Recent Advances and Innovation in Wildlife Population Estimation

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Wildlife".

Deadline for manuscript submissions: closed (1 September 2024) | Viewed by 3838

Special Issue Editor


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Guest Editor
School of Forestry and Natural Environment, Laboratory of Wildlife and Freshwater Fisheries, Aristotle University of Thessaloniki, Thessaloniki, Greecee
Interests: wildlife conservation; wildlife ecology; biodiversity monitoring; behavioral ecology; animal ecology; invasive species ecology and management
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Special Issue Information

Dear Colleagues, 

The estimation of population size is fundamental to wildlife management and conservation. Recently, high-tech devices have been used more frequently to monitor wild animals in an effort uncover behaviors that have until now been mysteries, but also to accurately assess biodiversity in remote areas.

The Special Issue aims to provide a forum for collating innovative techniques on wildlife population estimation. We welcome original research or review articles which focus on technology including (but not limited to) innovative wildlife monitoring techniques, such as camera traps, thermal cameras, implanting devices, satellite remote sensing, drones, environmental DNA (eDNA), acoustic sensors, etc. for use to conserve wildlife populations. In addition, papers from a wide range of disciplines, such as citizen science, artificial intelligence, deep neural networks, and machine learning are also welcome.

As this is a new and emerging research area, the knowledge on these topics will shed light on the most promising techniques in the realm of wildlife conservation going forward.

Prof. Dr. Dimitrios Bakaloudis
Guest Editor

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Keywords

  • wildlife technology
  • monitoring wildlife
  • new technology in wildlife conservation
  • wildlife ecology
  • wildlife surveys

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Published Papers (3 papers)

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Research

24 pages, 1855 KiB  
Article
Most Random-Encounter-Model Density Estimates in Camera-Based Predator–Prey Studies Are Unreliable
by Sean M. Murphy, Benjamin S. Nolan, Felicia C. Chen, Kathleen M. Longshore, Matthew T. Simes, Gabrielle A. Berry and Todd C. Esque
Animals 2024, 14(23), 3361; https://doi.org/10.3390/ani14233361 - 22 Nov 2024
Viewed by 550
Abstract
Identifying population-level relationships between predators and their prey is often predicated on having reliable population estimates. Camera-trapping is effective for surveying terrestrial wildlife, but many species lack individually unique natural markings that are required for most abundance and density estimation methods. Analytical approaches [...] Read more.
Identifying population-level relationships between predators and their prey is often predicated on having reliable population estimates. Camera-trapping is effective for surveying terrestrial wildlife, but many species lack individually unique natural markings that are required for most abundance and density estimation methods. Analytical approaches have been developed for producing population estimates from camera-trap surveys of unmarked wildlife; however, most unmarked approaches have strict assumptions that can be cryptically violated by survey design characteristics, practitioner choice of input values, or species behavior and ecology. Using multi-year datasets from populations of an unmarked predator and its co-occurring unmarked prey, we evaluated the consequences of violating two requirements of the random encounter model (REM), one of the first developed unmarked methods. We also performed a systematic review of published REM studies, with an emphasis on predator–prey ecology studies. Empirical data analysis confirmed findings of recent research that using detections from non-randomly placed cameras (e.g., on trails) and/or borrowing movement velocity (day range) values caused volatility in density estimates. Notably, placing cameras strategically to detect the predator, as is often required to obtain sufficient sample sizes, resulted in substantial density estimate inflation for both the predator and prey species. Systematic review revealed that 91% of REM density estimates in published predator–prey ecology studies were obtained using camera-trap data or velocity values that did not meet REM requirements. We suggest considerable caution making conservation or management decisions using REM density estimates from predator–prey ecology studies. Full article
(This article belongs to the Special Issue Recent Advances and Innovation in Wildlife Population Estimation)
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25 pages, 51247 KiB  
Article
CECS-CLIP: Fusing Domain Knowledge for Rare Wildlife Detection Model
by Feng Yang, Chunying Hu, Aokang Liang, Sheng Wang, Yun Su and Fu Xu
Animals 2024, 14(19), 2909; https://doi.org/10.3390/ani14192909 - 9 Oct 2024
Viewed by 1096
Abstract
Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed [...] Read more.
Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. Full article
(This article belongs to the Special Issue Recent Advances and Innovation in Wildlife Population Estimation)
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22 pages, 10343 KiB  
Article
Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China
by Wenjie Mao, Gang Li and Xiaowei Li
Animals 2024, 14(8), 1152; https://doi.org/10.3390/ani14081152 - 10 Apr 2024
Cited by 2 | Viewed by 1091
Abstract
To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object [...] Read more.
To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications. Full article
(This article belongs to the Special Issue Recent Advances and Innovation in Wildlife Population Estimation)
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