The American Fisheries Society APPLICATION FOR PROFESSIONAL CERTIFICATION

For applicants who completed their B.S. /B.A. (or equivalent) degree prior to July 1, 2002

All (typed) applications must be submitted electronically via email with the applicants name and “Certification Application” in the subject line to: Gail Goldberg, ggoldberg@fisheries.org. Mail payment and signed copy of declaration page to: AMERICAN FISHERIES SOCIETY-CERTIFICATION 5410 Grosvenor Lane, Suite 110 Bethesda, Maryland 20814-2199 If you have any questions, please contact (301) 897-8616X201 or ggoldberg@fisheries.org. Thank you for participating in the AFS Professional Certification Program! Name: Warren A. Mitchell Title: Fisheries Biologist Mailing Address: 271 Pinners Point Road Beaufort, NC 28516 Phone: 252.288.1881 Fax: N/A Email: warren.mitchell@noaa.gov Name, as it should appear in your certificate: Warren A. Mitchell APPLICATION FOR: ____X____ Certified Fisheries Professional (FP-C) (First time applicants for Certified Fisheries Professional designation)

_________ Certified Fisheries Professional (FP-C ESTABLISHED)

(First time applicants for Certified Fisheries Professional designation, applying as established fisheries professionals)

_________ Certified Fisheries Professional, Renewing (FP-C-RENEW)

(Previously certified Fisheries Professional applicants, renewing professional certification)

_________ Certified Fisheries Professional (FP-A to FP-C)

(Previously certified Associate Fisheries Professionals upgrading to Certified Fisheries Professional designation)

_________ Associate Fisheries Professional (FP-A)

(First time applicants for Associate Fisheries Professional designation) _________ Will accept certification as either Certified Fisheries

Professional or Associate Fisheries Professional, as granted by the Board after review. (First time applicants uncertain as to category because of professional and qualifying experience) (EITHER)

FP-C – Email an Application for Certification (all sections), a completed Professional Development Form and attach scanned official academic transcripts. Mail the Application fee in U.S. funds. Fee is $100 for AFS members and $200 for nonmembers. FP-C ESTABLISHED – Email an Application for Certification (omit section II), a completed Professional Development Form and attach documentation of the highest degree you have earned. Mail the Application fee in U.S. funds. Fee is $100 for AFS members and $200 for nonmembers. FP-C RENEW – Email this page, and a completed Professional Development Form. Mail the Application fee in U.S. funds. Fee is $50 for AFS members and $100 for nonmembers. If an applicant does not renew by December 31st of the year their certification expires, there is a penalty of $10 per year since the certification lapsed. FP-A to FP-C – Email an Application for Certification (omit section II), and a completed Professional Development Form. Mail the Application fee in U.S. funds. Fee is $50 for AFS members and $100 for nonmembers. Please document any degree obtained after original FP-A Application. FP-A – Email an Application for Certification (omit sections III and IV), and attach scanned official academic transcripts. Professional Development Form is not required. Mail the Application Fee in U.S. funds. Fee is $50 for AFS members and $100 for nonmembers. EITHER – Email an Application for Certification (all sections), a completed Professional Development Form, and attach SCAN of official academic transcripts. Mail the application fee in U. S. funds. Fee is $100 for AFS members and $200 for nonmembers.

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APPLICATION FOR PROFESSIONAL CERTIFICATION

Section I: EDUCATION

Title of Master's Thesis: Estimating run size of anadromous fishes in the Roanoke River, North Carolina, using hydroacoustics. Title of Doctoral Dissertation: N/A *If degree not obtained, indicate number of hours toward degree. Ten college mathematics credits were earned at PBSC (College Algebra, Statistics, Calculus II). All credits transferred to FL Tech. Four credits earned for Calculus II were officially applied towards the BS degree._

Disclaimers:

• Palm Beach State College was known as Palm Beach Community College during 1992-1994. • Hyperlinks included in this application were tested with the Google Chrome web browser,

November 2014.

Institutions

Dates

Attended

Degree*

Date

Degree Awarded

Majors

Minors

Palm Beach State College (PBSC)

Aug 1992

to Jun 1994

10 semester credits*

*

N/A

N/A

Florida Institute of Technology (FL Tech)

Aug 1993

to May 1997

BS

May 1997

Biological Oceanography

N/A

North Carolina State University

(NCSU)

Jan 2004

to Jul 2006

MS

Aug 2006

Fisheries and

Wildlife Sciences

Statistics

2

APPLICATION FOR PROFESSIONAL CERTIFICATION

Section II: MINIMUM COURSEWORK REQUIREMENTS This form is valid for individuals who obtained their B.A. /B.S (or equivalent) prior to July 1, 2002.

Please include only the minimum number of hours needed for each area. Read the Program Description and FAQ for descriptions of required coursework, available at www.fisheries.org/afs/certification.html. Course grades must be ‘C-’ or better to be acceptable, no pass/fail courses. Attach a scan of official transcripts (must include the legend). If coursework is deficient in any area, request the Guidelines for Satisfying

Coursework Deficiencies from AFS headquarters. Individuals applying as ‘FP-C Established’ do not complete this section.

Subject Area

School Course Number

Course Title

Semester

Hrs1

A. Fisheries and Aquatic Sciences. Four (4) courses, Two of which must be directly related to fisheries sciences.

NCSU NCSU NCSU FL TECH

ZO 726 MEA 750 ZO 592B OCN 3101

QUANTITATIVE FISHERIES MNGMT MARINE BENTHIC ECOLOGY SPTP-POPULATION ECOLOGY BIOLOGICAL OCEANOGRAPHY Deficient: “two… related to fisheries science;” see attachments.

3 3 3 3

B. Other Biological Sciences courses, which when added to the above courses must total 30 semester hours.

NCSU FL TECH NCSU FL TECH FL TECH FL TECH

ST 506 BIO 5030 GN 411 OCN 4104 BIO 3801 BIO 3510

SAMPLING ANIMAL POPULTNS CONSERVATION BIOLOGY PRINCIPLES OF GENETICS MARINE/ESTUARINE BENTHOS BIOMETRY INVERTEBRATE ZOOLOGY

3 3 4 3 3 4

TOTAL of A + B

32 of 30

C. Physical Sciences courses. Must total 15 semester hours.

FL TECH FL TECH FL TECH FL TECH FL TECH

OCN 4204 SPS 3040 OCN 3401 OCN 3301 CHM 2002

MARINE & ENVIRO POLLUTION FUND OF REMOTE SENSING PHYSICAL OCEANOGRAPHY GEOLOGICAL OCEANOGRAPHY ORGANIC CHEMISTRY 2

3 3 3 3 3

TOTAL of C

15 of 15

D. Mathematics and Statistics courses, which must include college algebra or calculus and one course in statistics. Must total 6 semester hours.

NCSU PBSC / FL TECH

ST 512 MAC2312 / MTH 1002

EXP STATISTICS FOR BIO SC II CALC-ANALYTIC GEOM II / CALCULUS 2

3 4

TOTAL of D

7 of 6

E. Communications courses. Must total 6 semester hours.

FL TECH FL TECH

COM 2223 COM 1101

SCI/TECH COMM COMPOSITION AND RHETORIC

3 3

TOTAL of E

6 of 6

1Semester hours = quarter hours x 2/3

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APPLICATION FOR PROFESSIONAL CERTIFICATION

Section III: QUALIFYING FULL-TIME EXPERIENCE (In chronological order, current employment first)

Employer _ NOAA National Marine Fisheries Service (Contractor: JHT, Inc.)____________________ ____ Period in this position: From: ____22 Feb 2010_(Part-time until 15 Aug 2011) To: ____ Current ______

(Day, Month, Year) (Day, Month, Year) Position title: Fisheries Biologist_____________________________________________ Description of specific duties and responsibilities as a fisheries professional (Specifically list how the job met the criteria for qualifying experience as described in the Program Description, available at www.fisheries.org/afs/certification.html): I am currently responsible for applied research project management, fisheries survey chief scientist duties, habitat mapping research cruise planning and supervisory operations, independent fisheries science research, and reef fish video analysis. I employ independent judgment and actions to organize teams of fisheries and hydrographic scientists while at sea. Recently published studies (see Section IV) have supported applied research goals related to improved scientific methodology and resource management practices. As an example of my work’s use in fisheries management, our team’s red snapper paper was recently submitted to the regional marine fisheries management process as a reference document (Mitchell et al. 2014, link, see SEDAR41-RD34). Employer __North Carolina State University, Center for Marine Sciences and Technology (CMAST) _____ Address _303 College Circle Morehead City, North Carolina 28557________________________________ Period in this position: From: __1 Sep 2006_To: _12 Aug 2011 (part-time 22 Feb 2010 to 12 Aug 2011)

(Day, Month, Year) (Day, Month, Year) Position title: _ North Carolina Sea Grant Marine Fisheries Fellow / Research Staff __________________ Description of specific duties and responsibilities as a fisheries professional As the 2006-2007 Marine Fisheries Fellow (North Carolina Sea Grant, hosted by Dr. Jeffery A. Buckel, NCSU CMAST), I was responsible for a 1-yr examination of NOAA Beaufort Bridgenet Icthyoplankton Sampling Program data. Independent research focused on long-term trend analysis via various univariate and multivariate methods. I employed independent judgment and actions during statistical design, data analysis, and reporting periods of the fellowship. Results were published in a peer-reviewed AFS journal (Taylor et al. 2009, link). During the period 2007-2011, I was employed as a professional research staff member in Dr. Buckel’s Marine and Estuarine Fisheries Ecology Laboratory. I was responsible for project coordination, applied research execution, and grant competition to fund reef fish and anadromous population studies. I employed independent judgment and actions while executing field seasons, data analysis periods, and document creation. In total, two peer-reviewed publications and three reports from the period were published (all listed in Section IV).

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Employer __Self-employed_______________________________________________________________ Address _271 Pinners Point Road Beaufort, NC 28516_________________________________________ Period in this position: From: ______1 Jun 2009___________To: _ 1 Sep 2011

(Day, Month, Year) (Day, Month, Year) Position title: Contracted researcher______________________________________________________ Description of specific duties and responsibilities as a fisheries professional Project: “Implementation of Electronic Logbooks on Headboats Operating in the U.S. South Atlantic”, under the direction of Mr. Kenneth Brennan, NOAA, NMFS, Beaufort, NC laboratory. Acting as an independent contractor, I collected fisheries landings data, analyzed trends, and authored a report to support a project evaluating costs and benefits of switching the NOAA NMFS Southeast Region Headboat Survey from a paper- to an electronic-based reporting system. I was solely responsible for study design, implementation, analysis, and reporting duties. The final report was used by the contracting research group to improve scientific methodology, and exists online (Brennan et al. 2011, link). In the context of fisheries management, the report assisted the now-complete implementation of electronic reporting across the U.S. south Atlantic and Gulf of Mexico coastal headboat vessel fleet.

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APPLICATION FOR PROFESSIONAL CERTIFICATION

Section IV: QUALIFYING, EXPERIENCE RELATED, PROFESSIONAL COMMUNICATIONS

Publications: Give complete citations -- author(s), year, title of paper, publication volume and number and pages. Cite no more than five of your most recent or significant publications. Mitchell , W. A., G. T. Kellison, N. M. Bacheler, J. C. Potts, C. M. Schobernd, and L. F. Hale. 2014.

Depth-related distribution of postjuvenile red snapper in southeastern U.S. Atlantic Ocean waters: ontogenic patterns and implications for management. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 6(1):142-155. Link.

Bacheler, N. M., Z. H. Schobernd, D. J. Berrane, C. M. Schobernd, W. A. Mitchell, and N. R. Geraldi.

2013. When a trap is not a trap: converging entry and exit rates and their effect on trap saturation of black sea bass (Centropristis striata). ICES Journal of Marine Science 70(4):873‐882. Link.

Bacheler, N. M., C. M. Schobernd, Z. H. Schobernd, W. A. Mitchell, D. J. Berrane, G. T. Kellison, and

M. J. M. Reichert. 2013. Comparison of trap and underwater video gears for indexing reef fish presence and abundance in the southeast United States. Fisheries Research 143:81‐88. Link.

Rudershausen, P. J., W. A. Mitchell, J. A. Buckel, E. H. Williams, and E. Hazen. 2010. Developing a

two‐step fishery‐independent design to estimate the relative abundance of deepwater reef fish: Application to a marine protected area off the southeastern United States coast. Fisheries Research 105:254‐260. Link.

Taylor, J. C., W. A. Mitchell, J. A. Buckel, H. J. Walsh, K. W. Shertzer, G. B. Martin and J. A. Hare.

2009. Relationships between larval and juvenile abundance of winter‐spawned fishes in North Carolina, USA. Marine and Coastal Fisheries 1:11‐20. Link.

Administrative reports: Give complete citations -- author(s), year, title, pages. Cite no more than five of your most recent or significant reports. Mitchell, W. A., and Southeast Acoustics Consortium (SEAC) co-organizers. 2012. SEAC Report 2012:

Report of the Inaugural Workshop and Activities of the SEAC. Public document, hosted by Florida International University Biscayne Bay Campus. Link on this page.

Brennan, K., W. Mitchell, E. Williams, and D. Gloeckner. 2011. Implementation of electronic logbooks

on headboats operating in the U.S. south Atlantic. Final report to the NOAA NMFS Marine Recreational Information Program For-Hire Workgroup. Link.

Mitchell, W. A., J. C. Taylor, J. A. Buckel, J. E. Hightower and T. Pratt. 2011. Final Report: Feasibility

of Using Mobile Hydroacoustic Surveys for Estimating Spawning Stock Size of Blueback Herring in Western Albemarle Sound, North Carolina. Final report to the Fishery Resource Grant program, North Carolina Sea Grant. Search project “2007-FEG-09” at this link.

