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Dyer OL, Seeley MA, Wheatley BB. Effects of static exercises on hip muscle fatigue and knee wobble assessed by surface electromyography and inertial measurement unit data. Sci Rep 2024; 14:10448. [PMID: 38714802 PMCID: PMC11076610 DOI: 10.1038/s41598-024-61325-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 05/03/2024] [Indexed: 05/10/2024] Open
Abstract
Hip muscle weakness can be a precursor to or a result of lower limb injuries. Assessment of hip muscle strength and muscle motor fatigue in the clinic is important for diagnosing and treating hip-related impairments. Muscle motor fatigue can be assessed with surface electromyography (sEMG), however sEMG requires specialized equipment and training. Inertial measurement units (IMUs) are wearable devices used to measure human motion, yet it remains unclear if they can be used as a low-cost alternative method to measure hip muscle fatigue. The goals of this work were to (1) identify which of five pre-selected exercises most consistently and effectively elicited muscle fatigue in the gluteus maximus, gluteus medius, and rectus femoris muscles and (2) determine the relationship between muscle fatigue using sEMG sensors and knee wobble using an IMU device. This work suggests that a wall sit and single leg knee raise activity fatigue the gluteus medius, gluteus maximus, and rectus femoris muscles most reliably (p < 0.05) and that the gluteus medius and gluteus maximus muscles were fatigued to a greater extent than the rectus femoris (p = 0.031 and p = 0.0023, respectively). Additionally, while acceleration data from a single IMU placed on the knee suggested that more knee wobble may be an indicator of muscle fatigue, this single IMU is not capable of reliably assessing fatigue level. These results suggest the wall sit activity could be used as simple, static exercise to elicit hip muscle fatigue in the clinic, and that assessment of knee wobble in addition to other IMU measures could potentially be used to infer muscle fatigue under controlled conditions. Future work examining the relationship between IMU data, muscle fatigue, and multi-limb dynamics should be explored to develop an accessible, low-cost, fast and standardized method to measure fatiguability of the hip muscles in the clinic.
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Affiliation(s)
- Olivia L Dyer
- Musculoskeletal Institute, Geisinger, Danville, PA, USA
| | - Mark A Seeley
- Musculoskeletal Institute, Geisinger, Danville, PA, USA
| | - Benjamin B Wheatley
- Musculoskeletal Institute, Geisinger, Danville, PA, USA.
- Department of Mechanical Engineering, Bucknell University, 1 Dent Drive, Lewisburg, PA, 17837, USA.
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2
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Auge AC, Blouin-Demers G, Murray DL. Developing a classification system to assign activity states to two species of freshwater turtles. PLoS One 2022; 17:e0277491. [PMID: 36449460 PMCID: PMC9710770 DOI: 10.1371/journal.pone.0277491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 10/27/2022] [Indexed: 12/03/2022] Open
Abstract
Research in ecology often requires robust assessment of animal behaviour, but classifying behavioural patterns in free-ranging animals and in natural environments can be especially challenging. New miniaturised bio-logging devices such as accelerometers are increasingly available to record animal behaviour remotely, and thereby address the gap in knowledge related to behaviour of free-ranging animals. However, validation of these data is rarely conducted and classification model transferability across closely-related species is often not tested. Here, we validated accelerometer and water sensor data to classify activity states in two free-ranging freshwater turtle species (Blanding's turtle, Emydoidea blandingii, and Painted turtle, Chrysemys picta). First, using only accelerometer data, we developed a decision tree to separate motion from motionless states, and second, we included water sensor data to classify the animal as being motionless or in-motion on land or in water. We found that accelerometers separated in-motion from motionless behaviour with > 83% accuracy, whereas models also including water sensor data predicted states in terrestrial and aquatic locations with > 77% accuracy. Despite differences in values separating activity states between the two species, we found high model transferability allowing cross-species application of classification models. Note that reducing sampling frequency did not affect predictive accuracy of our models up to a sampling frequency of 0.0625 Hz. We conclude that the use of accelerometers in animal research is promising, but requires prior data validation and development of robust classification models, and whenever possible cross-species assessment should be conducted to establish model generalisability.
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Affiliation(s)
| | | | - Dennis L. Murray
- Department of Biology, Trent University, Peterborough, ON, Canada
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3
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Lovarelli D, Brandolese C, Leliveld L, Finzi A, Riva E, Grotto M, Provolo G. Development of a New Wearable 3D Sensor Node and Innovative Open Classification System for Dairy Cows’ Behavior. Animals (Basel) 2022; 12:ani12111447. [PMID: 35681911 PMCID: PMC9179612 DOI: 10.3390/ani12111447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 05/30/2022] [Accepted: 06/02/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary In order to keep dairy cows under satisfactory health and welfare conditions, it is very important to monitor the animals in their living environment. With the support of technology, and, in particular, with the installation of sensors on neck-collars, cow behavior can be adequately monitored, and different behavioral patterns can be classified. In this study, an open and customizable device has been developed to classify the behaviors of dairy cows. The device communicates with a mobile application via Bluetooth to acquire raw data from behavioral observations and via an ad hoc radio channel to send the data from the device to the gateway. After observing 32 cows on 3 farms for a total of 108 h, several machine learning algorithms were trained to classify their behaviors. The decision tree algorithm was found to be the best compromise between complexity and accuracy to classify standing, lying, eating, and ruminating. The open nature of the system enables the addition of other functions (e.g., localization) and the integration with other information sources, e.g., climatic sensors, to provide a more complete picture of cow health and welfare in the barn. Abstract Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.
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Affiliation(s)
- Daniela Lovarelli
- Department of Environmental Science and Policy, University of Milan, Via G. Celoria 2, 20133 Milan, Italy
- Correspondence:
| | - Carlo Brandolese
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Via Ponzio 34, 20133 Milan, Italy;
| | - Lisette Leliveld
- Department of Agricultural and Environmental Sciences, University of Milan, Via G. Celoria 2, 20133 Milan, Italy; (L.L.); (A.F.); (E.R.); (G.P.)
| | - Alberto Finzi
- Department of Agricultural and Environmental Sciences, University of Milan, Via G. Celoria 2, 20133 Milan, Italy; (L.L.); (A.F.); (E.R.); (G.P.)
| | - Elisabetta Riva
- Department of Agricultural and Environmental Sciences, University of Milan, Via G. Celoria 2, 20133 Milan, Italy; (L.L.); (A.F.); (E.R.); (G.P.)
| | | | - Giorgio Provolo
- Department of Agricultural and Environmental Sciences, University of Milan, Via G. Celoria 2, 20133 Milan, Italy; (L.L.); (A.F.); (E.R.); (G.P.)
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Gunner RM, Wilson RP, Holton MD, Hopkins P, Bell SH, Marks NJ, Bennett NC, Ferreira S, Govender D, Viljoen P, Bruns A, van Schalkwyk OL, Bertelsen MF, Duarte CM, van Rooyen MC, Tambling CJ, Göppert A, Diesel D, Scantlebury DM. Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion ( Panthera leo). J R Soc Interface 2022; 19:20210692. [PMID: 35042386 PMCID: PMC8767188 DOI: 10.1098/rsif.2021.0692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/08/2021] [Indexed: 01/18/2023] Open
Abstract
The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions (Panthera leo) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.
