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Integrating multiple data sources in species distribution modeling: a framework for data fusion*

Overview of attention for article published in Ecology, March 2017
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9 X users

Citations

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171 Dimensions

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350 Mendeley
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Title
Integrating multiple data sources in species distribution modeling: a framework for data fusion*
Published in
Ecology, March 2017
DOI 10.1002/ecy.1710
Pubmed ID
Authors

Krishna Pacifici, Brian J. Reich, David A. W. Miller, Beth Gardner, Glenn Stauffer, Susheela Singh, Alexa McKerrow, Jaime A. Collazo

Abstract

The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the trade-off between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ('Shared', 'Correlation', 'Covariates') for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ('Single'). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions. This article is protected by copyright. All rights reserved.

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The data shown below were collected from the profiles of 9 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 350 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 <1%
United States 2 <1%
Netherlands 1 <1%
Germany 1 <1%
Unknown 344 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 85 24%
Student > Ph. D. Student 68 19%
Student > Master 53 15%
Student > Doctoral Student 29 8%
Student > Bachelor 16 5%
Other 36 10%
Unknown 63 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 144 41%
Environmental Science 85 24%
Mathematics 11 3%
Computer Science 8 2%
Earth and Planetary Sciences 7 2%
Other 22 6%
Unknown 73 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 April 2017.
All research outputs
#6,735,521
of 24,471,305 outputs
Outputs from Ecology
#2,923
of 6,817 outputs
Outputs of similar age
#103,069
of 315,615 outputs
Outputs of similar age from Ecology
#46
of 91 outputs
Altmetric has tracked 24,471,305 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 6,817 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.3. 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 315,615 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 66% of its contemporaries.
We're also able to compare this research output to 91 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 50% of its contemporaries.