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Bergen S, Huso MM, Duerr AE, Braham MA, Schmuecker S, Miller TA, Katzner TE. A review of supervised learning methods for classifying animal behavioural states from environmental features. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.14019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Silas Bergen
- Department of Mathematics and Statistics Winona State University Winona Minnesota USA
| | - Manuela M. Huso
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Corvallis Oregon USA
- Statistics Department Oregon State University Corvallis Oregon USA
| | - Adam E. Duerr
- Bloom Research Inc Los Angeles California USA
- West Virginia University Morgantown West Virginia USA
- Conservation Science Global, Inc West Cape May New Jersey USA
| | | | - Sara Schmuecker
- U.S. Fish and Wildlife Service Illinois‐Iowa Field Office Moline Illinois USA
| | - Tricia A. Miller
- West Virginia University Morgantown West Virginia USA
- Conservation Science Global, Inc West Cape May New Jersey USA
| | - Todd E. Katzner
- U.S. Geological Survey Forest and Rangeland Ecosystem Science Center Boise Idaho USA
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Zotos S, Stamatiou M, Vogiatzakis IN. Elusive species distribution modelling: The case of Natrix natrix cypriaca. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Introducing a new tool to derive animal behaviour from GPS data without ancillary data: The red-footed falcon in Italy as a case study. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Fayat R, Delgado Betancourt V, Goyallon T, Petremann M, Liaudet P, Descossy V, Reveret L, Dugué GP. Inertial Measurement of Head Tilt in Rodents: Principles and Applications to Vestibular Research. SENSORS (BASEL, SWITZERLAND) 2021; 21:6318. [PMID: 34577524 PMCID: PMC8472891 DOI: 10.3390/s21186318] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/03/2021] [Accepted: 09/13/2021] [Indexed: 12/21/2022]
Abstract
Inertial sensors are increasingly used in rodent research, in particular for estimating head orientation relative to gravity, or head tilt. Despite this growing interest, the accuracy of tilt estimates computed from rodent head inertial data has never been assessed. Using readily available inertial measurement units mounted onto the head of freely moving rats, we benchmarked a set of tilt estimation methods against concurrent 3D optical motion capture. We show that, while low-pass filtered head acceleration signals only provided reliable tilt estimates in static conditions, sensor calibration combined with an appropriate choice of orientation filter and parameters could yield average tilt estimation errors below 1.5∘ during movement. We then illustrate an application of inertial head tilt measurements in a preclinical rat model of unilateral vestibular lesion and propose a set of metrics describing the severity of associated postural and motor symptoms and the time course of recovery. We conclude that headborne inertial sensors are an attractive tool for quantitative rodent behavioral analysis in general and for the study of vestibulo-postural functions in particular.
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Affiliation(s)
- Romain Fayat
- Neurophysiologie des Circuits Cérébraux, Institut de Biologie de l’ENS (IBENS), Ecole Normale Supérieure, UMR CNRS 8197, INSERM U1024, Université PSL, 75005 Paris, France;
- Laboratoire MAP5, UMR CNRS 8145, Université Paris Descartes, 75006 Paris, France
| | | | - Thibault Goyallon
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, INRIA, 38330 Montbonnot-Saint-Martin, France; (T.G.); (L.R.)
| | - Mathieu Petremann
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Pauline Liaudet
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Vincent Descossy
- Preclinical Development, Sensorion SA, 34080 Montpellier, France; (V.D.B.); (M.P.); (P.L.); (V.D.)
| | - Lionel Reveret
- Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, INRIA, 38330 Montbonnot-Saint-Martin, France; (T.G.); (L.R.)
| | - Guillaume P. Dugué
- Neurophysiologie des Circuits Cérébraux, Institut de Biologie de l’ENS (IBENS), Ecole Normale Supérieure, UMR CNRS 8197, INSERM U1024, Université PSL, 75005 Paris, France;
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Ripperger SP, Carter GG. Social foraging in vampire bats is predicted by long-term cooperative relationships. PLoS Biol 2021; 19:e3001366. [PMID: 34555014 PMCID: PMC8460024 DOI: 10.1371/journal.pbio.3001366] [Citation(s) in RCA: 8] [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: 04/20/2021] [Accepted: 07/16/2021] [Indexed: 11/19/2022] Open
Abstract
Stable social bonds in group-living animals can provide greater access to food. A striking example is that female vampire bats often regurgitate blood to socially bonded kin and nonkin that failed in their nightly hunt. Food-sharing relationships form via preferred associations and social grooming within roosts. However, it remains unclear whether these cooperative relationships extend beyond the roost. To evaluate if long-term cooperative relationships in vampire bats play a role in foraging, we tested if foraging encounters measured by proximity sensors could be explained by wild roosting proximity, kinship, or rates of co-feeding, social grooming, and food sharing during 21 months in captivity. We assessed evidence for 6 hypothetical scenarios of social foraging, ranging from individual to collective hunting. We found that closely bonded female vampire bats departed their roost separately, but often reunited far outside the roost. Repeating foraging encounters were predicted by within-roost association and histories of cooperation in captivity, even when accounting for kinship. Foraging bats demonstrated both affiliative and competitive interactions with different social calls linked to each interaction type. We suggest that social foraging could have implications for social evolution if "local" within-roost cooperation and "global" outside-roost competition enhances fitness interdependence between frequent roostmates.
