Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models
International audience Occupancy models allow assessing species occurrence while accounting for imperfect detection. As with any statistical models, occupancy models rely on several assumptions amongst which (i) there should be no unmodelled heterogeneity in the detection probability and (ii) the sp...
Published in: | Ecological Modelling |
---|---|
Main Authors: | , , , |
Other Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2018
|
Subjects: | |
Online Access: | https://hal.science/hal-02329890 https://hal.science/hal-02329890/document https://hal.science/hal-02329890/file/1-s2.0-S0304380018302904-main.pdf https://doi.org/10.1016/j.ecolmodel.2018.09.002 |
id |
ftunivnantes:oai:HAL:hal-02329890v1 |
---|---|
record_format |
openpolar |
spelling |
ftunivnantes:oai:HAL:hal-02329890v1 2023-05-15T15:50:56+02:00 Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models Louvrier, Julie Chambert, Thierry Marboutin, Eric Gimenez, Olivier Centre d’Ecologie Fonctionnelle et Evolutive (CEFE) Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) Office National de la Chasse et de la Faune Sauvage (ONCFS) 2018-11 https://hal.science/hal-02329890 https://hal.science/hal-02329890/document https://hal.science/hal-02329890/file/1-s2.0-S0304380018302904-main.pdf https://doi.org/10.1016/j.ecolmodel.2018.09.002 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2018.09.002 hal-02329890 https://hal.science/hal-02329890 https://hal.science/hal-02329890/document https://hal.science/hal-02329890/file/1-s2.0-S0304380018302904-main.pdf doi:10.1016/j.ecolmodel.2018.09.002 http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess CC-BY-NC-ND ISSN: 0304-3800 Ecological Modelling https://hal.science/hal-02329890 Ecological Modelling, 2018, 387, pp.61-69. ⟨10.1016/j.ecolmodel.2018.09.002⟩ Occupancy models Detection heterogeneity Species imperfect detection False-positives Finite-mixture models [SDE.BE]Environmental Sciences/Biodiversity and Ecology info:eu-repo/semantics/article Journal articles 2018 ftunivnantes https://doi.org/10.1016/j.ecolmodel.2018.09.002 2023-03-01T01:39:26Z International audience Occupancy models allow assessing species occurrence while accounting for imperfect detection. As with any statistical models, occupancy models rely on several assumptions amongst which (i) there should be no unmodelled heterogeneity in the detection probability and (ii) the species should not be detected when absent from a site, in other words there should be no false positives (e.g., due to misidentification). In the real world, thesetwo assumptions are often violated. To date, models accounting simultaneously for both detection heterogeneity and false positives are yet to be developed. Here, we first show how occupancy models with false positives can be formulated as hidden Markov models (HMM). Second, benefiting from the HMM framework flexibility, we extend models with false positives to account for heterogeneity with finite mixtures. First, using simulations, we demonstrate that, as the level of heterogeneity increases, occupancy models accounting for both heterogeneity and misidentification perform better in terms of bias and precision than models accounting for misidentification only. Next, we illustrate the implementation of our new model to a real case study with grey wolves (Canis lupus) in France. We demonstrate that heterogeneity in wolf detection (false negatives) is mainly due to a heterogeneous sampling effort across space. In addition to providing a novel modeling formulation, this work illustrates the flexibility of HMM framework to formulate complex ecological models and relax important assumptions that are not always likely to hold. In particular, we show how to decompose the model structure in several simple components, in a way that provides much clearer ecological interpretation. Article in Journal/Newspaper Canis lupus Université de Nantes: HAL-UNIV-NANTES Ecological Modelling 387 61 69 |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
Occupancy models Detection heterogeneity Species imperfect detection False-positives Finite-mixture models [SDE.BE]Environmental Sciences/Biodiversity and Ecology |
spellingShingle |
Occupancy models Detection heterogeneity Species imperfect detection False-positives Finite-mixture models [SDE.BE]Environmental Sciences/Biodiversity and Ecology Louvrier, Julie Chambert, Thierry Marboutin, Eric Gimenez, Olivier Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
topic_facet |
Occupancy models Detection heterogeneity Species imperfect detection False-positives Finite-mixture models [SDE.BE]Environmental Sciences/Biodiversity and Ecology |
description |
International audience Occupancy models allow assessing species occurrence while accounting for imperfect detection. As with any statistical models, occupancy models rely on several assumptions amongst which (i) there should be no unmodelled heterogeneity in the detection probability and (ii) the species should not be detected when absent from a site, in other words there should be no false positives (e.g., due to misidentification). In the real world, thesetwo assumptions are often violated. To date, models accounting simultaneously for both detection heterogeneity and false positives are yet to be developed. Here, we first show how occupancy models with false positives can be formulated as hidden Markov models (HMM). Second, benefiting from the HMM framework flexibility, we extend models with false positives to account for heterogeneity with finite mixtures. First, using simulations, we demonstrate that, as the level of heterogeneity increases, occupancy models accounting for both heterogeneity and misidentification perform better in terms of bias and precision than models accounting for misidentification only. Next, we illustrate the implementation of our new model to a real case study with grey wolves (Canis lupus) in France. We demonstrate that heterogeneity in wolf detection (false negatives) is mainly due to a heterogeneous sampling effort across space. In addition to providing a novel modeling formulation, this work illustrates the flexibility of HMM framework to formulate complex ecological models and relax important assumptions that are not always likely to hold. In particular, we show how to decompose the model structure in several simple components, in a way that provides much clearer ecological interpretation. |
author2 |
Centre d’Ecologie Fonctionnelle et Evolutive (CEFE) Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD France-Sud )-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) Office National de la Chasse et de la Faune Sauvage (ONCFS) |
format |
Article in Journal/Newspaper |
author |
Louvrier, Julie Chambert, Thierry Marboutin, Eric Gimenez, Olivier |
author_facet |
Louvrier, Julie Chambert, Thierry Marboutin, Eric Gimenez, Olivier |
author_sort |
Louvrier, Julie |
title |
Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
title_short |
Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
title_full |
Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
title_fullStr |
Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
title_full_unstemmed |
Accounting for misidentification and heterogeneity in occupancy studies using hidden Markov models |
title_sort |
accounting for misidentification and heterogeneity in occupancy studies using hidden markov models |
publisher |
HAL CCSD |
publishDate |
2018 |
url |
https://hal.science/hal-02329890 https://hal.science/hal-02329890/document https://hal.science/hal-02329890/file/1-s2.0-S0304380018302904-main.pdf https://doi.org/10.1016/j.ecolmodel.2018.09.002 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
ISSN: 0304-3800 Ecological Modelling https://hal.science/hal-02329890 Ecological Modelling, 2018, 387, pp.61-69. ⟨10.1016/j.ecolmodel.2018.09.002⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecolmodel.2018.09.002 hal-02329890 https://hal.science/hal-02329890 https://hal.science/hal-02329890/document https://hal.science/hal-02329890/file/1-s2.0-S0304380018302904-main.pdf doi:10.1016/j.ecolmodel.2018.09.002 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess |
op_rightsnorm |
CC-BY-NC-ND |
op_doi |
https://doi.org/10.1016/j.ecolmodel.2018.09.002 |
container_title |
Ecological Modelling |
container_volume |
387 |
container_start_page |
61 |
op_container_end_page |
69 |
_version_ |
1766385964561727488 |