Cuthill, I. C. et al. The biology of color. Science 357, eaan0221 (2017).
Google Scholar
Caro, T. & Koneru, M. Towards an ecology of protective coloration. Biol. Rev. 96, 611–641 (2021).
Google Scholar
Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat. 139, S125–S153 (1992).
Google Scholar
Endler, J. A. Some general comments on the evolution and design of animal communication systems. Philos. Trans. R. Soc. Lond. Ser. B 340, 215–225 (1993).
Google Scholar
Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).
Google Scholar
Ödeen, A. & Håstad, O. The phylogenetic distribution of ultraviolet sensitivity in birds. BMC Evol. Biol. 13, 36 (2013).
Google Scholar
Lind, O., Mitkus, M., Olsson, P. & Kelber, A. Ultraviolet vision in birds: the importance of transparent eye media. Proc. R. Soc. Lond. Ser. B 281, 20132209 (2014).
Nicolaï, M. P. J., Shawkey, M. D., Porchetta, S., Claus, R. & D’Alba, L. Exposure to UV radiance predicts repeated evolution of concealed black skin in birds. Nat. Commun. 11, 2414 (2020).
Google Scholar
Stevens, M. & Cuthill, I. C. Hidden messages: are ultraviolet signals a special channel in avian communication? Bioscience 57, 501–507 (2007).
Google Scholar
Hausmann, F., Arnold, K. E., Marshall, N. J. & Owens, I. P. Ultraviolet signals in birds are special. Proc. R. Soc. Lond. Ser. B 270, 61–67 (2003).
Google Scholar
Eaton, M. D. & Lanyon, S. M. The ubiquity of avian ultraviolet plumage reflectance. Proc. R. Soc. Lond. Ser. B 270, 1721–1726 (2003).
Google Scholar
Gomez, D. & Théry, M. Influence of ambient light on the evolution of colour signals: comparative analysis of a Neotropical rainforest bird community. Ecol. Lett. 7, 279–284 (2004).
Google Scholar
Mullen, P. & Pohland, G. Studies on UV reflection in feathers of some 1000 bird species: are UV peaks in feathers correlated with violet-sensitive and ultraviolet-sensitive cones? Ibis 150, 59–68 (2008).
Google Scholar
Burns, K. J. & Shultz, A. J. Widespread cryptic dichromatism and ultraviolet reflectance in the largest radiation of Neotropical songbirds: Implications of accounting for avian vision in the study of plumage evolution. Auk 129, 211–221 (2012).
Google Scholar
Ödeen, A., Pruett-Jones, S., Driskell, A. C., Armenta, J. K. & Hastad, O. Multiple shifts between violet and ultraviolet vision in a family of passerine birds with associated changes in plumage coloration. Proc. R. Soc. Lond. Ser. B 279, 1269–1276 (2012).
Bleiweiss, R. Physical alignments between plumage carotenoid spectra and cone sensitivities in ultraviolet-sensitive (UVS) birds (Passerida: Passeriformes). Evolut. Biol. 41, 404–424 (2014).
Google Scholar
Lind, O. & Delhey, K. Visual modelling suggests a weak relationship between the evolution of ultraviolet vision and plumage coloration in birds. J. Evol. Biol. 28, 715–722 (2015).
Google Scholar
Bennett, A. T. D. & Cuthill, I. C. Ultraviolet vision in birds: what is its function? Vis. Res 34, 1471–1478 (1994).
Google Scholar
Doucet, S. M., Mennill, D. J. & Hill, G. E. The evolution of signal design in manakin plumage ornaments. Am. Nat. 169, S62–S80 (2007).
Google Scholar
Delhey, K. Revealing the colourful side of birds: spatial distribution of conspicuous plumage colours on the body of Australian birds. J. Avian Biol. 51, e02222 (2020).
Google Scholar
Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).
Google Scholar
Cooney, C. R. et al. Sexual selection predicts the rate and direction of colour divergence in a large avian radiation. Nat. Commun. 10, 1773 (2019).
Google Scholar
Miller, E. T., Leighton, G. M., Freeman, B. G., Lees, A. C. & Ligon, R. A. Ecological and geographical overlap drive plumage evolution and mimicry in woodpeckers. Nat. Commun. 10, 1602 (2019).
Google Scholar
Maia, R., Rubenstein, D. R. & Shawkey, M. D. Key ornamental innovations facilitate diversification in an avian radiation. Proc. Natl Acad. Sci. USA 110, 10687–10692 (2013).
Google Scholar
Stoddard, M. C. & Prum, R. O. How colorful are birds? Evolution of the avian plumage color gamut. Behav. Ecol. 22, 1042–1052 (2011).
