Estimating the Riverine Abundance of Green Sturgeon Using a Dual‐Frequency Identification Sonar

Mora, Ethan A.; Lindley, Steven T.; Erickson, Daniel L.; Klimley, A. Peter

doi:10.1080/02755947.2015.1017119

Cited by 23 publications

(30 citation statements)

References 26 publications

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“…As estimates of run size for each year, these values are uncorrected for the bias that was imparted due to species proportion, migration timing, and individual movement between sample sites during our surveys (Mora et al. ). We detected an average of 346 sturgeon each year, ranging from 220 sturgeon in 2011 to 526 sturgeon in 2014.…”

Section: Resultsmentioning

confidence: 99%

“…Our other modification from the methods of Mora et al. () allowed us to account for some of the potential bias inherent in the movement of individual sturgeon during the sample period (see Estimating migration patterns with telemetry below).…”

Section: Methodsmentioning

confidence: 99%

“…Mora et al. () described assumptions of our abundance estimation technique that, when violated, will impart bias to the final estimate. They recommended using individual‐based information describing migration patterns to correct for these potential sources of bias.…”

Section: Methodsmentioning

confidence: 99%

“…We then utilized the total number of detected sturgeon from the DIDSON transects (

\overset{T}{true}

; from Mora et al. : their equation ) to estimate the total number of individuals that had exited our study system before our abundance surveys

({\overset{N}{true}}_{E})

{\overset{N}{true}}_{E} = (\frac{\overset{false^}{T}}{1 - {\overset{false^}{P}}_{P}}) {\overset{P}{true}}_{P} .

…”

Section: Methodsmentioning

confidence: 99%

“…The variance of

{\overset{false^}{N}}_{A}

was calculated using the delta method as in Mora et al. (),

V ({\overset{false^}{N}}_{A}) = 2.470em2.470emtrue[{()(, {\overset{S}{true}}_{GS})}^{2} \overset{false^}{V} ({\bar{T}}_{G}) 2.470em2.470emtrue] + 2.470em2.470emtrue[{()(, {\overset{T}{true}}_{G})}^{2} \overset{false^}{V} ({\overset{false^}{S}}_{italicGS}) 2.470em2.470emtrue] + 2.470em2.470emtrue[\overset{false^}{V} ({\bar{T}}_{G}) \overset{false^}{V} ({\overset{false^}{S}}_{italicGS}) 2.470em2.470emtrue] .

…”

Section: Methodsmentioning

confidence: 99%

See 4 more Smart Citations

Estimating the Annual Spawning Run Size and Population Size of the Southern Distinct Population Segment of Green Sturgeon

et al. 2018

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The Southern Distinct Population Segment of Green Sturgeon Acipenser medirostris spawns in the Sacramento River, California, and is listed as a threatened species under the U.S. Endangered Species Act. We estimated the spawning run size and population size in 2010–2015 by using dual‐frequency identification sonar (DIDSON) sampling, underwater video camera species identification, and acoustic tag detections. Spawning run size varied from 336 to 1,236 individuals. We estimated the total population size to be 17,548 individuals (95% confidence interval [CI] = 12,614–22,482). The estimated number of adults was 2,106 (95% CI = 1,246–2,966), the estimated number of juveniles was 4,387 (95% CI = 2,595–6,179), and the estimated number of subadults was 11,055 (95% CI = 6,540–15,571). This study provides the first estimate of Sacramento River Green Sturgeon run size and initiates a time series of abundance that can inform Endangered Species Act recovery processes. Furthermore, these absolute abundance estimates provide a context for evaluating the significance of impacts, such as bycatch in coastal fisheries or entrainment in water diversions, where the number of impacted individuals is known.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…We then utilized the total number of detected sturgeon from the DIDSON transects (

\overset{T}{true}

; from Mora et al. : their equation ) to estimate the total number of individuals that had exited our study system before our abundance surveys

({\overset{N}{true}}_{E})

{\overset{N}{true}}_{E} = (\frac{\overset{false^}{T}}{1 - {\overset{false^}{P}}_{P}}) {\overset{P}{true}}_{P} .

