| Abstract |
This article is a continuation of the review of software for storing, processing and analysing data from camera traps. The review includes the latest software for processing data from camera traps that have appeared in recent years, as well as some already outdated solutions. For the first time, the topics of work of such software with video files and GIS are highlighted in Russian. We provide detailed descriptions of such thematic software as SpeedyMouse, TrophyRoom, ViXeN, camtrapR, CPW Photo Warehouse, Wild.ID, Camelot and ZSL CTAP. We briefly reviewed online services for camera trap data processing and web applications: eMammal, Snapshot Serengeti, Agouti, Western Shield, TRAPPER, as well as the global data repository for camera traps data – Wildlife Insights. The possibilities of working with camera traps in GIS are separately considered: e.g. plugins for QGIS (Geotag and Import Photos and eVis), as well as the GeoTagged Photos to Points tool for ArcGIS. Many advantages and disadvantages of the presented solutions, as well as some methods of working with them, are analysed. On the example of own studies in the Central Forest Nature Biosphere Reserve (Russia) a comparative analysis of various programs and approaches in the use of camera traps as a method of zoological studies was conducted. In addition, general recommendations were given on working with them for other Protected Areas. We identified the key characteristics of the programs for working with camera traps that are worth paying attention to when choosing a particular solution. For all of these software packets, there are hyperlinks for their free download, as well as hyperlinks to user manuals and tutorials. The purpose of the article was not only to identify the most optimal (from author's viewpoint) tools for working with the data of camera traps, but also to acquaint readers with the diversity of such means in world practice. |
| References |
Amin R., Wacher T. 2017. A new comprehensive package for the management and analysis of camera trap data for monitoring antelopes and other wild species. Gnusletter 34(2): 21–23.
Barrueto M., Clevenger A.P., Dorsey B., Ford A.T. 2013. A better solution for photo-classification, automatic storage and data input of camera data from wildlife crossing structures. In: International Conference on Ecology and Transportation (ICOET 2013). Scottsdale, Arizona, USA. P. 1–11.
Bolger D.T., Morrison T.A., Vance B., Lee D., Farid H. 2012. A computer-assisted system for photographic mark-recapture analysis. Methods in Ecology and Evolution 3(5): 813–822. DOI: 10.1111/j.2041-210X.2012.00212.x
Bubnicki J.W., Churski M., Kuijper D.P.J. 2016. TRAPPER: An open source web-based application to manage camera trapping projects. Methods in Ecology and Evolution 7(10): 1209–1216. DOI: 10.1111/2041-210X.12571
Burton A.C., Neilson E., Moreira D., Ladle A., Steenweg R., Fisher J.T., Bayne E., Boutin S. 2015. Wildlife camera trapping: A review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology 52(3): 675–685. DOI: 10.1111/1365-2664.12432
Efford M.G. 2015. Package 'secr': Spatially explicit capture-recapture. Version 2.9.3. Available from http://cran.r-project.org/web/packages/secr/secr.pdf
Fiske I.J., Chandler R.B. 2011. Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance. Journal of Statistical Software 43(10): 1–23. DOI: 10.18637/jss.v043.i10
Forrester T., McShea W.J., Keys R.W., Costello R., Baker M., Parsons A. 2013. eMammal – Citizen science camera trapping as a solution for broad-scale, long-term monitoring of wildlife populations. In: Sustainable Pathways: Learning from the Past and Shaping the Future, 98th ESA Annual Convention. Minneapolis, USA.
Harvey P. 2015. ExifTool: Read, write and edit meta information. Version 10.05. Available from http://www.sno.phy.queensu.ca/~phil/exiftool/
Hendry H., Mann C. 2018. Camelot – Intuitive software for camera-trap data management. Oryx 52(1): 15–15. DOI: 0.1017/S0030605317001818
Hines G., Swanson A., Kosmala M., Lintott C. 2015. Aggregating user input in ecology citizen science projects. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (Austin, Texas, 25–30 January 2015). Austin, Texas. P. 3975–3980.
Hines J.E. 2017. PRESENCE 12.1. Software to estimate patch occupancy and related parameters. Available from https://www.mbr-pwrc.usgs.gov/software/presence.html
Ivan J.S., Newkirk E.S. 2016. CPW Photo Warehouse: A custom database to facilitate archiving, identifying, summarizing and managing photo data collected from camera traps. Methods in Ecology and Evolution 7(4): 499–504. DOI: 10.1111/2041-210X.12503
Laake J.L. 2014. RMark: An R Interface for Analysis of Capture-Recapture Data with MARK. Available from http://cran.r-project.org/web/packages/RMark/index.html
López-Tello E., Mandujano S. 2017. Paquete camtrapR para gestionar datos de foto-trampeo: aplicación en la reserva de biosfera tehuacán-cuicatlán. Revista Mexicana de Mastozoología Nueva época 7(2): 13–37.
