Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data
Autocorrelation has been viewed as a problem in telemetry studies because sequential observations are not independent in time or space, therefore violating assumptions for statistical inference. Yet nearly all ecological and behavioural data are autocorrelated in both space and time. We argue that t...
Published in: | Philosophical Transactions of the Royal Society B: Biological Sciences |
---|---|
Main Authors: | , , , , , , |
Format: | Text |
Language: | English |
Published: |
The Royal Society
2010
|
Subjects: | |
Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894959 http://www.ncbi.nlm.nih.gov/pubmed/20566498 https://doi.org/10.1098/rstb.2010.0080 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:2894959 |
---|---|
record_format |
openpolar |
spelling |
ftpubmed:oai:pubmedcentral.nih.gov:2894959 2023-05-15T15:50:18+02:00 Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data Boyce, Mark S. Pitt, Justin Northrup, Joseph M. Morehouse, Andrea T. Knopff, Kyle H. Cristescu, Bogdan Stenhouse, Gordon B. 2010-07-27 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894959 http://www.ncbi.nlm.nih.gov/pubmed/20566498 https://doi.org/10.1098/rstb.2010.0080 en eng The Royal Society http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894959 http://www.ncbi.nlm.nih.gov/pubmed/20566498 http://dx.doi.org/10.1098/rstb.2010.0080 © 2010 The Royal Society Articles Text 2010 ftpubmed https://doi.org/10.1098/rstb.2010.0080 2013-09-03T02:02:30Z Autocorrelation has been viewed as a problem in telemetry studies because sequential observations are not independent in time or space, therefore violating assumptions for statistical inference. Yet nearly all ecological and behavioural data are autocorrelated in both space and time. We argue that there is much to learn about the structure of ecological and behavioural data from patterns of autocorrelation. Such patterns include periodicity in movement and patchiness in spatial data, which can be characterized by an autocorrelogram, semivariogram or spectrum. We illustrate the utility of temporal autocorrelation functions (ACFs) for analysing step-length data from GPS telemetry of wolves (Canis lupus), cougars (Puma concolor), grizzly bears (Ursus arctos) and elk (Cervus elaphus) in western Alberta. ACFs often differ by season, reflecting differences in foraging behaviour. In wilderness landscapes, step-length ACFs for predators decay slowly to apparently random patterns, but sometimes display strong daily rhythms in areas of human disturbance. In contrast, step lengths of elk are consistently periodic, reflecting crepuscular activity. Text Canis lupus Ursus arctos PubMed Central (PMC) Philosophical Transactions of the Royal Society B: Biological Sciences 365 1550 2213 2219 |
institution |
Open Polar |
collection |
PubMed Central (PMC) |
op_collection_id |
ftpubmed |
language |
English |
topic |
Articles |
spellingShingle |
Articles Boyce, Mark S. Pitt, Justin Northrup, Joseph M. Morehouse, Andrea T. Knopff, Kyle H. Cristescu, Bogdan Stenhouse, Gordon B. Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
topic_facet |
Articles |
description |
Autocorrelation has been viewed as a problem in telemetry studies because sequential observations are not independent in time or space, therefore violating assumptions for statistical inference. Yet nearly all ecological and behavioural data are autocorrelated in both space and time. We argue that there is much to learn about the structure of ecological and behavioural data from patterns of autocorrelation. Such patterns include periodicity in movement and patchiness in spatial data, which can be characterized by an autocorrelogram, semivariogram or spectrum. We illustrate the utility of temporal autocorrelation functions (ACFs) for analysing step-length data from GPS telemetry of wolves (Canis lupus), cougars (Puma concolor), grizzly bears (Ursus arctos) and elk (Cervus elaphus) in western Alberta. ACFs often differ by season, reflecting differences in foraging behaviour. In wilderness landscapes, step-length ACFs for predators decay slowly to apparently random patterns, but sometimes display strong daily rhythms in areas of human disturbance. In contrast, step lengths of elk are consistently periodic, reflecting crepuscular activity. |
format |
Text |
author |
Boyce, Mark S. Pitt, Justin Northrup, Joseph M. Morehouse, Andrea T. Knopff, Kyle H. Cristescu, Bogdan Stenhouse, Gordon B. |
author_facet |
Boyce, Mark S. Pitt, Justin Northrup, Joseph M. Morehouse, Andrea T. Knopff, Kyle H. Cristescu, Bogdan Stenhouse, Gordon B. |
author_sort |
Boyce, Mark S. |
title |
Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
title_short |
Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
title_full |
Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
title_fullStr |
Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
title_full_unstemmed |
Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
title_sort |
temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data |
publisher |
The Royal Society |
publishDate |
2010 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894959 http://www.ncbi.nlm.nih.gov/pubmed/20566498 https://doi.org/10.1098/rstb.2010.0080 |
genre |
Canis lupus Ursus arctos |
genre_facet |
Canis lupus Ursus arctos |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2894959 http://www.ncbi.nlm.nih.gov/pubmed/20566498 http://dx.doi.org/10.1098/rstb.2010.0080 |
op_rights |
© 2010 The Royal Society |
op_doi |
https://doi.org/10.1098/rstb.2010.0080 |
container_title |
Philosophical Transactions of the Royal Society B: Biological Sciences |
container_volume |
365 |
container_issue |
1550 |
container_start_page |
2213 |
op_container_end_page |
2219 |
_version_ |
1766385265564188672 |