Effects of capture depth on walleye hooking mortality during ice fishing

Abstract

Lyon CA, Davis JL, Fincel MJ, Chipps SR. 2022. Effects of capture depth on walleye hooking mortality during ice fishing. Lake Reserv Manage. 38:334–340.

Length-based regulations are a common tool used to limit fishing mortality by controlling the size of fish harvested. While such regulations are helpful in managing fish populations, mortality associated with catch-and-release fishing may negatively impact a fishery. We evaluated factors affecting hooking mortality of walleye (Sander vitreus) in 2 mainstem Missouri River reservoirs in South Dakota. Winter walleye hooking mortality was evaluated during the ice fishing season in February and March 2020. After capture, walleye (n = 55) were placed into holding pens for 12 to 72 h to monitor postrelease mortality. Hooking mortality was found to be 20% following angling. Capture depth, landing time, and time in pen were the most influential variables on probability of hooking mortality (p_m). We observed a sharp increase in p_m for walleye captured at depths from 10 to 12 m, where the probability of mortality for fish increased appreciably from 5 to 37%, respectively. Our findings indicate that hooking mortality during the ice fishing season can be substantial in lakes where walleye angling occurs at depths greater than 10 m.

Keywords:

• Hooking mortality
• ice fishing
• Sander vitreus
• South Dakota
• walleye

Length-based harvest regulations are a common tool that fisheries managers use to limit harvest mortality on preferred size classes of fish. However, due to size restrictions, the release of nonlegal fish can impact a fishery due to hooking mortality. Following implementation of length-based regulations in Alberta, Canada, percent of nonlegal walleye (Sander vitreus) killed as proportion of total yield increased from 1 to 44% during the summer and ranged from 27 to 79% (Sullivan 2003). Similarly, unpublished data in Reeves and Bruesewitz (2007) revealed losses from hooking mortality as a percentage of total kill by weight of walleye were as high as 52% on Mille Lacs Lake, Minnesota, and were attributed to a restrictive harvest slot limit and relatively high angler catch rates.

Stressors involved with catch-and-release angling such as hooking, landing, and handling of fish can be variable yet substantial sources of mortality (Payer et al. 1989, Reeves and Bruesewitz 2007). In North America, studies have shown that walleye hooking mortality can range from 1.1 to 31% (Fletcher 1987, Talmage and Staples 2011). Factors such as capture depth, hooking location, bleeding injuries, landing net design/mesh, and passively versus actively fished baits are known to influence hooking mortality (Schisler and Bergersen 1996, Barthel et al. 2003, Millard et al. 2005, Schreer et al. 2009). Fish size has been shown to influence hooking mortality (Loftus et al. 1988), but among walleye the relationship can be nonlinear (i.e., quadratic function; Reeves and Bruesewitz 2007).

Although the influences of capture depth and water temperature on postrelease hooking mortality of walleye have been documented (Graeb et al. 2005, Talmage and Staples 2011), the potential interactive effects between the 2 are largely unknown. In the Mississippi River, hooking mortality of sauger (S. canadensis) during the winter was found to be 26%, and increased with capture depth (Meerbeek and Hoxmeier 2011). On Lake Nipissing, Ontario, walleye hooking mortality during ice fishing season was relatively low at 7%, although the influence of capture depth was not evaluated (Twardek et al. 2018). A related study in Lake Nipissing showed that effects of air exposure on walleye during a simulated ice angling event had no effect on mortality and that catch-and-release regulations were useful in managing winter walleye fisheries (Logan et al. 2019).

To date, most studies involving walleye hooking mortality have been conducted on open water (Payer et al. 1989, Reeves and Bruesewitz 2007, Reeves and Staples 2011, Talmage and Staples 2011), though few have been done during the ice fishing portions of the year. While walleye hooking mortality appears to be low during the winter, the effects of capture depth during ice fishing are not well understood. During this study we expected to see an increase in release mortality in fish caught from deep water (greater than 10 m), as walleye mortality increased in capture depths exceeding 10 m (Schreer et al. 2009, Meerbeek and Hoxmeier 2011, Talmage and Staples 2011). Our objective was to evaluate the influence of capture depth on postrelease hooking mortality of walleye during ice fishing on 2 large, mainstem Missouri River reservoirs: Lake Oahe and Lake Sharpe.