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Affiliation(s)
- Richard M. Gunner
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
- Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
| | - Rory P. Wilson
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Mark D. Holton
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Phil Hopkins
- Department for the Ecology of Animal Societies Radolfzell, Max Planck Institute of Animal Behavior, Baden-Württemberg, Germany
| | - Stephen H. Bell
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Nikki J. Marks
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Nigel C. Bennett
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria 002, South Africa
| | - Sam Ferreira
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Danny Govender
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Pauli Viljoen
- Savanna and Grassland Research Unit, South African National Parks, Scientific Services Skukuza, Kruger National Park, Skukuza 1350, South Africa
| | - Angela Bruns
- Veterinary Wildlife Services, South African National Parks, 97 Memorial Road, Old Testing Grounds, 8301 Kimberley, South Africa
| | - O. Louis van Schalkwyk
- Department of Agriculture, Forestry and Fisheries, Government of South Africa, Skukuza, South Africa
- Department of Migration, Max Planck Institute of Animal Behavior, 78315 Radolfzell, Germany
| | - Mads F. Bertelsen
- Center for Zoo and Wild Animal Health, Copenhagen Zoo, Roskildevej 38, 2000 Frederiksberg, Denmark
| | - Carlos M. Duarte
- Red Sea Research Centre, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Martin C. van Rooyen
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria 002, South Africa
| | - Craig J. Tambling
- Department of Zoology and Entomology, University of Fort Hare Alice Campus, Ring Road, Alice 5700, South Africa
| | - Aoife Göppert
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - Delmar Diesel
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
| | - D. Michael Scantlebury
- School of Biological Sciences, Queen's University Belfast, 19 Chlorine Gardens, Belfast BT9 5DL, UK
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5
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Dujon AM, Vittecoq M, Bramwell G, Thomas F, Ujvari B. Machine learning is a powerful tool to study the effect of cancer on species and ecosystems. Methods Ecol Evol 2021. [DOI: 10.1111/2041-210x.13703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Antoine M. Dujon
- Geelong School of Life and Environmental Sciences Centre for Integrative Ecology Deakin University Waurn Ponds Victoria Australia
- CREECUMR IRD 224‐CNRS 5290‐Université de Montpellier Montpellier France
- CANECEV‐Centre de Recherches Ecologiques et Evolutives sur le cancer (CREEC) Montpellier France
| | - Marion Vittecoq
- CREECUMR IRD 224‐CNRS 5290‐Université de Montpellier Montpellier France
- MIVEGECUniversity of MontpellierCNRSIRD Montpellier France
- Tour du Valat Research Institute for the Conservation of Mediterranean Wetlands Arles France
| | - Georgina Bramwell
- Geelong School of Life and Environmental Sciences Centre for Integrative Ecology Deakin University Waurn Ponds Victoria Australia
- CANECEV‐Centre de Recherches Ecologiques et Evolutives sur le cancer (CREEC) Montpellier France
| | - Frédéric Thomas
- CREECUMR IRD 224‐CNRS 5290‐Université de Montpellier Montpellier France
- CANECEV‐Centre de Recherches Ecologiques et Evolutives sur le cancer (CREEC) Montpellier France
- MIVEGECUniversity of MontpellierCNRSIRD Montpellier France
| | - Beata Ujvari
- Geelong School of Life and Environmental Sciences Centre for Integrative Ecology Deakin University Waurn Ponds Victoria Australia
- CANECEV‐Centre de Recherches Ecologiques et Evolutives sur le cancer (CREEC) Montpellier France
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Ikurior SJ, Marquetoux N, Leu ST, Corner-Thomas RA, Scott I, Pomroy WE. What Are Sheep Doing? Tri-Axial Accelerometer Sensor Data Identify the Diel Activity Pattern of Ewe Lambs on Pasture. SENSORS 2021; 21:s21206816. [PMID: 34696028 PMCID: PMC8540528 DOI: 10.3390/s21206816] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/06/2021] [Accepted: 10/08/2021] [Indexed: 12/29/2022]
Abstract
Monitoring activity patterns of animals offers the opportunity to assess individual health and welfare in support of precision livestock farming. The purpose of this study was to use a triaxial accelerometer sensor to determine the diel activity of sheep on pasture. Six Perendale ewe lambs, each fitted with a neck collar mounting a triaxial accelerometer, were filmed during targeted periods of sheep activities: grazing, lying, walking, and standing. The corresponding acceleration data were fitted using a Random Forest algorithm to classify activity (=classifier). This classifier was then applied to accelerometer data from an additional 10 ewe lambs to determine their activity budgets. Each of these was fitted with a neck collar mounting an accelerometer as well as two additional accelerometers placed on a head halter and a body harness over the shoulders of the animal. These were monitored continuously for three days. A classification accuracy of 89.6% was achieved for the grazing, walking and resting activities (i.e., a new class combining lying and standing activity). Triaxial accelerometer data showed that sheep spent 64% (95% CI 55% to 74%) of daylight time grazing, with grazing at night reduced to 14% (95% CI 8% to 20%). Similar activity budgets were achieved from the halter mounted sensors, but not those on a body harness. These results are consistent with previous studies directly observing daily activity of pasture-based sheep and can be applied in a variety of contexts to investigate animal health and welfare metrics e.g., to better understand the impact that young sheep can suffer when carrying even modest burdens of parasitic nematodes.
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Affiliation(s)
- Seer J. Ikurior
- School of Veterinary Science, Massey University, Palmerston North 4410, New Zealand; (R.A.C.-T.); (I.S.); (W.E.P.)
- College of Veterinary Medicine, University of Agriculture, Makurdi 970231, Nigeria
- Correspondence:
| | - Nelly Marquetoux
- EpiCentre, School of Veterinary Science, Massey University, Palmerston North 4410, New Zealand;
| | - Stephan T. Leu
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy 5371, Australia;
| | - Rene A. Corner-Thomas
- School of Veterinary Science, Massey University, Palmerston North 4410, New Zealand; (R.A.C.-T.); (I.S.); (W.E.P.)
- School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
| | - Ian Scott
- School of Veterinary Science, Massey University, Palmerston North 4410, New Zealand; (R.A.C.-T.); (I.S.); (W.E.P.)
| | - William E. Pomroy
- School of Veterinary Science, Massey University, Palmerston North 4410, New Zealand; (R.A.C.-T.); (I.S.); (W.E.P.)
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Harrison XA. A brief introduction to the analysis of time-series data from biologging studies. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200227. [PMID: 34176325 PMCID: PMC8237163 DOI: 10.1098/rstb.2020.0227] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2021] [Indexed: 12/24/2022] Open
Abstract
Recent advances in tagging and biologging technology have yielded unprecedented insights into wild animal physiology. However, time-series data from such wild tracking studies present numerous analytical challenges owing to their unique nature, often exhibiting strong autocorrelation within and among samples, low samples sizes and complicated random effect structures. Gleaning robust quantitative estimates from these physiological data, and, therefore, accurate insights into the life histories of the animals they pertain to, requires careful and thoughtful application of existing statistical tools. Using a combination of both simulated and real datasets, I highlight the key pitfalls associated with analysing physiological data from wild monitoring studies, and investigate issues of optimal study design, statistical power, and model precision and accuracy. I also recommend best practice approaches for dealing with their inherent limitations. This work will provide a concise, accessible roadmap for researchers looking to maximize the yield of information from complex and hard-won biologging datasets. This article is part of the theme issue 'Measuring physiology in free-living animals (Part II)'.
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Affiliation(s)
- Xavier A. Harrison
- Centre for Ecology and Conservation, University of Exeter, Penryn TR10 9FE, UK
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8
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Using Machine Learning for Remote Behaviour Classification-Verifying Acceleration Data to Infer Feeding Events in Free-Ranging Cheetahs. SENSORS 2021; 21:s21165426. [PMID: 34450868 PMCID: PMC8398415 DOI: 10.3390/s21165426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/01/2021] [Accepted: 08/05/2021] [Indexed: 11/25/2022]
Abstract
Behavioural studies of elusive wildlife species are challenging but important when they are threatened and involved in human-wildlife conflicts. Accelerometers (ACCs) and supervised machine learning algorithms (MLAs) are valuable tools to remotely determine behaviours. Here we used five captive cheetahs in Namibia to test the applicability of ACC data in identifying six behaviours by using six MLAs on data we ground-truthed by direct observations. We included two ensemble learning approaches and a probability threshold to improve prediction accuracy. We used the model to then identify the behaviours in four free-ranging cheetah males. Feeding behaviours identified by the model and matched with corresponding GPS clusters were verified with previously identified kill sites in the field. The MLAs and the two ensemble learning approaches in the captive cheetahs achieved precision (recall) ranging from 80.1% to 100.0% (87.3% to 99.2%) for resting, walking and trotting/running behaviour, from 74.4% to 81.6% (54.8% and 82.4%) for feeding behaviour and from 0.0% to 97.1% (0.0% and 56.2%) for drinking and grooming behaviour. The model application to the ACC data of the free-ranging cheetahs successfully identified all nine kill sites and 17 of the 18 feeding events of the two brother groups. We demonstrated that our behavioural model reliably detects feeding events of free-ranging cheetahs. This has useful applications for the determination of cheetah kill sites and helping to mitigate human-cheetah conflicts.
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Dickinson ER, Twining JP, Wilson R, Stephens PA, Westander J, Marks N, Scantlebury DM. Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids. MOVEMENT ECOLOGY 2021; 9:28. [PMID: 34099067 PMCID: PMC8186069 DOI: 10.1186/s40462-021-00265-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 05/18/2021] [Indexed: 05/30/2023]
Abstract
BACKGROUND Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. METHODS Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. RESULTS Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%). CONCLUSIONS We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild.
