Article

Sergey S. Ogurtsov, Senior Researcher of the Central Forest State Nature Biosphere Reserve (172521, Russia, Tver Region, Nelidovo district, Zapovednyi settlement); Junior Researcher of the A.N. Severtsov Institute of Ecology and Evolution of the RAS (119071, Russia, Moscow, Leninsky Prospekt, 33); iD ORCID: https://orcid.org/0000-0002-0859-8954; e-mail: etundra@mail.ru

Ogurtsov S.S. 2023. Mammal population density estimation using camera traps based on a random encounter model: theoretical basis and practical recommendations. Nature Conservation Research 8(1): 1–23. https://dx.doi.org/10.24189/ncr.2023.007

Estimating the population density of mammals has long been one of the problematic tasks of both fundamental population ecology and practical programmes for their conservation and management. The majority of methods for population density estimation using camera traps are focused on individually marked species. This review paper presents the theoretical and practical foundations of a method, Random Encounter Model (REM), used for estimating the population density of unmarked mammal species using camera traps. Based on an extensive analysis of the literature and our personal practical experience, we discussed the theory and practice for the application of this method, as well as its strengths and weaknesses. In this method, if we know parameters of the effective detection zone of a camera trap (radius and angle), and the length of the day range, it is possible to correct the trapping rate (i.e. the number of independent trap events per total number of camera traps-nights) in order to calculate the population density of species. The effective detection zone of a camera trap is determined through modelling using computer vision algorithms. The mammal day range is calculated based on its activity level and travel speed, taking into account behavioural patterns based on machine learning models. For REM, a random or systematic design for the camera trap placements should be used. If camera traps are installed against trails or roads, appropriate correction factors must be applied. The effectiveness and reliability of REM has been confirmed by many independent population density estimates, including capture-recapture analyses, visual transect counts, and scat counts. To date, the implementation of REM and its extensions is presented in the R programming environment. It has been established that the main difficulties in the use of the REM are technical imperfections of the camera traps themselves, the relatively large required number of their stations (at least 50 or more), as well as long calibration work. For all these difficulties, possible solutions are proposed. In conclusion, practical recommendations are provided for the use of REM in studies in Protected Areas.

abundance, day range, monitoring, population size, REM, unmarked species

Received: 08.08.2022. Revised: 28.10.2022. Accepted: 06.11.2022.

Anile S., Ragni B., Randi E., Mattucci F., Rovero F. 2014. Wildcat population density on the Etna volcano, Italy: a comparison of density estimation methods. Journal of Zoology 293(4): 252–261. DOI: 10.1111/jzo.12141
Askerov E., Trepet S.A., Eskina T.G., Bibina K.V., Narkevich A.I., Pkhitikov A.B., Zazanashvili N., Akhmadova K. 2022. Estimation of the Population Densities of Species Prey or Competitor to the Leopard (Panthera pardus) in Hyrcan National Park, Azerbaijan. Biology Bulletin 49(7): 225–232. DOI: 10.1134/S1062359022070020
Balestrieri A., Ruiz-González A., Vergara M., Capelli E., Tirozzi P., Alfino S., Minuti G., Prigioni C., Saino N. 2016. Pine marten density in lowland riparian woods: a test of the Random Encounter Model based on genetic data. Mammalian Biology 81(5): 439–446. DOI: 10.1016/j.mambio.2016.05.005
Blake J.G., Mosquera D. 2014. Camera trapping on and off trails in lowland forest of eastern Ecuador: does location matter? Mastozoología Neotropical 21(1): 17–26.
