Evaluation of fisheries management and sustainability indicators can be supported by a reliable index of harvest rate. However, the most appropriate model that accounts for recreational fisheries is largely unknown. In order to adjust for these factors, generalized linear models were applied to data from shore-based recreational fishing surveys conducted in Western Australia between 2010 and 2016. Five candidate error distributions (lognormal, Gamma, Zero-Altered Gamma, Tweedie, and delta-lognormal) and seven independent variables (year, month, target species, fishing platform, fishers’ avidity, time of day, and day type) were examined for commonly caught nearshore teleost species. Zero-Altered Gamma and Tweedie models performed best overall, although model performance and explanatory variables varied between species. Standardized harvest rates for Australian herring (Arripis georgianus) declined from 1.88 ± 0.17 (mean ± s.e.) fish per fishing party per day) in 2010 to 0.86 ± 0.07 in 2016, while harvest rates for School whiting (Sillago spp.) increased from 0.44 ± 0.21 in 2010 to 0.94 ± 0.34 in 2016. The standardized harvest rates for both species generally showed less fluctuation among years and consistently had smaller errors than the raw harvest rates. Overall, the results suggest that the choice of error distribution, as well as explanatory variables, is species dependent when assessing shore-based fisheries. The approach used could easily be adapted to other recreational fisheries to establish reliable species-specific harvest rates that can detect variability against thresholds set in harvest strategies.
On-site surveys involving face-to-face interviews are implemented globally across many scientific disciplines. Incorporating new technologies into such surveys by using electronic devices is becoming more common and is widely viewed to be more cost-effective and accurate. However, Electronic Data Capture methods (EDC) when compared to traditional Paper-based Data Capture (PDC) are often implemented without proper evaluation of any changes in efficiency, especially from surveys in coastal and marine environments. A roving creel survey of recreational shore-based fishers in Western Australia in 2019 enabled a direct comparison between the two methods. Randomisation strategies were employed to ensure biases in using each technique were minimised. A total of 1,068 interviews with recreational fishers were undertaken with a total error rate of 5.1% (CI95%: 4.8–5.3%) for PDC and 3.1% (CI95%: 2.9–3.3%) for EDC. These results confirmed that EDC can reduce errors whilst increasing efficiency and decreasing cost, although some aspects of this platform could be improved with some streamlining. This study demonstrates how EDC can be successfully implemented in coastal and marine environments without compromising the randomised, stratified nature of a survey and highlights the cost-effectiveness of this method. Such findings can be widely applied to any discipline which uses face-to-face interviews for data collection.
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