Time series of ZOOSCAN images and results of zooplankton samples at Villefranche from 1966 to 2003

ZooScan with ZooProcess and Plankton Identifier (PkID) software is an integrated analysis system for acquisition and classification of digital zooplankton images from preserved zooplankton samples. Zooplankton samples are digitized by the ZooScan and processed by ZooProcess and PkID in order to detect, enumerate, measure and classify the digitized objects. Here we present a semi-automatic approach that entails automated classification of images followed by manual validation, which allows rapid and accurate classification of zooplankton and abiotic objects. We demonstrate this approach with a biweekly zooplankton time series from the Bay of Villefranche-sur-mer, France. The classification approach proposed here provides a practical compromise between a fully automatic method with varying degrees of bias and a manual but accurate classification of zooplankton. We also evaluate the appropriate number of images to include in digital learning sets and compare the accuracy of six classification algorithms. We evaluate the accuracy of the ZooScan for automated measurements of body size and present relationships between machine measures of size and C and N content of selected zooplankton taxa. We demonstrate that the ZooScan system can produce useful measures of zooplankton abundance, biomass and size spectra, for a variety of ecological studies.

Multi-temporal mapping of seagrass cover, species and biomass of the Eastern Banks, Moreton Bay, Australia, with links to shapefiles

The spatial and temporal dynamics of seagrasses have been studied from the leaf to patch (100 m**2) scales. However, landscape scale (> 100 km**2) seagrass population dynamics are unresolved in seagrass ecology. Previous remote sensing approaches have lacked the temporal or spatial resolution, or ecologically appropriate mapping, to fully address this issue. This paper presents a robust, semi-automated object-based image analysis approach for mapping dominant seagrass species, percentage cover and above ground biomass using a time series of field data and coincident high spatial resolution satellite imagery. The study area was a 142 km**2 shallow, clear water seagrass habitat (the Eastern Banks, Moreton Bay, Australia). Nine data sets acquired between 2004 and 2013 were used to create seagrass species and percentage cover maps through the integration of seagrass photo transect field data, and atmospherically and geometrically corrected high spatial resolution satellite image data (WorldView-2, IKONOS and Quickbird-2) using an object based image analysis approach. Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species. Maps and summary plots identified inter- and intra-annual variation of seagrass species composition, percentage cover level and above ground biomass. The methods provide a rigorous approach for field and image data collection and pre-processing, a semi-automated approach to extract seagrass species and cover maps and assess accuracy, and the subsequent empirical modelling of seagrass biomass. The resultant maps provide a fundamental data set for understanding landscape scale seagrass dynamics in a shallow water environment. Our findings provide proof of concept for the use of time-series analysis of remotely sensed seagrass products for use in seagrass ecology and management.

