Training and experience increase classification accuracy in white-tailed deer camera surveys
Jace R. Elliott![https://orcid.org/0000-0003-4432-4579](/media/client/orcid_16x16.png)
A College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL 36849, USA.
Wildlife Research 50(7) 568-580 https://doi.org/10.1071/WR22022
Submitted: 9 February 2022 Accepted: 18 August 2022 Published: 16 September 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: Use of camera trap data in wildlife research is reliant on accurate classification of animals at the species, sex–age category or individual level. One such example is white-tailed deer (Odocoileus virginianus) camera surveys, which are often conducted to produce demographic estimates used by managers to establish harvest goals for a population. Previous research suggests that misclassification of deer by sex–age category (e.g. adult male, adult female, fawn) is common in these surveys, and represents a source of bias that could misinform important management decisions.
Aim: To examine whether training material has an effect on classification accuracy of white-tailed deer and explore other observer-based, experiential factors as they relate to classification accuracy.
Methods: We developed and tested the efficacy of species-specific training material designed to reduce sex–age misclassifications associated with white-tailed deer images.
Key results: Exposure to training material resulted in the greatest improvement in classification accuracy of deer images compared with any other respondent-based factors we investigated. Other factors, such as professional experience as a wildlife biologist, field experience viewing white-tailed deer and experience viewing deer images from camera traps, were positively associated with classification accuracy of deer images.
Conclusions: Our findings suggest that training material has the ability to reduce misclassifications, leading to more accurate demographic estimates for white-tailed deer populations. We also found that prior experience using camera traps and familiarity with target species was positively related to classification accuracy.
Implications: Species-specific training material would provide a valuable resource to wildlife managers tasked with classifying animals at the species, sex–age category or individual level.
Keywords: camera survey, camera trap, classification accuracy, estimating abundance, observer error, Odocoileus virginianus, training material, white-tailed deer.
References
Ahrends, A, Rahbek, C, Bulling, MT, Burgess, ND, Platts, PJ, Lovett, JC, Kindemba, VW, Owen, N, Sallu, AN, Marshall, AR, Mhoro, BE, Fanning, E, and Marchant, R (2011). Conservation and the botanist effect. Biological Conservation 144, 131–140.| Conservation and the botanist effect.Crossref | GoogleScholarGoogle Scholar |
Azhar MAHB, Hoque S, Deravi F (2012) Automatic identification of wildlife using local binary patterns. In ‘IET Conference on Image Processing’. (IET Digital Library: London, UK)
Cohn, JP (2008). Citizen science: can volunteers do real research? BioScience 58, 192–197.
| Citizen science: can volunteers do real research?Crossref | GoogleScholarGoogle Scholar |
Curtis, PD, Boldgiv, B, Mattison, PM, and Boulanger, JR (2009). Estimating deer abundance in suburban areas with infrared-triggered cameras. Human–Wildilfe Conflicts 3, 116–128.
Danielsen, F, Jensen, PM, Burgess, ND, Altamirano, R, Alviola, PA, Andrianandrasana, H, Brashares, JS, Burton, AC, Coronado, I, Corpuz, N, Enghoff, M, Fjeldså, J, Funder, M, Holt, S, Hübertz, H, Jensen, AE, Lewis, R, Massao, J, Mendoza, MM, Ngaga, Y, Pipper, CB, Poulsen, MK, Rueda, RM, Sam, MK, Skielboe, T, Sørensen, M, and Young, R (2014). A multicountry assessment of tropical resource monitoring by local communities. BioScience 64, 236–251.
| A multicountry assessment of tropical resource monitoring by local communities.Crossref | GoogleScholarGoogle Scholar |
Darwall, WRT, and Dulvy, NK (1996). An evaluation of the suitability of non-specialist volunteer researchers for coral reef fish surveys. Mafia Island, Tanzania – a case study. Biological Conservation 78, 223–231.
| An evaluation of the suitability of non-specialist volunteer researchers for coral reef fish surveys. Mafia Island, Tanzania – a case study.Crossref | GoogleScholarGoogle Scholar |
Ditchkoff SS (2011) Anatomy and physiology. In ‘Biology and Management of White-tailed Deer’. (Ed. DG Hewitt) pp. 43–73. (CRC Press, Boca Raton, FL, USA)
Duquette, JF, Belant, JL, Svoboda, NJ, Beyer, DE, and Albright, CA (2014). Comparison of occupancy modeling and radiotelemetry to estimate ungulate population dynamics. Population Ecology 56, 481–492.
