Sea Surface Chlorophyll-a Concentration Research Articles

Bi-layered spring-neap variability of water masses in estuaries and the impact of human activities

Estuaries are one of the most important ecosystems in the world, which are economically developed and densely populated. However, the intricate hydrodynamic environment and frequent human activities within estuaries have left the spatiotemporal variability of water properties in these areas inadequately understood. Recently, based on in situ observations and numerical simulations, we found significant spring-neap variability of water mass properties in the Yangtze River Estuary, which exhibited a bi-layered vertical structure. In the Yangtze River Estuary, salinity could decrease (increase) over 4 psu during spring (neap) tides in the upper layer, and satellite observations confirmed that both sea surface chlorophyll-a concentration and particulate organic carbon concentration also showed significant spring-neap variabilities. Decreasing salinity in the upper layer induced a shoreward pressure gradient force in the lower layer, which caused shoreward advection of high salinity water from the deep ocean and resulted in salinity increasing up to 2 psu in the lower layer of the Yangtze River Estuary. Dynamical diagnoses proved that spring-neap variability of water mass properties were caused by the asymmetry of tidal currents via modulating the ratio of freshwater to seawater. Similar situations also occurred in the Mississippi River Estuary. Furthermore, constructions of dams and other hydraulic projects in the watershed could greatly alter the locations with significant spring-neap water masses variability through reducing the riverine sediment flux and thus, leading to the erosion of the tidal flats in estuaries. The above results highlight the important roles of tidal asymmetry and human activities in affecting spring-neap variabilities of water mass properties in estuaries.

El Niño–Southern Oscillation Diversity: Effect on Upwelling Center Intensity and Its Biological Response

The influence of El Niño–Southern Oscillation (ENSO) on productivity at upwelling systems has been extensively studied. However, in recent decades, ENSO diversity has been documented; there are more frequent events with anomalies in the central Pacific (CP) causing ecological impacts that are different from the canonical events centered in the eastern Pacific (EP). We hypothesize that ENSO effects on upwelling intensity and its biological response are highly dependent on ENSO diversity. Wavelet coherence was computed for monthly standardized anomalies of Ekman transport and sea surface Chlorophyll-a (Chl-a) concentration from eight upwelling centers located along the west coast of the Baja California peninsula (1993–2022). Ekman transport (ET) showed a non-linear association with ENSO at interannual periodicities between 1.2 and 7 years, as well as semiannual scale variability. Coherence between ENSO and ET showed a reduction in upwelling intensity during CP ENSO events and an increased upwelling intensity after EP El Niño events with regional differences. Our results reveal an influence of northern Pacific circulation that subsequently influences ENSO events during its development and its peak. We detected an influence of SST and wind anomalies on the ENSO–Ekman transport connection associated with the northern Pacific Meridional Mode pattern. The CP shows an important role in sea surface Chl-a concentration anomalies (1998–2022). We highlight the conceptual relevance of distinguishing ENSO diversity, with influential ecological effects associated with CP ENSO events.

Deep learning-based forecasting model for chlorophyll-a response to tropical cyclones in the Western North Pacific

