Morpho-Functional Adaptations to Digging in Australian Marsupials

Morpho-functional adaptations to digging in Australian marsupials

Meg Louise Martin

BSc (Hons)

A thesis submitted to Murdoch University

to fulfil the requirements for the degree of Doctor of Philosophy

August, 2020

“An understanding of the natural world and what’s in it is a source of not only a great curiosity but great fulfillment…”

Sir David Attenborough

Author’s declaration

I declare that this thesis is my own account of my research and contains as its main content work which has not previously been submitted for a degree at any tertiary education institute.

______

Meg Louise Martin

PhD Candidate

August 2020

Abstract

Digging behaviour has evolved across multiple lineages of Australian marsupials and monotremes, some of which are amongst the most specialised diggers in the world. These animals forage for subterranean food sources, while others dig extensive burrow systems for shelter. The scratch-diggings, in turn, assist in soil turn over, water infiltration, nutrient cycling and dispersal of fungi and seeds, thus playing important roles in ecosystem health.

Digging species are capable of generating high out-forces with their forelimbs to excavate soil. As form follows function, forelimb musculoskeletal morphology is expected to be driven by the forces that are imposed by their day-to-day activities, within the constraints imposed by phylogenetic background. In this thesis, I present four studies that quantitatively investigate intra- and inter-specific variation in forelimb adaptations to digging in Australian monotremes and marsupials to examine the link between skeletal morphology and muscle architecture. Representatives of all extant lineages of marsupials

(Diprotodontia, Dasyuromorphia, Peramelemorphia, Notoryctemorphia) and monotremes

(Monotremata) were used in a correlative study to examine the extent to which functional patterns of limb morphology are influenced by digging behaviour.

Study 1 examined ontogenetic development of muscle architecture (muscle mass (mm) and muscle physiological cross-sectional area (PCSA)) in the Quenda (Isoodon fusciventer).

This data demonstrated differential development of the muscles acting as main movers of the power-stroke during digging in comparison to recovery-stroke muscles for force production (PCSA) but on the whole suggested mechanical similarity throughout ontogeny in the sample. Study 2 examined the intraspecific relationship between the ontogenetic

iii development of muscle architecture and 2D and 3D measures of bone shape to reveal that the shape of the scapula, humerus and third metacarpal show significant covariation with muscle anatomy. However, the relationship was not well-represented by bone indices.

In study 3, the covariation between muscle PCSA and bone shape was quantified across a range of species. Bone shape was significantly different between species of different digging abilities; however, differences were not apparent after phylogenetic correction with the exception of the ulnar and third metacarpal shape. A significant link between muscles PCSA and shape was evident, especially for the scapula, humerus and third metacarpal. Study 4 extended the range of species examined for bone measures to reveal that ulnar shape and bone indices show significant differences between behaviour; this relationship was less evident in the scapula, humerus and third metacarpal.

Overall, this collective body of work has quantified the extent to which forelimb muscle architecture and bone shape covary. This thesis also highlights the importance of ontogeny in quantitative studies of muscle architecture, and provides novel models of analysis of post-cranial anatomy. This information furthers the understanding of the complex links between vertebrate form and function. The application of these results will assist in making inferences of the behaviour and ecology of extinct species and the roles they may have played within the Australian ecosystem through time and space.

Statement of candidate contributions

The work presented in this thesis was undertaken from June 2015 to August 2020 at

Murdoch University (six months pause of candidate May 2019-Dec 2019). Funding for this research was provided by Murdoch University and the Society of Graduate Women WA. This work is original and was carried out by myself, with contributions by my supervisory panel and other collaborators that have been acknowledged for their assistance in the production of the papers. Here, I detail the contributions of all authors towards each of the experimental chapters.

Chapter 2 is predominantly my own work. I performed the extensive literature review and drafted the manuscript. All authors assisted in drafting the manuscript and contributed to the design of the review. All authors approved the manuscript for publication. This chapter has been published in the Journal of Morphology.

Martin, M. L., Travouillon, K. J., Fleming, P. A. & Warburton, N. M. (2020), Review of the methods used for calculating physiological cross-sectional area (PCSA) for ecological questions. Journal of Morphology, 281, 778-789. doi: http://doi.org/10.1002/jmor.21139

Signatures of Co-Authors:

1. ______2. ______3. ______

Chapter 3 is predominantly my own work. I collected specimens, carried out muscle dissections, measured FL and pennation angles, undertook the statistical analysis, and drafted the manuscript, 1. NMW collected specimens, assisted in muscle dissections, produced muscle drawing figures and drafted the manuscript, 2. KJT drafted the manuscript, and 3. PAF assisted in statistical analysis and drafted the manuscript. All authors contributed

v to the experimental design and gave final approval for publication. This chapter has been published in the Journal of Morphology.

Martin, M. L., Warburton, N. M., Travouillon, K. J. & Fleming, P. A. (2019), Mechanical similarity across ontogeny of digging muscles in an Australian marsupial (Isoodon fusciventer). Journal of Morphology, 280, 423:435. doi: http://doi.org/10.1002/jmor.20954

Signatures of Co-Authors:

1. ______2. ______3. ______

Chapter 4 is predominantly my own work. I collected specimens, carried out all the scanning of specimens, undertook all landmarking, undertook the statistical analysis, and drafted the manuscript, 1. KJT drafted manuscript, 2. ES assisted in statistical analysis and drafted the manuscript, 3. PAF assisted in statistical analysis and drafted the manuscript, and 4. NMW collected specimens and drafted the manuscript. All authors contributed to the experimental design and gave final approval for publication. This chapter has been published in the Journal of Morphology.

Martin, M. L., Travouillon, K. J., Sherratt, E., Fleming, P. A. & Warburton, N. M. (2019),

Covariation between forelimb muscle anatomy and bone shape in an Australian scratch- digging marsupial: comparison of morphometric methods. Journal of Morphology, 280,

1900-1915. doi: http://doi.org/10.1002/jmor.21074

Signatures of Co-Authors:

1. ______2. ______3. ______

4. ______

Chapter 5 is predominantly my own work. I collected and sourced specimens, carried out muscle dissections, measured FL and pennation angles, carried out all the scanning and measuring of specimens, undertook all landmarking, undertook the statistical analysis and drafted the manuscript, NMW assisted in muscle dissections and drafted the manuscript,

KJT collected specimens and drafted the manuscript, PAF assisted in statistical analysis and drafted the manuscript. All authors contributed to the experimental design. This chapter has been written for submission to the Zoological Journal of the Linnaean Society.

Chapter 6 is predominantly my own work. I collected and sourced all specimens, carried out all the scanning of specimens, measured all the specimens, undertook all landmarking, undertook the statistical analysis, and drafted the manuscript, PAF assisted in statistical analysis and drafted the manuscript, KJT collected specimens and drafted the manuscript, and NMW collected specimens and drafted the manuscript.

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Acknowledgements

The completion of this thesis would not have been achieved without the help and support of many people during my PhD.

Firstly, I need to thank my amazing supervisors Prof. Trish Fleming, Dr Natalie

Warburton and Dr Kenny Travouillon for their support, guidance and patience throughout my PhD. Thank you for sharing your knowledge, expertise and investing endless amounts of time to help me complete this thesis. Trish, thank you for all your assistance and guidance with all my statistical analysis and for providing so much valuable feedback on all my manuscripts. Kenny, thank you for all your help at the museum, sourcing species, and for always correcting my spelling of scientific names! And Nat, I cannot thank you enough for all your encouragement, hugs and for sharing your passion for anatomy with me throughout the last five years. You have offered endless support, guidance and patience throughout this project. I could not have done this without you all, so thank you.

I am very thankful for the funders who have helped make this project possible; the

Australian Postgraduate Award, and the top-up award from Murdoch University, the

Graduate Women WA for the Mary Walters Scholarship that funded me to travel to Sydney to collect data, and the Murdoch University Postgraduate Travel Award that assisted in funding my travel to Washington DC to attend the International Congress of Vertebrate

Morphology conference.

I am grateful to all the museums that welcomed me and allowed me to spend so long in their collections. And thank you to all the wildlife centres and departments that kindly donated specimens to this study. I must also thank Dr Diana Nottle, Zsa Zsa Wong and Joe

Hong for all your assistance in the Anatomy labs. Also thank you to Dr Emma Sherratt at

UoA for your help and guidance teaching me all I needed to know in geomorph.

I cannot possibly thank all my friends by name, but thank you all for the endless support and encouragement. Especially, to the Murdoch postgrad team in the batcave, dungeon and beyond, thank you for all the cake, chocolate, hugs, cocktails, writing sessions and laughter you have provided over the years. I am so lucky to be part of such an amazing supportive little community, I could have not got through this PhD without you guys.

Thank you to my family for your constant love and support throughout my time as a student. Thank you for always being excited when I was excited, and encouraging me when I wasn’t feeling the best. Most importantly, thank you to my mum for reading through so many of my early drafts of my papers and finding all my grammatical errors.

And of course, thank you to my dog Sherlock for all the snuggles and being my hot water bottle through some long days and nights of writing! You weren’t always the best assistant (you slept through most of my thesis), but you were a great listener and always reminded me when to take a break and get some fresh air.

And lastly, the person who knows my project almost as well as I do, the person that started as my boyfriend, became my fiancé, and then married me throughout this hectic last

5 years, my husband, Alex. Thank you for your statistical and coding assistance (you were seriously a life saver!!), constant support, encouragement and for your endless tolerance and belief in me, you were my driving force. I love you.

Publications arising from this thesis

Journal publications

Martin, M. L., Travouillon, K. J., Sherratt, E., Fleming, P. A. & Warburton, N. M. (2019),

Covariation between forelimb muscle anatomy and bone shape in an Australian scratch- digging marsupial: comparison of morphometric methods. Journal of Morphology, 280,

1900-1915. doi: http://doi.org/10.1002/jmor.20954

Martin, M. L., Travouillon, K. J., Fleming, P. A. & Warburton, N. M. (2020), Review of the methods used for calculating Physiological cross-sectional area (PCSA) for ecological questions. Journal of Morphology, 281, 778-789. doi: http://doi.org/10.1002/jmor.21139

Manuscripts in preparation

Martin, M. L., Warburton, N. M., Travouillon, K. J. & Fleming, P. A., The covariation of bone shape and muscle anatomy; a comparative study of Australian digging marsupials.

Manuscript in preparation.

Conference oral presentations

Lane, M. L., Warburton, N. M., Fleming, P. A. & Travouillon, K. J. (2016) Grow bigger, dig deeper? Ontogenetic change in forelimb muscle anatomy and architecture in a bandicoot, oral presentation, International Congress of Vertebrate Morphology, Washington

DC, America.

Lane, M. L., Warburton, N. M., Fleming, P. A. & Travouillon, K. J. (2017) Forelimb adaptation to digging in the quenda, oral presentation, International Mammal Congress,

Perth, Australia.

Martin, M. L., Warburton, N. M., Travouillon, K. J., Sherratt, E. & Fleming, P. A. (2018)

Forelimb bone shape in a digging mammal, Australian Mammal Society Conference,

Brisbane, Australia.

Martin, M. L., Warburton, N. M., Travouillon, K. J. & Fleming, P. A. (2019) Who is best built for digging? Functional forelimb musculature in Australian marsupials, Australian

Mammal Society Conference, Sydney, Australia.

Conference poster presentation

Martin, M. L., Warburton, N. M., Travouillon, K. J. & Fleming, P. A. (2019) A comparative functional analysis of forelimb morphology in Australian marsupials

(Marsupialia), Society of Vertebrate Palaeontology Annual Meeting, Brisbane, Australia.

Invited presentations

Lane, M. L. (2016) Why wildlife is important to the environment, oral presentation,

National Science Week 24 Hours of Biology, Perth, Australia.

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Table of Contents

Author’s declaration ...... ii Abstract ...... iii Statement of candidate contributions ...... v Acknowledgements...... ix Publications arising from this thesis ...... xi Table of Contents ...... xiii List of Figures ...... xvii List of Tables ...... xxii List of abbreviations ...... xxiv Chapter 1 General Introduction ...... 1 1.1 Introduction ...... 1 1.1.1 Digging in mammals ...... 1 1.1.2 Australian marsupial radiation ...... 4 1.1.3 Digging repertoire in marsupials ...... 7 1.1.4 Marsupials act as ecosystem engineers ...... 8 1.2 Research aims ...... 11 1.3 Outline of the thesis ...... 12 Chapter 2 Review of methods used for calculating Physiological cross-sectional area (PCSA) for ecological questions ...... 15 2.1 Preface ...... 15 2.2 Abstract ...... 15 2.3 Introduction ...... 16 2.4 Issues of tissue preservation: fresh or fixed specimen’s ...... 18 2.5 The three components used to calculate PCSA ...... 20 2.5.1 Muscle volume (derived from muscle mass) ...... 22 2.5.2 Fascicle length ...... 24 2.5.3 Pennation angle ...... 29 2.6 PCSA in models versus in vivo measures of force ...... 32 2.7 PCSA between species ...... 34 2.8 Conclusions and recommendations ...... 35 Chapter 3 Mechanical similarity across ontogeny of digging muscles in an Australian marsupial (Isoodon fusciventer) ...... 37 3.1 Preface ...... 37

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3.2 Abstract ...... 37 3.3 Introduction ...... 38 3.4 Materials and methods ...... 42 3.4.1 Specimens: ...... 42 3.4.2 Muscle dissection: ...... 44 3.4.3 Statistical analysis: ...... 45 3.5 Results ...... 46 3.5.1 Is there a significant sex difference in forelimb development? ...... 46 3.5.2 Do digging power stroke muscles show greater positive allometry compared with recovery stroke muscles?...... 50 3.6 Discussion ...... 56 3.6.1 Isometry and some muscles of positive allometry in muscle mass throughout the forelimb ...... 56 3.6.2 Fibre length primarily shows isometry with total body mass ...... 57 3.6.3 Strong positive allometry in PCSA for majority of forelimb muscles ...... 58 3.6.4 Positive allometry in Fmax for one third of muscles ...... 60 3.6.5 No sex differences in forelimb muscle mass and architecture ...... 61 3.6.6 Further considerations ...... 61 3.6.7 Limitations of this study ...... 62 3.7 Conclusions ...... 62 3.8 Acknowledgements ...... 63 Chapter 4 Covariation between forelimb musculature and bone shape in an Australian scratch- digging marsupial: comparison of morphometric methods ...... 65 4.1 Preface ...... 65 4.2 Abstract ...... 65 4.3 Introduction ...... 66 4.4 Materials and methods ...... 70 4.4.1 Morphometric analyses ...... 71 4.4.2 Allometry in bone shape ...... 76 4.4.3 Covariation between forelimb muscle anatomy and bone shape ...... 76 4.5 Results ...... 77 4.5.1 Allometry in bone shape ...... 77 4.5.2 Covariation between bone shape and forelimb muscle anatomy ...... 84 4.6 Discussion ...... 87 4.6.1 Sex differences in bone shape change ...... 87 4.6.2 Indices associated with body mass and muscle anatomy ...... 88

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4.6.3 Landmark coordinates associated with body mass and muscle anatomy ...... 89 4.6.4 Comparison of indices and landmark coordinates reveals the strengths and limitations of both methods ...... 91 4.7 Conclusion ...... 92 4.8 Acknowledgements ...... 93 Chapter 5 The covariation of bone shape and muscle anatomy; a comparative study of Australian digging marsupials ...... 95 5.1 Abstract ...... 95 5.2 Introduction ...... 96 5.3 Material and methods ...... 100 5.3.1 Specimens ...... 100 5.3.2 Muscle PCSA ...... 102 5.3.3 Bone shape ...... 103 5.3.4 Phylogenetic signal...... 106 5.3.5 Methods for assessing the effect of phylogeny and digging ability on forelimb shape ...... 107 5.3.6 Methods for assessing covariation between forelimb bone shape and muscle PCSA 107 5.4 Results ...... 108 5.4.1 Phylogenetic signal...... 108 5.4.2 Muscle PCSA ...... 109 5.4.3 Bone indices ...... 112 5.4.4 Landmark coordinates ...... 115 5.4.5 Covariation between bone shape and muscle PCSA ...... 116 5.5 Discussion ...... 124 5.5.1 Muscle PCSA is significantly different between the three digging categories ...... 124 5.5.2 Bone indices ...... 126 5.5.3 Landmark coordinates ...... 126 5.5.4 Covariation between muscle PCSA and bone shape significant after phylogenetic correction ...... 128 5.5.5 Ulnar shape and ulnar bone indices show minimal covariation with muscle PCSA ... 129 5.5.6 Conservation management ...... 130 5.5.7 Limitations of this study ...... 130 5.6 Conclusion ...... 131 5.7 Acknowledgments ...... 132 Chapter 6 Phylogeny and ecology drive the shape of the forelimb in monotremes and marsupials ...... 133 6.1 Abstract ...... 133

6.2 Introduction ...... 134 6.3 Materials and methods ...... 136 6.3.1 Specimens ...... 136 6.3.2 Quantifying bone shape ...... 138 6.3.3 Phylogenetic signal...... 141 6.4 Results ...... 141 6.4.1 Indices variation ...... 141 6.4.2 Landmark coordinates variation ...... 145 6.5 Discussion ...... 151 6.5.1 Bone indices are great predictors of primary behaviour ...... 151 6.5.2 Landmark coordinates ...... 153 6.5.3 Limitations of this study ...... 158 6.6 Conclusion ...... 158 6.7 Acknowledgements ...... 159 Chapter 7 General discussion ...... 160 7.1 Summary ...... 160 7.2 Ontogenetic differences in Quenda ...... 160 7.3 Quantifying the link between muscle architecture and bone shape ...... 163 7.4 Association between ecology and bone shape ...... 166 7.5 The place of literature reviews ...... 172 7.6 Concluding remarks ...... 173 References ...... 174 Appendix Supplementary Materials...... 197 A.1 Supplements to Chapter 3 ...... 197 A.2 Supplements to Chapter 4 ...... 199 A.3 Supplements to Chapter 5 ...... 204 A.4 Supplements to Chapter 6 ...... 205

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List of Figures

Figure 1.1: Visual representation of a simple lever system seen in the forelimb...... 3 Figure 1.2: Phylogenetic trees of the marsupials and monotremes: modified from Mitchell et al. (2014) and Phillips and Penny (2010)...... 6 Figure 2.1: Simplified representation depicting the difference between (a) parallel architecture with fewer fascicles; however, longer fascicles and (b) pennate architecture with more fascicles; however, shorter fascicles...... 22 Figure 2.2: Photographs of methods of measuring fascicle length and pennation angle. (a) Modified from Cheng and Scott (2000); measurement of internal fascicle lengths (dotted lines) of Macca mulatta triceps long head. (b) flexor carpi ulnaris of the Quenda (Isoodon fusciventer). Thick line representing the line of action, and small line is the orientation of the fascicle. Pennation angle is measured between the two intersecting lines...... 25 Figure 3.1: Musculature of the forelimb muscles of the Quenda (Isoodon fusciventer). Each colour represents a different functional muscle group. (A) Superficial medial view altered from (Warburton et al., 2013b) and (B) superficial lateral view. BiB: biceps brachii, Bra: brachialis, Del acr & Del spi combined for Delt: deltoideus, ECR: extensor carpi radialis, ECU: extensor carpi ulnaris, EDC: extensor digitorum communis, EDL: extensor digitorum lateralis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, FDP: flexor digitorum profundus, FDS: flexor digitorum superficialis, Inf: infraspinatus, LtD: latissimus dorsi, Pec: pectoralis, PrT: pronator teres, Sub: subscapularis, Sup: supraspinatus, TFA: tensor fascia antebrachii, TMj: teres major, TLn & TMd combined for TrB: triceps brachii...... 43 Figure 3.2: Average (±1SD) proportions of total forelimb muscle mass represented by each of the nine functional groups. Functional groups are separated into muscles associated with either the power stroke or recovery stroke in scratch-digging and ordered from proximal to distal muscle. There were sex differences in distribution of muscle mass (t-test), and therefore the data are shown organised by sex where * p<0.05, ** p<0.01, *** p<0.001. Functional group abbreviations are defined in Table 3.2. Black bars: male, grey bars: female. Functional groups are ordered proximal to distal...... 48 Figure 3.3: Relationship between total body mass muscle architectural properties plotted on a log scale. Total muscle architectural properties are calculated by summing the muscle property values for all the individual muscles. (A) Total forelimb mass (g), (B) total fibre length (cm), (C) total PCSA (cm-2) and (D) total isometric force (Fmax) (Ncm-2). All the four muscle architectural properties showed a strong positive relationship with total body mass. Black squares: males, Grey diamonds: females...... 49 Figure 3.4: The 95% confidence intervals for scaling slopes of forelimb log architectural properties on log total body mass in Quenda (Isoodon fusciventer). Blue squares indicate the regression

slopes for muscle mass (mm); pink circles indicate regression slopes for PCSA; and purple diamonds indicate regression slopes for fibre length (FL). The error bars show the upper and lower 95% confidence intervals, and dashed lines indicate the relevant value for isometry. Muscles are separated into muscles associated with the power stroke or

