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The interactions between surface Kuroshio transport and the eddy field east of Taiwan using satellite altimeter data
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Huizan Wang1, 2, *, Quanhong Liu2, Hengqian Yan2, Bo Song2, Weimin Zhang2
Acta Oceanologica Sinica | 2019, 38(4) : 116 - 125
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Acta Oceanologica Sinica | 2019, 38(4): 116-125
• Articles •
The interactions between surface Kuroshio transport and the eddy field east of Taiwan using satellite altimeter data
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Huizan Wang1, 2, *, Quanhong Liu2, Hengqian Yan2, Bo Song2, Weimin Zhang2
Affiliations
  • 1 State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  • 2 College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
Published: 2019-04-25 doi: 10.1007/s13131-019-1417-3
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Based on the daily sea surface height and absolute geostrophic velocity data from 1993 to 2015 provided by the AVISO Center of French Space Agency, the surface Kuroshio transport east of Taiwan and its adjacent eddy field (sea surface height anomaly) were analyzed. Four main periods of the surface Kuroshio transport and eddy field east of Taiwan were obtained, which were used to reveal their interactions. The main conclusions are as follows: (1) Based on the wavelet analysis, the surface Kuroshio transport east of Taiwan and its nearby eddy field showed significant seasonal, annual and interannual periods. In addition to the obvious periods of 182 days (0.5 year) and 365 days (1 year), there were also more obvious periods of about 860 days (2.35 years) and 2 472 days (6.8 years) for the surface Kuroshio transport. There were also four more obvious periods corresponding to the eddy field of 200 days (0.55 year), 374 days (1 year), 889 days (2.43 years) and 2 374 days (6.5 years), although there were latitudinal variations. (2) Based on both the correlation and causal analysis, the correlation between the surface Kuroshio transport and the nearby eddy field over the above four periods was analyzed, and different Kuroshio-eddy interactions, with period and latitudinal variability, were revealed.

wavelet analysis  /  causal analysis  /  Kuroshio east of Taiwan  /  correlation  /  sea level anomaly
Huizan Wang, Quanhong Liu, Hengqian Yan, Bo Song, Weimin Zhang. The interactions between surface Kuroshio transport and the eddy field east of Taiwan using satellite altimeter data[J]. Acta Oceanologica Sinica, 2019 , 38 (4) : 116 -125 . DOI: 10.1007/s13131-019-1417-3
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1 Introduction
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As one of the major western boundary currents, the Kuroshio carries momentum and heat northward from the tropics to midlatitude regions. It plays a vital role in ocean circulation and affects climate and fisheries across the coastal regions of northeast Asia. The Kuroshio east of Taiwan shows tremendous variability (Jan et al., 2015), with an overall mean transport of 21.5 Sv (1 Sv=106 m3/s) (Johns et al., 2001).
Mesoscale eddies are nearly ubiquitous in the world’s ocean (Wang et al., 2003; Chelton et al., 2011; Nan et al., 2011a; Zheng et al., 2017), especially in the region over the subtropical countercurrent (STCC) (Hwang et al., 2004; Qiu and Chen, 2010a). Eddy trajectory statistics have indicated that eddies frequently intrude into the Kuroshio regime at two latitude bands, at the Luzon Strait and east of Taiwan (Lee et al., 2013; Cheng et al., 2017). Ma et al. (2016) showed that the feedback between ocean mesoscale eddies and the atmosphere is fundamental to the dynamics and control of the Kuroshio, highlighting the importance of eddies for the Kuroshio. When the westward-propagating mesoscale eddies, with an average propagation velocity of about 9 cm/s, encounter the east of Taiwan, they will interact with the Kuroshio, influencing its current and path (Hwang et al., 2004).
