Aquatic animal telemetry: A panoramic window into the underwater world

Hussey, Nigel E.; Kessel, Steven T.; Aarestrup, Kim; Cooke, Steven J.; Cowley, Paul D.; Fisk, Aaron T.; Harcourt, Robert; Holland, Kim N.; Iverson, Sara J.; Kocik, John F.; Flemming, Joanna Mills; Whoriskey, Fred

doi:10.1126/science.1255642

Cited by 1,085 publications

(981 citation statements)

References 100 publications

Supporting

Mentioning

973

Contrasting

Order By: Relevance

“…Conservation biology is entering a new era of innovation, with unprecedented growth across a range of techniques, from genetics and genomics to telemetry and remote sensing (Allendorf et al, 2010;Hussey et al, 2015). Rapid advances in the technology underpinning Unmanned Aerial Vehicles (UAVs also known as Unmanned Aircraft Systems or drones), are driving new and innovative environmental applications (Koh and Wich, 2012;Anderson and Gaston, 2013;Christie et al, 2016;Smith et al, 2016;Duffy et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Economical Custom-Built Drone for Assessing Whale Health

Pirotta

Smith²,

Ostrowski

et al. 2017

Front. Mar. Sci.

View full text Add to dashboard Cite

Drones or Unmanned Aerial Vehicles (UAVs) have huge potential to improve the safety and efficiency of sample collection from wild animals under logistically challenging circumstances. Here we present a method for surveying population health that uses UAVs to sample respiratory vapor, 'whale blow,' exhaled by free-swimming humpback whales (Megaptera novaeangliae), and coupled this with amplification and sequencing of respiratory tract microbiota. We developed a low-cost multirotor UAV incorporating a sterile petri dish with a remotely operated 'blow' to sample whale blow with minimal disturbance to the whales. This design addressed several sampling challenges: accessibility; safety; cost, and critically, minimized the collection of atmospheric and seawater microbiota and other potential sources of sample contamination. We collected 59 samples of blow from northward migrating humpback whales off Sydney, Australia and used high throughput sequencing of bacterial ribosomal gene markers to identify putative respiratory tract microbiota. Model-based comparisons with seawater and dronecaptured air demonstrated that our system minimized external sources of contamination and successfully captured sufficient material to identify whale blow-specific microbial taxa. Whale-specific taxa included species and genera previously associated with the respiratory tracts or oral cavities of mammals (e.g., Pseudomonas, Clostridia, Cardiobacterium), as well as species previously isolated from dolphin or killer whale blowholes (Corynebacteria, others). Many examples of exogenous marine species were identified, including Tenacibaculum and Psychrobacter spp. that have been associated with the skin microbiota of marine mammals and fish and may include pathogens. This information provides a baseline of respiratory tract microbiota profiles of contemporary whale health. Customized UAVs are a promising new tool for marine megafauna research and may have broad application in cost-effective monitoring and management of whale populations worldwide.

show abstract

Section: Introductionmentioning

confidence: 99%

An Economical Custom-Built Drone for Assessing Whale Health

Pirotta

Smith²,

Ostrowski

et al. 2017

Front. Mar. Sci.

View full text Add to dashboard Cite

show abstract

“…Groups of interest for marine spatial planning could include for example specific community-level functional groups (e.g., apex predators, Block et al, 2011), taxonomic groups (e.g., seabirds, Ronconi et al, 2012), species-at-risk (e.g., African penguins Spheniscus demersus, Ludynia et al, 2012), sub-populations (e.g., seabird colonies, Louzao et al, 2011; sea turtle breeding areas, Schofield et al, 2013) or specific life history phases, often divided further by sex (e.g., pupping female white sharks Carcharodon carcharias, Domeier and Nasby-Lucas, 2013). Our ability to study the space use of marine animals belonging to a specific group of-concern continues to expand with innovations in animal-attached biologging devices that record location and other ancillary data (Cooke, 2008;Hussey et al, 2015;Wilson et al, 2015). Importantly, how we use these individual-based data to define space use more broadly for the higher-level group to which the tracked animals belong, influences how we interpret the biological and management implications of the findings.…”

Section: Introductionmentioning

confidence: 99%

Individual-level Variation and Higher-level Interpretations of Space Use in Wide-ranging Species: An Albatross Case Study of Sampling Effects

et al. 2015

View full text Add to dashboard Cite

Marine ecologists and managers need to know the spatial extent of at-sea areas most frequented by the groups of wildlife they study or manage. Defining group-specific ranges and distributions (i.e., space use at the level of species, population, age-class, etc.) can help to identify the source or severity of common or distinct threats among different at-risk groups. In biologging studies, this is accomplished by estimating the space use of a group based on a sample of tracked individuals. A major assumption of these studies is consistency in individual movements among members of a group. The implications of scaling up individual-level tracking data to infer higher-level spatial patterns for groups (i.e., size and extent of areas used, overlap or segregation among groups) is not well documented for wide-ranging pelagic species with high potential for individual variation in space use. We present a case study exploring the effects of sampling (i.e., number and identity of individuals contributing to an analysis) on defining group-specific space use with year-round multi-colony tracking data from two highly vagile species, Laysan (Phoebastria immutabilis) and black-footed (P. nigripes) albatrosses. The results clearly demonstrate that caution is warranted when defining space use for a specific species-colony-period group based on datasets of small, intermediate, or relatively large sample sizes (ranging from n = 3-42 tracked individuals) due to a high degree of individual-level variation in movements. Overall, we provide further support to the recommendation that biologging studies aiming to define higher-level patterns in space use exercise restraint in the scope of inference, particularly when pooled Kernel Density Estimation (KDE) techniques are applied to small datasets for wide-ranging species. Transparent reporting in respect to the potential limitations of the data can in turn better inform both biological interpretations and science-based management decisions.

show abstract

“…Furthermore, increased understanding of the multiscale foraging behavior can yield important information on how the quality of habitat patches and resources vary in time, and can enable insights into the potential effects of habitat changes (Hussey et al. 2015). As we gain data from an increased sample of individual razorbills and guillemots we will prioritize the joint analysis of GPS and accelerometer data.…”

Section: Discussionmentioning

confidence: 99%

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Chimienti

Cornulier

Owen

et al. 2016

Ecology and Evolution

View full text Add to dashboard Cite

The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.

show abstract

Aquatic animal telemetry: A panoramic window into the underwater world

Cited by 1,085 publications

References 100 publications

An Economical Custom-Built Drone for Assessing Whale Health

An Economical Custom-Built Drone for Assessing Whale Health

Individual-level Variation and Higher-level Interpretations of Space Use in Wide-ranging Species: An Albatross Case Study of Sampling Effects

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

Contact Info

Product

Resources

About