For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic diversity, population dynamics, mechanisms of divergence and adaptation, and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics – the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, provides the opportunity to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV as an efficient and comprehensive method to collect phenomic data in ecological and evolutionary research. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can effectively capture phenomic-level data by taking pictures and analyzing them using CV. Next we describe the primary types of image-based data, review CV approaches for extracting them (including techniques that entail machine learning and others that do not), and identify the most common hurdles and pitfalls. Finally, we highlight recent successful implementations and promising future applications of CV in the study of phenotypes. In anticipation that CV will become a basic component of the biologist’s toolkit, our review is intended as an entry point for ecologists and evolutionary biologists that are interested in extracting phenotypic information from digital images.

For centuries, ecologists and evolutionary biologists have used images such as drawings, paintings, and photographs to record and quantify the shapes and patterns of life. With the advent of digital imaging, biologists continue to collect image data at an ever-increasing rate. This immense body of data provides insight into a wide range of biological phenomena, including phenotypic trait diversity, population dynamics, mechanisms of divergence and adaptation and evolutionary change. However, the rate of image acquisition frequently outpaces our capacity to manually extract meaningful information from the images. Moreover, manual image analysis is low-throughput, difficult to reproduce, and typically measures only a few traits at a time. This has proven to be an impediment to the growing field of phenomics - the study of many phenotypic dimensions together. Computer vision (CV), the automated extraction and processing of information from digital images, is a way to alleviate this longstanding analytical bottleneck. In this review, we illustrate the capabilities of CV for fast, comprehensive, and reproducible image analysis in ecology and evolution. First, we briefly review phenomics, arguing that ecologists and evolutionary biologists can most effectively capture phenomic-level data by using CV. Next, we describe the primary types of image-based data, and review CV approaches for extracting them (including techniques that entail machine learning and others that do not). We identify common hurdles and pitfalls, and then highlight recent successful implementations of CV in the study of ecology and evolution. Finally, we outline promising future applications for CV in biology. We anticipate that CV will become a basic component of the biologist’s toolkit, further enhancing data quality and quantity, and sparking changes in how empirical ecological and evolutionary research will be conducted.

1. Cryptic pigmentation of prey is often thought to evolve in response to predatormediated selection, but pigmentation traits can also be plastic, and change with respect to both abiotic and biotic environmental conditions. In such cases, identifying the presence of, and drivers of trait plasticity is useful for understanding the evolution of crypsis.2. Previous work suggests that cryptic pigmentation of freshwater isopods (Asellus aquaticus) has evolved in response to predation pressure by fish in habitats with varying macrophyte cover and coloration. However, macrophytes can potentially influence the distribution of pigmentation by altering not only habitat-specific predation susceptibility, but also dietary resources and abiotic conditions. The goals of this study were to experimentally test how two putative agents of selection, namely macrophytes and fish, affect the pigmentation of A. aquaticus, and to assess whether pigmentation is plastic, using a diet manipulation in a common garden.3. We performed two experiments: (a) in an outdoor mesocosm experiment, we investigated how different densities of predatory fish (0/30/60 three-spined stickleback [Gasterosteus aculeatus] per mesocosm) and macrophytes (presence/ absence) affected the abundance, pigmentation and body size structure of isopod populations. (b) In a subsequent laboratory experiment, we reared isopods in a common garden experiment on two different food sources (high/low protein content) to test whether variation in pigmentation of isopods can be explained by diet-based developmental plasticity. 4. We found that fish presence strongly reduced isopod densities, particularly in the absence of macrophytes, but had no effect on pigmentation or size structure of the populations. However, we found that isopods showed consistently higher pigmentation in the presence of macrophytes, regardless of fish presence or absence. | 613Journal of Animal Ecology LÜRIG et aL.

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