Description: Multiple investigators often generate data from seabed images within a single image set to reduce the time burden, particularly with the large photographic surveys now available to ecological studies. These data (annotations) are known to vary as a result of differences in investigator opinion on specimen classification and of human factors such as fatigue and cognition. These variations are rarely recorded or quantified, nor are their impacts on derived ecological metrics (density, diversity, composition). We compared the annotations of 3 investigators of 73 megafaunal morphotypes in ~28 000 images, including 650 common images. Successful annotation was defined as both detecting and correctly classifying a specimen. Estimated specimen detection success was 77%, and classification success was 95%, giving an annotation success rate of 73%. Specimen detection success varied substantially by morphotype (12-100%). Variation in the detection of common taxa resulted in significant differences in apparent faunal density and community composition among investigators. Such bias has the potential to produce spurious ecological interpretations if not appropriately controlled or accounted for. We recommend that photographic studies document the use of multiple annotators and quantify potential inter-investigator bias. Randomisation of the sampling unit (photograph or video clip) is clearly critical to the effective removal of human annotation bias in multiple annotator studies (and indeed single annotator works).

Description: Digital imaging is gaining more and more attention in marine environmental monitoring and exploration. Nowadays, mobile platforms such as autonomous underwater vehicles (AUVs) are equipped with high-resolution cameras that can collect gigabytes of digital images in a single dive. To extract quantitative and qualitative information from the accumulating image collections, annotation tools such as BIIGLE 2.0 have been proposed recently and have been established in the data analysis workflow. These tools run as web applications on a central server and can be accessed worldwide via the Internet. However, marine science and engineering are naturally associated with a high degree of mobility and, in some cases, limited resources or Internet access. Here we present a new application architecture for BIIGLE 2.0, which is particularly suitable for offshore deployment on a variety of platforms such as a server, workstation, laptop or even a small single-board computer such as a Raspberry Pi. We refer to the application architecture in combination with a mobile hardware platform as "BIIGLE2Go", which addresses the need for more flexibility and mobility in image annotation. We present and evaluate a first prototype for BIIGLE2Go, which runs as a mobile annotation system on a low-cost Raspberry Pi 3B.

Description: Cold-water coral (CWC) reefs are heterogeneous ecosystems comprising numerous microhabitats. A typical European CWC reef provides various biogenic microhabitats (within, on and surrounding colonies of coral species such as Lophelia pertusa, Paragorgia arborea and Primnoa resedaeformis, or formed by their remains after death). These microhabitats may be surrounded and intermixed with non-biogenic microhabitats (soft sediment, hard ground, gravel/pebbles, steep walls). To date, studies of distribution of sessile fauna across CWC reefs have been more numerous than those investigating mobile fauna distribution. In this study we quantified shrimp densities associated with key CWC microhabitat categories at the Røst Reef, Norway, by analysing image data collected by towed video sled in June 2007. We also investigated shrimp distribution patterns on the local scale (〈40 cm) and how these may vary with microhabitat. Shrimp abundances at the Røst Reef were on average an order of magnitude greater in biogenic reef microhabitats than in non-biogenic microhabitats. Greatest shrimp densities were observed in association with live Paragorgia arborea microhabitat (43 shrimp m−2, SD = 35.5), live Primnoa resedaeformis microhabitat (41.6 shrimp m−2, SD = 26.1) and live Lophelia pertusa microhabitat (24.4 shrimp m−2, SD = 18.6). In non-biogenic microhabitat, shrimp densities were 〈2 shrimp m−2. CWC reef microhabitats appear to support greater shrimp densities than the surrounding non-biogenic microhabitats at the Røst Reef, at least at the time of survey.

Description: Detecting objects in underwater image sequences and video frames automatically, requires the application of selected algorithms in consecutive steps. Most of these algorithms are controlled by a set of parameters, which need to be calibrated for an optimal detection result. Those parameters determine the effectivity and efficiency of an algorithm and their impact is usually well known. There are however further non-algorithmic impact factors (or hidden parameters), which bias the training of a machine learning system as well as the subsequent detection process and thus need to be well understood and taken into account. In the context of megafauna detection in benthic images, we investigate the effects of some of these parameters on our machine learning based detection system iSIS. The images to be analyzed were taken at the deep-sea, long-term observatory HAUSGARTEN in which five experts labeled seven distinct object classes as an annotation gold standard. We found, that the hidden parameters from imaging as well as the fusion of expert knowledge could partly be compensated and were able to achieve detection performances of 0.67 precision and 0.87 recall. Despite the efforts to compensate the hidden parameters, the detection performance was still varying across the image transect. This poses the potential occurrence of further hidden parameters not taken into account so far.