Materials and methods

Study area

Winter hooking mortality trials were conducted on Lake Oahe and Lake Sharpe, 2 mainstem Missouri River impoundments in South Dakota. Lake Oahe had a surface area of 1497 km², a mean depth of 19 m, and a maximum depth of 62 m. Lake Sharpe had a surface area of 230 km², a mean depth of 9.5 m, and a maximum depth of 24 m. The prey base for walleye in Lake Oahe was primarily rainbow smelt (Osmerus mordax) and cisco (Coregonus artedi; Fincel et al. 2014), while gizzard shad (Dorosoma cepedianum) was the primary prey base for walleye in Lake Sharpe (Wuellner et al. 2010). The walleye harvest regulation on Lake Oahe was a 4 fish daily limit with one over 508 mm. In contrast, Lake Sharpe had a 4 fish daily limit with a 381 mm minimum length limit, which was removed to no length restrictions during July and August, when the only size-related restriction is one walleye over 508 mm. This partial-year exemption is to reduce hooking mortality during months of high water temperatures (Lucchesi and Blackwell 2009).

During February 2020, winter hooking mortality trials took place on Lake Sharpe near Fort Thompson, South Dakota. Following warming events that led to unsafe ice conditions, trials were moved to Whitlock Bay on Lake Oahe in March 2020. Experienced anglers from South Dakota Game, Fish, and Parks staff were instructed to target both shallow (less than 10 m) and deep (greater than 10 m) water using active vertical jigging with artificial lures baited with dead minnows.

Hooking mortality protocol

The data collection protocol consisted of 4 time intervals, defined here by times T₀ to T₄. Stopwatches and smart phones were used to keep track of handling times. The first time interval (i.e., T₀ to T₁) began once an angler set the hook into a walleye (T₀) and ended once the walleye was pulled through the ice (T₁). During the second time interval (T₁ to T₂), a size-12 Monel jaw tag with a unique identification number (National Band and Tag Company, Inc. Newport, KY) was attached to the fish. Hooking location (mouth, gills, or throat), depth of capture (m), capture date, symptoms of barotrauma (e.g., bulging eyes, prolapsed swim bladder, egg extrusion), and signs of bleeding were noted and the fish were placed into a cooler containing ambient temperature lake water (T₂). The third time interval (T₂ to T₃) represented the amount of time to transport the fish to the holding pens. Upon arrival at the holding pens, fish were removed from the cooler (T₃) and placed into a holding pen (T₄), ending the handling protocol.

Holding pens were 2 m × 2 m × 10 m deep, constructed from 6.35 mm white nylon delta mesh with 7.93 mm black polyester rope borders (Christensen Net Works, Everson, WA). When inactive, nets were tied shut to prevent escape. Polyvinyl chloride frames (2 m × 2 m) were attached to the top and bottom of pens to aid in rigidity. Holes were drilled into the bottom frames and weights were tied on opposite corners to aid in submersion. Pens were placed in 8 to 9 m of water so the bottom frame would remain in contact with benthic sediment due to current.

Each week, holding pens were deployed on Monday and pulled on Friday. Fish were added to the pen throughout the week. Holding pens were then emptied and survival of walleye was determined. Mortality was defined as absence of opercular movement. After survival was determined, unique jaw tag numbers and total length (mm) were recorded for each fish.

