BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Arts and Sciences,College of Engineering,Graduate Stu dies,Lectures and Seminars,SMAST,STEM,Thesis/Dissertations DESCRIPTION:Department of Fisheries Oceanography PhD Dissertation Defense " Beyond the observer's gaze: an integrated approach to detection, estimatio n, and mitigation of observer and deployment effects in fisheries monitori ng" By: Debra Duarte AdvisorSteven X. Cadrin (UMass Dartmouth) Committee M embers Pingguo He (UMass Dartmouth), Gavin Fay (UMass Dartmouth), Geret De Piper (Texas A&M),  and Anna Malak Mercer (NOAA) Thursday April 30, 2026 1:00 PM SMAST East 102-103 836 S. Rodney French Blvd, New Bedford and via Zoom Abstract: Observers are deployed on commercial fishing trips to colle ct representative samples of discard rates. However, fishers may change th eir fishing behavior when an observer is onboard (i.e., “observer effect ”) or observer programs may over- or under-sample portions of the fleet (i.e., “deployment effect”). If the extent of these effects is substan tial, observer data will not be representative of unobserved trips, potent ially biasing the estimation of discards. This sampling bias can impact ca tch monitoring, stock assessments, and fishery management. The goal of thi s dissertation was to evaluate how well we can detect these types of effec ts, understand their impacts on catch and discard estimates, and explore m itigation strategies. The New England multispecies groundfish fishery was used as a test case throughout. Chapter 1 examined the performance of seve ral published methods for detecting an observer effect using a simulation of observer and deployment effects at varying sampling ratios (i.e., obser ver coverage) for several sample statistics. The simplest methods (t-test and F-test for difference of means and variances) provided an accurate but imprecise estimate of the observer effect size and only when there were n o deployment effects. A generalized linear mixed effects model (GLMM) was also not reliable for detecting small bias but was not confounded by deplo yment effects and was relatively robust to changing coverage rates. The mo st complicated tests involved comparing differences in trip characteristic s between subsequent trips for observed-unobserved and unobserved-unobserv ed pairs. These tests were able to detect smaller observer effects and wer e not confounded by deployment effects but were unreliable at high coverag e rates (>60%), producing both high false positive and false negative rate s. Sensitivity tests also showed differing detection accuracy as the distr ibution of the metric of interest changed. No single method was reliable a cross all conditions, indicating that the choice of method should depend o n the specific characteristics of the fishery. Chapter 2 compared the impa ct of observer and deployment effects on catch and discard estimates from multiple methods: stratified ratios, generalized additive models, generali zed linear models, and random forest models. Several methods were robust t o the impact of deployment effects, but the preferred model differed by sp ecies, and variability between iterations was high for some species. When an observer effect reduced only the proportion of catch discarded, models for estimating total catch were relatively unaffected, but discard estimat es were underestimated in all models. In contrast, when the observer effec t altered fishing behavior (e.g., fishing location or gear configuration), model estimates were biased for both catch and discards. Chapter 3 create d a framework for determining observer coverage needs to meet precision ta rgets for science and management. This framework was used to evaluate trad eoffs between observer coverage and integration of reference fleets with h igh fidelity data and fewer incentives to change behavior on observed trip s, such as electronic monitoring or cooperative research study fleets. The design of the program with respect to observer coverage (equal or unequal for reference fleet participants vs. non-participants) and discard estima tion (stratified or unstratified) was critical for accurate estimates, eve n in the absence of observer effects. A cohesive program must consider tra deoffs of data precision, logistics, quality, cost, and safety. These find ings underscore the importance of representative sampling, appropriate est imation models, and thoughtful design to produce accurate estimates for sc ience and management. Observer and deployment effects may be an inescapabl e outcome of deploying observers on a subset of fishing vessels, but there are viable options for dealing with them. Detection, estimation, and miti gation must be considered together rather than in isolation to avoid biase d estimates, which could lead to inaccurate assessments and errors in stoc k management. Join Meeting https://umassd.zoom.us/j/95408579777 Note: Meet ing ID and passcode required. Email contact to obtain For additional infor mation, please contact Callie Rumbut at c.rumbut@umassd.edu\nEvent page: /events/cms/dfo-phd-dissertation-defense-beyond-the- observers-gaze-.php\nEvent link: ٳٱ://ܳ.Ǵdz.ܲ//95408579777 X-ALT-DESC;FMTTYPE=text/html:

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Department of Fisheries Oceanog raphy

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PhD Dissertation Defense

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"Beyond the observer's gaze : an integrated approach to detection\, estimation\, and mitigation of obs erver and deployment effects in fisheries monitoring"

