scholarly journals The experience of low-resource fauna research by using camera traps

2021 ◽  
Vol 2021 (21) ◽  
pp. 114-124
Author(s):  
Denys Vishnevskyi ◽  
Keyword(s):  
Spatial Distribution ◽  
Daily Activity ◽  
Roe Deer ◽  
High Sensitivity ◽  
Camera Trap ◽  
Camera Traps ◽  
Local Conditions ◽  
Low Resource ◽  
Animal Populations ◽  
And Control

The tasks of managing animal populations (conservation, exploitation, and control) require reliable initial information for their implementation. This information includes a number of parameters: spatial distribution, annual and daily cycles of activity, abundance and its dynamics, ecosystem relationships, reproduction, nutrition, mortality, and others. Of this broad set, abundance and territorial distribution are of basic importance. They affect all other parameters. However, these indicators are the most sensitive to the influence of factors of qualification and motivation of the performer. The solution to this problem leads to searching for ways to unify methods in order to reduce errors in the assessment. One of the solutions to this problem is the introduction of technical means such as camera traps. Camera traps have become an increasingly popular tool in wildlife research. With its help, the following tasks are solved: assessment of the number and spatial distribution of animals, daily and seasonal activity, and much more. This tool avoids the factor of subjectivity. At the same time, in our conditions, the price of a camera trap is high for a researcher. However, the methodological requirements for the study require the use of more than ten cameras. Thus, it becomes necessary to comprehend low-resource research and the results that they can bring. During 2018, research was carried out in the territory of the Chornobyl Reserve using six camera traps. The placement of camera traps was not systematic, but corresponded to the diversity of habitats. The objects of research were such representatives of mammals as the elk, deer, wolf, roe deer, wild boar, fox, raccoon dog, and hare. They are the ones that make up the set of species that can be effectively captured by a camera trap. The following results were obtained: daily activity, spatial distribution, quantitative characteristics of groups. As the results have shown, even a small number of camera traps makes it possible to assess the presence of the largest animals and their daily activity. It should be noted that this group of species is of the greatest interest from the viewpoint of regulation and protection. Numerical parameters such as quantity and relative abundance cannot be used for estimation. This is due to the high sensitivity to local conditions.

scholarly journals Daily Activity Patterns and Co-Occurrence of Duikers Revealed by an Intensive Camera Trap Survey across Central African Rainforests

Animals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 2200
Author(s):  
Fructueux G. A. Houngbégnon ◽  
Daniel Cornelis ◽  
Cédric Vermeulen ◽  
Bonaventure Sonké ◽  
Stephan Ntie ◽  
...  
Keyword(s):  
Daily Activity ◽  
Activity Patterns ◽  
Camera Trap ◽  
Camera Traps ◽  
The Other ◽  
Species Pairs ◽  
Free Ranging ◽  
First Time ◽  
Daily Activity Patterns ◽  
Temporal Overlap

The duiker community in Central African rainforests includes a diversity of species that can coexist in the same area. The study of their activity patterns is needed to better understand habitat use or association between the species. Using camera traps, we studied the temporal activity patterns, and quantified for the first time the temporal overlap and spatial co-occurrence between species. Our results show that: (i) Two species are strongly diurnal: Cephalophus leucogaster, and Philantomba congica, (ii) two species are mostly diurnal: C.callipygus and C. nigrifrons, (iii) one species is strongly nocturnal: C.castaneus, (iv) and one species is mostly nocturnal: C.silvicultor. Analyses of temporal activities (for five species) identified four species pairs that highly overlapped (Δ^≥ 0.80), and six pairs that weakly overlapped (Δ^ between 0.06 and 0.35). Finally, co-occurrence tests reveal a truly random co-occurrence (plt > 0.05 and pgt > 0.05) for six species pairs, and a positive co-occurrence (pgt < 0.05) for four pairs. Positive co-occurrences are particularly noted for pairs formed by C.callipygus with the other species (except C. nigrifrons). These results are essential for a better understanding of the coexistence of duikers and the ecology of poorly known species (C. leucogaster and C. nigrifrons), and provide clarification on the activity patterns of C. silvicultor which was subject to controversy. Camera traps proved then to be a powerful tool for studying the activity patterns of free-ranging duiker populations.

