Analysing the natural population growth of a large marine mammal after a depletive harvest

M A Romero,G Svendsen,N A García,M Koen-Alonso,E A Crespo,M Ocampo Reinaldo,R González,M F Grandi,S L Dans

doi:10.1038/s41598-017-05577-6

Abstract

An understanding of the underlying processes and comprehensive history of population growth after a harvest-driven depletion is necessary when assessing the long-term effectiveness of management and conservation strategies. The South American sea lion (SASL), Otaria flavescens, is the most conspicuous marine mammal along the South American coasts, where it has been heavily exploited. As a consequence of this exploitation, many of its populations were decimated during the early 20th century but currently show a clear recovery. The aim of this study was to assess SASL population recovery by applying a Bayesian state-space modelling framework. We were particularly interested in understanding how the population responds at low densities, how human-induced mortality interplays with natural mechanisms, and how density-dependence may regulate population growth. The observed population trajectory of SASL shows a non-linear relationship with density, recovering with a maximum increase rate of 0.055. However, 50 years after hunting cessation, the population still represents only 40% of its pre-exploitation abundance. Considering that the SASL population in this region represents approximately 72% of the species abundance within the Atlantic Ocean, the present analysis provides insights into the potential mechanisms regulating the dynamics of SASL populations across the global distributional range of the species.

Highlights

We used Bayesian inference to integrate the time series of population abundances, commercial harvests, and fisheries bycatch into dynamic SSMs for the South American sea lion population from northern and central
We relied on previous studies to define plausible prior distribution for the parameters and to set the likelihood function. This is important for Bayesian inference because misspecification of prior distributions and the choice of an inappropriate likelihood function may result in unreliable posterior distributions for parameters[29, 30]
Using survey data avoids several problems that have been noted regarding the use of catch rates as a relative abundance indices, which is a common practice in fishery models[32, 33]