Zhang 2024a - Revision history

Tomamil: Tomamil moved page Review 931624371744 to Zhang 2024a

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Tomamil moved page Review 931624371744 to Zhang 2024a

Cynthiazhang at 16:34, 25 July 2024

2024-07-25T16:34:57Z

Cynthiazhang at 16:33, 25 July 2024

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Cynthiazhang at 18:10, 23 July 2024

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Cynthiazhang at 14:45, 23 July 2024

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Cynthiazhang: Cynthiazhang moved page Draft Zhang 430172805 to Review 931624371744

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Cynthiazhang moved page Draft Zhang 430172805 to Review 931624371744

Cynthiazhang at 19:41, 15 July 2024

2024-07-15T19:41:18Z

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Cynthiazhang at 19:30, 15 July 2024

2024-07-15T19:30:42Z

Show changes

@@ Line 185: / Line 185: @@
 ===='''Limitations'''====
-Although remote sensing data provides large spatial coverage and enables long-term analysis, Chl-''a'' data may not be precise due to cloudy conditions and other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Racault et al., 2015; Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also hindered the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, in situ data could be matched with satellite data for validation (Gittings et al., 2017).
+Although remote sensing data provides large spatial coverage and enables long-term analysis, ocean color satellites can not capture data below the surface of the water, and Chl-''a'' data may not be precise due to cloudy conditions and other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Racault et al., 2015; Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also affected the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, in situ data could be matched with satellite data for validation (Gittings et al., 2017).
 Moreover, remote sensing data products acquired from the ERDDAP database had inconsistent spatial and temporal resolutions, and not all variables had data available from 2003-2022 due to limited options. This study worked around this by standardizing resolutions through taking monthly, seasonal, and spatial averages and adjusting time periods for correlation and linear regression analysis.
@@ Line 195: / Line 195: @@
 ===='''Future research'''====
-Although remote sensing data provides large spatial coverage and enables long-term analysis, ocean color satellites can not capture data below the surface of the water, and Chl-''a'' data may not be precise due to cloudy conditions and other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Racault et al., 2015; Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also affected the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, in situ data could be matched with satellite data for validation (Gittings et al., 2017).
+Although this study stratified LI’s coastal waters into four categories and two regions according to b<sub>d</sub> and b<sub>p</sub> (Fig. 4, 5), the spatial distribution of phytoplankton phenology and abundance should be further investigated in smaller regions and at the coordinate level to identify areas vulnerable to fluctuations in phytoplankton growth. In the future, the spatial distribution of the strength of correlation coefficients between phenological metrics and ecological parameters and between Chl-''a'' levels and ecological parameters can be mapped as well (Zoljoodi et al., 2022). To capture a more comprehensive picture of phytoplankton bloom dynamics, remote sensing measurements could be combined or compared with in situ measurements as well (Gittings et al., 2017).
 Since this study found that phytoplankton species and PFTs may contribute to the timing and abundance of phytoplankton blooms due to their differing temperature tolerances and environmental preferences, more data for these two factors should be collected to identify the specific species and PFTs. Similarly, nutrient data in Region B or LI southeastern shores should be collected to determine the specific nutrients that primarily drive increased phytoplankton growth during upwelling, deep winter mixings, and nutrient runoff. In addition, this study also found that the Dilution-Recoupling Hypothesis from Behrenfeld (2010) may impact phytoplankton spatiotemporal distribution in Region B, so the factor of grazing pressure should be assessed in future studies.

@@ Line 195: / Line 195: @@
 ===='''Future research'''====
-Although this study stratified LI’s coastal waters into four categories and two regions according to b<sub>d</sub> and b<sub>p</sub> (Fig. 4, 5), the spatial distribution of phytoplankton phenology and abundance should be further investigated in smaller regions and at the coordinate level to identify areas vulnerable to fluctuations in phytoplankton growth. In the future, the spatial distribution of the strength of correlation coefficients between phenological metrics and ecological parameters and between Chl-''a'' levels and ecological parameters can be mapped as well (Zoljoodi et al., 2022). To capture a more comprehensive picture of phytoplankton bloom dynamics, remote sensing measurements could be combined or compared with in situ measurements as well (Gittings et al., 2017).
+Although remote sensing data provides large spatial coverage and enables long-term analysis, ocean color satellites can not capture data below the surface of the water, and Chl-''a'' data may not be precise due to cloudy conditions and other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Racault et al., 2015; Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also affected the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, in situ data could be matched with satellite data for validation (Gittings et al., 2017).
 Since this study found that phytoplankton species and PFTs may contribute to the timing and abundance of phytoplankton blooms due to their differing temperature tolerances and environmental preferences, more data for these two factors should be collected to identify the specific species and PFTs. Similarly, nutrient data in Region B or LI southeastern shores should be collected to determine the specific nutrients that primarily drive increased phytoplankton growth during upwelling, deep winter mixings, and nutrient runoff. In addition, this study also found that the Dilution-Recoupling Hypothesis from Behrenfeld (2010) may impact phytoplankton spatiotemporal distribution in Region B, so the factor of grazing pressure should be assessed in future studies.

