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Allocating conservation resources between areas where persistence of a species is uncertain

Overview of attention for article published in Ecological Applications, April 2011
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Title
Allocating conservation resources between areas where persistence of a species is uncertain
Published in
Ecological Applications, April 2011
DOI 10.1890/09-2075.1
Pubmed ID
Authors

McDonald-Madden, Eve, Chadès, Iadine, McCarthy, Michael A, Linkie, Matthew, Possingham, Hugh P, Eve McDonald-Madden, Iadine Chadès, Michael A. McCarthy, Matthew Linkie, Hugh P. Possingham, McCarthy, Michael A., Possingham, Hugh P.

Abstract

Research on the allocation of resources to manage threatened species typically assumes that the state of the system is completely observable; for example whether a species is present or not. The majority of this research has converged on modeling problems as Markov decision processes (MDP), which give an optimal strategy driven by the current state of the system being managed. However, the presence of threatened species in an area can be uncertain. Typically, resource allocation among multiple conservation areas has been based on the biggest expected benefit (return on investment) but fails to incorporate the risk of imperfect detection. We provide the first decision-making framework for confronting the trade-off between information and return on investment, and we illustrate the approach for populations of the Sumatran tiger (Panthera tigris sumatrae) in Kerinci Seblat National Park. The problem is posed as a partially observable Markov decision process (POMDP), which extends MDP to incorporate incomplete detection and allows decisions based on our confidence in particular states. POMDP has previously been used for making optimal management decisions for a single population of a threatened species. We extend this work by investigating two populations, enabling us to explore the importance of variation in expected return on investment between populations on how we should act. We compare the performance of optimal strategies derived assuming complete (MDP) and incomplete (POMDP) observability. We find that uncertainty about the presence of a species affects how we should act. Further, we show that assuming full knowledge of a species presence will deliver poorer strategic outcomes than if uncertainty about a species status is explicitly considered. MDP solutions perform up to 90% worse than the POMDP for highly cryptic species, and they only converge in performance when we are certain of observing the species during management: an unlikely scenario for many threatened species. This study illustrates an approach to allocating limited resources to threatened species where the conservation status of the species in different areas is uncertain. The results highlight the importance of including partial observability in future models of optimal species management when the species of concern is cryptic in nature.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 158 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Australia 4 3%
United Kingdom 2 1%
Brazil 2 1%
Spain 2 1%
Canada 1 <1%
Turkey 1 <1%
Iran, Islamic Republic of 1 <1%
India 1 <1%
Other 7 4%
Unknown 133 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 50 32%
Student > Ph. D. Student 30 19%
Student > Master 21 13%
Other 14 9%
Unspecified 12 8%
Other 31 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 46%
Environmental Science 57 36%
Unspecified 16 10%
Earth and Planetary Sciences 7 4%
Economics, Econometrics and Finance 2 1%
Other 4 3%

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 05 November 2016.
All research outputs
#6,544,097
of 8,620,037 outputs
Outputs from Ecological Applications
#1,627
of 1,789 outputs
Outputs of similar age
#191,879
of 275,733 outputs
Outputs of similar age from Ecological Applications
#64
of 72 outputs
Altmetric has tracked 8,620,037 research outputs across all sources so far. This one is in the 13th percentile – i.e., 13% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,789 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 3rd percentile – i.e., 3% 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 275,733 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.