THE IMPACT OF ENVIRONMENTAL CHANGES ON BLACK MARLIN, Makaira indica (Cuvier, 1832) ABUNDANCES IN THE EASTERN INDIAN OCEAN

Bram Setyadji, Zulkarnaen Fahmi

Abstract


Black marlin (Makaira indica) is commonly caught as frozen by-catch from Indonesian tuna longline fleets. Its contribution estimated 18% (~2,500 tons) from total catch in Indian Ocean. Relative abundance indices as calculated based on commercial catches are the input data for several to run stock assessment analyses that provide models to gather information useful information for decision making and fishery management, however, little are known about the influence of environmental factors to its abundance. In this paper, the abundance was represented as standardized index in order to eliminate any bias on other factors which might influence it. Data were collected from August 2005 to December 2017 through scientific observer program (2005-2017) and national observer program (2016-2017). Most of the vessels monitored were based in Benoa Port, Bali. Overall, time trends of abundance was fluctuated, although, there was increasing trend since 2010 then dropped significantly into relatively similar figure in 2005. Even though, Sea Surface Temperature (SST) and Sea Surface Height (SSH) were statistically significant when incorporating into the models, but it allegedly wasn’t the main driver in determining the abundance of black marlin. Instead, it was more likely driven by spatio-temporal factors (year and area) effect rather than environmental changes.

Keywords


impact; environmental factors; marlins; abundance; GLM

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References


Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

Akyol, O. (2013). The influence of the moon phase on the CPUEs of swordfish gillnet fishery in the Aegean Sea, Turkey. Turkish Journal of Fisheries and Aquatic Sciences, 13(2), 355–358.

Au, D. W. (1998). Protecting the reproductive value of swordfish, Xiphias gladius, and other billfishes. In: Barrett, Isadore, Oscar Sosa-Nishizaki, and Norman Bartoo (Eds.). Biology and Fisheries of Swordfish, Xiphias Gladius, 142, 219–226. Ensenada, Mexico 11-14 December 1994: U.S. Dep. Commer. NOAA. Tech. Rep. NMFS 142.

Brill, R. W., & Lutcavage, M. E. (2001). Understanding environmental influences on movements and depth distributions of tunas and billfishes can significantly improve population assessments. American Fisheries Society Symposium, 25, 179–198. American Fisheries Society.

Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed). New York: Springer.

Giner, G., & Smyth, G. K. (2016). statmod: Probability calculations for the inverse Gaussian distribution. ArXiv Preprint ArXiv:1603.06687.

Harrell Jr., F. E., Dupont, C., & Others. (2018). Hmisc: Harrell Miscellaneous. Retrieved from https://CRAN.R-project.org/package=Hmisc

Ichinokawa, M., & Brodziak, J. (2010). Using adaptive area stratification to standardize catch rates with application to North Pacific swordfish (Xiphias gladius). Fisheries Research, 106(3), 249–260.

Ijima, H. (2017). CPUE standardization of the Indian Ocean swordfish (Xiphias gladius) by Japanese longline fisheries: Using negative binomial GLMM and zero inflated negative binomial GLMM to consider vessel effect. Paper Presented on 15th Working Party on Billfish, San Sebastian, Spain, 10-14 September 2017, IOTC-2017-WPB15, 32 pp.

IOTC-WPB16. (2018). Report of the 16th Session of the IOTC Working Party on Billfish (Working Party Report No. IOTC–2018–WPB16–R[E]; p. 97 pp). Retrieved from Indian Ocean tuna Commission (IOTC) website: http://iotc.org/sites/default/files/documents/2018/11/IOTC-2018-WPB16-RE_FINAL_-_DO_NOT_MODIFY.pdf

Lan, K.-W., Shimada, T., Lee, M.-A., Su, N.-J., & Chang, Y. (2017). Using remote-sensing environmental and fishery data to map potential yellowfin tuna habitats in the tropical Pacific Ocean. Remote Sensing, 9(5), 444.

Lenth, R. (2018). emmeans: Estimated Marginal Means, aka Least-Squares Means. Retrieved from https://CRAN.R-project.org/package=emmeans

Lumban-Gaol, J., Leben, R. R., Vignudelli, S., Mahapatra, K., Okada, Y., Nababan, B., … Syahdan, M. (2015). Variability of satellite-derived sea surface height anomaly, and its relationship with Bigeye tuna (Thunnus obesus) catch in the Eastern Indian Ocean. European Journal of Remote Sensing, 48(1), 465–477.

