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 Bay trap net fishery (± 1 S.E.). The line represents the percent of the total lake trout caught that were morbid by month. The mean number of morbid lake trout can be inferred from total bycatch and proportion morbid values………………………………………81
Figure 7. Mean number walleye observed per trap net lift by month in the 2010 inner (IB) and outer (OB) Saginaw Bay trap net fisheries with error bars (± 1 S.E). Lines represent the percent of the total walleye caught that were morbid by month for the inner (solid line) and outer (dashed line) bay fisheries. The mean number of morbid walleye can be deduced from total bycatch and proportion morbid values…………………………………………………………...82
Figure 8. Partial dependence plots indicating predicted lake trout bycatch in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Soak time (days), b) Julian day…………………………………………………..83
Figure 9. Partial dependence plots indicating the predicted proportion of morbid lake trout in the outer Saginaw Bay trap net fishery from a logistic model fit as a function of variation in one variable while holding other variables at their mean: a) Surface water temperature ( oC), b) Target catch (kgs), c) Soak time (days), d) Trap net depth (m), e) Time of day, f) Wave height (m). Observed values are binned and represented by the closed circles. Predicted probabilities are represented by the line……………………………………………………………………….84
Figure 10. Partial dependence plots indicating predicted walleye bycatch in the inner Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Julian day, b) Soak time………………………………………………………….85
vii Figure 11. Partial dependence plots indicating the predicted probability of morbidity for walleye in the inner Saginaw Bay trap net fishery from a logistic model fit as a function of variation in one environmental or fishing practice variable while holding other variables at their mean: a) Trap net depth (m), b) Sort time (min), c) Soak time (days), and d) Surface water temperature ( oC). Observed values are binned and represented by the closed circles. Predicted probabilities are represented by the line…………………………………………………………86
Figure 12. Partial dependence plots indicating predicted walleye bycatch in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Julian day, b) Trap net depth (m)………………………………………………...87
Figure 13. Partial dependence plots indicating predicted lake whitefish discards in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Soak time (days), b) Julian day…………………………………………………..88
2 Figure 14. Relationship between the total number of lake trout caught daily (p = 0.27, r = 2 0.05), or number of morbid lake trout (p = 0.10, r = 0.10) and reported lake whitefish harvest (target catch) in the outer Saginaw Bay trap net fishery……...………………………………….89
2 Figure 15. Relationship between the total number of walleye caught daily (p < 0.001, r = 2 0.95), or number of morbid walleye (p = 0.002, r = 0.80), and reported daily harvest of yellow perch and channel catfish (Target catch) in the inner Saginaw Bay trap net fishery. Regression lines are denoted for total walleye caught with ── and for morbid walleye with -----………….90
Figure 16. Estimated number of caught or morbid walleye across all lifts for the 2010 inner Saginaw Bay trap net fishery. Estimates are based on daily reported harvest of yellow perch and channel catfish. Error bars represent regression based 95% confidence intervals……………...91
2 Figure 17. Relationship between the total number of walleye caught daily (p = 0.06, r = 2 0.13), or number of morbid walleye (p = 0.05, r = 0.14), and reported daily lake whitefish harvest (target catch) in the 2010 outer Saginaw Bay trap net fishery. Regression lines are denoted for total walleye caught with ── and for morbid walleye with -----…………………...92
Figure 18. Relationship between the total number of lake whitefish discarded daily and reported target catch (kilograms of lake whitefish per day) in the outer Saginaw Bay trap net 2 fishery (p = 0.004, r = 0.41). Regression line is denoted with ── .……………………………93
Figure 19. Box plots indicating soak time variation among trap net fisheries in outer and inner Saginaw Bay, 1836 Treaty Waters (CORA), and offshore Alpena, Michigan (Johnson et al. 2004a). The box represents the inter-quartile range (middle 50% of data), the upper boundary represents the 75 th percentile, and the lower boundary represents the 25 th percentile. The center line represents the median and the dots represent outliers in the data set..………………………94
viii COMMERCIAL BYCATCH AND ITS IMPORTANCE IN SAGINAW BAY, LAKE HURON FISHERIES MANAGEMENT
INTRODUCTION
Multiple issues confront Great Lakes fisheries today including the sustainability of fish
stocks, incidental catch and discard rates, rehabilitation of indigenous fishes, and conflict among
user groups (Ebener et al. 2008). This study highlights incidental or non-target catch (bycatch)
rates in the commercial trap net fishery of Saginaw Bay, Lake Huron. Bycatch has been
documented in some Great Lakes fisheries and is considered one of the largest impediments to
the recovery of native predators such as lake trout (Salvelinus namaycush ) and walleye (Sander vitreus ) within the lakes (Hansen 1999, Johnson et al. 2004b, Ebener et al. 2008). Bycatch has also increased user conflicts between recreational and commercial fishers since non-target catch from commercial fisheries (e.g., lake trout and walleye) can be the target of recreational fisheries. Additionally, many view the discarded portions of fishery catches as a waste of natural resources (Alverson et al. 1994, Crowder and Murawski 1998, Harrington et al. 2005). These concerns indicate that bycatch in commercial fisheries has the potential to compromise many of the efforts aimed at achieving Great Lakes fish community objectives.
In this chapter, I review 1) the definition and potential implications of fisheries bycatch,
2) the trends and management of commercial fisheries in the Great Lakes with an emphasis on
Saginaw Bay, and 3) the published literature on bycatch within Great Lakes fisheries.
Throughout each of these sections, I also highlight knowledge gaps concerning bycatch that should be addressed and that may help fishery managers achieve fish community objectives.
DEFINITION AND POTENTIAL IMPLICATIONS OF FISHERIES BYCATCH
1 Bycatch is the unintentional capture of organisms not directly targeted by fishing (Everett
1996, Harrington et al. 2005). Non-target organisms are often discarded because of low value or regulatory requirements, frequently resulting in injury or death (Harrington et al. 2005). The implications of bycatch must be considered from both a biological and socioeconomic perspective (Crowder and Murawski 1998). These often competing perspectives, as well as a general lack of knowledge regarding bycatch in region-specific fisheries, can contribute to management uncertainty and conflict among fishery user groups.
Biological perspective
The over-harvest of marine fish populations has long been recognized as a significant environmental issue. However, the current status and outlook for the future of these populations has been highly controversial. Some research has projected total collapse of all current marine fisheries (Worm et al. 2006), while other research has claimed these views to be alarmist and insist that fisheries management is working in some areas and many stocks are rebuilding or stable (Hilborn 2007, Worm et al. 2009). This controversy has been fueled, in part, by uncertainty regarding fishery exploitation rates. Uncertainty can cause marine fishery managers to incorrectly estimate fish population levels, and may ultimately lead to harvest strategies which do not accurately reflect available fish stock surpluses (Everett 1996, Crowder and Murawski
1998). Great Lakes fishery managers are often presented with similar uncertainty when attempting to estimate population levels and optimize harvest strategies.
Total fishing mortality (F) was defined by the Study Group on Unaccounted Mortality in
Fisheries (ICES 1995, 2005) as the sum of the following sub-components of F:
F = F C + F B + F D +F E + F O + F G + F A + F H
2 Where F is the sum of all direct and indirect fishing mortalities, F C is the landed catch including
commercial, recreational, and subsistence fisheries, F B is illegal and misreported landings, F D is
discard (bycatch) mortality, F E is mortality from fish that contact but escape fishing gear, F O is
mortality of fish that are captured by fishing gear but die before being landed on deck, F G is
mortality from ghost fishing gear (i.e., abandoned or lost fishing gear), F A is mortality a fish may be subject to from actively avoiding gear (stress or injury caused), and F H is mortality associated with the degradation of an aquatic environment as a result of fishing activity. Ideally, fishery managers would have accurate estimates of all the aforementioned sources of mortality in order to effectively manage a fishery resource. However, this is rarely, if ever the case.
Fishery landings (F C) are often reported to fishery management agencies. Therefore, fish stock assessment scientists and managers often have baseline estimates of this mortality source. Several other mortality sources (e.g., F A, F O) may be insignificant for many fisheries in terms of the summed F value (Alverson and Hughes 1996). However, discard or bycatch mortality (F D) may be a significant mortality source in many fisheries (Alverson et al. 1994,
NMFS 2004, Kelleher 2005) that is often unknown or unaccounted for (Chopin et al. 1996,
Johnson et al. 2004b). For example, in the Bering Sea in 1992, discard mortalities of rock sole
(Lepidopsetta bilineata ) and unidentified flounders (Pleuronectidae ) exceeded mortalities caused
by directed harvest (Alverson et al. 1994).
In Great Lakes fisheries, managers typically focus on targeted fishing mortality and sea
lamprey (Petromyzon marinus ) mortality (Hansen et al. 1996, Sitar et al. 1999, Irwin et al. 2009).
3 With few exceptions, an additional fishing mortality source that remains poorly quantified in the
Great Lakes is commercial fishing bycatch or discards. For example, the stock assessment
performed by Sitar et al. (1999) for lake trout in southern Lake Huron did not account for
commercially discarded lake trout as a mortality source. In more recent years, fisheries
managers have included bycatch mortality in some stock assessment models (Modeling
Subcommittee, Technical Fisheries Committee 2010). However, low mortality is often assumed
for non-target species captured, and varying fishing practices and environmental conditions
among locations may make this assumption questionable. For example, in the MH-2
management unit of 1836 Treaty Waters, lake trout discard mortality estimates are based on a
study which estimated a 12.2% mortality rate for trap net caught lake trout (Johnson et al. 2004a,
Modeling Subcommittee, Technical Fisheries Committee 2010). Alternatively, mortality rates
could be higher or lower in certain areas and during certain times of the year as many factors
(e.g., surface water temperature, capture depth, duration of net soak, handling time) may interact
to determine mortality of discarded fish. Bycatch mortality represents a potentially significant
source of uncertainty that should be accounted for to accurately assess population levels and
optimize harvest strategies (Chopin et al. 1996, Johnson et al. 2004b).
Discarded portions of fishery catches are implicated in the decline of several marine fish
stocks. For example, a 40% decline in Atlantic croaker (Micropogonias undulates ) population in
the Gulf of Mexico since the 1970s has been linked, at least in part, to the substantial amount of
croaker bycatch in the Gulf shrimp trawl fishery (Tillman 1992). Bycatch in the Gulf shrimp
fishery has also been implicated in population declines of red snapper (Lutjanus campechanus )
(Tillman 1992). Alternatively, large amounts of bycatch do not always result in stock declines.
In the northwest Atlantic, up to 108 million individual redfish ( Sebastes spp.) were reported
4 discarded by the commercial fishery from 1976 to 1980. However, these removals account for less than two percent of stock biomass (Atkinson 1984, Alverson et al. 1994). Thus, though still representing sizable numbers, discard mortality was apparently not a significant factor contributing to redfish population declines in the region (Alverson et al. 1994). Thus, seemingly high levels of bycatch may not have substantial population-level effects on a fish stock.
Therefore, fishery managers should input bycatch mortality estimates into stock assessment models to determine what, if any, population level effects, bycatch may have.
One of the principal fish community objectives for Lake Huron, as outlined in 1995 by the Lake Huron Committee of the Great Lakes Fishery Commission (GLFC), is to restore an ecologically balanced fish community dominated by top predators (e.g., lake trout and walleye)
(DesJardine et al. 1995). According to the GLFC, lake trout is the desired dominant salmonid species within the deeper, colder regions of the lake, whereas walleye is the desired dominant coolwater predator (DesJardine et al. 1995). Though recent years have seen gains in walleye abundance in Saginaw Bay (Fielder et al. 2007, 2010) and increasing numbers of wild-origin lake trout (Muir et al. 2012), rehabilitation objectives for these populations have not yet been attained (Fielder et al. 2010, Muir et al. 2012). Therefore, because bycatch and bycatch mortality have the potential to result in population level effects, GLFC fish community objectives may be compromised.
Socioeconomic perspective
A broad public consensus emerged in the 1990s that discarded portions of fishery catches are a waste of valuable natural resources (Crowder and Murawski 1998). From an economic perspective, Murawski (1996) argued that regulatory-induced discards of otherwise marketable species represent a loss of potential revenue to producers and supply for consumers.
5 Additionally, bycatch from one fishery may preclude another fishery from harvest, which may add greater cumulative value to society (Crowder and Murawski 1998). This can occur when commercial fisheries incidentally catch and discard highly valuable recreational species (e.g., shrimp trawls catching juvenile red snapper), when commercial fisheries discard non-target species that may be valuable to other commercial fisheries, or when recreational fishers catch and discard species valuable to the commercial industry. Alternatively, harvested non-target species can be important sources of income, particularly when target species decline in abundance (Crowder and Murawski 1998). Furthermore, when a fishery discards an undersized individual of a species it targets, often dead, the fishery is unable to harvest that individual once it has reached its targeted growth, reproductive, and monetary potential. This results in a fishery not optimizing its economic yield. From a nutritional perspective, Harrington et al. (2005) argued that discarding of fishery catches results in a substantial waste of potential food resources for humans. In addition to the potential biological implications, these economic and food resource implications should be considered when evaluating the impacts of fishery bycatch.
In the Great Lakes, some user groups (e.g., commercial and recreational fishermen) have expressed frustration regarding commercial bycatch. In large part, these complaints have been based on the biological or socioeconomic implications that may result from fishery discards. For example, in Saginaw Bay, Lake Huron recreational fishers have complained about the potential for walleye mortality from encountering commercial gear, potentially leading to decreases in the walleye population (Thomas M. Goneia, MDNR, Fisheries Division, personal communication and file letters). Commercial fishers are not currently permitted to harvest walleye in Saginaw
Bay, though the bay maintains a popular recreational fishery for the species. Concurrent with increases in walleye abundance from MDNR led stocking and rehabilitation programs (Fielder et
6 al. 2007, 2010), walleye recreational harvest had increased to about 300,000 walleye per year by
2009 (MNDR, unpublished data) after annually totaling around 50,000 in the early 90s (Rakoczy and Svoboda 1994). Recreational fishers argue that commercial walleye bycatch could compromise the walleye rehabilitation effort and the fishery. Alternatively, some commercial fishers in Saginaw Bay view walleye as a potential food resource for consumers and as a potentially valuable source of income (Thomas M. Goneia, MDNR, Fisheries Division, personal communication and file letters). Therefore, both user groups have expressed frustration about the potential biological and socioeconomic implications of fishery discards in Saginaw Bay.
Bycatch is a global issue affecting many fisheries (Kelleher 2005). The Food and
Agriculture Organization of the United Nations (FAO) outlined an international “Code of
Conduct for Responsible Fisheries,” which has been proffered by fisheries management agencies to reduce bycatch (Johnson et al. 2004b). Most notably, this report stated that to the extent practical, fisheries should attempt to “minimize the waste of catch of target and non-target species” and “develop selective, environmentally safe, and cost effective fishing gear and techniques” (Food and Agriculture Organization of the United Nations 1995). Given the potential magnitude and current uncertainty of bycatch and bycatch mortality in many fisheries, as well as the associated biological and socioeconomic implications, it is essential that fishers attempt to minimize bycatch and that fishery managers have accurate estimates of these rates in order to reach management objectives.
