Best bait for your buck: bait preference for camera trapping north Australian mammals

2015 ◽  
Vol 63 (6) ◽  
pp. 376 ◽  
Author(s):  
Rebecca L. Diete ◽  
Paul D. Meek ◽  
Kelly M. Dixon ◽  
Christopher R. Dickman ◽  
Luke K.-P. Leung
Keyword(s):  
Threatened Species ◽  
Peanut Butter ◽  
Camera Trap ◽  
Sesame Oil ◽  
Camera Traps ◽  
Camera Trapping ◽  
Visitation Rate ◽  
Capture Recapture ◽  
The Mean

Critical evaluations of bait attractiveness for camera trapping wildlife are scant even though use of the most attractive bait should improve detection of cryptic, threatened species. We aimed to determine the most attractive bait for camera trapping the northern hopping-mouse (Notomys aquilo) and sympatric mammals. We also tested the effectiveness of overhead camera trap orientation in identifying individual northern quolls (Dasyurus hallucatus) as this could be used to define a camera trap event for analysis purposes. Using white-flash camera traps, the attractiveness of four baits (peanut butter with oats, corn, sesame oil and sunflower kernels) and a control were compared for N. aquilo, D. hallucatus, the northern brown bandicoot (Isoodon macrourus) and the agile wallaby (Notamacropus agilis). Spot patterns of D. hallucatus were compared to determine the visitation rate of individuals. Peanut butter– and sesame oil–based baits were significantly more attractive to D. hallucatus, while I. macrourus strongly preferred the peanut butter bait. Bait type did not affect the mean number of events for N. aquilo or N. agilis. The consistently identifiable images of individual D. hallucatus were used to determine the optimal event delineator of 15 min. The improved techniques for camera trapping D. hallucatus should be valuable for future capture–recapture studies of this species. Camera trapping is a viable replacement for the ineffective method of indexing the abundance of N. aquilo using indirect signs.

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 An assessment of carnivore relative abundance and density in the eastern rainforests of Madagascar using remotely-triggered camera traps

Oryx ◽  
2010 ◽  
Vol 44 (2) ◽  
pp. 219-222 ◽  
Author(s):  
Brian Gerber ◽  
Sarah M. Karpanty ◽  
Charles Crawford ◽  
Mary Kotschwar ◽  
Johnny Randrianantenaina
Keyword(s):  
Relative Abundance ◽  
National Park ◽  
Camera Trap ◽  
Camera Traps ◽  
Domestic Dog ◽  
Likelihood Method ◽  
Ranomafana National Park ◽  
Carnivore Species ◽  
Cryptoprocta Ferox ◽  
Capture Recapture

AbstractDespite major efforts to understand and conserve Madagascar’s unique biodiversity, relatively little is known about the island’s carnivore populations. We therefore deployed 43 camera-trap stations in Ranomafana National Park, Madagascar during June–August 2007 to evaluate the efficacy of this method for studying Malagasy carnivores and to estimate the relative abundance and density of carnivores in the eastern rainforest. A total of 755 camera-trap nights provided 1,605 photographs of four endemic carnivore species (fossa Cryptoprocta ferox, Malagasy civet Fossa fossana, ring-tailed mongoose Galidia elegans and broad-striped mongoose Galidictus fasciata), the exotic Indian civet Viverricula indica and the domestic dog Canis familiaris. We identified 38 individual F. fossana and 10 individual C. ferox. We estimated density using both capture-recapture analyses, with a buffer of full mean-maximum-distance-moved, and a spatially-explicit maximum-likelihood method (F. fossana: 3.03 and 2.23 km-2, respectively; C. ferox: 0.15 and 0.17 km-2, respectively). Our estimated densities of C. ferox in rainforest are lower than published estimates for conspecifics in the western dry forests. Within Ranomafana National Park species richness of native carnivores did not vary among trail systems located in secondary, selectively-logged and undisturbed forest. These results provide the first assessment of carnivore population parameters using camera-traps in the eastern rainforests of Madagascar.

