Postdoctoral Associate in Quantitative Fisheries (Or Related Field)
Model selection in fisheries stock assessment models: can we disentangle observation and process uncertainty?
Fisheries stock assessment models are used to provide information to fishery managers about catch advice and ecosystem condition. State-space stock assessment models are the state-of-the-art methodology for this purpose and can simultaneously estimate observation and process uncertainty, quantities that traditionally had to be specified a priori in statistical catch-at-age frameworks. Although state-space assessment models have become increasingly popular in recent years, decisions regarding model structure (e.g., fishery selectivity, correlation in process uncertainty among ages or years) continue to rely on traditionally-used stock assessment model diagnostics (e.g., retrospective error, model residuals, etc.). Open questions remain, however, regarding whether traditional diagnostics guide analysts to correct decisions about stock assessment model structures in state-space settings. For example, state-space models have the ability to include many unobserved processes (e.g., survival, catch misreporting) that can greatly increase model fit at the cost of only 1-2 additional parameters. Commonly-used model selection criteria (e.g., AIC, likelihood ratio tests) have been shown to be unreliable in these cases because they do not take into account the high degree of flexibility of the unobserved processes (i.e., the random effects). Thus, common diagnostic tools and model selection techniques that are routinely applied to more traditional statistical catch-at-age models may be inappropriate for state-space models. Alternative diagnostics have been proposed elsewhere, primarily based on evaluating short-term prediction error, however these methods have not yet been evaluated in a stock assessment context. The objective of the study is to address the need for general guidelines on appropriate diagnostic tools and model selection techniques for state-space stock assessment models.
We are seeking a postdoctoral scientist to develop diagnostic tools for evaluating structural decisions when developing state-space stock assessment models. The research project will use an established operating model to generate simulated stock assessment datasets (e.g., fishery and survey catches) and test a variety of commonly-used and newly-developed diagnostic metrics. The goal will be to determine which diagnostics perform best at identifying the model with the lowest estimation error. The postdoctoral scientist will have the opportunity to develop their own diagnostic tests and apply the methods to real datasets, which will inform the 2023 State-space Assessment Methods Research Track (view NOAA’s Research Track page).
This is 2-year research opportunity; second year of funds contingent on federal budget.
The postdoc will be hosted by the NOAA Northeast Fisheries Science Center at the Woods Hole Laboratory in Woods Hole, MA (currently 100% remote due to COVID-19).
Minimum qualifications include:
- PhD in quantitative fisheries, statistics, applied mathematics, marine fisheries ecology, theoretical ecology, or a related field.
- Strong quantitative skills.
- Eligibility to work in the US.
- Experience in quantitative modeling, stock assessment, population dynamics, statistics, and computer programming (R, Template Model Builder, AD Model Builder).
Despite being an applied science project specifically within fisheries management, those with theoretical backgrounds or experience in fields that are seemingly removed from fisheries management (e.g., climate science, natural resource economics) are encouraged to apply.
The successful candidate will be motivated and capable of working independently and collaboratively. The successful candidate will be expected to give oral presentations at a range of scientific fora, as well as publish in peer reviewed written literature.
How To Apply
E-mail a cover letter describing your interest in the position, a CV, and the names and contact information of two references to Stacey Hansen (firstname.lastname@example.org). Inquiries regarding the position are also welcome. Review of candidates will continue until the position is filled.