Description: Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization. Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions. Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de . A login is required but freely available. Contact: nkessler@cebitec.uni-bielefeld.de

Description: Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N -dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application. Results: We applied WHIDE to a set of MBI recorded using the multitag fluorescence imaging Toponome Imaging System. The MBI show field of view in tissue sections from a colon cancer study and we compare tissue from normal/healthy colon with tissue classified as tumor. Our results show, that WHIDE efficiently reduces the complexity of the data by mapping each of the pixels to a cluster, referred to as Molecular Co-Expression Phenotypes and provides a structural basis for a sophisticated multimodal visualization, which combines topology preserving pseudocoloring with information visualization. The wide range of WHIDE's applicability is demonstrated with examples from toponome imaging, high content screens and MALDI imaging (shown in the Supplementary Material ). Availability and implementation: The WHIDE tool can be accessed via the BioIMAX website http://ani.cebitec.uni-bielefeld.de/BioIMAX/ ; Login: whidetestuser; Password: whidetest . Supplementary information: Supplementary data are available at Bioinformatics online. Contact: tim.nattkemper@uni-bielefeld.de

Description: Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004High latitudes are amongst the most sensitive ecosystems with respect to climate change, which prompted the launch of the first and only deep-sea long-term observatory beyond the polar circle, HAUSGARTEN (eastern Fram Strait), in 1999. An understanding of the abundance and spatial distribution of organisms is vital to assess the effects of global change. To map the distribution of megafaunal organisms deep-sea research relies strongly on the use of high-resolution cameras that are fitted to towed sledges, drop-down frames and remotely operated or autonomous underwater vehicles. Inevitably, such techniques generate large quantities of footage. The visual analysis of images is labour-intensive, time-consuming and subjective. Over recent years, the increase in computer power has facilitated advances to automate image analysis. Here, we present a novel approach for the automatic detection and classification of particular biological classes within image data. For this purpose, we applied machine-learning algorithms to two photographic transects from the HAUSGARTEN central station (2500m) taken by an ocean floor observation system in 2002 and 2004. Each transect contains some 700 photographs. The main goal is the automatic identification of important biological classes (e.g. sea cucumbers, sea lilies) to assess the densities of the most frequent organisms over time. So far, our system shows a promising performance in detecting sea cucumbers and sea lilies with sensitivities and positive predictive values between 75 - 80%.Results from manual analysis of 66 images taken at the central part of the transect indicate a significant decline in the mean density of sea cucumbers (Elpidia glacialis), sea lilies (Bathycrinus cf. carpenteri), burrow entrances and total megafaunal densities from 2002 to 2004 which concurs with a decrease in sea ice coverage, particulate flux to the sea floor, sediment-bound nutrients and pigments, microbial biomass and changes in meiofaunal community structure. Results from automated image analysis will increase the spatial resolution and statistical power of our analyses as it enables us to process larger quantities of images.

Description: Deep seafloor communities, especially those from the ice-covered Arctic, are subject to severe food limitation as the amount of particulate organic matter (POM) from the surface is attenuated with increasing depth. Here, we use naturally occurring stable isotope tracers (δ15N) to broaden our rudimentary knowledge of food web structure and the response of benthic organisms to decreasing food supplies along the bathymetric transect of the deep-sea observatory HAUSGARTEN. Encompassing five trophic levels, the HAUSGARTEN food web is among the longest indicating continuous recycling of organic material typical of food-limited deep-sea ecosystems. The δ15N signatures ranged from 3.0 for Foraminifera to 21.4 (±0.4) for starfish (Poraniomorpha tumida). The majority of organisms occupied the second and third trophic level. Demersal fish fed at the third trophic level, consistent with results from stomach contents analysis. There were significant differences in the δ15N signatures of different functional groups with highest δ15N values in predators/scavengers (13.2 ± 0.2) followed by suspension feeders (11.2 ± 0.2) and deposit feeders (10.2 ± 0.3). Depth (=increasing food limitation) affected functional groups in different ways. While the isotopic signatures of predators/ scavengers did not change, those of suspension feeders increased with depth, and the reverse was found for deposit feeders. In contrast to the results of other studies, the δ15N signatures in POM samples obtained below 800 m did not vary significantly with depths indicating that changes in δ15N values could not be held responsible for the depth-related δ15N signature changes observed for benthic consumers. However, the δ15N signatures of sediments decreased with increasing depth, which also explains the decrease found for deposit feeders. Suspension feeders may rely increasingly on particles trickling down the HAUSGARTEN slope and carrying higher δ15N signatures than the decreasing POM supplies, which elevates the δ15N value of their tissues. Our results imply that a depth-stratified approach should be taken to avoid a misinterpretation of data obtained at different depths.