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Identifying Determinants of Background Matching and Disruptive Colouration Using Computer Simulations and Humans As Predators

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Identifying Determinants of Background Matching and Disruptive Colouration Using Computer Simulations and Humans As Predators IDENTIFYING DETERMINANTS OF BACKGROUND MATCHING AND DISRUPTIVE COLOURATION USING COMPUTER SIMULATIONS AND HUMANS AS PREDATORS TOH KOK BEN BSc.(Hons), NUS A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF SCIENCE DEPARTMENT OF BIOLOGICAL SCIENCES NATIONAL UNIVERSITY OF SINGAPORE 2010 ACKNOWLEDGEMENT I owe my deepest gratitude to my supervisor, Dr. Peter Todd, who has been encouraging, guiding and supporting me throughout the past two years of this graduate research project. This thesis would not have been possible without his time and effort. I am indebted to many of my friends and/or the Marine Biology Laboratory members or alumni, including but not limited to Prof. Chou Loke Ming, Karenne Tun, Esther, Christina, Ywee Chieh, Ruth Neo, Lionel Ng, Yan Xiang, Jani, Lishi, Meilin, Lin Jin, Nicholas Yap, Lynette Loke, Denise Tan, Martin Chew, Yuchen, Nanthinee, Wee Foong, Juanhui and many more, for their suggestions, help and concern. Many of them also helped to proofread this thesis, which was a considerably tough job. Thanks to Prof. John Endler for his suggestions and encouragement. This project has also benefited tremendously from my predecessor Huijia, who has laid out the foundation of computer programs and experiment protocols in this thesis. I also like to thank the volunteers for this project, many of whom gave me encouragement and helped me in publicising the experiments. Special thanks to Ivy and Val who assisted me in recruiting volunteers. I would not be able to finish my experiments in time without their help. Finally, I am very grateful to have an extremely supportive family, especially my parents. i TABLE OF CONTENTS Acknowledgement i Table of Contents ii Summary iv List of Figures vi List of Tables ix Chapter 1 General Introduction 1 Chapter 2 The role of element size in background matching 17 2.1 Introduction 17 2.2 Materials and Method 24 2.2.1 Background 24 2.2.2 Morphs 26 2.2.3 General setup 27 2.2.4 Computer simulation and computer trial 28 2.2.5 Data analyses 31 2.3 Results 34 2.3.1 Survivorship for morph-background combinations 34 2.3.2 Net survivorship 39 2.3.3 Between-screen survivorship difference 41 2.4 Discussion 43 2.4.1 Tradeoff 44 2.4.2 Between-screen difference 47 2.4.3 Future directions 47 Chapter 3 Effects of density and contrast of elements in background matching 50 3.1 Introduction 50 3.2 Materials and methods 53 3.2.1 Experiment 1: Effects of elements density 53 3.2.2 Experiment 2: Effects of Elements contrast 55 3.2.3 General setup and computer trial 59 3.2.4 Data analyses 60 ii 3.3 Results 63 3.3.1 Experiment 1 – Density experiment 63 3.3.2 Experiment 2 – Contrast experiment 65 3.4 Discussion 68 3.4.1 Element density 68 3.4.2 Element contrast 70 Chapter 4 Developing a “disruption index” 73 4.1 Introduction 73 4.2 Materials and Methods 75 4.2.1 Disruptive index 75 4.2.2 Background 77 4.2.3 Morph and morph location 78 4.2.4 General setup and computer trial 80 4.2.5 Data analysis 81 4.3 Results 86 4.4 Discussion 88 Chapter 5 Conclusions 93 References 98 Appendix 106 iii SUMMARY In nature, many animals bear markings (or pattern elements) on their body to reduce detection through background matching and disruptive colouration, but studies of these strategies are surprisingly limited. This thesis investigated the role of element size, density and contrast in background matching, through the use of humans as predators in virtual computer simulations. In addition, this thesis also attempted to develop a disruptive index to quantify and predict survivorship of different-patterned morphs. Manipulative studies on how the determinants of colour pattern such as element size affect background matching are scarce. Experiment in Chapter 2 examined the role of element size in background matching using a ‘high resolution’ experiment comparing all combinations of eight virtual morphs and eight backgrounds with different element sizes. Using human predator search time as a measure of morph survivorship, a 3D surface graph (morph element size class × background element size class × search time) was produced, giving a detailed understanding of how morph and background element size affected survivorship. While predator search time was longest when the element size classes of morphs and backgrounds were similar, an inexact match still provided some protection. Search time was significantly higher in combinations where the element size of the morph was larger than that of the background and vice versa. This experiment demonstrates, for the first time, a convex tradeoff relationship in a habitat with two visually distinct backgrounds, i.e. generalists were potentially favoured over specialists when the difference between the two