Parasite exchange and hybridisation at a wild-feral-domestic interface

Smith, W. J. Bernhard; Jezierski, Michał T.; Dunn, Jenny C.; Clegg, Sonya M.

doi:10.1016/j.ijpara.2023.06.005

Cited by 4 publications

(2 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Equation (1), Precision (p): the proportion of samples that are positive among all samples that are positive in the detection result, where TP is True Positives and FP is False Positives, and the calculation formula is shown in Equation (2). Recall (r): the proportion of samples that are correctly detected as positive among all samples that are positive, where FN is False Negatives, and the formula is shown in Equation (3).…”

Section: Experimental Settings and Model Evaluation Indicatorsmentioning

confidence: 99%

“…In addition, overpopulation will negatively affect urban infrastructure due to increased excess of feces. Then, the feces of feral pigeons usually damage valuable buildings and statues and cause a huge economic loss [ 2 ]. Consequently, monitoring and quantifying feral pigeon populations is essential for evaluating their distribution and identifying instances of overpopulation, ultimately informing the design of effective intervention strategies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning

Guo,

He,

Lyu

et al. 2024

Animals

View full text Add to dashboard Cite

The overpopulation of feral pigeons in Hong Kong has significantly disrupted the urban ecosystem, highlighting the urgent need for effective strategies to control their population. In general, control measures should be implemented and re-evaluated periodically following accurate estimations of the feral pigeon population in the concerned regions, which, however, is very difficult in urban environments due to the concealment and mobility of pigeons within complex building structures. With the advances in deep learning, computer vision can be a promising tool for pigeon monitoring and population estimation but has not been well investigated so far. Therefore, we propose an improved deep learning model (Swin-Mask R-CNN with SAHI) for feral pigeon detection. Our model consists of three parts. Firstly, the Swin Transformer network (STN) extracts deep feature information. Secondly, the Feature Pyramid Network (FPN) fuses multi-scale features to learn at different scales. Lastly, the model’s three head branches are responsible for classification, best bounding box prediction, and segmentation. During the prediction phase, we utilize a Slicing-Aided Hyper Inference (SAHI) tool to focus on the feature information of small feral pigeon targets. Experiments were conducted on a feral pigeon dataset to evaluate model performance. The results reveal that our model achieves excellent recognition performance for feral pigeons.

show abstract

Section: Experimental Settings and Model Evaluation Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning

Guo,

He,

Lyu

et al. 2024

Animals

View full text Add to dashboard Cite

show abstract

Epidemiological investigation of Trichomonas gallinae in beijing, China

Jing,

Li,

et al. 2024

International Journal for Parasitology: Parasites and Wildlife

View full text Add to dashboard Cite

Use of anthropogenic landscapes in a wild Columba livia (Rock Dove) population

Smith,

Portugal,

Jezierski

2024

Ornithology

View full text Add to dashboard Cite

Despite the role of many domestic animals as model organisms, our understanding of their undomesticated conspecifics is comparatively poor. This limits our ability to infer the eco-evolutionary context of phenomena studied in the laboratory and to explore domestication. The domestic pigeon’s wild form is the Rock Dove (Columba livia). By studying 5 global positioning system–tagged wild Rock Doves in Scotland’s Outer Hebrides, we present the first individual-level assessment of undomesticated Rock Dove movements, revealing extensive use of anthropogenic habitats in both a roosting and foraging context. This association with humans provides tantalizing opportunities for research into the pre-domestication human–pigeon relationship. More generally, this work highlights the value of researching laboratory model species in nature.

show abstract

Parasite exchange and hybridisation at a wild-feral-domestic interface

Cited by 4 publications

References 73 publications

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning

Automatic Detection of Feral Pigeons in Urban Environments Using Deep Learning

Epidemiological investigation of Trichomonas gallinae in beijing, China

Use of anthropogenic landscapes in a wild Columba livia (Rock Dove) population

Contact Info

Product

Resources

About