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Integrating count and detection-nondetection data to model population dynamics

Overview of attention for article published in Ecology, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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13 tweeters
facebook
1 Facebook page

Citations

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51 Dimensions

Readers on

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177 Mendeley
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Title
Integrating count and detection-nondetection data to model population dynamics
Published in
Ecology, May 2017
DOI 10.1002/ecy.1831
Pubmed ID
Authors

Elise F. Zipkin, Sam Rossman, Charles B. Yackulic, J. David Wiens, James T. Thorson, Raymond J. Davis, Evan H. Campbell Grant

Abstract

As the spatial and temporal scale of ecological research expands, there is increasing need for methods that integrate multiple data types into a single analytical framework. Current work on this topic primarily focuses on combining capture-recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection-nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count versus detection-nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection-nondetection data (1995-2014) with newly collected count data (2015-2016) from a growing population of barred owls (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 177 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
South Africa 1 <1%
Unknown 174 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 49 28%
Student > Ph. D. Student 32 18%
Student > Master 32 18%
Other 9 5%
Student > Doctoral Student 7 4%
Other 27 15%
Unknown 21 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 91 51%
Environmental Science 41 23%
Unspecified 5 3%
Mathematics 2 1%
Social Sciences 2 1%
Other 4 2%
Unknown 32 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 06 June 2017.
All research outputs
#4,171,897
of 22,962,258 outputs
Outputs from Ecology
#1,949
of 6,560 outputs
Outputs of similar age
#73,675
of 310,798 outputs
Outputs of similar age from Ecology
#43
of 116 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,560 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.9. This one has gotten more attention than average, scoring higher than 70% 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 310,798 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 76% of its contemporaries.
We're also able to compare this research output to 116 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 62% of its contemporaries.