Rudershausen, P., W. A. Mitchell, J. A. Buckel, E. Hazen, E. Williams, and T. Burgess. 2009. Pilot

survey of deepwater reef fishes off North Carolina using a two-stage, adaptive design: Part II – use of chevron trapping. Final report to the Fishery Resource Grant program, North Carolina Sea Grant.

Mitchell, W. A., J. C. Taylor, G. B. Martin, K. W. Shertzer and J. A. Buckel. 2007. Analyses of

icthyoplankton ingress data from a long term monitoring program at Beaufort Inlet, North Carolina: Implications for fishery management. Final report to North Carolina Division of Marine Fisheries, in fulfillment of duties as the 2006 Marine Fisheries Fellow, North Carolina Sea Grant.

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Oral reports or presentations: Give year and title, and identify the audience. Cite no more than five of your most recent or significant presentations. Professional workshop, Southeast Acoustics Consortium Workshop and Forum, Panama City, FL, 1-2

April 2014. “Using multibeam sonar to inform the spatial extent of fisheries surveys: a case study using the Simrad ME70.”

Invited speaker, Fin-Addicts Fishing Club monthly meeting, Beaufort, NC, 20 Sep 2011. “The SouthEast

Fishery-Independent Survey: indexing reef fish abundance in the US South Atlantic.” Invited speaker, American Fisheries Society, Southern Division annual meeting, Tampa, FL, 14-18

January 2011. Symposium: Southeastern Reef Fishes. “A review of fishery‐independent sampling programs in southeastern U.S. coastal waters: where does a new effort fit in?” Podcast.

Invited speaker, South Atlantic Fishery Independent Monitoring Program Development Workshop.

Beaufort, NC, 17‐20 Nov 2009. “Why consider acoustics? Facts and results useful in designing a new survey.”

Speaker, American Fisheries Society, North Carolina Chapter annual meeting, Burlington, NC, 23-25

Feb 2009. “Feasibility of using mobile hydroacoustic surveys for estimating spawning stock size of blueback herring in Western Albemarle Sound, North Carolina.” Program and abstracts.

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APPLICATION FOR PROFESSIONAL CERTIFICATION

Section V: PROFESSIONAL INTERESTS AND GOALS

Affiliations in AFS (i.e., Division, Chapter, Section, Committee): Member, 2004 to present: Parent Society, Southern Division, NC Chapter Member, 2006 to present: Tidewater Chapter Member, 2001-2002, 2014: Florida Chapter Member, Fisheries Information & Technology Section Member, Marine Fisheries Section Member, Socioeconomics Section Member 2004-2006: NCSU Student Fisheries Society, subunit of the AFS NC Chapter Affiliations in other professional, scientific, or honorary societies: Founding member, 2011 to present: Southeast Acoustics Consortium, “a working group that brings together

academic institutions, federal and regional fisheries and environmental management agencies, and private industry that conduct acoustics research in the coastal environments of the US from North Carolina to Texas and the US Caribbean.” Link.

Association for the Sciences of Limnology and Oceanography (membership not current) Coastal and Estuarine Research Federation (membership not current) Services to AFS and other professional or scientific societies: Volunteer, 2006-Present: Take A Kid Fishing Foundation., link, “Volunteers… provide… disabled and

disadvantaged youth with an opportunity to go saltwater fishing while teaching them about conservation and our ocean environment.”

Co-president, 2005: NCSU Student Fisheries Society, subunit of the AFS NC Chapter. Advisory Committee member, 2005: AFS NC Chapter Education and Outreach Committee What are your goals as a fisheries professional? My goal is to pursue a competent, thoughtful, and honest career in fisheries science and the related natural sciences. I work to contribute meaningful scientific research, enable the responsible management of natural resources, and provide general benefits to society such as the education of younger generations. In seeking fisheries professional certification, I hope to expand my experiences within the AFS organizational structure (e.g., committee participation, hold office, education and outreach), and increase my abilities to communicate the science I am a part of.

Section VI: FISHERIES PROFESSIONAL CODE OF PRACTICES DECLARATION: As a fisheries professional, I will strive to conform to the American Fisheries Society Standards of Professional Conduct and support and promote the North American Fisheries Policy. I further attest that the information provided in this application, together with all attached documents is true to the best of my knowledge. If any part of the information provided herein is false, I understand that my certification will be revoked. Date Submitted: _21 November 2014_________________________ Applicants Name: __Warren A. Mitchell_________________________ Applicant Signature: _______________________________________ MAIL Application fee AND this page to:

AMERICAN FISHERIES SOCIETY-CERTIFICATION 5410 Grosvenor Lane, Suite 110 Bethesda, Maryland 20814-2199

8

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

The Professional Development Activity Form reflects professional development quality points (PDQPs) as listed on the following pages.

PDQPs: _Sep 2012 Beginning Documentation Date _Sep 2014 Ending Documentation Date

_31 of 20_ Category I & II

_20 of 20_ Category III _18 of 20_ Category IV

_4 of 20__ Category V _40 of 30_ TOTAL

FP-C – Professional Development Activity Form indicates a summary of Professional Development Quality Points for the previous 2 years. A total of 30 PDQPs over the previous 2 years are required. A minimum of 10 of these points, and a maximum of 20, must fall under categories I & II; a minimum of 10 of these points, and a maximum of 20 points, must fall under categories III, IV, and V. FP-C ESTABLISHED – Professional Development Activity Form indicates a summary of Professional Development Quality Points. Applicants may choose to document points over a previous 2 year or 5 year time frame. For the 5 year time frame, a total of 100 PDQPs are required; a minimum of 35 of these points, and a maximum of 60, must fall under categories I & II; a maximum of 35 points may fall under each of the remaining categories (III, IV, and V). For the 2 year time frame 40 PDQPs are required; a minimum of 14 of these points, and a maximum of 24 must fall under categories I and II; a minimum of 16 points must fall under categories III IV and V, and a maximum of 26 points may fall under two categories of the ‘III, IV, and V’ group. FP-C RENEW – Professional Development Activity Form indicates a summary of Professional Development Quality Points for the previous 5 years. A total of 100 points over the previous 5 years are required. A minimum of 35 of these points, and a maximum of 60, must fall under categories I & II. A maximum of 35 points may fall under each of the remaining categories (III, IV, and V). FP-A to FP-C – Professional Development Activity Form indicates a summary of Professional Development Quality Points for the previous 2 years. A total of 30 PDQPs over the previous 2 years are required. A minimum of 10 of these points, and a maximum of 20, must fall under categories I & II; a minimum of 10 of these points, and a maximum of 20 points, must fall under categories III, IV, and V. FP-A – First time FP-A applicants do not complete this form. EITHER – Professional Development Activity Form indicates a summary of Professional Development Quality Points for the previous 2 years. A total of 30 PDQPs over the previous 2 years are required. A minimum of 10 of these points, and a maximum of 20, must fall under categories I & II; a minimum of 10 of these points, and a maximum of 20 points, must fall under categories III, IV, and V.

AMERICAN FISHERIES SOCIETY

www.fisheries.org 9

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Category I: Continuing education-fisheries

Includes subjects directly related to fisheries science or management. Examples include fisheries management, habitat management, fisheries economics, fish diseases, aquaculture or fish culture, fisheries policy and law, aquatic ecology, etc.

Activities

PDQPs

Participation in courses or training programs sponsored or conducted by commercial organizations, professional organizations/agencies, employers, or universities

0.5 per hour of instruction

Attendance at annual or semiannual meetings or special conferences of professional societies, educational organizations, etc.

0.5 per hour of participation

Attendance at in-house meetings of employer involving education on new techniques or developments in the profession

0.5 per hour of participation

Completion of self-instruction audiovisuals

0.5 per hour of instruction

Attendance at seminars conducted by experts in the subject matter

0.5 per hour of instruction

Please record activities in the chart included below (you may include as many pages as needed).

Category I Activity Description

Provider

Date

PDQPs

Training workshop attendance, in support of the project: “Reducing uncertainty in stock assessment by expanding habitat mapping with AUVs: application to three NMFS fishery-independent survey programs.” Workshop objectives: qualify AUV operators, determine optimal survey methodology, develop operating and scheduling protocols and checklists, and compare positional accuracy with towed side scan system.

NOAA National Marine Fisheries Service

2-4 Sep 2014

10

10

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Category II: Continuing education – nonfisheries

Includes subjects that are not primarily fisheries oriented but are professionally enriching to the individual. Examples include computer science and statistics, managerial and leadership skills, public speaking, problem-solving, public relations, marketing, planning, and other related natural resource disciplines such as forestry, wildlife, etc.

Activities

PDQPs

Participation in courses or training programs sponsored or conducted by commercial organizations, professional organizations/agencies, employers, or universities

0.5 per hour of instruction

Attendance at annual or semiannual meetings or special conferences of professional societies, educational organizations, etc.

0.5 per hour of participation

Attendance at in-house meetings of employer involving education on new techniques or developments in the profession

0.5 per hour of participation

Completion of self-instruction audiovisuals

0.5 per hour of instruction

Attendance at seminars conducted by experts in the subject matter

0.5 per hour of instruction

Please record activities in the chart included below (you may include as many pages as needed).

Category II Activity Description

Provider

Date

PDQPs

Online continuing education course: NOAA Climate Connection Webinar. “Using the science of story to enhance climate writing.”

NOAA

1 Aug 2014

0.5

Online continuing education course: “Trends and Challenges in Communicating Science Effectively.”

NOAA/National Ocean Service

16 Apr 2014

0.5

Basic NOAA Hydrographic Training, Newport, OR.

NOAA/National Ocean Service

4-8 Mar 2013

20

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PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Category III: Oral communications in fisheries and nonfisheries subjects

Includes the development, preparation, and presentation of activities such as those described in categories II and I. Context is any public meeting that is open to the general public or a select group of invited participants. For fisheries subjects, the audience need not be fisheries professionals. For nonfisheries subjects, the audience must be fisheries professionals. Multiple presentations of the same or similar subject matter can only be credited once during a certification renewal period. Category III: Oral communications in fisheries and nonfisheries subjects

Activities

PDQPs

Author/coauthor of an oral or poster presentation at a professional meeting

7

Author/coauthor of an oral or poster presentation to a nonprofessional audience

7

Organizer/instructor of a short course or workshop

20

Instructor of a quarter- or semester-length course

10 points per credit maximum 30

Author/producer of self-instruction audiovisuals in fisheries

20

Please record activities in the chart included below (you may include as many pages as needed). Must include titles for presentations, courses, workshops, or audiovisuals

Category III Activity Description

Provider

Date

PDQPs

Biennial workshop, Southeast Acoustics Consortium Workshop and Forum, Panama City, FL, 1-2 April 2014. Group co-founder, planning committee member, session moderator, speaker.

Self-initiated, SEAC Planning Committee, 2011 to present

1-3 April 2014

20

12

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Category IV: Written communications Developing, writing, editing, reviewing, and publishing fisheries-oriented materials. The written material need not be published, but it must be readily available to professional and nonprofessional audiences.

Activities

PDQPs

Author/coauthor of peer-reviewed article or book chapter

15

Author/coauthor of a book/monograph

30

Editor/co editor of a book/monograph

15

Author/coauthor of non-peer-reviewed article in a magazine, brochure, newspaper, etc.

7

Author/coauthor of an agency publication or report

10

Reviewer or editor of an article that has been submitted for publication

3

Book reviewer for a professional publication

5

Please record activities in the chart included below (you may include as many pages as needed). Please include citations for all publications.

Category IV Activity Description

Provider

Date

PDQPs

Author: Mitchell, W. A., G. T. Kellison, N. M. Bacheler,

J. C. Potts, C. M. Schobernd, and L. F. Hale. 2014. Depth-related distribution of postjuvenile red snapper in southeastern U.S. Atlantic Ocean waters: ontogenic patterns and implications for management. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 6(1):142-155. Link.

American Fisheries Society

11 Jul 2014

15

Peer reviewer: Taylor, J. C., and E. Ebert. 2012.

Mapping coral reef fish schools and aggregations with high-frequency multibeam and split-beam sonars. Proceedings of Meetings on Acoustics 17:070041. Link.

Acoustical Society of America

23 Oct 2012

3

13

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Category V: Service Involves membership and active participation in fisheries or aquatic professional societies and organizations, and community service that draws on the individual’s professional expertise in fisheries. Community service may include contributions of professional expertise to civic groups, environmental organizations, government, etc. Points are given for each year served in multiple-year appointments.

Activities

PDQPs

Holding the highest office in an organization (including subdivisions), (e.g., president, director, chair, journal editor, etc.)

15

Holding the other offices in an organization (including subdivisions), (e.g., secretary, treasurer, associate editor, newsletter editor, Committee chair, etc.)

10

Committee Member 4

Mentor in the Hutton Junior Fisheries Biology Program

10

Please record activities in the chart included below (you may include as many pages as needed).

Category V Activity Description

Provider

Date

PDQPs

Community service: Helping children aged 6-18 to experience fishing and teach them about conservation and our ocean environment, especially conveying ethical fishing practices.

Take a Kid Fishing Foundation Link

Annually, one day in June

2

Community service: I lead the arrangement of food fish donations from SEFIS research cruises, including regulatory and food safety responsibilities. Donations have been provided locally to Hope Mission and the Morehead City Boys and Girls Club, and regionally to the Feeding America network of food banks.

