phd thesis on fisheries management

School of Aquatic and Fishery Sciences

• College of the Environment
• University of Washington

MS and PhD in Aquatic and Fisheries Science

Graduate degrees.

The School of Aquatic and Fishery Sciences (SAFS) offers two graduate degrees with an optional additional credential in data sciences:

Master of Science in Aquatic and Fishery Sciences (MS)

The following courses are required for all students in the SAFS graduate program.

• QSCI 482 , Statistical Inference in Applied Research, 5 credits
• FISH Current Topics aka “5-TEENS” (FISH 510-514), minimum of 2 courses/4 credits , Current Topics series, 2-5 credits
• FISH 521 , Research Proposal Writing, 4 credits
• FISH 522 , Hot Topics in Aquatic and Fishery Sciences 2 credits

SAFS M.S. students need a minimum of 45 credits to graduate, which will include a combination of courses, seminars, and thesis credits.

More information: https://fish.uw.edu/students/advising/forms-guidelines-handbooks/

UW Graduate School Degree Policies & Procedures: https://grad.uw.edu/policies-procedures/

Doctor of Philosophy in Aquatic and Fishery Sciences (PhD)

SAFS PhD students need a minimum of 90 credits to graduate, which will include a combination of courses, seminars, and thesis credits.

Data Science Option in Aquatic and Fishery Sciences

The School of Aquatic and Fishery Sciences Data Science Option (SAFS DSO) curriculum at the Master’s (M.S.) and Doctorate (Ph. D.) levels is designed to provide the breadth and depth of knowledge needed for a successful career at the interface of applied aquatic sciences and data science. The primary learning outcome for the SAFS DSO is for students to have professional working proficiency – gained through coursework – in data management, data visualization, software engineering (data focused), and statistical modeling.

Students complete courses from three out of four of the following areas. Each area lists current courses offered within SAFS and other departments on the UW Seattle campus that will satisfy the requirement in that area.

A minimum of 11 credits is required as follows

• 9 credits from courses in 3 of 4 topic areas below
• 2 credits of eScience seminar – minimum 2 quarters, 1 credit each

The 11 total credits for the DSO may be counted toward the minimum of 45 credits required for the SAFS MS degree or the 90 credits for the SAFS PhD degree. The 11+ data science option credits are not explicit requirements of the standard SAFS MS or PhD program.

DATA SCIENCE TOPIC AREAS:

Software Development for Data Science

• FISH 549 (3cr) Introduction to Environmental Data Science ( FISH 497A, WIN2021 will also apply )
• CSE 583 (4cr) Software Development for Data Scientists
• CHEM E 546 (3cr) Software Engineering for Molecular Data Scientists
• AMATH 583 (5cr) High Performance Scientific Computing
• M E 574 (3cr) Introduction to Applied Parallel Computing for Engineers

Data Management and/or Data Visualization

• FISH 554 (2cr) Beautiful Graphics in R
• FISH 546 (3cr) Bioinformatics for Environmental Sciences
• CSE 412 (4cr) Introduction to Data Visualization
• CSE 414 (4cr) Introduction to Database Systems
• CSE 544 (4cr) Principles of Database Systems
• HCDE 411/511 (4cr) Information Visualization

Department-Specific Course Options Related to Data Science

• FISH 558 (5cr) Decision Analysis in Natural Resource Management
• FISH 559 (5cr) Numerical Computing for the Natural Resources
• FISH 576 (2-5cr) Applied Stock Assessment I
• FISH 577 (2-5cr) Applied Stock Assessment II
• GENOME 569 (1.5cr) Bioinformatics Workflows for High-Throughput Sequencing Experiment

Advanced Statistics and/or Statistical Modeling

• FISH 458 (5cr) Advanced Ecological Modeling
• FISH 556 (5cr) Spatio-temporal Models for Ecologists
• FISH 560 (4cr) Applied Multivariate Statistics for Ecologists
• QERM 514 (4cr) Analysis of Ecological and Environmental Data I
• FISH 550 (4cr) Applied Time Series Analysis
• FISH 551 (4cr) Data and Resource-limited Methods in Fisheries Management
• FISH 555 (4cr) Age-Structured Models in Fisheries Stock Assessment
• FISH 557 (4cr) Demographic Estimation & Modeling
• ATM S 552 (3cr) Objective Analysis
• AMATH 582 (5cr) Computational Methods for Data Analysis
• AMATH 563 (5cr) Inferring Structure of Complex Systems
• AMATH 515 (5cr) Optimization: Fundamentals and Applications
• CSE/STAT 416 (4cr) Introduction to Machine Learning
• STAT 435 (4cr) Introduction to Statistical Machine Learning
• CSE 546 (4cr) Machine Learning
• STAT 535 (3cr) Statistical Learning: Modeling, Prediction, and Computing
• M E/E E 578 (4cr) Convex Optimization
• M E 599 (1-5cr) Special Topics: Machine Learning Control
• CSE 599 (1-5cr) Special Topics: Deep Reinforcement Learning
• Genome 559 (3cr) Introduction to Statistical and Computational Genomics

Research-Focussed Program

Students often begin their research project in the first quarter. Required coursework is minimal to allow for a self-designed plan of study tailored to support the research project. Most MS students complete the program with at least one publication and most PhD students graduate with multiple publications. 

Faculty Adviser

Faculty review all applicants to the graduate program and offer admission to work in their lab. On occasion, an applicant might receive an offer of admission to two different labs and would therefore get to choose. Faculty track student progress and provide mentorship. The adviser assists the student in planning initial coursework and may aid the development of a research program. The faculty adviser usually becomes the chairperson of the student’s supervisory committee or may assist in finding another appropriate faculty member who can supervise the student’s research.

Our current admissions cycle is for an Autumn Quarter start, and we begin accepting applications each year on September 1. The application submission deadline is November 15 for both domestic and international applicants.

Applicants are encouraged to contact potential advisers in the Fall, and definitely no later than February. You can review the online faculty profiles to determine whether your research background, interests, and objectives fit with one (or more) of the SAFS faculty and contact them accordingly. You do not need to have a specific research project in mind when you apply. However, you should have a clear idea of the type of research that you’d like to pursue and which SAFS faculty members are best suited to supervise your prospective research.

Please learn more about the admission process.

View more information about the Graduate School’s admissions policies and procedures at UW Office of Graduate Admissions . Admission and enrollment statistics can be found at Graduate School Statistics and Reports .

Funding is a critical aspect of admission. All of our graduate students are funded through four possible avenues: 1. research or teaching assistantships, 2. employer support, 3. other forms of aid (e.g., governmental sponsorship) or 4. SAFS fellowships.  Please learn more about how graduate students are funded in our program.

Degree Options

We hold MS and PhD students to the same standards – both are valuable; therefore, we have a unique structure allowing students that do not already hold a masters to seamlessly expand their thesis to a PhD with committee support. If you do not already hold a master’s by the time you begin the program, we ask that you apply through the UW Graduate School’s MS application portal but note on your application your intent to complete a PhD. 

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Ph.D. Rangeland, Wildlife and Fisheries Management

Our Doctor of Philosophy degree is designed primarily for students pursuing an academic or research-based career in ecology and/or natural resource management, with an emphasis in wildlife, fisheries, rangelands, or human dimensions and policy. This program trains scientists broadly in the field of wildlife biology, fisheries ecology, population biology, wildlife conservation and related fields. We emphasize the discovery and dissemination of knowledge that enhances biodiversity conservation, informs natural resource management and promotes the sustainable use of natural resources.

Program Details

• Degree: Ph.D. in Rangeland, Wildlife and Fisheries Management
• Credit Hours: A minimum of 64 hours is required on the degree plan for the Doctor of Philosophy for a student who has completed a master’s degree. A student who has completed a DDS/DMD, DVM or a MD at a U.S. institution is also required to complete a minimum of 64 hours. A student who has completed a baccalaureate degree but not a master’s degree will be required to complete a 96-hour degree plan.

Graduate Admissions

Texas A&M University is a Tier 1 research institution and a premier choice when pursuing a graduate degree thanks to our national academic ranking and high financial value. Joining the Aggie Family and gaining an advanced degree from Texas A&M puts students in the highest class of job candidates.

International Students

Official TOEFL scores are required for international applicants. Official scores must be sent by ETS directly to Texas A&M University using the school code: 6003. Visit the Graduate and Professional School website for a list of requirements, exemptions and other useful information regarding the TOEFL/IELTS Exams.

Admissions Decisions

You will receive an admissions decision either in a letter from the Texas A&M University Office of Admissions or from the department.

Scholarships and Financial Aid

By submitting a  financial aid application  and a  scholarship application , students are reviewed for all the types of financial aid that they are eligible for. Remember: each student’s financial aid eligibility may vary. The types of financial aid students are offered is based on the data provided on their financial aid and/or scholarship application.

Lauren Johnson

Academic Advisor IV

(979) 845-5712

[email protected]

Texas A&M AgriLife Extension Service | Texas A&M AgriLife Research | Texas A&M Forest Service | Texas A&M AgriLife Veterinary Medical Diagnostic Lab | College of Agriculture & Life Sciences

ORIGINAL RESEARCH article

A case study in connecting fisheries management challenges with models and analysis to support ecosystem-based management in the california current ecosystem.

• 1 Fisheries Resources Division, Southwest Fisheries Science Center, NOAA Fisheries, La Jolla, CA, United States
• 2 Institute of Marine Sciences, University of California, Santa Cruz, Santa Cruz, CA, United States
• 3 West Coast Regional Office, NOAA Fisheries, Portland, OR, United States
• 4 Cooperative Oxford Laboratory, Office of Science and Technology, NOAA Fisheries, Oxford, MD, United States
• 5 Conservation Biology Division, Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA, United States
• 6 Fisheries Ecology Division, Southwest Fisheries Science Center, NOAA Fisheries, Santa Cruz, CA, United States
• 7 Fishery Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, NOAA Fisheries, Seattle, WA, United States
• 8 Environmental Research Division, Southwest Fisheries Science Center, NOAA Fisheries, Monterey, CA, United States
• 9 Independent Consultant, Olympia, WA, United States
• 10 Pacific Islands Fisheries Science Center, NOAA Fisheries, Honolulu, HI, United States
• 11 Physical Sciences Laboratory, NOAA Earth System Research Laboratory, Boulder, CO, United States

One of the significant challenges to using information and ideas generated through ecosystem models and analyses for ecosystem-based fisheries management is the disconnect between modeling and management needs. Here we present a case study from the U.S. West Coast, the stakeholder review of NOAA’s annual ecosystem status report for the California Current Ecosystem established by the Pacific Fisheries Management Council’s Fisheries Ecosystem Plan, showcasing a process to identify management priorities that require information from ecosystem models and analyses. We then assess potential ecosystem models and analyses that could help address the identified policy concerns. We screened stakeholder comments and found 17 comments highlighting the need for ecosystem-level synthesis. Policy needs for ecosystem science included: (1) assessment of how the environment affects productivity of target species to improve forecasts of biomass and reference points required for setting harvest limits, (2) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between target and protected species, (3) identification of trophic interactions to better assess tradeoffs in the management of forage species between the diet needs of dependent predators, the resilience of fishing communities, and maintenance of the forage species themselves, and (4) synthesis of how the environment affects efficiency and profitability in fishing communities, either directly via extreme events (e.g., storms) or indirectly via climate-driven changes in target species availability. We conclude by exemplifying an existing management process established on the U.S. West Coast that could be used to enable the structured, iterative, and interactive communication between managers, stakeholders, and modelers that is key to refining existing ecosystem models and analyses for management use.

Introduction

Fish stocks do not live isolated from, but exist as part of an ecosystem, and their dynamics are intrinsically related to those of their habitat, prey, and predators, from environmental conditions to humans. In recognition of the need to assess the cumulative of effects and trade-offs of fisheries management actions considering these ecological interactions there has been a longstanding worldwide push for ecosystem-based fisheries management (EBFM, May et al., 1979 ; Pikitch et al., 2004 ; Link, 2010 ; Fogarty, 2014 ; Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ; Fulton, 2021 ). In the United States, scientists have been exploring and coordinating the use of ecosystem models to address ocean ecosystem science and management questions for over a decade ( Townsend et al., 2008 , 2014 , 2017 ; Link et al., 2010 ). The National Oceanic and Atmospheric Administration (NOAA), the U.S. federal agency responsible for marine ecosystem science and ecosystem-based fisheries management in Federal waters, has prioritized ecosystem modeling as necessary to better assess the trade-offs we make to maintain resilient and productive ecosystems, and to respond to climate, habitat, and ecological change ( National Marine Fisheries Service, 2016a , b ). Nevertheless, progress, in the United States and elsewhere, in using ecosystem models and analysis to guide fishery decision-making has been slow ( Skern-Mauritzen et al., 2016 ; Townsend et al., 2019 ).

One of the significant challenges to using information and ideas generated through ecosystem modeling is a lack of connection between modeling and management priorities ( Link et al., 2012 ). Ecosystem modelers are not necessarily asking the same questions of their models as those asked by legal mandates or by managers implementing those mandates. This disconnect between scientific interest and management needs may contribute to the perceived slow pace in the uptake and implementation of ecosystem-based management ( Hilborn, 2011 ; Cowan et al., 2012 ; Marshall et al., 2018 ). Townsend et al. (2019) suggests that scientists can better understand and tune models to address management priorities by working more closely with managers, within existing processes to implement legal mandates.

Indeed, establishment of an effective scientists-decision makers knowledge exchange has been recognized as a major challenge to successful science-based management of complex socio-ecological systems ( Cvitanovic et al., 2015 ). Frameworks for facilitating the uptake of scientific research in natural resources management, such as the System Approach Framework (SAF, Hopkins et al., 2011 ), structured decision making approaches ( Gregory et al., 2012 ), and integrative assessments (see review by Mach and Field, 2017 ) stress that ongoing two-way exchange of information between scientists and decision makers and participatory communication methods are key to facilitate uptake of scientific analysis for management of complex systems ( Lidström and Johnson, 2020 ). Use of scientific knowledge in support of decision-making is dependent on such knowledge being perceived as salient to the decision-makers ( Cvitanovic et al., 2015 ; Mach and Field, 2017 ). Iterative dialogue between scientists, managers and stakeholders can ensure scientific analysis and models are relevant to the decision-making process ( Hopkins et al., 2011 ; Cvitanovic et al., 2015 ; Mach and Field, 2017 ).

There are also technical issues that can limit use of ecosystem models in decision making. These have been widely discussed elsewhere ( Skern-Mauritzen et al., 2016 , 2018 ; Holsman et al., 2017 ; Schuwirth et al., 2019 ), but we synthesize them here. There needs to be sufficient data to develop a basic mechanistic understanding of the system ( Skern-Mauritzen et al., 2016 ; Schuwirth et al., 2019 ) and few research programs exist to empirically quantify processes at this level of complexity ( Wells et al., 2020 ). Such data requirements become more difficult to meet with increasing complexity of the approach being considered ( Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ), which in turn comes at the cost of greater estimation uncertainty ( Link et al., 2011 ). This uncertainty needs to be quantifiable and factored into management decisions ( Holsman et al., 2017 ; Skern-Mauritzen et al., 2018 ; Schuwirth et al., 2019 ). For tactical management applications, predictive performance of ecosystem models also needs to be sufficient for the model to be useful ( Skern-Mauritzen et al., 2018 ; Schuwirth et al., 2019 ). Thus, increases in estimation uncertainty need to be balanced by reductions in process uncertainty to maintain adequate predictive performance ( Link et al., 2011 ). Model output also needs to be at an appropriate temporal and spatial resolution to inform management ( Schuwirth et al., 2019 ). These issues, however, should not prevent the use of ecosystem-based approaches to improve the status quo and meet the needs of decision-makers for scientific information that considers feedback and interactions between multiple ecosystem components ( Patrick and Link, 2015 ; Skern-Mauritzen et al., 2018 ). The most appropriate ecosystem model will necessarily vary in complexity depending on the policy issue and data availability, and guidelines exist to inform choice of analytical tool (e.g., Weijerman et al., 2015 ; Holsman et al., 2017 ).

In this paper, we demonstrate a practical process, based within the framework of national laws and on the practices identified by Townsend et al. (2019) , to better connect ecosystem models and analyses with fisheries management ( Figure 1 ). We define ecosystem models and analyses as a broad suite of analytical tools which incorporate interactions between physical, biological, and/or human components of the ecosystem, ranging from empirical approaches to end-to-end ecosystem models. Fisheries in the exclusive economic zone off the U.S. West Coast are managed under the advice of the Pacific Fishery Management Council (PFMC). The PFMC established a regular process through which new ecosystem initiatives are co-developed to address ideas and issues that affect multiple species and fisheries ( Pacific Fishery Management Council (PFMC), 2013 ). In doing so, this process provided an avenue for managers, stakeholders, and scientists to work together to find solutions to policy issues, the type of forum identified as necessary by Townsend et al. (2019) . Here, we use the second PFMC ecosystem initiative, the PFMC’s stakeholder review of ecosystem status indicators, to identify emerging fisheries policy issues in the U.S. West Coast that require ecosystem information. This process echoes the Issue Identification step in the SAF framework, in which a policy issue is identified in collaboration with stakeholders so that the analytical tool can be developed for the specific decision context defined with stakeholders ( Dinesen et al., 2019 ). We then connect the management questions to existing ecosystem models by specifying how their output could address some of the concerns raised by stakeholders and decision-makers about future trade-offs expected for living marine resource management in the California Current Ecosystem (CCE).

Figure 1. Overview of the process to facilitate integration of ecosystem models and analyses into fisheries management proposed by Townsend et al. (2019) on left and adaptation of that process to address multiple issues as presented in this paper on right.

Materials and Methods

California current ecosystem status reports.

California Current ESRs are developed annually by the NOAA California Current Integrated Ecosystem Assessment (CCIEA) team. These reports focus on biophysical, economic and social indicators related to attributes such as abundance and population condition of key species, community composition and energy/material flows, extent and condition of habitat, and fisheries engagement and social vulnerability in coastal communities. In 2017, the PFMC formalized a process for technical review of individual indicators and analyses ( Box 1 ) so that new topics for in-depth technical assessment are identified annually in March and then reviewed in detail in September ( Pacific Fishery Management Council (PFMC), 2017b ; Box 1 ).

BOX 1. Glossary of terms and acronyms related to the United States West Coast approach to Ecosystem Based Fisheries Management.

Pacific Fishery Management Council (PFMC, or Council) – Management entity established under the Magnuson-Stevens Fishery Conservation and Management Act (MSA) responsible for advising the federal government on managing fisheries within the exclusive economic zone (EEZ) off the United States West Coast. Develops fishery management plans (FMPs) and fishery regulations to implement the FMPs. Advised by stakeholders (U.S. states and tribes, commercial and recreational fisheries participants, environmental and other non-governmental organizations, and the public) through Advisory Subpanels ( https://www.pcouncil.org/documents/2019/09/cop-2.pdf/ ), assisted with monitoring and analyses by Technical/Management Teams ( https://www.pcouncil.org/documents/2019/09/cop-3.pdf/ ) and Workgroups ( https://www.pcouncil.org/documents/2019/09/cop-8.pdf/ ) (including the Ecosystem Workgroup, EWG), and provided scientific advice by the Scientific and Statistical Committee (SSC) ( https://www.pcouncil.org/documents/2019/09/cop-4.pdf/ .

Fishery Ecosystem Plan (FEP) ( https://www.pcouncil.org/documents/2013/07/fep_final.pdf/ ) – PFMC’s formalized approach to Ecosystem Based Fisheries Management (EBFM) . Includes a process through which the PFMC takes up ecosystem initiatives to address ideas and issues that affect multiple species and fisheries.

California Current Ecosystem Status Report (ESR) (E.g., https://www.pcouncil.org/documents/2020/02/g-1-a-iea-team-report-1.pdf/ ) – Annual report to the PFMC providing an ecosystem overview outside of focal resource stocks and populations, considering how outside factors influence focal resources, identifying linkages between different ecosystem components. Prepared by the NOAA California Current Integrated Ecosystem Assessment (CCIEA) ( https://www. integratedecosystemassessment.noaa.gov/regions/california-current-region/index.html ).

Stakeholder review – Review of policy, regulatory, or scientific product by stakeholders and members of the public. Process followed under the second ecosystem initiative ( https://www.pcouncil.org/actions/initiative-2-coordinated-ecosystem-indicator-review/ ) to review the reliability and utility of existing ESR indicators, and identify desirable additions. Involved Council, its advisory bodies, the SSC, and public comment. Technical review – Review of scientific or analytic product by the SSC or its subcommittees. For the ESR, technical review involves an annual process of topic selection by the Council in conjunction with its advisory bodies and the CCIEA, followed by reviews by the SSC’s Ecosystem Based Management Subcommittee.

Methodology review – In-depth technical reviews of methods that are held periodically and as needed. Reviewers include members of the SSC and often outside experts, and reviews follow specific Terms of Reference (TOR) (E.g., https://www.pcouncil.org/documents/2018/06/terms-of-reference-for-the-methodology-review-process-for-groundfish-and-coastal-pelagic-species-for-2019-2020-june-2018.pdf/ ) that may also reflect established Council Operating Procedures (COP) ( https://www.pcouncil.org/documents/2019/09/cop-15.pdf/ ). Required for changes to assessment methods or forecasts, and used for other complex topics as warranted.

Management Strategy Evaluation (MSE) – A process and modeling framework used to assess performance of management strategies given uncertainty relative to a set of predefined management objectives.

PFMC Initiative to Review Indicators

While technical reviews of the statistical analyses and models are useful, they do not provide incentives for broad stakeholder and manager participation in ESR development and refinement. In 2015, the PFMC addressed this shortcoming by proposing a new ecosystem initiative, the “Coordinated Ecosystem Indicator Review” ( Figure 2 ). This initiative outlined a stakeholder review process ( Box 1 ) to address four questions ( Pacific Fishery Management Council (PFMC), 2017a ): (1) What can the PFMC reasonably expect to learn from, or monitor with, the existing indicators in the ESR? (2) How well do the existing indicators accomplish their intent, and are any redundant? (3) Are there alternate indicators, information, or analyses that may perform better in context? and (4) Are there additional ecosystem indicators that could help inform PFMC decision-making?

Figure 2. Overview of opportunities for stakeholder feedback (orange boxes) on ESR indicators and ecosystem models and analyses in the PFMC management process.

In early 2016, the PFMC hosted a series of webinars to present the ESR indicators and discuss the four questions detailed above. Webinars were open to the public and were widely advertised by the PFMC in advance during their meetings, on their website, through their ∼1500 address email list, and through notice in participating government publications. The PFMC compiled all comments and recommendations raised during the discussion portions of the webinars. From March to September 2016, the PFMC also directly solicited feedback on the initiative’s four focal questions from its scientific and technical advisors and stakeholders. Between the live webinars and the solicitation to review the recordings of the webinars, the PFMC received 88 comments and recommendations from stakeholders and the public.

Using Public Process Results to Refine Ecosystem Modeling Planning

In this paper, we consider how the ideas generated in the initiative’s public review process might be used in ecosystem modeling planning. From the 88 comments and recommendations ( Pacific Fishery Management Council (PFMC), 2016a , b ) we selected only those that emphasize the need for ecosystem-level understanding , which acknowledges that ESRs should include not only status and trends of different indicators, but also a synthesis of how indicators interact and affect one another. Comments were characterized as belonging to the ecosystem-level understanding theme if they related to interactions between ecosystem components. The interactions considered were (1) interactions between species, (2) interactions between fishers and species, (3) impacts of abiotic components on species, and (4) impacts of abiotic components on fisheries. The authors found 17 comments that matched these criteria and thus were salient to the ecosystem-level understanding theme and could be addressed through greater inclusion of ecosystem model outputs in the ESR or in other reports to or conversations with the PFMC. These are reported in Table 1 . Per Townsend et al. (2019) , for each comment, we identified the relevant policy issue, management objectives, and the existing management process that would be used to address the problem ( Table 1 ). In the Results and Discussion section, we describe in more detail the ecosystem-information needs highlighted in the 17 comments and assess which EBFM modeling activities could contribute to resolving the management concerns. We connect the policy issues highlighted in the stakeholder comments to specific models and analyses in Table 2 . In Table 2 we present existing modeling products, but also highlight the additional modeling needs required to improve management utility. Here, EBFM modeling activities are defined broadly as those models and analyses used to assess interactions between physical, biological, and/or human components of the ecosystem. These tools include a variety of empirical approaches, species distribution models, biophysical models, climate-informed population dynamic models, multispecies models, food web models, and end-to-end ecosystem models.

Table 1. Comments from the stakeholder review of ESR Indicators that could be informed by ecosystem models and analyses with relevant policy issues, management objectives, and existing management processes to address them.

Table 2. Overview of existing or potential modeling products that could be developed to address the specified comments from managers and stakeholders.

Environmental Drivers of Biological Productivity

The first set of ecosystem-level understanding comments ( Table 1 , Comments 1–8) highlighted the need for improved scientific advice on how climate, physical oceanography and biogeochemistry indicators are related to biological productivity (i.e., recruitment, mortality, or growth of target and protected species). Comments 2 to 5 emphasized the requirement for improved quantification of how oceanographic processes, and in particular upwelling, affect species of management concern, such as salmon or groundfish. Comment 6 suggested, given the cumulative and potentially synergistic impacts of a variety of climate drivers on a species’ productivity, a need for a more in-depth synthesis of how environmental conditions interact to affect biological components. Comments 7 and 8 stress the need to also assess the utility of seabirds as indicators of forage or salmon productivity. Ultimately, as reflected in Comment 1, stakeholders are interested in anticipating the risk of an undesirable outcome and minimizing its impact, and thus need relevant indicators for forecasting and risk assessment.

This topic was associated with the highest number of comments ( Table 1 ), perhaps because productivity indicators can inform the setting of species-specific harvest levels, one of the main management measures used by PMFC. Harvest levels are often dependent on a forecast of stock biomass and on reference points (fishing intensity or biomass thresholds that should not be crossed or targets to be achieved) derived from stock assessments. Use of climate-linked natural mortality in stock assessment can generate less variable reference points on which to base catch advice ( O’Leary et al., 2019 ). If predictive skill is sufficient, the integration of environmental indicators of recruitment can also improve estimates of reference points ( Basson, 1999 ). In addition, short-term recruitment forecasts can enable managers to alert fishing communities of potential changes in harvest levels, allowing for development of potential remediation strategies ( Tommasi et al., 2017b ). However, the added benefit of including environmental indicators into the estimation of stock-status depends on the species’ life-history type ( Haltuch et al., 2019b ). Environmentally informed short-term recruitment forecasts are particularly important for semelparous species like salmon, as there is no direct carryover of spawning biomass across years, or for forage species whose fishable biomass consists in large part of young age classes ( Tommasi et al., 2017c ). Catch advice for long-lived stocks may instead be more responsive to changes in natural mortality ( Bax, 1998 ).

