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Keywords:

  • marine mammals;
  • Antarctic seals;
  • crabeater seal;
  • leopard seal;
  • Ross seal;
  • Weddell seal;
  • census;
  • diurnal activity cycle;
  • census correction;
  • pack ice;
  • population model;
  • bootstrap

Abstract

Antarctic phocid seals and particularly the crabeater (Lobodon carcinophagus) have been observed to display a diurnal cycle in their propensity to haul out on pack ice where they are visible for census. The fact that they are not visible for much of the 24-h period means that density estimates made over broad geographic areas at various times of the day statistically confound this cycle with geographic variability. Limitation of census observations to times of peak haulout results in extreme logistical difficulties and/or considerable reduction in sample size upon which to base population estimates. Reduced sample size results in high variability in population estimates and broad confidence bands. To develop a model with which to correct density estimates for variability due to diurnal cycle, a series of stationary censuses at fixed locations in the Antarctic continental ice pack was made over significant fractions of several days. A unimodal polynomial model for the observed density variation in any one location was statistically significant; a similar model combining multiple locations with densities standardized to peak daily values was also significant. The latter model was used to make corrections for time of day to density estimates in three test data sets taken over broad geographic areas of the Antarctic. Statistical simulation (bootstrap) methods were used to determine if variances of corrected density estimates were lower than those based on uncorrected observations taken only during the peak haulout times of the day. Results were that 95% interval estimates for corrected densities were narrowed to between 40% and 61% of the uncorrected estimates. While there are additional possible sources of variation in haulout tendency, pending further data collection and analyses, the model developed represents a considerably more precise methodology than either averaging over haulout variability or limiting observations to peak daily periods.