A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France
National audience Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution...
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ftccsdartic:oai:HAL:hal-02787637v1 2023-05-15T15:49:59+02:00 A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France Louvrier, Julie Papaix, Julien Duchamp, Christophe Gimenez, Olivier Centre National de la Recherche Scientifique (CNRS) Biostatistique et Processus Spatiaux (BioSP) Institut National de la Recherche Agronomique (INRA) Office National de la Chasse et de la Faune Sauvage (ONCFS) Avignon, France 2019-05-13 https://hal.inrae.fr/hal-02787637 https://doi.org/10.1016/j.spasta.2020.100428 fr fre HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spasta.2020.100428 hal-02787637 https://hal.inrae.fr/hal-02787637 doi:10.1016/j.spasta.2020.100428 PRODINRA: 481123 WOS: 000540766200004 SPATIAL STATISTICS GdR Ecostat 2019 : Réunion annuelle du GDR Ecologie Statistique https://hal.inrae.fr/hal-02787637 GdR Ecostat 2019 : Réunion annuelle du GDR Ecologie Statistique, May 2019, Avignon, France. pp.100428, ⟨10.1016/j.spasta.2020.100428⟩ https://gdrecostat2019.sciencesconf.org/ Forecasting Hierarchical Bayesian modeling Measurement error Partial differential equations Spatio-temporal occupancy Species distribution models [SDV]Life Sciences [q-bio] [SDE]Environmental Sciences [MATH]Mathematics [math] [INFO]Computer Science [cs] info:eu-repo/semantics/conferenceObject Conference papers 2019 ftccsdartic https://doi.org/10.1016/j.spasta.2020.100428 2021-11-21T00:45:12Z National audience Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90's. Using partial differential equations for modeling species diffusion and growth in a fragmented landscape, we develop a mechanistic-statistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007-2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts. Conference Object Canis lupus Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Spatial Statistics 36 100428 |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
French |
topic |
Forecasting Hierarchical Bayesian modeling Measurement error Partial differential equations Spatio-temporal occupancy Species distribution models [SDV]Life Sciences [q-bio] [SDE]Environmental Sciences [MATH]Mathematics [math] [INFO]Computer Science [cs] |
spellingShingle |
Forecasting Hierarchical Bayesian modeling Measurement error Partial differential equations Spatio-temporal occupancy Species distribution models [SDV]Life Sciences [q-bio] [SDE]Environmental Sciences [MATH]Mathematics [math] [INFO]Computer Science [cs] Louvrier, Julie Papaix, Julien Duchamp, Christophe Gimenez, Olivier A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
topic_facet |
Forecasting Hierarchical Bayesian modeling Measurement error Partial differential equations Spatio-temporal occupancy Species distribution models [SDV]Life Sciences [q-bio] [SDE]Environmental Sciences [MATH]Mathematics [math] [INFO]Computer Science [cs] |
description |
National audience Species distribution models (SDMs) are important statistical tools for ecologists to understand and predict species range. However, standard SDMs do not explicitly incorporate dynamic processes like dispersal. This limitation may lead to bias in inference about species distribution. Here, we adopt the theory of ecological diffusion that has recently been introduced in statistical ecology to incorporate spatio-temporal processes in ecological models. As a case study, we considered the wolf (Canis lupus) that has been recolonizing Eastern France naturally through dispersal from the Apennines since the early 90's. Using partial differential equations for modeling species diffusion and growth in a fragmented landscape, we develop a mechanistic-statistical spatio-temporal model accounting for ecological diffusion, logistic growth and imperfect species detection. We conduct a simulation study and show the ability of our model to i) estimate ecological parameters in various situations with contrasted species detection probability and number of surveyed sites and ii) forecast the distribution into the future. We found that the growth rate of the wolf population in France was explained by the proportion of forest cover, that diffusion was influenced by human density and that species detectability increased with increasing survey effort. Using the parameters estimated from the 2007-2015 period, we then forecasted wolf distribution in 2016 and found good agreement with the actual detections made that year. Our approach may be useful for managing species that interact with human activities to anticipate potential conflicts. |
author2 |
Centre National de la Recherche Scientifique (CNRS) Biostatistique et Processus Spatiaux (BioSP) Institut National de la Recherche Agronomique (INRA) Office National de la Chasse et de la Faune Sauvage (ONCFS) |
format |
Conference Object |
author |
Louvrier, Julie Papaix, Julien Duchamp, Christophe Gimenez, Olivier |
author_facet |
Louvrier, Julie Papaix, Julien Duchamp, Christophe Gimenez, Olivier |
author_sort |
Louvrier, Julie |
title |
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
title_short |
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
title_full |
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
title_fullStr |
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
title_full_unstemmed |
A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France |
title_sort |
mechanistic-statistical species distribution model to explain and forecast wolf (canis lupus) colonization in south-eastern france |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://hal.inrae.fr/hal-02787637 https://doi.org/10.1016/j.spasta.2020.100428 |
op_coverage |
Avignon, France |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
SPATIAL STATISTICS GdR Ecostat 2019 : Réunion annuelle du GDR Ecologie Statistique https://hal.inrae.fr/hal-02787637 GdR Ecostat 2019 : Réunion annuelle du GDR Ecologie Statistique, May 2019, Avignon, France. pp.100428, ⟨10.1016/j.spasta.2020.100428⟩ https://gdrecostat2019.sciencesconf.org/ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.spasta.2020.100428 hal-02787637 https://hal.inrae.fr/hal-02787637 doi:10.1016/j.spasta.2020.100428 PRODINRA: 481123 WOS: 000540766200004 |
op_doi |
https://doi.org/10.1016/j.spasta.2020.100428 |
container_title |
Spatial Statistics |
container_volume |
36 |
container_start_page |
100428 |
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1766384990639095808 |