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Estimating abundance of an open population with anN-mixture model using auxiliary data on animal movements

Overview of attention for article published in Ecological Applications, April 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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9 tweeters

Citations

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Title
Estimating abundance of an open population with anN-mixture model using auxiliary data on animal movements
Published in
Ecological Applications, April 2018
DOI 10.1002/eap.1692
Pubmed ID
Authors

Alison C. Ketz, Therese L. Johnson, Ryan J. Monello, John A. Mack, Janet L. George, Benjamin R. Kraft, Margaret A. Wild, Mevin B. Hooten, N. Thompson Hobbs

Abstract

Accurate assessment of abundance forms a central challenge in population ecology and wildlife manage Many statistical techniques have been developed to estimate population sizes because populations change over time and space, and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi-state mark recapture methods using a hierarchical N-mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state-space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N-mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 35%
Researcher 14 30%
Student > Bachelor 4 9%
Other 3 7%
Professor 2 4%
Other 4 9%
Unknown 3 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 63%
Environmental Science 8 17%
Earth and Planetary Sciences 1 2%
Unknown 8 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 10 May 2018.
All research outputs
#2,901,568
of 12,960,324 outputs
Outputs from Ecological Applications
#686
of 2,224 outputs
Outputs of similar age
#91,423
of 347,989 outputs
Outputs of similar age from Ecological Applications
#26
of 47 outputs
Altmetric has tracked 12,960,324 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,224 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has gotten more attention than average, scoring higher than 63% 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 347,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 47 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.