@@ Line 185: / Line 185: @@
 ===='''Limitations'''====
-Although remote sensing data provides large spatial coverage and enables long-term analysis, Chl-''a'' data may not be precise due to other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also hindered the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, ''in situ ''data could be matched with satellite data for validation (Gittings et al., 2017).
+Although remote sensing data provides large spatial coverage and enables long-term analysis, Chl-''a'' data may not be precise due to cloudy conditions and other suspended terrigenous material present in bodies of water, especially coastal areas, that can interfere with the measurements of ocean color remote sensing satellites and hinder their accuracy (Racault et al., 2015; Aurin & Dierssen, 2012). Furthermore, not all missing values in the remote sensing data products for Chl-''a'' and the ecological parameters were qualified to be interpolated spatially and temporally, which may have also hindered the accuracy of the data. To evaluate the confidence in the use of these remote sensing products, in situ data could be matched with satellite data for validation (Gittings et al., 2017).
 Moreover, remote sensing data products acquired from the ERDDAP database had inconsistent spatial and temporal resolutions, and not all variables had data available from 2003-2022 due to limited options. This study worked around this by standardizing resolutions through taking monthly, seasonal, and spatial averages and adjusting time periods for correlation and linear regression analysis.
 Additionally, no public-access remote sensing or on-site nutrient data was found for areas outside of LIS. Thus, this study was only able to analyze the effects of TDN and TDP levels and ratios on Region A. Phytoplankton phenology and seasonal abundance in Region B exhibited strong correlations with high WS, low SST, and high PP, indicating increased phytoplankton growth from obtaining nutrients from upwelling, water mixings, and runoff (Table 3, 4), but specific nutrients that were involved in this process could not be identified.
 ===='''Future research'''====
-Although this study stratified LI’s coastal waters into four categories and two regions according to b<sub>d</sub> and b<sub>p</sub> (Fig. 4, 5), the spatial distribution of phytoplankton phenology and abundance should be further investigated in smaller regions and at the coordinate level to identify areas vulnerable to fluctuations in phytoplankton growth. In the future, the spatial distribution of the strength of correlation coefficients between phenological metrics and ecological parameters and between Chl-''a'' levels and ecological parameters can be mapped as well (Zoljoodi et al., 2022).
+Although this study stratified LI’s coastal waters into four categories and two regions according to b<sub>d</sub> and b<sub>p</sub> (Fig. 4, 5), the spatial distribution of phytoplankton phenology and abundance should be further investigated in smaller regions and at the coordinate level to identify areas vulnerable to fluctuations in phytoplankton growth. In the future, the spatial distribution of the strength of correlation coefficients between phenological metrics and ecological parameters and between Chl-''a'' levels and ecological parameters can be mapped as well (Zoljoodi et al., 2022). To capture a more comprehensive picture of phytoplankton bloom dynamics, remote sensing measurements could be combined or compared with in situ measurements as well (Gittings et al., 2017).
-Since this study found that phytoplankton species and PFTs may contribute to the timing and abundance of phytoplankton blooms due to their differing temperature tolerances and environmental preferences, more data for these two factors should be collected to identify the specific species and PFTs. In addition, nutrient data in Region B or LI southeastern shores should be collected to determine the specific nutrients that primarily drive increased phytoplankton growth during upwelling, deep winter mixings, and nutrient runoff.
+Since this study found that phytoplankton species and PFTs may contribute to the timing and abundance of phytoplankton blooms due to their differing temperature tolerances and environmental preferences, more data for these two factors should be collected to identify the specific species and PFTs. Similarly, nutrient data in Region B or LI southeastern shores should be collected to determine the specific nutrients that primarily drive increased phytoplankton growth during upwelling, deep winter mixings, and nutrient runoff. In addition, this study also found that the Dilution-Recoupling Hypothesis from Behrenfeld (2010) may impact phytoplankton spatiotemporal distribution in Region B, so the factor of grazing pressure should be assessed in future studies.
 Future research could also seek to investigate the long-term effects of decreased phytoplankton productivity on LI’s marine ecosystems and fisheries. Linear trend analysis produced strong decreasing trends of summer and autumn phytoplankton productivity in the recent 20 years, which can have implications for food web imbalances and trophic mismatch (Edwards & Richardson, 2004; Beaugrand et al., 2003).
@@ Line 201: / Line 203: @@
 ==Conclusion==
-The objective of this study was to identify regions across LI’s coastal waters with contrasting patterns in phytoplankton phenology, investigate the factors driving regional differences in phytoplankton phenology and seasonal abundance, and analyze their long-term patterns. Winter-spring (W-S) and summer-autumn (S-A) blooms in Region A, encompassing LIS and the southwestern shores of LI, peaked in late winter and late autumn and are primarily driven by P-rich river discharges and thermal stratification, respectively. Both W-S and S-A blooms in Region B, the southeastern shores of LI, initiate in autumn as they thrive from colder surface waters that deepen the mixed layer, increasing surface water nutrients and reducing grazing pressure. Although Region A had higher Chl-''a'' levels and greater access to nutrients throughout the year from river outlets, b<sub>d</sub> in Region A is 44 days shorter than Region B, likely due to more nutrient-limited phytoplankton in LIS. Seasonal variation in Chl-''a'' is attributed to sea surface temperature (SST) due to differing temperature tolerances of seasonal dominant species, high wind speeds that facilitate off-shelf transport, and N- and P-limitations. As a result, heightened autumn N-limitation and decreased summer SST led to reduced phytoplankton production in Region A in the past 20 years.
+The objective of this study was to identify regions across LI’s coastal waters with contrasting patterns in phytoplankton phenology, investigate the factors driving regional differences in phytoplankton phenology and seasonal abundance, and analyze their long-term patterns. By using 20-year remote sensing datasets covering large areas over time, this study provides a comprehensive view of phytoplankton phenology and abundance in Long Island’s coastal waters. Winter-spring (W-S) and summer-autumn (S-A) blooms in Region A, encompassing LIS and the southwestern shores of LI, peaked in late winter and late autumn and are primarily driven by P-rich river discharges and thermal stratification, respectively. Both W-S and S-A blooms in Region B, the southeastern shores of LI, initiate in autumn as they thrive from colder surface waters that deepen the mixed layer, increasing surface water nutrients and reducing grazing pressure. Although Region A had higher Chl-''a'' levels and greater access to nutrients throughout the year from river outlets, b<sub>d</sub> in Region A is 44 days shorter than Region B, likely due to more nutrient-limited phytoplankton in LIS. Seasonal variation in Chl-''a'' is attributed to sea surface temperature (SST) due to differing temperature tolerances of seasonal dominant species, high wind speeds that facilitate off-shelf transport, and N- and P-limitations. Consequently, heightened autumn N-limitation and decreased summer SST led to reduced phytoplankton production in Region A in the past 20 years. Results from this study enhance our understanding of the factors influencing phytoplankton phenology and abundance in Long Island’s coastal waters, which could help in predicting and managing harmful algal blooms.
 ==Acknowledgments==

