The history of wildlife camera trapping as a survey tool in Australia

2015 ◽  
Vol 37 (1) ◽  
pp. 1 ◽  
Author(s):  
Paul D. Meek ◽  
Guy-Anthony Ballard ◽  
Karl Vernes ◽  
Peter J. S. Fleming
Keyword(s):  
Quantitative Research ◽  
Reaction Times ◽  
Historical Review ◽  
Camera Trap ◽  
Recent Change ◽  
Camera Traps ◽  
Camera Trapping ◽  
Survey Tool ◽  
Trapping Methods ◽  
Effective Models

This paper provides an historical review of the technological evolution of camera trapping as a zoological survey tool in Australia. Camera trapping in Australia began in the 1950s when purpose-built remotely placed cameras were used in attempts to rediscover the thylacine (Thylacinus cynocephalus). However, camera traps did not appear in Australian research papers and Australasian conference proceedings until 1989–91, and usage became common only after 2008, with an exponential increase in usage since 2010. Initially, Australian publications under-reported camera trapping methods, often failing to provide fundamental details about deployment and use. However, rigour in reporting of key methods has increased during the recent widespread adoption of camera trapping. Our analysis also reveals a change in camera trap use in Australia, from simple presence–absence studies, to more theoretical and experimental approaches related to population ecology, behavioural ecology, conservation biology and wildlife management. Practitioners require further research to refine and standardise camera trap methods to ensure that unbiased and scientifically rigorous data are obtained from quantitative research. The recent change in emphasis of camera trapping research use is reflected in the decreasing range of camera trap models being used in Australian research. Practitioners are moving away from less effective models that have slow reaction times between detection and image capture, and inherent bias in detectability of fauna, to more expensive brands that offer faster speeds, greater functionality and more reliability.

Can camera trapping be used to accurately survey and monitor the Hastings River mouse (Pseudomys oralis)?

2016 ◽  
Vol 38 (1) ◽  
pp. 44 ◽  
Author(s):  
Paul D. Meek ◽  
Karl Vernes
Keyword(s):  
Small Mammals ◽  
Small Mammal ◽  
Survey Methods ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Common Element ◽  
Computer Assisted ◽  
Survey Tool ◽  
Wide Range

Camera trapping is increasingly recognised as a survey tool akin to conventional small mammal survey methods such as Elliott trapping. While there are many cost and resource advantages of using camera traps, their adoption should not compromise scientific rigour. Rodents are a common element of most small mammal surveys. In 2010 we deployed camera traps to measure whether the endangered Hastings River mouse (Pseudomys oralis) could be detected and identified with an acceptable level of precision by camera traps when similar-looking sympatric small mammals were present. A comparison of three camera trap models revealed that camera traps can detect a wide range of small mammals, although white flash colour photography was necessary to capture characteristic features of morphology. However, the accurate identification of some small mammals, including P. oralis, was problematic; we conclude therefore that camera traps alone are not appropriate for P. oralis surveys, even though they might at times successfully detect them. We discuss the need for refinement of the methodology, further testing of camera trap technology, and the development of computer-assisted techniques to overcome problems associated with accurate species identification.

The pitfalls of wildlife camera trapping as a survey tool in Australia

2015 ◽  
Vol 37 (1) ◽  
pp. 13 ◽  
Author(s):  
Paul D. Meek ◽  
Guy-Anthony Ballard ◽  
Peter J. S. Fleming
Keyword(s):  
Detection System ◽  
Objective Evaluation ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Camera Model ◽  
Survey Tool ◽  
Key Issues ◽  
Ecological Science ◽  
Technological Constraints