Statistical analysis

We used multivariate logistic regression to explore factors associated with walleye mortality (R function “glm,” specifying “family = binomial”; R Core Team 2015). The global regression model was expressed as $${\log }_e\left[\frac{p_m}{\left(1-p_m\right)}\right]={\beta }_0+{\beta }_1\left(R\right)+{\beta }_2\left(CD\right)+{\beta }_3\left(BL\right)+{\mathrm{\beta }}_4\left(L\right)+{\beta }_5\left(H\right)+{\beta }_6\left(TP\right)+$$ (1) $${\beta }_7\left(HL\right)+{\beta }_8\left(D\right)+{\beta }_9\left(TL\right)$$(1) where $p_m$ was equal to probability of mortality, $R$ was reservoir (0 = Lake Sharpe, 1 = Lake Oahe), $CD$ was capture depth, $BL$ was presence of bleeding (0 = no, 1 = yes),$L$ was landing time (T₀ to T₁),$H$ was handling time (T₁ to T₄), $TP$ was time in pen, $HL$ was hooking location (0 = mouth, 1 = throat, 2 = gills), $D$ was net pen fish density, and $TL$ was total length.

We evaluated potential multicollinearity among variables by examining the variance inflation factor (i.e., VIF > 5; Paul 2006). Backward Akaike information criterion (AIC) model selection was used to find the most parsimonious model. We used the Hosmer and Lemeshow goodness of fit test (P > 0.05) to assess model fit. Odds ratios were calculated by taking e to the ith logistic regression coefficient to assess importance of individual variables in final model ($e^{{\beta }_i}$; Rich et al. 2003). The lower bound (positive coefficient) or upper bound (negative coefficient) of confidence intervals (95%) for odds ratios was used to assess biological significance of variables (Rich et al. 2003). We calculated evidence ratios using model weights (w_i) for top models (ΔAICc < 2.0) to provide additional evidence for inferences concerning the actual best model (Burnham and Anderson 2002). To determine overall predictor variable support, we summed AICc weights (Σw_i) from top models that included each variable (MacKenzie et al. 2006).

Results

We captured 55 walleye during winter hooking mortality trials on Lake Sharpe (n = 34) and Lake Oahe (n = 21). Walleye hooking mortality during the ice fishing season was 20% (n = 11). Based on observed mortalities, 64% of walleye (n = 7) exhibited barotrauma symptoms. Walleye were caught in depths ranging from 2.4 to 14.0 m ($\overline{x}$= 11) and ranged in sizes from 217 to 461 mm ($\overline{x}$ = 342). Landing times ranged from 10 to 52 s ($\overline{x}$ = 26; Table 1). Two fish were foul-hooked in the throat and 2 fish bled following angling (Table 1).

Table 1. Mean parameter values used to assess factors affecting hooking mortality of walleye during ice fishing season on Lake Oahe and Lake Sharpe in 2020.

Variable	Mean value
Landing time (T0 to T1, s)	26.3 (1.4)
Capture depth (m)	10.9 (0.2)
Total length (mm)	342 (8.8)
Time in pen (days)	3.0 (0.1)
Pen density (fish/m^3)	0.4 (0.02)
Handling time (T0 to T4, s)	219 (25)
Barotrauma (n)	18
Foul-hooking (n)	2
Bleeding (n)	2

Values in parentheses represent 1 standard error.

The most parsimonious logistic model (top model) was found using an AIC (Akaike information criterion) backward selection logistic regression model. Candidate models revealed that walleye hooking mortality was influenced by capture depth, landing time, and time spent in a net pen (Table 2). Capture depth and time in pen had a positive relationship with probability of mortality, while landing time had a negative relationship. The top model provided adequate fit to the data (Hosmer–Lemeshow goodness of fit test; $\chi$² = 11, df = 8, P = 0.15). No evidence of multicollinearity was observed between independent variables in the global model (VIF < 5). The top model included only capture depth (CD; Table 2); thus, the probability of walleye mortality ($p_m$) was expressed as (2) $$p_m\mathrm{ }=\mathrm{ }\frac{e^{\left(-14.998+1.204\left(CD\right)\right)}}{1+\mathrm{ }{e}^{\left(-14.998+1.204\left(CD\right)\right)}}$$(2)

Table 2. Number of model parameters (K), Akaike’s information criterion (AICc) values, difference between AICc values, model weights (w_i), and evidence ratios for top logistic regression models evaluating probability of hooking mortality (${\mathrm{p}}_{\mathrm{m}}$) in Lake Oahe and Lake Sharpe, 2020.

