2 Course Information

2.1 Lectures Timing

Mondays and Wednesdays 9:00-10:50 am: In person in room FSH 213.

2.2 Office hours

Email instructors to set up meetings.

2.3 Credits

It is a 4-credit class with numerical grades. It is expected that students will work on assignments for about 8 hours per week. Given the limited in-class time, we expect active participation in all lectures and discussions.

2.4 Prerequisites

Basic statistics, statistical modeling, background in fisheries/wildlife science; if unclear of eligibility, please correspond with the instructor to obtain permission.

2.5 Specific Learning Goals

This class is intended to provide useful skills for your ongoing research.

Review types of fisheries-independent surveys and survey data products for ecosystem processes research, ecological studies, stock assessment, and forecasting within these disciplines.
Learn principles of sampling and survey designs, survey logistics and management, and survey data product estimation and application.
Analyze fishery-independent survey data using various statistical methods.
Plan research in survey science topics, e.g. uncertainty (observation error), survey continuity (catchability), effort optimization, flexible survey design, model-based estimators, simulations, statistical tools, and new technology.
Complete a research project using survey data.
Learn concepts and tools for long-term strategic planning of surveys, including adapting monitoring programs to our changing ecosystems as species distributions shift due to environmental change and ecological interactions.

2.6 Course Material

Course materials will be selected from journals, books, and other published scientific literature. These will be available as PDFs through the course google drive and listed in the lecture plan section below.

Lectures from each day’s class will be posted in the course google drive.

GitHub will be used in this course. If you have a user name you like us to reference or need help creating a GitHub username, please fill out this GitHub Username Collection google form.

Feedback makes this course better! Throughout the quarter, please let us know how we are doing and if we can answer any questions about the course so far. Use this Student Interest and Questions google form to anonymously or directly reach out to the instructors.

2.7 Course Format

Lectures: Lectures will take up approximately half of the in-class time. There will be two to three lectures per week given by instructors or visiting experts. Lectures will focus on a range of topics, described with examples from different survey programs around the world. Lecture slides will be made available on the course website for downloading and reviewing.

Literature review and discussion sections: Students (in groups of 1-3) will be responsible for presentations on relevant literature and leading subsequent discussions in class. Approximately one-quarter of in-class time will be used for these presentations and discussions. Presentations will include a summary of relevant scientific papers on a chosen survey-related topic, and all students will be expected to actively participate in discussions. A list of papers for student presentations and discussion will be provided by instructors, but students will be given the opportunity to propose a paper of their choice for presentations. The point of the discussion section is to read peer-reviewed literature and become familiar with current topics in survey science.

Survey data analysis: Each student will be responsible for one mid-course project to include survey data analysis on a data set of their choice (data sets from several actual surveys will be available, as needed). Analysis can involve estimation of standard design-based or model-based survey data products or could involve custom analysis for class projects.

Research plan and final paper: Half of the student’s grade is based on a final written research paper using survey data. Topics for the final paper will be proposed by students and will be presented for class discussion and feedback within the first 3 weeks of the quarter.

2.8 Grading

Students will be graded on 4 tasks:

1. Project plan - 1 page and 5 min presentation (10% weight)

DUE: Presentation January 14

Within the first days of the quarter, students will be responsible for planning a research project. Students can propose a project of their choice as long as the data for the project are from a fishery-independent survey. Project plans should be discussed with and accepted by instructors. Once accepted, students will be responsible for writing a 1-page project plan and for presenting the plan during the class. Instructors and students will provide feedback on the plan during the class discussion.

2. Literature review 20-30 min presentation on the survey topic (20%)

DUE: Presentation February 9

Students will be responsible for presentations on relevant literature and leading subsequent discussions. Literature review presentations will be conducted on Feb 9 or later, depending on the number of presentations. Papers for this literature review should be relevant to the final project. Students are advised to discuss potential papers for this review with instructors, but students will be given the opportunity to propose a paper(s) of their choice for presentations. The literature review presentation will be followed by a Q & A session and in-class discussion on the presented topics.