Tropical cyclones cause increases in sea surface chlorophyll-a concentration, which is important for studying variations in the regional marine environment. Precisely forecasting the variations of sea surface chlorophyll-a concentration induced by tropical cyclones remains a challenge. In this research, a bidirectional long short-term memory (BiLSTM) neural network deep learning model was applied to predict the variations of sea surface chlorophyll-a concentration induced by typhoons in the Western North Pacific (WNP). Typhoons occurring between 2011 and 2020 were used as training cases and those from 2021 to 2022 as forecasting and testing cases. The input variables of the deep learning model include the sea surface wind at 10 meters (U10 and V10), sea surface temperature anomaly (SSTA), and sea surface chlorophyll-a concentration. The output variable was the chlorophyll-a concentration one day after the passage of the typhoon. Data from the previous 7 days were used to predict the chlorophyll-a concentration one day after the typhoon's passage, and the rolling forecast method was employed to predict chlorophyll-a concentration in the following 7 days. To assess the impact of input variables on the model's forecasting performance, ablation experiments were conducted. The results showed that when using U10, V10, and chlorophyll-a from the previous seven days as inputs, the model exhibited the best overall forecasting performance. Taking Typhoons Chanthu, In-fa, and Malou as examples, the root mean square error (RMSE) for the forecast results are 0.0143 mg · m−3, 0.0087 mg · m−3, and 0.0030 mg · m−3, the mean absolute errors (MAE) are 0.0072 mg · m−3, 0.0074 mg · m−3, and 0.0025 mg · m−3, and the spatial anomaly correlation coefficients (ACC) are 0.9968, 0.9775, and 0.9721, respectively. The results reveal that the most accurate forecasting performance was observed during the mid-phase of the moderate-intensity Typhoon Muifa, with RMSE, MAE, and ACC values of 0.0040 mg · m−3, 0.0032 mg · m−3, and 0.9894, respectively. The BiLSTM neural network model had the best forecasting performance for typhoons of moderate intensity and during the mid-term phase. This is because moderate-intensity typhoons or the mature phase of any typhoon tend to have relatively stable and more predictable paths, resulting in better predictions of chlorophyll-a concentrations. In future work, we intend to extend our training and forecasting to typhoons of various intensities, aiming to further refine and enhance predictive performance.

Daily impact of the simultaneous passage of binary typhoons on sea surface chlorophyll-a concentration dynamics in the Northwestern Pacific

Typhoons are recognized as one of the most destructive meteorological phenomena, exerting significant influences on marine ecosystems. Sea surface chlorophyll-a concentration (CHL)an essential indicator of phytoplankton biomass, can be utilized to characterize the disturbances of typhoons on the marine ecosystem. However, it is challenging to investigate this impact at a daily scale due to the missing CHL remote sensing data caused by cloud cover. Given that concurrent passing typhoons may interact with CHL, this study analyzes the effect of the simultaneous passage of binary typhoons Tembin and Bolaven on CHL by using daily CHL reconstruction data, and investigates the role of ocean environmental factors in driving the dynamics of CHL, including sea surface temperature (SST), mixed layer depth (MLD), and sea surface height anomaly (SSHA). The results show that typhoons Tembin and Bolaven increase CHL with the maximum increment of ∼3.2 mg∙m−3 during 4–6 days after typhoons passage. The maximum change areas of CHL are distributed near the intersection of typhoon track of (32°N, 125.2°E), corresponding to the regions of greater variation in SST and MLD. During 15 days before and after typhoons (i.e., from 15 August to 15 September 2012), SST is negatively correlated with CHL (the correlation coefficient of −0.85) and MLD is positively correlated with CHL (the correlation coefficient of −0.80). SST immediately declines after typhoons with a maximum cooling of 7.8 deg. C, showing the decreased SST from ∼28 deg. C to ∼23 deg. C can promote phytoplankton growth. MLD deepens from 10 m to >25 m caused by typhoon-induced strong winds, allowing more nutrients to be transported from the subsurface layer to the euphotic layer for phytoplankton blooms. Furthermore, oceanic eddies captured by SSHA change from cyclonic to anticyclonic eddies accompanied by the beginning of CHL increases, and the largest CHL increases correspond to the distribution of pre-existing cyclonic eddies. It suggests that Tembin and Boravin promote phytoplankton growth to increase CHL by enhancing vertical mixing and upwelling to transport nutrients to the sea surface. These findings inspire us to rethink the daily effects of typhoons on CHL, with critical importance for predicting and managing the ecological consequences of typhoons in the ocean.

Exploring the relationship between environmental drivers and the manifestation of fibropapillomatosis in green turtles (Chelonia mydas) in eastern Brazil.

