A second site occupied by <i>Octopus tetricus</i> at high densities, with notes on their ecology and behavior

Scheel, David; Chancellor, Stephanie; Hing, Martin L.; Lawrence, Matthew; Linquist, Stefan; Godfrey‐Smith, Peter

doi:10.1080/10236244.2017.1369851

Cited by 31 publications

(26 citation statements)

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“…Camera traps also provide a unique advantage by recording the undisturbed behaviours of animals within their environment. This has resulted in the discovery of surprising ecological interactions, habitat ranges and social dynamics, among other insights (Meek, Vernes, & Falzon, ; Scheel et al., ). These advantages have led to a 50% annual growth in publications using camera trap methods to assess population sizes between 1998 and 2008 and the trend has persisted until 2015 (Burton et al., ; Rowcliffe, Field, Turvey, & Carbone, ).…”

Section: Introductionmentioning

confidence: 99%

Past, present and future approaches using computer vision for animal re‐identification from camera trap data

et al. 2019

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The ability of a researcher to re‐identify (re‐ID) an individual animal upon re‐encounter is fundamental for addressing a broad range of questions in the study of ecosystem function, community and population dynamics and behavioural ecology. Tagging animals during mark and recapture studies is the most common method for reliable animal re‐ID; however, camera traps are a desirable alternative, requiring less labour, much less intrusion and prolonged and continuous monitoring into an environment. Despite these advantages, the analyses of camera traps and video for re‐ID by humans are criticized for their biases related to human judgement and inconsistencies between analyses. In this review, we describe a brief history of camera traps for re‐ID, present a collection of computer vision feature engineering methodologies previously used for animal re‐ID, provide an introduction to the underlying mechanisms of deep learning relevant to animal re‐ID, highlight the success of deep learning methods for human re‐ID, describe the few ecological studies currently utilizing deep learning for camera trap analyses and our predictions for near future methodologies based on the rapid development of deep learning methods. For decades, ecologists with expertise in computer vision have successfully utilized feature engineering to extract meaningful features from camera trap images to improve the statistical rigor of individual comparisons and remove human bias from their camera trap analyses. Recent years have witnessed the emergence of deep learning systems which have demonstrated the accurate re‐ID of humans based on image and video data with near perfect accuracy. Despite this success, ecologists have yet to utilize these approaches for animal re‐ID. By utilizing novel deep learning methods for object detection and similarity comparisons, ecologists can extract animals from an image/video data and train deep learning classifiers to re‐ID animal individuals beyond the capabilities of a human observer. This methodology will allow ecologists with camera/video trap data to reidentify individuals that exit and re‐enter the camera frame. Our expectation is that this is just the beginning of a major trend that could stand to revolutionize the analysis of camera trap data and, ultimately, our approach to animal ecology.

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Section: Introductionmentioning

confidence: 99%

Past, present and future approaches using computer vision for animal re‐identification from camera trap data

et al. 2019

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“…We recently published a note describing discovery of an underwater site inhabited by unusually large numbers of gloomy octopuses (up to 15, on visits so far) in close proximity. 1 This is the second site of this kind discovered in the same area. 2 At both sites, octopuses live in dens excavated in a bed of discarded scallop shells.…”

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confidence: 87%

Octopus engineering, intentional and inadvertent

Scheel

Godfrey‐Smith

Linquist

et al. 2017

Communicative & Integrative Biology

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We previously published a description of discovery of a site where octopuses live in an unusually dense collection of individual dens near one another in a bed of scallop shells amid a rock outcrop. We believe the shell bed is an extended midden, accumulated over time by individual octopuses returning to their dens with food. Here we consider what aspects of material collection, den maintenance, and aggregation are intentional for the octopuses, versus inadvertent consequences of individual decisions. Collection of prey items, transport of prey to the den, den excavation, and collection and use of non-prey materials at the den appear to be intentional behaviors. The occurrence of many dens in close aggregation appears to be an inadvertent outcome of the availability of food and the risk of predation in the habitat. Popular media reports have described this site as an ‘city’ designed by octopuses, but that is not an accurate description of the site.

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“…In Australia, a diver filmed an octopus being mobbed and killed by a swarm of leatherjackets. 1 At a study site in Jervis Bay, Australia, a few colleagues and I have been recording video of gloomy octopuses (Octopus tetricus) interacting in the wild with predators, prey and with each other (Godfrey-Smith & Lawrence, 2012;Scheel et al, 2014Scheel et al, , 2016Scheel et al, , 2017. Audrianna Kinley (2015) reviewed video of predators approaching these octopuses at their dens.…”

Section: Introductionmentioning

confidence: 99%

Octopuses in wild and domestic relationships

Scheel

2018

Social Science Information

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People commonly interact with terrestrial domestic animals, such as dogs and cats, horses, cattle and goats, and birds. Thereby individuals of different species form animal–human bonds. We are now forming relationships with ocean animals in increasingly common ways through growing human populations, advances in technology such as SCUBA, ocean mapping, underwater instrumentation and advances in aquatic animal husbandry. Octopuses and humans share quite distant evolutionary ties and yet share aspects of sensory ability and intelligence. Octopuses thereby pose interesting challenges and conundrums for understanding animal–human relationships. I consider several reasons to expect that the evolution of octopuses, and of animal cognition generally among active and visually sophisticated animals, will favour traits that support relationships between individuals. The evolutionary outcome of animals capable of forming inter-individual relationships may thus be expected in any evolving biota with organisms of this kind. This article explores the ability of ocean and terrestrial animals to relate to one another in ways that are reciprocal, if not equally balanced, and illustrates this with the examples of octopuses.

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A second site occupied by Octopus tetricus at high densities, with notes on their ecology and behavior

Cited by 31 publications

References 11 publications

Past, present and future approaches using computer vision for animal re‐identification from camera trap data

Past, present and future approaches using computer vision for animal re‐identification from camera trap data

Octopus engineering, intentional and inadvertent

Octopuses in wild and domestic relationships

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