The effects of spatiotemporal scale on commercial fishery abundance index suitability,ICES Journal of Marine Science

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The effects of spatiotemporal scale on commercial fishery abundance index suitability
ICES Journal of Marine Science ( IF 3.1 ) Pub Date : 2021-06-17 , DOI: 10.1093/icesjms/fsab126
Jintao Wang _{1,

2,

3,

4,

5} , Robert Boenish ₆ , Xinjun Chen _{1,

2,

3,

4,

5} , Siquan Tian _{1,

2,

3,

4,

5} , JiangFeng Zhu _{1,

2,

3,

4,

5}

Affiliation

With consideration of sophisticated modern commercial fisheries, the commonly used metric catch per unit effort (CPUE) may not be a reasonable proxy for generating abundance indices (AIs) for all species. Presumably, spatiotemporal scale is a critical factor that affects the accuracy of local/aggregated AIs derived from spatial modelling approaches, thus it is necessary to evaluate how scale affects scientific estimates of abundance. We explored three commonly utilized AI proxies, including aggregated catch (CatchAI), aggregated effort (EffortAI), and CPUEAI from the perspective of accuracy and spatial representational ability using a neural network (NN) model at different spatiotemporal scales. As a case example, we grouped the Chinese fleet's Northwest Pacific neon flying squid (Ommastrephes bartramii) fishery dataset (2009–2018) at four spatial scales (0.25° × 0.25°, 0.5° × 0.5°, 1° × 1°, 2° × 2°) to construct monthly and annual resolution models. The results showed that for both simulated and real datasets, AIs based on catch data had better accuracy, consistency, and spatial representational ability compared to CPUE and effort-dependent AI models at all spatial scales. Relative to the finest spatial scale, only results from the model with 0.5° × 0.5° resolution preserved enough distributional detail to reflect the known migration route for O. bartramii. Model results exhibited large variation dependent on spatial scale, particularly amongst CPUEAI scenarios. We suggest that scale comparisons among potential proxies should be conducted prior to AIs being used for applications such as population trends in stock assessment.

更新日期：2021-06-17

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