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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.


The distribution of the means from 160 distributions that combined Q2–Q4 (5 experts scoring 32 sightings).The 160 distributions resulted from pooling linearly or logarithmically. The dotted line indicates the median and the shaded error indicates the interquartile range.
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fig-4: The distribution of the means from 160 distributions that combined Q2–Q4 (5 experts scoring 32 sightings).The 160 distributions resulted from pooling linearly or logarithmically. The dotted line indicates the median and the shaded error indicates the interquartile range.

Mentions: We summarise the distributions from linear pooling and logarithmic pooling by their means. The distributions of these means (Fig. 4) are similar to each other, which is consistent with the examples discussed earlier (Fig. 3). More importantly, the pooled distributions are considerably different to the distribution of the ‘best’ estimate for Q1 (Fig. 1A). The median is reduced from 0.79 to 0.68 (linear pooling) or 0.66 (logarithmic pooling), and the interquartile range (in both linear and logarithmic pooling) is approximately 0.3, which is 150% of the interquartile range for Q1. The interquartile range, as with all the questions, is centred evenly around the median. The pooled interquartile ranges are smaller than the interquartile range for Q4 (0.46), demonstrating that neither pooling processes extend the variance of the resulting distribution (and thus loose certainty) in order to represent the pooled responses.


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)

The distribution of the means from 160 distributions that combined Q2–Q4 (5 experts scoring 32 sightings).The 160 distributions resulted from pooling linearly or logarithmically. The dotted line indicates the median and the shaded error indicates the interquartile range.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-4: The distribution of the means from 160 distributions that combined Q2–Q4 (5 experts scoring 32 sightings).The 160 distributions resulted from pooling linearly or logarithmically. The dotted line indicates the median and the shaded error indicates the interquartile range.
Mentions: We summarise the distributions from linear pooling and logarithmic pooling by their means. The distributions of these means (Fig. 4) are similar to each other, which is consistent with the examples discussed earlier (Fig. 3). More importantly, the pooled distributions are considerably different to the distribution of the ‘best’ estimate for Q1 (Fig. 1A). The median is reduced from 0.79 to 0.68 (linear pooling) or 0.66 (logarithmic pooling), and the interquartile range (in both linear and logarithmic pooling) is approximately 0.3, which is 150% of the interquartile range for Q1. The interquartile range, as with all the questions, is centred evenly around the median. The pooled interquartile ranges are smaller than the interquartile range for Q4 (0.46), demonstrating that neither pooling processes extend the variance of the resulting distribution (and thus loose certainty) in order to represent the pooled responses.

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.