AI Article Synopsis
- Researchers developed a new method to estimate the density of Antarctic krill from video footage taken by a chinstrap penguin in Antarctica, utilizing a machine learning model to identify krill in the frames.
- The model achieved a 73% overall accuracy and identified krill frames with an 83% positive predictive value, helping to analyze the foraging behavior of the penguin.
- The estimated krill density varied between 2 to 307 krill per cubic meter, providing insights into prey availability that could impact predator foraging efficiency, and this technique could be applied to other marine predators as well.
Article Abstract
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 resolvable and assumes that mean krill length is known, and that the distribution of orientation angles of krill is uniform. From 1,932 images identified as containing krill, we manually identified a subset of 124 images from across the video record that contained resolvable and unresolvable krill necessary to estimate the resolvable range and imaged volume for the video sensor. Krill swarm density encountered by the penguins ranged from 2 to 307 krill·m-3 and mean density of krill was 48 krill·m-3 (sd = 61 krill·m-3). Mean krill biomass density was 25 g·m-3. Our frame-level image classifier model and krill density estimation method provide a new approach to efficiently process video-logger data and estimate krill density from 2D imagery, providing key information on prey aggregations that may affect predator foraging performance. The approach should be directly applicable to other marine predators feeding on aggregations of prey.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11232977 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0303633 | PLOS |
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