PITMAR History

Since year 2000 Neotropico Foundation takes and stores facial pictures of injured sea turtles. These specimens are rehabilitated in collaboration with Tenerife Island Council and its Wildlife Rescue Centre, called La Tahonilla. In year 2007 Neotropico Foundation registered PITMAR (Programa de Identificacion de Tortugas Marinas= Marine Turtle Identification Program by its acronym in spanish) as a copyrighted program.

To tell apart each turtle specimen, Neotropico Foundation uses a tagging technique known as photo-identification performed on the scale pattern of the temporal region of the turtle. This pattern is virtually unique in nature and combined with other data identifies individually each turtle from a potential population of hundreds of thousands.

The data set formed by the number of scales in this region, their shape and arrangement is unique to each turtle, as if it were a human fingerprint that only grows as the animal ages. Traumatic changes are possible in that area but the use of pictures from both head sides and the combination with the use of microchips make it the best system of universal tagging of sea turtles.

In PITMAR each point delimiting face scale polygons (called nodes) are used. The result is a cloud of points or nodes with a similar to a star constellation layout. With two correction factors (called marks), the algorithm homogenizes and compensates variations of the pattern over time and partially corrects mismatches caused by incorrect angle at the time of taking the pictures. Once the point clouds are compared, the program chooses the 15 registers with the highest number of matches and presents them to the operator for positive identifications.

If it is a new pattern, the specimen is not registered and the program can generate a new record with the information available.

If ia registered pattern is found, then it is a recapture and the data are stored in the sightings history for that turtle.

Anywhere in the world, with a simple digital camera and an Internet connection it will be able to know if a given turtle is registered and its history.

Decisions on international conservation measurements and law inforcement must be based on actual verifiable scientific data and the PITMAR provides free tools needed for this work.

Its potential spread covers tens of thousands of people all over the world becoming not only a program of scientific value but constituting a link for transnational collaboration and a helpful system for environmental awareness and education.

The PITMAR is freely accessible via internet, it is a non-invasive, non-traumatic, versatile and innovative system for tagging marine turtles.


REFERENCES used in the development of PITMAR (related to photo-identification techniques in zoology, sea turtle tagging, tag loss, etc).


Alibhai, S.K., Jewell, Z.C. & P.R. Law. 2008. A footprint technique to identify white rhino Ceratotherium simum at individual and species levels. Endang Species Res4:205–218.


Anderson, C.J.R., Roth, J.D., & J.M. Waterman. 2007. Can whisker spot patterns be used to identify individual polar bears?. Journal of Zoology 273: 333-339.


Arzoumanian, Z., Holmberg, J. & B. Norman. 2005. An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus. Journal of Applied Ecology 42: 999 – 1011.


Balazs, G. H. 1982. Factors affecting the retention of metal tags on sea turtles. Mar Turtle Newsl 20:11–14


Balazs, G. H. 1999. “Factors to consider in the tagging of sea turtles,” in Research and Management Techniques for the Conservation of Sea Turtles, K. L. Eckert, K. A. Bjorndal, F. A. Abreu-Grobois, and M. Donnelly, Eds., pp. 101–109, IUCN/SSC Marine Turtle Specialist Group, 1999.


Bansemer, C.S. & M.B Bennett. 2008. Multi-year validation of photographic identification of grey nurse sharks, Carcharias taurus, and applications for non-invasive conservation research. Marine and Freshwater Research 59: 322-331.


Beekmans, P.M., Whitehead, H.Huele, R., Steiner, L. & A.G. Steenbeek. 2005. Comparison of Two Computer-Assisted PhotoIdentification Methods Applied to Sperm Whales (Physeter macrocephalus). Journal: Aquatic Mammals , vol. 31, no. 2, pp. 243-247, 2005.


Bendik, N.F., Morrison, T.A., Gluesenkamp, A.G., Sanders, M.S. & L.J. O’Donnell. 2013. Computer-Assisted Photo Identification Outperforms Visible Implant Elastomers in an Endangered Salamander, Eurycea tonkawae. PLoS ONE 8(3): e59424.


Bennett, P., Keuper-Bennett, U. & G.H. Balazs. 2000. Photographic evidence for the regression of fibropapilloma afflicting green turtles at Honokawai, Maui, in the Hawaiian Islands. In: Kalb H, Wibbels T (eds) Proc 19th Annu Symp Sea Turtle Biol Cons NOAA Tech Memo NMFS-SEFSC-443:37–39


Bennet, P. & U. Keuper-Bennet. 2001. The use of subjective patterns in green turtle profiles to find matches in an image database. In Proceedings of the 21st annual symposium on sea turtle biology and conservation. 2005. Compiled by Michael Coyne & Randall D. Clark. NOAA Technical Memorandum NMFS-SEFSC 528.


Bjorndal,  K.A., Bolten, A.B., Lagueux, C.J. & A. Chaves. 1996.  Probability of tag loss in green turtles nesting at Tortuguero, Costa Rica. J Herpetol 30:566–571


Bloch, N. & D. J. Irschick. 2004.  Toe-Clipping Dramatically Reduces Clinging Performance in a Pad-Bearing Lizard (Anolis carolinensis). Journal of Herpetology, Vol. 37, No. 3, pp. 293–298, 2004.


