Modelado de idoneidad de hábitat mediante Bosque Aleatorio para Acanthurus spp. en arrecifes de coral filipinos: factores ambientales y proyecciones climáticas

Autores/as

DOI:

https://doi.org/10.47193/mafis.39420260011003

Palabras clave:

Bosque Aleatorio, idoneidad del hábitat, Acanthuridae, CMIP6, áreas marinas protegidas

Resumen

Los peces cirujano del Género Acanthurus (Familia Acanthuridae) son herbívoros clave desde el punto de vista ecológico en los arrecifes de coral del Indo-Pacífico. Sin embargo, sus requerimientos de hábitat y respuestas distributivas a los gradientes ambientales y al cambio climático aún no se conocen bien en Filipinas. Se utilizó un modelo de aprendizaje automático basado en Bosque Aleatorio (RF) para modelar la idoneidad de hábitat de seis especies de importancia comercial y ecológica: A. lineatus, A. nigricans, A. olivaceus, A. triostegus, A. xanthopterus y A. pyroferus, en los sistemas de arrecifes de coral filipinos. Los registros de presencia georreferenciados (n  352-714 por especie) de OBIS, GBIF y ReefBase se combinaron con diez predictores ambientales que abarcan la estructura del arrecife, las condiciones oceanográficas, la batimetría y las perturbaciones físicas. Los modelos RF lograron un AUC medio de 0,929 y un TSS de 0,805, lo que indica un excelente rendimiento predictivo. La cobertura de coral, la profundidad y la rugosidad fueron los predictores de hábitat dominantes en todas las especies. El hábitat adecuado actual varió de 6.890 km2 (A. olivaceus) a 11.350 km2 (A. xanthopterus). Bajo SSP5-8.5 para 2100, el hábitat proyectado disminuyó en 25,4-36,1%, impulsado principalmente por la pérdida de cobertura de coral inducida térmicamente. Acanthurus triostegus fue la más vulnerable (-36,1%) y A. xanthopterus la más resiliente (-25,4%). El análisis de superposición de múltiples especies identificó el Paso de la Isla Verde, el Arrecife Tubbataha y la Isla Apo como nodos de conservación prioritarios. El análisis de brechas reveló tres zonas de alta superposición no protegidas como candidatas para el establecimiento de nuevas Áreas Marinas Protegidas (AMP). Estos hallazgos proporcionan una base de evidencia espacial para la gestión de pesquerías de arrecife adaptadas al clima en todo el Indo-Pacífico.

Descargas

Los datos de descarga aún no están disponibles.

Referencias

Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B, Anderson RP. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography. 38 (5): 541-545. DOI: https://doi.org/10.1111/ecog.01132

Allouche O, Tsoar A, Kadmon R. 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol. 43 (6): 1223-1232. DOI: https://doi.org/10.1111/j.1365-2664.2006.01214.x

Bellwood DR, Hughes TP, Folke C, Nyström M. 2004. Confronting the coral reef crisis. Nature. 429 (6994): 827-833. DOI: https://doi.org/10.1038/nature02691

[BFAR] Bureau of Fisheries and Aquatic Resources. 2023. Philippine fisheries profile 2022. Quezon City (PH): BFAR.

Breiman L. 2001. Random forests. Mach Learn. 45 (1): 5-32. DOI: https://doi.org/10.1023/A:1010933404324

Burke L, Reytar K, Spalding M, Perry A. 2011. Reefs at risk revisited. Washington DC: World Resources Institute. 130 p.

Burkepile DE, Hay ME. 2008. Herbivore species richness and feeding complementarity affect community structure and function on a coral reef. Proc Natl Acad Sci USA. 105 (42): 16201-16206. DOI: https://doi.org/10.1073/pnas.0801946105

Carpenter KE, Springer VG. 2005. The center of the center of marine shore fish biodiversity: the Philippine Islands. Environ Biol Fishes. 72 (4): 467-480. DOI: https://doi.org/10.1007/s10641-004-3154-4

Cesar H, Burke L, Pet-Soede L. 2003. The economics of worldwide coral reef degradation. Arnhem (NL): Cesar Environmental Economics Consulting. 23 p.