Google Scholar
Cooney, C. R. et al. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature 542, 344–347 (2017).
Google Scholar
Felice, R. N. & Goswami, A. Developmental origins of mosaic evolution in the avian cranium. Proc. Natl Acad. Sci. USA 15, 555–560 (2018).
Google Scholar
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).
Google Scholar
Christin, S., Hervet, É. & Lecomte, N. Applications for deep learning in ecology. Methods Ecol. Evol. 10, 1632–1644 (2019).
Google Scholar
Lürig, M. D., Donoughe, S., Svensson, E. I., Porto, A. & Tsuboi, M. Computer vision, machine learning, and the promise of phenomics in ecology and evolutionary biology. Front. Ecol. Evol. 9, 642774 (2021).
Google Scholar
Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V. & Rueckert, D. Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46, 726–738 (2009).
Google Scholar
Baiker, M. et al. Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data. Med. Image Anal. 14, 723–737 (2010).
Google Scholar
Meijering, E. Cell segmentation: 50 years down the road. IEEE Signal Process. Mag. 29, 140–145 (2012).
Google Scholar
Kumar, Y. H. S., Manohar, N. & Chethan, H. K. Animal classification system: a block based approach. Procedia Computer Sci. 45, 336–343 (2015).
Google Scholar
Unger, J., Merhof, D. & Renner, S. Computer vision applied to herbarium specimens of German trees: testing the future utility of the millions of herbarium specimen images for automated identification. BMC Evol. Biol. 16, 248 (2016).
Google Scholar
Kohler, R. A segmentation system based on thresholding. Computer Graph. Image Process. 15, 319–338 (1981).
Google Scholar
Adams, R. & Bischof, L. Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 18, 641–647 (1994).
Google Scholar
Chan, T. F. & Vese, L. A. Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001).
Google Scholar
Boykov, Y. Y. & Jolly, M. P. Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. in Proceedings Eighth IEEE International Conference on Computer Vision (2001).
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv 1802, 02611 (2018).
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K. & Yuille, A. L. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. arXiv 1606, 00915 (2017).
Chen, L. C., Papandreou, G., Schroff, F. & Adam, H. Rethinking atrous convolution for semantic image segmentation. arXiv 1706, 05587 (2017).
Everingham, M. et al. The PASCAL Visual Object Classes challenge—a retrospective. Int. J. Computer Vis. 111, 98–136 (2015).
Google Scholar
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. in Advances In Neural Information Processing Systems (2012).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016).
Szegedy, C. et al. Going deeper with convolutions. arXiv 1409, 4842 (2014).
Google Scholar
Newell, A., Yang, K. & Deng, J. Stacked hourglass networks for human pose estimation. arXiv 1603, 06937 (2016).
Wei, S. E., Ramakrishna, V., Kanade, T. & Sheikh, Y. Convolutional pose machines. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016).
Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (2015).
Stoddard, M. C. & Prum, R. O. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of New World buntings. Am. Nat. 171, 755–776 (2008).
Google Scholar
Lynch, M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45, 1065–1080 (1991).
Google Scholar
Gomez, D. & Théry, M. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a Neotropical rainforest bird community. Am. Nat. 169, S42–S61 (2007).
Google Scholar
Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biol. Rev. Camb. Philos. Soc. 94, 1294–1316 (2019).
Google Scholar
Passarotto, A., Rodríguez‐Caballero, E., Cruz-Miralles, Á., Avilés Jesús, M. & Sheard, C. Ecogeographical patterns in owl plumage colouration: Climate and vegetation cover predict global colour variation. Glob. Ecol. Biogeogr. 31, 515–530 (2022).
Google Scholar
Bogert, C. M. Thermoregulation in reptiles, a factor in evolution. Evolution 3, 195–211 (1949).
Google Scholar
Galván, I., Rodríguez-Martínez, S., Carrascal, L. M. & Portugal, S. Dark pigmentation limits thermal niche position in birds. Funct. Ecol. 32, 1531–1540 (2018).
Google Scholar
Delhey, K., Dale, J., Valcu, M. & Kempenaers, B. Reconciling ecogeographical rules: rainfall and temperature predict global colour variation in the largest bird radiation. Ecol. Lett. 22, 726–736 (2019).
Google Scholar
Håstad, O., Victorsson, J. & Ödeen, A. Differences in color vision make passerines less conspicuous in the eyes of their predators. Proc. Natl Acad. Sci. USA 102, 6391–6394 (2005).