Blumenthal, J. M., Solomon, J.L.,  Bell, C.D.,  Austin, T.J.,  Ebanks-Petrie, G.,  Coyne, M.S.,  Broderick, A.C. & B. J. Godley. 2006.Satellite tracking highlights the need for international cooperation in marine Turtle management. Endangered Species Research. Esr 7: 1–11, 2006


Buonantony, D. 2008. An Analysis of Utilizing the Leatherback’s Pineal Spot for Photo-identification. Master of Environmental Management – CEM May 2008


Caci, G., Biscaccianti, A.B., Cistrone, L., Bosso, L., Garonna, A.B. &   D. Russo. 2013. Spotting the right spot: computer-aided individual identification of the threatened cerambycid beetle Rosalia alpine. Journal of Insect Conservation. August 2013, Volume 17, Issue 4, pp 787-795


Chassagneux, A., Jean, C., Bourjea, J. & S. Ciccione. 2013. Unraveling Behavioral Patterns of Foraging Hawksbill and Green Turtles Using Photo-Identification. Marine Turtle Newsletter 137:1-5.


De Urioste, J, Bethencourt, M.J. & H. Sicilia. 2014 (In Press).  Sea Turtle Photo-Identification. In “Advances in research techniques and conservation strategies for sea turtle”. Nova Science Publishers, Inc.


Dixon, D.R. 2003. A non-invasive technique for identifying individual badgers Meles meles. Mammal Review 33: 92-94.


Dunbar SG, Ito HE, Bahjri K, Dehom S y L. Salinas. 2014. Recognition of juvenile hawksbills Eretmochelys imbricata through face scale digitization and automated searching. Endang Species Res 26:137-146


Féliz, P., León, Y.M, Revuelta, O., Aucoin, S., Sofia, D. & R. Carreras. 2010. Photoidentification of juvenile hawksbills using facial scales. Poster presented at the 30th annual symposium on sea turtle biology and conservation.24-30 April 2010. Goa. India.


Frisch, A.J. & J.A. Hobbs. 2007. Photographic identification based on unique, polymorphic colour patterns: a novel method for tracking a marine crustacean. J Exp Mar Biol Ecol 351:294–299


Godley, B. J., Blumenthal, J. M., Broderick, A. C., Coyne, M. S., Godfrey, M. H., Hawkes, L. A. & M. J. Witt. 2007. Satellite tracking of sea turtles: Where have we been and where do we go next?. Endangered Species Research. Vol. 3: Preprint , 2007


Hendrickson, L.P. & J.R. Hendrickson. 1981. A new method for marking sea turtles? Marine Turtle Newsletter 19:6-7.


Hiby, L., Lundberg, T., Karlsson, O., Watkins, J., Jüssi, M., Jüssi, I. & B. Helander. 2007. Estimates of the size of the Baltic grey seal population based on photo-identification data. NAMMCO Sci. Publ. 6:163-175.


Huffard, C.L., Caldwell, R.L., DeLoach, N., Gentry, D.W., Humann, P., MacDonald, B., Moore, B., Ross, R., Uno, T. & S. Wong. 2008. Individually Unique Body Color Patterns in Octopus (Wunderpus photogenicus) Allow for Photoidentification. PLoS ONE 3(11): e3732.


Jean, C., Ciccione, S., Talma, E., Ballorain, K. & J. Bourjea. 2010. Photo-identification method for green and hawksbill turtles - First results from Reunion. Indian Ocean Turtle Newsletter No. 11


Karanth, K.U., Nichols, J.D., Kumar, N.S. & J.E. Hines. 2006. Assessing tiger population dynamics using photographic capture-recapture sampling. Ecology. 2006 Nov;87(11):2925-37.


Kelly, M. J. 2001. Computer-aided photograph matching in studies using individual identification: an example from Serengeti cheetahs. 440 Journal of Mammalogy, 82(2):440–449, 2001.


Langtimm, C. A., Beck, C. A., Edwards, H. H., Fick-Child, K. J., Ackerman, B. B., Barton, S. L. & W.C. Hartley. 2004. Survival estimates for Florida manatees from the photo-identification of individuals. Marine Mammal Science, 20: 438–463.


Limpus, C.J. 1992. Estimation of tag loss in marine turtle research. Wildl Res 19:457–469.


Lowe, D.G. 2004. Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, 60, 2 (2004) 91–110


Lloyd, J.R., Maldonado, M.A. & R. Stafford, 2012. Methods of Developing User-Friendly Keys to Identify Green Sea Turtles (Chelonia mydas L.) from Photographs. International Journal of Zoology, vol. 2012, Article ID 317568, 7 pages, 2012. doi:10.1155/2012/317568


McDonald, D., Dutton, P. H., Brandner, R. & S. Basford. 1996. Use of pineal spot ('pink spot') photographs to identify leatherback turtles. Herpetological Review 27 (1): 11-12. 1996.