Usefulness of stock productivity indicators to management decisions is also dependent on their availability relative to the timing of council decision making. Some environmentally based forecasts of salmon returns are dependent on ocean conditions during first ocean entry 2 or 3 years prior (e.g., Rupp et al., 2012 ; Burke et al., 2013 ), and thus rely on past, observed environmental covariates. This has facilitated their inclusion in some management-relevant salmon forecasts ( Burke et al., 2013 ; Litz and Hughes, 2020 ). However, for other species and salmon stocks (e.g., Satterthwaite et al., 2020 ) key indicators may need to be forecasted months to a year in advance to improve catch advice or even longer to inform stock status projections (e.g., for groundfish). Thanks to recent advancements in global climate prediction systems, forecasts of biologically relevant variables in coastal regions months to years in advance can be skillful in some regions ( Stock et al., 2015 ; Tommasi et al., 2017a , b ; Hervieux et al., 2019 ; Jacox et al., 2019a ; Park et al., 2019 ; Jacox et al., 2020 ). Integration of such forecasts with environmentally informed single species population dynamics models can enable managers to set more effective catch limits, but their utility will be dependent on how well management needs align with the regions and times with adequate forecast skill. For example, sea surface temperature (SST) forecast skill in the CCE is variable in space (with highest skill in more northern latitudes) and time (with highest skill for late winter and early spring forecasts) ( Jacox et al., 2019a ). In an evaluation of management performance for sardine, ecological and economic metrics were improved by SST forecasts only up to 4 months in advance, as forecast skill degraded at longer lead times ( Tommasi et al., 2017c ). This 4-month lead time may not be sufficient for inclusion of such approaches into current CPS management timelines ( Tommasi et al., 2017c ), but other applications may be able to leverage greater predictability for different seasons, lead times, regions, or environmental variables.

Use of environmental indicators of stock productivity to inform tactical decisions (e.g., catch advice) require adequate process understanding of the environment-species response, long time series of both biological and environmental variables at appropriate spatial and temporal scales, a strong effect of the environmental covariate on stock dynamics, and the ability to monitor and skillfully forecast the indicator ( Skern-Mauritzen et al., 2018 ; Haltuch et al., 2019b ). Improvements of management performance with the use of climate-enhanced stock assessments and environmental covariate-based harvest control rules (HCRs, e.g., Howell et al., 2021 ) as compared to other methods needs to be carefully evaluated with management strategy evaluation ( Haltuch et al., 2019b ). In some cases, application of survey-derived recruitment indicators may be more appropriate ( Walters and Collie, 1988 ). Nevertheless, since the stakeholder review of ESR indicators, some analyses and models that speak to the needs highlighted in Comments 1–8 have been developed and used to inform PFMC management decisions ( Table 2 ). We highlight those examples below and then discuss future research avenues.

For salmon, in 2017, the Council’s advisory bodies expressed concern about increasing variability in salmon escapements and worsening performance of forecasts ( Pacific Fishery Management Council (PFMC), 2017c ). This, along with earlier calls to investigate potential threshold values in indicators reported in the ESR ( Pacific Fishery Management Council (PFMC), 2015b ), prompted Satterthwaite et al. (2020) to investigate non-linear relationships between environmental covariates and forecast performance for Chinook salmon stocks of particular management concern. While mechanistic drivers of salmon demographic rates need to be investigated further before direct inclusion into pre-season forecast models, the work demonstrates that environmental indicators could be used indirectly to alert managers that forecast performance may be poor and that a precautionary approach may be warranted ( Satterthwaite et al., 2020 ).

Similar correlative approaches also inform the PFMC’s environmentally driven exploitation rates in the HCR for Pacific sardine ( Sardinops sagax , Pacific Fishery Management Council (PFMC), 2020 ), based on the recognition that the spawner-recruit relationship barely extended above the replacement line during cool periods, while indicating substantial compensation (surplus production) during warm periods ( Jacobson and MacCall, 1995 ). In this case, rather than the environmental indicator being included directly in the stock assessment to inform a short-term forecast of fishable biomass, an age-structured population dynamics model with an environment-recruitment link was first used to determine how the fishing mortality reference point depends on a temperature indicator, and then a management strategy evaluation (MSE) was employed to compare performance of different types of harvest control rules and potential environmental indicators ( Hurtado-Ferro and Punt, 2014 ). Temperature-dependent fishing mortality target reference points are also utilized for tactical management of cod ( Gadus morhua ) and whiting ( Merlangius merlangus ) in the Celtic Sea ( Howell et al., 2021 ).

A relationship between sea level and recruitment has also been identified and included in the 2019 assessment for sablefish ( Anoplopoma fimbria , Haltuch et al., 2019c ). However, inclusion of the environmental indicator in the stock assessment did not influence assessment output as it was consistent with survey length and compositions ( Haltuch et al., 2019c ). For long-lived species like groundfish that recruit into the fishery at older ages and for which recent recruits make up a smaller fraction of the biomass, a short-term forecast (sub-annual to annual) of fishable biomass is largely informed by the observed fishery and survey data. A short-term recruitment forecast may therefore not substantially improve short-term biomass forecast skill and derived management measures. For this life history type, environmentally informed recruitment forecasts may inform longer-term (2 years onward) projections of stock biomass or reference points. To date, projections have not considered environmental conditions; however, it may be beneficial to do so in cases like sablefish, for which an environment-recruitment relationship has been established. However, the environmental covariate would need to be forecasted with adequate skill.

At PFMC, efforts to improve our understanding of drivers of species productivity and the performance of biomass forecasts and projections will continue in the future and are of interest to managers and stakeholders ( Table 1 , comments 1–8). There are numerous avenues by which ecosystem science could contribute, which are highlighted below and in Table 2 . Improvements to ecosystem indicator development, such as the use of multivariate statistical techniques that reduce the dimensionality of a large set of covariates with minimal information loss, could refine inputs to existing environmentally driven forecasts (e.g., Rupp et al., 2012 ; Burke et al., 2013 ; Muhling et al., 2018 ). Exploratory statistical analyses based on improved ecological understanding of species interactions (e.g., trophic relationships) and employing a variety of data sources can also inform development of new productivity indicators (e.g., Tolimieri et al., 2018 ). Wells et al. (2017) , examining seabird diet and forage survey data, demonstrated that salmon survival decreases when common murre ( Uria aalge ) switch from foraging juvenile rockfish ( Sebastes spp. ) offshore to anchovy inshore following changes in upwelling. Where seabird data exists, such an analysis could be extended, as suggested by Comment 8, to inform development of indicators for salmon stocks in the Northern CCE. Similar statistical models could be used to identify ecosystem indicators, such as seabird abundance or reproductive success, that relate to forage fish abundance (Comment 7).

Ecosystem models capturing the mechanistic processes leading to changes in demographic rates are also a promising tool to develop indicators to inform forecasts. For instance, processes occurring during the critical early ocean entry period have long been thought to be a major driver of overall cohort abundance for salmon ( Pearcy, 1992 ; Beamish and Mahnken, 2001 ). Fiechter et al. (2015) developed a spatially explicit bioenergetics model of salmon linked to a configuration of the Regional Ocean Modeling System (ROMS) with biogeochemistry, and an index of juvenile salmon growth potential derived from this model was capable of describing a large proportion of variation in cohort strength ( Henderson et al., 2019 ). The ROMS-informed bioenergetic model enabled synthesis of how oceanographic indicators (including krill concentration) affect juvenile growth potential ( Fiechter et al., 2015 ). Then, a multivariate statistical technique was used to summarize the spatial variation in growth across years to inform a regression model of Central Valley Chinook salmon survival ( Henderson et al., 2019 ). This model could now provide projections of juvenile survival informing pre-season forecasts of Central Valley Chinook returns, and a similar approach could be expanded to other stocks and species.

It could also be fruitful for ecosystem modelers to turn their attention to factors operating later in the life cycle that could influence growth, maturation rates or mortality. For salmon, improved estimation of maturation rates and mortality can inform forecasts based on sibling regressions where the returns of younger age classes in the previous year are used to forecast returns of older ages from the same cohort in forecast years ( Peterman, 1982 ), as well as projections of future fishable biomass. Indeed, integration of an environmentally informed mortality parameterization in a population dynamics model of summer flounder ( Paralichthys dentatus ) in the U.S. East Coast resulted in improved biomass estimates ( O’Leary et al., 2018 ).

Development of the Henderson et al., 2019 ecosystem model and derived salmon productivity indicators were facilitated by advancements in ocean modeling of the CCE. Assimilation of observational oceanographic data with ROMS has now enabled the development of a fine-scale reconstruction of physical ocean conditions going back to 1980 ( Neveu et al., 2016 ) 1 . These capabilities were also essential for the development of key indicators of rockfish recruitment ( Schroeder et al., 2018 ), sablefish recruitment ( Tolimieri et al., 2018 ), petrale sole ( Eopsetta jordani ) recruitment ( Haltuch et al., 2020 ), and new indices of upwelling ( Jacox et al., 2018 ) or upwelling habitat compression ( Santora et al., 2020 ) that may be relevant to target and protected species.

However, as evidenced by Pacific sardine ( McClatchie et al., 2010 ; Jacobson and McClatchie, 2013 ; Zwolinski and Demer, 2019 ), correlative relationships can break down over time ( Myers, 1998 ). Thus, an adaptive process enabling regular re-evaluation of the relationships between environmental indicators and fish productivity needs to be in place if they are to inform management ( Skern-Mauritzen et al., 2016 ). For salmon, the PFMC has established processes for annual technical review of proposed changes to forecast methodology ( Pacific Fishery Management Council (PFMC), 2008 ). The existing ESR indicator technical review process could enable an annual re-evaluation of the correlative relationships between stocks and their environment and allow for periodic refinements to the oceanographic, ecosystem and statistical models used to estimate species responses to the environment. Predictions derived from ecosystem-based models (e.g., Henderson et al., 2019 ) might be considered as competing models of existing approaches, and the ESR process could also provide a platform where different approaches are discussed, compared, and potentially integrated in a forecast ensemble, as is regularly done in weather and climate forecasting ( Kirtman et al., 2014 ; Bauer et al., 2015 ).

Species Distributions and Their Overlap

The second set of ecosystem-level understanding comments (9 and 10), reflects the management need for more spatial distribution information to minimize the risk of interactions between fisheries and protected species, thereby increasing opportunities to fish for the target species ( Table 1 ). This information need has become particularly critical in recent years, as populations of protected predators (e.g., sea lions) in the CCE recover, increasing the potential for overlap with fisheries ( McClatchie et al., 2018 ). In addition, Comment 10 highlights the need to assess the links between changes in prey availability over space and predator distribution ( Table 1 ). Understanding spatiotemporal overlap between predators and potential prey species is particularly important for the development of a more ecosystem-focused approach to fisheries management ( Carroll et al., 2019 ; Link et al., 2020 ). A number of negative ecological and economic events occurring within the CCE in recent years, including, but not limited to, unusual mortality events for sea lions and seabirds ( Wells et al., 2013 ), and unprecedented whale entanglements ( Santora et al., 2020 ), were the result of changes in predator distribution linked to changes in forage availability and unprecedented environmental conditions. These incidents served to highlight the need for spatial tools mapping changes in species overlap in response to changes in environmental conditions.

Species Distribution Models (SDMs) are a common tool used to describe the distribution of species, often in relation to their environment, or in relation to space and time covariates that act as proxies for unobserved processes. These geostatistical models allow for the inclusion of multiple predictors and are flexible enough to capture complex or non-linear relationships between a species and its environment ( Guisan and Zimmermann, 2000 ; Elith and Leathwick, 2009 ; Norberg et al., 2019 ). SDMs developed using long observational time series can be used to describe the typical distributions of species. As such they have the potential to highlight anomalous changes in species distributions as a function of environmental change and to examine or anticipate how environmental conditions cause variability in species associations ( Carroll et al., 2019 ; Table 2 ), making them useful tools to address Comments 9 and 10. Indeed, SDMs have been applied in various management contexts worldwide although predominantly in terrestrial systems. SDMs can be used to assess historical or climatological distributions ( Valinia et al., 2014 ), dynamic distributions ( Stanton et al., 2012 ), or predict how species distributions will change over multiple forecast horizons, from short-term forecasts ( Payne et al., 2017 ) to climate change projections ( Briscoe et al., 2016 ). A spatiotemporal mixed-effects model (vector autoregressive spatiotemporal model) has become an important SDM for fisheries scientists who seek to develop accurate historical indices of abundance for use in stock assessment ( Thorson, 2019b ). SDMs have also been used to produce climatological prediction maps of marine mammals to assess risk from sonar operations ( Forney et al., 2012 ; Roberts et al., 2016 ; Robinson et al., 2017 ), to describe temperature-driven interannual variability in the distribution of Pacific hake ( Merluccius productus ) in the context of its joint management by the United States and Canada ( Malick et al., 2020a ), for spatial management planning (e.g., Leathwick et al., 2008 ; Valavanis et al., 2008 ; Esselman and Allan, 2011 ; Smith et al., 2020 ), and climate change impact assessments (e.g., Hazen et al., 2013 ; Kleisner et al., 2017 ). Of particular interest to managers is the use of SDMs to minimize interactions between fisheries and protected species or vulnerable life stages (e.g., Hobday et al., 2011 ; Howell et al., 2015 ; Lewison et al., 2015 and references therein, Druon et al., 2015 ; Little et al., 2015 ; Hazen et al., 2018 ).

Most SDMs have been applied in a historical context, to describe and understand drivers of past changes in species distribution and their overlap. An increasing number of studies are also using SDMs for climate change applications (e.g., Shelton et al., 2020 ), but use of SDMs to anticipate short- to medium-term (days to years) changes in species availability has only recently begun to receive attention (e.g., Kaplan et al., 2016 ; Thorson, 2019a ) despite the need for such products (Comment 9). Model-based distribution forecasts have been used to reduce unintended catch of southern bluefin tuna in the East Australia Current ( Hobday et al., 2010 ) and to explore reducing seabird interactions in the North Pacific Transition Zone ( Žydelis et al., 2011 ). Since the stakeholder review of ESR indicators, pioneering applications have also been developed for the CCE that address Comments 9 and 10, as outlined below and in Table 2 .

SDMs and satellite data or ocean model output are providing near-real time likelihoods of ship strike risk for blue whales ( Balaenoptera musculus ) in the California Current ( Hazen et al., 2017 ; Abrahms et al., 2019 ), and the ratio of catch to bycatch of protected species in the California swordfish fishery ( Brodie et al., 2018 ; Hazen et al., 2018 ; Welch et al., 2019 ; Table 2 ). The latter example, termed EcoCast, integrates predictions of habitat suitability for a target species (swordfish, Xiphias gladius ) and multiple bycatch species (blue sharks, Prionace glauca ; leatherback turtles, Dermochelys coriacea ; and California sea lions, Zalophus californianus ) to provide an integrated map of opportunity and risk. This tool is now fully operational ( Welch et al., 2019 ), providing daily predictions for use by fishery managers and fishers when deciding where to fish or adjust management regulations. (Operational, here, and throughout the paper is defined as in Welch et al. (2019) , “self-contained workflows that run automatically at a prescribed temporal frequency”). In the northern CCE, the J-SCOPE project uses a ROMS model with biogeochemistry and provides twice-annual seasonal forecasts that have shown skill for physical and biochemical conditions, including hypoxia, at lead times up to ∼4 months ( Siedlecki et al., 2016 ), and these are being used to forecast Pacific hake and sardine distributions and migration ( Kaplan et al., 2016 ; Malick et al., 2020b ) and inform the ESR ( Harvey et al., 2019 ).

Several recent advancements may allow for further development of SDMs to anticipate changes in species distributions and their overlap in the CCE at longer lead times (1–12 months), and thus expand their relevance for management applications (Comments 9 and 10). Advancements include improvements in the availability of output from global climate prediction systems at lead times up to a year, the configuration of regional ocean models to downscale such predictions for the CCE, and the implementation of SDMs that use ocean model fields as input (e.g., Brodie et al., 2018 ). Indeed, decision support tools at these longer lead times have been used to model the distribution of target and bycatch species in Australian fisheries up to 4 months in advance using output from global climate prediction systems ( Hobday et al., 2011 ; Eveson et al., 2015 ).

Continued development of such products would require further interactions between PFMC managers, stakeholders, and scientists to determine species, regions, and timeframes of interest, and to ensure that physical and ecological forecast skill aligns with management needs. The ESR technical review that has created opportunities to begin those discussions may continue to provide a forum moving forward. Predictions of extreme events may be of particular interest to managers, and several steps must be taken to evaluate whether such predictions can be useful. For example, temperature anomalies were predictable for some but not all periods of the persistent 2014–2016 CCE heatwave ( Jacox et al., 2019b ), and the ability of SDMs to capture species distribution shifts under these novel conditions, even with perfect environmental data, differs by SDM model type and species ( Becker et al., 2020 ; Muhling et al., 2020 ). Thus, more work is required to assess whether SDM forecast skill is adequate for management applications, as skillful forecasting of species distribution changes requires that both environmental conditions and species responses to those environmental changes are accurately predicted. This research will include the determination of which SDM architectures are best suited to anticipate changes in species distributions over the timescales most relevant to managers.

Trophic Interactions and Management Trade-Offs

Comments 11 and 12 highlight the need for ecosystem synthesis to examine the tradeoffs between protection of dependent predators, sustainability of fish populations, including both forage and the higher trophic level target species feeding on them, and the resilience of fishing communities ( Table 1 ). It is becoming apparent that trophic cascades resulting from variability in forage can have substantial and surprising consequences on coastal communities on the U.S. West Coast ( Wells et al., 2017 ; Santora et al., 2020 ). Therefore, in addressing Comments 11 and 12 ( Table 1 ), modeling frameworks enabling a broad approach to evaluating tradeoffs should be considered. Below, we focus on tools well suited to address these tradeoffs: end-to-end models, management strategy evaluation, and spatial modeling, and highlight specific examples of their application in the CCE to inform management issues. These examples are also reported in Table 2 and avenues for further research are discussed.

In the CCE, fishery managers must weigh the provision of adequate forage for dependent species against the importance of the CPS (sardine, squid, anchovy and mackerel) fishery to West Coast communities, while also safeguarding the forage species themselves. This balancing act is not unique to this region, and tradeoffs between forage fish harvest and predators have long been the focus of global analyses, modeling, and task forces (e.g., Cury et al., 2011 ; Smith et al., 2011 ; Pikitch et al., 2012 ). The need for consideration of trophic interactions in the management of CCE CPS fisheries was recognized early on by the PFMC, with the earliest information on trophic interactions informing management advice being derived from simple correlative relationships. For example, the first FMP passed by the PFMC, the 1978 Northern Anchovy FMP, included a cutoff parameter below which large-scale harvest was not allowed to provide adequate forage for brown pelicans ( Anderson et al., 1980 ). Information about similar trophic relationships were instrumental in the PFMC’s decision in the early 2000s to reduce the Allowable Biological Catch of shortbelly rockfish, a previously non-targeted species, based on the significance of pelagic juvenile shortbelly rockfish to seabirds, salmon and other higher trophic level predators.

End-to-end ecosystem models like Atlantis ( Fulton et al., 2011 ) and Ecopath with Ecosim ( Christensen and Walters, 2004 ), which model the entire food-web from plankton to top predators, can be used to assess the bottom-up effects of increased removals of forage fish on piscivorous fish species and protected species, such as marine mammals and seabirds, that depend on forage fish as prey, as well as the top-down impacts of increasing predator biomass on forage fish ( Table 2 ). Given the long-standing objective of ensuring adequate forage for predators in the CPS FMP, and consistent with the 1998 Ecosystem Principles Advisory Panel ( Ecosystem Principles Advisory Panel (EPAP), 1999 ), end-to-end ecosystem models became increasingly important to PFMC CPS management efforts during the early 2000s. For example, later amendments to the CPS FMP addressing krill management in the CCE were informed by both empirical data and insights from mass balance ecosystem models ( Field et al., 2006 ). Confronting the management needs with the limitations of both the data and the models was helpful in this effort, as a key outcome was the recognition that the apparent high consumption of krill by key predators was often inconsistent with (considerably greater than) the estimates of krill abundance and productivity ( Pacific Fishery Management Council (PFMC), 2009 ). Although the reasons for this inconsistency remain unknown, this limitation informed the decision to ultimately prohibit a directed CCE krill fishery in the absence of improved information for management.

Recognizing that models that include ecosystem processes and interactions are key to better informing the tradeoffs between forage needs and fisheries, among other things, the PFMC asked for a methodology review ( Box 1 ) of the California Current Atlantis model in June 2014 ( Pacific Fishery Management Council (PFMC), 2014 ; Kaplan and Marshall, 2016 ). The review process served as a platform to provide feedback on improvements required to increase utility of the model to management. For instance, the review noted that many of the management scenarios integrated in the CCE Atlantis model up to that point in time were not well aligned with specific PFMC management needs. Following the Atlantis methodology review and stakeholder review of ESR indicators, end-to-end ecosystem models for the CCE continue to be refined and have been used to evaluate long-term trophic impacts of U.S. West Coast groundfish fisheries ( Pacific Fishery Management Council (PFMC), 2015a ) and to assess the impacts of depleted forage species on predators ( Koehn et al., 2016 ; Kaplan et al., 2017 , 2019 ). The CCE Atlantis model can also be used to simulate the risks of climate-driven changes in the ocean environment, such as upwelling (Comments 3 and 4) and ocean acidification (Comment 13) on the CCE food-web, and it was linked to downscaled global climate projections to evaluate how the impacts of ocean acidification on benthic invertebrates may propagate through the CCE food-web and its fisheries ( Marshall et al., 2017 ; Table 2 ). Clearly, end-to-end ecosystem models including species interactions and environmental drivers can potentially be further applied to assess tradeoffs between the ecosystem and economic impacts of management decisions ( Table 2 ), but continued dialogue between managers and modelers is required to further tailor ecosystem models to answer management needs.

MSE has been widely used in fisheries management to highlight tradeoffs associated with alternative management actions, and to identify procedures that are robust to uncertainty ( Punt et al., 2016a ; ICES, 2021 ). MSE is therefore also an important tool to address Comments 11 and 12, related to how harvest rules for forage species may affect dependent predators, and vice versa. In simpler “one way” or bottom-up cases, an ecosystem model can be used to trace impacts of harvest rules on forage fish populations and fishery yields, and subsequently on predators. This was done in a recent herring MSE on the U.S. East Coast ( Deroba et al., 2019 ; Feeney et al., 2019 ). Complex “two way” cases trace top-down impacts of predators on forage fish as well as bottom-up impacts on predators and are of interest when investigating multispecies harvest control rules or ecosystem reference points need to be tested. Kaplan et al. (2021) discuss additional applications of ecosystem models within MSE, including as operating models, and to simulate monitoring, assessment, and harvest control rules.

In one MSE example from the CCE, Punt et al. (2016b) applied a models of intermediate complexity for ecosystem assessments (MICE, Plagányi et al., 2014 ) rather than an end-to-end ecosystem model to assess the impact of CPS harvest rules on dependent predators. MICE typically simulate prey-predator interactions, but on a smaller set of ecosystem components. These simpler multispecies models are useful for answering targeted management questions relative to a specific policy concern. Their lower complexity allows, as in stock assessment, for parameter estimation based on fits to data, and uncertainty quantification, making them well suited for MSEs, and more readily understood by management bodies familiar with stock assessment models. The Punt et al. (2016a) MSE examined the links between the forage base and higher trophic level species (Comment 11), specifically the links between the population dynamics of sardine and anchovy and of two protected predator species, brown pelican ( Pelecanus occidentalis ) and California sea lion. The MSE was able to assess the tradeoffs between fishing on CPS and protection of predators (Comment 12, Table 2 ) by testing performance of current harvest policies for sardine with respect to both fishery and conservation goals. The MICE was developed in parallel with the Atlantis and Ecopath models mentioned above, facilitating model development, comparison and engagement with managers ( Francis et al., 2018 ; Kaplan et al., 2019 ). Unlike the Ecopath or Atlantis applications, this MICE was able to quantify the performance of realistic management measures (including reproductive success and survival of protected species) while considering uncertainty in environmentally driven recruitment scenarios for sardine and anchovies as well as structural uncertainty regarding predator dependence on forage ( Punt et al., 2016b ).

Linking changes in the availability of forage species to higher trophic levels within particular geographic areas, the need highlighted by Comment 11, requires spatially explicit modeling for population dynamics of the species of interest. Both the CCE Atlantis and MICE model described above are spatial and can address Comment 11. However, the models have so far assumed a spatial distribution of forage species that remains constant over time. Considering the evidence for environmentally driven spatio-temporal variability in forage species ( Muhling et al., 2020 ), with impacts on predator demographic rates, particularly for central place foragers such as sea lions ( Fiechter et al., 2016 ), and on port-level availability to fishers ( Smith et al., 2021 ), a valuable goal for future research is the refinement of existing ecosystem models in the CCE to include environmentally driven changes in forage distribution, as has been done elsewhere (e.g., Coll et al., 2019 ; Moullec et al., 2019 ). Given their fine-scale representation of spatial movement processes, individual based models (IBMs) are also suited to evaluate impacts of varying prey dynamics on central-place predator distribution and foraging behavior. For example, a multi-species IBM model of sardine, anchovy, and sea lions coupled to a regional ocean model with biogeochemistry was used to examine the impacts of environmental variability and prey availability on sea lion feeding success in the central CCE ( Fiechter et al., 2016 ).

Interactions Between the Environment and Fishing Communities

The final set of comments (14–17) underscores the need for understanding how changes in climate variability, mediated via ecosystem processes, affect fishing communities ( Table 1 ). Climate change is expected to alter fish abundance and distribution ( Cheung et al., 2010 ; Morley et al., 2018 ) and PFMC advisory bodies are interested in evaluating the potential risk of shifting species availability to coastal communities (Comments 14–15). Fine scale oceanographic data from remote sensing and ocean models, in combination with spatially explicit survey, tagging or logbook data, has enabled development of SDMs for a variety of PFMC-managed species (e.g., Thorson et al., 2016 ; Shelton et al., 2020 ). When data on fisher behavior (e.g., trip distance) is available from logbooks, port-specific fishing grounds can be identified and target species availability from SDMs over the fishing grounds can be computed ( Rogers et al., 2019 ). Below, and in Table 2 we provide CCE-specific examples of how environmentally informed ecosystem models have been integrated with economic analyses to address comments 14–17, and what gaps remain.

With regards to the groundfish fishery in the CCE, indices of groundfish availability over distinct fishing grounds have been computed over the historical period ( Selden et al., 2019 ; Table 2 ) and integrated into the ESRs ( Harvey et al., 2019 ). To address comments 14–15, future work could develop such indices using environmentally informed SDMs to assess climate-induced shifts in economic opportunity (e.g., Smith et al., 2020 ), and project such changes into the future to assess the vulnerability of coastal communities to risk from climate change, as has been done for New England and Mid Atlantic fishing communities ( Rogers et al., 2019 ). When spatially explicit other ecosystem models can also inform port-level socio-economic indices. In the CCE, the spatially explicit structure of the Atlantis model allowed translation of the results assessing climate impacts on the CCE food-web and its fisheries ( Marshall et al., 2017 ) to port-based fishing communities and fleet-level economic effects ( Hodgson et al., 2018 ). These model results have been presented to the PFMC’s to inform an ongoing strategic initiative on the effects of climate variability and change on fish stocks and fishing communities ( Kaplan et al., 2018 ). By the explicit consideration of biological processes, end-to-end ecosystem models and MICE also have high potential to assess the cumulative effects of multiple environmental drivers (Comment 6, Table 2 ), e.g., under long-term climate change ( Ainsworth et al., 2011 ; Koenigstein et al., 2016 ).

