dafnee.isem-evolution.fr

Thank you to all of my wonderful co-authors, who are too numerous to shout out individually!

False negatives, i.e., an individual was resighted but erroneously marked as a first capture, dictated the optimal strategy in many cases. A 2% increase in the false negative rate translated to a 5% increase in the relative bias in abundance estimates. This rate is easy to compute in practice.

We fed the results into a custom optimization tool to discern the optimal strategy, that is, the optimal method for generating capture histories with an algorithm, for each dataset. We found that true automation was optimal when the algorithm matched images well.

Automating photo-ID creates tradeoffs between reducing effort and increasing estimation error. We explored these tradeoffs with a simulation study, informed by 39 photo-ID datasets representing 24 cetacean species.

False negatives, i.e., an individual was resighted but erroneously marked as a first capture, dictated the optimal strategy in many cases. A 2% increase in the false negative rate translated to a 5% increase in the relative bias in abundance estimates. This rate is easy to compute in practice.

We fed the results into a custom optimization tool to discern the optimal strategy, that is, the optimal method for generating capture histories with an algorithm, for each dataset. We found that true automation was optimal when the algorithm matched images well.

Automating photo-ID creates tradeoffs between reducing effort and increasing estimation error. We explored these tradeoffs with a simulation study, informed by 39 photo-ID datasets representing 24 cetacean species.

I’d love to be added if there’s space!

Exactly. Most procedures like this create more waste than they would have prevented.