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Coupled Active Systems Encode an Emergent Hunting Behavior in the Unicellular Predator Lacrymaria olor.

ABSTRACT: Many single-celled protists use rapid morphology changes to perform fast animal-like behaviors. To understand how such behaviors are encoded, we analyzed the hunting dynamics of the predatory ciliate Lacrymaria olor, which locates and captures prey using the tip of a slender "neck" that can rapidly extend more than seven times its body length (500 ?m from its body) and retract in seconds. By tracking single cells in real-time over hours and analyzing millions of sub-cellular postures, we find that these fast extension-contraction cycles underlie an emergent hunting behavior that comprehensively samples a broad area within the cell's reach. Although this behavior appears complex, we show that it arises naturally as alternating sub-cellular ciliary and contractile activities rearrange the cell's underlying helical cytoskeleton to extend or retract the neck. At short timescales, a retracting neck behaves like an elastic filament under load, such that compression activates a series of buckling modes that reorient the head and scramble its extensile trajectory. At longer timescales, the fundamental length of this filament can change, altering the location in space where these transitions occur. Coupling these fast and slow dynamics together, we present a simple model for how Lacrymaria samples the range of geometries and orientations needed to ensure dense stochastic sampling of the immediate environment when hunting to locate and strike at prey. More generally, coupling active mechanical and chemical signaling systems across different timescales may provide a general strategy by which mechanically encoded emergent cell behaviors can be understood or engineered.

SUBMITTER: Coyle SM

PROVIDER: S-EPMC7511173 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Coupled Active Systems Encode an Emergent Hunting Behavior in the Unicellular Predator Lacrymaria olor.

Coyle Scott M SM Flaum Eliott M EM Li Hongquan H Krishnamurthy Deepak D Prakash Manu M

Current biology : CB 20191031 22

Many single-celled protists use rapid morphology changes to perform fast animal-like behaviors. To understand how such behaviors are encoded, we analyzed the hunting dynamics of the predatory ciliate Lacrymaria olor, which locates and captures prey using the tip of a slender "neck" that can rapidly extend more than seven times its body length (500 μm from its body) and retract in seconds. By tracking single cells in real-time over hours and analyzing millions of sub-cellular postures, we find th ...[more]

PMID: 31679941

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Project description:BackgroundBiologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. One such example is the caching behaviour of the arctic fox (Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies.MethodsWe equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose (Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions.ResultsThe random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods.ConclusionsAccelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator-prey interactions and trophic networks.

Dataset Information

Coupled Active Systems Encode an Emergent Hunting Behavior in the Unicellular Predator Lacrymaria olor.

Publications

Coupled Active Systems Encode an Emergent Hunting Behavior in the Unicellular Predator Lacrymaria olor.

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