This study aimed to develop an ecological model of benthic macroinvertebrates (species abundance and composition) relative to the environmental parameters of water and sediment in Lagos lagoon using models based on their probability of occurrences. The data used were sampled from twenty-four (24) stations bi-monthly from September 2014 – July 2016 for benthic macroinvertebrates, sediment and water. At the study area, samples were collected in areas related to anthropogenic activities (wood logging, sand mining, land reclamation, construction sites and coastal residents) and areas not closely related to anthropogenic activities (mid-lagoons) as presence or absence of benthic macroinvertebrates. To obtain the predictive models, species distribution with environmental variables based on first-order polynomial equation function of linear regression model using least square adjustments equation to determine which variables were most relevant to the abundance of specific species. This also gave a good description of species abundance along gradients of environmental variables.
Results/Conclusions
The linear regression model used provided good results obtained for the abundance of nineteen (19) dominant macrobenthic species best fit linearly at R=0.94. The intra-validation analysis of observed values in the derived equation further eliminated deviated variables having test reliability most acceptable at a 95% confidence interval. The seasonal abundances of Brachyodontes puniceus, Donax acutangulus, Iphigenia laevigata, Nerita senegalensis, Turritella variegata, Crassostrea tulipa, Tympanotonus fuscatus, Perna perna were modelled and mapped. The new combination of methods provided results of abundance prediction of species having optimum habitat variables as moderate environmental variables which predicted more organism abundance in the wet season while on the contrary, peak environmental variables predicted more organism abundance in the dry season. The derived model proved to be appropriate for benthic macroinvertebrates species habitat suitability within a comparable seasonal scale and changing environmental variables.