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Estimating indices of range shifts in birds using dynamic models when detection is imperfect

Overview of attention for article published in Global Change Biology, May 2016
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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 (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

blogs
2 blogs
twitter
8 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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11 Dimensions

Readers on

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93 Mendeley
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Title
Estimating indices of range shifts in birds using dynamic models when detection is imperfect
Published in
Global Change Biology, May 2016
DOI 10.1111/gcb.13283
Pubmed ID
Authors

Matthew J. Clement, James E. Hines, James D. Nichols, Keith L. Pardieck, David J. Ziolkowski

Abstract

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multi-season correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997 to 2013 study period. Near-term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static-models. Occupancy models incorporating a finite mixture model on detection - a new extension to correlated detection occupancy models - were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology This article is protected by copyright. All rights reserved.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
Spain 2 2%
United Kingdom 2 2%
Japan 2 2%
Netherlands 1 1%
Switzerland 1 1%
Sweden 1 1%
Puerto Rico 1 1%
Latvia 1 1%
Other 0 0%
Unknown 78 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 32%
Student > Ph. D. Student 27 29%
Student > Master 10 11%
Student > Bachelor 6 6%
Professor > Associate Professor 4 4%
Other 16 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 52%
Environmental Science 32 34%
Unspecified 6 6%
Nursing and Health Professions 2 2%
Biochemistry, Genetics and Molecular Biology 2 2%
Other 3 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 25 August 2017.
All research outputs
#772,290
of 12,353,915 outputs
Outputs from Global Change Biology
#951
of 3,359 outputs
Outputs of similar age
#29,140
of 312,744 outputs
Outputs of similar age from Global Change Biology
#59
of 200 outputs
Altmetric has tracked 12,353,915 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,359 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.2. This one has gotten more attention than average, scoring higher than 71% 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 312,744 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 90% of its contemporaries.
We're also able to compare this research output to 200 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 68% of its contemporaries.