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
Caravaggi A., Zaccaroni M., Riga F., Schai-Braun S.C., Dick J.T.A., Montgomery W.I., Reid N. 2016. An invasive-native mammalian species replacement process captured by camera trap survey random encounter models. Remote Sensing in Ecology and Conservation 2(1): 45–58. DOI: 10.1002/rse2.11
Carbajal-Borges J.P., Godínez-Gómez O., Mendoza E. 2014. Density, abundance and activity patterns of the endangered Tapirus bairdii in one of its last strongholds in southern Mexico. Tropical Conservation Science 7(1): 100–114. DOI: 10.1177/194008291400700102
Carbone C., Christie S., Conforti K., Coulson T., Franklin N., Ginsberg J.R., Griffiths M., Holden J., Kawanishi K., Kinnaird M., Laidlaw R., Lynam A., Macdonald D.W., Martyr D., McDougal C., Nath L., O'Brien T., Seidensticker J., Smith D.J.L., Sunquist M., Tilson R., Wan Shahruddin W.N. 2001. The use of photographic rates to estimate densities of tigers and other cryptic mammals. Animal Conservation 4(1): 75–79. DOI: 10.1017/S1367943001001081
Chandler R.B., Royle J.A. 2013. Spatially explicit models for inference about density in unmarked or partially marked populations. Annals of Applied Statistics 7(2): 936–954. DOI: 10.1214/12- AOAS610
Chauvenet A.L.M., Gill R.M.A., Smith G.C., Ward A.I., Massei G. 2017. Quantifying the bias in density estimated from distance sampling and camera trapping of unmarked individuals. Ecological Modelling 350: 79–86. DOI: 10.1016/j.ecolmodel.2017.02.007.
Clevenger A.P., Purroy F.J., Pelton M.R. 1990. Movement and Activity Patterns of a European Brown Bear in the Cantabrian Mountains, Spain. Bears: Their Biology and Management 8: 205–211. DOI: 10.2307/3872920
Cusack J.J., Swanson A., Coulson T., Packer C., Carbone C., Dickman A.J., Kosmala M., Lintott C., Rowcliffe J.M. 2015a. Applying a random encounter model to estimate lion density from camera traps in Serengeti National Park, Tanzania. Journal of Wildlife Management 79(6): 1014–1021. DOI: 10.1002/jwmg.902
Cusack J.J., Dickman A.J., Rowcliffe J.M., Carbone C., MacDonald D.W., Coulson T., 2015b. Random versus game trail-based camera trap placement strategy for monitoring terrestrial mammal communities. PLoS ONE 10(5): e0126373. DOI: 10.1371/journal.pone.0126373
Di Bitetti M.S., Paviolo A., De Angelo C. 2014. Camera trap photographic rates on roads vs. off roads: location does matter. Mastozoología Neotropical 21(1): 37–46.
ENETwild Consortium, Grignolio S., Apollonio M., Brivio F., Vicente J., Acevedo P., Palencia P., Petrovic K., Keuling O. 2020. Guidance on estimation of abundance and density data of wild ruminant population: methods, challenges, possibilities. EFSA supporting publication 17(6): 1876E. DOI: 10.2903/sp.efsa.2020.EN-1876
ENETwild Consortium, Pascual-Rico R., Acevedo P., Apollonio M., Blanco-Aguiar J.A., Body G., Brivio F., Broz L., Cagnacci F., Casaer J., Delibes-Mateos M., Ferroglio E., Focardi S., Gieser T., Hahn N., Jansen P., Martínez-Jauregui M., Keuling O., Marvin G., Morelle K., Plis K., Podgorski T., Scandura M., Smith G., Vada R., Zanet S., Vicente J. 2021. Research protocols for designing studies/pilot trials to evaluate and to improve effectiveness of wild boar management in relation to African swine fever virus. Parma: European Food Safety Authority. 91 p.
