Multijurisdictional fisheries performance reporting: How Australia’s nationally standardised approach to assessing stock status compares

Australian marine wild-capture fisheries are managed by eight separate jurisdictions. Traditionally, fishery status reports have been produced separately by most of these jurisdictions, assessing the fish stocks they manage, and reporting on the effectiveness of their fisheries management. However, the format, the type of stock status assessments, the thresholds and terminology used to describe stock status and the classification frameworks have varied over time and among jurisdictions. These differences complicate efforts to understand stock status on a national scale. They also create potential misunderstanding among the wider community about how to interpret information on the status of fish stocks, and the fisheries management and science processes more generally. This is especially true when considering stocks that are shared across two or more jurisdictional boundaries. A standardised approach was developed in 2011 leading to production of the first national Status of key Australian fish stocks reports in 2012, followed by a second edition in 2014 (www.fish.gov.au). Production of these reports was the first step towards a broader national approach to reporting on the performance of Australian fisheries for target species and for wider ecosystem and socioeconomic consequences. This paper outlines the challenges associated with moving towards national performance reporting for target fish stocks and Australia’s successes so far. It also outlines the challenges ahead, in particular those relating to reporting more broadly on the status of entire fisheries. Comparisons are drawn between Australia and New Zealand and more broadly between Australia and other countries.

Characterization, development and multiplexing of microsatellite markers in three commercially exploited reef fish and their application for stock identification

Thirty-four microsatellite loci were isolated from three reef fish species; golden snapper Lutjanus johnii, blackspotted croaker Protonibea diacanthus and grass emperor Lethrinus laticaudis using a next generation sequencing approach. Both IonTorrent single reads and Illumina MiSeq paired-end reads were used, with the latter demonstrating a higher quality of reads than the IonTorrent. From the 1–1.5 million raw reads per species, we successfully obtained 10–13 polymorphic loci for each species, which satisfied stringent design criteria. We developed multiplex panels for the amplification of the golden snapper and the blackspotted croaker loci, as well as post-amplification pooling panels for the grass emperor loci. The microsatellites characterized in this work were tested across three locations of northern Australia. The microsatellites we developed can detect population differentiation across northern Australia and may be used for genetic structure studies and stock identification.

Stock assessment of whaler and hammerhead sharks (Carcharhinidae and Sphyrnidae) in Queensland

This stock assessment provides detailed results for the most common sharks encountered by Queensland commercial fishers. These sharks come from the whaler (Carcharhinidae) and hammerhead (Sphyrnidae) families and comprise sharpnose sharks (Rhizoprionodon taylori and R. oligolinx), the milk shark (R. acutus), the creek whaler (Carcharhinus fitzroyensis), the hardnose shark (C. macloti), the spot-tail shark (C. sorrah), the Australian blacktip shark (C. tilstoni), the common blacktip shark (C. limbatus), the spinner shark (C. brevipinna), bull and pigeye sharks (C. leucas and C. amboinensis), the winghead shark (Eusphyra blochii), the scalloped hammerhead (Sphyrna lewini) and the great hammerhead (S. mokarran). Reef sharks were excluded because fishery observer data indicated that they were largely spatially segregated from sharks caught in the inshore net fisheries. The three common species of reef sharks in Queensland, which are all whaler sharks, are the grey reef shark Carcharhinus amblyrhynchos, the blacktip reef shark C. melanopterus and the whitetip reef shark Triaenodon obesus.

Stout Whiting Fishery Summary : Queensland Total Allowable Catch for 2016

In Queensland, stout whiting are fished by Danish seine and fish otter-trawl methods between Sandy Cape and the Queensland-New South Wales border. The fishery is currently identified by a T4 symbol and is operated by two primary quota holders. Since 1997, T4 management has been informed by annual stock assessments in order to determine a total allowable commercial catch (TACC) quota. The TACC is assessed before the start of each fishing year using statistical methodologies. This includes evaluation of trends in fish catch-rates and catch-at-age frequencies against management reference points. The T4 stout whiting TACC for 2014 was adjusted down to 1150 t as a result of elevated estimates of fishing mortality and remained unchanged in 2015 (2013 TACC = 1350 t quota). Two T4 vessels fished for stout whiting in the 2015 fishing year, harvesting 663 t from Queensland waters. Annual T4 landings of stout whiting averaged about 713 t for the fishing years 2013–2015, with a maximum harvest in the last 10 fishing years of 1140 t and a maximum historical harvest of 2400 t in the 1995. Stout whiting catch rates from both Queensland and New South Wales were analysed for all vessels, areas and fishing gears. The 2015 catch rate index was equal to 0.85, down 15% compared to the 2010–2015 fishing year average (reference point =1). The stout whiting fish length and otolith weight frequencies indicated larger and older fish in the calendar year 2014. This data was translated to show improved measures of fish survival at about 38% per year and near the reference point of about 41%. Together, the stout whiting catch rate and survival indicators show the fishery was sustainable. Earlier population modelling conducted for the year 2013 also suggested the stock was sustainable, but the estimate was only marginally above the biomass for maximum sustainable yield. Irrespective, reasons for reduced catch rates should be examined further and interpreted with precaution, particularly given the TACC has been under-caught in many years. For setting of the 2016 TACC, alternate analyses and reference points were compared to address data uncertainties and provide options for quota change. The results were dependent on the stock indicator and harvest procedure used. Uncertainty in all TACC estimates should be considered as they were sensitive to the data inputs and assumptions. For the 2016 T4 fishing year, upper levels of harvest should be limited to 1000–1100 t following procedure equation 1, with target levels of harvest at 750–850 t for procedure equation 2. Use of these estimates to set TACC will depend on management and industry intentions.

