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Bycatch in the Saginaw Bay, Lake Huron Commercial Trap Net Fishery

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Bycatch in the Saginaw Bay, Lake Huron Commercial Trap Net Fishery BYCATCH IN THE SAGINAW BAY, LAKE HURON COMMERCIAL TRAP NET FISHERY By Eric MacMillan A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Fisheries and Wildlife 2011 ABSTRACT BYCATCH IN THE SAGINAW BAY, LAKE HURON COMMERCIAL TRAP NET FISHERY By Eric MacMillan This study provides species-specific catch and baseline mortality estimates of non-target species (bycatch) for the Saginaw Bay, Lake Huron commercial trap net fishery. Bycatch can represent a significant mortality source that is often unknown or unaccounted for. Bycatch and bycatch mortality rates in the Saginaw Bay commercial trap net fishery, which primarily targets lake whitefish ( Coregonus clupeaformis ), yellow perch ( Perca flavescens ), and channel catfish (Ictalurus punctatus ), are currently unknown. Throughout the 2010 fishing season, I observed onboard commercial trap net vessels and took species-specific counts of bycatch and bycatch mortality. The high levels of walleye (Sander vitreus ) catch and mortality observed within inner Saginaw Bay have not been previously documented in the Great Lakes. The levels of lake trout (Salvelinus namaycush ) catch observed in outer Saginaw Bay were within the range observed in previous studies, but mortality (percent) was higher than has been previously observed. Through the use of generalized linear models, this analysis also indicates factors that most influenced catch of non-target species including time of year and soak time (i.e., time interval between trap net lifts). Surface water temperature, trap net depth, and magnitude of target catch most influenced mortality. As evidenced by this and other studies that evaluate Great Lakes fisheries, the variable nature of bycatch abundance and mortality precludes generalizations across regions and years. This highlights the need for comprehensive bycatch monitoring throughout the Great Lakes. ACKNOWEDGEMENTS First of all, I’d like to thank the Saginaw Bay commercial fishermen. Tod, Forrest, Randy, Ben, Rich, Terry, Kenny from Bay Port Fish Company and Dana, Jerry, Denny, Josh, Tom, and Martin from Serafin Fisheries; you were all very gracious as I was collecting my data. I could not have done this without your patience and permission during my field season. I learned far more than I have written from my time on your boats and this cannot be understated. Also, I want to thank my committee members including Dr. Bill Taylor, Dr. Kendra Cheruvelil, and Dave Fielder. A special thanks goes to my advisor, Dr. Brian Roth, for his support and allowing (forcing) me to do my own thing throughout this project. Dr. Matt Catalano was of great help with my statistical analysis. Also, thanks goes to Kevin McDonnell, Brett Diffin, and Dan Wiefrich for helping with my data collection, video counts, and ArcMap. Tom Goneia and Tracy Kolb from the Michigan Department of Natural Resources were helpful with obtaining commercial and recreational fishing data and also understanding Saginaw Bay commercial fishery management. Mark Ebener of the Chippewa-Ottawa Resource Authority was also gracious in supplying data and sharing insight into Great Lakes trap net fishing. Thanks to my family; Mom, Dad, and Chris and finally, thanks to Abigail Lynch who has been there throughout for everything from editing to hearing me gripe. I could not have finished my degree without all of your support. I’d also like to thank my funding sources including the annual Dr. Howard A. Tanner Fisheries Excellence Fellowship through the Department of Fisheries and Wildlife at Michigan State University, the College of Agriculture and Natural Resources and Graduate School at Michigan State University, the Saginaw Bay Walleye Club, and Michigan Sea Grant. iii TABLE OF CONTENTS LIST OF TABLES………………………………………………………………………………..V LIST OF FIGURES……………………………………………………………………………..VII COMMERCIAL BYCATCH AND ITS IMPORTANCE IN SAGINAW BAY, LAKE HURON FISHERIES MANAGEMENT INTRODUCTION………………………………………………………………………………1 DEFINITION AND POTENTIAL IMPLICATIONS OF FISHERIES BYCATCH…...............1 TRENDS AND MANAGEMENT OF COMMERCIAL FISHERIES IN THE GREAT LAKES WITH AN EMPHASIS ON SAGINAW BAY, LAKE HURON…………………….................9 BYCATCH IN GREAT LAKES COMMERCIAL FISHERIES……………………...............17 CONCLUSIONS………….