This study employed an arithmetic mean model with sea surface temperature and chlorophyll data (2-month lag) to determine the projected impact of climate change on the immature albacore tuna habitat in the Indian Ocean. Albacore tuna fishing data from Taiwanese longline fishery from 2000 to 2016 were used. Standardization of the nominal catch per unit effort data was performed to prevent bias and overestimation resulting from various temporal and spatial factors. In the Indian Ocean, potential immature albacore habitats exhibited significant habitat suitability index (HSI) changes in response to future climate change levels. As the water stratifies in a projected warm climate, low HSI areas were enlarged, and potential immature albacore habitats exhibited a net southward shift. Although the CMIP5 climate model sea surface temperature projections generated different HSI patterns for immature albacore, our results from the ensemble median immature albacore habitat forecasts provided information useful for assessing risks and adaptation strategy options for albacore fishery resources under climate change. The trends of the potential immature albacore habitat distribution could also be cautiously used to inform resource stakeholders’ decisions.
Research Article
Projected climate change impact on immature albacore tuna habitats in the Indian Ocean by using sea surface temperature and chlorophyll data: Ensemble forecasting
https://doi.org/10.21203/rs.3.rs-1615080/v1
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AMM
CPUE
Ensemble forecasting
HSI
PHH
IPCC
RCP
Indian Ocean
The spatial distribution of fish stocks has been shifting globally since the end of the twentieth century (Cheung et al. 2013; Poloczanska et al. 2013). This has been attributed to recent climate change and extensive overfishing over the past half century, which has reduced fish stocks to historically low levels (Engelhard et al. 2014). The future impact of climate change on fisheries distribution in oceans worldwide is causing concern. Global climate change is expected to affect the properties of seawater (Gruber 2011), considerably altering the primary production of the oceans (Steinacher et al. 2010). These changes directly affect the spatial distribution of particular species and indirectly affect the ocean’s productivity, drastically modifying marine ecosystems (Brander 2007; Mondal et al. 2021). Hence, to achieve sustainable fisheries management, stakeholders must identify the dominant trends that drive changes in the spatial distribution and habitat preference of fish in relation to climate change (Brander 2010; Sumaila et al. 2011), because habitat is a key feature of any ecosystem (Sumaila et al. 2011). In this regard, the Intergovernmental Panel on Climate Change (IPCC) climate models can be used to explore the projected impact of climate change on marine resources. The marine science community has been using projected climate change scenarios under various Representative Concentration Pathway (RCP) conditions for qualitative and quantitative projections of marine ecosystem responses to environmental changes associated with the atmosphere. These projections have indicated the effect of climate change on fish and fisheries (Chang et al. 2021; Allison et al. 2009; Cochrane et al. 2009; Drinkwater et al. 2010; Blanchard et al. 2012; Doney et al. 2012; Merino et al. 2012). These changes can be either the shift of habitats toward higher latitude or reduction in the habitat preference of the current habitat zone. In this regard, we applied the ensemble forecasting method by using different IPCC climate models. Instead of using a single forecast, the average of multiple forecasts from multiple models was employed. This approach overcame the single-model error resulting from imperfect model formulation.
Albacore tuna (Thunnus alalunga) is a valuable commercial species (Ali et al. 2020) globally, and the longline fishery for this species in the Indian Ocean has a long history (since 1960). Albacore tuna is a highly migratory fish. The use of habitat models is a key approach to understanding any species’ habitat pattern and can be applied to examine tuna within the study area or a modeled corridor under projected climate change. Habitat suitability index (HSI) models, similar to arithmetic mean modeling (AMM), and geometric mean modeling are critical tools that have been used for sustainable species management, ecological impact assessment, and ecological restoration studies since 1996 (Van Der Lee et al. 2006). The conservation of species and habitats depends on locating the species’ habitat. Data standardization is a valuable approach for managing a large amount of data in HSI models. The primary indices of abundance for many of the world’s most valuable species (e.g., tunas) and vulnerable species (e.g., sharks) are based on catch and effort data collected from commercial and recreational fishers (Maunder et al. 2004). However, the accuracy of these indices can be affected through changes in catch rates over time. The trends of the nominal (N.) catch per unit effort (CPUE) can be influenced through several factors in addition to stock abundance, including the fishing location, target species, and environmental conditions. Therefore, standardizing the N. CPUE has become an essential step when studying fisheries management (Kim et al. 2015). In our previous study, we described the habitat preferences of immature albacore tuna and its critical role (Mondal et al. 2021). Extending that work, we identified the projected impact of climate change on the immature albacore tuna (average weight with less than 14 kg) in the present study.