Self-initiated

Up to seven deliveries per year

2

14

PROFESSIONAL DEVELOPMENT ACTIVITY FORM

DECLARATION: Having completed the Professional Development Activity Form, I hereby apply for professional status as a Certified Fisheries Professional. I attest that to the best of my knowledge the information contained in this application and any attached material is complete and true. If any part of the information provided herein is false, I understand that my certification will be revoked. As a fisheries professional, I will strive to conform to the American Fisheries Society Standards of Professional Conduct and support and promote the North American Fisheries Policy. Date Submitted: 21 Nov 2014_______________________ Applicants Name: _Warren A. Mitchell_______________________ (Name typed here shall serve as signature. Not valid unless signed. If not signed, please explain in an attached letter.) Applicant Signature: __________________________________________

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PROFESSIONAL DEVELOPMENT ACTIVITY FORM

Please use this chart as extra space for all professional development categories.

Category _______ Activity Description

Provider

Date

PDQPs

If you have questions or comments about the AFS Professional Certification Program, please contact Gail Goldberg at ggoldberg@fisheries.org or at 301-897-8616 x 201.

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Guidelines for Satisfying Coursework Deficiencies GUIDELINES FOR SATISFYING COURSEWORK DEFICIENCIES

Instructions: Only two course deficiencies can be satisfied in this

manner and only one in each area (A-Fisheries/Aquatics; B-Other

Biological Sciences; C-Physical Sciences; D-Quantitative

(Mathematics/Statistics); E-Communications; and F-Human

Dimensions. To satisfy a single course deficiency, three (3) out of

items (I-IV) must be checked. Statements from Warren Mitchell are in bold. Please see checks and text following items I-III. Name: Warren A. Mitchell Course deficiency: Fisheries/Aquatic Sciences

I. Letter from supervisor that directly alludes to specific deficiency and

comments on the proficiency of the applicant in that area (i.e.,

statistics, communications, fisheries, etc.).

Please see attached.

II. Add two years to the existing experience requirement (Ph.D. – 2

years; M.S. – 4 years; B.S. – 5 years) for each course deficiency.

My submitted professional work period spans September 2006 to November 2014, summing more 8 years since completion of a M.S. degree (August 2006).

III. Applicant must list the following to satisfy a deficiency in the

stated coursework area (papers must be submitted with the

application). Applicant must be the senior author of at least one

manuscript.

A. Fisheries/Aquatic Sciences

A1. Author of two (2) published (published paper, book, D-J report)

manuscripts dealing with fish (ecology, management, ichthyology,

toxicology, behavior, physiology, etc.). At least one manuscript must

be in a refereed journal.

I have authored more than two (2) manuscripts on fisheries science topics. All have been in refereed journals, and I am senior author on one (1) manuscript. Please see the attached papers.

17

B. Other Biological Sciences B1. Published two (2) manuscripts that deal with renewable aquatic resources. At least one manuscript must be in a refereed journal. C. Physical Sciences C1. Published two (2) manuscripts that involve physical sciences (water quality, flow, substrate, etc.). At least one manuscript must be in a refereed journal. D. Quantitative (Mathematics/Statistics) D1. Published two (2) manuscripts that have made statistical inference. At least one manuscript must be in a refereed journal. E. Communications E1. Published two (2) manuscripts, one in a refereed journal and; E2. Presented two (2) presentations; at least one must be a technical presentation at a scientific meeting. F. Human Dimensions F1. Published two (2) manuscripts that must be focused on socio- economic topics of natural resource science and management, preferably those issues and aspects that directly pertain to fisheries management. At least one manuscript must be in a refereed journal. 1V. Letter to the Board of Professional Certification from a colleague (of the applicant) who is a Certified Fisheries Professional and is not the applicant’s supervisor that directly addresses the specific coursework deficiency and how that deficiency has been satisfied.

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21 November 2014

United States Department of Commerce National Oceanic and Atmospheric Administration National Marine Fisheries Service Southeast Fisheries Science Center NOAA Beaufort Laboratory 101 Pivers Island Road Beaufort, NC 28516 USA

Dear Board of Professional Certification, I am writing to assist Warren Mitchell in his application for AFS Professional Certification, and to comment upon any perceived deficiency in his Fisheries and Aquatic Sciences coursework. I coordinate the program for which Warren works, and have first-hand knowledge of his strengths and weaknesses as a scientist. There is absolutely no doubt from myself or any of Warren’s coworkers that he is highly proficient in the fisheries science field. Four specific examples of his astute knowledge are:

1. Warren was lead author of a high-profile paper published in Marine and Coastal Fisheries earlier this year that describes the ontogenic shifts in depth for red snapper on the Southeast Coast of the USA. The paper would simply not have been possible without Warren’s strong command and knowledge of the fields of fisheries science, fisheries ecology, and fisheries management. He has also coauthored a number of additional scientific publications in fisheries science and ecology.

2. Warren is the fisheries acoustics lead for our research group and is tasked with the collection of

acoustic information on fish and fish habitats during research cruises. Warren stays current on the fisheries acoustics literature and has intimate knowledge of the benefits and drawbacks of the ways in which sound can be used to learn more about the ecology of myriad fish species.

3. Warren serves as chief scientist on 10- to 15-day research cruises each year that monitor the size, abundance, and diversity of reef fish species along the Southeast Coast of the USA. He oversees trap and video sampling that targets reef fish species, as well as the extraction of biological samples such as otoliths, reproductive tissues, diets, and muscle for DNA and mercury testing.

4. Warren spends significant time each year counting reef fish on videos, data which are used to develop fishery-independent indices of abundance for inclusion in stock assessments. Warren is responsible for identifying over 100 fish species on video, without the benefit of being able to count fin rays or rely on color patterns (everything is blue in deep water). Warren is an expert at using subtle characters and behaviors to identify myriad fish species on video.

While Warren may be technically “deficient” in fisheries coursework, he more than makes up for it with a broad understanding of fisheries science, ecology, and management, knowledge that is utilized for his job as a fisheries biologist each and every day. Thank you for considering his application for Professional Certification, and please feel free to contact me if you have any questions or need clarification. Regards, Nathan M. Bacheler, Ph.D. Coordinator, Southeast Fishery-Independent Survey National Marine Fisheries Service, Southeast Fisheries Science Center 101 Pivers Island Road, Beaufort, NC 28516, USA Phone: +1 252 838 0825; Email: nate.bacheler@noaa.gov

This article was downloaded by: [NOAA Central Library]On: 15 July 2014, At: 16:22Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Marine and Coastal Fisheries: Dynamics, Management,and Ecosystem SciencePublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/umcf20

Depth-Related Distribution of Postjuvenile Red Snapperin Southeastern U.S. Atlantic Ocean Waters: OntogenicPatterns and Implications for ManagementWarren A. Mitchella, G. Todd Kellisona, Nathan M. Bachelera, Jennifer C. Pottsa, Christina M.Schobernda & Loraine F. Haleb

a National Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort Laboratory,101 Pivers Island Road, Beaufort, North Carolina, USAb National Marine Fisheries Service, Southeast Fisheries Science Center, Panama CityLaboratory, 3500 Delwood Beach Road, Panama City, Florida, USAPublished online: 11 Jul 2014.

To cite this article: Warren A. Mitchell, G. Todd Kellison, Nathan M. Bacheler, Jennifer C. Potts, Christina M. Schobernd &Loraine F. Hale (2014) Depth-Related Distribution of Postjuvenile Red Snapper in Southeastern U.S. Atlantic Ocean Waters:Ontogenic Patterns and Implications for Management, Marine and Coastal Fisheries: Dynamics, Management, and EcosystemScience, 6:1, 142-155

To link to this article: http://dx.doi.org/10.1080/19425120.2014.920743

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Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 6:142–155, 2014American Fisheries Society 2014ISSN: 1942-5120 onlineDOI: 10.1080/19425120.2014.920743

ARTICLE

Depth-Related Distribution of Postjuvenile Red Snapperin Southeastern U.S. Atlantic Ocean Waters: OntogenicPatterns and Implications for Management

Warren A. Mitchell,* G. Todd Kellison, Nathan M. Bacheler, Jennifer C. Potts,and Christina M. SchoberndNational Marine Fisheries Service, Southeast Fisheries Science Center, Beaufort Laboratory,101 Pivers Island Road, Beaufort, North Carolina, USA

Loraine F. Hale1

National Marine Fisheries Service, Southeast Fisheries Science Center, Panama City Laboratory,3500 Delwood Beach Road, Panama City, Florida, USA

AbstractFor the economically and ecologically important Red Snapper Lutjanus campechanus, depth distribution patterns

across ontogeny are not well understood, particularly in the southeastern U.S. Atlantic Ocean (SEUSA). Using dataderived from two fishery-independent surveys targeting hardbottom habitats, we examined patterns of age- andlength-specific depth distributions of postjuvenile (age 1+) Red Snapper in the SEUSA. We also compared age andlength distributions between fishery-independent surveys and commercial hook-and-line catches to make inferencesabout gear-specific age and size selectivity, which could have implications for gear-specific interpretations of RedSnapper depth distribution patterns and for determining selectivity functions used in stock assessments. Older,larger Red Snapper were generally distributed throughout all depths, whereas the younger and smaller Red Snapperoccurred disproportionately in relatively shallow waters. For Red Snapper equal to or larger than 50 cm FL, we foundno evidence of a positive relationship between depth and age or length. Additionally, age and length distributionsof Red Snapper ≥ 50 cm FL did not differ between fishery-independent surveys and the commercial hook-and-linefishery. These results provide no support for assertions of greater abundances of older and larger Red Snapper indeeper SEUSA waters. As observed in this study for Red Snapper in SEUSA waters, we suggest that patterns ofincreasing age and size with depth for multiple reef-associated fish species in SEUSA and Gulf of Mexico waters maybe driven by younger and smaller fish occurring in shallower waters, and older and larger fish being distributed moreequally across depths. Analyses to test this hypothesis for multiple species would be informative for their assessmentand management and are recommended.

Foraging requirements, competitive interactions, and preda-tion risk vary throughout an animal’s lifetime (i.e., ontogeny)due to increases in body size and changes in behavior (Wernerand Gilliam 1984; Ludwig and Rowe 1990). Ontogenic habi-tat shifts are commonly observed for mobile species and are

Subject editor: Richard Brill, Virginia Institute of Marine Science, USA

*Corresponding author: warren.mitchell@noaa.gov1Deceased.Received October 21, 2013; accepted March 11, 2014

especially well documented for marine species (Dahlgren andEggleston 2000; Etherington et al. 2003; Snover 2008; Joneset al. 2010). For example, many marine reef fish species havelarvae that settle from pelagic waters to inshore benthic habitatsthat serve as nurseries (Parrish 1989; Nagelkerken et al. 2000;

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DEPTH-RELATED DISTRIBUTION OF POSTJUVENILE RED SNAPPER 143

Adams et al. 2006) and gradually shift to offshore habitats asolder juveniles and adults (Deegan 1993; Lindeman et al. 2000).

Successful assessment and management of marine fishspecies is predicated in part upon a clear understanding of on-togenic shifts in distribution patterns, driven, for example, bychanging habitat affinities with ontogeny. For instance, unknownlevels of immigration into, or emigration from, the unit stockduring ontogeny would be a clear violation of most parameteri-zations of stock assessment models (Hilborn and Walters 1992;Walters and Martell 2004). Moreover, changes in distributionpatterns during an individual’s lifetime can also result in a pat-tern of spatial overlap between fish and fishers that varies acrossontogeny, resulting in some ages or life stages of a species beingless vulnerable to fishing (i.e., subject to lower selectivity) thanother ages would (Bacheler et al. 2010). Incorrect assumptionsor estimates of the selectivity patterns of a fishery can result inhighly erroneous estimates of stock abundance and harvest rate(Myers and Hoenig 1997).

Red Snapper Lutjanus campechanus is an economically andecologically important species in the Gulf of Mexico (GOM)and in southeastern U.S. Atlantic Ocean waters (SEUSA). In theSEUSA, where Red Snapper depth distribution patterns acrossontogeny are not well understood, the Red Snapper fishery expe-rienced a complete closure in 2010 due to assessment results thatsuggested an unsustainable harvest and low spawning biomass(SEDAR 2009, 2010). Some SEUSA stakeholders have assertedthat relatively old and large Red Snapper are disproportionatelydistributed in relatively deep waters (see SEDAR 2010), whichhas implications for fishing sector-specific selectivity functionsused in recent Red Snapper stock assessments. For example,if older and larger Red Snapper were disproportionately dis-tributed in relatively deep waters, but the recreational or com-mercial hook-and-line fishery was centered in shallower waters,then a dome-shaped selectivity pattern (which assumes that thelargest, most fecund fish escape capture), as used for Red Snap-per in the GOM (Cowan 2011), might be more appropriate thana flat-top selectivity pattern (in which selectivity plateaus withincreasing age or length; Thorson and Prager 2011).

Here we examine patterns of age- and length-specific depthdistributions of postjuvenile (age 1+) Red Snapper in theSEUSA to inform future Red Snapper stock assessments and,more generally, broaden understanding of the ecology of thiseconomically and ecologically important species. Specifically,we used two fishery-independent data sets (both targeting hard-bottom habitats) to assess patterns of depth- and latitude-relatedvariation in ages, lengths, and CPUE for Red Snapper in SEUSAwaters. Additionally, to make inferences about gear-specific ageand size selectivity, we compared age and length distributionsfrom the two fishery-independent surveys with distributionsfrom the commercial hook-and-line fishery. Finally, becauserelatively old and large Red Snapper are thought to become pro-gressively less associated with hardbottom habitats (Szedlmayer2007; Gallaway et al. 2009; Cowan 2011), we assessed thesparse Red Snapper catch history and depth information avail-

able from a fishery-independent survey that included samplingof unstructured (nonhardbottom) habitats in SEUSA waters.