Observer programs
Onboard observer programs or short-term monitoring surveys have been implemented in many fisheries to reduce bycatch uncertainty and allow managers to address the potential biological and socioeconomic implications of bycatch (Liggins et al. 1997, Julian and Beeson
7 1998, Hall 1999, Johnson et al. 2004a, NMFS 2004). The primary objective of onboard monitoring is to record species and size-specific catch and bycatch data, as well as information regarding the disposition of incidentally caught organisms. Additionally, observers often record information such as fishing technique or gear used, fishing effort, and environmental conditions
(NMFS 2004).
Fisheries observer programs are subject to uncertainty on a number of levels. In most cases, economic and logistical constraints do not allow monitoring of all effort within a fishery unless the fishery may encounter federally protected species (NMFS 2004, Benoit and Allard
2009). Thus, most observer programs or surveys sample only a portion of the total effort (NMFS
2004), requiring the assumption that observed activities approximate a random sample of all activities within a fishery. If only a portion of the fishery is sampled, inferences made from bycatch monitoring may be biased (Benoit and Allard 2009). This bias can be related to observer deployment in which the observer is not randomly assigned to vessels or trips, or when the presence of the observer causes a fishing operation to change its activities (e.g., fishing location, gear type, effort, discard practice) (Liggins et al. 1997, Benoit and Allard 2009).
Observer programs in which participation by fishing vessels is voluntary may be especially subject to bias from observer deployment, as fishing operations that allow observers onboard may not be representative of fishing operations that do not volunteer for or refuse to participate in the observer program (Benoit and Allard 2009). Bias from the presence of an observer can most effectively be reduced by increasing sampling effort. However, when increased effort is impossible, observer programs can concentrate available resources among a small number of fishing operations. This may reduce the chance that fishers will alter practices in an effort to
8 deceive the observer as the observer is present a high percentage of the total fishing time. The
potential for observer bias must be considered when developing a fishery observer program.
Mitigation strategies
Once fisheries managers have obtained estimates of bycatch and bycatch mortality, attempts may be made to reduce these rates. Generally, fishery managers may employ two strategies for this purpose; reducing fishing effort or reducing bycatch per unit effort (Hall 1996).
Reducing fishing effort reduces the overall opportunities for bycatch since bycatch cannot occur when fishing does not occur. This practice operates on the assumption that bycatch is in some way proportional to total effort. If reducing fishing effort is not practical or is not economically feasible, reducing bycatch per unit effort may also be employed. However, this requires an understanding of the environmental variables or fishing practices that control the magnitude of bycatch and bycatch mortality. Several strategies are commonly implemented in order to reduce bycatch and bycatch mortality: 1) mesh size and gear restrictions, 2) time and area closures, 3) relocation of effort, 4) bycatch limits and quotas, or 5) incentives for using alternative gear types that reduce bycatch (Everett 1996, Johnson et al. 2004b). In the Great Lakes, fisheries managers have advocated for a combination of gear restrictions, bycatch limits and quotas, and incentives in order to limit bycatch (Johnson et al. 2004b).
TRENDS AND MANAGEMENT OF COMMERCIAL FISHERIES IN THE GREAT LAKES WITH AN EMPHASIS ON SAGINAW BAY, LAKE HURON
Bycatch in Great Lakes commercial fisheries emerged as an issue in 1968 when over
70,000 lake trout were taken incidentally in lake whitefish (Coregonus clupeaformis ) gill nets in
Michigan waters of Lake Michigan (Rybicki and Schneeberger 1990). Since that time, management agencies have adopted differing strategies for mitigating the potential problems
9 associated with bycatch. In order to understand these varying strategies and their implications for Great Lakes fishery resources, an exploration of management structure, fishing gear and fishery trends is warranted.
Management structure
Commercial fisheries in the Great Lakes have experienced tremendous change through the years in terms of the species harvested and the gear used for harvest. These changes are a result of changes in the composition of Great Lakes fish communities and the regulations imposed by fisheries management agencies. Great Lakes commercial fisheries management is complex given the numerous legal jurisdictions including state, provincial, tribal and First Nation
Aboriginal agencies. Through the years, these authorities have established and continuously modified fishery regulations in order to prevent overexploitation, promote sustainable resource use and enhance fishery productivity. The following is a brief summary of management structure for the upper Great Lakes (i.e., lakes Superior, Michigan and Huron) with a focus on
Saginaw Bay, Lake Huron. A thorough descriptive history of the evolution of management structure in the Great Lakes is provided by Brenden et al. (2012).
Fishery management authority of the upper Great Lakes is divided between the United
States and Canada. Management authority and fishery resource allocation in U.S. waters is further divided between tribal and state governments. For Michigan waters of the upper Great
Lakes, this was most recently reaffirmed with the 2000 Consent Decree between Native
American tribal governments, the US Fish and Wildlife Service, and the Michigan Department of Natural Resources (United States v. Michigan 2000). Michigan waters in the upper Great
Lakes are currently managed by zones and divided into state-commercial, tribal-commercial, and recreational fishing zones with fishery resources being allocated equally between Native
10 American tribes and the State of Michigan (Ebener et al. 2008, Brenden et al. 2012). The 2000
Consent Decree also established the Chippewa-Ottawa Resource Authority (CORA), which currently manages the tribal fishery in 1836 Treaty-ceded waters of the upper Great Lakes
(Brenden et al. 2012). The remaining portion of U.S. waters is managed by the states of
Michigan, Minnesota, and Wisconsin. Saginaw Bay is entirely contained within the Michigan waters of Lake Huron. Therefore, management of both the commercial and recreational fishery falls solely under the jurisdiction of the Michigan Department of Natural Resources (MDNR).
Each of these management authorities has varying regulations regarding species quotas, size restrictions and fishing techniques, locations and seasons. This complex suite of management agencies aims to appropriately manage fishery resources in the Great Lakes.
Fishing gear
The two most prevalent types of commercial fishing gear presently used in the Great
Lakes are gill nets (Figure 1) and trap nets (Figure 2). Gill nets consist of a vertical wall of netting or twine and capture fish by entanglement. Fish caught in gill nets often die, thus resulting in potentially high mortality rates of non-target species (Brenden et al. 2012).
Alternatively, trap nets are an entrapment gear where fish encounter vertical mesh netting and eventually get funneled into the pot of the net (Brenden et al. 2012). Trap nets are often preferred because of their high capture efficiency and also for the advantage that more fish are kept alive following capture when compared with gill nets (Brenden et al. 2012). Keeping captured fish alive presents two advantages when compared with gill nets. First, live capture results in a fresh product when fishers harvest the catch. Fish harvested from trap nets typically sell at higher prices when compared with gill net harvested fish (Ebener et al. 2008). Second, a higher proportion of bycatch can be released alive from trap nets when compared with gill nets
11 (Ebener et al. 2008). For example, in the Wisconsin waters of Lake Michigan from 1998 to
2000, gill nets accounted for approximately 35% of the lake whitefish harvest, but 96% of the incidental kill of lake trout. Alternatively, trap nets accounted for 60% of the lake whitefish harvest and only 3% of incidentally killed lake trout (Peeters 2001). Johnson et al. (2004a) estimated that a 100,000 kg commercial lake whitefish gill net fishery would kill over 10,000 kg of lake trout. Alternatively, based on the study’s observations, a 100,000 kg lake whitefish trap net fishery would only kill 1,420 kg of lake trout. Therefore, state fishery management agencies in the U.S. have preferred the use of trap nets since the 1990s (Brenden et al. 2012).
Fishery trends
Fishery trends in the Great Lakes have largely been dictated by target species abundance.
Overall, commercial fishery yields in the Great Lakes have declined consistently since the 1980s
(Brenden et al. 2012). However, yields specific to Lake Huron have observed a different trend, largely coinciding with fluctuations in lake whitefish population levels. With the initiation of sea lamprey control measures, lake whitefish populations have strongly rebounded from the low abundances observed in the 1970s and harvest has either met or exceeded goals set by fisheries agencies since the 1990s (Figure 3) (DesJardine et al. 1995, Baldwin et al. 2009). Historically, lake trout were also an important commercial species harvested in Lake Huron. However, due to a combination of overexploitation and sea lamprey predation, populations collapsed by the late
1940s (Hile 1949, Coble et al. 1990). Rehabilitation efforts ensued and in recent years, the lake has seen increasing numbers of wild-origin lake trout, though successful lake-wide rehabilitation of the species remains elusive (Muir et al. 2012). Michigan state-licensed commercial fishers are not currently allowed to harvest lake trout, though some tribal gill net fishers retain this right.
Tribal trap net vessels may harvest up to 45.5 kilograms (100 lbs) of lake trout per day (CORA
12 2009). Saginaw Bay commercial fishers are state-licensed and therefore, are not currently permitted to harvest lake trout.
Commercial fishery harvest specific to Saginaw Bay declined steadily starting in the early 1940s, but has recently become relatively stable (Figure 3) (Baldwin et al. 2009). The most harvested species in Saginaw Bay is lake whitefish, but other species including channel catfish
(Ictalurus punctatus ) and yellow perch (Perca flavescens ) are also harvested. Additionally,
Saginaw Bay once supported the largest commercial walleye fishery in Lake Huron and was second in the Great Lakes to only Lake Erie (Baldwin and Saalfield 1962, Hile 1995).
Commercial walleye harvest peaked in 1942 at over 950,000 kilograms, but collapsed in 1944 due to a series of year class failures from habitat degradation and intensive commercial exploitation (Baldwin and Saalfield 1962, Keller et al. 1987). Commercial harvest of the species was formally prohibited in Saginaw Bay in 1970 (Schneider 1977). Though commercial walleye harvest remains prohibited, a commercial trap net fishery currently operates in Saginaw Bay.
Commercial fishers operate both inner (depth < 10 m) and outer (depth < 40 m) bay trap nets (Figure 4). The main target species in the inner bay fishery include lake whitefish (in spring and fall), yellow perch, channel catfish, freshwater drum (Aplodinotus grunniens ), and catastomids. The outer bay fishery targets lake whitefish. In 2010, the Saginaw Bay trap net fishery harvested nearly 550,000 kilograms of fish (Thomas M. Goneia, MDNR, personnel communication and unpublished commercial harvest data). Of that total harvest, over 75% was lake whitefish with the majority being harvested by the outer bay fishery. Channel catfish
(84,000 kgs), freshwater drum (20,000 kgs), catastomid (20,000 kgs) and yellow perch (15,000 kgs) were also harvested (Thomas M. Goneia, MDNR, personnel communication and unpublished commercial harvest data). Lake whitefish are most important in terms of overall
13 economic value, with 31 to 43% of inner Saginaw Bay’s and nearly 100% of outer Saginaw
Bay’s commercial dockside value from 2001 to 2005 (MDNR, unpublished data). Additionally, though harvested in low numbers compared to some other fish species, yellow perch harvest made up 21 to 42% of total Saginaw Bay commercial dockside value from 2001 to 2005
(MDNR, unpublished data).
State-licensed Saginaw Bay commercial fishery policies and regulations
Regulatory authority of Michigan’s state-governed Lake Huron fishery resources lies with MDNR, and since 1966, Michigan has operated under a philosophy that emphasizes recreational over commercial fishing (Keller and Smith 1990, Rybicki and Schneeberger 1990,
Kocik and Jones 1999). This is partially because in the Great Lakes, it is generally believed that allocation of harvest to the recreational fishery provides larger economic benefits than allocation to the commercial fishery (Talhelm 1988, Bence and Smith 1999). In support of this philosophy,
MDNR implemented several fisheries management policies in the late 1960s that limits entry into the commercial fishery. These policies aim to: (1) preserve, protect and enhance the fishery resource itself; (2) make the commercial fishery an asset that contributes to the public good rather than being a liability; and (3) restore and improve the economic viability of the commercial fishing business (W.R. Crowe, MDNR memorandum 1968). Additionally, a Zone
Management Plan was implemented that reserved yellow perch, walleye, and lake trout for recreational use (Ebener et al. 2008). The Zone Management Plan banned gill nets in certain areas designated important for lake trout rehabilitation, and terminated commercial fishing for yellow perch (though not in Saginaw Bay), walleye, and lake trout throughout much of the upper
Great Lakes (Keller and Smith 1990). The Zone Management Plan reserved these species for recreational use in order to reduce conflict between recreational and commercial fishers (Ebener
14 et al. 2008) and enhance the recreational fishery. Michigan’s commercial fishing regulations are
guided by these policies.
Michigan’s state-licensed commercial fishing regulations were summarized by Brege and
Kevern (1978) and then were the subject of state legislation in 1994 (1994 Michigan Public Act
451). However, in addition to outlining current commercial regulations, PA 451 gave MDNR
the authority to suspend, abridge, extend, or modify any regulations outlined in the act. In
response, MDNR uses an adaptive approach allowing the agency to efficiently alter regulations
for the most effective management of the resource. Thus, a series of MDNR Administrative
Rule changes and Fisheries Orders have modified the regulations to what they are today.
Michigan’s state-licensed commercial fishing regulations are not published annually.
Here, I summarize the regulations for the commercial trap net fishery in Saginaw Bay as
of 2010. This summary is not exhaustive, but is intended to review regulations relevant to my
thesis. The following species must be immediately returned to the water whether they are caught
dead or alive: lake sturgeon (Acipenser fulvescens ), trout (all species), salmon (all species), lake
whitefish (under 43 cm (17 inches) in the inner bay and under 48 cm (19 inches) in the outer
bay), chubs (Coregonines), muskellunge (Esox masquinongy ), northern pike (Esox lucius ), largemouth bass (Micropterus salmoides ), smallmouth bass (Micropterus dolomieu ), sunfish
(Centrarchidae spp.), walleye, sauger (Sander canadensis ), and yellow perch (under 22 cm (8.5 inches)). Any undersized fish or species taken out of season, whether dead or alive, must be returned to the water immediately (MCL 324.47320). No commercial license in Saginaw Bay operates under a catch quota for any species.
As of April 2010, all commercial trap nets could be fished in waters up to 46 meters (150 feet) deep (Fisheries Order 243.10A). Mesh size restrictions are also enforced (MCL
15 324.47309). Trap net pot or crib mesh sizes cannot be smaller than 11.4 cm (4.5 inches) for nets fished outside of inner Saginaw Bay (the inner bay fishery). Additionally, these nets may not have lead mesh sizes less than 12.7 cm (5 inches). In inner Saginaw Bay, pot or crib mesh size cannot exceed 8.9 cm (3.5 inches) with no restriction on lead mesh size. There are no longer restrictions on depth of pot (MDNR Administrative Rule 299.821) and also no restrictions on height or length of lead. Seasonal and area closures are also in effect within Saginaw Bay (e.g.,
MDNR Administrative Rule 299.1074, MCL 324.47339). Most notably, a spawning closure is
st th in effect for lake whitefish from November 1 through November 30 each year. Fishers are required to report their daily catch, as well as the number of nets they lifted, on MDNR supplied catch reports each month.
