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Fleishman E, Cholewiak D, Gillespie D, Helble T, Klinck H, Nosal EM, Roch MA. Ecological inferences about marine mammals from passive acoustic data. Biol Rev Camb Philos Soc 2023; 98:1633-1647. [PMID: 37142263 DOI: 10.1111/brv.12969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023]
Abstract
Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.
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Affiliation(s)
- Erica Fleishman
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, 97331, USA
| | - Danielle Cholewiak
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, MA, 02543, USA
| | - Douglas Gillespie
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 9XL, UK
| | - Tyler Helble
- Naval Information Warfare Center Pacific, San Diego, CA, 92152, USA
| | - Holger Klinck
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA
| | - Eva-Marie Nosal
- Department of Ocean and Resources Engineering, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA
| | - Marie A Roch
- Department of Computer Science, San Diego State University, San Diego, CA, 92182, USA
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2
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Cubaynes HC, Clarke PJ, Goetz KT, Aldrich T, Fretwell PT, Leonard KE, Khan CB. Annotating very high-resolution satellite imagery: A whale case study. MethodsX 2023; 10:102040. [PMID: 36793672 PMCID: PMC9923222 DOI: 10.1016/j.mex.2023.102040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
The use of very high-resolution (VHR) optical satellites is gaining momentum in the field of wildlife monitoring, particularly for whales, as this technology is showing potential for monitoring the less studied regions. However, surveying large areas using VHR optical satellite imagery requires the development of automated systems to detect targets. Machine learning approaches require large training datasets of annotated images. Here we propose a standardised workflow to annotate VHR optical satellite imagery using ESRI ArcMap 10.8, and ESRI ArcGIS Pro 2.5., using cetaceans as a case study, to develop AI-ready annotations.•A step-by-step protocol to review VHR optical satellite images and annotate the features of interest.•A step-by-step protocol to create bounding boxes encompassing the features of interest.•A step-by-step guide to clip the satellite image using bounding boxes to create image chips.
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Affiliation(s)
| | - Penny Joanna Clarke
- British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom.,School of Engineering, The University of Edinburgh, Sanderson Building, Robert Stevenson Road, The King's Buildings, Edinburgh, EH9 3FB, United Kingdom
| | - Kimberly Thea Goetz
- Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA, Seattle, Washington, United States
| | - Tyler Aldrich
- Northeast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole, MA, United States
| | - Peter Thomas Fretwell
- British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, United Kingdom
| | - Kathleen Elise Leonard
- Protected Resources Division, Alaska Regional Office, National Marine Fisheries Service, NOAA, Anchorage, AK, United States
| | - Christin Brangwynne Khan
- Northeast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole, MA, United States
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3
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Ceballos V, Taggart C, Johnson H. Comparison of visual and acoustic surveys for the detection and dynamic management of North Atlantic right whales (
Eubalaena glacialis
) in Canada. CONSERVATION SCIENCE AND PRACTICE 2022. [DOI: 10.1111/csp2.12866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Affiliation(s)
| | | | - Hansen Johnson
- Oceanography Department Dalhousie University Halifax Nova Scotia Canada
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4
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Rodofili EN, Lecours V, LaRue M. Remote sensing techniques for automated marine mammals detection: a review of methods and current challenges. PeerJ 2022; 10:e13540. [PMID: 35757165 PMCID: PMC9220915 DOI: 10.7717/peerj.13540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 01/17/2023] Open
Abstract
Marine mammals are under pressure from multiple threats, such as global climate change, bycatch, and vessel collisions. In this context, more frequent and spatially extensive surveys for abundance and distribution studies are necessary to inform conservation efforts. Marine mammal surveys have been performed visually from land, ships, and aircraft. These methods can be costly, logistically challenging in remote locations, dangerous to researchers, and disturbing to the animals. The growing use of imagery from satellite and unoccupied aerial systems (UAS) can help address some of these challenges, complementing crewed surveys and allowing for more frequent and evenly distributed surveys, especially for remote locations. However, manual counts in satellite and UAS imagery remain time and labor intensive, but the automation of image analyses offers promising solutions. Here, we reviewed the literature for automated methods applied to detect marine mammals in satellite and UAS imagery. The performance of studies is quantitatively compared with metrics that evaluate false positives and false negatives from automated detection against manual counts of animals, which allows for a better assessment of the impact of miscounts in conservation contexts. In general, methods that relied solely on statistical differences in the spectral responses of animals and their surroundings performed worse than studies that used convolutional neural networks (CNN). Despite mixed results, CNN showed promise, and its use and evaluation should continue. Overall, while automation can reduce time and labor, more research is needed to improve the accuracy of automated counts. With the current state of knowledge, it is best to use semi-automated approaches that involve user revision of the output. These approaches currently enable the best tradeoff between time effort and detection accuracy. Based on our analysis, we identified thermal infrared UAS imagery as a future research avenue for marine mammal detection and also recommend the further exploration of object-based image analysis (OBIA). Our analysis also showed that past studies have focused on the automated detection of baleen whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar bears, sirenians, and mustelids.