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recovery stroke of scratch-digging as well as ordered as most proximally placed muscles to most distally placed muscles. Majority of the muscle masses and PCSA slopes are scaling above isometry, while the FL are scaling closer to isometry...... 51 Figure 3.5: Functional space plot (fibre length vs PCSA) for muscles in the forelimb of Quenda, Isoodon fusciventer. Normalised PCSA is normalised to body mass (0.67) and normalised FL is normalised to body mass (0.33). Data points are means with no error bars shown. Quadrant definitions were based off the literature (Channon et al., 2009; Allen et al., 2010; Dick & Clemente, 2016; Böhmer et al., 2018). High force is defined as high PCSA and shorter fibre length to produce large forces over a small working range. High power is defined as long fibres and high PCSA, therefore, muscles capable of producing high forces over a large working range. High shortening velocity is defined as relatively longer fibres with a small PCSA with the muscles produce a small force but contract over a longer distance. Generalised muscles have relatively small force production and fibre lengths...... 55 Figure 4.1: Linear measurements (solid lines) and landmarks (red points) used in the analysis to quantify shape variation on the forelimb. Dashed lines represent curves of semi-sliding landmarks. (A) 15 homologous landmarks and 81 semi-sliding landmarks for the scapula, and scapular length (SL) and glenoid cavity diameter (GC) linear measurements. (B) 41 homologous landmarks and 33 semi-sliding landmarks for the humerus, and diameter of the epicondyles (DEH), deltoid length of humerus (DLH), transverse diameter of humerus (TDH), functional humeral length (HL), cranial caudal diameter of humerus (CCDH) linear measurements. Scale bar represents 100mm...... 73 Figure 4.2: Linear measurements (solid lines) and landmarks (red points) used in the analysis to quantify shape variation on the forelimb. Dashed lines represent curves of semi-sliding landmarks. (A) 19 homologous landmarks and 57 semi-sliding landmarks for the ulna, and transverse diameter of the ulna (TDU), olecranon length (OL), total ulna length (UL) linear measurements. (B) 14 homologous landmarks and 37 semi-sliding landmarks for the third metacarpal. Scale bar represents 100mm...... 74 Figure 4.3: Results of the morphometric covariation analysis for the scapula (a b c d e) using landmark data. Results of the multivariate regression which visualises the patterns between size and shape (allometry) (a) and the results of the two-block partial least square (2b-PLS) between the forelimb landmark data and the muscle mass (b c) and PCSA (d e) of the associated forelimb muscles. Residual values of the landmark data and muscle data were used to control for the size component. Scatter plots of the first PLS axis describing the shape covariation between scapula and the PCSA of the forelimb muscles (b d). Forelimb bone shapes associated with each minimum and maximum of covariation are illustrated on the axis. Muscle loadings associated with the forelimb bone shape covariation are represented in the histogram (c e). Muscles are ordered from proximal to distal and colour coded by functional group (Table 4.1). In this and further figures, there are varying numbers of individuals between the bones due to some bones being too badly damaged to be analysed...... 80 Figure 4.4: Results of the analysis for the humerus (a b c d e) using landmark coordinates. Format as per Figure 4.3...... 81 Figure 4.5: Results of the analysis for the ulna (a b c d e) using landmark coordinates. Format as per Figure 4.3...... 82

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Figure 4.6: Results of the analysis for the third metacarpal (a b c d e) using landmark coordinates. Format as per Figure 4.3...... 83 Figure 5.1: Quenda, Isoodon fusciventer and an example of a foraging pit. Quenda nose poke searching for subterranean hypogeous fungi, invertebrates, plant roots and tubules. Photographs by Trish Fleming and Simon Cherriman...... 98 Figure 5.2: Phylogeny of Australian marsupials used in the study, based on (Mitchell et al., 2014; Kealy & Beck, 2017; Travouillon & Phillips, 2018). The three symbols indicate the species digging category; circles represent burrowing, triangles foraging and squares are non- digging species, while the colours represent the species phylogeny; dark grey Diprotodontia, light grey Dasyuromorphia and white Peramelemorphia. Number of specimens analysed per species indicated as n...... 99 Figure 5.3: Methods and analyses summary. All data was collected from specimens that were donated to Murdoch University or part of the Western Australian Museum collection. . 101 Figure 5.4: Scatterplot (B) of the first two principal components (phylogenetically corrected- purple; non-digging, blue- foraging, red- burrowing), and the loadings of the muscle PCSA proportion on PC1 (C) and PC2 (A). Muscle colours represent muscle functional groups (plots A & C); orange- scapular stabilisers, dark red- humeral retractors, medium red- elbow extensors, pink- carpal/digital flexors, dark blue- humeral protractors, medium blue- elbow flexors, light blue- carpal/digital extensors, light green- pronators. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, Rho; rhomboideus, SeV; serratus ventralis, LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, TrB: triceps brachii, Anc: anconeus, TFA; tensor fascia antebrachii, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, Sup: supraspinatus, BiB: biceps brachii, Bra: brachialis, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum longus, ECU: extensor carpi ulnaris, AbdL: abductor digiti longus, PrT: pronator teres, PrQ; pronator quadratus, Spr: supinator...... 111 Figure 5.5: The average, minimum and maximum values for each bone indices separated by species and separated by digging category (not phylogenetically corrected data). Using Kruskal- Wallis and Dunn’s pairwise comparison, each index was tested for a significant difference between the 11 species, and between the three digging categories. Letters and numbers represent the pairwise comparison results. Results show significant differences between species for all seven indices (P<0.001) and significant differences between the three digging categories (P<0.02)...... 115 Figure 5.6: Mean shapes of scapula, humerus, ulna and the third metacarpal for the three digging categories; burrowing, foraging and non-digging...... 116 Figure 5.7: Results of phylogenetic 2b-PLS analysis of four of the bone indices that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction: shoulder moment index (A-B), humerus transverse robustness index (C-D), humerus cranial-caudal robustness index (E-F) and epicondyle index (G-H). The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loadings associated with the PCSA axis values. Muscle abbreviations: LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps xix

brachii, Anc: anconeus, BiB: biceps brachii, Bra: brachialis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum lateralis, ECU: extensor carpi ulnaris, PrT: pronator teres...... 120 Figure 5.8: Results of the phylogenetic 2b-PLS analysis of the scapula landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, Rho: rhomboideus, SeV: serratus ventralis, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps brachii, BiB: biceps brachii...... 122 Figure 5.9: Results of the phylogenetic 2b-PLS analysis of the humerus landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps brachii, Anc: anconeus, BiB: biceps brachii, Bra: brachialis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum longus, ECU: extensor carpi ulnaris, AbdL: abductor digiti longus, PrT: pronator teres, Spr: supinator. 123 Figure 5.10: Results of the phylogenetic 2b-PLS analysis of the third metacarpal landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y- axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: FCR: flexor carpi radialis, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis...... 124 Figure 6.1: Box and whisker plots of each of the seven indices grouped by behavioural category. Results of one-way ANOVA and pair-wise comparison. Aquatic species were not included in the analysis due to only one species in the behavioural category. Behaviour groups with the same letter indicate they are significantly different from each other by the pair-wise comparison. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial, and blue: aquatic...... 144 Figure 6.2: Results of the PCAs performed on the morphometric data of the scapula and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), two terrestrial species (both diprotodontian) and no data for arboreal and aquatic categories...... 147

Figure 6.3: Results of the PCAs performed on the morphometric data of the humerus and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), two terrestrial species (both diprotodontian) and no data for arboreal and aquatic categories...... 148 Figure 6.4: Results of the PCAs performed on the morphometric data of the ulna and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), two terrestrial species (both diprotodontian) and no data for arboreal and aquatic categories...... 149 Figure 6.5: Results of the PCAs performed on the morphometric data of the third metacarpal and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), one terrestrial species (diprotodontian) and no data for arboreal and aquatic categories...... 150 Figure 7.1: Mean shapes of scapula, humerus, ulna and third metacarpal summarised for the four behaviour categories assessed in Chapter 6...... 167

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List of Tables

Table 2.1: Summary of alternative methods that can be used to calculate PCSA...... 21 Table 3.1: Summary of the four modes of digging in mammals. Fossorial animals (those that are morphologically or physiologically adapted for digging, yet are mostly active above ground) are categorised by their principal mode of excavating soil, of which, scratch- digging is the most common employed mode of digging in mammals. Subterranean animals (species that are highly specialised for living almost wholly underground) show further specialisations for scratch-digging and /or resort to other digging modes (Lessa & Thaeler, 1989)...... 39

Table 3.2: Average values of muscle mass (mm), fibre length (FL), pennation angle, physiological cross-sectional area (PCSA) and isometric force (Fmax) of each individual muscle calculated from 29 Quenda...... 47 Table 3.3: Regression analysis results for the log-transformed muscle architecture properties vs. log- transformed total body mass of the Quenda (Isoodon fusciventer) forelimb muscles. The lower and upper 95% confidence intervals demonstrate the scaling relationship- grey- shaded cells highlight where the confidence intervals range did not include the line representing isometry. Results with significant p-values are highlighted in bold which indicated that the muscle scaling relationship is significantly different to isometry. P-values with a * are p-values that are no longer significant using the false discovery rate method (FDR). Muscles are separated into muscles associated with the power stroke or the recovery stroke of scratch-digging, and within these two groups muscles are ordered as proximally to distally placed muscles. Functional group abbreviations- SS Scapula stabilisation; HR Humeral retraction; EE Elbow extension; CDF Carpal/digital flexion; PRO Pronators; HP Humeral protraction; EF Elbow flexion; CDE Carpal/digital extension; SUPI Supinators...... 52 Table 4.1: List of muscles, abbreviations and functional groups used for the study. Muscles ordered proximal to distal and colours represent muscle groups and are represented in Figures 4.3- 4.6...... 71 Table 4.2: Description of the seven indices of digging ability used in this study. Descriptions of how each related to the functional morphology of the forelimb...... 75 Table 4.3: Examining allometry: MANCOVA's of the three significant indices and the four forelimb bones (landmark coordinates) shape by size (Y ~ total body mass) and sex (Y ~ total body mass*sex)...... 78 Table 4.4: Results of the two-block partial least square (2b-PLS) analyses using (a) indices and (b) landmark coordinates...... 85 Table 5.1: Results of the initial statistical analysis on the “raw data”: 29 muscle PCSA (proportions of total forelimb PCSA), seven bone indices, and the Procrustes landmark coordinates for the scapula, humerus, ulna and third metacarpal. Univariate phylogenetic signal results of muscle PCSA and bone indices, and multivariate phylogenetic signal* of the landmark coordinates. Also, results of the one-way ANOVA to test if muscle PCSA, bone indices and

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shape measured using landmark coordinates are significantly different between the three digging categories...... 109 Table 5.2: Results of the principal component, phylogenetic signal and ANCOVA/phylogenetic ANCOVA to assess the effect of digging category on muscle PCSA (a), bone indices (b) and landmark coordinates (c). The first column explained variance for the first two principal components (PCs) from the PCA. Second column, results of the K-statistic and associated P-value of univariate on the first two PCs. Third and fourth columns, results of ANCOVAs (F

and P-values) and phylogenetic ANCOVAs (F and Pphy-value) for the first two PCs to test the effect of digging category on muscle PCSA and bone shape...... 113 Table 5.3: Results of the covariation between forelimb bone shape (measured using of each bone measured using both indices and Procrustes landmark coordinates associated muscle PCSA proportions. The analysis was repeated with phylogenetic correction...... 118 Table 6.1: Phylogenetic relationship between the 56 species used in the study and the classification of their primary behaviour. All species were measured to calculate the bone indices, and a subset of the bones were micro-CT scanned and analysed using landmark coordinates. Numbers in columns for Indices and Landmarks reflect sample sizes...... 137 Table 6.2: Lists of the abbreviations, calculations and descriptions of the seven indices and how they relate to the functional morphology of the forelimb...... 139 Table 6.3: Summary of the statistical analyses on the seven bone indices. Phylogenetic signal assessment on individual indices, and results of the one-way fixed-factor ANOVA and phylogenetic ANOVA carried out on each variable to determine whether significant differences existed between the mean values of the different locomotor types. Phylogenetic PCA with PC1 accounting for 65.3% of the total variance, and PC2 accounting for 19.6% total variance...... 142 Table 6.4: Details of the results of the principal components analysis, phylogenetic signal and ANCOVA/phylogenetic ANCOVA to assess the effect of primary behaviour on landmark coordinates. The first column explained variance for the first two principal components (PCs) from the PCA. Second column, results of the K-statistic and associated P-value of multivariate (denoted by * on Procrustes coordinates) and univariate on the first four PCs. Third and fourth columns, results of ANCOVAs (F and P-values) and phylogenetic ANCOVAs

(F and Pphy-value) for the first four PCs to test the effect of primary behavioural category on bone shape. All significant values indicated in bold...... 145

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List of abbreviations

2b-PLS Two block partial least square HTRI Humerus transverse robustness index AbdL Abductor digiti I longus IFA Index of fossorial ability Anc Anconeus Inf Infraspinatus ANCOVA Analysis of covariance LtD Latissimus dorsi ANOVA Analysis of variance MANCOVA Multivariate analysis of covariance BI Brachial index mb Total body mass BiB Biceps brachii mm Muscle mass Bra Brachialis MRI Magnetic resonance imaging CDE Carpal digital extension OmT Omotransversarius CDF Carpal digital flexion PaL Palmaris longus CT Computer tomography PBS Phosphate buffered saline Delt Deltoideus PCA Principal component analysis ECR Extensor carpi radialis PCSA Physiological cross-sectional area ECU Extensor carpi ulnaris Pec Pectoralis group EDC Extensor digitorum communis PRO Pronator EDL Extensor digitorum lateralis PrQ Pronator quadratus EE Elbow extension PrT Pronator teres EF Elbow flexion Rho Rhomboideus EI Epicondyle index SeV Serratus ventralis FCR Flexor carpi radialis SMI Shoulder moment index FCU Flexor carpi ulnaris Spr Supinator FDP Flexor digitorum profundus SS Scapula stabilisation FDS Flexor digitorum superficialis Sub Subscapularis FL Fibre length Sup Supraspinatus Fmax Maximum isometric force SUPI Supinators Formalin Formaldehyde TFA Tensor fascia antebrachii GM Geometric morphometrics TMj Teres major Humerus cranial-caudal robustness HCRI Tra Trapezius index HP Humeral protraction TrB Triceps brachii HR Humeral retraction URI Ulna robustness index

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Chapter 1 General Introduction

1.1 Introduction

1.1.1 Digging in mammals

Digging is a behaviour that is, important to a large diversity of mammals (Hildebrand,

1985; Moore et al., 2013), from the smallest examples such as the American shrew mole

(Neuotrichus gibbsii: 10–12 g), up to aardvarks (Orycteropus spp.: 40–100 kg) (Vizcaíno et al., 2001). Mammals specialised for digging use their digging abilities to raise young, hunt, escape predation and source food, although many of them to do not live underneath the surface (Shimer, 1903). Digging is classified into three categories that explain a suite of morphological and physiological adaptations; subterranean, fossorial and semi-fossorial.

Subterranean species such as marsupial moles (Notoryctes spp.), African mole-rats

(Batherygidae) and golden moles (Chrysochloridae) are highly specialised for living entirely underground. Fossorial species are morphologically adapted to digging and spend time underground; but, are primarily active above ground for foraging such as wombats

(Vombatidae), or badgers (Mustelidae). Semi-fossorial species live entirely above ground, although are morphologically adapted for digging, primarily for foraging (e.g., bandicoots:

Peramelemorphia, potoroos: Potoroidae, and echidnas: Tachyglossidae). These various groups show different morphological adaptations that reflect their habitual behaviours.

Digging mammals may also be categorised by their principal mode of excavating soil as described in Hildebrand (1985):

1) Scratch-digging is the most common mode of digging, in which animals employ

clawed forelimbs in a running motion to loosen the soil, using an alternating

limb cycle of retraction and protraction in a parasagittal plane. The hind feet

may then be used to displace the accumulated soil further (Moore et al.,

2013). Scratch-digging is used by wombats (Vombatidae), bandicoots and

bilbies (Peramelemorphia), potoroos (Potoroidae), badgers (Mustelidae), and

the majority of the other digging mammals (Martin et al., 2019b).

2) Chisel-tooth digging is a mode primarily employed by rodents that use their

elongated incisor teeth to gnaw at the soil during excavation of a tunnel

(Hildebrand, 1985; McIntosh & Cox, 2016b, 2016a).

3) Humeral rotation diggers, such as true moles (Talpidae) and echidnas

(Tachyglossidae) dig in a sprawling or abducted forelimb posture (Rose et al.,

2013). During digging, the abducted humerus rotates around its long axis as

the forelimb extends to sweep the soil behind (Hildebrand, 1985; Hopkins &

Davis, 2009).

4) Head-lift is employed by golden moles (Chrysochloridae), mole voles

(Cricetidae) and some mole-rats (Bathyergidae) in which they use their snout

and forelimbs to wedge open a tunnel in the substrate (Gasc et al., 1986; Stein,

2000).

Digging species have musculoskeletal adaptations to the forelimbs and head that are specific to their mode of digging and their environment. For scratch-diggers, a high out- force on the soil is achieved by enlarging the muscles of the forelimb specialised for the

power-stroke (thereby increasing the in-force). The out-force also increases as the ratio between the in-lever and the out-lever increases (Vizcaíno et al., 1999; Rose et al., 2014;

Olson et al., 2016). Migration of the attachment site of the main mover muscles away from their joint (fulcrum in a simple lever model) also acts to increase the in-lever length and/or decrease the out-lever length (Figure 1.1). Generally, the forelimbs of diggers are characterised as having massive triceps brachii muscles (in-force) that insert onto a relatively long olecranon process of the ulna (in-lever) (Lessa & Stein, 1992; Lagaria &

Youlatos, 2006; Moore et al., 2013), and robust, relatively short humeri and carpals/metacarpals (out-lever). Robust bones are capable of resisting high bending forces and thus the high forces imposed on the limb from the resistance of the soil (Rose et al.,

2014).

Figure 1.1: Visual representation of a simple lever system seen in the forelimb.

