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1. The scale of the whale: using video-tag data to evaluate sea-surface ice concentration from the perspective of individual Antarctic minke whales

   https://doi.org/10.1186/s40317-020-00218-8  

   Linsky, Jacob M. ; Wilson, Nicole ; Cade, David E. ; Goldbogen, Jeremy A. ; Johnston, David W. ; Friedlaender, Ari S. ( December 2020 , Animal Biotelemetry)

   Abstract Background Advances in biologging technology allow researchers access to previously unobservable behavioral states and movement patterns of marine animals. To relate behaviors with environmental variables, features must be evaluated at scales relevant to the animal or behavior. Remotely sensed environmental data, collected via satellites, often suffers from the effects of cloud cover and lacks the spatial or temporal resolution to adequately link with individual animal behaviors or behavioral bouts. This study establishes a new method for remotely and continuously quantifying surface ice concentration (SIC) at a scale relevant to individual whales using on-animal tag video data. Results Motion-sensing and video-recording suction cup tags were deployed on 7 Antarctic minke whales ( Balaenoptera bonaerensis ) around the Antarctic Peninsula in February and March of 2018. To compare the scale of camera-tag observations with satellite imagery, the area of view was simulated using camera-tag parameters. For expected conditions, we found the visible area maximum to be ~ 100m 2 which indicates that observations occur at an equivalent or finer scale than a single pixel of high-resolution visible spectrum satellite imagery. SIC was classified into one of six bins (0%, 1–20%, 21–40%, 41–60%, 61–80%, 81–100%) by two independent observers for the initial and final surfacing between dives. In the event of a disagreement, a third independent observer was introduced, and the median of the three observer’s values was used. Initial results ( n  = 6) show that Antarctic minke whales in the coastal bays of the Antarctic Peninsula spend 52% of their time in open water, and only 15% of their time in water with SIC greater than 20%. Over time, we find significant variation in observed SIC, indicating that Antarctic minke occupy an extremely dynamic environment. Sentinel-2 satellite-based approaches of sea ice assessment were not possible because of persistent cloud cover during the study period. Conclusion Tag-video offers a means to evaluate ice concentration at spatial and temporal scales relevant to the individual. Combined with information on underwater behavior, our ability to quantify SIC continuously at the scale of the animal will improve upon current remote sensing methods to understand the link between animal behavior and these dynamic environmental variables. 

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2. A Near‐Term Iterative Forecasting System Successfully Predicts Reservoir Hydrodynamics and Partitions Uncertainty in Real Time

   https://doi.org/10.1029/2019WR026138  

   Thomas, R. Quinn ; Figueiredo, Renato J. ; Daneshmand, Vahid ; Bookout, Bethany J. ; Puckett, Laura K. ; Carey, Cayelan C. ( November 2020 , Water Resources Research)

   Freshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real‐time iterative water temperature forecasting system (FLARE—Forecasting Lake And Reservoir Ecosystems). FLARE is composed of water temperature and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble‐based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475‐day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean square error (RMSE) of daily forecasted water temperatures was 1.13°C at the reservoir's near‐surface (1.0 m) for 7‐day ahead forecasts and 1.62°C for 16‐day ahead forecasts. The RMSE of forecasted water temperatures at the near‐sediments (8.0 m) was 0.87°C for 7‐day forecasts and 1.20°C for 16‐day forecasts. FLARE successfully predicted the onset of fall turnover 4–14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near‐sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open‐source system for lake and reservoir water quality forecasting to improve real‐time management.

