No effect of model distribution on long-term trends, even with underdispersion
International audience While many studies have illustrated the decline of animal populations—particularly of farmland birds—the statistical analyses, design, and protocols used have raised some concerns and criticism. Using a 27-year dataset (1996–2022) based on recording the number of skylarks (Ala...
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ftunivrochelle:oai:HAL:hal-04192086v1 2024-02-11T09:54:51+01:00 No effect of model distribution on long-term trends, even with underdispersion Schneider-Bruchon, Thomas Gaba, Sabrina Bretagnolle, Vincent Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC) La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) LTSER «Zone Atelier Plaine & Val de Sevre» France Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS) 2023 https://hal.science/hal-04192086 https://doi.org/10.1016/j.ecoinf.2023.102222 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoinf.2023.102222 hal-04192086 https://hal.science/hal-04192086 doi:10.1016/j.ecoinf.2023.102222 ISSN: 1574-9541 Ecological Informatics https://hal.science/hal-04192086 Ecological Informatics, 2023, 77, pp.102222. ⟨10.1016/j.ecoinf.2023.102222⟩ General linear mixed model Long-term trend Model distribution Underdispersion Bayesian information criterion Skylark [SDE.BE]Environmental Sciences/Biodiversity and Ecology [STAT.AP]Statistics [stat]/Applications [stat.AP] info:eu-repo/semantics/article Journal articles 2023 ftunivrochelle https://doi.org/10.1016/j.ecoinf.2023.102222 2024-01-23T23:34:09Z International audience While many studies have illustrated the decline of animal populations—particularly of farmland birds—the statistical analyses, design, and protocols used have raised some concerns and criticism. Using a 27-year dataset (1996–2022) based on recording the number of skylarks (Alauda arvensis) at 160 longitudinal count points, our study confronts two approaches commonly used to model long-term trends. The first uses a single model (based on a priori ecological knowledge), while the second is an a posteriori approach that relies on a multi-model selection among candidate models that account for probability distributions to describe the error structure. Here we investigate whether the statistical distribution of modelled variables and the method of including covariates in the model affect trend estimates. With a large amount of data and in the case of underdispersion, we found that the model distribution used had no impact on the estimation of the long-term trend. Moreover, adding confounding covariates did not change or improve the trend estimation, at least when data were obtained from a well-designed protocol (our case). In contrast to other studies reporting an effect of the model's distribution on long-term trends, especially in the presence of overdispersion, our results offer a new perspective on the presence of underdispersion, where simple models perform equally well as complex ones. Further research is now needed on multiple species data or on smaller data sets to check the generality of our findings. Article in Journal/Newspaper Alauda arvensis HAL - Université de La Rochelle Ecological Informatics 77 102222 |
institution |
Open Polar |
collection |
HAL - Université de La Rochelle |
op_collection_id |
ftunivrochelle |
language |
English |
topic |
General linear mixed model Long-term trend Model distribution Underdispersion Bayesian information criterion Skylark [SDE.BE]Environmental Sciences/Biodiversity and Ecology [STAT.AP]Statistics [stat]/Applications [stat.AP] |
spellingShingle |
General linear mixed model Long-term trend Model distribution Underdispersion Bayesian information criterion Skylark [SDE.BE]Environmental Sciences/Biodiversity and Ecology [STAT.AP]Statistics [stat]/Applications [stat.AP] Schneider-Bruchon, Thomas Gaba, Sabrina Bretagnolle, Vincent No effect of model distribution on long-term trends, even with underdispersion |
topic_facet |
General linear mixed model Long-term trend Model distribution Underdispersion Bayesian information criterion Skylark [SDE.BE]Environmental Sciences/Biodiversity and Ecology [STAT.AP]Statistics [stat]/Applications [stat.AP] |
description |
International audience While many studies have illustrated the decline of animal populations—particularly of farmland birds—the statistical analyses, design, and protocols used have raised some concerns and criticism. Using a 27-year dataset (1996–2022) based on recording the number of skylarks (Alauda arvensis) at 160 longitudinal count points, our study confronts two approaches commonly used to model long-term trends. The first uses a single model (based on a priori ecological knowledge), while the second is an a posteriori approach that relies on a multi-model selection among candidate models that account for probability distributions to describe the error structure. Here we investigate whether the statistical distribution of modelled variables and the method of including covariates in the model affect trend estimates. With a large amount of data and in the case of underdispersion, we found that the model distribution used had no impact on the estimation of the long-term trend. Moreover, adding confounding covariates did not change or improve the trend estimation, at least when data were obtained from a well-designed protocol (our case). In contrast to other studies reporting an effect of the model's distribution on long-term trends, especially in the presence of overdispersion, our results offer a new perspective on the presence of underdispersion, where simple models perform equally well as complex ones. Further research is now needed on multiple species data or on smaller data sets to check the generality of our findings. |
author2 |
Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC) La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) LTSER «Zone Atelier Plaine & Val de Sevre» France Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Schneider-Bruchon, Thomas Gaba, Sabrina Bretagnolle, Vincent |
author_facet |
Schneider-Bruchon, Thomas Gaba, Sabrina Bretagnolle, Vincent |
author_sort |
Schneider-Bruchon, Thomas |
title |
No effect of model distribution on long-term trends, even with underdispersion |
title_short |
No effect of model distribution on long-term trends, even with underdispersion |
title_full |
No effect of model distribution on long-term trends, even with underdispersion |
title_fullStr |
No effect of model distribution on long-term trends, even with underdispersion |
title_full_unstemmed |
No effect of model distribution on long-term trends, even with underdispersion |
title_sort |
no effect of model distribution on long-term trends, even with underdispersion |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04192086 https://doi.org/10.1016/j.ecoinf.2023.102222 |
genre |
Alauda arvensis |
genre_facet |
Alauda arvensis |
op_source |
ISSN: 1574-9541 Ecological Informatics https://hal.science/hal-04192086 Ecological Informatics, 2023, 77, pp.102222. ⟨10.1016/j.ecoinf.2023.102222⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ecoinf.2023.102222 hal-04192086 https://hal.science/hal-04192086 doi:10.1016/j.ecoinf.2023.102222 |
op_doi |
https://doi.org/10.1016/j.ecoinf.2023.102222 |
container_title |
Ecological Informatics |
container_volume |
77 |
container_start_page |
102222 |
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1790608144274030592 |