Welcome toChameleon

Pattern!

Background

• Pattern is thought to aid in camouflage, facilitate individual recognition, and signal health and condition• Complex elements of pattern have made objective quantification extremely difficult• For the first time, technological advances have allowed us to quantitatively analyze pattern in the veiled chameleon Chamaeleo calyptratus using two recently proposed methods: granularity analysis (GA) and fractal dimension analysis (FDA)

Objectives

• To identify relative costs and benefits of quantifying pattern via GA and FDA

• To determine which method is more appropriate for pattern analysis in the context of male-male competition in the veiled chameleon

• To use the respective analyses to determine if display pattern is correlated with contest outcome (i.e. winning or losing)

Methods

• High-definition digital photos were recorded from aggressive dyadic encounters and color-corrected to control for light variation• Three distinct events during dyadic

encounters were identified:• 1) beginning of trial (BEG)• 2) moment of determination (MOD)• 3) loser subjectively darkest (SD)

• With photos from each event, a total of five regions were sampled for full pattern representation

1) Casque 2) Vertical Stripe 3) Horizontal Stripe 4) Tail 5) Body

• Pattern images were subjected to both FDA and GA to compile the following:• 1) fractal dimension

(FD)• 2) total energy of image

(Etot)

• 3) energy of maximum band width (Emax)

• 4) proportion of total energy occupied by maximum band width (Eprop)

Starting Image

Fractal Dimension Analysis

(Threshold)

GranularityAnalysis

(Grayscale)

MATLABFracTop

Sample Graphs

1) Fractal Dimension• Using the free-access software

FracTop, FD was calculated utilizing the box-counting method– Images are partitioned into

squares of specific box sizes and the number of boxes occupied by a black pixel are counted

– The graphs produced from FracTop represent box size—they are not intrinsically representative of FD

• FD is used as a comparative measure—there is no known absolute meaning of a particular FD value– Relatively low FD is characteristic

of a “smooth” pattern

Starting Image

Fractal Dimension Analysis

(Threshold)

GranularityAnalysis

(Grayscale)

MATLABFracTop

2) Etot• Using MATLAB, each

grayscaled image was run through seven filters of varying size (band widths)

• Each band width occupies a unit on the x-axis of the resulting graphs, with the corresponding energy value on the y-axis

• Etot is the sum of the energies of each of the seven band widths – Shaded area

Starting Image

Fractal Dimension Analysis

(Threshold)

GranularityAnalysis

(Grayscale)

MATLABFracTop

3) Emax• Emax is the value of the

highest energy measurement – Starred peak

• It is associated with the band width that dominates the image– This value gives us some

idea of the size of the dominant pattern elements in a given image, allowing us to classify images as “coarse” or “fine”

Starting Image

Fractal Dimension Analysis

(Threshold)

GranularityAnalysis

(Grayscale)

MATLABFracTop

4) Eprop

• Eprop describes the strength of the dominant band width– An Eprop value

closer to one indicates a strong relationship, and vice versa

• Eprop = Emax/Etot

Starting Image

Fractal Dimension Analysis

(Threshold)

GranularityAnalysis

(Grayscale)

MATLABFracTop

Results

1)Winners have a higher FD in casques than losers at BEG

2)FD in horizontal stripe of losers is smaller at MOD and larger at SD

3) FD in body is larger in losers than winners at MOD

4) FD in body of losers is smaller at BEG and larger at MOD

5) Eprop in body of winners is smaller at BEG and larger at MOD

6) Eprop in body is larger in winners than losers at MOD

• FD and Eprop in the body regions were the most significant factors

• We generated images and ran them through both FDA and GA to gauge how FD and Eprop are physically manifested

EA C DB

FD and Eprop Values of Generated Images

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0.3

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B

CD E

• A positive correlation exists between increasing Eprop and probability of winning a contest, implying that consistent patterns are characteristic of a winning chameleon•Decreasing FD increases likelihood of winning, suggesting that smoother patterns can be associated with loser chameleons

Discussion

•Casques of winners have smoother pattern transitions than casques of losers before any aggressive interaction—this suggests the possibility of inherent differences between winners and losers •Body patterns were the most significant, potentially identifying the most informative vehicle for pattern communication in male veiled chameleons

Conclusion

•Other types of FDA exist, including mass-radius analysis, cumulative intersection analysis, and vectorized intersection method• Although the data gathered via box-counting FDA showed significant trends among winners and losers, the physical manifestation of what FDA actually measures is unclear• Further research is needed to identify the most appropriate method of FDA in the context of animal pattern quantification—therefore, GA should currently be employed as the standard method

Thank you for visiting Chameleon Pattern!

Please feel free to comment on this site or send an email to

megan.best92@gmail.com if you have any questions or feedback.

AcknowledgementsWe thank the Arizona State University School of Life Sciences Undergraduate Research Program, the Animal Behavior Society, the American Society for Ichthyologists and Herpetologists, and the Graduate and Professional Students Association for supporting this research. We thank Sarah Bruemmer and Brianna Bero-Buell for their contribution to this project. We thank Herbert Jelinek and David Cornforth for FracTop used in the fractal dimension analyses. Additionally, we thank Martin Stevens for providing the MATLAB code used in the granularity analyses.