METHODSTrap survey.—The Marine Resources Monitoring, As-

sessment, and Prediction (MARMAP) program of the SouthCarolina Department of Natural Resources has used chevronfish traps to index reef fish abundance in the SEUSA sincethe late 1980s (McGovern et al. 2002), supplemented withfunding from Southeast Area Monitoring and AssessmentProgram-South Atlantic (SEAMAP-SA) beginning in 2009.We analyzed MARMAP/SEAMAP-SA data from 1990 to2011, during which time chevron-fish-trap sampling wasconducted in a consistent manner (described below). In 2010,the National Marine Fisheries Service (NMFS) created theSouthEast Fishery-Independent Survey (SEFIS) to workcooperatively with MARMAP and SEAMAP-SA to increasefishery-independent sampling in the SEUSA; we also included2010–2012 SEFIS data in our analyses because samplingmethods were identical between the two survey programs.Hereafter, the MARMAP–SEAMAP-SA–SEFIS chevron trapsurvey is referred to as the “trap survey.”

Hardbottom sampling stations included in the analyses wereselected for sampling in one of three ways. First, most sites wererandomly selected from the MARMAP–SEAMAP-SA–SEFISsampling frame between Cape Hatteras, North Carolina, andPort St. Lucie, Florida. Second, some stations in the samplingframe were sampled opportunistically even though they were notrandomly selected for sampling in a given year. Third, new hard-bottom stations were added during the study period using infor-mation from fishers, charts, and historical survey information.These locations were investigated using vessel echosounders ordrop cameras and sampled if hardbottom was present. Samplingfor the trap survey occurred during daylight hours on one offour primary research vessels: MARMAP–SEAMAP-SA usedthe RV Palmetto (1990–2011), while SEFIS used the RV Savan-nah (2010–2012), the NOAA Ship Nancy Foster (2010), and theNOAA Ship Pisces (2011).

Chevron fish traps were deployed at each station sampled inthe trap survey. Chevron traps were constructed from plastic-coated, galvanized, 12.5-gauge wire (mesh size = 3.4 cm2), andwere shaped like an arrowhead that measured 1.7 × 1.5 × 0.6 mand had a total volume of 0.91 m3 (see Collins 1990). Each trapwas baited with 24 menhaden Brevoortia spp. and was typicallydeployed in a group of six traps. The minimum distance be-tween individual traps was 200 m to provide some measure ofindependence among traps. A soak time of 1.5 h was targeted foreach trap, and only those soaking for 0.8–2.5 h were included inthe analyses. Also, only traps deployed between 28◦N and 32◦Nwere included, which encompassed the historical “heart” of theSEUSA Red Snapper fishery (SEDAR 2009) and is consistentwith the geographic coverage of the longline survey describedbelow. Traps were deployed in spring through fall at depths

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TABLE 1. Cruise information for trap deployments in the MARMAP–SEAMAP-SA–SEFIS chevron trap survey that occurred south of 32◦N, 1990–2012, usedto elucidate the depth distribution of Red Snapper.

Number ofYear traps Start date End date Latitude (◦N) Depth (m)

1990 34 10 Jul 12 Jul 30.42–31.69 26–621991 35 30 Jul 15 Aug 30.42–31.69 27–551992 14 19 May 20 May 30.42–31.69 27–621993 107 21 Jul 12 Aug 30.43–31.74 16–571994 119 12 Jul 26 Oct 30.74–31.74 16–531995 180 17 Apr 26 Oct 29.94–31.74 16–551996 102 9 May 24 Jul 28.78–31.74 26–691997 139 22 Jul 28 Aug 28.27–31.74 16–741998 162 2 Jun 29 Jul 28.28–31.98 16–811999 56 13 Jul 6 Oct 29.93–31.39 15–602000 136 23 May 19 Oct 28.95–31.74 15–612001 79 10 Jul 24 Oct 30.52–31.64 14–672002 172 18 Jun 5 Nov 28.95–31.74 13–702003 65 3 Jun 11 Jun 28.95–31.54 34–622004 110 18 May 28 Oct 29.99–31.64 14–702005 127 14 Jun 20 Oct 28.95–31.74 15–692006 84 6 Jun 19 Oct 28.94–31.74 15–692007 115 26 Jun 13 Sep 28.95–31.74 15–732008 90 5 Jun 29 Sep 28.52–31.69 14–662009 158 2 Jun 8 Oct 28.52–31.74 15–702010 615 5 May 27 Oct 28.50–31.74 14–832011 492 21 May 26 Oct 28.08–31.74 14–852012 554 24 Apr 26 Sep 28.08–31.74 17–84

Overall 3,745 17 Apr 5 Nov 28.08–31.98 13–85

between 13 and 85 m (Table 1; Figure 1A). For some analyses,trap sets were grouped into three depth strata to be consistentwith longline sampling (see below), defined as <29.0 m,29.0–48.9 m, and ≥49.0 m. The shallow and intermediate depthstrata represented continental shelf waters, and the deep depthstratum represented shelf-break and deeper waters.

Upon capture, all Red Snapper were measured for FL andotoliths were removed and retained for aging. Sagittal otolithswere sectioned and aged using standard methodologies (Cowanet al. 1995; McInerny 2007; Stephen et al. 2011). Red Snappercaptured by the trap survey in 2012 were not aged in time tobe included here, so only lengths from 2012 Red Snapper wereincluded.

Georgia–Florida (GA-FL) longline survey.—This survey oc-curred in 2010 and early 2011. Protocols of longline samplingwere developed cooperatively by NMFS biologists and advo-cates from the commercial fishing community. Two commercialfishers were contracted to perform longline sampling using theirrespective fishing vessels (referred to as fishing vessels A andB). Each vessel was required to have at least one crew mem-ber during all surveys who possessed a demonstrated history(e.g., logbook data) of conducting bottom longline trips target-

ing Red Snapper in the study area. A NMFS Bottom LonglineObserver Program fisheries observer was present for all tripsto ensure agreed-upon sampling methodologies were followedand to lead data and sample collection efforts.

Longline sets targeting Red Snapper were allocated acrossthree depth strata in federal waters, defined a priori as <29.0 m,29.0–48.9 m, and ≥49.0 m (Table 2; Figure 1B), and eightlatitudinal “bands” defined by half-degree increments between28◦N and 32◦N (Figure 1B). Four longline sets were completedwithin each depth × latitude combination, for a total of 96 long-line sets. Hardbottom habitats were targeted, with the specificlocation of each longline set chosen by the vessel captain. Tomaximize consistency across depth strata within a latitude band,the same vessel and crew sampled all 12 sets within each band.Sampling occurred between September 24, 2010, and February2, 2011.

Contract vessels were rigged for bottom longline fishingaccording to specifications agreed upon by NMFS biologistsand contracted fishers. Mainlines were constructed of 3.2-mm-diameter stainless steel cable and were sufficiently long to dropfrom surface floats to the sea floor, accommodate 150 gangionsat 9.1–12.2-m spacings along the sea floor, and rise from the

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TABLE 2. Cruise information for the Red Snapper longline study, 2010–2011. Number of sets is the number of longline deployments made during each researchcruise.

Number ofVessel Year sets Start date End date Latitude (◦N) Depth (m)

A 2010 10 24 Sep 26 Sep 30.1–30.2 20–75A 2010 2 27 Sep 27 Sep 30.2–30.3 14–22A 2010 12 25 Oct 28 Oct 30.5–30.8 19–72A 2010 10 8 Nov 12 Nov 31.1–31.4 18–88A 2010 14 17 Nov 20 Nov 31.4–31.9 18–75B 2010 3 9 Dec 10 Dec 29.0–29.2 20–29B 2010 9 16 Dec 18 Dec 28.8–29.3 19–52B 2010 2 22 Dec 22 Dec 28.7–28.8 38–43B 2011 5 5 Jan 6 Jan 29.2–29.3 31–76B 2011 8 15 Jan 17 Jan 28.2–29.0 30–71B 2011 9 18 Jan 20 Jan 28.1–28.5 23–76B 2011 12 30 Jan 2 Feb 29.5–29.7 17–72

Overall 96 24 Sep 2 Feb 28.1–31.9 14–88

FIGURE 1. Study area in Georgia and Florida showing catches of Red Snapper in (A) the trap survey in 1990–2012 and (B) the longline survey in 2010–2011.Catch of Red Snapper is shown as either the number of individuals caught per trap or the number of individuals caught per longline set (150 hooks) and isrepresented by the open circles. Trap or longline sets where no Red Snapper were caught are shown by a gray × symbol. Gray lines are isobaths of 29 and 49 mdeep (corresponding to depth strata used in analysis) and horizontal lines delineate latitudinal bands (0.5◦N) used in stratification of longline sampling effort.

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sea floor to a second set of surface floats (total bottom longlinelength ≈ 1,500 m). Gangions originated in a medium snap clipconnected to a 4/0 swivel and were composed of 4.6 m of 300-lb-test (136 kg) monofilament line that was crimped at the snap clipand snelled to the hook. Hooks were offset circle hooks of sizes13/0, 14/0, and 15/0 (Mustad 39965, Mustad, Gjovik, Norway)and were fished in a systematically alternating pattern along themainline (e.g., 13/0, 14/0, 15/0, 13/0, . . . ). Hooks were baitedwith a mixture of imported Indian Oil Sardine Sardinella long-iceps and Atlantic Mackerel Scomber scombrus. Soak time wasdefined as the elapsed time between the last hook deployed andthe first hook retrieved, and was limited to 2 h. All sets wereinitiated (i.e., first hook deployed) between 0.5 h before sunriseand 0.5 h after sunset.

Trained fisheries observers recorded station-level informa-tion and characteristics of the catch, and collected biologicalsamples (e.g., otoliths). All Red Snapper were measured for FL.Otoliths were extracted, processed, and read as described abovefor Red Snapper captured by the trap survey. However, the num-ber of opaque zones was used as the unadjusted age of the fishfrom which the otolith was sampled, since longline samplingoccurred during the fall and winter when no annuli were beingdeposited (Baker et al. 2001; Allman et al. 2005).

Commercial hook-and-line data.—The third data source in-cluded in our analyses was the Trip Interview Program (TIP),which provided information on commercial fishery landings inthe SEUSA. The TIP program is a cooperative effort betweenNMFS and the various state fisheries agencies in the SEUSA.The TIP program uses port agents to sample the commercialcatch from fish being unloaded or already in storage containersat fish houses. Port agents collect length and weight informationfrom randomly selected individuals, as well as obtain biologi-cal samples such as otoliths or spines for aging. Moreover, portagents collect information regarding each fishing trip, includingthe general area and depth ranges where fishing occurred, and thetype and quantity of fishing gear used. Here, we focused on com-mercial hook-and-line catches of Red Snapper caught during atime period (1992–2009) when TIP sampling methods were gen-erally consistent and during which minimum size limits werein place for the commercial fishery. The hook-and-line catchesincluded samples between 28◦N and 32◦N to be consistent withthe geographic range of the trap and Georgia–Florida (GA-FL)longline surveys. Where available, depth ranges spanned the du-ration of a trip and were not specific to the location where a fishwas caught. Often range was not available and only minimumdepths were reported.

NMFS-SEFSC annual longline survey.—The NMFS South-east Fisheries Science Center (NMFS-SEFSC) Mississippi Lab-oratories has conducted standardized bottom longline surveysin the GOM, Caribbean, and SEUSA waters since 1995 (seeMitchell et al. 2004; Ingram et al. 2005) to generate fisheries-independent data for stock assessment purposes for multipletaxa, including shark, snapper, and grouper species. While sur-vey protocols have varied over time (Ingram et al. 2005), the

survey typically has employed 100-hook sets of baited, large(∼15/0) hooks in depths ranging from 9 to 366 m during July–September (Mitchell et al. 2004). The survey does not targetspecific bottom types or bottom features; however, randomlyselected set locations are examined with echosounders prior togear deployment and if the bottom profile appears prohibitivefor survey operations (e.g., containing hardbottom with verticalrelief) the set location is moved within 0.93 km of the orig-inal location or the station is eliminated if suitable bottom isnot found. Thus, gear deployment typically occurs partially orcompletely over unstructured bottom habitat. We assessed to-tal longline survey effort and Red Snapper catch from SEUSAwaters from 1995 to 2012.

Statistical analysis.—We investigated patterns in Red Snap-per age, length, and catch over the predictor variables of depth,latitude, gear type, and habitat type. Of the four described datasources, two presented data issues and were not used in allanalyses. Specifically, given the nonspecific nature of depthsincluded in the TIP database, commercial hook-and-line datafrom TIP were only used to analyze age- and length-frequencydistributions. Due to low Red Snapper sample size (see Results),data from the NMFS-SEFSC annual longline survey were onlyused to make inferences about Red Snapper habitat utilization.

For trap and GA-FL longline surveys, we assessed patternsof age- and length-specific depth distributions of Red Snap-per using two statistical methods. First, Kolmogorov–Smirnov(KS) two-sample tests were used to determine whether the ageor length distributions of Red Snapper caught in traps or onGA-FL longlines were different among the three depth strata(shallow: <29.0 m; middle: 29.0–48.9 m; deep: ≥49.0 m) sam-pled in this study. Kolmogorov–Smirnov tests are advantageousbecause they are sensitive to differences in location, dispersion,and skewness of two samples (Sokal and Rohlf 1995). Becauseonly two samples can be compared at a time, three KS tests wereused to compare Red Snapper ages (shallow versus deep, mid-dle versus deep, and shallow versus middle) and three additionaltests were used for Red Snapper lengths for each gear. We alsoused two-sample KS tests, separately for traps or GA-FL long-lines, to test for differences across depth strata in age and lengthdistributions of smaller (<50 cm FL) and larger (≥50 cm FL)Red Snapper. The 50-cm length was chosen as a cutoff betweensmaller and larger fish because it approximates the minimumsize limit (20 in [51 cm] TL) for Red Snapper in SEUSA watersprior to the 2010 fishery closure. Significance was accepted atP ≤ 0.05 for all statistical tests; a Bonferroni correction wasnot used to correct for multiple comparisons due to concernsabout the corresponding reduction in power to detect significanteffects (Perneger 1998).