3. Survey data analysis (20%)

DUE: 16 February

Survey data analysis will involve estimation of standard design-based and model-based survey data products (from provided simulated “true distributions”) or could involve custom analysis of survey data used for class projects. The format of the analysis presentation will be open and can include an analysis description with graphs and/or tables. Analyses will be graded separately, but can be included as part of the final paper or as an independent document. Data analysis will be due at the end of week 6 of the course.

4. Final project - up to 5-8 pages and 20-30 min presentation (50%)

Due: Presentation March 4-11; Paper March 13

Final project results will be presented in the form of a 20-30 minute in-class slide presentation. Students will receive feedback from instructors, and time for in-class discussion will be provided. Presentations will occur during the last 2 weeks of the course. Final 5 - 8 page paper will be due at the end of week 10 and graded during the week of finals.

2.9 Grading Scale

Learn more about the UW grading scale.

Percent	GPA	Letter
≥95	4	A
94	3.9
93	3.8	A-
92	3.7
91	3.6
90	3.5
89	3.4	B+
88	3.3
87	3.2
86	3.1
85	3	B
84	2.9
83	2.8	B-
82	2.7	B-
81	2.6
80	2.5
79	2.4	C+
78	2.3
77	2.2
76	2.1
75	2	C
74	1.9
73	1.8
72	1.7
<72	1.6- 0.0	E

2.10 Lecture plan

Lectures from each day’s class will be posted in the course google drive.

Please fill out a class evaluation at the end of the term!

