Towards Phytoplankton Parasite Detection Using Autoencoders

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological impact on phytoplankton bloom dynamics. To better understand their impact, we need improved detection methods to integrate phytoplankton parasite interactions in monitoring aquatic ecosystems. Automated imaging devices usually produce high amount of phytoplankton image data, while the occurrence of anomalous phytoplankton data is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity of the original and autoencoder-reconstructed samples. With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further compared with the supervised Faster R-CNN based object detector. With this supervised approach and the model trained on plankton species and anomalies, we were able to reach the highest F1 score of 0.86. However, the unsupervised approach is expected to be more universal as it can detect also unknown anomalies and it does not require any annotated anomalous data that may not be always available in sufficient quantities. Although other studies have dealt with plankton anomaly detection in terms of non-plankton particles, or air bubble detection, our paper is according to our best knowledge the first one which focuses on automated anomaly detection considering putative phytoplankton parasites or infections

A new algorithm using support vector machines to detect and monitor bloom-forming Pseudo-nitzschia from OLCI data

Pseudo-nitzschia spp. blooms are a recurrent problem in many coastal areas globally, imposing some significant threats to the health of humans, ecosystems and the economy. Monitoring programmes have been established, where feasible, to mitigate the impacts caused by Pseudo-nitzschia spp. and other harmful algae blooms. The detection of such blooms from satellite data could really provide timely information on emerging risks but the development of taxa-specific algorithms from available multispectral data is still challenged by coupled optical properties with other taxa and water constituents, availability of ground data and generalisation capabilities of algorithms. Here, we developed a new set of algorithms (PNOI) for the detection and monitoring of Pseudo-nitzschia spp. blooms over the Galician coast (NW Iberian Peninsula) from Sentinel-3 OLCI reflectances using a support vector machine (SVM). Our algorithm was trained and tested with reflectance data from 260 OLCI images and 4607 Pseudo-nitzschia spp. match up data points, of which 2171 were of high quality. The performance of the no bloom/bloom model in the independent test set was robust, showing values of 0.80, 0.72 and 0.79 for the area under the curve (AUC), sensitivity and specificity, respectively. Similar results were obtained by our below detection limit/presence model. We also present different model thresholds based on optimisation of true skill statistic (TSS) and F1-score. PNOI outperforms linear models, while its relationship with in situ chlorophyll-a concentrations is weak, demonstrating a poor correlation with the phytoplankton abundance. We showcase the importance of the PNOI algorithm and OLCI sensor for monitoring the bloom evolution between the weekly ground sampling and during periods of ground data absence, such as due to COVID-19

Automatic system for the detection and recognition of phytoplankton in digital microscope imaging

[Abstract] The quality of water can be compromised by the proliferation of toxic species of phytoplankton. When these blooms occur in rivers and reservoirs used for the water supply, this event can have a negative impact on human health. Currently, to determine the existence of risk, experts rudimentarily monitor phytoplankton populations by sampling and analysing the water. This analysis consits on the identification of dangerous species and its biologic volume. All in all, this process is long and tedious when the amount of samples that need to be analysed in order to obtain a quality and representative measure is taken into account, which also needs to be carried out periodically for each water source. The taxonomic process requires broad experience and training for the personnel involved. The automation of these tasks is highly desirable as it would free the experts from part of the work at the same time as that eliminates subjective factors that may impact in the overall quality of the process. In this work the intention is to help experts starting from images obtained directly from a conventional microscope, differentiating it from other similar works in the state of the art that use specific hardware. Computer vision techniques will be used to detect candidate individuals and artificial intelligence methods to recognise relevant phytoplankton species, that is, the toxic ones, distinguishing them from the rest of objects in the images like, for example, inorganic materials. Finally, the phytoplankton organisms will be classified to obtain a metric that counts the dangerous ones and so be able to analyse the quality of the water.[Resumo] A calidade da auga pode verse ameazada pola proliferación de especies tóxicas de fitoplancto. Cando estas proliferacións ocorren en ríos e encoros utilizados na subministración de auga potable este feito pode ter impactos negativos na saúde humana. Actualmente, para determinar a existencia de risco, os expertos monitorizan, de forma rudimentaria, as poboacións de fitoplancto mediante a recolección de mostras e a súa correspondente análise. Esta análise consiste na identificación das especies perigosas e o rexistro do seu volume biolóxico. Con todo, este proceso resulta longo e tedioso se se ten en conta a cantidade de mostras a analizar para poder ofrecer unhas métricas fiables e representativas, as cales se deben realizar periodicamente para cada unha das fontes de auga destinadas ao consumo. Así mesmo, o proceso taxonómico require unha ampla experiencia e formación específica do persoal involucrado. A automatización destas tarefas é moi desexable xa que libera aos expertos de parte do traballo, á vez que evita factores subxectivos que poidan influír na calidade global do proceso. Neste traballo preténdese axudar aos expertos partindo de imaxes obtidas directamente do microscopioconvencional, diferenciándoo de traballos similares do estado do arte que requiren hardware específico. Empregaranse técnicas de procesado de imaxe e visión artificial para detectar individuos candidatos e técnicas de intelixencia artificial para recoñecer as especies de fitoplancto relevantes, é dicir, as tóxicas, distinguíndoas do resto de obxectos nas imaxes, como, por exemplo, materiais inertes ou inorgánicos. Por último, os microogranismos de fitoplancto son clasificados para obter unha métrica que contabilice os perigosos e poder, así, analizar a calidade da auga.Traballo fin de grao (UDC.FIC). Enxeñaría informática. Curso 2018/201