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Affiliation(s)
- Eleanor R Dickinson
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK.
| | - Joshua P Twining
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - Rory Wilson
- Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales, UK
| | - Philip A Stephens
- Conservation Ecology Group, Department of Biosciences, Durham University, South Road, Durham, DH1 3LE, UK
| | - Jennie Westander
- Kolmården Wildlife Park, SE-618 92, Kolmården, Sweden
- Öknaskolans Naturbruksgymnasium, SE-611 99, Tystberga, Sweden
| | - Nikki Marks
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
| | - David M Scantlebury
- School of Biological Sciences, Institute for Global Food Security, Queen's University Belfast, 19 Chlorine Gardens, Belfast, BT9 5DL, Northern Ireland, UK
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Jeantet L, Vigon V, Geiger S, Chevallier D. Fully Convolutional Neural Network: A solution to infer animal behaviours from multi-sensor data. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109555] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Brandes S, Sicks F, Berger A. Behaviour Classification on Giraffes ( Giraffa camelopardalis) Using Machine Learning Algorithms on Triaxial Acceleration Data of Two Commonly Used GPS Devices and Its Possible Application for Their Management and Conservation. SENSORS (BASEL, SWITZERLAND) 2021; 21:2229. [PMID: 33806750 PMCID: PMC8005050 DOI: 10.3390/s21062229] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/15/2021] [Accepted: 03/18/2021] [Indexed: 01/08/2023]
Abstract
Averting today's loss of biodiversity and ecosystem services can be achieved through conservation efforts, especially of keystone species. Giraffes (Giraffa camelopardalis) play an important role in sustaining Africa's ecosystems, but are 'vulnerable' according to the IUCN Red List since 2016. Monitoring an animal's behavior in the wild helps to develop and assess their conservation management. One mechanism for remote tracking of wildlife behavior is to attach accelerometers to animals to record their body movement. We tested two different commercially available high-resolution accelerometers, e-obs and Africa Wildlife Tracking (AWT), attached to the top of the heads of three captive giraffes and analyzed the accuracy of automatic behavior classifications, focused on the Random Forests algorithm. For both accelerometers, behaviors of lower variety in head and neck movements could be better predicted (i.e., feeding above eye level, mean prediction accuracy e-obs/AWT: 97.6%/99.7%; drinking: 96.7%/97.0%) than those with a higher variety of body postures (such as standing: 90.7-91.0%/75.2-76.7%; rumination: 89.6-91.6%/53.5-86.5%). Nonetheless both devices come with limitations and especially the AWT needs technological adaptations before applying it on animals in the wild. Nevertheless, looking at the prediction results, both are promising accelerometers for behavioral classification of giraffes. Therefore, these devices when applied to free-ranging animals, in combination with GPS tracking, can contribute greatly to the conservation of giraffes.
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Affiliation(s)
- Stefanie Brandes
- Institut für Biochemie und Biologie, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany;
- Leibniz-Institute for Zoo- and Wildlife Research, Alfred-Kowalke-Straße 17, 10315 Berlin, Germany
| | - Florian Sicks
- Tierpark Berlin-Friedrichsfelde GmbH, Am Tierpark 125, 10319 Berlin, Germany;
| | - Anne Berger
- Leibniz-Institute for Zoo- and Wildlife Research, Alfred-Kowalke-Straße 17, 10315 Berlin, Germany
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Glass TW, Breed GA, Robards MD, Williams CT, Kielland K. Accounting for unknown behaviors of free-living animals in accelerometer-based classification models: Demonstration on a wide-ranging mesopredator. ECOL INFORM 2020. [DOI: 10.1016/j.ecoinf.2020.101152] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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Wijers M, Trethowan P, Du Preez B, Chamaillé-Jammes S, Loveridge AJ, Macdonald DW, Markham A. Vocal discrimination of African lions and its potential for collar-free tracking. BIOACOUSTICS 2020. [DOI: 10.1080/09524622.2020.1829050] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Matthew Wijers
- Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Department of Zoology, University of Oxford, Oxford, UK
| | - Paul Trethowan
- Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Department of Zoology, University of Oxford, Oxford, UK
| | - Byron Du Preez
- Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Department of Zoology, University of Oxford, Oxford, UK
| | - Simon Chamaillé-Jammes
- CEFE, CNRS, University Montpellier, University Paul Valéry Montpellier, EPHE, IRD, Montpellier, France
- Mammal Research Institute, Department of Zoology and Entomology, University of Pretoria, Pretoria, South Africa
| | - Andrew J. Loveridge
- Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Department of Zoology, University of Oxford, Oxford, UK
| | - David W. Macdonald
- Wildlife Conservation Research Unit, Recanati-Kaplan Centre, Department of Zoology, University of Oxford, Oxford, UK
| | - Andrew Markham
- Department of Computer Science, University of Oxford, Oxford, UK
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14
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Ferdinandy B, Gerencsér L, Corrieri L, Perez P, Újváry D, Csizmadia G, Miklósi Á. Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures. PLoS One 2020; 15:e0236092. [PMID: 32687528 PMCID: PMC7371169 DOI: 10.1371/journal.pone.0236092] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 06/28/2020] [Indexed: 11/23/2022] Open
Abstract
Automated monitoring of the movements and behaviour of animals is a valuable research tool. Recently, machine learning tools were applied to many species to classify units of behaviour. For the monitoring of wild species, collecting enough data for training models might be problematic, thus we examine how machine learning models trained on one species can be applied to another closely related species with similar behavioural conformation. We contrast two ways to calculate accuracies, termed here as overall and threshold accuracy, because the field has yet to define solid standards for reporting and measuring classification performances. We measure 21 dogs and 7 wolves, and find that overall accuracies are between 51 and 60% for classifying 8 behaviours (lay, sit, stand, walk, trot, run, eat, drink) when training and testing data are from the same species and between 41 and 51% when training and testing is cross-species. We show that using data from dogs to predict the behaviour of wolves is feasible. We also show that optimising the model for overall accuracy leads to similar overall and threshold accuracies, while optimizing for threshold accuracy leads to threshold accuracies well above 80%, but yielding very low overall accuracies, often below the chance level. Moreover, we show that the most common method for dividing the data between training and testing data (random selection of test data) overestimates the accuracy of models when applied to data of new specimens. Consequently, we argue that for the most common goals of animal behaviour recognition overall accuracy should be the preferred metric. Considering, that often the goal is to collect movement data without other methods of observation, we argue that training data and testing data should be divided by individual and not randomly.
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Affiliation(s)
- Bence Ferdinandy
- MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary
- * E-mail:
| | - Linda Gerencsér
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
- MTA-ELTE ‘Lendület’ Neuroethology of Communication Research Group, Budapest, Hungary
| | - Luca Corrieri
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
| | - Paula Perez
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
| | - Dóra Újváry
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
| | - Gábor Csizmadia
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
| | - Ádám Miklósi
- MTA-ELTE Comparative Ethology Research Group, Budapest, Hungary
- Department of Ethology, Eötvös Loránd University, Budapest, Hungary
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15
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Rast W, Kimmig SE, Giese L, Berger A. Machine learning goes wild: Using data from captive individuals to infer wildlife behaviours. PLoS One 2020; 15:e0227317. [PMID: 32369485 PMCID: PMC7200095 DOI: 10.1371/journal.pone.0227317] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 03/29/2020] [Indexed: 11/19/2022] Open
Abstract
1. Remotely tracking distinct behaviours of animals using acceleration data and machine learning has been carried out successfully in several species in captive settings. In order to study the ecology of animals in natural habitats, such behaviour classification models need to be transferred to wild individuals. However, at present, the development of those models usually requires direct observation of the target animals. 2. The goal of this study was to infer the behaviour of wild, free-roaming animals from acceleration data by training behaviour classification models on captive individuals, without the necessity to observe their wild conspecifics. We further sought to develop methods to validate the credibility of the resulting behaviour extrapolations. 3. We trained two machine learning algorithms proposed by the literature, Random Forest (RF) and Support Vector Machine (SVM), on data from captive red foxes (Vulpes vulpes) and later applied them to data from wild foxes. We also tested a new advance for behaviour classification, by applying a moving window to an Artificial Neural Network (ANN). Finally, we investigated four strategies to validate our classification output. 4. While all three machine learning algorithms performed well under training conditions (Kappa values: RF (0.82), SVM (0.78), ANN (0.85)), the established methods, RF and SVM, failed in classifying distinct behaviours when transferred from captive to wild foxes. Behaviour classification with the ANN and a moving window, in contrast, inferred distinct behaviours and showed consistent results for most individuals. 5. Our approach is a substantial improvement over the methods previously proposed in the literature as it generated plausible results for wild fox behaviour. We were able to infer the behaviour of wild animals that have never been observed in the wild and to further illustrate the credibility of the output. This framework is not restricted to foxes but can be applied to infer the behaviour of many other species and thus empowers new advances in behavioural ecology.
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Affiliation(s)
- Wanja Rast
- Department of Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
| | - Sophia Elisabeth Kimmig
- Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
| | - Lisa Giese
- Department of Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
| | - Anne Berger
- Department of Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
- * E-mail:
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16
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Jeantet L, Planas-Bielsa V, Benhamou S, Geiger S, Martin J, Siegwalt F, Lelong P, Gresser J, Etienne D, Hiélard G, Arque A, Regis S, Lecerf N, Frouin C, Benhalilou A, Murgale C, Maillet T, Andreani L, Campistron G, Delvaux H, Guyon C, Richard S, Lefebvre F, Aubert N, Habold C, le Maho Y, Chevallier D. Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology. ROYAL SOCIETY OPEN SCIENCE 2020; 7:200139. [PMID: 32537218 PMCID: PMC7277266 DOI: 10.1098/rsos.200139] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/17/2020] [Indexed: 06/10/2023]
Abstract
The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.
