↓ Skip to main content

On the reliability of N-mixture models for count data

Overview of attention for article published in Biometrics, July 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#46 of 1,646)
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
25 tweeters

Citations

dimensions_citation
146 Dimensions

Readers on

mendeley
307 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
On the reliability of N-mixture models for count data
Published in
Biometrics, July 2017
DOI 10.1111/biom.12734
Pubmed ID
Authors

Richard J. Barker, Matthew R. Schofield, William A. Link, John R. Sauer

Abstract

N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the "constant p" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.

Twitter Demographics

The data shown below were collected from the profiles of 25 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 307 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 307 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 70 23%
Student > Master 61 20%
Student > Ph. D. Student 59 19%
Student > Doctoral Student 19 6%
Student > Bachelor 18 6%
Other 42 14%
Unknown 38 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 143 47%
Environmental Science 79 26%
Mathematics 5 2%
Economics, Econometrics and Finance 3 <1%
Veterinary Science and Veterinary Medicine 2 <1%
Other 20 7%
Unknown 55 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2019.
All research outputs
#1,615,415
of 19,298,685 outputs
Outputs from Biometrics
#46
of 1,646 outputs
Outputs of similar age
#35,668
of 278,680 outputs
Outputs of similar age from Biometrics
#1
of 21 outputs
Altmetric has tracked 19,298,685 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,646 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,680 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.