Perspectives In Visual Imaging for Marine Biology and Ecology: From Acquisition to Understanding

Durden, Jennifer M.; Schoening, Timm; Althaus, Franziska; Friedman, Ariell; García, Rafael; Glover, Adrian G.; Greinert, Jens; Stout, Nancy Jacobsen; Jones, Daniel O. B.; Jordt, Anne; Kaeli, Jeffrey W.; Köser, Kevin; Kuhnz, Linda A.; Lindsay, Dhugal J.; Morris, Kirsty J.; Nattkemper, Tim Wilhelm; Osterloff, Jonas; Ruhl, Henry A.; Singh, Hanumant; Tran, Minh-Tien; Bett, Brian J.

doi:10.1201/9781315368597-2

Cited by 84 publications

(103 citation statements)

References 203 publications

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“…Autonomous underwater vehicles are an established technology in seafloor research (Durden et al. ) and appear to be an effective tool in science‐ and conservation‐driven studies both in shelf‐sea (Marzinelli et al. ) and deep‐sea (Morris et al.…”

Section: Introductionmentioning

confidence: 99%

“…Marine protected areas (MPAs) have long been suggested as a tool for maintaining and restoring biodiversity (Woodcock et al 2017), and the designation of numerous MPAs is now driving the need for better and more costeffective description and quantification of the biological assemblages present and their habitats. Autonomous underwater vehicles are an established technology in seafloor research (Durden et al 2016c) and appear to be an effective tool in science-and conservation-driven studies both in shelf-sea (Marzinelli et al 2015) and deepsea (Morris et al 2016) environments . They offer rapid, nondestructive data collection, access to a wide range of habitats, and reduced survey costs (Wynn et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle

Benoist

Morris

Bett

et al. 2019

Conservation Biology

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The number of marine protected areas (MPAs) has increased dramatically in the last decade and poses a major logistic challenge for conservation practitioners in terms of spatial extent and the multiplicity of habitats and biotopes that now require assessment. Photographic assessment by autonomous underwater vehicle (AUV) enables the consistent description of multiple habitats, in our case including mosaics of rock and sediment. As a case study, we used this method to survey the Greater Haig Fras marine conservation zone (Celtic Sea, northeast Atlantic). We distinguished 7 biotopes, detected statistically significant variations in standing stocks, species density, species diversity, and faunal composition, and identified significant indicator species for each habitat. Our results demonstrate that AUV‐based photography can produce robust data for ecological research and practical marine conservation. Standardizing to a minimum number of individuals per sampling unit, rather than to a fixed seafloor area, may be a valuable means of defining an ecologically appropriate sampling unit. Although composite sampling represents a change in standard practice, other users should consider the potential benefits of this approach in conservation studies. It is broadly applicable in the marine environment and has been successfully implemented in deep‐sea conservation and environmental impact studies. Without a cost‐effective method, applicable across habitats, it will be difficult to further a coherent classification of biotopes or to routinely assess their conservation status in the rapidly expanding global extent of MPAs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring mosaic biotopes in a marine conservation zone by autonomous underwater vehicle

Benoist

Morris

Bett

et al. 2019

Conservation Biology

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“…). Examples of aquatic ecology methods that are moving data collection from artisanal to more automated include: underwater photography using autonomous vehicles with some automated annotation for benthic community estimation (Durden et al ; Morris et al ; Schoening et al ), a sedimentation event sensor used to quantify organic matter deposition in lieu of sediment traps (McGill et al ), optical and acoustic sensors for identifying seabed habitats as opposed to interpolating physical point samples (Costa et al ; Flanagan ), and underway continuous flow cytometric samplers (Swalwell et al ) and the Scripps Plankton Camera (http://spc.ucsd.edu). Large interdisciplinary sampling efforts in aquatic ecology, such as ocean observatories (Favali et al ), components of the US Large Ecological Time Series (Hobbie et al ), and oceanic expeditions such as Tara Oceans (Hobbie et al ), are also contributing to Big Data generation and large‐scale comparisons.…”

Section: Challenge 1 Recognizing Big Datamentioning

confidence: 99%

Integrating “Big Data” into Aquatic Ecology: Challenges and Opportunities

Durden¹,

Luo²,

Alexander³

et al. 2017

Limnology and Oceanography Bulletin

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Got “Big Data”? Not sure how best to use it? Big Data is becoming an important facet of aquatic ecology, and researchers must learn to harness it to reap the rewards of using it. The benefits of using Big Data are many, and include advancements in scientific understanding at larger scales and higher resolution, applications to improving environmental management and policy, and public engagement. We aim to demystify the use of Big Data for individual scientists, and provide some food for thought for the aquatic ecology community on how to develop this sphere. To achieve this, we highlight six key challenges: (1) how to recognize if you have Big Data, (2) handling Big Data, (3) issues with classical analytical techniques, (4) verification of Big Data, (5) considerations for data sharing, and (6) community development of knowledge infrastructures. We then present approaches and tools which have been successfully applied to these challenges in aquatic ecology and other scientific fields.

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“…The aims were to bring together users of marine imaging tools from the fields of research and industry to discuss present and future practical, methodological and technological challenges. A multi-disciplinary review article of the current state-of-the-art methods in marine imaging was published by a group of commited participants following their presentation at the workshop (Durden et al 2016). …”

Section: Introductionmentioning

confidence: 99%