Resumen
Introducción: Coryphaena hippurus es una especie migratoria de interés comercial, que se encuentra en ambientes tropicales y subtropicales, prefiriendo zonas con un rango de temperatura entre 21 y 30 °C y salinidad cercana a 31 ppt. Aunque la tendencia poblacional del dorado es estable, la pesquería de este recurso está aumentando y ocupa importantes posiciones en la economía de países costeros del Pacífico Oriental Tropical, evidenciándose la necesidad de diseñar y fortalecer estrategias de conservación y mejorar el aprovechamiento de este recurso. Dada su ubicación en ambientes tropicales y subtropicales, la disponibilidad y distribución de este recurso podría verse afectado a futuro por el cambio climático. Objetivo: Analizar la distribución potencial actual y futura de C. hippurus bajo condiciones de cambio climático. Métodos: Se emplearon 10 algoritmos para modelar la distribución potencial e idoneidad de hábitat actual para C. hippurus a partir de la temperatura superficial del mar, la salinidad y velocidad de las corrientes, posteriormente se proyectaron estos resultados según el escenario de cambio climático más extremo. Resultados: Hubo buenos desempeños con todos los algoritmos empleados, pero se escogió el modelo generado con BIOCLIM (AUC: 0.89) pues además resuelve el inconveniente del sesgo espacial y temporal hallado en los registros de la especie. La región de mayor idoneidad de hábitat para C. hippurus coincide con los frentes oceánicos del Pacífico Oriental Tropical. En condiciones futuras de cambio climático extremo, el modelo de distribución de la especie indica una contracción, reubicación y expansión de hábitat hacia el sur de la línea ecuatorial. Conclusiones: En condiciones de cambio climático extremo, el modelo de distribución para C. hippurus sugiere un proceso de tropicalización de los ecosistemas marinos en el Pacífico Oriental Tropical para el año 2100.
Citas
Amorós, S., Gozzer, R., Melgar, V., & Rovegno, N. (2017). Peruvian mahi mahi fishery (Coryphaena hippurus) characterization and analysis of the supply chain. WWFMarine Program of WWF-Peru. https://doi.org/10.13140/RG.2.2.20284.74883
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., De Clerck, O., & Tittensor, D. (2018). Bio‐ORACLE v2.0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography, 27(3), 277–284. https://doi.org/10.1111/geb.12693
Asto, C., Chaigneau, A., & Gutiérrez, D. (2019). Spatio-temporal variability of the equatorial front in the eastern tropical Pacific from remote sensing salinity data (2010-2015). Deep-Sea Research Part II: Topical Studies in Oceanography, 169-170, 104640. https://doi.org/10.1016/j.dsr2.2019.104640
Ateweberhan, M., & McClanahan, T. R. (2010). Relationship between historical sea-surface temperature variability and climate change-induced coral mortality in the western Indian Ocean. Marine Pollution Bulletin, 60(7), 964–970. https://doi.org/10.1016/j.marpolbul.2010.03.033
Barbet-Massin, M., Jiguet, F., Albert, C. H., & Thuiller, W. (2012). Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution, 3(2), 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
Booth, T. H., Nix, H. A., Busby, J. R., & Hutchinson, M. F. (2014). Bioclim: The first species distribution modelling package, its early applications and relevance to most current MaxEnt studies. Diversity and Distributions, 20(1), 1–9. https://doi.org/10.1111/ddi.12144
Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification And Regression Trees. In G. Tiao (Ed.), The Wadsworth statistics/probability series (Vol. II). Chapman & Hall.