There is also a need to assess how extreme weather events directly affect safety of fishers (Comment 16, Table 1 ). Climate-change driven shifts in the frequency and strength of extreme weather events have the potential to directly affect the safety of commercial, recreational, and subsistence fishers. An active area of atmospheric research is concerned with how climate change may drive changes in storminess ( Knutson et al., 2010 ; Dominguez et al., 2012 ; Kossin et al., 2016 ; Mölter et al., 2016 ; Ornes, 2018 ; Swain et al., 2018 ; Teich et al., 2018 ). Fishers and boaters are among the most sophisticated consumers of weather forecast information ( Savelli and Joslyn, 2012 ; Finnis et al., 2019 ; Kuonen et al., 2019 ). Understanding how fishers respond to extreme weather events such as storms is essential to assessing the vulnerability of fishers and fishing communities to potential changes in storminess ( Sainsbury et al., 2018 ), as well as consideration of how fishery-specific management and regulatory incentives affect fishers’ safety by influencing the level of risk fishers take to catch and land their fish ( Pfeiffer and Gratz, 2016 ). Indeed, modeling work has shown that catch shares and other types of management that eliminate a race for fish and allow flexibility in the timing of trips decrease the propensity to take trips in hazardous weather ( Petursdottir et al., 2001 ; Pfeiffer and Gratz, 2016 ; Petesch and Pfeiffer, 2019 ; Pfeiffer, 2020 ). Hidden Markov Models provide a tool to uncover underlying fisher behavior from vessel tracking data, such as from vessel monitoring systems or automatic identification systems. Such models and data sources are being increasingly used to determine simple behavioral states of fishers, e.g., ‘fishing’ or ‘searching’ ( Joo et al., 2015 ), as well as to identify environmental factors that influence their behavior ( Watson et al., 2018 ). Future work may employ behavioral models informed by environmental conditions to examine how fisher behavior changes in response to adverse weather conditions, produce estimates of fishers’ risk tolerance, and help promote safety at sea by evaluating the change in risk from fishery policies and climate change ( Table 2 ).

Integration of environmental indicators with socio-economic models can also enable quantification of the impact of extreme events on fishing communities (Comment 17, Table 2 ). For instance, the 2014–2016 marine heatwave in the CCE triggered an unprecedented harmful algal bloom (HAB) ( McCabe et al., 2016 ; Ryan et al., 2017 ), leading to considerable economic losses in fisheries for Dungeness crab ( Metacarcinus magister ) ( Moore et al., 2019 ). To better alert communities of potential fisheries closures during HABs and mitigate their effects via adaptive actions, advisory bodies requested development of a HAB index at a localized scale and for a quantification of the economic impacts of HABs on fisheries participants (Comment 17). Moore et al. (2019) have developed a localized, community-specific index of lost fishing opportunity from HABs by computing the proportion of the Dungeness crab fishing season lost to HAB closures, which may be of interest to managers. In a follow-up study, Moore et al. (2020) , using regression models built from fishers’ survey data, found that individuals who were exposed to longer fisheries closures, as measured by the HAB index, suffered greater income losses. Moore et al. (2020) also identified potential adaptive actions to reduce the impact of HABs on Dungeness crab fishery participants. These actions include income diversification and fishing for alternate species or in alternate areas. In addition, Anderson et al. (2016) developed a model to provide nowcasts and 1–3 day forecasts of HABs for the California coast 2 by linking ROMS and satellite output to a statistical model of the likelihood of a toxic algal bloom. To better assess Comment 17 and assess the socioeconomic impacts of future shifts in HAB dynamics, future work could focus on developing more holistic models linking the socioeconomic analyses identifying the effects of HAB on fisheries described above to predictive HAB models.

For scientific information and analyses to directly support or affect public policies and regulations, the policymaking process should promote opportunities for scientists to engage with policymakers ( Hopkins et al., 2011 ; Cvitanovic et al., 2015 ). Although ecosystem modeling is relatively new to fisheries management, it has entered a policymaking space where the ongoing examination of the best scientific information available to analyze management questions is both expected by fisheries managers and required by law [16 U.S.C. §1851(a)(2), see also 16 U.S.C. §1362(2), §1386(a), and §1536(a)(2)]. U.S. federal fisheries management has a 40 + year history of discussing, debating, and improving fisheries science by bringing that science into the public arena and testing it through application to ongoing fisheries. Engaging in the existing policy making space of the fishery management council process allows ecosystem modelers to make that needed connection between modeling and management priorities.

In assessing the responses of managers and stakeholders to the review of ESR indicators, we demonstrated that policy needs for ecosystem science go beyond the setting and use of environmental indicators to improve forecasts of biomass and reference points required for the setting of harvest limits. Other uses of ecosystem models and analysis identified included: (1) assessment of shifts in the spatial distribution of target stocks and protected species to anticipate changes in availability and the potential for interactions between fisheries and protected species, (2) identification of trophic interactions to better assess tradeoffs between protection of dependent predators and resilience of fishing communities in the management of forage species and to holistically assess the impact of climate change on PFMC-managed species, and (3) synthesis of how the environment affects fishing communities, either via extreme events such as HABs or storms or via climate-driven changes in target species availability, to promote efficiency and profitability of fisheries. The identified policy needs largely reflect the broad aims of EBFM ( National Marine Fisheries Service, 2016a ) but were brought forward directly by managers and stakeholders operating in the CCE and thus are relevant to their experience and specific requirements and are more regionally actionable. By including a stakeholder review of ESR indicators into an existing policy discussion process, other regions could replicate our work to ensure that their ecosystem modeling complements legally mandated avenues for using best available science in management and for setting research priorities ( Box 1 , Pacific Fishery Management Council (PFMC), 2018 ). Given limited resources, the process here outlined could then be followed by an ecosystem risk assessment ( Holsman et al., 2017 ) to prioritize analyses and model development to focus on initially, as was done successfully by the U.S. Mid-Atlantic Fishery Management Council ( Gaichas et al., 2018 ).

While existing ecosystem modeling capabilities in the region can address many of the policy needs identified by the ESR comments ( Table 2 ), for some applications, improvements in ecosystem modeling capabilities are required to further the utility of ecosystem models and analyses to management needs ( Table 2 ). Comments 1 and 10 stressed the need to anticipate future changes in productivity or species interactions. While ecosystem models and analyses have shown skill for some species in predicting changes in productivity and distribution over the historical period using observed data or data assimilative ocean model output (e.g., Brodie et al., 2018 ; Tolimieri et al., 2018 ) and have in some cases been used to assess impacts of climate change ( Hazen et al., 2013 ; Haltuch et al., 2019a ), the skill of near term (months to years in advance) ecological forecasts needs to be tested to assess their utility to the setting of catch limits, biomass projections, or spatial management measures at the spatiotemporal scales that are relevant to managers. Development of forecasting capabilities for fish productivity or distribution changes would also benefit from expansion of the use of ecosystem models and analyses linked to oceanographic models to improve mechanistic understanding and to develop indicators with high explanatory power in modeling changes in species responses to environmental variability (e.g., Brodie et al., 2018 ; Tolimieri et al., 2018 ; Henderson et al., 2019 ). Utility of such methods should be assessed relative to current approaches and as part of a forecasting ensemble.

The ESR comments also show a clear desire on the part of managers and stakeholders to better assess the broader ecosystem impacts of management actions, particularly with regards to the tradeoffs between the forage needs of predators, fisheries for prey and predator species, and protections for non-target predator stocks. In light of the stakeholders’ and managers’ comments, ecosystem models have the potential to be used more routinely to assess the impact of changes in forage to dependent predators when linked to stock assessments (e.g., Drew et al., 2021 ) or MSE model output (e.g., Deroba et al., 2019 ), and to develop multispecies harvest control rules (HCRs) or ecosystem-level reference points ( Link, 2018 ; Fulton et al., 2019 ; Holsman et al., 2020 ). This is in addition to their demonstrated utility in addressing specific strategic questions, such as the role of krill in the ecosystem (e.g., Pacific Fishery Management Council (PFMC), 2009 ) or the impact of climate change on PFMC-managed species (e.g., Marshall et al., 2017 ). However, in some cases, model refinements to include more realistic fishing scenarios based on current harvest rules or more realistic responses to environmental variability, particularly with regards to changes in species distribution, may be required before implementation ( Table 2 ).

Many comments also acknowledged the need to better integrate human dimension considerations when assessing impacts of management policies on port-level socioeconomic metrics, particularly within the context of climate variability and change. While case studies for specific regions and fisheries have shown promising approaches (e.g., Plagányi et al., 2013 ; Rogers et al., 2019 ; Selden et al., 2019 ), further development of methods linking spatially explicit biological models to socioeconomic outcomes, as well as improved consideration of the diversity of harvesting portfolios ( Frawley et al., 2021 ), is required. In particular, links to on-shore community impacts, many of which are qualitative socio-cultural measures, have been neglected and may require direct consultation with communities rather than quantitative modeling ( Okamoto et al., 2020 ). This will necessitate further communication not only between ecosystem modelers and managers, but also between ecosystem modelers, managers, and (non-economic) social scientists. While the findings presented here can, in collaboration with managers and stakeholders, help refine ecosystem modeling planning, ecosystem model development for improved management applicability also needs to be balanced with research and development innovations to identify emerging information needs.

As ecosystem modeling insights evolve to more explicitly inform both tactical and strategic management, the means to better quantify and present uncertainty in such model outputs or scenarios will become more critical ( Link et al., 2012 ; Weijerman et al., 2015 ; Jacobsen et al., 2016 ; Haltuch et al., 2019b ). Combining information across approaches via model averaging or ensembles ( Marmion et al., 2009 ; Ianelli et al., 2016 ; Karp et al., 2019 ), or using Bayesian updating ( Staton and Catalano, 2019 ) or state-space models ( Fleischman et al., 2013 ) to formally integrate observations and modeled effects of drivers from multiple stages of a species life cycle may provide more reliable model output, and improved characterization of forecast uncertainty (i.e., model spread) on which to base decisions ( Ianelli et al., 2016 ). Agreement in the predictions of an ensemble of structurally different ecosystem models can also increase stakeholder confidence in the model results ( Jacobsen et al., 2016 ). MSE frameworks, which assess robustness of alternative management strategies to a range of uncertainties captured by a set of diverse operating models ( Punt et al., 2016a ), may be useful to both characterize uncertainty and communicate to stakeholders its impact on management performance.

Despite the growing need for ecosystem information and existing ecosystem modeling capabilities in the region potentially useful to the identified policy needs, only a few of these models or analyses have been implemented in management frameworks ( Table 2 ). With regards to stock assessment science, there is a well-established routine review process that has enabled continued feedback between managers and modelers, and model refinement aimed at improving utility to management issues. Indeed, our work demonstrates that most implementations of ecosystem analysis in the PFMC have been via the development of indices for single-species climate informed population dynamics models (i.e., salmon forecasts, sardine HCR, sablefish stock assessment) aimed at deriving better estimates of biomass and reference points on which to base harvest decisions. These models are embedded in the PFMC process: council advisory bodies are familiar with them, they are regularly used to set catch limits, and their limitations and potential improvements are routinely discussed during their review process. This has facilitated faster uptake of ecosystem consideration in the PFMC via this type of vetted models. However, examples from other regions have demonstrated that regular dialogue between ecosystem modelers and advisory bodies via existing management council processes can foster, gradually, adoption of new management approaches (e.g., Holsman et al., 2016 , 2019 ; Ianelli et al., 2019 ; Drew et al., 2021 ).

We suggest that in the PFMC and elsewhere, uses of ecosystem models and analyses could similarly be vetted and refined within the existing technical review, methodology review, stock assessment, and harvest setting process, or addressed in a more targeted review process such as for the Atlantis model in the CCE [ Kaplan and Marshall, 2016 , or as ‘key runs’ in ICES (2021) ]. As for stock assessment, such interactions between managers and ecosystem modelers should be iterative. As highlighted in Figure 3 , we propose that, for the PFMC, the annual technical review of ESR indicators, coupled with more in-depth methodology reviews when warranted, could serve as a forum for routine, iterative dialogue between managers and ecosystem modelers. This forum would enable discussion of ecosystem models and analyses showing potential utility but requiring further discussion on key details (e.g., species, timescales, and spatial scales of interest) with managers and stakeholders for implementation ( Figure 3 and Table 2 ). For those analyses and models that have already been reviewed or implemented, this forum would provide a platform for periodic review of model refinements or new applications. The manager-modelers idea sharing process here presented ( Figure 3 ) could enable the structured, iterative, and interactive communication between managers, stakeholders, and modelers that is key to refining existing ecosystem models and analyses for management use.

Figure 3. Overview of the PFMC forum for routine discussion of ecosystem models and analyses and the management process and policy issues informed by those models and analyses.

This paper explicitly looks at the comments on ESR indicators that pertained to an ecosystem-level understanding of fish stocks and fisheries. However, the comments that PFMC received on ESR indicators also ranged into questions about spatial management and links between climate variability and shifting stock distribution, extreme climate events, forecasting future risk, and about better understanding fishing community dependence on fishery resources and vulnerability to shifting stock availability. For natural resource managers, discussions of these wide-ranging questions and ideas are possible when working in an open, public process that involves stakeholders with diverse and sometimes competing goals. For ecosystem modelers, being open to the ideas that drive management processes and being willing to listen for how management processes communicate those ideas is key to successful connections between their models and management needs.

Several key aspects of our case study are present in other management systems for public trust resources, and this study may serve as a blueprint for matching models to management needs in a variety of policy making processes worldwide. To facilitate adoption of scientific knowledge in support of management decisions, existing natural resources management frameworks (e.g., Hopkins et al., 2011 ; Gregory et al., 2012 ; Mach and Field, 2017 ; Francis et al., 2018 ) highlight the need for continued, iterative engagement between scientists and decision makers. Here we find that both ecosystem scientists and managers have pre-existing tools in place, but nexus points between the science and management communities need to be present to foster information sharing and support the development of ecosystem models of interest and use to resource managers and the public. Development and use of ecosystem models should be guided by established best practices for model use (e.g., Collie et al., 2016 ; Punt et al., 2016a ), forums like ecosystem modeling workshops that focus on model improvements and information sharing (e.g., Weijerman et al., 2016 ; Townsend et al., 2017 ), and science integration templates like integrated ecosystem assessment (e.g., Levin et al., 2009 ; Harvey et al., 2020 ). Resource management processes that require regular assessments of key resources (stocks, habitats, protected species) and activities (fishing, conservation actions) foster the scientific data collection that supports ecosystem modeling. Management processes, like fishery management councils, that maintain space in their processes for discussing ecosystem science and EBFM signal their openness to considering and using new ecosystem information as it arises and can serve as forums to facilitate matchmaking between models and management needs (see Figures 2 , 3 ).

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author/s.

Author Contributions

DT, YR, HT, and CH developed the initial idea for this manuscript. DT, CH, SB, and SK reviewed and categorized comments from the Coordinated Ecosystem Indicators Review. All authors have contributed writing and editing.

This work was supported by the NOAA Climate Program Office’s Coastal and Ocean Climate Applications Program (NA17OAR4310268), Modeling, Analysis, Predictions, and Projections Program (NA17OAR4310108), and the NOAA Fisheries Office of Science and Technology.

The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect the views of NOAA or the Department of Commerce.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all the stakeholders, managers, and scientists who participated in the PFMC stakeholder review of ESR indicators as well as the participants of the NOAA NEMoW 5 workshop for useful suggestions on an early idea for the paper. We also thank J. Samhouri for helpful comments on an earlier draft of the manuscript and the two journal reviewers for insightful comments that helped improve the manuscript.

• ^ http://oceanmodeling.ucsc.edu/
• ^ https://www.cencoos.org/data/models/habs/forecast

Abrahms, B., Welch, H., Brodie, S., Jacox, M. G., Becker, E. A., Bograd, S. J., et al. (2019). Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Divers. Distrib. 25, 1182–1193. doi: 10.1111/ddi.12940

CrossRef Full Text | Google Scholar

Ainsworth, C., Samhouri, J., Busch, D., Cheung, W. W. L., Dunne, J., and Okey, T. A. (2011). Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. ICES J. Mar. Sci. 68, 1217–1229.

Google Scholar

Anderson, C. R., Kudela, R. M., Kahru, M., Chao, Y., Rosenfeld, L. K., Bahr, F. L., et al. (2016). Initial skill assessment of the California Harmful Algae Risk Mapping (C-HARM) system. Harmful Algae 59, 1–18. doi: 10.1016/j.hal.2016.08.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Anderson, D. W., Gress, F., Mais, K. F., and Kelly, P. R. (1980). “Brown Pelicans as anchovy stock indicators and their relationship to commercial fishing,” in California Cooperative Oceanic Fisheries. Reports XXI. La Jolla, CA.

Bailey, R. M., Carrella, E., Axtell, R., Burgess, M. G., Cabral, R. B., Drexler, M., et al. (2019). A computational approach to managing coupled human–environmental systems: the POSEIDON model of ocean fisheries. Sustain. Sci. 14, 259–275. doi: 10.1007/s11625-018-0579-9

Basson, M. (1999). The importance of environmental factors in the design of management procedures. ICES J. Mar. Sci. 56, 933–942.

Bauer, P., Thorpe, A., and Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature 525, 47–55. doi: 10.1038/nature14956

Bax, N. J. (1998). The significance and prediction of predation in marine fisheries. ICES J. Mar. Sci. 55, 997–1030.

Beamish, R. J., and Mahnken, C. (2001). A critical size and period hypothesis to explain natural regulation of salmon abundance and the linkage to climate and climate change. Prog. Oceanogr. 49, 423–437. doi: 10.1016/S0079-6611(01)00034-9

Becker, E. A., Carretta, J. V., Forney, K. A., Barlow, J., Brodie, S., Hoopes, R., et al. (2020). Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees. Ecol. Evol. 10, 5759–5784. doi: 10.1002/ece3.6316

Briscoe, N. J., Kearney, M. R., Taylor, C. A., and Wintle, B. A. (2016). Unpacking the mechanisms captured by a correlative species distribution model to improve predictions of climate refugia. Glob. Change Biol. 22, 2425–2439. doi: 10.1111/gcb.13280

Brodie, S., Jacox, M. G., Bograd, S. J., Welch, H., Dewar, H., Scales, K. L., et al. (2018). Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. 5:219. doi: 10.3389/fmars.2018.00219

Burke, B. J., Peterson, W. T., Beckman, B. R., Morgan, C., Daly, E. A., and Litz, M. (2013). Multivariate models of adult pacific salmon returns. PLoS One 8:e0054134. doi: 10.1371/journal.pone.0054134

Carroll, G., Holsman, K. K., Brodie, S., Thorson, J. T., Hazen, E. L., Bograd, S. J., et al. (2019). A review of methods for quantifying spatial predator–prey overlap. Glob. Ecol. Biogeogr. 28, 1561–1577. doi: 10.1111/geb.12984

Cheung, W. W. L., Lam, V. W. Y., Sarmiento, J. L., Kearney, K., Watson, R., Zeller, D., et al. (2010). Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Glob. Chang. Biol. 16, 24–35. doi: 10.1111/j.1365-2486.2009.01995.x

Christensen, V., and Walters, C. J. (2004). Ecopath with Ecosim: methods, capabilities and limitations. Ecol. Modell. 172, 109–139. doi: 10.1016/j.ecolmodel.2003.09.003

Coll, M., Pennino, M. G., Steenbeek, J., Sole, J., and Bellido, J. M. (2019). Predicting marine species distributions: complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Model. 405, 86–101.

Collie, J. S., Botsford, L. W., Hastings, A., Kaplan, I. C., Largier, J. L., Livingston, P. A., et al. (2016). Ecosystem models for fisheries management: finding the sweet spot. Fish Fish. 17, 101–125. doi: 10.1111/faf.12093

Cowan, J. H., Rice, J. C., Walters, C. J., Hilborn, R., Essington, T. E., Day, J. W., et al. (2012). Challenges for implementing an ecosystem approach to fisheries management. Mar. Coast. Fish. 4, 496–510. doi: 10.1080/19425120.2012.690825

Cury, P. M., Boyd, I. L., Bonhommeau, S., Anker-Nilssen, T., Crawford, R. J. M., Furness, R. W., et al. (2011). Global seabird response to forage fish depletion - one-third for the birds. Science 334, 1703–1706.

Cvitanovic, C., Hobday, A., van Kerkhoff, L., Wilson, S., Dobbs, K., and Marshall, N. A. (2015). Improving knowledge exchange among scientists and decision-makers to facilitate the adaptive governance of marine resources: a review of knowledge and research needs. Ocean Coast Manag. 112, 25–35.

Deroba, J. J., Gaichas, S. K., Lee, M.-Y., Feeney, R. G., Boelke, D., and Irwin, B. J. (2019). The dream and the reality: meeting decision-making time frames while incorporating ecosystem and economic models into management strategy evaluation. Can. J. Fish. Aquat. Sci. 76, 1112–1133. doi: 10.1139/cjfas-2018-0128

Dinesen, G. E., Neuenfeldt, S., Kokkalis, A., Andreas, L., Egekvist, J., Kristensen, K., et al. (2019). Cod and climate: a systems approach for sustainable fisheries management of Atlantic cod ( Gadus morhua ) in coastal Danish waters. J. Coast. Conserv. 23, 943–958. doi: 10.1007/s11852-019-00711-0

Dominguez, F., Rivera, E., Lettenmaier, D. P., and Castro, C. L. (2012). Changes in winter precipitation extremes for the western United States under a warmer climate as simulated by regional climate models. Geophys. Res. Lett. 39:L05803. doi: 10.1029/2011GL050762

Drew, K., Cieri, M., Schueller, A., Buchheister, A., Chagaris, D., Nesslage, G., et al. (2021). Balancing model complexity, data requirements, and management objectives in developing ecological reference points for atlantic menhaden. Front. Mar. Sci. 8:608059. doi: 10.3389/fmars.2021.608059

Druon, J. N., Fiorentino, F., Murenu, M., Knittweis, L., Colloca, F., Osio, C., et al. (2015). Modelling of European hake nurseries in the Mediterranean Sea: an ecological niche approach. Prog. Oceanogr. 130, 188–204.

Ecosystem Principles Advisory Panel (EPAP) (1999). Ecosystem-Based Fishery Management: A Report to Congress by the Ecosystem Principles Advisory Panel. Washington, D.C: U.S. Dept. of Commerce.

Elith, J., and Leathwick, J. R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. doi: 10.1146/annurev.ecolsys.110308.120159

Esselman, P. C., and Allan, J. D. (2011). Application of species distribution models and conservation planning software to the design of a reserve network for the riverine fishes of northeastern Mesoamerica. Freshw. Biol. 56, 71–88. doi: 10.1111/j.1365-2427.2010.02417.x

Eveson, J. P., Hobday, A. J., Hartog, J. R., Spillman, C. M., and Rough, K. M. (2015). Seasonal forecasting of tuna habitat in the Great Australian Bight. Fish. Res. 170, 39–49. doi: 10.1016/j.fishres.2015.05.008

Feeney, R. G., Boelke, D. V., Deroba, J. J., Gaichas, S., Irwin, B. J., and Lee, M. (2019). Integrating management strategy evaluation into fisheries management: advancing best practices for stakeholder inclusion based on an MSE for Northeast US Atlantic herring. Can. J. Fish. Aquat. Sci. 76, 1103–1111. doi: 10.1139/cjfas-2018-0125

Fiechter, J., Huckstadt, L. A., Rose, K. A., and Costa, D. P. (2016). A fully coupled ecosystem model to predict the foraging ecology of apex predators in the California Current. Mar. Ecol. Prog. Ser. 556, 273–285.

Fiechter, J., Huff, D. D., Martin, B. T., Jackson, D. W., Edwards, C. A., Rose, K. A., et al. (2015). Environmental conditions impacting juvenile Chinook salmon growth off central California: an ecosystem model analysis. Geophys. Res. Lett. 42, 2910–2917. doi: 10.1002/2015GL063046

Field, J. C., Francis, R. C., and Aydin, K. (2006). Top-down modeling and bottom-up dynamics: linking a fisheries-based ecosystem model with climate hypotheses in the Northern California Current. Prog. Oceanogr. 68, 238–270.

Finnis, J., Shewmake, J. W., Neis, B., and Telford, D. (2019). Marine forecasting and fishing safety: improving the fit between forecasts and harvester needs. J. Agromed. 24, 324–332. doi: 10.1080/1059924X.2019.1639576

Fleischman, S. J., Catalano, M. J., Clark, R. A., and Bernard, D. R. (2013). An age-structured state-space stock–recruit model for Pacific salmon ( Oncorhynchus spp.). Can. J. Fish. Aquat. Sci. 70, 401–414. doi: 10.1139/cjfas-2012-0112

Fogarty, M. J. (2014). The art of ecosystem-based fishery management. Can. J. Fish. Aquat. Sci. 71, 479–490.

Forney, K. A., Ferguson, M. C., Becker, E. A., Fiedler, P. C., Redfern, J. V., Barlow, J., et al. (2012). Habitat-based spatial models of cetacean density in the eastern Pacific Ocean. Endanger. Species Res. 16, 113–133. doi: 10.3354/esr00393

Francis, T. B., Levin, P. S., Punt, A. E., Kaplan, I. C., Varney, A., and Norman, K. (2018). Linking knowledge to action in ocean ecosystem management: the Ocean Modeling Forum. Elementa Sci. Anthrop. 6:83. doi: 10.1525/elementa.338

Frawley, T. H., Muhling, B. A., Brodie, S., Fisher, M. C., Tommasi, D., Le Fol, G., et al. (2021). Changes to the structure and function of an albacore fishery reveal shifting social-ecological realities for Pacific Northwest fishermen. Fish Fish. 22, 280–297. doi: 10.1111/faf.12519

Fulton, E. A. (2021). Opportunities to improve ecosystem-based fisheries management by recognizing and overcoming path dependency and cognitive bias. Fish Fish. 22, 428–448. doi: 10.1111/faf.12537

Fulton, E. A., Link, J. S., Kaplan, I. C., Savina-Rolland, M., Johnson, P., Ainsworth, C., et al. (2011). Lessons in modelling and management of marine ecosystems: the Atlantis experience. Fish Fish. 12, 171–188. doi: 10.1111/j.1467-2979.2011.00412.x

Fulton, E. A., Punt, A. E., Dichmont, C. M., Harvey, C. J., and Gorton, R. (2019). Ecosystems say good management pays off. Fish Fish. 20, 66–96. doi: 10.1111/faf.12324

Gaichas, S. K., DePiper, G. S., Seagraves, R. J., Muffley, B. W., Sabo, M. G., Colburn, L. L., et al. (2018). Implementing ecosystem approaches to fishery management: risk assessment in the US Mid-Atlantic. Front. Mar. Sci. 5:442. doi: 10.3389/fmars.2018.00442

Gregory, R., Failing, I., Harstone, M., Long, G., McDaniels, T., and Ohlson, D. (2012). Structured Decision Making: A Practical Guide to Environmental Management Choices. Hoboken, NJ: Wiley-Blackwell.

Guisan, A., and Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecol. Modell. 135, 147–186. doi: 10.1016/S0304-3800(00)00354-9

Haltuch, M. A., A’mar, Z. T., Bond, N. A., and Valero, J. L. (2019a). Assessing the effects of climate change on US West Coast sablefish productivity and on the performance of alternative management strategies. ICES J. Mar. Sci. 76, 1524–1542. doi: 10.1093/icesjms/fsz029

Haltuch, M. A., Brooks, E. N., Brodziak, J., Devine, J. A., Johnson, K. F., Klibansky, N., et al. (2019b). Unraveling the recruitment problem: a review of environmentally-informed forecasting and management strategy evaluation. Fish. Res. 217, 198–216.