Our previous study discussed the role of sea surface temperature (SST) and sea surface chlorophyll (2 month lag) SSC2 (Mondal et al. 2021). Studies have revealed SST to be a key predictor of the CPUE of albacore in surface waters (Chen et al. 2005; Phillips et al. 2014; Sagarminaga and Arrizabalaga 2014). SSC2 is an indicator of the presence of phytoplankton, which are the foundation of the aquatic food web. Small fish and invertebrate graze near phytoplankton, and albacore is attracted by these secondary producers (Bakun 1997; Olson et al. 1994; Polovina et al. 2001). In our preliminary study (Mondal et al. 2021) of immature albacore habitat modelling, we used only sea surface temperature (SST) and sea surface chlorophyll with 2 months lag (SSC2), which was focused. An AMM derived model with SST, and SSC2 was used to obtain a preliminary idea about the immature albacore distribution pattern in the Indian Ocean. Thus, we applied this developed model in our present study to assess the projected climate change impact on the immature albacore tuna habitat in the Indian Ocean because it showed a very good predictive performance in our previous study. We assumed that SST, and SSC2 worked as good proxies for broader environmental processes. Brief flowchart of the experiment is showed in table 1.
Table 1. Brief flowchart of the experiment.
2.1 Study location and fishery data
This study focused on the Indian Ocean from May to July of 2000 to 2016, because these 3 months were the most productive months during the study period of our previous study (Mondal et al. 2021). Fishery data were collected from 0° to 50°S and 20°E to 120°E. Taiwan’s Overseas Fisheries Development Council (OFDC) supplied the albacore tuna fishing data. Initially, 433,876 data points with a 1° × 1° spatial grid were applied in the present study. Fishery data consisted of the year, month, latitude, longitude, catch number, effort (number of hooks), and weight (dry or wet was not distinguished in the logbook). Monthly N. CPUE was calculated as the number of individuals caught per 103 hooks.
CPUE = (C / E)
where CPUE is the N. CPUE (in 103 hooks) in each 1° × 1° spatial point, C is the number of individuals caught, and E is the effort made (in number of hooks). Using 1° × 1° fishery data is always more effective than using 5° × 5° fishery data for understanding the habitat. This study also applied the commonly used generalized linear model (GLM) to standardize the N. CPUE to remove bias resulting from spatial and temporal factors. A model with four factors, namely year, month, latitude, and longitude, was used to construct the GLM for standardization as follows:
Log (CPUE + c) = µ + Year + Month + Latitude + Longitude + €
where CPUE is the N. CPUE; c is a constant, which is equal to 0.1% of the mean nominal catch rates and commonly used for standardization (Su et al. 2008; Lan et al. 2018); µ is the intercept; and € is a normally distributed variable with a mean equal to zero.
2.2 Environmental data
2.2.1 Historical environmental data
Historical environmental (SST and SSC2) data were collected from May to July of 2000 to 2016 from 0° to 50°S and 20° to 120°E (Table 2). Monthly SST data in °C were collected from the HadISST data set (https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdHadISST.html), which is produced at the Met Office Hadley Centre and has a spatial resolution of 1° × 1°. Monthly SSC2 data in mg/m−3 were collected from the biogeochemical hindcast for global ocean (https://resources.marine.copernicus.eu/products), which is produced at Mercator Ocean (Toulouse, France) and has a spatial resolution of 0.25° × 0.25°. Because the fishery and SST data had a 1° spatial grid, the SSC2 data were rescaled to the same spatial grid using MATLAB software (version 2019a).
