Academic Journal

Numbers or mass? Comparison of two theoretically different stage-based stock assessment models and their ability to model simulated and real-life stocks

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Numbers or mass? Comparison of two theoretically different stage-based stock assessment models and their ability to model simulated and real-life stocks
Συγγραφείς: Luke Batts, Cóilín Minto, Hans Gerritsen, Deirdre Brophy
Πηγή: Canadian Journal of Fisheries and Aquatic Sciences. 79:1605-1624
Στοιχεία εκδότη: Canadian Science Publishing, 2022.
Έτος έκδοσης: 2022
Θεματικοί όροι: 0106 biological sciences, 14. Life underwater, 01 natural sciences
Περιγραφή: Stage-based assessment models are a type of fisheries stock assessment model that offer an alternative middle ground between aggregate and compositional models. We compare the capabilities of two theoretically different stage-based assessment approaches: an implementation of a biomass-based delay-difference model first described in a theoretical paper by Schnute in 1987, and an implementation of the well-known numbers-based two-stage model Catch-Survey Analysis (CSA). Models were tested within a simulation framework as well as on the real stock of white-bellied anglerfish ( Lophius piscatorius) in the Celtic Seas and Northern Bay of Biscay. For the simulated stocks, estimates from the biomass-based two-stage models were close to the true values in certain scenarios, but were sensitive to selectivity assumptions and configuration of growth within the model. CSA was more robust to selectivity assumptions, performing well in all simulated stock scenarios. Overall, results indicated that CSA was a robust stock assessment model but with relatively low precision, whereas the Schnute model was precise but required growth and mean fish weight data unaffected by selectivity.
Τύπος εγγράφου: Article
Γλώσσα: English
ISSN: 1205-7533
0706-652X
DOI: 10.1139/cjfas-2021-0213
Rights: URL: https://creativecommons.org/licenses/by/4.0/deed.en_GB
Αριθμός Καταχώρησης: edsair.doi...........acdffbc91cfbba1bbfb30b7a7fd342a1
Βάση Δεδομένων: OpenAIRE
Περιγραφή
ISSN:12057533
0706652X
DOI:10.1139/cjfas-2021-0213