Haltuch, M. A., Johnson, K. F., Tolimieri, N., Kapur, M. S., and Castillo-Jordán, C. A. (2019c). Status of the Sablefish Stock in U.S. Waters in 2019. Portland, OR. Available online at: https://www.pcouncil.org/documents/2019/09/agenda-item-h-5-attachment-7-2.pdf/ .

Haltuch, M. A., Tolimieri, N., Lee, Q., and Jacox, M. G. (2020). Oceanographic drivers of petrale sole recruitment in the California Current Ecosystem. Fish Oceanogr. 29, 122–136. doi: 10.1111/fog.12459

Harvey, C. J., Garfield, N., Williams, G., and Tolimieri, N. (eds) (2020). California Current Integrated Ecosystem Assessment (CCIEA) California Current ecosystem status report, 2020. Report to the Pacific Fishery Management Council. Washington, DC: U.S. Department of Commerce.

Harvey, C. J., Garfield, N., Williams, G., Tolimieri, N., Schroeder, I., Andrews, K., et al. (2019). Ecosystem Status Report of the California Current for 2019: A Summary of Ecosystem Indicators Compiled by the California Current Integrated Ecosystem Assessment Team (CCEIA). Washington, DC: U.S. Department of Commerce, doi: 10.25923/p0ed-ke21 NOAA Technical Memorandum NMFS-NWFSC-149.

Hazen, E. L., Jorgensen, S., Rykaczewski, R. R., Bograd, S. J., Foley, D. G., Jonsen, I. D., et al. (2013). Predicted habitat shifts of Pacific top predators in a changing climate. Nat. Clim. Chang. 3, 234–238. doi: 10.1038/nclimate1686

Hazen, E. L., Palacios, D. M., Forney, K. A., Howell, E. A., Becker, E., Hoover, A. L., et al. (2017). WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current. J. Appl. Ecol. 54, 1415–1428. doi: 10.1111/1365-2664.12820

Hazen, E. L., Scales, K. L., Maxwell, S. M., Briscoe, D. K., Welch, H., Bograd, S. J., et al. (2018). EcoCast: a dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Sci. Adv. 4:EAAR3001.

Henderson, M., Fiechter, J., Huff, D. D., and Wells, B. K. (2019). Spatial variability in ocean-mediated growth potential is linked to Chinook salmon survival. Fish. Oceanogr. 28, 334–344. doi: 10.1111/fog.12415

Hervieux, G., Alexander, M. A., Stock, C. A., Jacox, M. G., Pegion, K., Becker, E., et al. (2019). More reliable coastal SST forecasts from the North American multimodel ensemble. Clim. Dyn. 53, 7153–7168. doi: 10.1007/s00382-017-3652-7

Hilborn, R. (2011). Future directions in ecosystem based fisheries management: a personal perspective. Fish. Res. 108, 235–239. doi: 10.1016/j.fishres.2010.12.030

Hobday, A. J., Hartog, J. R., Spillman, C. M., and Alves, O. (2011). Seasonal forecasting of tuna habitat for dynamic spatial management. Can. J. Fish. Aquat. Sci. 68, 898–911. doi: 10.1139/f2011-031

Hobday, A. J., Hartog, J. R., Timiss, T., and Fielding, J. (2010). Dynamic spatial zoning to manage southern bluefin tuna ( Thunnus maccoyii ) capture in a multi-species longline fishery. Fish. Oceanogr. 19, 243–253. doi: 10.1111/j.1365-2419.2010.00540.x

Hodgson, E. E., Kaplan, I. C., Marshall, K. N., Leonard, J., Essington, T. E., Busch, D. S., et al. (2018). Consequences of spatially variable ocean acidification in the California Current: lower pH drives strongest declines in benthic species in southern regions while greatest economic impacts occur in northern regions. Ecol. Modell. 383, 106–117. doi: 10.1016/j.ecolmodel.2018.05.018

Holsman, K., Samhouri, J., Cook, G., Hazen, E., Olsen, E., Dillard, M., et al. (2017). An ecosystem-based approach to marine risk assessment. Ecosyst. Health Sustain. 3:e01256. doi: 10.1002/ehs2.1256

Holsman, K. K., Ianelli, J., Aydin, K., and Punt, A. E. (2016). A comparison of fisheries biological reference points estimated from temperature-specific multi-species and single-species climate-enhanced stock assessment models. Deep Sea Res. Part II Top. Stud. Oceanogr. 134, 360–378. doi: 10.1016/j.dsr2.2015.08.001

Holsman, K. K., Haynie, A. C., Hollowed, A. B., Reum, J. C. P., Aydin, K., Hermannn, A. J., et al. (2020). Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun . 11:4579. doi: 10.1038/s41467-020-18300-3

Holsman, K. K., Ianelli, J., Aydin, K., and Spies, I. (2019). “2019 Climate-enhanced multi-species Stock Assessment for walleye pollock, Pacific cod, and arrowtooth flounder in the Eastern Bering Sea,” in Stock Assessment and Fishery Evaluation Report for the Groundfish Resources for the Bering Sea/Aleutian Islands Regions , Anchorage, AK: North Pacific Fishery Management Council.

Hopkins, T. S., Bailly, D., and Støttrup, J. G. (2011). A systems approach framework for coastal zones. Ecol. Soc. 16:25. doi: 10.5751/ES-04553-160425

Howell, D., Schueller, A. M., Bentley, J. W., Buchheister, A., Chagaris, D., Cieri, M., et al. (2021). Combining ecosystem and single-species modeling to provide ecosystem-based fisheries management advice within current management systems. Front. Mar. Sci. 7:607831. doi: 10.3389/fmars.2020.607831

Howell, E. A., Hoover, A., Benson, S. R., Bailey, H., Polovina, J. J., Seminoff, J. A., et al. (2015). Enhancing the TurtleWatch product for leatherback sea turtles, a dynamic habitat model for ecosystem-based management. Fish. Oceanogr. 24, 57–68. doi: 10.1111/fog.12092

Hurtado-Ferro, F., and Punt, A. E. (2014). Agenda Item I.1.b Revised Analyses Related to Pacific Sardine Harvest Parameters. Portland, OR.

Ianelli, J., Holsman, K. K., Punt, A. E., and Aydin, K. (2016). Multi-model inference for incorporating trophic and climate uncertainty into stock assessments. Deep Sea Res. Part II Top. Stud. Oceanogr. 134, 379–389.

Ianelli, J. N., Fissel, B., Holsman, K., Honkalehto, T., Kotwicki, S., Monnahan, C., et al. (2019). “Chapter 1: assessment of the walleye pollock stock in the Eastern Bering Sea,” in Stock Assessment and Fishery Evaluation Report for the Groundfish Resources of the Bering Sea/Aleutian Islands Regions (Anchorage, AK: North Pacific Fishery Management Council).

ICES (2021). “Working group on multispecies assessment methods,” in WGSAM; outputs from 2020 Meet-Ing , Copenhagen, 231. doi: 10.17895/ices.pub.7695 ICES Scientific Reports. 3:10.

Jacobsen, N. S., Essington, T. E., and Andersen, K. H. (2016). Comparing model predictions for ecosystem-based management. Can. J. Fish. Aquat. Sci. 73, 666–676.

Jacobson, L. D., and MacCall, A. D. (1995). Stock-recruitment models for Pacific sardine ( Sardinops sagax ). Can. J. Fish. Aquat. Sci. 52, 566–577. doi: 10.1139/f95-057

Jacobson, L. D., and McClatchie, S. (2013). Comment on temperature-dependent stock–recruit modeling for Pacific sardine ( Sardinops sagax ) in Jacobson and MacCall (1995), McClatchie et al. (2010), and Lindegren and Checkley (2013). Can. J. Fish. Aquat. Sci. 70, 1566–1569. doi: 10.1139/cjfas-2013-0128

Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y.-O., Brodie, S., et al. (2020). Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Prog. Oceanogr. 183:102307. doi: 10.1016/j.pocean.2020.102307

Jacox, M. G., Alexander, M. A., Stock, C. A., and Hervieux, G. (2019a). On the skill of seasonal sea surface temperature forecasts in the California Current System and its connection to ENSO variability. Clim. Dyn. 53, 7519–7533. doi: 10.1007/s00382-017-3608-y

Jacox, M. G., Tommasi, D., Alexander, M., Hervieux, G., and Stock, C. (2019b). Predicting the evolution of the 2014-16 California current system marine heatwave from an ensemble of coupled global climate forecasts. Front. Mar. Sci. 6:497. doi: 10.3389/fmars.2019.00497

Jacox, M. G., Edwards, C. A., Hazen, E. L., and Bograd, S. J. (2018). Coastal upwelling revisited: ekman, bakun, and improved upwelling indices for the U.S. West Coast. J. Geophys. Res. Ocean 123, 7332–7350. doi: 10.1029/2018JC014187

Joo, R., Salcedo, O., Gutierrez, M., Fablet, R., and Bertrand, S. (2015). Defining fishing spatial strategies from VMS data: insights from the world’s largest monospecific fishery. Fish. Res. 164, 223–230. doi: 10.1016/j.fishres.2014.12.004

Kaplan, I., Trainer, V., Jacox, M., and Siedlecki, S. (2018). ‘The State of the Art for Ecological Forecasting at Short-, Medium- and Longterm Time Frames’ [Slide presentation]. Pacific Fishery Management Council Climate and Communities Initiative. Available online at: https://www.pcouncil.org/actions/climate-and-communities-initiative/ (accessed July 13, 2020).

Kaplan, I. C., Williams, G. D., Bond, N. A., Hermann, A. J., and Siedlecki, S. A. (2016). Cloudy with a chance of sardines: forecasting sardine distributions using regional climate models. Fish. Oceanogr. 25, 15–27. doi: 10.1111/fog.12131

Kaplan, I. C., Francis, T. B., Punt, A. E., Koehn, L. E., Curchitser, E., Hurtado-Ferro, F., et al. (2019). A multi-model approach to understanding the role of Pacific sardine in the California Current food web. Mar. Ecol. Prog. Ser. 617–618, 307–321. doi: 10.3354/meps12504

Kaplan, I. C., Gaichas, S. K., Stawitz, C. C., Lynch, P. D., Marshall, K. N., Deroba, J. J., et al. (2021). Management strategy evaluation: allowing the light on the hill to illuminate more than one species. Front. Mar. Sci. 8:688.

Kaplan, I. C., Koehn, L. E., Hodgson, E. E., Marshall, K. N., and Essington, T. E. (2017). Modeling food web effects of low sardine and anchovy abundance in the California Current. Ecol. Modell. 359, 1–24. doi: 10.1016/j.ecolmodel.2017.05.007

Kaplan, I. C., and Marshall, K. N. (2016). A guinea pig’s tale: learning to review end-to-end marine ecosystem models for management applications. ICES J. Mar. Sci. 73, 1715–1724.

Karp, M. A., Blackhart, K., Lynch, P. D., Deroba, J., Hanselman, D., Gertseva, V., et al. (2019). Proceedings of the 13th National Stock Assessment Workshop: Model Complexity, Model Stability, and Ensemble Modeling. Washington, DC: U.S. Department of Commerce. NOAA Tech. Memo. NMFS-F/SPO-189.

Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., et al. (2014). The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Am. Meteorol. Soc. 95, 585–601. doi: 10.1175/BAMS-D-12-00050.1

Kleisner, K. M., Fogarty, M. J., McGee, S., Hare, J. A., Moret, S., Perretti, C. T., et al. (2017). Marine species distribution shifts on the U.S. Northeast Continental Shelf under continued ocean warming. Prog. Oceanogr. 153, 24–36. doi: 10.1016/j.pocean.2017.04.001

Knutson, T. R., McBride, J. L., Chan, J., Emanuel, K., Holland, G., Landsea, C., et al. (2010). Tropical cyclones and climate change. Nat. Geosci. 3, 157–163. doi: 10.1038/ngeo779

Koehn, L. E., Essington, T. E., Marshall, K. N., Kaplan, I. C., Sydeman, W. J., Szoboszlai, A. I., et al. (2016). Developing a high taxonomic resolution food web model to assess the functional role of forage fish in the California Current ecosystem. Ecol. Modell. 335, 87–100. doi: 10.1016/j.ecolmodel.2016.05.010

Koenigstein, S., Mark, F. C., Gößling-Reisemann, S., Reuter, H., and Poertner, H.-O. (2016). Modelling climate change impacts on marine fish populations: process-based integration of ocean warming, acidification and other environmental drivers. Fish Fish. 17, 972–1004. doi: 10.1111/faf.12155

Kossin, J. P., Emanuel, K. A., and Camargo, S. J. (2016). Past and projected changes in Western North Pacific tropical cyclone exposure. J. Clim. 29, 5725–5739. doi: 10.1175/JCLI-D-16-0076.1

Kuonen, J., Conway, F., and Strub, T. (2019). Relating Ocean condition forecasts to the process of end-user decision making: a case study of the oregon commercial fishing community. Mar. Technol. Soc. J. 53, 53–66. doi: 10.4031/MTSJ.53.1.1

Leathwick, J., Moilanen, A., Francis, M., Elith, J., Taylor, P., Julian, K., et al. (2008). Novel methods for the design and evaluation of marine protected areas in offshore waters. Conserv. Lett. 1, 91–102. doi: 10.1111/j.1755-263x.2008.00012.x

Levin, P. S., Fogarty, M. J., Murawski, S. A., and Fluharty, D. (2009). Integrated ecosystem assessments: developing the scientific basis for ecosystem-based management of the ocean. PLoS Biol. 7:e1000014. doi: 10.1371/journal.pbio.1000014

Lewison, R., Hobday, A. J., Maxwell, S., Hazen, E., Hartog, J. R., Dunn, D. C., et al. (2015). Dynamic ocean management: identifying the critical ingredients of dynamic approaches to ocean resource management. BioScience 65, 486–498. doi: 10.1093/biosci/biv018

Lidström, S., and Johnson, A. F. (2020). Ecosystem-based fisheries management: a perspective on the critique and development of the concept. Fish Fish. 21, 216–222.

Link, J., Ihde, T., Townsend, H., Osgood, K., Schirripa, M., Kobayashi, D., et al. (2010). Report of the 2nd National Ecosystem Modeling Workshop (NEMoW II), Bridging the Credibility Gap - Dealing with Uncertainty in Ecosystem Models. Washington, DC: U.S. Department of Commerce. NOAA Technical Memorandum NMFS-F/SPO-102.

Link, J. S. (2010). Ecosystem-Based Fishery Management: Confronting Tradeoffs. Cambridge, MA: Cambridge University Press.

Link, J. S. (2018). System-level optimal yield: increased value, less risk, improved stability, and better fisheries. Can. J. Fish. Aquat. Sci. 75, 1–16. doi: 10.1139/cjfas-2017-0250

Link, J. S., Bundy, A., Overholtz, W. J., Shackell, N., Manderson, J., Duplisea, D., et al. (2011). Ecosystem-based fisheries management in the Northwest Atlantic. Fish Fish. 12, 152–170.

Link, J. S., Huse, G., Gaichas, S., and Marshak, A. R. (2020). Changing how we approach fisheries: a first attempt at an operational framework for ecosystem approaches to fisheries management. Fish Fish. 21, 393–434. doi: 10.1111/faf.12438

Link, J. S., Ihde, T. F., Harvey, C. J., Gaichas, S. K., Field, J. C., Brodziak, J. K. T., et al. (2012). Dealing with uncertainty in ecosystem models: the paradox of use for living marine resource management. Prog. Oceanogr. 102, 102–114. doi: 10.1016/j.pocean.2012.03.008

Little, A. S., Needle, C. L., Hilborn, R., Holland, D. S., and Marshall, C. T. (2015). Real-time spatial management approaches to reduce bycatch and discards: experiences from Europe and the United States. Fish Fish. 16, 576–602.

Litz, M., and Hughes, K. M. (2020). Willapa Bay Coho Forecast Methodology. Pacific Fishery Management Council Briefing Book, March 2020, Agenda Item E.2, Attachment 1. Available online at: https://www.pcouncil.org/documents/2020/02/e-2-supplemental-attachment-1-willapa-bay-coho-forecast-methodology-electronic-only.pdf/ .

Mach, K. J., and Field, C. B. (2017). Toward the next generation of assessment. Annu. Rev. Environ. Resour. 42, 569–597.

Malick, M. J., Hunsicker, M. E., Haltuch, M. A., Parker-Stetter, S. L., Berger, A. M., and Marshall, K. N. (2020a). Relationships between temperature and Pacific hake distribution vary across latitude and life-history stage. Mar. Ecol. Prog. Ser. 639, 185–197.

Malick, M. J., Siedlecki, S. A., Norton, E. L., Kaplan, I. C., Haltuch, M. A., Hunsicker, M. E., et al. (2020b). Environmentally driven seasonal forecasts of Pacific hake distribution. Front. Mar. Sci. 7:578490. doi: 10.3389/fmars.2020.578490

Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R. K., and Thuiller, W. (2009). Evaluation of consensus methods in predictive species distribution modelling. Divers. Distrib. 15, 59–69. doi: 10.1111/j.1472-4642.2008.00491.x

Marshall, K. N., Kaplan, I. C., Hodgson, E. E., Hermann, A., Busch, D. S., McElhany, P., et al. (2017). Risks of ocean acidification in the California Current food web and fisheries: ecosystem model projections. Glob. Chang. Biol. 23, 1525–1539. doi: 10.1111/gcb.13594

Marshall, K. N., Levin, P. S., Essington, T. E., Koehn, L. E., Anderson, L. G., Bundy, A., et al. (2018). Ecosystem-based fisheries management for social–ecological systems: renewing the focus in the United States with next generation fishery ecosystem plans. Conserv. Lett 11:e12367. doi: 10.1111/conl.12367

May, R. M., Beddington, J. R., Clark, C. W., Holt, S. J., and Laws, R. M. (1979). Management of multispecies fisheries. Science 205, 267–277. doi: 10.1126/science.205 4403.267

McCabe, R. M., Hickey, B. M., Kudela, R. M., Lefebvre, K. A., Adams, N. G., Bill, B. D., et al. (2016). An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. 43, 10366–10376. doi: 10.1002/2016GL070023

McClatchie, S., Goericke, R., Auad, G., and Hill, K. (2010). Re-assessment of the stock–recruit and temperature–recruit relationships for Pacific sardine ( Sardinops sagax ). Can. J. Fish. Aquat. Sci. 67, 1782–1790. doi: 10.1139/F10-101

McClatchie, S., Vetter, R. D., and Hendy, I. L. (2018). Forage fish, small pelagic fisheries and recovering predators: managing expectations. Anim. Conserv . 21, 445–447. doi: 10.1111/acv.12421

Mölter, T., Schindler, D., Albrecht, A., and Kohnle, U. (2016). Review on the projections of future storminess over the North Atlantic European region. Atmosphere 7:60. doi: 10.3390/atmos7040060

Moore, K. M., Allison, E. H., Dreyer, S. J., Ekstrom, J. A., Jardine, S. L., Klinger, T., et al. (2020). Harmful algal blooms: identifying effective adaptive actions used in fishery-dependent communities in response to a protracted event. Front. Mar. Sci. 6:803. doi: 10.3389/fmars.2019.00803

Moore, S. K., Cline, M. R., Blair, K., Klinger, T., Varney, A., and Norman, K. (2019). An index of fisheries closures due to harmful algal blooms and a framework for identifying vulnerable fishing communities on the U.S. West Coast. Mar. Policy 110:103543. doi: 10.1016/j.marpol.2019.103543

Morley, J. W., Selden, R. L., Latour, R. J., Frölicher, T. L., Seagraves, R. J., Pinsky, M. L., et al. (2018). Projecting shifts in thermal habitat for 686 species on the North American continental shelf. PLoS One 13:e0196127. doi: 10.1371/journal.pone.0196127

Moullec, F., Barrier, N., Drira, S., Guilhaumon, F., Marsaleix, P., Somot, S., et al. (2019). An end-to-end model reveals losers and winners in a warming Mediterranean Sea. Front. Mar. Sci. 6:345. doi: 10.3389/fmars.2019.00345

Muhling, B. A., Brodie, S., Smith, J. A., Tommasi, D., Gaitan, C. F., Hazen, E. L., et al. (2020). Predictability of species distributions deteriorates under novel environmental conditions in the California Current System. Front. Mar. Sci. 7:589. doi: 10.3389/fmars.2020.00589

Muhling, B. A., Tommasi, D., Ohshimo, S., Alexander, M. A., and Dinardo, G. (2018). Regional-scale surface temperature variability allows prediction of Pacific bluefin tuna recruitment. ICES J. Mar. Sci. 75, 1341–1352. doi: 10.1093/icesjms/fsy017

Myers, R. A. (1998). When do environment–recruitment correlations work? Rev. Fish Biol. Fish. 8, 285–305. doi: 10.1023/A:1008828730759

National Marine Fisheries Service (2016a). Ecosystem-Based Fisheries Management Policy. National Marine Fisheries Service Policy 01-120 , Vol. 9. Available online at: https://www.fisheries.noaa.gov/resource/document/ecosystem-based-fisheries-management-policy (accessed May 23, 2016).

National Marine Fisheries Service (2016b). Ecosystem-Based Fisheries Management Road Map. National Marine Fisheries Service Procedure 01-120-01 , 50. Available online at: https://www.fisheries.noaa.gov/resource/document/ecosystem-based-fisheries-management-road-map (accessed November 17, 2016).

Neveu, E., Moore, A. M., Edwards, C. A., Fiechter, J., Drake, P., Crawford, W. J., et al. (2016). An historical analysis of the California Current circulation using ROMS 4D-Var: system configuration and diagnostics. Ocean Model. 99, 133–151. doi: 10.1016/j.ocemod.2015.11.012

Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., et al. (2019). A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels. Ecol. Monogr. 89:e01370. doi: 10.1002/ecm.1370

Okamoto, D. K., Poe, M. R., Francis, T. B., Punt, A. E., Levin, P. S., Shelton, A. O., et al. (2020). Attending to spatial social–ecological sensitivities to improve trade-off analysis in natural resource management. Fish Fish. 21, 1–12. doi: 10.1111/faf.12409

O’Leary, C. A., Miller, T. J., Thorson, J. T., and Nye, J. A. (2018). Understanding historical summer flounder ( Paralichthys dentatus ) abundance patterns through the incorporation of oceanography-dependent vital rates in Bayesian hierarchical models. Can. J. Fish. Aquat. Sci. 76, 1275–1294. doi: 10.1139/cjfas-2018-0092

O’Leary, C. A., Thorson, J. T., Miller, T. J., and Nye, J. A. (2019). Comparison of multiple approaches to calculate time-varying biological reference points in climate-linked population-dynamics models. ICES J. Mar. Sci. 77, 930–941. doi: 10.1093/icesjms/fsz215

Ornes, S. (2018). How does climate change influence extreme weather? Impact attribution research seeks answers. Proc. Natl. Acad. Sci. U.S.A. 115, 8232–8235. doi: 10.1073/pnas.1811393115

Pacific Fishery Management Council (PFMC) (2008). “Council operating procedure 15: salmon estimation methodology updates and review,” in Pacific Fishery Management Council , Portland, OR, 2.

Pacific Fishery Management Council (PFMC) (2009). “Management of krill as an essential component of the california current ecosystem: amendment 12 to the coastal pelagic species fishery management plan,” in Pacific Fishery Management Council , Portland, OR, 112.

Pacific Fishery Management Council (PFMC) (2013). “Pacific coast fishery ecosystem plan for the U.S. portion of the California current large marine ecosystem,” in Pacific Fishery Management Council , Portland, OR, 190.

Pacific Fishery Management Council (PFMC) (2014). Report on the Atlantis Model Review. Agenda Item H.1. Available online at: https://www.pcouncil.org/documents/2014/11/h-ecosystem-based-management-november-2014.pdf/ .

Pacific Fishery Management Council (PFMC) (2015a). Harvest Specifications and Management Measures for 2015-2016 and Biennial Periods Thereafter-Final Environmental Impact Statement. Available online at: https://www.pcouncil.org/documents/2015/01/2015-16-harvest-specifications-amendment-24-feis.pdf/ .

Pacific Fishery Management Council (PFMC) (2015b). Scientific and Statistical Committee Statement on Fisheries Ecosystem Plan Initiative Scoping. Agenda Item D.1.a, Supplemental SSC Report. Available online at: https://www.pcouncil.org/documents/2015/09/agenda-item-d-1-a-supplemental-ssc-report.pdf/ .

Pacific Fishery Management Council (PFMC) (2016a). “Agenda Item D.1: fishery ecosystem plan coordinated ecosystem indicator review initiative,” in 236th Session of the Pacific Fishery Management Council. Held: September 12-20, 2016 , Boise, ID.

Pacific Fishery Management Council (PFMC) (2016b). “Agenda Item D.2: update on coordinated ecosystem indicator review initiative,” in 233rd Session of the Pacific Fishery Management Council. Held: March 8-14, 2016 , Sacramento, CA.

Pacific Fishery Management Council (PFMC) (2017a). “Ecosystem initiatives appendix to the pacific coast fishery ecosystem Plan for the U.S,” in Portion of the California Current Large Marine Ecosystem. Pacific Fishery Management Council , Portland, OR, 20.

Pacific Fishery Management Council (PFMC) (2017b). Scientific and Statistical Committee Report on Future Council Meeting Agenda and Workload Planning. Agenda Item G.5.a, Supplemental SSC Report1. Available online at: https://www.pcouncil.org/documents/2017/11/agenda-item-g-5-a-supplemental-ssc-report-1-2.pdf .

Pacific Fishery Management Council (PFMC) (2017c). Scientific and Statistical Committee Report on the Review of 2016 Fisheries and Summary of 2017 Stock Abundance Forecasts. Agenda Item E.2.a, Supplemental SSC Report. Available online at: https://www.pcouncil.org/documents/2017/03/e2a_sup_ssc_rpt_mar2017bb.pdf/ .

Pacific Fishery Management Council (PFMC) (2018). Research and Data Needs. Portland, OR: Pacific Fishery Management Council, 226.

Pacific Fishery Management Council (PFMC) (2020). Status of the Pacific Coast Coastal Pelagic Species Fishery and Recommended Acceptable Biological Catches. Stock Assessment and Fishery Evaluation for 2019. Available online at: https://www.pcouncil.org/documents/2020/10/2019-cps-safe-june-2020.pdf/ .

Park, J.-Y., Stock, C. A., Dunne, J. P., Yang, X., and Rosati, A. (2019). Seasonal to Multiannual Marine Ecosystem Prediction With a Global Earth System Model. Science 365, 284–288. doi: 10.1126/science.aav6634

Patrick, W. S., and Link, J. S. (2015). Myths that continue to impede progress in ecosystem-based fisheries management. Fish 40, 155–160.

Payne, M. R., Hobday, A. J., MacKenzie, B. R., Tommasi, D., Dempsey, D. P., Fässler, S. M., et al. (2017). Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4:289.

Pearcy, W. G. (1992). Ocean Ecology of North Pacific Salmonids. Seattle: Washington Sea Grant Program.

Peterman, R. M. (1982). Model of salmon age structure and its use in preseason forecasting and studies of marine survival. Can. J. Fish. Aquat. Sci. 39, 1444–1452. doi: 10.1139/f82-195

Petesch, T., and Pfeiffer, L. (2019). Impacts of rationalization on exposure to high winds in Alaska’s Crab Fisheries. J. Agromed. 24, 364–373. doi: 10.1080/1059924X.2019.1646683

Petursdottir, G., Hannibalsson, O., and Turner, J. M. M. (2001). Safety at Sea as an Integral Part of Fisheries Management. FAO Fisheries Circular 966. Rome: Food and Agriculture Organization of the United Nations.