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Affiliation(s)
- Esteban N. Rodofili
- School of Natural Resources and Environment, University of Florida, Gainesville, FL, United States of America
| | - Vincent Lecours
- School of Natural Resources and Environment, University of Florida, Gainesville, FL, United States of America,School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, United States of America
| | - Michelle LaRue
- School of Earth and Environment, University of Canterbury, Christchurch, New Zealand,Department of Earth and Environmental Science, University of Minnesota, Minneapolis, MN, United States of America
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5
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Cubaynes HC, Fretwell PT. Whales from space dataset, an annotated satellite image dataset of whales for training machine learning models. Sci Data 2022; 9:245. [PMID: 35624202 PMCID: PMC9142526 DOI: 10.1038/s41597-022-01377-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/06/2022] [Indexed: 12/04/2022] Open
Abstract
Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and momentum. However, the development of this emerging technology relies on accurate automated systems to detect whales, which are currently lacking. Such detection systems require access to an open source library containing examples of whales annotated in satellite images to train and test automatic detection systems. Here we present a dataset of 633 annotated whale objects, created by surveying 6,300 km2 of satellite imagery captured by various very high-resolution satellites (i.e. WorldView-3, WorldView-2, GeoEye-1 and Quickbird-2) in various regions across the globe (e.g. Argentina, New Zealand, South Africa, United States, Mexico). The dataset covers four different species: southern right whale (Eubalaena australis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus). Measurement(s) | Whale detections in very high-resolution satellite imagery | Technology Type(s) | very high-resolution satellites and GIS software | Sample Characteristic - Organism | Megaptera novaeangliae • Balaenoptera physalus • Eschrichtius robustus • Eubalaena australis | Sample Characteristic - Location | Maui, United States • Peninsula Valdes, Argentina • Pelagos Sanctuary • Auckland Islands, New Zealand • Laguna San Ignacio, Mexico• Witsand, South Africa |
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Affiliation(s)
- Hannah C Cubaynes
- British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK. .,Scott Polar Research Institute, University of Cambridge, Lensfield Road, Cambridge, CB2 1ER, UK.
| | - Peter T Fretwell
- British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
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6
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Johannessen JED, Biuw M, Lindstrøm U, Ollus VMS, Martín López LM, Gkikopoulou KC, Oosthuizen WC, Lowther A. Intra-season variations in distribution and abundance of humpback whales in the West Antarctic Peninsula using cruise vessels as opportunistic platforms. Ecol Evol 2022; 12:e8571. [PMID: 35154653 PMCID: PMC8826076 DOI: 10.1002/ece3.8571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/27/2021] [Accepted: 01/06/2022] [Indexed: 11/20/2022] Open
Abstract
Fine-scale knowledge of spatiotemporal dynamics in cetacean distribution and abundance throughout the Western Antarctic Peninsula (WAP) is sparse yet essential for effective ecosystem-based management (EBM). Cruise vessels were used as platforms of opportunity to collect data on the distribution and abundance of humpback whales (Megaptera novaeangliae) during the austral summer of 2019/2020 in a region that is also important for the Antarctic krill (Euphausia superba) fishery, to assess potential spatiotemporal interactions for future use in EBM. Data were analyzed using traditional design-based line transect methodology and spatial density surface hurdle models fitted using a set of physical environmental covariates to estimate the abundance and distribution of whales in the area, and to describe their temporal dynamics. Our results indicate a rapid increase in humpback whale abundance in the Bransfield and Gerlache Straits through December, reaching a stable abundance by mid-January. The distribution of humpback whales appeared to change from a patchier distribution in the northern Gerlache Strait to a significantly concentrated presence in the central Gerlache and southern Bransfield Straits, followed by a subsequent dispersion throughout the area. Abundance estimates agreed well with previous literature, increasing from approximately 7000 individuals in 2000 to a peak of 19,107 in 2020. Based on these estimates, we project a total krill consumption of between 1.4 and 3.7 million tons based on traditional and contemporary literature on per capita krill consumption of whales, respectively. When taken in the context of krill fishery catch data in the study area, we conclude that there is minimal spatiotemporal overlap between humpback whales and fishery activity during our study period of November-January. However, there is potential for significant interaction between the two later in the feeding season, but cetacean survey efforts need to be extended into late season in order to fully characterize this potential overlap.