Cheung WW, Lam VW, Sarmiento JL, Kearney K, Watson R, Zeller D, Pauly D. 2009. Projecting global marine biodiversity impacts under climate change scenarios. Fish Fish. 10 (3): 235-251. DOI: https://doi.org/10.1111/j.1467-2979.2008.00315.x

Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. 2007. Random forests for classification in ecology. Ecology. 88 (11): 2783-2792. DOI: https://doi.org/10.1890/07-0539.1

Emslie MJ, Logan M, Williamson DH, Ayling AM, MacNeil MA, Ceccarelli D, Cheal AJ, Evans RD, Johns KA, Jonker MJ, et al. 2018. Expectations and outcomes of reserve network performance following re-zoning of the Great Barrier Reef Marine Park. Curr Biol. 25 (8): 983-992. DOI: https://doi.org/10.1016/j.cub.2015.01.073

González-Rivero M, Beijbom O, Rodriguez-Ramirez A, Bryant DE, Ganase A, Gonzalez-Marrero Y, Herrera-Reveles A, Kennedy EV, Kim CJ, Lopez-Marcano S, et al. 2020. Monitoring of coral reefs using artificial intelligence: a feasible and cost-effective approach. Remote Sens. 12 (3): 489. DOI: https://doi.org/10.3390/rs12030489

Greenwell BM. 2017. pdp: an R package for constructing partial dependence plots. R J. 9 (1): 421-436. DOI: https://doi.org/10.32614/RJ-2017-016

Hanley JA, McNeil BJ. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143 (1): 29-36. DOI: https://doi.org/10.1148/radiology.143.1.7063747

Hirzel AH, Le Lay G, Helfer V, Randin C, Guisan A. 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecol Model. 199 (2): 142-152. DOI: https://doi.org/10.1016/j.ecolmodel.2006.05.017

Hixon MA, Beets JP. 1993. Predation, prey refuges, and the structure of coral-reef fish assemblages. Ecol Monogr. 63 (1): 77-101. DOI: https://doi.org/10.2307/2937124

Hoegh-Guldberg O, Pendleton L, Kaup A. 2019. People and the changing nature of coral reefs. Reg Stud Mar Sci. 30: 100699. DOI: https://doi.org/10.1016/j.rsma.2019.100699

Hoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S. 2017. Coral reef ecosystems under climate change and ocean acidification. Front Mar Sci. 4: 158. DOI: https://doi.org/10.3389/fmars.2017.00158

Hughes TP. 1994. Catastrophes, phase shifts, and large-scale degradation of a Caribbean coral reef. Science. 265 (5178): 1547-1551. DOI: https://doi.org/10.1126/science.265.5178.1547

Hughes TP, Baird AH, Bellwood DR, Card M, Connolly SR, Folke C, Grosberg R, Hoegh-Guldberg O, Jackson JB, Kleypas J, et al. 2003. Climate change, human impacts, and the resilience of coral reefs. Science. 301 (5635): 929-933. DOI: https://doi.org/10.1126/science.1085046

Hughes TP, Graham NAJ, Jackson JBC, Mumby PJ, Steneck RS. 2010. Rising to the challenge of sustaining coral reef resilience. Trends Ecol Evol. 25 (11): 633-642. DOI: https://doi.org/10.1016/j.tree.2010.07.011

Klaassen M, Marques TA, Alves F, Fernandez M. 2025. Trends in marine species distribution models: a review of methodological advances and future challenges. Ecography. 2026: e07702. DOI: https://doi.org/10.1002/ecog.07702

Liaw A, Wiener M. 2002. Classification and regression by randomForest. R News. 2 (3): 18-22.

Liu C, Berry PM, Dawson TP, Pearson RG. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography. 28 (3): 385-393. DOI: https://doi.org/10.1111/j.0906-7590.2005.03957.x

Logan CA, Dunne JP, Eakin CM, Donner SD. 2014. Incorporating adaptive responses into future projections of coral bleaching. Glob Change Biol. 20 (1): 125-139. DOI: https://doi.org/10.1111/gcb.12390

Mayer M, Gaffert P, Muller K. 2023. kernelshap: Kernel SHAP for explaining predictive models. R package version 0.3.0. [accessed 2024 Jan 15]. https://CRAN.R-project.org/package=kernelshap.

