Dataset Information

From skylight input to behavioural output: A computational model of the insect polarised light compass.

ABSTRACT: Many insects navigate by integrating the distances and directions travelled on an outward path, allowing direct return to the starting point. Fundamental to the reliability of this process is the use of a neural compass based on external celestial cues. Here we examine how such compass information could be reliably computed by the insect brain, given realistic constraints on the sky polarisation pattern and the insect eye sensor array. By processing the degree of polarisation in different directions for different parts of the sky, our model can directly estimate the solar azimuth and also infer the confidence of the estimate. We introduce a method to correct for tilting of the sensor array, as might be caused by travel over uneven terrain. We also show that the confidence can be used to approximate the change in sun position over time, allowing the compass to remain fixed with respect to 'true north' during long excursions. We demonstrate that the compass is robust to disturbances and can be effectively used as input to an existing neural model of insect path integration. We discuss the plausibility of our model to be mapped to known neural circuits, and to be implemented for robot navigation.

SUBMITTER: Gkanias E

PROVIDER: S-EPMC6638774 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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From skylight input to behavioural output: A computational model of the insect polarised light compass.

Gkanias Evripidis E Risse Benjamin B Mangan Michael M Webb Barbara B

PLoS computational biology 20190718 7

Many insects navigate by integrating the distances and directions travelled on an outward path, allowing direct return to the starting point. Fundamental to the reliability of this process is the use of a neural compass based on external celestial cues. Here we examine how such compass information could be reliably computed by the insect brain, given realistic constraints on the sky polarisation pattern and the insect eye sensor array. By processing the degree of polarisation in different direct ...[more]

PMID: 31318859

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Project description:Nitrogen (N) presents an important challenge for sustainability. Human intervention in the global nitrogen cycle has been pivotal in in providing goods and services to society. However, release of N beyond its intended societal use has many negative health and environmental consequences. Several systems modeling approaches have been developed to understand the trade-offs between the beneficial and harmful effects of N. These efforts include life cycle modeling, integrated management practices and sustainability metrics for individuals and communities. However, these approaches do not connect economic and ecological N flows in physical units throughout the system, which could better represent these trade-offs for decision-makers. Physical Input-Output Table (PIOT) based models present a viable complementary solution to overcome this limitation. We developed a N-PIOT for Illinois representing the interdependence of sectors in 2002, using N mass units. This allows studying the total N flow required to produce a certain amount of N in the final product. An Environmentally Extended Input Output (EEIO) based approach was used to connect the physical economic production to environmental losses; allowing quantification of total environmental impact to support agricultural production in Illinois. A bottom up approach was used to develop the N-PIOT using Material Flow Analysis (MFA) tracking N flows associated with top 3 commodities (Corn, Soybean and Wheat). These three commodities cover 99% of N fertilizer use in Illinois. The PIOT shows that of all the N inputs to corn production the state exported 68% of N embedded in useful products, 9% went to animal feed manufacturing and only 0.03% was consumed directly within the state. Approximately 35% of N input to soybean farming ended up in animal feed. Release of N to the environment was highest from corn farming, at about 21.8% of total N fertilizer inputs, followed by soybean (9.2%) and wheat farming (4.2%). The model also allowed the calculation of life cycle N use efficiency for N based on physical flows in the economy. Hence, PIOTs prove to be a viable tool for developing a holistic approach to manage disrupted biogeochemical cycles, since these provide a detailed insight into physical flows in economic systems and allow physical coupling with ecological N flows.

Project description:BackgroundAnimal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors.MethodsWe address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition.ResultsThe overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture.ConclusionsMagnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.

Dataset Information

From skylight input to behavioural output: A computational model of the insect polarised light compass.

Publications

From skylight input to behavioural output: A computational model of the insect polarised light compass.

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