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All issues Volume 38 (2025) Aquat. Living Resour., 38 (2025) 21 References
Open Access
Issue
Aquat. Living Resour.
Volume 38, 2025
Article Number 21
Number of page(s) 10
DOI https://doi.org/10.1051/alr/2025019
Published online 09 December 2025
  • Akia S, Amandé M, Gaertner D. 2023. Towards “glocalised” management of tuna stocks based on causation between a stock and its component belonging temporally to local Exlusive Economic Zones. Aquat Living Resour 36: 1–13. [Google Scholar]
  • Breston L, Leonardis EJ, Quinn LK, Tolston M, Wiles J, Chiba AA. 2021. Convergent cross sorting for estimating dynamic coupling. Sci Rep 11:20374. [Google Scholar]
  • Bustos E, Robotham H, Rodriguez G, Ther-Ríos F, Muñoz J, Andrade L, Barahona N, Clement A. 2022. Indicators for the determination of the status and trends of king’s littleneck clam fishery (Ameghinomya antiqua, P.P. King, 1832) in Los Lagos Region, Chile: An integral sustainable approach. Mar Pol 143: 1–12. [Google Scholar]
  • Canales C, Adasme N, Sánchez N. Curiel J, Mardones M, Barahona N, Quiroz JC, Araya P, Molinet C, Hernriquez J, Roa R, Diaz M, Cares L. 2019. Estrategias de manejo de la pesquería de almeja Venus Antiqua en la Región de Los Lagos y la Región de Aysén del general Carlos Ibañez del Campo, Informe Final, Proyecto FIPA 2018–32, Valparaiso, Chile, 2019, p. 274. https://www.subpesca.cl/fipa/613/articles-102252_informe_final.pdf [accessed January 10 2024]. [Google Scholar]
  • Chang CW, Ushio M, Hsieh CH. 2017. Empirical dynamic modelling for beginners. Ecol Res 32: 785–796. [Google Scholar]
  • Chang CW, Ye H, Miki T, Deyle ER, Souissi S, Anneville O, Adrian R, Chiang RY, Ichise S, Kumagai M, Matsuzaki SI, Shiah FK, Wu JT. Hsieh C-H, Sugihara G. 2020. Longterm warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks. Glob Change Biol 26: 6413–6423. [Google Scholar]
  • Chang CW, Miki T, Ushio M, Kes PJ, Lu HP, Hsieh CH. 2021. Reconstructing large interaction networks from empirical time series data. Ecol Lett 24: 2763–2774. [Google Scholar]
  • Clark AT, Ye H. Isbell F, Deyle ER, Cowles J, Tilman GD, Sugihara G. 2015. Spatial convergent cross mapping to detect causal relationships from short time series. Ecology 96: 1174–1181. [CrossRef] [PubMed] [Google Scholar]
  • DeAngelis DL, Yurek S. 2015. Equation-free modelling unravels the behavior of complex ecological systems. www.pnas.org/cgi/doi/10.1073/PNAS.1503154112. [Google Scholar]
  • Deyle ER, Fogarty M, Hsieh CH, Kaufman L, MacCall AD, Munch SB, Perreti CT, Ye H, Sugihara G. 2013. Predicting climate effects on Pacific sardine. PNAS 110: 6430–6435. [Google Scholar]
  • Deyle ER, May RM, Munch SB, Sugihara G. 2016. Tracking and forecasting ecosystem interactions in real time. Proc R Soc B 283:2015–2258. [Google Scholar]
  • Deyle ER, Maher MC, Hernandez RD, Basu S, Sugihara G. 2016b. Global environmental drivers of influenza. Proc Natl Acad Sci USA 113: 13081–13086. [Google Scholar]
  • Froese R. 2004. Keep it simple: three indicators to deal with overfishing, Fish Fish 5:86–91. [Google Scholar]
  • Garcia SM, Cochrane KL. 2005. Ecosystem approach to fisheries: a review of implementation guidelines. ICES J Mar Sci 62: 311–318. [CrossRef] [Google Scholar]
  • Glaser SM, Ye H, Maunder M, MacCall A, Fogarty M, Liu H, Sugihara G. 2011. Detecting and forecasting complex nonlinear dynamics in spatally structured catch per unit effort time series for North Pacific albacore (Thunnus alalunga). Can J Fish Aquat Sci 68: 400–412. [Google Scholar]
  • Glaser SM, Fogarty MJ, Liu H, Altman I, Hsieh CH, Kaufman L, MacCall AD, Rosenberg AA, Ye H, Sugihara G. 2014. Complex dynamics may limit prediction in marine fisheries. Fish Fish 15: 616–633. [Google Scholar]
  • Granger CWJ. 1969 Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37: 424–438. [Google Scholar]
  • Hoyle SD, Campbell RA, Ducharme-Barth ND, Gruss A, Moore BR, Thorson JT, TremblayBroyer L, Winker H, Zhou S, Maunder MN. 2024. Catch per unit effort modelling for stock assessment: A summary of good practices. Fish Res 269: 1–21. [Google Scholar]
