Assessing a Threatened Fish Species under Budgetary Constraints: Evaluating the Use of Existing Monitoring Data

Daniel C. Gwinn, Charles R. Todd, Paul Brown, Taylor L. Hunt, Gavin Butler, Adrian Kitchingman, John D. Koehn, Brett Ingram

Research output: Contribution to journalArticle

Abstract

Because of the high costs of collecting field data, many species recovery and management plans do not include a monitoring feedback component to measure the success of interventions and refine management strategies. Here, we demonstrate how leveraging existing monitoring data can provide broad-scale, cost-effective information about a threatened fish species, the Murray Cod Maccullochella peelii, which is of cultural and recreational importance in Australia. We applied a Bayesian hierarchical model of abundance to Murray Cod catch data collected as part of broad-scale, general condition monitoring in the Murray–Darling Basin. The model uses replicated sampling at spatially independent sites to disentangle the confounding effects of detection probability and abundance on catch data. We demonstrate the reliability of the analysis for determining trends in abundance with a simulation study, and we show that basinwide abundance of Murray Cod declined by over 50% between 2010 and 2013. We found that detection probability of Murray Cod can vary substantially across space and through time, suggesting that accounting for variable detection will be important in any future evaluation of Murray Cod populations. This study highlights variable detection as an issue in monitoring regimes and demonstrates a method for the cost-effective use of existing monitoring data to evaluate species abundance trends.

Original languageEnglish
Pages (from-to)315-327
Number of pages13
JournalNorth American Journal of Fisheries Management
Volume39
Issue number2
DOIs
Publication statusPublished - 1 Apr 2019

Fingerprint Dive into the research topics of 'Assessing a Threatened Fish Species under Budgetary Constraints: Evaluating the Use of Existing Monitoring Data'. Together they form a unique fingerprint.

  • Cite this