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 The four phenological metrics—timing of initiation (b<sub>i</sub>), peak (b<sub>p</sub>), termination (b<sub>t</sub>), and duration (b<sub>d</sub>) of annual phytoplankton growth periods—were extracted from each year in the 20-year period 2003-2022 for each Chl-''a'' data coordinate from the ESA OC-CCI product using Microsoft Excel and following the threshold criterion and cumulative sum of anomalies methods adapted from Racault et al. (2015) as shown in Figure 2.
-First, a threshold value, which acts as a baseline to determine the main phytoplankton growth period, was chosen by testing previous studies’ threshold values on 20 random samples of climatological series of Chl-''a'' across LI’s coastal waters: the median Chl-''a'' concentration plus 5% to 20% of the maximum concentration (Brody et al., 2013; Zoljoodi et al., 2022; Racault et al., 2015). The median + 10% of the maximum concentration was chosen as the best threshold value as it fell below the major peaks but above most of the small peaks of Chl-''a'' climatologies (Zoljoodi et al., 2022). This chosen threshold is consistent with previous studies as Brody et al. (2013) found that thresholds of median + 10% to 15% of the maximum concentration accurately predicted initiation timing of phytoplankton blooms in subtropical regions after testing six thresholds of 5% to 30%. Furthermore, the 10% threshold was applied for the Red Sea, a body of water in a subtropical climate zone, like New York (Racault et al., 2015).
+First, a threshold value, which acts as a baseline to determine the main phytoplankton growth period, was chosen by testing previous studies’ threshold values on 20 random samples of climatological series of Chl-''a'' across LI’s coastal waters: the median Chl-''a'' concentration plus 5% to 20% of the maximum concentration (Brody et al., 2013; Zoljoodi et al., 2022; Racault et al., 2015). Graphing the four thresholds (median + 5%, median + 10%, median + 15%, median + 20%) with 20 random samples of Chl-''a'' climatological series (one random coordinate was chosen for each year in the 20-year period 2003-2022), the median + 10% of the maximum concentration was chosen as the best threshold value as it fell below the major peaks but above most of the small peaks of Chl-''a'' climatologies (Zoljoodi et al., 2022). This chosen threshold is consistent with previous studies as Brody et al. (2013) found that thresholds of median + 10% to 15% of the maximum concentration accurately predicted initiation timing of phytoplankton blooms in subtropical regions after testing six thresholds of 5% to 30%. Furthermore, the 10% threshold was applied for the Red Sea, a body of water in a subtropical climate zone, like New York (Racault et al., 2015).
-The threshold was subtracted from the 8-day composites of Chl-''a'', creating 8-day Chl-''a'' anomalies. Chl-''a'' concentrations below the threshold have an anomaly value less than 0, while concentrations above the threshold have an anomaly value greater than 0. The cumulative sum of anomalies was taken over 8-day periods starting at the beginning of the climatology, eliminating high-frequency noise and revealing trends of persistent increases or decreases of Chl-''a'' (Lozowski et al., 1989).
+The threshold was subtracted from the 8-day composites of Chl-''a'', creating 8-day Chl-''a'' anomalies, revealing the times of year when Chl-''a'' levels deviate from the established norm. Chl-''a'' concentrations below the threshold have an anomaly value less than 0, whereas concentrations above the threshold have an anomaly value greater than 0. By adding positive and negative anomalies, the cumulative sum of anomalies, taken over 8-day periods starting at the beginning of the climatology, eliminates high-frequency noise and reveals trends of persistent increases or decreases of Chl-''a'' (Lozowski et al., 1989).