Some key differences exist between the regulations for state-licensed commercial fishers in Michigan waters of the Great Lakes versus those for tribal fishers in 1836 Treaty-ceded waters. Most notably, tribal fishers are permitted to use gill nets in some areas whereas state- licensed commercial fishers are not (with the exception of large mesh gill nets in Saginaw Bay to target common carp ( Cyprinus carpio ). Additionally, tribal trap net fishers are permitted a
th longer season for lake whitefish as their seasonal spawning closure occurs from November 6
th through November 29 (CORA 2009). In 1836 Treaty waters, tribal trap net fishermen may harvest up to 23 kgs (50 lbs) of undersized lake whitefish and up to 45.5 kgs (100 lbs) of lake trout over 43 cm per vessel per day as long as those lake trout are not caught in a refuge. In contrast, state-licensed commercial fishers must discard all sub-legal lake whitefish and all live or dead lake trout regardless of size. Finally, tribal fishers are not allowed to have unattended or abandoned nets in 1836 Treaty waters. An unattended net is one that has been tagged by an
16 enforcement officer for at least four days in which the commercial fisher has not lifted.
Abandoned nets are those that have not been utilized or tended by the fisher for 14 days.
Unattended and abandoned nets may be seized by enforcement officers. At present, state-
licensed fishers are not required to tend nets with any specificity. These regulation differences
may account for some of the variation in bycatch and bycatch mortality rates, as will be
explained later, between the Lake Huron state-licensed commercial fishery and the tribal-
licensed commercial fishery.
The fishery in practice
While state regulations provide some restrictions on trap net configuration and operation, much is determined by individual fishers. In Saginaw Bay, significant variation exists in net structure and fishing practice between and within operations. Inner Saginaw Bay trap nets have
8.9 cm or less mesh, range in height from 1.2 to 5.5 m in the pot and have leads ranging from
120 to 370 m in length with mesh sizes ranging from 3.8 to 8.9 cm. These nets are fished predominantly in water less than 10 m (30 ft) deep. Six fishing operations work in inner
Saginaw Bay. A total of 292 trap nets are licensed to fish in these waters, though a large percentage of these nets are not fished each year nor are they fished continuously.
Outer bay nets have 11.4 cm mesh in the pot, have pots that range in height from 6.1 to
7.3 m, and have leads ranging from 370 to 460 m. These nets are fished in depths between 20 and 40 m during most months and target lake whitefish. During the fall, fishers often move their nets into shallower water to harvest pre-spawn lake whitefish close to shore. Two fishing operations work in outer Saginaw Bay. One operation holds a license for 11 trap nets while the other holds a license for 10 trap nets.
BYCATCH IN GREAT LAKES COMMERCIAL FISHERIES
17 Bycatch in Great Lakes commercial fisheries is not well understood. Most bycatch studies in the Great Lakes have been relatively short-term “snap shots” (Eshenroder 1980,
Schneeberger et al. 1982, Smith 1988, Copes and McComb 1992, Gallinat et al. 1997, Peck
1997, Peeters 2001, Johnson et al. 2004a), though some management agencies (e.g., CORA and
Ontario Ministry of Natural Resources (OMNR)) maintain long-term bycatch monitoring observer programs. In Lake Huron, OMNR and CORA operate fisheries observer programs for their provincial and tribal-licensed commercial fisheries, respectively (Adam Cottrill, OMNR, personal communication; Mark Ebener, CORA, personal communication). There is currently no long-term observer program in place for Michigan state-licensed commercial fisheries.
Most studies examining Great Lakes commercial fisheries bycatch have investigated fisheries targeting lake whitefish where lake trout is the principal bycatch species (e.g.,
(Eshenroder 1980, Smith 1988, Johnson et al. 2004a)). No published literature currently exists that examines bycatch in shallow water Great Lakes commercial fisheries targeting warm and cool water species such as yellow perch and channel catfish. Specifically, the magnitude, species composition and factors influencing bycatch in inner Saginaw Bay’s commercial trap net fishery have never been evaluated. Based on anecdotal evidence from commercial and recreational fishers (Thomas M. Goneia, MDNR, Fisheries Division, personal communication and file letters), and the warm to coolwater fish assemblage of the shallow portion of the bay
(Fielder and Thomas 2006, Fielder et al. 2008), walleye is likely the principal bycatch species of concern in this fishery.
Lake Trout
Lake trout bycatch and bycatch mortality rates in Great Lakes commercial trap net fisheries targeting lake whitefish vary among seasons and areas. Schneeberger et al. (1982)
18 observed an average of 75 lake trout caught and 1.1 lake trout dead per lift in Lake Huron.
These observations occurred over 31 trap net lifts in September and October of 1980. In Lake
Michigan, Smith (1988) observed an average of 72 and 88 lake trout caught and 5.5 and 2.3 lake trout dead per trap net lift in 1985 and 1986, respectively. These observations occurred over 76 lifts in 1985 and 136 lifts in 1986. However, seasonal differences were observed with 17 dead lake trout observed per lift in August of 1985 over nine lifts observed. Schorfhaar and Peck
(1993) observed an average of 7.19 lake trout caught and 0.26 lake trout dead per lift in Lake
Superior over 1,012 trap net lifts observed from 1983-1989. Johnson et al. (2004a) observed an average of 14.66 lake trout caught and 0.98 lake trout dead per lift in northern Lake Huron over
96 lifts in 1998 and 1999. The highest bycatch and mortality rates were observed in July with
62.5 lake trout caught and 5.5 lake trout dead observed per lift though only four lifts were observed during this month. August observed the next highest rates with 48.6 lake trout caught and 4.4 lake trout dead observed per lift, though only five lifts were observed during this month.
CORA maintains a long term bycatch monitoring program for its tribal commercial fishery in
1836 Treaty-ceded waters. In northern Lake Huron from 2004 to 2009 (Mark Ebener, CORA, unpublished data), an average of 18.2 lake trout were caught per trap net lift. August saw the highest bycatch rates (34.3/lift) whereas May had the lowest rates (3.56/lift) of all months sampled (May through September). CORA observers do not record data concerning survival of lake trout during sampling, but based on observations made onboard commercial fishing vessels, tribal biologists generally assume that most lake trout are released alive from tribal trap nets
(Mark Ebener, CORA, personal communication). As illustrated, considerable variation in lake trout catch and mortality has been observed among trap net fisheries in the Great Lakes.
19 However, little research exists to illustrate what factors (i.e., environmental conditions and
fishing practices) contribute to this variability.
Lake whitefish
Release of sub-legal lake whitefish in Great Lakes trap net fisheries has been previously
documented. Copes and McComb (1992) observed an average of 110 sub-legal lake whitefish
caught over 31 commercial trap net lifts in Lake Michigan. However, on average, only 3.4 per
lift were floating and assumed dead following release. Schorfhaar and Peck (1993) observed
lower catch rates overall, but considerable variation among years and sites in Lake Superior.
Overall, an average of 12.2 sub-legal lake whitefish were caught per lift, though average catches
from all fishing grounds ranged from 3.9 in 1989 to 25.5 in 1985. At one site an average of 40
sub-legal lake whitefish (n = 51 lifts) were observed caught in 1985. Schorfhaar and Peck
(1993) observed an average of 0.07 dead sub-legal lake whitefish per lift. Smith (1988) rarely
observed sub-legal lake whitefish in trap nets and hypothesized that most undersized fish
escaped or that the operations monitored in his study did not set nets in habitats frequented by
juvenile lake whitefish. Johnson et al. (2004a) also observed relatively low catch rates of sub-
legal lake whitefish with 100 caught over 96 lifts. In 1836 Treaty waters from 2004 to 2009, an
average of 12.9 caught and 0.59 dead sub-legal lake whitefish were observed (Mark Ebener,
CORA, unpublished data), though considerable seasonal variability exists. In May, an average
of 91.4 caught and 4.2 dead sub-legal lake whitefish were observed. However, during this time period only ten lifts were observed in May and all occurred in 2004. July had the next highest sub-legal lake whitefish rates with an average of 21.1 caught and 1.28 dead per lift. Similar to lake trout observations, the magnitude and mortality rates of lake whitefish discarding have
20 exhibited high variability in different regions of the Great Lakes. However, the underlying cause of this variability has not been fully explored.
Factors influencing bycatch discard survival
Several stressors may contribute to bycatch mortality in Great Lakes commercial trap nets. First, fish are subject to stressors from confinement in the trap net pot. For example, contact with other fish or the trap net itself may result in physical trauma. Trauma becomes more likely with greater densities of fish (Breder 1976, Schneeberger et al. 1982). Secondly, fish generally occupy thermal strata near their optimum temperature, (Fry 1947, Christie and Regier
1988), which may not be possible when confined within a trap net pot. The incidence of fish caught by their gills in trap net mesh (gilling) may increase with fish density (Schneeberger et al.
1982). Several studies have indicated that gilling is the primary cause of mortality for both target and non-target species in Great Lakes trap nets (Schneeberger et al. 1982, Smith 1988,
Schorfhaar and Peck 1993).
Bycatch mortality may also be affected by the length of confinement within a trap net
(i.e., soak time). The longer a fish is confined within a net the more stressors it may be subject to. However, studies that evaluated trap net soak times did not indicate this to be a factor contributing to bycatch mortality. Grinstead (1970) found that the mortality of fish caught in trap nets did not increase significantly with soak time, though soak times in that study only ranged between 1 and 7 days. Smith (1988) also found that soak time did not influence mortality of fish caught in Lake Michigan commercial trap nets. Soak times in the Smith (1988) study averaged 4 to 6 days, but ranged as long as 10 to 12 days. He suggested this result could have been because fish escaped. Patriarche (1968) demonstrated that longer soak times increased the likelihood of escape from trap nets. However, underwater observations of a trap net in Lake
21 Huron suggested that lake whitefish and lake trout do not exhibit behavior suggesting they were trying to escape (Rutecki et al. 1983). No studies have examined the effects of soak times longer than 12 days on trap net bycatch mortality. However, in Saginaw Bay, fisheries managers have expressed concern regarding the recent trend of prolonged soak times (i.e., > 12 days) in the trap net fishery (Thomas M. Goneia, MDNR, Fisheries Division, personal communication).
When the trap net is lifted from depth, fish may be subject to several additional stressors which may eventually lead to mortality. These include increased physical trauma, increased gilling, temperature shock, and barotrauma. Scheeberger et al. (1982) described how schooling lake whitefish in a trap net pot can crowd together as the net is lifted, resulting in increased physical trauma from contact with other fish. The study also observed increased gilling as fish moved in panicked fashion or became restricted in their movements because of other fish.
Temperature shock may also play a role in mortality as temperature at the surface may be much warmer than temperature at depth (Olla et al. 1998). However, Copes and McComb (1992) did not find that the difference between bottom and surface water temperatures significantly influenced mortality of discarded sub-legal lake whitefish in a Lake Michigan trap net fishery.
Barotrauma, the physical damage caused by the sudden decrease in ambient pressure as the fish is brought to the surface from deeper depths (Schreer et al. 2009), may also be a significant factor determining survival of discarded fish, particularly for physoclistous fish (e.g., walleye and yellow perch, smallmouth bass). Barotrauma can cause both internal and external injuries including a bloated swim bladder, stomach and anal eversion, bulging of the eyes, inability to maintain equilibrium, hemorrhaging, organ torsion, and formation of gas bubbles in the circulatory system, gills, heart and brain (Feathers and Knable 1983, Morrissey et al. 2005,
Hannah and Matteson 2007, Gravel and Cooke 2008, Scheer et al. 2009). Barotrauma effects
22 have been demonstrated in as little as 6.1 m depths for walleye, with the incidence of barotrauma
increasing with depth (Schreer et al. 2009). All of these stressors may interact and increase the
likelihood for mortality.
After the trap net has been lifted from depth, fish are subject to more potential stressors
which may lead to mortality. These include prolonged exposure to surface water temperatures,
air exposure, and trauma from close contact with other fish as they are densely packed in the net
at the water’s surface. Surface water temperature has been shown to be one of the most
influential factors determining survival of released angler caught fish (Hoffman et al. 1996,
Graeb et al. 2005, Reeves and Bruesewitz 2007, Schramm et al. 2010). Following release, angler
caught and trap net caught fish may be subject to the same stressors. Schramm et al. (2010)
found that walleye mortality increased rapidly within the surface water temperature range of 15–
20 oC. Similarly, Graeb et al. (2005) found that post-release mortality of tournament caught walleye increased from 18% at 14 oC to 79% at 19 oC. Several studies have shown a positive correlation between water temperature and mortality in salmonids (Klein 1965, Dotson 1982,
Nuhfer and Alexander 1992), though a study looking at hooking mortality of lake trout in the upper Great Lakes did not find that surface water temperature had a significant effect on lake trout survival (Loftus et al. 1988). Air exposure has been shown to increase stress and mortality in captured fish (Davis 2002). Giomi et al. (2008) observed consistently higher mortalities of non-target species caught by demersal trawling gear with warm summer air temperatures
(temperature: 28 oC) with 96% mortality, when compared to cool winter temperatures
(temperature: 2 oC) with 2% mortality. Additionally, Copes and McComb (1992) found that crowding at the surface of the water during a trap net lift was the primary factor determining
23 survival of sublegal lake whitefish. This was thought primarily to be due to increased scaling, battering, and general stress with higher fish densities in the trap net pot.
Once fish are dipped out of the trap net, they are subject to additional stressors including continued air exposure and trauma from contact with other fish or the sorting table. Rough water conditions (i.e., high current or wave action) may also increase mortality (Goeman 1991, Davis
2002). After fish are discarded overboard, elevated surface water temperatures may remain a stressor as discarded fish are often unable to immediately return to preferred water temperature and depth following release. This is often the result of barotrauma (Scheer et al. 2009), but can also result from the numerous other stressors outlined above. Additionally, floating fish are vulnerable to sea bird predation as has been observed in other Great Lakes trap net fisheries
(Copes and McComb 1992). All of these potential stressors interact to determine survival of discarded fish.
CONCLUSIONS
Bycatch and bycatch mortality within Saginaw Bay and other Great Lakes fisheries has the potential to compromise fish community objectives and conflict with recreational use.
Additionally, from both a biological and socioeconomic perspective, many view bycatch mortality as a waste of valuable natural resources (Alverson et al. 1994, Crowder and Murawski
1998, Harrington et al. 2005). While the transition from gill nets to trap nets in many Great
Lakes fisheries has greatly reduced bycatch mortality, mortality of non-target species in trap nets still occurs. Bycatch rates exhibit high variability among trap net fisheries in the Great Lakes.
Therefore, a greater understanding of the magnitude of bycatch and bycatch mortality in all
Great Lakes fisheries would benefit management of recreational and commercial fish stocks.
