Standardizing harvest rates of finfish caught by shore-based recreational fishers

Abstract

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.

Comparing the efficiency of paper-based and electronic data capture during face-to-face interviews

“…There are numerous platforms for shore-based recreational fishing within this area, including groynes, natural rocky outcrops, intertidal reef platforms, jetties, and sandy beaches. A suite of nearshore finfish species is targeted by shore-based recreational fishers in this area [ 25 , 26 ].…”

“…A 5-month roving creel survey of recreational shore-based fishers was undertaken in the Perth Metropolitan area between Ocean Reef and Woodman Point in 2010, and then annually from 2014–2019 ( Fig 1 ). The randomised, stratified survey design follows well-documented protocols [ 27 ] and has been implemented consistently for all surveys [ 25 , 26 ]. Standardised questionnaires for each survey interview captures 27 fields related to the survey day (i.e.…”

Toward sustainable harvest strategies for marine fisheries that include recreational fishing

“…While RF is generally challenging to monitor, shore-based catch and effort are particularly difficult to quantify due to the large and often unknown number of access points and broad spatial scale of potential effort. The activity is therefore frequently overlooked or omitted from stock assessments and HSs (Hartill et al, 2012;Hyder et al, 2014Hyder et al, , 2018Hyder et al, , 2020Smallwood et al, 2012;Tate et al, 2020).…”

Spatiotemporal Variability of Fishery‐Dependent Indices for the Declining Louisiana Southern Flounder Fishery

“…Fishery-independent data are collected using systematic and random survey designs that attempt to keep spatial, temporal, and effort elements consistent so as to gather unbiased abundance data that allow for proportionality between survey catch rates and stock abundance to be reasonably assumed (Hilborn and Walters 1992;Hubert and Fabrizio 2007). The nonrandom aspects of commercial and recreational fisheries, along with any regulatory changes in the fishery through time, can lead to nonproportionality between fishery catch rates and stock abundance, which creates biases when interpreting fishery-dependent data as a measure of stock abundance (Gr üss et al 2019;Tate et al 2020). Additionally, the nonlinear relationship between catchability and stock abundance (Crecco and Overholtz 1990;Wilberg et al 2009) can potentially lead to hyperstability, in which indices of stock abundance ostensibly appear stable while a decline in stock size is occurring (Hilborn and Walters 1992).…”

“…Despite the inferential limitations of fishery-dependent data as a measure of stock abundance, broad applications exist for the use of fishery-dependent data in assessing fish populations. A classic example of applying fisherydependent data as a source of stock size is through the standardization of CPUE to remove the factors not related to changes in abundance; this approach is often used in stock assessments when fishery-independent data are lacking or unavailable (Winker et al 2013;Okamura et al 2018;Gr üss et al 2019;Tate et al 2020). Another popular application of fishery-dependent data includes characterizing the spatial structure of fish distributions and habitats (Pilar-Fonseca et al 2014;Pennino et al 2016;Sculley and Brodziak 2020).…”