On Ignorance Regions and Spatial Aspects for South American sea lion (Otaria byronia) Operational Interaction with the Artisan Gillnet Fishery in Chile

  • Milan StehlíkEmail author
  • Jean Paul Maidana
  • Claudia Navarro Villarroel
  • Maritza Sepúlveda
  • Doris Oliva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11386)


The South Pacific Hake (Merluccius gayi) is the main artisan gillnet fishery in central Chile and the South American sea lion (SASL, Otaria byronia) has a strong operational interaction with this fishery. This was analyzed in paper [2] and several issues, like ignorance regions and spatial variation of such conflicts have not been studied in detail. In this paper we provide such analyses in order to complement the study. In particular, we show that construction of ignorance regions and its boundaries in the parameter space for SASL, dist, season4 and depth variables of interest could play an important role in order to characterize and possibly to eliminate the inaccuracies in the results and decisions that could be made.

We also checked Ripley’s K and Moran’s I in order to conduct spatial analysis using GIS tools. This may help to determine areas of conflict and how these vary in time and space. The interactions with sea lions are not a determining factor in the variation of artisan fishery catches and such observations are important for managing fisheries interactions and protection of marine species.


South Pacific Hake (Merluccius gayiSouthern sea lion (Otaria byroniaIgnorance regions Spatial analysis 



This study was supported by the Undersecretariat for Fisheries and Aquaculture, Chilean Government [Grant number 2013-115-DAP-35, “Characterization of the effects of principal artisan fisheries on marine ecosystems”], and Fondo de Investigación Pesquera [Grant number FIP 2014-29 “Population estimates for sea lions in Regions V, VI, VII and VIII”]. The authors acknowledge Tamara Martínez and Pablo Couve for collecting the SASL data in the field. Milan Stehlík acknowledges the support of project Fondecyt Regular No. 1151441 and LIT-2016-1-SEE-023 mODEC. Jean Paul Maidana acknowledges the support of the PhD. grant FIB-UV from the Universidad de Valparaíso. Maritza Sepúlveda acknowledges the support of the Iniciativa Científica Milenio from Chile’s Ministerio de Economía, Fomento y Turismo. The authors were supported by the bilateral projects Bulgaria - Austria, 2016 2019, Feasible statistical modelling for extremes in ecology and finance, Contract number 01/8, 23/08/2017 and WTZ Project No. BG 09/2017.


  1. 1.
    Agresti, A.: Foundations of Linear and Generalized Linear Models. Wiley Series in Probability and Statistics. Wiley, Hoboken (2015)zbMATHGoogle Scholar
  2. 2.
    Sepúlveda, M., et al.: Factors affecting the operational interaction between the South American sea lions and the artisan gillnet fishery in Chile. Fish. Res. 201, 147–152 (2018)CrossRefGoogle Scholar
  3. 3.
    Vansteelandt, S., Goetghebeur, E.: Analyzing the sensitivity of generalized linear models to incomplete outcomes via the IDE algorithm. J. Comput. Graph. Stat. 10(4), 656–672 (2001)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Potocký, R., Villarroel, C.N., Sepúlveda, M., Luna, G., Stehlík, M.: Random elements on lattices: review and statistical applications. In: AIP Conference Proceedings, vol. 1863, p. 100007 (2017).
  5. 5.
    Python Software Foundation. Python Language Reference, version 2.7.
  6. 6.
    The Comprehensive R Archive Network (CRAN).
  7. 7.
    Google Maps (2018).
  8. 8.
    Seabold, S., Perktold, J.: Statsmodels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Linz Institute of Technology (LIT) and Department of Applied StatisticsJohannes Kepler University in LinzLinzAustria
  2. 2.School of Mathematical and Statistical SciencesArizona State UniversityTempeUSA
  3. 3.Instituto de EstadísticaUniversidad de ValparaísoValparaísoChile
  4. 4.Facultad de IngenieríaUniversidad Andrés BelloViña del MarChile
  5. 5.Instituto de Biología, Centro de Investigación y Gestión de Recursos Naturales (CIGREN), Facultad de CienciasUniversidad de ValparaísoValparaísoChile
  6. 6.Núcleo Milenio INVASALConcepciónChile

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