Satellite and in situ observations (Yang et al., 1999; Hwang et al., 2004; Qiu and Chen, 2010b) and simulated results (Geng et al., 2016, 2018; Kuo et al., 2017) have indicated the existence of eddy-Kuroshio interactions. Interpretations have included the effects of the Kuroshio on eddies, which appear to decay near the western boundary, and how eddies drive Kuroshio variability (Chang and Oey, 2011; Nan et al., 2011b; Ma and Wang, 2014; Lien et al., 2014; Tsai et al., 2015; Chang et al., 2015; Delman et al., 2015). Kuroshio variations in the East China Sea (ECS) are induced by mesoscale eddies east of Taiwan (Ichikawa et al., 2008; Hsin et al., 2011), and eddies with a radii larger than 150 km are strong enough to significantly alter the Kuroshio (Zheng et al., 2011). The impact of eddies on the Kuroshio volume transport has long been recognized: eddies from the STCC play an important role in affecting Kuroshio transports both at interannual and seasonal time scales (Qiu and Chen, 2010a), and approaching anticyclonic (cyclonic) eddies result in an increase (reduction) of the Kuroshio transport (Yang et al., 1999; Zhang et al., 2001; Lee et al., 2013). The westward propagating eddy can also trigger the Kuroshio to meander or intrude (Waseda et al., 2002; Miyazawa et al., 2004; Hsu et al., 2016; Yin et al., 2017). Anticyclones can also intensify sea surface and pycnocline slopes across the Kuroshio, while cyclones weaken these slopes, particularly east of Taiwan (Tsai et al., 2015). Furthermore, mesoscale eddies can be shed from the Kuroshio loop (Zhang et al., 2017; Liu et al., 2017).
Most of these described results led to the conclusion that anticyclonic eddies correspond to an increase in transport and cyclonic eddies are related to transport reduction. However, contrasting results have also been reported (Zhang et al., 2001). Yan et al. (2016) found that both anticyclonic and cyclonic eddies can strengthen or weaken the Kuroshio volume transport. The relationship between the mesoscale eddy field and the Kuroshio is more complicated and even partially contradictory because of the scarcity of ocean observational data and nonlinear phenomena (Zheng et al., 2011).
Previous research on Kuroshio and eddy variability have often regarded the Kuroshio and the eddies east of Taiwan as a whole and focused less on their different characteristics at different latitudes. Concurrently, to a certain extent, the observations have been limited in time resolution, generally more than 7 days, and the length of time series, generally less than 10 years. Most of the studies have been model-based, and it is unclear if their results are fully applicable to real eddy-Kuroshio interaction (Geng et al., 2016).
Although many works have shed light on the variations of mesoscale eddies or the Kuroshio east of Taiwan, few studies have discussed the interaction between the eddy field and Kuroshio over different time scales, especially the effect of the Kuroshio on the nearby eddy field. In addition, a longer record measurement period will be needed to resolve the variations in the Kuroshio and eddies. Different Kuroshio-eddy interactions over varying time periods and latitudes remains to be quantified and further studied.
The objective of this paper is to characterize the variability in the Kuroshio and eddy field (sea level anomalies) east of Taiwan, understand the correlation between the sea level anomaly filed (westward-propagating mesoscale eddies) and the Kuroshio, and to unravel the cause-effect relation between them .
This paper is organized as follows: Section 2 describes the data source and methodology. Section 3 analyzes the periods of the surface Kuroshio and nearby eddy field. In Section 4, the correlation and cause-effect relation between the sea level anomaly field (westward-propagating mesoscale eddies) and the Kuroshio are revealed. The results are summarized in Section 5.
2 Data source and methodology
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2.1 Satellite altimeter data
The satellite-derived daily sea level anomaly (SLA) data and surface current data (MADT_UV) (a total of 8 400 d) with delayed quality control were provided by the AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic) data center, the data was merged from multi-satellite altimeter data of T/P, Jason-1/2 and Geosat and other orbiting satellites. The product was gridded to 0.25°×0.25° and a time interval of 1 d. The time ranges from January 1993 to December 2015, a total of 23 years with 8 400 d. The study area is east of Taiwan (22.5°–25°N, 121°–125°E).
2.2 Surface Kuroshio intensity (SKI)
Surface current data is also provided by AVISO center to identify the Kuroshio axis and compute the surface Kuroshio intensity (SKI).
As a strong western boundary current, the most prominent feature of the Kuroshio is its velocity. The Kuroshio axis is defined as the line of the maximum surface velocity along the Kuroshio path.
