What drives the recruitment of European sardine in Atlanto-Iberian waters (SW Europe)? Insights from a 22-year analysis

The European sardine (Sardina pilchardus) is the most abundant and socio-economically important small pelagic fish species in Western Iberia Upwelling Ecosystem. As a result of a long series of low recruitments, sardine biomass off Western Iberia has greatly reduced since the 2000s. Recruitment of small pelagic fish is mainly dependent on environmental factors. In order to identify the key drivers of sardine recruitment, it is essential to understand its temporal and spatial variability. To achieve this goal, a comprehensive suite of atmospheric, oceanographic, and biological variables spanning 1998-2020 (22 years) were extracted from satellite datasets. These were then related to in situ recruitment estimates obtained from yearly spring acoustic surveys carried out along two different hotspots of sardine recruitment of the southern Iberian sardine stock (NW Portugal and Gulf of Cadiz). Sardine recruitment in Atlanto-Iberian waters appears to be driven by distinct combinations of environmental factors, although sea surface temperature was identified as the main driver in both regions. Physical conditions that favour larval feeding and retention, such as shallower mixed layers and onshore transport, were also seen to play a vital role in modulating sardine recruitment. Furthermore, high sardine recruitment in NW Iberia was associated with optimal conditions in the winter (January-February). In contrast, recruitment strength of sardine off the Gulf of Cadiz were associated with the optimal conditions during late autumn and spring. The results from this work provide valuable insights to further understand the dynamics of sardine off Iberia, with potential to contribute to the sustainable management of sardine stocks in Atlanto-Iberian waters, particularly under climate change.

Gap-filling of ocean color over the tropical Indian Ocean using Monte-Carlo method

Continuous remote-sensed daily fields of ocean color now span over two decades; however, it still remains a challenge to examine the ocean ecosystem processes, e.g., phenology, at temporal frequencies of less than a month. This is due to the presence of significantly large gaps in satellite data caused by clouds, sun-glint, and hardware failure; thus, making gap-filling a prerequisite. Commonly used techniques of gap-filling are limited to single value imputation, thus ignoring the error estimates. Though convenient for datasets with fewer missing pixels, these techniques introduce potential biases in datasets having a higher percentage of gaps, such as in the tropical Indian Ocean during the summer monsoon, the satellite coverage is reduced up to 40% due to the seasonally varying cloud cover. In this study, we fill the missing values in the tropical Indian Ocean with a set of plausible values (here, 10,000) using the classical Monte-Carlo method and prepare 10,000 gap-filled datasets of ocean color. Using the Monte-Carlo method for gap-filling provides the advantage to estimate the phenological indicators with an uncertainty range, to indicate the likelihood of estimates. Quantification of uncertainty arising due to missing values is critical to address the importance of underlying datasets and hence, motivating future observations.

A perfect storm: An anomalous offshore phytoplankton bloom event in the NE Atlantic (March 2009)

While primary productivity is more stable in oceanic regions, it may vary to a great extent with the proximity to coasts, where mesoscale processes may intertwine and shape phytoplankton community composition and biomass. Sometimes, this may lead to the development of anomalous phytoplankton blooms (i.e., episodic blooms that exceed several times the average phytoplankton biomass). A massive bloom observed off the Western Iberian Coast (SW Europe) during March 2009 prompted a full investigation on its spatial and temporal extent, its causes, and its potential impact on the ecosystem. Results revealed that the March 2009 bloom was both novel in terms of biomass in a regional context and one of the largest anomalous blooms until now described in terms of relative magnitude. Its causes were due to a concurrence of long-term (deep winter MLD) and short-term factors (coastal upwelling, sudden changes in the water column, consistent offshore water transport). Its impact on the regional ecosystem is difficult to gauge, although the high concentrations of particulate organic carbon at surface during the bloom period suggests that it may have had a significant local impact. Since climate change is expected to increase the frequency and intensity of extreme weather events, it is possible that anomalous blooms will also become more frequent, expanding their role in shaping carbon export and food webs. These results are crucial for the monitoring of the Western Iberian Coast and are applicable to other complex coastal upwelling regions where phytoplankton biomass and variability have a crucial link to fisheries.

Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

Spatiotemporal Variability in Phytoplankton Bloom Phenology in Eastern Canadian Lakes Related to Physiographic, Morphologic, and Climatic Drivers

Phytoplankton bloom monitoring in freshwaters is a challenging task, particularly when biomass is dominated by buoyant cyanobacterial communities that present complex spatiotemporal patterns. Increases in bloom frequency or intensity and their earlier onset in spring were shown to be linked to multiple anthropogenic disturbances, including climate change. The aim of the present study was to describe the phenology of phytoplankton blooms and its potential link with morphological, physiographic, anthropogenic, and climatic characteristics of the lakes and their watershed. The spatiotemporal dynamics of near-surface blooms were studied on 580 lakes in southern Quebec (Eastern Canada) over a 17-year period by analyzing chlorophyll-a concentrations gathered from MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images. Results show a significant increase by 23% in bloom frequency across all studied lakes between 2000 and 2016. The first blooms of the year appeared increasingly early over this period but only by 3 days (median date changing from 6 June to 3 June). Results also indicate that high biomass values are often reached, but the problem is seldom extended to the entire lake surface. The canonical correlation analysis between phenological variables and environmental variables shows that higher frequency and intensity of phytoplankton blooms and earlier onset date occurred for smaller watersheds and higher degree-days, lake surface area, and proportion of urban zones. This study provides a regional picture of lake trophic state over a wide variety of lacustrine environments in Quebec, a detailed phenology allowing to go beyond local biomass assessments, and the first steps on the development of an approach exploiting regional trends for local pattern assessments.

Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

Phenology of Phytoplankton Blooms in a Trophic Lake Observed from Long-Term MODIS Data

Phytoplankton phenology critically affects elements biogeochemical cycles, ecosystem structure, and productivity. However, our understanding about the phenological process and driving mechanism is still very limited due to the shortage of long-term observation data. We used all available daily MODIS-Aqua data from 2003 to 2017 to determine bloom start dates (BSDs) in a typical trophic lake (Lake Taihu) and investigate how phytoplankton BSDs respond to climate change and environmental factors. The results indicate that BSDs have advanced 29.9 days for the entire Lake Taihu from 2003 to 2017. Spatially, an earlier phytoplankton bloom was recorded in the northern bays and the littoral regions than in the center of open water. Air temperature, wind speed, and N/P ratio (N, total nitrogen; P, total phosphorus) were three important factors affecting phytoplankton phenology. Multiple linear correlation showed that air temperature, wind speed, and N/P ratio in Spring could explain 59.9% variability of BSDs for Lake Taihu. This study provides a quantitative assessment of phytoplankton phenological shifts and elucidates the inter-relationship between phenology parameters and environmental factors, thus improving our understanding on the potential impact of climate change and eutrophication on lake ecosystems. The starting earlier and lasting longer of phytoplankton are consistent with the expected effects of climate warming on aquatic ecosystem in recent decades, which will bring new challenges for algal bloom management in eutrophic Lake Taihu.

Detection of climate change-driven trends in phytoplankton phenology

The timing of the annual phytoplankton spring bloom is likely to be altered in response to climate change. Quantifying that response has, however, been limited by the typically coarse temporal resolution (monthly) of global climate models. Here, we use higher resolution model output (maximum 5 days) to investigate how phytoplankton bloom timing changes in response to projected 21st century climate change, and how the temporal resolution of data influences the detection of long-term trends. We find that bloom timing generally shifts later at mid-latitudes and earlier at high and low latitudes by ~5 days per decade to 2100. The spatial patterns of bloom timing are similar in both low (monthly) and high (5 day) resolution data, although initiation dates are later at low resolution. The magnitude of the trends in bloom timing from 2006 to 2100 is very similar at high and low resolution, with the result that the number of years of data needed to detect a trend in phytoplankton phenology is relatively insensitive to data temporal resolution. We also investigate the influence of spatial scales on bloom timing and find that trends are generally more rapidly detectable after spatial averaging of data. Our results suggest that, if pinpointing the start date of the spring bloom is the priority, the highest possible temporal resolution data should be used. However, if the priority is detecting long-term trends in bloom timing, data at a temporal resolution of 20 days are likely to be sufficient. Furthermore, our results suggest that data sources which allow for spatial averaging will promote more rapid trend detection.

