverine nutrients and air temperature (Ma et al., 2013). The correlation coefficient (r = 0.37) is not as good as for the climatological results (Fig. 3). The significant discrepancy between the model results and observations could be attributed to various factors, including oversimplification of river inputs.
Both the observed and modeled monthly mean surface salinity, temperature, and DO in the inner bay show strong seasonal cycles with low (high) values in summer (winter) (Fig. 6). In addition, the modeled DO shows very low values in summer during 2013 and 2015, probably due to the larger river outflow (Fig. 6a, Table 1). Similar results are observed in the outer bay (Fig. 7) with higher salinity values (Fig. 7a). The model simulates well the observed patterns of the surface salinity, temperature, and DO, although underestimates the observed values to some extent. For salinity, the reason for the small correlation coefficients (r = 0.22, 0.19) may be the temporal mismatch between monthly mean modeling results and daily sampling data. The model performance for DO in the inner bay (r = 0.35) is not as good as in the outer bay (r = 0.52). The pollutants from the coast may contribute to this difference because of our model without the impacts of wastewater (Wang et al., 2021a).
3.3 The seasonal variability of salinity, DO and nutrients on the transections
Figure 8 shows the monthly mean salinity and DO along transection A, which display the strong seasonal variations. In winter, the river discharge mainly influences salinity and DO within the inner bay (Figs 8a and b). A larger DO horizontal gradient arises in the inner bay while a uniform DO distribution exists in the outer bay (Fig. 8b). In summer, the river plume spreads to the outer bay with lower surface salinity (Fig. 8c). A lower DO value occurs at the bottom of the outer bay (Fig. 8d) because the stratified water column inhibits the DO vertical exchange between the river plume and seawater. The riverine water transports more nutrients seaward and stimulates the phytoplankton growth, which is converted to oxygen-consuming materials after dying. In general, the simulated distributions of DO show a decrease from the inner bay to the outer bay (Figs 8b and d).
Nutrient concentrations are higher in the inner bay and lower in the outer bay (Fig. 9). Larger monthly mean ${{\mathrm{NH}}}_4^{+}$, ${{\mathrm{NO}}}_3^{-} $ and ${{\mathrm{PO}}}_4^{3-} $ arise in the inner bay during winter due to smaller river discharge (Figs 9a, b and c). In the outer bay, these nutrient concentrations are low and vertically uniform due to the enhanced mixing in the water column during winter. In summer, the monthly mean ${{\mathrm{NH}}}_4^{+} $, ${{\mathrm{NO}}}_3^{-} $ and ${{\mathrm{PO}}}_4^{3-} $ show an intense vertical gradient in both the inner and outer bay which is affected by river plumes (Figs 9d, e and f). In addition, lower ${{\mathrm{NH}}}_4^{+} $, ${{\mathrm{NO}}}_3^{-} $ and ${{\mathrm{PO}}}_4^{3-} $ are found in the bottom of the outer bay during summer.
Figure 10 shows the vertical distribution of salinity and DO along transection B. For both the monthly mean salinity and DO, stronger stratification is seen in summer (Figs 10c and d) than in winter (Figs 10a and b), probably due to the larger river discharge in summer. For the monthly mean ${\mathrm{NH}}_4^+ $, ${\mathrm{NO}}_3^- $ and ${\mathrm{PO}}_4^{3-} $ along transection B, similar vertical distributions are found in winter and summer (not shown).
4 Discussions
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Hypoxia has been widely reported in estuaries and shelf regions due to water eutrophication and changes in physical forcing (Breitburg et al., 2018; Li et al., 2021; Scully, 2013; Zhang et al., 2018). In Qinzhou Bay, the observed data showed that oxygen concentrations larger than 5 mg/L in the inner bay responded to moderate enrichment (Lu et al., 2021; Zhang et al., 2019a). Zhang et al. (2019a) suggested that the nutrients mainly came from rivers, and were affected by precipitation, temperature, and high irradiation. Wang et al. (2021b) suggested that submarine groundwater discharge played an important role in dissolved inorganic nutrient sources. Moreover, Kaiser et al. (2013) found that the tidal currents dispersed land-derived nutrients offshore into Beibu Gulf, leading to low concentrations near the estuary.