1.1.2 Australian marsupial radiation

Australian marsupials (Marsupialia) fill a diverse range of ecological niches and habitats. Taxonomically they are categorised into four extant orders (Figure 1.2) (Aplin &

Archer, 1987; Mitchell et al., 2014), and one extinct order Yalkaparidontia (Black et al.,

2012). Dasyuromorphia comprises of insectivorous and carnivorous marsupials such as quolls (Dasyurus), dunnarts (Sminthopsis), the Tasmanian devil (Sarcophillus harrisii), the numbat (Myrmecobius fasciatus), and the recently extinct Thylacine (Thylacinus cynocephalus – extinct in 1936). The body masses of Dasyuromorphia range from the smallest living marsupial, the long-tailed planigale (Planigale ingrami) of 4 g, up to the largest living carnivorous marsupials the Tasmanian devil at more than 8 kg; Thylacine reached up to 35 kg (Van Dyck & Strahan, 2008; Kealy & Beck, 2017). Bandicoots and bilbies

(Peramelemorphia) are primarily omnivorous marsupials that are endemic to Australia and

Papua New Guinea (Warburton & Travouillon, 2016) together with two species of recently extinct pig-footed bandicoots (Chaeropodidae- extinct in ~1950s: Travouillon et al., 2019).

Australian bandicoots’ range in body size from the Shark bay bandicoot (130-170 g:

Perameles bougainville) to the northern brown bandicoot (males up to 3100 g: Isoodon macrourus) (Warburton & Travouillon, 2016). Diprotodontia is the largest and most ecologically diverse marsupial order that includes the principally herbivorous kangaroos and wallabies (Macropodidae), bettongs and potoroos (Potoroidae), possums

(Phalangeriformes), koala (Phascolarctidae) and wombats (Vombatidae) (Meredith et al.,

2009). Diprotodontia was named by Owen in 1899 corresponding to a dental arrangement that the lower medial incisors are procumbent and enlarged that is, diagnostic of the order

(Kirsch, 1977). Finally, Notoryctemorphia is the least speciose but most distinct Australian

marsupial order that includes two extant species of highly specialised, subterranean marsupial moles (Notoryctes caurinus and N. typhlops) and one fossil species (Naraboryctes philcreaseri) (Warburton, 2006; Archer et al., 2011).

The oldest Australian marsupial (Djarthia murgonensis) is dated to the early Eocene, approximately 55 million years ago, from Tingamarra Local Fauna in southeastern

Queensland (Godthelp et al., 1999; Beck et al., 2008). Early examples of Dasyuromorphia and Peramelemorphia were probably also present around the early Eocene, with the

Diprotodontia not appearing until the late Oligocene. Notoryctemorphia and

Yalkaparidontia have been traced back to the early Miocene (Black et al., 2012). The

Australian marsupials have been in relative isolation for 35 million years since the Australian continent separated from Antarctica, and during that time have undergone an adaptive radiation to occupy a wide range of ecological niches over a large range of habitats across

Australia (Archer, 1984; Kemp, 2005; Black et al., 2012). The relationships between the four orders of marsupials have historically been a matter of debate (Archer, 1976b, 1976a;

Kirsch, 1977; Szalay, 1982). However, a range of molecular studies (Amrine-Madsen et al.,

2003; Nilsson et al., 2004; Phillips et al., 2006; Springer et al., 2009) have confirmed that

Dasyuromorphia and Peramelemorphia are sister groups.

Figure 1.2: Phylogenetic trees of the marsupials and monotremes: modified from Mitchell et al. (2014) and Phillips and Penny (2010).

Marsupials have the unusual reproductive strategy of a short gestation followed by a relatively long period of lactation; the majority of their development therefore takes place post-partum (Tyndale-Biscoe & Renfree, 1987). At the time of parturition, the neonate must crawl unaided from the urogenital opening to the pouch and attach itself to a nipple (Sears

& Janis, 2004). To accomplish the crawl to the pouch (a distance approximately ten times the neonate's body length), many marsupials exhibit precocious development of the forelimbs (Gemmell et al., 2002; Sears & Janis, 2004). This is true of all marsupials except for bandicoots (Sears & Janis, 2004; Cooper & Steppan, 2010; Garland et al., 2017). During the crawl, the disproportionally large forelimbs alternate movements, dragging the neonate in a

“swimming” wiggle with the hindlimbs hanging passively behind. The propulsive force of this crawl is generated from the muscles in the back and shoulders, which are attached to the shoulder arch. The shoulder arch is an extensive cartilaginous shoulder girdle that anchors the limb to the skeleton and provides skeletal support (Sánchez-Villagra & Maier, 2003). The

contrasting development of marsupial forelimb and hindlimbs at birth is extremely different to that of placentals, and therefore most likely a derived trait atypical for vertebrates

(Weisbecker et al., 2008; Weisbecker, 2011). This precocial development of the forelimbs has been suggested to limit forelimb shape diversity and locomotor adaptations in comparison to placentals (i.e., lack of species adapted to flight with the development of wings, or adapted to swimming with the development of fins). This is thought to be due to the functional requirements as a neonate imposing developmental constraints on marsupial forelimb evolution (Sánchez-Villagra & Maier, 2003; Sears & Janis, 2004; Weisbecker &

Warton, 2006; Weisbecker et al., 2008; Cooper & Steppan, 2010).

In bandicoots (Permelidae), however, a backwards-facing pouch means that the pathway for newborns is relatively short (approximately 1cm in Isoodon macrourus).

Newborn bandicoots do not have a defined crawling motion or well-developed forelimb, but rather, they utilise a ‘snake-like wiggle’ with minimal forelimb movement (Gemmell et al.,

2002; Garland et al., 2017). This suggests that bandicoots may have less developmental constraints on their forelimbs that other marsupials (Sears & Janis, 2004; Cooper & Steppan,

2010; Bennett & Goswami, 2011), which may have resulted in the potential for more divergent forelimb anatomy in this group (Garland et al., 2017).

1.1.3 Digging repertoire in marsupials

Digging marsupials generally employ a scratch-digging mode of digging, although there is variation in how this mode of digging is used (i.e., foraging, burrowing, tunnelling) and therefore differences in musculoskeletal adaptations are seen between species

(Warburton, 2006; Rose et al., 2014). The most specialised and only subterranean (truly

fossorial) marsupials are the marsupial moles (Notoryctes caurinus Thomas 1920 and N. typhlops Stirling 1891). These species are extremely adapted to their fossorial lifestyle, having no eyes or external ears, and with a highly modified body plan and limb structure

(Warburton, 2006). Of the other scratch-digging marsupials, only a few dig burrows for shelter; the burrowing bettong (also known in Western Australia as the boodie; Bettongia lesueur), the greater bilby (Macrotis lagotis) and the three species of wombats

(Vombatidae) (Read et al., 2008; Fleming et al., 2014); few studies have investigated the adaptations of these animals (Saber, 2013; Warburton et al., 2013b). The remaining scratch- digging species that do not burrow, such as bandicoot, numbats and potoroos, all scratch the topsoil to source shallow sub-surface food items such as tubules, fungi bodies, termites, invertebrates, roots. Little is known about the forelimb anatomy of these animals of varying digging abilities, therefore, additions to the literature will assist in further understanding the adaptations and differences that occur within Australian marsupials.

1.1.4 Marsupials act as ecosystem engineers

Ecosystem engineers are species that have a strong effect on ecological communities by changing the environment as a result of their normal behaviour, which in turn modifies or maintain the habitat for other species (Davies et al., 2019). Digging animals can do this in several ways; by (1) increasing soil turnover, (2) capturing organic matter in the soil, (3) assisting with water infiltration and (4) dispersing fungi and seeds to assist with recruitment in plant communities (Fleming et al., 2014). Digging mammals act as bioturbators by breaking up and mixing the soil that in turn improves the soil health by integrating organic matter into the mix. Burrowing marsupials can move a substantial amount of soil per kilogram body mass. For example, it has been calculated that bilbies and boodies move up 8

to 30 tonnes per individual each year (Newell, 2008), while an individual 40 kg northern hairy-nosed wombat may move up to 560 tonnes a year (Löffler & Margules, 1980).

Foraging species such as bandicoots and woylies (with average body mass between 1.2-1.6 kg) can move between two to three tonnes a year (Mallick et al., 1997; Garkaklis et al.,

2004; Valentine et al., 2013). Their diggings assist to capture and incorporate organic matter into the soil and to help alleviate soil compaction (Eldridge & Mensinga, 2007), as well as improve the water penetration and therefore reducing water run-off and erosion. The resulting increase of soil nutrients then assists in fungal and seed recruitment (Murphy et al., 2005), while the act of digging assists in dispersion (Garkaklis et al., 2004; Newell, 2008;

Eldridge & James, 2009; Fleming et al., 2014). Non-burrowing animals have also been documented to benefit through using burrows that have been constructed by fossorial mammals (Kinlaw, 1999; Read et al., 2008; Hofstede & Dziminski, 2017). For example, in

Australia, the burrows of bilbies, bettongs, wombats and rabbits are used as a refuge by small rodent species (particularly Pseudomys spp. Read et al., 2008), brush-tailed mulgara

(Dasycercus blythi: Hofstede & Dziminski, 2017), short-beaked echidna (Tachyglossus aculeatus: Wilkinson et al., 1998), and reptiles including carpet pythons (Morelia spilota metcalfei: Heard et al., 2004), pygmy blue-tongue lizards (Tiliqua adelaidensis: Milne et al.,

2003), sand goannas (Varanus gouldii: Hofstede & Dziminski, 2017; Dawson et al., 2019) and many desert lizards. Invertebrates such as ants have also been recorded using burrows as areas of foraging (Read et al., 2008). The disappearance of these digging mammals would have large consequences on a range of plants and animals, and thus the overall health of the Australian ecosystem.

The mechanisms leading to the Late Quaternary megafaunal extinctions (LQE) in

Australia are highly contested between scientists as the largest mass extinctions coincided with human colonisation. Models of these events fall into three categories; (1) climatic changes restricted habitats for megafauna species (Wroe et al., 2013), (2) the spread of humans from Africa negatively affected the megafauna by exploiting species naivety and/or modifying the animals’ ecosystems (Flannery, 1990; Saltré et al., 2016; Andermann et al.,

2020), and (3) a combination of human exploitation and climate-driven environmental changes (Koch & Barnosky, 2006; Wroe & Field, 2006; Saltré et al., 2019). The LQE was devastating for larger body sized animals, with the entire loss of all animals over 100kg, almost total extinction of animals above 32kg, but had less of an impact on smaller animals that were less than 10kg (Koch & Barnosky, 2006). Regardless of the cause of the LQE, humans have impacted the natural world, and it is predicted that all areas of the world will enter a second wave of extinctions (Andermann et al., 2020).

Over the last 200 years, there has been further decline in the number of Australian digging mammals due to habitat loss, introduced predators and competitors, altered fire regimes and disease (Abbott, 2008; Fleming et al., 2014). Australia has had the highest record of extinction of mammal species than any other area of the world (McKenzie et al.,

2007). Species in the ‘critical weight range’ (CWR; Burbidge & McKenzie, 1989) with body masses between 35g and 5kg, are most at risk, especially from predation by the introduced fox (Vulpes vulpes) and cats (Felis catus). Medium-sized mammals are also at risk due to altered fire regimes, habitat destruction and fragmentation, and competition for resources from introduced species (McKenzie et al., 2007; Abbott, 2008). Unfortunately, almost all of the Australian digging marsupials fall into this size category, and the majority have faced a

significant decline in abundance and distribution range (Fleming et al., 2014). In the absence of ecosystem engineers, ecosystem function has been shown and/or inferred to decline

(Eldridge & James, 2009; Fleming et al., 2014). Although introduced species may contribute towards some of these environmental processes (i.e., European rabbit Oryctolagus cuniculus or feral pigs Sus scrofa), their actions are unlikely to match those of species that are native and have a long evolutionary relationship with the land (James et al., 2011). Understanding the critical roles that digging marsupials play in our ecosystem is vital so that appropriate conservation and management strategies can be implemented to help protect Australian ecosystems.

1.2 Research aims

The general aim of my research was to investigate the varied adaptations of the skeletal morphology and muscle architecture of the marsupial forelimb for digging using quantitative methods. I examined the relationships between forelimb bone and muscle morphology in a more quantitative method than previously attempted, using multiple lineages of digging marsupials as natural experiments of the adaptation to digging ecologies through

A. intra-specific analyses of a) the ontogenetic patterns of development of muscle

architecture and b) the quantitative relationship between muscle architecture and

bone anatomy in Quenda Isoodon fusciventer.

B. examining inter-specific variation in the functional relationships between bone

morphology and muscle architecture across a range of Australian marsupials

exhibiting different levels of digging behaviour.

Specifically, I addressed the following research questions:

1. Do the muscles that are main movers during the power stroke of scratch-digging

show a steeper allometric growth compared to muscles associated with the

recovery stroke? (Chapter 3)

2. Does the shape of the long bones and associated muscle anatomy covary?

(Chapter 4)

3. Can quantitative measures of bone shape predict forelimb muscle morphology

(measured by PCSA)? (Chapter 5)

4. Does morphological variation in the forelimb reflects the three digging categories

(burrowing, foraging, and non-digging species)? (Chapter 5)

5. Does lifestyle influence the shape of the forelimb? (Chapter 6)

1.3 Outline of the thesis

Chapter 2 of this thesis is a standalone literature review of the methods used in the literature to calculate physiological cross-sectional area (PCSA). The review summarises previous literature on methods of collecting muscle architecture data to calculate PCSA and offers suggestions on how to best perform these measurements, and how to quantify the muscle architectural properties. The paper is a published general set of guidelines for researchers looking at measuring PCSA. This chapter has been published in the Journal of

Morphology (see Martin et al., 2020).

Chapter 3 is the first data chapter presented in this thesis. This study investigates the ontogeny of forelimb muscle development. Intraspecific variation, including ontogenetic

trends, has often been overlooked when considering a functional adaptation of musculoskeletal systems for digging in the literature. Here, I compare the development of

“power stroke muscles” in comparison to “recovery stroke muscles” to understand the ontogenetic development of the Quenda forelimb. This chapter has been published in the

Journal of Morphology (see Martin et al., 2019b).

Chapter 4 further investigates intraspecific variation with body size by examining the covariation between the forelimb musculature and the forelimb bone shape within I. fusciventer. This chapter explores two methods of quantifying bone shape and discusses which methods are most appropriate to muscle force production in a single species. This chapter has been published in the Journal of Morphology (see Martin et al., 2019a).

Chapter 5 tests whether forelimb long bone shape is influenced by digging ability

(burrowing, foraging, non-digging) in 11 species across different phylogenetic lineages of

Australian marsupials, and secondly, to assess the capacity for forelimb long bone shape to predict forelimb muscle PCSA. This chapter quantifies bone shape using both two- dimensional bone indices and three-dimensional landmark coordinates. At the time of submission, this chapter has been written to be submitted to the Biological Journal of the

Linnaean Society for publication.

Chapter 6 presents a large comparative study of forelimb bone anatomy across the different Australian monotreme and marsupial phylogenetic lineages, using both two- dimensional bone indices and three-dimensional landmark coordinates to quantify bone shape. Based on the understanding developed in the previous chapters regarding bone shape influenced by digging ability in 11 species, this chapter tests if lifestyle influences the

forelimb bone shape, and if so, what morphological adaptations are related to these lifestyles in 56 Australian marsupials and monotremes.

Chapter 7 is a general discussion of the findings of the four experimental chapters and considers findings in the context of the thesis research questions. This chapter also discusses the limitations of the thesis and the benefits of this research for further exploration.

Chapter 2 Review of methods used for calculating

Physiological cross-sectional area (PCSA) for ecological

questions

2.1 Preface

This chapter has been published in Journal of Morphology:

2.2 Abstract

This review examines literature that used physiological cross-sectional area (PCSA) as a representative measure of an individual muscle’s maximal isometric force production.

PCSA is used to understand the muscle architecture and how a trade-off between muscle force and muscle contractile velocity reflect adaptations of the musculoskeletal system as a reflection of functional demands. Over the decades, methods have been developed to measure muscle volume, fascicle lengths, and pennation angle to calculate PCSA. The advantages and limitations of these methods (especially the inclusion/elimination of pennation angle) are discussed frequently; however, these method descriptions are scattered throughout the literature. Here we reviewed and summarised the different approaches to collecting and recording muscle architectural properties to subsequently calculate PCSA. By critically discussing the advantages and limitations of each methodology,

we aim to provide readers with an overview of repeatable methods to assess muscle architecture. This review may serve as a guide to facilitate readers searching for the appropriate techniques to calculate PCSA and measure muscle architecture to be applied in ecomorphology research.

2.3 Introduction

Movement powered by muscles is one of the characteristic features of animals. In vertebrates, the action of skeletal muscles on the (principally) bony endoskeleton accomplishes movement of body segments and the body as a whole, as an animal interacts with the environment. As would be expected, the muscle properties (physiology and anatomy) throughout an animal’s body vary according to the functional demands of the animal’s behaviours (Bergmann & Hare-Drubka, 2015). Together, the muscle properties with the arrangement of the skeleton will then determine the nature of locomotion and other movements such as food manipulation and processing, and thus will reflect animal ecology

(Higham et al., 2011).

Muscle function and performance are reflected in muscle anatomy. Striated muscle consists of long and slender muscle fibres, which are bundled together into fascicles and subsequently muscle bellies (Bergmann & Hare-Drubka, 2015). Muscle contractions occur when there is an activation of the tension-generating sites within the muscle fibre.

Repeating units of interdigitating thick myosin and thin actin protein filaments, called sarcomeres, are the site of muscle contraction. Shortening of each sarcomere occurs via the formation of cross-bridges between myosin heads and actin molecules. The cycle of cross- bridge formation, contraction, detachment, and reattachment is powered by the energy

release of ATP (Hildebrand et al., 1985; McNeill, 2003). A muscle’s contractile property is determined by the number of fascicles, the fascicle length, the angle of the fascicles relative to the axis of force production (pennation angle), and the size and types of muscle fibres.

Longer fascicles have more sarcomeres in a series, which results in a greater contractile velocity, and thus a greater potential for speed and movement (Sacks & Roy, 1982;

Hildebrand et al., 1985; Lieber & Fridén, 2000). Force production capacity is a function of how many sarcomeres are within a muscle (i.e., number of fibres within a muscle), which will determine the number of cross-bridges that are formed and, in-turn, increases the force that the muscles can exert (Wickiewicz et al., 1983; Gans & de Vree, 1987). The cross- sectional area of the muscle, therefore, reflects the capacity for force production (Gans & de

Vree, 1987). Physiological cross-sectional areas (hereafter PCSA) is a representative measure of ‘muscle force’ while fascicle length reflects the ‘length excursion’ of the muscle; both measures are used to determine how much work (force x distance) can be done by an individual muscle and how much power (work/time) can be produced (Payne et al., 2005;

Allen et al., 2010; Allen et al., 2014). For a given muscle volume, both muscle force and working range cannot be maximised at the same time (due to force-velocity relationship); therefore specialisation in accordance with functional demands will occur (Rosin &

Nyakatura, 2017).

Physiological cross-sectional area is applied extensively in ecological research. As PCSA is proportional to maximum isometric force production, it is used to compare muscle force production between species, as well as to investigate the trade-off between force generation (PCSA) and capacity for shortening length changes. Various aspects of animal biology, including bite force (Becerra et al., 2014), running (Michilsens et al., 2009; Leischner

et al., 2018), digging (Lagaria & Youlatos, 2006; Moore et al., 2013; Martin et al., 2019b), diet (Taylor & Vinyard, 2004), and flight (Maniakas & Youlatos, 2012; Yang et al., 2015), have been investigated by applying PCSA as a quantitative estimate of muscle force. In the discipline of anthropology, comparative PCSA has been applied to questions of hominoid evolution, to assess bite force in relation to diet (Strait et al., 2009; Strait et al., 2010), and hindlimb adaptation in the evolution of bipedal locomotion (Thorpe et al., 1999; Payne et al., 2006). The calculation of PCSA appears incongruous with the interpretation of fossils; however, modelling of muscle fibre arrangement and volume has been facilitated through comparative approaches between living and extinct species (Perry et al., 2015; Perry, 2018).

This review explores and summarises different methods used to calculate PCSA to assist researchers to make a logical and informed decision on applications of PCSA for their research.