| Comparison of occupancy modeling and radiotelemetry to estimate ungulate population dynamics.Crossref | GoogleScholarGoogle Scholar |
Efford, MG, and Fewster, RM (2013). Estimating population size by spatially explicit capture–recapture. Oikos 122, 918–928.
| Estimating population size by spatially explicit capture–recapture.Crossref | GoogleScholarGoogle Scholar |
Efford MG, Borchers DL, Byrom AE (2009) Density estimation by spatially explicit capture–recapture: likelihood-based methods. In ‘Modeling Ddemographic Processes in Marked Populations, Environmental and Ecological Statistics. Vol. 3’. (Eds DL Thomson, EG Cooch, MJ Conroy) pp. 255–269. (Springer Science & Business Media, LLC: Berlin, Germany)
Gálvez, N, Guillera-Arroita, G, Morgan, BJT, and Davies, ZG (2016). Cost-efficient effort allocation for camera-trap occupancy surveys of mammals. Biological Conservation 204, 350–359.
| Cost-efficient effort allocation for camera-trap occupancy surveys of mammals.Crossref | GoogleScholarGoogle Scholar |
Garel, M, Cugnasse, J-M, Gaillard, J-M, Loison, A, Santosa, Y, and Maublanc, M-L (2005). Effect of observer experience on the monitoring of a mouflon population. Acta Theriologica 50, 109–114.
| Effect of observer experience on the monitoring of a mouflon population.Crossref | GoogleScholarGoogle Scholar |
Gunnlaugsson, T, and Sigurjónsson, J (1990). A note on the problem of false positives in the use of natural marking data for abundance estimation. Report of the International Whaling Commission 12, 143–145.
Hosmer DW, Lemeshow S, Sturdivant RX (2013) ‘Applied Logistic Regression.’ (John Wiley and Sons: Hoboken, NJ, USA)
Jacobson, HA, Kroll, JC, Browning, RW, Koerth, BH, and Conway, MH (1997). Infrared-triggered cameras for censusing white-tailed deer. Wildlife Society Bulletin 25, 547–556.
Johansson, O, Samelius, G, Wikberg, E, Chapron, G, Mishra, C, and Low, M (2020). Identification errors in camera-trap studies result in systematic population overestimation. Scientific Reports 10, 6393.
| Identification errors in camera-trap studies result in systematic population overestimation.Crossref | GoogleScholarGoogle Scholar |
Karanth, KU, Nichols, JD, Kumar, NS, and Hines, JE (2006). Assessing tiger population dynamics using photographic capture–recapture sampling. Ecology 87, 2925–2937.
| Assessing tiger population dynamics using photographic capture–recapture sampling.Crossref | GoogleScholarGoogle Scholar |
Katrak-Adefowora, R, Blickley, JL, and Zellmer, AJ (2020). Just-in-time training improves accuracy of citizen scientist wildlife identifications from camera trap photos. Citizen Science: Theory and Practice 5, 8.
| Just-in-time training improves accuracy of citizen scientist wildlife identifications from camera trap photos.Crossref | GoogleScholarGoogle Scholar |
Keever, AC, McGowan, CP, Ditchkoff, SS, Acker, PK, Grand, JB, and Newbolt, CH (2017). Efficacy of N-mixture models for surveying and monitoring white-tailed deer populations. Mammal Research 62, 413–422.
| Efficacy of N-mixture models for surveying and monitoring white-tailed deer populations.Crossref | GoogleScholarGoogle Scholar |
Kelly, MJ (2001). Computer-aided photograph matching in studies using individual identification: an example from serengeti cheetahs. Journal of Mammalogy 82, 440–449.
| Computer-aided photograph matching in studies using individual identification: an example from serengeti cheetahs.Crossref | GoogleScholarGoogle Scholar |
Kelly, MJ, Noss, AJ, DiBitetti, MS, Maffei, L, Arispe, RL, Paviolo, A, DeAngelo, CD, and DiBlanco, YE (2008). Estimating puma densities from camera trapping across three study sites: Bolivia, Argentina, and Belize. Journal of Mammalogy 89, 408–418.
| Estimating puma densities from camera trapping across three study sites: Bolivia, Argentina, and Belize.Crossref | GoogleScholarGoogle Scholar |
Koerth, BH, and Kroll, JC (2000). Bait type and timing for deer counts using cameras triggered by infrared monitors. Wildlife Society Bulletin 28, 630–635.
Koerth, BH, McKown, CD, and Kroll, JC (1997). Infrared-Triggered camera versus helicopter counts of white-tailed deer. Wildlife Society Bulletin 25, 557–562.
