Estimating Length-Weight Relationship and Condition Factor of Tripletail, Lobotes surinamensis Using a Linear Mixed-Effects Modelling Approach

Estimating the length-weight relationship and condition factor in fish relies on the quantitative traits in length and weight, with the expression of the two not only influenced by genetics but also heavily influenced by environmentally-linked variables. Yet, the conventional estimation of the length-weight parameters typically does not incorporate such information about the individuals. In the present study, we estimated length-weight relationship and the condition factor of Lobotes surinamensis using a linear mixed-effects modelling approach. Length and weight data were collected between 2019 and 2021 from Malindi-Ungwana Bay region in different seasons. A total of six models, including a generalised linear model (GLM), a linear regression model (LM), and four linear mixed-effects models (LMMs), with the latter incorporating the spatio-temporal information for the individuals, were evaluated. The LMM with intercepts and slopes randomly varying among fishing grounds was selected based on AIC values. For the overall dataset, the model estimated a (intercept) = 0.032, with a 95% confidence interval (CI) of 0.010–0.101, and b (slope) = 2.814 (CI: 2.526–3.098), indicating isometric growth. Samples from the small-scale fishery (SSF) and semi-industrial fishery (SIF) had isometric and negative allometry growths, respectively. The b value of the combined dataset estimated using the default LM indicated negative allometry. The overall dataset had a condition factor, K = 1.631 (CI: 1.595–1.667). The present results provide a basic description of the length-weight relationship and condition factor of L. surinamensis within Kenya and highlight the usefulness of LMMs in parameter estimations.