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 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.

scholarly journals Identifying individual cougars (Puma concolor) in remote camera images – implications for population estimates

2018 ◽  
Vol 45 (3) ◽  
pp. 274 ◽  
Author(s):  
Peter D. Alexander ◽  
Eric M. Gese
Keyword(s):  
Puma Concolor ◽  
Camera Traps ◽  
Camera Trapping ◽  
Field Methods ◽  
Density Estimates ◽  
North West ◽  
Photo Identification ◽  
Matching Process ◽  
Capture Recapture ◽  
Remote Camera

Context Several studies have estimated cougar (Puma concolor) abundance using remote camera trapping in conjunction with capture–mark–recapture (CMR) type analyses. However, this methodology (photo-CMR) requires that photo-captured individuals are individually recognisable (photo identification). Photo identification is generally achieved using naturally occurring marks (e.g. stripes or spots) that are unique to each individual. Cougars, however, are uniformly pelaged, and photo identification must be based on subtler attributes such as scars, ear nicks or body morphology. There is some debate as to whether these types of features are sufficient for photo-CMR, but there is little research directly evaluating its feasibility with cougars. Aim We aimed to examine researchers’ ability to reliably identify individual cougars in photographs taken from a camera-trapping survey, in order to evaluate the appropriateness of photo-CMR for estimating cougar abundance or CMR-derived parameters. Methods We collected cougar photo detections using a grid of 55 remote camera traps in north-west Wyoming, USA. The photo detections were distributed to professional biologists working in cougar research, who independently attempted to identify individuals in a pairwise matching process. We assessed the level to which their results agreed, using simple percentage agreement and Fleiss’s kappa. We also generated and compared spatially explicit capture–recapture (SECR) density estimates using their resultant detection histories. Key results There were no cases where participants were in full agreement on a cougar’s ID. Agreement in photo identification among participants was low (n = 7; simple agreement = 46.7%; Fleiss’s kappa = 0.183). The resultant SECR density estimates ranged from 0.7 to 13.5 cougars per 100 km2 (n = 4; s.d. = 6.11). Conclusion We were unable to produce reliable estimates of cougar density using photo-CMR, due to our inability to accurately photo-tag detected individuals. Abundance estimators that do not require complete photo-tagging (i.e. mark–resight) were also infeasible, given the lack of agreement on any single cougar’s ID. Implications This research suggested that there are substantial problems with the application of photo-CMR to estimate the size of cougar populations. Although improvements in camera technology or field methods may resolve these issues, researchers attempting to use this method on cougars should be cautious.

scholarly journals How many giraffes are there? A comparison of abundance estimators at Ongava Game Reserve, Namibia

2021 ◽  
Author(s):  
Christophe Bonenfant ◽  
Ken Stratford ◽  
Stephanie Periquet
Keyword(s):  
Population Size ◽  
Detection Probability ◽  
Wildlife Conservation ◽  
Camera Trap ◽  
Biological Data ◽  
Camera Traps ◽  
Population Abundance ◽  
Visit Frequency ◽  
Game Reserve ◽  
Capture Recapture

Camera-traps are a versatile and widely adopted tool to collect biological data in wildlife conservation and management. If estimating population abundance from camera-trap data is the primarily goal of many projects, what population estimator is suitable for such data needs to be investigated. We took advantage of a 21 days camera-trap monitoring on giraffes at Onvaga Game Reserve, Namibia to compare capture-recapture (CR), saturation curves and N-mixture estimators of population abundance. A marked variation in detection probability of giraffes was observed in time and between individuals. Giraffes were also less likely to be detected after they were seen at a waterhole with cameras (visit frequency of f = 0.25). We estimated population size to 119 giraffes with a Cv = 0.10 with the best CR estimator. All other estimators we a applied over-estimated population size by ca. -20 to >+80%, because they did not account for the main sources of heterogeneity in detection probability. We found that modelling choices was much less forgiving for N-mixture than CR estimators. Double counts were problematic for N-mixture models, challenging the use of raw counts at waterholes to monitor giraffes abundance.

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 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.

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