ISSN: 2536-7099
Model: Open Access/Peer Reviewed
DOI: 10.31248/JASVM
Start Year: 2016
Email: jasvm@integrityresjournals.org
https://doi.org/10.31248/JASVM2026.639 | Article Number: 15EE24CF8 | Vol.11 (2) - April 2026
Received Date: 08 February 2026 | Accepted Date: 25 March 2026 | Published Date: 30 April 2026
Authors: Kperegbeyi, J. I.* , Nwadiolu, R. and Ewododhe, A. C. A.
Keywords: regression models., linear function, Breeder, predictor., Egg weight, Haugh unit
This study was conducted to evaluate egg external and internal quality traits of the West African Black Mud Tortoise. A total of Eighty (80) eggs were collected from (Pelusios castaneus). Four model specifications (linear, double-log, semi-log and exponential functions) were evaluated to determine the best-fitting regression of egg weight on various egg traits. The data showed mean values of 18.84±1.24g, 8.02±0.26g, 28.30±2.40mm and 76.13±3.62% for egg weight (EWT), albumen weight (AWT), albumen length (ALT) and haugh unit (HU), respectively. Analysis indicated that mean yolk weight (YWT), yolk height (YHT), yolk width (YWD), yolk index (YI), shell weight (SWT) and shell thickness (STH) were 7.14±0.15g, 2.16±0.03mm, 4.07±0.13mm, 0.44±0.01, 3.68±0.13g and 0.25±0.01mm, respectively. Findings revealed mean egg length (ELT) (33.89±1.14mm), egg width (EWD) (25.79±1.41mm) and egg shape index (ESI) (1.20±0.02). EWT data demonstrated a non-linear trend. Evaluation of fit quality across four functional forms resulted in coefficient of determination (R2) values of 78.68 % (linear), 78.35% (semi-log), 65.80% (double-log), and 71.30% (exponential), respectively, indicating that the linear model provided the strongest fit. AWT was the most significant predictor of EWT. The results indicate that a linear function is the most suitable, providing the best fit for modelling the EWT curve of Pelusios castaneus in this study. In conclusion, data analysis shows that EWT is the key factor influencing egg quality and reproductive performance in Pelusios castaneus.
| Anatolyev, S. (2019). Many instruments and/or regressors: A friendly guide. Journal of Economic Surveys, 33(2), 689-726. https://doi.org/10.1111/joes.12295 |
||||
| Blake, S., Cabrera, F., Cruz, S., Ellis‐Soto, D., Yackulic, C. B., Bastille‐Rousseau, G., ... & Deem, S. L. (2024). Environmental variation structures reproduction and recruitment in long‐lived mega‐herbivores: Galapagos giant tortoises. Ecological Monographs, 94(2), e1599. https://doi.org/10.1002/ecm.1599 |
||||
| Booth, D. T., Dunstan, A., Bell, I., Reina, R., & Tedeschi, J. (2020). Low male production at the world's largest green turtle rookery. Marine Ecology Progress Series, 653, 181-190. https://doi.org/10.3354/meps13500 |
||||
| Booth, D. T., Staines, M. N., & Reina, R. D. (2022). Sand characteristics do not influence hatching success of nests at the world's largest green turtle rookery. Australian Journal of Zoology, 69(4), 113-124. https://doi.org/10.1071/ZO21050 |
||||
| Card, L. E, & Nesheim, M. G. (1975). Poultry Production. 11th edition, Lea and Febiger, Philadelphia, Pp. 291-293. | ||||
| Eustace, A., Esser, L. F., Mremi, R., Malonza, P. K., & Mwaya, R. T. (2021). Protected areas network is not adequate to protect a critically endangered East Africa Chelonian: Modelling distribution of pancake tortoise, Malacochersus tornieri under current and future climates. PLoS One, 16(1), e0238669. https://doi.org/10.1371/journal.pone.0238669 |
||||
| Gatto, C. R., & Reina, R. D. (2022). A review of the effects of incubation conditions on hatchling phenotypes in non squamate reptiles. Journal of Comparative Physiology B, 192(2), 207-233. https://doi.org/10.1007/s00360-021-01415-4 |
||||
| Guedes, P., dos Santos, Y., Matilde, E., Alves, J., Rato, C., & Rocha, R. (2023). Phylogenetic relationships of the West African mud turtle (Pelusios castaneus) on the islands of São Tomé and Príncipe, West Central Africa. Amphibia-Reptilia, 44(3), 391-397. https://doi.org/10.1163/15685381-bja10145 |
||||
