Correcting Bias Introduced by Aerial Counts in Angler Effort Estimation

Smucker, Byran J.; Lorantas, Robert M.; Rosenberger, James L.

doi:10.1577/m09-193.1

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…Smucker et al. () corrected bias in aerial counts on river fisheries in Pennsylvania by integrating creel information on the angler‐to‐nonangler ratio and verifying the aerial sightability of anglers and boats. Smallwood et al.…”

Section: Discussionmentioning

confidence: 99%

Angler Effort Estimates from Instantaneous Aerial Counts: Use of High‐Frequency Time‐Lapse Camera Data to Inform Model‐Based Estimators

Askey

Ward

Godin

et al. 2018

N American J Fish Manag

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Due to the logistics of monitoring remote and diffuse fisheries, typically only a small portion of the annual fishing effort can be observed, and a random sample is often impractical. We used a large data set (over 250,000 observations) from time-lapse cameras placed at 53 lakes across the province of British Columbia, Canada, to better understand the relative influence of different temporal strata (time of day, day type, and month) on the relative number of fishing boats observed on lakes. The high temporal frequency data available from cameras were used to evaluate alternative effort monitoring and estimation strategies for aerial surveys that are commonly used in recreational fisheries. We tested the predictive performance of simple mean count expansion factors versus generalized linear mixed models (GLMMs) under random and stratified (targeting high-effort strata) sampling regimes. Out-of-sample cross validation showed that the current sampling protocol and estimation method (mean count expansion) used for aerial surveys in British Columbia have low predictive power and are biased. A stratified sampling design combined with the GLMM estimator was an efficient strategy that could easily be applied to effort monitoring programs using aircraft. Moreover, the GLMM approach is much more flexible to interpret data obtained from nonstandard sampling regimes and can estimate angler effort over any temporal scale. We outline several summary statistics on accuracy, precision, and bias of estimates at different sample sizes so that managers can evaluate expected precision versus sampling effort in their management jurisdictions. Furthermore, we suggest that our general approach of using camera data to parameterize predictive models applied to data from other low-frequency effort monitoring methods (such as aircraft) can easily be replicated elsewhere.

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Section: Discussionmentioning

confidence: 99%

Angler Effort Estimates from Instantaneous Aerial Counts: Use of High‐Frequency Time‐Lapse Camera Data to Inform Model‐Based Estimators

Askey

Ward

Godin

et al. 2018

N American J Fish Manag

View full text Add to dashboard Cite

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“…Spatial layout and waterbody size can affect a clerk's ability to interview and accurately count anglers, which would affect the accuracy and precision of estimates of effort and catch. Numerous researchers have investigated the complexities associated with conducting creel surveys on large (>10,000 ha) waterbodies (e.g., Soupir et al, 2006;McGlennon and Kinloch, 1997;Smucker et al, 2010). Large waterbodies, which are best sampled using a ''bus route" approach (Robson and Jones, 1989; 2011; Martin, 2013).…”

Section: Introductionmentioning

confidence: 99%

Angler effort and catch within a spatially complex system of small lakes

et al. 2014

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a b s t r a c tSpatial layout of waterbodies and waterbody size can affect a creel clerk's ability to intercept anglers for interviews and to accurately count anglers, which will affect the accuracy and precision of estimates of effort and catch. This study aimed to quantify angling effort and catch across a spatially complex system of 19 small (<100 ha) lakes, the Fremont lakes. Total (±SE) angling effort (hours) on individual lakes ranged from 0 (0) to 7,137 (305). Bank anglers utilized 18 of the 19 lakes, and their mean (±SE) trip lengths (hours) ranged from 0.80 (0.31) to 7.75 (6.75), depending on the waterbody. In contrast, boat anglers utilized 14 of the 19 lakes, and their trip lengths ranged from 1.39 (0.24) to 4.25 (0.71), depending on the waterbody. The most sought fishes, as indexed by number of lakes on which effort was exerted, were anything (17 of 19 lakes), largemouth bass Micropterus salmoides (15 of 19 lakes), and channel catfish Ictalurus punctatus (13 of 19 lakes). Bluegill Lepomis machrochirus, crappie Pomoxis spp., and largemouth bass were caught most frequently across the lakes, but catch rates varied considerably by lake. Of the 1,138 parties interviewed, most parties (93%) visited a single lake but there were 77 (7%) parties that indicated that they had visited multiple lakes during a single day. The contingent of parties that visited more than one lake a day were primarily (87%) bank anglers.. The number of lake-to-lake connections made by anglers visiting more than one waterbody during a single day was related to catch rates and total angling effort. The greater resolution that was achieved with a lake specific creel survey at Fremont lakes revealed a system of lakes with a large degree of spatial variation in angler effort and catch that would be missed by a coarser, system-wide survey that did not differentiate individual lakes.

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