This understanding would increase stock assessment accuracy and improve harvest strategy
24 evaluation. A more comprehensive bycatch monitoring program throughout the Great Lakes would serve this purpose. Bycatch monitoring programs are already in place in the Canadian waters of Lake Huron and 1836 Treaty-ceded waters. These programs can serve as an example to other agencies (i.e., MDNR) that do not currently have observer programs in place. Observers should accompany commercial fishers throughout the Great Lakes to determine species-specific counts and mortality estimates of bycatch. Additionally, relatively little is known about what environmental conditions and fishing practices may contribute to bycatch mortality in Great
Lakes fisheries. If bycatch and bycatch mortality were of an unacceptable magnitude, as determined by fishery managers, this information would be necessary to recommend strategies intended to reduce these rates. Therefore, observers would also need to record data concerning gear attributes, water depth, fishing location, air and water temperature, soak time, and other factors that may influence bycatch mortality. Ultimately, a more comprehensive monitoring program would decrease uncertainty and help fishery management agencies work towards achieving fish community objectives.
25 BYCATCH IN THE SAGINAW BAY, LAKE HURON COMMERCIAL TRAP NET FISHERY
INTRODUCTION
Bycatch is the unintentional capture of organisms not directly targeted by fishing (Everett
1996, Harrington et al. 2005). Non-target organisms are often discarded because of low value or regulatory requirements, often resulting in injury or death (Harrington et al. 2005). Bycatch is a global issue affecting many fisheries (Kelleher 2005), and may represent upwards of 40% of global marine catches (Davies et al. 2009). Bycatch has been documented in some Great Lakes fisheries and is considered one of the largest impediments to the recovery of native predators including lake trout (Salvelinus namaycush ) and walleye (Sander vitreus ) (Hansen 1999, Johnson et al. 2004b, Ebener et al. 2008) within the lakes. Bycatch has also increased user conflict between recreational and commercial fishers because non-target catch from commercial fisheries
(e.g., lake trout and walleye) can be targeted by recreational fisheries. Additionally, many view the discarded portions of fishery catches as wasteful uses of natural resources (Alverson et al.
1994, Crowder and Murawski 1998, Harrington et al. 2005). Because of these concerns, bycatch in commercial fisheries has the potential to compromise efforts aimed at achieving fish community objectives.
Most studies examining Great Lakes commercial fishery bycatch have investigated fisheries targeting lake whitefish (Coregonus clupeaformis ) where lake trout are the principal bycatch species (Eshenroder 1980, Schneeberger et al. 1982, Smith 1988, Rybicki and
Schneeberger 1990, Schorfhaar and Peck 1993, Gallinat et al. 1997, Peck 1997, Peeters 2001,
Johnson et al. 2004a). However, bycatch in the commercial lake whitefish fishery in outer
Saginaw Bay, Lake Huron has never been evaluated. Additionally, no published literature currently exists that examines bycatch in shallow water Great Lakes commercial fisheries
26 targeting warm and cool water species such as yellow perch (Perca flavescens ) and channel catfish (Ictalurus punctatus ). A commercial fishery targeting these species exists in inner
Saginaw Bay, but bycatch has never been quantified.
Bycatch in Great Lakes commercial fisheries emerged as an issue in the late 1960s when in 1968, over 70,000 lake trout were taken incidentally in lake whitefish gill nets in Michigan waters of Lake Michigan (Rybicki and Schneeberger 1990). Fish caught in gill nets often die, thus resulting in high mortality rates of non-target species (Brenden et al. 2012). In response, fishery management agencies in the U.S. have preferred the use of trap nets since the 1990s
(Brenden et al. 2012). Trap nets are an entrapment gear in which fish encounter vertical mesh netting, swim to deeper water along this netting and eventually get funneled into the pot of the net (Brenden et al. 2012) (Figure 2). Trap nets are often preferred because of their high capture efficiency and because a higher percentage of fish are kept alive following capture when compared with gill nets (Ebener et al. 2008). For example, in the Wisconsin waters of Lake
Michigan from 1998 to 2000, gill nets accounted for approximately 35% of the lake whitefish harvest, but 96% of lake trout discard mortality. Alternatively, trap nets accounted for 60% of the lake whitefish harvest and only 3% of lake trout discard mortality (Peeters 2001). Johnson et al. (2004a) estimated that a 100,000 kg commercial whitefish gill net fishery would kill over
10,000 kg of lake trout bycatch, but identical harvest of lake whitefish in trap nets would only kill 1,420 kg of lake trout. Therefore, though trap nets typically result in less bycatch mortality than gill nets, some may still occur.
Discard or bycatch mortality may be a large source of mortality in many fisheries
(Alverson et al. 1994, NMFS 2004, Kelleher 2005) that is often unknown or unaccounted for
(Chopin et al. 1996, Johnson et al. 2004b). Discarded portions of fishery catches are implicated
27 in the decline of several marine fish stocks. For example, a 40% decline in Atlantic croaker
(Micropogonias undulates ) population in the Gulf of Mexico since the 1970s has been linked to the substantial amount of croaker bycatch in the Gulf shrimp trawl fishery (Tillman 1992).
Bycatch in shrimp fisheries is also implicated in population declines of red snapper ( Lutjanus
campechanus ) (Tillman 1992).
In Great Lakes fisheries, managers typically focus stock assessment mortality estimates
on targeted fishing and sea lamprey (Petromyzon marinus ) mortality (Hansen et al. 1996, Sitar et al. 1999, Irwin et al. 2009). With few exceptions, commercial fishing bycatch is poorly quantified throughout the Great Lakes. For example, Sitar et al. (1999) fit an age-structured model to assess lake trout populations in southern Lake Huron, but did not account for commercially discarded lake trout as a mortality source. In more recent years, fisheries managers have included bycatch mortality in some stock assessment models (Modeling
Subcommittee, Technical Fisheries Committee 2010). However, low mortality is assumed for certain gear types, and varying fishing practices and environmental conditions among fisheries make this assumption questionable. Given the potential magnitude and current uncertainty of bycatch and bycatch mortality in many fisheries, as well as the associated biological and socioeconomic implications, it is essential that fishery managers have accurate estimates of these rates in order to accurately assess population levels and optimize harvest strategies.
Bycatch within Great Lakes commercial fisheries has the potential to conflict with fish population rehabilitation and management goals. One of the principal fish community objectives for Lake Huron, as outlined in 1995 by the Lake Huron Committee of the Great Lakes Fishery
Commission, is to restore an ecologically balanced fish community dominated by top predators
(e.g., lake trout and walleye) (DesJardine et al. 1995). Though recent years have seen gains in
28 walleye abundance (Fielder et al. 2007, 2010) and increasing numbers of wild-origin lake trout
(Muir et al. 2012), rehabilitation goals for these populations have not yet been reached (Fielder et al. 2010, Muir et al. 2012). If bycatch mortality were to result in population level affects, rehabilitation efforts and thus, fish community objectives may be compromised.
From an economic and food resource perspective, many view discarded portions of fishery catches as a waste of valuable natural resources (Alverson et al. 1994, Crowder and
Murawski 1998, Harrington et al. 2005). Murawski (1996) argued that regulatory induced discards of otherwise marketable species represent a loss of potential revenue to producers and supply for consumers. Alternatively, harvested non-target species can be an important source of income for producers, particularly when target species decline in abundance (Crowder and
Murawski 1998). Furthermore, when a fishery discards an undersized individual of a species it targets, often dead, the fishery is unable to harvest that individual once it has reached its targeted growth, reproductive, and monetary potential. This results in a fishery not optimizing its economic yield. From a nutritional perspective, Harrington et al. (2005) argued that discarding of fishery catches results in a substantial waste of potential food resources for humans. In addition to the potential biological implications, these economic and food resource implications should be considered when evaluating the impacts of fishery bycatch.
In the Great Lakes, some user groups (e.g., commercial and recreational fishermen) have expressed frustration regarding commercial bycatch. In large part, these complaints have been based on the biological, economic, or food resource implications that may result from fishery discards. For example, in Saginaw Bay, Lake Huron recreational fishers have complained about the potential for walleye mortality from encountering commercial gear, potentially leading to decreases in the walleye population (Thomas M. Goneia, MDNR, Fisheries Division, personal
29 communication and file letters). Commercial fishers are not currently permitted to harvest walleye in Saginaw Bay, though the bay maintains a popular recreational fishery for the species.
Concurrent with increases in walleye abundance from MDNR led stocking and rehabilitation programs (Fielder et al. 2007, 2010), walleye recreational harvest had increased to about 300,000 individuals per year by 2009 (MNDR, unpublished data) after annually totaling around 50,000 in the early 90s (Rakoczy and Svoboda 1994). Recreational fishers argue that commercial walleye bycatch could compromise this fishery and ongoing rehabilitation efforts. Alternatively, some commercial fishers in Saginaw Bay view walleye as a potential food resource for consumers and as a potentially valuable source of income (Thomas M. Goneia, MDNR, Fisheries Division, personal communication and file letters).
Bycatch and bycatch mortality within the Saginaw Bay commercial fishery has the potential to compromise fish community objectives and conflict with recreational use.
Additionally, from both an economic and food resource perspective, many view bycatch mortality as a waste of valuable natural resources (Alverson et al. 1994, Crowder and Murawski
1998, Harrington et al. 2005). The species composition and abundance of bycatch and bycatch mortality in the Saginaw Bay, Lake Huron commercial fishery has never been evaluated.
Therefore, a greater understanding of the magnitude of bycatch and bycatch mortality in the fishery, as well as the factors that influence these rates, would benefit management of Saginaw
Bay fish stocks. This understanding would increase stock assessment accuracy, improve harvest strategy evaluation, and help managers achieve fish community objectives.
Objectives
This research had three primary objectives: 1) determine the quantity and monthly variation of lake trout, walleye, undersized target species, and other non-target species caught in
30 the Saginaw Bay commercial trap net fishery and estimate mortality rates for these species, 2) determine how environmental factors and fishing practices influence the magnitude, species composition, and mortality of bycatch, and 3) determine seasonal bycatch and bycatch mortality totals and compare these totals to population metrics.
METHODS
I evaluated the magnitude of and factors influencing bycatch and bycatch mortality in
Saginaw Bay’s commercial trap net fishery by observing onboard Michigan state-licensed commercial fishing vessels and recording species-specific counts of all non-target species caught. Although all non-target species were quantified, most of my effort and analysis focused on walleye and lake trout due to their ecological and economic importance to the system.
STUDY AREA
Saginaw Bay is a large embayment contained within the Michigan waters of Lake Huron.
The bay is separated into an inner and outer portion by a line between Point Au Gres, Michigan and Sand Point, Michigan (Figure 4). The inner bay is shallow with a mean depth of 4.6 m and a maximum depth of 14 m. The outer bay is deeper with a mean depth of 15.6 m and a maximum depth of 40.5 m (Beeton et al. 1967). The inner bay is considered eutrophic with productivity declining towards the outer bay (Fielder and Thomas 2006).
A state-licensed commercial trap net fishery currently operates in Saginaw Bay. The fishery operates under the jurisdiction of the Michigan Department of Natural Resources
(MDNR) and is prohibited from harvesting walleye and lake trout. Commercial fishers operate both inner (depth < 10 m) and outer (depth < 40 m) bay trap nets (Figure 4). The main target species in the inner bay fishery include lake whitefish (in colder months), yellow perch and channel catfish. The outer bay fishery targets lake whitefish.
31 FIELD SAMPLING Sampling selection
I observed onboard with two fishing operations in Saginaw Bay from May through
August 2010 (Table 1). Both operations employed inner and outer bay trap nets. During this time, I observed 67 inner bay trap net lifts and 91 outer bay trap net lifts. This corresponds to
11% of the lifts that occurred in inner Saginaw Bay during this time and 38% of the lifts that occurred in the outer bay. Sampling effort was dependent on fishing effort (i.e., I could not observe when and where fishers were not fishing) and on weather. I sampled across a range of fishing tactics (e.g., varying soak times or net depths) and environmental conditions (e.g., varying surface water temperatures or sea conditions) in order to obtain a representative sample of varying abiotic conditions within Saginaw Bay. This allowed me to observe catch trends and determine which factors most influenced bycatch and bycatch mortality rates.
Observer bias has been documented in other fishery observer programs in which fishers may alter their fishing practices in an effort to deceive an observer (Liggins et al. 1997, Benoit and Allard 2009). In an effort to reduce observer bias, I concentrated my effort with a small number of fishing operations (i.e., two operations) and was present a high percentage of the time, so that fishers would be less likely to alter their fishing practices.
OBJECTIVE 1 – Fish counts
All catch was video recorded for later analysis in the lab. For both inner and outer bay trap net lifts, all fish caught onboard were placed on one sorting table on the vessel deck. A video camera was strategically placed above the sorting table so that all fish caught from each trap net could be recorded.
Two individuals made independent species-specific counts of each video record. Counts were only made of species that were discarded (discarded species differ by operation). If video
32 counts from the two individuals were not within 15% of each other, a third individual recorded
species specific counts for that lift. I was able to obtain independent duplicate counts within a
15% error rate for all videos. For each video record, the smaller count was used to produce a
more conservative estimate of bycatch.
During each lift, I recorded the number of lake trout and walleye that were floating at the
surface of the water following release. These fish were defined as morbid and were unable to
maintain an upright position or swim away following release (Schmalz and Staples 2011). This
metric has been used by other state management agencies (i.e., Minnesota Department of Natural
Resources) as a measure of initial mortality following release from fishing nets (Schmalz and
Staples 2011). This assumption of mortality for floating fish is justified because of the numerous
stressors fish are subject to when at the surface of the water which may contribute to mortality.
Most notably, elevated surface water temperature remains a stressor as fish are unable to return
to preferred water temperature following release. Surface water temperature has been shown to
be one of the most influential factors determining survival of released angler caught fish with
higher temperatures increasing mortality (Hoffman et al. 1996, Graeb et al. 2005, Reeves and
Bruesewitz 2007, Schramm et al. 2010). Additionally, floating fish are vulnerable to sea bird
predation as has been observed in other Great Lakes trap net fisheries (Copes and McComb
1992). I estimated fish morbidity on 55 inner bay lifts and 89 outer bay lifts.
OBJECTIVE 2 – Environmental and fishing practice variables
I recorded environmental and fishing practice data to relate to bycatch metrics.
Environmental and fishing practice variables were selected that had the potential to influence bycatch and bycatch mortality rates. Variables were also selected to avoid multicollinearity
(e.g., I did not evaluate both surface water temperature and air temperature as they were
33 significantly correlated). I recorded trap net depth, the number of days since the trap net had last
been lifted (soak time), and trap net location. Remote combination temperature/light loggers
were attached to trap nets to monitor temperature and light levels at trap net depth throughout the
sampling season and to corroborate soak times reported by fishers. Soak times could be
corroborated because light and temperature levels increased rapidly on the loggers when a net
was lifted. Trap net location was recorded with a hand-held GPS unit. Other recorded
environmental variables included wave height and surface water temperature. I also recorded the
time of day and the time it took to sort through the catch (sort time). Finally, I obtained daily
harvest of target species for inner bay trap nets from MDNR catch reports and obtained harvest
of lake whitefish for each outer bay trap net lift directly from fishers. Lake whitefish harvest
from the outer bay was then corroborated with MDNR catch reports.