Pfeiffer, L. (2020). How storms affect fishers’ decisions about going to sea. ICES J. Mar. Sci. 77, 2753–2762.

Pfeiffer, L., and Gratz, T. (2016). The effect of rights-based fisheries management on risk taking and fishing safety. Proc. Natl. Acad. Sci. U.S.A. 113, 2615–2620. doi: 10.1073/pnas.1509456113

Pikitch, E. K., Boersma, P. D., Boyd, I. L., Conover, D. O., et al. (2012). “Little fish, big impact: managing a crucial link in ocean food webs,” in Lenfest Ocean Program , Washington, DC.

Pikitch, E. K., Santora, C., Babcock, E. A., Bakun, A., Bonfil, R., Conover, D. O., et al. (2004). Ecosystem-based fishery management. Science 305, 346–347.

Plagányi, ÉE., Punt, A. E., Hillary, R., Morello, E. B., Thébaud, O., Hutton, T., et al. (2014). Multispecies fisheries management and conservation: tactical applications using models of intermediate complexity. Fish Fish. 15, 1–22. doi: 10.1111/j.1467-2979.2012.00488.x

Plagányi, ÉE., van Putten, I., Hutton, T., Deng, R. A., Dennis, D., Pascoe, S., et al. (2013). Integrating indigenous livelihood and lifestyle objectives in managing a natural resource. Proc. Natl. Acad. Sci. U.S.A. 110, 3639–3644.

Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. A., and Haddon, M. (2016a). Management strategy evaluation: best practices. Fish Fish. 17, 303–334. doi: 10.1111/faf.12104

Punt, A. E., MacCall, A. D., Essington, T. E., Francis, T. B., Hurtado-Ferro, F., Johnson, K. F., et al. (2016b). Exploring the implications of the harvest control rule for Pacific sardine, accounting for predator dynamics: a MICE model. Ecol. Modell. 337, 79–95. doi: 10.1016/j.ecolmodel.2016.06.004

Roberts, J. J., Best, B. D., Mannocci, L., Fujioka, E., Halpin, P. N., Palka, D. L., et al. (2016). Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico. Sci. Rep. 6:22615. doi: 10.1038/srep22615

Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E., and Lundquist, C. J. (2017). A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4:421. doi: 10.3389/fmars.2017.00421

Rogers, L. A., Griffin, R., Young, T., Fuller, E., St. Martin, K., and Pinsky, M. L. (2019). Shifting habitats expose fishing communities to risk under climate change. Nat. Clim. Chang 9, 512–516. doi: 10.1038/s41558-019-0503-z

Rupp, D. E., Wainwright, T. C., Lawson, P. W., and Peterson, W. T. (2012). Marine environment-based forecasting of coho salmon ( Oncorhynchus kisutch ) adult recruitment. Fish. Oceanogr. 21, 1–19. doi: 10.1111/j.1365-2419.2011.00605.x

Ryan, J. P., Kudela, R. M., Birch, J. M., Blum, M., Bowers, H. A., Chavez, F. P., et al. (2017). Causality of an extreme harmful algal bloom in Monterey Bay, California, during the 2014–2016 northeast Pacific warm anomaly. Geophys. Res. Lett. 44, 5571–5579. doi: 10.1002/2017GL072637

Sainsbury, N. C., Genner, M. J., Saville, G. R., Pinnegar, J. K., O’Neill, C. K., Simpson, S. D., et al. (2018). Changing storminess and global capture fisheries. Nat. Clim. Chang. 8, 655–659. doi: 10.1038/s41558-018-0206-x

Santora, J. A., Mantua, N. J., Schroeder, I. D., Field, J. C., Hazen, E. L., Bograd, S. J., et al. (2020). Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat. Commun. 11:536. doi: 10.1038/s41467-019-14215-w

Satterthwaite, W. H., Andrews, K. S., Burke, B. J., Gosselin, J. L., Greene, C. M., Harvey, C. J., et al. (2020). Ecological thresholds in forecast performance for key United States West Coast Chinook salmon stocks. ICES J. Mar. Sci. 77, 1503–1515. doi: 10.1093/icesjms/fsz189

Savelli, S., and Joslyn, S. (2012). Boater safety: communicating weather forecast information to high-stakes end users. Weather. Clim. Soc. 4, 7–19. doi: 10.1175/WCAS-D-11-00025.1

Schroeder, I. D., Santora, J. A., Bograd, S. J., Hazen, E. L., Sakuma, K. M., Moore, A. M., et al. (2018). Source water variability as a driver of rockfish recruitment in the California Current Ecosystem: implications for climate change and fisheries management. Can. J. Fish. Aquat. Sci. 76, 950–960. doi: 10.1139/cjfas-2017-0480

Schuwirth, N., Borgwardt, F., Domisch, S., Friedrichs, M., Kattwinkel, M., Kneis, D., et al. (2019). How to make ecological models useful for environmental management. Ecol. Modell. 411:108784.

Selden, R. L., Thorson, J. T., Samhouri, J. F., Bograd, S. J., Brodie, S., Carroll, G., et al. (2019). Coupled changes in biomass and distribution drive trends in availability of fish stocks to US West Coast ports. ICES J. Mar. Sci. 77, 188–199. doi: 10.1093/icesjms/fsz211

Shelton, A. O., Sullaway, G. H., Ward, E. J., Feist, B. E., Somers, K. A., et al. (2020). Redistribution of salmon populations in the northeast Pacific ocean in response to climate. Fish Fish. 22, 503–517. doi: 10.1111/faf.12530

Siedlecki, S. A., Kaplan, I. C., Hermann, A. J., Nguyen, T. T., Bond, N. A., Newton, J. A., et al. (2016). Experiments with Seasonal Forecasts of ocean conditions for the Northern region of the California Current upwelling system. Sci. Rep. 6:27203. doi: 10.1038/srep27203

Skern-Mauritzen, M., Olsen, E., and Huse, G. (2018). Opportunities for advancing ecosystem-based management in a rapidly changing, high latitude ecosystem. ICES J. Mar. Sci. 75, 2425–2433. doi: 10.1093/icesjms/fsy150

Skern-Mauritzen, M., Ottersen, G., Handegard, N. O., Huse, G., Dingsør, G. E., Stenseth, N. C., et al. (2016). Ecosystem processes are rarely included in tactical fisheries management. Fish Fish. 17, 165–175. doi: 10.1111/faf.12111

Smith, A. D. M., Brown, C. J., Bulman, C. M., Fulton, E. A., Johnson, P., Kaplan, I., et al. (2011). Impacts of fishing low-trophic level species on marine ecosystems. Science 333, 1147–1150.

Smith, J. A., Tommasi, D., Sweeney, J., Brodie, S., Welch, H., Hazen, E. L., et al. (2020). Lost opportunity: quantifying the dynamic economic impact of time-area fishery closures. J. Appl. Ecol. 57, 502–513. doi: 10.1111/1365-2664.13565

Smith, J. A., Muhling, B., Sweeney, J., Tommasi, D., Pozo Buil, M., Fiechter, J., et al. (2021). The potential impact of a shifting Pacific sardine distribution on U.S. West Coast landings. Fish. Oceanogr. doi: 10.1111/fog.12529

Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P., and Reşit Akçakaya, H. (2012). Combining static and dynamic variables in species distribution models under climate change. Methods Ecol. Evol. 3, 349–357. doi: 10.1111/j.2041-210X.2011.00157.x

Staton, B. A., and Catalano, M. J. (2019). Bayesian information updating procedures for Pacific salmon run size indicators: evaluation in the presence and absence of auxiliary migration timing information. Can. J. Fish. Aquat. Sci. 76, 1719–1727. doi: 10.1139/cjfas-2018-0176

Stock, C. A., Pegion, K., Vecchi, G. A., Alexander, M. A., Tommasi, D., Bond, N. A., et al. (2015). Seasonal sea surface temperature anomaly prediction for coastal ecosystems. Prog. Oceanogr. 137, 219–236. doi: 10.1016/j.pocean.2015.06.007

Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A. (2018). Increasing precipitation volatility in twenty-first-century California. Nat. Clim. Chang. 8, 427–433. doi: 10.1038/s41558-018-0140-y

Teich, T., Groll, N., and Weisse, R. (2018). Long-term statistics of potentially hazardous sea states in the North Sea 1958–2014. Ocean Dyn. 68, 1559–1570. doi: 10.1007/s10236-018-1210-4

Thorson, J. T. (2019a). Forecast skill for predicting distribution shifts: a retrospective experiment for marine fishes in the Eastern Bering Sea. Fish Fish. 20, 159–173. doi: 10.1111/faf.12330

Thorson, J. T. (2019b). Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments. Fish. Res. 210, 143–161. doi: 10.1016/j.fishres.2018.10.013

Thorson, J. T., Pinsky, M. L., and Ward, E. J. (2016). Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity. Methods Ecol. Evol. 7, 990–1002. doi: 10.1111/2041-210X.12567

Tolimieri, N., Haltuch, M. A., Lee, Q., Jacox, M. G., and Bograd, S. J. (2018). Oceanographic drivers of sablefish recruitment in the California Current. Fish. Oceanogr. 27, 458–474. doi: 10.1111/fog.12266

Tommasi, D., Stock, C. A., Alexander, M. A., Yang, X., Rosati, A., and Vecchi, G. A. (2017a). Multi-annual Climate predictions for fisheries: an assessment of skill of sea surface temperature forecasts for large marine ecosystems. Front. Mar. Sci. 4:201. doi: 10.3389/fmars.2017.00201

Tommasi, D., Stock, C. A., Hobday, A. J., Methot, R., Kaplan, I. C., Eveson, J. P., et al. (2017b). Managing living marine resources in a dynamic environment: the role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49. doi: 10.1016/j.pocean.2016.12.011

Tommasi, D., Stock, C. A., Pegion, K., Vecchi, G. A., Methot, R. D., Alexander, M. A., et al. (2017c). Improved management of small pelagic fisheries through seasonal climate prediction. Ecol. Appl. 27, 378–388. doi: 10.1002/eap.1458

Townsend, H., Aydin, K., Holsman, K., Harvey, C., Kaplan, I., Hazen, E., et al. (2017). Report of the 4th National Ecosystem Modeling Workshop (NEMoW 4): Using Ecosystem Models to Evaluate Inevitable Trade-offs. Washington, D.C: U.S. Department of Commerce. NOAA Tech. Memo. NMFS-F/SPO-173.

Townsend, H., Harvey, C. J., deReynier, Y., Davis, D., Zador, S. G., Gaichas, S., et al. (2019). Progress on implementing ecosystem-based fisheries management in the United States Through the use of ecosystem models and analysis. Front. Mar. Sci. 6:641. doi: 10.3389/fmars.2019.00641

Townsend, H. M., Harvey, C., Aydin, K. Y., Gamble, R., Grüss, A., Levin, P. S., et al. (2014). Report of the 3rd National Ecosystem Modeling Workshop (NEMoW 3): Mingling Models for Marine Resource Management – Multiple Model Inference. Washington, D.C: U.S. Department of Commerce. NOAA Technical Memorandum NMFS-F/SPO-149.

Townsend, H. M., Link, J. S., Osgood, K. E., Gedamke, T., Watters, G. M., Polovina, J. J., et al. (2008). National Marine Fisheries Service Report of the National Ecosystem Modeling Workshop (NEMoW). Washington, D.C: U.S. Department of Commerce. NOAA Technical Memorandum NMFS-F/SPO-87, 93.

Valavanis, V. D., Pierce, G. J., Zuur, A. F., Palialexis, A., Saveliev, A., Katara, I., et al. (2008). Modelling of essential fish habitat based on remote sensing, spatial analysis and GIS. Hydrobiologia 612, 5–20. doi: 10.1007/s10750-008-9493-y

Valinia, S., Englund, G., Moldan, F., Futter, M. N., Köhler, S. J., Bishop, K., et al. (2014). Assessing anthropogenic impact on boreal lakes with historical fish species distribution data and hydrogeochemical modeling. Glob. Change Biol. 20, 2752–2764. doi: 10.1111/gcb.12527

Walters, C. J., and Collie, J. S. (1988). Is research on environmental factors useful to fisheries management? J. Fish. Aquat. Sci. 45, 1848–1854.

Watson, J. R., Fuller, E. C., Castruccio, F. S., and Samhouri, J. F. (2018). Fishermen follow fine-scale physical ocean features for finance. Front. Mar. Sci. 5:46. doi: 10.3389/fmars.2018.00046

Weijerman, M., Fulton, E. A., Janssen, A. B. G., Kuiper, J. J., Leemans, R., Robson, B. J., et al. (2015). How models can support ecosystem-based management of coral reefs. Prog. Oceanogr. 138, 559–570.

Weijerman, M., Link, J. S., Fulton, E. A., Olsen, E., Townsend, H., Gaichas, S., et al. (2016). Atlantis ecosystem model summit: report from a workshop. Ecol. Modell. 335, 35–38. doi: 10.1016/j.ecolmodel.2016.05.007

Welch, H., Hazen, E. L., Bograd, S. J., Jacox, M. G., Brodie, S., Robinson, D., et al. (2019). Practical considerations for operationalizing dynamic management tools. J. Appl. Ecol. 56, 459–469. doi: 10.1111/1365-2664.13281

Wells, B. K., Huff, D. D., Burke, B. J., Brodeur, R. D., Santora, J. A., Field, J. C., et al. (2020). Implementing ecosystem-based management principles in the design of a salmon ocean ecology program. Front. Mar. Sci. 7:342. doi: 10.3389/fmars.2020.00342

Wells, B. K., Santora, J. A., Henderson, M. J., Warzybok, P., Jahncke, J., Bradley, R. W., et al. (2017). Environmental conditions and prey-switching by a seabird predator impact juvenile salmon survival. J. Mar. Syst. 174, 54–63. doi: 10.1016/j.jmarsys.2017.05.008

Wells, B. K., Schroeder, I. D., Santora, J. A., Hazen, E. L., Bograd, S. J., Bjorkstedt, E. P., et al. (2013). State of the California Current 2012-2013: no such thing as an ‘average’ year. Calif. Coop 54, 37–71.

Zwolinski, J. P., and Demer, D. A. (2019). Re-evaluation of the environmental dependence of Pacific sardine recruitment. Fish. Res. 216, 120–125. doi: 10.1016/j.fishres.2019.03.022

Žydelis, R., Lewison, R. L., Shaffer, S. A., Moore, J. E., Boustany, A. M., Roberts, J. J., et al. (2011). Dynamic habitat models: using telemetry data to project fisheries bycatch. Proc. R. Soc. B 278, 3191–3200. doi: 10.1098/rspb.2011.0330

CrossRef Full Text | PubMed Abstract | Google Scholar

Keywords : ecosystem-based fisheries management, ecosystem modeling, fisheries science, fisheries management, natural resource management

Citation: Tommasi D, deReynier Y, Townsend H, Harvey CJ, Satterthwaite WH, Marshall KN, Kaplan IC, Brodie S, Field JC, Hazen EL, Koenigstein S, Lindsay J, Moore K, Muhling B, Pfeiffer L, Smith JA, Sweeney J, Wells B and Jacox MG (2021) A Case Study in Connecting Fisheries Management Challenges With Models and Analysis to Support Ecosystem-Based Management in the California Current Ecosystem. Front. Mar. Sci. 8:624161. doi: 10.3389/fmars.2021.624161

Received: 30 October 2020; Accepted: 31 May 2021; Published: 30 June 2021.

Reviewed by:

Copyright © 2021 Tommasi, deReynier, Townsend, Harvey, Satterthwaite, Marshall, Kaplan, Brodie, Field, Hazen, Koenigstein, Lindsay, Moore, Muhling, Pfeiffer, Smith, Sweeney, Wells and Jacox. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Desiree Tommasi, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

University of Florida

Fisheries and Aquatic Sciences

Program information.

Director: T. “Red” Baker III Graduate Coordinator: D. Behringer

Since 1937 the Change to School of Forest, Fisheries and Geomatics Sciences has prepared students for professional careers caring for natural resources. We emphasize the role of people in managing both terrestrial and aquatic systems, to produce the myriad of benefits and services they provide. Our faculty have a broad range of interests, including ecology, economics/policy, and recreation/education, and are united by an interest in environmental resources, rather than by traditional academic discipline. The School is composed of three programmatic areas: Fisheries and Aquatic Sciences, Forest Resources and Conservation, and Geomatics. Combined, these programs offer seven different degree options (including two professional masters degrees), as well as concentrations and certificates in a diversity of specific areas.

The School’s program in Fisheries and Aquatic Sciences leads to the Master of Science, Master of Fisheries and Aquatic Sciences (nonthesis), and Doctor of Philosophy degrees with a program in Fisheries and Aquatic Sciences. Minimum requirements for these degrees are given in the Graduate Degrees section of this catalog.

The Fisheries and Aquatic Sciences program also offers a combination bachelor’s/master’s degree program. Contact the academic coordinator for information.

The School of Forest, Fisheries and Geomatics Sciences program in Fisheries and Aquatic Sciences conducts research, teaching, and extension programs in four broad areas:

• Sustainable fisheries
• Aquaculture
• Aquatic animal health
• Conservation and management of aquatic environments

Faculty encompass both freshwater and marine environments, as well as managed aquaculture systems. Collaborators include the UF College of Veterinary Medicine, National Biological Survey, National Marine Fisheries Service, Harbor Branch Oceanographic Institute, Mote Marine Laboratory, the US Geologic Survey, the Florida Fish and Wildlife Conservation Commission, and others. Academic programs are structured to emphasize direct engagement of students with faculty. Further information, including specific degree options, faculty biographies, and information on the admissions process, is available at https://ffgs.ifas.ufl.edu .

Degrees Offered with a Major in Fisheries and Aquatic Sciences

• without a concentration
• concentration in Ecological Restoration
• concentration in Geographic Information Systems
• concentration in Natural Resource Policy and Administration
• concentration in Wetland Sciences
• concentration in Geographic Information Systems

Requirements for these degrees are given in the Graduate Degrees section of this catalog.

School of Forest Resources and Conservation Courses

Geomatics departmental courses.

Fisheries and Aquatic Sciences Program Courses

Forest Resources and Conservation Program Courses

College of Agricultural and Life Sciences Courses

Fisheries & Aquatic sciences (PHD)

SLO 1     Knowledge         Describe and explain key concepts, theories and information in their discipline.

SLO 2     Knowledge         Apply the scientific method and the appropriate methodologies to the generation of new knowledge.

SLO 3     Skills      Communicate effectively in both written and oral form.

SLO 4     Skills      Develop and execute proper experimental or sampling designs.

SLO 5     Skills      Utilize critical thinking to evaluate spoken and written communications.

SLO 6     Professional Behavior    Work in teams with peers; interact honestly, ethically and with cultural sensitivity; translate skills, knowledge and motivation into observable behaviors related to success in specific situations.

Fisheries & Aquatic sciences (MFAS)

SLO 2     Knowledge         Apply the appropriate methodologies to the synthesis of existing knowledge.

SLO 4     Skills      Develop and execute proper project design.

fisheries & Aquatic Sciences (MS)

SLO 1     Knowledge         Describe and explain key concepts, theories and information into their discipline.

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• Graduate School
• Agricultural and veterinary sciences
• Agriculture, forestry, and fisheries
• Fisheries sciences

Fisheries management

Parent category, graduate degree programs in fisheries management, doctor of philosophy in integrated studies in land and food systems (phd).

The doctoral (PhD) program in Integrated Studies in Land and Food Systems creates opportunities for students to develop and strengthen research capabilities and advanced knowledge. Students must plan and complete a doctoral thesis resulting in an original scholarly contribution to knowledge in...

Doctor of Philosophy in Oceans and Fisheries (PhD)

The Program is full-time, consisting of courses and research, designed to train marine and freshwater scientists in basic and applied research that will help foster healthy marine and freshwater ecosystems and sustainable resource use. The Program draws on the broad and extensive expertise of...

Master of Science in Integrated Studies in Land and Food Systems (MSc)

The MSc program in Integrated Studies in Land and Food Systems provides opportunities for students to broaden their knowledge base and gain research experiences.  ILSFS students work on diverse and often interdisciplinary research topics that address priority food systems questions and challenges...

Master of Science in Oceans and Fisheries (MSc)

The Program is designed to train marine and freshwater scientists to undertake basic and applied research that will help foster healthy marine and freshwater ecosystems and sustainable resource use. Students broaden their interdisciplinary expertise and acquire professional experience in areas...

UBC Researchers conducting research in Fisheries management

Christensen, villy, institute for the oceans and fisheries, faculty of science.

Faculty (G+PS eligible/member)

Fisheries management; Global change biology; Ecosystem function

Student & Alumni Stories in Fisheries management

Erika Gavenus

Doctor of Philosophy in Resources, Environment and Sustainability (PhD)

Interrogating Crown-imposed governance of First Nations’ fisheries through the lens of food justice

Roshni Mangar

Understanding the fishers to change the fishery: who is involved in bottom trawl fisheries in Asia, and why?

Kaitlyn Zinn

Doctor of Philosophy in Forestry (PhD)

Cumulative effects of recreational catch-and-release, temperature, and infectious agents on Chinook salmon: from marine environments to spawning grounds

Academic Units in Fisheries management

Faculty of land and food systems, french name, french description, explore our wide range of course-based and research-based program options.

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Texas A&M University Catalogs

Doctor of philosophy in rangeland, wildlife and fisheries management.

The Doctor of Philosophy degree in Rangeland, Wildlife, and Fisheries Management is designed primarily for students pursuing an academic or research career in natural resource management and ecology, with an emphasis in wildlife, fisheries, rangelands, or human dimensions and policy. The RWFM curriculum aims to provide students with a multi-disciplinary toolkit grounded in cutting-edge science to address an array of questions confronting the management of natural resources in Texas, the nation, and beyond. With the goal of conserving our natural legacy in a dynamic and fluid social-ecological system, students are provided an educational foundation that equips them to address complexity across multiple social-ecological systems and scales. This program involves intensive research, guided coursework, and a resulting dissertation demonstrating superior knowledge and understanding of the subject area.

Steps to Fulfill a Doctoral Program

Program Requirements

• Student's Advisory Committee

Degree Plan

Transfer of credit, research proposal, preliminary examination, preliminary examination format, preliminary examination scheduling, preliminary examination grading, failure of the preliminary examination, retake of failed preliminary examination, final examination, final examination grading, dissertation, student’s advisory committee.

After receiving admission to graduate studies and enrolling, the student will consult with the head of their major or administrative department (or chair of the intercollegiate faculty) concerning appointment of the chair of the advisory committee. The student’s advisory committee will consist of  no fewer than four members of the graduate faculty  representative of the student’s several fields of study and research, where the chair or co-chair must be from the student’s department (or intercollegiate faculty, if applicable), and  at least one or more of the members must have an appointment to a department other than the student’s major department . The outside member for a student in an interdisciplinary degree program must be from a department different from the chair of the student’s committee.

The chair, in consultation with the student, will select the remainder of the advisory committee. Only graduate faculty members located on Texas A&M University campuses may serve as chair of a student’s advisory committee. Other Texas A&M University graduate faculty members located off-campus may serve as a member or co-chair (but not chair), with a member as the chair.

If the chair of a student’s advisory committee voluntarily leaves the University and the student is near completion of the degree and wants the chair to continue to serve in this role, the student is responsible for securing a current member of the University Graduate Faculty, from the student’s academic program and located near the Texas A&M University campus site, to serve as the co-chair of the committee. The Department Head or Chair of Intercollegiate faculty may request in writing to the Associate Provost and Dean of the Graduate and Professional School that a faculty member who is on an approved leave of absence or has voluntarily separated from the university, be allowed to continue to serve in the role of chair of a student’s advisory committee without a co-chair for up to one year. The students should be near completion of the degree. Extensions beyond the one year period can be granted with additional approval of the Dean.

The committee members’ signatures on the degree plan indicate their willingness to accept the responsibility for guiding and directing the entire academic program of the student and for initiating all academic actions concerning the student. Although individual committee members may be replaced by petition for valid reasons, a committee cannot resign  en masse . The chair of the committee, who usually has immediate supervision of the student’s research and dissertation or record of study, has the responsibility for calling all meetings of the committee. The duties of the committee include responsibility for the proposed degree plan, the research proposal, the preliminary examination, the dissertation or record of study and the final examination. In addition, the committee, as a group and as individual members, is responsible for counseling the student on academic matters, and, in the case of academic deficiency, initiating recommendations to the Graduate and Professional School.

The student’s advisory committee will evaluate the student’s previous education and degree objectives. The committee, in consultation with the student, will develop a proposed degree plan and outline a research problem which, when completed, as indicated by the dissertation (or its equivalent for the degree of Doctor of Education or the degree of Doctor of Engineering), will constitute the basic requirements for the degree. The degree plan must be filed with the Graduate and Professional School prior to the deadline imposed by the student’s college and no later than 90 days prior to the preliminary examination.

This proposed degree plan should be submitted through the online Document Processing Submission System located on the website  http://ogsdpss.tamu.edu . A minimum of 64 hours is required on the degree plan for the Doctor of Philosophy for a student who has completed a master’s degree. A student who has completed a DDS/DMD, DVM or a MD at a U.S. institution is also required to complete a minimum of 64 hours. A student who has completed a baccalaureate degree but not a master’s degree will be required to complete a 96-hour degree plan. Completion of a DDS/DMD, DVM or MD degree at a foreign institution requires completion of a minimum of 96 hours for the Doctor of Philosophy. A field of study may be primarily in one department or in a combination of departments. A degree plan must carry a reasonable amount of 691 (Research). A maximum of 9 hours of 400-level undergraduate courses may be used toward meeting credit-hour requirements for the Doctor of Philosophy.

Additional coursework may be added by petition to the approved degree plan by the student’s advisory committee if it is deemed necessary to correct deficiencies in the student’s academic preparation. No changes can be made to the degree plan once the student’s Request for Final Examination is approved by the Graduate and Professional School.

Approval to enroll in any professional course (900-level) should be obtained from the head of the department (or Chair of the intercollegiate faculty, if applicable) in which the course will be offered before including such a course on a degree plan.

No credit may be obtained by correspondence study, by extension or for any course of fewer than three weeks duration.

For non-distance degree programs, no more than 50 percent of the non-research credit hours required for the program may be completed through distance education courses.

To receive a graduate degree from Texas A&M University, students must earn one-third or more of the credits through the institution’s own direct instruction. This limitation also applies to joint degree programs. 

Courses for which transfer credits are sought must have been completed with a grade of B or greater and must be approved by the student’s advisory committee and the Graduate and Professional School. These courses must not have been used previously for another degree. Except for officially approved cooperative doctoral programs, credit for thesis or dissertation research or the equivalent is not transferable. Credit for “internship” coursework in any form is not transferable. Courses taken in residence at an accredited U.S. institution or approved international institution with a final grade of B or greater will be considered for transfer credit if, at the time the courses were completed, the courses would be accepted for credit toward a similar degree for a student in degree-seeking status at the host institution. Credit for coursework taken by extension is not transferable. Coursework  in which no formal grades are given or in which grades other than letter grades (A or B) are earned (for example, CR, P, S, U, H, etc.) is not accepted for transfer credit . Credit for coursework submitted for transfer from any college or university must be shown in semester credit hours, or equated to semester credit hours.