Week	Date	Lecture	Instructor	Description	Readings
1	Jan 5	1	Kotwicki (Wassermann)	Course outline and introductions. Overview of fisheries dependent and independent data collection, types of fisheries surveys, and other survey science topics. Describe potential class project. Provide examples. Guest Lectures: Susanne McDermott (AFSC), Denise McKelvey (AFSC)	Required: - Wakabayashi, Bakkala, & Alton. 1985. Methods of the U.S.-Japan demersal trawl surveys, p. 7-29. In R. G. Bakkala and K. Wakabayashi (editors), Results of cooperative U.S.-Japan groundfish investigations in the Bering Sea during May-August 1979. - Rago. 2005. Fishery independent sampling: survey techniques and data analysis. pp 201-215 in: Musick, J.A. and Bonfil R. (eds): Management techniques for elasmobranch fisheries. Optional: - Gunderson (1993). Surveys of fisheries resources. - National Research Council 2000. Improving the Collection, Management, and Use of Marine Fisheries Data. https://doi.org/10.17226/9969. - Hilborn & Walters (1992). Quantitative fisheries stock assessment: Choice, dynamics and uncertainty.
1	Jan 7	2	Kotwicki (Markowitz)	Overview of survey data products for ecosystem processes research, ecological studies, stock assessment, and forecasting. Guest Lectures: Kayla Ualesi (IPHC), Ebett Siddon (NOAA)	Required: - Lynch, Methot, & Link (eds.). 2018. Implementing a Next Generation Stock Assessment Enterprise. An Update to the NOAA Fisheries Stock Assessment Improvement Plan. - Read parts that refer to the survey data products and use of survey data in assessment - Cochran. 1977. Sampling techniques (3rd ed.) - Feel free to omit proofs. Pay attention to considerations when designing stratified random survey. - https://apps-afsc.fisheries.noaa.gov/Plan_Team/2022/assessments.htm Skim below to explore differences between design- and model-based estimation: - Berg, Nielsen, & Kristensen, 2014. Evaluation of alternative age-based methods for estimating relative abundance from survey data in relation to assessment models. - Thorson, Shelton, Ward, & Skaug. 2015. Geostatistical delta‐generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. - Thorson. 2018 Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Optional: - Somerton, Munro, & Weinberg (2007). Whole‐gear efficiency of a benthic survey trawl for flatfish. - Ailloud and Hoenig. 2019. A general theory of age–length keys: combining the forward and inverse keys to estimate age composition from incomplete data. - Ono, et al. 2015. The importance of length and age composition data in statistical age-structured models for marine species. - Dickson. 1993. Estimation of the capture efficiency of trawl gear. I: Development of a theoretical model. - Brodie et al. 2020. Trade‐offs in covariate selection for species distribution models: a methodological comparison. - Carroll et al. 2019. A review of methods for quantifying spatial predator–prey overlap. - Kotwicki & Lauth. 2013. Detecting temporal trends and environmentally‐driven changes in the spatial distribution of ground‐ fishes and crabs on the eastern Bering Sea shelf. https://doi. org/10.1016/j.dsr2.2013.03.017.
2	Jan 12	3	Hicks (Markowitz)	Design principles, sampling designs, logistics and estimation. Guest presentation. Case study for implementing survey design (Zack Oyafuso).	Optional: - Jason Conner, Stan Kotwicki, Kotaro Ono, and Lewis A.K. Barnett. 2025. The sensitivity of fisheries-independent survey indices to decisions of sampling design and intensity and the mitigation of biased precision estimators for systematic sampling. Canadian Journal of Fisheries and Aquatic Sciences. 82: 1-22. https://doi.org/10.1139/cjfas-2024-0405 - Cochran, W.G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons, New York - Gunderson, D.R. (1993). Surveys of Fisheries Resources. John Wiley & Sons, New York, NY, 248 pages - Hankin, D. G., Mohr, M. S., & Newman, K. B. (2019). Sampling theory: For the ecological and natural resource sciences. Oxford University Press. https://doi.org/10.1093/oso/9780198815792.001.0001. - Thompson, S.K. (2012) Sampling. 3rd Edition, Wiley, Hoboken. https://doi.org/10.1002/9781118162934 - Oyafuso, Z. S., Barnett, L. A. K., Siple, M. C., & Kotwicki, S. (2022). A flexible approach to optimizing the Gulf of Alaska groundfish bottom trawl survey design for abundance estimation. - Oyafuso, Z. S., Barnett, L. A., & Kotwicki, S. (2021). Incorporating spatiotemporal variability in multispecies survey design optimization addresses trade-offs in uncertainty. ICES Journal of Marine Science, 78(4), 1288-1300.
2	Jan 14	4	Barnett (Markowitz)	Student presentations on project plan: 5 minutes presentation, 5-10 minutes for discussion. Some general topics a. Design of a new survey from the ground up. b. Analysis of existing survey data (e.g. present survey data product and its uncertainty, use of survey data in assessments, role of uncertainty in assessment). c. Literature review of a survey topic under active research. Examples of literature overview. Opportunity for students to ask questions about class projects.	Install software for class using this installation guide: https://github.com/afsc-gap-products/UW-FISH572-coursework/blob/main/coursework/simulations/install_model_based_software.pdf