Fibropapillomatosis (FP) is a disease characterized by epithelial tumors that can impede life-sustaining activities of sea turtles, especially green turtles (Chelonia mydas). FP is caused by a herpesvirus, but environmental factors are also thought to play a role in triggering FP tumor growth. In this study, we evaluate the epidemiology of FP tumors in green turtles along the coast of Espírito Santo, Brazil, a region where juvenile green turtles are known to aggregate with high FP prevalence. A dataset comprising 2024 beach-cast green turtles recorded through daily beach surveys on 400 km of coastline from 2018 to 2021 (inclusive) was evaluated. FP tumors were recorded in 40.9% of the individuals in this dataset, and presence of FP tumors was predicted by individual variables (presence of marine leeches, stranding code, curved carapace length, body mass-size residual) and characteristics of the stranding site (distance to nearest metallurgical plant, mean sea surface salinity (SSS), annual range of sea surface temperature (SST)). Additionally, a second dataset comprising detailed information about the size and anatomical distribution of tumors in 271 green turtles with FP from the same region was evaluated. Hierarchical clustering analysis revealed these turtles could be classified in three groups according to the anatomical distribution of their tumors, and in turn the group to which each turtle was assigned could be predicted by the study period (2010-2014 vs. 2018-2022) and by characteristics of the stranding/capture site (green turtle stranding density, mean sea surface chlorophyll-a concentration, mean SSS, mean SST, annual range of SST). These results corroborate that individual and environmental factors play a significant role driving FP epidemiology. Furthermore, the results suggest that rather than behaving as a single entity, FP may be seen as a mosaic of distinct anatomical patterns that are not necessarily driven by the same environmental factors.

The Catch of Little Tuna (Euthynnus sp.) in Relation to Thermal Front in the Makassar Strait

The Makassar Strait waters area is a potential area for the utilization of marine fishery resources in Indonesia especially for large pelagic fisheries because it is one of the migration trajectories of little tuna (Euthynnus sp.). In order to optimally utilize tuna resources, remote sensing technology based on oceanographic satellite imagery can be relied upon to facilitate identification, prediction, and spatial and temporal mapping of potential fishing areas. This study aims to study the phenomenon of oceanography (thermal front) as an indicator of potential for little tuna fishing areas in the Makassar Strait. Primary data was obtained through in-situ data collection (direct observation in the field by following fishing operations using the purse seine, while secondary data consisting of oceanographic data such as sea surface temperature and sea surface chlorophyll-a concentrations sourced from satellite imagery data (Aqua satellite with MODIS sensor). The distribution of the thermal front was analyzed using ArcGIS 10.4 software to determine the relationship between little tuna fishing productivity and the oceanographic phenomenon. Based on the analysis results, total of 61 positions for little tuna fishing using a purse seine, five (5) fishing positions were in the thermal front area, where the catch of little tuna is higher than the average catch outside the area. These results indicate that the thermal front can be used as an indicator of potential for little tuna fishing areas in the Makassar Strait.

Effects of Biotic and Abiotic Factors on the Spatiotemporal Distribution of Round Scad (Decapterus maruadsi) in the Hainan Island Offshore Area

Fishery resource and environmental data from four surveys conducted in the Hainan Island offshore area from March 2021 to June 2022 were used to investigate the spatiotemporal distribution of round scad (Decapterus maruadsi). A generalized additive model was applied to explore the relationships among abundance and distribution, also biotic and abiotic factors (i.e., temperature, salinity, chlorophyll-a concentration, water depth, phytoplankton abundance, zooplankton abundance, and jack mackerel (Trachurus japonicas) abundance). Round scad abundance (average 67.17 kg/km2) showed distinct spatial and seasonal differences around Hainan Island, with highest abundance in summer (171.72 kg/km2) and lowest abundance in spring (3.06 kg/km2). The optimal model revealed that jack mackerel abundance, sea bottom temperature, sea surface temperature, and latitude very significantly (p < 0.01) affected round scad distribution. Jack mackerel abundance (mainly in the range 0–50 kg/km2) was positively correlated with round scad distribution. The distribution showed a dome-shaped relationship with bottom water temperature in the range 18–30 °C, with maximum abundance at 24 °C. The distribution was negatively correlated with surface water temperature in the range 22–30 °C. Sea surface chlorophyll-a concentration and longitude were significantly correlated (p < 0.05) with round scad distribution. The results provide theoretical support to further investigate the formation mechanism of round scad fishing grounds and to enrich knowledge of pelagic fish abundance in the continental-shelf waters of the northern South China Sea.

