Patterns in the temporal complexity of global chlorophyll concentration.

Decades of research have relied on satellite-based estimates of chlorophyll-a concentration to identify oceanographic processes and plan in situ observational campaigns; however, the patterns of intrinsic temporal variation in chlorophyll-a concentration have not been investigated on a global scale. Here we develop a metric to quantify time series complexity (i.e., a measure of the ups and downs of sequential observations) in chlorophyll-a concentration and show that seemingly disparate regions (e.g., Atlantic vs Indian, equatorial vs subtropical) in the global ocean can be inherently similar. These patterns can be linked to the regularity of chlorophyll-a concentration change and the likelihood of anomalous events within the satellite record. Despite distinct spatial changes in decadal chlorophyll-a concentration, changes in time series complexity have been relatively consistent. This work provides different metrics for monitoring the global ocean and suggests that the complexity of chlorophyll-a time series can be independent of its magnitude.

Sub-monthly prediction of harmful algal blooms based on automated cell imaging.

Harmful algal blooms (HABs) are an increasing threat to global fisheries and human health. The mitigation of HABs requires management strategies to successfully forecast the abundance and distribution of harmful algal taxa. In this study, we attempt to characterize the dynamics of 2 phytoplankton genera (Pseudo-nitzschia spp. and Dinophysis spp.) in Narragansett Bay, Rhode Island, using empirical dynamic modeling. We utilize a high-resolution Imaging FlowCytobot dataset to generate a daily-resolution time series of phytoplankton images and then characterize the sub-monthly (1-30 days) timescales of univariate and multivariate prediction skill for each taxon. Our results suggest that univariate predictability is low overall, different for each taxon and does not significantly vary over sub-monthly timescales. For all univariate predictions, models can rely on the inherent autocorrelation within each time series. When we incorporated multivariate data based on quantifiable image features, we found that predictability increased for both taxa and that this increase was apparent on timescales >7 days. Pseudo-nitzschia spp. has distinctive predictive dynamics that occur on timescales of around 16 and 25 days. Similarly, Dinophysis spp. is most predictable on timescales of 25 days. The timescales of prediction for Pseudo-nitzschia spp. and Dinophysis spp. could be tied to environmental drivers such as tidal cycles, water temperature, wind speed, community biomass, salinity, and pH in Narragansett Bay. For most drivers, there were consistent effects between the environmental variables and the phytoplankton taxon. Our analysis displays the potential of utilizing data from automated cell imagers to forecast and monitor harmful algal blooms.

Quantitative Analysis of the Trade-Offs of Colony Formation for Trichodesmium.

There is considerable debate about the benefits and trade-offs for colony formation in a major marine nitrogen fixer, Trichodesmium. To quantitatively analyze the trade-offs, we developed a metabolic model based on carbon fluxes to compare the performance of Trichodesmium colonies and free trichomes under different scenarios. Despite reported reductions in carbon fixation and nitrogen fixation rates for colonies relative to free trichomes, we found that model colonies can outperform individual cells in several cases. The formation of colonies can be advantageous when respiration rates account for a high proportion of the carbon fixation rate. Negative external influence on vital rates, such as mortality due to predation or micronutrient limitations, can also create a net benefit for colony formation relative to individual cells. In contrast, free trichomes also outcompete colonies in many scenarios, such as when respiration rates are equal for both colonies and individual cells or when there is a net positive external influence on rate processes (i.e., optimal environmental conditions regarding light and temperature or high nutrient availability). For both colonies and free trichomes, an increase in carbon fixation relative to nitrogen fixation rates would increase their relative competitiveness. These findings suggest that the formation of colonies in Trichodesmium might be linked to specific environmental and ecological circumstances. Our results provide a road map for empirical studies and models to evaluate the conditions under which colony formation in marine phytoplankton can be sustained in the natural environment. IMPORTANCE Trichodesmium is a marine filamentous cyanobacterium that fixes nitrogen and is an important contributor to the global nitrogen cycle. In the natural environment, Trichodesmium can exist as individual cells (trichomes) or as colonies (puffs and tufts). In this paper, we try to answer a longstanding question in marine microbial ecology: how does colony formation benefit the survival of Trichodesmium? To answer this question, we developed a carbon flux model that utilizes existing published rates to evaluate whether and when colony formation can be sustained. Enhanced respiration rates, influential external factors such as environmental conditions and ecological interactions, and variable carbon and nitrogen fixation rates can all create scenarios for colony formation to be a viable strategy. Our results show that colony formation is an ecologically beneficial strategy under specific conditions, enabling Trichodesmium to be a globally significant organism.

