Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus)
Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distanc...
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ftdoajarticles:oai:doaj.org/article:97ddb54f5b4b4d298cbca5189d71cbe6 2024-01-07T09:42:21+01:00 Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) Douglas B. Sigourney Samuel Chavez-Rosales Paul B. Conn Lance Garrison Elizabeth Josephson Debra Palka 2020-01-01T00:00:00Z https://doi.org/10.7717/peerj.8226 https://doaj.org/article/97ddb54f5b4b4d298cbca5189d71cbe6 EN eng PeerJ Inc. https://peerj.com/articles/8226.pdf https://peerj.com/articles/8226/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.8226 2167-8359 https://doaj.org/article/97ddb54f5b4b4d298cbca5189d71cbe6 PeerJ, Vol 8, p e8226 (2020) Bayesian model Jagam Generalized Additive Model (GAM) Tweedie distribution Fin whales Density surface model Medicine R Biology (General) QH301-705.5 article 2020 ftdoajarticles https://doi.org/10.7717/peerj.8226 2023-12-10T01:50:34Z Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance ... Article in Journal/Newspaper Balaenoptera physalus Fin whale Directory of Open Access Journals: DOAJ Articles Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) PeerJ 8 e8226 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Bayesian model Jagam Generalized Additive Model (GAM) Tweedie distribution Fin whales Density surface model Medicine R Biology (General) QH301-705.5 |
spellingShingle |
Bayesian model Jagam Generalized Additive Model (GAM) Tweedie distribution Fin whales Density surface model Medicine R Biology (General) QH301-705.5 Douglas B. Sigourney Samuel Chavez-Rosales Paul B. Conn Lance Garrison Elizabeth Josephson Debra Palka Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
topic_facet |
Bayesian model Jagam Generalized Additive Model (GAM) Tweedie distribution Fin whales Density surface model Medicine R Biology (General) QH301-705.5 |
description |
Density surface models (DSMs) are an important tool in the conservation and management of cetaceans. Most previous applications of DSMs have adopted a two-step approach to model fitting (hereafter referred to as the Two-Stage Method), whereby detection probabilities are first estimated using distance sampling detection functions and subsequently used as an offset when fitting a density-habitat model. Although variance propagation techniques have recently become available for the Two-Stage Method, most previous applications have not propagated detection probability uncertainty into final density estimates. In this paper, we describe an alternative approach for fitting DSMs based on Bayesian hierarchical inference (hereafter referred to as the Bayesian Method), which is a natural framework for simultaneously propagating multiple sources of uncertainty into final estimates. Our framework includes (1) a mark-recapture distance sampling observation model that can accommodate two team line transect data, (2) an informed prior for the probability a group of animals is at the surface and available for detection (i.e. surface availability) (3) a density-habitat model incorporating spatial smoothers and (4) a flexible compound Poisson-gamma model for count data that incorporates overdispersion and zero-inflation. We evaluate our method and compare its performance to the Two-Stage Method with simulations and an application to line transect data of fin whales (Balaenoptera physalus) off the east coast of the USA. Simulations showed that both methods had low bias (<1.5%) and confidence interval coverage close to the nominal 95% rate when variance was propagated from the first step. Results from the fin whale analysis showed that density estimates and predicted distribution patterns were largely similar among methods; however, the coefficient of variation of the final abundance estimate more than doubled (0.14 vs 0.31) when detection variance was correctly propagated into final estimates. An analysis of the variance ... |
format |
Article in Journal/Newspaper |
author |
Douglas B. Sigourney Samuel Chavez-Rosales Paul B. Conn Lance Garrison Elizabeth Josephson Debra Palka |
author_facet |
Douglas B. Sigourney Samuel Chavez-Rosales Paul B. Conn Lance Garrison Elizabeth Josephson Debra Palka |
author_sort |
Douglas B. Sigourney |
title |
Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
title_short |
Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
title_full |
Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
title_fullStr |
Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
title_full_unstemmed |
Developing and assessing a density surface model in a Bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (Balaenoptera physalus) |
title_sort |
developing and assessing a density surface model in a bayesian hierarchical framework with a focus on uncertainty: insights from simulations and an application to fin whales (balaenoptera physalus) |
publisher |
PeerJ Inc. |
publishDate |
2020 |
url |
https://doi.org/10.7717/peerj.8226 https://doaj.org/article/97ddb54f5b4b4d298cbca5189d71cbe6 |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Gam |
geographic_facet |
Gam |
genre |
Balaenoptera physalus Fin whale |
genre_facet |
Balaenoptera physalus Fin whale |
op_source |
PeerJ, Vol 8, p e8226 (2020) |
op_relation |
https://peerj.com/articles/8226.pdf https://peerj.com/articles/8226/ https://doaj.org/toc/2167-8359 doi:10.7717/peerj.8226 2167-8359 https://doaj.org/article/97ddb54f5b4b4d298cbca5189d71cbe6 |
op_doi |
https://doi.org/10.7717/peerj.8226 |
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PeerJ |
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8 |
container_start_page |
e8226 |
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1787423301279154176 |