Using the surface geostrophic velocity, the SKI ($IN{T_{{v_{\rm{g}}}}} $) can be calculated from
$IN{T_{{v_{\rm{g}}}}}{\rm{ = }}\int_{{X_{\rm W}}}^{{X_{\rm E}}} {{v_{\rm{g}}}} ({{x, y, t}}){\rm d}x, $
where x, y and t are longitude, latitude and time, respectively; XW and XE are the western and eastern integral limits; and vg is the surface current from AVISO (MADT_UV). XW follows the eastern coastline of Taiwan; however, the eastern integral limit, XE, is set to the position of Kuroshio axis +1 (plus one longitude) (Hsin et al., 2013; Wang et al., 2018).
The identification of the Kuroshio axis was based on the surface currents by Ambe et al. (2004). Wang et al. (2018) identified the daily axis of the Kuroshio east of Taiwan and investigated the variation in axes position and SKI for different latitudes based on the 23-year (1993–2015) satellite-derived daily surface current data. The daily SKI dataset is based on the method of Wang et al. (2018).
2.3 Selection of related eddy field
To obtain the continuous time series of the mesoscale eddies approaching the Kuroshio east of Taiwan, the regional sea level anomaly field (22.5°–25°N, 123°–124°E) was used to characterize the mesoscale eddies (Fig. 1). The region selected for the eddy field was a compromise between the areas approaching the Kuroshio region and mixing with the Kuroshio. The advantage of the SLA field is that it is continuous compared to the mesoscale eddies. In this study, the time series of the mean SLA averaged from 123°E to 124°E was used as the time series of eddy field for each latitude.
2.4 Wavelet analysis
Wavelet analysis is a widely-used method to analyze localized variations of power within a time series (Torrence and Compo, 1998; Yu et al., 2007). By decomposing a time series into the time–frequency space, both the dominant modes of variability and how those modes vary over time can be determined. For a detailed introduction to wavelet analysis, please refer to Torrence and Compo (1998).
In this study, wavelet analysis was used to calculate the wavelet power spectra at different latitudes respectively for the time series of the SKI and mean sea level anomalies. By calculating the power spectra at different latitudes, four corresponding obvious seasonal and interannual periods were identified.
2.5 Correlation analysis
Previous studies have showed a good correlation between the Kuroshio volume transport east of Taiwan and the sea surface height (Yang et al., 2014), and found a maximum correlation of 0.83 between PCM-1 Kuroshio transport and sea surface height anomalies at 23.9°N, 123.2°E (Gawarkiewicz et al., 2011).
To understand the correlation between the sea level anomaly field (westward-propagating mesoscale eddies) and SKI, we first calculated the correlation coefficient between the two variables. Except for the correlation between the surface Kuroshio intensity and the eddy at the same time, the time-lagged correlation between the surface Kuroshio intensity and the eddy fields for different lag times were also calculated. Using these calculations, we obtained the correlation coefficients for surface Kuroshio intensity and eddy across a range of latitudes for different lag times and periods.
After computing the correlation coefficients, the significance of the correlation coefficient should be tested. The statistic obeys the t distribution with a degree of freedom of n–2. The correlation coefficient is derived from the formula for the critical value:
${r_\alpha } = \frac{{{t_\alpha }}}{{\sqrt {t_\alpha ^2 + \left( {n - 2} \right)} }}.$
Based on the significant level of $\alpha = 0.05$, degree of freedom of n=8 400, and checking t distribution threshold table to obtain ${t_\alpha } = 1.96$, we determined that the correlation coefficient passed the test if ${r_\alpha } > 0.021\;4$ based on Eq. (2).
2.6 Causal analysis
Recently, a rigorous yet concise formula has been derived to evaluate information flow, and hence the causality in a quantitative sense, between time series (Liang, 2014). To assess the importance of a resulting causality, the time series need to be normalized. The normalization is achieved by distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate, and noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient (Liang, 2015).
Using the method provided in Liang (2015), take two series X1 and X2, for example. Cij is the sample covariance between Xi and Xj, and Ci, dj is that between Xi and ${\dot X_j}$, ${\dot X_j}$ being the difference approximation of dXj/dt using the Euler forward scheme (Liang, 2014). In addition, $\frac{{{\rm d}{H_1}}}{{{\rm d}t}}$ is the rate of change of the marginal entropy of X1. It can be decomposed into two parts, $\frac{{{\rm d}H_1^*}}{{{\rm d}t}}$ and $\frac{{{\rm d}H_1^{\rm noise}}}{{{\rm d}t}}$.
