Improved central place foraging modeling: importance of landscape context and modeling strategies in the case of a French sweat bee

Authors

David Sheeren (ORCID: https://orcid.org/0000-0001-8016-5355)
Sylvain Moulhérat (ORCID: https://orcid.org/0000-0003-2451-018X)
Anouk Glad (ORCID: https://orcid.org/0000-0001-5810-189X)
Matthieu Moulherat
Émilie Andrieu (ORCID: https://orcid.org/0000-0002-0021-4573)
Annie Ouin (ORCID: https://orcid.org/0000-0001-7046-2719)

Institutions

Institut National Polytechnique de Toulouse (FR)
École Nationale Supérieure Agronomique de Toulouse (FR)
Dynamiques et écologie des paysages agriforestiers (FR)

Publication Details

Journal: Ecological Modelling
Published: 2025-06-16
DOI: https://doi.org/10.1016/j.ecolmodel.2025.111208
Primary Topic: Plant and animal studies
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Agence Nationale de la Recherche

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article

Improved central place foraging modeling: importance of landscape context and modeling strategies in the case of a French sweat bee

David Sheeren, Sylvain Moulhérat, Anouk Glad, Matthieu Moulherat et al.

Jun 16, 2025

Ecological Modelling

Plant and animal studies

article

Improved central place foraging modeling: importance of landscape context and modeling strategies in the case of a French sweat bee

David Sheeren, Sylvain Moulhérat, Anouk Glad, Matthieu Moulherat, Émilie Andrieu, Annie Ouin

June 16, 2025article en

Abstract

No abstract available for this paper.

Ecological ModellingVol. 508

Institut National Polytechnique de Toulouse (FR), École Nationale Supérieure Agronomique de Toulouse (FR), Dynamiques et écologie des paysages agriforestiers (FR)

Agence Nationale de la Recherche

Climate action

Openalex Percentile: Top 89%

Plant and animal studies

DOI Open Access PDF OpenAlex

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Improved central place foraging modeling: importance of landscape context and modeling strategies in the case of a French sweat bee — David Sheeren, Sylvain Moulhérat, et al. · Ecological Modelling (2025) | TGRS Research Map | TGRS