The seasonal fluctuations and heterogeneously spatial distributions may complicate the monitoring of DO, leading to contradictory conclusions when interpreting data from different sources (Capet et al., 2013). The numerical models are important complements that help understand the important factors causing the variability of DO (Fennel et al., 2013; Fennel and Laurent, 2018; Laurent et al., 2017; Yu et al., 2015b). Although our model underestimates DO in winter (Fig. 4d), the results show that DO is sensitive to temporal changes in river discharge (Figs 6c and 7c). Because of the uncertainty in estimating groundwater discharge and surface runoff along the shoreline, it is usually difficult to obtain an accurate river discharge (Du et al., 2019). To examine the influence of river discharge, wind and tide on DO distribution, we used the monthly nutrient concentrations, climatological monthly freshwater discharge, tide and wind to force the model as a control run (denoted as CTRL). In contrast, another four sensitivity experiments (Table 2) were carried out to demonstrate how each factor contributes to the DO distribution in Qinzhou Bay. Each experiment was conducted for one year, and the monthly averaged outputs were analyzed.
4.1 The impact of river discharges on DO distribution
The river nutrient concentration and stratification play substantial and comparable roles in the interannual variability of hypoxia (Obenour et al., 2012). The impact of river discharge on the seasonal variability of DO had already been discussed by Scully (2013), who found that the increases in river discharge led to an increase in hypoxic volumes, independent from the associated biological response to higher nutrient delivery. The joint effect of river plume and shelf benthic waters results in a notable pycnocline, which weakens the mixing over the water column. The stratification induced by river discharge can form a physical bound that affects the organic matter inputs and respiratory oxygen consumption and inhibits the DO diffusion from river plumes (Cui et al., 2019; Hetland and DiMarco, 2008). The DO may reduce as a result of stratification and oxygen consumption (Yu et al., 2015a).
To examine the impact of variable river discharges on DO distribution in Qinzhou Bay, we carried out a set of sensitivity experiments with different riverine inputs (Table 2) while preserving the nutrient loading, and compared the difference with the control experiment. During winter, the effects of river discharge mainly appear in the inner bay (Figs 11a and b). As the river discharge varies, the oxygen concentrations reduce with lower discharge (Fig. 11a) and increase with higher discharge (Fig. 11b). During summer, the river discharge mainly causes the variability of bottom dissolved oxygen (Figs 11c and d). The oxygen concentration in the water column below 5 m increases by about 0.15 mg/L with lower discharge (Fig. 11c), whereas it reduces by more than 0.05 mg/L with higher discharge (Fig. 11d). These results suggest that the higher river discharge could be the reason for the lower DO in the summer of 2013 and 2015 (Fig. 6c). Particularly, the variation of DO in the inner bay is more sensitive to the increase in river discharge and nutrient loading. Scully (2013) suggested that increases in river discharge lead to an increase in stratification and nutrient delivery, both of which tend to decrease the DO below the pycnocline. On the opposite, a reduction in river discharge means a decrease in stratification and nutrient delivery, both of which tend to increase the DO below the pycnocline. Our results followed these mechanisms in summer, but not in winter. This is possible due to the advective fluxes associated with the discharge variations. The DO increase is limited at low discharge because of decreased advective fluxes.
4.2 The impacts of tides and winds on DO distribution
The tidally induced and wind-driven circulation can modulate the mixing and stratification (Li et al., 2020). To evaluate the role of tide and wind in the seasonal variation of DO, two sensitivity experiments including no tide (Notide) and no wind (Nowind) were carried out. During winter, the results from the CTRL-Notide experiment indicate that the tidal forcing reduces (increases) the surface (subsurface) DO in the inner bay, while it reduces DO in the whole water column in the outer bay (Fig. 12a). During summer, the tidal forcing reduces (increases) DO in the upper (bottom) water in both the inner and outer bays (Fig. 12c). Compared with the effect of river discharge (Fig. 11), the tide forcing leads to a unified vertical distribution of DO by enhancing the vertical mixing of seawater, especially in summer when the stratification is stronger (Fig.12c). The seasonal variation of winds strongly influences the seasonal cycle of water exchange, and the varying wind-driven circulation interacts with the plume to jointly regulate the transport of nutrients and detritus, water vertical mixing, and residence time (Li et al., 2021). The results from the CTRL-Nowind experiment indicate that winds enhance DO in the whole water column in winter, especially in the outer bay (Fig. 12b); during summer, DO increases in the upper water in the inner bay while it reduces in the bottom water in the outer bay, which could be caused by the wind-driven currents that restrict the seaward advection of DO (Fig. 12d).
