Long‐Term Changes in Recreational Catch Inequality in a Trout Stream

Seekell, David A.; Brosseau, Chase J.; Cline, Timothy J.; Winchcombe, Raymond J.; Zinn, Lee J.

doi:10.1080/02755947.2011.642491

Cited by 13 publications

(30 citation statements)

References 20 publications

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“…There is much more variability in the raw data in the period prior to the transition than in the period after the transition. This is because mean catch is tightly correlated with the variance and skewness of the distribution of catch (cf., Seekell 2011;Seekell et al 2011a). The high variance at the first attractor in the raw data is a function of the inherent high variability when fish catch is high.…”

Section: Discussionmentioning

confidence: 99%

Evidence of alternate attractors from a whole-ecosystem regime shift experiment

et al. 2013

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Ecosystems sometimes shift between different states or dynamic regimes. Theory attributes these shifts to multiple ecosystem attractors. However, documenting multiple ecosystem attractors is difficult, particularly at spatial and temporal scales relevant to ecosystem management. We manipulated the fish community of a lake with the goal of causing trophic cascades and shifting the food web from a planktivoredominated state to an alternate piscivore-dominated state. We evaluated evidence that the shifts in the fish community comprise alternate attractors using two complementary approaches. First, we calculated phase space trajectories to visualize the shift between attractors. Second, we computed generalized autoregressive conditional heteroskedasticity (GARCH) models and the Brock-Dechert-Scheinkman (BDS) test for linearity. The reconstructed phase space trajectories show the system departing a point attractor, entering a limit cycle, and then shifting to a new point attractor. The GARCH and BDS results indicate that linear explanations are not sufficient to explain the observed patterns. The results provide evidence for alternate attractors based on high-frequency time series of field measurements.

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Section: Discussionmentioning

confidence: 99%

Evidence of alternate attractors from a whole-ecosystem regime shift experiment

et al. 2013

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“…Seekell et al (2011a) provided a detailed description of the survey methodology. Briefly, these data represent a complete census of recreational fishing in this fishery.…”

Section: Methodsmentioning

confidence: 99%

“…Records were submitted after each trip. We used records with a party size of one angler to minimize potential confounding factors associated with variable group sizes (Seekell et al 2011a). We believe that the potential for confounding variability from substitutability or targeting of different species in influencing catching power or harvesting decisions was minimal because 99% of the fish caught were Brown Trout Arlinghaus et al 2007).…”

Section: Methodsmentioning

confidence: 99%

“…These methods are described in detail by Seekell et al (2011a). Briefly, Lorenz curves and Gini coefficients assess the relationship between the cumulative proportion of fish harvested and the cumulative proportion of effort.…”

Section: Methodsmentioning

confidence: 99%

“…We plotted Lorenz curves from catch distributions reported by Seekell et al (2011a) for this fishery as a basis for comparison with our harvest distribution Lorenz curves. Pearson's productmoment correlation coefficients between harvest inequality (as represented by Gini coefficients), harvest per trip, S, and year were calculated as basic tests for trend; we calculated correlations for these variables to understand how they co-vary.…”

Section: Methodsmentioning

confidence: 99%

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Can Management Reduce Harvest Inequality in Recreational Fisheries?

Seekell

Cline

Winchcombe

2013

N American J Fish Manag

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Harvest inequality, a situation in which most of the fish are harvested by a disproportionately small number of anglers, is a characteristic of most recreational fisheries. Harvest inequality develops when a few anglers harvest a very large number of fish or when many anglers harvest few fish. Identifying the cause of harvest inequality is critical to understanding the potential for management to reduce the inequality. Management efforts aimed at reducing the top anglers’ take will have only a modest impact if the harvest inequality is actually caused by many anglers harvesting no fish. We measured harvest inequality in 20 years of creel census data from a trout stream in southeastern New York. We calculated Lorenz curve asymmetry coefficients (S) to test whether harvest inequality was attributable to small harvests by many anglers or large harvests by a few anglers. Harvest inequality in the fishery was consistently high and the S‐value was always less than 1.0, indicating that harvest inequality was caused by many anglers harvesting no fish rather than by few anglers harvesting many fish. This influence becomes stronger with increased harvest. We conclude that management is unlikely to influence the magnitudes of harvest inequality in recreational fisheries because regulations do not target the principal cause of harvest inequality.Received May 10, 2012; accepted October 29, 2012

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Using Limited Data to Identify Optimal Bag and Size Limits to Prevent Overfishing

Moreau

Matthias

2018

N American J Fish Manag

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Bag and size limits are commonly used in recreational fisheries management, but these regulations are often treated as separate management tools. This effectively overlooks how bag and size limits can be simultaneously used to achieve multiple management outcomes (e.g., reduce exploitation, prevent overfishing, maximize angler acceptance, etc.). Our objectives were to combine data‐limited stock assessment methods with an angler catch simulation and a yield‐per‐recruit model to assess the effectiveness of bag and size limits to decrease exploitation rates and improve the spawning potential ratio (SPR). We then applied these methods to the Kipawa Lake Walleye Sander vitreus fishery that has experienced overfishing and poor fishing quality. Using data‐limited assessment methods, the exploitation rate was estimated at 0.45 (95% CI = 0.32–0.59) and the population was overfished (mean SPR = 0.06; 95% CI = 0.02–0.13). Bag limits significantly reduced total harvest when extremely restrictive (i.e., reduced to one fish per angler from the current limit of six), but changes in bag limits alone were not sufficient to prevent overharvest because SPR remained below 0.35. Size limits could be used to prevent overharvest with narrow harvest slots (up to a 14‐cm slot range with a minimum harvestable size greater than 32 cm) or large minimum size limits (>52 cm) at the current bag limit of six. When bag limits were reduced to one or two fish per day, harvest windows could be 3–13 cm larger and minimum length limits could be 3–12 cm lower to prevent overharvest. This analysis outlines a relatively simple and effective method that can be applied using data commonly collected in annual agency surveys to predict which regulatory combinations can be used to prevent overharvest, reduce exploitation rates, and maximize angler satisfaction and acceptance of regulations. Finally, the data and model code are included in the Supplement and can be easily applied to other data limited fisheries.

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Long‐Term Changes in Recreational Catch Inequality in a Trout Stream

Cited by 13 publications

References 20 publications

Evidence of alternate attractors from a whole-ecosystem regime shift experiment

Evidence of alternate attractors from a whole-ecosystem regime shift experiment

Can Management Reduce Harvest Inequality in Recreational Fisheries?

Using Limited Data to Identify Optimal Bag and Size Limits to Prevent Overfishing

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