Table 2. Environmental data sources and their descriptions.
|
Environmental data |
Temporal resolution |
Spatial resolution |
Source |
Time period |
|
Sea surface temperature |
Monthly |
10 x 10 |
AQUA-MODIS |
2000-2016 |
|
Sea surface chlorophyll (2 months lag) |
Monthly |
0.250 x 0.250 |
COPERNICUS |
2.2.2 Projected climate change data
All projected environmental data were collected from the IPCC climate models under RCP scenarios 2.6, 4.5, and 8.5. Environmental variables were downloaded from the Earth System Grid Federation. RCP 2.6, 4.5, and 8.5 were characterized using the stabilization without overshoot pathway to 4.5 W m−2 (400 ppm CO2 equivalent) and the rising radiative forcing pathway leading to 8.5 W m−2 (1370 ppm CO2 equivalent), respectively. RCP 4.5 is a scenario involving long-term global emissions of greenhouse gases, short-lived species, and land use and land cover, which stabilizes radiative forcing at 4.5 W/m2 (approximately 650 ppm CO2 equivalent) in the year 2100 without ever. We used the average of different IPCC climate models to reduce the maximum bias resulting from using a single climate model (Table 2). The data were collected for May to July of 2040, 2070, and 2100 to analyze the short-term, medium-term, and long-term effects of projected climate change on the distribution pattern of immature albacore tuna under both RCP scenarios. In this study, all climate model data were interpolated to a spatial grid of 1° by using MATLAB software (version 2019a) to match the 1° spatial grid of the fishery data. The six atmosphere–oceanic general circulation models (AOGCMs) used in this study are listed in Table 3.
Table 3. Sources of projected environmental data from different climate models.
|
Institute |
Code |
Resolution |
Parameters |
|
Institute Pierre Simon Laplace |
IPSL |
10 x 10 |
SST, SSC2 |
|
Geophysical Fluid Dynamics Laboratory |
GFDL |
0.3-10 x 10 |
SST, SSC2 |
|
Commonwealth Scientific and Industrial Research Organization |
CSIRO |
1.50 x 10 |
SST, SSC2 |
|
Hadley Center Global Environment Model |
HadGEM |
0.3-10 x 10 |
SST, SSC2 |
|
Max Plank Institute for Meteorology |
MPI |
10 x 10 |
SST, SSC2 |
|
Canadian Earth System Model |
CanSEM |
1.50 x 10 |
SST, SSC2 |
2.3 Model development
Suitability index (SI) curves were constructed for each parameter with the standardized immature albacore CPUE by using smoothing spline regression to identify the relationship between immature albacore relative abundance and their varying environmental preferences (Haghi et al. 2013). This approach elucidated the relationship between standardized CPUE (S. CPUE) and environmental variables. In the regression analysis, the S. CPUE was the dependent variable, and all environmental parameters were the explanatory variables. The SI for albacore was established by applying the S. CPUE and all environmental variables and was then normalized as follows (Tian et al. 2009; Lee et al. 2020):
where Ymax and Ymin, respectively, are the maximum and minimum number of observations of the S. CPUE or environmental variables, and Y is the predicted value from Ymax to Ymin; thus, the SI values can range only from zero to one.
The SI value was calculated using the summed frequency distribution of the S. CPUE of each class, and SI values were assumed to be between zero and one. The midpoints of each environmental variable class interval were used as the observed values to fit the SI models, and, finally, the relationship between the SI and environmental variables was calculated using the following formula (Chen et al. 2009; Lee et al. 2018; Lee et al. 2019):
where m denotes the response variable (environmental parameters) and α and β are fixed by applying the nonlinear least squares estimate to minimize the residual between the SI observation and SI function.
After construction of the SI curves, an AMM-based HSI model was constructed as follows by using the average and standard deviation of SST and SSC2 for the S. CPUE:
HSIAMM = (SST + SSC2)/2
An HSI of more than 0.6 was considered as high (Mondal et al. 2021).
2.4 Ensemble HSI forecasting
The expected frequency distribution of the combined forecasts is not assumed in consensus forecasting, but a measure of the central tendency (e.g., the mean or median) is produced for ensemble forecasting to reduce the uncertainty in future habitat projections under climate change (Araujo et al. 2005; Araujo and New 2007). The mean prediction is more sensitive to outliers than the median. Thus, consensus forecasting was employed to select AMM with SST and SSC2 to calculate the habitat predictions derived from the six AOGCM models. The median of the ensemble forecasts used to reduce inter model variance (Lefebvre and Goosse 2008) relating to projected climate change impact on the habitat of immature tuna in the Indian Ocean. Ensemble prediction was performed using the caret Ensemble package of R (version 1.2.1335). The monthly prediction of the HSI was mapped on a 1° × 1° spatial grid by using ArcGIS (version 10.2) for all three scenarios under all three time periods (2040, 2070, and 2100).
