Zanellato Gabriel L, Pagnossin Gabriel A, Failla Mauricio, Masello Juan F
Fundación Soberanía Cinco Saltos Río Negro Argentina.
Universidad Nacional de Río Negro General Roca Río Negro Argentina.
Ecol Evol. 2024 Aug 13;14(8):e70172. doi: 10.1002/ece3.70172. eCollection 2024 Aug.
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 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.
统计动物种群数量是理解生态过程的基础。通过统计可以估算特定时间点动物种群的规模，这对于理解种群动态变化至关重要。然而，对于数量庞大的种群，统计工作耗时费力，尤其是手动进行时。在此，我们利用卷积神经网络（CNN）对222张照片中的巢穴入口总数进行统计，这些照片涵盖了世界上已知最大的鹦鹉目（鸟类）栖息地。我们的研究地点是位于阿根廷巴塔哥尼亚东北部埃尔孔多尔村附近、面向大西洋的悬崖上的最大穴居鹦鹉栖息地。我们还旨在研究该栖息地中巢穴入口沿悬崖的分布情况。为此，我们使用了三种CNN架构，即U-Net、ResUnet和DeepLabv3。U-Net架构表现最佳，整个栖息地平均统计出59,842个穴居鹦鹉巢穴入口，测试斑块上的平均绝对误差为2.7个巢穴入口，计算方式为每个斑块实际计数与预测计数的差值。与20多年前在埃尔孔多尔栖息地进行的一项研究相比，CNN架构还发现了巢穴入口沿悬崖分布的显著差异。我们发现，巢穴入口分布的显著变化是长期以来人类对埃尔孔多尔穴居鹦鹉栖息地造成干扰的可测量结果。鉴于埃尔孔多尔穴居鹦鹉栖息地的至关重要性，该栖息地集中了全球71%的该物种数量，我们主张在鹦鹉达到其适应能力极限之前，必须减少这种干扰程度。
