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An investigation into multi-component warning colouration

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posted on 2024-07-12, 06:17 authored by Christopher John Irving

Butterflies and moths present conspicuous colour patterns that signal toxicity to predators. The existence of redundancies (warning signals on different body parts) in conspicuous signals is a theoretical conundrum, as they are thought to interfere with predator avoidance learning. Here, we investigate warning colour components in Australian Lepidopterans using three approaches. First, we scored the absence and presence of redundant warning signals across Australian lepidopterans. Then, we reviewed warning colouration methodologies to assess the appropriateness of frequently used quantification techniques. We implemented this knowledge in the final phase, through quantifying warning signal size in both static and dynamic contexts. Redundant signals (warning colours on both wing and abdomen) were found in ~44% of warning-coloured moths, and ~32% of warning-coloured butterflies. Literature analysis revealed that warning signals are generally quantified in a static context, despite broad acknowledgment that signalling is dynamic. Our novel signal-measuring method, tested on the Amata nigriceps moth, confirmed that context (static and dynamic) affects signal appearance, and that one signal element can receive higher selection pressure than another in a colouration pattern. Our results may explain the persistence of overall signal variation, due to the relaxed selection of specific components, as well as redundant signals.

Funding

Australian Research Council - DP220102323

Australian Research Council - DP190101028

History

Table of Contents

Chapter 1: Spots and stripes? How common are redundant warning colour signals in Australian Lepidoptera -- Chapter 2: Static and dynamic warning colours - a review of current methodology -- Chapter 3: The function of individual colour pattern elements in Amata nigriceps warning colouration -- Final conclusions and future directions -- Supplementary material -- References

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

Master of Research

Department, Centre or School

School of Natural Sciences

Year of Award

2023

Principal Supervisor

Marie Herberstein

Additional Supervisor 1

Liisa Hamalainen

Additional Supervisor 2

George E. Binns

Rights

Copyright: The Author Copyright disclaimer: https://www.mq.edu.au/copyright-disclaimer

Language

English

Extent

81 pages

Former Identifiers

AMIS ID: 296287

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