Hydrochemical Atlas of the Arctic Ocean

Introduction: Chemical composition of water determines its physical properties and character of processes proceeding in it: freezing temperature, volume of evaporation, density, color, transparency, filtration capacity, etc. Presence of chemical elements in water solution confers waters special physical properties exerting significant influence on their circulation, creates necessary conditions for development and inhabitance of flora and fauna, and imparts to the ocean waters some chemical features that radically differ them from the land waters (Alekin & Liakhin, 1984). Hydrochemical information helps to determine elements of water circulation, convection depth, makes it easier to distinguish water masses and gives additional knowledge of climatic variability of ocean conditions. Hydrochemical information is a necessary part of biological research. Water chemical composition can be the governing characteristics determining possibility and limits of use of marine objects, both stationary and moving in sea water. Subject of investigation of hydrochemistry is study of dynamics of chemical composition, i.e. processes of its formation and hydrochemical conditions of water bodies (Alekin & Liakhin 1984). The hydrochemical processes in the Arctic Ocean are the least known. Some information on these processes can be obtained in odd publications. A generalizing study of hydrochemical conditions in the Arctic Ocean based on expeditions conducted in the years 1948-1975 has been carried out by Rusanov et al. (1979). The "Atlas of the World Ocean: the Arctic Ocean" contains a special section "Hydrochemistry" (Gorshkov, 1980). Typical vertical profiles, transects and maps for different depths - 0, 100, 300, 500, 1000, 2000, 3000 m are given in this section for the following parameters: dissolved oxygen, phosphate, silicate, pH and alkaline-chlorine coefficient. The maps were constructed using the data of expeditions conducted in the years 1948-1975. The illustrations reflect main features of distribution of the hydrochemical elements for multi-year period and represent a static image of hydrochemical conditions. Distribution of the hydrochemical elements on the ocean surface is given for two seasons - winter and summer, for the other depths are given mean annual fields. Aim of the present Atlas is description of hydrochemical conditions in the Arctic Ocean on the basis of a greater body of hydrochemical information for the years 1948-2000 and using the up-to-date methods of analysis and electronic forms of presentation of hydrochemical information. The most wide-spread characteristics determined in water samples were used as hydrochemical indices. They are: dissolved oxygen, phosphate, silicate, pH, total alkalinity, nitrite and nitrate. An important characteristics of water salt composition - "salinity" has been considered in the Oceanographic Atlas of the Arctic Ocean (1997, 1998). Presentation of the hydrochemical characteristics in this Hydrochemical Atlas is wider if compared with that of the former Atlas (Gorshkov, 1980). Maps of climatic distribution of the hydrochemical elements were constructed for all the standard depths, and seasonal variability of the hydrochemical parameters is given not only for the surface, but also for the underlying standard depths up to 400 m and including. Statistical characteristics of the hydrochemical elements are given for the first time. Detailed accuracy estimates of initial data and map construction are also given in the Atlas. Calculated values of mean-root deviations, maximum and minimum values of the parameters demonstrate limits of their variability for the analyzed period of observations. Therefore, not only investigations of chemical statics are summarized in the Atlas, but also some elements of chemical dynamics are demonstrated. Digital arrays of the hydrochemical elements obtained in nodes of a regular grid are the new form of characteristics presentation in the Atlas. It should be mentioned that the same grid and the same boxes were used in the Atlas, as those that had been used by creation of the US-Russian climatic Oceanographic Atlas. It allows to combine hydrochemical and oceanographic information of these Atlases. The first block of the digital arrays contains climatic characteristics calculated using direct observational data. These climatic characteristics were not calculated in the regions without observations, and the information arrays for these regions have gaps. The other block of climatic information in a gridded form was obtained with the help of objective analysis of observational data. Procedure of the objective analysis allowed us to obtain climatic estimates of the hydrochemical characteristics for the whole water area of the Arctic Ocean including the regions not covered by observations. Data of the objective analysis can be widely used, in particular, in hydrobiological investigations and in modeling of hydrochemical conditions of the Arctic Ocean. Array of initial measurements is a separate block. It includes all the available materials of hydrochemical observations in the form, as they were presented in different sources. While keeping in mind that this array contains some amount of perverted information, the authors of the Atlas assumed it necessary to store this information in its primary form. Methods of data quality control can be developed in future in the process of hydrochemical information accumulation. It can be supposed that attitude can vary in future to the data that were rejected according to the procedure accepted in the Atlas. The hydrochemical Atlas of the Arctic Ocean is the first specialized and electronic generalization of hydrochemical observations in the Arctic Ocean and finishes the program of joint efforts of Russian and US specialists in preparation of a number of atlases for the Arctic. The published Oceanographic Atlas (1997, 1998), Atlas of Arctic Meteorology and Climate (2000), Ice Atlas of the Arctic Ocean prepared for publication and Hydrochemical Atlas of the Arctic Ocean represent a united series of fundamental generalizations of empirical knowledge of Arctic Ocean nature at climatic level. The Hydrochemical Atlas of the Arctic Ocean was elaborated in the result of joint efforts of the SRC of the RF AARI and IARC. Dr. Ye. Nikiforov was scientific supervisor of the Atlas, Dr. R. Colony was manager on behalf of the USA and Dr. L. Timokhov - on behalf of Russia.

Benthic and substrate cover data derived from a time series of photo-transect surveys for the Eastern Banks, Moreton Bay Australia, 2004-2015

This paper describes seagrass species and percentage cover point-based field data sets derived from georeferenced photo transects. Annually or biannually over a ten year period (2004-2015) data sets were collected using 30-50 transects, 500-800 m in length distributed across a 142 km**2 shallow, clear water seagrass habitat, the Eastern Banks, Moreton Bay, Australia. Each of the eight data sets include seagrass property information derived from approximately 3000 georeferenced, downward looking photographs captured at 2-4 m intervals along the transects. Photographs were manually interpreted to estimate seagrass species composition and percentage cover (Coral Point Count excel; CPCe). Understanding seagrass biology, ecology and dynamics for scientific and management purposes requires point-based data on species composition and cover. This data set, and the methods used to derive it are a globally unique example for seagrass ecological applications. It provides the basis for multiple further studies at this site, regional to global comparative studies, and, for the design of similar monitoring programs elsewhere.