DATA ANALYSIS
OBJECTIVE 1 – Fish counts
I evaluated month-to-month variation in bycatch and morbidity rates with non-parametric
statistics. Non-parametric statistics were necessary because they do not assume a normal
distribution (Freund and Wilson 2003) and catch data often exhibit over-dispersion and high
variability (Clark 1974, Power and Moser 1999, Ward and Myers 2005, Pradhan and Leung
2006, Claramunt et al. 2009). Monthly bycatch and morbidity rates, along with catch rates in the
inner versus outer bay fisheries, were compared using Kruskal-Wallis (omnibus) and Mann-
Whitney tests ( post hoc ). Mean catch and the mean number of morbid lake trout and walleye per lift were calculated for each month of the sampling season.
OBJECTIVE 2 – Environmental and fishing practice variables
34 I fit generalized linear models (GLMs) to evaluate the importance of varying environmental conditions and fishing practices in determining the magnitude of walleye, lake trout and sub-legal lake whitefish bycatch and the probability of walleye and lake trout morbidity. Data from the two operations was pooled as one of the goals of the analysis was to determine specifically what operational differences between the two operations most controlled bycatch and morbidity. Generalized linear models allow for nonlinear relationships between the independent variables (e.g., surface water temperature, soak time) and the dependent variable
(i.e., quantity of bycatch or morbid fish per lift). These models also accommodate the non- normal distribution of the dependent variable (Ward and Myers 2005) and overcome the basic problem that transforming the data to make it linear also changes the variance (Bolker 2008). As noted, net catch data often exhibit high variability and over-dispersion (Clark 1974, Power and
Moser 1999, Ward and Myers 2005, Pradhan and Leung 2006, Claramunt et al. 2009), thus
GLMs were used because they have less restrictive distributional assumptions than ordinary linear models. Bycatch or the number of morbid fish per lift was the dependent variable and environmental conditions or fishing practices were independent variables in each model. The two most common GLMs are Poisson regression for count data (e.g., net catch rates) and logistic regression for survival data (Bolker 2008).
For examining the effect of different environmental conditions and fishing practices on the magnitude of lake trout, walleye, and sub-legal lake whitefish caught (bycatch), I fit GLMs assuming a negative binomial distribution. Literature on the analysis of net catch data indicate that the negative binomial is often a reasonable probability distribution to use for analysis of catch data as these data often exhibit over-dispersion (Clark 1974, Power and Moser 1999, Ward and Myers 2005, Pradhan and Leung 2006, Claramunt et al. 2009). Additionally, preliminary
35 analysis that also involved testing the Poisson and geometric distributions indicated that assuming the negative binomial probability distribution provided the best fit to the observed data.
The negative binomial probability distribution can be expressed in several different forms. The form I used is given by Bolker (2008) in which the probability P of catching x number of lake trout or walleye is assumed to follow a negative binomial distribution with mean catch µ:
k x ()k + x − !1 k u P()x : k : = ⋅ ⋅ ()k − !1 x! k + k + where x is the random variable, k is the over-dispersion (or shape) parameter describing the underlying heterogeneity, and u is the expected mean number of counts (i.e., mean catch). It should be noted that a smaller k means more variance. As k becomes large, the variance approaches the mean and the distribution approaches the Poisson distribution (Bolker 2008).
I used the glm.nb function within the MASS package in R Statistical Package 2.9.2
(hereafter referred to as R; R Development Core Team 2009) for the GLM analysis. I evaluated the importance and effect of three independent variables: trap net depth (m) (Depth ), soak time
(days) (Soak ), and time of year (Julian day) (Day ). These variables were considered because they likely influence bycatch and because I wanted a model consisting of variables that can actively be controlled by fishers or managers when developing strategies to reduce bycatch.
These variables were also selected in order to avoid multicollinearity. I performed stepwise regression analysis to determine the best fitting model and evaluated relative model fit with or without variables included using Akaike’s Information Criterion (AIC). Lower AIC values indicated better model fit and differences in AIC value > 4 showed significantly different model fits (Burnham and Anderson 2002). Once I determined the best fitting model, I then removed or added individual variables to determine the effect each variable had on model fit. The difference
36 in AIC value from the best fitting model, once a variable is added or removed, can be considered an indicator of the relative importance of that variable in the model (Minami et al. 2007).
Alternatively, the difference in AIC value from the best fitting model once a variable not included in the best fitting model is added can indicate the degree to which that variable detracted from the best model’s fit. Overall goodness of fit for the best model was evaluated by comparing the AIC value from the best model to the null model (i.e., the model that did not include any variables). I then constructed partial dependence plots to predict the incidence of walleye or lake trout bycatch as a function of an individual variable (e.g., the number of walleye caught based on trap net depth). This analysis was meant to graphically depict how one variable may influence bycatch rates if all other variables are held constant. I held all variables, except the variable of interest, at their mean values. I then predicted bycatch, based on the best model’s parameters, over the observed range of the variable of interest. Observed bycatch rates were also graphically displayed in order to illustrate model fit as a function of individual variables.
Logistic regression is often used for survival data (Bolker 2008) and was applicable to evaluate the importance and effect of different predictors on bycatch morbidity for walleye in the inner bay fishery and lake trout in the outer bay fishery. Logistic regression provides a method for modeling a binary response variable (i.e., morbidity or survival of bycatch) as a function of one or more explanatory variables (i.e., varying environmental conditions or fishing practices)
(Bewick et al. 2005). I used the glm function in the stats package in R (R Development Core
Team 2009) for this purpose. I evaluated the importance and effect of the following independent variables for lake trout in the outer bay: Surface water temperature ( oC) (Water ), wave height (m)
(Wave ), soak time (days) (Soak ), trap net depth (m) (Depth ), time of day (Time ), and target catch
(kgs) (Catch ). For walleye caught in the inner bay, I evaluated the importance and effect of the
37 following independent variables: Surface water temperature ( oC), wave height (m), soak time
(days), trap net depth (m), time of day, and sort time (minutes) (Sort ). Target catch by lift was not available for the inner bay. Therefore, sort time was used as a proxy for target catch as the time it takes to sort through the catch is dependent on total catch (Figure 5). The best model was determined using stepwise regression analysis and relative model fit with or without a predictor variable was evaluated, as it was for GLM analysis of walleye and lake trout bycatch, with AIC. Predicted probabilities were calculated to determine the effect individual variables have on walleye or lake trout morbidity (e.g., the probability of morbidity based on trap net depth), (Scheer et al. 2009). This analysis was meant to visually depict how one variable influenced the probability of morbidity if the effect of all other variables was held constant. For this purpose, I held all variables except the variable of interest at their mean value. I then predicted the probability of morbidity based on the best fitting model’s parameters over the observed range of the variables of interest. Binned observed probabilities of morbidity, in which the number of morbid walleye or lake trout observed over a specified range was divided by the total number caught over that range, were also graphically displayed in order to illustrate model fit for each variable.
OBJECTIVE 3 – Estimation of seasonal totals
To determine seasonal totals of bycatch and bycatch morbidity in the trap net fishery, I employed one of two methods: 1) I statistically related the observed number of incidentally caught or morbid fish to target catch rates. This assumed that high target catches would lead to high bycatch rates. I aimed to determine if there was a significant relationship between bycatch, or the number of morbid walleye or lake trout, and target catch. Target species considered in the inner bay were yellow perch and channel catfish whereas the target species considered in the
38 outer bay was lake whitefish. I performed a simple linear regression relating total bycatch per day, or the number of morbid fish per day, to target catch per day reported by commercial fishers to MDNR. If there was a significant linear relationship between the dependent and independent variable (i.e., p < 0.05), this linear model was used to extrapolate bycatch and morbid fish observations to the entire fishing season based on daily harvest for unobserved trap net lifts reported to MDNR. I established 95% confidence intervals for year and monthly estimates based on regression parameter variance. 2) Alternatively, I multiplied the mean number of incidentally caught or morbid walleye or lake trout observed per lift by month by the total number of lifts that occurred in that month as reported to MDNR. This method is limited to calculations within the fishing season I observed. This method may also introduce bias into the estimates as different fisher’s catch rates varied, thus their bycatch rates, may vary substantially. I then compared my seasonal estimates to recreational harvest and overall stock size, both of which were estimated by
MDNR.
RESULTS
OBJECTIVE 1 – Fish counts
Over the 67 lifts observed in the inner bay at least 16,000 fish were discarded compared to 3,700 fish discarded (over 91 lifts observed) in the outer bay (Table 2).
Lake Trout – Significantly more lake trout were caught in the outer bay fishery as opposed to the inner bay (Mann Whitney test, P < 0.001). I observed only 15 lake trout caught in the inner bay fishery (N = 67 lifts) but 3,582 in the outer bay over all lifts (N = 91 lifts). I observed 1,225 morbid lake trout in the outer bay (N = 89 lifts) but only three morbid lake trout in the inner bay
(N = 55 lifts). Low lake trout catch rates in the inner bay preclude further analysis in this area.
39 In the outer bay, over all months sampled, I observed an average of 39 lake trout caught per lift. Lake trout bycatch was highest in August (x = 72/lift, Figure 6) and lowest in June (x =
3.1/lift). July had the second highest bycatch rates (x = 45.1/lift). June had the lowest bycatch rates when compared with all other months of the sampling season (Mann Whitney test, P <
0.001, Table 3). July and August appear to have higher bycatch rates than May (x = 18.8/lift), but rates were not significantly different (Mann Whitney test, P = 0.277, 0.476, respectively).
July and August did not have significantly different bycatch rates (Mann Whitney test, P =
0.3659). I observed an average of 13.8 morbid lake trout per lift. Overall, 39.2% of all lake trout caught were morbid. The number of morbid lake trout per lift was highest in August (x =
20.8/lift), followed by July (x = 20.1/lift). May had a significantly higher number of morbid lake trout per lift (x = 0.4/lift) than June (x = 0.1/lift) (Mann Whitney test, P = 0.046, Table 3), but significantly lower rates than July and August (Mann Whitney test, P = 0.024, 0.006, respectively). June also had significantly fewer morbid lake trout than July and August per lift
(Mann Whitney test, P < 0.001). The number of morbid lake trout observed per lift did not differ significantly between July and August (Mann Whitney test, P = 0.260).
Walleye – Overall, significantly more walleye were caught per trap net lift in the inner bay fishery as opposed to the outer bay (Mann Whitney test P < 0.001, Figure 7). Over all lifts in inner bay, I observed 8,532 walleye (N = 67 lifts), whereas I observed 109 in the outer bay (N =
91 lifts). I observed significantly more morbid walleye per lift in the inner bay fishery as opposed to the outer bay fishery (Mann Whitney test, P < 0.001, Figure 7). I observed 3,286 morbid walleye in the inner bay fishery (N = 55 lifts) and 99 morbid walleye in the outer bay fishery (N = 89 lifts).
40 In the inner bay, over all months sampled, I observed an average of 127 walleye caught per lift. Walleye bycatch was highest in May (x = 280.9/lift) and decreased each month to a low in August (x = 8.3/lift) (Table 3, Figure 7). The largest catch in one lift (1130 walleye) was observed in May and no more than 144 walleye were observed caught in a single lift in any other month. On average, I observed 60 morbid walleye per lift in the inner bay. This number corresponds to a 42% morbidity rate across all lifts. The number of morbid walleye per lift was highest in May (x = 130.9/lift) and decreased each month thereafter. May had a marginally higher number of morbid walleye per lift than June (x = 27.4/lift) (Mann Whitney test, P = 0.086,
Table 3), but did have significantly more morbid walleye per lift compared with July (x =
10.0/lift) (Mann Whitney test, P = 0.022). May also observed the highest number of morbid walleye in a single lift (448 walleye). Lifts in June had significantly more morbid walleye than
July in the inner bay fishery (Mann Whitney test, P = 0.002). I did not compare the number of morbid walleye from August in the inner bay fishery to other months because only two lifts were observed for morbid walleye during this month.
In the outer bay, over all months sampled, I observed an average of 1.23 walleye caught per lift in the outer bay. Walleye bycatch in the outer bay was highest in May (x = 5.0/lift,
Figure 7) and decreased each month to a low in August (x = 0.4/lift). June and July did not have significantly different bycatch rates (x = 1.3, 1.1/lift, respectively) (Mann Whitney test, P =
0.213, Table 3). The highest number of walleye caught in one lift occurred in May (15 walleye).
Most lifts in the outer bay fishery did not contain walleye (N = 55). Over all months sampled, I observed an average of 1.11 morbid walleye per lift in the outer bay. The number of morbid walleye observed per lift was highest in May (x = 4/lift, Figure 7), and decreased each month to a low in August (x = 0.4/lift). May had significantly more morbid walleye per lift than July (x =
41 1.1/lift) and August (x = 0.4/lift) (Mann Whitney test, P = 0.046, 0.004, respectively), though not significantly more than June (x = 1.2/lift) (Mann Whitney test, P = 0.123). June and July did not have significantly different numbers of morbid walleye per lift in the outer bay (Mann Whitney test, P = 0.224), though both June and July had significantly more morbid walleye per lift than
August (Mann Whitney test, P = 0.014, 0.038, respectively, Table 3). The highest number of morbid walleye observed in one lift in the outer bay fishery occurred in May (n = 15). Almost all (91%) walleye caught in the outer bay were morbid upon release.
Lake whitefish – Overall, an average of 36.4 sub-legal lake whitefish per lift were discarded in the outer bay. The number of discarded lake whitefish was highest in July (x = 49.04/lift) and lowest in August (x = 21.65). However, lake whitefish discard rates did not differ significantly among months (Kruskal-Wallis test, P = 0.11).
Other bycatch – Other incidentally caught species are listed in Table 2 for both the inner and outer bay fishery. The most numerous non-target species discarded in the inner bay, other than walleye, was freshwater drum (n = 3,363), and white sucker (n = 3,154). I also observed two double-crested cormorants and three common loons captured in the inner bay. The most numerous incidentally caught species outside of lake trout and walleye in the outer bay fishery was burbot (n = 21). I also observed 2,036 discarded sub-legal lake whitefish in the outer bay fishery over 56 lifts.
OBJECTIVE 2 – Environmental and fishing practice variables
Lake trout – Soak time and Julian day were the most important variables determining lake trout catch because the model that included these variables best fit the observed lake trout catch data from the outer bay (Table 4; Soak = 0.22, Day = 0.02, intercept = -2.32). Lake trout bycatch increased with soak time and Julian day (Figure 8). Soak time was the most important variable
42 determining lake trout bycatch ( AIC = 31, Table 4) and Julian day was the second most important ( AIC = 17). Trap net depth did not influence lake trout bycatch rates ( AIC = < 1).
Surface water temperature was the most important variable determining lake trout morbidity in the outer bay ( AIC = 301, Table 5), but the model that included all variables considered (i.e., surface water temperature, target catch, soak time, trap net depth, time of day, and wave height) best predicted lake trout morbidity (Table 5; Water = 0.54, Catch = < 0.001,
Soak = 0.07, Depth = -0.09, Time = 0.002, Wave = 0.8, intercept = -10.35). Lake trout morbidity increased with surface water temperature and target catch in the outer bay (Figure 9).