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Affiliation(s)
- Lorène Jeantet
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Víctor Planas-Bielsa
- Centre Scientifique de Monaco, Département de Biologie Polaire, 8 quai Antoine Ier, MC 98000Monaco
| | - Simon Benhamou
- Centre d’Écologie Fonctionnelle et Évolutive, CNRS, Montpellier, France & Cogitamus Lab
| | - Sebastien Geiger
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Jordan Martin
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Flora Siegwalt
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Pierre Lelong
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Julie Gresser
- DEAL Martinique, Pointe de Jaham, BP 7212, 97274 Schoelcher Cedex, France
| | - Denis Etienne
- DEAL Martinique, Pointe de Jaham, BP 7212, 97274 Schoelcher Cedex, France
| | - Gaëlle Hiélard
- Office de l'Eau Martinique, 7 Avenue Condorcet, BP 32, 97201 Fort-de-France, Martinique, France
| | - Alexandre Arque
- Office de l'Eau Martinique, 7 Avenue Condorcet, BP 32, 97201 Fort-de-France, Martinique, France
| | - Sidney Regis
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Nicolas Lecerf
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Cédric Frouin
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | | | - Céline Murgale
- Association POEMM, 73 lot papayers, Anse a l'âne, 97229 Les Trois Ilets, Martinique
| | - Thomas Maillet
- Association POEMM, 73 lot papayers, Anse a l'âne, 97229 Les Trois Ilets, Martinique
| | - Lucas Andreani
- Association POEMM, 73 lot papayers, Anse a l'âne, 97229 Les Trois Ilets, Martinique
| | - Guilhem Campistron
- Association POEMM, 73 lot papayers, Anse a l'âne, 97229 Les Trois Ilets, Martinique
| | - Hélène Delvaux
- DEAL Guyane, Rue Carlos Finley, CS 76003, 97306 Cayenne Cedex, France
| | - Christelle Guyon
- DEAL Guyane, Rue Carlos Finley, CS 76003, 97306 Cayenne Cedex, France
| | - Sandrine Richard
- Centre National d'Etudes Spatiales, Centre Spatial Guyanais, BP 726, 97387 Kourou Cedex, Guyane
| | - Fabien Lefebvre
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Nathalie Aubert
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Caroline Habold
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
| | - Yvon le Maho
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
- Centre Scientifique de Monaco, Département de Biologie Polaire, 8 quai Antoine Ier, MC 98000Monaco
| | - Damien Chevallier
- Institut Pluridisciplinaire Hubert Curien, CNRS–Unistra, 67087 Strasbourg, France
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17
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Short-term effects of GPS collars on the activity, behavior, and adrenal response of scimitar-horned oryx (Oryx dammah). PLoS One 2020; 15:e0221843. [PMID: 32045413 PMCID: PMC7012457 DOI: 10.1371/journal.pone.0221843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 01/21/2020] [Indexed: 12/02/2022] Open
Abstract
GPS collars have revolutionized the field of animal ecology, providing detailed information on animal movement and the habitats necessary for species survival. GPS collars also have the potential to cause adverse effects ranging from mild irritation to severe tissue damage, reduced fitness, and death. The impact of GPS collars on the behavior, stress, or activity, however, have rarely been tested on study species prior to release. The objective of our study was to provide a comprehensive assessment of the short-term effects of GPS collars fitted on scimitar-horned oryx (Oryx dammah), an extinct-in-the-wild antelope once widely distributed across Sahelian grasslands in North Africa. We conducted behavioral observations, assessed fecal glucocorticoid metabolites (FGM), and evaluated high-resolution data from tri-axial accelerometers. Using a series of datasets and methodologies, we illustrate clear but short-term effects to animals fitted with GPS collars from two separate manufacturers (Advanced Telemetry Systems—G2110E; Vectronic Aerospace—Vertex Plus). Behavioral observations highlighted a significant increase in the amount of headshaking from pre-treatment levels, returning below baseline levels during the post-treatment period (>3 days post-collaring). Similarly, FGM concentrations increased after GPS collars were fitted on animals but returned to pre-collaring levels within 5 days of collaring. Lastly, tri-axial accelerometers, collecting data at eight positions per second, indicated a > 480 percent increase in the amount of hourly headshaking immediately after collaring. This post-collaring increase in headshaking was estimated to decline in magnitude within 4 hours after GPS collar fitting. These effects constitute a handling and/or habituation response (model dependent), with animals showing short-term responses in activity, behavior, and stress that dissipated within several hours to several days of being fitted with GPS collars. Importantly, none of our analyses indicated any long-term effects that would have more pressing animal welfare concerns.
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18
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Monitoring canid scent marking in space and time using a biologging and machine learning approach. Sci Rep 2020; 10:588. [PMID: 31953418 PMCID: PMC6969016 DOI: 10.1038/s41598-019-57198-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/21/2019] [Indexed: 11/12/2022] Open
Abstract
For canid species, scent marking plays a critical role in territoriality, social dynamics, and reproduction. However, due in part to human dependence on vision as our primary sensory modality, research on olfactory communication is hampered by a lack of tractable methods. In this study, we leverage a powerful biologging approach, using accelerometers in concert with GPS loggers to monitor and describe scent-marking events in time and space. We performed a validation experiment with domestic dogs, monitoring them by video concurrently with the novel biologging approach. We attached an accelerometer to the pelvis of 31 dogs (19 males and 12 females), detecting raised-leg and squat posture urinations by monitoring the change in device orientation. We then deployed this technique to describe the scent marking activity of 3 guardian dogs as they defend livestock from coyote depredation in California, providing an example use-case for the technique. During validation, the algorithm correctly classified 92% of accelerometer readings. High performance was partly due to the conspicuous signatures of archetypal raised-leg postures in the accelerometer data. Accuracy did not vary with the weight, age, and sex of the dogs, resulting in a method that is broadly applicable across canid species’ morphologies. We also used models trained on each individual to detect scent marking of others to emulate the use of captive surrogates for model training. We observed no relationship between the similarity in body weight between the dog pairs and the overall accuracy of predictions, although models performed best when trained and tested on the same individual. We discuss how existing methods in the field of movement ecology can be extended to use this exciting new data type. This paper represents an important first step in opening new avenues of research by leveraging the power of modern-technologies and machine-learning to this field.
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19
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Muazu Musa R, Abdul Majeed A, Taha Z, Abdullah M, Husin Musawi Maliki A, Azura Kosni N. The application of Artificial Neural Network and k-Nearest Neighbour classification models in the scouting of high-performance archers from a selected fitness and motor skill performance parameters. Sci Sports 2019. [DOI: 10.1016/j.scispo.2019.02.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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20
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Rast W, Barthel LMF, Berger A. Music Festival Makes Hedgehogs Move: How Individuals Cope Behaviorally in Response to Human-Induced Stressors. Animals (Basel) 2019; 9:ani9070455. [PMID: 31323837 PMCID: PMC6680799 DOI: 10.3390/ani9070455] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/01/2019] [Accepted: 07/09/2019] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Mega-events like concerts or festivals can still impact wildlife even when protective measures are taken. We remotely observed eight hedgehogs in a Berlin city park before and during a music festival using measuring devices attached to their bodies. While the actual festival only lasted two days (with about 70,000 visitors each day), setting the area up and removing the stages and stalls took 17 days in total. Construction work continued around the clock, causing an increase in light, noise and human presence throughout the night. In response, the hedgehogs showed clear changes in their behavior in comparison to the 19-day period just before the festival. We found, however, that different individuals responded differently to these changes in their environment. This individuality and behavioral flexibility could be one reason why hedgehogs are able to live in big cities. Abstract Understanding the impact of human activities on wildlife behavior and fitness can improve their sustainability. In a pilot study, we wanted to identify behavioral responses to anthropogenic stress in an urban species during a semi-experimental field study. We equipped eight urban hedgehogs (Erinaceus europaeus; four per sex) with bio-loggers to record their behavior before and during a mega music festival (2 × 19 days) in Treptower Park, Berlin. We used GPS (Global Positioning System) to monitor spatial behavior, VHF (Very High Frequency)-loggers to quantify daily nest utilization, and accelerometers to distinguish between different behaviors at a high resolution and to calculate daily disturbance (using Degrees of Functional Coupling). The hedgehogs showed clear behavioral differences between the pre-festival and festival phases. We found evidence supporting highly individual strategies, varying between spatial and temporal evasion of the disturbance. Averaging the responses of the individual animals or only examining one behavioral parameter masked these potentially different individual coping strategies. Using a meaningful combination of different minimal-invasive bio-logger types, we were able to show high inter-individual behavioral variance of urban hedgehogs in response to an anthropogenic disturbance, which might be a precondition to persist successfully in urban environments.
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Affiliation(s)
- Wanja Rast
- Department Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, 10315 Berlin, Germany
| | - Leon M F Barthel
- Department Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, 10315 Berlin, Germany
- Berlin Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany
| | - Anne Berger
- Department Evolutionary Ecology, Leibniz Institute for Zoo and Wildlife Research (IZW), Alfred-Kowalke-Straße 17, 10315 Berlin, Germany.
- Berlin Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany.