2.4 Issues of tissue preservation: fresh or fixed specimen’s

Before determining methods with which to measure muscle architecture and calculate

PCSA, the preservation of specimen tissue needs to be considered. Preservation of specimens influences the decisions of later methods, particularly muscle mass. The decision to fix specimens in formaldehyde (hereafter formalin) will depend on where the specimen was sourced and its condition.

Formalin is a reliable chemical fixative; however, it is known to cause morphological changes in the tissue (Fox et al., 1985) that are relevant to the calculation of PCSA. Mass is known to decrease (Cheng & Scott, 2000) and fascicles shrink (shorten) as part of the fixation process. Some studies have shown the decrease in muscle mass to be insignificant

(Cutts, 1988), while other studies found a 7% decrease in mass (Schremmer, 1967; Cheng &

Scott, 2000) up to a 14% decrease (Kikuchi & Kuraoka, 2014). Correction factors are available to counteract the shrinkage and produce a PCSA score from fixed material. Kikuchi and Kuraoka (2014) suggest that muscle mass should be adjusted to compensate for a decrease of 14%, while fascicle length should be adjusted for a decrease in length of 9-13% after formalin fixation. An alternative to formalin and a fixative often used in histology or in preparation for digital imaging is Bouin’s fluid as it minimises shrinkage of the specimens

(Metscher, 2009; Schenk et al., 2013; Siebert et al., 2015; Sombke et al., 2015).

The benefit of fixing is to preserve specimens that may be too damaged to dissect fresh (e.g., roadkill in Martin et al., 2019b). Often specimens have been used for other studies and may already be embalmed in formalin; therefore, for consistency, all specimens used within the study should be preserved in the same manner to ensure that potential decrease in mass and length will be similar across all muscles. The impact of fixation caused shrinkage needs to be considered when functional interpretations are made based on PCSA values in comparison to previously published results.

Another factor to be aware of when using cadavers is the influence of rigor mortis on joint angles (Kawakami et al., 1998). Rigor mortis causes slow contractions in muscle fascicles that are not released due to a lack of ATP to break the binding (Bendall, 1951;

Davies, 1963), and thus depending on the timing of embalming/dissection, muscles may be fixed in a state of partial muscle contraction (Martin et al., 2001). Rigor mortis may be difficult to control, unless the specimen is fresh frozen immediately after death, and therefore needs to be taken into account when measuring fascicle length (i.e., freezing

specimens in a consistent position immediately after death) or the potential removal of pennation angle in the calculation.

2.5 The three components used to calculate PCSA

PCSA as a representative measure for muscle force was established in quantitative muscle architecture studies by Gans and Bock (1965) and Gans and de Vree (1987). The measures used: muscle volume (2.5.1), fascicle length (2.5.2), and pennation angle (2.5.3).

Muscle mass (g) x Pennation angle (cosθ) PCSA (cm2) = Muscle density g cm−3x fascicle length(cm)

To estimate the maximum isometric force (Fmax) and instantaneous power of a muscle (both are key indicators of muscle functional performance) PCSA is multiplied by the maximum isometric stress. Two alternative maximum tetanic tensions for mammalian muscle are available; 22.5N/cm2 (Powell et al., 1984; Lieber & Fridén, 2000) calculated from guinea pig hindlimbs (Rodentia) or 30N/cm2 (Wells, 1965; Woledge et al., 1985; Medler,

2002) calculated from rats (Rodentia).

Measuring these variables differs according to specimen type and researcher preferences, as well as the time and facilities available. Below, we discuss the different methods and present the advantages and limitations of the different methodologies

(summarised in Table 2.1).

Table 2.1: Summary of alternative methods that can be used to calculate PCSA. Component of Alternative Advantages Limitations Examples PCSA methods Preservation of Fresh (commonly - Time-efficient - Potential for dehydration 1, 2 specimen stored frozen and - No shrinkage of - Fresh muscles fragile thawed for architecture dissection) Formalin - Preserves damage to - Shrinkage of muscle mass 3, 4 specimens and fibre length - Use of museum preserved - Expense of formalin and specimens ethanol Muscle mass Wet muscle mass - Minimal equipment - Extra requirements of 5, 6 (mm) needed spraying throughout - Most common therefore dissection more comparable in - Potential variation in literature hydration Dry muscle mass - Accuracy: no effect of - Time consuming 7, 8 hydration - Require extra equipment (oven) Fascicle length Muscle belly - Time-efficient - Inaccurate for majority of 8 (FL) length - Minimal equipment muscles required - Too simplified Incision along - Time-efficient - Potential damage to 9, 10 plane of fascicles - Minimal equipment muscle required - Difficult to see the whole fibre length - Bias of ‘random selection’ Layer-wise - High-quality 3D model - Expense of microscribe 11, 12 fascicle - Time consuming dissection Acid to digest - Loosens connective tissue - Expenses of acid 13, 14 connective tissue to separate fibres - Time-consuming - Random selection of fibres Digital dissection - Accurate measure of fibre - Expensive 15 length - Time-consuming - Non-destructive Pennation Pennation angle - Significant effect on PCSA - Bias of ‘random selection’ 16, 17 angle (θ) if angle above 30 degrees - Potential in introduce error Angle set to Zero - Time-efficient - Under-estimating PCSA 18, 19 - Generally <30 degrees thus cosθ approx. 1 - Minimal effect on PCSA 1: (Cuff et al., 2016), 2: (Dick & Clemente, 2016), 3: (Böhmer et al., 2018), 4: (Martin et al., 2019b), 5: (Marchi et al., 2018), 6: (Olson et al., 2018), 7: (Lagaria & Youlatos, 2006), 8: (Langenbach & Weijs, 1990), 9: (Lamas et al., 2014), 10: (Rose et al., 2016), 11: (Nyakatura & Stark, 2015), 12: (Rosin & Nyakatura, 2017), 13: (Hartstone‐Rose et al., 2012), 14: (Leischner et al., 2018), 15: (Dickinson et al., 2019), 16: (Allen et al., 2010), 17: (Böhmer et al., 2018), 18: (Myatt et al., 2012), 19: (Thorpe et al., 1999). Abbreviations: FL, fascicle length; PCSA, physiological cross-sectional area.

2.5.1 Muscle volume (derived from muscle mass)

As the potential force production of a muscle is dependent on the number of fascicles working together in parallel, the total muscle volume will be a reflection of muscle force production, to the extent that large muscles will generally have a larger number of muscle fascicles. Muscle volume is usually calculated from the mass of muscles, which is easier to measure. The muscle density constant (1.0597 g cm-3) determined by Mendez and Keys

(1960) for fresh dog and rabbit skeletal muscle or the density constant (1.0564 g cm-3) by

Murphy and Beardsley (1974) using cat skeletal muscle, has been used extensively throughout the literature to convert muscle mass to muscle volume. The density constant is often simplified to 1.06g cm-3 in the literature.

Figure 2.1: Simplified representation depicting the difference between (a) parallel architecture with fewer fascicles; however, longer fascicles and (b) pennate architecture with more fascicles; however, shorter fascicles.

Either dry muscle mass or wet muscle mass is sufficient when calculating PCSA, providing a consistent approach is undertaken throughout the study to avoid variation due to the state of hydration. Consistency is vital to ensure comparisons between muscle groups or between individual animals are valid.

The dry muscle mass technique involves drying isolated muscles in a low-temperature oven to remove all moisture (Langenbach & Weijs, 1990; Lagaria & Youlatos, 2006). This method has the benefit of ensuring that the moisture content of all muscles is consistent and is beneficial when specimens are at varying levels of hydration (i.e., older specimens may have dried out over time). The drying period often takes between 24-48 hours

(Warburton et al., 2013b), and requires an air-drying oven that can maintain a constant temperature over a long period of time. Using dry muscle mass also limits the comparisons that can be made with PCSA scores calculated with wet muscle mass, as dry mass is lighter than wet mass.

The wet method can lead to inconsistencies in muscle masses, especially when using specimens of varying states of preservation and dehydration. Wet weights, however, seem to be preferred and the most common method in the literature. The measurement of wet muscle mass requires periodically moistening of the specimen (either phosphate-buffered saline (PBS) or water) during dissection to avoid desiccation that would lead to inconsistent results and the immediate weighing of each muscle after removal to avoid dehydration. An alternative is to fully rehydrate muscles before weighing by soaking them in PBS then gently blotting dry with paper towel before weighing (Burkholder et al., 1994; Eng et al., 2008), or storing muscles in 70% ethanol immediately after dissection (Böhmer et al., 2018). Using wet mass does allow for a wider comparison of the literature compared to dried mass.

2.5.2 Fascicle length

In the calculation of PCSA, fascicle length is used to represent the number of sarcomeres in a series within the muscle fibres (Michilsens et al., 2009). If comparing two muscles of equal mass, but different length fascicles, the muscle with longer fascicles will have a faster shortening velocity as there are more sarcomeres in series, and therefore a greater contractile velocity (Figure 2.1) (Lieber & Fridén, 2000; Azizi et al., 2008; Kikuchi,

2010).

Five methods are used to measure fascicle and fibre length in the literature: (a) measuring the muscle belly length (origin to insertion), (b) making an incision down the muscles line of action/fascicle plane (depending on fascicle architecture) to measure individual fascicles, (c) layer-wise fascicle dissection, (d) using partial chemical digestion to allow the fibres to separate for measurement, and (e) digital dissection to measure fascicles without destroying the specimen.

(a) Measuring the muscle belly length (origin to insertion) was historically, used as the estimate of fascicle length. This is unlikely to be a true representation of fascicle length, especially for larger muscles, as very few fascicles run the entire length of the muscle (Pasi &

Carrier, 2003; Lagaria & Youlatos, 2006; Moore et al., 2013). Several architectural investigations on human upper and lower limb muscles have shown that even the most parallel orientated muscles (muscle composed of fascicles extending parallel to the muscle force-generating axis) have fibres that only extend around 60% of the muscle length (Lieber

& Fridén, 2000). The use of total muscle belly length is, therefore, a significant over- estimation of fascicle length. While this method provides a quick measure of muscle length, it is not recommended for measuring fascicle length due to this over-estimation. 24

(b) Making an incision down the muscle line of action/fascicle plane (depending on fascicle architecture) to measure individual fascicles provides a relatively quick method of collection and reasonably accurate measure of fascicle length (Oishi et al., 2008; Moore et al., 2013; Rose et al., 2013; Lamas et al., 2014; Rupert et al., 2015; Bribiesca-Contreras et al.,

2019). A limitation of this method is that making incisions in the muscle belly ultimately destroys fascicles, and therefore it can be difficult to measure intact fascicles. This method requires precision dissecting skills to ensure fascicles are not destroyed and accurate fascicle lengths can be measured. For strap muscles, a careful incision from the origin to insertion along the muscle belly can reveal complete muscle fascicles, which then are measured using calipers, ruler or digitally using photography. For pennate-fibred muscles, the incision must be made along the plane of the fascicles to expose the internal fascicle lengths (Figure 2.2a).

An incision from origin to insertion of a pennate muscle would result in bisecting the fascicles, resulting in much shorter fascicle length measurements.

Figure 2.2: Photographs of methods of measuring fascicle length and pennation angle. (a) Modified from Cheng and Scott (2000); measurement of internal fascicle lengths (dotted lines) of Macca mulatta triceps long head. (b) flexor carpi ulnaris of the Quenda (Isoodon fusciventer). Thick line representing the line of action, and small line is the orientation of the fascicle. Pennation angle is measured between the two intersecting lines.

(c) Layer-wise fascicle dissection involves digitising the paths of individual muscle fascicles using a Microscribe (handheld 3D digitiser) to produce a 3D muscle model.

Individual fascicles are removed carefully, then the groove that remains after fascicle removal is digitised for the length and orientation (Stark et al., 2013; Nyakatura & Stark,

2015; Rosin & Nyakatura, 2017; Sullivan et al., 2019). To monitor potential bone movement throughout the dissection process, screws are placed in palpable bony landmarks as a reference for the 3D reconstruction (Stark et al., 2013; Nyakatura & Stark, 2015), or the specimen is pinned to a surface with metal pins that are digitised for reference (Rosin &

Nyakatura, 2017). This method of fascicle measurement produces a high-quality 3D model of the muscle without the use of microCT or diceCT; however, this method requires a substantial amount of time (especially for larger muscles) and dissection skills (especially for the small muscles).

(d) Partial chemical digestion of the muscle belly is by far the most commonly used method in the literature for the measurement of fascicle length (e.g., Herrel et al., 2008;

Fabre et al., 2017; Lowie et al., 2018; Lowie et al., 2019; Leonard et al., 2020); however, it must be noted this method provides a measurement of fibre length rather than fascicle length. Isolated muscle bellies are placed in nitric or sulphuric acid (10-30%) or acetic acid

(when the other acids are unavailable; Leischner et al., 2018), which dissolves the intervening collagenous connective tissue and facilitates the separation of individual muscle fibres (Hermanson & Hurley, 1990; Hermanson & Cobb, 1992; Hartstone‐Rose et al., 2012).

The typical digestion period is 24–48 hours (Santana et al., 2010) with a minimum of 45 min to 12 hours (Bergmann & Hare-Drubka, 2015; Dickinson et al., 2019) or maximum 3–10 days

(Law et al., 2016; de Souza Junior et al., 2018). Digestion time is variable due to the

concentration of acid used and the size and thickness of the muscles. The first-time digesting muscles requires close attention to ensure muscles are not over-digested and, therefore, become too soft to use. Digestion is commonly left at room temperature (often an assumption, as many studies do not state temperature), although a few studies have stated they heat the muscles and acid at 60–70 oC (Hartstone-Rose et al., 2019; Deutsch et al., 2020). Following digestion, muscles are rinsed and placed in 50% aqueous glycerine solution for 1-2hours before the muscle can be teased apart and individual muscle fascicles measured (Herrel et al., 2008). The disadvantages of using chemical digestion to measure fascicle lengths is that digestion can be a time-consuming process and that nitric/sulphuric acid and chemical disposal is an expense to the study.

(e) Digital dissection using high-resolution diffusible iodine-based contrast enhanced computer tomography (DiceCT) or high-resolution microCT techniques can also be used to measure fibre lengths. While dissection or partial chemical digestion is destructive and irreversible, digital imaging technologies provide a method of non-destructively visualising the reconstructed fascicle architecture (Dickinson et al., 2019; Nyakatura et al., 2019).

Specimens are first soaked in 10% sucrose dissolved in distilled water to minimise deformation, then immersed in a low concentration iodine solution (<1.0%–10%) (Gignac et al., 2016; Dickinson et al., 2019; Sullivan et al., 2019), which increases the density of the muscular tissues and increases the contrast between the muscles and the connective tissues. Soaking times can be extensive and vary substantially, depending on specimen size and the concentration of the staining solution. Invertebrates and vertebrate embryos with a low staining concentration can exhibit excellent contrast overnight (<24hours), in comparison to larger vertebrates that take several weeks to months to stain with a low

concentration of stain solution (Gignac et al., 2016), with the average soaking time between

4–7 weeks. After soaking, specimens are CT scanned (Dickinson et al., 2018; Dickinson et al.,

2019; Fahn-Lai et al., 2020). Numerous programs can be used to segment, visualise and quantify digital scans; free software such as 3Dslicer, ParaView, Trackvis, Cloud2, or licensed software such as Amira or Avizo. Software programs are available that digitally discriminate between individual fascicles (i.e., Amira, VGMax, BitPlane) and calculate fascicle lengths.

Studies comparing gross dissection and digitally-dissected fascicle lengths have found comparable results, with some muscles (e.g., more complex muscles such as temporalis) showing more variability than others (Kupczik et al., 2015; Dickinson et al., 2018). Free software tools are available for segmentation and visualisation; however, digital imaging still requires access to specialised equipment, can be expensive (access to machines and high- quality software) and is potentially time-consuming for large specimens due to the preparation process in comparison to other methods of collecting fascicle length via dissection. Digital scans are beneficial as the muscle topology is kept intact and the specimens can be returned to a wet collection and used for further research.

For methods, b–e, typically a minimum of five to ten fascicles/fibres are measured from different sites throughout the muscles to account for variation (Payne et al., 2006;

McGowan et al., 2008; Williams et al., 2008; Moore et al., 2013; Rose et al., 2013; Allen et al., 2014; Lamas et al., 2014; Cox & Baverstock, 2016). Ideally, superficial fascicles/fibres can be avoided as they tend to be longer than the deeper fascicles (Cheng & Scott, 2000;

Kikuchi, 2010). Once fascicles are removed, the lengths are measured using a ruler or callipers (Martin et al., 2019a), or digitally measured after photography using programs such as ImageJ (Cox & Baverstock, 2016; Boettcher et al., 2019).

Arguably, either fascicle length by making an incision to expose deep fascicles, or by partial digestion to measure fibre lengths, are acceptable methods of collecting fascicle/fibre length. Large muscles are time-consuming to digest and require large amounts of acid, therefore, dissection along the plane of the fascicles may be more economical.

Layer-wise fascicle dissection is an extremely time-consuming method; therefore, it may only be suited to studies focussing on a few specific muscles, in comparison to an entire limb of muscles. Smaller muscles are easily digested and results in less damaged fibres, therefore digestion may be the best option for fibre length collection. Digital dissection is an excellent method to calculate PCSA without destroying specimens utilising iodine staining and microCT/ DiceCT, however, is remains expensive at this point in time and the soaking times for visualisation can be extensive, especially for larger specimens.

2.5.3 Pennation angle

Pennation describes the arrangement of muscle fascicles attaching obliquely to the muscle tendon, compared to strap muscles where fascicles are arranged in parallel to the long axis of the muscle belly. The advantage of a pennate arrangement is increased numbers of fascicles within a muscle of a given volume (McNeill, 1968; Azizi et al., 2008; Lee et al.,

2015). ‘Pennation angle’ refers to the angle at which the muscle fascicles lies relative to the axis of force production, or ‘force-generating axis’ (Gans & Bock, 1965) (see Figure 2.2b).

Pennation angle has two main physiological effects on the relationship between force production and shortening velocity. An increase in the pennation angle results in greater

PCSA by accommodating a greater cross-sectional area of muscle fascicles attaching to a tendon (i.e., more fascicles packed into an area). At the same time, more obtuse pennation angles will result in shorter fascicles (due to the arrangement of muscle origins and 29

insertions) and consequently, the muscle will have a slower maximum shortening velocity

(Gans & Bock, 1965; Lieber & Fridén, 2000; Lorenz & Campello, 2012).

Measuring pennation angle once the muscles have been dissected involves measuring the angles from either high-quality photographs (Channon et al., 2009; Olson et al., 2016;

Martin et al., 2019b), or using a protractor over the surface of the muscle belly (McGowan et al., 2008; Allen et al., 2014; Rupert et al., 2015; de Souza Junior et al., 2018; Butcher et al., 2019). This method of collecting pennation angle reduces the pennation angles to one plane. Multiple individual measures of angle are required to account for the internal variation of the muscle and therefore minimise measurement error (Scott & Winter, 1991).

Measuring pennation angle from photographs has the advantages that it can be measured at a later date (i.e., it is not time-dependent), and that it may utilise computer programs such as Image J (freeware), to measure from a high-quality photograph. Taking photographs that distinctly show the angles can be difficult, and also requires the muscle belly to be fully exposed and all covering fascia removed. The protractor on the surface of the muscle allows for quick measurement of the angle but may be less precise.

Many authors choose to exclude pennation angle from their PCSA calculation (e.g.,

Furuuchi et al., 2013; Fabre et al., 2017; Goh et al., 2017; Hartstone-Rose et al., 2018; Lowie et al., 2018; Hartstone-Rose et al., 2019). First, excluding pennation was justified as an angle below 30o has little effect on the calculation potential force as the cosine of the angle is typically close to 1: cos(10)=0.98, cos(20)=0.94, cos(30)=0.87 (Thorpe et al., 1999; Payne et al., 2006). However, Channon et al. (2009) recorded a maximum of 39o pennation in gibbon hindlimb muscles, and exclusion of pennation angle would, therefore, have resulted in a reduction of approximately 22% in the PCSA.

