Modelling the sound production of narwhals using a point process framework with memory effects
Obtaining an adequate description of the behaviour of narwhals in a pristine environment is important to understand natural behaviour as well as providing the means to determine potential changes in behaviour directly or indirectly caused by human activity. Based on Acousonde™ data from five narwhal...
Published in: | The Annals of Applied Statistics |
---|---|
Main Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://curis.ku.dk/portal/da/publications/modelling-the-sound-production-of-narwhals-using-a-point-process-framework-with-memory-effects(120bb2b3-2972-4071-bf52-d3d1779c576d).html https://doi.org/10.1214/20-AOAS1379 https://curis.ku.dk/ws/files/254663915/MODELLING_THE_SOUND_PRODUCTION_OF_NARWHALS_USING.pdf http://www.scopus.com/inward/record.url?scp=85098279965&partnerID=8YFLogxK |
id |
ftcopenhagenunip:oai:pure.atira.dk:publications/120bb2b3-2972-4071-bf52-d3d1779c576d |
---|---|
record_format |
openpolar |
spelling |
ftcopenhagenunip:oai:pure.atira.dk:publications/120bb2b3-2972-4071-bf52-d3d1779c576d 2024-06-09T07:45:40+00:00 Modelling the sound production of narwhals using a point process framework with memory effects Søltoft-Jensen, Aleksander Heide-Jørgensen, Mads Peter Ditlevsen, Susanne 2020 application/pdf https://curis.ku.dk/portal/da/publications/modelling-the-sound-production-of-narwhals-using-a-point-process-framework-with-memory-effects(120bb2b3-2972-4071-bf52-d3d1779c576d).html https://doi.org/10.1214/20-AOAS1379 https://curis.ku.dk/ws/files/254663915/MODELLING_THE_SOUND_PRODUCTION_OF_NARWHALS_USING.pdf http://www.scopus.com/inward/record.url?scp=85098279965&partnerID=8YFLogxK eng eng info:eu-repo/semantics/openAccess Søltoft-Jensen , A , Heide-Jørgensen , M P & Ditlevsen , S 2020 , ' Modelling the sound production of narwhals using a point process framework with memory effects ' , Annals of Applied Statistics , vol. 14 , no. 4 , pp. 2037-2052 . https://doi.org/10.1214/20-AOAS1379 Autoregressive process Behavioural data of marine mammals Buzz and call Ecology Logistic regression with memory Narwhal Point process article 2020 ftcopenhagenunip https://doi.org/10.1214/20-AOAS1379 2024-05-16T11:29:18Z Obtaining an adequate description of the behaviour of narwhals in a pristine environment is important to understand natural behaviour as well as providing the means to determine potential changes in behaviour directly or indirectly caused by human activity. Based on Acousonde™ data from five narwhals in Scoresby Sound, this paper aims at modelling buzzing and calling rates of East Greenland narwhals as functions of time, space and, possibly, autoregressive memory. Both buzzing and calling are sounds produced by narwhals. Buzzing is a way for the whale to navigate and locate prey using echolocation, while calling is associated with social communication between whales. Logistic regression models without and with autoregressive components are compared based on AIC and comparatively assessed using diagnostics from point process theory. Adding an autoregressive component appears to improve the models, and further improvements for the buzzing model are made with a non-GLM extension. Effects of extrinsic covariates and memory are presented and interpreted. Buzzing occurs at deeper depths, and initiations of buzzes are separated by refractory periods. A possible feeding area is identified. Calling occurs closer to the surface, and, while the probability of calling in general is lower than buzzing, it is more likely that calls are clustered together rather than spread randomly. Article in Journal/Newspaper East Greenland Greenland narwhal* Scoresby Sound University of Copenhagen: Research Greenland Scoresby ENVELOPE(162.750,162.750,-66.567,-66.567) The Annals of Applied Statistics 14 4 |
institution |
Open Polar |
collection |
University of Copenhagen: Research |
op_collection_id |
ftcopenhagenunip |
language |
English |
topic |
