Hernández-Flores A et al. (2018), Bioeconomic modeling for a small-scale sea cucu...

ECB-ART-46036

PLoS One 2018 Jan 01;131:e0190857. doi: 10.1371/journal.pone.0190857.

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Bioeconomic modeling for a small-scale sea cucumber fishery in Yucatan, Mexico.

Hernández-Flores A , Cuevas-Jiménez A , Poot-Salazar A , Condal A , Espinoza-Méndez JC .

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Due to the heavy exploitation of holothurians over the last few decades, it is necessary to implement fishing regulations aimed at reversing this situation. Holothurians require specific regulations that take into account their biology and ecology. Their behavior to group and form patches as a strategy for feeding, defense and reproduction, makes them vulnerable to overfishing. The higher the population density, the higher the catchability coefficient, and because they are sedentary organisms, the catchability does not change significantly until the density is very low. Hence, the stock assessment of holothurians can be improved by analyzing their spatial distribution. This paper proposes a stock assessment technique that considers the spatial distribution pattern of the sea cucumber Isostichopus badionotus from Yucatan, Mexico. A bioeconomic spatial model was developed to explain the interactions between fishing effort allocation, quasi-profits and the population in the short term. Because of the high price of the species and the low production costs, artisanal fishers preferred to maximize short-term quasi-profits, rather than the long-term benefits they could gain with low fishing mortality rates.

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Genes referenced: LOC100887844 LOC100893907

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Mercier, Endogenous and exogenous control of gametogenesis and spawning in echinoderms. 2009, Pubmed, Echinobase

	Fig 1. General conceptual spatial-bioeconomic model for the sea cucumber fishery.The three subsystems are connected in such a way that the system functions endogenously.
	Fig 2. Ordinary kriging models.a) Ordinary kriging models displayed in relief (3D) image format for: March and August 2013. The lower case letters indicate different patches and the colors represent different density ranges; b) Spatial distribution of sea cucumber density in patch a, as observed in March and August 2013; c) Profiles of sea cucumber density drawn along the same linear transect (from point A to B) for the two periods of study (March and August 2013).
	Fig 3. Density-dependent catchability curve of I. badionotus in patch a.Dashed lines show the changes in catchability at three densities: 0.025, 0.10 and 0.25 individuals m-2.
	Fig 4. Total abundance of I. badionotus and cumulative catch trajectories depicted by the bioeconomic simulation model.Total sea cucumber abundance and cumulative catch trajectories reproduced by the bioeconomic simulation model (depletion model) for patch a. The blue dots show the observed abundance calculated using the ordinary kriging technique, on 31 March 2013 and five months later (26 August 2013).
	Fig 5. Trajectories of density (D) predicted by the dynamic bioeconomic model.Trajectories of density (D) predicted by the dynamic bioeconomic model for each of the eight density levels in patch a. All densities converge at 0.05 (±0.01) individuals m-2 in August. The images at the bottom show the spatial arrangement of density from April to September (on the first day of each month).
	Fig 6. Spatial allocation of fishing effort in patch a as portrayed by the bioeconomic model.Each color represents a density level. The trajectory of total density (D) fell from 0.16 to 0.04 individuals m-2 in six months.