Secondly, measurements of pennation angle only capture the angle at a fixed time

(i.e., resting pennation angle). As such, the cosine of the angle only captures the component of the force that is, acting in the direction of the principal line of action, as some force is not transmitted along the muscle axis (Blanco & Patek, 2014). This is dependent on the position the muscle was fixed in when measured.

Third, multipennate muscles present complex pennation measurements, and some authors have stated the musculature is too complex to accurately measure pennation

(Furuuchi et al., 2013). For example, jaw muscles change pennation angle dramatically across different gape angles, and this is also true for other complex muscles (Anapol &

Barry, 1996). This causes a complicated variation imparted in the rotational complex that is difficult to resolve in comparison to a simpler linear contractile muscle found in other areas of the body. Therefore, to calculate the pennation angle in one plane is not an adequate measure for masticatory muscles, and it has been argued to be more accurate to calculate

PCSA without pennation angle (Hartstone-Rose et al., 2018; Hartstone-Rose et al., 2019).

Scott and Winter (1991) suggested the importance of a dynamic pennation angle

(measuring pennation on moving muscle), which encompasses the angle of pennation through the range of movement of the muscle. This would be the most accurate measure of pennation, and therefore force production. In vivo recordings of the pennation angle at specific joint angles (muscle tensions and lengths) are now available via ultrasound sonography (Aagaard et al., 2001), although the incorporation of these measurements is complex, and this approach is unavailable for cadavers or fixed samples.

The decision to include a measure of pennation angle in the calculation of PSCA is largely dependent on the specific muscle and the applications of the data. For muscles with

a high pennation angle (i.e., greater than 30o), omission is likely to introduce relatively large error into the calculation, and therefore pennation angle is an important component of the

PCSA calculation. Complex muscles (e.g., masticatory muscles) should also have pennation omitted from the PCSA calculation as the three dimensionalities of the system cannot be accounted for.

2.6 PCSA in models versus in vivo measures of force

Although PCSA is widely used, few studies have directly compared PCSA values calculated from cadaver specimens with in vivo measures of muscle force. This is partly because in vivo forces are difficult to measure, especially in live non-human specimens. Bite force is the most commonly reported in vivo force measures, but assumes that the animal is exerting the maximum force possible. Wild-caught specimens often aggressively snap at an object approaching them; however, this may not be the case in all species (Davis et al.,

2010; Becerra et al., 2011). Gape angle is the other limiting factor with in vivo bite forces, with wider gape angles expecting to generate lower bite forces in mammals (Santana,

2016).

Studies comparing PCSA via dissection and in vivo bite forces commonly find that PCSA is a good predictor of in vivo bite force (e.g., Herrel et al., 2008; Huber et al., 2008; Van

Daele et al., 2009; Davis et al., 2010; Mara et al., 2010; Santana et al., 2010; Kolmann et al.,

2015; Ginot et al., 2018; Meyers et al., 2018), although a few studies obtained greater values for PCSA via dissection than in vivo measurements (Becerra et al., 2011; Becerra et al., 2014). To mimic the in vivo bite data, three-dimensional bite models or static bite force models are used to calculate the bite force from the PCSA via dissection. These models use

3D coordinates of the origin and insertion of the muscles, their PCSA, and the 3D coordinates of the point of application of the bite (Herrel et al., 1998; Davis et al., 2010;

Santana et al., 2010). These types of models allow for many iterations to be run to generate the optimum bite force, similar to when in vivo bite forces are measured at varied gape angles (Santana, 2016).

Limb systems are also commonly modelled in the literature to integrate the musculature and skeleton. These models can be musculoskeletal models that use muscle moment arms to investigate the effectiveness of a muscle(s) to contribute to a particular motion over a range of configurations, i.e., muscle effectiveness in different movements of locomotion (Sherman et al., 2013). These models can be built using PCSA values collected by physical dissection (Charles et al., 2016; Goh et al., 2017), or via digital dissection from microCT, CT or Magnetic resonance imaging (MRI) (Blemker & Delp, 2005), or more commonly they are built using 3D scans or from published descriptions of the origins and insertions of muscles, and then estimated muscle forces are generated from the computer models (Regnault & Pierce, 2018). Finite Element Analysis (FEA) predicts the performance of bone (and other structures) by quantifying the distribution of stresses and strains within a morphological structure (i.e., within the limb) that are caused by external forces (muscles)

(Dumont et al., 2009). The benefits of such modelling techniques can then be applied to skeletal remains of extinct species, and further, that these can be integrated with functional studies of extant species to provide a deeper understanding of the mechanical basis of their locomotion (Orr, 2016).

2.7 PCSA between species

The benefit of PCSA is that it can be used for intra- or inter-specific studies to investigate variation, functional morphology, or ecomorphology. PCSA calculated using the same methodology means that direct comparisons between species can be made. As PCSA calculations are dependent on size (as muscle volume is a large component of the PCSA calculation) animals with larger body masses and absolute larger muscles will have greater

PCSAs. Implementing a correction for body mass can remove the effect of body mass and allow direct comparison of the relative distribution of musculature forces between species to investigate functional adaptation to their environment or ecology. A few methods of body mass correction are used in the literature. First is the use of residual values from a regression between the muscle properties (i.e., muscle mass, fascicle length, or PCSA) and total body mass (Leischner et al., 2018; Böhmer et al., 2019). Second, data can be normalised to geometric similarity (i.e., lack of change in muscle properties with ontogenetic increase in body mass) by scaling the muscle properties by body mass (mb);

1.0 0.33 0.67 muscle mass by mb , fascicle length by mb and PCSA by mb (Allen et al., 2010; Rupert et al., 2015; Dick & Clemente, 2016; Olson et al., 2018; Butcher et al., 2019; Nyakatura et al.,

2019). PCSA can also be used to produce a functional space plot, which is PCSA plotted against fascicle length (Payne et al., 2005; Allen et al., 2010; Dick & Clemente, 2016; Böhmer et al., 2018; Olson et al., 2018; Butcher et al., 2019). This will separate out the muscles that are specialised for different actions (i.e., (a) force specialised: large PCSA and short fascicles;

(b) displacement specialists: small PCSA and long fascicles; (c) powerful: large PCSA and long fascicles; and (d) generalists: small PCSA and short fascicles) and is used to investigate the structure-function trade-off between PCSA and fascicle length for a muscle with a given

volume (Böhmer et al., 2018). These functional space plots can be used to compare the function trade-offs between species of differing postures or locomotor behaviour.

2.8 Conclusions and recommendations

This review summarises the methods used to calculate PCSA. The method chosen to calculate PCSA is likely to depend on the preservation of the specimen (which will influence whether dry or wet mass can be measured) and the size of the target muscles (which will dictate the necessity to digest the intramuscular connective tissue or more simply to incise along the plane of the fascicles to expose the fascicles). Pennation angle is entirely dependent on the area of the body the muscle is taken from and therefore, the inclusion of pennation angle in PCSA calculations should be considered; pennation angles should be included for muscles with simple linear contractile properties, but pennation angles below

30o make very little difference to force estimates. Reviewing PCSA methods will help ensure researchers understand the benefits and limitations of different approaches and can make informed decisions about their methods. This will ensure that PCSA calculations are comparable between studies and with as little error possible. If not using standard methods, sufficient detail needs to be given in methodology to ensure researches can repeat your methods and be aware of the assumptions that have been made.

Chapter 3 Mechanical similarity across ontogeny of

digging muscles in an Australian marsupial (Isoodon

fusciventer)

3.1 Preface

This chapter has been published in Journal of Morphology:

3.2 Abstract

Many mammals dig, either during foraging to access subsurface food resources, or in creating burrows for shelter. Digging requires large forces produced by muscles and transmitted to the soil via the skeletal system; thus, fossorial mammals tend to have characteristic modifications of the musculoskeletal system that reflect their digging ability.

Bandicoots (Marsupialia: Peramelidae) scratch-dig mainly to source food, searching for subterranean food items including invertebrates, seeds, and fungi. They have musculoskeletal features for digging, including shortened, robust forelimb bones, large muscles, and enlarged muscle attachment areas. Here, we compared changes in the ontogenetic development of muscles associated with digging in the Quenda (Isoodon fusciventer). We measured muscle mass (mm), pennation angle, and fibre length (FL) to calculate physiological cross-sectional area (PCSA; a proxy of maximum isometric force) as 37

well as estimate the maximum isometric force (Fmax) for 34 individuals ranging in body size from 124 to 2,390 g. Males grow larger than females in this bandicoot species, however, we found negligible sex differences in mass-specific mm, PCSA or FL for our sample. Majority of the forelimb muscles PCSA showed a positive allometric relationship with total body mass, while mm and FL in the majority of forelimb muscles showed isometry. Mechanical similarity was tested, and two-thirds of forelimb muscles maximum isometric forces (Fmax) scaled with isometry; therefore, the forelimb is primarily mechanical similar through ontogeny.

PCSA showed a significant difference between scaling slopes between the main movers in the power stroke, and the main movers of the recovery stroke of scratch-digging. This suggests that some forelimb muscles grow with positive allometry, especially those associated with the power stroke of digging. Intraspecific variation in PCSA is rarely considered in the literature, and thus this is an important study quantifying changes in muscle architectural properties with growth in a mammalian model of scratch-digging.

3.3 Introduction

Many animals scratch-dig in search of shallow subterranean food, or to construct burrows to shelter themselves and their young from environmental extremes and predators

(Shimer, 1903). Fossorial and semi-fossorial mammals are categorised by their principal mode of excavating soil (Table 3.1). Of these, scratch-digging, in which soil loosened by clawed forelimbs using a running motion (using an alternating limb cycle of retraction - power stroke, and protraction - recovery stroke) in a parasagittal plane, is the most common mode of digging employed in mammals. Scratch-digging mammals are characterised by morphological modifications of their forelimbs that allow them to produce large out-forces that act against the resistance of the soil (Hildebrand, 1985; Rose et al., 2013). 38

Table 3.1: Summary of the four modes of digging in mammals. Fossorial animals (those that are morphologically or physiologically adapted for digging, yet are mostly active above ground) are categorised by their principal mode of excavating soil, of which, scratch-digging is the most common employed mode of digging in mammals. Subterranean animals (species that are highly specialised for living almost wholly underground) show further specialisations for scratch-digging and /or resort to other digging modes (Lessa & Thaeler, 1989). Description Actions Examples References

Scratch- Soil loosened by • Humeral retraction e.g., Bandicoots and (Hildebrand, 1985; digging clawed forelimbs using • Elbow extension bilbies Vassallo, 1998; a running motion in a • Carpal flexion (Peramelemorphia), Moore et al., 2013; parasagittal plane. • Digit flexion wombats (Vombatidae), Warburton et al., potoroos (Potoroidae), 2013b; Olson et al., badgers (Mustelidae), 2016) armadillos (Dasypodidae), groundhogs (Sciuridae)

Chisel- Gnaw soil with incisors • Incisors increased e.g., Rodents (Hildebrand, 1985; tooth to excavate their procumbency (Octodontids, Hopkins & Davis, digging burrows. • Jaw adductors Ctenomyids), mole-rats 2009; McIntosh & • Jaw stabilisation (Bathyergidae) Cox, 2016a, 2016b) Humeral- Sprawling, abducted • Humeral rotation e.g., Moles (Talpidae), (Barnosky, 1981; rotation forelimb posture, and • Elbow extension echidnas (Tachyglossidae) Hopkins & Davis, involves a rotation of • Manus held 2009) the humerus while the between forelimb extends to supination and sweep the soil behind. pronation Head-lift Snout and forelimbs to • Head elevation e.g., Golden moles (Hildebrand, 1985; digging wedge open the tunnel • Push firmly down (Chrysochloridae ), mole Gasc et al., 1986; where they with the forelimbs voles (Cricetidae), mole- Stein, 2000) permanently reside. rats (Bathyergidae)

If limb segments are modelled as simple lever systems, the mechanical advantage is dependent on the ratio of in-lever length to out-lever length and the magnitude of the in- force (Hildebrand, 1985). As a result, the limb bones in scratch-digging mammals are short and robust to reduce the out-lever length, main mover muscles during the power stroke of scratch-digging are enlarged to increase the magnitude of the in-force, and the attachment sites of these muscles are moved further away from the joint (centre of rotation) to increase the in-lever length (Shimer, 1903; Hildebrand, 1985; Lagaria & Youlatos, 2006; Warburton et al., 2013b; Rose et al., 2014). Forelimb muscles involved in humeral stabilisation, humeral 39

retraction, and elbow extension are capable of producing large out-forces during the power stroke (Hildebrand, 1985; Lagaria & Youlatos, 2006). For example, Quenda Isoodon fusciventer have large and powerful muscles associated with humeral retraction, elbow extension, and carpal and digital flexion (Warburton et al., 2013b). The bones of the forelimb have enlarged muscle attachment areas, relatively long in-levers, such as an elongate olecranon process for the insertion of the elbow extensors, and the humerus, radius and ulna are short and robust (Warburton et al., 2013b). These specialisations provide improved mechanical advantage for scratch-digging.

Muscle ultrastructure reflects contractile velocity and force production, and is therefore also likely to demonstrate specialisations for digging. Fibre length (FL) represents the number of sarcomeres in a series (Michilsens et al., 2009), and consequently longer fibres have faster shortening velocity (Lieber & Fridén, 2000; Azizi et al., 2008; Kikuchi,

2010). In digging animals such as badgers (Taxidea taxus; Moore et al., 2013), groundhogs

(Marmota monax; Rupert et al., 2015), and armadillos (Dasypus novemcinctus; Olson et al.,

2016), longer fibres in muscles such as the latissimus dorsi, pectoralis, triceps brachii and teres major provide rapid fibre contraction during the power stroke of scratch-digging. The force a muscle produces is influenced by the arrangement of the muscle fibres (number of sarcomeres in parallel) (Lieber & Ward, 2011; Rupert et al., 2015). Physiological cross- sectional area (PCSA) is calculated from muscle mass (mm) and muscle architecture data; fibre length (FL) and pennation angle (the angle between the line of action of the muscle and the fibres), and is considered a better representation of muscle force production than muscle mass alone (Moore et al., 2013). As PCSA is a representation of maximum isometric force production for individual muscles, the PCSA values of muscles in a limb can be used to

interpret biological functions in correlation with skeletal morphology (Thorpe et al., 1999;

Myatt et al., 2012). Scratch-digging mammals have relatively large PCSA values for muscles used as main movers in the power stroke, such as the triceps brachii long head, which reflect a greater number of fibres per unit volume of muscles; this allows them to produce a greater force for scratch-digging (Moore et al., 2013; Rose et al., 2013; Rupert et al., 2015;

Olson et al., 2016).

Bandicoots (Peramelidae) are typically omnivorous and employ scratch-digging to search for subterranean food sources. When moving, bandicoots employ an asymmetrical half-bound gait similar to many other marsupial taxa (Hildebrand, 1977; Bennett & Garden,

2004). This mode of locomotion, being powered primarily by the hindlimbs, allows bandicoots to retain specialised forelimbs for digging (Gordon & Hulbert, 1989; Bennett &

Garden, 2004). Quenda are commonly found throughout south-west Western Australia. The

Quenda produces an average of 45 foraging pits per day (Valentine et al., 2013) as they search for subterranean food sources. Each dig is conical in shape, measuring 35–135 mm in depth, and an average-sized (1.4 kg) Quenda is estimated to move 2.8 tons of soil per year.

As the presence of digging mammals is known to improve soil quality, the presence of bandicoots has the potential to greatly impact their local ecological community, and these animals are therefore considered ecosystem engineers (Valentine et al., 2013; Fleming et al., 2014).

Scaling muscle architectural properties (such as individual mm, FL, or PCSA) against total body mass are commonly used to investigate locomotor adaptations, or used to describe intraspecific biomechanical constraints across a range of body sizes (Allen et al.,

2010; Allen et al., 2014; Lamas et al., 2014; Picasso, 2015) or among species (Bertram &

Biewener, 1990; Cuff et al., 2016). A scaling muscle architecture study has not been used to investigate Australian digging marsupials. We compared ontogenetic changes in the forelimb muscles in Isoodon fusciventer, testing the prediction that muscles that are main movers during the power stroke of scratch-digging would show steeper allometric growth compared with the muscles that are more associated with the recovery stroke of scratch- digging, reflecting modifications associated with large out-force production in these scratch- digging mammals.

3.4 Materials and methods

3.4.1 Specimens:

Quenda were recently raised to species level based on cranio-dental morphology and molecular data compared to the other sub-species in Isoodon obesulus (Travouillon &

Phillips, 2018) (Figure 3.1). Their ability to adapt to an urban environment has come with risks, as Quenda are susceptible to dog attacks and collisions with cars; these were the primary reasons of death for the specimens collected. Bandicoot cadaver specimens (n=34) were collected from Kanyana Wildlife Rehabilitation Centre (Lesmurdie, Western Australia), or opportunistically collected throughout the Perth metropolitan area (Regulation 17 licence

SF010344). Details of specimens are presented in supplementary Table S3.1. Quenda are sexually dimorphic in body size (body mass mb range; males: 500–1,850g, females: 400–

1,200g) (Warburton & Travouillon, 2016), and therefore we were mindful of potential sex differences in our analyses.

Figure 3.1: Musculature of the forelimb muscles of the Quenda (Isoodon fusciventer). Each colour represents a different functional muscle group. (A) Superficial medial view altered from (Warburton et al., 2013b) and (B) superficial lateral view. BiB: biceps brachii, Bra: brachialis, Del acr & Del spi combined for Delt: deltoideus, ECR: extensor carpi radialis, ECU: extensor carpi ulnaris, EDC: extensor digitorum communis, EDL: extensor digitorum lateralis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, FDP: flexor digitorum profundus, FDS: flexor digitorum superficialis, Inf: infraspinatus, LtD: latissimus dorsi, Pec: pectoralis, PrT: pronator teres, Sub: subscapularis, Sup: supraspinatus, TFA: tensor fascia antebrachii, TMj: teres major, TLn & TMd combined for TrB: triceps brachii.

Specimens were stored frozen (–18oC), and defrosted for 48 hours in refrigerated

o conditions (4 C) prior to dissection. Total body mass (mb) was recorded (± 0.1g; Phoenix

Scales Ltd., West Bromwich, UK) and then specimens were skinned, eviscerated, and embalmed in a solution of 10% formalin and 4% glycerol for 7 days before transfer to 70% ethanol for storage until muscle dissection. Specimens were preserved in formalin for the study due to the nature of specimen collection; roadkill specimens have substantial damage, therefore fixing the specimens allowed the forelimbs to be used in the study. Entire bodies were fixed with the forelimbs and hindlimbs fixed in the resting anatomical position (as close to a 90-degree angle that could be achieved) to minimise the effect of changing

(shortening or lengthening) the lengths of the muscles and therefore muscle architecture.

All muscle architectural measurements were corrected using correction factors published for fixed muscle tissue (Kikuchi & Kuraoka, 2014).

3.4.2 Muscle dissection:

Muscle dissections were performed using standard dissection techniques and followed muscle descriptions (names, origins and insertions) and methods by Warburton et al. (2013b). Right forelimbs were dissected unless damage had occurred, in which case the left forelimb was dissected. Individual muscles were isolated and removed, and muscle mass recorded (mm, ±0.001g; Mettler Toledo BB240 Precision Balance, Toronto Surplus &

Scientific Inc., Richmond Hill Canada).

Photographs of isolated muscle bellies (Olympus Stylus Tough TG-3 Digital Camera,

Olympus Australia, Pty Ltd., Notting Hill, Australia) were used to determine pennation angle.

