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Incorporating fragmentation and non-native species into distribution models to inform fluvial fish conservation

Overview of attention for article published in Conservation Biology, December 2017
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Mentioned by

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3 tweeters

Citations

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19 Dimensions

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79 Mendeley
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Title
Incorporating fragmentation and non-native species into distribution models to inform fluvial fish conservation
Published in
Conservation Biology, December 2017
DOI 10.1111/cobi.13024
Pubmed ID
Authors

Andrew T. Taylor, Monica Papeş, James M. Long

Abstract

Fluvial fishes face increased imperilment from anthropogenic activities, but the specific factors contributing most to range declines are often poorly understood. For example, the shoal bass (Micropterus cataractae) is a fluvial-specialist species experiencing continual range loss, yet how perceived threats have contributed to range loss is largely unknown. We employed species distribution models (SDMs) to disentangle which factors are contributing most to shoal bass range loss by estimating a potential distribution based on natural abiotic factors and by estimating a series of current, occupied distributions that also incorporated variables characterizing land cover, non-native species, and fragmentation intensity (no fragmentation, dams only, and dams and large impoundments). Model construction allowed for interspecific relationships between non-native congeners and shoal bass to vary across fragmentation intensities. Results from the potential distribution model estimated shoal bass presence throughout much of their native basin, whereas models of current occupied distribution illustrated increased range loss as fragmentation intensified. Response curves from current occupied models indicated a potential interaction between fragmentation intensity and the relationship between shoal bass and non-native congeners, wherein non-natives may be favored at the highest fragmentation intensity. Response curves also suggested that free-flowing fragment lengths of > 100 km were necessary to support shoal bass presence. Model evaluation, including an independent validation, suggested models had favorable predictive and discriminative abilities. Similar approaches that use readily-available, diverse geospatial datasets may deliver insights into the biology and conservation needs of other fluvial species facing similar threats. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 79 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 24%
Student > Ph. D. Student 17 22%
Researcher 12 15%
Student > Doctoral Student 9 11%
Student > Bachelor 8 10%
Other 7 9%
Unknown 7 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 39%
Environmental Science 25 32%
Engineering 5 6%
Biochemistry, Genetics and Molecular Biology 2 3%
Computer Science 1 1%
Other 3 4%
Unknown 12 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 07 October 2020.
All research outputs
#12,075,803
of 19,033,718 outputs
Outputs from Conservation Biology
#3,157
of 3,481 outputs
Outputs of similar age
#159,466
of 283,660 outputs
Outputs of similar age from Conservation Biology
#44
of 51 outputs
Altmetric has tracked 19,033,718 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,481 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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 283,660 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.