Feng et al. (2014) suggested that winds influence hypoxic by changing the vertical and horizontal distributions of the low salinity and the high chlorophyll water on the shelf. The upwelling-favorable winds reduce stratification in nearshore regions and enhance the mixing of the water column (Yu et al., 2015a). The wind speed and direction in Qinzhou Bay have pronounced seasonal variability, i.e., northerly (southerly) with a higher (lower) speed in winter (summer). To further examine the respective effects of winds, we investigate the seasonal change in currents induced by winds (Fig. 13). During winter, the wind-driven currents are seaward dominant at the surface (Fig. 13a) and east-northward dominant at the bottom (Fig. 13b) in the outer bay, which is favorable to the seaward transport of DO (Fig. 12b). In inner bay, the wind-driven currents is weaker and has a smaller effect on DO (Fig. 12b). During summer, the wind-driven currents are landward dominant at the surface (Fig. 13c) and westward dominate at the bottom (Fig. 13d), which inhibits the seaward transport of DO (Fig. 12b).
5 Conclusions
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In this study, a numerical model is used to investigate the spatial and seasonal variabilities of dissolved oxygen in Qinzhou Bay, as well as the key dynamic processes. The model captures well the main features and seasonal variation of surface nutrients but underestimates dissolved oxygen during winter due to the complex physical processes. Comparisons with MODIS data demonstrate that high chlorophyll concentrations in Qinzhou Bay persist during the warm season, while the concentrations are low during the cold season. During summer, the river plumes induce strong stratification which inhibits DO vertical exchange and thus leads to lower DO in the bottom of the outer bay. In contrast, the tide forcing enhances the mixing of seawater and thus leads to higher DO in the bottom of both the inner and outer bay during summer. Furthermore, the enhancement of mixing induced by winds, the wind-driven currents are favorable to the offshore transport of DO in winter while they inhibit it in summer, leading to higher (lower) DO in the outer (inner) bay in winter and higher (lower) DO in the inner (outer) bay in summer. In this paper, however, the relative importance of nitrogen and phosphorus on the variability of DO and the effect of nutrient enrichment on the long-term evolution of DO are not examined yet due to the lack of observations. Therefore, further research on these issues, including conducting more field monitoring and numerical modeling for Qinzhou Bay, is necessary. This will be our focus for future work.
Acknowledgements
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We want to express our gratitude to Liuqian Yu for her assistance in the preparation of this article. We thank Katja Fennel and Arnaud Laurent for providing the code of ROMS_fennel. The numerical simulations are supported by the High-Performance Computing Division in the South China Sea Institute of Oceanology, Chinese Academy of Sciences.
Funding
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  • The Major Projects of the National Natural Science Foundation of China under contract No. U20A20105; the Guangdong Key Project under contract No. 2019BT2H594; the National Key Research and Development Program of China under contract No. 2022YFC3105000; the State Key Laboratory of Tropical Oceanography Independent Research Fund under contract No. LTOZZ2103; the Guangxi Key Laboratory of Marine Environmental Change and Disaster in Beibu Gulf, Beibu Gulf University under contract No. 2023KF01.
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Article Info
doi: 10.1007/s13131-023-2243-1
  • Receive Date：2023-02-21
  • Online Date：2025-11-19
  • Published：2024-06-25
Article Data
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  • Received：2023-02-21
  • Accepted：2023-06-16
Funding
The Major Projects of the National Natural Science Foundation of China under contract No. U20A20105; the Guangdong Key Project under contract No. 2019BT2H594; the National Key Research and Development Program of China under contract No. 2022YFC3105000; the State Key Laboratory of Tropical Oceanography Independent Research Fund under contract No. LTOZZ2103; the Guangxi Key Laboratory of Marine Environmental Change and Disaster in Beibu Gulf, Beibu Gulf University under contract No. 2023KF01.
Affiliations
    1 State Key Laboratory of Tropical Oceanography , South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China
    2 Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
    3 Guangxi Key Laboratory of Marine Environmental Change and Disaster in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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