 The derivatives of the cumulative sum of anomalies were computed, and the main growth period was defined as the time with the highest Chl-''a'' peak and when derivatives were above 0. The dates when the derivatives fall above and below 0 represent b<sub>i</sub> and b<sub>t</sub>, respectively. The number of days of the growth period translates to b<sub>d</sub>. The date of maximum Chl-''a'' concentration in the growth period translates to b<sub>p</sub>.
@@ Line 83: / Line 83: @@
 ==== '''Spatial distribution: stratifying coordinates and years''' ====
-For each coordinate from the ESA OC-CCI Chl-''a'' product, each of the four phenological metrics was averaged across the 20 year-period, and their spatial distributions were mapped (Fig. 3). These coordinates were stratified according to the bimodal distribution of b<sub>d</sub> (Fig 4a), forming Region A with b<sub>d</sub> shorter than the median b<sub>d</sub> of 72 days, encompassing LIS and the southwestern shores of LI, and Region B with b<sub>d</sub> longer than 72 days, encompassing the southeastern shores of LI and the open seas, separated by transition-like boundaries between the two regions indicated by the lighter red and blue coordinates (Fig. 4b).
+For each coordinate from the ESA OC-CCI Chl-''a'' product, each of the four phenological metrics was averaged across the 20 year-period, and their spatial distributions were mapped (Fig. 3). These coordinates were stratified according to the bimodal distribution of b<sub>d</sub>, which exhibits two peaks separated by the median b<sub>d</sub> of 72 days (Fig 4a), forming Region A with b<sub>d</sub> shorter than 72 days, encompassing LIS and the southwestern shores of LI, and Region B with b<sub>d</sub> longer than 72 days, encompassing the southeastern shores of LI and the open seas, separated by transition-like boundaries between the two regions indicated by the lighter red and blue coordinates (Fig. 4b).
 The years of each coordinate were further categorized into winter-spring (W-S) or summer-autumn (S-A) according to the season that b<sub>p</sub> falls into since main growth periods were found in both the first and second half of the year after establishing phenology (Fig. 5). Moreover, W-S and S-A phytoplankton blooms have been studied as two different entities for LI and the U.S. northeast coast (George et al, 2015; Anderson & Taylor, 2001; Gobler et al., 2006; Record et al., 2019). As a result, four groups are formed: W-S blooms in Region A (Region A<sub>w-s</sub>), S-A blooms in Region A (Region A<sub>s-a</sub>), W-S blooms in Region B (Region B<sub>w-s</sub>), and S-A blooms in Region B (Region B<sub>s-a</sub>).
@@ Line 104: / Line 104: @@
 For each of the four groups, regression analysis was performed in Microsoft Excel (Version 2404) to identify correlations between annual phenological metrics (b<sub>i</sub>, b<sub>p</sub>, b<sub>t</sub>, b<sub>d</sub>) from 2011-2019 and monthly means of the ecological parameters (SST, WS, PAR, SSS, PP, TDN, TDP, TDN:TDP) in the corresponding year. Correlation coefficients were also calculated to find relationships between seasonal means of Chl-''a'' levels and those of the eight ecological parameters.
-In Region A and Region B, linear trend analysis was performed to detect interannual trends from 2003-2022 in phenological metrics and seasonal and monthly averages of Chl-''a'' levels and those of factors that showed significant correlation coefficients from regression analysis.
+In Region A and Region B, linear trend analysis was performed to detect interannual trends from 2003-2022 in phenological metrics and seasonal and monthly averages of Chl-''a'' levels and those of factors that showed significant correlation coefficients from regression analysis. Significance of all correlations were determined by computing the p-value from the t-statistic.
 ==Results==