Second, linear models were used to test whether the meanage or length of Red Snapper caught in each trap or on eachGA-FL longline was related to depth or latitude. Mean age orlength for each trap and longline set was used in these analysesdue to the potential lack of independence of fish caught in asingle collection. Mean age or length was used as the response

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variable and depth and latitude were included as continuouspredictor variables. Only traps or longlines that caught RedSnapper were included in these analyses.

Linear models were also used to test whether Red Snappercatch rates from traps or GA-FL longlines varied as a func-tion of depth and latitude. The response variables were the log-transformed number of Red Snapper caught per trap or longline.Catch per trap or longline (hereafter, CPUE) was not standard-ized by soak time because preliminary linear models indicatedno relationship between soak time and catch per trap (P = 0.07)or longline (P = 0.52). Depth and latitude were included in bothlinear models as predictor variables. Depth was included as acategorical variable with three levels (shallow, middle, or deep),and latitude was included as a continuous variable. We alsotested for interactions in Red Snapper CPUE between depth andlatitude to determine whether Red Snapper depth distributionvaried by latitude.

To make inferences about gear-specific age and length se-lectivities, KS tests were used to compare age and length dis-tributions of Red Snapper collected in the trap survey, GA-FLlongline survey, and commercial hook-and-line fishery. Becausethe commercial hook-and-line fishery data excluded fish smallerthan the regulatory minimum size limit (most recently, ∼50 cm),we limited comparisons to fish ≥ 50 cm FL.

Finally, to make inferences about the extent to which RedSnapper occurred in association with unstructured (nonhardbot-tom) habitats, we assessed the number of Red Snapper collectedby the NMFS-SEFSC annual longline survey (1995–2012; N =789 longline sets) in SEUSA waters.

RESULTSA total of 3,745 chevron traps were deployed south of 32◦N

in the trap survey in 1990–2012 (Table 1; Figure 1). Mean ± SEsoak time was 1.64 ± 0.02 h and depths ranged from 13 to 85 m.In terms of GA-FL longline sampling, a total of 96 sets werecompleted in 2010–2011 (Table 2; Figure 1). Vessel A sampledthe northern four latitude bands earlier in the study than VesselB, which sampled the southern four latitude bands (Table 2).Longlines were set in waters 14–88 m deep (Table 2). Meansoak time was 0.40 ± 0.02 h, and no sets had soak times thatexceeded 2 h. From the TIP sampling database, we included 253commercial hook-and-line fishing trips from 1992 to 2009 thatcaught at least one Red Snapper (catch: 8.8 ± 0.8 [mean ± SE];range, 1–71). A total of 668 Red Snapper were caught in the trapsurvey, 220 were caught in the GA-FL longline study, and 2,233were sampled from the commercial hook-and-line fishery.

FIGURE 2. (A, C) Age and (B, D) length of Red Snapper caught in shallow (<29.0 m; open circles), middle (29.0–48.9 m; gray circles), or deep (≥49.0 m;black circles) depth strata by (A, B) the trap survey in 1990–2012 and (C, D) the longline survey in 2010–2011.

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There was an effect of depth on Red Snapper age and lengthdistributions in the trap survey, but not in the GA-FL longlinesurvey (Figure 2). In the trap survey, age distributions weresignificantly different between the middle and deep depth zones(one-tailed KS two-sample distribution tests: P < 0.01), butnot between the shallow and deep zones or shallow and middlezones (KS tests: P > 0.05). Length distributions from trapswere significantly different for all depth-zone combinations (KStests: P < 0.001). For both ages and lengths, older and larger fishcollected in the trap survey occurred in higher proportions in thedeep depth zone compared with the shallow and middle depthzones (Figure 2A, B). In contrast, there was no evidence thatRed Snapper caught in the deep stratum were older or larger thanthose from the shallow or middle strata in the GA-FL longlinesurvey (KS tests: P > 0.80; Figure 2C, D).

For smaller (<50 cm FL) Red Snapper, age distributions offish collected in the trap survey were significantly different be-tween shallow and middle depth zones, and between shallow anddeep depth zones, and greater proportions of younger fish werefound in the shallow zone (Figure 3A). For smaller Red Snappercollected by GA-FL longline, limited samples sizes (n = 0 col-lected in the shallow depth zone and n = 1 collected in each ofthe middle and deep zones; Figure 3C) prohibited informative

statistical comparisons of age distributions as a function of depthzones. For smaller Red Snapper caught in traps, there were sig-nificant differences in length distributions across the three depthzones (KS test: P < 0.001); the smallest Red Snapper occurredin higher proportions in the shallow depth zone than in the mid-dle and deep depth zones (Figure 4A). For smaller (<50 cmFL) Red Snapper caught in the GA-FL longline survey, lengthdistributions did not differ across depth zones as no fish < 45 cmFL were caught (Figure 4C).

For larger (≥50 cm FL) Red Snapper, age and length distri-butions across the three depth zones were similar in both thetrap (KS tests: P > 0.50) and GA-FL longline surveys (KStest: P > 0.80) (Figures 3B, D and 4B, D). Using the linearmodeling approach, there was no effect of depth on mean ageor length of Red Snapper caught in either the trap or GA-FLlongline survey (P > 0.05 for all tests; Figure 5). Mean age andlength of Red Snapper were positively related to latitude in theGA-FL longline survey (P < 0.01), but not in the trap survey(P > 0.05).

The CPUE of Red Snapper was variable (Figure 6), rangingfrom 0 to 28 in the trap survey (0.18 ± 0.02 [mean ± SE]) andfrom 0 to 19 in the GA-FL longline survey (2.4 ± 0.4). Over-all, 270 traps (7%) caught Red Snapper, whereas 41 GA-FL

FIGURE 3. Age frequency histograms for (A, C) smaller (<50 cm FL) or (B, D) larger (≥50 cm FL) Red Snapper caught in shallow (<29.0 m; open circles),middle (29.0–48.9 m; gray circles), or deep (≥49.0 m; black circles) depth strata by (A, B) the trap survey in 1990–2012 or (C, D) the longline survey in 2010–2011.

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FIGURE 4. Length frequency histograms for (A, C) smaller (<50 cm FL) or (B, D) larger (≥50 cm FL) Red Snapper caught in shallow (<29.0 m; open circles),middle (29.0–48.9 m; gray circles), or deep (≥49.0 m; black circles) depth strata by (A, B) the trap survey in 1990–2012 or (C, D) the longline survey in 2010–2011.

longlines (43%) caught Red Snapper. Highest CPUE in bothsurveys occurred just north of Cape Canaveral between 28.5◦

and 29.5◦N (Figure 1). Log-transformed Red Snapper CPUEwas not related to depth in either survey (P > 0.05), but wassignificantly and negatively related to latitude in both surveys(P < 0.01). Although differences in CPUE were not statisticallysignificant across depth zones, mean CPUE was 30% higher inshallow depths than in deep depths in the trap survey and 94%higher in the GA-FL longline survey (Figure 6).

Red Snapper caught in the trap survey (age: 3.6 ± 1.5 years[mean ± SE]; length: 48 ± 10 cm FL) were generally youngerand smaller than those caught by the commercial hook-and-linefishery (age: 4.8 ± 3.9 years; length: 59 ± 10 cm FL) or the GA-FL longline survey (age: 5.4 ± 2.4 years; length: 66 ± 10 cm FL;Figure 7). However, the reason for the difference in mean age orsize appeared to be due to traps catching a higher proportion ofyounger, smaller Red Snapper than the other gears, as opposedto traps missing the older, larger fish (Figure 7). Age and lengthdistributions of fish ≥ 50 cm FL did not differ between the trapsurvey, GA-FL longline survey, and the commercial hook-and-line fishery (KS test; P > 0.80 for all comparisons).

The NMFS-SEFSC annual longline survey database (1995–2012; N = 789 longline sets) contained records of 16 Red Snap-per collected in SEUSA waters (age range, 2–27 years; length

range, 55–90 cm FL; depth of capture range, 31–80 m; depthrange sampled, 6–232 m; Figure 8).

DISCUSSIONWe found evidence of depth-related variation in Red Snapper

age, length, and CPUE in SEUSA waters; greater proportionsof older and larger fish occurred in deeper waters and CPUEdecreased nominally with depth. However, the depth-relatedvariation in Red Snapper age and length was driven byyounger and smaller fish occurring disproportionately inshallower waters, as opposed to older and larger fish occurringdisproportionately in deeper waters. In essence, younger andsmaller fish (<50 cm FL, approximating the minimum sizelimit for Red Snapper in SEUSA waters before the 2010 fisheryclosure) occurred predominantly in relatively shallow waters.For Red Snapper ≥ 50 cm FL, we found no evidence of apositive relationship between depth and Red Snapper age orlength. Thus, within the depths where surveys occurred (to85 m for the hardbottom-targeted trap survey, 88 m for thehardbottom-targeted GA-FL longline survey, and 232 m forthe nonhardbottom-targeted NMFS-SEFSC annual longlinesurvey), these results provide no support for assertions ofgreater abundances of older and larger Red Snapper in deeper

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FIGURE 5. (A, C) Mean age and (B, D) fork length of Red Snapper caught in various depths in (A, B) the trap survey in 1990–2012 or (C, D) the longline surveyin 2010–2011. Mean age and fork length were calculated for each trap or longline collection to avoid pseudoreplication, and trend lines indicate linear regressionfit.

SEUSA waters. It is possible that relatively older and larger RedSnapper inhabit SEUSA hardbottom habitats in waters deeperthan 88 m, and thus the depths sampled by the trap and GA-FLlongline surveys were insufficient to document older and largerfish. However, we believe this possibility to be unlikely giventhat (1) the commercial fishers who performed the GA-FLlongline survey essentially had an objective of identifying olderand larger Red Snapper in deeper waters, and yet chose tosample in depths no greater than 88 m, (2) the NMFS-SEFSCannual longline survey did not record a significant abundanceof Red Snapper in SEUSA waters deeper than 88 m, as wascommon in the GOM sampling (Mitchell et al. 2004), and(3) Red Snapper have been only infrequently observed indeep-water (>50 m) studies off SEUSA coasts (see Quattriniand Ross 2006; Sedberry et al. 2006; Harter et al. 2009).

Our findings on the relationship between depth and RedSnapper age or length are consistent with conclusions reportedin SEDAR 2010 (see their Figure 2.9.1a, b) based on analyses ofage- and length-with-depth data pooled from multiple SEUSAfishery-independent and fishery-dependent data sources, butpotentially contrast with patterns exhibited by Red Snapperin the GOM. In the GOM, shallower continental shelf waters,particularly in association with artificial habitat, are dominated

by relatively young (age 2–4) fish (Gitschlag et al. 2003;Szedlmayer 2007; Gallaway et al. 2009), while older, larger fishare captured more frequently in deeper (>50 m) habitats fartherfrom shore (Mitchell et al. 2004; Henwood et al. 2005; Allmanand Fitzhugh 2007; Gallaway et al. 2009). The apparent lackof a positive age- or length-with-depth relationship for larger(exploited size) Red Snapper in the SEUSA could be a naturalphenomenon, perhaps due to the narrow width of the continentalshelf in SEUSA waters (∼55–75 km off Florida and Georgiacoasts), or to a greater availability of reef habitat on the SEUSAcontinental shelf relative to the GOM (Cowan 2011). Alterna-tively, the lack of an age- or length-with-depth relationship forlarger Red Snapper could be a result of fishing exploitation, inwhich a majority of older, larger individuals have been removedfrom the population, precluding the observation of a positiveage- or length-with-depth pattern that would be apparent ifa greater proportion of older and larger individuals existed(Lindeman et al. 2000). The truncated age distribution of theRed Snapper landed during the study was consistent with thefindings of SEDAR (2010), in which a majority of fish wereassigned to ages 3, 4, 5, or 6, and few older fish (Figure 2). Giventhat Red Snapper are a relatively long-lived species (maximumreported age in the study area = 54 years: SEDAR 2010), these

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FIGURE 6. Mean catch (number of fish) of Red Snapper in shallow (<29.0 m),middle (29.0–48.9 m), and deep (≥49.0 m) depth strata from (A) the trap surveyin 1990–2012 and (B) the longline survey in 2010–2011. Error bars indicate± 1 SE.

results are consistent with age-truncation patterns expected inheavily exploited populations (Hsieh et al. 2006). Repeating thisstudy in subsequent years, following increases in SEUSA RedSnapper population size and age structure anticipated to resultfrom the current fishery closure, should allow differentiation be-tween the competing, but not mutually exclusive, explanationsof habitat availability versus fishing exploitation underlying

the apparent lack of a positive age- or length-with-depthrelationship for larger Red Snapper in SEUSA waters.