Mellin C, Delean S, Caley J, Edgar G, Meekan M, Pitcher CR, Przeslawski R, Williams A, Bradshaw CJ. 2012. Effectiveness of biological surrogates for predicting patterns of marine biodiversity: a global meta-analysis. PLoS ONE. 6 (6): e20141. DOI: https://doi.org/10.1371/journal.pone.0020141

Monk J, Ierodiaconou D, Versace VL, Bellgrove A, Harvey E, Rattray A, Laurenson L, Quinn GP. 2010. Habitat suitability for marine fishes using presence-only modelling and multibeam sonar. Mar Ecol Prog Ser. 420: 157-174. DOI: https://doi.org/10.3354/meps08858

Muallil RN, Deocadez MR, Martinez RJS, Panga FM, Atrigenio MP, Aliño PM. 2020. Negative trophic relationship between parrotfish biomass and algal cover on Philippine coral reefs. Reg Stud Mar Sci. 39: 101471. DOI: https://doi.org/10.1016/j.rsma.2020.101471

Muallil RN, Mamauag SS, Cababaro JT, Arceo HO, Aliño PM. 2014. Catch trends in Philippine small-scale fisheries over the last five decades: the fishers’ perspectives. Mar Policy. 47: 110-117. DOI: https://doi.org/10.1016/j.marpol.2014.02.008

Mumby PJ, Hastings A, Edwards HJ. 2007. Thresholds and the resilience of Caribbean coral reefs. Nature. 450 (7166): 98-101. DOI: https://doi.org/10.1038/nature06252

Phillips SJ, Dudik M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl. 19 (1): 181-197. DOI: https://doi.org/10.1890/07-2153.1

Pickens BA, Carroll R, Schirripa MJ, Forrestal F, Friedland KD, Taylor JC. 2021. A systematic review of spatial habitat associations and modeling of marine fish distribution: a guide to predictors, methods, and knowledge gaps. PLoS ONE. 16 (5): e0251818. DOI: https://doi.org/10.1371/journal.pone.0251818

Pittman SJ, Costa BM, Battista TA. 2009. Using lidar bathymetry and boosted regression trees to predict the diversity and abundance of fish and corals. J Coast Res. (10053): 27-38. DOI: https://doi.org/10.2112/SI_53_3

Prasad AM, Iverson LR, Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 9 (2): 181-199. DOI: https://doi.org/10.1007/s10021-005-0054-1

Pratchett MS, Bay LK, Gehrke PC, Koehn JD, Osborne K, Pressey RL, Sweatman HP, Wachenfeld D. 2011. Contribution of climate change to degradation and loss of critical fish habitats in Australian marine and freshwater environments. Mar Freshw Res. 62 (9): 1062-1081. DOI: https://doi.org/10.1071/MF10303

Probst P, Boulesteix AL. 2017. To tune or not to tune the number of trees in random forest? J Mach Learn Res. 18 (181): 1-18.

[RA 9522] Republic Act No. 9522. 2009. An act to amend certain provisions of Republic Act No. 3046, as amended by Republic Act No. 5446, to define the archipelagic baseline of the Philippines, and for other purposes. Manila (PH): Congress of the Philippines.

Reaka-Kudla ML. 1997. The global biodiversity of coral reefs: a comparison with rain forests. In: Reaka-Kudla ML, Wilson DE, Wilson EO, editors. Biodiversity II. Washington DC: Joseph Henry Press. p. 83-108.

Roberts DR, Bahn V, Ciuti S, Boyce MS, Elith J, Guillera-Arroita G, Hauenstein S, Lahoz-Monfort JJ, Schröder B, Thuiller W, et al. 2017. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography. 40 (8): 913-929. DOI: https://doi.org/10.1111/ecog.02881

Sillero N, Barbosa AM. 2021. Common mistakes in ecological niche models. Int J Geogr Inf Sci. 35 (2): 213-226. DOI: https://doi.org/10.1080/13658816.2020.1798968

Thuiller W, Lafourcade B, Engler R, Araújo MB. 2009. BIOMOD - a platform for ensemble forecasting of species distributions. Ecography. 32 (3): 369-373. DOI: https://doi.org/10.1111/j.1600-0587.2008.05742.x

Valavi R, Elith J, Lahoz-Monfort JJ, Guillera-Arroita G. 2019. blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models. Methods Ecol Evol. 10 (2): 225-232. DOI: https://doi.org/10.1111/2041-210X.13107

Warton DI, Shepherd LC. 2010. Poisson point process models solve the pseudo-absence problem for presence-only data in ecology. Ann Appl Stat. 4 (3): 1383-1402. DOI: https://doi.org/10.1214/10-AOAS331

Publicado

31-07-2026

Número

Sección

Documentos de Investigación Originales

Cómo citar

Perante, N. C. (2026). Modelado de idoneidad de hábitat mediante Bosque Aleatorio para Acanthurus spp. en arrecifes de coral filipinos: factores ambientales y proyecciones climáticas. Marine and Fishery Sciences (MAFIS), 39(4). https://doi.org/10.47193/mafis.39420260011003