Addressing the reliability of data-poor stock assessment methods to provide advice on the status of small-scale fisheries
- Autores: John Gabriel Ramírez Téllez
- Directores de la Tesis: Francesc Maynou Hernández (dir. tes.), Marta Coll Montón (codir. tes.)
- Lectura: En la Universitat de Barcelona ( España ) en 2019
- Idioma: español
- Tribunal Calificador de la Tesis: José M. Bellido Millán (presid.), Nicolás Luís Gutiérrez de los Santos (secret.), Beatriz Guijarro González (voc.)
- Programa de doctorado: Programa de Doctorado en Ciencias del Mar por la Universidad de Barcelona y la Universidad Politécnica de Catalunya
- Materias:
- Enlaces
- Tesis en acceso abierto en: TESEO
- Resumen
Small-scale fishers are often identified as key players in the recovery of overexploited fish and invertebrate stocks supplying food for rural people and contributing to achieving healthy marine ecosystems. Stocks harvested by small-scale fisheries tend to be largely unassessed, but methods based on the data-limited toolbox exist that help provide information on exploitation status for fished stocks that do not have historical time series of catches, as usually occur in small-scale fisheries. Many of the data-limited methods follow length-based assessment approaches, which assume steady state, use at least length structure derived from fishery and knowledge on the life history parameters of the fished species. Accordingly, this thesis aimed at addressing the reliability of data-poor stock assessment methods in providing advice on the status of small-scale fisheries lacking knowledge of catch history. The data-rich bottom trawl fishery for European hake (Merluccius merluccius) in GSA 06 (Northwest Mediterranean Sea) was assumed as data-limited. This case study allowed me to test the performance of the pseudo-cohort Virtual Population Analysis (VPA) when input data are considered well known and unbiased. The same fishery but held in GSA 01 (Southwest Mediterranean Sea) was used as data-limited case study to introduce the uncertainty derived from parameterizing the length-based spawning potential ratio (LB-SPR) model with two contrasting growth hypotheses. Acknowledging challenges faced by stock assessment of small-scale fisheries around the world, I considered high input-data bias and large outputs uncertainty. The effect of biases in fishery data and uncertainty in life-history parameters on the outputs of the pseudo-cohort VPA model was explored by assessing the small-scale Wayuu fisheries for lane snapper (Lutjanus synagris) and white grunt (Haemulon plumierii) in the northern Colombian Caribbean Sea. An extreme, but common, case of uncertainty in small-scale fisheries was explored through assessing the beach fishery of the Peruvian grunt (Anisotremus scapularis) in the central coast of Peru on the Pacific Ocean, holding poor information on life-history parameters and catches. My findings indicate that the pseudo-cohort VPA may offer useful information regarding the exploitation trend but the absolute values of the indicators do not accurately express the fishing mortality and stock size among years for the European hake. The SPR estimates for this species is not specially linked to the growth hypothesis, and estimates of the ratio of fishing mortality to natural mortality (F/M) and the SPR value depend on the sample size and representation of the stock structure. The contribution of the information derived from the participatory monitoring of small-scale fisheries in Colombia, instead of using only official fishery data, largely demonstrated an improving picture of the exploitation of the lane snapper and white grunt. The uncertainty related to estimates of the von Bertalanffy growth parameters and natural mortality of the Peruvian grunt could be addressed but an accurate definition of SPR was not straightforwardly achieved. This thesis highlights that the data-limited methods assuming a steady state might contribute to defining the status of the small-scale fisheries. However, the stock status is importantly affected by bias in the input data, the available knowledge on the assessed fisheries and how fishery fit the model assumptions.
