Modeling discontinuous growth in reared Panulirus ornatus: a generalized additive model and cox proportional hazard model approach

Foo, Chuan Hui

Abstract

Crustaceans exhibit discontinuous growth as they shed hard shells periodically. Fundamentally, the growth of crustaceans is typically assessed through two key components, length increase after molting (LI) and time intervals between consecutive molts (TI). In this article, we propose a unified likelihood approach that combines a generalized additive model and a Cox proportional hazard model to estimate the parameters of LI and TI separately in crustaceans. This approach captures the observed discontinuity in individuals, providing a comprehensive understanding of crustacean growth patterns. Our study focuses on 75 ornate rock lobsters (Panulirus ornatus) off the Torres Strait in northeastern Australia. Through a simulation study, we demonstrate the effectiveness of the proposed models in characterizing the discontinuity with a continuous growth curve at the population level. 2023 American Institute of Mathematical Sciences. All rights reserved.

References

R. Wahyudin, A. Hakim, Y. Qonita, M. Boer, A. Farajallah, A. Mashar, et al., Lobster diversity of Palabuhanratu Bay, South Java, Indonesia with new distribution record of Panulirus ornatus, P. polyphagus and Parribacus antarcticus, Aquacult. Aquarium, Conserv. Legis., 10 (2017), 308–327.

A. Indarjo, G. Salim, T. I. Maryanto, L. A. Linting, M. Firdaus, Growth patterns and mortality of lobster Panulirus ornatus from the catch of bottom gill net fishers in the western waters of Tarakan Island, HAYATI J. Biosci., 30 (2023), 532–542. http://dx.doi.org/10.4308/hjb.30.3.532-542

P. Briones-Fourza´n, E. Lozano-A´ lvarez, Lobsters: Ocean icons in changing times, ICES J. Mar. Sci., 72 (2015), i1–i6. https://doi.org/10.1093/icesjms/fsv111

M. Pandolfi, M. F. Scaia, M. P. Fernandez, Sexual dimorphism in aggression: Sexspecific fighting strategies across species, Front. Behav. Neurosci., 15 (2021), 1–12. https://doi.org/10.3389/fnbeh.2021.659615

M. C. Ooi, E. F. Goulden, G. G. Smith, A. R. Bridle, Haemolymph microbiome of the cultured spiny lobster Panulirus ornatus at different temperatures, Sci. Rep., 9, (2019), 1–13. https://doi.org/10.1038/s41598-019-39149-7

N. M. Noordin, C. Zeng, P. C. Southgate, Survival, molting pattern, and growth of early blue swimmer crab, Portunus pelagicus, juveniles fed diets containing varying levels of cholesterol, J. World Aquacult. Soc., 51, (2020), 255–265. https://doi.org/10.1111/jwas.12623

I. Somers, Y. G. Wang, A simulation model for evaluating seasonal closures in Australia’s multispecies Northern Prawn Fishery, North American, North Am. J. Fish. Manage., 17 (1997), 114–130.

T. Miller, S. G. Smith, Modeling crab growth and population dynamics: Insights from the Blue Crab Conference, Bull. Mar. Sci., 72 (2003), 537–541.

C. H. Foo, Parameter estimation of laboratory-reared Panulirus ornatus, SN Appl. Sci., 2 (2020), 1–6. https://doi.org/10.1007/s42452-020-2612-8

Y. J. Chang, C. L. Sun, Y. Chen, S. Z. Yeh, Modelling the growth of crustacean species, Rev. Fish Biol. Fish., 22 (2012), 157–187. https://doi.org/10.1007/s11160-011-9228-4

G. C. Rilling, E. D. Houde, Regional and temporal variability in growth and mortality of bay anchovy, Anchoa mitchilli, larvae in Chesapeake Bay, Fish. Bull., 97 (1999), 555–569. https://doi.org/10.2307/1353087

T. Russo, P. Baldi, A. Parisi, G. Magniffco, S. Mariani, S. Cataudella, Levy processes and stochastic von Bertalanffy models of growth, with application to fish population analysis, J. Theor. Biol., 258 (2009), 521–529. https://doi.org/10.1016/j.jtbi.2009.01.033

D. G. Kleinbaum, M. Klein, Survival Analysis A Self-Learning Text, third edition, Springer, 2012. https://doi.org/10.1007/978-1-4419-6646-9

P. Hougaard, Multivariate non-parametric estimates, in Analysis of Multivariate Survival Data, Springer, (2000), 442–482. https://doi.org/10.1007/978-1-4612-1304-8

C. H. Foo, Crustacean growth estimation from tag-recapture data, J. Adv. Res. Dyn. Control Syst., 11 (2019), 1859–1867.

M. Tableman, J. S. Kim, Survival analysis using S: Analysis of time-to-event data, CRC Press, (2003).

17. D. Schoenfeld, Partial residuals for the proportional hazards regression model, Biometrika, 69

(1982), 239–241. https://doi.org/10.1093/biomet/69.1.239