How to snap your cat: optimum lures and their placement for attracting mammalian predators in arid Australia

2015 ◽  
Vol 42 (1) ◽  
pp. 1 ◽  
Author(s):  
J. L. Read ◽  
A. J. Bengsen ◽  
P. D. Meek ◽  
K. E. Moseby
Keyword(s):  
Arid Zone ◽  
Activity Patterns ◽  
Capture Rate ◽  
Camera Trap ◽  
Camera Traps ◽  
Monitoring And Control ◽  
Feral Cats ◽  
Behavioural Responses ◽  
Visitation Rates ◽  
And Control

Context Automatically activated cameras (camera traps) and automated poison-delivery devices are increasingly being used to monitor and manage predators such as felids and canids. Maximising visitation rates to sentry positions enhances the efficacy of feral-predator management, especially for feral cats, which are typically less attracted to food-based lures than canids. Aims The influence of camera-trap placement and lures were investigated to determine optimal monitoring and control strategies for feral cats and other predators in two regions of semi-arid South Australia. Methods We compared autumn and winter capture rates, activity patterns and behaviours of cats, foxes and dingoes at different landscape elements and with different lures in three independent 6 km × 3 km grids of 18 camera-trap sites. Key results Neither visual, olfactory or audio lures increased recorded visitation rates by any predators, although an audio and a scent-based lure both elicited behavioural responses in predators. Cameras set on roads yielded an eight times greater capture rate for dingoes than did off-road cameras. Roads and resource points also yielded highest captures of cats and foxes. All predators were less nocturnal in winter than in autumn and fox detections at the Immarna site peaked in months when dingo and cat activity were lowest. Conclusions Monitoring and management programs for cats and other predators in arid Australia should focus on roads and resource points where predator activity is highest. Olfactory and auditory lures can elicit behavioural responses that render cats more susceptible to passive monitoring and control techniques. Dingo activity appeared to be inversely related to fox but not cat activity during our monitoring period. Implications Optimised management of feral cats in the Australian arid zone would benefit from site- and season-specific lure trials.

scholarly journals The impacts of camera placement on species detectability in an Indonesian terrestrial community

2021 ◽  
Author(s):  
Eric Van Dam
Keyword(s):  
Body Size ◽  
Data Collection ◽  
Study Design ◽  
Detection Probability ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Placement ◽  
Species Detection ◽  
Animal Populations ◽  
Selective Placement

<p>Ecologists have increasingly favoured the use of camera traps in studies of animal populations and their behaviour. Because camera trap study design commonly implements non-random selective placement, we must consider how this placement strategy affects the integrity of our data collection. Selective placement of camera traps have the benefits of 1) maximizing the probability of encounter events by sampling habitats or microhabitats of known significance to a focus or closely-related species and 2) reducing data collection and maintenance effort in the field by situating cameras along more easily-accessible landscape features. Introducing a non-random survey method, such as selective placement, into a project studying a species or community that also expresses non-random habitat use may lead to unintentionally biased data and inaccurate results. By using a paired on-trail/off-trail camera-trap study design, my aim is to investigate potential differences in popular ecological indices, species detection probability (p) using multi-method occupancy models, and intraspecific temporal activity for a terrestrial community in Gunung Palung National Park in Indonesian Borneo. Differences in detection probability between on and off-trail cameras were compared against species characteristics (including body size, diet, and taxonomic group) to find potential correlations. While several species exhibited a significant difference in detection probability between cameras placed on foot trails and those placed randomly off-trail, there was no measured community trend. This stresses my conclusion further that a non-random study design leaves results open to bias from unknown patterns in detection due to underlying variation in behaviour and microhabitat use. Selective placement may be effective for increasing detection probability for some species but can also lead to substantial bias if the features selected for are not explicitly taken into account within the analysis or balanced with a control in the study design. In addition, a positive interactive effect was found between on trail species detection and body size for the terrestrial omnivore guild, and three species presented significant variation in temporal activity between camera placement types. This provides evidence that camera placement not only affects species state parameters and indices but has a noticeable impact on behavioural observations that require accountability as well.</p>

scholarly journals Efficient Pipeline for Automating Species ID in new Camera Trap Projects