Minami, M., Lennert-Cody, C. E., Gao, W., & Roman-Verdesoto, M. (2007). Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing. Fisheries Research, 84(2), 210–221.

Nakamura, I. (1985). Billfishes of the world. An annotated and illustrated catalogue of marlins, sailfishes, spearfishes and swordfishes known to date. FAO Species Catalogue; FAO Fisheries Synopsis, 5(125), 65.

QGIS Developer Team. (2018). QGIS Geographic Information System. Retrieved from Open Source Geospatial Foundation Project website: http://qgis.osgeo.org/

R Core Team. (2018). R: A Language and Environment for Statistical Computing. Retrieved from https://www.R-project.org/

Rathnasuriya, M. I. G., Gunasekara, S. S., Haputhanthri, S. S. K., & Rajapaksha, J. K. (2016). Environmental preferences of Billfish in Bay of Bengal: A case study in longline fishery of Sri Lanka. Paper Presented at 14th Working Party on Billfish, Victoria, Seychelles, 6-10 September 2016. IOTC-WPB14-2016-10_Rev1, 18.

Sadiyah, L., Dowling, N., & Prisantoso, B. I. (2012). Developing recommendations for undertaking CPUE standardisation using observer program data. Indonesian Fisheries Research Journal, 18(1), 19–33.

Sajeevan, M. K. (2013). Evaluation of the effect of lunar cycle, monsoon and spatial differences on billfishes. Paper Presented at 11th Working Party on Billfish, La Reunion, France, 18-22 September 2013. IOTC–2013–WPB11–20, 17.

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.

Setyadji, B., Andrade, H. A., & Proctor, C. H. (2018). Standardization of Catch Per Unit Effort with High Proportion of Zero Catches: An Application to Black Marlin Istiompax indica (Cuvier, 1832) Caught by The Indonesian Tuna Longline Fleet in The Eastern Indian Ocean. Turkish Journal of Fisheries and Aquatic Sciences, 19(2), 119–129.

Setyadji, B., Jumariadi, J., & Nugraha, B. (2012). Catch estimation and size distribution of billfishes landed in Port of Benoa, Bali. Indonesian Fisheries Research Journal, 18(1), 35–40.

Su, N.-J., Sun, C.-L., Punt, A. E., Yeh, S.-Z., & DiNardo, G. (2011). Modelling the impacts of environmental variation on the distribution of blue marlin, Makaira nigricans, in the Pacific Ocean. ICES Journal of Marine Science, 68(6), 1072–1080.

Su, N.-J., Sun, C.-L., Punt, A. E., Yeh, S.-Z., & DiNardo, G. (2015). Environmental influences on seasonal movement patterns and regional fidelity of striped marlin Kajikia audax in the Pacific Ocean. Fisheries Research, 166, 59–66.

Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (Fourth). Retrieved from http://www.stats.ox.ac.uk/pub/MASS4

Wang, S.-P. (2017). CPUE standardization of swordfish (Xiphias gladius) caught by Taiwanese longline fishery in the Indian Ocean. Paper Presented on 15th Working Party on Billfish, San Sebastian, Spain, 10-14 September 2017, IOTC–2017–WPB15–17, 28 pp.

Wang, S.-P., & Nishida, T. (2013). Correlations between environmental factors and CPUEs of blue marlin (Makaira mazara) and striped marlin (Kajikia audax) caught by Taiwanese longline fishery in the Indian Ocean. Paper Presented at 11th Working Party on Billfish, La Reunion, France, 18-22 September 2013. IOTC–2013–WPB11–22 Rev_2, 14.

West, A. P. (2004). Aspects of the early life history of billfish off Kona, Hawaii (PhD Thesis).

Yokoi, H., Semba, S., Satoh, K., & Tsutomu, N. (2016). Standardization of catch rate for black marlin (Istiompax indica) exploited by the Japanese tuna longline fisheries in the Indian Ocean (1971-2015). Paper Presented on 14th Working Party on Billfish, Victoria, Seychelles, 6-10 September 2016, IOTC–2016–WPB14–19_Rev1, 17 pp.

Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.




DOI: http://dx.doi.org/10.15578/ifrj.26.1.2020.41-49


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