Camera trapping is a relatively new addition to the wildlife survey repertoire in Australia. Its rapid adoption has been unparalleled in ecological science, but objective evaluation of camera traps and their application has not kept pace. With the aim of motivating practitioners to think more about selection and deployment of camera trap models in relation to research goals, we reviewed Australian camera trapping studies to determine how camera traps have been used and how their technological constraints may have affected reported results and conclusions. In the 54 camera trapping articles published between 1991 and 2013, mammals (86%) were studied more than birds (10%) and reptiles (3%), with small to medium-sized mammals being most studied. Australian camera trapping studies, like those elsewhere, have changed from more qualitative to more complex quantitative investigations. However, we found that camera trap constraints and limitations were rarely acknowledged, and we identified eight key issues requiring consideration and further research. These are: camera model, camera detection system, camera placement and orientation, triggering and recovery, camera trap settings, temperature differentials, species identification and behavioural responses of the animals to the cameras. In particular, alterations to animal behaviour by camera traps potentially have enormous influence on data quality, reliability and interpretation. The key issues were not considered in most Australian camera trap papers and require further study to better understand the factors that influence the analysis and interpretation of camera trap data and improve experimental design.

Pits or pictures: a comparative study of camera traps and pitfall trapping to survey small mammals and reptiles

2019 ◽  
Vol 46 (2) ◽  
pp. 104 ◽  
Author(s):  
Shannon J. Dundas ◽  
Katinka X. Ruthrof ◽  
Giles E. St.J. Hardy ◽  
Patricia A. Fleming
Keyword(s):  
Community Composition ◽  
Small Mammals ◽  
Survey Methods ◽  
Infrared Camera ◽  
Camera Traps ◽  
Camera Trapping ◽  
Survey Method ◽  
Pitfall Traps ◽  
Pitfall Trapping ◽  
Trapping Methods

Context Camera trapping is a widely used monitoring tool for a broad range of species across most habitat types. Camera trapping has some major advantages over other trapping methods, such as pitfall traps, because cameras can be left in the field for extended periods of time. However, there is still a need to compare traditional trapping methods with newer techniques. Aims To compare trap rates, species richness and community composition of small mammals and reptiles by using passive, unbaited camera traps and pitfall traps. Methods We directly compared pitfall trapping (20-L buried buckets) with downward-facing infrared-camera traps (Reconyx) to survey small reptiles and mammals at 16 sites within a forested habitat in south-western Australia. We compared species captured using each method, as well as the costs associated with each. Key results Overall, we recorded 228 reptiles, 16 mammals and 1 frog across 640 pitfall trap-nights (38.3 animal captures per 100 trap-nights) compared to 271 reptiles and 265 mammals (for species likely to be captured in pitfall traps) across 2572 camera trap nights (20.8 animal captures per 100 trap-nights). When trap effort is taken into account, camera trapping was only 23% as efficient as pitfall trapping for small reptiles (mostly Scincidae), but was five times more efficient for surveying small mammals (Dasyuridae). Comparing only those species that were likely to be captured in pitfall traps, 13 species were recorded by camera trapping compared with 20 species recorded from pitfall trapping; however, we found significant (P<0.001) differences in community composition between the methods. In terms of cost efficacy, camera trapping was the more expensive method for our short, 4-month survey when taking the cost of cameras into consideration. Conclusions Applicability of camera trapping is dependent on the specific aims of the intended research. Camera trapping is beneficial where community responses to ecosystem disturbance are being tested. Live capture of small reptiles via pitfall trapping allows for positive species identification, morphological assessment, and collection of reference photos to help identify species from camera photos. Implications As stand-alone techniques, both survey methods under-represent the available species present in a region. The use of more than one survey method improves the scope of fauna community assessments.

scholarly journals Applying camera traps to detect and monitor introduced mammals on oceanic islands

Oryx ◽  
2020 ◽  
pp. 1-8
Author(s):  
Lucas Lamelas-López ◽  
Iván Salgado
Keyword(s):  
Introduced Species ◽  
Large Scale ◽  
Camera Trap ◽  
Oceanic Islands ◽  
Camera Traps ◽  
Camera Trapping ◽  
Sampling Effort ◽  
Terrestrial Mammals ◽  
Management Actions ◽  
Survey Technique