Pennation angles were measured on photographs using the ‘angle’ tool in ImageJ (Version

1.49) (Abramoff et al., 2004) at 10 randomised locations throughout the muscle belly.

Muscles were placed in 30% nitric acid for 24h to loosen the connections between fibres, then washed in water and soaked in 30% glycerol for 1–2h. This process allowed individual muscle fibres to be teased apart using blunt forceps. Superficial fibres were avoided as they tended to be longer, therefore 10 deeper fibres were randomly selected for measurement of FL. PCSA was calculated:

푚푚 (g) x pennation angle (cos θ) PCSA (cm2) = muscle density (g/cm3) x FL (cm)

where an estimate of vertebrate muscle density (1.06 g/cm3) (Mendez & Keys, 1960) was used as the muscle density constant. To permit for the most accurate estimate of the maximum isometric force (Fmax) (Williams et al., 2008; Moore et al., 2013), we multiplied the PCSA (cm2) by a maximum isometric stress of 30 N/cm-2 (Medler, 2002).

3.4.3 Statistical analysis:

Twenty-nine muscles were classified for statistical analyses into nine functional groups

(Table 3.2). Muscles involved in scapula stabilisation (SS), humeral retraction (HR), elbow extension (EE), carpal/digital flexion (CF) and pronation (PRO) were considered to be active during the power stroke of scratch-digging, whereas the functional groups involved in humeral protraction (HP), elbow flexion (EF), carpal/digital extension (CE), and supination

(SUP) were classified as active during the recovery stroke (Figure 3.1). While we acknowledge that many muscles will have more than one action, this simplified view was taken to allow comparison between muscles classified as main movers in the power stroke vs. recovery stroke during scratch-digging.

The four muscle architectural properties (mm, FL, PCSA, Fmax) were log10-transformed and analysed against log10-total body mass (mb). We tested for potential sex differences in the slopes of these relationships by Analysis of Covariance (ANCOVA), with sex (males: 0, females: 1) as a fixed factor and mb as the covariate. To calculate the slopes of the relationships between the muscle architectural properties and mb, we used reduced major axis (RMA) (Model II) regression in PAST version 3.16 (Paleontological Statistics; http://folk.uio.no/ohammer/past/) (Hammer et al., 2001). All scaling regressions were assessed using Shapiro-Wilk tests (Quinn & Keough, 2002). Correlation coefficients

(Pearson’s Moment correlation) and upper and lower bounds of the 95% confidence intervals were calculated to evaluate the data spread around each regression. In order to consider different properties as geometrically similar, and thus infer the scaling pattern, the measures must be scaled following the rules of isometry. Muscle properties were

1.0 considered isometric for masses (mm) and force (Fmax) if they scale with mb , areas (i.e.,

0.67 0.33 PCSA) if they scale to mb , and lengths (i.e., FL) if they scale to mb . The slopes (β) of the individual muscles were tested against the null hypotheses of β=1 (mm), β=0.67 (PCSA), or

β=0.33 (FL) by calculating the t ratio = (β-1.0, 0.67 or 0.33)/standard error of the slope.

To test if the muscles are mechanically similar throughout ontogeny, the slope of

Fmax with total body mass was tested against the null hypothesis of the slope of 1.0 (Rose,

2014). We estimated the false discovery rate (FDR) to determine which slopes were significantly different from the geometric slope (Benjamini & Hochberg, 1995) to control for type 1 errors. Mann-Whitney U test was used to compare the slopes (β) of the power stroke and recovery stroke muscles.

Values are presented as means ± 1SD throughout.

3.5 Results

3.5.1 Is there a significant sex difference in forelimb development?

In our sample, male Quenda specimens (1,041±667g, range 124–2,390g, n=21) were not significantly larger than females (810±356g, range 208–1,320g, n=13) (t32= -1.15 p=0.259). The mean total forelimb mass for our sample of Quenda was 43.2±29.6g (male) and 31.2g±15.0 g (female), which accounts for 4.0±0.5% (male) and 3.8±0.3% (female) of the total body mass. There was no significant sex difference in total forelimb mass (t32= -1.65 p=0.109).

Table 3.2: Average values of muscle mass (mm), fibre length (FL), pennation angle, physiological cross-sectional area (PCSA) and isometric force (Fmax) of each individual muscle calculated from 29 Quenda. Abb. Funct. 1 Muscle mass Fibre length Pennation PCSA Fmax Trapezius Trap SS 3.94 ± 2.49 1.81 ± 0.83 28 ± 7 1.82 ± 1.10 54.57 ± 32.48 Omotransversarius OmT SS 1.05 ± 0.68 1.69 ± 0.60 11 ± 9 0.57 ± 0.33 17.06 ± 10.14 Rhomboideus Rho SS 2.11 ± 1.71 1.46 ± 0.54 26 ± 11 1.13 ± 0.65 33.98 ± 19.21 Serratus ventralis SeV SS 5.83 ± 4.36 1.84 ± 0.68 12 ± 10 2.90 ± 2.29 87.08 ± 67.62 Latissimus dorsi LtD HR 3.19 ± 2.17 1.88 ± 0.94 22 ± 8 1.53 ± 0.87 45.78 ± 25.89 Pectoralis Pec HR 9.85 ± 6.63 1.85 ± 0.70 27 ± 10 4.18 ± 2.29 125.33 ± 67.93 Deltoideus Delt HP 0.50 ± 0.33 1.03 ± 0.30 29 ± 9 0.38 ± 0.22 11.44 ± 6.36 Infraspinatus Inf HR 1.45 ± 1.03 0.94 ± 0.22 25 ± 5 1.24 ± 0.72 37.15 ± 21.41 Teres major TMj HR 1.14 ± 0.78 1.07 ± 0.51 21 ± 7 0.91 ± 0.50 27.21 ± 14.77 Subscapularis Sub HR 1.63 ± 1.16 0.89 ± 0.22 24 ± 12 1.50 ± 0.90 44.88 ± 26.63 Triceps brachii TrB EE 4.99 ± 3.41 1.36 ± 0.44 23 ± 7 3.12 ± 1.88 93.54 ± 55.74 Anconeus Anc EE 0.19 ± 0.14 0.85 ± 0.28 NA 0.20 ± 0.13 6.13 ± 3.83 Tensor fascia antebrachii TFA EE 0.81 ± 0.70 1.53 ± 0.72 NA 0.49 ± 0.35 14.57 ± 10.49 Flexor carpi radialis FCR CDF 0.26 ± 0.20 0.78 ± 0.19 19 ± 7 0.29 ± 0.18 8.56 ± 5.22 Flexor carpi ulnaris FCU CDF 0.25 ± 0.20 0.68 ± 0.19 35 ± 7 0.27 ± 0.19 8.09 ± 5.70 Palmaris longus PaL CDF 0.20 ± 0.16 0.82 ± 0.20 20 ± 6 0.20 ± 0.14 5.98 ± 4.08 Flexor dig. superficialis FDS CDF 0.21 ± 0.14 0.95 ± 0.23 20 ± 11 0.18 ± 0.12 5.49 ± 3.53 Flexor dig. profundus FDP CDF 1.76 ± 1.26 0.98 ± 0.24 25 ± 7 1.41 ± 0.83 42.36 ± 24.51 Pronator teres PrT PRO 0.20 ± 0.13 0.74 ± 0.13 24 ± 6 0.22 ± 0.13 6.52 ± 3.95 Pronator quadratus PrQ PRO 0.05 ± 0.03 0.40 ± 0.09 NA 0.11 ± 0.07 3.22 ± 2.21 Supraspinatus Sup HP 1.80 ± 1.22 1.07 ± 0.27 25 ± 5 1.41 ± 0.82 42.37 ± 24.28 Biceps brachii BiB HP/EF 0.56 ± 0.41 1.06 ± 0.30 16 ± 6 0.47 ± 0.28 13.96 ± 8.32 Brachialis Bra EF 0.68 ± 0.45 1.13 ± 0034 22 ± 7 0.51 ± 0.28 15.33 ± 8.32 Extensor carpi radialis ECR CDE 0.55 ± 0.34 1.05 ± 0.32 22 ± 6 0.46 ± 0.27 13.95 ± 8.00 Extensor dig. communis EDC CDE 0.38 ± 0.25 1.01 ± 0.18 19 ± 8 0.33 ± 0.20 10.02 ± 6.03 Extensor dig. lateralis EDL CDE 0.09 ± 0.07 0.86 ± 0.27 10 ± 10 0.10 ± 0.08 3.11 ± 2.45 Extensor carpi ulnaris ECU CDE 0.16 ± 0.10 0.72 ± 0.19 16 ± 8 0.22 ± 0.16 6.63 ± 4.84 Abductor digiti I longus AbdL CDE 0.10 ± 0.07 0.68 ± 0.22 NA 0.15 ± 0.11 4.40 ± 3.17 Supinator Spr SUPI 0.10 ± 0.07 0.57 ± 0.20 NA 0.18 ± 0.19 5.35 ± 5.71 Standard deviations displayed and the abbreviations of all the muscles that are used throughout the study. NA in pennation angle represents muscles that display parallel fibres and no pennation.

Abbreviations: Functional classification: SS Scapula stabilisation; HR humeral retraction; EE elbow extension; CDF carpal/digital flexion; PRO pronators; HP humeral protraction; EF elbow flexion; CDE carpal/digital extension; SUPI supinators.

Forelimb muscle mass was distributed in a proximal-distal gradient, with larger

muscles positioned closer to the shoulder joint (Table 3.2). Nearly half of the forelimb mm

was attributed to humeral retraction (HR, 40.4±3.36%), while scapula stabilisation (SS,

29.2±3.46%), elbow extension (EE, 13.6±1.25%) and carpal/digital flexion (CDF, 6.03±0.58%) contributed the next three largest proportions of the forelimb mass; the other five functional groups each contributed less than 5% of the forelimb mass (Figure 3.2). Males and females had a slightly different distribution of muscles throughout the limb (Figure 3.2).

Compared with females, males had relatively heavier forearm muscles: elbow flexors (EF) and extensors (EE), carpal/digital flexors (CDF), humeral protractors (HP) and the pronators

(PRO).

Figure 3.2: Average (±1SD) proportions of total forelimb muscle mass represented by each of the nine functional groups. Functional groups are separated into muscles associated with either the power stroke or recovery stroke in scratch-digging and ordered from proximal to distal muscle. There were sex differences in distribution of muscle mass (t-test), and therefore the data are shown organised by sex where * p<0.05, ** p<0.01, *** p<0.001. Functional group abbreviations are defined in Table 3.2. Black bars: male, grey bars: female. Functional groups are ordered proximal to distal.

There were negligible sex differences in forelimb muscle architectural properties

(Analysis of Covariance testing for sex differences in the scaling relationships with mb for each of the three muscle architecture measures (mm, FL, PCSA) of the 29 individual forelimb

muscles; Figure 3.3). Only one relationship (of the total 87 total measurements analysed) showed a significant sex effect (muscle mass of the tensor fascia antebrachii (TFA); p=0.027), which had a significantly steeper slope (positive allometry) in males than females.

Sex was included as a factor in the analyses of slopes to account for any potential sex differences, although the sex effect will not be described in any further detail.

Figure 3.3: Relationship between total body mass muscle architectural properties plotted on a log scale. Total muscle architectural properties are calculated by summing the muscle property values for all the individual muscles. (A) Total forelimb mass (g), (B) total fibre length (cm), (C) total PCSA (cm-2) and (D) total isometric force (Fmax) (Ncm-2). All the four muscle architectural properties showed a strong positive relationship with total body mass. Black squares: males, Grey diamonds: females.

3.5.2 Do digging power stroke muscles show greater positive allometry compared with

recovery stroke muscles?

Forelimb muscle masses (mm) were strongly correlated with mb; 26 of the 29 muscles had correlation coefficient values r >0.90 (Table 3.3). The slopes for all of the muscles except flexor carpi radialis (FCR) were greater than 1, and only half the muscles had 95% confidence intervals that included the line of geometric similarity (β=1) (Figure 3.4). The slopes for six muscles (all classed as main movers in the power stroke of scratch-digging) were statistically different from β=1.

Compared with the muscle mass values, measures of FL, generally showed weaker correlations with mb (most muscles had r<0.6; Table 3.3). The majority of forelimb muscles had slopes steeper than β=0.33 (the value reflecting geometric similarity for this linear measure) indicating they may be developing with positive allometry. Only five of these muscles were statistically significantly different from a slope of 0.33 (Figure 3.4); four muscles (of 20) were associated with the power stroke, and one (of nine) in the recovery stroke of scratch-digging.

Figure 3.4: The 95% confidence intervals for scaling slopes of forelimb log architectural properties on log total body mass in Quenda (Isoodon fusciventer). Blue squares indicate the regression slopes for muscle mass (mm); pink circles indicate regression slopes for PCSA; and purple diamonds indicate regression slopes for fibre length (FL). The error bars show the upper and lower 95% confidence intervals, and dashed lines indicate the relevant value for isometry. Muscles are separated into muscles associated with the power stroke or recovery stroke of scratch-digging as well as ordered as most proximally placed muscles to most distally placed muscles. Majority of the muscle masses and PCSA slopes are scaling above isometry, while the FL are scaling closer to isometry.

Values of PCSA for all muscles were significantly correlated with mb, with the majority of the muscles (25/29) having correlation coefficient values >0.8 (Table 3.3). Of the 29 muscles, 18 had slopes that were significantly different from 0.67 (geometric similarity for this area measure) indicating that they increased with strong positive allometry. Both muscles that are main movers in the power stroke, and muscles associated with the recovery stroke of digging, showed positive allometry.

1 Table 3.3: Regression analysis results for the log-transformed muscle architecture properties vs. log-transformed total body mass of the Quenda (Isoodon 2 fusciventer) forelimb muscles. The lower and upper 95% confidence intervals demonstrate the scaling relationship- grey-shaded cells highlight where the 3 confidence intervals range did not include the line representing isometry. Results with significant p-values are highlighted in bold which indicated that the 4 muscle scaling relationship is significantly different to isometry. P-values with a * are p-values that are no longer significant using the false discovery rate 5 method (FDR). Muscles are separated into muscles associated with the power stroke or the recovery stroke of scratch-digging, and within these two groups 6 muscles are ordered as proximally to distally placed muscles. Functional group abbreviations- SS Scapula stabilisation; HR Humeral retraction; EE Elbow 7 extension; CDF Carpal/digital flexion; PRO Pronators; HP Humeral protraction; EF Elbow flexion; CDE Carpal/digital extension; SUPI Supinators. Fmax

mm v mb FL v mb PCSA v mb v mb

p Abbv. Slope Std. range (95% p Slope Std. range (95% P Slope Std. range (95% Funct. r (β) err. β CI) β=1 r (β) err. β CI) β=0.33 r (β) err. β CI) p β=0.67 β=1

Trapezius Trap SS 0.979 1.059 0.012 0.968-1.140 0.348 0.602 0.548 0.050 0.377-0.679 0.015* 0.796 0.877 0.074 0.662-1.060 0.767 0.002

Omotransversarius OmT SS 0.961 1.117 0.025 1.034-1.225 0.180 0.589 0.501 0.043 0.353-0.630 0.018* 0.809 0.964 0.084 0.750-1.129 0.272 0.037*

Rhomboideus Rho SS 0.955 1.099 0.028 1.000-1.205 0.389 0.592 0.419 0.030 0.261-0.551 0.020* 0.914 0.903 0.035 0.793-1.007 0.022 0.012

Serratus ventralis SeV SS 0.962 1.256 0.031 1.119-1.360 0.002 0.648 0.504 0.039 0.348-0.622 0.002 0.833 1.082 0.094 0.844-1.261 0.034* 0.366

Latissimus dorsi LtD HR 0.986 1.135 0.01 1.054-1.197 0.001 0.533 0.646 0.079 0.441-0.828 0.024* 0.840 0.962 0.072 0.738-1.140 0.145 0.043*

Pectoralis Pec HR 0.965 1.135 0.023 1.016-1.262 0.078 0.696 0.492 0.033 0.362-0.597 0.001 0.865 0.867 0.050 0.709-1.005 0.315 0.003

Deltoideus Delt HR 0.964 1.029 0.02 0.912-1.121 0.865 0.624 0.402 0.026 0.259-0.511 0.069 0.876 0.864 0.046 0.734-0.967 0.254 0.002

Infraspinatus Inf HR 0.985 1.165 0.011 1.076-1.259 <0.001 0.567 0.323 0.019 0.192-0.410 0.505 0.954 1.018 0.024 0.915-1.109 <0.001 0.605

Teres major TMj HR 0.975 1.091 0.015 0.997-1.191 0.139 0.630 0.461 0.034 0.246-0.640 0.044* 0.895 0.880 0.040 0.780-0.967 0.098 0.004

Subscapularis Sub HR 0.975 1.114 0.016 0.994-1.224 0.055 0.455 0.337 0.024 0.200-0.425 0.869 0.935 1.024 0.035 0.920-1.145 <0.001 0.522

Triceps brachii TrB EE 0.983 1.078 0.01 0.984-1.176 0.094 0.484 0.429 0.037 0.275-0.522 0.095 0.894 0.956 0.048 0.790-1.090 0.017 0.065

Anconeus Anc EE 0.884 1.113 0.071 0.952-1.248 0.858 0.547 0.426 0.033 0.268-0.527 0.345 0.800 0.935 0.083 0.760-1.056 0.447 0.016

Tensor fascia antebrachii TFA EE *0.972 1.224 0.022 1.097-1.345 0.001 0.589 0.556 0.053 0.379-0.701 0.009 0.869 0.993 0.064 0.801-1.152 0.033 0.119

Flexor carpi radialis FCR CDF 0.819 0.991 0.085 0.827-1.132 0.068 0.613 0.302 0.015 0.206-0.372 0.930 0.737 0.867 0.090 0.683-1.006 0.773 0.001

Flexor carpi ulnaris FCU CDF 0.967 1.195 0.025 1.049-1.333 0.009 0.543 0.354 0.023 0.230-0.439 0.757 0.911 1.082 0.052 0.929-1.225 0.001 0.859

Palmaris longus PaL CDF 0.966 1.156 0.024 1.027-1.295 0.036* 0.632 0.320 0.016 0.217-0.391 0.652 0.933 0.977 0.032 0.846-1.108 0.001 0.165

Flexor dig. Superficialis FDS CDF 0.941 1.099 0.036 0.934-1.238 0.602 0.647 0.325 0.016 0.250-0.386 0.180 0.881 0.912 0.049 0.727-1.077 0.087 0.014

Flexor dig. Profundus FDP CDF 0.98 1.148 0.014 1.039-1.254 0.003 0.745 0.371 0.016 0.261-0.455 0.017* 0.939 0.887 0.025 0.766-0.982 0.006 0.003

Pronator teres PrT PRO 0.957 1.066 0.025 0.943-1.200 0.714 0.641 0.237 0.009 0.173-0.287 0.187 0.873 1.057 0.070 0.789-1.258 0.010 0.068

Pronator quadratus PrQ PRO 0.833 1.001 0.081 0.749-1.172 0.095 0.534 0.347 0.023 0.242-0.441 0.001 0.737 0.879 0.093 0.593-1.068 0.836 0.003

Supraspinatus Sup HP 0.985 1.103 0.01 1.020-1.192 0.016* 0.422 0.353 0.027 0.223-0.440 0.662 0.923 1.022 0.041 0.873-1.145 0.001 0.421

Biceps brachii BiB HP/EF 0.974 1.119 0.017 1.028-1.213 0.053 0.455 0.365 0.028 0.198-0.477 0.390 0.913 1.025 0.046 0.889-1.135 0.001 0.387

Brachialis Bra EF 0.975 1.02 0.014 0.919-1.128 0.887 0.542 0.393 0.029 0.270-0.480 0.183 0.921 0.881 0.031 0.782-0.975 0.028 0.004

Extensor carpi radialis ECR CDE 0.983 1.055 0.01 0.966-1.146 0.286 0.218 0.392 0.039 0.276-0.432 0.456 0.911 1.045 0.049 0.838-1.216 0.001 0.534

Extensor dig. communis EDC CDE 0.96 1.053 0.023 0.922-1.194 0.827 0.217 0.237 0.014 0.147-0.574 0.051 0.942 1.029 0.031 0.881-1.173 <0.001 0.608

Extensor dig. lateralis EDL CDE 0.925 1.21 0.056 1.050-1.347 0.143 0.172 0.430 0.047 0.250-0.574 0.190 0.826 1.247 0.130 1.013-1.432 0.008 0.814

Extensor carpi ulnaris ECU CDE 0.978 1.12 0.015 1.025-1.212 0.030* 0.173 0.389 0.039 0.213-0.534 0.001 0.941 1.216 0.044 1.083-1.344 <0.001 0.056

Abductor digiti I longus AbdL CDE 0.959 1.124 0.027 0.984-1.283 0.184 0.336 0.425 0.042 0.253-0.564 0.397 0.887 1.053 0.062 0.860-1.219 0.005 0.462

Supinator Spr SUPI 0.959 1.071 0.024 0.947-1.173 0.614 0.235 0.345 0.050 0.282-0.426 0.136 0.873 1.057 0.070 0.789-1.258 0.010 0.397

9 The slope of isometric force (Fmax) was significantly correlated with mb and about a

10 third of the muscles (11/29) had significantly different slopes from 1.0 (mechanical

11 similarity). Ten of the muscles statistically different from mechanical similarity were active

12 during the power stroke, while brachialis (BRA) was the only muscle active during the

13 recovery stroke.