Lewandowski, E, and Specht, H (2015). Influence of volunteer and project characteristics on data quality of biological surveys. Conservation Biology 29, 713–723.
| Influence of volunteer and project characteristics on data quality of biological surveys.Crossref | GoogleScholarGoogle Scholar |
Lovell, S, Hamer, M, Slotow, R, and Herbert, D (2009). An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveys. Biodiversity and Conservation 18, 3295–3307.
| An assessment of the use of volunteers for terrestrial invertebrate biodiversity surveys.Crossref | GoogleScholarGoogle Scholar |
McCarthy, MS, Després-Einspenner, M-L, Farine, DR, Samuni, L, Angedakin, S, Arandjelovic, M, Boesch, C, Dieguez, P, Havercamp, K, Knight, A, Langergraber, KE, Wittig, RM, and Kühl, HS (2019). Camera traps provide a robust alternative to direct observations for constructing social networks of wild chimpanzees. Animal Behaviour 157, 227–238.
| Camera traps provide a robust alternative to direct observations for constructing social networks of wild chimpanzees.Crossref | GoogleScholarGoogle Scholar |
McCoy, JC, Ditchkoff, SS, and Steury, TD (2011). Bias associated with baited camera sites for assessing population characteristics of deer. The Journal of Wildlife Management 75, 472–477.
| Bias associated with baited camera sites for assessing population characteristics of deer.Crossref | GoogleScholarGoogle Scholar |
McKinley WT (2002) Evaluating infrared camera and other census techniques for white-tailed deer in Mississippi. M.Sc. Thesis, Mississippi State University, MS, USA.
Meek, PD, Ballard, G-A, and Fleming, PJS (2015). The pitfalls of wildlife camera trapping as a survey tool in Australia. Australian Mammalogy 37, 13–22.
| The pitfalls of wildlife camera trapping as a survey tool in Australia.Crossref | GoogleScholarGoogle Scholar |
Mendoza, E, Martineau, PR, Brenner, E, and Dirzo, R (2011). A novel method to improve individual animal identification based on camera-trapping data. The Journal of Wildlife Management 75, 973–979.
| A novel method to improve individual animal identification based on camera-trapping data.Crossref | GoogleScholarGoogle Scholar |
Moeller, AK, Lukacs, PM, and Horne, JS (2018). Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere 9, e02331.
| Three novel methods to estimate abundance of unmarked animals using remote cameras.Crossref | GoogleScholarGoogle Scholar |
Morrison, TA, Yoshizaki, J, Nichols, JD, and Bolger, DT (2011). Estimating survival in photographic capture–recapture studies: overcoming misidentification error. Methods in Ecology and Evolution 2, 454–463.
| Estimating survival in photographic capture–recapture studies: overcoming misidentification error.Crossref | GoogleScholarGoogle Scholar |
Neuman, TJ, Newbolt, CH, Ditchkoff, SS, and Steury, TD (2016). Microsatellites reveal plasticity in reproductive success of white-tailed deer. Journal of Mammalogy 97, 1441–1450.
| Microsatellites reveal plasticity in reproductive success of white-tailed deer.Crossref | GoogleScholarGoogle Scholar |
Newbolt, CH, and Ditchkoff, SS (2019). Misidentification error associated with classifications of white-tailed deer images. Wildlife Society Bulletin 43, 527–536.
| Misidentification error associated with classifications of white-tailed deer images.Crossref | GoogleScholarGoogle Scholar |
Newman C, Buesching CD, Macdonald DW (2003) Validating mammal monitoring methods and assessing the performance of volunteers in wildlife conservation—“Sed quis custodiet ipsos custodies?”. Biological Conservation 113, 189–197.
Oliveira-Santos, LGR, Zucco, CA, Antunes, PC, and Crawshaw, PG (2010). Is it possible to individually identify mammals with no natural markings using camera-traps? A controlled case-study with lowland tapirs. Mammalian Biology 75, 375–378.
| Is it possible to individually identify mammals with no natural markings using camera-traps? A controlled case-study with lowland tapirs.Crossref | GoogleScholarGoogle Scholar |
Palmer, MS, Swanson, A, Kosmala, M, Arnold, T, and Packer, C (2018). Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys. African Journal of Ecology 56, 791–803.
| Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys.Crossref | GoogleScholarGoogle Scholar |
Parsons, AW, Goforth, C, Costello, R, and Kays, R (2018). The value of citizen science for ecological monitoring of mammals. PeerJ 6, e4536.
