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The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake

Overview of attention for article published in Conservation Biology, November 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Average Attention Score compared to outputs of the same age and source

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12 X users

Citations

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26 Dimensions

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95 Mendeley
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Title
The Value of Using Feasibility Models in Systematic Conservation Planning to Predict Landholder Management Uptake
Published in
Conservation Biology, November 2014
DOI 10.1111/cobi.12403
Pubmed ID
Authors

AYESHA I. T. TULLOCH, VIVITSKAIA J. D. TULLOCH, MEGAN C. EVANS, MORENA MILLS

Abstract

Understanding the social dimensions of conservation opportunity is crucial for conservation planning in multiple-use landscapes. However, factors that influence the feasibility of implementing conservation actions, such as the history of landscape management, and landholders' willingness to engage are often difficult or time consuming to quantify and rarely incorporated into planning. We examined how conservation agencies could reduce costs of acquiring such data by developing predictive models of management feasibility parameterized with social and biophysical factors likely to influence landholders' decisions to engage in management. To test the utility of our best-supported model, we developed 4 alternative investment scenarios based on different input data for conservation planning: social data only; biological data only; potential conservation opportunity derived from modeled feasibility that incurs no social data collection costs; and existing conservation opportunity derived from feasibility data that incurred collection costs. Using spatially explicit information on biodiversity values, feasibility, and management costs, we prioritized locations in southwest Australia to control an invasive predator that is detrimental to both agriculture and natural ecosystems: the red fox (Vulpes vulpes). When social data collection costs were moderate to high, the most cost-effective investment scenario resulted from a predictive model of feasibility. Combining empirical feasibility data with biological data was more cost-effective for prioritizing management when social data collection costs were low (<4% of the total budget). Calls for more data to inform conservation planning should take into account the costs and benefits of collecting and using social data to ensure that limited funding for conservation is spent in the most cost-efficient and effective manner.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Australia 3 3%
United Kingdom 2 2%
Botswana 1 1%
France 1 1%
United States 1 1%
Unknown 87 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 20%
Researcher 19 20%
Student > Master 15 16%
Other 9 9%
Student > Bachelor 5 5%
Other 14 15%
Unknown 14 15%
Readers by discipline Count As %
Environmental Science 32 34%
Agricultural and Biological Sciences 25 26%
Earth and Planetary Sciences 4 4%
Social Sciences 4 4%
Computer Science 2 2%
Other 8 8%
Unknown 20 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 August 2015.
All research outputs
#5,301,843
of 25,058,660 outputs
Outputs from Conservation Biology
#2,028
of 4,022 outputs
Outputs of similar age
#57,788
of 269,398 outputs
Outputs of similar age from Conservation Biology
#28
of 41 outputs
Altmetric has tracked 25,058,660 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,022 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.0. This one is in the 49th percentile – i.e., 49% 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 269,398 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 78% of its contemporaries.
We're also able to compare this research output to 41 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.