Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns

Abstract Quantifying the distribution of daily activity is an important component of behavioral ecology. Historically, it has been difficult to obtain data on activity patterns, especially for elusive species. However, the development of affordable camera traps and their widespread usage has led to...

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Main Authors: Greg B. Distiller, David L. Borchers, Rebecca J. Foster, Bart J. Harmsen
Format: Article
Language:English
Published: Wiley 2020-10-01
Series:Ecology and Evolution
Subjects:
Online Access:https://doi.org/10.1002/ece3.6822
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spelling doaj-7cd1b22011cc4a068b215d3bb4bf83fa2021-04-02T21:30:43ZengWileyEcology and Evolution2045-77582020-10-011020118261183710.1002/ece3.6822Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patternsGreg B. Distiller0David L. Borchers1Rebecca J. Foster2Bart J. Harmsen3Department of Statistical Sciences Centre for Statistics in Ecology, Environment and Conservation (SEEC) University of Cape Town Cape Town South AfricaCentre for Research into Ecological and Environmental Modelling School of Mathematics and Statistics University of St Andrews St Andrews UKPanthera New York NY USAPanthera New York NY USAAbstract Quantifying the distribution of daily activity is an important component of behavioral ecology. Historically, it has been difficult to obtain data on activity patterns, especially for elusive species. However, the development of affordable camera traps and their widespread usage has led to an explosion of available data from which activity patterns can be estimated. Continuous‐time spatial capture–recapture (CT SCR) models drop the occasion structure seen in traditional spatial and nonspatial capture–recapture (CR) models and use the actual times of capture. In addition to estimating density, CT SCR models estimate expected encounters through time. Cyclic splines can be used to allow flexible shapes for modeling cyclic activity patterns, and the fact that SCR models also incorporate distance means that space–time interactions can be explored. This method is applied to a jaguar dataset. Jaguars in Belize are most active and range furthest in the evening and early morning and when they are located closer to the network of trails. There is some evidence that females have a less variable pattern than males. The comparison between sexes demonstrates how CT SCR can be used to explore hypotheses about animal behavior within a formal modeling framework. SCR models were developed primarily to estimate and model density, but the models can be used to explore processes that interact across space and time, especially when using the CT SCR framework that models the temporal dimension at a finer resolution.https://doi.org/10.1002/ece3.6822activity patternsbehavioral ecologycontinuous‐time spatial capture–recapturespatial capture–recapturetemporal partitioning
collection DOAJ
language English
format Article
sources DOAJ
author Greg B. Distiller
David L. Borchers
Rebecca J. Foster
Bart J. Harmsen
spellingShingle Greg B. Distiller
David L. Borchers
Rebecca J. Foster
Bart J. Harmsen
Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
Ecology and Evolution
activity patterns
behavioral ecology
continuous‐time spatial capture–recapture
spatial capture–recapture
temporal partitioning
author_facet Greg B. Distiller
David L. Borchers
Rebecca J. Foster
Bart J. Harmsen
author_sort Greg B. Distiller
title Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
title_short Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
title_full Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
title_fullStr Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
title_full_unstemmed Using Continuous‐Time Spatial Capture–Recapture models to make inference about animal activity patterns
title_sort using continuous‐time spatial capture–recapture models to make inference about animal activity patterns
publisher Wiley
series Ecology and Evolution
issn 2045-7758
publishDate 2020-10-01
description Abstract Quantifying the distribution of daily activity is an important component of behavioral ecology. Historically, it has been difficult to obtain data on activity patterns, especially for elusive species. However, the development of affordable camera traps and their widespread usage has led to an explosion of available data from which activity patterns can be estimated. Continuous‐time spatial capture–recapture (CT SCR) models drop the occasion structure seen in traditional spatial and nonspatial capture–recapture (CR) models and use the actual times of capture. In addition to estimating density, CT SCR models estimate expected encounters through time. Cyclic splines can be used to allow flexible shapes for modeling cyclic activity patterns, and the fact that SCR models also incorporate distance means that space–time interactions can be explored. This method is applied to a jaguar dataset. Jaguars in Belize are most active and range furthest in the evening and early morning and when they are located closer to the network of trails. There is some evidence that females have a less variable pattern than males. The comparison between sexes demonstrates how CT SCR can be used to explore hypotheses about animal behavior within a formal modeling framework. SCR models were developed primarily to estimate and model density, but the models can be used to explore processes that interact across space and time, especially when using the CT SCR framework that models the temporal dimension at a finer resolution.
topic activity patterns
behavioral ecology
continuous‐time spatial capture–recapture
spatial capture–recapture
temporal partitioning
url https://doi.org/10.1002/ece3.6822
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