2019 ◽  
Vol 3 ◽  
Author(s):  
Sara Beery ◽  
Dan Morris ◽  
Siyu Yang ◽  
Marcel Simon ◽  
Arash Norouzzadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Annotation ◽  
Activity Patterns ◽  
Camera Trap ◽  
Camera Traps ◽  
High Rate ◽  
Species Classification ◽  
Data Set ◽  
Training Time ◽  
Animal Populations

Camera traps are heat- or motion-activated cameras placed in the wild to monitor and investigate animal populations and behavior. They are used to locate threatened species, identify important habitats, monitor sites of interest, and analyze wildlife activity patterns. At present, the time required to manually review images severely limits productivity. Additionally, ~70% of camera trap images are empty, due to a high rate of false triggers. Previous work has shown good results on automated species classification in camera trap data (Norouzzadeh et al. 2018), but further analysis has shown that these results do not generalize to new cameras or new geographic regions (Beery et al. 2018). Additionally, these models will fail to recognize any species they were not trained on. In theory, it is possible to re-train an existing model in order to add missing species, but in practice, this is quite difficult and requires just as much machine learning expertise as training models from scratch. Consequently, very few organizations have successfully deployed machine learning tools for accelerating camera trap image annotation. We propose a different approach to applying machine learning to camera trap projects, combining a generalizable detector with project-specific classifiers. We have trained an animal detector that is able to find and localize (but not identify) animals, even species not seen during training, in diverse ecosystems worldwide. See Fig. 1 for examples of the detector run over camera trap data covering a diverse set of regions and species, unseen at training time. By first finding and localizing animals, we are able to: drastically reduce the time spent filtering empty images, and dramatically simplify the process of training species classifiers, because we can crop images to individual animals (and thus classifiers need only worry about animal pixels, not background pixels). drastically reduce the time spent filtering empty images, and dramatically simplify the process of training species classifiers, because we can crop images to individual animals (and thus classifiers need only worry about animal pixels, not background pixels). With this detector model as a powerful new tool, we have established a modular pipeline for on-boarding new organizations and building project-specific image processing systems. We break our pipeline into four stages: 1. Data ingestion First we transfer images to the cloud, either by uploading to a drop point or by mailing an external hard drive. Data comes in a variety of formats; we convert each data set to the COCO-Camera Traps format, i.e. we create a Javascript Object Notation (JSON) file that encodes the annotations and the image locations within the organization’s file structure. 2. Animal detection We next run our (generic) animal detector on all the images to locate animals. We have developed an infrastructure for efficiently running this detector on millions of images, dividing the load over multiple nodes. We find that a single detector works for a broad range of regions and species. If the detection results (as validated by the organization) are not sufficiently accurate, it is possible to collect annotations for a small set of their images and fine-tune the detector. Typically these annotations would be fed back into a new version of the general detector, improving results for subsequent projects. 3. Species classification Using species labels provided by the organization, we train a (project-specific) classifier on the cropped-out animals. 4. Applying the system to new data We use the general detector and the project-specific classifier to power tools facilitating accelerated verification and image review, e.g. visualizing the detections, selecting images for review based on model confidence, etc. The aim of this presentation is to present a new approach to structuring camera trap projects, and to formalize discussion around the steps that are required to successfully apply machine learning to camera trap images. The work we present is available at http://github.com/microsoft/cameratraps, and we welcome new collaborating organizations.

scholarly journals Quantifying daily activity patterns of the spotted-tailed quoll (Dasyurus maculatus) using camera trap data from a stronghold population in south-eastern New South Wales

2019 ◽  
Vol 41 (2) ◽  
pp. 283 ◽  
Author(s):  
Stephanie K. Courtney Jones ◽  
Katarina M. Mikac
Keyword(s):  
Daily Activity ◽  
New South ◽  
Activity Patterns ◽  
New South Wales ◽  
Camera Trap ◽  
Camera Traps ◽  
Activity Levels ◽  
South Eastern ◽  
South Wales ◽  
Daily Activity Patterns

