Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes
Abstract Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, cou...
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Wiley
2024-08-01
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| Series: | Ecology and Evolution |
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| Online Access: | https://doi.org/10.1002/ece3.70172 |
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| author | Gabriel L. Zanellato Gabriel A. Pagnossin Mauricio Failla Juan F. Masello |
| author_facet | Gabriel L. Zanellato Gabriel A. Pagnossin Mauricio Failla Juan F. Masello |
| author_sort | Gabriel L. Zanellato |
| collection | DOAJ |
| description | Abstract Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time‐consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest‐entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north‐eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest‐entrances along the cliff with the colony. For this, we used three CNN architectures, U‐Net, ResUnet, and DeepLabv3. The U‐Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest‐entrances across the colony, with a mean absolute error of 2.7 nest‐entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. We show that the strong changes observed in the distribution of nest‐entrances are a measurable effect of a long record of human‐induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation. |
| format | Article |
| id | doaj-art-1a2d117b0fba46cbb52e52e4a062ed3f |
| institution | OA Journals |
| issn | 2045-7758 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-1a2d117b0fba46cbb52e52e4a062ed3f2025-08-20T01:55:49ZengWileyEcology and Evolution2045-77582024-08-01148n/an/a10.1002/ece3.70172Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of PsittaciformesGabriel L. Zanellato0Gabriel A. Pagnossin1Mauricio Failla2Juan F. Masello3Fundación Soberanía Cinco Saltos Río Negro ArgentinaUniversidad Nacional de Río Negro General Roca Río Negro ArgentinaProyecto Patagonia Noreste Balneario El Cóndor Río Negro ArgentinaDepartment of Evolutionary Population Genetics Bielefeld University Bielefeld GermanyAbstract Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time‐consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest‐entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north‐eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest‐entrances along the cliff with the colony. For this, we used three CNN architectures, U‐Net, ResUnet, and DeepLabv3. The U‐Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest‐entrances across the colony, with a mean absolute error of 2.7 nest‐entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. We show that the strong changes observed in the distribution of nest‐entrances are a measurable effect of a long record of human‐induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation.https://doi.org/10.1002/ece3.70172artificial intelligenceburrow nestingcolonycomputer visionconvolutional neural networksmachine learning |
| spellingShingle | Gabriel L. Zanellato Gabriel A. Pagnossin Mauricio Failla Juan F. Masello Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes Ecology and Evolution artificial intelligence burrow nesting colony computer vision convolutional neural networks machine learning |
| title | Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes |
| title_full | Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes |
| title_fullStr | Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes |
| title_full_unstemmed | Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes |
| title_short | Using convolutional neural networks to count parrot nest‐entrances on photographs from the largest known colony of Psittaciformes |
| title_sort | using convolutional neural networks to count parrot nest entrances on photographs from the largest known colony of psittaciformes |
| topic | artificial intelligence burrow nesting colony computer vision convolutional neural networks machine learning |
| url | https://doi.org/10.1002/ece3.70172 |
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