RAPID : research on automated plankton identification

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.When Victor Hensen deployed the first true plankton1 net in 1887, he and his colleagues were attempting to answer three fundamental questions: What planktonic organisms are present in the ocean? How many of each type are present? How does the plankton’s composition change over time? Although answering these questions has remained a central goal of oceanographers, the sophisticated tools available to enumerate planktonic organisms today offer capabilities that Hensen probably could never have imagined.This material is based upon work supported by the National Science Foundation under Grants OCE-0325018, OCE-0324937, OCE-0325167 and OCE-9423471, and the European Union under grants Q5CR-2002-71699, MAS3-ct98-0188, and MAS2-ct92-0015

Introduction to the Special Volume on "Ecology and Ecological Modeling in R"

The third special volume in the "Foometrics in R" series of the Journal of Statistical Software collects a number of contributions describing statistical methodology and corresponding implementations related to ecology and ecological modelling. The scope of the papers ranges from theoretical ecology and ecological modelling to statistical methodology relevant for data analyses in ecological applications.

Carbon cycling in a Patagonian fjord: Strength of biological vs. physical pump

Póster presentado en la 2nd International Ocean Research Conference, celebrada en Barcelona del 17 al 21 de noviembre de 2014.Understanding the role of the pelagic “biological and physical pump” in carbon cycling is critical to climate change adaptation and mitigation efforts especially in coastal areas characterized by intense biogeochemical cycling. Fjords are among the few coastal regions that appear to be net annual sinks for atmosphericCO2. Vertical profiles of temperature and salinity were collected with a CTD revealing intense vertical structure in the water column that could be summarised as 2 layers with a transition-mixing region .Spatio-temporal sampling of physical and biogeochemical (C system, nutrients, Phyto- and Zooplankton, in situ dissolved pCO2) parameters was conducted in Comau Fjord (Puerto Montt-Chile) during Austral spring from the surface and deep layers. Spatial variation in water column structure in the fjord was minimal, however the depth of the upper layer varies probably depending on the surface-water inputs. Surface waters had significantly lower pCO2 values compared to the atmosphere and deeper water. Concentrations of suspended material and chlorophyll a were higher deeper in the water column, suggesting concentration process of material across the halocline. Overall, concentrations of particulate matter and mesozooplankton (during the study period), were low compared to many mid-latitude regions, and near absent in the vicinity of the 2 rivers entering the fjord. The low surface water pCO2 values suggest negative air-water CO2fluxes predominates within Comau Fjord during Austral spring. This preliminary study suggests that the geochemical properties of watershed and the low [DIC] of surface-water inputs, i.e., the physical pump, seems to play an important role in this region.This work was supported by the project 2013CL0013 funded by the CSIC, Fundacion Endesa and Fundación San Ignacio del Huinay. Funding was also provided by the Spanish Ministry of Sciences and Innovation (JAE DOCTORES 2010 contract for E.P.M., JAE PREDOCTORAL scholarship for S.T. and S.F.) and part-funded bythe European Union (European Social Fund, ESF2007-2013) and the Spanish Ministry for Economy and Competitiveness.Peer Reviewe

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

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

An Instrument for Rapid Mesozooplankton Monitoring at Ocean Basin Scale

The development and testing of a new imaging and classification system for mesozooplankton sampling over very large spatial and temporal scales is reported. The system has been evaluated on the Atlantic Meridional Transect (AMT), acquiring nearly one million images of planktonic particles over a transect of 13,500km. These images have been acquired at a flow rate of 12.5L per minute, in near-continuous underway mode from the ships seawater supply and in discrete mode using integrated vertical net haul samples. The aim of this development is to produce an instrument capable of delivering autonomously acquired and processed data on the biomass and taxonomic distribution of mesozooplankton over ocean-basin scales, in or near real-time, so that data are immediately available without the need for significant amounts of post-cruise processing and analysis. The hardware and image acquisition and processing software system implemented to support this development, together with some preliminary results from AMT21, are described. The images acquired during this Atlantic cruise comprise microplankton, mesoplankton, fish larvae and sampling artefacts (air bubbles, detritus, etc.), and were classified to one of 7 pre-defined taxonomic classes with 67% success