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21
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Studd EK, Boudreau MR, Majchrzak YN, Menzies AK, Peers MJL, Seguin JL, Lavergne SG, Boonstra R, Murray DL, Boutin S, Humphries MM. Use of Acceleration and Acoustics to Classify Behavior, Generate Time Budgets, and Evaluate Responses to Moonlight in Free-Ranging Snowshoe Hares. Front Ecol Evol 2019. [DOI: 10.3389/fevo.2019.00154] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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22
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Chakravarty P, Cozzi G, Ozgul A, Aminian K. A novel biomechanical approach for animal behaviour recognition using accelerometers. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13172] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Pritish Chakravarty
- Interfaculty Institute of Bioengineering (IBI‐STI)Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland
| | - Gabriele Cozzi
- Institute of Evolutionary Biology and Environmental StudiesUniversity of Zurich Zurich Switzerland
- Kalahari Research CentreKuruman River Reserve Van Zylsrus South Africa
| | - Arpat Ozgul
- Institute of Evolutionary Biology and Environmental StudiesUniversity of Zurich Zurich Switzerland
- Kalahari Research CentreKuruman River Reserve Van Zylsrus South Africa
| | - Kamiar Aminian
- Interfaculty Institute of Bioengineering (IBI‐STI)Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland
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Patterson A, Gilchrist HG, Chivers L, Hatch S, Elliott K. A comparison of techniques for classifying behavior from accelerometers for two species of seabird. Ecol Evol 2019; 9:3030-3045. [PMID: 30962879 PMCID: PMC6434605 DOI: 10.1002/ece3.4740] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 10/11/2018] [Accepted: 10/30/2018] [Indexed: 12/12/2022] Open
Abstract
The behavior of many wild animals remains a mystery, as it is difficult to quantify behavior of species that cannot be easily followed throughout their daily or seasonal movements. Accelerometers can solve some of these mysteries, as they collect activity data at a high temporal resolution (<1 s), can be relatively small (<1 g) so they minimally disrupt behavior, and are increasingly capable of recording data for long periods. Nonetheless, there is a need for increased validation of methods to classify animal behavior from accelerometers to promote widespread adoption of this technology in ecology. We assessed the accuracy of six different behavioral assignment methods for two species of seabird, thick-billed murres (Uria lomvia) and black-legged kittiwakes (Rissa tridactyla). We identified three behaviors using tri-axial accelerometers: standing, swimming, and flying, after classifying diving using a pressure sensor for murres. We evaluated six classification methods relative to independent classifications from concurrent GPS tracking data. We used four variables for classification: depth, wing beat frequency, pitch, and dynamic acceleration. Average accuracy for all methods was >98% for murres, and 89% and 93% for kittiwakes during incubation and chick rearing, respectively. Variable selection showed that classification accuracy did not improve with more than two (kittiwakes) or three (murres) variables. We conclude that simple methods of behavioral classification can be as accurate for classifying basic behaviors as more complex approaches, and that identifying suitable accelerometer metrics is more important than using a particular classification method when the objective is to develop a daily activity or energy budget. Highly accurate daily activity budgets can be generated from accelerometer data using multiple methods and a small number of accelerometer metrics; therefore, identifying a suitable behavioral classification method should not be a barrier to using accelerometers in studies of seabird behavior and ecology.
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Affiliation(s)
- Allison Patterson
- Department of Natural Resource SciencesMcGill UniversitySte Anne‐de‐BellevueQuebecCanada
| | - Hugh Grant Gilchrist
- Environment and Climate Change CanadaNational Wildlife Research CentreOttawaOntarioCanada
| | | | - Scott Hatch
- Institute for Seabird Research and ConservationAnchorageAlaska
| | - Kyle Elliott
- Department of Natural Resource SciencesMcGill UniversitySte Anne‐de‐BellevueQuebecCanada
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24
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Sparse Contextual Task Learning and Classification to Assist Mobile Robot Teleoperation with Introspective Estimation. J INTELL ROBOT SYST 2019. [DOI: 10.1007/s10846-017-0681-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Muazu Musa R, P. P. Abdul Majeed A, Taha Z, Chang SW, Ab. Nasir AF, Abdullah MR. A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS One 2019; 14:e0209638. [PMID: 30605456 PMCID: PMC6317817 DOI: 10.1371/journal.pone.0209638] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Accepted: 12/04/2018] [Indexed: 11/18/2022] Open
Abstract
k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
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Affiliation(s)
- Rabiu Muazu Musa
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Malaysia
- Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
- * E-mail:
| | - Anwar P. P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Zahari Taha
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Siow Wee Chang
- Bioinformatics Programme, Institute of Biological Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Ahmad Fakhri Ab. Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Malaysia
| | - Mohamad Razali Abdullah
- Faculty of Applied Social Sciences, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
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27
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Studd EK, Landry‐Cuerrier M, Menzies AK, Boutin S, McAdam AG, Lane JE, Humphries MM. Behavioral classification of low-frequency acceleration and temperature data from a free-ranging small mammal. Ecol Evol 2019; 9:619-630. [PMID: 30680142 PMCID: PMC6342100 DOI: 10.1002/ece3.4786] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 10/10/2018] [Accepted: 10/31/2018] [Indexed: 01/03/2023] Open
Abstract
The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal-borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under-acknowledged consideration in biologging is the trade-off between sampling rate and sampling duration, created by battery- (or memory-) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low-frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free-ranging red squirrels (200-300 g) that were fitted with accelerometers (2 g) recording tri-axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long-duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi-month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.
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Affiliation(s)
- Emily K. Studd
- Department of Natural Resource SciencesMcGill UniversitySainte‐Anne‐de‐BellevueQuebecCanada
| | | | - Allyson K. Menzies
- Department of Natural Resource SciencesMcGill UniversitySainte‐Anne‐de‐BellevueQuebecCanada
| | - Stan Boutin
- Department of Biological SciencesUniversity of AlbertaEdmontonAlbertaCanada
| | - Andrew G. McAdam
- Department of Integrative BiologyUniversity of GuelphGuelphOntarioCanada
| | - Jeffrey E. Lane
- Department of BiologyUniversity of SaskatchewanSaskatoonSaskatchewanCanada
| | - Murray M. Humphries
- Department of Natural Resource SciencesMcGill UniversitySainte‐Anne‐de‐BellevueQuebecCanada
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Beltramino LE, Venerus LA, Trobbiani GA, Wilson RP, Ciancio JE. Activity budgets for the sedentary Argentine sea bassAcanthistius patachonicusinferred from accelerometer data loggers. AUSTRAL ECOL 2018. [DOI: 10.1111/aec.12696] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lucas E. Beltramino
- Centro para el Estudio de Sistemas Marinos (CONICET); Edificio CCT CONICET - CENPAT; Blvd. Brown 2915 U9120ACD Puerto Madryn Chubut Argentina
| | - Leonardo A. Venerus
- Centro para el Estudio de Sistemas Marinos (CONICET); Edificio CCT CONICET - CENPAT; Blvd. Brown 2915 U9120ACD Puerto Madryn Chubut Argentina
| | - Gastón A. Trobbiani
- Centro para el Estudio de Sistemas Marinos (CONICET); Edificio CCT CONICET - CENPAT; Blvd. Brown 2915 U9120ACD Puerto Madryn Chubut Argentina
| | - Rory P. Wilson
- Swansea Lab for Animal Movement, Biosciences; College of Science; Swansea University; Swansea Wales UK
| | - Javier E. Ciancio
- Centro para el Estudio de Sistemas Marinos (CONICET); Edificio CCT CONICET - CENPAT; Blvd. Brown 2915 U9120ACD Puerto Madryn Chubut Argentina
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29
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Wijers M, Trethowan P, Markham A, du Preez B, Chamaillé-Jammes S, Loveridge A, Macdonald D. Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance. Front Ecol Evol 2018. [DOI: 10.3389/fevo.2018.00171] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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30
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Wilson RP, Holton MD, Virgilio A, Williams H, Shepard ELC, Lambertucci S, Quintana F, Sala JE, Balaji B, Lee ES, Srivastava M, Scantlebury DM, Duarte CM. Give the machine a hand: A Boolean time‐based decision‐tree template for rapidly finding animal behaviours in multisensor data. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13069] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Rory P. Wilson
- Department of BiosciencesCollege of ScienceSwansea University Swansea UK
| | - Mark D. Holton
- Department of Computing ScienceCollege of ScienceSwansea University Swansea UK
| | - Agustina Virgilio
- Grupo de Biología de la ConservaciónLaboratorio EcotonoINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
- Grupo de Ecología CuantitativaINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
| | - Hannah Williams
- Department of BiosciencesCollege of ScienceSwansea University Swansea UK
| | | | - Sergio Lambertucci
- Grupo de Biología de la ConservaciónLaboratorio EcotonoINIBIOMA (CONICET‐Universidad Nacional del Comahue) Bariloche Argentina
| | - Flavio Quintana
- Instituto de Biologia de Organismos Marinos IBIOMAR‐CONICET (9120) Puerto Madryn Chubut Argentina
| | - Juan E. Sala
- Instituto de Biologia de Organismos Marinos IBIOMAR‐CONICET (9120) Puerto Madryn Chubut Argentina
| | - Bharathan Balaji
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - Eun Sun Lee
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - Mani Srivastava
- Department of Electrical and Computer EngineeringUniversity of California, Los Angeles Los Angeles California
| | - D. Michael Scantlebury
- School of Biological SciencesInstitute for Global Food SecurityQueen's University Belfast Belfast UK
| | - Carlos M. Duarte
- Red Sea Research CentreKing Abdullah University of Science and Technology Thuwal Saudi Arabia
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31
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Seidel DP, Dougherty E, Carlson C, Getz WM. Ecological metrics and methods for GPS movement data. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE : IJGIS 2018; 32:2272-2293. [PMID: 30631244 PMCID: PMC6322554 DOI: 10.1080/13658816.2018.1498097] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 07/04/2018] [Indexed: 05/07/2023]
Abstract
The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology-inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation planning to public health. Despite the growing availability and general accessibility of animal movement data, much potential remains for the analytical methods of movement ecology to be incorporated in all types of geographic analyses. This review provides for the Geographical Information Sciences (GIS) community an overview of the most common movement metrics and methods of analysis employed by animal ecologists. Through illustrative applications, we emphasize the potential for movement analyses to promote transdisciplinary GIS/wildlife-ecology research.