Model	K	AICc	ΔAICc	wi	Evidence ratio
Capture depth	2	47.1	0.0	0.5	1.0
Capture depth + landing time	3	47.6	0.5	0.4	1.3
Capture depth + landing time + time in pen	4	49.1	2.0	0.2	2.7

Based on odds ratios, a 1 m increase in capture depth was associated with at least a 43% (1.43/1) increase in the probability of mortality (Table 3).

Table 3. Logistic model variables predicting probability of hooking mortality ($p_m$) in walleye during winter of 2020 in Lake Oahe and Lake Sharpe.

	Logistic coefficient	S.E.	Odds ratio	95% CI (LL, UL)	P > chi squared
Intercept	–15.0	5.00			<0.01
Capture depth	1.20	0.43	3.33	1.43, 7.76	<0.01

Discussion

We found that hooking mortality of walleye exhibited a sharp increase at capture depths exceeding 10 m (Fig. 1). At 9 m, predicted probability of mortality was 1.5%, but at 11 m the probability rose to 15%. All walleye that died during our study were caught in water exceeding 10 m. The 2 deepest caught fish (∼14 m) had a 92% probability of mortality and subsequently died. A study conducted on the St. Lawrence River found that walleye had 50% probability of mortality at capture depths of 9.5 m (Schreer et al. 2009). A similar study in Rainy Lake, Minnesota, found that hooking mortality of walleye increased in water depths of ∼10 m, as probability of mortality doubled from 9 to 12 m (Talmage and Staples 2011). Sauger experience significant increases in hooking mortality at capture depths greater than 9 m (Meerbeek and Hoxmeier 2011).

Figure 1. Probability of hooking mortality ($p_m$) of walleye caught in Lake Oahe and Lake Sharpe in winter of 2020 as a function of capture depth (m). Dashed lines indicate 95% confidence intervals.

A likely mechanism for hooking mortality of fish caught in deep water is barotrauma. Barotrauma is a physical injury associated with rapid decompression as fish are brought up from deep water (Schreer et al. 2009), causing harmful injuries that include prolapsed swim bladder, hemorrhaging, loss of equilibrium, and bloating (Rummer and Bennett 2005, Gravel and Cooke 2008, Schreer et al. 2009, Eberts et al. 2018). During previous marine and freshwater hooking mortality studies, barotrauma rates in angled fish varied from 20 to 80% (capture depths ranging from 1 to 60 m; Rummer and Bennett 2005, Gravel and Cooke 2008, Brown et al. 2010). An ice angling study for walleye on Lake Nipissing revealed a 22% rate of barotrauma for fish caught at depths of 6 to 12 m, although it was not associated with increased mortality rate (Twardek et al. 2018). We observed barotrauma incidence of 33% among walleye caught at depths ranging from 7.6 to 14 m.

The likelihood of barotrauma is also dependent on the rate of ascent during angling. In yellow perch (Perca flavescens), increased ascent rate decreases survival (Keniry et al. 1996). In a hooking mortality study on Australasian snapper (Pagrus auratus), ascent rates of 0.4 m/s had no influence on mortality (Stewart 2008). Ascent rates of 1.0 m/s caused 80% barotrauma incidence in red snapper (Lutjanus campechanus), which could lead to substantial mortality (Rummer and Bennett 2005). Decreased landing times in the winter likely contributed to a greater incidence of barotrauma in our study and could be a more important factor affecting hooking-related mortality than season, population attributes, or reservoir conditions.

Using net pens to monitor walleye mortality is easy to implement, but overcrowding of fish in holding pens can contribute to lower survival rates (Portz et al. 2006). In studies with walleye, fish are typically held for a period of 120 h to monitor mortality (Reeves and Bruesewitz 2007, Talmage and Staples 2011). Although most hooking mortality occurs within the first 24 h (Muoneke and Childress 1994), at high stocking density it can be difficult to determine whether mortality is attributed to angling practices or stressors associated with prolonged confinement (Portz et al. 2006). In a study of walleye hooking mortality in Porcupine Bay, Washington, Fletcher (1987) observed low mortality (1.1%) of walleyes held in holding pens for 12 d (288 h) at fish densities of 1.1 to 1.2 fish/m³. Average walleye density in our holding pens was notably lower (0.3 fish/m³) than that reported by Fletcher (1987) and never exceeded 20 fish per pen (0.5 fish/m³).

