Temporal and regional variability in the skin microbiome of humpback whales along the Western Antarctic Peninsula

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Applied and Environmental Microbiology 84 (2018): e02574-17, doi:10.1128/AEM.02574-17.The skin is the first line of defense between an animal and its environment, and disruptions in skin-associated microorganisms can be linked to an animal's health and nutritional state. To better understand the skin microbiome of large whales, high-throughput sequencing of partial small subunit ribosomal RNA genes was used to study the skin-associated bacteria of 89 seemingly healthy humpback whales (Megaptera novaeangliae) sampled along the Western Antarctic Peninsula (WAP) during early (2010) and late (2013) austral summers. Six core genera of bacteria were present in 93% or more of all humpback skin samples. A shift was observed in the average relative abundance of these core genera over time, with the emergence of four additional core genera corresponding to a decrease in water temperature, possibly caused by seasonal or foraging related changes in skin biochemistry that influenced microbial growth, or other temporal-related factors. The skin microbiome differed between whales sampled at several regional locations along the WAP, suggesting that environmental factors or population may also influence the whale skin microbiome. Overall, the skin microbiome of humpback whales appears to provide insight into animal and environmental-related factors and may serve as a useful indicator for animal health or ecosystem alterations.This project was supported by 67 donors to the “Whale Bacterial Buddies” crowdfunded project supported by WHOI, the Edna Bailey Sussman Fund, and the Michael K. Orbach Enrichment Fund awarded to K. C. Bierlich

CNN Training Imagery and Labels Subset 2

This zip file includes the second subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network

CNN Validation Imagery and Labels

This zip file includes the validation images used for training this CNN. The label file for all validation data is included. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network

Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry

The flourishing application of drones within marine science provides more opportunity to conduct photogrammetric studies on large and varied populations of many different species. While these new platforms are increasing the size and availability of imagery datasets, established photogrammetry methods require considerable manual input, allowing individual bias in techniques to influence measurements, increasing error and magnifying the time required to apply these techniques. Here, we introduce the next generation of photogrammetry methods utilizing a convolutional neural network to demonstrate the potential of a deep learning‐based photogrammetry system for automatic species identification and measurement. We then present the same data analysed using conventional techniques to validate our automatic methods. Our results compare favorably across both techniques, correctly predicting whale species with 98% accuracy (57/58) for humpback whales, minke whales, and blue whales. Ninety percent of automated length measurements were within 5% of manual measurements, providing sufficient resolution to inform morphometric studies and establish size classes of whales automatically. The results of this study indicate that deep learning techniques applied to survey programs that collect large archives of imagery may help researchers and managers move quickly past analytical bottlenecks and provide more time for abundance estimation, distributional research, and ecological assessments

CNN Test Imagery and Labels

This zip file includes the test images used for reported metrics along with the via_region_data.json label file that has the mask shapes in JSON format as needed for the neural network. Species specific .json files are also included

CNN Training Imagery and Labels Subset 1

This zip file includes the first subset of training images used for training this CNN. This data is only subset for ease of upload and download but should be combined by the user on their local machine. The full label file for all training data is included with both subsets and is identical. That file "via_region_data.json" has the mask shapes in and species IDs in a JSON format as needed for the neural network