Through the following equations:
${T_{2 \to 1}} = \frac{{{C_{11}}{C_{12}}{C_{2, d1}} - C_{12}^2{C_{1, d1}}}}{{C_{11}^2{C_{22}} - {C_{11}}C_{12}^2}}, $
${Z_{2 \to 1}} \equiv |{T_{2 \to 1}}| + |\frac{{{\rm d}H_1^*}}{{{\rm d}t}}| + |\frac{{{\rm d}H_1^{\rm noise}}}{{{\rm d}t}}|, $
${\tau _{2 \to 1}} = {T_{2 \to 1}}/{Z_{2 \to 1}},$
we have drawn the causal relationship between the surface Kuroshio intensity and the eddy field. The method provided in Liang (2015) provided a mechanism for normalizing the results so that the interaction between the two were directly compared using ${\tau _{2 \to 1}}$ and ${\tau _{1 \to 2}}$.
This study adopted the method provided by Liang (2015) to evaluate the causal relationship between time series of the sea level anomaly filed (westward-propagating mesoscale eddies) and the SKI. Furthermore, we were able to reveal these causal relationships between the surface Kuroshio intensity and eddy field at different latitudes over different periods.
3 Variation of surface Kuroshio intensity and eddy field
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3.1 Variation of surface Kuroshio intensity
The time series of surface Kuroshio intensity from January 1993 to December 2015 are analyzed by wavelet analysis described above, and its wavelet power spectrums for different latitudes are obtained. Taking the Kuroshio at latitudes of 23°N and 25°N as examples, the corresponding wavelet power spectra are provided in Figs 2 and 3. Color indicates the significance of the period, with colors closer to red indicating more pronounced periodicity. The thin black line is the cone of influence (COI), and the power spectrum outside the curve is not considered due to the boundary effect (Yu et al., 2007). From these figures, there are clear seasonal and interannual variations in the SKI. When the wavelet analysis method is used to identify a periodic wave in a time series, in addition to passing a significance test, the periodic wave time series must be within a COI.
Based on the average power spectrum at different latitudes from 22.5°N to 25°N, e.g., in Figs 2c and 3c, the power spectra of the SKI time series at all latitudes were obtained. In Fig. 4, periods of 182 days (0.5 year, the semiannual period), 365 days (1 year, the annual period), 860 days (2.35 years, the first interannual period), 2 472 days (6.8 years, the second interannual period) are obvious. Furthermore, different periods occur at different latitudes in the SKI time series. The periodicity of the semiannual period and 2.35-year periods at the lower latitudes (22.5°–24°N) is more pronounced than that in the higher latitudes (24°–25°N) east of Taiwan. The annual period and 6.8-year period in each latitude have good periodicity and the 6.8-year period at the lower latitudes (22.7°–23.9°N) is most significant. It should be noted that many previous studies (Zhang et al., 2001) have shown a 100-day variability in the Kuroshio based on the PCM1 data at about 24.5°N, which is also observed in Fig. 3 (the period is 115 days accurately). However, the 100-day period is not as significant as other periods, such as the semiannual or annual cycle.
Once these data were obtained, a significance test was performed, and the power spectrum is shown in Fig. 5a. The blank areas are the regions that failed the test, where the results do not meet the significance threshold. The remaining regions passed the test, and are considered significant.
3.2 Variation of eddy field
The power spectra for the time series of the eddy fields to the east of the Kuroshio (sea level anomalies) at different latitudes were also obtained based on wavelet analysis. The corresponding wavelet power spectra of eddy fields at latitudes of 23°N and 25°N were obtained (Figs 6 and 7). As shown in these figures, there are seasonal and interannual variations in the eddy fields east of Taiwan.
Based on the average power spectra at different latitudes from 22.5°N to 25°N, e.g., Figs 6c and 7c, the time series power spectra for eddy fields at all latitudes east of Taiwan were obtained (Fig. 5b). In addition, a significant test of the wavelet power spectrum of the eddy field was performed using the same method as described in Section 2.5. Finally, a comparison of wavelet power spectra between SKI and eddy field is shown in Fig. 5, where the blank areas are insignificant and the remaining areas passed the significance test.