Target catch was the second most important variable determining morbidity( AIC = 55).
Additionally, lake trout morbidity was positively related to soak time ( AIC = 42), time of day
( AIC = 36) and wave height ( AIC = 22) but negatively related to trap net depth ( AIC = 41).
Walleye – Julian day and soak time were the most important variables determining walleye catch in the inner bay as the model that included these variables best fit the observed walleye catch data (Table 6; Day = -0.03, Soak = 0.04, intercept = 9.32). Walleye bycatch in the inner bay was negatively related to Julian day but positively related to soak time (Figure 10). Julian day was the most important variable determining walleye bycatch ( AIC = 37, Table 6) and soak time was the second most important ( AIC = 10). Trap net depth was not significant a significant predictor of bycatch rates ( AIC = 1). As determined through stepwise logistic regression analysis, the model that included trap net depth, sort time, soak time and surface water temperature best predicted walleye morbidity in the inner bay (Table 7; Depth = 0.56, Sort =
0.02, Soak = -0.02, Water = 0.05, intercept = -5.43). Walleye morbidity in the inner bay increased with trap net depth and sort time (Figure 11). Trap net depth was the most important variable determining walleye morbidity (Table 7, AIC = 249) and sort time was second most
43 important ( AIC = 81). Additionally, walleye morbidity decreased with soak time ( AIC = 14) and increased with surface water temperature ( AIC = 4), though these variables were not as important as either depth or sort time.
The model that included Julian day and trap net depth best fit the observed walleye catch data from the outer bay (Table 8; Day = -0.02, Depth = -0.19, intercept = 9.31). Walleye catch rates in the outer bay were negatively related to Julian day and trap net depth (Figure 12). Julian day was the most important variable determining walleye bycatch (Table 8, AIC = 21) and trap net depth was the second most important ( AIC = 5). Soak time was not an important variable
( AIC = < 1) determining walleye bycatch per lift in outer bay trap nets. Logistic regression analysis was not conducted to determine which factors most influence the proportion of morbid walleye in outer bay trap net lifts because of the high frequency of visible barotrauma.
Lake whitefish – The model that included Julian day and trap soak time best fit the observed sub- legal lake whitefish catch data from the outer bay (Table 9; Day = -0.01, Depth = 0.10, intercept
= 5.44). Discarding of lake whitefish in the outer bay was positively related to soak time and negatively related to Julian day (Figure 13). Soak time was the most important variable determining lake whitefish discards ( AIC = 13, Table 9) and Julian day was the second most important ( AIC = 7). Trap net depth was not an important variable determining lake whitefish discards ( AIC = 1).
OBJECTIVE 3 – Seasonal totals
2 Lake trout – There was not a significant relationship between lake trout bycatch (p = 0.27, r =
2 0.05, Figure 14), or the number of morbid lake trout (p = 0.10, r = 0.10), observed per day in the outer bay fishery and daily harvest of lake whitefish. Therefore, I used the second method described under Objective 3 of the methods section to estimate seasonal totals of number of
44 caught and morbid lake trout. Throughout the sampling season, I estimated that just over 9,110 lake trout were caught and that 2,980 of those were morbid (Table 8). Incidental catch of lake trout peaked in August (4540) with the number morbid peaking in July (1310). June had the fewest number caught (169) and morbid (7) lake trout.
Walleye – In the inner bay, there was a significant linear relationship between the number of walleye caught per day and the daily combined yellow perch and channel catfish harvest (p <
2 0.001, r = 0.95, Figure 15). Therefore, I used the first method outlined under Objective 3 in the methods and estimated seasonal bycatch totals based on reported daily harvest of yellow perch and channel catfish. The highest bycatch rates were estimated to occur in April and October when catch of yellow perch and channel catfish was highest (Figure 16). Total estimated walleye bycatch for the 2010 inner bay trap net fishery was just over 212,400 individuals with a
95% confidence interval of [122000, 327000]. Estimated walleye bycatch for my sampling season was just over 51,200 individuals with 95% confidence interval [21800, 90600].
There was also significant linear relationship between the number of morbid walleye observed per day in the inner bay and the combined daily yellow perch and channel catfish
2 harvest (p = 0.002, r = 0.80, Figure 15). Therefore, I used the first method and estimated seasonal totals of morbid walleye based on reported daily harvest of yellow perch and channel catfish. The highest number of morbid walleye was estimated to occur in April and October when yellow perch and channel catfish harvest was highest (Figure 16). The total estimated number of morbid walleye for the 2010 inner bay trap net fishery was just under 102,000 individuals with a 95% confidence interval of [26900, 253000]. The estimated number of morbid walleye for my sampling season was just under 23,500 walleyes with a 95% confidence interval [3630, 75600].
45 In the outer bay, there was a marginally significant negative linear relationship between
2 2 walleye bycatch (p = 0.06, r = 0.13, Figure 17), or the number of morbid walleye (p = 0.05, r =
0.14), observed per day in the outer bay fishery and daily harvest of lake whitefish. Therefore, I used the second method described under Objective 3 in the methods section to estimate seasonal totals of the number caught and morbid walleye. The highest number of incidentally caught and morbid walleye was estimated to occur in May (190 and 152 fish, respectively, Table 10).
Estimated seasonal totals were 379 incidentally caught and 330 morbid walleye.
Lake whitefish – There was a significant linear relationship between target catch and discarding
2 of lake whitefish by day (p = 0.004, r = 0.41, Figure 18). Therefore, I used the first method described to estimate the total number of lake whitefish discarded for the 2010 fishing season in outer Saginaw Bay. Total estimated lake whitefish discards was just over 10,200 with a 95% confidence interval of [1860, 19800].
DISCUSSION
This study provides catch and baseline mortality estimates of non-target species for the
Saginaw Bay commercial trap net fishery. The levels of walleye catch and mortality observed within inner Saginaw Bay were higher than previously observed in the Great Lakes. Though mortality rates were highly variable, as many as 448 morbid walleye were observed in one lift.
The levels of lake trout catch were within the range observed in previous studies, but mortality was higher than has been previously observed (Mark Ebener, CORA, unpublished data no date,
Smith 1988, Schorfhaar and Peck 1993, Johnson et al. 2004a). This analysis also indicates factors that most influence catch and mortality of non-target species within the bay; time of year and soak time most influenced catch rates whereas surface water temperature, trap net depth, and magnitude of target catch most influenced bycatch morbidity. As evidenced by this and other
46 studies that evaluate the Great Lakes trap net fisheries (Mark Ebener, CORA, unpublished data,
Eshenroder 1980, Schneeberger et al. 1982, Smith 1988, Rybicki and Schneeberger 1990,
Schorfhaar and Peck 1993, Gallinat et al. 1997, Peck 1997, Peeters 2001, Johnson et al. 2004a), the variable nature of bycatch abundance and bycatch mortality precludes generalizations across all regions and years. This highlights the need for comprehensive bycatch monitoring throughout the Great Lakes.
FISH COUNTS
Lake trout – The most abundant non-target species caught in outer Saginaw Bay was lake trout
(Table 2). This result was expected based on other studies from the Great Lakes lake whitefish trap net fishery and the fish community of Lake Huron’s deeper, colder waters (e.g., (Eshenroder
1980, Schorfhaar and Peck 1993, DesJardine et al. 1995, Johnson et al. 2004a). On average, I observed 39.0 lake trout per lift in the outer bay. Lake trout catches in previous studies have ranged as high as an average of 88.0 per lift in Lake Michigan (Smith 1988), but as low as 7.2 per lift in Lake Superior (Schorfhaar and Peck 1993). However, in Lake Huron, Johnson et al.
(2004a) observed an average of only 14.7 lake trout caught per lift. Similarly, catch rates from outer Saginaw Bay more than doubled those observed from 2004 to 2009 in the 1836 Treaty
Waters of Lake Huron. CORA biologists observed an average of 18.2 lake trout caught per lift during these years (Mark Ebener, CORA, unpublished data). Therefore, the lake trout catch rates observed in Saginaw Bay were higher than recent observations from Lake Huron, but within the range that has been observed from studies in other Great Lakes.
The overall lake trout morbidity rate observed in outer Saginaw Bay (13.8/lift) was higher than has been observed in previous studies (Schneeberger et al. 1982, Smith 1988,
Schorfhaar and Peck 1993, Johnson et al. 2004a) and in recent observations from the 1836
47 Treaty waters of Lake Huron (Mark Ebener, CORA, unpublished data). No other studies have observed overall lake trout mortality higher than 5.5 per lift (Smith 1988). Schorfhaar and Peck
(1993) observed less than one dead lake trout per lift over all months sampled. Similarly, CORA biologists rarely observe lake trout mortality and assume 0.5 to 0.75 lake trout die per lift (Mark
Ebener, CORA, personal communication). Johnson et al. (2004a) estimated that just over 4 lake trout died per lift from May through August in another Michigan state-licensed commercial fishery. Overall lake trout mortality from this study was higher than has been previously observed in Great Lakes trap net fisheries.
The pattern of monthly variation in lake trout morbidity observed, with the highest morbidity rates observed in July and August, was consistent with observations from other trap net fisheries in the Great Lakes. For example, Smith (1988) observed 17 dead lake trout per lift in August of 1985, and lower rates in other months. Johnson et al. (2004a) also observed the highest mortality rates in late summer, with 5.5 dead in July and 4.4 in August. However, the magnitude of mortality observed by Smith (1988) and Johnson et al. (2004a) was much lower than was observed in outer Saginaw Bay in 2010.
Walleye – The most abundant bycatch species in the inner bay fishery was walleye (Table 2).
High walleye catch rates were expected based on the rapid increase in walleye reproductive success and adult abundance that have occurred in Saginaw Bay since 2003 (Fielder et al. 2007,
2010). In addition to the high walleye catch rates, no other study has observed mortality of walleye, or any other non-target species, in such large numbers (e.g., Mark Ebener, CORA, unpublished data, Smith 1988, Schorfhaar and Peck 1993, Johnson et al. 2004a). However, prior to this research, no study had evaluated walleye mortality in Great Lakes trap nets targeting cool to warm water species, precluding meaningful comparison.
48 Lake whitefish – Over all lifts observed, commercial fishers discarded an average of 36.4 sub- legal lake whitefish per lift in the outer bay. This is substantially lower than discard rates observed by Copes and McComb (1992) who observed an average of 110 discarded lake whitefish per lift. However, discard rates from this study in outer Saginaw Bay were higher than those observed by Schorfhaar and Peck (1993) who observed an average of 12.2 per lift and
Smith (1988) who rarely observed sub-legal lake whitefish in trap nets. Smith (1988) suspected the sub-legal lake whitefish were either not in the areas of operation or they slipped through the large mesh pot when it was lifted. Discard rates in Saginaw Bay tripled those recently observed in 1836 Treaty Waters of Lake Huron where an average of 12.9 sub-legal lake whitefish were observed per lift from 2004 to 2009 (Mark Ebener, CORA, unpublished data).
Overall, lake trout and lake whitefish discard rates were within the range that has been observed in other studies focused on bycatch in Great Lakes trap net fisheries, but the magnitude of walleye bycatch was unprecedented (Eshenroder 1980, Schneeberger et al. 1982, Smith 1988,
Rybicki and Schneeberger 1990, Schorfhaar and Peck 1993, Gallinat et al. 1997, Peck 1997,
Peeters 2001, Johnson et al. 2004a). Also, the magnitude and percent morbidity for both lake trout and walleye in the Saginaw Bay trap net fishery was higher than has been previously documented in the Great Lakes.
ENVIRONMENTAL AND FISHING PRACTICE VARIABLES
It is beyond the scope of this study to define acceptable levels of bycatch or bycatch mortality. However, if Saginaw Bay fishery managers conclude that the level of bycatch and bycatch mortality are unacceptable, they may implement mitigation strategies to reduce these rates. This may be accomplished by reducing fishery effort or by reducing bycatch per unit of fishery effort (Hall 1996). Reducing bycatch or bycatch mortality per unit effort would require
49 an understanding of the factors that influence those rates. Objective 2 addresses which factors most influenced bycatch and bycatch mortality rates in the Saginaw Bay trap net fishery.
Therefore, this analysis can indicate ways in which bycatch and bycatch mortality may be reduced. These factors may also explain some of the differences observed between Saginaw Bay and other Great Lakes trap net fisheries.
Julian day – Time of year was the most important variable determining walleye bycatch (Table
6) and the second most important factor determining lake trout bycatch (Table 4) and the number of discarded lake whitefish (Table 9). I was able to sample from May through August whereas other studies have considered bycatch rates in other months of the fishing season as well. As this study and others have shown (e.g., (Hamley and Howley 1985)), bycatch rates can exhibit seasonal variation. Therefore, this difference could account for differences observed in overall bycatch rates between this and other studies in Lake Huron (Mark Ebener, CORA, unpublished data, Johnson et al. 2004a). Though overall bycatch in this study differed from others, monthly variation was observed and was consistent with observations from other trap net fisheries in the
Great Lakes. For example, I observed average lake trout catch rates to be highest at the end of the sampling season with 72 per lift in August and 45 per lift in July. In the 1836 Treaty Waters of Lake Huron, the highest lake trout bycatch rates were observed in August with an average of
34 observed per lift (Mark Ebener, CORA, unpublished data). Johnson et al. (2004a) also observed the highest lake trout bycatch rates during the late summer months with averages of
62.5 caught per lift in July and 48.6 caught per lift in August. Therefore, overall, lake trout catch rates in this study differed from others, but the pattern of monthly variation was similar (i.e., July and August had highest lake trout catch rates).
50 Walleye catch rates were highest in the beginning of the sampling season in both the inner and outer bay, though catch rates in the inner bay were far higher than those in the outer bay (Figure 7). The decrease in walleye bycatch in the inner bay as the sampling season progressed tracks walleye life history dynamics as adults generally move into deeper and cooler water during the summer months (Colby et al. 1979). For example, in Lake Erie, the unusually warm summer of 2005 produced exceptional walleye fisheries in the cool east basin, but poor fisheries in the warmer west and central basins (Roseman et al. 2012). In Saginaw Bay, Fielder and Thomas (2006) noted that walleye from Saginaw Bay often move up and down the Michigan coast line of Lake Huron’s main basin, particularly during the summer months. Inner bay trap net catch rates likely decreased after May because the nets were not fished in depths greater than
10 meters and most walleye had likely moved beyond this depth after May. I expected that these fish would then be caught in higher numbers in the deeper and cooler waters of the outer bay.
However, walleye bycatch rates in the outer bay fishery also decreased with each month. Outer bay trap nets were set between 20 and 40 meter depths. Thus, it is possible that most walleye did not move into such deep depths during the sampling season. Similarly, Fielder and Thomas
(2006) analyzed Saginaw Bay walleye movement, but did not show any evidence that walleye ventured into the deeper waters of outer Saginaw Bay or the central main basin.