Abstract The introduction of mammal predators has been a major cause of species extinctions on oceanic islands. Eradication is only possible or cost-effective at early stages of invasion, before introduced species become abundant and widespread. Although prevention, early detection and rapid response are the best management strategies, most oceanic islands lack systems for detecting, responding to and monitoring introduced species. Wildlife managers require reliable information on introduced species to guide, assess and adjust management actions. Thus, a large-scale and long-term monitoring programme is needed to evaluate the management of introduced species and the protection of native wildlife. Here, we evaluate camera trapping as a survey technique for detecting and monitoring introduced small and medium-sized terrestrial mammals on an oceanic island, Terceira (Azores). Producing an inventory of introduced mammals on this island required a sampling effort of 465 camera-trap days and cost EUR 2,133. We estimated abundance and population trends by using photographic capture rates as a population index. We also used presence/absence data from camera-trap surveys to calculate detection probability, estimated occupancy rate and the sampling effort needed to determine species absence. Although camera trapping requires large initial funding, this is offset by the relatively low effort for fieldwork. Our findings demonstrate that camera trapping is an efficient survey technique for detecting and monitoring introduced species on oceanic islands. We conclude by proposing guidelines for designing monitoring programmes for introduced species.

scholarly journals Improving Terrestrial Squamate Surveys with Camera-Trap Programming and Hardware Modifications

Animals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 388 ◽  
Author(s):  
D. J. Welbourne ◽  
A. W. Claridge ◽  
D. J. Paull ◽  
F. Ford
Keyword(s):  
Focal Length ◽  
Time Lapse ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Infrared Sensor ◽  
Vertebrate Fauna ◽  
The World ◽  
Lens Focal Length

Camera-traps are used widely around the world to census a range of vertebrate fauna, particularly mammals but also other groups including birds, as well as snakes and lizards (squamates). In an attempt to improve the reliability of camera-traps for censusing squamates, we examined whether programming options involving time lapse capture of images increased detections. This was compared to detections by camera-traps set to trigger by the standard passive infrared sensor setting (PIR), and camera-traps set to take images using time lapse in combination with PIR. We also examined the effect of camera trap focal length on the ability to tell different species of small squamate apart. In a series of side-by-side field comparisons, camera-traps programmed to take images at standard intervals, as well as through routine triggering of the PIR, captured more images of squamates than camera-traps using the PIR sensor setting alone or time lapse alone. Similarly, camera traps with their lens focal length set at closer distances improved our ability to discriminate species of small squamates. With these minor alterations to camera-trap programming and hardware, the quantity and quality of squamate detections was markedly better. These gains provide a platform for exploring other aspects of camera-trapping for squamates that might to lead to even greater survey advances, bridging the gap in knowledge of this otherwise poorly known faunal group.

scholarly journals A picture is worth a thousand words: the application of camera trapping to the study of birds

2008 ◽  
Vol 18 (S1) ◽  
pp. S144-S162 ◽  
Author(s):  
Timothy G. O'Brien ◽  
Margaret F. Kinnaird
Keyword(s):  
Finite Mixture Models ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Individual Identification ◽  
Nest Defence ◽  
Primary Forest ◽  
Sampling Effort ◽  
Observation Process ◽  
Capture Recapture

AbstractThis study reviews the use of remotely triggered still cameras, known as camera traps, in bird research and suggests new methods useful for analyzing camera trap data. Camera trapping may be most appropriate for large, ground-dwelling birds, such as cracids and pheasants. Recent applications include documentation of occurrence of rare species and new species records, nest predation studies and behavioural studies including nest defence, frugivory, seed dispersal, and activity budgets. If bird postures are analyzed, it may be possible to develop behavioural time budgets. If birds are marked or individually identifiable, abundance may be estimated through capture-recapture methods typically used for mammals. We discourage use of relative abundance indices based on trapping effort because of the difficulty of standardizing surveys over time and space. Using the Great Argus Pheasant Argus argusianus, a cryptic, terrestrial, forest bird as an example, we illustrate applications of occupancy analysis to estimate proportion of occupied habitat and finite mixture models to estimate abundance when individual identification is not possible. These analyses are useful because they incorporate detection probabilities < 1 and covariates that affect the sample site or the observation process. Results are from camera trap surveys in the 3,568 km2 Bukit Barisan Selatan National Park, Indonesia. We confirmed that Great Argus Pheasants prefer primary forest below 500 m. We also find a decline in occupancy (6–8% yr−1). Point estimates of abundance peak in 2000, followed by a sharp decline. We discuss the effects of rarity, detection probability and sampling effort on accuracy and precision of estimates.