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Affiliation(s)
- Simon P. Ripperger
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
- Smithsonian Tropical Research Institute, Balboa, Ancón, Panamá
- Museum für Naturkunde, Leibniz-Institute for Evolution and Biodiversity Science, Berlin, Germany
| | - Gerald G. Carter
- Department of Evolution, Ecology and Organismal Biology, The Ohio State University, Columbus, Ohio, United States of America
- Smithsonian Tropical Research Institute, Balboa, Ancón, Panamá
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Wildlife Monitoring on the Edge: A Performance Evaluation of Embedded Neural Networks on Microcontrollers for Animal Behavior Classification. SENSORS 2021; 21:s21092975. [PMID: 33922753 PMCID: PMC8123074 DOI: 10.3390/s21092975] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 12/26/2022]
Abstract
Monitoring animals’ behavior living in wild or semi-wild environments is a very interesting subject for biologists who work with them. The difficulty and cost of implanting electronic devices in this kind of animals suggest that these devices must be robust and have low power consumption to increase their battery life as much as possible. Designing a custom smart device that can detect multiple animal behaviors and that meets the mentioned restrictions presents a major challenge that is addressed in this work. We propose an edge-computing solution, which embeds an ANN in a microcontroller that collects data from an IMU sensor to detect three different horse gaits. All the computation is performed in the microcontroller to reduce the amount of data transmitted via wireless radio, since sending information is one of the most power-consuming tasks in this type of devices. Multiples ANNs were implemented and deployed in different microcontroller architectures in order to find the best balance between energy consumption and computing performance. The results show that the embedded networks obtain up to 97.96% ± 1.42% accuracy, achieving an energy efficiency of 450 Mops/s/watt.
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Kadar JP, Ladds MA, Day J, Lyall B, Brown C. Assessment of Machine Learning Models to Identify Port Jackson Shark Behaviours Using Tri-Axial Accelerometers. SENSORS 2020; 20:s20247096. [PMID: 33322308 PMCID: PMC7763149 DOI: 10.3390/s20247096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/08/2020] [Accepted: 12/09/2020] [Indexed: 01/08/2023]
Abstract
Movement ecology has traditionally focused on the movements of animals over large time scales, but, with advancements in sensor technology, the focus can become increasingly fine scale. Accelerometers are commonly applied to quantify animal behaviours and can elucidate fine-scale (<2 s) behaviours. Machine learning methods are commonly applied to animal accelerometry data; however, they require the trial of multiple methods to find an ideal solution. We used tri-axial accelerometers (10 Hz) to quantify four behaviours in Port Jackson sharks (Heterodontus portusjacksoni): two fine-scale behaviours (<2 s)-(1) vertical swimming and (2) chewing as proxy for foraging, and two broad-scale behaviours (>2 s-mins)-(3) resting and (4) swimming. We used validated data to calculate 66 summary statistics from tri-axial accelerometry and assessed the most important features that allowed for differentiation between the behaviours. One and two second epoch testing sets were created consisting of 10 and 20 samples from each behaviour event, respectively. We developed eight machine learning models to assess their overall accuracy and behaviour-specific accuracy (one classification tree, five ensemble learners and two neural networks). The support vector machine model classified the four behaviours better when using the longer 2 s time epoch (F-measure 89%; macro-averaged F-measure: 90%). Here, we show that this support vector machine (SVM) model can reliably classify both fine- and broad-scale behaviours in Port Jackson sharks.
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Affiliation(s)
- Julianna P. Kadar
- Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia;
- Correspondence:
| | - Monique A. Ladds
- Marine Ecosystems Team, Wellington University, Wellington 6012, New Zealand;
| | - Joanna Day
- Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Sydney, NSW 2088, Australia;
| | - Brianne Lyall
- Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush Veterinary Centre, Midlothian EH25 9RG, UK;
| | - Culum Brown
- Department of Biological Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, NSW 2109, Australia;
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Abstract
Reliable information about wildlife is absolutely important for making informed management decisions. The issues with the effectiveness of the control and monitoring of both large and small wild animals are relevant to assess and protect the world’s biodiversity. Monitoring becomes part of the methods in wildlife ecology for observation, assessment, and forecasting of the human environment. World practice reveals the potential of the joint application of both proven traditional and modern technologies using specialized equipment to organize environmental control and management processes. Monitoring large terrestrial animals require an individual approach due to their low density and larger habitat. Elk/moose are such animals. This work aims to evaluate the methods for monitoring large wild animals, suitable for controlling the number of elk/moose in the framework of nature conservation activities. Using different models allows determining the population size without affecting the animals and without significant financial costs. Although, the accuracy of each model is determined by its postulates implementation and initial conditions that need statistical data. Depending on the geographical, climatic, and economic conditions in each territory, it is possible to use different tools and equipment (e.g., cameras, GPS sensors, and unmanned aerial vehicles), a flexible variation of which will allow reaching the golden mean between the desires and capabilities of researchers.
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