Camera trapping in ecology: A new section for wildlife research

Fisher, Jason T.

doi:10.1002/ece3.9925

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Targeted efforts to surveil free-ranging deer mice and white-footed mice will be necessary to evaluate natural SARS-CoV-2 infection and transmission in these species as well as their potential to serve as an intermediate host for other animals like white-tailed deer. Identifying approaches to quantify potential intra- and inter-species interactions in wildlife (e.g., GIS, remote sensing, camera traps, habitat and population modeling) will also be critical to assessing transmission risks [ 56 , 57 , 58 ].…”

Section: Discussionmentioning

confidence: 99%

Lack of SARS-CoV-2 Viral RNA Detection among a Convenience Sampling of Ohio Wildlife, Companion, and Agricultural Animals, 2020–2021

Ehrlich

Madden

McBride

et al. 2023

Animals

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in humans in late 2019 and spread rapidly, becoming a global pandemic. A zoonotic spillover event from animal to human was identified as the presumed origin. Subsequently, reports began emerging regarding spillback events resulting in SARS-CoV-2 infections in multiple animal species. These events highlighted critical links between animal and human health while also raising concerns about the development of new reservoir hosts and potential viral mutations that could alter the virulence and transmission or evade immune responses. Characterizing susceptibility, prevalence, and transmission between animal species became a priority to help protect animal and human health. In this study, we coalesced a large team of investigators and community partners to surveil for SARS-CoV-2 in domestic and free-ranging animals around Ohio between May 2020 and August 2021. We focused on species with known or predicted susceptibility to SARS-CoV-2 infection, highly congregated or medically compromised animals (e.g., shelters, barns, veterinary hospitals), and animals that had frequent contact with humans (e.g., pets, agricultural animals, zoo animals, or animals in wildlife hospitals). This included free-ranging deer (n = 76 individuals), free-ranging mink (n = 57), multiple species of bats (n = 59), and other wildlife in addition to domestic cats (n = 275) and pigs (n = 184). In total, we tested 792 individual animals (34 species) via rRT-PCR for SARS-CoV-2 RNA. SARS-CoV-2 viral RNA was not detected in any of the tested animals despite a major peak in human SARS-CoV-2 cases that occurred in Ohio subsequent to the peak of animal samplings. Importantly, we did not test for SARS-CoV-2 antibodies in this study, which limited our ability to assess exposure. While the results of this study were negative, the surveillance effort was critical and remains key to understanding, predicting, and preventing the re-emergence of SARS-CoV-2 in humans or animals.

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Section: Discussionmentioning

confidence: 99%

Lack of SARS-CoV-2 Viral RNA Detection among a Convenience Sampling of Ohio Wildlife, Companion, and Agricultural Animals, 2020–2021

Ehrlich

Madden

McBride

et al. 2023

Animals

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“…In the last five decades, camera traps have become an established method of studying and monitoring wildlife populations (Fisher, 2023; Sollmann, 2018). Camera traps are an easy-to-operate, accessible, relatively affordable, and low-impact method of monitoring wildlife.…”

Section: Introductionmentioning

confidence: 99%

Dead zones, obstructions, and missed captures: a novel approach for determining the Effective Capture Area for remote camera traps

Carswell,

Avgar,

Street

et al. 2024

Preprint

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1. Camera traps have become cemented as an important tool of wildlife research, yet, their utility is now extending beyond researchers, as cameras can contribute to more inclusive methods of place-based wildlife management. From recent advances in analytics and technology, camera trap-based density estimates of wildlife is an emerging field of research. Most camera trap-based density methods require an estimate of the area monitored by each camera, a relatively novel parameter that may be highly variable and is rarely quantified in literature. 2. Here, we developed and tested a standardized field and analytical method allowing us to predict the probability of photographic capture as it varies within the camera viewshed. We investigated how capture probability changes due to environmental influences, i.e., vegetation structure, ambient temperature, speed of subject, time of day, in addition to internal factors from cameras themselves, i.e., sensitivity settings, number of photos taken, and camera trap brand. We then use our method to gain standardized, accurate, and predictable estimates of the area a camera monitors, the Effective Capture Area (ECA). 3. We found that ECAs in our study areas are heavily influenced by location-specific environmental factors, i.e., vegetation structure, technological delays associated with cameras themselves, i.e., refractory period, and custom internal camera settings, i.e., sensitivity, number of photographs taken. We also found that the ECAs computed using our methodology are substantially smaller than reported values in the literature. 4. Imprecision surrounding camera trap viewshed areas can create propagating bias when implementing viewshed-based density estimators. Our method and Effective Capture Area calculation may help increase the reliability of camera trap-based density estimation methods, provide a framework to help improve camera-trap occupancy modelling, and contribute to more accessible wildlife management practices.

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“…Thus, the identification of wildlife species is essential for species diversity detection and the conservation of rare and endangered wildlife. Currently, camera traps are the mainstream method for wildlife species monitoring [3][4][5]. However, manual identification of monitoring images is known to suffer from the challenges of high intensity and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition

Zhang,

Zhang

2023

Animals

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Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model’s generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.

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Camera trapping in ecology: A new section for wildlife research

Cited by 5 publications

References 40 publications

Lack of SARS-CoV-2 Viral RNA Detection among a Convenience Sampling of Ohio Wildlife, Companion, and Agricultural Animals, 2020–2021

Lack of SARS-CoV-2 Viral RNA Detection among a Convenience Sampling of Ohio Wildlife, Companion, and Agricultural Animals, 2020–2021

Dead zones, obstructions, and missed captures: a novel approach for determining the Effective Capture Area for remote camera traps

DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition

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