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Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients

Overview of attention for article published in PLOS ONE, June 2015
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

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3 news outlets
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6 X users

Citations

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33 Dimensions

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96 Mendeley
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Title
Linking Dynamic Habitat Selection with Wading Bird Foraging Distributions across Resource Gradients
Published in
PLOS ONE, June 2015
DOI 10.1371/journal.pone.0128182
Pubmed ID
Authors

James M. Beerens, Erik G. Noonburg, Dale E. Gawlik

Abstract

Species distribution models (SDM) link species occurrence with a suite of environmental predictors and provide an estimate of habitat quality when the variable set captures the biological requirements of the species. SDMs are inherently more complex when they include components of a species' ecology such as conspecific attraction and behavioral flexibility to exploit resources that vary across time and space. Wading birds are highly mobile, demonstrate flexible habitat selection, and respond quickly to changes in habitat quality; thus serving as important indicator species for wetland systems. We developed a spatio-temporal, multi-SDM framework using Great Egret (Ardea alba), White Ibis (Eudocimus albus), and Wood Stork (Mycteria Americana) distributions over a decadal gradient of environmental conditions to predict species-specific abundance across space and locations used on the landscape over time. In models of temporal dynamics, species demonstrated conditional preferences for resources based on resource levels linked to differing temporal scales. Wading bird abundance was highest when prey production from optimal periods of inundation was concentrated in shallow depths. Similar responses were observed in models predicting locations used over time, accounting for spatial autocorrelation. Species clustered in response to differing habitat conditions, indicating that social attraction can co-vary with foraging strategy, water-level changes, and habitat quality. This modeling framework can be applied to evaluate the multi-annual resource pulses occurring in real-time, climate change scenarios, or restorative hydrological regimes by tracking changing seasonal and annual distribution and abundance of high quality foraging patches.

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The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Brazil 1 1%
Unknown 93 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 25 26%
Researcher 15 16%
Student > Ph. D. Student 15 16%
Student > Bachelor 9 9%
Other 5 5%
Other 11 11%
Unknown 16 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 35%
Environmental Science 27 28%
Social Sciences 3 3%
Engineering 2 2%
Earth and Planetary Sciences 2 2%
Other 4 4%
Unknown 24 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 28. 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 17 October 2015.
All research outputs
#1,191,194
of 22,815,414 outputs
Outputs from PLOS ONE
#15,831
of 194,701 outputs
Outputs of similar age
#15,950
of 264,049 outputs
Outputs of similar age from PLOS ONE
#502
of 6,718 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 194,701 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done particularly well, scoring higher than 91% 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 264,049 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 6,718 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 92% of its contemporaries.