    

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3. Time series of high-frequency sensor data measuring water temperature, dissolved oxygen, conductivity, specific conductance, total dissolved solids, chlorophyll a, phycocyanin, fluorescent dissolved organic matter, and turbidity at discrete depths in Beaverdam Reservoir, Virginia, USA in 2016-2022

   https://doi.org/10.6073/pasta/4182de376fde52e15d493fdd9f26d0c7  

   Carey, Cayelan C. ; Breef-Pilz, Adrienne ; Bookout, Bethany J. ; McClure, Ryan P. ; Wynne, Jacob H. ( February 2023 , Environmental Data Initiative)

   We monitored water quality in Beaverdam Reservoir (Vinton, Virginia, USA, 37.31288, -79.8159) with high-frequency (10-minute and 15-minute) sensors in 2016-2022. All variables were measured at the deepest site of the reservoir adjacent to the dam. Beaverdam Reservoir is owned and managed by the Western Virginia Water Authority as a secondary drinking water source for Roanoke, Virginia. This data package is comprised of 2 data sets: BVR_sensor_string_2016_2020.csv and BVR_platform_data_2020_2022.csv. BVR_sensor_string_2016_2022.csv consists of a temperature profile at ~1-meter intervals from the surface of the reservoir to 10.5 m below the water, complemented by a dissolved oxygen logger at 5 m or 10 m depending on the time of year. A sonde measuring temperature, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, dissolved oxygen, fluorescent dissolved organic matter, and turbidity was deployed at ~1.5 m depth. This initial data set spans 2016 to 2020, with no additional data collection beyond the last observation. The second data set is BVR_platform_data_2020_2022.csv, with data collection still ongoing. This data set contains 1) a temperature string with 13 temperature sensors ~1 m apart from the surface to 0.5 m above the sediments of the reservoir; 2) two oxygen sensors, one in the middle of the string and one sensor above the sediments; and 3) a pressure sensor just above the sediments. The same sonde from the first 2016-2020 data set is also included in this 2020-2022 data set, deployed at 1.5 m below the surface. The temperature string with the thermistors, dissolved oxygen sensor, and pressure sensor are permanently fixed to the platform and water level changes around them. In the methods we describe how to add a depth measurement to each observation. 

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4. Studying animal locomotion with multiple data loggers: quantifying time drift between tags

   https://doi.org/10.1186/s40317-024-00363-4  

   White, Connor F. ; Lauder, George V. ( April 2024 , Animal Biotelemetry)

   Temporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h⁻¹) than the variance between tags (σ = 0.015 s·h⁻¹), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h⁻¹) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

    

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5. High‐resolution measurements of the epipelagic and mesopelagic ocean by a profiling vehicle equipped with environmental sensors and a broadband echosounder

   https://doi.org/10.1002/lom3.10532  

   Grassian, Benjamin ; Roman, Christopher ; Warren, Joseph D. ; Casagrande, Dave ( January 2023 , Limnology and Oceanography: Methods)

   Collecting detailed surveys of the environmental and biological distributions in the epipelagic and mesopelagic ocean is important for understanding the basic processes that govern these expansive habitats and influence the earth system at large. Common ocean sampling platforms (e.g., net systems, moored, and shipboard sensors), are often unable to resolve marine biota at scales comparable to the variability associated with their own behavior or that of their physical environment. Newer approaches using mobile robotic systems carrying multiple environmental sensors have enabled detailed interrogation of the fine and sub‐mesoscale distribution of animals and have provided more context for the water column structure. We integrated a dual‐frequency broadband split‐beam echosounder (Simrad EK80 with 70 and 200 kHz transducers) into the Wire Flyer profiling vehicle to achieve concurrent hydrographic and acoustic sections in the midwater environment (0–1000 m) at novel scales. The Wire Flyer provides high‐resolution repeat profiling (0–2.5 m s⁻¹up and down velocity) within specified water column depth bands typically spanning 300–400 m. This system can provide acoustic backscatter data at depths unavailable to shipboard surveys due to attenuation limits and can be operated in tandem with conventional shipboard echosounders to provide overlapping acoustic coverage with concurrent hydrographic sections. The side‐looking transducer orientation, as opposed to the traditional vertically oriented arrangement on ships, samples orthogonal to the vehicle's profiling survey path and provides a direct measurement of animal distributions in the horizontal. The processed data have demonstrated the system's capacity to track migrating layers and resolve coherent biological patches and single targets in the horizontal, rising seafloor gas plumes, and scattering layer distributions tightly coupled to measured sub‐mesoscale features such as strong vertical oxygen gradients.

    

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