backgrounds was small. Experiments in Chapter 3 aimed to improve our current understanding of the importance of element contrast and element density in background matching. Survivorship patterns of two morphs (low and high density or contrast) on 15 backgrounds from low to high density iv or contrast were obtained, again using human as predators. Element contrast was found to be much more important than element density. However, the lack of element density effect on search times could have been due to differential fragmentation of background elements by the prey morphs placed in different locations, as suggested by the large variation in predator search times. The effects of disrupted edge length, number of marginal elements and variation in area of marginal elements on disruptive colouration were previously untested. Using these factors, Chapter 4 focused on developing a novel index that may quantify the degree of disruptive colouration and predict a morph’s survivorship based on morph pattern or morph location. Correlation tests and linear models showed that a higher disruptive index led to lower survivorship, the opposite of what was predicted. Instead of disruptive colouration, the index was found to reflect how element geometry affected background matching. Additionally, morph location was shown to be as important as morph pattern in predicting survivorship. These findings demonstrated the complexity of background matching and disruptive colouration and would be important for future studies. v LIST OF FIGURES Figure 1.1: Terms and definitions related to visual camouflage (modified from Stevens and Merilaita (2009a)). 4 Figure 1.2: Examples of 5 sub-principles of disruptive colouration: (a) differential blending, (b) maximum disruptive contrast, (c) disruptive marginal pattern, (d) disruption of surface through false edges and (e) coincident disruptive colouration. (Pictures taken with permission from Stevens and Merilaita (2009b), copyrighted to Stevens & Merilaita/Phil. Trans B.) 7 Figure 2.1: The three possibilities of (simple) relationship between survivorship of prey patterns on background A and background B. Adapted and modified from Sheratt et al. (2007). 20 Figure 2.2: Samples of backgrounds used in the experiment. Background 1 had the smallest elements size while Background 8 had the largest. 25 Figure 2.3: Samples of virtual morphs used in the experiment. The morphs are random samples of the backgrounds. 26 Figure 2.4: The cotton tent where volunteers performed their computer trials. 28 Figure 2.5: Surface graph showing the mean search time of each morph and background combination. 35 Figure 2.6: Surface graph showing the standard deviation of the mean search time of each morph and background combination. 36 Figure 2.7: Mean search time of combinations with element size class difference of 0 to 7. Combinations with morph element size class larger than the background’s were separated from those with background element size class larger than the morph’s. As background and morph ESC for ESCD 0 group were similar, the two bars (Morph <= Background and Morph >= Background) are identical. Mean ± 95% CI is presented. 38 Figure 2.8: Mean search time of morph types 1 to 8 when presented against backgrounds 1 and, in separate occasions, backgrounds 8. Labels beside the points refer to the morph type. Mean ± SE is presented. 40 Figure 2.9: Mean search time of morph types 3 to 6 when presented against backgrounds 3 and, in separate occasions, backgrounds 6. Labels beside the points refer to the morph type. Mean ± SE is presented. 41 Figure 3.1: Examples of virtual prey with 30%, 50% and 70% green pixels. These images were created using Sheratt et al.’s (2007) methods. 52 Figure 3.2: Samples of backgrounds used in the experiment. Background 1 had the lowest element density while Background 15 had the highest. 54 vi Figure 3.3: Samples of morphs used in the experiment. 55 Figure 3.4: Samples of backgrounds used in the experiment. Background 1 has lowest elements contrast while background 8 has highest. Samples shown are not to scale. 57 Figure 3.5: Samples of morphs used in the experiment. 58 Figure 3.6: A) The hypothetical survivorship pattern of the LD morph and HD morph when plotted against background density. Another possibility of survivorship pattern of HD morph is also plotted here, with a label of “HD morph (2)”. B) Survivorship patterns of the all morphs flipped horizontally when the mean search time was plotted against element density difference. 61 Figure 3.7: Mean search time of low (LD) and high (HD) density morphs when set against backgrounds with different element densities. Element densities of LD and HD morphs are approximately equal to 140 and 350 elements per 750 × 750 pixels. Element density difference hence refers to the absolute difference of element density of the morph and the background. Mean ± 1 SE is presented. 64 Figure 3.8: Mean search time of LC and HC morphs when set against backgrounds with different contrast.
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