Importantly, the trap survey and GA-FL 2010 longline sur-vey, which were used to assess age and length distributions,targeted hardbottom habitats. In the GOM, older and largerRed Snapper are thought to become progressively less associ-ated with hardbottom habitats, venturing instead over nonstruc-tured, soft-sediment habitats (Szedlmayer 2007; Gallaway et al.2009; Cowan 2011). Thus, Red Snapper are regularly collectedin GOM waters by the NMFS-SEFSC annual longline survey,which occurs partially or entirely over nonstructured habitats,to an extent that Red Snapper annual abundance indices aregenerated from that survey for use in GOM stock assessments(Ingram and Pollack 2012). (Note that catch rates in the GOMvary spatially between the eastern and western regions suchthat rates are considerably lower in the eastern GOM, althoughcatches appear to have increased in recent years [Ingram andPollack 2012; Figures 1, 2]). In contrast to GOM sampling ef-forts, only 16 Red Snapper were collected by the NMFS-SEFSCannual longline survey in SEUSA waters over an 18-year pe-riod during which 789 longline sets were completed. That theNMFS-SEFSC annual longline survey effectively targets RedSnapper in GOM waters but rarely catches them in SEUSA wa-ters could be explained by (1) regional (GOM versus SEUSA)differences in Red Snapper ontogenic habitat utilization pat-terns, such that Red Snapper in SEUSA waters do not becomeless affiliated with hardbottom or structured habitats as they in-crease in age or size, (2) fewer Red Snapper per unit of preferredhabitat area in SEUSA versus GOM waters, in which density-dependent processes cause Red Snapper to use nonreef habitatsdisproportionately in GOM (relative to SEUSA) waters, (3) ageneral dearth of older and larger fish in SEUSA waters relativeto GOM waters (Cowan 2011), or (4) some combination thereof.Nevertheless, these results suggest that Red Snapper were notwidely distributed over nonstructured, soft-sediment habitatsin SEUSA waters. Thus, the hardbottom-targeted surveys fromwhich data were analyzed for this study were appropriate for as-sessing patterns of depth- and latitude-related variation in ages,lengths, and CPUE for Red Snapper in SEUSA waters.

Our ability to make inferences about patterns of age- andlength-specific depth distributions of Red Snapper is a functionof the fishery-independent gears (chevron trap and longline)used in the surveys we utilized, as well as the seasonality ofthose surveys and the depths over which they occurred. From agear standpoint, longlines are used in the GOM to survey RedSnapper (Mitchell et al. 2004; Henwood et al. 2005) and, inouter shelf and upper slope depths, generally sample greaterproportions of older fish than do other gears (SEDAR 2005).The GA-FL longline gear used in this study was chosen at thespecific recommendation of industry members, with a reason-ing that the gear had been successful historically in targetingrelatively large Red Snapper in SEUSA continental shelf-breakwaters. Thus, we believe the combination of longline and trapgears we employed (the latter of which more effectively sampled

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FIGURE 7. Histograms of (A, C, E) ages and (B, D, F) lengths of Red Snapper caught across all depths and latitudinal bands by (A, B) the trap survey in1990–2012, (C, D) longline survey in Georgia and Florida in 2010–2011, and (E, F) commercial hook-and-line sampling in 1992–2009.

smaller fish than did longlines) was appropriate for assessingpatterns of age- and length-specific depth distributions of RedSnapper over the depths covered in the surveys. From a sea-sonality standpoint, sampling in the trap (April–November) orGA-FL longline (September–February) survey occurred duringall months but March. Given the similarity in patterns of age-and length-specific depth distributions generated from each sur-vey and the generally differing seasonality of those surveys, itis unlikely that patterns of Red Snapper age- and length-specific

depth distributions vary considerably by season in SEUSA wa-ters, and thus, it is unlikely our results were biased by the season-ality of the fishery-independent surveys we used. From a depthstandpoint, while it is possible that relatively high abundancesof relatively old and large Red Snapper occur in waters deeperthan those covered by the surveys we used (13 to 88 m for thehardbottom-targeted trap and GA-FL longline surveys and upto 232 m for the nonhardbottom-targeted NMFS-SEFSC annuallongline survey), surveys in the GOM indicate that a majority of

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FIGURE 8. Number of Red Snapper caught per gear deployment in the NMFS-SEFSC annual longline survey, 1995–2012; N = 789 longline sets.

Red Snapper are caught in depths shallower than 92 m (Mitchellet al. 2004), suggesting the depths covered in this study wereappropriate for assessing patterns of age- and length-specificdepth distributions of Red Snapper in SEUSA waters.

While assessing depth variation in gear-specific CPUE wasnot a main objective of this study, we noted that the high vari-ability of CPUE values generated in this study was likely a resultof multiple factors, including potential Red Snapper schoolingbehavior (e.g., McDonough and Cowan 2007) and variability inhabitat quality and quantity within the study area. The signifi-cant latitudinal trend in CPUE observed in the GA-FL longlinesurvey, in which greater CPUE values occurred in the southernend of the study area, was consistent with prior observationsof a SEUSA Red Snapper population centered off north-centralFlorida (SEDAR 2010).

From a selectivity standpoint, our comparison of age andsize distributions between the commercial hook-and-linesector and the trap and GA-FL longline surveys suggests thatlarger Red Snapper are not underselected by the commercialhook-and-line sector, thus providing no justification for theuse of a dome-shaped selectivity function for the Red Snappercommercial hook-and-line sector in SEUSA waters, such as

was used in stock assessments for Red Snapper in GOM waters(Cowan 2011). Similarly, our finding of no evidence of apositive relationship between depth and larger (exploited size)Red Snapper age or length provides no support for the use ofa dome-shaped selectivity function for Red Snapper in anyhook-and-line fishery sector (e.g., commercial, recreational, for-hire) in waters where those sectors are focused. However, giventhat younger and smaller Red Snapper appear to occur dispro-portionately in shallower waters (Figures 3, 4), while older andlarger Red Snapper appear to be distributed equally throughoutthe depth range assessed in this study, it is possible that a spatialfocus of sector-specific fishing pressure in shallower waters(e.g., as likely occurs for the recreational and for-hire sectors)could result in decreased overall selectivity of older and largerfish (given lower fishing pressure on the fish in deeper depths).This could potentially contribute to a dome-shaped selectivitypattern. Additionally, if the lack of an age- or length-with-depthrelationship for larger Red Snapper in the SEUSA is a resultof fishing exploitation (in which older and larger Red Snapperhave been selectively removed from deeper waters by fishing),then increases in SEUSA Red Snapper population size and agestructure expected to occur due to ongoing South Atlantic Fish-ery Management Council actions could result in dome-shapedselectivity functions becoming more appropriate.

Finally, in terms of ontogeny, it is generally accepted thatmany marine fish species exhibit shifts to deeper depths (andhabitats) as they increase in age and size, resulting in posi-tive relationships between depth and mean age and size. Withinthe SEUSA and GOM, such ontogenic shifts occur for multi-ple species with estuarine juvenile phases (e.g., Spot Leiosto-mus xanthurus, Atlantic Croaker Micropogonias undulatus, par-alichthyid flounders, and Gag Mycteroperca microlepis: SEDAR2006a, 2006b), for at least one fully marine species (HogfishLachnolaimus maximus: Collins and McBride 2011), and po-tentially for Red Porgy Pagrus pagrus (DeVries 2005). In thisstudy, we found no evidence of a positive relationship betweendepth and Red Snapper age or length once smaller fish wereexcluded from the analyses, suggesting that ontogenic depthor habitat shifts cease to occur once a critical age or size isreached. We suggest this phenomenon (cessation of increasingdepth with ontogeny once a critical age or size is obtained)may be common and perhaps widespread for reef-associatedfish species in SEUSA and GOM waters—e.g., snappers (Lut-janidae), groupers (Serranidae), and grunts (Haemulidae)—andrecommend analyses of existing data sets, where possible, to testthis hypothesis. Examining populations in which age truncationdue to harvest does not occur may be particularly useful.

ACKNOWLEDGMENTSWe thank Captain M. Egner, Captain J. Klostermann, J. Case,

and the captains and crews of the RV Palmetto, RV Savannah,NOAA Ship Nancy Foster, and NOAA Ship Pisces. We alsothank MARMAP and SEFIS staff and numerous volunteers for

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making the field work possible. We acknowledge scientific con-tributions by D. Carr, S. Gulak, J. Lewis, J. Patterson, and B.White; the statistical advice of K. Shertzer; and early draft re-views by J. Ballenger, A. Chester, M. Grace, W. Ingram, P.Marraro, W. Patterson, M. Reichert, R. Rindone, K. Shertzer,K. Simonsen, and T. Smart. Five anonymous reviewers greatlyimproved drafts of this manuscript. We thank M. Grace andNMFS-SEFSC Pascagoula Laboratory colleagues for providingaccess to and details of NMFS-SEFSC annual longline surveydata. We also thank Feeding America network staff E. Dark-atsh, K. Langan, R. Thomas, and A. Voyles, as well as G.Pack, C. Phillips, and J. Polston, for assisting with food do-nations. Research funds were provided by the National MarineFisheries Service, including awards NA11NMF4540174 andNA11NMF4350043 to the South Carolina Department of Natu-ral Resources MARMAP and SEAMAP-SA programs. Mentionof trade names or commercial companies is for identificationpurposes only and does not imply endorsement by the NationalMarine Fisheries Service. This paper is dedicated to the memoryof our valued colleague, Lori Hale.

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Szedlmayer, S. T. 2007. An evaluation of the benefits of artificial habitatsfor Red Snapper, Lutjanus campechanus, in the northeast Gulf of Mexico.Proceedings of the Gulf and Caribbean Fisheries Institute 59:223–230.

Thorson, J. T., and M. H. Prager. 2011. Better catch curves: incorporating age-specific natural mortality and logistic selectivity. Transactions of AmericanFisheries Society 140:356–366.

Walters, C. J., and S. J. D. Martell. 2004. Fisheries ecology and management.Princeton University Press, Princeton, New Jersey.

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When a trap is not a trap: converging entry and exit rates and theireffect on trap saturation of black sea bass (Centropristis striata)

Nathan M. Bacheler*, Zeb H. Schobernd, David J. Berrane, Christina M. Schobernd, Warren A. Mitchell,and Nathan R. GeraldiSoutheast Fisheries Science Center, National Marine Fisheries Service, 101 Pivers Island Road, Beaufort, NC 28516, USA

*Corresponding author: tel: +1 252 838 0825; fax: +1 252 728 8784; e-mail: nate.bacheler@noaa.gov

Bacheler, N. M., Schobernd, Z. H., Berrane, D. J., Schobernd, C. M., Mitchell, W. A., and Geraldi, N. R. 2013. When a trap is not a trap: converging entryand exit rates and their effect on trap saturation of black sea bass (Centropristis striata) – ICES Journal of Marine Science, 70: 873–882.

Received 12 February 2013; accepted 1 April 2013; advance access publication 27 May 2013.

Catch rates are often used to index the abundance of marine organisms, but catch saturation (i.e. declining catch rate as fishing timeincreases) can decouple catch and abundance. Researchers have struggled to account for saturation when using trap catch to infer popu-lation dynamics. We used the underwater video to document entries and exits of black sea bass (Centropristis striata) from chevron traps(n ¼ 26) to quantify catch saturation. Black sea bass catch varied between 3 and 188 individuals for soak times of �90 min. Overall, 3564black sea bass entered the traps and 1826 exited; therefore, over half (51%) of black sea bass entering traps exited before traps were retrieved.Black sea bass catch rates were non-linear and asymptotic for most (81%) trap samples, despite short soak times. Moreover, catch saturationoccurred at 50 min, when the entry rate declined and the exit rate increased to a point where their confidence intervals overlapped. Severallines of evidence suggest that the level of black sea bass catch once saturation occurred may be positively related to true abundance, butadditional research is needed to more fully test this hypothesis.

Keywords: chevron trap, index of abundance, reef fish, saturate, snapper-grouper.

IntroductionModern fisheries stock assessments rely on sound fishery-dependent (i.e. harvest) and fishery-independent (i.e. survey)data. To be useful, surveys must generate an unbiased estimate ofabundance such that catches are proportional to the actual abun-dance of a species across a landscape (Kimura and Somerton,2006). However, catch and abundance can become uncoupled if,for instance, catchability (i.e. efficiency of the gear) varies acrossspace, time, habitats, or environmental conditions (Hilborn andWalters, 1992; Pollock et al., 2002; Stoner, 2004). In some situations,it may be possible to standardize the catch rates when environmentalconditions across the study area vary temporally or spatially(Maunder and Punt, 2004), but rarely will this result in constantcatchability because unidentified and uncontrollable factors willstill have a significant influence (MacKenzie et al., 2006).

Catch rates are often assumed to be constant over the amount oftime a particular fishing gear is fished (Hamley, 1975). For trawls,this assumption is likely valid because trawl durations tend to berelatively short and there is typically sufficient space in the codendfor organisms to accumulate (Ragonese et al., 2001). It is more

tenuous, however, to assume constant catch rates for traps, gillnets,or longlines because soak times can vary from hours to days and thearea, volume, or amount of gear is typically much more limited(Rodgveller et al., 2008). A variety of approaches have been usedto determine if saturation is occurring, but the most common hasbeen to periodically retrieve the gear, count the enmeshed ortrapped animals, and redeploy the gear without removing any indi-viduals from the gear. These studies have documented the presenceof saturation over the time-scale of hours (Miller, 1979; Powles andBarans, 1980; Kennelly, 1989) to days (Munro, 1974; Morrissy, 1975;Dalzell and Aini, 1992).

