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Inferring species interactions through joint mark-recapture analysis

Overview of attention for article published in Ecology, February 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • Average Attention Score compared to outputs of the same age and source

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13 tweeters
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1 Facebook page

Citations

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13 Dimensions

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46 Mendeley
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Title
Inferring species interactions through joint mark-recapture analysis
Published in
Ecology, February 2018
DOI 10.1002/ecy.2166
Pubmed ID
Authors

Charles B. Yackulic, Josh Korman, Michael D. Yard, Maria Dzul

Abstract

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark-recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark-recapture models that allow for joint modeling of interacting species. Here, we introduce a two-species mark-recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub (Gila cypha) and introduced rainbow trout (Oncorhynchus mykiss) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long-term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark-recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 33%
Researcher 13 28%
Student > Master 6 13%
Student > Bachelor 3 7%
Other 2 4%
Other 3 7%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 57%
Environmental Science 9 20%
Nursing and Health Professions 1 2%
Arts and Humanities 1 2%
Earth and Planetary Sciences 1 2%
Other 1 2%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 09 May 2018.
All research outputs
#3,426,852
of 21,022,292 outputs
Outputs from Ecology
#1,679
of 6,440 outputs
Outputs of similar age
#67,299
of 292,987 outputs
Outputs of similar age from Ecology
#50
of 91 outputs
Altmetric has tracked 21,022,292 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,440 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has gotten more attention than average, scoring higher than 73% 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 292,987 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% 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 is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.