Satellite‐Observed Interannual Variations in Sea Surface Chlorophyll‐a Concentration in the Yellow Sea Over the Past Two Decades

AbstractOcean chlorophyll‐a concentration is a reliable indicator of phytoplankton biomass that plays an important role in controlling the marine ecosystem. Here, we investigated the interannual variations in sea surface chlorophyll‐a concentration (SSC) in the Yellow Sea and underlying mechanisms with 22 yr (1998–2019) of satellite ocean color observations. Results indicated that SSC showed a positive trend (0.039 mg m−3 yr−1 on average) during 1998–2010 and a negative trend (−0.058 mg m−3 yr−1 on average) during 2011–2019. Similar SSC trends occurred in the four seasons. These trends had larger magnitudes in inshore waters than in offshore waters. SSC variations were primarily driven by rainfall and anthropogenic nutrient emissions with a possibly secondary contribution from sea surface temperature (SST). During 1998–2010, increased rainfall and anthropogenic nutrient emissions largely enhanced the terrestrial nutrient inputs into the Bohai Sea and Yellow Sea. Coincidentally, decreased SST might have led to a weakened stratification in summer, favoring upward turbulent diffusion of deeper waters rich in nutrients and chlorophyll‐a. These processes resulted in significant increases of surface nutrient concentrations and SSC. During 2011–2019, the opposite processes occurred to generate decreased SSC. The current study highlights the change of SSC trends in the Yellow Sea in the first two decades of the 21st century and physical processes at work, enriching our understanding of ocean chlorophyll‐a dynamics in the global shelf seas.

Application of the random forest algorithm for mapping potential fishing zones of Rastrelliger kanagurta off the east coast of peninsular Malaysia

Indian mackerel, Rastrelliger kanagurta, play an important role in the marine fisheries of Malaysia, and their distribution is reported to be influenced by various oceanographic conditions. In this study, a modeling framework of machine learning techniques (random forest [RF]) and spatial analysis with geographic information systems (hotspot analysis) was used to predict and map potential fishing zones of R. kanagurta in the exclusive economic zone (EEZ) waters off the east coast of peninsular Malaysia. The RF model was constructed with a training dataset (n = 2535) and externally validated by a testing dataset (n = 1087). Model validation using Lin’s concordance correlation coefficient (CCC) on the testing dataset indicated good agreement (CCC = 0.811) between the predicted and observed catch per unit effort (CPUE) of R. kanagurta. The variable importance plot revealed the relative importance of four predictors of potential habitat, ranked in decreasing order of importance: sea surface height anomaly (SSHA), eddy kinetic energy (EKE), sea surface temperature (SST) and surface chlorophyll-a (CHL) concentration. Partial dependence shows that R. kanagurta prefers a habitat with the following parameters: SSHA -0.05–0.20 m, EKE < 0.02 m2/s2 or > 0.07 m2/s2, SST 30–31 °C and CHL 0.1–0.3 mg/m3. Spatial distribution of predicted CPUE hotspots were found to be closely related to the thermal fronts, which influenced by the monsoonal wind and occurrence of upwelling event in the study area. Our study indicates that cost-effective satellite-derived products can be used to predict potential fishing zones of R. kanagurta and provide useful information on relationship between environmental factors and CPUE of R. kanagurta.