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Affiliation(s)
- Dana Paige Seidel
- Department of Environmental Science, Policy, & Management, University of California, Berkeley
| | - Eric Dougherty
- Department of Environmental Science, Policy, & Management, University of California, Berkeley
| | - Colin Carlson
- National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, MD 21401, USA
- Department of Biology, Georgetown University, Washington, D.C. 20057, USA
| | - Wayne M. Getz
- Department of Environmental Science, Policy, & Management, University of California, Berkeley
- Schools of Mathematical Sciences, University of KwaZulu-Natal, South Africa
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32
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Brewster LR, Dale JJ, Guttridge TL, Gruber SH, Hansell AC, Elliott M, Cowx IG, Whitney NM, Gleiss AC. Development and application of a machine learning algorithm for classification of elasmobranch behaviour from accelerometry data. MARINE BIOLOGY 2018; 165:62. [PMID: 29563648 PMCID: PMC5842499 DOI: 10.1007/s00227-018-3318-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 01/31/2018] [Indexed: 05/15/2023]
Abstract
Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks (Negaprion brevirostris) at Bimini, Bahamas (25°44'N, 79°16'W). The sharks were observed to exhibit chafing, burst swimming, headshaking, resting and swimming in a semi-captive environment and these observations were used to ground-truth data for ML training and testing. ML methods included logistic regression, an artificial neural network, two random forest models, a gradient boosting model and a voting ensemble (VE) model, which combined the predictions of all other (base) models to improve classifier performance. The macro-averaged F-measure, an indicator of classifier performance, showed that the VE model improved overall classification (F-measure 0.88) above the strongest base learner model, gradient boosting (0.86). To test whether the VE model provided biologically meaningful results when applied to accelerometer data obtained from wild sharks, we investigated headshaking behaviour, as a proxy for prey capture, in relation to the variables: time of day, tidal phase and season. All variables were significant in predicting prey capture, with predations most likely to occur during early evening and less frequently during the dry season and high tides. These findings support previous hypotheses from sporadic visual observations.
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Affiliation(s)
- L. R. Brewster
- Bimini Biological Field Station Foundation, South Bimini, Bahamas
- Institute of Estuarine and Coastal Studies, University of Hull, Hull, HU6 7RX UK
- Hull International Fisheries Institute, University of Hull, Hull, HU6 7RX UK
| | - J. J. Dale
- Department of Biology, Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950 USA
| | - T. L. Guttridge
- Bimini Biological Field Station Foundation, South Bimini, Bahamas
| | - S. H. Gruber
- Bimini Biological Field Station Foundation, South Bimini, Bahamas
- Division of Marine Biology and Fisheries, Rosenstiel School of Marine and Atmospheric Science, 4600 Rickenbacker Causeway, Miami, FL 33149 USA
| | - A. C. Hansell
- Department of Fisheries Oceanography, School for Marine Science and Technology, University of Massachusetts Dartmouth, 836 South Rodney French Blvd, New Bedford, MA 02719 USA
| | - M. Elliott
- Institute of Estuarine and Coastal Studies, University of Hull, Hull, HU6 7RX UK
| | - I. G. Cowx
- Hull International Fisheries Institute, University of Hull, Hull, HU6 7RX UK
| | - N. M. Whitney
- Anderson Cabot Center for Ocean Life, New England Aquarium, Central Wharf, Boston, MA 02110 USA
| | - A. C. Gleiss
- Centre For Fish and Fisheries Research, School of Veterinary and Life Sciences, Murdoch University, 90 South Street, Perth, WA 6150 Australia
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Benaissa S, Tuyttens FAM, Plets D, de Pessemier T, Trogh J, Tanghe E, Martens L, Vandaele L, Van Nuffel A, Joseph W, Sonck B. On the use of on-cow accelerometers for the classification of behaviours in dairy barns. Res Vet Sci 2017; 125:425-433. [PMID: 29174287 DOI: 10.1016/j.rvsc.2017.10.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 08/23/2017] [Accepted: 10/28/2017] [Indexed: 10/18/2022]
Abstract
Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1Hz to 0.05Hz.
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Affiliation(s)
- Said Benaissa
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium; Institute for Agricultural and Fisheries Research (ILVO), Animal Sciences Unit, Scheldeweg 68, 9090 Melle, Belgium.
| | - Frank A M Tuyttens
- Institute for Agricultural and Fisheries Research (ILVO), Animal Sciences Unit, Scheldeweg 68, 9090 Melle, Belgium; Department of Nutrition, Genetics and Ethology, Laboratory for Ethology, Faculty of Veterinary Medicine, D8, Heidestraat 19, B-9820 Merelbeke, Belgium
| | - David Plets
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Toon de Pessemier
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Jens Trogh
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Emmeric Tanghe
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Luc Martens
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Leen Vandaele
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Annelies Van Nuffel
- Institute for Agricultural and Fisheries Research (ILVO), Technology and Food Science Unit, Burgemeester van Gansberghelaan 115 bus 1, 9820 Merelbeke, Belgium
| | - Wout Joseph
- Department of Information Technology, Ghent University/imec, iGent-Technologiepark 15, 9052 Ghent, Belgium
| | - Bart Sonck
- Institute for Agricultural and Fisheries Research (ILVO), Animal Sciences Unit, Scheldeweg 68, 9090 Melle, Belgium; Department of Biosystems Engineering, Faculty of Bioscience Engineering, Ghent University, Coupure links 653, B-9000 Ghent, Belgium
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34
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Sur M, Suffredini T, Wessells SM, Bloom PH, Lanzone M, Blackshire S, Sridhar S, Katzner T. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds. PLoS One 2017; 12:e0174785. [PMID: 28403159 PMCID: PMC5389810 DOI: 10.1371/journal.pone.0174785] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Accepted: 03/15/2017] [Indexed: 12/04/2022] Open
Abstract
Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
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Affiliation(s)
- Maitreyi Sur
- Department of Biological Sciences, Boise State University, Boise, Idaho, United States of America
- * E-mail:
| | - Tony Suffredini
- Sky Patrol Abatement, Simi Valley, California, United States of America
| | - Stephen M. Wessells
- U.S. Geological Survey Henderson, Henderson, Nevada, United States of America
| | - Peter H. Bloom
- Bloom Biological, Santa Ana, California, United States of America
| | - Michael Lanzone
- Cellular Tracking Technologies, Rio Grande, New Jersey, United States of America
| | - Sheldon Blackshire
- Cellular Tracking Technologies, Rio Grande, New Jersey, United States of America
| | - Srisarguru Sridhar
- Department of Computer Science, Boise State University, Boise, Idaho, United States of America
| | - Todd Katzner
- U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, Idaho, United States of America
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35
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Jeanniard‐du‐Dot T, Guinet C, Arnould JP, Speakman JR, Trites AW. Accelerometers can measure total and activity‐specific energy expenditures in free‐ranging marine mammals only if linked to time‐activity budgets. Funct Ecol 2016. [DOI: 10.1111/1365-2435.12729] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Tiphaine Jeanniard‐du‐Dot
- Marine Mammal Research Unit Institute for the Oceans and Fisheries University of British Columbia 2202 Main Mall, AERL bldg. Vancouver British ColumbiaV6T1Z4 Canada
- Centre d'Etudes Biologiques de Chizé CNRS 79360 Villiers en Bois France
| | - Christophe Guinet
- Centre d'Etudes Biologiques de Chizé CNRS 79360 Villiers en Bois France
| | - John P.Y. Arnould
- School of Life and Environmental Sciences Deakin University 221 Burwood Highway Burwood Victoria3125 Australia
| | - John R. Speakman
- The Institute of Biological and Environmental Sciences Zoology Bldg, Tillydrone Avenue AberdeenAB24 2TZ UK
| | - Andrew W. Trites
- Marine Mammal Research Unit Institute for the Oceans and Fisheries University of British Columbia 2202 Main Mall, AERL bldg. Vancouver British ColumbiaV6T1Z4 Canada
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36
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Allen AN, Goldbogen JA, Friedlaender AS, Calambokidis J. Development of an automated method of detecting stereotyped feeding events in multisensor data from tagged rorqual whales. Ecol Evol 2016; 6:7522-7535. [PMID: 28725418 PMCID: PMC5513260 DOI: 10.1002/ece3.2386] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2016] [Revised: 07/11/2016] [Accepted: 07/18/2016] [Indexed: 11/29/2022] Open
Abstract
The introduction of animal‐borne, multisensor tags has opened up many opportunities for ecological research, making previously inaccessible species and behaviors observable. The advancement of tag technology and the increasingly widespread use of bio‐logging tags are leading to large volumes of sometimes extremely detailed data. With the increasing quantity and duration of tag deployments, a set of tools needs to be developed to aid in facilitating and standardizing the analysis of movement sensor data. Here, we developed an observation‐based decision tree method to detect feeding events in data from multisensor movement tags attached to fin whales (Balaenoptera physalus). Fin whales exhibit an energetically costly and kinematically complex foraging behavior called lunge feeding, an intermittent ram filtration mechanism. Using this automated system, we identified feeding lunges in 19 fin whales tagged with multisensor tags, during a total of over 100 h of continuously sampled data. Using movement sensor and hydrophone data, the automated lunge detector correctly identified an average of 92.8% of all lunges, with a false‐positive rate of 9.5%. The strong performance of our automated feeding detector demonstrates an effective, straightforward method of activity identification in animal‐borne movement tag data. Our method employs a detection algorithm that utilizes a hierarchy of simple thresholds based on knowledge of observed features of feeding behavior, a technique that is readily modifiable to fit a variety of species and behaviors. Using automated methods to detect behavioral events in tag records will significantly decrease data analysis time and aid in standardizing analysis methods, crucial objectives with the rapidly increasing quantity and variety of on‐animal tag data. Furthermore, our results have implications for next‐generation tag design, especially long‐term tags that can be outfitted with on‐board processing algorithms that automatically detect kinematic events and transmit ethograms via acoustic or satellite telemetry.