2.5 Identification of potential habitat hotspots (PHH)
The locations of habitat hotspots were determined using environmental probability indices, specifically the high-probability areas for immature albacore tuna in the Indian Ocean. In this study, SST and SSC2 were used to predict the potential habitat hotspots (PHHs). The probability was estimated using histogram graphs to show the relations between total S.CPUE and environmental variables, as well as the relationships between total fishing effort and the variables. At first, probability indices were calculated by dividing total S.CPUE at a particular interval of the histogram by the maximum total S.CPUE and fishing efforts were calculated using the same approach for each variable. After that, the average of probability indices from both SST and SSC2 interval ranges was then calculated. In connection to a certain interval of the variables, the maximum probability value reflects the highest frequency fishing effort of and total S.CPUE. This indicates the best environmental conditions (hot spots). Finally, the average of the probability indices from the interval ranges of all variables was calculated. A probability value for the probability index of more than 0.7 indicated habitat hotspots. The average SST and SSC2 probability was used to generate a probability map for all interval ranges of the oceanographic conditions.
where PHI is the PHI; PI-S.CPUE is the mean probability index for immature albacore tuna based on the relationship between the S. CPUE and the two environmental parameters for each interval; PI-F is the mean probability index based on the relationship between fishing frequency and the environmental parameters; S.CPUEij is the value of the S. CPUE in relation to environmental parameter i for class interval j; S.CPUEimax is the maximal S. CPUE for each environmental parameter; Fij is the value of fishing frequency in relation to environmental parameter i for class interval j; Fimax is the maximal fishing frequency for each environmental parameter; and n is the total number of variables.
3.1 Changes in the HSI under projected climate change scenarios
Figure 1 depicts the changes in the HSI under different RCP scenarios at different times. The high HSI (>0.6) zone was mainly between 30° and 35°S. Longitudinally, this was restricted to 60° to 100°E. In 2040, a high HSI zone was observed with a similar pattern as that of 2016 under all RCP scenarios. However, a longitudinal extension of the high HSI zone up to 50°E was noted in 2040 under RCP 2.6. In 2070, the high HSI zone was approximately 35°S under RCP 2.6 and 4.5. Again, longitudinally, the high HSI zone was between 60° and 100°E. The situation was slightly different in RCP 8.5. The high HSI zone exhibited a slight southward shift in 2070. At the end of 2100, the high HSI remained between 30° and 35°S under RCP 2.6 and 4.5. However, a clear change was observed in RCP 8.5. The magnitude of the HSI was between only 0.6 and 0.7. In addition, this region was almost touching 40°S. Longitudinally, this zone was between 65° and 80°E.
Figure 2 illustrates the latitudinal shift in the ensemble HSI under different RCP scenarios at different times. In 2016, the HSI was high (30°–33°S). In RCP 2.6 at all three times, a high HSI was observed within this zone with only a slight change; the HSI magnitude ranged from 0.55 to 0.65. In RCP 4.5 at all three times, a high HSI was observed within this zone only and with only a slight change; the HSI magnitude ranged from 0.4 to 0.5. In both cases (RCP 2.6 and 4.5) and years (2040 and 2070), a significant reduction in the HSI was observed after 35°S only. In RCP 8.5, the high HSI in 2040 and 2070 exhibited little significant latitudinal change except a slight change in HSI magnitude. However, in 2100, the HSI magnitude was drastically reduced to under 0.3, and the HSI was the highest at 37°S. In 2100, the HSI magnitude of the area 31° to 33°S was less than 0.3 in RCP 8.5, which was much lower than that in RCP 2.6 (>0.5) and 4.5 (>0.4).
3.2 Identification of PHHs and predicted changes
The optimal ranges of SST, and SSC2 were found 17.5-18.5°C, 0.07-0.09 mgm-3 in our previous study. Figure 3a-d illustrated the total effort and total immature albacore S.CPUE and in relation to SST and SSC2 during the study period. Immature albacore tended to remain near the place with a high probability index of 0.7 (preferred SST and SSC2) and to congregate in waters with a low probability index of less than 0.7. This relationship was statistically significant under piecewise linear regression (R2 = 0.931, p < 0.01). In the regression, the first equation was written as CPUE = a (intercept) + b (slope).PHH (x = PHH), when x ≤ 71 (x = 71 indicates the point where the slopes changes), and the second equation was written as CPUE = (a − 71b) + (a + b) PHH, when x >71. Figure 4 depicts the relationship between the total immature albacore S. CPUE and the probability indices of fishing grounds under piecewise linear regression.
