A study on the ecological environment influenced by the sex ratio of lampreys based on logistic and dynamic analysis model

Qihua Wu; Simin Liu; Binying Huang

doi:10.54097/06y0v085

Authors

Qihua Wu
Simin Liu
Binying Huang

DOI:

https://doi.org/10.54097/06y0v085

Keywords:

Lamprey Sex Ratios, Logistic Growth Mode, Dynamic Analysis Model, Ecosystem Stability, Species Abundance and Distribution.

Abstract

Lamprey sex determination is influenced by factors such as larval growth rate and food availability. When food is plentiful, males make up approximately half of the population. However, in conditions of food scarcity, the proportion of males can increase dramatically, reaching up to 78%. This study investigates how changes in lamprey sex ratios can affect the abundance and distribution of the species, and ultimately, the stability of the ecosystem. This study developed a logistic growth model to simulate the ecosystem of the Great Lakes, using differential equations to analyze the temporal changes in lamprey populations over time. In addition, this study established a dynamic analysis model to predict future population trends based on varying environmental conditions. This study results show that an increased proportion of males leads to exponential growth in the lamprey population, which in turn significantly depletes the available food supply and reduces predator numbers. This imbalance causes large fluctuations in habitat occupancy, reproductive rates, and the overall health of the ecosystem. This research underscores the importance of understanding how sex ratio variability can influence ecosystem dynamics and species distribution, providing valuable insights into maintaining ecological balance in changing environments.

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References

[1] Hansen MJ, Madenjian CP, Slade JW, Steeves TB, Almeida PR, Quintella BR. Population ecology of the sea lamprey (Petromyzon marinus) as an invasive species in the Laurentian Great Lakes and an imperiled species in Europe[J]. Agricultural Sciences, 2016, 26: 509–535.

[2] Kennedy RJ, Campbell W, Gallagher K, Evans D. River lamprey present an unusual predation threat to Atlantic salmon smolts in Lough Neagh, Northern Ireland [J]. Journal of Fish Biology, 2020, 97(4): 1265–1267.

[3] Gellatly C. The Sex Ratio: A Biological and Statistical Conundrum [J]. Current Biology: CB, 2020, 30(20): R1261–R1263.

[4] Johnson NS, Swink WD, Brenden TO. Field study suggests that sex determination in sea lamprey is directly influenced by larval growth rate [J]. Proceedings. Biological Sciences, 2017, 284(1851): 20170262.

[5] Lennox RJ, Bravener GA, Lin HY, Madenjian CP, Muir AM, Remucal CK, Robinson KF, Rous AM, Siefkes MJ, Wilkie MP, Zielinski DP, Cooke SJ. Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change [J]. Global Change Biology, 2020, 26(3): 1118–1137.

[6] Qureshi S, Yusuf A, Aziz S. Fractional numerical dynamics for the logistic population growth model under Conformable Caputo: A case study with real observations [J]. Physica Scripta, 2021, 96(11): 114002.

[7] Tsoularis A, Wallace J. Analysis of logistic growth models [J]. Mathematical Biosciences, 2002, 179(1): 21–55.

[8] Tsoularis A, Wallace J. Analysis of logistic growth models [J]. Mathematical biosciences, 2002, 179(1): 21-55.

[9] Murakami K, Sato M, and Kubota M, et al. Plant Robots: Harnessing Growth Actuation of Plants for Locomotion and Object Manipulation [J]. Advanced Science, 2024: 2405549.

[10] Lee J, Lee Y J, Ha J. Denosumab in Osteoporosis: Predicting Long-Term Efficacy beyond 10 Years [J]. Journal of Bone Metabolism, 2024, 31(3): 246.

[11] Chowell G, Bleichrodt A, Dahal S, Tariq A, Roosa K, Hyman JM, Luo R. GrowthPredict: A toolbox and tutorial-based primer for fitting and forecasting growth trajectories using phenomenological growth models[J]. Scientific Reports, 2024, 14(1): 1630.

[12] Sylla A, Chevillon C, Djidjiou-Demasse R, et al. Understanding the transmission of bacterial agents of sapronotic diseases using an ecosystem-based approach: A first spatially realistic metacommunity model [J]. PLOS Computational Biology, 2024, 20(9): e1012435.

[13] Mahanty C, Patro S G K, Rathor S, et al. Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques[J]. Scientific Reports, 2024, 14(1): 18039.