| Hays, G. C., Shimada, T., & Schofield, G. (2022). A review of how the biology of male sea turtles may help mitigate female-biased hatchling sex ratio skews in a warming climate. Marine Biology, 169(7), 89. https://doi.org/10.1007/s00227-022-04074-3 |
||||
| Hunter, E. A., Loope, K. J., Drake, K. K., Hanley, K., Jones Jr, D. N., Shoemaker, K. T., & Rostal, D. C. (2021). Warming conditions boost reproductive output for a northern gopher tortoise population. Endangered Species Research, 46, 215-226. https://doi.org/10.3354/esr01155 |
||||
| Knizat, P. (2024). Functional linear regression: A case study from the food industry. Economic Review (Ekonomicke ronhl'ady), 53(1), 22-31. https://doi.org/10.53465/ER.2644-7185.2024.1.22-31 |
||||
| Kperegbeyi, J. I., & Akaine, F. (2025). Phenotypic correlation between external and internal egg quality traits of pancake tortoise (Malacochersus tornieri). International Journal of Bioscience and Agrological Research, 9(1): 1-7. https://doi.org/10.70382/hijbar.v09i1.015 |
||||
| Kperegbeyi, J. I., Onwumere-Idolor, S. O., & Okhale, O. E. (2025). Repeatability estimate of egg weight, egg number and body weight of tortoise (Pelusios casteneus) breeds in lowland ecological zone, Nigeria. Journal of Science Innovation and Technology Research, 9(9), 18-26. https://doi.org/10.70382/ajsitr.v9i9.040 |
||||
| Mayor, P., Hidalgo, S., El Bizri, H. R., & Morcatty, T. Q. (2023). Ovarian cycle, reproductive performance and breeding seasonality of Amazonian yellow-footed tortoises (Chelonoidis denticulatus) in the wild. Theriogenology Wild, 2, 100022. https://doi.org/10.1016/j.therwi.2023.100022 |
||||
| Morris, J. S. (2015). Functional regression. Annual Review of Statistics and Its Application, 2(1), 321-359. https://doi.org/10.1146/annurev-statistics-010814-020413 |
||||
| Odiko, A. E., Chidi, D., Agbo, D. O., & Abushe, P. O. (2021). Proximate and mineral composition of Pelusios castaneus (West African mud turtle) flesh sold in Edo State, Nigeria. Adan Journal of Agriculture, 2(1), 156-169. https://doi.org/10.36108/adanja/1202.20.0151 |
||||
| Pakdemiril, M. (2024). Implicit functional transformation method for perturbed and unperturbed differential equations. Journal of Science and Arts, 25(2), 247-258. https://doi.org/10.46939/J.Sci.Arts-25.2-a02 |
||||
| Phillott, A. D., & Godfrey, M. H. (2020). Assessing the evidence of 'infertile'sea turtle eggs. Endangered species research, 41, 329-338. https://doi.org/10.3354/esr01032 |
||||
| Phillott, A. D., Godfrey, M. H., & Avens, L. I. (2021). Distinguishing between fertile and infertile sea turtle eggs. Marine Turtle Newsletter, (162), 18-21. | ||||
| Ramalho, E. A., & Ramalho, J. J. S. (2017). Moment-based estimation of nonlinear regression models with boundary outcomes and endogeneity, with applications to nonnegative and fractional responses. Econometric Reviews, 36(4), 397-420. https://doi.org/10.1080/07474938.2014.976531 |
||||
| Rubin, J., Mariani, L., Smith, A., & Zee, J. (2023). Ridge regression for functional form identification of continuous predictors of clinical outcomes in glomerular disease. Glomerular Diseases, 3(1), 47-55. https://doi.org/10.1159/000528847 |
||||
| Rugiero, L., (2021). Differences in reproductive success in young and old females of a mediterranean spur-thighed tortoise population (Testudo graeca). Animals, 11(2), 467. https://doi.org/10.3390/ani11020467 |
||||
| SAS (2018). Statistical Analysis System Institute. User's Guide Version 9.2. S.A.S. Institute | ||||
| Southern Delta University (SDU) Meteorological Station Report Zonal Office, Ozoro, 2025. | ||||
| Stanford, C. B., Iverson, J. B., Rhodin, A. G., van Dijk, P. P., Mittermeier, R. A., Kuchling, G., Berry, K. H., Bertolero, A., Bjorndal, K. A., Blanck, T. E., & Walde, A. D. (2020). Turtles and tortoises are in trouble. Current Biology, 30(12), R721-R735. https://doi.org/10.1016/j.cub.2020.04.088 |
||||
| Stemle, L. (2022). First georeferenced report of a non-native west african mud turtle, Pelusios castaneus (Schweigger 1812), in Florida. Reptiles and Amphibians, 29(1), 150-151. https://doi.org/10.17161/randa.v29i1.16436 |
||||