scholarly journals A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest

2021 ◽  
pp. 299-310
Author(s):  
Mateusz Choiński ◽  
Mateusz Rogowski ◽  
Piotr Tynecki ◽  
Dries P. J. Kuijper ◽  
Marcin Churski ◽  
...  
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Species Recognition ◽  
Population Monitoring ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Classification Model ◽  
Wildlife Monitoring ◽  
Species Classification

AbstractCamera traps are used worldwide to monitor wildlife. Despite the increasing availability of Deep Learning (DL) models, the effective usage of this technology to support wildlife monitoring is limited. This is mainly due to the complexity of DL technology and high computing requirements. This paper presents the implementation of the light-weight and state-of-the-art YOLOv5 architecture for automated labeling of camera trap images of mammals in the Białowieża Forest (BF), Poland. The camera trapping data were organized and harmonized using TRAPPER software, an open-source application for managing large-scale wildlife monitoring projects. The proposed image recognition pipeline achieved an average accuracy of 85% F1-score in the identification of the 12 most commonly occurring medium-size and large mammal species in BF, using a limited set of training and testing data (a total of 2659 images with animals).Based on the preliminary results, we have concluded that the YOLOv5 object detection and classification model is a fine and promising DL solution after the adoption of the transfer learning technique. It can be efficiently plugged in via an API into existing web-based camera trapping data processing platforms such as e.g. TRAPPER system. Since TRAPPER is already used to manage and classify (manually) camera trapping datasets by many research groups in Europe, the implementation of AI-based automated species classification will significantly speed up the data processing workflow and thus better support data-driven wildlife monitoring and conservation. Moreover, YOLOv5 has been proven to perform well on edge devices, which may open a new chapter in animal population monitoring in real-time directly from camera trap devices.

Estimating macropod grazing density and defining activity patterns using camera-trap image analysis

2018 ◽  
Vol 45 (8) ◽  
pp. 706 ◽  
Author(s):  
Helen R. Morgan ◽  
Guy Ballard ◽  
Peter J. S. Fleming ◽  
Nick Reid ◽  
Remy Van der Ven ◽  
...  
Keyword(s):  
Vegetation Change ◽  
Activity Patterns ◽  
Time Lapse ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Absolute Abundance ◽  
Deposition Rates ◽  
Pellet Counts ◽  
Density Methods

Context When measuring grazing impacts of vertebrates, the density of animals and time spent foraging are important. Traditionally, dung pellet counts are used to index macropod grazing density, and a direct relationship between herbivore density and foraging impact is assumed. However, rarely are pellet deposition rates measured or compared with camera-trap indices. Aims The aims were to pilot an efficient and reliable camera-trapping method for monitoring macropod grazing density and activity patterns, and to contrast pellet counts with macropod counts from camera trapping, for estimating macropod grazing density. Methods Camera traps were deployed on stratified plots in a fenced enclosure containing a captive macropod population and the experiment was repeated in the same season in the following year after population reduction. Camera-based macropod counts were compared with pellet counts and pellet deposition rates were estimated using both datasets. Macropod frequency was estimated, activity patterns developed, and the variability between resting and grazing plots and the two estimates of macropod density was investigated. Key Results Camera-trap grazing density indices initially correlated well with pellet count indices (r2=0.86), but were less reliable between years. Site stratification enabled a significant relationship to be identified between camera-trap counts and pellet counts in grazing plots. Camera-trap indices were consistent for estimating grazing density in both surveys but were not useful for estimating absolute abundance in this study. Conclusions Camera trapping was efficient and reliable for estimating macropod activity patterns. Although significant, the relationship between pellet count indices and macropod grazing density based on camera-trapping indices was not strong; this was due to variability in macropod pellet deposition rates over different years. Time-lapse camera imagery has potential for simultaneously assessing herbivore foraging activity budgets with grazing densities and vegetation change. Further work is required to refine the use of camera-trapping indices for estimation of absolute abundance. Implications Time-lapse camera trapping and site-stratified sampling allow concurrent assessment of grazing density and grazing behaviour at plot and landscape scale.