Mandujano S. 2017. Monitoreo de la biodiversidad de mamíferos en áreas naturales protegidas empleando cámaras-trampa: sugerencias de herramientas para la gestión y el análisis numérico de las fotos. Paraquaria Natural 5(1): 22–31.
McShea W.J., Forrester T., Costello R., He Z., Kays R. 2016. Volunteer-run cameras as distributed sensors for macrosystem mammal research. Landscape Ecology 31(1): 55–66. DOI: 10.1007/s10980-015-0262-9
Niedballa J., Sollmann R., Courtiol A., Wilting A. 2016. camtrapR: An R package for efficient camera trap data management. Methods in Ecology and Evolution 7(12): 1457–1462. DOI: 10.1111/2041-210X.12600
Norouzzadeh M.S., Nguyen A., Kosmala M., Swanson A., Palmer M.S., Packer C., Clune J. 2018. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proceedings of the National Academy of Sciences of the United States of America 115(25): E5716–E5725. DOI: 10.1073/pnas.1719367115
Ogurtsov S.S., Volkov V.P., Zheltukhin A.S. 2017. Review of some actual methods of storage, processing and analysis of data from camera traps in zoological research. Nature Conservation Research 2(1): 73–98. DOI: 10.24189/ncr.2017.057 [In Russian]
Orrell T., Nicolson D. 2018. ITIS Global: The Integrated Taxonomic Information System (version Jun 2017). In: Y. Roskov, G. Ower, T. Orrell, D. Nicolson, N. Bailly, P.M. Kirk, T. Bourgoin, R.E. DeWalt, W. Decock, A. De Wever, E. van Nieukerken, J. Zarucchi, L. Penev (Eds.): Species 2000 & ITIS Catalogue of Life (24 September 2018). Naturalis, Leiden (Netherlands): Species 2000. Available from www.catalogueoflife.org/col.
R Development Core Team. 2018. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available from http://www.R-project.org
Ramachandran P., Devarajan K. 2018. ViXeN: An open-source package for managing multimedia data. Methods in Ecology and Evolution 9(3): 785–792. DOI: 10.1111/2041-210X.12892
Rovero F., Zimmermann F. 2016. Camera Trapping for Wildlife Research. Exeter: Pelagic Publishing, UK. 433 p.
Rowcliffe J.M., Carbone C. 2008. Surveys using camera traps: Are we looking to a brighter future? Animal Conservation 11(3): 185–186. DOI: 10.1111/j.1469-1795.2008.00180.x
Scotson L., Johnston L.R., Iannarilli F., Wearn O.R., Mohd-Azlan J., Wong W., Gray T.N.E., Dinata Y., Suzuki A., Willard C.E., Frechette J., Loken B., Steinmetz R., Moßbrucker A.M., Gopalasamy R.C., Fieberg J. 2017. Best practices and software for the management and sharing of camera trap data for small and large scales studies. Remote Sensing in Ecology & Conservation 3(3): 158–172. DOI: 10.1002/rse2.54
Spoelstra K. 2016. SpeedyMouse 2.2 for the analysis of camera trap images. Software Manual. DOI: 10.13140/RG.2.1.4076.5528.
Steenweg R., Hebblewhite M., Kays R., Ahumada J., Fisher J.T., Burton C., Townsend S.E., Carbone A., Rowcliffe M., Whittington J., Brodie J., Royle A., Switalski A., Clevenger A.P., Heim N., Rich L.N. 2016. Scaling-up camera traps: Monitoring the planet's biodiversity with networks of remote sensors. Frontiers in Ecology and the Environment 15(1): 26–34. DOI: 10.1002/fee.1448
Swanson A., Kosmala M., Lintott C., Packer C. 2016. A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology 30(3): 520–531. DOI: 10.1111/cobi.12695
Swinnen K.R.R., Reijniers J., Breno M., Leirs H. 2014. A novel method to reduce time investment when processing videos from camera trap studies. PLoS ONE 9(6): e98881. DOI: 10.1371/journal.pone.0098881
Tobler M. 2015. Camera Base. Version 1.7. User Guide. 38 p.
Wong W.-M., Kachel S. 2016. Camera trapping: Advancing the technology. In: T. McCarthy, D. Mallon, P.J. Nyus (Eds.): Snow leopards. Biodiversity of the world: Conservation from genes to landscapes. London, UK: Academic Press. P. 383–394. DOI: 10.1016/B978-0-12-802213-9.00028-6
Young S., Rode-Margono J., Amin R. 2018. Software to facilitate and streamline camera trap data management: A review. Ecology and Evolution 8(19): 9947–9957. DOI: 10.1002/ece3.4464
Zheltukhin A.S., Ogurtsov S.S. 2018. Camera traps in the monitoring of forest mammals and birds. Tver: Polypress. 54 с. [In Russian]
|