Habitat map of Danajon Bank, Philippines, derived from a high-spatial-resolution multi-spectral satellite image and georeferenced point intercept transect and spot-check survey field data, using an object-based image classification method

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.

Morphometrics of seagrasses at species level, Moreton Bay, Australia determined from core samples collected in 2012-2013

Seagrass meadows are important marine carbon sinks, yet they are threatened and declining worldwide. Seagrass management and conservation requires adequate understanding of the physical and biological factors determining carbon content in seagrass sediments. Here, we identified key factors that influence carbon content in seagrass meadows across several environmental gradients in Moreton Bay, SE Queensland. Sampling was conducted in two regions: (1) Canopy Complexity, 98 sites on the Eastern Banks, where seagrass canopy structure and species composition varied while turbidity was consistently low; and (2) Turbidity Gradient, 11 locations across the entire bay, where turbidity varied among sampling locations. Sediment organic carbon content and seagrass structural complexity (shoot density, leaf area, and species specific characteristics) were measured from shallow sediment and seagrass biomass cores at each location, respectively. Environmental data were obtained from empirical measurements (water quality) and models (wave height). The key factors influencing carbon content in seagrass sediments were seagrass structural complexity, turbidity, water depth, and wave height. In the Canopy Complexity region, carbon content was higher for shallower sites and those with higher seagrass structural complexity. When turbidity varied along the Turbidity Gradient, carbon content was higher at sites with high turbidity. In both regions carbon content was consistently higher in sheltered areas with lower wave height. Seagrass canopy structure, water depth, turbidity, and hydrodynamic setting of seagrass meadows should therefore be considered in conservation and management strategies that aim to maximize sediment carbon content.

Faecal pellet production of copepoda collected in water depth up to 30 meters in the eastern Mediterranean Sea in Oktober 2008 during SES_GR2

Mesozooplankton is collected by vertical tows within the Black sea water body mass layer in the NE Aegean, using a WP-2 200 µm net equipped with a large non-filtering cod-end (10 l). Macrozooplankton organisms are removed using a 2000 µm net. A few unsorted animals (approximately 100) are placed inside several glass beaker of 250 ml filled with GF/F or 0.2 µm Nucleopore filtered seawater and with a 100 µm net placed 1 cm above the beaker bottom. Beakers are then placed in an incubator at natural light and maintaining the in situ temperature. After 1 hour pellets are separated from animals and placed in separated flasks and preserved with formalin. Pellets are counted and measured using an inverted microscope. Animals are scanned and counted using an image analysis system. Carbon- Specific faecal pellet production is calculated from a) faecal pellet production, b) individual carbon: Animals are scanned and their body area is measured using an image analysis system. Body volume is then calculated as an ellipsoid using the major and minor axis of an ellipse of same area as the body. Individual carbon is calculated from a carbon- total body volume of organisms (relationship obtained for the Mediterranean Sea by Alcaraz et al. (2003) divided by the total number of individuals scanned and c) faecal pellet carbon: Faecal pellet length and width is measured using an inverted microscope. Faecal pellet volume is calculated from length and width assuming cylindrical shape. Conversion of faecal pellet volume to carbon is done using values obtained in the Mediterranean from: a) faecal pellet density 1,29 g cm**3 (or pg µm**3) from Komar et al. (1981); b) faecal pellet DW/WW=0,23 from Elder and Fowler (1977) and c) faecal pellet C%DW=25,5 Marty et al. (1994).

Mass estimates and morphometric meassurements of Discoaster species and Sphenolithus from Ceara Rise ODP Site 927

Mass estimates for Late Miocene and Pliocene (8.6-3.25 Ma) Discoaster species and Sphenolithus are determined using samples of the equatorial Atlantic (Ceara Rise: ODP Site 927). Based on morphometric measurements, 3D computer models were created for 11 Discoaster species and their volumes calculated. From these, shape factors (ks) were derived to allow calculation of mass for different-sized discoasters and Sphenolithus abies. The mass estimates were then used to calculate the contribution of nannofossils to the total nannofossil carbonate. The discoaster contribution ranges from 10% to 40%, with a decreasing trend through the investigated interval. However, our estimates of total nannofossil carbonate from size-corrected abundance data are consistently 30-50% lower than estimates from grain-size measurement; this suggests that data based on mass estimates need to be interpreted with caution.

Abundance and biomass of phytoplankton in the western part of the Black Sea in September 1991 during the NATO Programme Hydroblack

The samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS-BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective - 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000). Total phytoplankton abundance was calculated as sum of taxon-specific abundances. Total phytoplankton biomass was calculated as sum of taxon-specific biomasses. The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).