Data from: how to generate and test hypotheses about colour: insights from half a century of guppy research
Data files
Nov 08, 2023 version files 56.31 MB
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Arima1.s1.16.7.75_flipped.bmp
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Arima1.s2.16.7.75_flipped.bmp
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Arima5.12.07.75_flipped.bmp
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Aripo1.s1.4.5.78.bmp
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Aripo1.s2.4.5.78.bmp
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Aripo1.s3.4.5.78.bmp
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Aripo1.s4.4.5.78.bmp
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Aripo6.s1.2.3.78.bmp
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Aripo6.s2.02.03.78.bmp
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Aripo6.s3.02.03.78.bmp
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colormesh_data.csv
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Notes.txt
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README.md
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Spot_area_and_number.csv
Abstract
Colouration facilitates evolutionary investigations in nature because the interaction between genotype, phenotype and environment is relatively accessible. In a landmark set of studies, Endler addressed this complexity by demonstrating that the evolution of male Trinidadian guppy colouration is shaped by the local balance between selection for mate attractiveness versus crypsis. This became a textbook paradigm for how antagonistic selective pressures may determine evolutionary trajectories in the wild. Recent studies have, however, challenged the generality of this paradigm. Here we respond to these challenges by reviewing five important yet underappreciated factors that contribute to colour pattern evolution: (1) inter-population variation in female preference, (2) differences in how predators versus conspecifics view males, (3) biased assessment of pigmentary versus structural colouration, (4) the importance of accounting for multi-species predator communities, and (5) the importance of considering the multivariate genetic architecture and multivariate context of selection. We elaborate upon these points specifically in relation to two papers that illustrate the challenges raised by the recent body of work on guppies. More broadly, we emphasise the depth of consideration necessary for testing evolutionary hypotheses using complex multi-trait phenotypes such as guppy colour patterns.
README: Data from: how to generate and test hypotheses about colour: insights from half a century of guppy research
File Name | Data Summary |
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Spot area and number.csv | average area in mm2 of orange, black, and yellow color on body, body area, and number of spots of each color per fish. |
Colormesh_data.csv | raw RBG values for each pixel sampled by colormesh. |
Aripo1.s1.4.5.78.bmp | group photo for Aripo 1 population (low predation). |
Aripo1.s2.4.5.78.bmp | " |
Aripo1.s3.4.5.78.bmp | " |
Aripo1.s4.4.5.78.bmp | " |
Aripo6.s1.2.3.78.bmp | group photo for Aripo 6 population (high predation). |
Aripo6.s2.02.03.78.bmp | " |
Aripo6.s3.02.03.78.bmp | " |
Arima1.s1.16.7.75.bmp | group photo for Arima 1 population (high predation). |
Arima1.s2.16.7.75.bmp | " |
Arima5.12.07.75.bmp | group photo for Arima 5 population (low predation). |
A description of column headings
Spot area and number.csv
- pop = population
- River = river, Aripo or Arima
- pred = predation regime (low predation or high predation)
- black_avg = average area (mm2) of black spots on that fish
- orange_avg = average area (mm2) of orange spots on that fish
- yellow_avg = average area (mm2) of yellow spots on that fish (not used in analysis)
- black_N = number of black spots on that fish
- orange_N = number of orange spots on that fish
- yellow_N = number of yellow spots on that fish (not used in analysis)
- Body_area = area (mm2) of body + tail fin
Colormesh_data.csv
- photo = fish measured. population.photograph.fish (A1=Aripo1, A6=Aripo6, Arima1=Arima1, Arima5=Arima5). "s1" indicates photo s1 (or s2, s3, etc). Number indicates fish counting from upper right corner, moving down the column, then top of next column to the right, etc.
- unwrp = unwarped photographed actually used to measure RGB values in colormesh.
- r_interior1 = red value of first pixel (0-1)
- g_interior1 = green value of first pixel (0-1)
- b_interior1 = blue value of first pixel (0-1)
- ...
- ...
- r_perimeter1 = red value of pixel at first landmark digitized to outline the fish.
- ...
- ...
- ...
- b_perimeter58 = blue value of pixel at last (58th) landmark digitized to outline fish.
Methods
For more detail, refer Notes.txt file.