Iterative human and automated identification of wildlife images
1.Steenweg, R. et al. Scaling-up camera traps: monitoring the planet’s biodiversity with networks of remote sensors. Front. Ecol. Environ. 15, 26–34 (2017).Article
Google Scholar
2.Rich, L. N. et al. Assessing global patterns in mammalian carnivore occupancy and richness by integrating local camera trap surveys. Global Ecol. Biogeogr. 26, 918–929 (2017).Article
Google Scholar
3.Barnosky, A. D. et al. Has the Earth’s sixth mass extinction already arrived? Nature 471, 51–57 (2011).Article
Google Scholar
4.Ahumada, J. A. et al. Wildlife insights: a platform to maximize the potential of camera trap and other passive sensor wildlife data for the planet. Environ. Conserv. 47, 1–6 (2020).MathSciNet
Article
Google Scholar
5.Norouzzadeh, M. S. et al. Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning. Proc. Natl Acad. Sci. 115, E5716–E5725 (2018).Article
Google Scholar
6.Miao, Z. et al. Insights and approaches using deep learning to classify wildlife. Sci. Rep. 9, 8137 (2019).Article
Google Scholar
7.Liu, Z. et al. Large-scale long-tailed recognition in an open world. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 2537–2546 (IEEE, 2019).8.Liu, Z. et al. Open compound domain adaptation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 12406–12415 (IEEE, 2020).9.Hautier, Y. et al. Anthropogenic environmental changes affect ecosystem stability via biodiversity. Science 348, 336–340 (2015).Article
Google Scholar
10.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).Article
Google Scholar
11.Ripple, W. J. et al. Conserving the world’s megafauna and biodiversity: the fierce urgency of now. Bioscience 67, 197–200 (2017).Article
Google Scholar
12.Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).Article
Google Scholar
13.O’Connell, A. F., Nichols, J. D. & Karanth, K. U. Camera Traps in Animal Ecology: Methods and Analyses (Springer Science & Business Media, 2010).14.Burton, A. C. et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J. Appl. Ecol. 52, 675–685 (2015).Article
Google Scholar
15.Kays, R., McShea, W. J. & Wikelski, M. Born-digital biodiversity data: millions and billions. Divers. Distrib. 26, 644–648 (2020).Article
Google Scholar
16.Swanson, A. et al. Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna. Sci. Data 2, 1–14 (2015).Article
Google Scholar
17.Ahumada, J. A. et al. Community structure and diversity of tropical forest mammals: data from a global camera trap network. Philos. Trans. R. Soc. B Biol. Sci. 366, 2703–2711 (2011).Article
Google Scholar
18.Pardo, L. E. et al. Snapshot Safari: a large-scale collaborative to monitor Africa’s remarkable biodiversity. South Africa J. Sci. https://doi.org/10.17159/sajs.2021/8134 (2021).19.Anderson, T. M. et al. The spatial distribution of African savannah herbivores: species associations and habitat occupancy in a landscape context. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150314 (2016).Article
Google Scholar
20.Palmer, M., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article
Google Scholar
21.Tabak, M. A. et al. Machine learning to classify animal species in camera trap images: applications in ecology. Methods Ecol. Evol. 10, 585–590 (2019).Article
Google Scholar
22.Whytock, R. C. et al. Robust ecological analysis of camera trap data labelled by a machine learning model. Methods Ecol. Evol 12, 1080–1092 (2021).Article
Google Scholar
23.Beery, S., Van Horn, G. & Perona, P. Recognition in terra incognita. In Proc. European Conference on Computer Vision (ECCV) 456–473 (IEEE, 2018).24.Tabak, M. A. et al. Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2. Ecol. Evol. 10, 10374–10383 (2020).Article
Google Scholar
25.Shahinfar, S., Meek, P. & Falzon, G. How many images do I need? Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring. Ecol. Inform. 57, 101085 (2020).Article
Google Scholar
26.Norouzzadeh, M. S. et al. A deep active learning system for species identification and counting in camera trap images. Methods Ecol. Evol. 12, 150–161 (2020).Article
Google Scholar
27.Willi, M. et al. Identifying animal species in camera trap images using deep learning and citizen science. Methods Ecol. Evol. 10, 80–91 (2019).Article
Google Scholar
28.Schneider, S., Greenberg, S., Taylor, G. W. & Kremer, S. C. Three critical factors affecting automated image species recognition performance for camera traps. Ecol. Evol. 10, 3503–3517 (2020).Article
Google Scholar
29.Kays, R. et al. An empirical evaluation of camera trap study design: how many, how long and when? Methods Ecol. Evol. 11, 700–713 (2020).Article
Google Scholar
30.Prach, K. & Walker, L. R. Four opportunities for studies of ecological succession. Trends Ecol. Evol. 26, 119–123 (2011).Article
Google Scholar
31.Mech, L. D., Isbell, F., Krueger, J. & Hart, J. Gray wolf (Canis lupus) recolonization failure: a Minnesota case study. Can. Field-Nat. 133, 60–65 (2019).Article
Google Scholar
32.Taylor, G. et al. Is reintroduction biology an effective applied science? Trends Ecol. Evol. 32, 873–880 (2017).Article
Google Scholar
33.Clavero, M. & Garcia-Berthou, E. Invasive species are a leading cause of animal extinctions. Trends Ecol. Evol. 20, 110 (2005).Article
Google Scholar
34.Caravaggi, A. et al. An invasive-native mammalian species replacement process captured by camera trap survey random encounter models. Remote Sens. Ecol. Conserv. 2, 45–58 (2016).Article
Google Scholar
35.Arjovsky, M., Bottou, L., Gulrajani, I. & Lopez-Paz, D. Invariant risk minimization. Preprint at https://arxiv.org/abs/1907.02893 (2019).36.Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 3320–3328 (IEEE, 2014).37.Deng, J. et al. ImageNet: a large-scale hierarchical image database. In Proc. 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).38.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution and protection. Science https://doi.org/10.1126/science.1246752 (2014).39.Liu, W., Wang, X., Owens, J. & Li, Y. Energy-based out-of-distribution detection. In Advances in Neural Information Processing Systems (eds Larochelle, H. et al.) 21464–21475 (Curran Associates, 2020).40.Lee, D.-H. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning, ICML, Vol. 3 (2013).41.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).42.Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Preprint at https://arxiv.org/abs/1503.02531 (2015).43.Gaynor, K. M., Daskin, J. H., Rich, L. N. & Brashares, J. S. Postwar wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness. Anim. Conserv. 24, 510–522 (2020).Article
Google Scholar
44.Paszke, A. et al. in Advances in Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 8024–8035 http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (Curran Associates, 2019)45.Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. Preprint at https://arxiv.org/abs/2002.05709 (2020).46.He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R. Momentum contrast for unsupervised visual representation learning. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 9729–9738 (IEEE, 2020).47.Xiao, T., Wang, X., Efros, A. A. & Darrell, T. What should not be contrastive in contrastive learning. Preprint at https://arxiv.org/abs/2008.05659 (2020). More