Catch saturation is typically attributed to declining entry ratesinto traps or gillnets as more individuals are caught and spacebecomes limited, competition or territoriality increases, or thebait is consumed (Kennedy, 1951; Richards et al., 1983; Olin et al.,2004). However, recent research has focused on the importance ofexit rates, especially in the trap gear. For instance, greater than90% of American lobster (Homarus americanus) entering lobstertraps exited before the trap was retrieved, likely due to agonistic be-havioural interactions in and around traps (Karnofsky and Price,

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1989; Jury et al., 2001). Similarly high exit rates have been found forblue crabs (Callinectes sapidus; Sturdivant and Clark, 2011), andCole et al. (2004) showed that saturation occurred in two individualtraps for blue cod (Parapercis colias) when exit rates eventuallyincreased enough to offset entry rates. Determining the exact mech-anism causing trap saturation is important because it directly affectswhether or not catch reflects actual abundance (Beverton andHolt, 1954).

In this study, we used underwater video to quantify the temporalpatterns of entries, exits, and catch for black sea bass (Centropristisstriata) in a multispecies trap survey on the southeast US continentalshelf. Black sea bass are an economically important protogynousspecies found along the coast of Massachusetts through Floridaand in the Gulf of Mexico, USA (Wenner et al., 1986). Black seabass are a demersal reef fish species that tends to aggregate with con-specifics. In the southeastern United States, the successful assess-ment and management of black sea bass relies, in part, on reliablefishery-independent survey trap data (SEDAR, 2011). The objec-tives of our study were to (i) determine if black sea bass catches satu-rated within soak times typical for the trap survey (i.e. 90 min), and(ii) determine whether saturation occurred due to declining entryrates, increasing exit rates, or both. These results help us understandthe catch dynamics of a passive fishing gear and provide clues aboutwhy trap catch may saturate.

MethodsData collectionSampling for this study occurred in Atlantic Ocean continental shelfwaters of the southeastern United States (hereafter, “SEUS”), specif-ically in waters off Georgia and Florida. Sampling targeted reef fishthat typically associate with hard substrates, which occur as scatteredpatches within the dominant sand and mud substrate in the SEUS(Fautin et al., 2010). Patches of hard substrates in the SEUS arediverse and consist of flat limestone pavement, ledges, rocky out-croppings, or reefs, often colonized by various types of attachedbiota (Kendall et al., 2008; Schobernd and Sedberry, 2009).

Sampling was conducted by the SouthEast Fishery-IndependentSurvey (SEFIS), a US National Marine Fisheries Service fishery-in-dependent sampling programme, using a simple random samplingdesign. Eachyear, the number of stations is randomly selected from asampling frame of �2600 hardbottom stations maintained bySEFIS and the Marine Resources Monitoring, Assessment, andPrediction (MARMAP) programme of the South CarolinaDepartment of Natural Resources. Twenty-three stations includedin the analyses below (88%) were randomly selected from the sam-pling frame, and the remaining three hardbottom stations werenewly found and sampled opportunistically. All sampling for thisstudy occurred during daylight hours between April andSeptember 2012 aboard the RV “Savannah” (Table 1).

Chevron fish traps, also known as arrowhead or Madeira traps,were deployed at each station selected for sampling in this study.Fish traps are a versatile gear for reef fish because they (i) can fish un-attended, (ii) are suitable for most bottom types and depths, (iii) areinexpensive and robust, and (iv) often catch fish alive so that indivi-duals caught as byctach can be returned to the water unharmed(Miller, 1990). Fish traps are commonly used around the world toindex the abundance of various types of fish and invertebratespecies (e.g. Recksiek et al., 1991; Evans and Evans, 1996; Joneset al., 2003; Wells et al., 2008; Rudershausen et al., 2010).

We used chevron fish traps during annual fishery-independentsurveys conducted by SEFIS. Chevron traps were constructedfrom plastic-coated galvanized 12.5 gauge wire (mesh size ¼3.4 × 3.4 cm) and were shaped like an arrowhead measuring1.7 m × 1.5 m × 0.6 m, with a total volume of 0.91 m3 (Figure 1).The funnel of each trap was constructed from hexagonal wiremesh �3.4 cm in diameter, and the mouth opening of eachchevron trap was shaped like a teardrop measuring �18 cm wideand 45 cm long (Figure 1). Each trap was weighted using cylindricalsteel rods weighing a total of 11 kg and baited with 24 menhaden(Brevoortia spp.; approximate weight of each individual ¼ 180 g),16 of which were attached to four freely accessible stringers andthe other 8 placed loosely inside the trap. A stainless steel cablebridle on the side of each chevron trap was connected to twosurface buoys by 8-mm diameter polypropylene lines used fortrap retrieval. Usually, chevron fish traps were deployed individuallyin a group of six traps, with each trap soaking for �90 min. Theminimum distance between individual traps was at least 200 m toprovide some measure of independence between traps.

Each trap was deployed with a high-definition video camera(Figure 1). A GoPro Herow camera was attached to the side of thetrap, looking inward towards the mouth opening of the trap, sothat reef fish entries and exits could be recorded (Figure 1). Waterclarity was always high enough to see the mouth opening andinside of the trap clearly (i.e. ≥2 m). Cameras were turned on andset to record before the trap was deployed and recording wasstopped when the trap was retrieved, so that video captured theentire time the trap fished.

Video analysisIn April 2012, we placed inward-looking GoPro cameras on the firsttwo traps of every trap set. Video data collected from cameras duringthis cruise suggested that black sea bass were an ideal species uponwhich to focus this research because they were often caught inlarge numbers, along with relatively few individuals of otherspecies (e.g. tomtate Haemulon aurolineatum, grey triggerfishBalistes capriscus; Table 2). In the remaining research cruises in2012, we generally targeted our deployments of inward-lookingcameras to stations where we expected to catch black sea bassbased on data from previous years. In all, 120 videos of trap deploy-ments documenting entries and exits of reef fish species fromchevron fish traps were collected in May–September 2012. Videoswere excluded from analysis if they (i) were missing video segmentsbetween trap deployment and retrieval or any video files werecorrupt, (ii) were too dark at some point during deployment sothat fish entries or exits may have been missed, (iii) did not catchany black sea bass, (iv) caught potential black sea bass predatorssuch as gag (Mycteroperca microlepis) or red snapper (Lutjanus cam-pechanus), or (v) caught a large number of individuals of speciesother than black sea bass.

For each video included in the analysis, we recorded the timewhen the trap landed on the bottom, the time when the trap retrievalbegan, and the time that the trap exited the water. For all analyses,soak time was defined as the time that elapsed between when thetrap landed on the bottom (and began fishing) to the time whentrap retrieval process commenced (i.e. trap began to lift off thebottom). We only included in our analyses entries and exits occur-ring until the trap retrieval process began, thus excluding a poten-tially limited number of entries and exits occurring during trapretrieval.

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The times of each individual entry and exit for black sea bass andall other species were recorded during the entire soak time. Toqualify as an entry or exit, an individual fish must have crossed itsentire body past the plane of the trap mouth opening (Figure 1).We could not distinguish individuals within a species, so the sameindividual may have entered and exited the trap multiple times.Sand perch (Diplectrum formosum) and bank sea bass(Centropristis ocyurus) were excluded from all analyses becausethey were often small enough to enter and exit through the meshof the trap. The number of individuals of each species in the trapat any given time during the soak time was calculated as the

cumulative number of entries minus the cumulative number ofexits for each minute of soak time.

Data analysisWe were first interested in whether black sea bass catch (i.e. numberof fish in the trap when retrieval began) was related to the totalnumber of entries, the total number of exits, or the exit rate ofblack sea bass in the chevron trap survey. The exit rate was calculatedas the per-capita rate of black sea bass exiting the trap once theyentered, i.e. the proportion of fish entering that ultimately exited.General linear models were used to test the relationship between

Figure 1. (a) Chevron trap fitted with high-definition, inward-looking video camera, which was used to examine the entry and exit rates of black seabass (C. striata) in GA and FL, USA, 2012. (b) Still image from the underwater video camera showing chevron trap mouth opening used to quantifythe entry and exit rates of black sea bass (pictured).

Table 1. Station-level information for each of the 26 trap video samples included in the analysis of entry and exit rates of black sea bass (C.striata) from chevron fish traps.

Trap number Cruise Date (2012) Soak time (min) Depth (m) BSB catch Total entries Total exits Exit rate (%) Other species

1 1 27 April 83 24 69 106 37 35 02 1 27 April 85 24 28 43 15 35 33 1 27 April 88 23 124 273 149 55 24 1 27 April 86 24 88 242 154 64 115 1 29 April 80 42 3 4 1 25 26 1 30 April 81 52 3 4 1 25 87 2 23 May 81 24 89 206 117 57 518 2 23 May 84 24 64 168 104 62 09 2 25 May 77 37 5 12 7 58 810 2 26 May 79 23 18 55 37 67 111 2 28 May 82 29 11 14 3 21 112 2 29 May 91 27 41 192 151 79 513 2 29 May 88 26 85 143 58 41 214 3 12 July 85 41 57 73 16 22 1015 3 12 July 89 42 34 46 12 26 116 4 25 August 76 32 24 39 15 38 1017 4 25 August 82 37 103 144 41 28 6418 4 27 August 82 20 85 167 82 49 119 4 31 August 90 33 71 99 28 28 420 5 19 September 81 17 35 114 79 69 821 5 19 September 91 18 92 222 130 59 3022 5 19 September 106 19 164 324 160 49 1023 5 19 September 99 19 188 361 173 48 3324 5 19 September 108 19 122 193 71 37 425 5 20 September 89 19 3 3 0 0 026 5 20 September 88 20 183 270 87 32 16

Soak time was defined as the time that elapsed between when the trap landed on the bottom (and began fishing) to the time when the trap retrieval processcommenced (i.e. trap began to lift off the bottom). BSB catch is the number of black sea bass contained in the trap when the trap retrieval process began. Otherspecies is the total number of individuals of all species, not including black sea bass, caught in the trap.

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black sea bass catch and total entries, total exits, or exit rate. We usedR 2.14.1 (R Development Core Team, 2011) for this and all subse-quent analyses.

We next tested for the evidence of trap saturation by examiningthe relationship between the number of individuals in the trapand the soak time for each trap included in the analysis. We deter-mined the shape of the relationship using a power function of theform (Preston, 1962):

y = axb, (1)

where y is the number of black sea bass in the trap, x is soak time, andaandbare parameters estimated by the model. We were particularlyinterested in estimates of the constant b. When b was ,1, then theweight of evidence suggested that the relationship between indivi-duals in the trap and soak time was non-linear and saturating;when b was not significantly smaller than 1, then the evidence didnot indicate a non-linear, saturating relationship.

We also determined whether catch rate was invariant across therange of soak times in our study, an assumption that must be metwhen using catch rate to index abundance. We calculated thenumber of black sea bass in each individual trap each minutedivided by the total soak time of that trap up to that point, and deter-mined the shape of the relationship using a quadratic equation of theform (Zar, 1999):

z = ax2 + bx + c, (2)

where z is the mean catch min21, x is soak time, and a, b, and c areconstants. Here, when a was significantly different from zero, therewas evidence to suggest that the relationship between catch rate andsoak time was non-linear; a significant b value would indicate a slopedifferent from zero.

To determine if saturation resulted from declining entry rates, in-creasing exit rates, or both, we related the mean number of entriesand exits min21 (across all traps) to soak time. We then used quad-ratic equations (as described above) to determine the form of therelationship between mean entry or exit rate and soak time, withthe response variable z here equal to the mean number of entries orexits min21 across all traps. We then plotted the model-based esti-mates of mean entries and exits min21, as well as their respective95% confidence intervals. The soak time at which the confidenceintervals first overlapped was considered to be the point at whichmean entries and exits were statistically indistinguishable.

ResultsTwenty-six videos of trap deployments documenting black sea bassentries and exits were included in the analysis. These videos wererecorded during five research cruises in Georgia and Florida, USA,continental shelf waters between 27 April and 20 September 2012(Table 1; Figure 2). Depth varied from 17 to 52 m (mean+ s.d.,27.5+ 9.3) and soak time ranged from 76 to 108 min (mean+s.d., 86.6+ 7.9).

Figure 2. Map of the study area showing where 26 trap video sampleswere collected in GA and FL, USA, 2012. The number of trap samplesincluded in the analysis is provided next to each group of open circles;note that symbols overlap. Grey bathymetry lines indicate 20- and 60-mdepth contours.

Table 2. Total entries and exits of fish species caught in all 26 chevron traps combined in GA and FL, from the time the trap landed on thebottom and began fishing until the trap retrieval process began.

Common name Scientific name Entries Exits Per cent caught Per cent escaped

Black sea bass Centropristis striata 3564 1826 49 51Spottail pinfish Diplodus holbrookii 4 2 50 50Tomtate Haemulon aurolineatum 305 140 54 46Pinfish Lagodon rhomboides 47 19 60 40Vermilion snapper Rhomboplites aurorubens 21 8 62 38Red porgy Pagrus pagrus 3 1 67 33Pigfish Orthopristis chrysoptera 3 1 67 33

Stenotomus spp. 47 7 85 15Grey triggerfish Balistes capriscus 27 2 93 7Whitespotted soapfish Rypticus maculatus 2 0 100 0

Bank sea bass (C. ocyurus) and sand perch (D. formosum) were excluded from analysis because they were commonly seen entering or exiting through the meshof the trap.

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More black sea bass (n ¼ 3564) entered the 26 traps than anyother species (Table 2). The number of black sea bass exiting thetraps (n ¼ 1826) was also considerably greater than any otherspecies. The overall exit rate for black sea bass was 51%, meaningthat less than half (i.e. 49%) of the black sea bass entering thetraps were eventually caught. The overall black sea bass exit ratewas higher than the rate for any other species (which ranged from0 to 50%), but 7 of 10 species had exit rates greater than 30%(Table 2). Given the high sample sizes for black sea bass but noother species, we limited subsequent analyses to black sea bass only.