3	Jan 19		Martin Luther King Jr. Observed Holiday
3	Jan 21	5	Kotwicki (Markowitz)	Overview of current hot topics in survey science: Uncertainty (observation error), Survey continuity (flexibility and catchability), multispecies optimisation, flexible survey design, model-based estimation of survey data products, simulations, statistical tools and new technology. Combining surveys. Absolute indices of abundance (catchability). Changes in technology affect continuity but also improve estimates. Using observation from fishing vessels. Second part of the class will consist of discussion topics picked by students after the lecture. We will pick 4 topics of interest to students, prioritize them and discuss them in detail.	Required: - WGISDAA WKUSER 2022 planning document - Godø. 1994. Factors affecting the reliability of groundfish abundance estimates from bottom trawl surveys. In Fernö & Olsen (Eds.), Marine fish behaviour in capture and abundance estimation. - O’Leary, Thorson, Ianelli & Kotwicki. 2020. Adapting to climate‐driven distribution shifts using model‐based indices and age composition from multiple surveys in the walleye pollock (Gadus chalcogrammus) stock assessment. - Oyafuso, Barnett & Kotwicki. Incorporating spatiotemporal variability in multispecies survey design optimization addresses trade-offs in uncertainty. Skim over: - ICES. 2020. ICES Workshop on unavoidable survey effort reduction (WKUSER). http://doi.org/10.17895/ices.pub.7453 - ICES. 2023. ICES Workshop on unavoidable survey effort reduction 2 (WKUSER 2). (in press) - Kotwicki, Ianelli & Punt. 2014. Correcting density‐dependent effects in abundance estimates from bottom trawl surveys. https:// doi.org/10.1093/icesjms/fst208 Optional: - Jones, et al. 2021. Estimates of availability and catchability for select rockfish species based on acoustic-optic surveys in the Gulf of Alaska. - Rooper et al. 2020. Estimating habitat-specific abundance and behavior of several groundfishes using stationary stereo still cameras in the southern California Bight. - Kotwicki et al. 2018. Combining data from bottom trawl and acoustic surveys to estimate an index of abundance for semipelagic species. https://doi.org/10.1139/cjfas‐2016‐0362 - Walline. 2007. Geostatistical simulations of eastern Bering Sea walleye pollock spatial distributions, to estimate sampling precision. - Kilfoil et al, 2020. Using unmanned aerial vehicles and machine learning to improve sea cucumber density estimation in shallow habitats.
4	Jan 26	6	Hicks (Wassermann)	Examples of combining surveys into one platform and/or combining different data types across platforms to improve survey products. Combining acoustic/trawl data. Calibrating a longline survey to trawl survey observations. Main topics: changes to survey, calibration, scientific and legal process. Guest Lectures: Ray Webster, Sophia Wassermann
4	Jan 28	7	Hicks (Wassermann)	General considerations for using survey data in stock assessments and a few examples from recent assessments. Guest Lecture: Ian Stewart (IPHC)
5	Feb 2	8	Barnett (Markowitz)	Model-based approaches for standardizing both abundance and compositional data. Guest Lecture: Eric Ward (NWFSC)	Required: -Anderson, S. C., Ward, E. J., English, P. A., & Barnett, L. A. (2022). sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. BioRxiv, 2022-03. https://doi.org/10.1101/2022.03.24.485545 Optional: -Shelton, A.O., Thorson, J.T., Ward, E.J. and Feist, B.E., 2014. Spatial semiparametric models improve estimates of species abundance and distribution. Canadian Journal of Fisheries and Aquatic Sciences, 71(11), pp.1655-1666. -Thorson, J.T., Shelton, A.O., Ward, E.J. and Skaug, H.J., 2015. Geostatistical delta-generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. ICES Journal of Marine Science, 72(5), pp.1297-1310. -Maunder, M.N. and Punt, A.E. 2004. Standardizing Catch and Effort Data: A Review of Recent Approaches. Fisheries Research 70(2-3): 141-159. https://www.sciencedirect.com/science/article/abs/pii/S0165783604001638 -Thorson, J.T. and Haltuch, M.A., 2019. Spatiotemporal analysis of compositional data: increased precision and improved workflow using model-based inputs to stock assessment. Canadian Journal of Fisheries and Aquatic Sciences, 76(3), pp.401-414. -Commander CJC, Barnett LAK, Ward EJ, Anderson SC, Essington TE. The shadow model: how and why small choices in spatially explicit species distribution models affect predictions. PeerJ. 2022 Feb 14;10:e12783. doi: 10.7717/peerj.12783.
5	Feb 4	9	Barnett (Wassermann)	Student data simulation and analysis with design- and model-based approaches
6	Feb 9	10	Kotwicki (Markowitz)	Student Led Literature Review (20min/10min)