As shown in Fig. 5b, the eddy field east of Taiwan has four obvious periods: 200 days (0.55 year, semiannual period), 374 days (1 year, annual period), 889 days (2.43 years, first interannual period) and 2 374 days (6.5 years, second interannual period), generally corresponding to the four periods of the SKI (shown in Fig. 5a). There are also variations across latitudes in the same period. In addition, in the first period, the Kuroshio period ranged from 122 days to 192 days, and the eddy period ranged from 160 days to 240 days. The period of the two show an approximate correspondence.
4 Correlation analysis and cause analysis
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4.1 Correlation analysis
In this study, we used the correlation analysis method described in Section 2.5 to calculate correlation coefficients between the time series of the SKI and the time series of eddy fields at different latitudes for the four different periods respectively. By using the time-lagged correlation analysis method, the correlation coefficients between the SKI and eddy fields for different lag time are calculated. A negative time lag (τ<0), indicates that the time of eddy field is earlier than that of the SKI, i.e., the eddy field leads the SKI. In contrast, a positive time lag (τ>0) indicates that the time of eddy field is later than that of the surface Kuroshio intensity, i.e., the time of eddy field t+τ corresponds to the SKI time t. Finally, a zero time lag indicates that the Kuroshio and eddy are in the same period. The correlation coefficient of the semiannual period between two series is shown in Fig. 8. Figure 9a shows this correlation coefficient after significance testing.
Figure 9 shows the correlation coefficient of all periods after significance testing. Blank areas are those that did not pass the significance test. It should be noted that the first interannual period of the Kuroshio in Fig. 7a did not pass the significance testing between 24.5°–25°N. In other words, the first interannual period is only obvious in the middle region, so only the results for 22.5°–24.5°N are shown in Fig. 9c. As shown, there are differences in the correlation coefficients at different latitudes. For example, the correlation coefficient in the semiannual and annual periods is relatively high at the 23.3°–25°N latitudes, indicating that the correlation between the Kuroshio and the eddy in this region is also high. The correlation coefficient of the first interannual period (~2.4 year-period) at the 22.7°–24.1°N latitudes is relatively large and negative, which indicates a high correlation between the Kuroshio and the eddy in this region, but with the opposite sign. The second interannual period (~6.5 year-period) has a high correlation at all latitudes (22.5°–25°N). Clearly, varying lag times have different correlation coefficients. For the semiannual period and first interannual period (2.4 year period), the correlation seems to be systemic at the line where the lag time is zero. For the second interannual period, when the time lag is negative (τ<0), which means the time of eddy field time is earlier than that of the SKI, the correlation is mainly positive, whereas a positive time lag (τ>0) corresponds to a primarily negative correlation.
4.2 Causal analysis
The causal analysis method described in Section 2.6 was applied to the SKI and eddies over the Kuroshio. We obtained the effects of the Kuroshio on the eddy field over the four periods and vice-versa. The lag time meaning is similar to the lag correlation analysis in Section 4.1. A schematic diagram of the first-period Kuroshio influence on eddies is provided as an example in Fig. 10.
To test the interaction between the two, we compare the absolute value of the effect of Kuroshio on the eddy in the four periods, i.e., ${\tau _{12}}$. The impact of eddies on the Kuroshio, i.e., ${\tau _{21}}$, was also compared. In the semiannual period, during the test of the impact of the Kuroshio on the eddy, $|\frac{{{\tau _{12}}}}{{{\tau _{21}}}}| \text{≥} 1$ means that the data passed the test, while $|\frac{{{\tau _{12}}}}{{{\tau _{21}}}}| < 1$ indicates a failure to pass the test. For the impact of the eddy on the Kuroshio, $|\frac{{{\tau _{21}}}}{{{\tau _{12}}}}| \text{≥} 1$ means that the data passes the test, while $|\frac{{{\tau _{21}}}}{{{\tau _{12}}}}|< 1$ indicates a failure to pass the test. Figure 11 shows effect of Kuroshio on eddy field and Figure 12 shows the influence of eddy field on the Kuroshio in all periods. The blank regions in Figs 11 and 12 indicate the failed tests.
As shown in Figs 11 and 12, the interaction between the Kuroshio and eddy field vary over periods and latitudes. For example, the influence of eddies on the Kuroshio is obvious in the semiannual period, primarily at the higher latitudes (23.9°–25°N) east of Taiwan. In the annual period, the Kuroshio has a greater impact on the eddy field at the high latitudes (23.9°–25°N). In the first interannual period (~2.4-year period), the eddy field has impact on the SKI. In the second interannual period (~6.5-year period), the eddy field has a larger impact on the SKI than SKI has on the eddy field.
