Pub Date : 2021-02-19DOI: 10.21203/RS.3.RS-195951/V1
Liangbo Wang, F. Diakogiannis, Scott Mills, Nigel Bajema, I. Atkinson, G. Bishop-Hurley, E. Charmley
Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model ( ResUnet-1d ) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.
{"title":"A noise robust automatic radiolocation animal tracking system","authors":"Liangbo Wang, F. Diakogiannis, Scott Mills, Nigel Bajema, I. Atkinson, G. Bishop-Hurley, E. Charmley","doi":"10.21203/RS.3.RS-195951/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-195951/V1","url":null,"abstract":"Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model ( ResUnet-1d ) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":"9 1","pages":"1-12"},"PeriodicalIF":2.7,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-15DOI: 10.21203/RS.3.RS-224086/V1
M. Modest, Ladd M. Irvine, V. Andrews‐Goff, William T. Gough, D. Johnston, D. Nowacek, L. Pallin, A. Read, R. T. Moore, A. Friedlaender
Background Despite exhibiting one of the longest migrations in the world, half of the humpback whale migratory cycle has remained unexamined. Until now, no study has provided a continuous description of humpback whale migratory behavior from a feeding ground to a calving ground. We present new information on satellite-derived offshore migratory movements of 16 Breeding Stock G humpback whales from Antarctic feeding grounds to South American calving grounds. Satellite locations were used to demonstrate migratory corridors, while the impact of departure date on migration speed was assessed using a linear regression. A Bayesian hierarchical state–space animal movement model (HSSM) was utilized to investigate the presence of Area Restricted Search (ARS) en route. Results 35,642 Argos locations from 16 tagged whales from 2012 to 2017 were collected. The 16 whales were tracked for a mean of 38.5 days of migration (range 10–151 days). The length of individually derived tracks ranged from 645 to 6381 km. Humpbacks were widely dispersed geographically during the initial and middle stages of their migration, but convened in two convergence regions near the southernmost point of Chile as well as Peru’s Illescas Peninsula. The state–space model showed almost no instances of ARS along the migratory route. The linear regression assessing whether departure date affected migration speed showed suggestive but inconclusive support for a positive trend between the two variables. Results suggestive of stratification by sex and reproductive status were found for departure date and route choice. Conclusions This multi-year study sets a baseline against which the effects of climate change on humpback whales can be studied across years and conditions and provides an excellent starting point for the investigation into humpback whale migration.
{"title":"First description of migratory behavior of humpback whales from an Antarctic feeding ground to a tropical calving ground","authors":"M. Modest, Ladd M. Irvine, V. Andrews‐Goff, William T. Gough, D. Johnston, D. Nowacek, L. Pallin, A. Read, R. T. Moore, A. Friedlaender","doi":"10.21203/RS.3.RS-224086/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-224086/V1","url":null,"abstract":"Background Despite exhibiting one of the longest migrations in the world, half of the humpback whale migratory cycle has remained unexamined. Until now, no study has provided a continuous description of humpback whale migratory behavior from a feeding ground to a calving ground. We present new information on satellite-derived offshore migratory movements of 16 Breeding Stock G humpback whales from Antarctic feeding grounds to South American calving grounds. Satellite locations were used to demonstrate migratory corridors, while the impact of departure date on migration speed was assessed using a linear regression. A Bayesian hierarchical state–space animal movement model (HSSM) was utilized to investigate the presence of Area Restricted Search (ARS) en route. Results 35,642 Argos locations from 16 tagged whales from 2012 to 2017 were collected. The 16 whales were tracked for a mean of 38.5 days of migration (range 10–151 days). The length of individually derived tracks ranged from 645 to 6381 km. Humpbacks were widely dispersed geographically during the initial and middle stages of their migration, but convened in two convergence regions near the southernmost point of Chile as well as Peru’s Illescas Peninsula. The state–space model showed almost no instances of ARS along the migratory route. The linear regression assessing whether departure date affected migration speed showed suggestive but inconclusive support for a positive trend between the two variables. Results suggestive of stratification by sex and reproductive status were found for departure date and route choice. Conclusions This multi-year study sets a baseline against which the effects of climate change on humpback whales can be studied across years and conditions and provides an excellent starting point for the investigation into humpback whale migration.","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":"1-16"},"PeriodicalIF":2.7,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47908137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-11DOI: 10.1186/s40317-021-00233-3
