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Behavior underpins the predictive power of a trait-based model of butterfly movement.
Evans, Luke C; Sibly, Richard M; Thorbek, Pernille; Sims, Ian; Oliver, Tom H; Walters, Richard J.
Afiliación
  • Evans LC; School of Biological Sciences University of Reading Reading UK.
  • Sibly RM; School of Biological Sciences University of Reading Reading UK.
  • Thorbek P; Syngenta Jealott's Hill International Research Centre Bracknell UK.
  • Sims I; BASF SE, APD/EE Limburgerhof Germany.
  • Oliver TH; Syngenta Jealott's Hill International Research Centre Bracknell UK.
  • Walters RJ; School of Biological Sciences University of Reading Reading UK.
Ecol Evol ; 10(7): 3200-3208, 2020 Apr.
Article en En | MEDLINE | ID: mdl-32273981
ABSTRACT
Dispersal ability is key to species persistence in times of environmental change. Assessing a species' vulnerability and response to anthropogenic changes is often performed using one of two

methods:

correlative approaches that infer dispersal potential based on traits, such as wingspan or an index of mobility derived from expert opinion, or a mechanistic modeling approach that extrapolates displacement rates from empirical data on short-term movements.Here, we compare and evaluate the success of the correlative and mechanistic approaches using a mechanistic random-walk model of butterfly movement that incorporates relationships between wingspan and sex-specific movement behaviors.The model was parameterized with new data collected on four species of butterfly in the south of England, and we observe how wingspan relates to flight speeds, turning angles, flight durations, and displacement rates.We show that flight speeds and turning angles correlate with wingspan but that to achieve good prediction of displacement even over 10 min the model must also include details of sex- and species-specific movement behaviors.We discuss what factors are likely to differentially motivate the sexes and how these could be included in mechanistic models of dispersal to improve their use in ecological forecasting.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ecol Evol Año: 2020 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Ecol Evol Año: 2020 Tipo del documento: Article