FishSizer: Software solution for efficiently measuring larval fish size

Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter- and intra-observer variability. We developed an open-source software solution to semi-automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast-based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy.publishedVersio

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

publishedVersio

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.publishedVersionPaid Open Acces

Classification of pig calls produced from birth to slaughter according to their emotional valence and context of production

Vocal expression of emotions has been observed across species and could provide a non-invasive and reliable means to assess animal emotions. We investigated if pig vocal indicators of emotions revealed in previous studies are valid across call types and contexts, and could potentially be used to develop an automated emotion monitoring tool. We performed an analysis of an extensive and unique dataset of low (LF) and high frequency (HF) calls emitted by pigs across numerous commercial contexts from birth to slaughter (7414 calls from 411 pigs). Our results revealed that the valence attributed to the contexts of production (positive versus negative) affected all investigated parameters in both LF and HF. Similarly, the context category affected all parameters. We then tested two different automated methods for call classification; a neural network revealed much higher classification accuracy compared to a permuted discriminant function analysis (pDFA), both for the valence (neural network: 91.5%; pDFA analysis weighted average across LF and HF (cross-classified): 61.7% with a chance level at 50.5%) and context (neural network: 81.5%; pDFA analysis weighted average across LF and HF (cross-classified): 19.4% with a chance level at 14.3%). These results suggest that an automated recognition system can be developed to monitor pig welfare on-farm.publishedVersio

Applying artificial intelligence methods to detect and classify fish calls from the northern gulf of mexico

Passive acoustic monitoring is a method that is commonly used to collect long-term data on soniferous animal presence and abundance. However, these large datasets require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract points of interest. In this study, an energy detector and subsequent pre-trained neural network were used to detect and classify six fish call types from a long-term dataset collected in the northern Gulf of Mexico. The development of this two-step methodology and its performance are the focus of this paper. The energy detector by itself had a high recall rate (>84%), but very low precision; however, a subsequent neural network was used to classify detected signals and remove noise from the detections. Image augmentation and iterative training were used to optimize classification and compensate for the low number of training images for two call types. The classifier had a relatively high average overall accuracy (>87%), but classifier average recall and precision varied greatly for each fish call type (recall: 39–91%; precision: 26–94%). This coupled methodology expedites call extraction and classification and can be applied to other datasets that have multiple, highly variable calls

Applying artificial intelligence methods to detect and classify fish calls from the northern gulf of Mexico

publishedVersio

FishSizer: Software solution for efficiently measuring larval fish size

Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter- and intra-observer variability. We developed an open-source software solution to semi-automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast-based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy

Geographically distinct blue whale song variants in the Northeast Pacific

The Northeast Pacific (NEP) population of blue whales Balaenoptera musculus musculus is currently managed as a single stock. We investigated the fine-scale frequency characteristics of 1 NEP blue whale song unit, the B call. We analyzed B calls from passive acoustic data collected between 2010 and 2013 at 2 low-latitude sites, Palmyra Atoll and the Hawaiian Islands, and 3 higher-latitude sites, off southern California, off Washington state and in the Gulf of Alaska. Frequency measurements were extracted along the contour of the third harmonic from each call, and data from each region were compared. Calls from the Gulf of Alaska and Hawai‘i presented a downshift in frequency, beginning just past the midway point of the contour, which was not present in calls recorded from southern California or Palmyra Atoll. Calls from Washington displayed intermediate characteristics between those from the other 2 high-latitude sites. Cluster analysis resulted in consistent grouping of call contours from Washington and southern California, in what we termed the NEP B1 variant, while contours from Hawai‘i and the Gulf of Alaska were grouped together, as a NEP B2 variant. Frequency differences were also observed among the variants; the Gulf of Alaska displayed the highest frequency on average, followed by Washington, then south ern California. Consistent with other studies, a yearly decline in the frequency of B calls was also observed. This discovery of at least 2 geographically distinct variants provides the first evidence of vocally distinct subpopulations within the NEP, indicating the possibility of a need for finer scale population segmentation.HARP data collection was supported by the Chief of Naval Operations CNO-N45 (Frank Stone, Ernie Young and Bob Gisiner) and the US Pacific Fleet Environ mental Monitoring Program (Chip Johnson). Data from ACO were provided by James Potemra, Bruce Howe and Fernando Santiago-Mandujano from the University of Hawai‘i. ACO was supported by research and instrumentation grants from the National Science Foundation to the University of Hawai‘i. A.C.R. was funded by a Texas A&M University at Galveston Graduate Fellowship

FishSizer: Software solution for efficiently measuring larval fish size

Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter- and intra-observer variability. We developed an open-source software solution to semi-automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast-based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy

FishSizer: Software solution for efficiently measuring larval fish size

Length and depth of fish larvae are part of the fundamental measurements in many marine ecology studies involving early fish life history. Until now, obtaining these measurements has required intensive manual labor and the risk of inter- and intra-observer variability. We developed an open-source software solution to semi-automate the measurement process and thereby reduce both time consumption and technical variability. Using contrast-based edge detection, the software segments images of a fish larva into “larva” and “background.” Length and depth are extracted from the “larva” segmentation while taking curvature of the larva into consideration. The graphical user interface optimizes workflow and ease of usage, thereby reducing time consumption for both training and analysis. The software allows for visual verification of all measurements. A comparison of measurement methods on a set of larva images showed that this software reduces measurement time by 66%–78% relative to commonly used software. Using this software instead of the commonly used manual approach has the potential to save researchers from many hours of monotonous work. No adjustment was necessary for 89% of the images regarding length (70% for depth). Hence, the only workload on most images was the visual inspection. As the visual inspection and manual dimension extraction works in the same way as currently used software, we expect no loss in accuracy