A method to estimate prey density from single-camera images: a case study with chinstrap penguins and Antarctic krill dataset ...
Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap pen...
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| Format: | Dataset |
| Language: | unknown |
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Zenodo
2024
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| Online Access: | https://dx.doi.org/10.5281/zenodo.10883788 https://zenodo.org/doi/10.5281/zenodo.10883788 |
| Summary: | Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap penguin (Pygoscelis antarcticus) from Cape Shirreff, Livingston Island, Antarctica to develop a novel approach for estimating the density of Antarctic krill (Euphausia superba) encountered during foraging activities. Using the open-source Video and Image Analytics for a Marine Environment (VIAME), we trained a neural network model to identify video frames containing krill. Our image classifier has an overall accuracy of 73%, with a positive predictive value of 83% for prediction of frames containing krill. We then developed a method to estimate the volume of water imaged, thus the density (N·m-3) of krill, in the 2-dimensional images. The method is based on the maximum range from the camera where krill remain visibly ... |
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