Brosse, S., Guegan, J. F., Tourenq, J. N., & Lek, S. (1999). The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecological Modelling, 120(2-3), 299–311. https://doi.org/10.1016/S0304-3800(99)00110-6
Brown, J. L. (2011). SDMtoolbox 2.0 User Guide. www.sdmtoolbox.org
Brown, J. L., Bennett, J. R., & French, C. M. (2017). SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ, 5(7), e4095. https://doi.org/10.7717/peerj.4095
Cai, L. N., Xu, L. L., Tang, D. L., Shao, W. Z., Liu, Y., Zuo, J. C., & Ji, Q. Y. (2020). The effects of ocean temperature gradients on bigeye tuna (Thunnus obesus) distribution in the equatorial eastern Pacific Ocean. Advances in Space Research, 65(12), 2749–2760. https://doi.org/10.1016/j.asr.2020.03.030
Cheung, W. W. L., Pinnegar, J., Merino, G., Jones, M. C., & Barange, M. (2012a). Review of climate change impacts on marine fisheries in the UK and Ireland. Aquatic Conservation: Marine and Freshwater Ecosystems, 22(3), 368–388. https://doi.org/10.1002/aqc.2248
Cheung, W. W. L., Meeuwig, J. J., Feng, M., Harvey, E., Lam, V. W. H., Langlois, T., Slawinski, D., Sun, C., & Pauly, D. (2012b). Climate-change induced tropicalisation of marine communities in Western Australia. Marine and Freshwater Research, 63(5), 415–427. https://doi.org/10.1071/MF11205
Coelho, M. T. P., Diniz‐Filho, J. A., & Rangel, T. F. (2019). A parsimonious view of the parsimony principle in ecology and evolution. Ecography, 42(5), 968–976. https://doi.org/10.1111/ecog.04228
Collette, B., Acero, A., Amorim, A. F., Boustany, A., Canales-Ramirez, C. Cardenas, G., Carpenter, K. E., de Oliveira-Leite, J., Di Natale, A., Fox, W., Fredou, F. L., Graves, J., Viera-Hazin, F. H., Juan-Jorda, M., Minte-Vera, C., Miyabe, N., Montano-Cruz, R., Nelson, R., Oxenford, H., … Yanez, E. (2011). Coryphaena hippurus. The IUCN Red List of Threatened Species 2011. https://doi.org/http://dx.doi.org/10.2305/IUCN.UK.2011-2.RLTS.T154712A4614989.en
Cortés, J. (2012). Marine biodiversity of an eastern tropical pacific oceanic island, Isla del Coco, Costa Rica. Revista de Biologia Tropical, 60(S3), 131–185.
D’Croz, L., & O’Dea, A. (2007). Variability in upwelling along the Pacific shelf of Panama and implications for the distribution of nutrients and chlorophyll. Estuarine, Coastal and Shelf Science, 73(1-2), 325–340. https://doi.org/10.1016/j.ecss.2007.01.013
Dapp, D., Arauz, R., Spotila, J. R., & O’Connor, M. P. (2013). Impact of Costa Rican longline fishery on its bycatch of sharks, stingrays, bony fish and olive ridley turtles (Lepidochelys olivacea). Journal of Experimental Marine Biology and Ecology, 448, 228–239. https://doi.org/10.1016/j.jembe.2013.07.014
Duffy, L. M., Kuhnert, P. M., Pethybridge, H. R., Young, J. W., Olson, R. J., Logan, J. M., Goñi, N., Romanov, E., Allain, V., Staudinger, M. D., Abecassis, M., Choy, C. A., Hobday, A. J., Simier, M., Galván-Magaña, F., Potier, M., & Ménard, F. (2017). Global trophic ecology of yellowfin, bigeye, and albacore tunas: Understanding predation on micronekton communities at ocean-basin scales. Deep-Sea Research Part II: Topical Studies in Oceanography, 140(March), 55–73. https://doi.org/10.1016/j.dsr2.2017.03.003
EcoCommons Australia. (2022). EcoCommons Australia – the platform of choice for ecological and environmental modelling. https://doi.org/https://doi.org/10.47486/PL108
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
Evans, K., Arrizabalaga, H., Brodie, S., Chang, C. T., Llopiz, J., Phillips, J. S., & Weng, K. (2020). Comparative research on ocean top predators by CLIOTOP: Understanding shifts in oceanic biodiversity under climate change. Deep Sea Research Part II: Topical Studies in Oceanography, 175, 104822. https://doi.org/10.1016/j.dsr2.2020.104822