@@ Line 49: / Line 49: @@
 ====Objectives====
-In this study, phenological metrics were established across LI’s coastal waters using remote sensing data, and regions with contrasting patterns in phenology were identified and compared. In each region, the study aimed to investigate the physical, chemical, and climatic factors driving phytoplankton phenology and seasonal Chl-''a'' abundance. Lastly, the interannual trends of phenological metrics and Chl-''a'' levels and their significant driving factors were investigated.
+This study aims (1) to uncover the patterns in phytoplankton phenology across Long Island’s coastal waters, thereby contributing to our understanding of marine ecosystem health; (2) to investigate the physical, chemical, and climatic factors that influence phytoplankton phenology and seasonal Chl-''a'' abundance, which could help in predicting and managing harmful algal blooms; and (3) to scrutinize the interannual trends of phenological metrics and Chl-''a'' levels and their significant driving factors, providing insights that could inform future research and conservation efforts.
 ==Methods==

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	==Introduction==		==Introduction==
−
−	~~==== Phytoplankton in Long Island’s (LI) coastal waters ====~~
	Phytoplankton, also known as microscopic algae, are naturally found at the surface of oceans and play a major role in marine ecosystem health and functioning. Forming the base of aquatic food webs, they support marine life and recycle nutrients by assimilating metabolic waste from other organisms, such as nitrogen (N) and phosphorus (P), from the environment as a nutrient source (Falkowski et al., 2012; Banta et al., 2004). On the other hand, their excessive growth—attributable to nutrient loading from rainfall runoff or river discharge—can lead to harmful algal blooms (HABs) in which uncontrolled growth of algal colonies contribute to hypoxia, poisoning syndromes, and the loss of biodiversity (Anderson et al., 2021; Vaquer-Sunyer & Duarte, 2008). Furthermore, the timing of phytoplankton blooms is essential to ensure food availability during the feeding window of higher trophic level organisms (Edwards & Richardson, 2004; Beaugrand et al., 2003). Since phytoplankton significantly impacts marine ecosystems, their growth is monitored and, in some cases, regulated on a need-basis to restore biological balance.		Phytoplankton, also known as microscopic algae, are naturally found at the surface of oceans and play a major role in marine ecosystem health and functioning. Forming the base of aquatic food webs, they support marine life and recycle nutrients by assimilating metabolic waste from other organisms, such as nitrogen (N) and phosphorus (P), from the environment as a nutrient source (Falkowski et al., 2012; Banta et al., 2004). On the other hand, their excessive growth—attributable to nutrient loading from rainfall runoff or river discharge—can lead to harmful algal blooms (HABs) in which uncontrolled growth of algal colonies contribute to hypoxia, poisoning syndromes, and the loss of biodiversity (Anderson et al., 2021; Vaquer-Sunyer & Duarte, 2008). Furthermore, the timing of phytoplankton blooms is essential to ensure food availability during the feeding window of higher trophic level organisms (Edwards & Richardson, 2004; Beaugrand et al., 2003). Since phytoplankton significantly impacts marine ecosystems, their growth is monitored and, in some cases, regulated on a need-basis to restore biological balance.