Foster R.J., Harmsen B.J. 2012. A critique of density estimation from camera-trap data. Journal of Wildlife Management 76(2): 224–236. DOI: 10.1002/jwmg.275
Gamelon M., Filli F., Sæther B.E., Herfindal I. 2020. Multi-event capture-recapture analysis in Alpine chamois reveals contrasting responses to interspecific competition, within and between populations. Journal of Animal Ecology 89(10): 2279–2289. DOI: 10.1111/1365-2656.13299
Garland L., Neilson E., Avgar T., Bayne E., Boutin S. 2020. Random encounter and staying time model testing with human volunteers. Journal of Wildlife Management 84(6): 1179–1184. DOI: 10.1002/jwmg.21879
Garrote G., Pérez de Ayala R., Álvarez A., Martín J., Ruiz M., de Lillo S., Simón M. 2021. Improving the random encounter model method to estimate carnivore densities using data generated by conventional camera-trap design. Oryx 55(1): 99–104. DOI: 10.1017/S0030605318001618
Gilbert N.A., Clare J.D.J., Stenglein J.L., Zuckerberg B. 2020. Abundance estimation of unmarked animals based on camera-trap data. Conservation Biology 35(1): 88–100. DOI: 10.1111/cobi.13517
Glen A.S., Cockburn S., Nichols M., Ekanayake J., Warburton B. 2013. Optimising camera traps for monitoring small mammals. PLoS ONE 8(6): e67940. DOI: 10.1371/journal.pone.0067940
Harmsen B.J., Foster R.J., Silver S., Ostro L., Doncaster C.P. 2010. Differential use of trails by forest mammals and the implications for camera-trap studies: A case study from Belize. Biotropica 42(1): 126–133. DOI: 10.1111/j.1744-7429.2009.00544.x
Hendry H., Mann C. 2018. Camelot – intuitive software for camera-trap data management. Oryx 52(1): 15. DOI: 10.1017/S0030605317001818
Hernandez-Blanco J.A., Rozhnov V.V., Lukarevskiy V.S., Naidenko S.V., Chistopolova M.D., Sorokin P.A., Litvinov M.N., Kotlyar A.K. 2013. Spatially explicit capture-recapture method (SECR, SPACECAP): A new approach to determination of the Amur tiger (Panthera tigris altaica) population density by means of camera-traps. Doklady Biological Sciences 453: 365–368. DOI: 10.1134/S0012496613060033
Hobbs M.T., Brehme C.S. 2017. An improved camera trap for amphibians, reptiles, small mammals, and large invertebrates. PLoS ONE 12(10): e0185026. DOI: 10.1371/journal.pone.0185026
Hofmeester T.R., Rowcliffe J.M., Jansen P.A. 2017. A simple method for estimating the effective detection distance of camera traps. Remote Sensing in Ecology and Conservation 3(2): 81–89. DOI: 10.1002/rse2.25
Howe E.J., Buckland S.T., Després-Einspenner M.L., Kühl H.S. 2017. Distance sampling with camera traps. Methods in Ecology and Evolution 8(11): 1558–1565. DOI: 10.1111/2041-210X.12790
Hutchinson J.M.C., Waser P.M. 2007. Use, misuse and extensions of “ideal gas" models of animal encounter. Biological Reviews 82(3): 335–359. DOI: 10.1111/j.1469-185X.2007.00014.x
Jayasekara D., Mahaulpatha D., Miththapala S. 2021. Population density estimation of meso-mammal carnivores using camera traps without the individual recognition in Maduru Oya National Park, Sri Lanka. Hystrix 32(2): 137–146. DOI: 10.4404/hystrix-00452-2021
Jennelle C.S., Runge M.C., MacKenzie D.I. 2002. The use of photographic rates to estimate densities of tigers and other cryptic mammals: a comment on misleading conclusions. Animal Conservation Forum 5(2): 119–120. DOI: 10.1017/S1367943002002160
Jensen P.O., Wirsing A.J., Thornton D.H. 2022. Using camera traps to estimate density of snowshoe hare (Lepus americanus): a keystone boreal forest herbivore. Journal of Mammalogy 103(3): 693–710. DOI: 10.1093/jmammal/gyac009
Jones J.P., Asner G.P., Butchart S.H., Karanth K.U. 2013. The “why", “what" and “how" of monitoring for conservation. In: D.W. Macdonald, K.J. Willis (Eds.): Key Topics in Conservation Biology 2. Oxford: Wiley-Blackwell. P. 327–343. DOI: 10.1002/9781118520178.ch18