Autoregressive process Behavioural data of marine mammals Buzz and call Ecology Logistic regression with memory Narwhal Point process |
spellingShingle |
Autoregressive process Behavioural data of marine mammals Buzz and call Ecology Logistic regression with memory Narwhal Point process Søltoft-Jensen, Aleksander Heide-Jørgensen, Mads Peter Ditlevsen, Susanne Modelling the sound production of narwhals using a point process framework with memory effects |
topic_facet |
Autoregressive process Behavioural data of marine mammals Buzz and call Ecology Logistic regression with memory Narwhal Point process |
description |
Obtaining an adequate description of the behaviour of narwhals in a pristine environment is important to understand natural behaviour as well as providing the means to determine potential changes in behaviour directly or indirectly caused by human activity. Based on Acousonde™ data from five narwhals in Scoresby Sound, this paper aims at modelling buzzing and calling rates of East Greenland narwhals as functions of time, space and, possibly, autoregressive memory. Both buzzing and calling are sounds produced by narwhals. Buzzing is a way for the whale to navigate and locate prey using echolocation, while calling is associated with social communication between whales. Logistic regression models without and with autoregressive components are compared based on AIC and comparatively assessed using diagnostics from point process theory. Adding an autoregressive component appears to improve the models, and further improvements for the buzzing model are made with a non-GLM extension. Effects of extrinsic covariates and memory are presented and interpreted. Buzzing occurs at deeper depths, and initiations of buzzes are separated by refractory periods. A possible feeding area is identified. Calling occurs closer to the surface, and, while the probability of calling in general is lower than buzzing, it is more likely that calls are clustered together rather than spread randomly. |
format |
Article in Journal/Newspaper |
author |
Søltoft-Jensen, Aleksander Heide-Jørgensen, Mads Peter Ditlevsen, Susanne |
author_facet |
Søltoft-Jensen, Aleksander Heide-Jørgensen, Mads Peter Ditlevsen, Susanne |
author_sort |
Søltoft-Jensen, Aleksander |
title |
Modelling the sound production of narwhals using a point process framework with memory effects |
title_short |
Modelling the sound production of narwhals using a point process framework with memory effects |
title_full |
Modelling the sound production of narwhals using a point process framework with memory effects |
title_fullStr |
Modelling the sound production of narwhals using a point process framework with memory effects |
title_full_unstemmed |
Modelling the sound production of narwhals using a point process framework with memory effects |
title_sort |
modelling the sound production of narwhals using a point process framework with memory effects |
publishDate |
2020 |
url |
https://curis.ku.dk/portal/da/publications/modelling-the-sound-production-of-narwhals-using-a-point-process-framework-with-memory-effects(120bb2b3-2972-4071-bf52-d3d1779c576d).html https://doi.org/10.1214/20-AOAS1379 https://curis.ku.dk/ws/files/254663915/MODELLING_THE_SOUND_PRODUCTION_OF_NARWHALS_USING.pdf http://www.scopus.com/inward/record.url?scp=85098279965&partnerID=8YFLogxK |
long_lat |
ENVELOPE(162.750,162.750,-66.567,-66.567) |
geographic |
Greenland Scoresby |
geographic_facet |
Greenland Scoresby |
genre |
East Greenland Greenland narwhal* Scoresby Sound |
genre_facet |
East Greenland Greenland narwhal* Scoresby Sound |
op_source |
Søltoft-Jensen , A , Heide-Jørgensen , M P & Ditlevsen , S 2020 , ' Modelling the sound production of narwhals using a point process framework with memory effects ' , Annals of Applied Statistics , vol. 14 , no. 4 , pp. 2037-2052 . https://doi.org/10.1214/20-AOAS1379 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1214/20-AOAS1379 |
container_title |
The Annals of Applied Statistics |
container_volume |
14 |
container_issue |
4 |
_version_ |
1801375131980791808 |