preprints.org > biology and life sciences > anatomy and physiology > doi: 10.20944/preprints202010.0565.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 26 October 2020 / Approved: 27 October 2020 / Online: 27 October 2020 (21:04:35 CET)

Lake Malombe fish stocks have been depleted by chronic overfishing. Various management approaches (co-management, command control, and ecosystem-based management to fisheries) have been used to manage the fishery. However, the lack of an accurate predictive model has hampered their success. Therefore, we developed and tested a time series model for Lake Malombe fishery. The seasonal fish biomass and CPUE trends were first observed and both were non-stationary. The second-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), and Akaike information criterion (AIC) were estimated, which led to the identification and construction of autoregressive integrated moving average (ARIMA) models, suitable in explaining the time series and forecasting. The results showed that ARIMA (1,2,1) provided a better prediction than its counterparts. The model satisfactorily predicted that by 2032, both fish biomass and CPUE will decrease to 3204.6 tons and 59.672 respectively, signifying the potential threat to Lake Malombe fishery. The model justified the necessity of taking precautionary measures to avoid the total collapse of the fishery.

ARIMA, CPUE, Fish biomass landings, Forecasting, Lake Malombe, Time series approach

Biology and Life Sciences, Anatomy and Physiology

* All users must log in before leaving a comment