Courses used toward a degree at another institution may not be applied for graduate credit. If the course to be transferred was taken prior to the conferral of a degree at the transfer institution, a letter from the registrar at that institution stating that the course was not applied for credit toward the degree must be submitted to the Graduate and Professional School.

Grades for courses completed at other institutions are not included in computing the GPA. An official transcript from the university at which transfer courses are taken must be sent directly to the Office of Admissions.

The general field of research to be used for the dissertation should be agreed on by the student and the advisory committee at their first meeting, as a basis for selecting the proper courses to support the proposed research.

As soon thereafter as the research project can be outlined in reasonable detail, the dissertation research proposal should be completed. The research proposal should be approved at a meeting of the student’s advisory committee, at which time the feasibility of the proposed research and the adequacy of available facilities should be reviewed. The approved proposal, signed by all members of the student’s advisory committee, the head of the student’s major department (or chair of the intercollegiate faculty, if applicable), must be submitted to the Graduate and Professional School at least 20 working days prior to the submission of the Request for the Final Examination.

Compliance issues must be addressed if a graduate student is performing research involving human subjects, animals, infectious biohazards and recombinant DNA. A student involved in these types of research should check with the Office of Research Compliance and Biosafety at (979) 458-1467 to address questions about all research compliance responsibilities. Additional information can also be obtained on the website  http:// rcb.tamu.edu .

Examinations

The student’s major department (or chair of the interdisciplinary degree program faculty, if applicable) and their advisory committee may require qualifying, cumulative or other types of examinations at any time deemed desirable. These examinations are entirely at the discretion of the department and the student’s advisory committee.

The preliminary examination is required. The preliminary examination for a doctoral student shall be given no earlier than a date at which the student is within 6 credit hours of completion of the formal coursework on the degree plan (i.e., all coursework on the degree plan except 681, 684, 691 or other graduate courses specifically designated as S/U in the course catalog). The student should complete the Preliminary Examination no later than the end of the semester following the completion of the formal coursework on the degree plan.

The objective of preliminary examination is to evaluate whether the student has demonstrated the following qualifications:

a.     a mastery of the subject matter of all fields in the program;

b.     an adequate knowledge of the literature in these fields and an ability to carry out bibliographical research;

c.     an understanding of the research problem and the appropriate methodological approaches.

The format of the preliminary examination shall be determined by the student’s department (or interdisciplinary degree program, if applicable) and advisory committee, and communicated to the student in advance of the examination. The exam may consist of a written component, oral component, or combination of written and oral components.

The preliminary exam may be administered by the advisory committee or a departmental committee; herein referred to as the examination committee.

Regardless of exam format, a student will receive an overall preliminary exam result of pass or fail. The department (or interdisciplinary degree program, if applicable) will determine how the overall pass or fail result is determined based on the exam structure and internal department procedures. If the exam is administered by the advisory committee, each advisory committee member will provide a pass or fail evaluation decision.

Only one advisory committee substitution is allowed to provide an evaluation decision for a student’s preliminary exam, and it cannot be the committee chair.

If a student is required to take, as a part of the preliminary examination, a written component administered by a department or interdisciplinary degree program, the department or interdisciplinary degree program faculty must:

a.     offer the examination at least once every six months. The departmental or interdisciplinary degree program examination should be announced at least 30 days prior to the scheduled examination date.

b.     assume the responsibility for marking the examination satisfactory or unsatisfactory, or otherwise graded, and in the case of unsatisfactory, stating specifically the reasons for such a mark.

c.     forward the marked examination to the chair of the student’s advisory committee within one week after the examination.

Students are eligible for to schedule the preliminary examination in the Academic Requirements Completion System (ARCS) if they meet the following list of eligibility requirements:

Student is registered at Texas A&M University for a minimum of one semester credit hour in the long semester or summer term during which any component of the preliminary examination is held. If the entire examination is held between semesters, then the student must be registered for the term immediately preceding the examination.

An approved degree plan is on file with the Graduate and Professional School prior to commencing the first component of the examination.

Student’s cumulative GPA is at least 3.000.

Student’s degree plan GPA is at least 3.000.

At the end of the semester in which at least the first component of the exam is given, there are no more than 6 hours of coursework remaining on the degree plan (except 681, 684, 691 or other graduate courses specifically designated as S/U in the course catalog). The head of the student’s department (or Chair of the Interdisciplinary Degree Program, if applicable) has the authority to approve a waiver of this criterion.

Credit for the preliminary examination is not transferable in cases where a student changes degree programs after passing a preliminary exam.

If a written component precedes an oral component of the preliminary exam, the chair of the student’s examination committee is responsible for making all written examinations available to all members of the committee. A positive evaluation of the preliminary exam by all members of a student’s examination committee with at most one dissension is required to pass a student on their preliminary exam.

The student’s department will promptly report the results of the Preliminary Examination to the Graduate and Professional School via the Academic Requirements Completion System (ARCS) within 10 working days of completion of the preliminary examination.

If an approved examination committee member substitution (one only) has been made, their approval must be submitted to the Graduate and Professional School via ARCS. The approval of the designated department approver is also required on the request.

After passing the required preliminary oral and written examinations for a doctoral degree, the student must complete the final examination within four years of the semester in which the preliminary exam is taken. Exams taken in between terms will expire at the end of the term that ended prior to the exam. For example, a preliminary exam taken and passed during the Fall 2023 semester will expire at the end of the Fall 2027 semester. A preliminary exam taken in the time between the Summer and Fall 2023 semesters will expire at the end of the Summer 2027 semester.

First Failure

Upon approval of a student’s examination committee (with no more than one member dissenting), and approval of the Department and Graduate and Professional School, a student who has failed a preliminary examination may be given one re-examination. In accordance with Student Rule 12.5, the student’s department head or designee, intercollegiate faculty, or graduate advisory committee should make a recommendation to the student regarding their scholastic deficiency.

Second Failure

Upon failing the preliminary exam twice in a doctoral program, a student is no longer eligible to continue to pursue the PhD in that program/major. In accordance with Student Rule 12.5.3 and/or 12.5.4, the student will be notified of the action being taken by the department as a result of the second failure of the preliminary examination.

Adequate time must be given to permit a student to address inadequacies emerging from the first preliminary examination. The examination committee must agree upon and communicate to the student, in writing, an adequate time-frame from the first examination (normally six months) to retest, as well as a detailed explanation of the inadequacies emerging from the examination. The student and committee should jointly negotiate a mutually acceptable date for this retest.  When providing feedback on inadequacies, the committee should clearly document expected improvements that the student must be able to exhibit in order to retake the exam.  The examination committee will document and communicate the time-frame and feedback within 10 working days of the exam that was not passed.

Candidates for the doctoral degrees must pass a final examination by deadline dates announced in the  Graduate and Professional School Calendar  each semester. A doctoral student is allowed only one opportunity to take the final examination.

No unabsolved grades of D, F, or U for any course can be listed on the degree plan. The student must be registered for any remaining hours of 681, 684, 691 or other graduate courses specifically designated as S/U in the course catalog during the semester of the final exam. No student may be given a final examination until they have been admitted to candidacy and their current official cumulative and degree plan GPAs are 3.00 or better.

Refer to the  Admission to Candidacy  section of the graduate catalog for candidacy requirements.

A request to schedule the final examination must be submitted to the Graduate and Professional School via ARCS a minimum of 10 working days in advance of the scheduled date. Any changes to the degree plan must be approved by the Graduate and Professional School prior to the submission of the request for final examination.

The student’s advisory committee will conduct this examination. Only one committee member substitution is allowed with the approval of the Graduate and Professional School. If the substitution is for the sole external member of the advisory committee - with an appointment to a department other than the student's major department - then the substitute must also be external to the student's major department. In extenuating circumstances, with the approval of the Graduate and Professional School, an exception to this requirement may be granted.

The final examination is not to be administered until the dissertation or record of study is available in substantially final form to the student’s advisory committee, and all concerned have had adequate time to review the document.  Whereas the final examination may cover the broad field of the candidate’s training, it is presumed that the major portion of the time will be devoted to the dissertation and closely allied topics. Persons other than members of the graduate faculty may, with mutual consent of the candidate and the chair of the advisory committee, be invited to attend a final examination for an advanced degree. A positive vote by all members of the graduate committee with at most one dissension is required to pass a student on their exam. A department can have a stricter requirement provided there is consistency within all degree programs within a department. Upon completion of the questioning of the candidate, all visitors must excuse themselves from the proceedings.

The student’s department will promptly report the results of the Final Examination to the Graduate and Professional School via the Academic Requirements Completion System (ARCS) within 10 working days of completion of the final examination. The Graduate and Professional School will be automatically notified via ARCS of any cancellations.

A positive evaluation of the final exam by all members of a student’s advisory committee with at most one dissension is required to pass a student on their final exam. If an approved committee member substitution (1 only) has been made, their approval must be submitted to the Graduate and Professional School via ARCS.

The dissertation,  which must be a candidate's original work demonstrates the ability to perform independent research . Whereas acceptance of the dissertation is based primarily on its scholarly merit, it must also exhibit creditable literary workmanship. Dissertation formatting must be acceptable to the Graduate and Professional School as outlined in the Guidelines for Theses, Dissertations, and Records of Study.

After successful defense and approval by the student’s advisory committee and the head of the student’s major department (or chair of intercollegiate faculty, if applicable), a student must submit the dissertation in electronic format as a single PDF file to https://etd.tamu.edu/ . Additionally, a dissertation approval form with original signatures must be received by the Graduate and Professional School through the Academic Requirements Completion System (ARCS). Both the PDF file and the completed ARCS approval form must be received by the deadline.

Deadline dates for submitting are announced each semester or summer term in the Graduate and Professional School Calendar (see Time Limit statement). These dates also can be accessed via the  Graduate and Professional School website .

Each student who submits a document for review is assessed a one-time thesis/dissertation processing fee through Student Business Services. This processing fee is for the thesis/dissertation services provided. After commencement, dissertations are digitally stored and made available through the Texas A&M Libraries.

A dissertation that is deemed unacceptable by the Graduate and Professional School because of excessive corrections will be returned to the student’s department head or chair of the intercollegiate faculty . The manuscript must be resubmitted as a new document, and the entire review process must begin anew. All original submittal deadlines must be met during the resubmittal process to graduate.

Additional Requirements

Continuous registration, admission to candidacy.

• 99-Hour Cap on Doctoral Degree

Application for Degree

A student who enters the doctoral degree program with a baccalaureate degree must spend one academic year plus one semester in resident study at Texas A&M University. A student who holds master’s degree when they enter a doctoral degree program must spend one academic year in resident study. One academic year may include two adjacent regular semesters or one regular semester and one adjacent 10-week summer semester. The third semester is not required to be adjacent to the one year. Enrollment for each semester must be a minimum of 9 credit hours each to satisfy the residence requirement. A minimum of 1 credit hour must be in a non-distance education delivery mode. Semesters in which the student is enrolled in all distance education coursework will not count toward fulfillment of the residence requirement.

To satisfy the residence requirement, the student must complete a minimum of 9 credit hours per semester or 10-week summer semester in resident study at Texas A&M University for the required period. A student who enters a doctoral degree program with a baccalaureate degree may fulfill residence requirements in excess of one academic year (18 credit hours) by registration during summer sessions or by completion of a less-than-full course load (in this context a full course load is considered 9 credit hours per semester).

Students who are employed full-time while completing their degree may fulfill total residence requirements by completion of less-than-full time course loads each semester. In order to be considered for this, the student is required to submit a Petition for Waivers and Exceptions along with verification of employment to the Graduate and Professional School. An employee should submit verification of employment at the time they submit the degree plan. See  Registration .

See  Residence Requirements .

All requirements for doctoral degrees must be completed within a period of ten consecutive calendar years for the degree to be granted.   A course will be considered valid until 10 years after the end of the semester in which it is taken. Graduate credit for coursework more than ten calendar years old at the time of the final oral examination may not be used to satisfy degree requirements.

After passing the required preliminary oral and written examinations for a doctoral degree, the student must complete the final examination within four years of the semester in which the preliminary exam is taken. Exams taken in between terms will expire at the end of the term that ended prior to the exam. For example, a preliminary exam taken and passed during the fall 2019 semester will expire at the end of the fall 2023 semester. A preliminary exam taken in the time between the summer and fall 2019 semesters will expire at the end of the summer 2023 semester.

A final corrected version of the dissertation or record of study in electronic format as a single PDF file must be cleared by the Graduate and Professional School within one year of the semester in which the final exam is taken. Exams taken in between terms will expire at the end of the term that ended prior to the exam. For example, a final exam taken and passed during the fall 2022 semester will expire at the end of the fall 2023 semester. A final exam taken in the time between the summer and fall 2022 semesters will expire at the end of the summer 2023 semester. Failure to do so will result in the degree not being awarded.

A student in a program leading to a Doctor of Philosophy who has completed all coursework on a degree plan other than 691 (research) are required to be in continuous registration until all requirements for the degree have been completed. See  Continuous Registration Requirements .

To be admitted to candidacy for a doctoral degree, a student must have:

• completed all formal coursework on the degree plan with the exception of any remaining 681, 684, 690 and 691, or 791.
• a 3.0 Graduate GPA and a Degree Plan GPA of at least 3.0 with no grade lower than C in any course on the degree plan,
• passed the preliminary examination (written and oral portions),
• submitted an approved dissertation proposal,
• met the residence requirements. The final examination will not be authorized for any doctoral student who has not been admitted to candidacy.

A student is required to possess a competent command of English. For English language proficiency requirements, see the Admissions section of this catalog. The doctoral (PhD) foreign language requirement at Texas A&M University is a departmental option, to be administered and monitored by the individual departments of academic instruction.

99-Hour Cap on Doctoral Degrees

In Texas, public colleges and universities are funded by the state according to the number of students enrolled. In accordance with legislation passed by the Texas Legislature, the number of hours for which state universities may receive subvention funding at the doctoral rate for any individual is limited to 99 hours. Texas A&M and other universities will not receive subvention for hours in excess of the limit.

Institutions of higher education are allowed to charge the equivalent of non-resident tuition to a resident doctoral student who has enrolled in 100 or more semester credit hours of doctoral coursework.

Doctoral students at Texas A&M have seven years to complete their degree before being charged out-of-state tuition. A doctoral student who, after seven years of study, has accumulated 100 or more doctoral hours will be charged tuition at a rate equivalent to out-of-state tuition. Please note that the tuition increases will apply to Texas residents as well as students from other states and countries who are currently charged tuition at the resident rate. This includes those doctoral students who hold GAT, GANT, and GAR appointments or recipients of competitive fellowships who receive more than $1,000 per semester. Doctoral students who have not accumulated 100 hours after seven years of study are eligible to pay in-state tuition if otherwise eligible.

Doctoral students who exceed the credit limit will receive notification from the Graduate and Professional School during the semester in which they are enrolled and exceeding the limit in their current degree program. The notification will explain that the State of Texas does not provide funding for any additional hours in which a student is enrolled in excess of 99 hours. Texas A&M University will recover the lost funds by requiring students in excess of 99 hours to pay tuition at the non-funded, non-resident rate. This non-funded, non-resident tuition rate status will be updated for the following semester and in all subsequent semesters until receipt of a doctoral degree. Please see the  Tuition Calculator  at the non-resident rate for an example of potential charges.

The following majors are exempt from the 99-Hour Cap on Doctoral Degrees and have a limit of 130 doctoral hours:

• Biochemistry and Molecular Biophysics
• Biomedical Sciences
• Clinical Psychology
• Counseling Psychology
• Epidemiology and Environmental Health
• Genetics and Genomics
• Health Services Research
• Medical Sciences
• Microbiology
• Neurosciences (College of Medicine)
• Oral and Craniofacial Biomedical Sciences
• Pharmaceutical Sciences
• Public Health Sciences
• School Psychology

For information on applying for your degree, please visit the  Graduation  section.

Doctor of Philosophy (Ph.D.)

Admission and course requirements for the Ph.D. degree in Wildlife and Fisheries Science as well as Committee, research, thesis and examination information.

The objectives of the PhD degree are:

• to attain the highest level of scholarship and independent research in one of the three subject matter areas within the Department of Ecosystem Science and Management,
• to conduct original research in a scholarly manner which represents a significant contribution to knowledge within the scope of the Department's programs, and
• to develop a proficiency in a basic scientific discipline in relation to one of the Department's subject matter areas.

The PhD candidate must develop and demonstrate the ability to conceive and conduct independent research. The degree is designed to produce a scientist proficient in scientific principles and capable of academic teaching and/or scholarly research.

Following admission to residency, even if course work and thesis research have been initiated, full acceptance in the PhD program requires passing the Qualifying Examination early in the residency period. The student must be registered during the semester that the Qualifying Exam is administered.

The minimum admissions requirements established by Penn State University's Graduate School and the Department of Ecosystem Science and Management.

The course and credit requirements stipulated by the Department of Ecosystem Science and Management, in conjunction with the Graduate School of Penn State University.

Ph.D. Dissertation Research information.

Ph.D. Advisory Committee appointment, membership, and responsibilities information.

Ph.D. Final Oral Examination (Dissertation Defense) information.

PDF document, 207.4 KB

PDF document, 356.9 KB

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Professor in Fisheries biology, group leader

On general level, I am interested about the role of science in solving environmental societal problems. An interesting narrative about biology may create motivation to solve the problems, but solution needs also the identification of cost-effective management options that are practically applicable. Decision analysis is a way to identify the most justified solutions, and to estimate how likely it is that the desired aims are actually achieved. I am favoring the Bayesian approach to risk assessment and decision analysis, because to me it Is a scientific description of a learning process: the posterior distributions of one study could, and should, be the prior probabilities of the next study. Such learning chains can be effective tools to focus the science on most essential policy questions.

I am interested in:

· Bayesian risk and decision analysis

· Interdisciplinary probabilistic modeling

· Fish stock assessment and fisheries management

· Fisheries and environmental management problems

Sakari's publications on his TUHAT pages

Follow Sakari on Twitter

Follow Sakari on Facebook

Contact info

Email: [email protected]

Tel: +358 50 330 9233

I am teaching on the following courses: ECGS 014 Diagnosis of environmental problems in aquatic ecosystems, ECGS-151 Introduction to decision analysis and probabilistic integrated modelling ECGS-017 Fisheries management, 519111 Writing of a scientific proposals

Postdoctoral researcher, PhD in Environmental sciences

My background is in aquatic sciences: I have MSc in limnology and fisheries science from the University of Helsinki (2007). During my PhD I studied Bayesian methods and risk and decision analysis. The PhD thesis, titled as "Bayesian network applications for environmental risk assessment " (2014), draws together the risk and decision analytic work I have conducted around the eutrophication (Lehikoinen et al. 2014 ) and oil spill risks (Lehikoinen et al. 2013 and 2015 , Jolma et al. 2014 ) of the Gulf of Finland, using Bayesian Networks as the analytical tool and platform for knowledge integration. In 2015 - 2016 I was working as a post doc in the Swedish University of Agricultural Sciences (SLU), Institute of Coastal Research. There I focused on machine learning, applying Bayesian network classifiers for heterogeneous ecological and environmental data to identify key factors determining the status of two coastal fish indicators of the Baltic Sea (Lehikoinen et al. 2019 ). After returning to FEM group in the end of the year 2016 I have been involved in the projects 30MILES (principal investigator), GOHERR and COMPLETE. Currently my main project is WISE where, with a multidisciplinary consortium, we analyze the resilience of the Finnish society against divergent “wicked” (lacking a clear optimal solution) social-environmental disruptions and develop instruments to improve the resilience.

As the result of working all these years as part of two highly multidisciplinary research communities: the FEM group and the Kotka Maritime Research Center (where my office is located), my world view have been influenced by many other scientific disciplines such as sociology, engineering, environmental economy, geography etc. I could say cross-disciplinary communication is one of my special skills. In our projects I have also had the possibility to work with stakeholders representing different sectors, which have been extremely useful and educative. My perspective to integrative modelling has widened from the data and model coupling only to also the social aspects of knowledge integration, covering for example the elicitation of stakeholders’ values (Laurila-Pant et al., a submitted manuscript) and thinking about problem structuring (Parviainen et al. 2019 ).

I am the 1st supervisor of three PhD students, Mirka Laurila-Pant, Emilia Luoma, and Lauri Ronkainen and teach on the courses Introduction to decision analysis and Bayesian inference and Diagnosis of environmental problems in aquatic ecosystems .

Annukka's publications on her TUHAT pages

Follow Annukka on  ResearchGate and Twitter

Email: [email protected]

Visit Annukka's web pages

Doctoral student

• Risk governance
• Shipping and corporate social responsibility
• Bayesian analysis

Tuuli is working as a researcher in CEARCTIC project

Tuuli's publications on her TUHAT pages

Tuuli's curriculum vitae on LinkedIn

Email: [email protected]

Doctoral student, MSc Aquatic Sciences

• Participatory modelling
• Environmental valuation
• Fish stock assessment

My research takes a probabilistic view on how to set the management objectives and the role of valuation in environmental management problems. As the environmental management aims to improve the ecosystem health and promote sustainable use of natural resources, we need to measure the state as well as to define the desirable and undesirable status of the system. However it is not always straightforward how and in which perspective the impacts on the environment should be valued, therefore I am developing a probabilistic Bayesian approaches to quantify the uncertainty about the management objectives as we as the methods used for measuring the prevailing status of the system.

See Mirka's publications on her TUHAT pages

Email: mirka.laurila-pant[at] helsinki.fi

Follow Mirka on Twitter

Doctoral student, MSSc in Sociology

• Ecosystem approach to fisheries
• Environmental governance
• Science, technology, and society studies

Suvi works as a researcher in  BONUS GOHERR project

Suvi's publications on her TUHAT pages

Email: [email protected]

• Ecological risk assessment
• Causal networks
• Marine minerals

My PhD research focuses on the environmental impacts of seabed mineral extraction, and understanding how seafloor exploitation affects marine ecosystems. I am interested in how adverse effects of human activities may be estimated prior to disturbance, and how impact assessments may be improved using a causal approach.

In my work, I am using Bayesian networks to examine the ecological risks of seabed mining, and the magnitude of the potential impacts. My work uses shallow water mineral concretions in the Baltic Sea as a case study to examine the impacts of seabed mining. As these minerals consitute an understudied habitat type, I am also examining the ecological role of mineral concretions in order to infer the potential impacts of their removal. In addition, I am interested in how we perceive the impacts to remote environments, such as the deep sea, and how our values for these environments guide decision-making in natural resource governance.

Laura works as a researcher in Smartsea project

Laura's publications on her TUHAT pages

Follow Laura on Twitter

Email: laura.m.kaikkonen [at] helsinki.fi

Doctoral student, MSc Geography

· Baltic Sea Environment

· Bayesian Networks

· Stakeholder Involvement

· Sustainable Decision Making

My PhD is about stakeholder involvement and decision making in the environmental problem solving in the Baltic Sea. The Baltic Sea is a unique and vulnerable ecosystem facing various environmental threats and my PhD focuses on biofouling management of the ships, sustainable boating, and oil spills. When trying to solve complex environmental problems, interdisciplinary research is highly needed and thus used in my thesis as well. I use Bayesian networks as a method because they are visual, easy to use and can contain both qualitative and quantitative data from different sources. Therefore they are usable in solving complex interdisciplinary problems.

The stakeholder involvement is important in decision making to make sustainable and fair decisions. However, it is not always easy to know how the stakeholders should be involved. My thesis will tackle this problem and show some approaches to involve stakeholders. Finally, the Bayesian models formed here can be used to better understand these complex environmental problems and ideally, in the future, the models can be used in the decision making as well.

Emilia works as a reseracher in the COMPLETE project.

Email: [email protected]

Doctoral student, MSc in Fisheries science

•  Aquatic ecology
• Environmental management
• Systems analysis
• Bayesian networks

Social and ecological problems are complex per se , not to mention when these two are combined together in one analysis. Humans pose direct and indirect impact on ecosystems, such as fishing, which creates feedbacks. But how do we react to these feedbacks? Social-ecological systems can be seen as a large network, consisting of variables describing the behavior of the system. If we modify the state of one variable in the network or make a decision about the other, what are the causal consequences? My interest is in exploring these networks, by slicing them into smaller sub-systems and trying to figure it out how they work. With relevant indicators, I aim to assess the most critical parts of the system and discuss the dos and don’ts on the management perspective.

Lauri works as a researcher in the COMPLETE project.

Email: [email protected]

Email: [email protected]

See Sampsa's publications on his TUHAT profile.

Jani's TUHAT pages.

Master's student

• Marine resource management
• Fisheries, Marine Spatial Planning
• Baltic Sea & Arctic

Magnus is working on his Master's thesis titled Fisheries management, social dimensions of the Individual Transferable Quota system.

Email: [email protected]

MSc Student

My Masters thesis aims to build a Bayesian model to analyze biomass fluctuation correlations between biologically similar fish species and stocks in the northern Atlantic, which, if functional, could be used to estimate several stock biomasses by observing and analyzing one stock. This could, in turn, lower the costs and resources needed for future stock assessments. In my bachelors degree in aquatic sciences, mainly fisheries and fish biology, I focused on both biological and anthropogenic factors that contribute to fish stock collapses and slow recovery of collapsed stocks.

My Master’s thesis focuses on selection of target species that are potentially harmful alien species in the Baltic Sea invading via ship ballast water. I'm using the Bayesian approach, which reveals the amount of uncertainty concerning the correct classification based on the criteria currently in use. This study may be helpful in the management of ship ballast water.

Eduardo Maeda

Mika Rahikainen

Riikka Venesjärvi

Inari Helle

Post-doctoral researcher, Environmental and Ecological Statistics Group, Univeristy of Helsinki

I work as a postdoctoral researcher in the Environmental and Ecological Statistics group at the University of Helsinki.

My background is in ecology and environmental engineering, and I have a PhD in Aquatic sciences. I am interested in the interactions between humans and ecosystems: What kinds of impacts human activities have on ecosystems and what we can do to mitigate these impacts. I have studied these topics especially from the environmental risk assessment and decision analysis perspective in the Baltic Sea by using Bayesian methods. Currently, I study oil spill risks in the Arctic with my HELSUS Fellow funding . I also work with non-indigenous species and biofouling issues in the COMPLETE project .

I am interested in inter- and transdisciplinary research, and I aim at producing knowledge that is relevant for the society and can be used to support decision-making.

See Inari's personal webpages here.

Marine Risk Governance Research Group

MARISK is a newly founded research group within the Ecosystems and Environment Research Group led by professor Päivi Haapasaari. The group works in close collaboraton with FEM researchers, specializing on the risk governance issues of e.g.  fisheries and shipping.

School of Natural Resources and Environment

Previous dissertations and theses .