M. O’Brien, D. Secor
{"title":"Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight","authors":"M. O’Brien, D. Secor","doi":"10.1186/s40317-021-00233-3","DOIUrl":"https://doi.org/10.1186/s40317-021-00233-3","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2021-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-021-00233-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46974912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-29DOI: 10.1186/s40317-020-00228-6
J. Daniels, E. Brunsdon, G. Chaput, H. Dixon, H. Labadie, J. Carr
{"title":"Quantifying the effects of post-surgery recovery time on the migration dynamics and survival rates in the wild of acoustically tagged Atlantic Salmon Salmo salar smolts","authors":"J. Daniels, E. Brunsdon, G. Chaput, H. Dixon, H. Labadie, J. Carr","doi":"10.1186/s40317-020-00228-6","DOIUrl":"https://doi.org/10.1186/s40317-020-00228-6","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-020-00228-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-07DOI: 10.1186/s40317-021-00229-z
Jessica A. Keller, D. Morley, J. Herbig, P. Barbera, M. Feeley, A. Acosta
{"title":"Under pressure: comparing in situ and boat tagging methods using time-to-event analyses","authors":"Jessica A. Keller, D. Morley, J. Herbig, P. Barbera, M. Feeley, A. Acosta","doi":"10.1186/s40317-021-00229-z","DOIUrl":"https://doi.org/10.1186/s40317-021-00229-z","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-021-00229-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65849624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-07DOI: 10.1186/s40317-020-00225-9
B. E. Lewis Baida, A. Swinbourne, J. Barwick, S. Leu, W. Van Wettere
{"title":"Technologies for the automated collection of heat stress data in sheep","authors":"B. E. Lewis Baida, A. Swinbourne, J. Barwick, S. Leu, W. Van Wettere","doi":"10.1186/s40317-020-00225-9","DOIUrl":"https://doi.org/10.1186/s40317-020-00225-9","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":"9 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2021-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65849578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-05DOI: 10.1186/s40317-020-00226-8
M. Føre, E. Svendsen, F. Økland, A. Gräns, J. A. Alfredsen, B. Finstad, R. Hedger, I. Uglem
{"title":"Heart rate and swimming activity as indicators of post-surgical recovery time of Atlantic salmon (Salmo salar)","authors":"M. Føre, E. Svendsen, F. Økland, A. Gräns, J. A. Alfredsen, B. Finstad, R. Hedger, I. Uglem","doi":"10.1186/s40317-020-00226-8","DOIUrl":"https://doi.org/10.1186/s40317-020-00226-8","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-020-00226-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47445831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1186/s40317-020-00221-z
Ian G. Brosnan, D. Welch
{"title":"A model to illustrate the potential pairing of animal biotelemetry with individual-based modeling","authors":"Ian G. Brosnan, D. Welch","doi":"10.1186/s40317-020-00221-z","DOIUrl":"https://doi.org/10.1186/s40317-020-00221-z","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-020-00221-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42178364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-22DOI: 10.1186/s40317-020-00220-0
R. Skubel, Kenady Wilson, Y. Papastamatiou, Hannah J. Verkamp, J. Sulikowski, Daniel Benetti, N. Hammerschlag
{"title":"A scalable, satellite-transmitted data product for monitoring high-activity events in mobile aquatic animals","authors":"R. Skubel, Kenady Wilson, Y. Papastamatiou, Hannah J. Verkamp, J. Sulikowski, Daniel Benetti, N. Hammerschlag","doi":"10.1186/s40317-020-00220-0","DOIUrl":"https://doi.org/10.1186/s40317-020-00220-0","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":"8 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2020-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-020-00220-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65849531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.1186/s40317-020-00222-y
Patrick J. Burke, J. Mourier, T. Gaston, J. Williamson
{"title":"Novel use of pop-up satellite archival telemetry in sawsharks: insights into the movement of the common sawshark Pristiophorus cirratus (Pristiophoridae)","authors":"Patrick J. Burke, J. Mourier, T. Gaston, J. Williamson","doi":"10.1186/s40317-020-00222-y","DOIUrl":"https://doi.org/10.1186/s40317-020-00222-y","url":null,"abstract":"","PeriodicalId":37711,"journal":{"name":"Animal Biotelemetry","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s40317-020-00222-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44217553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：