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A statistical model for monitoring shell disease in inshore lobster fisheries: A case study in Long Island Sound

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  • Kisei R Tanaka
  • Samuel L Belknap
  • Jared J Homola
  • Yong Chen

Abstract

The expansion of shell disease is an emerging threat to the inshore lobster fisheries in the northeastern United States. The development of models to improve the efficiency and precision of existing monitoring programs is advocated as an important step in mitigating its harmful effects. The objective of this study is to construct a statistical model that could enhance the existing monitoring effort through (1) identification of potential disease-associated abiotic and biotic factors, and (2) estimation of spatial variation in disease prevalence in the lobster fishery. A delta-generalized additive modeling (GAM) approach was applied using bottom trawl survey data collected from 2001–2013 in Long Island Sound, a tidal estuary between New York and Connecticut states. Spatial distribution of shell disease prevalence was found to be strongly influenced by the interactive effects of latitude and longitude, possibly indicative of a geographic origin of shell disease. Bottom temperature, bottom salinity, and depth were also important factors affecting the spatial variability in shell disease prevalence. The delta-GAM projected high disease prevalence in non-surveyed locations. Additionally, a potential spatial discrepancy was found between modeled disease hotspots and survey-based gravity centers of disease prevalence. This study provides a modeling framework to enhance research, monitoring and management of emerging and continuing marine disease threats.

Suggested Citation

  • Kisei R Tanaka & Samuel L Belknap & Jared J Homola & Yong Chen, 2017. "A statistical model for monitoring shell disease in inshore lobster fisheries: A case study in Long Island Sound," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
    • Handle: RePEc:plo:pone00:0172123
    DOI: 10.1371/journal.pone.0172123
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    1. Rachel L Pullan & Peter W Gething & Jennifer L Smith & Charles S Mwandawiro & Hugh J W Sturrock & Caroline W Gitonga & Simon I Hay & Simon Brooker, 2011. "Spatial Modelling of Soil-Transmitted Helminth Infections in Kenya: A Disease Control Planning Tool," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 5(2), pages 1-11, February.
    2. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    3. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
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