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Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Overview of attention for article published in Global Change Biology, September 2013
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Mentioned by

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2 tweeters

Citations

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63 Dimensions

Readers on

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129 Mendeley
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1 CiteULike
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Title
Probabilistic accounting of uncertainty in forecasts of species distributions under climate change
Published in
Global Change Biology, September 2013
DOI 10.1111/gcb.12294
Pubmed ID
Authors

Seth J. Wenger, Nicholas A. Som, Daniel C. Dauwalter, Daniel J. Isaak, Helen M. Neville, Charles H. Luce, Jason B. Dunham, Michael K. Young, Kurt D. Fausch, Bruce E. Rieman

Abstract

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species' range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1-42.5 thousand km; this was predicted to decline to 0.5-7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.

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 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Spain 3 2%
Brazil 3 2%
Switzerland 2 2%
Canada 2 2%
Finland 1 <1%
South Africa 1 <1%
Mexico 1 <1%
China 1 <1%
Other 2 2%
Unknown 108 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 40 31%
Student > Ph. D. Student 35 27%
Student > Master 13 10%
Student > Doctoral Student 8 6%
Other 7 5%
Other 18 14%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 58 45%
Environmental Science 48 37%
Earth and Planetary Sciences 6 5%
Engineering 4 3%
Social Sciences 1 <1%
Other 2 2%
Unknown 10 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 September 2013.
All research outputs
#7,721,368
of 12,353,915 outputs
Outputs from Global Change Biology
#2,891
of 3,359 outputs
Outputs of similar age
#78,015
of 147,784 outputs
Outputs of similar age from Global Change Biology
#60
of 74 outputs
Altmetric has tracked 12,353,915 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
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 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 147,784 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 74 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.