Remote sensing in shallow lake ecology

Shallow lakes are an important ecological and socio-economic resource. However, the impact of human pressures, both at the lake and catchment scale, has precipitated a decline in the ecological status of many shallow lakes, both in the UK, and throughout Europe. There is now, as direct consequence, unprecedented interest in the assessment and monitoring of ecological status and trajectory in shallow lakes, not least in response to the European Union Water Framework Directive (2000/60/EC). In this context, the spatially-resolving and panoramic data provided by remote sensing platforms may be of immense value in the construction of effective and efficient strategies for the assessment and monitoring of ecological status in shallow lakes and, moreover, in providing new, spatially-explicit, insights into the function of these ecosystems and how they respond to change. This thesis examined the use of remote sensing data for the assessment of (i) phytoplankton abundance and species composition and (ii) aquatic vegetation distribution and ecophysiological status in shallow lakes with a view to establishing the credence of such an approach and its value in limnological research and monitoring activities. High resolution in-situ and airborne remote sensing data was collected during a 2-year sampling campaign in the shallow lakes of the Norfolk Broads. It was demonstrated that semi-empirical algorithms could be formulated and used to provide accurate and robust estimations of the concentration of chlorophyll-a, even in these optically-complex waters. It was further shown that it was possible to differentiate and quantify the abundance of cyanobacteria using the biomarker pigment C-phycocyanin. The subsequent calibration of the imagery obtained from the airborne reconnaissance missions permitted the construction of diurnal and seasonal regional-scale time-series of phytoplankton dynamics in the Norfolk Broads. This approach was able to deliver unique spatial insights into the migratory behaviour of a potentially-toxic cyanobacterial bloom. It was further shown that remote sensing can be used to map the distribution of aquatic plants in shallow lakes, importantly including the extent of submerged vegetation, which is central to the assessment of ecological status. This research theme was subsequently extended in an exploration of the use of remote sensing for assessing the ecophysiological response of wetland plants to nutrient enrichment. It was shown that remote sensing metrics could be constructed for the quantification of plant vigour. The extrapolation of these techniques enabled spatial heterogeneity in the ecophysiological response of Phragmites australis to lake nutrient enrichment to be characterised and assisted the formulation of a mechanistic explanation for the variation in reedswamp performance in these shallow lakes. It is therefore argued that the spatially synoptic data provided by remote sensing has much to offer the assessment, monitoring and policing of ecological status in shallow lakes and, in particular, for facilitating the development of pan-European scale lake surveillance capabilities for the Water Framework Directive (2000/60/EC). It is also suggested that remote sensing can make a valuable contribution to furthering ecological understanding and, most significantly, in enabling ecosystem processes and functions to be examined at the lake-scale

Estimating phytoplankton size classes from their inherent optical properties

Phytoplankton plays a massive role in the regulation of greenhouse gases, with different functional types affecting the carbon cycle differently. The most practical way of synoptically mapping the ocean’s phytoplankton communities is through remote sensing with the aid of ocean-optics algorithms. This thesis is a study of the relationships between the Inherent Optical Properties (IOPs) of the ocean and the physical constituents within it, with a special focus on deriving phytoplankton size classes. Three separate models were developed, each focusing on a different relationship between absorption and phytoplankton size classes, before being combined into a final ensemble model. It was shown that all of the developed models performed better than the baseline model, which only estimates the mean values per size class, and that the results of the final ensemble model is comparable to, and performs better than, most other published models on the NOMAD dataset

Semi-automated image analysis for the identification of bivalve larvae from a Cape Cod estuary