Figure 5 presents the changes in PHHs under different RCP scenarios at different times. The high PHH (>0.6) zone was mainly between 30° and 35°S. Longitudinally, this was between 60° and 110°E. In 2040, a high PHH was observed with a similar pattern to that in 2016 under all RCP scenarios. We noted a longitudinal extension of a high HSI zone to 50°E to almost 120°E under all three RCP 2.6 scenarios. A high PHH remained detectable in the area between 30° and 35°S at the end of 2100 under RCP scenario; in RCP 8.5, the PHH was at 35°S at this time. In both cases, the longitude zone for the high PHHs was between 60° and 100°E. We observed different results for 2100 in RCP 8.5. PHHs with a magnitude of more than 0.6 were observed in few locations, only near 40°S and between 60° and 80°E. In RCP 8.5, high PHHs were concentrated within restricted zone.
4.1 Projected impacts of climate change on immature albacore habitats
In the southern Indian Ocean, hydrography and topography combine to create ecologically rich areas (“hot spots”) for commercially critical pelagic species including albacore tuna. Using proxy environmental variables such as SST and SSC, the most productive albacore habitats, which are likely frontal zones, can be determined (Polovina et al. 2001; Zainuddin et al. 2004). In our previous study, we examined the spatial pattern of immature albacore tuna in the Indian Ocean by using remotely sensed SST and SSC2, the spatial pattern being a valuable tool for examining the dynamic nature of environmental variables on a basin-wide scale.
The expected effects of climate change on the immature albacore’s habitat distribution in the Indian Ocean were revealed in the current study. Projected changes in the location of preferred SST and SSC2 were observed from the median ensembles of the CMIP5 AOGCMs in three RCP scenarios, with varying spatial and temporal warming patterns (Figure 6). Demonstrated through the use of AMM (Mondal et al. 2021) habitat model predictions, differences in regional changes in the preferred SST and SSC2 patterns resulted in pronounced spatial changes in potential immature albacore habitats (Figure 6), with the regions of moderate to high habitat suitability contracting and expanding relative to current habitat distributions. Projected immature albacore habitats revealed a net reduction in spatial area as well as a southward habitat shift (Figure 6), possibly because of varying degrees of projected climate change. Other empirical modeling studies examining the potential future distributions of various marine resources under global warming scenarios are consistent with the results of our inferred spatial responses in potential albacore habitats in this study (Cheung et al. 2010; Hollowed et al. 2013; Jones and Cheung 2015). The immature albacore tuna, similar to other marine species, is extremely sensitive to SST variations in feeding environments, because these changes affect their physiology, growth, and survival (Ichii et al. 2009, 2011; Vijai et al. 2014; Nikolic et al. 2016; Nikolic et al. 2017; Zainuddin et al. 2006; Zainuddin et al. 2008). In our previous study (Mondal et al. 2021) the area between 30° and 40°S was the feeding ground of immature albacore tuna during the study period. In our present study, we determined that the projected immature albacore habitats matched the determined feeding areas of immature albacore in the Indian Ocean and were similarly substantially affected through zonal change relating to preferred SST and SSC2 (Figure 6). The optimal SST range (17.5°C) of the immature albacore feeding grounds exhibited a southward expansion from 2016 to 2100 in RCP 8.5 but was almost the same from 2016 to 2100 in RCP 2.6 and 4.5. Following this trend, the high HSI zone also exhibited a southward shift by the end of 2100 in RCP 8.5. Moreover, in this scenario, a drastic reduction in the high HSI zone was observed, which was probably attributable to the presence of optimal SST and SSC2 in that particular area (south of 35°S; 65°–80°E; Figure 6).
Changes in the high PHHs were also connected to the spatial change of the preferred SST and SSC2 (Figure 7-9). Similarly, the trend of the HSI also exhibited a southward shift and net reduction in high PHH zones by the end of 2100 in RCP 8.5 (Figure 9).
