Commercial Harvesting Has Driven the Evolution of Camouflage in an Alpine Plant

Yang, Ning; Stevens, Martin; Sun, Hang

doi:10.1016/j.cub.2020.10.078

Cited by 22 publications

(20 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To explore the importance of blue flower color and considering bee pollinator color vision, it is interesting to compare reflectance spectra of plants from different latitudes and geographic regions. Datasets from Australia (Dyer et al, 2012;Shrestha et al, 2019a,b), Nepal (Shrestha et al, 2014), Norway (Arnold et al, 2009(Arnold et al, , 2010, and Brazil (Camargo et al, 2019) provide an accessible comparison, because similar data collection methods were employed in the various studies, although some abiotic factors including background and/or light can vary with increasing altitude (Niu et al, 2020). The floral spectra from these studies were expressed in a bee color space, a geometrical interpretation allowing for modeling colors as perceived by an animal observer (Figure 8) (Kelber et al, 2003;Renoult et al, 2017); this model is implemented using a visual namogram (Stavenga et al, 1993) to model spectral sensitivity functions (Figure 7) for typical bee photoreceptors (350, 440, and 540 nm), using standard foliage background and open midday illumination (Judd et al, 1964), enabling the calculation of a spectral locus for the main flower color for each plant species (Figure 8).…”

Section: In Search Of Blue: Spectral Analysis and Bee Color Spacementioning

confidence: 99%

Fragmentary Blue: Resolving the Rarity Paradox in Flower Colors

Dyer

Jentsch²,

Burd³

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

Blue is a favored color of many humans. While blue skies and oceans are a common visual experience, this color is less frequently observed in flowers. We first review how blue has been important in human culture, and thus how our perception of blue has likely influenced the way of scientifically evaluating signals produced in nature, including approaches as disparate as Goethe’s Farbenlehre, Linneaus’ plant taxonomy, and current studies of plant-pollinator networks. We discuss the fact that most animals, however, have different vision to humans; for example, bee pollinators have trichromatic vision based on UV-, Blue-, and Green-sensitive photoreceptors with innate preferences for predominantly short-wavelength reflecting colors, including what we perceive as blue. The subsequent evolution of blue flowers may be driven by increased competition for pollinators, both because of a harsher environment (as at high altitude) or from high diversity and density of flowering plants (as in nutrient-rich meadows). The adaptive value of blue flowers should also be reinforced by nutrient richness or other factors, abiotic and biotic, that may reduce extra costs of blue-pigments synthesis. We thus provide new perspectives emphasizing that, while humans view blue as a less frequently evolved color in nature, to understand signaling, it is essential to employ models of biologically relevant observers. By doing so, we conclude that short wavelength reflecting blue flowers are indeed frequent in nature when considering the color vision and preferences of bees.

show abstract

Section: In Search Of Blue: Spectral Analysis and Bee Color Spacementioning

confidence: 99%

Fragmentary Blue: Resolving the Rarity Paradox in Flower Colors

Dyer

Jentsch²,

Burd³

et al. 2021

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…2017b; Niu et al. 2021). By using ImageJ, CamoEvo and its GA can easily be integrated with a variety of image generation and analysis tools, in addition to other open source plugins and toolboxes such as MICA, Acuity View, and QCPA (Troscianko and Stevens 2015; Caves and Johnsen 2018; van den Berg et al.…”

Section: Figurementioning

confidence: 99%

“…CamoEvo is designed to be advanced, customizable, and user friendly, acting as a tool for animal color research. (Bonney et al 2014;Troscianko et al 2017b;Niu et al 2021). By using ImageJ, CamoEvo and its GA can easily be integrated with a variety of image generation and analysis tools, in addition to other open source plugins and toolboxes such as MICA, Acuity View, and QCPA (Troscianko and Stevens 2015;Caves and Johnsen 2018;van den Berg et al 2020).…”

mentioning

confidence: 99%

CamoEvo: An open access toolbox for artificial camouflage evolution experiments

Hancock

Troscianko

2022

Evolution

View full text Add to dashboard Cite

Camouflage research has long shaped our understanding of evolution by natural selection, and elucidating the mechanisms by which camouflage operates remains a key question in visual ecology. However, the vast diversity of color patterns found in animals and their backgrounds, combined with the scope for complex interactions with receiver vision, presents a fundamental challenge for investigating optimal camouflage strategies. Genetic algorithms (GAs) have provided a potential method for accounting for these interactions, but with limited accessibility. Here, we present CamoEvo, an open-access toolbox for investigating camouflage pattern optimization by using tailored GAs, animal and egg maculation theory, and artificial predation experiments. This system allows for camouflage evolution within the span of just 10-30 generations (∼1-2 min per generation), producing patterns that are both significantly harder to detect and that are optimized to their background. CamoEvo was built in ImageJ to allow for integration with an array of existing open access camouflage analysis tools. We provide guides for editing and adjusting the predation experiment and GA as well as an example experiment. The speed and flexibility of this toolbox makes it adaptable for a wide range of computer-based phenotype optimization experiments.

show abstract

“…An online game was created to replicate the field trials in a computer-based search task, using custom Javascript code, adapted from code previously-used in similar citizen science experiments [77], and made freely available to play on internet browsers (at: http:// field hares. senso ryeco logy.…”

Section: Online Experiments Designmentioning

confidence: 99%

Generalist camouflage can be more successful than microhabitat specialisation in natural environments

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Crypsis by background-matching is a critical form of anti-predator defence for animals exposed to visual predators, but achieving effective camouflage in patchy and variable natural environments is not straightforward. To cope with heterogeneous backgrounds, animals could either specialise on particular microhabitat patches, appearing cryptic in some areas but mismatching others, or adopt a compromise strategy, providing partial matching across different patch types. Existing studies have tested the effectiveness of compromise strategies in only a limited set of circumstances, primarily with small targets varying in pattern, and usually in screen-based tasks. Here, we measured the detection risk associated with different background-matching strategies for relatively large targets, with human observers searching for them in natural scenes, and focusing on colour. Model prey were designed to either ‘specialise’ on the colour of common microhabitat patches, or ‘generalise’ by matching the average colour of the whole visual scenes. Results In both the field and an equivalent online computer-based search task, targets adopting the generalist strategy were more successful in evading detection than those matching microhabitat patches. This advantage occurred because, across all possible locations in these experiments, targets were typically viewed against a patchwork of different microhabitat areas; the putatively generalist targets were thus more similar on average to their various immediate surroundings than were the specialists. Conclusions Demonstrating close agreement between the results of field and online search experiments provides useful validation of online citizen science methods commonly used to test principles of camouflage, at least for human observers. In finding a survival benefit to matching the average colour of the visual scenes in our chosen environment, our results highlight the importance of relative scales in determining optimal camouflage strategies, and suggest how compromise coloration can succeed in nature.

show abstract

Commercial Harvesting Has Driven the Evolution of Camouflage in an Alpine Plant

Cited by 22 publications

References 26 publications

Fragmentary Blue: Resolving the Rarity Paradox in Flower Colors

Fragmentary Blue: Resolving the Rarity Paradox in Flower Colors

CamoEvo: An open access toolbox for artificial camouflage evolution experiments

Generalist camouflage can be more successful than microhabitat specialisation in natural environments

Contact Info

Product

Resources

About