Author Posting. © Association for the Sciences of Limnology and Oceanography, 2012. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Limnology and Oceanography: Methods 10 (2012): 538-554, doi:10.4319/lom.2012.10.538.Machine-learning methods for identifying planktonic organisms are becoming well-established. Although similar morphologies among species make traditional image identification methods difficult for larval bivalves, species-specific shell birefringence patterns under polarized light permit identification by color and texture-based features. This approach uses cross-polarized images of bivalve larvae, extracts Gabor and color angle features from each image, and classifies images using a Support Vector Machine. We adapted this method, which was established on hatchery-reared larvae, to identify bivalve larvae from a series of field samples from a Cape Cod estuary in 2009. This method had 98% identification accuracy for four hatchery-reared species. We used a multiplex polymerase chain reaction (PCR) method to confirm field identifications and to compare accuracies to the software classifications. Image classification of larvae collected in the field had lower accuracies than both the classification of hatchery species and PCR-based identification due to error in visually classifying unknown larvae and variability in larval images from the field. A six-species field training set had the best correspondence to our visual classifications with 75% overall agreement and individual species agreements from 63% to 88%. Larval abundance estimates for a time-series of field samples showed good correspondence with visual methods after correction. Overall, this approach represents a cost- and time-saving alternative to molecular-based identifications and can produce sufficient results to address long-term abundance and transport-based questions on a species-specific level, a rarity in studies of bivalve larvae.This project was supported by an award to S. Gallager and C. Mingione Thompson from the Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National Ocean Service, National Oceanic and Atmospheric Administration and a grant from Woods Hole Oceanographic Institution’s Coastal Ocean Institute

A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems

Species-specific patterns in bivalve larval supply to a coastal embayment

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2011Larval supply is an important process linking reproductive output to recruitment of benthic marine invertebrates. Few species-specific studies of bivalve larvae have been performed due to the lack of suitable methods for species identification. This thesis focused on applying a method to identify larvae from field samples from Waquoit Bay, MA using shell birefringence patterns. This method was then used to address variability in larval supply for three bivalve species on weekly, tidal, and hourly scales. Sampling weekly for six months during two years showed large variability in larval concentrations on this time scale. Abundances of most species were related to bay temperature, and species distributions among sampling sites were indicative of transport potential and population coherence. Greater growth of larvae in 2009 compared to 2007 was attributed to more wind-induced mixing and better food availability in 2009. Integrative samples over each tidal event for a 14-day period demonstrated that larvae were mostly constrained by water masses. During a period when there were sharp tidal signals in temperature and salinity, larval concentrations were higher in bay water compared to coastal waters on incoming tides. After a storm event, water mass properties were less distinct between tidal events and a semidiurnal signal in larval concentrations was no longer apparent. The timing of periods of high larval concentrations did not always coincide with periods of highest water mass flux reducing net export in some cases. On an hourly scale, the vertical distribution of larvae affected by water column stratification and strength of tidal flow. Strong currents and a fresh upper layer both prevented larvae from concentrating at the surface. There was little evidence of peaks in larval concentrations associated with a given tidal period. Species-specific data can provide new perspectives on larval transport. For the three species studied, Anomia simplex, Guekensia demissa, and Mercenaria mercenaria, different source areas, patterns for growth, and potential for export were observed. Applying species-specific identification methods to future studies of bivalve larval transport has the potential to relate larval abundance to settlement patterns, an important component of larval ecology and shellfish management.Funding for this thesis research was provided by an award to from NOAA Sea Grant to S. Gallager (grant NA06OAR417002), an award to S. Gallager and C. Mingione Thompson from the Estuarine Reserves Division, Office of Ocean and Coastal Resource Management, National Ocean Service, National Oceanic and Atmospheric Administration (grant NA07NOS42000024), the WHOI Academic Programs Office, a WHOI Coastal Ocean Institute student grant, the WHOI Biology Department, and the WHOI Ocean Life Institute

Validation methods for plankton image classification systems

In recent decades, the automatic study and analysis of plankton communities using imaging techniques has advanced significantly. The effectiveness of these automated systems appears to have improved, reaching acceptable levels of accuracy. However, plankton ecologists often find that classification systems do not work as well as expected when applied to new samples. This paper proposes a methodology to assess the efficacy of learned models which takes into account the fact that the data distribution (the plankton composition of the sample) can vary between the model building phase and the production phase. As opposed to most validation methods that consider the individual organism as the unit of validation, our approach uses a validation‐by‐sample, which is more appropriate when the objective is to estimate the abundance of different morphological groups. We argue that, in these cases, the base unit to correctly estimate the error is the sample, not the individual. Thus, model assessment processes require groups of samples with sufficient variability in order to provide precise error estimates