Multinomial<i>N</i>‐Mixture Models Improve the Applicability of Electrofishing for Developing Population Estimates of Stream‐Dwelling Smallmouth Bass

Mollenhauer, Robert; Brewer, Shannon K.

doi:10.1080/02755947.2016.1254127

Cited by 15 publications

(21 citation statements)

References 54 publications

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Section: Conservation Challengesmentioning

confidence: 99%

“…In cases where multiple environmental and ecological alterations may have played a role in black bass population declines, disentangling the factors most responsible can be challenging. Innovative riverscape level modeling efforts pro vide promising advancements towards identifying drivers of range loss and declining abundance (Mollenhauer and Brewer 2017;Taylor et al 2018b). Additional modeling efforts and fine scale ecological studies are needed to illuminate more precisely the mechanisms by which land use and water use influence black bass persistence.…”

Section: Land and Water Usementioning

confidence: 99%

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Conservation of Black Bass Diversity: An Emerging Management Paradigm

et al. 2019

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Black bass Micropterus spp. are quintessential North American sportfishes that support economically valuable fisheries and act as keystone predators within aquatic ecosystems. Despite their prominence among North American fish fauna, a number of taxonomic designations are unresolved and novel forms continue to be identified within drainages of the southeastern USA. We review the current understanding of black bass diversity, including distributions, evolutionary histories, and phylogenetic relationships. We also provide a brief overview of the major paradigms that have been applied to black bass management and highlight an emerging focus on the conservation of black bass diversity. Black bass diversity is threatened by anthropogenic land and water use, fragmentation of fluvial habitats, historic and contemporary stocking of non‐native congeners, and climate change. Successful conservation of black bass diversity requires that management agencies prioritize the protection of native species, forms, and lineages within and across jurisdictional boundaries. Collaboration among scientists and resource managers is needed to develop practical ways to ameliorate current problems created by past and present anthropogenic alterations, while also preparing for future challenges like global climate change.

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Section: Conservation Challengesmentioning

confidence: 99%

Section: Land and Water Usementioning

confidence: 99%

Conservation of Black Bass Diversity: An Emerging Management Paradigm

et al. 2019

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“…This finding can also provide an insight on population surveys of larger waterbodies in which depletion methods are not logistically feasible but capture probabilities are presumably low. Although depletion methods may not be an option, managers should be aware that the power to detect temporal trends would likely be compromised with a standardized sampling protocol of capture data collection, and other field techniques (e.g., mark–recapture) and innovative analytical approaches might be needed (Mollenhauer and Brewer ; Mycko et al. ).…”

Section: Discussionmentioning

confidence: 99%

“…However, the single‐pass method cannot typically infer capture probabilities of individuals, which can vary over time and space (Letcher et al. ; Mollenhauer and Brewer ), making temporal and spatial comparisons of single‐pass data challenging.…”

mentioning

confidence: 99%

Can Single‐Pass Electrofishing Replace Three‐Pass Depletion for Population Trend Detection?

2018

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Since changes in climate and land use operate at broad spatial scales, efficient monitoring of temporal trends in fisheries resources over large geographic areas is vital to appropriate management. We compared the statistical power of single‐ versus three‐pass electrofishing surveys in detecting temporal trends of age‐1 and older Brook Trout Salvelinus fontinalis populations in western North Carolina. Empirical estimates of abundance and capture probabilities were obtained from annual three‐pass depletion surveys at 14 headwater stream sites between 2012 and 2017. The CVs in abundance averaged 26% (SD = 14.2%) across study sites, and the mean capture probability per pass was 0.72 (range = 0.57–0.84, SD = 0.09). Captures from single‐pass sampling and abundance estimates from three‐pass removal sampling were highly correlated (r2 = 0.98). In simulations, under the range of years sampled (5–25 years) and annual declines (2.5–7.5%) considered, the power to detect temporal trends was similar (∆ power < 0.1) between the two methods when five or more sites were monitored. An additional set of simulations with varying capture probabilities demonstrated that differences in power between the two methods increased as mean capture probabilities decreased (0.8, 0.5, and 0.2) accompanied by larger variation in capture probabilities among the samples, indicating results obtained with Brook Trout populations in North Carolina might not be applicable to other habitat types or species. Variation in fish abundance did not affect the difference in power between the two methods. Single‐pass electrofishing surveys can be an efficient survey method to monitor temporal population trends for habitat types and species characterized with high capture probabilities and low variation among samples. However, single‐pass data would not typically allow for inferences of capture probabilities and thus abundance. This can be problematic when environmental factors vary and when data sets collected using different protocols are compared. This trade‐off should be carefully considered when designing monitoring programs.

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“…N ‐mixture modeling is used by ecologists interested in measuring organismal abundance while accounting for detection uncertainty (Kéry and Royle ). An aquatic‐based N ‐mixture model study of Smallmouth Bass Micropterus dolomieu in Oklahoma relied upon 48‐h electrofishing intervals to note the presence and abundance of Smallmouth Bass per a variable area of observation (Mollenhauer and Brewer ). Data collection under this type of design is subject to temporal variation as one experiences delayed observation between electrofishing events and is also prone to spatial variation as the observation area becomes larger and less certain.…”

Section: Biology Ecology and Abiotic Habitat Factors Influencing Spmentioning

confidence: 99%

N‐Mixture Modeling of River Herring Egg Abundance and Distribution in the Tributaries of the Hudson River

Kowalik

Eakin

2019

Mar Coast Fish

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N‐mixture models are often employed to estimate latent organismal abundance while concurrently accounting for detection probability. Our study offers a novel means for simultaneously measuring abundance, detection probability, and gear efficiency by focusing on a previously understudied and relatively immobile subject (river herring eggs). Custom‐designed egg mats accommodating two individual collecting surfaces were deployed in two tributaries of the Hudson River (Fall Kill and Black Creek, New York) to collect anadromous river herring eggs during the spawn. Mats were orientated approximately parallel to streamflow under a stratified random sampling design. Strata were defined as three equidistant spatial segments measured from a given tributary's confluence with the main stem of the Hudson River to its first impassable barrier to fish migration. In total, 93 sites were surveyed, with the majority of eggs being detected within the upper two‐thirds of each respective tributary. Throughout the course of the sampling season, an average of 1,585 eggs per egg mat subsampling event was recovered from the upper two strata of Black Creek, and 2,619 eggs per subsampling event were recovered from the upper two strata of the Fall Kill. In Black Creek, an egg density of 568 eggs per 58.1‐cm2 egg mat subsample was observed over an average deployment duration of 3.7 d at a detection rate of 82%. In the Fall Kill, an egg density of 1,222 eggs per 58.1‐cm2 subsample was observed over an average deployment duration of 3.9 d at a detection rate of 92%. The N‐mixture negative binomial model outperformed Poisson and zero‐inflated Poisson N‐mixture models in estimating river herring egg abundance using Akaike's information criterion model comparison indices. In terms of iteration processing time, N‐mixture modeling using the “unmarked” package in R proved to be more efficient than Bayesian‐based hierarchical modeling processed through the “jagsUI” package.

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MultinomialN‐Mixture Models Improve the Applicability of Electrofishing for Developing Population Estimates of Stream‐Dwelling Smallmouth Bass

Cited by 15 publications

References 54 publications

Conservation of Black Bass Diversity: An Emerging Management Paradigm

Conservation of Black Bass Diversity: An Emerging Management Paradigm

Can Single‐Pass Electrofishing Replace Three‐Pass Depletion for Population Trend Detection?

N‐Mixture Modeling of River Herring Egg Abundance and Distribution in the Tributaries of the Hudson River

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