Is land condition a useful indicator of soil organic carbon stock in Australia?

The grazing lands of northern Australia contain a substantial soil organic carbon (SOC) stock due to the large land area. Manipulating SOC stocks through grazing management has been presented as an option to offset national greenhouse gas emissions from agriculture and other industries. However, research into the response of SOC stocks to a range of management activities has variously shown positive, negative or negligible change. This uncertainty in predicting change in SOC stocks represents high project risk for government and industry in relation to SOC sequestration programs. In this paper, we seek to address the uncertainty in SOC stock prediction by assessing relationships between SOC stocks and grazing land condition indicators. We reviewed the literature to identify land condition indicators for analysis and tested relationships between identified land condition indicators and SOC stock using data from a paired-site sampling experiment (10 sites). We subsequently collated SOC stock datasets at two scales (quadrat and paddock) from across northern Australia (329 sites) to compare with the findings of the paired-site sampling experiment with the aim of identifying the land condition indicators that had the strongest relationship with SOC stock. The land condition indicators most closely correlated with SOC stocks across datasets and analysis scales were tree basal area, tree canopy cover, ground cover, pasture biomass and the density of perennial grass tussocks. In combination with soil type, these indicators accounted for up to 42% of the variation in the residuals after climate effects were removed. However, we found that responses often interacted with soil type, adding complexity and increasing the uncertainty associated with predicting SOC stock change at any particular location. We recommend that caution be exercised when considering SOC offset projects in northern Australian grazing lands due to the risk of incorrectly predicting changes in SOC stocks with change in land condition indicators and management activities for a particular paddock or property. Despite the uncertainty for generating SOC sequestration income, undertaking management activities to improve land condition is likely to have desirable complementary benefits such as improving productivity and profitability as well as reducing adverse environmental impact.

Rising temperatures increased recruitment of brown tiger prawn (Penaeus esculentus) in Moreton Bay (Australia)

Abiotic factors are fundamental drivers of the dynamics of wild marine fish populations. Identifying and quantifying their influence on species targeted by the fishing industry is difficult and very important for managing fisheries in a changing climate. Using multiple regression, we investigated the influence of both temperature and rainfall on the variability of recruitment of a tropical species, the brown tiger prawn (Penaeus esculentus), in Moreton Bay which is located near the southern limit of its distribution on the east coast of Australia. A step-wise selection between environmental variables identified that variations in recruitment from 1990 to 2014 were best explained by a combination of temperature and spawning stock biomass. Temperature explains 35% of recruitment variability and spawning stock biomass 33%. This analysis suggests that increasing temperatures have increased recruitment of brown tiger prawn in Moreton Bay.

Investigating bias in recreational fishing surveys: Fishers listed in public telephone directories fish similarly to their unlisted counterparts

Several recent offsite recreational fishing surveys have used public landline telephone directories as a sampling frame. Sampling biases inherent in this method are recognised, but are assumed to be corrected through demographic data expansion. However, the rising prevalence of mobile-only households has potentially increased these biases by skewing raw samples towards households that maintain relatively high levels of coverage in telephone directories. For biases to be corrected through demographic expansion, both the fishing participation rate and fishing activity must be similar among listed and unlisted fishers within each demographic group. In this study, we tested for a difference in the fishing activity of listed and unlisted fishers within demographic groups by comparing their avidity (number of fishing trips per year), as well as the platform used (boat or shore) and species targeted on their most recent fishing trip. 3062 recreational fishers were interviewed at 34 tackle stores across 12 residential regions of Queensland, Australia. For each fisher, data collected included their fishing avidity, the platform used and species targeted on their most recent trip, their gender, age, residential region, and whether their household had a listed telephone number. Although the most avid fishers were younger and less likely to have a listed phone number, cumulative link models revealed that avidity was not affected by an interaction of phone listing status, age group and residential region (p > 0.05). Likewise, binomial generalized linear models revealed that there was no interaction between phone listing, age group and avidity acting on platform (p > 0.05), and platform was not affected by an interaction of phone listing status, age group, and residential region (p > 0.05). Ordination of target species using Bray-Curtis dissimilarity indices found a significant but irrelevant difference (i.e. small effect size) between listed and unlisted fishers (ANOSIM R < 0.05, p < 0.05). These results suggest that, at this time, the fishing activity of listed and unlisted fishers in Queensland is similar within demographic groups. Future research seeking to validate the assumptions of recreational fishing telephone surveys should investigate fishing participation rates of listed and unlisted fishers within demographic groups.