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Modeling Trends from North American Breeding Bird Survey Data: A Spatially Explicit Approach

Overview of attention for article published in PLoS ONE, December 2013
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  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

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4 tweeters

Citations

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21 Dimensions

Readers on

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99 Mendeley
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Title
Modeling Trends from North American Breeding Bird Survey Data: A Spatially Explicit Approach
Published in
PLoS ONE, December 2013
DOI 10.1371/journal.pone.0081867
Pubmed ID
Authors

Florent Bled, John Sauer, Keith Pardieck, Paul Doherty, J. Andrew Royle

Abstract

Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Brazil 2 2%
Latvia 1 1%
Chile 1 1%
Canada 1 1%
Japan 1 1%
Germany 1 1%
Unknown 88 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 36 36%
Student > Ph. D. Student 16 16%
Student > Master 15 15%
Student > Bachelor 7 7%
Other 6 6%
Other 14 14%
Unknown 5 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 60%
Environmental Science 24 24%
Medicine and Dentistry 2 2%
Business, Management and Accounting 1 1%
Psychology 1 1%
Other 3 3%
Unknown 9 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 August 2018.
All research outputs
#6,899,113
of 13,333,056 outputs
Outputs from PLoS ONE
#59,978
of 142,364 outputs
Outputs of similar age
#93,638
of 249,823 outputs
Outputs of similar age from PLoS ONE
#2,701
of 7,650 outputs
Altmetric has tracked 13,333,056 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 142,364 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.1. This one has gotten more attention than average, scoring higher than 56% 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 249,823 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 61% of its contemporaries.
We're also able to compare this research output to 7,650 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 63% of its contemporaries.