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Affiliation(s)
| | | | - Ulf Lindstrøm
- Department of Arctic BiologyThe Arctic University of TromsøTromsøNorway
- Institute of Marine ResearchTromsøNorway
| | | | | | - Kalliopi C. Gkikopoulou
- Sea Mammal Research UnitSchool of BiologyScottish Ocean InstituteUniversity of St AndrewsSt AndrewsUK
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7
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Nelms SE, Alfaro-Shigueto J, Arnould JPY, Avila IC, Bengtson Nash S, Campbell E, Carter MID, Collins T, Currey RJC, Domit C, Franco-Trecu V, Fuentes MMPB, Gilman E, Harcourt RG, Hines EM, Hoelzel AR, Hooker SK, Johnston DW, Kelkar N, Kiszka JJ, Laidre KL, Mangel JC, Marsh H, Maxwell SM, Onoufriou AB, Palacios DM, Pierce GJ, Ponnampalam LS, Porter LJ, Russell DJF, Stockin KA, Sutaria D, Wambiji N, Weir CR, Wilson B, Godley BJ. Marine mammal conservation: over the horizon. ENDANGER SPECIES RES 2021. [DOI: 10.3354/esr01115] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Marine mammals can play important ecological roles in aquatic ecosystems, and their presence can be key to community structure and function. Consequently, marine mammals are often considered indicators of ecosystem health and flagship species. Yet, historical population declines caused by exploitation, and additional current threats, such as climate change, fisheries bycatch, pollution and maritime development, continue to impact many marine mammal species, and at least 25% are classified as threatened (Critically Endangered, Endangered or Vulnerable) on the IUCN Red List. Conversely, some species have experienced population increases/recoveries in recent decades, reflecting management interventions, and are heralded as conservation successes. To continue these successes and reverse the downward trajectories of at-risk species, it is necessary to evaluate the threats faced by marine mammals and the conservation mechanisms available to address them. Additionally, there is a need to identify evidence-based priorities of both research and conservation needs across a range of settings and taxa. To that effect we: (1) outline the key threats to marine mammals and their impacts, identify the associated knowledge gaps and recommend actions needed; (2) discuss the merits and downfalls of established and emerging conservation mechanisms; (3) outline the application of research and monitoring techniques; and (4) highlight particular taxa/populations that are in urgent need of focus.
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Affiliation(s)
- SE Nelms
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
| | - J Alfaro-Shigueto
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
- Facultad de Biologia Marina, Universidad Cientifica del Sur, Lima, Perú
| | - JPY Arnould
- School of Life and Environmental Sciences, Deakin University, Burwood, VIC 3125, Australia
| | - IC Avila
- Grupo de Ecología Animal, Departamento de Biología, Facultad de Ciencias Naturales y Exactas, Universidad del Valle, Cali, Colombia
| | - S Bengtson Nash
- Environmental Futures Research Institute (EFRI), Griffith University, Nathan Campus, 170 Kessels Road, Nathan, QLD 4111, Australia
| | - E Campbell
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
| | - MID Carter
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
| | - T Collins
- Wildlife Conservation Society, 2300 Southern Blvd., Bronx, NY 10460, USA
| | - RJC Currey
- Marine Stewardship Council, 1 Snow Hill, London, EC1A 2DH, UK
| | - C Domit
- Laboratory of Ecology and Conservation, Marine Study Center, Universidade Federal do Paraná, Brazil
| | - V Franco-Trecu
- Departamento de Ecología y Evolución, Facultad de Ciencias, Universidad de la República, Uruguay
| | - MMPB Fuentes
- Marine Turtle Research, Ecology and Conservation Group, Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - E Gilman
- Pelagic Ecosystems Research Group, Honolulu, HI 96822, USA
| | - RG Harcourt
- Department of Biological Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - EM Hines
- Estuary & Ocean Science Center, San Francisco State University, 3150 Paradise Dr. Tiburon, CA 94920, USA
| | - AR Hoelzel
- Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK
| | - SK Hooker
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
| | - DW Johnston
- Duke Marine Lab, 135 Duke Marine Lab Road, Beaufort, NC 28516, USA
| | - N Kelkar
- Ashoka Trust for Research in Ecology and the Environment (ATREE), Royal Enclave, Srirampura, Jakkur PO, Bangalore 560064, Karnataka, India
| | - JJ Kiszka
- Department of Biological Sciences, Coastlines and Oceans Division, Institute of Environment, Florida International University, Miami, FL 33199, USA
| | - KL Laidre
