Dataset Information

Quantifying free behaviour in an open field using k-motif approach.

ABSTRACT: Quantification and parametrisation of movement are widely used in animal behavioural paradigms. In particular, free movement in controlled conditions (e.g., open field paradigm) is used as a "proxy for indices of baseline and drug-induced behavioural changes. However, the analysis of this is often time- and labour-intensive and existing algorithms do not always classify the behaviour correctly. Here, we propose a new approach to quantify behaviour in an unconstrained environment: searching for frequent patterns (k-motifs) in the time series representing the position of the subject over time. Validation of this method was performed using subchronic quinpirole-induced changes in open field experiment behaviours in rodents. Analysis of this data was performed using k-motifs as features to better classify subjects into experimental groups on the basis of behaviour in the open field. Our classifier using k-motifs gives as high as 94% accuracy in classifying repetitive behaviour versus controls which is a substantial improvement compared to currently available methods including using standard feature definitions (depending on the choice of feature set and classification strategy, accuracy up to 88%). Furthermore, visualisation of the movement/time patterns is highly predictive of these behaviours. By using machine learning, this can be applied to behavioural analysis across experimental paradigms.

SUBMITTER: Konings M

PROVIDER: S-EPMC6934846 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Quantifying free behaviour in an open field using k-motif approach.

Könings Marein M Blokpoel Mark M Kapusta Katarzyna K Claassen Tom T Buitelaar Jan K JK Glennon Jeffrey C JC Bielczyk Natalia Z NZ

Scientific reports 20191227 1

Quantification and parametrisation of movement are widely used in animal behavioural paradigms. In particular, free movement in controlled conditions (e.g., open field paradigm) is used as a "proxy for indices of baseline and drug-induced behavioural changes. However, the analysis of this is often time- and labour-intensive and existing algorithms do not always classify the behaviour correctly. Here, we propose a new approach to quantify behaviour in an unconstrained environment: searching for f ...[more]

PMID: 31882743

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Project description:Estimating migration parameters of individuals and populations is vital for their conservation and management. Studies on animal movements and migration often depend upon location data from tracked animals and it is important that such data are appropriately analyzed for reliable estimates of migration and effective management of moving animals. The Net Squared Displacement (NSD) approach for modelling animal movement is being increasingly used as it can objectively quantify migration characteristics and separate different types of movements from migration. However, the ability of NSD to properly classify the movement patterns of individuals has been criticized and issues related to study design arise with respect to starting locations of the data/animals, data sampling regime and extent of movement of species. We address the issues raised over NSD using tracking data from 319 moose (Alces alces) in Sweden. Moose is an ideal species to test this approach, as it can be sedentary, nomadic, dispersing or migratory and individuals vary in their extent, timing and duration of migration. We propose a two-step process of using the NSD approach by first classifying movement modes using mean squared displacement (MSD) instead of NSD and then estimating the extent, duration and timing of migration using NSD. We show that the NSD approach is robust to the choice of starting dates except when the start date occurs during the migratory phase. We also show that the starting location of the animal has a marginal influence on the correct quantification of migration characteristics. The number of locations per day (1-48) did not significantly affect the performance of non-linear mixed effects models, which correctly distinguished migration from other movement types, however, high-resolution data had a significant negative influence on estimates for the timing of migrations. The extent of movement, however, had an effect on the classification of movements, and individuals undertaking short- distance migrations can be misclassified as other movements such as sedentary or nomadic. Our study raises important considerations for designing, analysing and interpreting movement ecology studies, and how these should be determined by the biology of the species and the ecological and conservation questions in focus.

Dataset Information

Quantifying free behaviour in an open field using k-motif approach.

Publications

Quantifying free behaviour in an open field using k-motif approach.

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