Temporal synchrony among juvenile marine fishes and potential climate and environmental drivers in the Bay of Biscay

The existence of synchronous fluctuations among sympatric species is an important property of a community, potentially reducing temporal stability of ecosystem services. Yet, community synchrony and its potential drivers have rarely been studied in marine ecosystems. Here, we analyzed a 14-year time-series (2007–2020) of fish abundance data collected by a scientific beam trawl survey in the Bay of Biscay, a temperate marine ecosystem located in the North-east Atlantic. We used dynamic factor analysis to reveal common trends in abundance among juvenile demersal marine fishes, and to identify potential environmental drivers. Our results revealed synchronous changes in juvenile fish abundance among the 12 marine species examined in the study. This strong temporal coherence was likely related to the influence of climate and environmental factors, notably sea bottom temperature and the East Atlantic Pattern. We also found some support for alternative drivers of early-life fish dynamics, namely the North Atlantic Oscillation and sea surface chlorophyll-a concentration, the later suggesting bottom-up trophic control. In the context of ongoing environmental changes, our findings raise questions about the future stability of the demersal marine fish community in the Bay of Biscay.

Sudden Track Turning of Typhoon Prapiroon (2012) Enhanced the Upper Ocean Response

Due to the change in environmental steering flow influenced by the surrounding synoptic systems, typhoon tracks often manifest sudden turnings, potentially prolonging the residence time of typhoon wind forcing and, thus, exerting a remarkable upper ocean response. Typhoon Prapiroon (2012) in the western North Pacific, had a very complex track and underwent two sudden-turning stages over its lifespan. On the basis of satellite and Argo float observations, this paper studies the surface and subsurface ocean environmental responses to Prapiroon. The observations show that the oceanic responses during the two sudden-turning stages of Prapiroon were much more remarkable than those in the straight-moving stage, including significant sea surface temperature (SST) cooling (~7 °C), sea surface chlorophyll-a (Chl-a) concentration increase (&gt;0.30 mg m−3), and sea surface height anomaly (SSHA) reduction (&lt;−50 cm), compared with those in the straight-moving stage, with SST cooling weaker than 3 °C, Chl-a concentration increase less than 0.05 mg m−3, and SSHA reduction less than −10 cm. By employing the three-dimensional Price–Weller–Pinkel (3DPWP) model to conduct a series of sensitivity experiments, we separate the contribution of the typhoon track’s sudden turnings to the upper ocean response and find that the relative contributions of the two sudden turnings to SST cooling (sea surface salinity salinification) reached 38.4% (23.5%) and 46.8% (28.0%), respectively. In addition, the model experiments further show that the sudden turning could also induce stronger upwelling in the subsurface ocean. Our results demonstrate that typhoon track sudden turning could result in more kinetic energy input into the upper ocean, enhancing the physical and biological responses in the upper ocean.

Satellite-detected phytoplankton blooms in the Japan/East Sea during the past two decades: Magnitude and timing

The Japan/East Sea (JES) is known as a mid-latitude “Miniature Ocean” that features multiscale oceanic dynamical processes. Using principal component analysis (PCA), we investigate the variability of the sea surface chlorophyll-a concentration (SSC) and its bloom timing in the JES based on satellite remote sensing products spanning 1998–2019. The JES SSC exhibits strong seasonal variability and blooms twice annually. The spring bloom is induced under combined factors of increased photosynthetically active radiation (PAR), weakened wind speeds and sea ice melting, and terminated by the enhanced stratification. The fall bloom is induced by destratification and active dynamic processes (such as upwelling and front), and terminated by decreased PAR. The interannual variability of spring and fall bloom occur along the northwestern coast of the JES and in the deep Japan Basin, respectively. The positive SSC anomalies along the northwestern coast of the JES in spring is associated with more sea ice in the previous winter, weaker wind speed, and stronger stratification induced by the El Niño events. No significant relationship has been found between the fall bloom and the El Niño events. The bloom timing is controlled by the critical depth hypothesis. The initiation/termination timing of spring blooms has shifted earlier by 0.37/0.45 days, and the counterpart of fall blooms has shifted 0.49/1.28 days earlier per year. The duration and magnitude are independent with each other for spring bloom at interannual time scale. In contrast, they are positively correlated for fall bloom, because of both bloom timing and magnitude are dominated by active oceanic dynamical processes in fall.

