Bird Research Innovation: Using Camera Traps to Identify Hidden Species.

Introduction

Bird study is one of the topics that is gaining popularity, especially in terms of biodiversity conservation. The correct observation strategy is critical for generating accurate and dependable data. However, many bird species are difficult to observe directly, either because they are sensitive to observers' presence or because their habitats are difficult to access. As a birdwatcher, I frequently encounter this difficulty when attempting to obtain data on bird populations and behavior in the outdoors.

Installation of camera traps to monitor terrestrial birds

In solution to this challenge, camera traps have developed as a new instrument that has important implications for bird study methodologies. Camera traps, which were originally utilized for mammal studies, are increasingly being used in avifauna research. According to (Delisle et al. 2021), using camera traps in ornithology not only broadens the scope of research, but also reveals previously unknown insights into bird behavior. Camera traps, which can take photographs automatically without human intervention, are an effective non-invasive method for watching bird species hidden in their natural habitats.

Using camera traps for observation not only overcomes the limits of traditional approaches, but also allows for more extensive and reliable data collecting. In a research in the Sumatran forest, for example, we discovered that camera traps may detect the presence of endemic bird species that are rarely or nearly never seen. The use of this technology allows us to study these species in ways that were previously unimaginable, enhancing our understanding of avian variation and the environments they inhabit.

Why are camera traps effective in bird research?

Direct observation presents particular issues for academics and birdwatchers, particularly when working in difficult-to-access places or studying bird species that prevent humans. Conventional observation methods, such as the use of binoculars or field observation, are usually ineffective for fully capturing bird activity, particularly for species that are active at night or live in isolated areas. In such a situation, camera traps appear as a highly effective and uncommon option since they can collect photographs and movies without interfering birds' natural behavior.

Camera traps are particularly efficient, as they are activated by movement or thermal activity, allowing them to monitor bird behavior for 24 hours without the presence of humans at the observation station. According to Vaughan et al. (2022), camera traps collect bird population data more rapidly and efficiently than manual approaches, while also minimizing disturbances to their habitats. This is especially relevant for bird species that are vulnerable to environmental changes or human activity.

In addition, camera traps enable non-invasive data collection. As birdwatchers, we are very concerned about the impact on natural habitats and bird behavior. Camera traps can be strategically placed throughout habitats without disrupting the ecosystem or animal behavior. Camera traps can also be used to record bird species that are difficult to see using traditional methods, such as nocturnal birds or those that are only active during specific times of the day. Oliver et al. (2023) conducted a similar investigation, which validates the effectiveness of this strategy in taking photos of shy or endangered birds without requiring direct human participation.

Case Study: Use of Camera Traps for Hidden Bird Species.

Camera traps have proven to be quite effective in uncovering hidden bird species that were previously difficult to identify using direct observation methods, although its use in ornithology remains limited. Camera traps have progressively started to be used for Sumatran tropical forest studies to study bird activity in isolated locations such as swamp forests and difficult-to-reach mountains, though they have not yet become the major method. Camera traps are useful for collecting photographs and videos of rare and elusive bird species, including some endemic species that are rarely reported.

Rolls partridge species caught on FFI camera trap.

One of the most significant discoveries is the successful capturing of photos about the Hoogerwerf's pheasant (Lophura hoogerwerfi), a highland endemic species of northern Sumatra, by the Fauna Flora Indonesia (FFI) camera trap in 2013. This indicates the importance of camera traps in offering new insights into the presence and ecology of bird species that would otherwise be overlooked by conventional methods. Camera traps also allow researchers to observe bird movement and activity without having to spend hours in the field, which is an important advantage in bird research studies in difficult or dangerous locations.

Problems and Challenges of Using Camera Traps for Bird Research

Although video traps have given several benefits in bird study, they also pose various obstacles and constraints that field researchers must solve. One of the most common obstacles is the technological and logistical limits that come with installing and maintaining cameras in birds' natural environments. Extreme weather conditions, such as heavy rain and high humidity, can impact the effectiveness of trap cameras in tropical places like as the Sumatra rainforest. Temperature can harm the camera, and dew or condensation on the lens can reduce image quality. According to Franceschi et al.'s (2022) research, camera traps set in densely vegetated habitats are at high risk of being obscured by leaves or branch movements, which might result in false triggers or irrelevant photos.

Furthermore, a thorough preparation will be needed while placing camera traps. The exact location of the camera installation is critical for obtaining accurate data. However, installing cameras in expansive or difficult-to-access places can be a logistical difficulty. In our investigation in the Aceh mountain forest, for example, it was difficult to put up camera traps in the steep and thick terrain, restricting our observation range. This is worsened by limited battery power and camera memory cards that frequently run out before the needed data is collected, resulting in an important factor that could threaten data collecting continuity.

Beyond from technical problems, analyzing data from camera traps may become very complicated. Camera traps often generate a vast number of photos, many of which are useless since they are activated by non-target motions such as wind or tiny animals. To manually examine such a massive amount of data, substantial time and resources are required. In this regard, artificial intelligence (AI) software is rapidly being used to speed up data processing. According to Yang et al. (2024), AI can assist identify bird species from hundreds of photos captured by camera traps; however, this technology is still in development and requires greater accuracy, particularly in distinguishing rare or unusual bird species.

The Future of Using Camera Traps for Bird Research

The use of video traps in bird study is evolving and has enormous potential for the future. Camera traps, a becoming developed device, allow researchers to collect data more effectively and without disturbing the animals under study. The rapid development of technology such as more sensitive sensors that detect movement and AI-based picture identification has created new prospects for bird studies. In the future, driven by artificial intelligence trap cameras may improve the capacity to automatically identify bird species, even in low-light circumstances or against complicated background images.

