Migratory Pathways and Connectivity in Asian Houbara Bustards: Evidence from 15 Years of Satellite Tracking

Information on migratory pathways and connectivity is essential to understanding population dynamics and structure of migrant species. Our manuscript uses a unique dataset, the fruit of 103 individual Asian houbara bustards captured on their breeding grounds in Central Asia over 15 years and equipped with satellite transmitters, to provide a better understanding of migratory pathways and connectivity; such information is critical to the implementation of biologically sound conservation measures in migrant species. At the scale of the distribution range we find substantial migratory connectivity, with a clear separation of migration pathways and wintering areas between western and eastern migrants. Within eastern migrants, we also describe a pattern of segregation on the wintering grounds. But at the local level connectivity is weak: birds breeding within the limits of our study areas were often found several hundreds of kilometres apart during winter. Although houbara wintering in Arabia are known to originate from Central Asia, out of all the birds captured and tracked here not one wintered on the Arabian Peninsula. This is very likely the result of decades of unregulated off-take and severe habitat degradation in this area. At a time when conservation measures are being implemented to safeguard the long-term future of this species, this study provides critical data on the spatial structuring of populations

The Emirates at 2050: Balancing Development and Environmental Stewardship

The United Arab Emirates (UAE) faces the challenge of balancing rapid economic development with environmental preservation and conservation in the Anthropocene era. The nation’s unique ecosystems, characterized by arid desert, rugged mountains, and diverse marine habitats, are vulnerable to disturbances such as urbanization, habitat degradation, groundwater extraction and climate change. To chart a more sustainable course for the Emirates by 2050, the paper proposes policy recommendations such as adopting a national strategy for sustainable development, strengthening environmental policies, investing in urban planning and design, promoting sustainable water management, encouraging use of nature-based solutions, addressing climate change, fostering environmental education, supporting research in environmental sciences, encouraging national and regional cooperation, promoting sustainable business practices in the private sector, and monitoring the progress of environmental policies. By embracing a vision of development that respects the natural environment and safeguards its plant and animal life, the UAE can demonstrate its commitment and serve as a model for other nations to follow, becoming a shining example of responsible development by 2050

An IoT System Using Deep Learning to Classify Camera Trap Images on the Edge

Camera traps deployed in remote locations provide an effective method for ecologists to monitor and study wildlife in a non-invasive way. However, current camera traps suffer from two problems. First, the images are manually classified and counted, which is expensive. Second, due to manual coding, the results are often stale by the time they get to the ecologists. Using the Internet of Things (IoT) combined with deep learning represents a good solution for both these problems, as the images can be classified automatically, and the results immediately made available to ecologists. This paper proposes an IoT architecture that uses deep learning on edge devices to convey animal classification results to a mobile app using the LoRaWAN low-power, wide-area network. The primary goal of the proposed approach is to reduce the cost of the wildlife monitoring process for ecologists, and to provide real-time animal sightings data from the camera traps in the field. Camera trap image data consisting of 66,400 images were used to train the InceptionV3, MobileNetV2, ResNet18, EfficientNetB1, DenseNet121, and Xception neural network models. While performance of the trained models was statistically different (Kruskal–Wallis: Accuracy H(5) = 22.34, p p = 0.0168), there was only a 3% difference in the F1-score between the worst (MobileNet V2) and the best model (Xception). Moreover, the models made similar errors (Adjusted Rand Index (ARI) > 0.88 and Adjusted Mutual Information (AMU) > 0.82). Subsequently, the best model, Xception (Accuracy = 96.1%; F1-score = 0.87; F1-Score = 0.97 with oversampling), was optimized and deployed on the Raspberry Pi, Google Coral, and Nvidia Jetson edge devices using both TenorFlow Lite and TensorRT frameworks. Optimizing the models to run on edge devices reduced the average macro F1-Score to 0.7, and adversely affected the minority classes, reducing their F1-score to as low as 0.18. Upon stress testing, by processing 1000 images consecutively, Jetson Nano, running a TensorRT model, outperformed others with a latency of 0.276 s/image (s.d. = 0.002) while consuming an average current of 1665.21 mA. Raspberry Pi consumed the least average current (838.99 mA) with a ten times worse latency of 2.83 s/image (s.d. = 0.036). Nano was the only reasonable option as an edge device because it could capture most animals whose maximum speeds were below 80 km/h, including goats, lions, ostriches, etc. While the proposed architecture is viable, unbalanced data remain a challenge and the results can potentially be improved by using object detection to reduce imbalances and by exploring semi-supervised learning

Year and location of capture of Asian houbara bustards fitted with satellite transmitters.

<p>*In brackets are the numbers of satellite tagged birds that completed at least one outward migration following capture, or survived more than 6 months (resident birds).</p

First outward migration routes of Asian houbara bustards captured during the breeding season and followed by satellite tracking.

<p>First outward migration routes of Asian houbara bustards captured during the breeding season and followed by satellite tracking.</p

Wintering grounds of migrant Asian houbara bustards coming from breeding areas distributed across the greater part of the range.

<p>Wintering grounds of migrant Asian houbara bustards coming from breeding areas distributed across the greater part of the range.</p

Migration paths and wintering ranges of juvenile Asian houbara bustards originating from Central Kazakhstan and Jungar Basin with reference to wintering ranges of adults.

<p>Migration paths and wintering ranges of juvenile Asian houbara bustards originating from Central Kazakhstan and Jungar Basin with reference to wintering ranges of adults.</p