Under increased temperatures, the expected poleward relocation of prospective immature albacore habitats could indirectly induce the poleward retreat of favorable feeding environments of immature albacore tuna in the Indian Ocean. Different physical processes in the ocean are closely linked to and influenced by upper ocean temperatures, which has biological implications (Barange and Perry 2009; Daw et al. 2009). Vertical mixing is one of these temperature-dependent physical processes that is crucial in regulating nutrient delivery and light availability for marine biological productivity (Ayers and Lozier 2010). Because vertical mixing weakens under warm stratified conditions, projected ocean warming in the future is likely to enhance biological production off the southern zone of the Indian Ocean, providing a possible complementary scenario for the predicted southward shift of the immature albacore’s PHH in the Indian Ocean. This study only employed SST and SSC2 to obtain a preliminary idea of the projected impact, but other key factors such as salinity, current, dissolved oxygen, and primary productivity may also play critical roles in the determination of the projected climate change impact. We plan to examine these factors in the next step of our research.
4.2 Potential implications for albacore fisheries
The future availability of albacore resources to fisheries may be affected through the prospective albacore habitat responses under varying levels of ocean warming. Changes in seasonal and spatial SST trends, for example, could lead to changes in fishing activity timing and location. Furthermore, when the immature albacore’s optimal habitat SST increases in the current fishing location, future fishing grounds are projected to retreat poleward in comparison with their current locations. These intertwined temporal and spatial shifts in the potential immature albacore fishing grounds could, in turn, have far-reaching socioeconomic consequences. Fishing industries would likely face greater operational costs as a result of reduced fishing grounds farther from the coasts and consumers would be affected through consequent market shifts.
4.3 Limitations of the study and future research directions
Although the results of this study provide some useful insights into resource management and adaptation strategy evaluation, these habitat predictions were subject to uncertainty arising from global warming estimates that might vary across IPCC-class climate models and RCP estimates (Taylor et al. 2011). The species habitat model algorithm and the amount of available immature albacore fisheries data used to create the base habitat model could also be causes of uncertainty in immature albacore habitat forecasts. For a more robust resolution of immature albacore habitat responses to climate change in the future, investigating various habitat model techniques, assembling longer time-series fisheries data, and using available SST forecasts from a broader pool of IPCC-class CMIP5 climate models would be beneficial. We plan to implement these approaches in the next step of our research.
The current investigation indicated the expected effects of climate change on the habitat distribution of young albacore in the Indian Ocean. The median ensembles of the CMIP5 AOGCMs projected changes in the position of preferred SST and SSC2 in three RCP scenarios with variable geographical and temporal warming trends. In the Indian Ocean, potential immature albacore habitats exhibited significant HSI changes in response to projected levels of climate change. When the water stratifies in a warm climate in the future, low HSI areas could be enlarged, and potential squid habitats may exhibit net southward shift. Although the CMIP5 climate model SST projections resulted in different HSI patterns for the immature albacore, our results from the ensemble median immature albacore habitat forecasts have provided information useful for assessing risks and formulating adaptation strategies for albacore fishery resources under climate change. Therefore, this effort connoted the intact excellent performance and fruitful outcomes of the executed models and techniques for such kind of assessment and forecasting to replicate predicted changes and anomaly, and therefore, we would like to advocate such more studies over the region. The trends of the potential immature albacore habitat distribution could likewise be cautiously used to inform resource stakeholders’ decisions.
Acknowledgements
We thank the team members of the Fisheries Agency and the OFDC of Taiwan for their assistance with data preparation. We are grateful to the Council of Agriculture, Taiwan, R.O.C., for the grant we received. We will also like to thank Wallac English editing company for the English editing of the full manuscript.
Conflict of interest
The authors declare no conflicts of interest.
Author contributions
Conceptualization, S.M. and M.-A.L.; methodology, M.-A.L. and S.M.; software, S.M.; validation, M.-A.L.; formal analysis, S.M.; investigation, M.-A.L., J. H. and Y.-C.W.; resources, M.-A.L., M.-L. N. and Y.-C.W.; writing—original draft preparation, S.M.; writing—review and editing, M.-A.L., visualization, Y.-C.W., A.-B. S., B.-K.M. All authors have read and agreed to the published version of the manuscript.
Funding
This research was financed by Taiwan Council of Agriculture (106AS-10.1.5-FA-F1 (4);107AS-9.1.5-FA-F1 (4)) and Taiwan Ministry of Science and Technology (MOST105-2611-M-019-011).
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