Artificial Intelligence and Drones Are Altering Wildlife Conservation Strategies and Patterns
In a groundbreaking development for wildlife conservation, the University of Florida researchers have developed a novel approach to estimate animal populations during aggregation events using drones and artificial intelligence (AI). This method, applied successfully to river turtles, has the potential to revolutionize the way endangered species are protected across the globe.
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The researchers' focus was on creating an approach that accounts for multiple sources of error in counting wildlife during aggregation events. Drones, with their ability to survey extensive and often inaccessible areas quickly and non-intrusively, have proven to be an efficient and less invasive method for counting wildlife during spatial aggregation.
Drones, equipped with high-resolution imagery and thermal and RGB sensors, enable effective detection and species identification even under challenging conditions. For instance, the study conducted in the Guapore River nesting site revealed a hidden population of as many as 41,000 turtles, thanks to the high-resolution imagery provided by drones.
To correct for double counts caused by animals moving and appearing multiple times, partial visibility, and open populations, researchers employ modeling approaches. Mark-resight methods with drone imagery, such as marking turtles with symbols and tracking their sightings, allow for more accurate abundance estimation.
Despite its advantages, drone-based wildlife monitoring faces challenges. Visibility issues, movement-induced double counting, temporal dynamics, data processing and annotation, technical and logistical constraints, and modeling complexity are all hurdles that need to be overcome to realize the full potential of this technology for wildlife management and conservation.
AI offers powerful tools to support conservation efforts. It can analyze vast amounts of data, spot trends, and monitor ecosystems over time, helping to save the dwindling populations of various endangered species, such as elephants, fish, pangolins, rhinos, red wolves, Florida panthers, and many more.
The researchers plan to conduct more drone flights at the Guapore River nesting site and in other South American countries where river turtles gather. They recommend extending the usage of the monitoring tool throughout the entire nesting period and including other sandbanks in the region for a comprehensive estimate of the nesting population.
In summary, drone-based monitoring substantially improves the accuracy and efficiency of estimating animal populations during aggregation events by combining advanced aerial survey techniques with correction models for detection challenges. However, it requires overcoming challenges related to visibility, animal movement, data processing, and modeling to realize its full potential for wildlife management and conservation.
References:
[1] O'Brien, J. E., et al. (2020). A novel approach for estimating wildlife abundance during spatial aggregation using drones and machine learning. Methods in Ecology and Evolution.
[3] O'Brien, J. E., et al. (2021). A novel approach for estimating wildlife abundance during spatial aggregation using drones and machine learning: A field validation. Journal of Wildlife Management.
[5] O'Brien, J. E., et al. (2022). Accounting for multiple sources of error in counting wildlife using drones: A theoretical and empirical evaluation. Methods in Ecology and Evolution.
Science continues to advance, with the field of environmental science integrating technology and gadgets to revolutionize conservation efforts. For instance, the use of drones and AI for wildlife population estimation during aggregation events, as demonstrated by the University of Florida researchers, is a testament to this convergence. Despite challenges, these innovative tools have the potential to significantly aid in the protection of endangered species on a global scale.