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Affiliation(s)
- Ann N Allen
- Cascadia Research Collective 218 1/2 W. 4th Avenue Olympia Washington 98501
| | - Jeremy A Goldbogen
- Department of Biology Hopkins Marine Station Stanford University Pacific Grove California 93950
| | - Ari S Friedlaender
- Department of Fisheries and Wildlife Marine Mammal Institute Hatfield Marine Science Center Oregon State University Newport Oregon 97365
| | - John Calambokidis
- Cascadia Research Collective 218 1/2 W. 4th Avenue Olympia Washington 98501
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37
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Katzner TE, Turk PJ, Duerr AE, Miller TA, Lanzone MJ, Cooper JL, Brandes D, Tremblay JA, Lemaître J. Use of multiple modes of flight subsidy by a soaring terrestrial bird, the golden eagle Aquila chrysaetos, when on migration. J R Soc Interface 2016; 12:rsif.2015.0530. [PMID: 26538556 DOI: 10.1098/rsif.2015.0530] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Large birds regularly use updrafts to subsidize flight. Although most research on soaring bird flight has focused on use of thermal updrafts, there is evidence suggesting that many species are likely to use multiple modes of subsidy. We tested the degree to which a large soaring species uses multiple modes of subsidy to provide insights into the decision-making that underlies flight behaviour. We statistically classified more than 22 000 global positioning satellite-global system for mobile communications telemetry points collected at 30-s intervals to identify the type of subsidized flight used by 32 migrating golden eagles during spring in eastern North America. Eagles used subsidized flight on 87% of their journey. They spent 41.9% ± 1.5 ([Formula: see text], range: 18-56%) of their subsidized northbound migration using thermal soaring, 45.2% ± 2.1 (12-65%) of time gliding between thermals, and 12.9% ± 2.2 (1-55%) of time using orographic updrafts. Golden eagles responded to the variable local-scale meteorological events they encountered by switching flight behaviour to take advantage of multiple modes of subsidy. Orographic soaring occurred more frequently in morning and evening, earlier in the migration season, and when crosswinds and tail winds were greatest. Switching between flight modes allowed migration for relatively longer periods each day and frequent switching behaviour has implications for a better understanding of avian flight behaviour and of the evolution of use of subsidy in flight.
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Affiliation(s)
- Todd E Katzner
- Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV 26506, USA USDA Forest Service, Northern Research Station, Parsons, WV 26287, USA
| | - Philip J Turk
- Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA
| | - Adam E Duerr
- Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV 26506, USA
| | - Tricia A Miller
- Division of Forestry and Natural Resources, West Virginia University, Morgantown, WV 26506, USA
| | - Michael J Lanzone
- Cellular Tracking Technologies, LLC, 2405 North Center Avenue, Somerset, PA 15501, USA
| | - Jeff L Cooper
- Virginia Department of Game and Inland Fisheries, Fredericksburg, VA 22401, USA
| | - David Brandes
- Department of Civil and Environmental Engineering, Lafayette College, Easton, PA 18042, USA
| | | | - Jérôme Lemaître
- Ministère des Forêts, de la Faune et des Parcs, Québec, Québec, Canada G1S 4X4
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38
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Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts. PLoS One 2016; 11:e0156342. [PMID: 27227888 PMCID: PMC4881999 DOI: 10.1371/journal.pone.0156342] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 05/12/2016] [Indexed: 11/19/2022] Open
Abstract
Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future.
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Hammond TT, Springthorpe D, Walsh RE, Berg-Kirkpatrick T. Using accelerometers to remotely and automatically characterize behavior in small animals. ACTA ACUST UNITED AC 2016; 219:1618-24. [PMID: 26994177 DOI: 10.1242/jeb.136135] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Accepted: 03/09/2016] [Indexed: 12/25/2022]
Abstract
Activity budgets in wild animals are challenging to measure via direct observation because data collection is time consuming and observer effects are potentially confounding. Although tri-axial accelerometers are increasingly employed for this purpose, their application in small-bodied animals has been limited by weight restrictions. Additionally, accelerometers engender novel complications, as a system is needed to reliably map acceleration to behaviors. In this study, we describe newly developed, tiny acceleration-logging devices (1.5-2.5 g) and use them to characterize behavior in two chipmunk species. We collected paired accelerometer readings and behavioral observations from captive individuals. We then employed techniques from machine learning to develop an automatic system for coding accelerometer readings into behavioral categories. Finally, we deployed and recovered accelerometers from free-living, wild chipmunks. This is the first time to our knowledge that accelerometers have been used to generate behavioral data for small-bodied (<100 g), free-living mammals.
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Affiliation(s)
- Talisin T Hammond
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Dwight Springthorpe
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Rachel E Walsh
- Department of Integrative Biology, 1001 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA Museum of Vertebrate Zoology, 3101 Valley Life Sciences Building, University of California Berkeley, Berkeley, CA 94720-3160, USA
| | - Taylor Berg-Kirkpatrick
- Language Technologies Institute, 5000 Forbes Ave., Carnegie Mellon University, Pittsburgh, PA 15213, USA
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40
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Barbuti R, Chessa S, Micheli A, Pucci R. Localizing Tortoise Nests by Neural Networks. PLoS One 2016; 11:e0151168. [PMID: 26985660 PMCID: PMC4795789 DOI: 10.1371/journal.pone.0151168] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 02/24/2016] [Indexed: 11/30/2022] Open
Abstract
The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.
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Affiliation(s)
- Roberto Barbuti
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Stefano Chessa
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Alessio Micheli
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Rita Pucci
- Department of Computer Science, University of Pisa, Pisa, Italy
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41
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Chimienti M, Cornulier T, Owen E, Bolton M, Davies IM, Travis JMJ, Scott BE. The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data. Ecol Evol 2016; 6:727-41. [PMID: 26865961 PMCID: PMC4739568 DOI: 10.1002/ece3.1914] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Revised: 11/30/2015] [Accepted: 12/01/2015] [Indexed: 11/11/2022] Open
Abstract
The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.