| The value of citizen science for ecological monitoring of mammals.Crossref | GoogleScholarGoogle Scholar |
Perry, JR, Sumner, S, Thompson, C, and Hart, AG (2021). ‘Citizen identification’: online learning supports highly accurate species identification for insect-focused citizen science. Insect Conservation and Diversity 14, 862–867.
| ‘Citizen identification’: online learning supports highly accurate species identification for insect-focused citizen science.Crossref | GoogleScholarGoogle Scholar |
Ratnieks, FLW, Schrell, F, Sheppard, RC, Brown, E, Bristow, OE, and Garbuzov, M (2016). Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers. Methods in Ecology and Evolution 7, 1226–1235.
| Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers.Crossref | GoogleScholarGoogle Scholar |
Rovero, F, Zimmermann, F, Berzi, D, and 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, 148–156.
| “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.Crossref | GoogleScholarGoogle Scholar |
Rowcliffe, JM, and Carbone, C (2008). Surveys using camera traps: are we looking to a brighter future? Animal Conservation 11, 185–186.
| Surveys using camera traps: are we looking to a brighter future?Crossref | GoogleScholarGoogle Scholar |
Royle JA, Chandler RB, Sollmann R, Gardner B (Eds) (2014) ‘Spatial capture–recapture.’ (Elsevier: Waltham, MA, USA)
Sikes, RS, and Gannon, WL (2011). Guidelines of the American Society of Mammalogists for the use of wild mammals in research. Journal of Mammalogy 92, 235–253.
Silveira, L, Jácomo, ATA, and Diniz-Filho, JAF (2003). Camera trap, line transect census and track surveys: a comparative evaluation. Biological Conservation 114, 351–355.
| Camera trap, line transect census and track surveys: a comparative evaluation.Crossref | GoogleScholarGoogle Scholar |
Speed, CW, Meekan, MG, and Bradshaw, CJA (2007). Spot the match – wildlife photo-identification using information theory. Frontiers in Zoology 4, 2.
| Spot the match – wildlife photo-identification using information theory.Crossref | GoogleScholarGoogle Scholar |
Steger, C, Butt, B, and Hooten, MB (2017). Safari science: assessing the reliability of citizen science data for wildlife surveys. Journal of Applied Ecology 54, 2053–2062.
| Safari science: assessing the reliability of citizen science data for wildlife surveys.Crossref | GoogleScholarGoogle Scholar |
Stevick, PT, Palsbøll, PJ, Smith, TD, Bravington, MV, and Hammond, PS (2001). Errors in identification using natural markings: rates, sources, and effects on capture–recapture estimates of abundance. Canadian Journal of Fisheries and Aquatic Sciences 58, 1861–1870.
| Errors in identification using natural markings: rates, sources, and effects on capture–recapture estimates of abundance.Crossref | GoogleScholarGoogle Scholar |
Swanson, A, Kosmala, M, Lintott, C, and Packer, C (2016). A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conservation Biology 30, 520–531.
| A generalized approach for producing, quantifying, and validating citizen science data from wildlife images.Crossref | GoogleScholarGoogle Scholar |
van der Wal, R, Sharma, N, Mellish, C, Robinson, A, and Siddharthan, A (2016). The role of automated feedback in training and retaining biological recorders for citizen science. Conservation Biology 30, 550–561.
| The role of automated feedback in training and retaining biological recorders for citizen science.Crossref | GoogleScholarGoogle Scholar |
Wearn, OR, and Glover-Kapfer, P (2019). Snap happy: camera traps are an effective sampling tool when compared with alternative methods. Royal Society Open Science 6, 181748.
| Snap happy: camera traps are an effective sampling tool when compared with alternative methods.Crossref | GoogleScholarGoogle Scholar |
Weckel, M, Rockwell, RF, and Secret, F (2011). A modification of Jacobson et al.’s (1997) individual branch-antlered male method for censusing white-tailed deer. Wildlife Society Bulletin 35, 445–451.
| A modification of Jacobson et al.’s (1997) individual branch-antlered male method for censusing white-tailed deer.Crossref | GoogleScholarGoogle Scholar |
Yoshizaki, J, Pollock, KH, Brownie, C, and Webster, RA (2009). Modeling misidentification errors in capture–recapture studies using photographic identification of evolving marks. Ecology 90, 3–9.
| Modeling misidentification errors in capture–recapture studies using photographic identification of evolving marks.Crossref | GoogleScholarGoogle Scholar |