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Resumen
Predator/parasitoid functional response is one of the main tools used to study predation behavior, and in assessing the potential of biological control candidates. It is generally accepted that predator learning in prey searching and manipulation can produce the appearance of a type III functional response. Holling proposed that in the presence of alternative prey, at some point the predator would shift the preferred prey, leading to the appearance of a [ver mas...]
dc.contributor.authorBruzzone, Octavio Augusto
dc.contributor.authorAguirre, María Belén
dc.contributor.authorHill, Jorge Guillermo
dc.contributor.authorVirla, Eduardo Gabriel
dc.contributor.authorLogarzo, Guillermo Alejandro
dc.date.accessioned2023-08-09T13:54:53Z
dc.date.available2023-08-09T13:54:53Z
dc.date.issued2022-02
dc.identifier.issn2045-7758
dc.identifier.otherhttps://doi.org/10.1002/ece3.8593
dc.identifier.urihttp://hdl.handle.net/20.500.12123/14891
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/10.1002/ece3.8593
dc.description.abstractPredator/parasitoid functional response is one of the main tools used to study predation behavior, and in assessing the potential of biological control candidates. It is generally accepted that predator learning in prey searching and manipulation can produce the appearance of a type III functional response. Holling proposed that in the presence of alternative prey, at some point the predator would shift the preferred prey, leading to the appearance of a sigmoid function that characterized that functional response. This is supported by the analogy between enzyme kinetics and functional response that Holling used as the basis for developing this theory. However, after several decades, sigmoidal functional responses appear in the absence of alternative prey in most of the biological taxa studied. Here, we propose modeling the effect of learning on the functional response by using the explicit incorporation of learning curves in the parameters of the Holling functional response, the attack rate (a), and the manipulation time (h). We then study how the variation in the parameters of the learning curves causes variations in the shape of the functional response curve. We found that the functional response product of learning can be either type I, II, or III, depending on what parameters act on the organism, and how much it can learn throughout the length of the study. Therefore, the presence of other types of curves should not be automatically associated with the absence of learning. These results are important from an ecological point of view because when type III functional response is associated with learning, it is generally accepted that it can operate as a stabilizing factor in population dynamics. Our results, to the contrary, suggest that depending on how it acts, it may even be destabilizing by generating the appearance of functional responses close to type I.eng
dc.formatapplication/pdfes_AR
dc.language.isoenges_AR
dc.publisherWileyes_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/es_AR
dc.sourceEcology and Evolution 2 (2) : e8593. (February 2022)es_AR
dc.subjectDepredadoreses_AR
dc.subjectPredatorseng
dc.subjectControl Biológicoes_AR
dc.subjectBiological Controleng
dc.subjectRelaciones Predador Presaes_AR
dc.subjectPredator Prey Relationseng
dc.subjectParasitoideses_AR
dc.subjectParasitoidseng
dc.titleRevisiting the influence of learning in predator functional response, how it can lead to shapes different from type IIIes_AR
dc.typeinfo:ar-repo/semantics/artículoes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.typeinfo:eu-repo/semantics/publishedVersiones_AR
dc.rights.licenseCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)es_AR
dc.description.origenEEA Barilochees_AR
dc.description.filFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentinaes_AR
dc.description.filFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentinaes_AR
dc.description.filFil: Aguirre, María Belén. Fundación para el Estudio de Especies Invasivas; Argentinaes_AR
dc.description.filFil: Aguirre, María Belén. Consejo Nacional de Investigaciones Científicas y Tecnológicas. Fundación para el Estudio de Especies Invasivas; Argentinaes_AR
dc.description.filFil: Hill, Jorge Guillermo. Universidad Nacional de Tucumán. Facultad de Agronomía y Zootecnia; Argentinaes_AR
dc.description.filFil: Virla, Eduardo G. Fundación Miguel Lillo. Instituto de Entomología; Argentinaes_AR
dc.description.filFil: Virla, Eduardo G. Consejo Nacional de Investigaciones Científicas y Técnicas. Planta Piloto de Pocesos Industriales Microbiológicos (PROIMI-Biotecnología); Argentinaes_AR
dc.description.filFil: Logarzo, Guillermo Alejandro. Fundación para el Estudio de Especies Invasivas; Argentinaes_AR
dc.subtypecientifico


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