@@ Line 28: / Line 28: @@
 ====Phenological Metrics ====
-As objective metrics that can be acquired from each data pixel, the four phenological metrics, the timing of initiation, termination, peak, and duration—or the start date, end date, date of maximum Chl-''a'' concentration, and length of annual phytoplankton growth periods—serve as important ecological indicators to understand seasonal dynamics and variation of phytoplankton blooms (Platt & Sathyendranath, 2008). Using Chl-''a'' remote sensing data, phenological metrics can be derived at a low cost and high spatial and temporal resolutions, allowing long-term patterns and variability in phenology to be observed. These metrics were used in past studies to identify and analyze driving factors, recent trends, and regional patterns of phytoplankton phenology and bloom intensity (Gittings et al., 2017; Zoljoodi et al., 2022; Record et al., 2019)
+As objective metrics that can be acquired from each data pixel, the four phenological metrics, the timing of initiation, termination, peak, and duration—or the start date, end date, date of maximum Chl-''a'' concentration, and length of annual phytoplankton growth periods—serve as important ecological indicators to understand seasonal dynamics and variation of phytoplankton blooms (Platt & Sathyendranath, 2008). Using Chl-''a'' remote sensing data, phenological metrics can be derived at a low cost and high spatial and temporal resolutions, allowing long-term patterns and variability in phenology to be observed. These metrics were used in past studies to identify and analyze driving factors, recent trends, and regional patterns of phytoplankton phenology and bloom intensity (Gittings et al., 2017; Zoljoodi et al., 2022; Record et al., 2019).
 ==== '''Precipitation and river discharge contribute to phytoplankton growth by altering nutrient levels and the Redfield ratio''' ====
@@ Line 45: / Line 45: @@
 ====Impacts of climate change on phytoplankton threaten marine ecosystem health====
-''' '''Threats to marine ecosystems are emerging as ongoing changes in climate and environmental conditions, especially global warming, are significantly altering phytoplankton phenology and abundance, while organisms at higher trophic levels were shown to respond differently to increased temperatures (Edwards & Richardson, 2004). After using principal component analysis to group survey data measurements of different plankton species in the North Sea into their respective functional types, trophic mismatch was present as functional types varied significantly in their shifts in seasonality, with the timing of peak meroplankton, a secondary consumer, shifting earlier in the year by 27 days from 1958 to 2002 (p < 0.001), while that of diatoms, a primary producer, staying relatively static (Edwards & Richardson, 2004). Moreover, the effects of long-term decreases in North Sea phytoplankton from rising temperatures are evident as phytoplankton fluctuations strongly covaried with cod recruitment, explaining 27.87% of variation from principal component analysis (Beaugrand et al., 2003). The survival indexes of larval cod and plankton were also significantly correlated (r = 0.52, p < 0.001), indicating that lower cod survival is largely attributed to reduced phytoplankton productivity (Beaugrand et al., 2003).
+Threats to marine ecosystems are emerging as ongoing changes in climate and environmental conditions, especially global warming, are significantly altering phytoplankton phenology and abundance, while organisms at higher trophic levels were shown to respond differently to increased temperatures (Edwards & Richardson, 2004). After using principal component analysis to group survey data measurements of different plankton species in the North Sea into their respective functional types, trophic mismatch was present as functional types varied significantly in their shifts in seasonality, with the timing of peak meroplankton, a secondary consumer, shifting earlier in the year by 27 days from 1958 to 2002 (p < 0.001), while that of diatoms, a primary producer, staying relatively static (Edwards & Richardson, 2004). Moreover, the effects of long-term decreases in North Sea phytoplankton from rising temperatures are evident as phytoplankton fluctuations strongly covaried with cod recruitment, explaining 27.87% of variation from principal component analysis (Beaugrand et al., 2003). The survival indexes of larval cod and plankton were also significantly correlated (r = 0.52, p < 0.001), indicating that lower cod survival is largely attributed to reduced phytoplankton productivity (Beaugrand et al., 2003).
