Ocean fronts construct spatial zonation in microfossil assemblages

Abstract

Aim Integration of macroecology and palaeoecology is an important trend in understanding rapidly changing marine ecosystems. However, the spatial mismatch between these two data types has led to difficulties in interpretation, particularly for short‐lived phytoplankton and their microfossils. Fronts are narrow transition zones between distinct water masses and play an essential role in partitioning phytoplankton assemblages in the ocean. Whether they also delimit microfossil assemblages deposited at the sea floor is unclear. We examined the correlation between quasi‐stationary mesoscale fronts and the spatial distribution of microfossils (diatoms, dinoflagellates and silicoflagellates) in the Bohai, Yellow and East China Seas, to establish a causal link between microfossil assemblages and the factors controlling pelagic species assemblages on continental shelves. Location China. Time period 2003–2015. Major taxa studied Phytoplankton. Methods Front locations were determined using gradient analysis of monthly satellite sea surface temperature (SST) for 2003–2015. Microfossil assemblages were classified using two‐way indicator species analysis of the relative abundance of 345 species collected from surface sediments at 126 sites. The relationships between frontal patterns and microfossil assemblages were evaluated by superimposing maps of front location, microfossil distribution and environmental features in the main water masses and by canonical correspondence analysis. Results Ten major fronts and four primary microfossil assemblages were identified. Analyses of the spatial patterns of fronts, microfossil assemblages, SST, salinity and nutrients revealed that the fronts partitioned the microfossils into assemblage types corresponding to the physicochemical features of the water masses. Main conclusions Microfossil species assemblages and their indicator species are separated by mesoscale fronts and are correlated with water properties. Producing base maps of microfossil assemblages in relationship to SST fronts enables examination of the importance of quasi‐stationary mesoscale fronts in constructing microfossil patterns on continental shelves. The results displayed potential for the interpretation sediment core data and their integration with the macroecological context.

Ocean Temperature and Color Frontal Zones in the Gulf of Mexico: Where, When, and Why

“…Of these, the gradient‐based method is characterized by its simplicity (e.g., Breaker et al., 2005; Kostianoy et al., 2004). The widely used Canny edge detection method (Canny, 1986; Castelao & Wang, 2014; Castelao et al., 2006; Jones et al., 2012; Wall et al., 2008; Wang et al., 2015; Zhang et al., 2019) and the Belkin & O’Reilly algorithm (BOA, Belkin & O'Reilly, 2009; Dodge et al., 2014; Guo et al., 2017; Lin et al., 2019; Liu & Hou, 2012; Liu et al., 2018; Oh et al., 2020; Sagarminaga & Arrizabalaga, 2014; Wei et al., 2020; Woodson et al., 2012; Zeng et al., 2014) are classified as gradient‐based methods. The histogram method is characterized by its robustness and worldwide validation (e.g., Belkin et al., 2009; Cayula &Cornillon, 1992, 1995, 1996; Kahru et al., 2012, 2018; Svendsen et al., 2020; Tseng et al., 2014; Ullman & Cornillon, 1999, 2000, 2001; Wall et al., 2008).…”

Unraveling environmental drivers of chlorophyll seasonal and interannual variability in the East China Sea

“…Compared with the coastal waters and the sea adjacent to large river mouths, the offshore southern ECS shelf waters between 50 and 200 m have relatively lower averaged Chl-a (~0.5 mg m −3 ), but display The eigenvectors of EOF analysis of the SeaWiFS and MODISA Chl-a anomaly fields identify a similar spatial pattern in that significant variance occurs in a triangular area located in the mid-shelf south of the 32°N ECS, between the 50and 100-m isobath (Figures 5A, B). This triangular area reminded us of the position of the three fronts in the ECS (Belkin et al, 2009;Chen, 2009;Huang et al, 2010;Liu et al, 2018): the Zhejiang-Fujian front, which is the boundary between the TWC and the ECS coastal current, formed along the 50-m isobath, the Yangtze Bank Ring front surrounds the Yangtze Bank (Shoal) caused by the huge fresh discharge of the Changjiang River, and the Kuroshio SST front. Time series of Chl-a averaged for the region (123-125°E, 28-30°N box) where the EOF spatial coefficients are larger than 0.015 (Figure 5D) indicates a similar temporal variability to that of EOF PC-1 (Figure 5C), which both indicated significant interannual variabilities.…”

“…The likely process for this phenomenon is the ocean fronts. Ocean fronts separate distinct water masses, resulting in sharp physical, chemical, and phytoplankton species differences at the front separated area (Belkin et al, 2009;Liu et al, 2018). The front variability significantly affects its barrier area.…”

The Application of an Artificial Neural Network to Quantify Anthropogenic and Climatic Drivers in Coastal Phytoplankton Shift

“…As the raw data accumulate, the use of statistical models to advance our understanding of ecological dynamics is becoming increasingly important. Several conventional statistical approaches, including multiple linear regression (MLR) (Liu et al, 2022), principal component analysis (PCA) (Yuan et al, 2020), canonical correspondence analysis (CCA) (Liu et al, 2018) have been widely applied for a long time in palaeoecological studies. However, increasing evidence indicates that many ecological issues possess the characteristics of nonlinearity, time variation, and multiple forcing mechanisms Multiple Factors Driving Phytoplankton Shift (Andersen et al, 2009), and these conventional approaches to data analysis have presented limitations in revealing inner relationships and providing results prediction.…”