Effects of oceanographic environment on the distribution and migration of Pacific saury (Cololabis saira) during main fishing season

The Pacific saury (Cololabis saira) is one of the most commercially important pelagic fishes in Asia–Pacific countries. The oceanographic environment, especially the Oyashio Current, significantly affects the distribution of Pacific saury, and may lead to variations in their migration route and the formation of fishing grounds in Japanese coastal region and the high seas. In this study, six oceanographic factors, sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface salinity (SSS), sea surface height (SSH), mixed layer depth (MLD), and eddy kinetic energy (EKE), were associated with the monthly catch per unit effort 1 (monthly CPUE1, ton/vessel) and the monthly CPUE2 (ton/day) of Pacific saury from Chinese fishing vessels during the optimal fishing periods (September–November) in 2014–2017. The gradient forest analysis showed that the performance of monthly CPUE1 was higher than monthly CPUE2 and SST was the most important oceanographic factor influencing monthly CPUE1, followed by EKE. The generalized additive model indicated that SST, SSH, and EKE negatively affected monthly CPUE1, whereas SSC, SSS, and MLD induced dome-shaped increases in monthly CPUE1. The distributions of fishing locations are likely to form along Offshore Oyashio current and meanders, especially in October and November. Synchronous trends in the relationship between the intrusion area of the Oyashio and relative abundance variation index suggest that an increase in the intrusion area of the Oyashio causes more Pacific saury to migrate to the Japanese coastal region, and vice versa. These findings extend our understanding of the effects of the oceanographic environment on Pacific saury.

Fishery Resource Evaluation in Shantou Seas Based on Remote Sensing and Hydroacoustics

The Shantou-Taiwan shoal fishing ground in southeastern China supports a significant population of pelagic fish, which play a key role in the marine ecosystem. An acoustic survey was carried out using a digital scientific echosounder in June 2019. In this paper, the spatial distribution of pelagic fish is analyzed based on acoustic data using geostatistical analysis tools. Meanwhile, the relationship between fish density from acoustic data and sea surface environment factors were evaluated by using generalized additive models (GAMs) based on the satellite-based oceanographic data of sea surface temperature, sea surface chlorophyll-a concentration, sea surface height and sea surface wind. The results showed the following: (1) Fish density and acoustic biomass have strong spatial correlation; the optimal model for acoustic biomass is exponential and the optimal model for fish density is gaussian; based on optimal model, spatial interpolation analysis of fish density and acoustic biomass was performed using the ordinary kriging method, and the higher values of density and acoustic biomass were located in the central and eastern parts of the study area. The total fish density and acoustic biomass is 2.56 × 1010 ind. and 1908.99 m2/m, respectively. (2) In vertical distribution, fish gradually move to the middle and lower layers of water during daytime, and gather in the middle and upper layers of water at night. (3) The variance explanation rate of GAM was 88.2% which indicates that the model has an excellent fitting degree, and the results of GAM showed that longitude, sea surface temperature (SST), sea surface wind (SSW), and sea surface height (SSH) had significant effects on fish density. Results of this study were meaningful for understanding the distribution of fishery resources, and as a guide for fish management in the Shantou offshore water.

Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.