14 The Quenda forelimb had some muscles that had relatively greater PCSA and long FL

15 that indicated the capacity for greater power (Figure 3.5). The pectoralis (Pec), triceps

16 brachii (TrB) and serratus ventralis (SeV) occupied the high-power quadrant, while no

17 muscles were in the force-specialised quadrant. While the larger muscles acting across the

18 shoulder joint are expected to be more powerful, we saw that the largest elbow extensor,

19 triceps brachii (TrB), was more powerful that the shoulder stabilisers and humeral retractors

20 such as the latissimus dorsi (LtD) and rhomboideus (Rho).

21 22 Figure 3.5: Functional space plot (fibre length vs PCSA) for muscles in the forelimb of Quenda, 23 Isoodon fusciventer. Normalised PCSA is normalised to body mass (0.67) and normalised FL is 24 normalised to body mass (0.33). Data points are means with no error bars shown. Quadrant definitions 25 were based off the literature (Channon et al., 2009; Allen et al., 2010; Dick & Clemente, 2016; 26 Böhmer et al., 2018). High force is defined as high PCSA and shorter fibre length to produce large 27 forces over a small working range. High power is defined as long fibres and high PCSA, therefore, 28 muscles capable of producing high forces over a large working range. High shortening velocity is 29 defined as relatively longer fibres with a small PCSA with the muscles produce a small force but 30 contract over a longer distance. Generalised muscles have relatively small force production and fibre 31 lengths. 32

33 The scaling slopes of the muscles associated with the power stroke of digging, and

34 muscles associated with the recovery stroke of digging were not significantly different for

35 the muscles masses (Zn=20,9= 0.660, p= 0.509) and FL (Zn=20,9= 0.825, p= 0.409) however the

36 scaling slopes of PCSA showed a significant difference between the muscles associated with

37 the two phases of scratch-digging (Zn=20,9= -2.71, p= 0.007) with shallower slopes for muscles

38 active during the power stroke of scratch-digging.

39 3.6 Discussion

40 We have presented the first study on ontogenetic scaling of the forelimb muscle

41 architectural properties in a digging mammal, using 34 Quenda across a large range of body

42 masses. In our study, the majority of muscle PCSA values scaled with positive allometry,

43 while mm and FL primarily showed isometry. During growth, there appears to be a strong

44 investment in muscles associated with the power stroke of scratch-digging, and these

45 muscles account for 90% of the total forelimb muscle mass (range between 88-91%).

46 3.6.1 Isometry and some muscles of positive allometry in muscle mass throughout the

47 forelimb

48 The majority of the muscle masses in the Quenda forelimb scaled with isometry

49 suggesting that relative muscle size does not change throughout body size growth.

50 Previously published studies have found that generally, muscles tend to scale with positive

51 allometry during ontogenetic growth, although findings of isometric scaling are often

52 common (McGowan et al., 2008; Allen et al., 2010; Lamas et al., 2014; Marchi et al., 2018).

53 Positive allometry of muscles is often found in hindlimbs where the interpretation has been

54 that more forceful hindlimb muscles are required for behaviour such as escape locomotion

55 (Marchi et al., 2018). Given the strong anatomical patterns consistent with adaptation to

56 digging in the Quenda (Warburton et al., 2013b), we might have expected that more

57 muscles would scale with positive allometry. Consistent with this hypothesis, we found that

58 the six muscles scaled with positive allometry were all muscles that acted as the main

59 movers in the power stroke of scratch-digging. Latissimus dorsi (LtD) and serratus ventralis

60 (SeV) were two of the largest proximal placed muscles, and grew with positive allometry.

61 These two muscles would allow the forelimb to retract with a large force against the

62 resistance of the soil, and to stabilise the scapula during the power stroke of digging

63 respectively.

64 In the distal forelimb, Quenda have relatively massive carpal and digital flexor

65 muscles. This is consistent with patterns found in other scratch-digging mammals such as

66 the American badger (Taxidea taxus), in order to stabilise the carpus during the power

67 stroke of scratch-digging (Moore et al., 2013; Warburton et al., 2013b). In Quenda, the large

68 flexor digitorum profundus (FDP) grew with positive allometry and thus would be able to

69 produce an increasingly large force as the animal grows in comparison to the smaller

70 muscles of the forearm that grew with isometry. The flexor digitorum profundus (FDP) is the

71 largest muscles of the forearm, and its increasing relative mass is indicative of its primary

72 role in carpal and digital flexion against the resistance of the soil during digging.

73 3.6.2 Fibre length primarily shows isometry with total body mass

74 We found that FL primarily scaled with isometry, and of the five muscles in which FL

75 scaled with positive allometry were not confined to either the power-stroke (n=4) or

76 recovery phases (n=1) of scratch-digging. This is consistent with other ontogenetic studies

77 that have found that FL generally scales isometrically within species (Allen et al., 2010).

78 However, as FL is proportional to shortening velocity, longer fibres shorten at greater

79 velocity and therefore are capable of greater power generation, difference in FL between

80 species have been linked to differences in function (Lieber & Ward, 2011; Rose et al., 2016).

81 It has been suggested that positive scaling of FL is often associated with muscles for which

82 there is a strong functional signal for a particular behaviour (i.e., differing diets, different

83 locomotor styles) (Leischner et al., 2018). As such, we might expect that if larger Quenda are

84 digging to a greater extent that FL should have increased with positive allometry. We found

85 that FL of pectoralis (Pec) and serratus ventralis (SeV) have positive allometry with body

86 mass. These muscles have relatively long fibres generally, and thus the fact that these scale

87 with positive allometry suggests that they become capable of producing relatively greater

88 power as the animal grows. FL also scaled with positive allometry in tensor fascia

89 antebrachii (TFA), pronator quadratus (PrQ) and extensor carpi ulnaris (ECU), suggesting

90 that these muscles rely on FL and therefore shortening velocity for greater power

91 generation.

92 3.6.3 Strong positive allometry in PCSA for majority of forelimb muscles

93 The majority of muscles had positive allometry of PCSA consistent with other studies

94 (McGowan et al., 2008; Cuff et al., 2016; Dick & Clemente, 2016). This suggests that a

95 combination of factors are involved with the change in muscle force generation as Quenda

96 grow. The 18 muscles that were significantly different from isometry (i.e., scaled with

97 positive allometry) included half the muscles classified as main movers of the power stroke

98 (9/20) and all of the muscles associated with the recovery stroke of scratch-digging (9/9).

99 As animals grow larger, there are likely to be additional pressures on force production

100 for other behaviour including locomotion and the need for balancing large scratch-digging

101 forces with antagonistic forces to help stabilise joints. Positive allometry in PCSA has been

102 found in a range of other studies on ontogenetic scaling of muscles (Allen et al., 2010; Lamas

103 et al., 2014) and is generally interpreted as larger animals requiring relatively greater forces

104 than predicted to support a heavier body mass and muscles involved in locomotion.

105 Interestingly, all the muscles associated with the recovery stroke showed positive

106 allometry, but not all muscles associated with the power-stroke of scratch-digging did. This

107 significant difference in slope was the opposite of our hypothesis that the main movers of

108 the power stroke of scratch-digging would be more likely to grow with positive allometry.

109 While at first, this may appear to suggest differential investment in force production, it

110 could reflect the action of muscles in different regions of the limb. The muscles in the

111 recovery stroke, in general, are smaller in mass, but may increase their potential force

112 production by modifying their pennation angle in order to increase the number of fibres into

113 a given volume (Zajac, 1992). The carpal and digital flexors and extensors (CDF and CDE) had

114 isometric growth of FL, however, generally have a pennation angle of greater than 15

115 degrees. This allows them to produce a large force from their relatively small muscle mass.

116 In contrast, the main movers of the power stroke have a greater capacity to increase

117 potential force production by increasing muscle mass and/or fibre length.

118 A trade-off between force (PCSA) and speed of contraction (FL) was seen between the

119 smaller forearm muscles (force specialised), and the larger muscles closer to the shoulder

120 that showed relatively longer FL in the Quenda. This trade-off is common, for example, in

121 jaw muscles (Hartstone‐Rose et al., 2012) and hindlimbs (Marchi et al., 2018) as larger

122 animals have different functional pressures than smaller species. The positive allometry in

123 PCSA of the primate’s forelimbs combined with isometry in FL, suggests that larger species

124 rely on stronger forearms while smaller species have relatively faster and more flexible

125 forearms (Leischner et al., 2018). A similar case was made for interspecific variation in the

126 forelimb muscles of felids (Cuff et al., 2016); however, after a phylogenetic correction was

127 performed, these results were not significant. Our results were consistent with the

128 literature and shows the trade-off which allows relatively smaller muscles to be force

129 specialised.

130 3.6.4 Positive allometry in Fmax for one third of muscles

131 The isometric hypothesis states that for mechanical similarity, isometric force (N)

132 scales with total body mass to the power of 1.0 (Economos, 1982; Kokshenev, 2008; Rose,

133 2014). One third (11/29) of Quenda forelimb muscles scaled with positive allometry

134 including half of the power stroke muscles, and one recovery stroke of digging muscles. The

135 remaining two thirds of forelimb muscles in the Quenda are mechanically similar and thus

136 force production remains the same through ontogeny.

137 In Quenda, we primarily found mechanical similarity across the range of total body

138 sizes. This suggests that smaller body size is not necessarily a disadvantage for smaller

139 animals. In contrast, studies on jackrabbits (Lepus californicus), musk-ox (Ovibos

140 moschatus), and capuchin monkeys (Cebus spp.) have found that juveniles appear to be

141 ‘over-built’ in their musculoskeletal features to compensate for their smaller overall body

142 size (Torzilli et al., 1981; Carrier, 1983; Heinrich et al., 1999; Young, 2005). These studies

143 have principally related to hindlimbs and thus reflect adaptation for locomotion. Although

144 the isometric force production of small Quenda is absolutely lower than the large

145 individuals, it is unclear to what extent this relates to their digging performance. Data

146 regarding the propensity to dig, the depth of diggings and the substrate selection between

147 small and large individuals might reveal differential behaviour that could be associated with

148 muscle development.

149 3.6.5 No sex differences in forelimb muscle mass and architecture

150 There was a negligible difference between males and females in forelimb architecture,

151 although sexual dimorphism is known for this species (Warburton & Travouillon, 2016).

152 Differential muscle growth is reported in species where male-male competition involves

153 fighting with the forelimbs (Warburton et al., 2013a; Martin et al., 2018). However, in

154 bandicoot species, male-male fighting involves primarily biting (Stodart, 1966; Johnson,

155 1989), and is reflected in dimorphism in the dentition for some species, including the

156 Quenda (Flores et al., 2013; Travouillon et al., 2015; Warburton & Travouillon, 2016).

157 Negligible sexual dimorphism in forelimb musculature in the Quenda is therefore not

158 unexpected.

159 3.6.6 Further considerations

160 The method of determining if two properties are considered geometrically similar

161 (isometric pattern) or scale with positive or negative allometry has been fairly consistent

162 throughout the literature. The most commonly used method is using 95% confidence

163 intervals to assess the spread of data points around the regression line, and are classed as

164 isometric if the upper and lower confidence intervals include the relevant value for

165 isometry, i.e., mass: 1.0, lengths: 0.33, areas (volumetric property): 0.67 (Allen et al., 2010;

166 Lamas et al., 2014). In comparison, raw values of mm, FL and PCSA are transformed by the

167 value of isometry so that predicted regression slopes would be equal to 1.0 (Leischner et al.,

168 2018), or alternatively, the raw values are tested against the value of isometry (our

169 study)(Cuff et al., 2016). The second less common method requires the calculated slope to

170 be statistically tested against the isometric slope (geometric similarity). Scaling studies will

171 have multiple measures per individual, therefore associated type 2 errors, which are not 61

172 controlled when the 95% confidence intervals is the only method of assessing the allometric

173 relationship with the size component (Benjamini & Hochberg, 1995). We suggest using a

174 statistical test to assess the validity of claims of geometric similarity, or positive/negative

175 allometry in addition to using the 95% confidence intervals in further scaling studies.

176 3.6.7 Limitations of this study

177 Using wild-derived specimens that died of natural causes or as a result of a road

178 accident or dog/cat attack allowed us to collect a large sample size, although there was

179 variation in decomposition and damage to the bodies. Specimens were limited to those in

180 the best condition and steps were taken to reduce these sources of variation. Specimens

181 were frozen as soon as possible to stop decomposition; however, time of death to freezing

182 in some roadkill specimens was longer than euthanised animals. Fixation was required due

183 to the nature of the specimens; to ensure the decrease of mm and FL and allow comparison

184 to fresh-thawed studies, the data was corrected using the correction factors proposed by

185 Kikuchi and Kuraoka (2014). We also note that some animals were from areas where

186 supplementary feeding (i.e., by members of the public) is reported, and as such these

187 animals were potentially overweight (Hillman et al., 2017). Despite these limitations, wild-

188 derived specimens allowed the collection of a larger number of individuals that would

189 otherwise have been available, and thus to provide a robust test for intraspecific variation

190 that is, often lacking in comparable studies.

191 3.7 Conclusions

192 Here we present the first ontogenetic scaling study of forelimb muscle properties for

193 scratch-digging mammal. In this marsupial, we found that the force production may be the

194 driving factor in larger individuals benefitting from stronger forelimb muscles, while the

195 smaller individuals rely on relatively faster or more flexible forelimbs for their scratch-

196 digging. The mechanical similarity hypothesis was accepted in two third of the forelimb

197 muscles as the maximum isometric force (N) scaled with isometry (slope 1.0). The other

198 third of muscles (half of the main movers of scratch-digging) scaled with positive allometry.

199 This suggests that forelimb muscles force production remains the same through ontogeny

200 for two thirds of the muscles. We have also made recommendations for methods of scaling

201 assessment for future studies.

202 Our study emphasises the importance of intraspecific variation in morphological

203 studies and quantified the ontogenetic muscle architecture. Our results propose questions

204 around the functional behaviours of juveniles in comparison to adults, such as habitat

205 selection and scratch-digging frequencies that can be ultimately tested by behavioural

206 studies.

207 3.8 Acknowledgements

208 The authors would like to thank the two anonymous reviewers that provided detailed

209 comments and suggestions to improve the manuscript. We thank Kanyana Wildlife

210 Rehabilitation Centre for access to specimens. This study was carried out with the technical

211 support at Murdoch University, and we thank Diana Nottle, Zsa Zsa Wong and Joe Hong for

212 their assistance, and Natasha Tay for assisting in the preparation of the animals. Financial

213 support was provided by Murdoch University.

Chapter 4 Covariation between forelimb musculature

and bone shape in an Australian scratch-digging

marsupial: comparison of morphometric methods

4.1 Preface

This chapter has been published in Journal of Morphology:

Martin, M. L., Travouillon, K. J., Sherratt, E., Fleming, P. A. & Warburton, N. M. (2019),

Covariation between forelimb muscle anatomy and bone shape in an Australian scratch- digging marsupial: comparison of morphometric methods. Journal of Morphology, 280,

1900-1915. doi: http://doi.org/10.1002/jmor.21074

4.2 Abstract

The close association between muscle and bone is broadly intuitive; however, details of the covariation between the two has not been comprehensively studied. Without a quantitative understanding of how muscle anatomy influences bone shape, it is difficult to draw conclusions of the significance of many morphological traits of the skeleton. In this study, we investigated these relationships in the Quenda (Isoodon fusciventer), a scratch- digging marsupial. We quantified the relationships between forelimb muscle anatomy and bone shape for animals representing a range of body masses (124-1,952g) using two-block partial least square analyses. Muscle anatomy was quantified as muscle mass and physiological cross-sectional area (PCSA), and we used two morphometric methods to characterise bone shape: seven indices of linear bone proportions, and landmarks analysis. 65

Bone shape was significantly correlated with body mass, reflecting allometric bone growth.

Of the seven bone indices, only shoulder moment index (SMI) and ulna robustness index

(URI) showed a significant covariation with muscle anatomy. Stronger relationships between muscle anatomy and forelimb bone shape were found using the landmark coordinates: muscle mass and PCSA were correlated with the geometric shape of the scapula, humerus and third metacarpal, but to a lesser extent with the shape of the ulna. Overall, our data show that landmark coordinates are more sensitive than bone indices to capturing shape changes evident throughout ontogeny, and are, therefore, a more appropriate method to investigate covariation with forelimb muscle anatomy. Single-species studies investigating ontogeny require refined methods to accurately develop an understanding of the important relationships between muscle force generation and bone shape re-modelling. Landmark analyses provide such a method.

4.3 Introduction

An animal’s musculoskeletal system is shaped and altered as the individual grows in response to mechanical forces associated with movement (Currey, 2013). Differences in the musculoskeletal system between groups and species reflect adaptations to particular movements and behaviours, and thus also represent an animal species’ ecological niche. For example, arboreal species demonstrate strong elbow, wrist and digital flexors to meet the mechanical forces imposed by brachiation (Leischner et al., 2018). In contrast, cursorial species have proximal pelvic limb muscles enlarged and specialised for force generation, proximal thoracic limb muscles specialised for action over a large range of motion (Williams et al., 2008). For an individual animal, such relationships will also vary as the individual increases in body mass during growth, and/or changes aspects of its behaviour over time. 66

Characteristics of behaviour are particularly evident in the bony and muscular anatomy of digging mammals. In addition to the requirements for locomotion, these animals also demonstrate specialisations for digging. Species that are highly specialised for scratch-digging and burrowing (semi-fossorial and fossorial animals) have enlarged muscles associated with the power stroke (i.e., humeral retractors, elbow extensors and carpal/digital flexors) as they are required to match the mechanical resistance of the substrate they dig through (Moore et al., 2013; Warburton et al., 2013b). These muscular changes are then reflected in their bony anatomy. For example, the marsupial mole

(Marsupialia: Notoryctemorphia) shows some of the more extreme specialisations for fossorial behaviour; their forelimb bones reflect their highly-specialised digging motion of

‘sand-swimming’ in which they constantly back-fill their burrow (Warburton, 2006). All forelimb bones of the marsupial mole show specialisations: the scapula is elongated and narrow, which reflects a reduction on muscles attaching to the vertebral column to allow for more posterior rotation, while the humerus and ulna are short and robust to assist the forelimb in withstanding the large muscular force and reaction forces acting against the bone during the digging motion. These patterns are observable in extant and extinct

Notoryctes (Warburton, 2006; Beck et al., 2016). Scratch-digging mammals show similar forelimb adaptions, although in less extreme forms: shortened and robust bones, enlarged distal epicondyles, and increased bone surface areas providing increased surface area for enlarged muscle attachment (Tenrecoidea: Salton & Sargis, 2008; Cingulates: Milne et al.,

2009; Mustelidae: Rose et al., 2014; Myrmecophagidae: Sesoko et al., 2015). Such specialisations of the forelimbs provide the mechanical advantage necessary for digging.

