A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data.
Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-mod...
Main Authors: | , , , , , , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
eScholarship, University of California
2023
|
Subjects: | |
Online Access: | https://escholarship.org/uc/item/20x8387c |
id |
ftcdlib:oai:escholarship.org:ark:/13030/qt20x8387c |
---|---|
record_format |
openpolar |
spelling |
ftcdlib:oai:escholarship.org:ark:/13030/qt20x8387c 2024-04-28T08:22:50+00:00 A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Zhang, Wei Chipperfield, Joseph Illian, Janine Dupont, Pierre Milleret, Cyril de Valpine, Perry Bischof, Richard 2023-01-01 application/pdf https://escholarship.org/uc/item/20x8387c unknown eScholarship, University of California qt20x8387c https://escholarship.org/uc/item/20x8387c public Ecology, vol 104, iss 1 NIMBLE Poisson point process area search binomial point process continuous sampling non-invasive genetic sampling spatial capture-recapture wolverine Animals Bayes Theorem Wild Probability Population Density Norway article 2023 ftcdlib 2024-04-03T14:14:44Z Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway. Article in Journal/Newspaper Gulo gulo University of California: eScholarship |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
NIMBLE Poisson point process area search binomial point process continuous sampling non-invasive genetic sampling spatial capture-recapture wolverine Animals Bayes Theorem Wild Probability Population Density Norway |
spellingShingle |
NIMBLE Poisson point process area search binomial point process continuous sampling non-invasive genetic sampling spatial capture-recapture wolverine Animals Bayes Theorem Wild Probability Population Density Norway Zhang, Wei Chipperfield, Joseph Illian, Janine Dupont, Pierre Milleret, Cyril de Valpine, Perry Bischof, Richard A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
topic_facet |
NIMBLE Poisson point process area search binomial point process continuous sampling non-invasive genetic sampling spatial capture-recapture wolverine Animals Bayes Theorem Wild Probability Population Density Norway |
description |
Spatial capture-recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway. |
format |
Article in Journal/Newspaper |
author |
Zhang, Wei Chipperfield, Joseph Illian, Janine Dupont, Pierre Milleret, Cyril de Valpine, Perry Bischof, Richard |
author_facet |
Zhang, Wei Chipperfield, Joseph Illian, Janine Dupont, Pierre Milleret, Cyril de Valpine, Perry Bischof, Richard |
author_sort |
Zhang, Wei |
title |
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
title_short |
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
title_full |
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
title_fullStr |
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
title_full_unstemmed |
A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. |
title_sort |
flexible and efficient bayesian implementation of point process models for spatial capture-recapture data. |
publisher |
eScholarship, University of California |
publishDate |
2023 |
url |
https://escholarship.org/uc/item/20x8387c |
genre |
Gulo gulo |
genre_facet |
Gulo gulo |
op_source |
Ecology, vol 104, iss 1 |
op_relation |
qt20x8387c https://escholarship.org/uc/item/20x8387c |
op_rights |
public |
_version_ |
1797584191035015168 |