Farrell, E. R., Boustany, A. M., Halpin, P. N., & Hammond, D. L. (2014). Dolphinfish (Coryphaena hippurus) distribution in relation to biophysical ocean conditions in the northwest Atlantic. Fisheries Research, 151, 177–190. https://doi.org/10.1016/j.fishres.2013.11.014
França, S., & Cabral, H. N. (2016). Predicting fish species distribution in estuaries: Influence of species’ ecology in model accuracy. Estuarine, Coastal and Shelf Science, 180, 11–20. https://doi.org/10.1016/j.ecss.2016.06.010
Friedman, J. (1991). Multivariate Adaptive Regression Splines. The Annals of Statistics, 19(1), 1–67. http://www.jstor.org/stable/2241837
Furukawa, S., Tsuda, Y., Nishihara, G. N., Fujioka, K., Ohshimo, S., Tomoe, S., Nakatsuka, N., Kimura, H., Aoshima, T., Kanehara, H., Kitagawa, T., Chiang, W. C., Nakata, H., & Kawabe, R. (2014). Vertical movements of Pacific bluefin tuna (Thunnus orientalis) and dolphinfish (Coryphaena hippurus) relative to the thermocline in the northern East China Sea. Fisheries Research, 149, 86–91. https://doi.org/10.1016/j.fishres.2013.09.004
García-Roselló, E., Guisande, C., Heine, J., Pelayo-Villamil, P., Manjarrés-Hernández, A., González Vilas, L., González-Dacosta, J., Vaamonde, A., & Granado-Lorencio, C. (2014). Using ModestR to download, import and clean species distribution records. Methods in Ecology and Evolution, 5(7), 708–713. https://doi.org/10.1111/2041-210X.12209
Grados, C., Chaigneau, A., Echevin, V., & Dominguez, N. (2018). Upper ocean hydrology of the Northern Humboldt Current System at seasonal, interannual and interdecadal scales. Progress in Oceanography, 165, 123–144. https://doi.org/10.1016/j.pocean.2018.05.005
Guisan, A., Thuiller, W., & Zimmermann, N. E. (2017). Habitat Suitability and Distribution Models. Cambridge University Press. https://doi.org/10.1017/9781139028271
Hallgren, W., Beaumont, L., Bowness, A., Chambers, L., Graham, E., Holewa, H., Laffan, S., Mackey, B., Nix, H., Price, J., Vanderwal, J., Warren, R., & Weis, G. (2016). The biodiversity and climate change virtual laboratory: Where ecology meets big data. Environmental Modelling and Software, 76, 182–186. https://doi.org/10.1016/j.envsoft.2015.10.025
Hastie, T., Tibshirani, R., & Buja, A. (1994). Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89(428), 1255–1270. https://doi.org/10.1080/01621459.1994.10476866
Hayhoe, K., Edmonds, J., Kopp, R. E., LeGrande, A. N., Sanderson, B. M., Wehner, M. F., & Wuebbles, D. J. (2017). Climate models, scenarios, and projections. In D. J. Wuebbles, D.W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, & T. K. Maycock (Eds.), Climate Science Special Report: Fourth National Climate Assessment (Vol. I, pp. 133–160). https://doi.org/10.7930/J0WH2N54
Herrera-Montiel, S. A., Coronado-Franco, K. V., & Selvaraj, J. J. (2019). Predicted changes in the potential distribution of seerfish (Scomberomorus sierra) under multiple climate change scenarios in the Colombian Pacific Ocean. Ecological Informatics, 53, 100985. https://doi.org/10.1016/j.ecoinf.2019.100985
Hijmans, R. J., Phillips, S., Leathwick, J., & Elith, J.(2017). dismo: Species Distribution Modeling (“R Package”). https://cran.r-project.org/web/packages/dismo/
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression. In N. A. C. Cressie et al. (Eds.), Wiley Series In Probability And Statistics. (2th ed). Wiley-Interscience Publication.
IATTC (Inter-American Tropical Tuna Commission). (2005). Annual Report of the Inter-American Tropical Tuna Commission.
