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Fine‐resolution conservation planning with limited climate‐change information

Overview of attention for article published in Conservation Biology, November 2016
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)

Mentioned by

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7 tweeters

Citations

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13 Dimensions

Readers on

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30 Mendeley
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Title
Fine‐resolution conservation planning with limited climate‐change information
Published in
Conservation Biology, November 2016
DOI 10.1111/cobi.12793
Pubmed ID
Authors

Payal Shah, Mindy L. Mallory, Amy W. Ando, Glenn R. Guntenspergen

Abstract

Climate change induced uncertainties in future spatial patterns of conservation-related outcomes make it difficult to implement standard conservation planning paradigms. A recent study translates Markowitz's risk diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However this method is information intensive and requires a large number of climate scenarios for carrying out fine-resolution conservation planning. We develop an iterative portfolio analysis technique that enables conservation agents to allocate scarce conservation resources across a desired level of sub-regions in a planning landscape in the absence of sufficient number of climate scenarios. We use a case study of the Prairie Pothole Region to show that lack of sufficient future climate information prevents a conservation agent from attaining the most efficient risk-return conservation outcomes. Our study highlights that the difference in expected conservation returns can be as large as 30% between conservation planning with limited climate change information and full climate change information, even when using the most efficient iterative approach. However, our iterative approach enables a decision maker to do finer-resolution portfolio allocation with limited available climate change forecasts in a manner that obtains the best possible risk-return combinations. We illustrate that a conservation agent can reduce the expected loss in conservation outcomes owing to limited climate change information by 17% by using our recommended iterative approach compared to other approaches. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 33%
Researcher 5 17%
Other 4 13%
Professor > Associate Professor 2 7%
Professor 2 7%
Other 4 13%
Unknown 3 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 37%
Environmental Science 8 27%
Economics, Econometrics and Finance 3 10%
Social Sciences 2 7%
Computer Science 1 3%
Other 2 7%
Unknown 3 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 11 March 2017.
All research outputs
#5,551,035
of 18,922,407 outputs
Outputs from Conservation Biology
#2,138
of 3,477 outputs
Outputs of similar age
#83,366
of 270,994 outputs
Outputs of similar age from Conservation Biology
#51
of 57 outputs
Altmetric has tracked 18,922,407 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 3,477 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.6. This one is in the 38th percentile – i.e., 38% 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 270,994 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 68% of its contemporaries.
We're also able to compare this research output to 57 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.