Evaluation of Electrofishing Catch per Unit Effort for Indexing Fish Abundance in Florida Lakes

Hangsleben, Matt; Allen, Micheal S.; Gwinn, Daniel C.

doi:10.1080/00028487.2012.730106

Cited by 34 publications

(27 citation statements)

References 23 publications

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“…For example, Hangsleben et al . () investigated variation in p of largemouth bass ( Micropterus salmoides ) sampled with boat electrofishing in Florida lakes. Their results suggested that p could vary through time and among lakes for unknown reasons between 0.01 and 0.13.…”

Section: Violations Of the Assumption Of Constant Detectionmentioning

confidence: 97%

“…Hangsleben, Allen & Gwinn, 2013; see Appendix S1 in Supporting Information for a stylised example of how variable p can impact inference about N). Furthermore, the literature suggests that the probability of spurious conclusions about fish abundance can increase dramatically with only small variations in p (Archaux, Henry & Gimenez, 2012; Hangsleben et al, 2013). For example, Hangsleben et al (2013) investigated variation in p of largemouth bass (Micropterus salmoides) sampled with boat electrofishing in Florida lakes.…”

Section: Violations Of the Assumption Of Constant Detectionmentioning

confidence: 99%

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Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions

Gwinn

Beesley

et al. 2015

Freshwater Biology

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SUMMARY1. Relationships between river flow characteristics and fish community/population dynamics (i.e. flow-ecology relationships) underpin methods to determine and monitor environmental water allocations. Quantifying these relationships can be difficult, and consequently, most environmental flow strategies for fish conservation in Australian rivers are based on general flow-ecology relationships as opposed to statistical predictions. 2. Of those studies that have investigated relationships between flow and fish, most have not accounted for incomplete and variable detection of fish by the sampling methods, thus making the implicit assumption that sampling efficiency is invariant. This important assumption is rarely met, leading to inconsistent research findings and spurious results, and a reliance on generic flow-ecology principles for defining flow management strategies. 3. We illustrate how and when detection probability varies when sampling freshwater fish and the consequences to scientific inference about fish-flow relationships. Methods for accounting for imperfect detection of fish are identified and tools to increase the efficiency of experimental designs while reducing sampling cost are discussed. These tools include methods for borrowing information among experimental components and simulation techniques to optimise sampling designs. 4. We argue that, due to the very nature of sampling designs to quantify flow-ecology relationships (e.g. sampling at different flow magnitudes/regimes), the challenge of imperfect detectability is particularly relevant to environmental flow science. We encourage the broader adoption of methods that account for imperfect detection to improve inference about fish-flow relationships and increase the successful application of environmental flows for managing fish communities.

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Section: Violations Of the Assumption Of Constant Detectionmentioning

confidence: 97%

Section: Violations Of the Assumption Of Constant Detectionmentioning

confidence: 99%

Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions

Gwinn

Beesley

et al. 2015

Freshwater Biology

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“…Sampling gears with the greatest efficiency (i.e., greatest CPUE or catch per person‐hour) often are selected without consideration of accuracy, precision, or variation in catchability (i.e., fraction of the fish stock collected per unit of effort) related to relative abundance estimates (see Hangsleben et al. ; Gwinn et al. ; and Tyszko et al.…”

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confidence: 99%

Evaluation of Unbaited Hoop Nets for Simultaneously Assessing Channel Catfish and Flathead Catfish populations in the Minnesota River

