Barcoding and its application for visualizing ecological dynamics

Time is perceived to be unidirectional and continuous in the philosophy of science. This continuity can play a crucial role in time series analysis as events are generally seen as an outcome of the past, or subject to events that occurred previously in time. In this study, we describe an ordinal approach to perceiving ecological time series – one that relies on pattern formation with both antecedent and future events. Our approach defines a limited set of structural shapes that can occur for past, present, and future time points. Such a library of all possible shapes can then be used for novel approaches to data visualization and time series analysis. We applied this method to simple ecological models and then to natural time series data for measles cases in London and the phytoplankter Pseudo-nitzschia spp. in Narragansett Bay, Rhode Island. Alternative perspectives on time series representation can strengthen our ability to identify important patterns in dynamics and effectively discriminate between similar time series. When used in conjunction with conventional line-plots, barcodes can be tailored to demonstrate the presence or absence of specific structural patterns or features. Our results show that data exploration without the assumption of time series continuity can yield important and novel insight into the behavior of ecological systems

Expanding understanding of optical variability in Lake Superior with a 4-year dataset

Lake Superior is one of the largest freshwater lakes on our planet, but few optical observations have been made to allow for the development and validation of visible spectral satellite remote sensing products. The dataset described here focuses on coincidently observing inherent and apparent optical properties along with biogeochemical parameters. Specifically, we observe remote sensing reflectance, absorption, scattering, backscattering, attenuation, chlorophyll concentration, and suspended particulate matter over the ice-free months of 2013–2016. The dataset substantially increases the optical knowledge of the lake. In addition to visible spectral satellite algorithm development, the dataset is valuable for characterizing the variable light field, particle, phytoplankton, and colored dissolved organic matter distributions, and helpful in food web and carbon cycle investigations. The compiled data can be freely accessed at https://seabass.gsfc.nasa.gov/archive/URI/Mouw/LakeSuperior/

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 \u3e7 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

Meeting Mentoring Needs in Physical Oceanography: An Evaluation of the Impact of MPOWIR

After a decade of program offerings, the Mentoring Physical Oceanography Women to Increase Retention (MPOWIR) program initiated a community-wide survey to (1) assess the impact MPOWIR has had on retention of women in the field of physical oceanography, and (2) gauge where needs are being met and where gaps still exist. To investigate the impact of MPOWIR, we compare MPOWIR participants with male and female cohorts that did not participate in MPOWIR but were at a similar career stage. The survey results indicate MPOWIR has had a substantial impact by aiding individuals in finding and developing mentoring relationships. MPOWIR women are far more likely to have a mentor, and they report having mentors in addition to their advisors, indicating proactive seeking of mentoring relationships. Survey results identify many unmet mentoring needs for both men and women, but MPOWIR participants appear to be receiving more from their mentoring relationships than their non-MPOWIR cohorts. The majority of survey respondents reported there were challenges to achieving career goals, but MPOWIR participants were significantly more likely to have attained their career goals, even though they had received their PhDs more recently. Eighty-eight percent of survey respondents with PhDs were employed in oceanography, irrespective of participation in MPOWIR. MPOWIR women indicate the program has had a large impact on their lives, with the greatest effect being expansion of professional networks and exposure to professional development skills. Senior participants in the program (who serve as mentors to junior scientists) also reported significant professional and personal growth from being involved. Data obtained independently of the survey show that, of the 173 women who have participated in MPOWIR, the recent PhDs are predominantly in postdoctoral positions as expected, but for participants receiving their PhDs prior to 2012, an impressive 80% are in faculty or university/government/nonprofit research positions. Thus, MPOWIR appears to have had an important impact on retention and career satisfaction of its participants

Deriving inherent optical properties from decomposition of hyperspectral non-water absorption