Google Scholar
Lind, O., Henze, M. J., Kelber, A. & Osorio, D. Coevolution of coloration and colour vision? Philos. Trans. R. Soc. Lond. Ser. B 372, 20160338 (2017).
Google Scholar
Zhao, H., Shi, J., Qi, X., Wang, X. & Jia, J. Pyramid scene parsing network. arXiv 01105, 2017 (1612).
Zoph, B. et al. Rethinking pre-training and self-training. arXiv 2006, 06882 (2020).
Chang, Y. L. & Li, X. Adaptive image region-growing. IEEE Trans. Image Process. 3, 868–872 (1994).
Google Scholar
Fan, J., Yau, D. K. Y., Elmagarmid, A. K. & Aref, W. G. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. Image Process. 10, 1454–1466 (2001).
Google Scholar
Joulin, A., van der Maaten, L., Jabri, A. & Vasilache, N. Learning visual features from large weakly supervised data. arXiv 1511, 02251 (2015).
Hestness, J. et al. Deep learning scaling is predictable, empirically. arXiv 1712, 00409 (2017).
Hudson, L. N. et al. Inselect: automating the digitization of natural history collections. PLoS ONE 10, e0143402 (2015).
Google Scholar
Hussein, B. R., Malik, O. A., Ong, W.-H. & Slik, J. W. F. Semantic segmentation of herbarium specimens using deep learning techniques. in Computational Science and Technology (2020).
Cordts, M. et al. The Cityscapes dataset for semantic urban scene understanding. arXiv 01685, 2016 (1604).
Deng, J. et al. ImageNet: a large-scale hierarchical image database. in 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009).
Andriluka, M., Pishchulin, L., Gehler, P. & Schiele, B. 2D human pose estimation: new benchmark and state of the art analysis. in 2014 IEEE Conference on Computer Vision and Pattern Recognition (2014).
Bradski, G. The OpenCV Library. Dr Dobb’s J. Softw. Tools 120, 122–125 (2000).
Ruder, S. An overview of gradient descent optimization algorithms. arXiv 1609, 04747 (2016).
Kingma, D. P. & Ba, J. L. ADAM: a method for stochastic optimisation. arXiv 1412, 6980 (2014).
Google Scholar
Loshchilov, I. & Hutter, F. SGDR: stochastic gradient descent with warm restarts. arXiv 1608, 03983 (2016).
Abadi, M. et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv 1603, 04467 (2016).
He, Y. et al. Code for: Deep learning image segmentation reveals patterns of UV reflectance evolution in passerine birds. https://doi.org/10.5281/zenodo.6916988 (2022).
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv 1207, 0580 (2012).
van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014).
Google Scholar
Lee, J. S. Digital image smoothing and the signam filter. Computer Vis., Graph., Image Process. 24, 255–269 (1983).
Google Scholar
Haralick, R. M., Sternberg, S. R. & Zhuang, X. Image analysis using mathematical morphology. IEEE Trans. Pattern Anal. Mach. Intell. 9, 532–550 (1987).
Google Scholar
Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, Cybern. 9, 62–66 (1979).
Google Scholar
Sezgin, M. & Sankur, B. Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13, 146–165 (2004).
Google Scholar
Kass, M., Witkin, A. & Terzopoulos, D. Snakes: active contour models. Int. J. Computer Vis. 1, 321–331 (1988).
Google Scholar
Coffin, D. DCRAW V. 9.27. https://www.cybercom.net/~dcoffin/dcraw/ (2016).
Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).
Google Scholar
He, Y. PhenoLearn v.1.0.1. https://doi.org/10.5281/zenodo.6950322 (2022).
Hijmans, R. J. raster: geographic data analysis and modeling. R package version 3.4-5. https://CRAN.R-project.org/package=raster (2020).
Maia, R., Gruson, H., Endler, J. A., White, T. E. & O’Hara, R. B. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods Ecol. Evolution 10, 1097–1107 (2019).
Google Scholar
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).
Google Scholar
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
Google Scholar
Jablonski, N. G. & Chaplin, G. Human skin pigmentation as an adaptation to UV radiation. Proc. Natl Acad. Sci. USA 107, 8962–8968 (2010).
Google Scholar
Beckmann, M. et al. glUV: a global UV-B radiation data set for macroecological studies. Methods Ecol. Evol. 5, 372–383 (2014).
Google Scholar
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).
Google Scholar
Ödeen, A., Håstad, O. & Alström, P. Evolution of ultraviolet vision in the largest avian radiation—the passerines. BMC Evol. Biol. 11, 313 (2011).
Google Scholar
Hadfield, J. D. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).
Google Scholar
Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. Lond. Ser. B 281, 20140298 (2014).
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar
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