Zoo/PhytoImage version 5.4-0 : Analyse d'images de plancton assistée par ordinateur

L'analyse d'échantillons zooplanctoniques ou phytoplanctoniques est traditionnellement associée à de longues et fastidieuses séances de comptage des particules fixées de plancton sous binoculaire et avec des vapeurs de formaldéhyde flottant autour. Bien que cette image du planctonologiste restera probablement pendant un certain temps, il semble y avoir une autre façon de recueillir des données sur le zooplancton : l'analyse assistée par ordinateur d'images numériques de plancton. Toute une gamme de matériel pour prendre des photos de nos animaux, à la fois in situ et/ou à partir d'échantillons fixés, est maintenant disponible : FlowCAM, OPC laser, VPR, Zooscan, ... (plus, à venir, l'holocam, Sipper, Zoovis, bouée HAB, ...), sans oublier l'utilisation d'un appareil photo numérique sur binoculaire ou avec un macro objectif. Cependant, les images numériques de zooplancton sont à peine utilisables en tant que telles : elles doivent être analysées de manière à extraire des attributs biologiquement et écologiquement significatifs à partir des pixels. Un logiciel permettant de réaliser une telle analyse est donc indispensable. Zoo/PhytoImage a pour objectif de fournir une solution puissante et riche en fonctionnalités logicielles pour utiliser les images de zooplancton ou phytoplancton provenant d'origines diverses et les transformer en une table de mesures utilisables (c'est-à-dire, les abondances, les spectres de taille totaux et partiels, les biomasses totales et partielles, .. .). Zoo/PhytoImage n'est pas fermé à l'un des dispositifs cités précédemment, et n'est pas un produit commercial. Il est distribué gratuitement (licence GPL, distribuée à travers son site web, http://www.sciviews.org/zooimage) et est ouvert, ce qui signifie qu'il fournit un cadre général pour importer des images, les analyser et exporter les résultats à partir et vers un grand nombre de systèmes. Donc, tout le monde peut utiliser Zoo/PhytoImage... mais mieux encore, chaque développeur peut également y contribuer! L'approche Open Source de câblage de nombreux développeurs à travers le monde dans un projet commun a déjà montré son efficacité : Linux, Apache, mais aussi R ou ImageJ dans le domaine des statistiques et de l'analyse d'image respectivement, sont de bons exemples. Zoo/PhytoImage est basé sur ImageJ et R, et fonctionne sur Linux ... mais il peut aussi être exécuté sur Windows, Mac OS ou diverses Unixes1. La meilleure qualification de Zoo/PhytoImage est sa 'réutilisation'. Il est né en réutilisant diverses caractéristiques de logiciels existants comme ImageJ, ou R, et fournit lui-même des composants réutilisables, au bénéfice des utilisateurs et des développeurs. Zoo/PhytoImage peut être utilisé sur des images acquises dans différentes situations : in situ (comme le VPR ou la bouée HAB) ou dans un laboratoire (échantillons fixés numérisés avec le Zooscan, par exemple). Le cadre général de Zoo/PhytoImage est conçu de manière à ce que le logiciel soit capable de traiter efficacement des images de caractéristiques et d'origines diverses. Par conséquent, ce n'est pas un système rationalisé et rigide. Il est plutôt constitué d'un ensemble d'applications différentes et personnalisables rassemblées en un seul système. Ce manuel utilisateur vous guidera dans votre première utilisation de Zoo/PhytoImage. Ce manuel décrit la version actuelle de ZooImage (5.4-0), qui sera une version publique! Il est adapté aux besoins de nos partenaires: UMONS, IFREMER, Belspo, ULCO et LISIC. 4/5 du code est commun avec la version 3.0-5, qui est publique et téléchargeable à partir du site du CRAN (http://cran.r-project.org)