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Related Concept Videos

Topographical Estimation of Visual Population Receptive Fields by fMRI06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

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It is important to obtain unbiased estimates of visual population receptive fields (pRFs) by functional magnetic resonance imaging. We use mild regularization constraints to estimate pRF topography without a-priori assumptions about pRF shape, allowing us to choose specific pRF models post-hoc. This is particularly advantageous in subjects with visual-pathway...
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Estimating Population Standard Deviation01:26

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Estimating Population Mean with Known Standard Deviation01:16

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Topographical Estimation of Visual Population Receptive Fields by fMRI
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A method to estimate pre-exploitation population size.

Catherine M Foley1, Heather J Lynch2

  • 1Hawai'i Institute of Marine Biology, University of Hawai'i at Mānoa, P.O. Box 1346, Kāne'ohe, HI 96744, USA.

Conservation Biology : the Journal of the Society for Conservation Biology
|August 29, 2019
PubMed
Summary
This summary is machine-generated.

Antarctic fur seal populations on South Georgia have rebounded significantly since commercial exploitation. Historical data suggests pre-exploitation abundance was 2.5 million females, providing crucial context for current conservation efforts.

Keywords:
Antarctic fur sealGeorgia del SurSouth Georgiaapproximate Bayesian computationcaptura comercialcommercial harvestingcómputo bayesiano aproximadoecología históricafoca antárticagreen listhistorical ecologylista verdelíneas base cambiantesshifting baselines

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Area of Science:

  • Ecology
  • Conservation Biology
  • Historical Ecology

Background:

  • Antarctic fur seals (Arctocephalus gazella) on South Georgia faced near extinction due to over 100 years of commercial exploitation.
  • Rebounding populations are now sometimes viewed as a nuisance, necessitating an understanding of their historical abundance to avoid shifting baseline syndrome.

Purpose of the Study:

  • To estimate the pre-exploitation abundance of Antarctic fur seals on South Georgia.
  • To provide historical context for current population assessments and conservation targets.
  • To inform efforts for developing a green list of recovering species.

Main Methods:

  • Reconstructed the fur seal harvest from 1786 to 1908 using ship logbooks and historical records.
  • Interpolated missing harvest data using a generalized linear model.
  • Employed an approximate Bayesian computation framework and a stochastic age-structured population model.

Main Results:

  • Estimated the pre-exploitation abundance of Antarctic fur seals on South Georgia at 2.5 million females (95% CI 1.5-3.5 million).
  • This estimate suggests current population levels may be comparable to pre-exploitation conditions.
  • Highlights the ecological consequences of current fur seal numbers on the island.

Conclusions:

  • Understanding historical population baselines is essential for defining species recovery and setting appropriate conservation targets.
  • Archival records are vital for reconstructing past ecological states and informing present-day conservation.
  • Current Antarctic fur seal populations may reflect a return to pre-exploitation ecological conditions.