In spite of the Julian day trends, catch rates in this study varied significantly within months. Walleye catches in May ranged as high as 1130 but as low as 12. Lake trout catches in
July ranged as high as 253, but as low as 2. This large variation was expected as these species tend to aggregate because of temperature preferences or reproduction (Colby et al. 1979, Dawson et al. 1997) and aggregation often results in high variability in net catches (Clark 1974, Minami et al. 2007).
51 Soak time – Soak time affected both catch and morbidity rates. Soak time was the most important factor determining lake trout (Table 4) bycatch and lake whitefish discard rates (Table
9) and the second most important factor determining walleye bycatch rates (Table 6). With some exceptions, longer soak times led to higher bycatch rates. However, the effect of soak time on trap net catches observed in other studies is inconsistent. Grinstead (1970) observed that increases in soak time led to increased total catch, but progressively fewer fish were caught by the net on each succeeding day. Hamley and Howley (1985) observed that catches increased approximately in proportion to soak times of 1 to 3 days, but not beyond. They noted that many fish escape trap nets and that the likelihood of escape increases with longer soak times. Smith
(1988), however, did not find that soak time was a significant factor explaining catch rates. He attributed this to a lack of variation in soak times and the potential for escape.
For lake trout in the outer bay, morbidity increased with soak time (Figure 9). This was expected because the longer fish are trapped in a net, the more stressors they would likely be subject to. However, this also assumes that fish are captured immediately and do not escape.
Additionally, as this study and others (Grinstead 1970, Hamley and Howley 1985) have indicated, longer soak times can lead to increased catch. Therefore, longer soak times likely led to increased crowding in the net and increased physical trauma (Breder 1976, Schneeberger et al.
1982).
The effect of soak time on walleye morbidity in the inner bay, on the other hand, was inconclusive. Soak time was the second least important factor determining walleye morbidity
(Table 7) and longer soak times led to slightly decreased morbidity rates (Figure 11). Similarly,
Grinstead (1970) found that mortality of fish caught in trap nets did not increase significantly with soak time. Smith (1988) also found that soak time did not influence mortality of fish caught
52 in Lake Michigan commercial trap nets. Further research is necessary to determine the effect, if any, of soak time on walleye survival in Great Lakes trap nets.
It is important to note that prior to this research, soak times longer than 12 days had never been documented for Great Lakes trap net fisheries (Mark Ebener, CORA, unpublished data,
Grinstead 1970, Hamley and Howley 1985, Smith 1988, Johnson et al. 2004a). Specifically, many of the soak times observed in this study were longer than was observed by Johnson et al.
(2004a) in Lake Huron and longer than has been recently observed in trap net fisheries in the
1836 Treaty waters of northern Lake Huron (Mark Ebener, CORA, unpublished data) (Figure
19). While soak times in this study ranged from 2 to 42 days, these other Lake Huron based studies derived conclusions from observed soak times no longer than ten days. In addition, soak times recorded as part of this research may not be representative of other operations. Commercial fishers were not required to report soak time in 2010. Therefore, it is unclear how soak times collected in my study compare to other operations in Saginaw Bay or soak times from other years.
Soak times may be extended for several reasons. Weather plays a significant role, as lifting trap nets in rough weather is both dangerous and can result in ineffective gear because anchor lines can detach. As this study demonstrates, extended soak times, that may result from poor weather, can lead to higher catch rates (Figures 8, 10, 13). Higher catch rates, in turn, can result in extended on-deck processing (sort) time (Figure 5) which precludes opportunities to lift other nets. Thus, extended soak times for some nets can occur with limited days available to lift trap nets on Saginaw Bay.
Trap net depth – Trap net depth was the most important factor determining walleye morbidity in the inner bay with morbidity increasing with depth (Figure 11, Table 7). This increased
53 probability of morbidity with depth likely was due to barotrauma effects. Several studies have similarly concluded that the probability of fish mortality from barotrauma increases with depth of capture (Feathers and Knable 1983, Gitschlag and Renaud 1994, St. John and Syers 2005,
Schreer et al. 2009). For example, Feather and Knable (1983) observed that mortality of largemouth bass brought from 9 m was approximately 25% but was almost 50% for fish brought from 27 m. Similarly, Schreer et al. (2009) found that barotrauma effects on physoclistous angler caught fish (i.e., smallmouth bass, walleye, and yellow perch) increased with depth. For walleye, they observed barotrauma to occur in depths as shallow as 6.1 m, and that at 9.5 m, incidence of barotrauma reached 50%. The majority of the trap net lifts that I observed in inner
Saginaw Bay (93%) occurred in the 6 – 9.5 m depth range with the probability of morbidity increasing over this range. In the outer bay, I observed noticeable evidence of barotrauma on the majority of fish (e.g., bloated swim bladder, stomach eversion, bulging eyes, hemorrhaging, and the inability to maintain equilibrium). I observed 109 walleye caught over the 91 lifts observed in the outer bay, and 91% of those were morbid upon release. On the other hand, Johnson et al.
(2004a) observed a 15% mortality rate for walleye released from Great Lakes trap nets targeting lake whitefish. Scheer et al. (2009) found that 90% of physoclistous species (i.e., walleye, yellow perch, and smallmouth bass) caught by anglers in 15-17.5 meter depths were subject to barotrauma. All outer bay trap nets in Saginaw Bay were set in 20 to 40 m depths and therefore, all walleye caught were subject to significant barotrauma affects. The much higher morbidity observed in outer Saginaw Bay when compared with Johnson et al. (2004a) can be attributed to differences in depth as their study only observed one walleye caught in water deeper than 20 meters.
54 In contrast to walleye, my analysis indicated that lake trout morbidity decreased with increasing depth. Lake trout have physostomous swim bladders. Therefore, I did not expect depth to influence lake trout morbidity. More investigation is needed to determine the mechanism of this result and whether this holds true in other areas or years.
Surface water temperature – For both lake trout in the outer bay and walleye in the inner bay morbidity increased with surface water temperature (Figures 9 and 11). For lake trout, surface water temperature was the most important factor determining morbidity (Table 5). For walleye, magnitude of target catch and depth were most influential to morbidity and surface water temperature was of secondary importance (Table 7). Consistent with these results, other studies have shown that increased surface water temperature decreases survival in captured salmonids
(Klein 1965, Dotson 1982, Nuhfer and Alexander 1992) and angler caught walleye (Hoffman et al. 1996, Graeb et al. 2005, Reeves and Bruesewitz 2007, Schramm et al. 2010). Temperatures greater than 15.5 oC are generally unsuitable for lake trout survival (Maclean et al. 1990). In July and August, the outer bay surface water temperatures were greater than 15.5 oC on all lifts observed and greater than 20 oC on 97% of the lifts observed. Consequently, prolonged exposure to high temperatures likely increased lake trout morbidity. Similarly, in observations of tournament caught walleye, Schramm et al. (2010) found that walleye mortality was positively related to measures of water temperature and that mortality increased rapidly within the temperature range of 15-20 oC.
Target catch – For both walleye in the inner bay and lake trout in the outer bay, target catch was the second most important factor determining morbidity (Tables 5 and 7) with higher target catches leading to increased morbidity (Figures 9 and 11). Higher target catches likely resulted in increased morbidity due to over-crowding and longer sort times. Previous studies indicate that
55 increased densities of fish in a trap net associated with high catch rates can result in more physical trauma and decreased survival (Breder 1976, Schneeberger et al. 1982). For example,
Copes and McComb (1992) observed that crowding at the surface of the water during the lifting process was the primary factor influencing whether sub-legal lake whitefish were morbid following release. Increased target catches also result in longer sort times (Figure 5). Therefore, fish are subject to the numerous stressors of a trap net lift (e.g., air exposure, high surface water temperature, increased crowding and associated trauma) for a longer period of time with high catches.
GLM analysis indicated that target catch was one of the more important factors determining bycatch morbidity. However, some exceptions existed. For example, the observed probability of morbidity for lake trout caught with lake whitefish catches ranging from 7,000 to
8,000 kilograms was lower than the observed probability of morbidity for lake trout caught in lifts with lake whitefish catches ranging from 2,000 to 6,000 kilograms (Figure 11). The inconsistency between target catch and lake trout morbidity is likely confounded by surface water temperature, the more important factor determining lake trout morbidity. The lifts with the largest catches had an average surface water temperature of 18.6oC. Alternatively, the lifts with catches ranging from 2,000 to 6,000 kilograms had an average surface water temperature of
21.2 oC. Therefore, for the lifts with the highest catches, surface water temperatures were likely low enough to counteract the effect target catch may have had on morbidity.
OBJECTIVE 3 – SEASONAL TOTALS
Simple linear regression indicated that walleye bycatch or bycatch morbidity and target catch (i.e., harvest of yellow perch and channel catfish) were tightly correlated (Figure 15).
There was a significant relationship between harvest of lake whitefish and lake whitefish
56 discards (Figure 18). Using this relationship, catch (for walleye and lake whitefish) and morbidity (for walleye) estimates were made for the entire fishing season, including months in which I was unable to sample. This estimation procedure assumes that the linear relationship between bycatch and target catch is maintained during months of the year in which I did not sample. This is a reasonable assumption because walleye and yellow perch occupy similar thermal strata in the warmer inshore waters of the Great Lakes (DesJardine et al. 1995).
However, estimates outside of my sampling season obtained using this method should be viewed as hypotheses testable by further study. Though the magnitude of bycatch and bycatch morbidity may change from year to year based on stochastic recruitment processes, variable fishing practices and environmental variability, the relative magnitude of bycatch and bycatch morbidity by month is likely to remain consistent across years.
Linear regression did not indicate a significant relationship between lake trout bycatch and morbidity and lake whitefish harvest (Figure 14). Therefore, seasonal lake trout estimates were based on monthly mean catches of observed lifts and the number of lifts reported to MDNR each month. Since monthly bycatch rates differed, this method did not allow bycatch estimation for months outside of my sampling season. Therefore, lake trout bycatch and morbidity estimates should be viewed as baseline estimates specific to my sampling season. Estimates for the entire fishing season would require more comprehensive monitoring during all months of the fishing season.
Walleye bycatch estimates for the 2010 inner bay trap net fishery totaled just over
212,000 individuals and the total estimated number of morbid walleye was just under 102,000 individuals. Total estimated walleye recreational harvest for 2010 in Saginaw Bay was close to
173,000 individuals (Tracy Kolb, MDNR, unpublished data). Therefore, based on my estimates,
57 the inner bay trap net fishery could account for the equivalent of 59% of the recreational fishery harvest for 2010. However, based on 95% confidence intervals derived from simple linear regression, trap net incidental harvest could amount to as low as 15% or as high as 135% of recreational harvest. In 2009, the Saginaw Bay walleye stock was estimated to total 4.48 million age-2+ fish (Dave Fielder, MDNR, unpublished data). Therefore, I estimate that incidental harvest represents 2.3% of the Saginaw Bay stock, but could be as low as 1.0% or as high as
5.6% based on 95% confidence intervals. These comparisons are informative, but they do not indicate the affect walleye bycatch mortality may have on the Saginaw Bay walleye stock.
These estimates should be input into Saginaw Bay walleye stock assessment models to determine the biological effect of bycatch mortality from the trap net fishery. This analysis is currently being conducting by MDNR biologists (Dave Fielder, MDNR, personal communication).
Lake trout bycatch morbidity from the outer bay during my sampling season was estimated to total 2,980 fish. Lake trout recreational harvest for MH-4, the management unit that contains Saginaw Bay, was estimated at 1,690 fish in 2010 (MDNR, unpublished data).
Therefore, based on my estimates, the outer bay trap net fishery incidentally kills nearly double the equivalent of recreational harvest. However, this assessment must be tempered by the low recreational effort for lake trout in this part of Lake Huron (MDNR, unpublished data). A more valuable comparison would be to estimate lake trout bycatch mortality as a function of total lake trout population in Saginaw Bay. However, lake trout population estimates specific to Saginaw
Bay are unavailable.
Morbidity estimates produced by this study are likely conservative. I did not account for sinking fish or delayed mortality. Though not quantified, some decomposing fish were observed sinking once released overboard. Additionally, delayed discard mortality has been shown to
58 occur as long as 30 days after initial release (Davis and Olla 2001). Fish may be subject to stressors that do not result in immediate mortality, but may eventually result in mortality from predation, physiological stress or disease (Davis 2002). Therefore, walleye and lake trout mortality could be much higher. Alternatively, mortality could be lower as all morbid fish may not die. Some may revive after the initial trauma associated with capture and release (Dotson et al. 2009), although this scenario is less likely than delayed mortality because of the numerous stressors, as outlined previously, fish are subject to at the surface of the water. As a result, these estimates are likely conservative as I did not account for all potential sources of mortality.
STUDY LIMITATIONS AND FUTURE RESEARCH
This analysis evaluates the magnitude, and factors that influence, bycatch rates and morbidity in the Saginaw Bay commercial trap net fishery. Multiple factors such as time of year, surface water temperature, trap net depth and soak time influence catch and mortality rates. The interaction of these factors complicates analysis and potential management action. Higher observer coverage is necessary for more accurate estimates of the magnitude and variability of bycatch in the fishery. Intensive study over the full fishing season for multiple years with multiple fishing operations would provide a more robust data set because bycatch rates are likely to exhibit inter- and intra-annual fluctuations due to differences in fishing practices and abundance of target and non-target species. For example, observations from months of the fishing season in which I was unable to sample (i.e., March through April, September through
December) would greatly increase accuracy of total bycatch estimates and overall understanding of trends. Higher observer coverage may also decrease the likelihood for observer bias as this would decrease the likelihood fishers would alter fishing practices to deceive observers.
Additionally, there are operations within the inner bay fishery that I was unable to observe.
59 These operations may have different fishing practices, and consequently different bycatch rates, from those I observed. Deroba and Bence (2011) found that in 1836 Treaty-ceded Waters, the most variability in commercial gill net catch per effort was explained by undefined differences between fishing license holders. Therefore, increased observer coverage of other fishing operations would prove useful.
Future research should attempt to quantify bycatch and bycatch mortality of undersized or undesirable target species. Due to logistical restraints, this was not possible during this study, with the exception of lake whitefish discards in the outer bay. From my qualitative observations, catch and morbidity of undersized or otherwise undesirable yellow perch appears to be particularly high, though this was not quantified. Discarded sub-legal lake whitefish mortality has been shown in other studies to be minimal (Smith 1988, Copes and McComb 1992,
Schorfhaar and Peck 1993, Johnson et al. 2004a, Ebener et al. 2008). For example, Copes and
McComb (1992) observed an average of 110 sub-legal lake whitefish caught over 31 commercial trap net lifts in Lake Michigan. However, on average, only 3.4 of those caught were floating and assumed dead following release. Survival of discarded sub-legal target species could be evaluated for the Saginaw Bay fishery as well.
Future research could also devise methods to account for sources of mortality not considered in this study (i.e., sinking fish and delayed mortality). More than one observer onboard each fishing vessel, for instance, would allow estimation of the number of sinking fish.
