Skip to content
Research Article Open access CC BY 4.0

Numerical Simulations and Fish Diversity in Relation to Physico-chemical Parameters of Four Aquatic Stations in Badagry Division, Lagos, Nigeria

Mekuleyi Gabriel Olarinde, Joseph Olufemi Olabode, Wusu Ashiribo Senapon, Badmos Lateef Akorede, Anetekhai Martins Agenuma

Asian Journal of Fisheries and Aquatic Research · pp. 24–35 · Published 12 May 2023

10.9734/ajfar/2023/v22i4578

Abstract

Aim: This study investigated common fish assemblages in four aquatic stations (Gbaji, Topo, Agboju and Ajegunle) in relation to physico-chemical parameters of its water and as well simulates numerical values of the physico-chemical parameters for year 2024. Methodology: Data were collected and computed using standard methods and algorithm machine learning model respectively. Statistical tools employed include ANOVA and Pearson correlation (r) test. All the parameters (except turbidity, chemical oxygen demand and biochemical oxygen demand) were within standard permissible limits. Results: The results of Pearson’s correlation analysis showed that increase in amount of fish species in the sampling stations was significantly and strongly linked to the decrease in value of biochemical oxygen demand (r = −0.571, P=.05), total dissolved solids (r= −0.696, P=.05) and conductivity (r=−0.882, P<0.01) while increase in number of fish species was significantly and strongly linked to the increase in value of dissolved oxygen(r= 0.991, P=.05).  However, salinity (r= 0.387), sulphate (r = 0.212) and turbidity (r= 0.282) had weak positive relationship with the number of fish species. The prediction by the machine learning algorithm modeling system revealed that at Agboju, total hardness would exceed permissible limit in February and September, carbondioxide (C02) in February, May and June, 2024.On the other hand, level of C02 at Ajegunle would exceed permissible limit in January, May, August and September, and total hardness in November, 2024. For both Agboju and Ajegunle, turbidity and chemical oxygen demand level would exceed limits throughout the months in year 2024. Conclusion: Hence, effects of higher parameters could results in declination of distribution of demersal fish species which often shuttle between the water bottom and surface.

Fish diversity numerical simulations correlation water quality

References (13)

  1. 1 Bioaccumulation of some heavy metals in tilapia fish relevant to their concentration in water and sediment of Wadi Hanifah, Saudi Arabia
  2. 2 Évaluation des caractéristiques physico-chimiques de l'eau dans certaines stations du lac Tanganyika, en Afrique, avec un accent particulier sur les objectifs de la piscicoles
  3. 3 Response of periphyton community to salinity gradient in tropical estuary, Niger Delta
  4. 4 Studies on heavy metals and fish health indicators in Malapterurus electricus from Lekki Lagoon, Lagos, Nigeria [DOI]
  5. 5 Some Aspects of the Physicochemistry and Heavy Metal Content of Water, Sediment and Cynothrissa mento (Regan, 1917) from Badagry Creek, Lagos, Nigeria [DOI]
  6. 6 MULTIVARIATE ANALYSIS OF FISH SPECIES AND ENVIRONMENTAL FACTORS IN MARINE COASTAL WATERS OF THE GULF OF GUINEA, SOUTHWEST NIGERIA [DOI]
  7. 7 Variations in the macrobenthic invertebrates in relation to physico-chemical characteristics and WQI in Lake Qarun, Egypt [DOI]
  8. 8 Physicochemical influence on the spatial distribution of faecal bacteria and polychaetes in the Densu Estuary, Ghana [DOI]
  9. 9 ICHTHYODIVERSITY IN RELATION TO PHYSICO-CHEMICAL PARAMETERS OF RIVER SWAT.
  10. 10 Spatial dynamics in physicochemistry and bacterio- and myco-plankton assemblages of Imo River in a Niger Delta community in Nigeria [DOI]
  11. 11 ASSESSMENT OF PHYSICO-CHEMICAL CHARACTERISTICS AND HEAVY METAL CONCENTRATIONS OF STUBBS CREEK, IBENO, AKWA IBOM, NIGERIA [DOI]
  12. 12 American Public Health Association (APHA) [DOI]
  13. 13 African Journal of Environmental Science and Technology [DOI]

Cited by 0

No indexed citations yet.

Related research

Article metrics

Real usage data collected on this platform.

6

Page views

0

PDF downloads

7

Outbound clicks

0

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.