Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates
International audience Knowledge of population sizes in delimited spatial regions is crucial for most ecological research. Data from population surveys are collected with strip, line, or point transects sampling. These data are positively skewed and spatially autocorrelated, which makes estimation o...
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Online Access: | https://hal.archives-ouvertes.fr/hal-00959334 https://doi.org/10.1002/env.2240 |
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ftccsdartic:oai:HAL:hal-00959334v1 2023-05-15T15:56:00+02:00 Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates Bellier, Edwige Monestiez, Pascal, Certain, Grégoire Chadœuf, Joel Bretagnolle, Vincent Biostatistique et Processus Spatiaux (BioSP) Institut National de la Recherche Agronomique (INRA) Institute of Marine Research Bergen (IMR) University of Bergen (UiB) Centre d'études biologiques de Chizé (CEBC) Centre National de la Recherche Scientifique (CNRS) French Government (MEDD); University of La Rochelle; Communaute de Commune de La Rochelle; Provence Alpes Cotes d'Azur province; Institut National pour la Recherche Agronomique (INRA) 2013 https://hal.archives-ouvertes.fr/hal-00959334 https://doi.org/10.1002/env.2240 en eng HAL CCSD Wiley info:eu-repo/semantics/altIdentifier/doi/10.1002/env.2240 hal-00959334 https://hal.archives-ouvertes.fr/hal-00959334 doi:10.1002/env.2240 PRODINRA: 257516 WOS: 000327247100005 ISSN: 1180-4009 EISSN: 1099-095X Environmetrics https://hal.archives-ouvertes.fr/hal-00959334 Environmetrics, Wiley, 2013, 24 (7), pp.476-488. ⟨10.1002/env.2240⟩ hierarchical model Poisson block kriging distribution free nested covariance function non-stationarity block bootstrap [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2013 ftccsdartic https://doi.org/10.1002/env.2240 2021-11-21T03:32:12Z International audience Knowledge of population sizes in delimited spatial regions is crucial for most ecological research. Data from population surveys are collected with strip, line, or point transects sampling. These data are positively skewed and spatially autocorrelated, which makes estimation of uncertainty in the population size difficult. Thus, we propose a novel spatial-based estimator from a hierarchical spatial model for count data where the inhomogeneous animal density is decomposed into a deterministic trend related to potential habitat and a stationary latent field modeled by geostatistics. An empirical estimate of the latent variable is obtained including corrective terms for non-stationarity and variance resulting from a Poisson distribution of sightings. A novel block kriging estimator that takes into account both non-stationarity and the nature of count data is derived to obtain a spatial estimate of animal total abundance and variance of errors of prediction. From spatial simulated count data and real count data of common guillemot wintering in the Bay of Biscay (France), we compare mean population size and variance estimates obtained from our model-based approach to the design-based estimator (i.e., block bootstrap). The novel Poisson block kriging estimates greatly reduces uncertainty of population size estimates while block bootstrap provides larger uncertainties. Article in Journal/Newspaper common guillemot Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Environmetrics 24 7 476 488 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
hierarchical model Poisson block kriging distribution free nested covariance function non-stationarity block bootstrap [SDE]Environmental Sciences |
spellingShingle |
hierarchical model Poisson block kriging distribution free nested covariance function non-stationarity block bootstrap [SDE]Environmental Sciences Bellier, Edwige Monestiez, Pascal, Certain, Grégoire Chadœuf, Joel Bretagnolle, Vincent Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
topic_facet |
hierarchical model Poisson block kriging distribution free nested covariance function non-stationarity block bootstrap [SDE]Environmental Sciences |
description |
International audience Knowledge of population sizes in delimited spatial regions is crucial for most ecological research. Data from population surveys are collected with strip, line, or point transects sampling. These data are positively skewed and spatially autocorrelated, which makes estimation of uncertainty in the population size difficult. Thus, we propose a novel spatial-based estimator from a hierarchical spatial model for count data where the inhomogeneous animal density is decomposed into a deterministic trend related to potential habitat and a stationary latent field modeled by geostatistics. An empirical estimate of the latent variable is obtained including corrective terms for non-stationarity and variance resulting from a Poisson distribution of sightings. A novel block kriging estimator that takes into account both non-stationarity and the nature of count data is derived to obtain a spatial estimate of animal total abundance and variance of errors of prediction. From spatial simulated count data and real count data of common guillemot wintering in the Bay of Biscay (France), we compare mean population size and variance estimates obtained from our model-based approach to the design-based estimator (i.e., block bootstrap). The novel Poisson block kriging estimates greatly reduces uncertainty of population size estimates while block bootstrap provides larger uncertainties. |
author2 |
Biostatistique et Processus Spatiaux (BioSP) Institut National de la Recherche Agronomique (INRA) Institute of Marine Research Bergen (IMR) University of Bergen (UiB) Centre d'études biologiques de Chizé (CEBC) Centre National de la Recherche Scientifique (CNRS) French Government (MEDD); University of La Rochelle; Communaute de Commune de La Rochelle; Provence Alpes Cotes d'Azur province; Institut National pour la Recherche Agronomique (INRA) |
format |
Article in Journal/Newspaper |
author |
Bellier, Edwige Monestiez, Pascal, Certain, Grégoire Chadœuf, Joel Bretagnolle, Vincent |
author_facet |
Bellier, Edwige Monestiez, Pascal, Certain, Grégoire Chadœuf, Joel Bretagnolle, Vincent |
author_sort |
Bellier, Edwige |
title |
Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
title_short |
Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
title_full |
Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
title_fullStr |
Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
title_full_unstemmed |
Reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
title_sort |
reducing the uncertainty of wildlife population abundance: model-based versus design-based estimates |
publisher |
HAL CCSD |
publishDate |
2013 |
url |
https://hal.archives-ouvertes.fr/hal-00959334 https://doi.org/10.1002/env.2240 |
genre |
common guillemot |
genre_facet |
common guillemot |
op_source |
ISSN: 1180-4009 EISSN: 1099-095X Environmetrics https://hal.archives-ouvertes.fr/hal-00959334 Environmetrics, Wiley, 2013, 24 (7), pp.476-488. ⟨10.1002/env.2240⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1002/env.2240 hal-00959334 https://hal.archives-ouvertes.fr/hal-00959334 doi:10.1002/env.2240 PRODINRA: 257516 WOS: 000327247100005 |
op_doi |
https://doi.org/10.1002/env.2240 |
container_title |
Environmetrics |
container_volume |
24 |
container_issue |
7 |
container_start_page |
476 |
op_container_end_page |
488 |
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1766391487427248128 |