Version 1
: Received: 3 August 2020 / Approved: 4 August 2020 / Online: 4 August 2020 (04:58:51 CEST)
How to cite:
Jardim, E.; Azevedo, M.; Brodziak, J.; N. Brooks, E.; F. Johnson, K.; Klibansky, N.; P. Millar, C.; Minto, C.; Mosqueira, I.; D.M. Nash, R.; Vasilakopoulos, P.; K. Wells, B. Operationalizing Ensemble Models for Scientific Advice to Fisheries Management. Preprints2020, 2020080078. https://doi.org/10.20944/preprints202008.0078.v1
Jardim, E.; Azevedo, M.; Brodziak, J.; N. Brooks, E.; F. Johnson, K.; Klibansky, N.; P. Millar, C.; Minto, C.; Mosqueira, I.; D.M. Nash, R.; Vasilakopoulos, P.; K. Wells, B. Operationalizing Ensemble Models for Scientific Advice to Fisheries Management. Preprints 2020, 2020080078. https://doi.org/10.20944/preprints202008.0078.v1
Jardim, E.; Azevedo, M.; Brodziak, J.; N. Brooks, E.; F. Johnson, K.; Klibansky, N.; P. Millar, C.; Minto, C.; Mosqueira, I.; D.M. Nash, R.; Vasilakopoulos, P.; K. Wells, B. Operationalizing Ensemble Models for Scientific Advice to Fisheries Management. Preprints2020, 2020080078. https://doi.org/10.20944/preprints202008.0078.v1
APA Style
Jardim, E., Azevedo, M., Brodziak, J., N. Brooks, E., F. Johnson, K., Klibansky, N., P. Millar, C., Minto, C., Mosqueira, I., D.M. Nash, R., Vasilakopoulos, P., & K. Wells, B. (2020). Operationalizing Ensemble Models for Scientific Advice to Fisheries Management. Preprints. https://doi.org/10.20944/preprints202008.0078.v1
Chicago/Turabian Style
Jardim, E., Paris Vasilakopoulos and Brian K. Wells. 2020 "Operationalizing Ensemble Models for Scientific Advice to Fisheries Management" Preprints. https://doi.org/10.20944/preprints202008.0078.v1
Abstract
There are uncertainties associated with every phase of the stock assessment process, ranging from the collection of data, assessment model choice, model assumptions and interpretation of risk to the implementation of management advice. The dynamics of fish populations are complex, and our incomplete understanding of those dynamics (and limited observations of important mechanisms) necessitate that models are simpler than nature. The aim is for the model to capture enough of the dynamics to accurately estimate trends and abundance and to provide advice to managers about sustainable harvests. The \textit{status quo} approach to assessment modelling has been to identify the `best' model, based on diagnostics and model selection criteria, and to generate advice from that model, mostly ignoring advice from other model configurations regardless of how closely they performed relative to the chosen model. We review the suitability of the ensemble modelling paradigm to more fully capture uncertainty in stock assessment model building and the provision of advice. We recommend further research to evaluate potential gains in modelling performance and advice from the use of ensemble modelling, while also suggesting revisions to the formal process for reviewing models and providing advice to management bodies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.