Combined Effects of Hydrological Drought and Reduced Food Availability on the Decline of the Little Penguins in South Australia

Droughts in many regions of the world are increasing in frequency and severity which, coupled with effects from anthropogenic water extraction and diversion, are reducing river discharges. Yet to date, few studies have investigated the impacts of hydrological droughts (i.e., reduced river outflows to the ocean) on seabirds. Here, we examined the consequences of the “Millennium Drought” on the local decline of an iconic Australian seabird, the little penguin (Eudyptula minor). We analysed monthly and annual penguin numbers in relation to river outflow, rainfall, the characteristics of the coastal waters (sea surface temperatures and chlorophyll-a concentrations), and local abundance of key predators and prey species. We found a negative association between monthly penguin numbers and both sea surface temperatures and river outflow. Annual penguin numbers were positively associated with southern garfish numbers (our local indicator of food availability) but negatively associated with annual chlorophyll-a concentrations. Our findings emphasizing the need for further research into the effect of hydrological droughts on seabird populations and for improved river management that account for potential downstream impacts on the coastal environment receiving freshwater from rivers.

Temporal variability of satellite chlorophyll-a as an ecological resilience indicator in the central region of the Gulf of California

Multiple publications over the past decades argue that the Gulf of California is a region with high biological diversity and productivity. However, ecosystem resilience to climate disturbances and anthropogenic stressors in the Gulf of California remains poorly explored. One method to assess ecosystem resilience based on ecological indicators is the analysis of continuous records of critical environmental variables. Here we analyze satellite time-series of sea surface chlorophyll-a concentration (Chl-a) over the past two decades (1997–2020) and hydrographic data obtained from the central Gulf of California (2005–2019) to detect abrupt transitions (tipping points) and shifts in the temporal trends and their association with the most prominent modes of climate variability in the northeastern Pacific. In addition, based on the critical “slow-down theory”, we estimated the autocorrelation time (AcT) and standard deviation (SD) of satellite sea surface Chl-a, as a resilience indicator (RI), to monitor whether early-warning signals anticipate any impacts of climate change. We observed a clear negative trend in SST in the pre-2012 period, related to decadal and multiannual modes of variability of the Pacific decadal Oscillation (PDO), El Niño Modoki (EMI), and Pacific Meridional Mode (PMMSST). In contrast, a positive SST trend in the period post-2012 to 2017 was associated with the multiannual warming event in the northeastern Pacific that peaked during the intense 2015–2016 El Niño. These trends differentially regulate the Chl-a response during the cold (November to April) and warm (June to October) seasons, in line with the shift of regime in 2012. The critical transition early-warning signal depicted better consistency in the use of increasing SD in Chl-aSat time series, but still, AcT provides an effective predictor of a slowdown in most cases. GAM results showed that the main mode of climate variability that affects Chl-a was PMMSST. EMI, NPGO, and PDO modes had a less significant influence on Chl-a than PMMSST. The monitoring of high-frequency satellite records in the Gulf of California central region provided insight into temporal trends and their association with modes of climate variability. It represents an indicator of the effectiveness of the application of RIs for resilience monitoring that can be used to inform resource management decisions.

Carbon and nitrogen dynamics in the coastal Sea of Japan inferred from 15 years of measurements of stable isotope ratios of &amp;lt;i&amp;gt;Calanus sinicus&amp;lt;/i&amp;gt;

