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.

Intraseasonal predictability of natural phytoplankton population dynamics.

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22-year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992-2014) to test whether and how aggregating phytoplankton into multi-species assemblages can improve predictability of their temporal dynamics. Using a non-parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 µm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole-community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.

Networks of Causal Linkage Between Eigenmodes Characterize Behavioral Dynamics of Caenorhabditis elegans.

Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called "eigenworms"). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create "interaction profiles" to represent an individual's behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons.

Tracking changes in behavioural dynamics using prediction error.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded 'attractor' of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach-defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error-may be of broad interest and relevance to behavioural researchers working with video-derived time series.