IPCC (Intergovernmental Panel on Climate Change). (2014a). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. V. Barros et al. (Eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://ipcc-wg2.gov/AR5/images/uploads/WGIIAR5-PartB_FINAL.pdf
IPCC (Intergovernmental Panel on Climate Change). (2014b). Climate Change 2014: Mitigation of Climate Change. In Working Group III Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/CBO9781107415416
Isaza-Toro, E., Giraldo, A., Josephraj, J., & Ortíz-ferrín, O. O. (2020). Standardization of small purse seiner fishing effort and its relation to fishing grounds in the eastern tropical sector of the Eastern Pacific Ocean. Regional Studies in Marine Science, 39, 101432. https://doi.org/10.1016/j.rsma.2020.101432
Lehodey, P. (2001). The pelagic ecosystem of the tropical Pacific Ocean: Dynamic spatial modelling and biological consequences of ENSO. Progress in Oceanography, 49(1-4), 439–468. https://doi.org/10.1016/S0079-6611(01)00035-0
MacLeod, C. D., Mandleberg, L., Schweder, C., Bannon, S. M., & Pierce, G. J. (2008). A comparison of approaches for modelling the occurrence of marine animals. Hydrobiologia, 612(1), 21–32. https://doi.org/10.1007/s10750-008-9491-0
Manel, S., Williams, H. C., & Ormerod, S. (2001). Evaluating presence-absence models in ecology : The need to account for prevalence. Journal of Applied Ecology, 38, 921–931. https://doi.org/10.1080/09613210110101185
Martínez-Ortiz, J., Aires-Da-silva, A. M., Lennert-Cody, C. E., & Maunder, M. N. (2015). The ecuadorian artisanal fishery for large pelagics: Species composition and spatio-temporal dynamics. PLoS ONE, 10(8), 1–29. https://doi.org/10.1371/journal.pone.0135136
Melo-Merino, S. M., Reyes-Bonilla, H., & Lira-Noriega, A. (2020). Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence. Ecological Modelling, 415(2), 108837.https://doi.org/10.1016/j.ecolmodel.2019.108837
Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography, 36(10), 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
Merten, W., Appeldoorn, R., & Hammond, D. (2016). Movement dynamics of dolphinfish (Coryphaena hippurus) in the northeastern Caribbean Sea: Evidence of seasonal re-entry into domestic and international fisheries throughout the western central Atlantic. Fisheries Research, 175, 24–34. https://doi.org/10.1016/j.fishres.2015.10.021
Nieblas, A. E., Demarcq, H., Drushka, K., Sloyan, B., & Bonhommeau, S. (2014). Front variability and surface ocean features of the presumed southern bluefin tuna spawning grounds in the tropical southeast Indian Ocean. Deep-Sea Research Part II: Topical Studies in Oceanography, 107, 64–76. https://doi.org/10.1016/j.dsr2.2013.11.007
Olson, R. J. J., Young, J. W. W., Ménard, F., Potier, M., Allain, V., Goñi, N., Logan, J. M. M., & Galván-Magaña, F. (2016). Bioenergetics, trophic ecology, and niche separation of tunas. Advances in Marine Biology, 35(74), 199–344. https://doi.org/10.1016/bs.amb.2016.06.002
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Quinteros-Malpartida, S., & Ñiquen-Carranza, M. (2016). Plan de acción nacional para la conservación y manejo del recurso perico (Coryphaena hippurus) en el Perú (PAN PERICO - PERÚ). En Resolución Viceministerial 81-2016-PRODUCE/DVPA (p. 41). Ministerio de la Producción.
Rajesh, K. M., Rohit, P., & Abdussamad, E. M. (2016). Fishery, diet composition and reproductive biology of the dolphinfish Coryphaena hippurus (Linnaeus, 1758) off Karnataka, south-west coast of India. Indian Journal of Fisheries, 63(4), 35–40. https://doi.org/10.21077/ijf.2016.63.4.60190-06
Ridgeway, G. (2007). Generalized boosted models: A guide to the gbm package. Compute, 1(4), 1–12. https://doi.org/10.1111/j.1467-9752.1996.tb00390.x
Ripley, B. D. (1994). Neural networks and flexible regression and discrimination. Journal of Applied Statistics, 21, 39–57.
