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On the robustness of N‐mixture models

Overview of attention for article published in Ecology, June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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22 X users

Citations

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119 Dimensions

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178 Mendeley
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Title
On the robustness of N‐mixture models
Published in
Ecology, June 2018
DOI 10.1002/ecy.2362
Pubmed ID
Authors

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

Abstract

N-mixture models provide an appealing alternative to mark-recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model's assumptions. We consider the effects of three violations of assumptions which might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities. This article is protected by copyright. All rights reserved.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 178 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 42 24%
Student > Master 26 15%
Student > Ph. D. Student 25 14%
Other 11 6%
Student > Doctoral Student 10 6%
Other 27 15%
Unknown 37 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 75 42%
Environmental Science 48 27%
Mathematics 3 2%
Engineering 2 1%
Computer Science 1 <1%
Other 9 5%
Unknown 40 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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
#2,701,155
of 26,306,521 outputs
Outputs from Ecology
#1,287
of 7,044 outputs
Outputs of similar age
#52,440
of 345,796 outputs
Outputs of similar age from Ecology
#39
of 86 outputs
Altmetric has tracked 26,306,521 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,044 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.3. This one has done well, scoring higher than 81% 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 345,796 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 84% of its contemporaries.
We're also able to compare this research output to 86 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.