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By: Debra Du arte

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Advisor
Steven X. Cadrin (UMass Dartmouth)

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Commi ttee Members

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Pingguo He (UMass Dartmouth)\, Gavin Fay (UMass Dartm outh)\, Geret DePiper (Texas A&M)\,  and Anna Malak Mercer (NOAA)

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Thursday April 30\, 2026

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1:00 PM

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SMAST East 102-103

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836 S. Rodney French Blvd\, New Bedford

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and via Zoom

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Ab stract:

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Observers are deployed on commercial fishing trips to coll ect representative samples of discard rates. However\, fishers may change their fishing behavior when an observer is onboard (i.e.\, “observer eff ect”) or observer programs may over- or under-sample portions of the fle et (i.e.\, “deployment effect”). If the extent of these effects is sub stantial\, observer data will not be representative of unobserved trips\, potentially biasing the estimation of discards. This sampling bias can imp act catch monitoring\, stock assessments\, and fishery management. The goa l of this dissertation was to evaluate how well we can detect these types of effects\, understand their impacts on catch and discard estimates\, and explore mitigation strategies. The New England multispecies groundfish fi shery was used as a test case throughout.

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Chapter 1 examined the p erformance of several published methods for detecting an observer effect u sing a simulation of observer and deployment effects at varying sampling r atios (i.e.\, observer coverage) for several sample statistics. The simple st methods (t-test and F-test for difference of means and variances) provi ded an accurate but imprecise estimate of the observer effect size and onl y when there were no deployment effects. A generalized linear mixed effect s model (GLMM) was also not reliable for detecting small bias but was not confounded by deployment effects and was relatively robust to changing cov erage rates. The most complicated tests involved comparing differences in trip characteristics between subsequent trips for observed-unobserved and unobserved-unobserved pairs. These tests were able to detect smaller obser ver effects and were not confounded by deployment effects but were unrelia ble at high coverage rates (>60%)\, producing both high false positive and false negative rates. Sensitivity tests also showed differing detection a ccuracy as the distribution of the metric of interest changed. No single m ethod was reliable across all conditions\, indicating that the choice of m ethod should depend on the specific characteristics of the fishery.

\n< p>Chapter 2 compared the impact of observer and deployment effects on catc h and discard estimates from multiple methods: stratified ratios\, general ized additive models\, generalized linear models\, and random forest model s. Several methods were robust to the impact of deployment effects\, but t he preferred model differed by species\, and variability between iteration s was high for some species. When an observer effect reduced only the prop ortion of catch discarded\, models for estimating total catch were relativ ely unaffected\, but discard estimates were underestimated in all models. In contrast\, when the observer effect altered fishing behavior (e.g.\, fi shing location or gear configuration)\, model estimates were biased for bo th catch and discards.

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Chapter 3 created a framework for determini ng observer coverage needs to meet precision targets for science and manag ement. This framework was used to evaluate tradeoffs between observer cove rage and integration of reference fleets with high fidelity data and fewer incentives to change behavior on observed trips\, such as electronic moni toring or cooperative research study fleets. The design of the program wit h respect to observer coverage (equal or unequal for reference fleet parti cipants vs. non-participants) and discard estimation (stratified or unstra tified) was critical for accurate estimates\, even in the absence of obser ver effects. A cohesive program must consider tradeoffs of data precision\ , logistics\, quality\, cost\, and safety. These findings underscore the i mportance of representative sampling\, appropriate estimation models\, and thoughtful design to produce accurate estimates for science and managemen t. Observer and deployment effects may be an inescapable outcome of deploy ing observers on a subset of fishing vessels\, but there are viable option s for dealing with them. Detection\, estimation\, and mitigation must be c onsidered together rather than in isolation to avoid biased estimates\, wh ich could lead to inaccurate assessments and errors in stock management.\n

Join Meeting

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Note: Meeting ID and passcode required. Email contact to obtain

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For additional information\, please contact Callie Rumbut at 

Event page: https://w ww.umassd.edu/events/cms/dfo-phd-dissertation-defense-beyond-the-observers -gaze-.php
Event link:

DTSTAMP:20260419T173305 DTSTART;TZID=America/New_York:20260430T130000 DTEND;TZID=America/New_York:20260430T140000 LOCATION:SMAST East 102-103 SUMMARY;LANGUAGE=en-us:DFO PhD Dissertation Defense: Beyond the observer's gaze UID:1cd1ba00b886045972768afb3ad89456@www.umassd.edu END:VEVENT END:VCALENDAR