Bleeding intensity can contribute substantially to mortality of fishes that are hooked during angling (Schisler and Bergersen 1996). Presence of bleeding occurred at a low frequency during the winter angling season (n = 2). This could be due to decreased metabolic rate and blood flow associated with colder water temperatures during the winter (Egginton 1997). There were also few instances of foul-hooking during the study. Two of the 55 total fish were foul-hooked (both hooked in throat). The low frequency of foul-hooking observed is most likely due to use of active gears (crankbaits and vertical jigging). Passively fished baits can result in high injury rates that lead to increased mortality (Schill 1996). One fish during our study was caught using passive gears; that fish did not suffer foul-hooking/injury because of passive angling and was included in the analysis. None of the foul-hooked fish in our study (3.6%) died from angling. As a result, hooking location was not a determining factor in hooking mortality.

Walleye in our study experienced handling times from less than a minute to almost 15 min. In simulated angling conditions on Lake Nipissing, researchers found no mortality when walleye were exposed to intervals of snow (140 ± 104 s) and air exposure (176 ± 68 s; Logan et al. 2019). Although handling time was not a significant variable in our model, cold air temperatures during ice fishing have the potential to magnify stressors on walleye. Forty-five seconds of air exposure can change surface temperatures of fish, leading to freezing damage to the gills and eyes (Twardek et al. 2018). In a study on Elbow Lake, Ontario, Canada, skin temperature of largemouth bass (Micropterus salmoides) had a positive relationship with windchill temperature (LaRochelle et al. 2021).

The effect of fish size on hooking-related mortality can vary among species. A meta-analysis by Hühn and Arlinghaus (2011) showed that mortality was similar among fish that were smaller or larger than a “typical” minimum size. Similar findings have been reported for walleye, where hooking mortality was unrelated to fish size (Talmage and Staples 2011, this study). In rainbow trout (Oncorhynchus mykiss), hooking mortality increased with fish length (Schisler and Bergersen 1996), and may be related to larger fish being more likely to “fight” to exhaustion (Reeves and Bruesewitz 2007). We saw similar results, as total length did not have an impact on the probability of mortality for walleye in our study.

Our study likely underestimated potential hooking mortality in walleye because active gears were used and injuries (e.g., bleeding and foul-hooking) were minimal. Passively fished gears and live bait can increase the incidence of injuries in fish and thus increase postrelease mortality (Bartholomew and Bohnsack 2005). Results from this study indicate that hooking mortality should be considered when implementing length-based regulations, especially when (where) walleye angling occurs at depths greater than 10 m. Catch-and-release angling for walleyes during the winter months could have an important impact on fishing mortality related to greater capture depths and potentially faster landing times (e.g., higher incidence of barotrauma).

Acknowledgments

We thank Gene Galinat, Cameron Goble, Bill Miller, Jeremy Kientz, Greg Simpson, Blaise Bursell, Tanner Davis, Seth Fopma, and Dalton Flahaven for logistical support and assistance in the field and laboratory. Region 2 staff from South Dakota Game, Fish & Parks aided with data collection on hooking mortality.

Funding

Funding for this study was provided by Federal Aid in Sport Fish Restoration, Project F-15-R-53, Study 1543, administered through South Dakota Department of Game, Fish, and Parks. All animals used in this study were housed according to animal use and care guidelines established by South Dakota State University (Animal Welfare Assurance Number 16-055A). The South Dakota Cooperative Fish and Wildlife Research Unit is jointly supported by the US Geological Survey, South Dakota State University, and South Dakota Department of Game, Fish & Parks. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US government.

Data availability

The datasets analyzed for the study are available from the corresponding author on reasonable request.