Monitoring global fishing activity in proximity to seamounts using automatic identification systems

Kerry, Christopher R.; Exeter, Owen M.; Witt, Matthew J.

doi:10.1111/faf.12647

Cited by 11 publications

(8 citation statements)

References 161 publications

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“…However, choosing between different formulations for these drivers (i.e., depending on distance from the coast, d coast , or depth of the seafloor, z bot ) was only possible by testing plausible functional forms and retaining those leading to the largest improvements against empirical data. While this selection was not exhaustive, our final formulation is consistent with recent observations, such as the effect of seamount on fishing effort distributions (Kerry et al, 2022), and the historical deepening of fishing as technology progresses (Watson and Morato, 2013).…”

Section: Discussionsupporting

confidence: 61%

“…Commercially exploited fish often aggregate near seamounts and other shallow features, resulting in the local establishment of fisheries (e.g. 57% of longlining activity, Kerry et al (2022)). The coarse resolution of BOATS prevents a direct representation of seamounts.…”

Section: Heterogeneous Catchabilitymentioning

confidence: 99%

“…Here, q min = 0.8 is the minimum efficiency of gears targeting pelagic resources (see Appendix D), and x mean and x max respectively 2372 and 5750 m depth, based on the median and deepest depths of seamounts where fishing activity occurs (Kerry et al, 2022). The depth of the fishing grounds z bot is determined from ETOPO at 1/10th resolution (Amante and Eakins, 2009), coarsened by taking the shallowest depth in each 1 o model grid cell, as described above.…”

Section: Heterogeneous Catchabilitymentioning

confidence: 99%

“…Appendix C: Spatial variation of costs Appendix D: Spatial variation of catchability Technology coefficients varies between gears (Palomares and Pauly, 2019), and gears are predominantly used in separate regions of the global ocean (Kroodsma et al, 2018;Kerry et al, 2022). Ultimately this can lead to spatially heterogeneous catchability of fish resource.…”

Section: In μMmentioning

confidence: 99%

“…In parallel, recent studies have shed new light on large-scale aspects of global marine ecosystems and fisheries. Reconstructions of industrial fishing effort by Global Fishing Watch (GFW, Kroodsma et al (2018)) highlighted spatial variations in fishing costs (Sala et al, 2018) and revealed the importance of seamounts in concentrating fishing activity, especially for pelagic fisheries in the High Seas (Kerry et al, 2022). New reconstructions of fishing effort that include artisanal and industrial sectors provide more nuanced insights on the development of regional fisheries (Rousseau et al, 2019(Rousseau et al, , 2024.…”

mentioning

confidence: 99%

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BOATSv2: New ecological and economic features improve simulations of High Seas catch and effort

Guiet,

Bianchi,

Scherrer

et al. 2024

Preprint

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Abstract. Climate change and industrial fishing have profound effects on marine ecosystems. Numerical models that capture key features of fish biomass dynamics and its interaction with fishing can help assess the biogeochemical and socio-economic consequences of these impacts. However, these models have significant biases and do not include many processes known to be relevant. Here we describe an updated version of the BiOeconomic mArine Trophic Size-spectrum (BOATS) model for global fish and fisheries studies. The model incorporates new ecological and economic features designed to ameliorate prior biases. Recent improvements include reduction of fish growth rates in iron-limited high-nutrient low-chlorophyll regions, and the ability to simulate fisheries management. Novel features described here include a separation of pelagic and demersal fish communities to provide an expanded representation of ecological diversity, and spatially variable fishing costs and catchability for more realistic fishing effort dynamics. We also introduce a new set of observational diagnostics designed to evaluate the model beyond the boundary of large marine ecosystems. Following a multi-step parameter selection, the updated BOATSv2 model shows comparable performance to the original model in coastal ecosystems, accurately simulating catch, biomass and fishing effort. The revised model provides a markedly improved representation of fisheries in the High Seas, largely correcting the biases of the original version, including excessive high-sea catches and too rapid deepening of fishing effort over time. The updated model code is available for simulating both historical and future scenarios.

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Section: Discussionsupporting

confidence: 61%

Section: Heterogeneous Catchabilitymentioning

confidence: 99%

Section: Heterogeneous Catchabilitymentioning

confidence: 99%