For estimation of delayed mortality, other studies have either constructed holding cages (Dotson et al. 2009), transported catch to laboratories (Johnson et al. 2004a) or evaluated tagged and recaptured fish (Kaimmer and Trumble 1998). Additionally, some floating or apparently morbid fish may recover. Analysis of what percentage of those fish that are morbid upon initial release
60 actually die would increase certainty of mortality estimates. Similar to analysis of delayed mortality, observations of apparently morbid fish contained in holding tanks could serve this purpose (Dotson et al. 2009). Future research should also collect data on biological parameters of all bycatch such as age, length, and sex. This information would be valuable as managers are implementing these data into age-structured population models.
I observed some multicollinearity between seemingly unrelated explanatory variables.
For example, in the inner bay, there was a significant relationship between soak time and trap net
2 depth (p = 0.02, r = 0.32). However, this was unavoidable given that observations were made under normal fishing conditions. In future research, experimental control in which biologists set their own nets to manipulate potentially influential factors (e.g., soak time, time of day) could test variables individually. Davis (2002) advocated for a combination of laboratory and field experiments to predict bycatch mortality as a function of environmental variables. This could eliminate multicollinearity, but could also decrease the validity of the analysis because observations would not be conducted under normal fishing conditions.
CONCLUSIONS AND MANAGEMENT IMPLICATIONS
This is the first analysis of bycatch and bycatch mortality in Saginaw Bay’s commercial trap net fishery. Walleye bycatch mortality in the inner bay and lake trout bycatch mortality in the outer bay were substantial when compared with recreational harvest. However, this study did not address the effects bycatch may have on the Saginaw Bay walleye or the southern Lake
Huron lake trout stocks. Bycatch estimates should be added into existing stock assessment models in order to determine the potential effect commercial bycatch may have on these populations. Sitar et al. (1999) fit an age-structured population model to assess lake trout stocks in southern Lake Huron and estimated total annual mortality from estimates of fishing
61 (recreational and commercial), sea lamprey (Petromyzon marinus ) and natural mortality.
However, they did not include commercial bycatch as a potential source of fishing mortality.
This could cause managers to over-estimate harvestable stock surpluses and, consequently, impede rehabilitation efforts.
More recently, fisheries managers in the Great Lakes have included estimates of discard mortality in stock assessments. For example, in the MH-2 management unit of 1836 Treaty
Waters, lake trout discard mortality estimates are based on the Johnson et al. (2004a) study which estimated a 12.2% mortality rate for trap net caught lake trout (Modeling Subcommittee,
Technical Fisheries Committee 2010). However, my study observed a much higher lake trout morbidity rate of 39.2% over all outer bay lifts observed. Similarly, in the inner bay, overall walleye morbidity was 42.0%. This level of mortality is unprecedented in Great Lakes trap nets.
Therefore, the assumed lake trout mortality rate from trap nets used in stock assessment models may not be valid for all locations or years.
This analysis also highlights some of the more influential factors (e.g., time of year, soak time, trap net depth, surface water temperature, target catch) determining bycatch and bycatch mortality. These factors should be considered when developing strategies to reduce bycatch.
Because time of year was consistently an important factor determining bycatch rates, seasonal effort restrictions may prove useful for reducing bycatch. However, the viability of the commercial industry must also be considered. For example, May yielded the highest walleye bycatch rates in inner Saginaw Bay. May also had the highest yield of yellow perch and channel catfish of all months sampled. Therefore, reducing commercial effort in May would greatly decrease fishery yields.
62 Soak time was also an important factor determining bycatch rates and lake trout morbidity. Given the unprecedented soak times observed in this study, limiting soak times may prove useful for reducing lake trout and walleye catch, and lake trout morbidity. Tribal fishers in
1836 Treaty Waters of Lake Huron are required by CORA to tend their nets. Nets cannot be left unutilized or unattended for 14 or more days in 1836 Treaty Waters (CORA 2009). A similar requirement may prove useful for reducing bycatch mortality in Saginaw Bay’s commercial fishery. However, similar to the potential problems associated with seasonal effort restrictions, managers should consider how practical soak time restrictions will be. Often times, long soak times are unavoidable as weather prevents lifting of trap nets. Additionally, enforcing soak time restrictions may be difficult. Therefore, mitigation strategies such as soak time or seasonal restrictions should be implemented that reduce bycatch and mortality, but these strategies should also consider the viability of the commercial fishery and also how practical they may be to enforce.
The Food and Agriculture Organization of the United Nations outlined an international
“Code of Conduct for Responsible Fisheries” which has been proffered by fisheries management agencies to reduce bycatch (Johnson et al. 2004b). Most notably, this report stated that to the extent practical, fisheries should attempt to “minimize the waste of catch of target and non-target species” and “develop selective, environmentally safe, and cost effective fishing gear and techniques” (Food and Agriculture Organization of the United Nations 1995). A higher proportion of fish can be released alive from trap nets when compared with gill nets (Johnson et al. 2004a, Ebener et al. 2008). Therefore, the transition from gill nets to trap nets in many Great
Lakes fisheries has greatly reduced bycatch mortality. However, as this study indicates, some bycatch mortality still occurs in trap nets. Even if further bycatch reduction strategies are
63 implemented, some bycatch and bycatch mortality will remain unavoidable because target and non-target species often occupy the same temporal and spatial strata. Given this reality and the socioeconomic implications of fishery discards, an evaluation of discard policy may be warranted. CORA permits trap net fishers to harvest up to 45.5 kilograms (100 lbs) of legal- sized lake trout per vessel per day (CORA 2009). A similar strategy may be applicable to the
Michigan state-licensed commercial fishery.
The biological, economic and food resource implications of fishery discards in the Great
Lakes, and specifically Saginaw Bay, have yet to be fully evaluated. As this study indicates, bycatch and bycatch mortality occurs in the Saginaw Bay trap net fishery. Based on the differences observed between this and other Great Lakes trap net bycatch studies, generalizations regarding the levels of bycatch or bycatch mortality should not be made among fisheries or years. Therefore, a greater understanding of the magnitude and species composition of bycatch and bycatch mortality in all Great Lakes fisheries should be a management priority. A more comprehensive bycatch monitoring program throughout the Great Lakes would serve this purpose. Ultimately, this would decrease uncertainty and help fishery management agencies achieve fish community objectives.
64
APPENDICES
65 TABLES
Table 1. Number of lifts observed onboard Saginaw Bay commercial trap net vessels May through August 2010.
May June July August Total lifts Inner bay 22 26 13 6 67 Outer bay 5 24 39 23 91
66 Table 2. Non-target species observed caught in the May through August 2010 Saginaw Bay trap net fishery (TNTC=Too numerous to count).
Number caught Common name Scientific name Inner bay Outer bay Walleye Sander vitreus 8532 109 Lake trout Salvelinus namaycush 15 3582 Freshwater drum Aplodinotus grunniens 3363 0 Largemouth bass Micropterus salmoides 1 0 Shorthead redhorse Moxostoma macrolopidotum 264 0 Longnose gar Lepisosteus osseus 1 0 Smallmouth bass Micropterus dolomieu 25 0 Quillback Carpiodes cyprinus 354 0 Burbot Lota lota 0 21 Rainbow trout Oncorhynchus mykiss 3 0 Chinook salmon Oncorhynchus tshawytscha 0 1 Black crappie Poxomis nigromaculatus 2 0 White sucker Catostomus commersonii 3154 1 Common carp Cyprinus carpio 119 0 Gizzard shad Dorosoma cepedianum 78 0 Whiteperch Morone americana TNTC 0 Bowfin Amia calva 6 0 Northern pike Esox lucius 71 0 Chain pickerel Esox niger 1 0 Double crested cormorant Phalacrocorax auritus 2 0 Common loon Gavia immer 3 0 N = 67 lifts N = 91 lifts
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.
Inner Bay Outer Bay Walleye Lake trout Walleye Month comparison Total Morbid Total Morbid Total Morbid May/June 0.009 0.086 <0.001 0.046 0.050 0.123 May/July <0.001 0.022 0.277 0.024 0.019 0.046 May/August <0.001 NA 0.476 0.006 0.002 0.004 June/July <0.001 0.002 <0.001 <0.001 0.213 0.224 June/August <0.001 NA <0.001 <0.001 0.012 0.014 July/August 0.019 NA 0.366 0.260 0.036 0.038
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).
Variables ± Variable AIC Difference in AIC from best model null w/o all 816.6 51.68 Soak, Day None 764.92 0 Soak, Day, Depth w/ Depth 764.23 0.69 Soak w/o Day 781.47 16.55 Day w/o Soak 795.5 30.58
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).
Variables ± Variable AIC Difference in AIC from best model null w/o all 4186 589.1 Water, Catch, Soak, Depth, Time, Wave None 3596.9 0 Water, Catch, Soak, Depth, Time w/o Wave 3619.1 22.2 Water, Catch, Soak, Depth, Wave w/o Time 3632.8 35.9 Water, Catch, Soak, Time, Wave w/o Depth 3637.6 40.7 Water, Catch, Depth, Time, Wave w/o Soak 3639.3 42.4 Water, Soak, Depth, Time, Wave w/o Catch 3651.9 55 Catch, Soak, Depth, Time, Wave w/o Water 3898.2 301.3
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).
Variables ± Variable AIC Difference in AIC from best model null w/o all 782.8 82.08 Day, Soak None 700.72 0 Day, Soak, Depth w/ Depth 701.89 1.17 Day w/o Soak 710.23 9.51 Soak w/o Day 737.91 37.19
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.
Variables ± Variable AIC Difference in AIC from best model null w/o all 10650 381 Depth, Sort, Soak, Water None 10269 0 Depth, Sort, Soak, Water, Time w/ Time 10270 1 Depth, Sort, Soak, Water, Wave w/ Wave 10270 1 Depth, Sort, Soak w/o Water 10273 4 Depth, Sort, Water w/o Soak 10283 14 Water, Soak, Depth w/o Sort 10350 81 Sort, Soak, Water w/o Depth 10518 249
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 the model. Day = Time of year (Julian day), Soak = Soak time (days), Depth = Trap net depth (meters).
Variables ± Variable AIC Difference in AIC from best model null w/o all 260.2 29.01 Day, Depth None 231.19 0 Day, Depth, Soak w/ Soak 232.04 0.85 Day w/o Depth 236.23 5.04 Depth w/o Day 252.6 21.41
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).
Variables ± Variable AIC Difference in AIC from best model null w/o all 518.7 18.83 Soak, Day None 499.87 0 Soak, Day, Depth w/ Depth 501.2 1.33 Soak w/o Day 506.54 6.67 Day w/o Soak 512.59 12.72
74 Table 10. Estimated bycatch and morbid individuals for the May through August 2010 Saginaw Bay trap net fishery based on onboard observations.
Outer Bay Lake trout Walleye Lake whitefish Catch Morbid Catch Morbid Discards May 714 15 190 152 1338 June 169 7 72 65 NA July 3694 1646 90 86 4021 August 4536 1311 27 27 1364 Totals 9113 2979 379 330 6723
75 FIGURES
Shore Deep Water
Lead Weights
Figure 1. Great Lakes gill net (Brenden et al. 2012).
76
Wooden Flagged Heart Float s Float Tunnel Anchor Buoy
Pipe Weight Pot Lead Anchor
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 per lift) 2 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 Bay trap net fishery (± 1 S.E.). The line represents the percent of the total lake trout caught that were morbid by month. The mean number of morbid lake trout can be inferred from total bycatch and proportion morbid values.
81
Figure 7. Mean number walleye observed per trap net lift by month in the 2010 inner (IB) and outer (OB) Saginaw Bay trap net fisheries with error bars (± 1 S.E). Lines represent the percent of the total walleye caught that were morbid by month for the inner (solid line) and outer (dashed line) bay fisheries. The mean number of morbid walleye can be deduced from total bycatch and proportion morbid values.
82
Figure 8. Partial dependence plots indicating predicted lake trout bycatch in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Soak time (days), b) Julian day.
83
Figure 9. Partial dependence plots indicating the predicted proportion of morbid lake trout in the outer Saginaw Bay trap net fishery from a logistic model fit as a function of variation in one variable while holding other variables at their mean: a) Surface water temperature ( oC), b) Target catch (kgs), c) Soak time (days), d) Trap net depth (m), e) Time of day, f) Wave height (m). Observed values are binned and represented by the closed circles. Predicted probabilities are represented by the line.
84
Figure 10. Partial dependence plots indicating predicted walleye bycatch in the inner Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Julian day, b) Soak time.
85
Figure 11. Partial dependence plots indicating the predicted probability of morbidity for walleye in the inner Saginaw Bay trap net fishery from a logistic model fit as a function of variation in one environmental or fishing practice variable while holding other variables at their mean: a) Trap net depth (m), b) Sort time (min), c) Soak time (days), and d) Surface water temperature (oC). Observed values are binned and represented by the closed circles. Predicted probabilities are represented by the line.
86
Figure 12. Partial dependence plots indicating predicted walleye bycatch in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Julian day, b) Trap net depth (m).
87
Figure 13. Partial dependence plots indicating predicted lake whitefish discards in the outer Saginaw Bay trap net fishery from a generalized linear model fit using the negative binomial probability distribution as a function of variation in one variable while holding the other variable at its mean: a) Soak time (days), b) Julian day.
88
2 Figure 14. Relationship between the total number of lake trout caught daily (p = 0.27, r = 0.05), 2 or number of morbid lake trout (p = 0.10, r = 0.10) and reported lake whitefish harvest (target catch) in the outer Saginaw Bay trap net fishery.
89
2 Figure 15. Relationship between the total number of walleye caught daily (p < 0.001, r = 0.95), 2 or number of morbid walleye (p = 0.002, r = 0.80), and reported daily harvest of yellow perch and channel catfish (Target catch) in the inner Saginaw Bay trap net fishery. Regression lines are denoted for total walleye caught with ── and for morbid walleye with -----.
90
Figure 16. Estimated number of caught or morbid walleye across all lifts for the 2010 inner Saginaw Bay trap net fishery. Estimates are based on daily reported harvest of yellow perch and channel catfish. Error bars represent regression based 95% confidence intervals.
91
2 Figure 17. Relationship between the total number of walleye caught daily (p = 0.06, r = 0.13), 2 or number of morbid walleye (p = 0.05, r = 0.14), and reported daily lake whitefish harvest (target catch) in the 2010 outer Saginaw Bay trap net fishery. Regression lines are denoted for total walleye caught with ── and for morbid walleye with -----.
92
Figure 18. Relationship between the total number of lake whitefish discarded daily and reported target catch (kilograms of lake whitefish per day) in the outer Saginaw Bay trap net fishery (p = 2 0.004, r = 0.41). Regression line is denoted with ── .
93
Figure 19. Box plots indicating soak time variation among trap net fisheries in outer and inner Saginaw Bay, 1836 Treaty Waters (CORA), and offshore Alpena, Michigan (Johnson et al. 2004a). The box represents the inter-quartile range (middle 50% of data), the upper boundary th th represents the 75 percentile, and the lower boundary represents the 25 percentile. The center line represents the median and the dots represent outliers in the data set.
94
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