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Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates

Overview of attention for article published in Global Change Biology, September 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 (81st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
73 Dimensions

Readers on

mendeley
170 Mendeley
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Title
Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current, past, and future climates
Published in
Global Change Biology, September 2016
DOI 10.1111/gcb.13454
Pubmed ID
Authors

Paul D. Mathewson, Lucas Moyer-Horner, Erik A. Beever, Natalie J. Briscoe, Michael Kearney, Jeremiah M. Yahn, Warren P. Porter

Abstract

How climate constrains species' distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modelling of endotherm distributions remains limited in the current literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat-balance model, to convert macroclimate data to pika-specific surface-activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface-activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8-19% less habitat loss in response to annual temperature increases of ~3-5°C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate-change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate-imposed restrictions on activity. This more complete understanding is necessary to inform climate-adaptation actions, management strategies, and conservation plans. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 170 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
South Africa 1 <1%
Portugal 1 <1%
Spain 1 <1%
China 1 <1%
Unknown 164 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 34 20%
Student > Ph. D. Student 33 19%
Researcher 31 18%
Student > Bachelor 12 7%
Other 10 6%
Other 26 15%
Unknown 24 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 77 45%
Environmental Science 47 28%
Medicine and Dentistry 3 2%
Earth and Planetary Sciences 3 2%
Immunology and Microbiology 2 1%
Other 9 5%
Unknown 29 17%

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 26 August 2016.
All research outputs
#1,676,094
of 12,353,915 outputs
Outputs from Global Change Biology
#1,662
of 3,359 outputs
Outputs of similar age
#48,837
of 267,172 outputs
Outputs of similar age from Global Change Biology
#81
of 126 outputs
Altmetric has tracked 12,353,915 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% 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 50% 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 267,172 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 81% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.