Jourdain N.O.A.S., Cole D.J., Ridout M.S., Rowcliffe J.M. 2020. Statistical Development of Animal Density Estimation Using Random Encounter Modelling. Journal of Agricultural, Biological, and Environmental Statistics 25(2): 148–167. DOI: 10.1007/s13253-020-00385-4
Karanth K.U., Nichols J.D., Seidenstricker J., Dinerstein E., Smith J.L.D., McDougal C., Johnsingh A.J.T., Chundawat R.S., Thapar V. 2003. Science deficiency in conservation practice: the monitoring of tiger populations in India. Animal Conservation Forum 6(2): 141–146. DOI: 10.1017/S1367943003003184
Kastrikin V.A., Podolskii S.A., Babykina M.S. 2020. A new method for calculating the population density of terrestrial animals using camera traps with assessment of roe deer (Capreolus pygargus Pallas, 1771) (Cervidae, Mammalia) population density in the Khingan Nature Reserve as an example. Povolzhskiy Journal of Ecology 3: 307–317. DOI: 10.35885/1684-7318-2020-3-307-317 [In Russian]
Kavčić K., Palencia P., Apollonio M., Vicente J., Šprem N. 2021. Random encounter model to estimate density of mountain-dwelling ungulate. European Journal of Wildlife Research 67(5): 87. DOI: 10.1007/s10344-021-01530-1
Kelly M.J. 2008. Design, evaluate, refine: camera trap studies for elusive species. Animal Conservation 11(3): 182–184. DOI: 10.1111/j.1469-1795.2008.00179.x
Kelly M.J., Holub E.L. 2008. Camera Trapping of Carnivores: Trap Success Among Camera Types and Across Species, and Habitat Selection by Species, on Salt Pond Mountain, Giles County, Virginia. Northwestern Naturalist 15(2): 249–262. DOI: 10.1656/1092-6194(2008)15[249:CTOCTS]2.0.CO;2
Landau L.D., Akhiezer A.I., Lifshitz E.M. 1965. General physics course: Mechanics. Molecular physics. Moscow: Nauka Publishing. 405 p. [In Russian]
Larrucea E.S., Brussard P.F., Jaeger M.M., Barrett R.H. 2007. Cameras, coyotes, and the assumption of equal detectability. Journal of Wildlife Management 71(5): 1682–1689. DOI: 10.2193/2006-407
Loonam K.E., Ausband D.E., Lukacs P.M., Mitchell M.S., Robinson H.S. 2021. Estimating abundance of an unmarked, low-density species using cameras. Journal of Wildlife Management 85(1): 87–96. DOI: 10.1002/jwmg.21950
Lucas T.C.D., Moorcroft E.A., Freeman R., Rowcliffe J.M., Jones K.E. 2015. A generalised random encounter model for estimating animal density with remote sensor data. Methods in Ecology and Evolution 6(5): 500–509. DOI: 10.1111/2041-210X.12346
Luo G., Wei W., Dai Q., Ran J. 2020. Density Estimation of Unmarked Populations Using Camera Traps in Heterogeneous Space. Wildlife Society Bulletin 44(1): 173–181. DOI: 10.1002/wsb.1060
MacKenzie D.I., Nichols J.D., Lachman G.B., Droege S., Royle J.A., Langtimm C.A. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83(8): 2248–2255. DOI: 10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2
Mann G.K., O'Riain M.J., Parker D.M. 2015. The road less travelled: assessing variation in mammal detection probabilities with camera traps in a semi-arid biodiversity hotspot. Biodiversity and Conservation 24(3): 531–545. DOI: 10.1007/s10531-014-0834-z
Manzo E., Bartolommei P., Rowcliffe J.M., Cozzolino R. 2012. Estimation of population density of European pine marten in central Italy using camera trapping. Acta Theriologica 57(2): 165–172. DOI: 10.1007/s13364-011-0055-8
Marcon A., Battocchio D., Apollonio M., Grignolio S. 2019. Assessing precision and requirements of three methods to estimate roe deer density. PLoS ONE 14(10): e0222349. DOI: 10.1371/journal.pone.0222349
Marcon A., Bongi P., Battocchio D., Apollonio M. 2020. REM: performance on a high-density fallow deer (Dama dama) population. Mammal Research 65(3): 835–841. DOI: 10.1007/s13364-020-00522-x
Massei G., Coats J., Lambert M.S., Pietravalle S., Gill R., Cowan D. 2017. Camera traps and activity signs to estimate wild boar density and derive abundance indices. Pest Management Science 74(4): 853–860. DOI: 10.1002/ps.4763
