Improvements in estimating an acoustic target strength–length relationship for hoki (Macruronus novaezeladiae)

Dunford, Adam J.; O’Driscoll, Richard L.; Oeffner, Johannes

doi:10.1016/j.fishres.2014.09.008

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Recently, Dunford et al (2015) proposed an alternative method of deriving TS-size equations, calculating regression coefficients in the linear domain (σ bs ) first using nonlinear weighted regression and then transforming regression coefficients to the linear (TS) domain. Dunford et al (2015) suggested the use of their method because mean TS calculated in the linear domain differs from that calculated in the logarithmic domain; however, our method also correctly calculates a mean in the linear domain before transforming to the logarithmic domain.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Dunford et al (2015) proposed an alternative method of deriving TS–size equations, calculating regression coefficients in the linear domain (σ bs ) first using nonlinear weighted regression and then transforming regression coefficients to the linear (TS) domain. Dunford et al (2015) suggested the use of their method because mean TS calculated in the linear domain differs from that calculated in the logarithmic domain; however, our method also correctly calculates a mean in the linear domain before transforming to the logarithmic domain. Furthermore, given the large body of literature (Love 1971; Frouzova et al 2005; Simmonds and MacLannen 2005; Brooking and Rudstam 2009; Pollum and Rose 2016) in which mean TS (calculated in the linear domain) has been regressed on fish L , we are confident that our methods produce meaningful TS–size equations for Gizzard Shad.…”

Section: Discussionmentioning

confidence: 99%

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Gizzard Shad Target Strength‐to‐Body Size Equations at Multiple Hydroacoustic Frequencies

et al. 2021

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Species-specific target strength (TS)-to-length (L) and TS-to-weight (W) equations can reduce bias in biomass estimates from hydroacoustic surveys, yet these equations do not exist for most fishes. Equations specific to the Gizzard Shad Dorosoma cepedianum, a wide-ranging and often highly abundant prey fish in North American reservoirs and rivers, do not exist. Herein, we sought to develop TS-L and TS-W equations for Gizzard Shad by insonifying free-swimming individuals of known sizes (36-209 mm TL) in a net cage at three transducer frequencies (70, 120, and 200 kHz). We derived TS-size relationships using major-axis regression (MAR) and leastsquares regression (LSR), comparing our resultant TS-L equations to a commonly used multispecies equation (Love 1971). To determine how our Gizzard Shad-specific equations affected estimates of prey fish biomass, we conducted mobile hydroacoustic surveys in four small, shallow Midwestern reservoirs and then estimated biomass using each equation. In general, for TS-L equations, MAR produced the highest estimates of biomass, followed by LSR and then the multispecies equation. Similarly, the TS-W equation derived using MAR produced greater biomass estimates than the equation using LSR. Our findings highlight the value of using species-specific TS-size equations over multispecies equations in ecosystems dominated by a single fish species (e.g., Gizzard Shad in Midwestern reservoirs). They also demonstrate the value of using MAR to develop TS-size equations,

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Gizzard Shad Target Strength‐to‐Body Size Equations at Multiple Hydroacoustic Frequencies

et al. 2021

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“…Hoki are in the Merlucciidae family, made up of 24 species and 5 Genera, distributed throughout the Atlantic, eastern Pacific, Tasmania and New Zealand, often in sub-antarctic waters (Alyling & Cox, 1982). As an abundant commercial finfish species in New Zealand waters, hoki is New Zealand's largest fishery and is exported all over the world (Dunford et al, 2015;McKenzie, 2017). Hoki are widely distributed throughout New Zealand Exclusive Economic Zone at depths from 50 -1000m, although they are typically caught between 400-600m in southern regions, preferring cooler water temperatures (Hamer et al, 2012).…”

Section: Hoki (Macruronus Novaezelandiae Hector 1871)mentioning

confidence: 99%

Using Ecological Niche Modelling to Predict Climate Change Responses of Ten Key Fishery Species in Aotearoa New Zealand

Brooks¹

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<p>The long-term sustainability and security of food sources for an increasing human population will become more challenging as climate change alters growing and harvesting conditions. Significant infrastructure changes could be required to continue to supply food from traditional sources. Fisheries remain the only major protein supply directly harvested from the wild. This likely makes it the most sensitive primary sector to climate change. Overfishing is an additional concern for harvested species. There is a need to anticipate how marine species may respond to climate change to help inform how management might best be prepared for shifting distributions and productivity levels. The most common response of mobile marine species to changes in climate is an alteration of their geographic distributions and/or range shifts. Predicting changes to a species’ range could promote timely development of more sustainable harvest strategies. Additionally, these predictions could reduce potential conflict when different management areas experience increasing or decreasing catches. Ecological Niche Modelling (ENM) is a helpful approach for predicting the response of key fishery species to climate change scenarios. The overall aim of this research was to use the maximum entropy method, Maxent, to perform ENM on 10 commercially important fishery species, managed under the Quota management system in Aotearoa (New Zealand). Occurrence data from trawl surveys were used along with climate layers from Bio-ORACLE to estimate the species niche and then predict distributions in four different future climate scenarios, called Representative Concentration Pathway Scenarios (RCPS), in both 2050 and 2100. With little consensus over the best settings and way to apply the Maxent method, hundreds of variations were tried for each species, and the best model chosen from trial experimentation. In general, Maxent performed well, with evaluation metrics for best models showing little omission error and good discriminatory ability. There was, however, considerable variation between the different species responses to the future climate scenarios. Consistent with other studies, species able to tolerate sub-tropical or temperate conditions tended to expand southward, while subantarctic species generally contracted within their preferred environment. The increasing emissions or ‘business as usual’ climate change scenario consistently presented the most extreme difference from modern predictions. Northern regions of prediction, where sub-tropical or temperate species increased in probability of presence, were often highly uncertain due to novel conditions in future environments. Southern regions were usually less uncertain. Surface temperature consistently influenced base models more so than any other covariates considered, with the exception of bathymetry. Some predictions showed common areas of relative stability, such as hoki and ling on the southern Chatham Rise, potentially indicating future refugia. The preservation of habitats in the putative refugia may be important for long-term fisheries resilience. Furthermore, most species that showed large predicted declines are currently heavily harvested and managed. Overfishing could compound the effects of climate change and put these fisheries at serious risk of collapse. Identification of potential refugial areas could aid strategy adjustments to fishing practice to help preserve stock viability. Additionally, when some species shift, there are areas where new fisheries may emerge. This study offers a perspective of what future distributions could be like under different climate scenarios. The ENM predicts that the ‘business as usual’ scenario, where ‘greenhouse gas’ emissions continue to rise throughout the century, will have a negative impact on multiple aspects of distribution. However, in a reduced emissions scenario, less extreme range shifts are predicted. This study has provided a predictive approach to how fisheries in Aotearoa might change. The next step is to determine whether there is any evidence for the beginning of these changes and to consider how fisheries might best adapt.</p>

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Influence on management advice of fishers acoustics—10 year review of blue grenadier monitoring

et al. 2016

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Improvements in estimating an acoustic target strength–length relationship for hoki (Macruronus novaezeladiae)

Cited by 7 publications

References 12 publications

Gizzard Shad Target Strength‐to‐Body Size Equations at Multiple Hydroacoustic Frequencies

Gizzard Shad Target Strength‐to‐Body Size Equations at Multiple Hydroacoustic Frequencies

Using Ecological Niche Modelling to Predict Climate Change Responses of Ten Key Fishery Species in Aotearoa New Zealand

Influence on management advice of fishers acoustics—10 year review of blue grenadier monitoring

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