Photographic Mark-Recapture: Applications and Utility for the Study of an Endangered Aquatic Salamander, Eurycea tonkawae

Nathan F. Bendik

PhotoID• Using photographs to identify individual animals that have

unique natural marks• Manually (by-eye)• Computer-assisted

Example organisms

Common name Photo ID method Reported accuracy N photos Year published

Marbled and Spotted salalamanders manual not given 91 1985

Daruma Pond frogs manual not given 196 2003

Moroccan rock lizard manual 100% 99 2004

Blue Ridge Two-Lined salamander manual 94% 270 2004

Western Diamondback rattlesnakes manual 99% 261 2004

Spotted salamanders pattern-mapping + manual 100% 654 2006

Eastern tiger salamanders manual? (not given) not given 1511 2007

Marbled salamanders custom algorithm and software 95-99% 1008 2008

Leatherback sea turtles custom software (SIFT) 100% 613 2008

Green-eyed tree frogs manual 62% 59 2009

Common wall lizards I3S 98% 1043 2010

Long-tailed salamanders manual not given 4000 2011

Jewelled geckos manual 100% 855 2012

Southern Red-Bellied toads Wild-ID (manual) 90% (99%) 492 2012

Indian Gliding lizards I3S 100% 59 2013

Why photo ID?

Why PhotoID (for E. tonkawae)?

• Less invasive than Visible Implant Elastomer tags • Cheaper over the long run• Faster in the field• Easier to implement

Automated Matching Programs

Wild-ID

• Wild-ID• Pros:

• Simple: provide photos folder and a database folder, and it scores the photos

• No manual input of data for each photo- FAST• Can easily add more photos later to a saved project db• Allows user to “accept” or “reject” up to 20 potential matches• Saves sessions so you can quit matching and return any time

• Cons:• No individual metadata• Workflow is not “smart”• Brute force: takes all photos in folder and scores them sequentially (by

name) in one direction, i.e., B to A, but not A to B• Cannot parse out known non-matches• Requires file naming scheme and significant manipulation in R to process

data and combine with metadata

Bolger, D. T., T. A. Morrison, B. Vance, D. Lee, and H. Farid. 2012. A computer-assisted system for photographic mark–recapture analysis. Methods in Ecology and Evolution 3:813–822.

StripeSpotter

• StripeSpotter• Pros:

• Workflow is intuitively structured with capture-recapture in mind• Includes 2 matching algorithms• Extracts EXIF data (e.g. date, time, exposure)• Allows inclusion of individual metadata for each record such as sex,

location, etc.• .csv output convenient and detailed- very useful for post-processing

data

• Cons:• Many steps; may be cumbersome for large photo databases

1. Select animal’s body using mouse via rectangle selection tool2. Save new animal to database3. To match, user must click “identify animal” for each individual and 4. Use the rectangle selection tool

• Does not save session history (remember where you left off or don’t quit mid-session)

M. Lahiri, C. Tantipathananandh, R. Warungu, D.I. Rubenstein, T.Y. Berger-Wolf. Biometric Animal Databases from Field Photographs: Identification of Individual Zebra in the Wild. Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR 2011), Trento, Italy, 2011

I3S: Interactive Individual Identification System

• I3S: Interactive Individual Identification System • Pros:

• Works well where other programs may perform poorly (e.g. less complex patterns but easily distinguishable spots)

• Matching seems fast• Easy to check images one at a time (e.g. against a large database) to

ID an animal

• Cons:• Must generate fingerprint file for each photo (lots of clicking)• Capture-recapture data processing must be done manually (either as

you are matching or based on the resulting database).

Van Tienhoven, A.M., Den Hartog, J.E., Reijns, R.A., & Peddemors, V.M. 2007. A computer-aided program for pattern-matching natural marks on the spotted raggedtooth shark Carcharias taurus (Rafinesque, 1810). Journal of Applied Ecology 44:273–280.

Applications for E. tonkawae• Standard mark-recapture data analysis (pop. size, survival…)• Analysis of movement patterns • Change in body condition of individuals

Bendik, N. F., and A. G. Gluesenkamp. 2013. Body length shrinkage in an endangered amphibian is associated with drought. Journal of Zoology 290:35–41.

PhotoID: Validation• Compared visible implant elastomer tags (VIEs) to computer-

assisted photoID (752 VIE-tagged individuals, 1367 photos)• Used scores from Wild-ID

Bendik, N. F., T. A. Morrison, A. G. Gluesenkamp, M. S. Sanders, and L. J. O’Donnell. 2013. Computer-Assisted Photo Identification Outperforms Visible Implant Elastomers in an Endangered Salamander, Eurycea tonkawae. PLoS ONE 8:e59424.

Error Rates• VIE• 1.9% False Rejections• 1.8% False Acceptances

• Computer-assisted PhotoID• 0.76% False Rejections• 0 False Acceptances

Change in similarity score over time

Field setup

Software

Advantages • Photographs are easy to obtain and cheap • Less invasive than toe clipping, VIEs, PIT tagging• Can be a lot faster in the field (time to take one photo vs.

inject three tiny elastomer blobs in a two-inch salamander)• Can be more accurate (but not necessarily)

Disadvantages• Animals can be hard to photograph; need good quality

photographs• Lighting• Sharpness• Consistent angle

• Changing natural marks increase error rates• Growth• Injury

• Requires more computer time (summore–alotmore)• Back problems• Eye strain• CTS

Acknowledgements• Collaborators

Tom Morrison, Andy Gluesenkamp, Mark Sanders, Lisa O’Donnell, Kira McEntire

• Field Assistance Blake Sissel, Matt Westbrook, Liza Colucci, Mike Colucci, Laurie

Dries, Heather Perry, Leah Gluesenkamp, Beth Bendik, Alisha Shah, Helen Snook, Todd Jackson, Melanie Pavlas-Snyder, COA Interns

• Technical AssistanceBennett Vance, Josh O’Brien, Rob Clayton

• Megan Chesser and Danny Martin

Photo dataset

Years #Individuals #Captures#Matching photo pairs (recaptures)

Error Rates

VIE FRR VIE FARVIE error sample size per iteration

Photo FRR Photo FARPhotoID error sample

size per iteration

Low quality 2007 473 965 896 0.0190 (0.0003)

0.0178 (0.0002) 554

0.1591 (0.0001) 0.0132 (0.0004) 264

High quality2008-2010 742 1367 1090

0.0076 (0.0002) 0.0000 (0.0000) 356