Wearable reproductive trackers: quantifying a key life history event remotely
Abstract Advancements in biologging technology allow terabytes of data to be collected that record the location of individuals but also their direction, speed and acceleration. These multi-stream data sets allow researchers to infer movement and behavioural patterns at high spatiotemporal resolution...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | Animal Biotelemetry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40317-022-00298-8 |
| _version_ | 1798042529314111488 |
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| author | Luke Ozsanlav-Harris Larry R. Griffin Mitch D. Weegman Lei Cao Geoff M. Hilton Stuart Bearhop |
| author_facet | Luke Ozsanlav-Harris Larry R. Griffin Mitch D. Weegman Lei Cao Geoff M. Hilton Stuart Bearhop |
| author_sort | Luke Ozsanlav-Harris |
| collection | DOAJ |
| description | Abstract Advancements in biologging technology allow terabytes of data to be collected that record the location of individuals but also their direction, speed and acceleration. These multi-stream data sets allow researchers to infer movement and behavioural patterns at high spatiotemporal resolutions and in turn quantify fine-scale changes in state along with likely ecological causes and consequences. The scope offered by such data sets is increasing and there is potential to gain unique insights into a suite of ecological and life history phenomena. We use multi-stream data from global positioning system (GPS) and accelerometer (ACC) devices to quantify breeding events remotely in an Arctic breeding goose. From a training set of known breeders we determine the movement and overall dynamic body acceleration patterns indicative of incubation and use these to classify breeding events in individuals with unknown reproductive status. Given that researchers are often constrained by the amount of biologging data they can collect due to device weights, we carry out a sensitivity analysis. Here we explore the relative merits of GPS vs ACC data and how varying the temporal resolution of the data affects the accuracy of classifying incubation for birds. Classifier accuracy deteriorates as the temporal resolution of GPS and ACC are reduced but the reduction in precision (false positive rate) is larger in comparison to recall (false negative rate). Precision fell to 94.5%, whereas recall didn’t fall below 98% over all sampling schedules tested. Our data set could have been reduced by c.95% while maintaining precision and recall > 98%. The GPS-only classifier generally outperformed the ACC-only classifier across all accuracy metrics but both performed worse than the combined GPS and ACC classifier. GPS and ACC data can be used to reconstruct breeding events remotely, allowing unbiased, 24-h monitoring of individuals. Our resampling-based sensitivity analysis of classifier accuracy has important implications with regards to both device design and sampling schedules for study systems, where device size is constrained. It will allow researchers with similar aims to optimize device battery, memory usage and lifespan to maximise the ability to correctly quantify life history events. |
| first_indexed | 2024-04-11T22:36:43Z |
| format | Article |
| id | doaj.art-666b30472f7947f4985a2c75e2724f15 |
| institution | Directory Open Access Journal |
| issn | 2050-3385 |
| language | English |
| last_indexed | 2024-04-11T22:36:43Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Animal Biotelemetry |
| spelling | doaj.art-666b30472f7947f4985a2c75e2724f152022-12-22T03:59:11ZengBMCAnimal Biotelemetry2050-33852022-08-0110111510.1186/s40317-022-00298-8Wearable reproductive trackers: quantifying a key life history event remotelyLuke Ozsanlav-Harris0Larry R. Griffin1Mitch D. Weegman2Lei Cao3Geoff M. Hilton4Stuart Bearhop5Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of ExeterWildfowl & Wetlands TrustDepartment of Biology, University of SaskatchewanState Key Laboratory of Urban and Regional Ecology, Research Centre for Eco-Environmental Sciences, Chinese Academy of SciencesWildfowl & Wetlands TrustCentre for Ecology and Conservation, College of Life and Environmental Sciences, University of ExeterAbstract Advancements in biologging technology allow terabytes of data to be collected that record the location of individuals but also their direction, speed and acceleration. These multi-stream data sets allow researchers to infer movement and behavioural patterns at high spatiotemporal resolutions and in turn quantify fine-scale changes in state along with likely ecological causes and consequences. The scope offered by such data sets is increasing and there is potential to gain unique insights into a suite of ecological and life history phenomena. We use multi-stream data from global positioning system (GPS) and accelerometer (ACC) devices to quantify breeding events remotely in an Arctic breeding goose. From a training set of known breeders we determine the movement and overall dynamic body acceleration patterns indicative of incubation and use these to classify breeding events in individuals with unknown reproductive status. Given that researchers are often constrained by the amount of biologging data they can collect due to device weights, we carry out a sensitivity analysis. Here we explore the relative merits of GPS vs ACC data and how varying the temporal resolution of the data affects the accuracy of classifying incubation for birds. Classifier accuracy deteriorates as the temporal resolution of GPS and ACC are reduced but the reduction in precision (false positive rate) is larger in comparison to recall (false negative rate). Precision fell to 94.5%, whereas recall didn’t fall below 98% over all sampling schedules tested. Our data set could have been reduced by c.95% while maintaining precision and recall > 98%. The GPS-only classifier generally outperformed the ACC-only classifier across all accuracy metrics but both performed worse than the combined GPS and ACC classifier. GPS and ACC data can be used to reconstruct breeding events remotely, allowing unbiased, 24-h monitoring of individuals. Our resampling-based sensitivity analysis of classifier accuracy has important implications with regards to both device design and sampling schedules for study systems, where device size is constrained. It will allow researchers with similar aims to optimize device battery, memory usage and lifespan to maximise the ability to correctly quantify life history events.https://doi.org/10.1186/s40317-022-00298-8AccelerometerBiologgingGPSIncubationMovement patternsNest survival |
| spellingShingle | Luke Ozsanlav-Harris Larry R. Griffin Mitch D. Weegman Lei Cao Geoff M. Hilton Stuart Bearhop Wearable reproductive trackers: quantifying a key life history event remotely Animal Biotelemetry Accelerometer Biologging GPS Incubation Movement patterns Nest survival |
| title | Wearable reproductive trackers: quantifying a key life history event remotely |
| title_full | Wearable reproductive trackers: quantifying a key life history event remotely |
| title_fullStr | Wearable reproductive trackers: quantifying a key life history event remotely |
| title_full_unstemmed | Wearable reproductive trackers: quantifying a key life history event remotely |
| title_short | Wearable reproductive trackers: quantifying a key life history event remotely |
| title_sort | wearable reproductive trackers quantifying a key life history event remotely |
| topic | Accelerometer Biologging GPS Incubation Movement patterns Nest survival |
| url | https://doi.org/10.1186/s40317-022-00298-8 |
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