Catch per unit effort modelling for stock assessment: A summary of good practices

Hoyle, Simon D.; Campbell, Robert A.; Ducharme-Barth, Nicholas D.; Grüss, Arnaud; Moore, Bradley R.; Thorson, James T.; Tremblay-Boyer, Laura; Winker, Henning; Zhou, Shijie; Maunder, Mark N.

doi:10.1016/j.fishres.2023.106860

Cited by 14 publications

(7 citation statements)

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“…It is uncertain whether these areas are unfished due to low CPUE or other reasons. Including such regions in the modeling process will introduce additional uncertainty since estimating abundance in these areas will rely on limited data (Hoyle et al, 2024). One approach to mitigate this concern is to confine the spatial scope to a "core" fishery, comprising the regions where the majority of the catch is obtained (Campbell, 2015;Hoyle et al, 2024).…”

Section: Spatial Effectmentioning

confidence: 99%

“…Spatial effects are able to smooth the effect of multiple unaccounted drivers and can be modeled using geostatistics (INLA) and 2D smoothing splines of GAM (Paradinas et al, 2023). Modeling spatial effects in continuous space is one of the key appeals of the GAM approach for CPUE standardization (Table 2 of Hoyle et al, 2024). However, as indicated in Section 2.3.2.1, spatial factor was treated as a random effect for all analyses of this study.…”

Section: Spatial Effectmentioning

confidence: 99%

“…One key advantage of spatiotemporal models such as VAST is that they allow a straightforward computation of "predict-then-aggregate" indices of relative abundance (sensu Hoyle et al, 2024). Since VAST imputes fish density in unsampled locations informed by a distance-based covariance function, the index can be computed from the entire spatial domain at each time step.…”

Section: Statistical Modelsmentioning

confidence: 99%

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Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

Yuan,

Xu,

et al. 2024

Front. Mar. Sci.

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IntroductionWorldwide coastal fish resources face severe threats from fisheries overexploitation. However, the evaluation of abundance trends in most coastal fisheries is constrained by limited data. This study took blackmouth croaker (Atrobucca nibe), a stock depleted by coastal trawl fishery in southwestern Taiwan, as an example to showcase the development of a relative abundance index from data-limited fishery (only landing data were available).MethodsThis study employed unique data sourcing from voyage data recorders (VDRs) to estimate fishing effort (in combination with landing data to estimate the catch per unit effort, CPUE) that demonstrated the potential application in global data-limited fisheries and assessed alternative approaches for predictors of fishery-targeting practices to condition effort for producing more accurate metrics of relative abundance. The nominal CPUE was standardized using three statistical models: generalized linear model, generalized additive model (GAM), and vector-autoregressive spatiotemporal models (VASTs) with two treatments of each of the four effects: environmental (sea temperature, salinity, density of mixing layer, seafloor temperature, and chlorophyll), vessel, spatial, and targeting effects. A total of 15 models were designed and compared for these effects, and their explanatory power (EP) was evaluated using cross-validation R2 and other metrics.Results and discussionResults indicated that the targeting effect exerted the most significant influence on standardization and was suggested to be addressed through the principal component analysis (PCA) approach. Both vessel and spatial effects demonstrated considerable influence, whereas the environmental effect exhibited a limited impact, possibly due to the small fishing area in this study. Regarding models’ EP, given the nonlinear nature of the PCA algorithm and environmental data, the study highlighted the superiority of the GAM over linear-based models. However, incorporating nonlinear features in VAST (M15) makes it the most effective model in terms of predictive power in this study. Concerning the stock status, despite variations in relative CPUE trends among major models, a general declining trend since 2015 signals the potential decline of the blackmouth stock and urges fishery managers to consider further design of management measures.

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Section: Spatial Effectmentioning

confidence: 99%

Section: Spatial Effectmentioning

confidence: 99%

Section: Statistical Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

Yuan,

Xu,

et al. 2024

Front. Mar. Sci.

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“…While numerous different models are available for catch rate standardisation (Hoyle et al., 2024), there is increasing recognition of the critical role of spatial structure in species' dynamics, extinction risks and recovery potential (Wilson et al., 2023). In this context.…”

Section: Introductionmentioning

confidence: 99%

Coupling state‐of‐the‐art modelling tools for better informed Red List assessments of marine fishes