Matiukhina D.S., Vitkalova A.V., Rybin A.N., Aramilev V.V., Shevtsova E.I., Miquelle D.G. 2016. Camera-trap monitoring of Amur Tiger (Panthera tigris altaica) in southwest Primorsky Krai, 2013–2016: preliminary results. Nature Conservation Research 1(3): 36–43. DOI: 10.24189/ncr.2016.025
Moeller A.K., Lukacs P.M., Horne J.S. 2018. Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9(8): e02331. DOI: 10.1002/ecs2.2331
Morales J.M., Ellner S.P. 2002. Scaling up animal movements in heterogeneous landscapes: The importance of behavior. Ecology 83(8): 2240–2247. DOI: 10.1890/0012-9658(2002)083[2240:SUAMIH]2.0.CO;2
Morellet N., Gaillard J.M., Hewison A.J.M., Ballon P., Boscardin Y., Duncan P., Klein F., Maillard D. 2007. Indicators of ecological change: new tools for managing populations of large herbivores. Journal of Applied Ecology 44(3): 634–643. DOI: 10.1111/j.1365-2664.2007.01307.x
Nakashima Y., Fukasawa K., Samejima H. 2018. Estimating animal density without individual recognition using information derivable exclusively from camera traps. Journal of Applied Ecology 55(2): 735–744. DOI: 10.1111/1365-2664.13059
Nakashima Y., Hongo S., Akomo-Okoue E.F. 2020. Landscape-scale estimation of forest ungulate density and biomass using camera traps: applying the REST model. Biological Conservation 241: 108381. DOI: 10.1016/j.biocon.2019.108381
Nichols J., Williams B. 2006. Monitoring for conservation. Trends in Ecology and Evolution 21(12): 668–673. DOI: 10.1016/j.tree.2006.08.007
Nickerson B.S., Parks L.C. 2019. Estimating population density of black-tailed deer in Northwestern Washington using camera traps and a Random Encounter Model. Management Report. Available from https://doi.org/10.13140/RG.2.2.28655.18083
O'Brien T.G., Kinnaird M.F., Wibisono H.T. 2003. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Animal Conservation 6(2): 131–139. DOI: 10.1017/S1367943003003172
Ogurtsov S.S. 2023. Estimating brown bear population density and abundance using camera traps in the Central Forest State Nature Reserve (West-European Russia). Nature Conservation Research 8(2). DOI: 10.24189/ncr.2023.008 [In Russian]
Ogurtsov S.S., Zheltukhin A.S. 2022. Camera traps monitoring program for large and medium-sized mammals on the example of the Central Forest Nature Reserve. In: Mammals in a changing world: current problems of theriology. Moscow: KMK Scientific Press Ltd. P. 257. [In Russian]
Palencia P. 2021. trappingmotion: integrate camera-trapping in movement and behavioural studies. R package version 0.1.1. Available from https://github.com/PabloPalencia/trappingmotion
Palencia P., Vicente J., Barroso P., Barasona J.Á., Soriguer R.C., Acevedo P. 2019. Estimating day range from camera-trap data: the animals' behaviour as a key parameter. Journal of Applied Ecology 309(3): 182–190. DOI: 10.1111/jzo.12710
Palencia P., Rowcliffe J.M., Vicente J., Acevedo P. 2021a. Assessing the camera trap methodologies used to estimate density of unmarked populations. Journal of Applied Ecology 58(8): 1583–1592. DOI: 10.1111/1365-2664.13913
Palencia P., Fernández-López J., Vicente J., Acevedo P., 2021b. Innovations in movement and behavioural ecology from camera traps: day range as model parameter. Methods in Ecology and Evolution 12(7): 1201–1212. DOI: 10.1111/2041-210X.13609
Palencia P., Barroso P., Vicente J., Hofmeester T.R., Ferreres J., Acevedo P. 2022. Random encounter model is a reliable method for estimating population density of multiple species using camera traps. Remote Sensing in Ecology and Conservation 8(5): 670–682. DOI: 10.1002/rse2.269
Pettigrew P., Sigouin D., St-Laurent M. 2021. Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities. Ecology and Evolution 11(12): 7879–7889. DOI: 10.1002/ece3.7619
