Environmental and Ecological Statistics

, Volume 20, Issue 4, pp 591–608 | Cite as

Promising the moon? Evaluation of indigenous and lunar fishing calendars using semiparametric generalized mixed models of recreational catch data

  • Ben C. Stevenson
  • Russell B. MillarEmail author


Previous studies that have investigated relationships between the lunar cycle and recreational fishing success have all suffered from various problems—most notably, the failure to account for potential confounders in a statistically rigorous manner. We propose methods to account for season, fisher identity, fishing effort, day, and variation in biomass, all of which have previously either been omitted or handled in an ad hoc way. These are applied to two sets of data on recreational fishing of the snapper Pagrus auratus in New Zealand. In addition to estimating effects due to lunar phase, we also implement these methods to analyse the performance of a lunar-based indigenous M\({{\bar{\mathrm{a}}}}\)ori fishing calendar. Recreational fishers in New Zealand often make use of such calendars in order to predict fishing success on specific days, however little is known about the performance of such predictions or whether they hold any practical use to the everyday angler. A relationship between lunar phase and fishing success is identified, as well as support for some aspects of the M\({{\bar{\mathrm{a}}}}\)ori fishing calendar predictions. The magnitudes of these effects are small, however, casting doubt on the practical relevance of lunar based fishing predictions. In addition to the known seasonal trend associated with annual migration, an unexpected second trend is detected, and postulated to be associated with intense local fishing pressure over the summer vacation period.


Nonlinear regression Recreational fishing Seasonal effects  Snapper Pagras auratus 



We wish to thank Bill Hohepa and OceanFun Publishing Ltd. for the provision of historical fishing calendar predictions, and two anonymous reviewers for their comments and suggestions which helped to improve the original manuscript.


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of StatisticsThe University of AucklandAucklandNew Zealand
  2. 2.Centre for Research into Ecological and Environmental ModellingUniversity of St Andrews, The Observatory, Buchanan GardensSt AndrewsUnited Kingdom

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