6	Feb 11	11	Kotwicki (Wassermann)	Explore total variance of a survey, sources of error, estimation methods, minimizing error, and sampling effort optimization. Survey design planning in a changing environment.	Required reading: Kotwicki, S. and Ono, K., 2019. The effect of random and density‐dependent variation in sampling efficiency on variance of abundance estimates from fishery surveys. Fish and Fisheries, 20:760-774. Skim through: Smith, S.J., 1997. Bootstrap confidence limits for groundfish trawl survey estimates of mean abundance. Canadian Journal of Fisheries and Aquatic Sciences, 54(3), pp.616-630. Berg, C.W., Nielsen, A. and Kristensen, K., 2014. Evaluation of alternative age-based methods forestimating relative abundance from survey data in relation to assessment models. Thorson, J. T., Shelton, A. O., Ward, E. J., & Skaug, H. J. (2015). Geostatistical delta‐generalized linear mixed models improve precision for estimated abundance indices for West Coast groundfishes. Thorson, J..T. 2019 Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fisheries Research, 2014, 143-161. Francis, C.R., Hurst, R.J. and Renwick, J.A., 2003. Quantifying annual variation in catchability for commercial and research fishing. Fishery Bulletin, 101(2), pp.293-304. Optional reading: Taylor Approximation and the Delta Method (wherever) Shao, J.,1989.The efficiency and consistency of approximations to the jackknife variance estimators. J.Am. Stat. Assoc. 84,114. Stewart, I.J. and Hamel, O.S., 2014. Bootstrapping of sample sizes for length-or age-composition data used in stock assessments. Canadian Journal of Fisheries and Aquatic Sciences, 71(4), pp.581-588.
7	Feb 16		President’s Day Observed Holiday	President’s Day Observed Holiday (Survey Data Analysis due)
7	Feb 18	12	Hicks (Markowitz)	Acoustic Techniques for Fishery and Ecosystem Surveys Guest Lecture: John Horne (UW/SAFS) Engaging Industry Guest Lecture: Sarah Webster, John Harms, Noelle Yochum, Dan Carney
8	Feb 23	13	Kotwicki (Markowitz)	International perspective Guests & panel discussion: Richard O’Driscoll (NIWA), Olav Rune Godø (IMR Norway - retired), Colm Lordan (ICES, ACOM chair)	Lipsky, A., Silva, A., Gilmour, F., Arjona, Y., Hogan, F., Lloret, J., Bolser, D., Haase, S., Oesterwind, D., ten Brink, T., Roach, M., & Ford, K. (2024). Fisheries independent surveys in a new era of offshore wind energy development. ICES Journal of Marine Science, 82(3). https://doi.org/10.1093/icesjms/fsae060 Liu, Y., Chen, Y., & Cheng, J. (2009). A comparative study of optimization methods and conventional methods for sampling design in fishery-independent surveys. ICES Journal of Marine Science, 66(9), 1873–1882. https://doi.org/10.1093/icesjms/fsp157 Maureaud, A. A., Frelat, R., Pécuchet, L., Shackell, N., Mérigot, B., Pinsky, M. L., et al. (2021). Are we ready to track climate-driven shifts in marine species across international boundaries? – A global survey of scientific bottom trawl data. Global Change Biology, 27(2), 220–236.
8	Feb 25	14	Barnett (Wassermann)	Advanced Technologies. Platforms, technologies and provision of data for stock assessment. Guests & panel discussion: Robert Levine (AFSC), Kresimir Williams (AFSC), Ole Shelton (NWFSC)
9	Mar 2	15	Kotwicki (Markowitz)	Future of surveys (Kotwicki)	WKUSER3 report
9	Mar 4	16	Barnett (Wassermann)	Student Presentations
10	Mar 9	17	Hicks (Markowitz)	Student Presentations
10	Mar 11	18	Kotwicki (Wassermann)	Student Presentations Class Evaluation URL: https://uw.iasystem.org/survey/268860

2.11 Academic integrity

Plagiarism, cheating, and other misconduct are serious violations of your contract as a student. We expect that you will know and follow the University’s policies on cheating and plagiarism. Any suspected cases of academic misconduct will be handled according to University regulations. More information can be found here.

For this course, plagiarism is defined as figures and legends that are identical or eerily similar to those of other students. You should absolutely work together, get advice and tips from other students, and help each other (this is the essence of being a successful and helpful scientist), but the final project must be your own work.

2.12 Religious accommodation policy

Washington state law requires that UW develop a policy for accommodation of student absences or significant hardship due to reasons of faith or conscience, or for organized religious activities. For more information, including instructions for requesting accommodations, see the UW Religious Accommodations Policy. Accommodations must be requested within the first two weeks of this course using the Religious Accommodations Request form.

2.13 Disability accommodations

To request academic accommodations due to a disability, please contact Disability Resources for Students: 448 Schmitz, (206)543-8924 (V/TTY). If you have a letter from Disability Resources for Students indicating that you have a disability which requires academic accommodations, please present the letter to the instructor so we can discuss the accommodations needed for this class.