Efficiency of presence-absence sampling used to estimate the mean abundance of fish eggs

Yamada, Sakutarō; Zenitani, Hiromu

doi:10.1111/j.1444-2906.2005.01031.x

“…The latter was considered only briefly because of less convincing results (Royle and Nichols 2003), but has since been modified or extended and applied to studies of terrestrial species (Royle 2004, Royle and Link 2005, Kery et al 2005. Similar methods have been proposed for aggregated populations, but have assumed known parameters that describe the aggregation pattern and sampling efficiency or detectability (Mangel andSmith 1990, Yamada andZenitani 2005).…”

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confidence: 99%

Estimating abundance from detection?nondetection data for randomly distributed or aggregated elusive populations

Zhou¹,

Griffiths²

2007

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Estimating abundance is important in many ecological studies in order to understand the spatial and temporal dynamics of a population, which can assist in management and conservation. However, direct estimates of abundance can be difficult and expensive to obtain, particularly for wide-ranging, rare or elusive species. An alternative Á estimating from detection-nondetection data Á is a challenging but alluring concept to ecologists since the cost and effort of a study can be greatly reduced. This paper describes a method for estimating the abundance of randomly distributed or aggregated populations by using binary data where the probability of detection is less than one. The performances of the models were evaluated by computer simulations comprising 1620 cases. The results show that the accuracy of the abundance estimates increases as the sampling rate, efficiency of survey method, and the number of repeated surveys increase, whereas the accuracy declines as individuals become more aggregated. For a randomly distributed population, using a sampling rate of 0.05 in a survey method with a detection probability of 0.5, and repeating surveys three times provides sufficient accuracy of abundance. For an aggregated population, to achieve reasonably accurate abundance estimates the sampling rate should be doubled and each cell should be repeatedly surveyed on 4 to 6 occasions.

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“…The latter was considered only briefly because of less convincing results (Royle and Nichols 2003), but has since been modified or extended and applied to studies of terrestrial species (Royle 2004, Royle and Link 2005, Kery et al 2005, Royle et al 2005). Similar methods have been proposed for aggregated populations, but have assumed known parameters that describe the aggregation pattern and sampling efficiency or detectability (Mangel and Smith 1990, Yamada and Zenitani 2005).…”

mentioning

confidence: 99%

Estimating abundance from detection–nondetection data for randomly distributed or aggregated elusive populations

Zhou

¹

,

Griffiths²

2007

View full text Add to dashboard Cite

Estimating abundance is important in many ecological studies in order to understand the spatial and temporal dynamics of a population, which can assist in management and conservation. However, direct estimates of abundance can be difficult and expensive to obtain, particularly for wide-ranging, rare or elusive species. An alternative Á estimating from detection-nondetection data Á is a challenging but alluring concept to ecologists since the cost and effort of a study can be greatly reduced. This paper describes a method for estimating the abundance of randomly distributed or aggregated populations by using binary data where the probability of detection is less than one. The performances of the models were evaluated by computer simulations comprising 1620 cases. The results show that the accuracy of the abundance estimates increases as the sampling rate, efficiency of survey method, and the number of repeated surveys increase, whereas the accuracy declines as individuals become more aggregated. For a randomly distributed population, using a sampling rate of 0.05 in a survey method with a detection probability of 0.5, and repeating surveys three times provides sufficient accuracy of abundance. For an aggregated population, to achieve reasonably accurate abundance estimates the sampling rate should be doubled and each cell should be repeatedly surveyed on 4 to 6 occasions.

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“…Research articles on the ME of the IDP are primarily focused on the spatial distribution of the marine fishes' ichthyoplankton density using the presence-absence (detection and nondetection) data (Syfert et al 2013;Ogburn and Forward 2012;Palialexis et al 2011;MacLeod et al 2008). The other main research topics are related to sampling effectiveness, occurrences, habitat distribution, abundance and community of a marine species using the monitoring survey data (Zwolinski et al 2011;Phillips et al 2009;Yamada and Zenitani 2005;Royle and Nichols 2003;Cyr et al 1992;Mangel and Smith 1990). The ME research of the IDP is very limited even though it is very useful and important for the uncertainty analysis of the statistical inference using the finite sampling data (Katasanevakis et al 2012;Stauffer et al 2002).…”

Section: Introductionmentioning

confidence: 99%

Ichthyoplankton Detection Proportion and Margin of Error for the Scomber japonicus in Korean Coastal Seas

Lee¹,

Cho²

2017

Ocean and Polar Research

0

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: The probability distribution of ichthyoplankton is important for enhancing the precision of sampling while reducing unnecessary surveys. To estimate the ichthyoplankton detection proportion (IDP) and its margin of error (ME), the monitoring information of the chub mackerel's (Scomber japonicus) ichthyoplankton presence-absence sampling data has been were collected over approximately 30 years (from 1982 to 2011) in the Korean coastal seas. Based on the computed spatial distributions of the mackerel's IDP and ME, the confidence interval (CI) range, defined as 2 ME, decreases from approximately 80% to 40% as the sample size n increases from 4 to 24 and the ME is approximately 40% in the typical (seasonal survey) case n = 4 per year. The IDP and ME off Jeju Island are relatively high at the 0.5-degree smoothing level. After increasing the spatial smoothing level to 1.0-degree, the ME decreased, and the spatial distribution pattern also changed due to the over-smoothing effects. In this study, the 0.5-degree smoothing is more suitable for the distribution pattern than the 1.0-degree smoothing level. The area of the high IDP and the low ME on the mackerel's ichthyoplankton was similar to the estimated spawning ground in the Korean peninsula. This information could contribute to enhancing for the spawning ecology surveys.

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Efficiency of presence-absence sampling used to estimate the mean abundance of fish eggs

Cited by 3 publications

References 3 publications

Estimating abundance from detection?nondetection data for randomly distributed or aggregated elusive populations

Estimating abundance from detection?nondetection data for randomly distributed or aggregated elusive populations

Estimating abundance from detection–nondetection data for randomly distributed or aggregated elusive populations

Ichthyoplankton Detection Proportion and Margin of Error for the Scomber japonicus in Korean Coastal Seas

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