"Songs of the Wild: Estimating Bird Populations Through Acoustic Monitoring"

Acoustic Monitoring Image By e360yale.edu

*Heri Tarmizi

These devices can be left in place for days, weeks, or even months, continuously capturing sound data without the need for human presence.

Acoustic monitoring has emerged as a powerful tool for estimating bird populations, particularly for nocturnal or cryptic species that are challenging to detect using traditional visual methods. Automated recording devices (ARDs) capture bird songs and calls over extended periods, providing a wealth of data that, when analyzed correctly, can yield accurate estimates of population size and density. This essay explores the application of acoustic monitoring in ornithology, details the methodologies and formulas used to process acoustic data, and discusses the advantages and challenges associated with this innovative approach.

Application of Acoustic Monitoring

Acoustic monitoring involves deploying ARDs in the field to record avian vocalizations. These devices can be left in place for days, weeks, or even months, continuously capturing sound data without the need for human presence. This method is particularly advantageous for monitoring species that are active at night, such as owls, or those that inhabit dense vegetation where visual detection is difficult, like many passerines and tropical birds.

        1. Deployment and Data Collection:

• Site Selection: Choosing appropriate locations for ARDs is crucial. Sites should represent the habitat types of interest and be distributed systematically across the study area to ensure comprehensive coverage.
• Recording Schedule: ARDs can be programmed to record continuously or at specific times, such as dawn and dusk, when bird vocal activity peaks. The choice depends on the target species and study objectives.
• Data Storage and Retrieval: Modern ARDs are equipped with large storage capacities and power sources that allow for long deployment periods. Data are retrieved periodically or at the end of the study.

        2. Data Processing:

• Preprocessing: Recorded audio files are first processed to remove non-avian sounds, such as wind or anthropogenic noise. This step is often automated using software that can filter out irrelevant frequencies.
• Species Identification: Sophisticated software and machine learning algorithms are used to identify bird species from their vocalizations. Tools like Raven Pro, Song Scope, and Kaleidoscope Pro are commonly employed for this purpose.
• Temporal Analysis: The timing and frequency of vocalizations are analyzed to understand patterns of activity and detect the presence of different species.

Estimating Bird Populations from Acoustic Data

The core challenge in acoustic monitoring is translating the recorded vocalizations into population estimates. Several statistical models and formulas can be used to achieve this.

       1. Occupancy Modeling:

• Concept: Occupancy modeling estimates the probability that a species is present at a given site based on detection/non-detection data. This method accounts for imperfect detection, recognizing that not all individuals will be detected even if they are present.
• Formula: The basic occupancy model is expressed as: $$\psi =\frac{n_{\text{occupied}}}{n_{\text{sites}}}$$ where $\psi$ is the occupancy probability, $n_{\text{occupied}}$ is the number of sites where the species was detected, and $n_{\text{sites}}$ is the total number of surveyed sites.
• Application: By analyzing the presence of vocalizations across multiple recording sites and times, researchers can estimate the probability of occupancy for each species.

      2. Density Estimation Using Distance Sampling:

• Concept: Distance sampling involves estimating the density of calling birds by measuring the distance of each detected call from the ARD. This method assumes that detection probability decreases with distance from the recorder.
• Formula: The density (D) of calling birds is calculated as: $$D=\frac{n}{2wL\widehat{P}}$$where $n$ is the number of calls detected, $w$ is the effective listening radius, $L$ is the total length of the sampling period, and $\widehat{P}$ is the estimated detection probability, often derived from a detection function.
• Application: By recording the distance and frequency of calls, researchers can estimate the density of bird populations in the study area.

        3. Acoustic Spatial Capture-Recapture (ASCR):

• Concept: ASCR combines traditional capture-recapture models with spatial information to estimate population size. This method is particularly effective for species with distinct vocalizations that can be individually identified.
• Formula: The ASCR model integrates spatial coordinates and detection histories to estimate population size ($N$): $$N=\frac{C}{\widehat{p}}$$where $C$ is the number of unique individuals detected, and $\widehat{p}$ is the estimated detection probability.
• Application: ASCR requires identifying individual birds from their calls, which can be challenging but feasible for species with unique vocal signatures.

Advantages of Acoustic Monitoring

1. Non-Invasive: Unlike capture methods, acoustic monitoring does not physically interact with birds, reducing stress and potential harm.
2. Continuous Monitoring: ARDs can operate continuously, providing data across different times and conditions, which is difficult to achieve with human observers.
3. Long-Term Data: Acoustic monitoring allows for the collection of long-term data, essential for studying population trends and the impacts of environmental changes.
4. Remote Areas: ARDs can be deployed in remote or inaccessible areas, enabling the study of species and habitats that are difficult to monitor otherwise.

Challenges and Limitations

1. Species Identification: Accurately identifying species from vocalizations can be challenging, particularly in diverse avian communities or for species with similar calls. Advances in machine learning and bioacoustics are addressing these challenges, but there is still room for improvement.
2. Detection Probability: Estimating detection probability is complex, as it can be influenced by various factors such as background noise, weather conditions, and the distance from the ARD.
3. Data Management: Acoustic monitoring generates vast amounts of data, requiring significant storage, processing power, and specialized software for analysis.
4. Cost and Accessibility: While the cost of ARDs is decreasing, initial setup and maintenance can still be expensive, and access to sophisticated analysis software may be limited in some regions.

Case Studies and Applications

1. Nocturnal Bird Monitoring: Studies on owl populations have benefited significantly from acoustic monitoring. For instance, Marques et al. (2013) used ARDs to estimate the density of the endangered Blakiston's Fish Owl (Bubo blakistoni) in Japan, providing crucial data for conservation efforts.
2. Tropical Rainforest Birds: In dense tropical forests where visual detection is challenging, acoustic monitoring has proven effective. A study by Aide et al. (2013) used ARDs to monitor bird diversity in Puerto Rico, revealing changes in species composition over time.
3. Migratory Bird Stopovers: Acoustic monitoring has been used to study migratory bird stopover sites. Buler and Dawson (2014) deployed ARDs along the Gulf of Mexico to estimate the abundance and diversity of migratory songbirds, aiding in the identification of critical stopover habitats.

Conclusion

Acoustic monitoring represents a significant advancement in ornithological research, offering a non-invasive, efficient, and effective method for estimating bird populations, particularly for elusive and nocturnal species. By leveraging advanced recording devices and sophisticated data analysis techniques, researchers can gain deeper insights into bird population dynamics and their responses to environmental changes. Despite its challenges, the continued development and application of acoustic monitoring hold great promise for bird conservation and biodiversity studies.

References

1. Marques, T. A., Thomas, L., Fancy, S. G., & Buckland, S. T. (2007). Improving estimates of bird density using multiple covariate distance sampling. The Auk, 124(4), 1229-1243.
2. Aide, T. M., Corrada-Bravo, C., Campos-Cerqueira, M., Milan, C., & Vega, G. (2013). Real-time bioacoustics monitoring and automated species identification. PeerJ, 1, e103.
3. Buler, J. J., & Dawson, D. K. (2014). Radar analysis of fall bird migration stopover sites in the northeastern U.S. The Condor, 116(3), 357-370.
4. Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001). Introduction to Distance Sampling: Estimating Abundance of Biological Populations. Oxford University Press.