New preprint: we advertise DAFNEE, a database of academia-friendly eco-evo-archaeo journals. 1/6
#AcademicPublishing #ecoevo #archaeology #EthicalPublishing #SocietyJournals #DiamondOpenAccess
doi.org/10.32942/X24...

Gray whale numbers plummet to their lowest level in 50 years. Now far from their recovered level
www.seattletimes.com/seattle-news...

Shoutout to my incredible lab mate, Martin! Such a rad paper!

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.

I’m thrilled to see my dissertation’s second chapter published in Conservation Biology. In the paper, we found that researchers can use high-performance identification algorithms to reduce the cost of population assessments without biasing abundance estimates. tinyurl.com/ne8tbpw3

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.

Hi everyone! I'm a PhD candidate studying new tools for assessing stocks of whales and dolphins. Here is the first chapter of my dissertation, where we explore the strengths and weaknesses of a multispecies individual identification algorithm. besjournals.onlinelibrary.wiley.com/doi/full/10....