- Polar Science Center, APL, University of Washington, 1013 NE 40th Street, Seattle, WA 98105, USA
| | - JC Mangel
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- ProDelphinus, Jose Galvez 780e, Miraflores, Perú
| | - H Marsh
- James Cook University, Townsville, QLD 48111, Australia
| | - SM Maxwell
- School of Interdisciplinary Arts and Sciences, University of Washington Bothell, Bothell WA 98011, USA
| | - AB Onoufriou
- School of Biology, University of St Andrews, Fife, KY16 8LB, UK
- Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - DM Palacios
- Marine Mammal Institute, Hatfield Marine Science Center, Oregon State University, Newport, OR, 97365, USA
- Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97330, USA
| | - GJ Pierce
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
- Instituto de Investigaciones Marinas, Consejo Superior de Investigaciones Cientificas, Eduardo Cabello 6, 36208 Vigo, Pontevedra, Spain
| | - LS Ponnampalam
- The MareCet Research Organization, 40460 Shah Alam, Malaysia
| | - LJ Porter
- SMRU Hong Kong, University of St. Andrews, Hong Kong
| | - DJF Russell
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, Fife, KY16 8LB, UK
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, Fife, KY16 8LB, UK
| | - KA Stockin
- Animal Welfare Science and Bioethics Centre, School of Veterinary Science, Massey University, Private Bag 11-222, Palmerston North, New Zealand
| | - D Sutaria
- School of Interdisciplinary Arts and Sciences, University of Washington Bothell, Bothell WA 98011, USA
| | - N Wambiji
- Kenya Marine and Fisheries Research Institute, P.O. Box 81651, Mombasa-80100, Kenya
| | - CR Weir
- Ketos Ecology, 4 Compton Road, Kingsbridge, Devon, TQ7 2BP, UK
| | - B Wilson
- Scottish Association for Marine Science, Oban, Argyll, PA37 1QA, UK
| | - BJ Godley
- Centre for Ecology and Conservation, University of Exeter, Cornwall, TR10 9EZ, UK
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Moore MJ, Rowles TK, Fauquier DA, Baker JD, Biedron I, Durban JW, Hamilton PK, Henry AG, Knowlton AR, McLellan WA, Miller CA, Pace RM, Pettis HM, Raverty S, Rolland RM, Schick RS, Sharp SM, Smith CR, Thomas L, der Hoop JMV, Ziccardi MH. REVIEW: Assessing North Atlantic right whale health: threats, and development of tools critical for conservation of the species. DISEASES OF AQUATIC ORGANISMS 2021; 143:205-226. [PMID: 33629663 DOI: 10.3354/dao03578] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Whaling has decimated North Atlantic right whales Eubalaena glacialis (NARW) since the 11th century and southern right whales E. australis (SRW) since the 19th century. Today, NARWs are Critically Endangered and decreasing, whereas SRWs are recovering. We review NARW health assessment literature, NARW Consortium databases, and efforts and limitations to monitor individual and species health, survival, and fecundity. Photographs are used to track individual movement and external signs of health such as evidence of vessel and entanglement trauma. Post-mortem examinations establish cause of death and determine organ pathology. Photogrammetry is used to assess growth rates and body condition. Samples of blow, skin, blubber, baleen and feces quantify hormones that provide information on stress, reproduction, and nutrition, identify microbiome changes, and assess evidence of infection. We also discuss models of the population consequences of multiple stressors, including the connection between human activities (e.g. entanglement) and health. Lethal and sublethal vessel and entanglement trauma have been identified as major threats to the species. There is a clear and immediate need for expanding trauma reduction measures. Beyond these major concerns, further study is needed to evaluate the impact of other stressors, such as pathogens, microbiome changes, and algal and industrial toxins, on NARW reproductive success and health. Current and new health assessment tools should be developed and used to monitor the effectiveness of management measures and will help determine whether they are sufficient for a substantive species recovery.
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Affiliation(s)
- Michael J Moore
- Woods Hole Oceanographic Institution, Woods Hole MA 02543, USA Co-authors' addresses given in a supplement; www.int-res.com/articles/suppl/d143p205_supp.pdf
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9
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The Potential of Satellite Imagery for Surveying Whales. SENSORS 2021; 21:s21030963. [PMID: 33535463 PMCID: PMC7867100 DOI: 10.3390/s21030963] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/26/2021] [Accepted: 01/28/2021] [Indexed: 11/16/2022]
Abstract
The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use.
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