Activity levels of spotted-tailed quolls were investigated using camera traps over 12 months. There were 33 independent camera trap photos with 17 individual quolls identified. Latency to initial detection was 40 days. Quolls were nocturnal/crepuscular, spending 35% of the day they were detected active. Highest activity levels were recorded in summer.

scholarly journals The impacts of camera placement on species detectability in an Indonesian terrestrial community

2021 ◽  
Author(s):  
Eric Van Dam
Keyword(s):  
Body Size ◽  
Data Collection ◽  
Study Design ◽  
Detection Probability ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Placement ◽  
Species Detection ◽  
Animal Populations ◽  
Selective Placement

<p>Ecologists have increasingly favoured the use of camera traps in studies of animal populations and their behaviour. Because camera trap study design commonly implements non-random selective placement, we must consider how this placement strategy affects the integrity of our data collection. Selective placement of camera traps have the benefits of 1) maximizing the probability of encounter events by sampling habitats or microhabitats of known significance to a focus or closely-related species and 2) reducing data collection and maintenance effort in the field by situating cameras along more easily-accessible landscape features. Introducing a non-random survey method, such as selective placement, into a project studying a species or community that also expresses non-random habitat use may lead to unintentionally biased data and inaccurate results. By using a paired on-trail/off-trail camera-trap study design, my aim is to investigate potential differences in popular ecological indices, species detection probability (p) using multi-method occupancy models, and intraspecific temporal activity for a terrestrial community in Gunung Palung National Park in Indonesian Borneo. Differences in detection probability between on and off-trail cameras were compared against species characteristics (including body size, diet, and taxonomic group) to find potential correlations. While several species exhibited a significant difference in detection probability between cameras placed on foot trails and those placed randomly off-trail, there was no measured community trend. This stresses my conclusion further that a non-random study design leaves results open to bias from unknown patterns in detection due to underlying variation in behaviour and microhabitat use. Selective placement may be effective for increasing detection probability for some species but can also lead to substantial bias if the features selected for are not explicitly taken into account within the analysis or balanced with a control in the study design. In addition, a positive interactive effect was found between on trail species detection and body size for the terrestrial omnivore guild, and three species presented significant variation in temporal activity between camera placement types. This provides evidence that camera placement not only affects species state parameters and indices but has a noticeable impact on behavioural observations that require accountability as well.</p>

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.

Daily activity pattern of reintroduced giant anteaters (Myrmecophaga tridactyla): effects of seasonality and experience

Mammalia ◽  
2017 ◽  
Vol 81 (1) ◽  
Author(s):  
Yamil E. Di Blanco ◽  
Karina L. Spørring ◽  
Mario S. Di Bitetti
Keyword(s):  
Activity Pattern ◽  
Daily Activity ◽  
Radio Telemetry ◽  
Kernel Density ◽  
Camera Traps ◽  
Nocturnal Activity ◽  
Age Classes ◽  
Local Conditions ◽  
Daily Activity Pattern ◽  
Seasonal Shifts

AbstractWe assessed the effect of seasonality and intrinsic conditions on daily activity pattern of giant anteaters reintroduced in the Iberá Reserve, Argentina. During 2007–2012 we gathered 159 24-h focal samples on 15 radio-marked individuals (11 captive-reared, four wild-reared; seven adults, eight juveniles), 216 records of beginning and end of activity bouts on 20 individuals, and 454 camera-traps records (3345 trap-days). We estimated the daily hours of activity, the percentage of diurnal and nocturnal activity, and the daily activity range and time overlap using time as a circular variable in kernel density estimations. We assessed differences between seasons, sexes, age classes, and types of rearing. The average daily hours of activity was 8:43 h. Camera-traps and radio-telemetry showed similar results. Animals exhibited both diurnal (60–65%) and nocturnal (40–35%) activity. The higher probability for being active ranged within 09:00–03:00 h. Anteaters spent more hours active and were more nocturnal during summer. Activity was highly overlapped between sexes, and wild-reared individuals were more nocturnal than captive-reared ones. Seasonal shifts in daily activity highlights the importance of thermoregulation as a selective factor in this species. The giant anteater is a cathemeral species with flexibility to accommodate its activity pattern to local conditions or experience.

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