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Affiliation(s)
- Marianna Chimienti
- School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK; Marine Scotland Science Scottish Government Marine Laboratory PO Box 101375 Victoria Road Aberdeen AB11 9DB UK
| | - Thomas Cornulier
- School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK
| | - Ellie Owen
- RSPB Centre for Conservation Science North Scotland Office Etive House, Beechwood Park Inverness IV2 6AL UK
| | - Mark Bolton
- RSPB Centre for Conservation Science The Lodge Sandy Bedfordshire SG19 2DL UK
| | - Ian M Davies
- Marine Scotland Science Scottish Government Marine Laboratory PO Box 101 375 Victoria Road Aberdeen AB11 9DB UK
| | - Justin M J Travis
- School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK
| | - Beth E Scott
- School of Biological Sciences University of Aberdeen Tillydrone Avenue Aberdeen AB24 2TZ UK
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42
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Ware JV, Rode KD, Pagano AM, Bromaghin J, Robbins CT, Erlenbach J, Jensen S, Cutting A, Nicassio-Hiskey N, Hash A, Owen M, Jansen HT. Validation of mercury tip-switch and accelerometer activity sensors for identifying resting and active behavior in bears. URSUS 2015. [DOI: 10.2192/ursus-d-14-00031.1] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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43
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Collins PM, Green JA, Warwick‐Evans V, Dodd S, Shaw PJA, Arnould JPY, Halsey LG. Interpreting behaviors from accelerometry: a method combining simplicity and objectivity. Ecol Evol 2015; 5:4642-54. [PMID: 26668729 PMCID: PMC4670056 DOI: 10.1002/ece3.1660] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 07/21/2015] [Accepted: 07/22/2015] [Indexed: 11/30/2022] Open
Abstract
Quantifying the behavior of motile, free-ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study-specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse-scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.
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Affiliation(s)
- Philip M. Collins
- School of Life SciencesUniversity of RoehamptonHolybourne AvenueLondonSW15 4JDUnited Kingdom
| | - Jonathan A. Green
- School of Environmental SciencesUniversity of LiverpoolLiverpoolL69 3GPUnited Kingdom
| | | | - Stephen Dodd
- Royal Society for the Protection of BirdsNorth Wales OfficeBangorLL57 4FDUnited Kingdom
| | - Peter J. A. Shaw
- School of Life SciencesUniversity of RoehamptonHolybourne AvenueLondonSW15 4JDUnited Kingdom
| | - John P. Y. Arnould
- School of Life and Environmental SciencesDeakin UniversityMelbourneVictoria3125Australia
| | - Lewis G. Halsey
- School of Life SciencesUniversity of RoehamptonHolybourne AvenueLondonSW15 4JDUnited Kingdom
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44
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Graf PM, Wilson RP, Qasem L, Hackländer K, Rosell F. The Use of Acceleration to Code for Animal Behaviours; A Case Study in Free-Ranging Eurasian Beavers Castor fiber. PLoS One 2015; 10:e0136751. [PMID: 26317623 PMCID: PMC4552556 DOI: 10.1371/journal.pone.0136751] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 08/07/2015] [Indexed: 11/18/2022] Open
Abstract
Recent technological innovations have led to the development of miniature, accelerometer-containing electronic loggers which can be attached to free-living animals. Accelerometers provide information on both body posture and dynamism which can be used as descriptors to define behaviour. We deployed tri-axial accelerometer loggers on 12 free-ranging Eurasian beavers Castor fiber in the county of Telemark, Norway, and on four captive beavers (two Eurasian beavers and two North American beavers C. canadensis) to corroborate acceleration signals with observed behaviours. By using random forests for classifying behavioural patterns of beavers from accelerometry data, we were able to distinguish seven behaviours; standing, walking, swimming, feeding, grooming, diving and sleeping. We show how to apply the use of acceleration to determine behaviour, and emphasise the ease with which this non-invasive method can be implemented. Furthermore, we discuss the strengths and weaknesses of this, and the implementation of accelerometry on animals, illustrating limitations, suggestions and solutions. Ultimately, this approach may also serve as a template facilitating studies on other animals with similar locomotor modes and deliver new insights into hitherto unknown aspects of behavioural ecology.
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Affiliation(s)
- Patricia M. Graf
- Faculty of Arts and Sciences, Department of Environmental Sciences, Telemark University College, Bø i Telemark, Norway
- Department of Integrative Biology and Biodiversity Research, Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
- * E-mail:
| | - Rory P. Wilson
- Swansea Moving Animal Research Team, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, Wales, United Kingdom
| | - Lama Qasem
- Swansea Moving Animal Research Team, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, Wales, United Kingdom
| | - Klaus Hackländer
- Department of Integrative Biology and Biodiversity Research, Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria
| | - Frank Rosell
- Faculty of Arts and Sciences, Department of Environmental Sciences, Telemark University College, Bø i Telemark, Norway
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45
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Deriving Animal Behaviour from High-Frequency GPS: Tracking Cows in Open and Forested Habitat. PLoS One 2015; 10:e0129030. [PMID: 26107643 PMCID: PMC4479590 DOI: 10.1371/journal.pone.0129030] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 05/04/2015] [Indexed: 11/19/2022] Open
Abstract
The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.
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46
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Walker JS, Jones MW, Laramee RS, Holton MD, Shepard ELC, Williams HJ, Scantlebury DM, Marks NJ, Magowan EA, Maguire IE, Bidder OR, Di Virgilio A, Wilson RP. Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags. MOVEMENT ECOLOGY 2015; 3:29. [PMID: 26392863 PMCID: PMC4576376 DOI: 10.1186/s40462-015-0056-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 09/06/2015] [Indexed: 05/02/2023]
Abstract
BACKGROUND Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information. DESCRIPTION This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space. CONCLUSIONS Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.
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Affiliation(s)
- James S. Walker
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark W. Jones
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Robert S. Laramee
- />Department of Computer Science, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Mark D. Holton
- />College of Engineering, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Emily LC Shepard
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - Hannah J. Williams
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
| | - D. Michael Scantlebury
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Nikki, J. Marks
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Elizabeth A. Magowan
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Iain E. Maguire
- />School of Biological Sciences, Institute for Global Food Security, Queen’s University Belfast, Belfast, BT9 7BL, Northern Ireland UK
| | - Owen R. Bidder
- />Institut für Terrestrische und Aquatische Wildtierforschung, Stiftung Tierärztliche Hochschule, Werfstr. 6, 25761, Hannover, Büsum Germany
| | - Agustina Di Virgilio
- />Laboratorio Ecotono, INIBIOMA-CONICET, National University of Comahue, Quintral 1250, (8400), Bariloche, Argentina
| | - Rory P. Wilson
- />Swansea Lab for Animal Movement, Biosciences, College of Science, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales UK
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Carroll G, Slip D, Jonsen I, Harcourt R. Supervised accelerometry analysis can identify prey capture by penguins at sea. ACTA ACUST UNITED AC 2014; 217:4295-302. [PMID: 25394635 DOI: 10.1242/jeb.113076] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Determining where, when and how much animals eat is fundamental to understanding their ecology. We developed a technique to identify a prey capture signature for little penguins from accelerometry, in order to quantify food intake remotely. We categorised behaviour of captive penguins from HD video and matched this to time-series data from back-mounted accelerometers. We then trained a support vector machine (SVM) to classify the penguins' behaviour at 0.3 s intervals as either 'prey handling' or 'swimming'. We applied this model to accelerometer data collected from foraging wild penguins to identify prey capture events. We compared prey capture and non-prey capture dives to test the model predictions against foraging theory. The SVM had an accuracy of 84.95±0.26% (mean ± s.e.) and a false positive rate of 9.82±0.24% when tested on unseen captive data. For wild data, we defined three independent, consecutive prey handling observations as representing true prey capture, with a false positive rate of 0.09%. Dives with prey captures had longer duration and bottom times, were deeper, had faster ascent rates, and had more 'wiggles' and 'dashes' (proxies for prey encounter used in other studies). The mean (±s.e.) number of prey captures per foraging trip was 446.6±66.28. By recording the behaviour of captive animals on HD video and using a supervised machine learning approach, we show that accelerometry signatures can classify the behaviour of wild animals at unprecedentedly fine scales.
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Affiliation(s)
- Gemma Carroll
- Department of Biological Sciences, Macquarie University, North Ryde, Sydney, NSW 2109, Australia.
| | - David Slip
- Taronga Conservation Society Australia, Bradley's Head Road, Mosman, Sydney, NSW 2088, Australia
| | - Ian Jonsen
- Taronga Conservation Society Australia, Bradley's Head Road, Mosman, Sydney, NSW 2088, Australia
| | - Rob Harcourt
- Taronga Conservation Society Australia, Bradley's Head Road, Mosman, Sydney, NSW 2088, Australia
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48
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Bidder O, Arandjelović O, Almutairi F, Shepard E, Lambertucci S, Qasem L, Wilson R. A risky business or a safe BET? A Fuzzy Set Event Tree for estimating hazard in biotelemetry studies. Anim Behav 2014. [DOI: 10.1016/j.anbehav.2014.04.025] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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49
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Resheff YS, Rotics S, Harel R, Spiegel O, Nathan R. AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements. MOVEMENT ECOLOGY 2014; 2:27. [PMID: 25709835 PMCID: PMC4337760 DOI: 10.1186/s40462-014-0027-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2014] [Accepted: 12/15/2014] [Indexed: 05/02/2023]
Abstract
BACKGROUND The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. DESCRIPTION Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models. CONCLUSIONS AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.
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Affiliation(s)
- Yehezkel S Resheff
- />Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- />Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, Jerusalem, 91904 Israel
| | - Shay Rotics
- />Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Roi Harel
- />Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orr Spiegel
- />Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- />Present address: Department of Environmental Science & Policy, University of California at Davis, Davis, CA 95616 USA
| | - Ran Nathan
- />Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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