The ability of a researcher to re‐identify (re‐ID) an individual animal upon re‐encounter is fundamental for addressing a broad range of questions in the study of ecosystem function, community and population dynamics and behavioural ecology. Tagging animals during mark and recapture studies is the most common method for reliable animal re‐ID; however, camera traps are a desirable alternative, requiring less labour, much less intrusion and prolonged and continuous monitoring into an environment. Despite these advantages, the analyses of camera traps and video for re‐ID by humans are criticized for their biases related to human judgement and inconsistencies between analyses. In this review, we describe a brief history of camera traps for re‐ID, present a collection of computer vision feature engineering methodologies previously used for animal re‐ID, provide an introduction to the underlying mechanisms of deep learning relevant to animal re‐ID, highlight the success of deep learning methods for human re‐ID, describe the few ecological studies currently utilizing deep learning for camera trap analyses and our predictions for near future methodologies based on the rapid development of deep learning methods. For decades, ecologists with expertise in computer vision have successfully utilized feature engineering to extract meaningful features from camera trap images to improve the statistical rigor of individual comparisons and remove human bias from their camera trap analyses. Recent years have witnessed the emergence of deep learning systems which have demonstrated the accurate re‐ID of humans based on image and video data with near perfect accuracy. Despite this success, ecologists have yet to utilize these approaches for animal re‐ID. By utilizing novel deep learning methods for object detection and similarity comparisons, ecologists can extract animals from an image/video data and train deep learning classifiers to re‐ID animal individuals beyond the capabilities of a human observer. This methodology will allow ecologists with camera/video trap data to reidentify individuals that exit and re‐enter the camera frame. Our expectation is that this is just the beginning of a major trend that could stand to revolutionize the analysis of camera trap data and, ultimately, our approach to animal ecology.

Much confusion in genome biology results from conflation of possible meanings of the word “function.” We suggest that, in this connection, attention should be paid to evolutionary biologists and philosophers who have previously dealt with this problem. We need only decide that although all genomic structures have effects, only some of them should be said to have functions. Although it will very often be difficult or impossible to establish function (strictly defined), it should not automatically be assumed. We enjoin genomicists in particular to pay greater attention to parsing biological effects.

Integrating the study of human diversity into the human evolutionary sciences requires substantial revision of traditional conceptions of a shared human nature. This process may be made more difficult by entrenched, 'folkbiological' modes of thought. Earlier work by the authors suggests that biologically naive subjects hold an implicit theory according to which some traits are expressions of an animal's inner nature while others are imposed by its environment. In this paper, we report further studies that extend and refine our account of this aspect of folkbiology. We examine biologically naive subjects' judgments about whether traits of an animal are 'innate', 'in its DNA' or 'part of its nature'. Subjects do not understand these three descriptions to be equivalent. Both innate and in its DNA have the connotation that the trait is species-typical. This poses an obstacle to the assimilation of the biology of polymorphic and plastic traits by biologically naive audiences. Researchers themselves may not be immune to the continuing pull of folkbiological modes of thought.

The proposal that the concept of innateness expresses a 'folk biological ' Innateness and Folk BiologyIt is a truism that the term 'innate' is vague and ambiguous. According to ethologist Patrick Bateson, "At least six meanings are attached to the term: present at birth; a behavioral difference caused by a genetic difference; adapted over the course of evolution; unchanging throughout development; shared by all members of a species; and not learned. … Say what you mean (even if it uses a bit more space) rather than unintentionally confuse your readers by employing a word such as innate that carries so many different connotations" (Bateson, 1991, p. 21-22). The rejection of the term 'innate' . Acknowledgements: Griffiths and Linquist were supported by Australian Research Council grant FF0457917. We thank Patrick Bateson, Ron Mallon, Matteo Mameli and Shaun Nicholls for helpful comments on an earlier draft.

A promising recent development in molecular biology involves viewing the genome as a miniecosystem, where genetic elements are compared to organisms and the surrounding cellular and genomic structures are regarded as the local environment. Here we critically evaluate the prospects of Ecological Neutral Theory (ENT), a popular model in ecology, as it applies at the genomic level. This assessment requires an overview of the controversy surrounding neutral models in community ecology. In particular, we discuss the limitations of using ENT both as an explanation of community dynamics and as a null hypothesis. We then analyze a case study in which ENT has been applied to genomic data. Our central finding is that genetic elements do not conform to the requirements of ENT once its assumptions and limitations are made explicit. We further compare this genome-level application of ENT to two other, more familiar approaches in genomics that rely on neutral mechanisms: Kimura's Molecular Neutral Theory and Lynch's Mutational Hazard Model. Interestingly, this comparison reveals that there are two distinct concepts of neutrality associated with these models which we dub 'fitness-neutrality' and 'competitive neutrality'. This distinction helps to clarify the various roles for neutral models in genomics, for example, in explaining the evolution of genome size.