Abstract. Both nitrogen and carbon dynamics have changed in the Sea of Japan. We hypothesized that the carbon and nitrogen stable isotope ratios (δ13C and δ15N) of the copepod Calanus sinicus could record changes in the coastal environment of the Sea of Japan. Consequently, these isotope ratios were monitored during the spring at four stations from 2006 to 2020 to identify the changes in carbon and nitrogen dynamics. The δ13C values ranged from −24.7 ‰ to −15.0 ‰ and decreased from the spring bloom (February–March) to the post-bloom (June–July) seasons. These variations were attributed to changes in the physiology of both C. sinicus and phytoplankton δ13C contents. The δ15N values range from 2.8 ‰ to 8.8 ‰, indicating that C. sinicus is a secondary producer; the tendency of the δ15N values to increase from the bloom to the post-bloom seasons was attributable to an increase in the δ15N of phytoplankton. A generalized linear model (GLM) approach indicated that &gt;70 % of the variations in δ13C can be explained by sea surface temperature (SST), sea surface chlorophyll a concentration (SSC), carbon:nitrogen ratio of C. sinicus (C/N ratio), and geographic differences. The residuals of δ13C in the GLM decreased yearly (−0.035 ‰ yr−1). The GLM for δ15N of C. sinicus indicated that δ15N varies with the stage or sex in addition to SST, SSC, C/N ratio, and geographic differences. The δ15N values of female C. sinicus and stage V copepodites were the lowest and highest, respectively. The residuals of δ15N in the GLM did not exhibit a significant interannual trend. These results suggest that the carbon isotope ratio in the secondary producer has linearly changed in the coastal Sea of Japan over the past 15 years. Moreover, the changes in carbon dynamics of this area have been recorded and observed to impact the marine ecosystem, while the nitrogen dynamics have not been recorded despite the increasing nitrogenous nutrient inputs in this sea.

Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Harmful algal blooms (HABs) events have a serious impact on marine fisheries and marine management. They occur globally with high frequency and are characterized by a long duration and difficult governance. HABs incidents have occurred in the South China Sea (SCS), and the frequency of occurrence has been on the rise in recent decades. Predicting the long-term chlorophyll-a (Chl-a) concentration has the potential to facilitate long-term monitoring and early warning of HABs events. Currently, long-term predictions of ocean circulation and temperature are common, while long-term predictions of marine biochemistry are still in their infancy. Traditional Chl-a prediction methods have problems, such as low accuracy and the inability to carry out long-term predictions. This research improved the CNN-LSTM model by combining spatio-temporal features to predict Chl-a concentrations. This model can extract both the temporal and spatial features of Chl-a, expand the dataset, and improve the prediction accuracy and training speed. The predictions were made using a Chl-a dataset for the Reed Tablemount in the SCS. The time series of Chl-a used was the satellite data of NASA's official website from January 2002 to June 2020. The results indicate that the predictions of the CNN-LSTM model are better than those of the LSTM and SARIMA models. The five-year long-term rolling prediction of Chl-a was carried out, and the three-year Pearson correlation coefficient reached 0.5. The novelty of this study is the realization of a three-year long-term prediction of Chl-a concentrations. The Mann-Kendall trend test method and the least square method were used to fit the straight line to detect the trend of the five-year predicted value and the true value, respectively. The results indicated that the prediction value and true value of the sea surface Chl-a from 2015 to 2020 both exhibited an overall upward trend. In addition, the prediction performance of the model in large-scale prediction is better than that in small-scale prediction.

Detecting the feeding habitat zone of albacore tuna (Thunnus alalunga) in the southern Indian Ocean using multisatellite remote sensing data

Remote sensing is an effective method for identifying potential feeding habitat zones. Spatial and temporal distribution patterns of albacore (ALB) tuna were studied using Taiwan longline fisheries data from 2009 to 2014. A generalized additive model (GAM) was used to compile a fishery database and statistically explore the relationship between distribution and environmental factors. Sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface salinity (SSS), sea surface height (SSH), mixed layer depth (MLD), and eddy kinetic energy (EKE) were examined. The results indicated SST as the largest contributor on ALB distribution, followed by SSS and SSC. Catches per unit effort (CPUE) values indicated that ALB tuna were primarily distributed at 20°E–110°E and 25°S–40°S. The SST and SSS ranges for higher ALB abundance were 17–19 °C and 35.1-35.5 psu, respectively. SSC was positively related to CPUE as a result of ALB tuna habits in the current location during the study period (0.1–0.25 mg m-3). The predicted CPUE indicated that the potential feeding habitat zones were 35°S–40°S and 25°S–30°S during March and April–August, respectively. These findings provide preliminary insight into the key environmental features affecting the ALB distribution in the southern Indian Ocean.