Meekan,  M.G., Speed, C.W., Planes, S., McLean, C. & C. Bradshaw. 2008. Population monitoring for whale sharks (Rhincodon typus). Report prepared for the Australian Government Department of the Environment, Water, Heritage and the Arts. Australian Institute of Marine Science, Townsville. 195 pp.


Mrosovsky, N. & M.H. Godfrey. 2003. Living tag, living reputation. Marine Turtle Newsletter 99: 3-4.


Apparent sea turtle mortality due to flipper tags


Niest, E.K., Burns, D. & P. Harrison. 2010. Fluke Matcher: A computer-aided matching system for humpback whale (Megaptera novaeangliae) flukes. MARINE MAMMAL SCIENCE, 26(3): 744–756 (July 2010).


Olson, P.A. 2009. Blue whale photo-identification from IWC IDCR/SOWER surveys. IWCSC/62/SH29.


Pauwels, E.J., de Zeeuw, P. M. & D. M. Bounantony. 2008. Leatherbacks Matching by Automated Image Recognition. Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects Lecture Notes in Computer Science Volume 5077, 2008, pp 417-425.


Reisser, J., Proietti, M., Kinas, P. & I. Sazima. 2008. Photographic identification of sea turtles: method description and validation, with an estimation of tag loss. Endangered Species Research.Vol. 5: 73–82, 2008.


Richardson, A., Herbst, L.H., Bennett, P.A., Keuper-Bennett, U., 2000. Photo-identification of Hawaiian green sea turtles. In: Abreu-Grobois, F.A., Briseno-Duenas, R., Marquez, R., Sarti, L. (Eds.), Proceedings of the 18th International Symposium on Sea Turtle Biology and Conservation. NOAA Technical Memorandum NMFS-SEFSC-436, p. 293.


Rowat, D.,  Speed, C.W.,  Meekan, M.G.,  Gore, M.A. & C.J.A. Bradshaw. 2009. Population abundance and apparent survival of the Vulnerable whale shark Rhincodon typus in the Seychelles aggregation. Fauna & Flora International, Oryx, 43(4), 591–598


Runyan, A.L. & P. A. Meylan. 2005. PIT Tag Retention in Trachemys and PseudemysHerpetological Review , 2005, 36(1), 45–47.


Sacchi, R., Scali, S., Fasola, M., & P. Galeotti, 2007. The numerical encoding of scale morphology highly improves photographic identification in lizards. Acta Herpetologica 2: 27-35.


Sacchi, R., Scali, S.,  Pellitteri-Rosa, D.,  Pupin, F.,  Gentilli, A., Tettamanti, S., Cavigioli, L.,  Racina, L.,  Maiocchi, V.,  Galeotti, P. & M. Fasola. 2010. Photographic identification in reptiles: a matter of scales. Amphibia-Reptilia, Volume 31, Number 4, 2010 , pp. 489-502(14).


Schofield, G., Katselidis, K.A., Pantis, J.D., 2004. Assessment of photo-identification and GIS as a technique to collect in-water information about loggerhead sea turtles in Laganas Bay, Zakynthos Greece. Proceedings of the Twenty-fourth Annual. Symposium on Sea Turtle Biology & Conservation U.S. Dept. Commerce. NOAA Tech. Memo. NMFS-SEFSC.


Schofield, G., Katselidis, K.A., Dimopoulos, P. & J. D. Pantis. 2008. Investigating the viability of photo-identification as an objective tool to study endangered sea turtle populations. Journal of Experimental Marine Biology and Ecology 360 (2008) 103–108


Sherley, R.B., Burghardt, T., Barham, P.J., Campbell, N. & I.C. Cuthill. 2010. Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus. Endang Species Res 11:101-111.


Smyth, B. & S. Nebel. 2013. Passive Integrated Transponder (PIT) Tags in the Study of Animal Movement. Nature Education Knowledge 4(3):3


Van Dam, R.P. & C.E. Diez. 1999. Differential tag retention in Caribbean hawksbill turtles. Chelonian Conserv Biol 3: 225–229


White, M., 2006. Photo-recognition: a technique used to identify individual loggerhead turtles in the marine environment. In: Frick, M., Panagopoulou, A., Rees, A.F., Williams, K. (Eds.), Book of Abstracts. Twenty-sixth Annual Symposium on Sea Turtle Biology and Conservation. International Sea Turtle Society, Athens, Greece, Crete, Greece, p. 376.


Wright, K. M. 2001. Surgical techniques. Pp. 273 – 283 in: Wright, K. M. and Whitaker, B. R. (eds.) Amphibian Medicine and Captive Husbandry.  Krieger Publishing Company, Malabar, Florida.


Yasuda, T. & A. Nobuaki. 2005. Fine-Scale Tracking of Marine Turtles Using GPS-Argos PTTs. Zoological Science 22(5):547-553. 2005


Zeeuw, P. M., Pauwels, E.J., Ranguelova, E.B, Buonantony, D.M. & S.A. Eckert. 2010. Computer assisted photo identification of Dermochelys coriacea. In proceedings of: International Conference on Pattern Recognition (ICPR) 2010.