……………………………………………………………………24 BYCATCH IN THE SAGINAW BAY, LAKE HURON COMMERCIAL TRAP NET FISHERY INTRODUCTION……………………………………………………………………………..26 METHODS…………………………………………………………………………………….31 RESULTS……………………………………………………………………………...............39 DISCUSSION………………………………………………………………………………….46 APPENDICES TABLES……………………………………………………………………………………….66 FIGURES……………………………………………………………………………................76 LITERATURE CITED…………………………………………………………………………..95 iv LIST OF TABLES Table 1. Number of lifts observed onboard Saginaw Bay commercial trap net vessels May through August 2010……………………………………………………………………………..66 Table 2. Non-target species observed caught in the May through August 2010 Saginaw Bay trap net fishery (TNTC=Too numerous to count)…………………………………………..67 Table 3. P-values from Mann Whitney tests comparing bycatch or morbid bycatch per trap net lift between months for the May through August 2010 Saginaw Bay fishery……………….68 Table 4. Model AIC values when a variable is added to or removed from the best model (in bold) used in estimating the number of lake trout caught in outer Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in the model. Day = Time of year (Julian day), Soak = Soak time (days), Depth = Trap net depth (meters)…………………………………………………………………………………………..69 Table 5. Model AIC values when a variable is added to or removed from the best model (in bold) used in predicting the proportion of morbid lake trout in outer Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in the model. Water = surface water temperature ( oC), Catch = total catch (lbs), Soak = soak time (days), Depth = trap net depth (meters), Time = time of day, Wave = wave height (feet)…………………………..………………………………………………………………….70 Table 6. Model AIC values when a variable is added to or removed from the best model (in bold) used in estimating the number of walleye caught in inner Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in the model. Day = Time of year (Julian day), Soak = Soak time (days), Depth = Trap net depth (meters)…………………………………………………………………………………………..71 Table 7. Model AIC values when a variable is added to or removed from the best model (in bold) used in predicting the proportion of morbid walleye in inner Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in the model. Depth = trap net depth (meters), Sort = sort time (minutes), Water = surface water temperature ( oC), Soak = soak time (days), Wave = wave height (feet), Time = time of day………………………………………...……………………………………………………...72 Table 8. Model AIC values when a variable is added to or removed from the best model (in bold) used in estimating the number of walleye caught in outer Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in v the model. Day = Time of year (Julian day), Soak = Soak time (days), Depth = Trap net depth (meters)…………………………………………………………………………………………..73 Table 9. Model AIC values when a variable is added to removed from the best model (in bold) used in estimating the number of discarded lake whitefish from outer Saginaw Bay trap nets as observed May through August 2010. The difference in AIC value when a variable is subtracted from the best model can be considered an indicator of the relative importance of the variable in the model. Day = Time of year (Julian day), Soak = Soak time (days), Depth = Trap net depth (meters)…………………………………………………………………………….….74 Table 10. Estimated bycatch and morbid individuals for the May through August 2010 Saginaw Bay trap net fishery based on onboard observations…………………………………...75 vi LIST OF FIGURES Figure 1. Great Lakes gill net (Brenden et al. 2012)……………………………………….76 Figure 2. Great Lakes trap net (Brenden et al. 2012)............................................................77 Figure 3. Commercial fish harvest (in metric tons) of all species in Lake Huron (total) and Saginaw Bay from 1920-2006 (Baldwin et al. 2009)……………………………………………78 Figure 4. Michigan state-licensed commercial fishing grounds in Saginaw Bay, Lake Huron........................................................................................................................................….79 Figure 5. Relationship between sort time (minutes) and lake whitefish harvest (kilograms 2 per lift) in the outer Saginaw Bay trap net fishery May through August 2010 (p < 0.001, r = 0.88). Regression line is denoted with ── ……………………………………………………...80 Figure 6. Mean number incidentally caught lake trout observed per trap net lift by month in the outer Saginaw
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