 Global warming also threatens marine ecosystems as it increases the intensity of HABs, like ''Cochlodinium polykrikoides'' blooms. Using the Theil-Sen trend estimator and Mann-Kendall test on data from previously published datasets, remote sensing satellites, and ''in situ'' stations, Griffith et al. (2019) revealed that warming temperatures since 1982 have increased the severity of ''C. polykrikoides'' blooms worldwide, increasing their cell doubling rates, peak cell density, and bloom duration along coasts of the US, South Korea, and Japan.
@@ Line 51: / Line 51: @@
 ====Objectives====
-''' '''In this study, phenological metrics were established across LI’s coastal waters using remote sensing data, and regions with contrasting patterns in phenology were identified and compared. In each region, the study aimed to investigate the physical, chemical, and climatic factors driving phytoplankton phenology and seasonal Chl-''a'' abundance. Lastly, the interannual trends of phenological metrics and Chl-''a'' levels and their significant driving factors were investigated.
+In this study, phenological metrics were established across LI’s coastal waters using remote sensing data, and regions with contrasting patterns in phenology were identified and compared. In each region, the study aimed to investigate the physical, chemical, and climatic factors driving phytoplankton phenology and seasonal Chl-''a'' abundance. Lastly, the interannual trends of phenological metrics and Chl-''a'' levels and their significant driving factors were investigated.
 ==Methods==
@@ Line 114: / Line 114: @@
 The average phytoplankton seasonalities of W-S and S-A blooms in Region A and Region B were graphed by averaging at 8-day intervals of Chl-''a'' data across all years of the coordinates in each of the four groups. Phenological metrics, estimated using the threshold criterion and cumulative sum of anomalies methods adapted from Racault et al. (2015), were also averaged across all years of each category’s coordinates to calculate the average phytoplankton phenology. T-tests and ANOVA tests followed by Dunnett’s C post-hoc tests were performed to find any differences in average phytoplankton phenology and seasonality between groups.
-Phenological metrics between W-S and S-A blooms and between Region A and B of W-S and S-A blooms were found to be significantly different (p < 0.05), except for b<sub>d</sub> between Region B<sub>w-s</sub> and Region B<sub>s-a</sub> (Table 2). The growth periods of Region B<sub>w-s</sub> and Region B<sub>s-a</sub> both fell ar  ound late autumn and early winter, as shown in the overlapping appearance of their seasonalities, while Region A<sub>w-s</sub> and Region A<sub>s-a</sub> were more differentiated, falling around late winter and late summer, respectively (Fig. 6). Overall, b<sub>p</sub> of W-S blooms in Region A is 20.27 days later than Region B, while b<sub>p</sub> of S-A blooms in Region A is 50.12 days earlier than Region B (Table 2).
+Phenological metrics between W-S and S-A blooms and between Region A and B of W-S and S-A blooms were found to be significantly different (p < 0.05), except for b<sub>d</sub> between Region B<sub>w-s</sub> and Region B<sub>s-a</sub> (Table 2). The growth periods of Region B<sub>w-s</sub> and Region B<sub>s-a</sub> both fell around late autumn and early winter, as shown in the overlapping appearance of their seasonalities, while Region A<sub>w-s</sub> and Region A<sub>s-a</sub> were more differentiated, falling around late winter and late summer, respectively (Fig. 6). Overall, b<sub>p</sub> of W-S blooms in Region A is 20.27 days later than Region B, while b<sub>p</sub> of S-A blooms in Region A is 50.12 days earlier than Region B (Table 2).
 Chl-''a ''levels were also significantly different between W-S and S-A blooms and between Region A and B. Overall, Region A had more than two times greater annual Chl-''a'' levels than Region B at an average mean difference of 2.22 mg/m<sup>3</sup> (p < 0.05). Within Region A, Chl-''a ''levels of W-S blooms had higher Chl-''a'' concentrations than S-A blooms at a mean difference of 0.33 mg/m<sup>3</sup> (p < 0.05). However, there was no significance in Chl-''a'' levels between W-S and S-A blooms in Region B.