Semi-analytical algorithms (SAAs) developed for multispectral ocean color sensors have benefited from a variety of approaches for retrieving the magnitude and spectral shape of inherent optical properties (IOPs). SAAs generally follow two approaches: 1) simultaneous retrieval of all IOPs, resulting in pre-defined bio-optical models and spectral dependence between IOPs and 2) retrieval of bulk IOPs (absorption and backscattering) first followed by decomposition into separate components, allowing for independent retrievals of some components. Current algorithms used to decompose hyperspectral remotely-sensed reflectance into IOPs follow the first strategy. Here, a spectral deconvolution algorithm for incorporation into the second strategy is presented that decomposes at-w(λ) from in situ measurements and estimates absorption due to phytoplankton (aph(λ)) and colored detrital material (adg(λ)) free of explicit assumptions. The algorithm described here, Derivative Analysis and Iterative Spectral Evaluation of Absorption (DAISEA), provides estimates of aph(λ) and adg(λ) over a spectral range from 350 to 700 nm. Estimated aph(λ) and adg(λ) showed an average normalized root mean square difference of \u3c30% and \u3c20%, respectively, from 350 to 650 nm for the majority of optically distinct environments considered. Estimated Sdg median difference was \u3c20% for all environments considered, while distribution of Sdg uncertainty suggests that biogeochemical variability represented by Sdg can be estimated free of bias. DAISEA results suggest that hyperspectral satellite ocean color data will improve our ability to track biogeochemical processes affiliated with variability in adg(λ) and Sdg free of explicit assumptions

The impact of mpowir a decade of investing in mentoring women in physical oceanography

MPOWIR (Mentoring Physical Oceanography Women to Increase Retention) is a US communityinitiated and community-led mentoring program aimed at improving the retention of women physical oceanographers in academic and/or research positions. This article describes the MPOWIR program elements designed by the US physical oceanography community, quantifies the participation in these programs, describes MPOWIR’s impact to date, and outlines future directions. An examination of surveys to date indicates that MPOWIR, several years after its implementation, is having a positive impact on the retention of junior women in physical oceanography, primarily by giving them a broad professional network and focused mentoring

A Satellite Assessment of Environmental Controls of Phytoplankton Community Size Structure

Phytoplankton play a key role as the base of the marine food web and a crucial component in the Earth\u27s carbon cycle. There have been a few regional studies that have utilized satellite‐estimated phytoplankton functional type products in conjunction with other environmental metrics. Here we expand to a global perspective and ask, what are the physical drivers of phytoplankton composition variability? Using a variety of satellite‐observed ocean color products and physical properties spanning 1997–2015, we characterize spatial and temporal variability in phytoplankton community size structure in relation to satellite‐based physical drivers. We consider the relationships globally and by major thermal regimes (cold and warm), dominant size distribution, and chlorophyll concentration variability. Globally, euphotic depth is the most important parameter driving phytoplankton size variability and also over the majority of the high‐latitude ocean and the central gyres. In all other regions, size variability is driven by a balance of light and mode of nutrient delivery. We investigated the relationship between size composition and chlorophyll concentration and the physical drivers through correlation analysis. Changes in size composition over time are regionally varying and explained by temporal shifts in the varying physical conditions. These changes in phytoplankton size composition and the varying underlying physical drivers will ultimately impact carbon export and food web processes in our changing ocean

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 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

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

Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite‐Based Observations in Detecting Outbreaks

Rotavirus is the most common cause of diarrheal disease among children under 5. Especially in South Asia, rotavirus remains the leading cause of mortality in children due to diarrhea. As climatic extremes and safe water availability significantly influence diarrheal disease impacts in human populations, hydroclimatic information can be a potential tool for disease preparedness. In this study, we conducted a multivariate temporal and spatial assessment of 34 climate indices calculated from ground and satellite Earth observations to examine the role of temperature and rainfall extremes on the seasonality of rotavirus transmission in Bangladesh. We extracted rainfall data from the Global Precipitation Measurement and temperature data from the Moderate Resolution Imaging Spectroradiometer sensors to validate the analyses and explore the potential of a satellite‐based seasonal forecasting model. Our analyses found that the number of rainy days and nighttime temperature range from 16°C to 21°C are particularly influential on the winter transmission cycle of rotavirus. The lower number of wet days with suitable cold temperatures for an extended time accelerates the onset and intensity of the outbreaks. Temporal analysis over Dhaka also suggested that water logging during monsoon precipitation influences rotavirus outbreaks during a summer transmission cycle. The proposed model shows lag components, which allowed us to forecast the disease outbreaks 1 to 2 months in advance. The satellite data‐driven forecasts also effectively captured the increased vulnerability of dry‐cold regions of the country, compared to the wet‐warm regions