The combination of camera traps with real-time analytic tools is one of the upcoming technologies with the potential to transform the landscape of bird studies. This technology allows researchers to monitor and evaluate data instantly after capturing photographs or videos, reducing the need to manually download and process the data. This is especially beneficial in vulnerable ecosystems or conservation areas where bird population fluctuations must be monitored quickly and accurately. According to Laughlin et al. (2023), using camera traps with real-time data processing has aided in the early detection of risks such as unlawful hunting or habitat changes, allowing conservation actions to be applied more swiftly.

In addition, global collaboration among researchers is promoting the widespread use of camera traps as a major tool in bird study. Cloud technology allows academics all around the world to exchange photos and data from camera traps. This encourages worldwide collaboration, increases the area of study, and allows data from a variety of environments to be collected and processed together. As a bird researcher, I saw this development as an excellent chance to advance our understanding of bird species' global distribution and migration patterns. Initiatives like these increase data participation and accuracy by allowing professionals from across the world to verify the data.

However, while the future of camera traps is encouraging, there are several problems that must be overcome. Increasingly advanced technology requires increased expenditures, both in terms of equipment installation and people training to operate and analyze the generated data. Additionally, while AI has enormous potential, the technology is not yet impeccable. Species misidentification still occurs, particularly among bird species with comparable physical variants. This has become one of the areas that need more attention, as faults in data processing might lead to incorrect results.

The Use of Camera Traps for Bird Conservation

Camera traps can help bird conservation efforts by delivering valuable data for monitoring endangered species populations, estimating species distribution, and understanding bird behavior in habitats that are natural. Camera traps enable researchers to acquire more precise information on the abundance of bird species in many difficult-to-access areas without having to be physically present at the site. This helps to monitor bird species that are sensitive to human presence or reside in remote places.

Camera trap data may also be used to inform endangered species conservation initiatives, such as habitat restoration and environmental impact decreases. Camera traps assist detect places critical to species survival, such as breeding grounds and migration pathways. Thus, camera traps not only aid in scientific study but also give compelling information to assist campaigning for bird habitat conservation on both a local and global scale.

Promoting global collaboration among research institutes, universities, and non-governmental organizations (NGOs). By building solid collaborations, we can exchange resources such as camera trap devices, training, and data. Researchers should also encourage the use of emerging technologies, such as artificial intelligence (AI) and big data analysis, to increase data processing efficiency from camera traps. The application of artificial intelligence in species recognition can minimize the time necessary to examine photos while also improving identification accuracy. 

Recommendations to Improve the Use of Camera Traps in Bird Research

To maximize the potential of camera traps in bird study, researchers and conservation organizations should consider numerous guidelines.

1. Researchers ought to participate in training and instruction on camera trap technology and data processing. Many new or less experienced researchers may struggle to use the technology and software used in this study.
2. Promoting global collaboration among research institutes, universities, and non-governmental organizations (NGOs). Through building solid collaborations, we can exchange resources such as camera trap devices, training, and data. 
3. Researchers should also encourage the use of emerging technologies, such as artificial intelligence (AI) and big data analysis, to increase data processing efficiency from camera traps. The application of artificial intelligence in species recognition can minimize the time necessary to examine photos while also improving identification accuracy. 
4. Researchers should encourage community participation in the data collection program using camera traps. Local communities can be involved in the installation and maintenance of camera traps, as well as in data collection and analysis. This will not only raise public awareness about the importance of bird conservation, but it can also expand the reach of research. Successful community-based initiatives have been proven to produce high-quality data and help protect endangered bird species.

Reference

Delisle, Z. J., Flaherty, E. A., Nobbe, M. R., Wzientek, C. M., and Swihart, R. K.. 2021. Next-Generation Camera Trapping: Systematic Review of Historic Trends Suggests Keys to Expanded Research Applications in Ecology and Conservation. Frontiers in Ecology and Evolution, 9. ISSN 2296-701X

Fauna & Flora International. 2013. Rare pheasant snapped in Sumatra. accessed on date 21 October 2024. https://phys.org/news/2013-11-rare-pheasant-snapped-sumatra.html

Franceschi, I. C., Dornas, R. A., Lermen, I. S., Coelho, A. V. P., et al.. 2024. Camera trap surveys of Atlantic Forest mammals: A data set for analyses considering imperfect detection (2004–2020). Ecology 105(5):e4298. DOI: 10.1002/ecy.4298.

Laughlin, L.A.,  Freeman, H. M., Blevins, C. A., Depuy, V. E., and et al.. 2023. Assessing efficacy of cellular transmission technology in camera trapping for wild life research. Wildlife Society Bulletin 2023;47:e1491. https://doi.org/10.1002/wsb.149. 

Oliver, R. Y., Fabiola Iannarilli 1 2, Jorge Ahumada 4, and Eric Fegraus 4. 2023. Camera trapping expands the view into global biodiversity and its change. Philosophical Transactions B 378(1881):20220232. DOI: 10.1098/rstb.2022.0232.

Vaughan, P. M., Buettel, J. C., and Brook, B. W.. 2022. Investigating avian behaviour using opportunistic camera-trap imagery reveals an untapped data source. 21(1):3-12. https://doi.org/10.2326/osj.21.3

Yang, F., Shen, N.. and Xu, F.. 2024. Automatic Bird Species Recognition from Images with Feature Enhancement and Contrastive Learning. Applied Sciences 14(10):4278. DOI: 10.3390/app14104278.