RESUMEN
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.
RESUMEN
Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.
RESUMEN
In August 2018, the harmful algae species Margalefidinium polykrikoides bloomed to levels previously unobserved in the open waters of Narragansett Bay, Rhode Island, in a transient but intense bloom. Detected by an Imaging FlowCytobot providing hourly data, it is characterized by a time span of less than a week and patchiness with sub-daily oscillations in concentration. The highest concentrations are recorded at lower salinity and higher temperature, suggesting the bloom may have developed in the upper bay and was transported south. The proportion of chains increased during the height of the bloom, and many of the images contained 4-cells per chain. The development of the bloom was favored by optimal temperature and salinity conditions as well as increased nitrogen coincident with greater precipitation and river flow. The period preceding bloom formation also saw a sharp decrease in the dominating large chain-forming diatom Eucampia sp. and highly abundant Skeletonema spp., thus reducing competition over resources for the slow-growing M. polykrikoides. The height of the bloom was reached during the lowest tidal range of the month when the turbulence and water displacement were lower. This time series highlights an out-of-the-ordinary bloom's environmental and biological conditions and the importance of frequent sampling during known favorable conditions.