Pfeffer S.E., Spitzer R., Allen A.M., Hofmeester T.R., Ericsson G., Widemo F., Singh N.J., Cromsigt J.P.G.M. 2018. Pictures or pellets? Comparing camera trapping and dung counts as methods for estimating population densities of ungulates. Remote Sensing in Ecology Conservation 4(2): 173–183. DOI: 10.1002/rse2.67
Pollock K.H., Nichols J.D., Simons T.R., Farnsworth G.L., Bailey L.L., Sauer J.R. 2002. Large scale wildlife monitoring studies: statistical methods for design and analysis. Environmetrics 13(2): 105–119. DOI: 10.1002/env.514
Pop I.M., Bereczky L., Chiriac S., Iosif R., Nita A., Popescu V.D., Rozylowicz L. 2018. Movement ecology of brown bears (Ursus arctos) in the Romanian Eastern Carpathians. Nature Conservation 26: 15–31. DOI: 10.3897/natureconservation.26.22955
Popova E., Ahmed A., Stepanov I., Zlatanova D., Genov P. 2017. Estimating brown bear population density with camera traps in Central Balkan Mountain, Bulgaria. Annuaire de l'Université de Sofia “St. Kliment Ohridski" Faculte de Biologie 103(4): 145–151.
Ramsey D.S., Caley P.A., Robley A. 2015. Estimating population density from presence–absence data using a spatially explicit model. Journal of Wildlife Management 79(3): 491–499. DOI: 10.1002/jwmg.851
Reed D.H., O'Grady J.J., Brook B.W., Ballou J.D., Frankham R. 2003. Estimates of minimum viable population sizes for vertebrates and factors influencing those estimates. Biological Conservation 113(1): 23–34. DOI: 10.1016/S0006-3207(02)00346-4
Rovero F., Marshall A.R. 2009. Camera trapping photographic rate as an index of density in forest ungulates. Journal of Applied Ecology 46(5): 1011–1017. DOI: 10.1111/j.1365-2664.2009.01705.x
Rovero F., Zimmermann F. 2016. Camera trapping for wildlife research. Exeter: Pelagic Publishing Ltd. 320 p.
Rovero F., Zimmermann F., Berzi D., Meek P. 2013. “Which camera trap type and how many do I need?" A review of camera features and study designs for a range of wildlife research applications. Hystrix 24(2): 148–156. DOI: 10.4404/hystrix-24.2-8789
Rowcliffe J.M. 2020. REM analysis using camtools. Available from https://github.com/MarcusRowcliffe/camtools
Rowcliffe J.M. 2021. Protocol for generating distance data from camera trap images using a simple computer vision approach, CTtracking V0.3.2. Available from https://github.com/MarcusRowcliffe/CTtracking
Rowcliffe J.M., Field J., Turvey S.T., Carbone C. 2008. Estimating animal density using camera traps without the need for individual recognition. Journal of Applied Ecology 45(4): 1228–1236. DOI: 10.1111/j.1365-2664.2008.01473.x
Rowcliffe J.M., Carbone C., Jansen P.A., Kays R., Kranstauber B. 2011. Quantifying the sensitivity of camera traps: an adapted distance sampling approach. Methods in Ecology and Evolution 2(5): 464–476. DOI: 10.1111/j.2041-210X.2011.00094.x
Rowcliffe J.M., Carbone C., Kays R., Kranstauber B., Jansen P.A. 2012. Bias in estimating animal travel distance: the effect of sampling frequency. Methods in Ecology and Evolution 3(4): 653–662. DOI: 10.1111/j.2041-210X.2012.00197.x
Rowcliffe J.M., Kays R., Carbone C., Jansen P.A. 2013. Clarifying assumptions behind the estimation of animal density from camera trap rates. Journal of Wildlife Management 77(5): 876. DOI: 10.1002/jwmg.533
Rowcliffe J.M., Kays R., Kranstauber B., Carbone C., Jansen P.A. 2014. Quantifying levels of animal activity using camera trap data. Methods in Ecology and Evolution 5(11): 1170–1179. DOI: 10.1111/2041-210X.12278
Rowcliffe J.M., Jansen P.A., Kays R., Kranstauber B., Carbone C. 2016. Wildlife speed cameras: measuring animal travel speed and day range using camera traps. Remote Sensing in Ecology and Conservation 2(2): 84–94. DOI: 10.1002/rse2.17
Royle J.A., Chandler R.B., Sollmann R., Gardner B. 2013. Spatial capture-recapture. Academic Press. 577 p.
