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Keywords:

  • conservation evaluation;
  • endangered species;
  • fishing;
  • ocean;
  • coastal;
  • fish

ABSTRACT

  1. Many sharks are listed as data deficient in assessment and management reports owing to a lack of basic life history data, making decisions about conservation management difficult. As such, the collection of these data is a priority. However, rare or threatened species with populations that are already small can be difficult to sample. Thus there is a need for techniques that permit the use of small sample sizes to provide preliminary information on life history parameters such as age and growth.
  2. In this study, growth curves were fitted to length-at-age data for five rare or difficult to sample sharks from north-eastern Australia: Carcharhinus coatesi (n = 56), Carcharhinus fitzroyensis (n = 39), Carcharhinus macloti (n = 37), Eusphyra blochii (n = 14) and Hemipristis elongata (n = 14) to provide the first estimates of growth for these species. Vertebral centra from field collections were aged to obtain length-at-age data, and partial age adjustments were used to increase the precision of age estimates. In addition, back calculation techniques were applied to add interpolated data to fill gaps in the growth curves caused by missing length classes.
  3. Back calculation techniques did not substantially alter the growth curves of the species, which had an even spread of data across size classes (C. fitzroyensis, E. blochii and H. elongata). However, the back calculation techniques considerably improved the growth curves for C. coatesi and C. macloti where juveniles were missing from the samples.
  4. Small sample sizes often provide a barrier to conducting growth studies because of the perception that growth estimates can only be obtained from ‘large’ sample sizes. However, by including individuals across all of the species length classes and maximizing the use of all available exogenous information using back calculation and partial age adjustments, as well as through judicious choice of growth models, it is possible to obtain practical estimates of growth, even when sample sizes are extremely limited. Copyright © 2012 John Wiley & Sons, Ltd.