Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns. In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline‐wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications. We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitoring and discovery and inference from genetic data. Increased use of unsupervised learning for discovery and visualization of clusters and gaps, simplification of multi‐step analysis pipelines, and integration of machine learning into graduate and postgraduate training are all likely in the near future.

Tong et al. comment on the accuracy of the dating analysis presented in our work on the phylogeny of insects and provide a reanalysis of our data. They replace log-normal priors with uniform priors and add a "roachoid" fossil as a calibration point. Although the reanalysis provides an interesting alternative viewpoint, we maintain that our choices were appropriate.

We present our current phylogenetic hypothesis on the phylogeny of Trichoptera, generated from an analysis of over 7000 nucleotides from 18S and 28S rRNA, EF-1α, COI, and CAD. We corroborate our earlier hypotheses, with results that include a monophyletic Annulipalpia, Integripalpia, Brevitentoria, and Plenitentoria. Monophyly of Psychomyioidea, Pseudoneureclipsidae, and Grumichellinae were confirmed. The "Spicipalpian" families were again found to be paraphyletic, and most closely related to Integripalpia. Ptilocolepidae was not found to be monophyletic, but support for its paraphyly was so weak that we interpret our results as unresolved. We interpret our measures of branch support, and present a collapsed phylogeny that more conservatively represents our current hypothesis. We discuss how these data can eventually be merged into other sources of data, such as COI barcode data and transcriptomes, and suggest that a single huge analysis of all data, with all taxa, is unnecessary if analyses can be phylogenetically subdivided into many separate parts, using transcriptome data to fix the deepest nodes, and allowing faster evolving data to be more appropriately targeted to nodes closer to the tips of the tree.