Rozhnov V.V., Yachmennikova A.A., Naidenko S.V., Hernandez-Blanco J.A., Chistopolova M.D., Sorokin P.A., Dobrynin D.V., Sukhova O.V., Poyarkov A.D., Dronova N.A., Trepet S.A., Pkhitikov A.B., Pshegusov R.Kh., Magomedov M.R.D. 2018. Monitoring of the Persian leopard and other large cats. Moscow: KMK Scientific Press Ltd.121 p. [In Russian]
Schaus J., Uzal A., Gentle L.K., Baker P.J., Bearman-Brown L., Bullion S., Gazzard A., Lockwood H., North A., Reader T., Scott D.M., Sutherland C.S., Yarnell R.W. 2020. Application of the Random Encounter Model in citizen science projects to monitor animal densities. Remote Sensing in Ecology and Conservation 6(4): 514–528. DOI: 10.1002/rse2.153
Seber G.A.F. 1982. The estimation of animal abundance and related parameters. New York: Macmillan Pub. Co. 672 p.
Seryodkin I.V., Kostyria A.V., Goodrich J.M. 2014. Daily and seasonal movements of brown bear in the Sikhote-Alin. Bulletin of Tver State University. Series: Biology and Ecology 4: 233–240. [In Russian]
Soutyrina S.V., Riley M.D., Goodrich J.M., Seryodkin I.V., Miquelle D.G. 2013. A population estimate of amur tigers using camera traps. Vladivostok: Dalnauka. 156 p. [In Russian]
Steenweg R., Hebblewhite M., Kays R., Ahumada J., Fisher J.T., Burton C., Townsend S.E., Carbone C., Rowcliffe J.M., Whittington J., Brodie J., Royle J.A., Switalski A., Clevenger A.P., Heim N., Rich L.N. 2017. 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
Wearn O.R., Glover-Kapfer P. 2017. Camera-trapping for conservation: a guide to best-practices. WWF Conservation Technology Series 1(1). Woking: WWF-UK. 181 p.
Wearn O.R., Bell T.E.M., Bolitho A., Durrant J., Haysom J.K., Nijhawan S., Thorley J., Rowcliffe M. 2022. Estimating animal density for a community of species using information obtained only from camera‐traps. Methods in Ecology and Evolution 13(10): 2248–2261. DOI: 10.1111/2041-210x.13930
Zaumyslova O.Yu., Bondarchuk S.N. 2017. Assessment of the Long-tailed Goral (Naemorhedus caudatus: Bovidae) population status in the Sikhote-Alin Reserve using camera-traps. Nature Conservation Research 2(Suppl. 1): 151–163. DOI: 10.24189/ncr.2017.024 [In Russian]
Zero V.H., Sundaresan S.R., O'Brien T.G., Kinnaird M.F. 2013. Monitoring an Endangered savannah ungulate, Grevy's zebra Equus grevyi: choosing a method for estimating population densities. Oryx 47(3): 410–419. DOI: 10.1017/S0030605312000324
Zwerts J.A., Stephenson P.J., Maisels F., Rowcliffe M., Astaras C., Jansen P.A., van der Waarde J., Sterck L.E.H.M., Verweij P.A., Bruce T., Brittain S., van Kuijk M. 2021. Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors. Conservation Science and Practice 3(12): e568. DOI: 10.1111/csp2.568