5 Conclusions
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Based on the satellite remote sensing data provided by AVISO center from January 1993 to December 2015, a total of 8 400 days, SKI and eddy field (sea level anomaly) data from adjacent waters (22.5°–25°N, 123°–124°E) in the east of Taiwan were analyzed using wavelet analysis. The results indicate there are four main periods of SKI and eddy field east of Taiwan. The correlation and causal relationship between the SKI and the nearby eddy field over the four corresponding periods have been studied. Our conclusions are as follows.
(1) Based on wavelet analysis, the SKI east of Taiwan shows significant seasonal, annual and interannual periods. In addition to the obvious periods of 182 days (0.5 year, the semiannual period) and 365 days (1 year, the annual period), there is also more obvious period about 860 days (2.35 years, the first interannual period) and 2 472 days (6.8 years, the second interannual period) for the SKI. Different latitudes correspond to different periods. The periodicity of the semiannual period and 2.35-year periods at the lower latitudes (22.5°–24°N) was more pronounced than that in the higher latitudes (24°–25°N) east of Taiwan. The annual and 6.8-year periods have obvious periodicity in all latitudes (22.5°–25°N), and the annual period has the most obvious periodicity at the high latitudes (24.5°–25°N). The period of the 6.8-year period is the most significant at the middle and low latitudes (22.7°–23.9°N).
(2) There are significant periods of eddy field east of the Kuroshio at 200 days (0.54 year, the semiannual period), 374 days (1 year, the annual period), 889 days (2.43 years, the first interannual period), and 2 374 days (6.5 years, the second interannual period), generally corresponding to the four periods of the SKI; similarly, there are differences at different latitudes in the same period. Four cycles have obvious periodicity for the whole latitudinal range of the study area (22.5°–25°N). Among them, the period of the semiannual period is the most obvious at the low latitudes (22.5°–23.5°N). The period of the annual period is most obvious in the middle and low latitudes (22.5°–24.5°N) and the 2.43-year period has more obvious periodicity at middle latitudes (23°–24°N). The 6.5-year period has obvious periodicity at the middle and low latitudes (22.5°–23.7°N).
(3) The correlations between the Kuroshio and eddy field are different at different latitudes of different periods. Correlations found in the semiannual period and annual period are more obvious at middle latitudes (23.3°–25°N). The correlation coefficient of the first interannual period (~2.4 year-period) at the latitudes 22.7°–24.1°N is relatively large and negative. The second interannual period (~6.5 year-period) had a high correlation in all latitudes (22.5°–25°N). Finally, with different lag times, the correlation coefficients are different.
(4) The causal relationship between the SKI and the eddy field varied with latitude and period. In the semiannual period, the influence of eddies on the Kuroshio is obvious, while in the annual period, the Kuroshio had a greater impact on the eddy field. In the first interannual period (~2.4-year period), the eddy field has impact on the surface Kuroshio intensity. In the second interannual period (~6.5-year period), the eddy field has a larger impact on the SKI than SKI has on the eddy field.
Acknowledgements
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We thank Guihua Wang and Xiang San Liang for helpful conversations. The altimeter products used in this study were produced by Ssalto/Duacs and distributed by AVISO, with support from CNES (http://www.aviso.oceanobs.com/duacs/).
Funding
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  • The National Key R&D Program of China under contract No. 2018YFC1406202; the National Natural Science Foundation of China under contract Nos 41206002, 41706021, 41775053 and 91428206; the China Postdoctoral Science Foundation under contract No. 2014M551711.
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doi: 10.1007/s13131-019-1417-3
  • Receive Date：2018-02-08
  • Online Date：2026-03-31
  • Published：2019-04-25
Article Data
Affiliations
History
  • Received：2018-02-08
  • Accepted：2018-06-19
Funding
The National Key R&D Program of China under contract No. 2018YFC1406202; the National Natural Science Foundation of China under contract Nos 41206002, 41706021, 41775053 and 91428206; the China Postdoctoral Science Foundation under contract No. 2014M551711.
Affiliations
    1 State Key Laboratory of Satellite Ocean Environment Dynamics (SOED), Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
    2 College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, 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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