000135991 001__ 135991
000135991 005__ 20250908131436.0
000135991 0247_ $$2doi$$a10.1088/2632-072X/ad459e
000135991 0248_ $$2sideral$$a138952
000135991 037__ $$aART-2024-138952
000135991 041__ $$aeng
000135991 100__ $$aPayrató-Borrás, Claudia
000135991 245__ $$aBeyond the aggregated paradigm: phenology and structure in mutualistic networks
000135991 260__ $$c2024
000135991 5060_ $$aAccess copy available to the general public$$fUnrestricted
000135991 5203_ $$aMutualistic relationships, where species interact to obtain mutual benefits, constitute an essential component of natural ecosystems. The use of ecological networks to represent the species and their ecological interactions allows the study of structural and dynamic patterns common to different ecosystems. However, by neglecting the temporal dimension of mutualistic communities, relevant insights into the organization and functioning of natural ecosystems can be lost. Therefore, it is crucial to incorporate empirical phenology -the cycles of species' activity within a season- to fully understand the impact of temporal variability on network architecture. In this paper, by using empirical datasets together with a set of synthetic models, we propose a framework to characterize the phenology of plant-pollinator communities and assess how it reshapes their portrayal as a network. Analyses of three empirical cases reveal that non-trivial information is missed when representing the network of interactions as static, which leads to overestimating the value of fundamental structural features. We discuss the implications of our findings for mutualistic relationships and intra-guild competition for common resources. We show that recorded interactions and species' activity duration are pivotal factors in accurately replicating observed patterns within mutualistic communities. Furthermore, our exploration of synthetic models underscores the system-specific character of the mechanisms driving phenology, increasing our understanding of the complexities of natural ecosystems.
000135991 536__ $$9info:eu-repo/grantAgreement/ES/DGA/E36-23R-FENOL$$9info:eu-repo/grantAgreement/ES/MICINN/PID2020-115800GB-I00
000135991 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000135991 592__ $$a0.641$$b2024
000135991 593__ $$aArtificial Intelligence$$c2024$$dQ2
000135991 593__ $$aInformation Systems$$c2024$$dQ2
000135991 593__ $$aComputer Science Applications$$c2024$$dQ2
000135991 593__ $$aComputer Networks and Communications$$c2024$$dQ2
000135991 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000135991 700__ $$0(orcid)0000-0002-9769-8796$$aGracia-Lázaro, Carlos
000135991 700__ $$aHernández, Laura
000135991 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000135991 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000135991 773__ $$g5, 2 (2024), e025013 [17 pp.]$$tJournal of Physics: Complexity$$x2632-072X
000135991 8564_ $$s1194631$$uhttps://zaguan.unizar.es/record/135991/files/texto_completo.pdf$$yVersión publicada
000135991 8564_ $$s610591$$uhttps://zaguan.unizar.es/record/135991/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000135991 909CO $$ooai:zaguan.unizar.es:135991$$particulos$$pdriver
000135991 951__ $$a2025-09-08-12:57:38
000135991 980__ $$aARTICLE