  • Hsieh CH, Glaser SM, Lucas AJ, Sugihara G. 2005. Distinguishing random environmental fluctuations from ecological catastrophes for the North Pacific Ocean. Nature 435: 336–340. [Google Scholar]
  • Huang X, Stärz M, Gohl K, Knorr G, Lohmann G. 2017. Climate model results of various COSMOS Miocene experiments in format. NetCDF PANGAEA. https://doi.org/10.1594/PANGAEA.873850. [Google Scholar]
  • Jerez G, Ponce F, Rivas D. 1991. Análisis de la explotación del recurso almeja (Venus antiqua antiqua) King y Broderip, 1835) en la Bahía de Ancud, Chile. Invest Pesq (Chile) 36: 3–16. https://bibliotecadigital.ciren.cl/items/96c2a647-47a2-4fc4-ae76509dc13226fa [accessed January 10 2024]. [Google Scholar]
  • Ma H, Aihara K, Chen L. 2014. Detecting causality from nonlinear dynamics with short-term time series. Sci Rep 4: 1–10. [Google Scholar]
  • Mardones M, Techeira C, Vicencio C. 2018. Reporte Técnico, Informe. Implementación metodológica de la evaluación de stock en recursos bentónicos implementados por Planes de Manejo, Proyecto: Programa de Seguimiento de las Pesquerías Bentónicas Bajo Planes de Manejo, Subsecretaría de Economía y EMT, Valparaíso, Chile, 2018, p. 71. https://www.researchgate.net/publication/336902592[accessed January 10 2024]. [Google Scholar]
  • Maunder M, Punt A. 2004. Standardizing catch and effort data: a review of recent approaches. Fish Res 70: 141–150. [CrossRef] [Google Scholar]
  • Mirshojaeian H, Kaneko S. 2012. Causality between pillars of sustainable development: global stylized facts or regional phenomena. Ecol Indicat 14: 197–201. [Google Scholar]
  • MolluscaBase, Venus antiqua P.P. King, 1832. https://www.marinespecies.org/aphia.php?p=taxdetails&id=544286. (accessed 12 March 2024). [Google Scholar]
  • Runge J, Bathaiany S, Bollts E, Campus-Valls G, Coumou D, Deyle E, Glymour C, Kretschmers M, Mahecha MD, Muñoz-Marí J, van Nes EH, Peters J, Quax R, Reichstein M, Scheffer M, Schölkopf B, Spirtes P, Sugihara G, Sun J, Zhang K, Zscheischler J. 2019. Inferring causation from time series in Earth system sciences. iNat Commun 10: 25–53. [Google Scholar]
  • Sugihara G, May RM, Ye H, Hsieh CH, Deyle E, Fogarty M, Munch S. 2012. Detecting causality in complex ecosystems. Science 338: 496–500. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  • Sugihara G, May RM. 1990. Nonlinear for as a way of distinguishing chaos from measurement error in time series. Nature 344: 734–741. [Google Scholar]
  • Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philos Trans R Soc A 348: 477–495. [Google Scholar]
  • Takens F. Detecting strange attractors in turbulence, in: D.A. Rand, L.S. Young (Eds.), Dynamical Systems and Turbulence, Springer Berlin, Heidelberg, 1981, pp. 366–381. [Google Scholar]
  • Tsonis AA, Deyle E, Ye H. (Eds.), Advances in Nonlinear Geosciences. Springer International Publishing AG, 2018. [Google Scholar]
  • van Nes EH, Scheffer M, Brovkin V, Lenton TM, Ye H, Deyle, E, Sugihara G. 2015. Causal feedbacks in climate change. Nat Clim Change 5: 445–448. [Google Scholar]
  • Wang JY, Kuo TC, Hsieh CH. 2020. Causal effects of population dynamics and environmental changes on spatial variability of marine fishes. Nat Commun 11: 2635. [Google Scholar]
  • Whitney H. 1936. Differentiable manifolds. Annals Math. Second Series 37: 645–680. [Google Scholar]
  • Ye H, Clark A, Deyle E, Sugihara G. 2016. rEDM: an R package for empirical dynamic modeling and convergent cross-mapping. Sci Rep 5: https://ha0ye.github.io/rEDM/articles/rEDM.html. [Google Scholar]
  • Ye H, Deyle ER, Gilarranz LJ, Sugihara G. 2015b. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci Rep 5: 1–9. [Google Scholar]
  • Zhang BG, Li W, Shi Y, Liu X, Chen L. 2017. Detecting causality from short time-series data based on prediction of topologically equivalent attractors. BMC Syst Biol 11(suppl 7). https://doi.org/10.1186/s12918-017-0512-3. [Google Scholar]

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