…”

Section: Methodsmentioning

confidence: 99%

“…The variance of

{\overset{false^}{N}}_{A}

was calculated using the delta method as in Mora et al. (),

V ({\overset{false^}{N}}_{A}) = 2.470em2.470emtrue[{()(, {\overset{S}{true}}_{GS})}^{2} \overset{false^}{V} ({\bar{T}}_{G}) 2.470em2.470emtrue] + 2.470em2.470emtrue[{()(, {\overset{T}{true}}_{G})}^{2} \overset{false^}{V} ({\overset{false^}{S}}_{italicGS}) 2.470em2.470emtrue] + 2.470em2.470emtrue[\overset{false^}{V} ({\bar{T}}_{G}) \overset{false^}{V} ({\overset{false^}{S}}_{italicGS}) 2.470em2.470emtrue] .

…”

Section: Methodsmentioning

confidence: 99%

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Estimating the Annual Spawning Run Size and Population Size of the Southern Distinct Population Segment of Green Sturgeon

et al. 2018

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Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay

Coleman,

Fox,

Horne

et al. 2024

Mar Coast Fish

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ObjectiveOnce thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.MethodsData from mobile side‐scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid‐August to late October. Side‐scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.ResultIn 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.ConclusionIn both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.

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Improved Fish Counting Method Accurately Quantifies High‐Density Fish Movement in Dual‐Frequency Identification Sonar Data Files from a Coastal Wetland Environment

Eggleston

Milne²,

Ramsay

et al. 2020

N American J Fish Manag

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There are many ways to quantify fish movement through shallow‐water habitats, but most noninvasive methods (e.g., visual counts) are not effective in turbid coastal wetland waters of the Great Lakes. Dual‐frequency identification sonar (DIDSON) technology (Sound Metrics) offers a noninvasive, hydroacoustic‐based approach to characterize fish movement in wetlands and other habitats by collecting highly detailed fish movement data regardless of light and water quality conditions. High‐resolution data can be analyzed to estimate fish movement in areas where visual observations are difficult. However, enumerating a complex mix of fish sizes by manually counting fish visible in echogram files requires training and is very time consuming. Therefore, four counting techniques were tested to estimate fish abundance from DIDSON echograms that were collected at a hydrologically reconnected coastal wetland in the Great Lakes. Briefly, the four counting methods were (1) manually viewing the entire length of the echogram (full‐hour manual count), (2) manually viewing subsections of the echogram before generating fish estimates by per‐minute average (subsample manual count), (3) using Echoview automated software to generate automated estimates, and (4) using DIDSON viewer software to generate automated estimates. Over 800 echogram‐hours were recorded over a 9‐month period at an open‐flow water control structure connecting a coastal wetland to a tributary to Lake Erie. Commercial fish tracking software (Echoview) and custom software scripts from Milne Technologies were used to semi‐automate fish count estimates for a small subset of data. Semi‐automated software counts were compared to manual counts of identical data files to assess differences in accuracy, cost, processing time, and counter effort. Semi‐automated fish count estimates using Echoview and custom pre‐ and postprocessing software scripts did not differ from baseline manual counts, suggesting that the semi‐automated count process could be a reliable tool to increase efficiency when processing large DIDSON data sets.

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Estimating the Riverine Abundance of Green Sturgeon Using a Dual‐Frequency Identification Sonar

Cited by 23 publications

References 26 publications

Estimating the Annual Spawning Run Size and Population Size of the Southern Distinct Population Segment of Green Sturgeon

Estimating the Annual Spawning Run Size and Population Size of the Southern Distinct Population Segment of Green Sturgeon

Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay

Improved Fish Counting Method Accurately Quantifies High‐Density Fish Movement in Dual‐Frequency Identification Sonar Data Files from a Coastal Wetland Environment

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