Sindt

2018

N American J Fish Manag

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Fisheries managers desire efficient sampling methods with sufficient precision for monitoring ictalurid catfish populations. For this study, I conducted single unbaited hoop‐net surveys in the Minnesota River to evaluate the efficacy of simultaneously assessing stock‐size (≥280‐mm) Channel Catfish Ictalurus punctatus and quality‐size (≥510‐mm) Flathead Catfish Pylodictis olivaris populations. I compared catch rates and length‐frequency distributions among months (April–September) and used resampling procedures to estimate the number of hoop‐net samples needed to achieve desirable precision of catch rate estimates and adequate statistical power. Catch rates (mean ± SE) were 2.1 ± 0.3 fish/net‐night for Channel Catfish and 0.8 ± 0.1 fish/net‐night for Flathead Catfish. Resampling procedures demonstrated that desirable precision (i.e., relative SE ≤ 25) and adequate statistical power (≥0.80 with α = 0.10) to detect 50% reductions in mean catch rates with two‐tailed t‐tests could be achieved for both species with as few as 68 hoop‐net samples during August. Unbaited hoop nets are effective for simultaneously assessing Channel Catfish and Flathead Catfish populations in the Minnesota River, and conducting surveys during August provides the best balance between catch rate and precision while minimizing perceived environmental and behavioral biases. However, the amount of sampling effort needed to detect small population changes (<50%) and to collect sufficient sample sizes for age and growth analyses may be logistically prohibitive. This study emphasizes the importance of evaluating precision and statistical power of sampling methods so that fisheries managers can identify the most suitable methods for their specific objectives.

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“…Similarly, fish detection increased while abundance decreased with increasing water temperature. This example is not unique; Hangsleben et al (2013) found that annual variability in capture efficiency of boat electrofishing, calculated from the capture of marked and unmarked individuals, coincided with reduced confidence in the utility of catch per unit effort of large-bodied fishes in large lentic systems. Thus, these and other environmental factors affect detection probability of other gear types, seasons, and environments, thus affecting the practitioners' ability to effectively draw inference to habitat relationships and trends in abundance if detection probability is not accounted for in the sampling design.…”

Section: Discussionmentioning

confidence: 99%

“…We based λ on the average estimated species-specific abundance estimates, and variation in p y was simulated as a random draw from a beta distribution and parameterized (a, b) using estimates of species-specific detection probability determined from the Bayesian mixture models. In post-population change years, catch was simulated where site-specific abundance λ was doubled, catch ĩ NBin(2λp y , k) (Hangsleben et al 2013). We compared species-specific estimates among an nual (1 yr) sampling schedules pre-and post-…”

Section: Objective 2: Soak Timementioning

confidence: 99%

Estimating abundance of endangered fish by eliminating bias from non-constant detectability

Stewart¹,

Butler²,

Harris³

et al. 2017

Endang. Species. Res.

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Worldwide, approximately half of all freshwater fish are threatened with extinction or lack sufficient data to classify their conservation status. We focused on 3 such species endemic to southeastern Arizona, USA, and Sonora, Mexico: Yaqui topminnow Poeciliopsis occidentalis sonoriensis, Yaqui chub Gila purpurae, and beautiful shiner Cyprinella formosa. These species, like many others, require accurate estimates and trends of abundance to characterize their conservation status. Hence, sampling must be designed appropriately. We used historical data to determine precision and minimum number of traps necessary to estimate abundance with relative precision ≤25. Next, we examined alternative trap soak times to improve sampling efficiency. We then incorporated variables influencing detectability to produce unbiased abundance estimates. Finally, we simulated how ignoring biases from heterogeneous detectability affects abundance indices and the ability to detect changes in abundance. We found catch estimated from the historical design, which assumed constant detectability, had much variability, thereby requiring 40 traps pond −1. A 4 h soak duration increased catch for 2 species and detection probability for all. Detectability increased while abundance decreased with water temperature (all 3 species). Detection of beautiful shiner declined as abundance increased with depth. Our simulations indicated that if detectability varies but is assumed constant (i.e. ignoring detection probability), the probability of finding population change when none occurred (i.e. Type I error) is 50%, and the probability of detecting true population change (i.e. power) declines to ~0.75. Ignoring variable detectability in count data can misrepresent species status, habitat relationships, and ultimately mislead conservation and stewardship of endangered species.

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Evaluation of Electrofishing Catch per Unit Effort for Indexing Fish Abundance in Florida Lakes

Cited by 34 publications

References 23 publications

Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions

Imperfect detection and the determination of environmental flows for fish: challenges, implications and solutions

Evaluation of Unbaited Hoop Nets for Simultaneously Assessing Channel Catfish and Flathead Catfish populations in the Minnesota River

Estimating abundance of endangered fish by eliminating bias from non-constant detectability

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