Grüss,

Winker,

Thorson

et al. 2024

Journal of Applied Ecology

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In the face of biodiversity loss worldwide, it is paramount to quantify species' extinction risk to guide conservation efforts. The International Union for the Conservation of Nature (IUCN)'s Red List is considered the global standard for evaluating extinction risks. IUCN criteria also inform national extinction risk assessments. Bayesian models, including the state‐of‐the‐art JARA (‘Just Another Red List Assessment’) tool, deliver probabilistic statements about species falling into extinction risk categories, thereby enabling characterisation and communication of uncertainty in extinction risk assessments. We coupled the state‐of‐the‐art VAST (‘Vector Autoregressive Spatio‐Temporal’) modelling tool and JARA, for better informed Red List assessments of marine fishes. In this framework, VAST is fitted to scientific survey catch rate data to provide indices to JARA whose uncertainty is propagated to JARA outcomes suggesting extinction risk categories (under the population reduction criterion). In addition, VAST delivers a valuable habitat assessment to better understand what may be driving extinction risk in the study region. Here, we demonstrate the coupled VAST‐JARA modelling framework by applying it to five contrasting North Sea species, with or without a quantitative stock assessment and with different conservation statuses according to the latest global Red List assessments. The North Sea application coupled with previous assessments and studies suggest that, among the three elasmobranchs, starry ray is in most need of urgent research (and conservation actions where appropriate), followed by spurdog, while lesser‐spotted dogfish is increasing in biomass. Moreover, both the VAST‐JARA modelling framework and previous research indicate that, while European plaice is not of conservation concern, cod has likely met the IUCN criteria for being listed as Endangered recently. Synthesis and applications. The predictions of the VAST‐JARA modelling framework for North Sea species, including JARA output and VAST habitat assessment, constitute valuable supporting information to make interpretations based on Red List guidelines, which will help decision‐makers in their next North Sea Red List assessment. We foresee applications of the modelling framework to assist Red List assessments of numerous marine fishes worldwide. Our modelling framework has many potential advantageous uses, including informing resource management about climate change impacts on species' extinction risks.

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“…For example, GLMs were utilised by Francis (2001) and Coburn et al (2002) to standardise orange roughy catch-per-unit-effort (CPUE) on the Chatham Rise and CPUE for New Zealand black oreo. GLMs for CPUE standardisation employ the same principles as more classical forms of SDMs (Hoyle et al, 2024). However, GLMs for CPUE standardisation often do not consider environmental effects on fish density and instead usually focus on covariates that influence fish catchability (e.g., net size, trawl duration).…”

Section: Review Of Sdms For Inferencementioning

confidence: 99%

Using Spatio-Temporal Species Distribution Models to Explore the Effects of Reef-Forming Coral on Seamount-Associated Fishes in New Zealand

Jarvis-Child

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<p><strong>Species distribution models (SDMs) are important tools for exploring the complex associations between species and their habitats to support marine conservation and management. In New Zealand, orange roughy and black oreo fisheries are characterised in part by their association with seamounts. New Zealand seamounts are also home to reef-forming corals. However, direct linkages between seamount-associated fishes and reef-forming corals are uncertain; that is, the extent to which coral might provide biogenic structures for shelter, feeding, spawning, or recruitment in New Zealand waters. This research aims to investigate the degree of association between two seamount-associated fish species, orange roughy (Hoplostethus atlanticus) and black oreo (Allocyttus niger), and reef-forming corals, using vector autoregressive spatio-temporal (VAST) models fitted to available monitoring data. To achieve this, we characterised the available data and single-species VAST models of data collected by observers onboard commercial bottom trawlers were selected. These models included the probability of encountering reef-forming coral as an environmental covariate. To allow for the inclusion of this environmental covariate, other (binomial) VAST models were developed and cross-validated for reef-forming coral, with alternative methods of addressing spatial bias in the coral data investigated as a secondary objective. Four models (i.e., four fish cases) were developed for orange roughy and black oreo during their spawning and non-spawning seasons. The VAST models for fishes were two-stage delta models that combined spatio-temporal estimations of probability of encounter and positive catch rate to predict fish spatio-temporal density patterns in New Zealand waters. These models accounted for unmeasured (latent) spatial and spatio-temporal variation, random vessel effects, and known environmental covariates. The effect of reef-forming coral on fishes was assessed by: (1) comparing the Akaike information criterion (AIC) of VAST models that did or did not include reef-forming coral as a covariate; (2) computing the percent deviance explained by reef-forming coral; (3) investigating the statistical significance of the reef-forming coral effect on fish probability of encounter and positive catch rate; and (4) analysing the marginal effect plots for the reef-forming coral covariate. Results indicated that AIC decreased for spawning orange roughy and non-spawning black oreo, which suggests that coral improved model parsimony in these two cases. However, the percent deviance explained by reef-forming coral was very small for the four cases considered. Across the four fish cases, reef-forming coral had a consistently significant effect on fish positive catch rates but not on fish probabilities of encounter. Ultimately, linkages between reef-forming coral and orange roughy and black oreo during their spawning and non-spawning seasons remain quite unclear as marginal effects plots often suggested no meaningful relationship and were associated with considerable uncertainty. This is not to say, however, that this dynamic does not exist, but rather that more targeted work is required in New Zealand to understand interactions between key fish species and their habitat.</strong></p>

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Catch per unit effort modelling for stock assessment: A summary of good practices

Cited by 14 publications

References 159 publications

Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

Coupling state‐of‐the‐art modelling tools for better informed Red List assessments of marine fishes

Using Spatio-Temporal Species Distribution Models to Explore the Effects of Reef-Forming Coral on Seamount-Associated Fishes in New Zealand

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