scholarly journals A Camera-Trap Home-Range Analysis of the Indian Leopard (Panthera pardus fusca) in Jaipur, India

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1600
Author(s):  
Swapnil Kumbhojkar ◽  
Reuven Yosef ◽  
Abhinav Mehta ◽  
Shrey Rakholia
Keyword(s):  
Home Range ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Home Ranges ◽  
Panthera Pardus ◽  
Range Analysis ◽  
Minimum Convex Polygon ◽  
Median Percentage ◽  
Home Range Analysis

The suitability of the camera trap–retrap method was explored for identifying territories and studying the spatial distribution of leopards (Panthera pardus fusca) in the Jhalana Reserve Forest, Jaipur, India. Data from two years (November 2017 to November 2019, N = 23,208 trap-hours) were used to provide estimates of minimum home-range size and overlap. We conducted home-range analysis and estimation, using the minimum convex polygon (MCP) method with geographic information system (GIS) tools. We are aware of the limitations and advantages of camera trapping for long-term monitoring. However, the limitations of the research permit allowed only the use of camera traps to estimate the home ranges. A total of 25 leopards were identified (male = 8, female = 17). No territorial exclusivity was observed in either of the sexes. However, for seven females, we observed familial home-range overlaps wherein daughters established home ranges adjacent to or overlapping their natal areas. The median home range, as calculated from the MCP, was 305.9 ha for males and 170.3 ha for females. The median percentage overlap between males was 10.33%, while that between females was 3.97%. We concluded that camera trapping is an effective technique to map the territories of leopards, to document inter- and intraspecific behaviors, and to elucidate how familial relationships affect dispersal.

High variation in camera trap-model sensitivity for surveying mammal species in northern Australia

2018 ◽  
Vol 45 (7) ◽  
pp. 578 ◽  
Author(s):  
Jaime Heiniger ◽  
Graeme Gillespie
Keyword(s):  
Detection Probability ◽  
High Sensitivity ◽  
Camera Trap ◽  
Camera Traps ◽  
Northern Australia ◽  
Mammal Species ◽  
Survey Tool ◽  
Significant Difference ◽  
Event Trigger ◽  
The Difference

Context The use of camera traps as a wildlife survey tool has rapidly increased, and understanding the strengths and weaknesses of the technology is imperative to assess the degree to which research objectives are met. Aims We evaluated the differences in performance among three Reconyx camera-trap models, namely, a custom-modified high-sensitivity PC850, and unmodified PC850 and HC550. Methods We undertook a controlled field trial to compare the performance of the three models on Groote Eylandt, Northern Territory, by observing the ability of each model to detect the removal of a bait by native mammals. We compared variation in detecting the known event, trigger numbers, proportion of false triggers and the difference in detection probability of small to medium-sized mammals. Key results The high-sensitivity PC850 model detected bait take 75% of the time, as opposed to 33.3% and 20% for the respective unmodified models. The high-sensitivity model also increased the detection probability of the smallest mammal species from 0.09 to 0.34. However, there was no significant difference in detection probability for medium-sized mammals. Conclusions Despite the three Reconyx camera models having similar manufacturer-listed specifications, they varied substantially in their performance. The high-sensitivity model vastly improved the detection of known events and the detection probability of small mammals in northern Australia. Implications Failure to consider variation in camera-trap performance can lead to inaccurate conclusions when multiple camera models are used. Consequently, researchers should carefully consider the parameters and capabilities of camera models in study designs. Camera models and their configurations should be reported in methods, and variation in detection probabilities among different models and configurations should be incorporated into analyses.

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