Length, width, shape regularity, and chain structure: time series analysis of phytoplankton morphology from imagery.

Functional traits are increasingly used to assess changes in phytoplankton community structure and to link individual characteristics to ecosystem functioning. However, they are usually inferred from taxonomic identification or manually measured for each organism, both time consuming approaches. Instead, we focus on high throughput imaging to describe the main temporal variations of morphological changes of phytoplankton in Narragansett Bay, a coastal time-series station. We analyzed a 2-yr dataset of morphological features automatically extracted from continuous imaging of individual phytoplankton images (~ 105 million images collected by an Imaging FlowCytobot). We identified synthetic morphological traits using multivariate analysis and revealed that morphological variations were mainly due to changes in length, width, shape regularity, and chain structure. Morphological changes were especially important in winter with successive peaks of larger cells with increasing complexity and chains more clearly connected. Small nanophytoplankton were present year-round and constituted the base of the community, especially apparent during the transitions between diatom blooms. High inter-annual variability was also observed. On a weekly timescale, increases in light were associated with more clearly connected chains while more complex shapes occurred at lower nitrogen concentrations. On an hourly timescale, temperature was the determinant variable constraining cell morphology, with a general negative influence on length and a positive one on width, shape regularity, and chain structure. These first insights into the phytoplankton morphology of Narragansett Bay highlight the possible morphological traits driving the phytoplankton succession in response to light, temperature, and nutrient changes.

A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications.

Light emerging from natural water bodies and measured by radiometers contains information about the local type and concentrations of phytoplankton, non-algal particles and colored dissolved organic matter in the underlying waters. An increase in spectral resolution in forthcoming satellite and airborne remote sensing missions is expected to lead to new or improved capabilities for characterizing aquatic ecosystems. Such upcoming missions include NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; the NASA Surface Biology and Geology designated observable mission; and NASA Airborne Visible/Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) airborne missions. In anticipation of these missions, we present an organized dataset of geographically diverse, quality-controlled, high spectral resolution inherent and apparent optical property (IOP-AOP) aquatic data. The data are intended to be of use to increase our understanding of aquatic optical properties, to develop aquatic remote sensing data product algorithms, and to perform calibration and validation activities for forthcoming aquatic-focused imaging spectrometry missions. The dataset is comprised of contributions from several investigators and investigating teams collected over a range of geographic areas and water types, including inland waters, estuaries, and oceans. Specific in situ measurements include remote-sensing reflectance, irradiance reflectance, and coefficients describing particulate absorption, particulate attenuation, non-algal particulate absorption, colored dissolved organic matter absorption, phytoplankton absorption, total absorption, total attenuation, particulate backscattering, and total backscattering. The dataset can be downloaded from https://doi.org/10.1594/PANGAEA.902230 (Casey et al., 2019).

Evidence of Environmental Changes Caused by Chinese Island-Building.

This paper quantifies environmental effects of island-building operations in the South China Sea, which result from dredging and can negatively impact marine flora, fauna, and ecosystems. The extent of the damage caused by island-creation is believed to be large, as the South China Sea reefs support the largest concentration of marine biodiversity on Earth. Through use of satellite imagery, we investigate the island-construction on Mischief Reef in the South China Sea, showing backscatter increases of up to 350% in waters surrounding the reef, with plumes of excess sediment exceeding 250 km2 at times during island-construction, and the cumulative area impacted by dredging exceeding 1,200 km2. Comparison of satellite-derived chlorophyll-a, backscatter, absorption and remote sensing reflectance at 412 nm suggest that dredging activities led to a decrease in biological health of the region resulting from the smothering of natural benthic habitats and reef complexes with sediment. We anticipate this ex post facto quantification of the connectivity between island-construction, large particulate plumes and a decrease in absorption related to marine life in the water column to establish a starting point for further study into ecosystem impact. The potential associations between these damages and a long-term reduction in ocean life and resources could serve inter-governmental bodies with a baseline metric for evaluating the level of damage caused. This may result in both forward-looking deterrent policies that limit island-building as well as backward-looking compensation.

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.