Shcheglovitova, M., & Anderson, R. P. (2013). Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecological Modelling, 269, 9–17. https://doi.org/10.1016/j.ecolmodel.2013.08.011
Soberón, J. (2007). Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters, 10(12), 1115–1123. https://doi.org/10.1111/j.1461-0248.2007.01107.x
Strychar, K. B., & Sammarco, P. W. (2009). Exaptation in corals to high seawater temperatures: Low concentrations of apoptotic and necrotic cells in host coral tissue under bleaching conditions. Journal of Experimental Marine Biology and Ecology, 369(1), 31–42. https://doi.org/10.1016/j.jembe.2008.10.021
Svendsen, M. B. S., Domenici, P., Marras, S., Krause, J., Boswell, K. M., Rodriguez-Pinto, I., Wilson, A. D. M., Kurvers, R. H. J. M., Viblanc, P. E., Finger, J. S., & Steffensen, J. F. (2016). Maximum swimming speeds of sailfish and three other large marine predatory fish species based on muscle contraction time and stride length: A myth revisited. Biology Open, 5(10), 1415–1419. https://doi.org/10.1242/bio.019919
Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240(4857), 1285–1293. https://doi.org/10.1126/science.3287615
Thuiller, W., Lafourcade, B., & Araujo, M. (2012). The Presentation Manual for BIOMOD. Université Joseph Fourier.
Thuiller, W., Lafourcade, B., Engler, R., & Araújo, M. B. (2009). BIOMOD - A platform for ensemble forecasting of species distributions. Ecography, 32(3), 369–373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., De Clerck, O., Troupin, K., Mineur, F., & De Clerck, O. (2012). Bio-ORACLE: a global environmental dataset for marine species distribution modelling. Global Ecology and Biogeography, 21(2), 272–281. https://doi.org/10.1111/j.1466-8238.2011.00656.x
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J. F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., & Rose, S. K. (2011). The representative concentration pathways: An overview. Climatic Change, 109(1), 5–31. https://doi.org/10.1007/s10584-011-0148-z
Veloz, S. D. (2009). Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36(12), 2290–2299. https://doi.org/10.1111/j.1365-2699.2009.02174.x
Venables, W. N., & Dichmont, C. M. (2004). GLMs, GAMs and GLMMs: An overview of theory for applications in fisheries research. Fisheries Research, 70(2-3 SPEC. ISS.), 319–337. https://doi.org/10.1016/j.fishres.2004.08.011
Verneil, A., Franks, P. J. S., & Ohman, M. D. (2019). Frontogenesis and the creation of fine‐scale vertical phytoplankton structure. Journal of Geophysical Research: Oceans, 124(3), 1509–1523. https://doi.org/10.1029/2018JC014645
Vetter, V. M. S., Tjaden, N. B., Jaeschke, A., Buhk, C., Wahl, V., Wasowicz, P., & Jentsch, A. (2018). Invasion of a legume ecosystem engineer in a cold biome alters plant biodiversity. Frontiers in Plant Science, 9, 715. https://doi.org/10.3389/fpls.2018.00715
Whoriskey, S., Arauz, R., & Baum, J. K. (2011). Potential impacts of emerging mahi-mahi fisheries on sea turtle and elasmobranch bycatch species. Biological Conservation, 144(6), 1841–1849. https://doi.org/10.1016/j.biocon.2011.03.021
Zúñiga-Flores, M. S., Ortega-García, S., & Klett-Traulsen, A. (2008). Interannual and seasonal variation of dolphinfish (Coryphaena hippurus) catch rates in the southern Gulf of California, Mexico. Fisheries Research, 94(1), 13–17. https://doi.org/10.1016/j.fishres.2008.06.003
Comentarios
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2024 Revista de Biología Tropical