The number of black sea bass caught ranged from 3 to 188 indi-viduals (mean+ s.d., 68.8+ 54.8; Table 1). Between 3 and 361(mean+ s.d., 135.3+ 105.2) black sea bass entered the traps, andbetween 0 and 173 exited from individual traps (mean+ s.d.,66.5+ 58.6). On a trap-specific basis, the black sea bass exit ratefor individual traps ranged from 0% (3 entries, 0 exits) to 79%(192 entries, 151 exits; Table 1). There was a positive relationshipbetween total entries and black sea bass catch (linear model:slope+ s.e. ¼ 1.77+ 0.15; F1,24 ¼ 137.5; p , 0.01; r2 ¼ 0.85), aswell as between total exits and black sea bass catch (linear model:slope+ s.e. ¼ 0.77+ 0.15; F1,24 ¼ 26.1; p , 0.01; r2 ¼ 0.50;Figure 3). However, the exit rate of black sea bass from traps was un-related to black sea bass catch (linear model: slope+ s.e. ¼ 0.06+0.07; F1,24 ¼ 0.8; p ¼ 0.39; r2 ¼ 0.03; Figure 3). All linear modelsexhibited constant variance and normally distributed residuals.

The temporal pattern of black sea bass in the trap was non-linearand asymptotic for 21 of the 26 (81%) traps, despite the relativelyshort soak times of less than 2 h (Table 3; Figure 4). The mostdrastic example was one trap that caught 99 black sea bass indivi-duals in the first 5 min, but only 84 additional black sea bass inthe remaining 84 min. Saturation did not occur for black sea bassin five traps with eventual black sea bass catches of 3, 3, 11, 85,and 103 individuals, as indicated by b values in Equation (1) thatwere not significantly less than 1 (Table 3). Consequently, therelationship between soak time and overall mean catch min21 ofblack sea bass was non-linear [Equation (2), a+ s.e. ¼ 0.002+0.0002; p , 0.001), with catch decreasing as soak time increased(b+ s.e. ¼ 20.03+ 0.002; p , 0.001; Figure 5).

The pattern of entries and exits through time was variable amongtraps (Figure 6). For most traps, entry rates (i.e. the slope of cumu-lative entries to soak time) were higher earlier than later during thesoak time, suggesting a declining entry rate over time. Some traps,however, appeared to have relatively constant entry rates throughtime. Exit rates were similarly variable, with some increasing,some decreasing, and some remaining constant over time.

When all traps were combined, mean entries min21 declinednon-linearly (a+ s.e. ¼ 0.006+ 0.003; p ¼ 0.04) from �2 to 1black sea bass entries min21, at which point the mean number ofentries over time plateaued (Figure 7). Black sea bass mean exitsmin21 increased non-linearly from 0 to an asymptote around 1exit min21 (a+ s.e. ¼ 20.002+ 0.0003; p , 0.01). The 95% con-fidence intervals for mean entries and mean exits min21 began over-lapping at 50 min, suggesting that mean entries and exits werestatistically indistinguishable after a soak time of 50 min.

DiscussionBlack sea bass are an economically and ecologically importantspecies in the SEUS, and their successful management depends in

Figure 3. The relationship between total black sea bass (C. striata)catch and black sea bass (a) entries, (b) exits, or (c) exit rate (i.e. per centof all entering black sea bass that exited before trap retrieval).

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part on reliable fishery-independent survey trap data (SEDAR,2011). We found that black sea bass trap catch rates tended todecline over relatively short periods (i.e. ,2 h), resulting in catchsaturation. Moreover, catch saturation occurred when the decliningentry rate converged statistically with an increasing exit rate, after asoak time of 50 min. These results improve our understanding of thedynamics of fish traps and shed light on the mechanisms causingcatch saturation, which must be understood to determine whethertrap catch is proportional to true abundance (Kimura andSomerton, 2006).

Black sea bass catches in chevron fish traps saturated relativelyquickly. In 21 of 26 traps in our study, catch was non-linearly andasymptotically related to soak times of less than 2 h. Theoretically,trap catches will eventually saturate because there is finite spaceinside a trap that will eventually become filled with animals,which would not permit additional individuals from entering(Bennett, 1974; Austin, 1977; Miller, 1990). Other trap surveysand commercial trap fishers soak traps for much longer periods(e.g. days to weeks), and trap saturation by reef fish has beenobserved over these longer time frames (Munro et al., 1971;Munro, 1974; Dalzell and Aini, 1992), but there are some notableexceptions. Powles and Barans (1980) used repeated diver countsof fish in traps to show that black sea bass and Stenotomus spp.catches saturated in two different types of (non-chevron) fishtraps over the course of 3–12 h. Also, trap catches of blue codwere shown to saturate in 30–60 min in New Zealand, but samplesizes were very low (n ¼ 2 traps; Cole et al., 2004). Similarly,Bacheler et al. (2013a) used long-term chevron trapping data in a re-gression modelling framework to show that black sea bass catches

were similar regardless of soak times between 50 and 150 min.The fact that black sea bass catch saturated in our study is expectedbased on theory, but the most noteworthy finding is how quickly thesaturation process occurred and, more importantly, why it oc-curred.

Figure 4. Number of black sea bass (C. striata) in each of the 26 chevrontrap samples from the time the trap landed on the bottom (time ¼ 0)until the trap retrieval process began (which varied between 76 and108 min). “Black sea bass individuals in trap” was calculated as thecumulative number of entries minus the cumulative number of exits foreach minute of soak time.

Figure 5. Mean black sea bass (C. striata) catch per unit effort (i.e.number of individuals in the trap divided by the soak time; solid line) forall 26 videos collected in GA and FL waters, 2012. Dashed line indicatesthe quadratic model fit.

Table 3. Estimates and s.e. of the b parameter from power modelfits to the relationship between black sea bass catch (C. striata) andsoak time for each of the 26 chevron traps included in the analysis.

Trap number BSB catch b s.e. p-value

1 69 0.87 0.04 0.0012 28 0.89 0.03 0.0013 124 0.44 0.03 ,0.0014 88 0.69 0.06 ,0.0015 3 1.94 0.14 .0.056 3 1.27 0.13 .0.057 89 0.46 0.01 ,0.0018 64 0.36 0.03 ,0.0019 5 0.18 0.04 ,0.00110 18 0.70 0.06 ,0.00111 11 1.74 0.08 .0.0512 41 0.13 0.02 ,0.00113 85 0.51 0.02 ,0.00114 57 0.46 0.01 ,0.00115 34 0.83 0.03 ,0.00116 24 0.60 0.03 ,0.00117 103 1.11 0.05 .0.0518 85 1.38 0.05 .0.0519 71 0.17 0.01 ,0.00120 35 0.44 0.03 ,0.00121 92 0.49 0.02 ,0.00122 164 0.55 0.03 ,0.00123 188 0.72 0.03 ,0.00124 122 0.91 0.02 ,0.00125 3 0.23 0.02 ,0.00126 183 0.21 0.01 ,0.001

BSB catch is the number of black sea bass contained in the trap when the trapretrieval process began.

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Intraspecific aggressive or territorial behaviours in and aroundtraps is one common explanation for catch saturation, where oneor a few dominant individuals defend traps against potentialentrants (Jury et al., 2001). However, in those cases, the ultimatelevel of catch in traps is typically low and saturates at approximatelythe same level of catch regardless of the variability in actual abun-dance across a landscape (Addison and Bell, 1997). In our study,black sea bass catch saturated regardless of the magnitude of thecatch and no aggressive interactions among black sea bass indivi-duals were observed on video (i.e. similar to Sturdivant and Clark,2011), suggesting that intraspecific interactions around the trapwere not responsible for black sea bass catch saturation. The meanlength of black sea bass was also similar among traps in our study,

indicating that size differences were not responsible for the factthat traps saturated at very different levels of catch.

Black sea bass catch did not asymptote due to an entry rate thatapproached zero, as is commonly assumed, but rather due to aslowly declining entry rate combined with an increasing exit rate.After soak times of 50 min, the entry rate declined and the exitrate increased enough that the two approximately offset eachother, resulting in an asymptotic catch. Cole et al. (2004) similarlyshowed that saturation occurred for blue cod when exit rates even-tually increased enough to offset entry rates. Our results imply thatbait consumption was not responsible for trap saturation becauseblack sea bass were still entering traps regularly (i.e. mean of 1 fishmin21) even when saturation had already occurred, and catch

Figure 6. Cumulative number of entries (solid lines) and exits (dashed lines) for black sea bass (C. striata) within each of the 26 trap collectionsincluded in the analysis. The vertical distance between the two lines represents the number of black sea bass in the trap. Here, traps are plotted inorder of descending catch, with the largest catch in the top left panel and the smallest in the bottom right. Note different y-axis scales.

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rates of reef fish species in unbaited traps may in some instances ac-tually be higher than baited traps (Munro et al., 1971). Regardless,bait was never completely consumed in the traps so fish attractionto the traps was always present, even in traps with the highest catch.

Black sea bass catch, once saturation occurred, is likely positivelyrelated to true abundance. In our study, catch of black sea bass insidethe trap appeared to be positively related to the number of black seabass visible on video immediately outside the trap; when catch waslarge, many black sea bass were simultaneously observed outside thetrap, and when catch was low, few black sea bass were observedoutside the trap (N. M. Bacheler, pers. obs.). Also, the long-termMARMAP chevron trap index of abundance for black sea bass washighly correlated with many other fishery-independent and fishery-dependent indices in the region (SEDAR, 2011), suggesting thatcatch was likely proportional to abundance in the trap survey. Ifblack sea bass catch was proportional to abundance, it suggeststhat chevron traps catch a constant proportion of black sea bassavailable at each site. More research is clearly required to test thenovel hypothesis that black sea bass catch (at saturation) is propor-tional to true abundance.

Black sea bass catch in five chevron fish traps did not exhibit astatistically significant non-linear, asymptotic relationship. Thereare two potential reasons for these results. The first explanation isthat catch may not have truly saturated, implying that the catch inthese traps would continue to increase if soak time increased; ifthis is the case, longer soak times may be needed in some instancesto fully understand the saturation process. Alternatively, determin-ing whether the relationship between catch and soak time is linear ornon-linear is likely dependent on the level of catch, and type I errorrates are likely higher at low levels of catch (Zar, 1999). In three of thefive non-saturating traps, black sea bass catch was low, suggestingthat low statistical power may have also contributed to the lack ofnon-linear fits for these three traps.

Across all traps, 51% of the black sea bass entering traps escapedbefore the traps were retrieved. The high overall exit rate was muchhigher than reported elsewhere in the literature for fish (e.g. Coleet al., 2004), with the single exception of Munro et al. (1971), whoreported the exit rates of up to 50% for reef fish in the Caribbeanbut only after much longer soak times (i.e. 14 d). In this regard,our results are more consistent with work on American lobsters,where exit rates were high (i.e. 76–94%; Karnofsky and Price,1989; Jury et al., 2001). High exit rates may also partially explainwhy the frequency of occurrence for many reef fish species washigher on video than traps in recent fishery-independent surveysin the SEUS (Bacheler et al., 2013b).

There were three limitations of our approach. First, knowingactual black sea bass abundance around the trap would haveallowed a direct comparison between catch at saturation and abun-dance, but determining actual abundance was not possible in ourstudy. Future studies should attempt to estimate abundance over abroader area around the trap using divers or additional underwatervideo cameras. Second, stronger inference could have been made ifsoak times had been longer. We only allowed traps to soak for�90 min so that our results would directly apply to long-termfishery-independent survey collections in the region. Longer soaktimes would have allowed us to understand the temporal dynamicsof the saturation process more fully, particularly when catch was low(Fogarty and Addison, 1997). Third, we excluded traps that caughtpotential predators and multiple competitors, so that we couldreduce the influence of interspecific interactions on our under-standing of the saturation process (Bacheler et al., 2013a). A morecomplete understanding of the mechanisms causing trap saturationcould have been captured had interspecific interactions been expli-citly examined.

Gear saturation decouples the often-assumed linear relationshipbetween catch and abundance because catch rates decline until thepoint when catch stops increasing (Miller, 1990). In some situations,it may be possible to model catch instead of catch per unit effort asthe response variable in standardization models and include soaktime as a predictor variable in the model (Maunder and Punt,2004; Bacheler et al., 2013a). But ultimately whether catch orcatch per unit effort should be used to index abundance willdepend on a clearer understanding of the mechanisms causinggear saturation (Beverton and Holt, 1954). We provided some evi-dence against some of the more common hypotheses on mechan-isms causing trap saturation, such as aggressive behaviours andbait consumption, and suggest that the level of black sea basscatch once saturation occurred may be positively related to trueabundance. Clearly, more research is needed to more fully test thishypothesis. Such information will increase our ability to accuratelydescribe population dynamics using fishery-dependent andfishery-independent survey data.

AcknowledgementsWe thank the captain and the crew of the RV “Savannah”, staff fromthe Marine Resources Monitoring, Assessment, and Prediction pro-gramme, and many volunteers for making the fieldwork possible.We also thank P. Raley for assisting with camera placement. Webenefited greatly from discussions with J. Buckel, L. Coggins, andK. Shertzer, and reviews were provided by M. Burton, A. Chester,A. Hohn, T. Kellison, P. Marraro, and K. Shertzer. The use oftrade, product, industry, or firm names, products, software, ormodels, whether commercially available or not, is for informativepurposes only and does not constitute an endorsement by the US

Figure 7. Quadratic model-based mean black sea bass (C. striata)entries (solid black line) into, or exits (solid grey line) from, chevron fishtraps min21 across all 26 trap collections included in the analysis, 2012.Dotted lines indicate 95% confidence intervals around the entry (black)or exit rate (grey).

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