Xiaoxing Bian Ph.D. Dissertation: A Multi-Dimensional Assessment on the Co-Existence of the Snow Leopard and Local Pastoralist Communities in the Changtang, China Chair/Faculty Advisor: Dr. Vanessa Hull, Wildlife Ecology and Conservation

Deisi "Vanessa" Luna Celino Ph.D. Dissertation: Fire in the Peruvian Andes: Agricultural Burns, Key Actors' Perceptions, and Changes in Community-Based Management Co-chairs/Faculty Advisors: Dr. Karen Kainer, Forest, Fisheries, & Geomatics Sciences, and Dr. Bette Loiselle, Wildlife Ecology and Conservation

Sinomar Ferreira Da Fonseca Junior Ph.D. Dissertation: Indigenous Mobilization for Rights in Resistance to Infrastructure Projects in the Brazilian Amazon Co-chairs/Faculty Advisors: Dr. Stephen Perz, Sociology, and Dr. Catherine M. Tucker, Anthropology

Ashpreet Kaur Ph.D. Dissertation: Zero Waste Programs in Higher Education Institutions: Understanding Factors Influencing Campus Waste Diversion and Minimization Co-chairs/Faculty Advisors: Dr. Overdevest, Sociology, and Dr. Stephen Perz, Sociology

Matthew Richardson Ph.D. Dissertation: Quantitative Frameworks for Oyster Reef Monitoring and Restoration in the Big Bend of Florida Chair/Faculty Advisor: Dr. Bill Pine, Wildlife Ecology and Conservation/Forest, Fisheries, & Geomatics Sciences

Hui Zhao Ph.D. Dissertation: Sustaining Urban Agriculture: The Role of Ecosystem Service and the Public Involvement Chair/Faculty Advisor: Dr. Jianxiao Qiu, Forest, Fisheries, & Geomatics Sciences

Fernando Noriega Betancourt Ph.D. Dissertation: Spatiotemporal Dynamics of Marine Vessels & Humpback Whale Habitat in Bahia De Banderas, Mexico, Alongside Societal Analysis of Vessel Captains' Attitudes & Perceptions Toward Whale Conservation Chair/Faculty Advisor: Dr. Vince LeCours, Forest, Fisheries, & Geomatics Sciences

Amanda Muni-Morgan Ph.D. Dissertation: Investigating the Composition and Reactivity of Dissolved Organic Matter From Urban Environs: Implications for Blooms of Karenia Brevis and Pyrodinium Bahamense Chair/Faculty Advisor: Dr. Mary Lusk, Soil, Water, and Ecosystem Sciences

Natalia Uribe Castaneda Ph.D. Dissertation: Community Engagement in Coral Reef Restoration Chair/Faculty Advisor: Dr. Martin Main, Wildlife Ecology and Conservation

LeClare, Shelby M.S. Thesis: Effects of an Invasive Top Predator on Ecosystem Structure and Function: A Comparison of the Greater Everglades Graminoid Marsh Food Web Before and After the Burmese Python Invasion Chair/Faculty Advisor: Ben Baiser, Wildlife Ecology and Conservation

Castro, Ernesto Bastos Viveiros De Ph.D. Dissertation: A Path to Nature Conservation: The Role of Mega Trails in Connecting Hikers, Communities, and Landscapes Chair/Faculty Advisor: Taylor Stein, Forest Resources and Conservation

Dawson, DeVant’e Ph.D. Dissertation: Investigating Potential Bioindicators of Health & Environmental Stress in the Cnidarian Holobiont Experiencing Multiple Stressors Chair/Faculty Advisor: Julie Meyer, Soil, Water, and Ecosystem Sciences

Donovan, Megan Ph.D. Dissertation: Advancing Protected Agriculture as a Climate Risk Mitigation Strategy in the Southeastern United States Chair/Faculty Advisor: Sam Smidt, Soil, Water, and Ecosystem Sciences

Episcopio-Sturgeon, Diane Ph.D. Dissertation: Case Studies Exploring Social Science Approaches to Charismatic Species Conservation Chair/Faculty Advisor: Vanessa Hull, Wildlife Ecology and Conservation

Gengler, Nicholas Ph.D. Dissertation: A Global Examination of the Spatial Extent of Landscape Effects and a Regional Examination of How Climate Effects Vary Across Trophic Levels Chair/Faculty Advisor: Lyn Branch, Wildlife Ecology and Conservation

Jones, Maggie Ph.D. Dissertation: Savannas in a Changing World: Effects of Environmental Change on Tree-herbivore Interactions in African Savannas Chair/Faculty Advisor: Robert McCleery, Wildlife Ecology and Conservation

Pappo, Emily Ph.D. Dissertation: Evaluating Climate Resilience of Arabica Coffee (Coffea arabica) Agroecosystems Chair/Faculty Advisor: Luke Flory, Agronomy

Perry, Diane Ph.D. Dissertation: Developing Decision Support Tools for Socioecological Systems: a Cast Study of Florida Stock Enhanced Freshwater Recreational Fisheries Chair/Faculty Advisor: Ed Camp, Fisheries and Aquatic Sciences

Rodofili, Esteban Ph.D. Dissertation: Semi-Automated Analysis of Satellite and UAS Imagery to Inform Marine Mammal Detection, Migration Route Studies and Marine Protected Area Design Chair/Faculty Advisor: Vince LeCours, Fisheries and Aquatic Sciences

Schneider, Owen Ph.D. Dissertation: Restoration Effects, Community Assembly Dynamics, and Flammability of the Rare and Imperiled Plant Communities on Long Pine Key in Everglades National Park Chair/Faculty Advisor: Ben Baiser, Wildlife Ecology and Conservation

Sibiya, Muzi Ph.D. Dissertation: Shrub Encroachment and Large Herbivores as Drivers of Avian Communities in African Savannas Chair/Faculty Advisor: Robert Fletcher, Wildlife Ecology and Conservation

Talla Kouete, Marcel Ph.D. Dissertation: Microbial Diversity of Central African Amphibians and its Relationship to Pathogens Chair/Faculty Advisor: David Blackburn, Florida Museum of Natural History and Zoology

Howley, Samantha M.S. Thesis: River Reversals and the Metabolic Regimes of Florida's Springs Chair/Faculty Advisor: Matt Cohen, Forest Resources and Conservation

Lucinda Fisher M.S. Thesis: Effects of Soil Amendment Incorporation on Soil-Water Relationships and Turfgrass Quality in Florida Chair/Faculty Advisor: Eban Bean, Agricultural and Biological Engineering

Markee, Amanda M.S. Thesis: Weaving Connections from Genotype to Phenotype: A Characterization of Silk Production in the Luna Moth (Actias luna) Chair/Faculty Advisor: Akito Kawahara, Entomology and Nematology

Borden, Jesse Ph.D. Dissertation: Complex Impacts of Anthropogenic Habitat Alteration on Wildlife Chair/Faculty Advisor:  Luke Flory , Agronomy 

Borsum, Scott Ph.D. Dissertation: The Development of Oyster Resource Production and Governance: A Harbinger For Seafood Production Systems Chair/Faculty Advisor:  Ed Camp , School of Forest, Fisheries and Geomatics Sciences

Botta, Robert Ph.D. Dissertation: Enhancing the usage of regional economic analyses within resource management: Case studies of Florida aquatic resources Chair/Faculty Advisor:  Ed Camp , School of Forest, Fisheries and Geomatics Sciences

Brinton, Amanda Ph.D. Dissertation: Puerto Rico's Solid Waste System: Stakeholder Dynamics, Relationships and Events Chair/Faculty Advisor:  Tim Townsend , Environmental Engineering Sciences

De Oliveira Jordao, Carolina Ph.D. Dissertation: Saving the Rainforest? Challenges of being and acting as NGOs in the Amazon Chair/Faculty Advisor: Bob Buschbacher , School of Forest, Fisheries and Geomatics Sciences

Diaz, Renata Ph.D. Dissertation: Of Rodents and Randomness: Macroecological Approaches to Community Structure Chair/Faculty Advisor:  Morgan Ernest , Wildlife Ecology and Conservation

Enloe, Carolyn Ph.D. Dissertation: Population Genomic Structure and Geographic Variation in Florida's Coastal Seaside Sparrows Chair/Faculty Advisor:  Rebecca Kimball , Biology

Feyers, Shane Ph.D. Dissertation: System Mechanics of Sustainable Nature-Based Tourism: Producers, Consumers, and Facilitative Actors Chair/Faculty Advisor:  Taylor Stein , School of Forest, Fisheries and Geomatics Sciences

Khazan, Emily Ph.D. Dissertation: Thermal Physiology and Community Ecology of Butterflies of the Colombian Andes Co-chairs/Faculty Advisors: Jaret Daniels , Florida Museum of Natural History;  Bette Loiselle , Wildlife Ecology and Conservation

Lefler, Forrest Ph.D. Dissertation: Diversity and Management of Cyanobacteria in Tropical and Subtropical Fresh Waters Chair/Faculty Advisor: Dail Laughinghouse , Fort Lauderdale Research and Education Center

Montero Alvarez, Pamela Ph.D. Dissertation: Conservation Strategies in the Contemporary Amazon: Community-Based Tourism, Networks, and Actions among Conservation Organizations Chair/Faculty Advisor: Stephen Perz , Sociology; co-advisor: Angelica Almeyda , Center for Latin American Studies

Moreno Garcia, Pablo Ph.D. Dissertation: Variation in plant-pollinator and host-parasite networks along spatial and temporal gradients Chair/Faculty Advisor:  Ben Baiser , Wildlife Ecology and Conservation

Padilla Paz, Sergio Ph.D. Dissertation: Human Dimensions of Morelet's Crocodile in Campeche, Mexico  Chair/Faculty Advisor: Steve Johnson , Wildlife Ecology and Conservation

Pilnick, Aaron Ph.D. Dissertation: Intensive Aquaculture of the Long-Spinded Sea Urchin Diadema Antillarum with Restoration Considerations Chair/Faculty Advisor:  Josh Patterson , Fisheries and Aquatic Sciences

Roth, Jamila Ph.D. Dissertation: Effects of Environmental Change on Seagrass Reslience and Seagrass-Herbivore Interactions Chair/Faculty Advisor:  Laura Reynolds , Soil, Water, and Ecosystem Sciences

Stokes, Gretchen Ph.D. Dissertation: Assessing global inland fisheries with integrated socio-ecological approaches Co-chairs/Faculty Advisors: Tom Frazer, Fisheries and Aquatic Sciences; Sam Smidt, Soil, Water, and Ecosystem Sciences

Webb, Elizabeth Ph.D. Dissertation: Land-Climate Feedbacks in the Arctic-Boreal Zone Chair/Faculty Advisor: Jeremy Lichstein , Biology 

Zlotnik, Sam Ph.D. Dissertation: Feeding Behavior and Mouthpart Development in Juvenile Leaf-Footed Bugs Chair/Faculty Advisor: Christine Miller , Entymology

DiMaggio, Kylee M.S. Thesis: The fitness consequences of human-wildlife interaction on common bottlenose dolphins in Sarasota Bay, Florida Co-chairs/Faculty Advisors:  Miguel Acevedo , Wildlife Ecology and Conservation;  Madan Oli , Wildlife Ecology and Conservation

Hardin, Alizé M.S. Thesis: Historical Ecology of Seagrass Meadows along the Gulf Coast of Florida: Environmental Trends in Body Size, Taphonomy and Predation Chair/Faculty Advisor: Michal Kowalewski , Florida Museum of Natural History

Littell, Joe M.S. Non-thesis Paper: Crown-of-Thorns Starfish (Acanthaster spp.) and the Great Barrier Reef Chair/Faculty Advisor: Steve Johnson , Wildlife Ecology and Conservation

Barchiesi, Stefano Ph.D. Dissertation: From Ecosystem Restoration to Nature-based Solution: the Case of an Endangered Wetland of International Importance in Costa Rica  Advisor:  Christine Angelini , Environmental Engineering

Basham, Edmund Ph.D. Dissertation: Vertical Stratification of Tropical Forest Biodiversity across Multiple Scales of Space and Time Advisor:  Brett Scheffers , Wildlife Ecology and Conservation

Bishop, Nichole Ph.D. Dissertation: Nutritional Ecology of the Critically Endangered Central American River Turtle, Dermatemys Mawii Advisor:  Ray Carthy , Wildlife Ecology and Conservation

Claunch, Natalie Ph.D. Dissertation: Physiological Legacies of Reptile Invasions Advisor:  Christina Romagosa , Wildlife Ecology and Conservation

Garcia V., Angelica Ph.D. Dissertation: Factors Affecting Cultural Perceptions of Palms and the Sustainability of Their Management in Madre de Dios, Peru Advisor:  Stephen Perz , Sociology  

Hecht, Kirsten Ph.D. Dissertation: A Mixed-Methods Investigation of Public Engagement Activities by Herpetologists Advisors:  Katie Stofer , Agricultural Education and Communication; Max Nickerson, Florida Museum of Natural History

Lehmensiek, May Ph.D. Dissertation: Occupation Displacement of Commercial Fishers - Case Studies from Florida and Brazil Advisor:  Kai Lorenzen , Fisheries and Aquatic Sciences

Lowe, Ben Ph.D. Dissertation: Human dimensions of global environmental change: The influence of religion on perceptions and responses to climate change, fisheries management, and biodiversity conservation management, and Biodiversity Conservation Advisor:  Susan Jacobson , Wildlife Ecology and Conservation

Stelling, Benjamin Ph.D. Dissertation: Phytoplankton Composition & Abundance along Depth & Seasonal Gradients in the South Atlantic Bight off the Coast of Cape Canaveral, FL Advisor:  Edward Phlips , Fisheries and Aquatic Sciences

Walker, Julie Ph.D. Dissertation: Effects of Climate Change on Coastal Wetland Ecosystems and the Implications for Their Future Management Advisors:  Todd Osborne , Soil and Water Sciences;  Christine Angelini , Environmental Engineering Sciences

Bandara, Manuja Pabasara M.S. Thesis: Role of Metaphors in Communicating Threat of Climate Change: An Experimental Study on Climate Change Communication Advisor:  Christine Overdevest , Sociology 

Carneiro, Celine M.S. Thesis: Genomic Insight into the Demographic History and Structure of the Grasshopper Sparrow (Ammodramus Savannarum) Advisor:  James Austin , Wildlife Ecology and Conservation          

Catizone, Dan  M.S. Thesis: Ecology of the Ornate Diamondback Terrapin (Malaclemys terrapin macrospilota) in St. Joseph Bay, FL  Advisor:  Christina Romagosa , Wildlife Ecology and Conservation

Farris, Seth M.S. Thesis: Improving the Use of Ecological Indicators Using Hierarchical Modeling Advisor:  Frank Mazzotti , Wildlife Ecology and Conservation        

Hartfelder, Jack M.S. Thesis: Megaherbivore Distributions and Their Effects on Savanna Herbivore Community Structure Advisor:  Rob Fletcher , Wildlife Ecology and Conservation

Logan, Tracey M.S. Thesis: Generation of in vitro cell lines, maintenance of lines, and creation of database Advisor:  Robert Ossiboff , Comparative, Diagnostic, and Population Medicine       

Maleko, Philipp M.S. Thesis: Filling Knowledge Gaps for Two Declining East Asian-Australasian Flyway Shorebirds: Nordmann's Greenshanks and Common Redshanks Advisor:  Abby Powell , Wildlife Ecology and Conservation

Merriell, Brandon M.S. Thesis: Evaluating the Demographics and Dynamics of Florida Panthers with an Integrated Population Model Advisor: Madan Oli , Wildlife Ecology and Conservation

Moreno, Melissa “Mel” M.S. Thesis: Big Changes in the Big Bend: A data management and shoreline analysis study Advisor:  Bill Pine , Wildlife Ecology and Conservation

Poongavanan, Jenicca  M.S. Thesis: A Glimpse into the Reproducibility of Scientific Papers in Movement Ecology: How Are We Doing? Advisor:  Mathieu Basille , Wildlife Ecology and Conservation

Rash, Rebecca M.S. Thesis: Environmental and Occupational Risk Factors for Stingray Puncture Injuries in Cedar Key Clam Farmers Advisor:  Andy Kane , Environmental and Global Health

Scherneck, Sam M.S. Thesis: International survey explores how risk assessment tools for invasive species can be utilized successfully Advisors: Deah Lieurance , Agronomy;  Luke Flory , Agronomy

Bedoya Duran, Maria Juliana Ph.D. Dissertation: Privately protected areas, shade coffee, and the conservation of ground birds and medium to large mammals in the western Andes of Colombia Advisor:  Lyn Branch , Wildlife Ecology and Conservation

Bledsoe, Ellen B.A. Mount Holyoke College Research: The Role of Patch- and Landscape-Level Processes in Shaping Desert Rodent Communities Advisor:  Morgan Ernest , Wildlife Ecology and Conservation

DeLong, Alia N Ph.D. Dissertation: Farmer Decision-Making: A New Generation of New and Beginning Farmers Advisor:  Marilyn Swisher , Family, Youth and Community Sciences 

Durland Donahou, Allison Ph.D. Dissertation: Effects of Domestication on the Guppy Poecilia reticulata with Implications for Invasiveness Advisor:  Jeff Hill , Fisheries and Aquatic Science

Esbach, Michael Ph.D. Dissertation: Hunting for Justice: Cofan Subsistence, Sustainability, and Self-Determination in the Ecuadorian Amazon Advisor:  Bette Loiselle , Wildlife Ecology and Conservation

Harris, Holden E Ph.D. Dissertation: Viability and Effectiveness of a Commercial Fishery to Mitigate Invasive Lionfish in the Northern Gulf of Mexico Advisor:  Mike Allen , Fisheries and Aquatic Sciences

Hightower, Jessica  Ph.D. Dissertation: The Response of Bird Communities to Logging, Fragmentation, and Conversion to Oil Palm Plantations in Borneo Advisor:  Rob Fletcher , Wildlife Ecology and Conservation

Jamal, Fatemah  Ph.D. Dissertation: Ecology and Systematics of Irregular Echinoids (Echinoids, Echinodermata) and Associated Pea Crabs (Crustacea, Arthropoda) from the Gulf of Mexico and Bahamas  Advisor:  Michal Kowalewski , Florida Museum of Natural History

Kim, Kwanmok   Ph.D. Dissertation: How does Refuge Shape, Abundance, and Arrangement Affect Species Diversity in an Oyster Reef System Advisor:  Peter Frederick , Wildlife Ecology and Conservation

Kirk, Lily Ph.D. Dissertation: Metabolism in Subtropical Lowland Rivers Advisor:  Matt Cohen , Forest Resources and Conservation

Marconi, Sergio Ph.D. Dissertation: Disentangling the Role of Ecological Drivers on Forest Biological Dimensions across Scales Advisor:  Ethan White , Wildlife Ecology and Conservation

McEachron, Lucas Ph.D. Dissertation: Reef Fish Spatial Distributions Throughout the Florida Keys in the Context of Matrix Effects, Trophic Dynamics, and Complementary Modeling Techniques Advisor:  Robert Fletcher , Wildlife Ecology and Conservation

McGrew, Alicia Ph.D. Dissertation: Taxonomic, Phylogenetic, and Size-Based Approaches to Characterizing Aquatic Community Structure Advisor:  Ben Baiser , Wildlife Ecology and Conservation

Mtsetfwa, Fezile B.S., M.S. University of Swaziland Research: Bat Conservation in African Savannas Advisor:  Robert McCleery , Wildlife Ecology and Conservation

Nhleko, Zoliswa Nombulelo   Ph.D. Dissertation: Understanding and Responding to South Africa's White Rhino Poaching Crisis Advisor:  Robert McCleery , Wildlife Ecology and Conservation

Nunez Godoy, Cristina Ph.D. Dissertation: Improving Payments for Ecosystem Service Programs in a Global Deforestation Hotspot: The Gran Chaco of Argentina Advisors:  Elizabeth Pienaar , Wildlife Ecology and Conservation;  Lyn Branch , Wildlife Ecology and Conservation

Peralta, Percy Ph.D. Dissertation: What Incentivizes Eco-Friendly Coffee Practices in the Peruvian Tropical Andes?: Socioeconomic and Environmental Sustainability of Smallholder Coffee Production in the Satipo Province, Peru Advisor:  Karen Kainer , Forest Resources and Conservation

Pinheiro, Felipe Machado  Ph.D. Dissertation: Silvopastoral Management of the Brazilian Drylands: Soil Carbon Sequestration and Farmer-Managed Restoration of Native Vegetation Advisor:  P.K. Nair , Forest Resources and Conservation

Poli, Caroline Ph.D. Dissertation: The Roles of Habitat and Individual-Based Traits for Understanding Survival and Connectivity across Landscapes Advisor:  Rob Fletcher , Wildlife Ecology and Conservation

Srivathsa, Arjun S. Ph.D. Dissertation: Saving the Underdogs: a Multi-Scale Approach to Inform Strategies for Conserving the Endangered Asiatic Wild Dog Advisor:  Madan Oli , Wildlife Ecology and Conservation

Toh, Kok Ben Ph.D. Dissertation: Predicting Malaria: Models and Their Applications Advisor:  Denis Valle , Forest Resources and Conservation

White Rose, Elizabeth Ph.D. Dissertation: Population Characteristics, Movement Behavior, and Resource Selection of Florida Burrowing Owls Breeding in Suburban and Pastureland Habitats Advisor:  Raoul Boughton , Wildlife Ecology and Conservation

De Vito, Lauren M.S. Thesis: Land and vegetation change from regional to local scales in Ecuador  Advisor:  Matt Cohen , Forest Resources and Conservation

Eastman, Scott M.S. Thesis: A Comparative Study of the Loggerhead Sea Turtle (Caretta caretta) Nesting on Undeveloped and Developed Beaches in Northeast Florida Advisor:  Raymond Carthy , Wildlife Ecology and Conservation

McCallister, Lorna M.S. Thesis: Effects of Land Use and Land Cover Change on Insect and Avian Pollinator Communities. Advisor:  Robert McCleery , Wildlife Ecology and Conservation 

Smithers, Cherice M.S. Thesis: Drivers of Diversity and Composition of Native Bee Communities In Fire-Maintained Pine Savanna Advisor:  Ben Baiser , Wildlife Ecology and Conservation

Alvarez Aleman, Anmari Ph.D. Dissertation: Population Genetics and Conservation of the West Indian Manatee (Trichechus manatus) in Cuba Advisor: Tom Frazer, Fisheries and Aquatic Sciences/SNRE

Baudoin Farah, C. Andrea B.S. University of Sao Paulo, M.S. AgroParis Tech Ph.D. Dissertation: " Volver a hablar con la gente del agua ": Meanders of indigenous autonomy in the TIPNIS, Bolivia Advisor:  Stephen Perz , Sociology

Brown, Hannah O. Ph.D. Dissertation: Collaborating for Oyster Sustainability: A mixed-methods analysis of stakeholder communication and preference for future management outcomes on the Gulf Coast Advisor:  Susan Jacobson , Wildlife Ecology and Conservation

Dahl, Kristen Ph.D. Dissertation: Life History and Ecology of Invasive Lionfish Populations in the Northern Gulf of Mexico: Impacts to Native Reef Fish Communities and their Potential Mitigation Advisor:  Will Patterson , SFRC/FAS

Diaz Toribio, Milton B.S. Universidad Veracruzana, M.S. Instituto de Ecologia Ph.D. Dissertation: Community Dynamics and Under-Ground Functional Trait Responses of Plants in Pine Savannas after Fire Suppression Advisor:  Francis "Jack" Putz , Biology

Dobbins, Michael B.A., B.S. & M.S. University of Alabama Ph.D. Dissertation: Anthropogenic Impacts on Tropical Mammals Advisor:  Eben Broadbent , School of Forest Resources and Conservation

Haro-Carrion, Xavier Ph.D. Dissertation: Land and vegetation change from regional to local scales in Ecuador  Advisor:  Jane Southworth , Geography

Hilsenroth, Jana B.S. University of Tampa Ph.D. Dissertation: Changing Seas: Potential Effects of Increasing Ocean Temperatures on the Black Pearl Industry in French Polynesia Advisor: Tom Frazer, USF

Kadagi, Nelly Isigi Ph.D. Dissertation: Contextualizing Socio-Ecological Interactions in Recreational and Artisanal Fisheries: Implications for Sustainable Use and Management of Billfish in the Western Indian Ocean  Advisor:  Rob Ahrens , Fisheries and Aquatic Sciences

Malone, Kristen Ph.D. Dissertation: Ground-Nesting Birds in Southeastern Pine Savanna: Predation and Habitat Management  Advisor:  Kathryn Sieving , Wildlife Ecology and Conservation

Maynard, Lily Ph.D. Dissertation: Are Zoos Conservation Leaders? Using Organizational Conservation Identity and Social Network Analysis to Assess Zoos’ Collective Impact Advisor:  Susan Jacobson , Wildlife Ecology and Conservation

Meiners, Joan Ph.D. Dissertation: Data Dynamics of Bee Biodiversity: Understanding Native Bee Ecology Advisor: Tom Frazer, Fisheries and Aquatic Sciences

Singer, Randal Ph.D. Dissertation: An Interdisciplinary Approach to Increasing Biodiversity Collections' Sustainability using Computational and Survey Methods  Advisor:  Larry Page , Florida Museum of Natural History

Taylor, Shawn Ph.D. Dissertation: Forecasting Plant Phenology: an Assessment of Data Sources and Estimators, and a Fully Automated Implementation Advisor:  Ethan White , Wildlife Ecology and Conservation

Epperly, Haley B.S. Oregon State University  M.S. Thesis: Effects of woody vegetation in savannas on animal diversity and behavior Advisor:  Robert McCleery , Wildlife Ecology and Conservation

Gearhart, Justin B.S. University of Florida M.S. Thesis: Longleaf Pine Sensitivity to Interannual Variability in Precipitation in Fire-Maintained and Fire-Excluded Stands Advisor:  Jeremy Lichstein , Biology

Hill, Geena B.S. Kent State University M.S. Thesis: Ecological Dynamics of Lepidoptera in a Changing World, with a Focus on a Critically Endangered Butterfly  Advisor:  Jaret Daniels , Entomology and Nematology

Mosso, Clara M.S. Thesis: Urban Expansion Into Native Forests in Patagonia, Argentina: Assessing Environmental Policy Challenges at National And Local Levels Advisor:  Mark Hosteler , Wildlife Ecology and Conservation  

Pride, Lillian B.S. Iowa State University M.S. Thesis: Living Mulch and Microirrigation for Runoff and Erosion Reduction during Bare-root Strawberry Transplant Establishment Advisor:  Carlene Chase , Horticulture Sciences

Quincy, Kaitlyn M.S. Thesis: The Ecology and Management of West Indian Marsh Grass (Hymenachne amplexicaulis) in Florida Wetlands Advisor:  Stephen Enloe , Agronomy

Adler, Jennifer Ph.D. Dissertation: Water's Story: an interdisciplinary approach combining springs science, communications, and environmental education Advisor: Tom Frazer, Fisheries and Aquatic Sciences/SNRE

Ávila, Susana Hervas Ph.D. Dissertation: Understanding Stakeholder Conflict in the Gulf of Mexico Red Snapper Fishery  Advisor:  Kai Lorenzen , Fisheries and Aquatic Science

Bailey, Karen Ph.D. Dissertation: Understanding drivers and consequences of adaptation to drought in Swaziland Advisor:  Bob McCleery , Wildlife Ecology and Conservation

Chidakel, Alex  Ph.D. Dissertation: Institutions, Governance, and the Economic Performance of Protected Areas in Southern Africa Advisor:  Brian Child , Geography

Desormeaux, Amanda Ph.D. Dissertation: Quantification and Source Identification of Nitrate Leached in the Vadose Zone of a Karst Springshed Advisor:  James Jawitz , Soil and Water Sciences

Fahey, Catherine  Ph.D. Dissertation: The Effects of Plant Invasion and Drought on Plant-Soil Interactions Advisor:  Luke Flory , Agronomy

Fiorini, Ana Carolina Oliveira  Ph.D. Dissertation:  Brazilian Forest Code Instruments Used to Promote Atlantic Forest Restoration: A Study in Rio Claro Municipality Advisor:  Jack Putz , Department of Biology 