Global and local factors driving the phenology of Alexandrium minutum (Halim) blooms and its toxicity

The dinoflagellate Alexandrium minutum is a toxic bloom-forming species distributed worldwide. The mechanisms driving and promoting the species blooms and their toxicity are studied and presented here. Most previously published work focuses on local and/or short-term scales. In this study, a broad temporal and spatial approach is addressed using time series covering several sites over several years and combining environmental variables and A. minutum abundances from the French English Channel - Atlantic coasts. Data were explored by means of phenology and threshold analysis. The A. minutum bloom characteristics are defined. Only one bloom per year is measured and it may reach more than a million of cells L^-1. Bloom period extends from April to October and the bloom length ranges from two weeks to six months. In the ecosystems studied, water temperature and river flow, as regional and local factors respectively, are the main environmental drivers influencing the magnitude, growth rate and length of the blooms. Bloom toxicity is linked to the bloom maximum abundance and river flow. This work provides new knowledge for further managing tools for A. minutum blooms in the ecosystems studied.

Estimating the onset of spring from a complex phenology database: trade-offs across geographic scales

Phenology is an important indicator of ecological response to climate change. Yet, phenological responses are highly variable among species and biogeographic regions. Recent monitoring initiatives have generated large phenological datasets comprised of observations from both professionals and volunteers. Because the observation frequency is often variable, there is uncertainty associated with estimating the timing of phenological activity. "Status monitoring" is an approach that focuses on recording observations throughout the full development of life cycle stages rather than only first dates in order to quantify uncertainty in generating phenological metrics, such as onset dates or duration. However, methods for using status data and calculating phenological metrics are not standardized. To understand how data selection criteria affect onset estimates of springtime leaf-out, we used status-based monitoring data curated by the USA National Phenology Network for 11 deciduous tree species in the eastern USA between 2009 and 2013. We asked, (1) How are estimates of the date of leaf-out onset, at the site and regional levels, influenced by different data selection criteria and methods for calculating onset, and (2) at the regional level, how does the timing of leaf-out relate to springtime minimum temperatures across latitudes and species? Results indicate that, to answer research questions at site to landscape levels, data users may need to apply more restrictive data selection criteria to increase confidence in calculating phenological metrics. However, when answering questions at the regional level, such as when investigating spatiotemporal patterns across a latitudinal gradient, there is low risk of acquiring erroneous results by maximizing sample size when using status-derived phenological data.

Seasonality of North Atlantic phytoplankton from space: impact of environmental forcing on a changing phenology (1998-2012)

Seasonal pulses of phytoplankton drive seasonal cycles of carbon fixation and particle sedimentation, and might condition recruitment success in many exploited species. Taking advantage of long-term series of remotely sensed chlorophyll a (1998-2012), we analyzed changes in phytoplankton seasonality in the North Atlantic Ocean. Phytoplankton phenology was analyzed based on a probabilistic characterization of bloom incidence. This approach allowed us to detect changes in the prevalence of different seasonal cycles and, at the same time, to estimate bloom timing and magnitude taking into account uncertainty in bloom detection. Deviations between different sensors stressed the importance of a prolonged overlap between successive missions to ensure a correct assessment of phenological changes, as well as the advantage of semi-analytical chlorophyll algorithms over empirical ones to reduce biases. Earlier and more intense blooms were detected in the subpolar Atlantic, while advanced blooms of less magnitude were common in the Subtropical gyre. In the temperate North Atlantic, spring blooms advanced their timing and decreased in magnitude, whereas fall blooms delayed and increased their intensity. At the same time, the prevalence of locations with a single autumn/winter bloom or with a bimodal seasonal cycle increased, in consonance with a poleward expansion of subtropical conditions. Changes in bloom timing and magnitude presented a clear signature of environmental factors, especially wind forcing, although changes on incident photosynthetically active radiation and sea surface temperature were also important depending on latitude. Trends in bloom magnitude matched changes in mean chlorophyll a during the study period, suggesting that seasonal peaks drive long-term trends in chlorophyll a concentration. Our results link changes in North Atlantic climate with recent trends in the phenology of phytoplankton, suggesting an intensification of these impacts in the near future.