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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.


Two examples of pooling Q2–Q4 linearly and logarithmically. The triangle distributions are from responses to Q2, Q3 and Q4.In (B), “Q2–Q4 combined” is the consensus distribution from pooling these three triangle distributions. This process is carried out for all sightings for all experts.
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fig-3: Two examples of pooling Q2–Q4 linearly and logarithmically. The triangle distributions are from responses to Q2, Q3 and Q4.In (B), “Q2–Q4 combined” is the consensus distribution from pooling these three triangle distributions. This process is carried out for all sightings for all experts.

Mentions: Having established that asking Q2–Q4 more fully explores the different factors that might influence whether a sighting is valid, we need to consider how to combine these three responses. Linear and logarithmic pooling provide a very similar distribution to each other when the variation among Q2–Q4 are similar, see the example in Fig. 3A. When the variation among Q2–Q4 is larger, there is a more noticeable difference between the two pooling methods, especially in the bounds, see the example in Fig. 3B. These differences will be compounded once we pool the consensus distribution for each expert. For now we combine Q2–Q4 for each sighting, from each expert, and compare the resulting means (the peak of the distribution) from these 160 pooled opinions.


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)

Two examples of pooling Q2–Q4 linearly and logarithmically. The triangle distributions are from responses to Q2, Q3 and Q4.In (B), “Q2–Q4 combined” is the consensus distribution from pooling these three triangle distributions. This process is carried out for all sightings for all experts.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-3: Two examples of pooling Q2–Q4 linearly and logarithmically. The triangle distributions are from responses to Q2, Q3 and Q4.In (B), “Q2–Q4 combined” is the consensus distribution from pooling these three triangle distributions. This process is carried out for all sightings for all experts.
Mentions: Having established that asking Q2–Q4 more fully explores the different factors that might influence whether a sighting is valid, we need to consider how to combine these three responses. Linear and logarithmic pooling provide a very similar distribution to each other when the variation among Q2–Q4 are similar, see the example in Fig. 3A. When the variation among Q2–Q4 is larger, there is a more noticeable difference between the two pooling methods, especially in the bounds, see the example in Fig. 3B. These differences will be compounded once we pool the consensus distribution for each expert. For now we combine Q2–Q4 for each sighting, from each expert, and compare the resulting means (the peak of the distribution) from these 160 pooled opinions.

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.