Glodzik, Katie  Ph.D. Dissertation: Impacts of Saltwater Intrusion and Hydrologic Change to Salt Marsh and Coastal Forest of Florida's Big Bend  Advisor:  David Kaplan , Environmental Engineering Sciences

Ludgate, Nargiza Ph.D. Dissertation: Gender Roles in Household Water Resource Management in Water-Scarce Countries: Does Greywater Treatment Technology Empower Rural Women in Jordan? Advisor:  Sandra Russo , International Center

Muyengwa, Shylock  Ph.D. Dissertation: Elite Capture of Community Based Natural Resources Management Projects in Southern Africa Advisor:  Brian Child , Geography

Pinheiro, Paula Soares  Ph.D. Dissertation: Decentralization of rights to communities in the co-management of natural resources in the Lower Juruá Extractive Reserve, central-west Brazilian Amazon: implications for natural resource conservation Advisor:  Stephen Perz , Sociology and Criminology & Law 

Rubino, Elena  Ph.D. Dissertation: Incentivizing Rhinoceros   Conservation among Private Landowners in South Africa Advisor:  Elizabeth Pienaar , Wildlife Ecology and Conservation 

Rueda, Farah Carrasco Ph.D. Dissertation: Land-use Change and Bat Biodiversity: Understanding Patterns, Drivers, and Impacts of Mitigation Efforts  Advisor:  Bette Loiselle , Wildlife Ecology and Conservation

Schuman, Carrie Ph.D. Dissertation: Ecosystem Service Provision by the Eastern Oyster, Crassostrea virginica, within the St. Augustine Region of Florida Advisor:  Shirley Baker , Fisheries and Aquatic Sciences

Shapiro, Julie  Ph.D. Dissertation: Bats in a Changing World: Where They Go, What They Bring, and How They Impact People  Advisor:  Bob McCleery , Wildlife Ecology and Conservation

Somjee, Ummat  Ph.D. Dissertation: The Hidden Consequences of Exaggerated Sexually Selected Weapons  Advisor:  Christine Miller , Entomology and Nematology

Wilkinson, Krystan Ph.D. Dissertation: Understanding Risk Mitigation by Common Bottlenose Dolphins (Tursiops Truncatus) in Sarasota Bay, Florida through Analysis of Group Size, Habitat Use, and Perinatal Movements Advisor:  Bill Pine , Wildlife Ecology and Conservation

Adair, Robyn M.S. Thesis: Field Evaluation of Cover Crops for Nematode and Weed Management in Florida and Haiti Advisor:  Marilyn Swisher , Family, Youth, and Community Sciences;  Carlene Chase , Horticulture Sciences

Borden, Jesse  M.S. Thesis: Ecological Disturbances and Canopy Communities Advisor:  Brett Scheffers , Wildlife Ecology and Conservation

Duan, Leilei M.S. Thesis: Examine Urban Allocation Outcomes with Sea Level Rise Scenarios in City of Tampa Advisor:  Paul Zwick , College of Design, Construction & Planning

Hill, Melissa  M.S. Thesis: Determining Property Owners' Attitudes of Adopting Conservation Easements to Protect Sea Turtle Nesting Habitat Advisor:  Martha Monroe , School of Forest Resources and Conservation

Maneval, Paul  M.S. Thesis: Influences of genotype, nursery design, and location on the growth of Acropora cervicornis fragments Advisor:  Tom Frazer , Fisheries and Aquatic Sciences/SNRE

Scott, Raymond  M.S. Thesis: Non-thesis Project Advisor: Tom Frazer, Fisheries and Aquatic Sciences/SNRE

Smith, Austin M.S. Thesis: Niche Modeling of Released Chukar Partridge ( Alectoris chukar ) in North America Advisor:  Wendell Crooper , Forest Resources and Conservation 

Szabo, Andrea  M.S./J.D.: Non-thesis Project Advisor:  Alison Adams , School of Forest Resources and Conservation 

Thomas, Shelby M.S. Thesis: More than the Eye Can See: Gross and Radiographic Observations of Shell-Boring Parasites Cliona, Diplothyra, and Polydora in Eastern Oyster, Crassostrea Virginica, Reveal Dynamics of Shell Damage Advisor:  Andy Kane , Emerging Pathogens Institute

Veras Mena, Daniel  M.S. Thesis: Influence of Sponges on the Performance of Nursery-reared Staghorn Coral ( Acropora cervicornis ) Advisor: Donald Behringer , Fisheries and Aquatic Sciences

Avila-Segura, Laura  Ph.D. Dissertation: Stingless Bee Foraging Ecology in Tropical Agricultural Landscapes  Advisor: Glenn Hall, Entomology and Nematology 

Guan, Jing Ph.D. Dissertation: Quantifying the Effects of Epiphytic Algae on the Growth of a Submersed Macrophyte Advisor: Tom Frazer, Fisheries and Aquatic Sciences/SNRE

Hallett, Matt Ph.D. Dissertation: Population Size, Spatial Distribution, Space Use, Human/Wildlife Conflict, Protection and Planning Related to Jaguars in Guyana, South America Advisor: John Blake, Wildlife Ecology and Conservation

Hernandez, Felipe  Ph.D. Dissertation: Anthropogenic Factors as Disruptors of Ecosystem Health: Effects of Contaminants in Raccoons ( Procyon lotor ) and Pathogen Pollution by Invasive Feral Swine ( Sus scrofa ) in Southeastern United States Advisor: Sam Wisely, Wildlife Ecology and Conservation

Karelus, Dana Ph.D. Dissertation: Black Bear Home Ranges and Habitat Use Advisor: Madan Oli, Wildlife Ecology and Conservation

Marquez Garcia, Marcela Andrea Ph.D. Dissertation: Attitudes and Factors Influencing Behavior Change of Wine Producers Toward the Conservation of Chile's Mediterranean Habitat Advisor: Susan Jacobson, Wildlife Ecology and Conservation 

McCarthy, James Ph.D. Dissertation: The Dynamics of Corporate Sustainability and Profitability Advisor: Robert Ries, Construction Management

Rios Marin, Maria Constanza  Ph.D. Dissertation: From Individual Decisions to Collective Action for Biodiversity Conservation: Networks of Reserves of the Civil Society in Colombia Advisor: Karen Kainer, Forest Resources and Conservation, Latin American Studies 

Stanton, Richard   Ph.D. Dissertation: Effects of Shrub Encroachment in Grass-Dominated Biomes on Vertebrate Communities Advisor: Rob Fletcher, Wildlife Ecology and Conservation

Van Treese, Jeffrey Ph.D. Dissertation: Environmental Horticulture Advisor: Andrew Koeser, Landscape Conservation and Ecology

Wilson, Chris Ph.D. Dissertation: Ecology and Management of Soil Carbon on Ranchlands of Florida, USA Advisor: Luke Flory, Agronomy

Berkebile, Nathan M.S. Thesis: Ecology and Distribution of the Florida Sea Cucumber,  Holothuria floridana , in Seagrass and Hard-bottom Communities of the Florida Keys Advisor: Donald Behringer, Fisheries and Aquatic Sciences

Hyman, Alex  M.S. Thesis: Seagrass Ecology  Advisor: Michal Kawalewski, Florida Museum of Natural History

Lastinger, Cody M.S. Non-Thesis: Improving Hack and Squirt Technologies for Management of Woody Invasive Plants in Florida  Advisor: Stephen Enloe, Center for Aquatic and Invasive Plants

Loggins, Annie M.S. Thesis: Savanna Vegetation Shapes Small Mammal Communities and Foraging Behaviors Advisor: Bob McCleery, Wildlife Ecology and Conservation

McBride, Jennifer  M.S. Thesis: Submerged Aquatic Vegetation Growth in Spring-Fed Rivers   Advisor: Matt Cohen, Forest Resources and Conservation

Mulindahabi, Felix M.S. Thesis: Assessment of the Impacts of the Conservation of Protected Areas to the Improvement of Livelihoods of Adjacent Communities of the Nyungwe National Park, Rwanda Advisor: Brian Child, Geography

Rodrigues, Camila  M.S. Thesis: Livelihood Strategies of Family Farmers across the Amazon Frontier of Malo Grosse  Advisor: Stephen Perz, Sociology 

Wang, Jinghui M.S. Thesis: Economic Analysis of Tomato Production with Anaerobic Soil Disinfestation   Advisor: Zhifeng Gao, Food and Resource Economics

Anderson, C. Jane Ph.D. Dissertation: Ecology and Impacts of Introduced Non-Human Primate Populations in Florida Advisor: Steve Johnson, Wildlife Ecology and Conservation

Anderson, John D.   Ph.D. Dissertation: Ecosocial Determinants of Environmental Exposure to Diarrheal Disease Advisor: Richard Rheingans, Environmental & Global Health

Chaves Didier, Willandia  Ph.D. Dissertation: Wild Meat Consumption in the Central Amazon, Brazil: Evaluating Drivers and Conservation Strategies  Advisor: Katie Sieving, Wildlife Ecology and Conservation

Crandall, Chelsey Ph.D. Dissertation: Stakeholder Engagement in Fisheries Management Advisor: Kai Lorenzen, Fisheries and Aquatic Sciences

Haase, Catherine  Ph.D. Dissertation: Effects of the Spatial Configuration of Forage and Thermal Habitats on the Behavior of the Florida Manatee Advisor: Rob Fletcher, Wildlife Ecology and Conservation

Fang, Yu Ph.D. Dissertation: Human Population Spatio-Temporal Distribution Embedded Within Water Networks  Advisor: Jim Jawitz, Soil and Water Science 

Knowles, Hal Ph.D. Dissertation: From Development Density to Consumption Complexity: Using Visualization Tools and Nonlinear Analyses to Compare Resource Efficiency in Urban Communities and Buildings Advisor: Mark Hostetler, Wildlife Ecology and Conservation

Laing, Joelle Ph.D. Dissertation: Relationships between Sediment Redox Potential and Vegetation Characteristics in Florida Spring Systems: Implications for Restoration Advisor: Tom Frazer, School of Natural Resources and Environment

Littles, Chanda Ph.D. Dissertation: An Investigation of Wintering Florida Manatee Population Dynamics at Multiple Scales Advisor: Tom Frazer, School of Natural Resources and Environment

Montes, Nancy Ph.D. Dissertation: Analyzing the Distribution of Recreational Boating off the Coast of Northeast Florida to Determine its Implications for the Conservation of the North Atlantic Right Whale Advisor: Bob Swett, Forest Resources and Conservation

Nunez-Regueiro, Mauricio Ph.D. Dissertation: Regional and Global Effects of Biofuel Production and Expansion  Advisor: Rob Fletcher, Wildlife Ecology and Conservation

Sobreiro da Silva, Thaissa Ph.D. Dissertation: Indigenous Mobilization and Multi-Local Livelihood Strategies in the Middle Rio Negro, Amazonas, Brazil Advisor: Stephen Perz, Sociology and Criminology and Law

Souza, Thiago Do Val S. Ph.D. Dissertation: Recreation Classification, Tourism Demand and Economic Impact Analyses in Protected Areas of Brazil Advisor:  Brijesh Thapa, Dept of Tourism, Recreation, and Sport Management

Vincent, Christopher Ph.D. Dissertation: Wind and Light Stress in Papaya as Influenced by Intercropping: Stress Priming and Photosynthetic Acclimation Advisor: Bruce Schaffer, Horticultural Sciences, Tropical REC

West, Thales Ph.D. Dissertation: Deforestation and Payment for Environmental Services Advisor: Jack Putz, Biology

Williams, Rebecca J. Ph.D. Dissertation: The Gift of More Time: The Influence of Eco-Stove Improved Cookstoves on Women's Time Poverty and Agency in Indigenous Lenca Communities in Intibuca, Honduras Advisor: Sandra Russo, Women's Studies

Xu, Yiming Ph.D. Dissertation: Predicting Soil Properties and Soil Quality by Remote Sensing and GIS Techniques in South India  Advisor: Scott Smith, Forest Resources & Conservation

Archer, Jan-Michael M.S. Thesis: A Systematic Review of Forest Bird Occurrence in North American Forest Fragments and the Built Environment  Advisor: Mark Hostetler, Wildlife Ecology and Conservation

Burja, Kristina M.S. Thesis: Conservation Education at Tsavo West National Park, Kenya: A Case Study Advisor: Martha Monroe, Forest Resources and Conservation

Cummings, Katy M.S. Thesis: Ecological Fidelity of Death Assemblages: Can Mollusks be Used to Assess Changes in Seagrass Ecosystems? Advisors: Michael Kowalewski, Florida Museum of Natural History; Tom Frazer, Fisheries and Aquatic Sciences

Gelin Spessot, Maria Laura    M.S. Thesis: Response of Pumas ( Puma concolor ) to the Migration of Guanacos ( Lama Guanicoe ) in Patagonia Advisor: Lyn Branch, Wildlife Ecology and Conservation

Gottlieb, Isabel M.S. Thesis: Implications of Future Biofuels Expansion on Avian Communities in the Southeastern U.S.  Advisor: Rob Fletcher, Wildlife Ecology and Conservation

Hiatt, Drew  M.S. Thesis: Invasive Plant Populations and Co-Occurring Native Species Vary in Phenotypic Plasticity Advisor: Luke Flory, Agronomy 

Langston, Jaqueline M.S. Thesis: The Effect of the Non-Native Mayan Cichlid  Cichlasoma Urophthalmus  on the Nesting and Parental Care of the Spotted Sunfish  Lepomis Punctatus Advisor: Tom Frazer, School of Natural Resources and Environment

NeSmith, Julienne M.S. Thesis: Tree Regeneration Response to Cogon Grass Invasion Along a Soil Moisture Gradient Advisor: Luke Flory, Agronomy

Nunez, Leroy M.S. Thesis: Molecular Analyses of Three Non-Indigenous Squamate Species in Florida: Testing Various Hypotheses Regarding Species Introductions Advisor: Kenneth Krysko, Florida Museum of Natural History

Nunez Godoy, Cristina M.S. Thesis: Wildlife Friendly Certification: A Case Study of Patagonian Cashmere Producers and Buyers  Advisor: Karen Kainer, Forest Resources and Conservation, Latin American Studies

Timpe, Kelsie M.S. Thesis: How are Dams Changing the Ecohydrology of Amazonian Rivers? A Comprehensive Review of Environmental Flows Management in the Brazilian Amazon Advisor: David Kaplan, Environmental Engineering Sciences

Cao, Baijing Ph.D. Dissertation: Soil Carbon Modeling Along Ecological, Climatic, and Biotic Trajectories at a Continental Scale Advisor: Sabine Grunwald, Soil and Water Science

Gonzalez, Oscar Ph.D. Dissertation: Bird-Flowering Plant Networks in Andean Montane Forests Advisor: Bette Loiselle, Wildlife Ecology and Conservation

Livengood, Elisa Ph.D. Dissertation: Sustainability of the Global Ornamental Fish Trade through Management, Education, Health and Welfare of the Resource Advisor:  Frank Chapman, Fisheries and Aquatic Sciences

Luo, Jiexuan Ph.D. Dissertation: Nitrogen Mass Balance at University of Florida Campus Advisor: George Hochmuth, Soil and Water Science

Mavah, Germain Ph.D. Dissertation: Governance for Sustaining Natural Resources: Effectiveness of Community-Based Wildlife Management in the Republic of Congo Advisor: Brian Child, Geography and Center for African Studies

Monaghan, Kelly Ph.D. Dissertation: Sustainable Agriculture and Urban-Community Food Systems Advisor: Mickie Swisher, Family, Youth and Community Sciences

Murillo, Oscar Ph.D. Dissertation: Demography and Population Dynamics of Peregrine Falcons ( Falco peregrinus ) in South Scotland Advisor: Madan Oli, Wildlife Ecology and Conservation

Nifong, Rachel Ph.D. Dissertation: Analysis of the Relationships Between the Stoichiometry of Whole Ecosystem Metabolism and Primary Producers Advisor: Matt Cohen, Forest Resources and Conservation

Yuan, Jing  Ph.D. Dissertation: Metrics of Pattern Loss and Ecosystem Change in the Ridge and Slough Mosaic of the Everglades Advisor: Matt Cohen, Forest Resources and Conservation

Bauman, Michael L. M.S. Thesis: Where Should the Forests Grow: Socio-Ecological Forest Conservation Planning Using Biodiversity, Landholder, and Agency-Based Priorities in Los Santos, Panama Advisor: Stephanie Bohlman, Forest Resources and Conservation

Bouchillon, Rachel M.S. Thesis: Florida Gulf Coast Oysters and Freshwater River Flow Modeling Advisor: Bill Pine, Wildlife Ecology and Conservation

Delaney, John Patrick M.S. Thesis: Using GIS to Assess Nest Site Selection and Nest Abundance by American Alligators ( Alligator mississippiensis ) in Three Central Florida Lakes Advisor: Franklin Percival, Wildlife Ecology and Conservation

Dornisch, Vanessa M.S. Thesis: Florida Beach Users’ Perceptions of Beach Ownership, Erosion Management, and Sea Level Rise Advisor: Bob Swett, Fisheries and Aquatic Sciences

Grade, Aaron M. M.S. Thesis: Consequences of Anthropogenic Road Noise on Avian Communities Advisor: Katie Sieving, Wildlife Ecology and Conservation

Lane, Christian M.S. Thesis: Sturgeon Nutrition Advisor: Frank Chapman, Fisheries and Aquatic Sciences

Larios, Kalindhi M.S. Thesis: Florida Wildfires During the Holocene Climatic Optimum Advisor: Stefan Gerber, Soil and Water Science

Mortimer, Jessica M.S. Thesis: The Development and Comparative Analysis of a Rapid Herbaceous Monitoring Method in Lowveld Savanna, South Africa Advisor: Brian Child, Geography, Center for African Studies

Presser, Jackson M.S. Thesis: GIS-Based Risk Model for Prediction/Detection of  Pythium insidiosum  in the Environment Advisor: Erica Goss, Plant Pathology, Emerging Pathogens Institute

Puls, Danielle M.S. Thesis: Determining if the Mucilage Caused by a Specific Cyanobacterium is Inhibiting Sponge Function/Killing Sponge Species in the Florida Bay Advisor: Donald Behringer, Fisheries and Aquatic Sciences

Quintana, Yasmin  M.S. Thesis: Fishing Mortality Assessment from Giant Cichlid Artisanal Fishery in Lake Peten Itza, Guatemala Advisor: Mike Allen, Fisheries and Aquatic Sciences

Rojas Bonzi, Viviana B. M.S. Thesis: Phylogenetics of the Red-Footed Tortoise Advisor: Jim Austin, Wildlife Ecology and Conservation

List of theses and dissertations in the UF Library database.

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College of Agriculture & Natural Resources Department of Fisheries and Wildlife

Thesis and dissertation lists.

MS Thesis List 2010-2017

PhD Dissertation List 2010-2017

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PHD IN AQUACULTURE AND FISHERIES MANAGEMENT

GRADUATE PROFILE

A PhD graduate in Aquaculture and Fisheries management will be able to:

• Teach aquaculture and fisheries courses in universities and other institutions.
• Provide advisory/ consultancy works to policy makers, investors and other relevant stakeholders in aquaculture and fisheries
• Devise management plans to meet national needs of using aquatic resources in a sustainable manner
• Apply appropriate technologies for the efficient and sustainable utilization of fisheries resources 
• Adapt the contemporary scientific knowledge on aquaculture and fisheries practices to ensure sustainable utilization of fisheries resources
• Facilitate the link between higher institutions, communities, and other stakeholders towards the common goals of fighting community problems and for sustainable utilization of the fisheries and other aquatic resources
• Develop and implement aquaculture projects and business ventures

ADMISSION REQUIREMENTS

Admissions to the program are competitive based on the following criteria:

• Applicants need to have Master Degree in Aquaculture and/or Fisheries, Biology, Animal Sciences, Wetlands Management, Environmental Sciences and other related fields which will be determined by the Department Graduate Committee (DGC) of Department of Biology
• Applicants need to have an MSc cumulative grade point average (cGPA) of 3.00 and above from accredited higher learning institution
• Submit a preliminary synopsis of planned PhD research for review and approval, and admission depends on availability and consent of advisor
• Successful performance (i.e. a minimum of 50 %) in the aptitude examination that encompass basic knowledge on subject matter, language test, basic computer skills, research ethics and others. The purpose of the examination is to ascertain whether a candidate is capable of the independent and thoughtful research required for the PhD program.
• Having publication(s) in (a) reputable journal(s) is an added advantage
• Based on the DGC assessment, candidates whose MSc background are not directly or related to Fisheries and Aquaculture will be required to take the bridge courses from the MSc program in Aquaculture and Fisheries before becoming eligible to register for the PhD courses proper (see Section 16).

DURATION OF THE STUDY PROGRAM

Duration of the study is 4 years. A candidate who fails to complete within the set schedule must present an acceptable justification in consultation with his/her research supervisor(s) for possible extension as per the existing JU regulation pertaining to the case.

GRADUATION REQUIREMENTS

A candidate must fulfill the following requirements of the School of Graduate studies (SGS) of Jimma University (JU) for graduation:

• A candidate should have a minimum course work of 12 credit hours and dissertation work of 12 credit hours.
• A Cumulative Grade Point Average (cGPA) of 3.00 must be obtained in the course works 
• No less than “B” grade(s) in any course(s) taken
• If there is a chance to link a candidate’s project to foreign universities a candidate in the sandwich scheme is required to spend at least 1 ½ years at the Department, Jimma University. Six credit hours of the 12 required graduate courses may be completed by residence courses taken at an accredited University or institute other than Jimma University. However, the equivalence of the courses taken abroad will be evaluated against the course list of the launched program.
• The Doctoral Dissertation shall constitute individual effort in academic pursuits to identify and analyze problems by applying sound methodology. A Doctoral Dissertation shall constitute the partial fulfillment of the requirement to the PhD Degree program. To be eligible for graduation, a candidate should:
• Publish at least one article in reputable peer reviewed journals.
• Submit at least one manuscript to a publisher and produce evidence of submission from editorial board
• Make oral paper presentation at one national or international level
• Present a compiled monograph for final open public defense of his/her Doctoral Dissertation.
• However, special cases can be entertained to permit a candidate to defend the Doctoral Dissertation by getting approval/advice from the candidate’s advisory board in consultation with respective supervisor(s) regardless of the criteria listed in a & b above.
• A candidate must successfully defend his/her Dissertation. If the student’s dissertation is rated “Fail”, he/she may be allowed to re-correct his/her work in a maximum duration of 6 months grace period.
• The result of the Dissertation evaluation will be an average of one external examiner (hereafter defined as a relevant professional outside Jimma University, either from Ethiopia or abroad) and one internal examiner (hereafter defined as a relevant professional from within Jimma University). A student must first submit the thesis, in an acceptable form, to the Advisor, who will return it, with comments, within six weeks. The revised Doctoral thesis may then be passed to the Advisory Committee which will then determine whether the Thesis is ready for submission.  The student’s advisor(s) in consultation with the Advisory Committee recommend potential external and internal examiners. The public oral defense of the thesis is arranged by the Department and conducted according to rules and regulations of Jimma University.

DEGREE NOMENCLATURE

Upon successful completion of the program, the candidate will be awarded a Doctor of Philosophy (PhD) Degree in Biology (Aquaculture and Fisheries Management) ; In Amharic: የፍልስፍና ዶክትሬት ዲግሪ በስነ-ሕይወት (አኳካልቸር ና የዓሳ ሀብት አስተዳደር)

LIST OF COURSES

COURSE BREAKDOWN

Based on the DGC assessment, candidates whose MSc background are not directly or related to Fisheries and Aquaculture will be required to take the following bridge courses from the MSc program in Aquaculture and Fisheries before becoming eligible to register for the PhD courses proper during Year I Semester I. Consequently, for such students the course work duration before proceeding to the PhD Dissertation will be one and half academic year. These courses are considered part of the student’s PhD training and thus appear on the student’s academic transcript. The course schedule for students taking bridge courses will accordingly extend from Year I Semester I through Year II Semester I i.e. one and half academic year.

Year I Semester I ( for students taking bridge courses )

Year I    Semester I    (Year I Semester II, for students taking bridge courses )

Year I    Semester II (Year II Semester I, for students taking bridge courses )

Year II    Semester I (Year II Semester II, for students taking bridge courses )

Cooperative Fish and Wildlife Research Units ========== --> Units/Headquarters Headquarters Alabama Alaska Arizona Arkansas California Colorado Florida Georgia Hawaii Idaho Indiana Iowa Kansas Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Fish Montana Wildlife Nebraska Nevada New Mexico New York North Carolina Oklahoma Oregon Pennsylvania South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Fish Wisconsin Wildlife Wyoming Twitter Facebook Instagram Tumblr Cooperative Fish and Wildlife Research Units Program Georgia Cooperative Fish And Wildlife Research Unit --> Education, Research and Technical Assistance for Managing Our Natural Resources

Theses/dissertations.

www.usgs.gov says

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Master of Natural Resources (M.N.R.)

CNR | Graduate Studies Office

Physical Address: 975 W. 6th Street Moscow, Idaho

Mailing Address: 875 Perimeter Drive MS 1142 Moscow, ID 83844-1142

Phone: 208-885-1505

Email: [email protected]

Fish and Wildlife Science and Management

The Fish and Wildlife Science and Management option of the MNR degree at The University of Idaho is a non-thesis option designed for:

• Professionals who want to advance their careers in fish and wildlife management by obtaining a graduate degree.
• Students desiring a career in fish and wildlife management who would benefit from a curriculum that includes policy and social aspects of fish and wildlife management as well as traditional graduate level courses in the fish and wildlife sciences.

Our program can be completed 100% online, on campus or any combination of online and on campus.

For detailed information on course requirements see the course catalogue .

For more information, please email the graduate research office .

What makes us unique?

Our Fish and Wildlife Department faculty ranked 4th out of 33 U.S. in terms of research productivity in a 2016 study published in PLOS One, CNR ranked 1st in Value and 5th for program quality for Natural Resources and Conservation — USA Today. The College of Natural Resources has been a leader in natural resources education for over 100 years.

• 30 credit, non-thesis program designed for working professionals, but open to all.
• Can be completed in three semesters.
• Online or in-person.
• Support for veterans and active service members.
• Pay only Idaho graduate resident tuition for the Online program.
• No GRE required.
• Apply year-round .

Program Outcomes

This flexible graduate program will provide students with advanced knowledge and competency in:

• Ecology, genetics, habitat management and conservation, and advanced biology courses related to a student’s field of interest
• Quantitative and statistical methods
• Environmental and natural resource policy and management
• Communication skills (oral and written) necessary for being an effective fish and wildlife management professional

Core required courses include:

• FISH 510 Advanced Fish and Wildlife Management* (3 cr) -OR- NRS 555 Human Dimensions of Natural Resources (3 cr)
• FISH/WLF 598 Internship (2 cr) plus NR 599 Non-thesis Master's Research (2 cr) -OR- FISH/WLF 502 Directed Study Project (4 cr)
• FOR 546 Science Synthesis and Communication (3 cr)
• WLF 506 External Speaker Seminar (1 cr)

* Important Note: FISH 510 is a virtual course, which requires students to join a Zoom meeting at a scheduled time two-three times a week.

• Fisheries or Wildlife biologist
• Fisheries or Wildlife manager
• Aquaculture research technician
• Fisheries or Wildlife research technician
• Wildlife Law Enforcement Officer or Conservation Officer
• Aquaculture research manager or program director
• Endangered species biologist
• Fish and Wildlife communication or education specialist
• Environmental consultant
• Park ranger