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Assessing uncertainty in sighting records: an example of the Barbary lion.

Lee TE, Black SA, Fellous A, Yamaguchi N, Angelici FM, Al Hikmani H, Reed JM, Elphick CS, Roberts DL - PeerJ (2015)

Bottom Line: We find that asking experts to provide scores for these three aspects resulted in each sighting being considered more individually, meaning that this new questioning method provides very different estimated probabilities that a sighting is valid, which greatly affects the outcome from an extinction model.We consider linear opinion pooling and logarithm opinion pooling to combine the three scores, and also to combine opinions on each sighting.We find the two methods produce similar outcomes, allowing the user to focus on chosen features of each method, such as satisfying the marginalisation property or being externally Bayesian.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematical Institute, University of Oxford , UK.

ABSTRACT
As species become rare and approach extinction, purported sightings can be controversial, especially when scarce management resources are at stake. We consider the probability that each individual sighting of a series is valid. Obtaining these probabilities requires a strict framework to ensure that they are as accurately representative as possible. We used a process, which has proven to provide accurate estimates from a group of experts, to obtain probabilities for the validation of 32 sightings of the Barbary lion. We consider the scenario where experts are simply asked whether a sighting was valid, as well as asking them to score the sighting based on distinguishablity, observer competence, and verifiability. We find that asking experts to provide scores for these three aspects resulted in each sighting being considered more individually, meaning that this new questioning method provides very different estimated probabilities that a sighting is valid, which greatly affects the outcome from an extinction model. We consider linear opinion pooling and logarithm opinion pooling to combine the three scores, and also to combine opinions on each sighting. We find the two methods produce similar outcomes, allowing the user to focus on chosen features of each method, such as satisfying the marginalisation property or being externally Bayesian.

No MeSH data available.


Sightings with experts’ opinions (weighted according to expertise) pooled linearly and logarithmically.The darker lines correspond to more recent sightings.
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fig-5: Sightings with experts’ opinions (weighted according to expertise) pooled linearly and logarithmically.The darker lines correspond to more recent sightings.

Mentions: The averages of Q1, are represented as a triangle distribution, see Fig. 5. The range of these distributions covers a significantly larger range than do both linear and logarithm pooling. This may imply that Q1 received larger bounds than did Q2–Q4, but as previously seen (Fig. 3), linear and logarithm pooling tends to narrow the bounds, meaning that the pooled opinion is stronger than any experts’ opinion on its own. This follows the intuition that opinions from several experts provide a result that we have more confidence in.


Assessing uncertainty in sighting records: an example of the Barbary lion.

Lee TE, Black SA, Fellous A, Yamaguchi N, Angelici FM, Al Hikmani H, Reed JM, Elphick CS, Roberts DL - PeerJ (2015)

Sightings with experts’ opinions (weighted according to expertise) pooled linearly and logarithmically.The darker lines correspond to more recent sightings.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562256&req=5

fig-5: Sightings with experts’ opinions (weighted according to expertise) pooled linearly and logarithmically.The darker lines correspond to more recent sightings.
Mentions: The averages of Q1, are represented as a triangle distribution, see Fig. 5. The range of these distributions covers a significantly larger range than do both linear and logarithm pooling. This may imply that Q1 received larger bounds than did Q2–Q4, but as previously seen (Fig. 3), linear and logarithm pooling tends to narrow the bounds, meaning that the pooled opinion is stronger than any experts’ opinion on its own. This follows the intuition that opinions from several experts provide a result that we have more confidence in.

Bottom Line: We find that asking experts to provide scores for these three aspects resulted in each sighting being considered more individually, meaning that this new questioning method provides very different estimated probabilities that a sighting is valid, which greatly affects the outcome from an extinction model.We consider linear opinion pooling and logarithm opinion pooling to combine the three scores, and also to combine opinions on each sighting.We find the two methods produce similar outcomes, allowing the user to focus on chosen features of each method, such as satisfying the marginalisation property or being externally Bayesian.

View Article: PubMed Central - HTML - PubMed

Affiliation: Mathematical Institute, University of Oxford , UK.

ABSTRACT
As species become rare and approach extinction, purported sightings can be controversial, especially when scarce management resources are at stake. We consider the probability that each individual sighting of a series is valid. Obtaining these probabilities requires a strict framework to ensure that they are as accurately representative as possible. We used a process, which has proven to provide accurate estimates from a group of experts, to obtain probabilities for the validation of 32 sightings of the Barbary lion. We consider the scenario where experts are simply asked whether a sighting was valid, as well as asking them to score the sighting based on distinguishablity, observer competence, and verifiability. We find that asking experts to provide scores for these three aspects resulted in each sighting being considered more individually, meaning that this new questioning method provides very different estimated probabilities that a sighting is valid, which greatly affects the outcome from an extinction model. We consider linear opinion pooling and logarithm opinion pooling to combine the three scores, and also to combine opinions on each sighting. We find the two methods produce similar outcomes, allowing the user to focus on chosen features of each method, such as satisfying the marginalisation property or being externally Bayesian.

No MeSH data available.