The relationship between bone shape and muscle size in vertebrates reflects the forces acting on the musculoskeletal system as the body performs motions (Currey, 2013).

One measure could, therefore, be used to predict the other. Morphometrics and associated multivariate statistical analyses used to quantify biological shape and assess covariation largely follow two approaches (Adams et al., 2013). First, traditional morphometrics uses linear measurements and derived ratios/indices, which are useful in assessing bony proportions. These methods are particularly suited to studying articulated structures.

Second, advances in computing power have allowed rapid development of geometric morphometrics using coordinate-based methods (in two- or three-dimensions) to characterise shape; such methods are increasingly common practice for quantifying individual bone shapes (Zelditch et al., 2012). Understanding the relative value of each approach in the context of quantifying covariation with muscle anatomy would be helpful in guiding future work.

Unfortunately, muscles and bones are often studied separately, with quantitative studies that match these tissues being rare; the exceptions have focused on the skull, investigating bite force and feeding strategies (e.g., Fabre et al., 2014; Cornette et al., 2015;

Noback & Harvati, 2015; Fabre et al., 2018; Sella-Tunis et al., 2018). There has been a general lack of post-cranial studies, and very few studies have investigated the quantitative relationship between bones and muscles for digging mammals (Warburton et al., 2013b; e.g., Böhmer et al., 2018). Of those studies that have investigated interactions between muscles and bones, the majority have used linear bone measurements and associated ratios/indices to make inferences about muscle anatomy (e.g., Böhmer et al., 2018), while others have used landmark coordinates without clear extrapolation to muscle anatomy

(e.g., Carlon, 2014; McCabe et al., 2017). Also lacking in the literature are studies that explicitly compare the two methods of assessing bone shape (indices and landmark coordinates) (e.g., Bernal, 2007; Bonnan et al., 2008; Breno et al., 2011).

We carried out morphometric analysis of the forelimb of the Quenda (Isoodon fusciventer) to quantify the relationship between the shape of forelimb long bone and associated muscle anatomy (i.e., physiological cross-sectional area; PCSA) to test the extent to which these two parameters covary. Furthermore, we test indices and landmark coordinates as methods for quantifying bone shape, to determine which method provides the better insight into muscle force production. Specifically, we predict that shape covariation will be greater between the landmark coordinates and muscle PCSA as this method captures the precise geometry of bones, and thus conveys more shape information in comparison with the bone indices.

Quenda employ their forelimbs for scratch-digging to search for subterranean food items (e.g., invertebrates, bulbs and fungi fruiting bodies) as well as to construct short burrows and nests for shelter (Gordon & Hulbert, 1989; Long, 2009; Warburton &

Travouillon, 2016). The species displays adaptations of both muscle and bone anatomy associated with scratch-digging (Warburton et al., 2013b). This study builds on previous findings that showed Quenda force generation capacity (PCSA) showed differential growth between main movers of the recovery stroke and the power stroke of scratch-digging

(Martin et al., 2019b). We, therefore, predicted that muscles associated with the power- stroke of digging will have disproportionate influence on shape change of the long bones. To account for allometric effects, we repeated analyses on the residuals of linear regressions of muscle forces (PCSA) and bone shape data against body mass. This study contributes to our

understanding of how forelimb muscle anatomy influences bone shape, and how these structures functionally covary in a digging marsupial as a reflection of the mechanical demands of scratch-digging.

4.4 Materials and methods

The Quenda (Isoodon fusciventer), formerly listed as a subspecies of Southern Brown

Bandicoot (I. obesulus fusciventer), has recently been raised to species level based on cranio-dental morphology and molecular data (Travouillon & Phillips, 2018). We sampled 32

Quenda specimens: 19 males (1041 ± 667 g) and 13 females (810 ± 356 g). Males can reach larger body mass than females (males: 500-1800g, females: 400-1200g; Warburton &

Travouillon, 2016), although the difference in body mass in our data set was not significant

(t32= -1.15, p=0.259). The specimens were from either Kanyana Wildlife Rehabilitation

Centre, Lesmurdie, Western Australia, or opportunistically collected throughout the Perth metropolitan area (Regulation 17 licence SF010344; accidental deaths including road collisions, pool drownings and domestic pet attack). As some specimens were often injured, forelimb bones were sometimes too badly damaged to be analysed; consequently, the number of specimens per bone varied.

Muscles were dissected and measured for mass and PCSA; these data have been fully analysed and published separately (Martin et al., 2019b) (Table 4.1). Muscle mass is a representation of overall muscle size, while PCSA is a representation of maximum isometric force production for individual muscles. PCSA is generally considered a more appropriate measure of muscle force potential to interpret biological functions in correlation with skeletal morphology (Thorpe et al., 1999; Myatt et al., 2012).

Table 4.1: List of muscles, abbreviations and functional groups used for the study. Muscles ordered proximal to distal and colours represent muscle groups and are represented in Figures 4.3-4.6. Muscle Abbreviation Usage in 2b-PLS with indices Functional group

Trapezius Tra - Scapula stabilisation (SS) Omotransversarius OmT - Scapula stabilisation (SS) Rhomboideus Rho - Scapula stabilisation (SS) Serratus ventralis SeV - Scapula stabilisation (SS) Supraspinatus Sup BI, SMI Humeral protraction (HP) Latissimus dorsi LtD SMI, HTRI, HCRI Humeral retraction (HR) Pectoralis group Pec BI, SMI, HTRI, HCRI Humeral retraction (HR) Deltoideus Del SMI, HTRI, HCRI Humeral retraction (HR) Infraspinatus Inf BI, SMI Humeral retraction (HR) Teres major TMj BI, SMI Humeral retraction (HR) Subscapularis Sub BI, SMI Humeral retraction (HR) Biceps brachii BiB BI, SMI, HTRI, HCRI, URI Elbow flexion (EF) Brachialis Bra BI, HTRI, HCRI, URI Elbow flexion (EF) Triceps brachii TrB BI, SMI, HTRI, HCRI, EI, IFA Elbow extension (EE) Anconeus Anc BI, EI. IFA Elbow extension (EE) Tensor fascia antebrachii TFA IFA Elbow extension (EE) Flexor carpi radialis FCR BI, EI, URI Carpal/digital flexion (CDF) Flexor carpi ulnaris FCU BI, EI, URI Carpal/digital flexion (CDF) Palmaris longus PaL BI, EI, URI Carpal/digital flexion (CDF) Flexor digitorum FDS BI, EI, URI Carpal/digital flexion (CDF) superficialis Flexor digitorum profundus FDP BI, EI, URI Carpal/digital flexion (CDF) Pronator teres PrT BI, EI, URI Pronators (PRO) Pronator quadratus PrQ URI Pronators (PRO) Extensor carpi radialis ECR BI, EI, URI Carpal/digital extension (CDE) Extensor digitorum EDC BI, EI, URI Carpal/digital extension (CDE) communis Extensor digitorum lateralis EDL BI, EI, URI Carpal/digital extension (CDE) Extensor carpi ulnaris ECU BI, EI, URI Carpal/digital extension (CDE) Abductor digiti I longus AbdL URI Carpal/digital extension (CDE) Supinator Spr BI, URI Supinators (SUPI) Abbreviations: BI, brachial index; EI, epicondyle index; HCRI, humerus cranial-caudal robustness index; HTRI, humerus transverse robustness index; IFA, index of fossorial ability; SMI, shoulder moment index; URI, ulna robustness index.

4.4.1 Morphometric analyses

The scapula, humerus, ulna and third metacarpal for each specimen were scanned using a Skyscan 1176 scanner (Bruker microCT; Centre of Microscopy, Characterisation and

Analysis, The University of Western Australia) at a resolution of 35μm, with a 0.2-mm aluminium filter, voltage 45kv and amperage 550μA. Scans were reconstructed using the software NRecon (Burker microCT) that produces stacks of images that were visualised in

CTAn (Burker microCT) as a 3D digital volume. The volumes were processed via a threshold approach to digitally dissect the bone, and surface models of the bones were exported as .ply files.

Indices: Seventeen linear variables of the scapula, humerus, ulna, and third metacarpal were measured on skeletal material to the nearest 0.01mm using PES digital callipers (Figure 4.1 & Figure 4.2) that were combined to calculate seven functional indices that represent attributes of the forelimb bones and the potential mechanical efficiency for digging (Table 4.2). The indices have developed in the literature as means of characterising limb specialisations, primarily used to distinguish between species, and predict different styles of digging and ecology (i.e., diggers, occasional diggers, generalised, burrowers, and cursorial) (Hopkins & Davis, 2009). Indices are ratios, and therefore control for size variation in the sample (Klingenberg, 2016).

Figure 4.1: Linear measurements (solid lines) and landmarks (red points) used in the analysis to quantify shape variation on the forelimb. Dashed lines represent curves of semi-sliding landmarks. (A) 15 homologous landmarks and 81 semi-sliding landmarks for the scapula, and scapular length (SL) and glenoid cavity diameter (GC) linear measurements. (B) 41 homologous landmarks and 33 semi-sliding landmarks for the humerus, and diameter of the epicondyles (DEH), deltoid length of humerus (DLH), transverse diameter of humerus (TDH), functional humeral length (HL), cranial caudal diameter of humerus (CCDH) linear measurements. Scale bar represents 100mm.

Figure 4.2: Linear measurements (solid lines) and landmarks (red points) used in the analysis to quantify shape variation on the forelimb. Dashed lines represent curves of semi-sliding landmarks. (A) 19 homologous landmarks and 57 semi-sliding landmarks for the ulna, and transverse diameter of the ulna (TDU), olecranon length (OL), total ulna length (UL) linear measurements. (B) 14 homologous landmarks and 37 semi-sliding landmarks for the third metacarpal. Scale bar represents 100mm.

Table 4.2: Description of the seven indices of digging ability used in this study. Descriptions of how each related to the functional morphology of the forelimb. Calculation Description References†

Brachial index (BI) BI= (UL-OL)/HL x 100 Indication of relative distal out- 4, 5 lever length

Shoulder moment index (SMI) SMI= DLH/HL x 100: Indication of the mechanical 1, 2, 3, 4, 5 advantage of the pectoral muscles and deltoid to act across the shoulder joint

Humerus transverse HTRI= TDH/HL x 100: Indication of the robustness of the 1, 2, 3, 4, 5 robustness index (HTRI) humerus

Humerus cranial-caudal HCRI= CCDH/HL x 100 Indication of the robustness of the 5 robustness index (HCRI) humerus

Epicondyle index (EI) EI= DEH/HL x 100: Indication of width available for the 1, 2, 3, 4, 5 flexor, pronator and supinator muscles of the forearm to attach

Ulna robustness index (URI) URI= TDU/(UL-OL) x 100: Indication of the robustness of the 1, 4, 5 forearm

Index of fossorial ability (IFA) IFA= OL/(UL-OL) x 100: Indication of mechanical 1, 2, 3, 4, 5 advantage of the triceps in the elbow extension and is considered a good indicator of fossoriality

Note: Descriptions of how each related to the functional morphology of the forelimb. Abbreviations: CCDH, cranial caudal diameter of humerus; DEH, diameter of the epicondyles; DLH, deltoid length of humerus; HL, humeral length; OL, olecranon length; TDH, transverse diameter of humerus; TDU, transverse diameter of the ulna; UL, ulna length. †References: 1(Elissamburu & de Santis, 2011), 2(Elissamburu & Vizcaíno, 2004), 3(Lagaria & Youlatos, 2006), 4(Rose et al., 2014), 5(Warburton et al., 2013b)

Landmark coordinates: The software IDAV Landmark Editor v.3.6 (Wiley et al., 2005) was used to place homologous landmarks and curves of semi-sliding landmarks to the bones

(Figure 4.1 & Figure 4.2; detailed descriptions of the individual landmarks are listed in supplementary Table S4.1-S4.4). Landmark coordinates analyses were completed in R (R

Development Core Team, 2018) using the package geomorph v.3.0.4 (Adams & Otárola-

Castillo, 2013; Adams et al., 2018). Landmarks were aligned using Generalised Procrustes

Superimposition to remove the effects of size, position and orientation, leaving only shape

variation (Rohlf & Slice, 1990). During Procrustes superimposition, the semi-sliding landmarks were permitted to slide along their tangent directions to minimise the Procrustes distance between specimens.

4.4.2 Allometry in bone shape

In order to address potential sex differences and allometric relationships between bone shape and muscles, we used a multivariate analysis of covariance (MANCOVA) model testing bone shape (indices and landmark coordinates as dependent variables in two separate analyses) against log-transformed body mass, with sex as an independent factor

(implemented with function procD.lm in geomorph; R Development Core Team, 2018). The models were evaluated for statistical significance with Goodall’s F-test (Goodall, 1991) using a permutation procedure (1000 permutations).

4.4.3 Covariation between forelimb muscle anatomy and bone shape

To quantify the strength of covariation between forelimb muscle anatomy (mass and

PCSA) and bone shape (indices and landmark coordinates), we carried out a series of two- block partial least squares (2b-PLS) analyses (Rohlf & Corti, 2000) implemented using two.b.pls function in geomorph. This method identifies axes in two multivariate datasets

(‘blocks’) that explain the covariance between them and quantifies the degree of association between the two blocks by constructing pairs of variables that are linear combinations of the variables within each of the two blocks. The linear combinations construct new variables that account for as much of the covariation as possible between the two blocks. The results of the 2b-PLS analysis are an r-PLS value: the correlation coefficient between the scores of

projected values from the two blocks, and an associated p-value: empirically calculated from the resampling procedure.

First, 2b-PLS was performed individually for the seven indices against log-transformed muscle anatomy including only muscles associated with the bone or the index (listed in

Table 4.1); and for landmark coordinates each forelimb bone individually against log- transformed muscle anatomy. These tests examined the strength of covariation due to allometry.

Second, to exclude the effects of allometry (Klingenberg, 2016), the above analyses were repeated using residuals calculated from linear regressions of the bone shape data

(indices, landmarks) and muscle anatomy (mass and PCSA) against log-transformed body mass. These were calculated using resid(lm) (R Development Core Team, 2018) for each index and muscle anatomy, and using procD.lm in geomorph for landmark shape data.

4.5 Results

4.5.1 Allometry in bone shape

Indices

Of the seven indices, only three showed significant allometric associations with body mass (Table 4.3). The brachial index (BI), humerus cranial-caudal robustness index (HCRI) and the index of fossorial ability (IFA) were associated with body mass, while the remaining four indices showed no significant association. HCRI was the only index to show an interaction for sex, although there was no significant interaction term between body mass and sex, indicating that the allometric slopes between males and females were the same.

Table 4.3: Examining allometry: MANCOVA's of the three significant indices and the four forelimb bones (landmark coordinates) shape by size (Y ~ total body mass) and sex (Y ~ total body mass*sex). Df SS MS R2 F P a. Indices Brachial index (BI) shape Log(body mass) 1 167.67 167.67 0.23 8.64 0.011 Sex 1 10.48 10.48 0.01 0.54 0.437 Log(body mass) x Sex 1 1.13 1.13 <0.01 0.06 0.748 Residuals 28 543.40 19.41 Total 31 722.67 Humerus cranial-caudal robustness index (HCRI) shape Log(body mass) 1 25.40 25.40 0.43 25.19 0.001 Sex 1 4.94 4.94 0.08 4.90 0.006 Log(body mass) x Sex 1 0.11 0.11 <0.01 0.11 0.644 Residuals 28 28.23 1.01 Total 31 58.68 Index of fossorial ability (IFA) shape Log(body mass) 1 66.10 66.10 0.23 8.73 0.007 Sex 1 0.58 0.58 <0.01 0.08 0.765 Log(body mass) x Sex 1 10.06 10.06 0.04 1.33 0.185 Residuals 28 211.95 7.57 Total 31 288.79 b. Landmark coordinates Scapular landmark shape Log(body mass) 1 0.01 0.01 0.14 2.43 0.007 Sex 1 0.01 0.01 0.07 1.24 0.130 Log(body mass) x Sex 1 <0.01 <0.01 0.06 1.12 0.161 Residuals 13 0.05 <0.01 Total 16 0.06 Humeral landmark shape Log(body mass) 1 0.01 0.01 0.16 5.09 0.001 Sex 1 <0.01 <0.01 0.03 1.11 0.149 Log(body mass) x Sex 1 <0.01 <0.01 0.02 0.74 0.515 Residuals 26 0.05 <0.01 Total 29 0.07 Ulnar landmark shape Log(body mass) 1 <0.01 <0.01 0.08 2.32 0.042 Sex 1 <0.01 <0.01 0.02 0.56 0.834 Log(body mass) x Sex 1 <0.01 <0.01 0.04 1.04 0.297 Residuals 26 0.05 <0.01 Total 29 0.06 Third landmark metacarpal shape Log(body mass) 1 0.01 0.01 0.09 2.91 0.001 Sex 1 <0.01 0.01 0.05 1.61 0.027 Log(body mass) x Sex 1 <0.01 <0.01 0.03 0.93 0.352 Residuals 28 0.09 <0.01 Total 31 0.10 Note: Degrees of freedom (Df), sums of squares (SS), coefficient of determination (R2), and the F ratio and associated P- value. Bold values indicate p-values less than 0.05. Remaining indices (p>0.05) can be found in Table S4.5.

Landmarks coordinates

MANCOVA showed significant allometric associations between body mass and bone shapes for the landmark coordinates for all four bones (Table 4.3). Body mass explained between 8-16% of the shape variation, while sex explained less than 7% of the shape variation (Figure 4.3a, 4.4a, 4.5a, 4.6a). Allometry in scapular shape was primarily evident in the cranial and caudal borders as well as the shape and relative size of the glenoid cavity and coracoid process. The humerus primarily changed in the deltopectoral crest and the epicondyles. Shape variation in the ulna was primarily in the trochlear notch. The third metacarpal showed minimal shape change to the proximal end of the bone, although this shape change was strongly associated with body mass.

Figure 4.3: Results of the morphometric covariation analysis for the scapula (a b c d e) using landmark data. Results of the multivariate regression which visualises the patterns between size and shape (allometry) (a) and the results of the two-block partial least square (2b-PLS) between the forelimb landmark data and the muscle mass (b c) and PCSA (d e) of the associated forelimb muscles. Residual values of the landmark data and muscle data were used to control for the size component. Scatter plots of the first PLS axis describing the shape covariation between scapula and the PCSA of the forelimb muscles (b d). Forelimb bone shapes associated with each minimum and maximum of covariation are illustrated on the axis. Muscle loadings associated with the forelimb bone shape covariation are represented in the histogram (c e). Muscles are ordered from proximal to distal and colour coded by functional group (Table 4.1). In this and further figures, there are varying numbers of individuals between the bones due to some bones being too badly damaged to be analysed.

Figure 4.4: Results of the analysis for the humerus (a b c d e) using landmark coordinates. Format as per Figure 4.3.

Figure 4.5: Results of the analysis for the ulna (a b c d e) using landmark coordinates. Format as per Figure 4.3.

Figure 4.6: Results of the analysis for the third metacarpal (a b c d e) using landmark coordinates. Format as per Figure 4.3.

Only the third metacarpal showed a significant sex difference in the change of shape

(4.8% of variance). The interaction terms between body mass and sex for all four bones were not significant, which indicates that allometric slopes did not differ between the sexes.

Due to these negligible sex differences, the sexes were pooled for all further analyses.

4.5.2 Covariation between bone shape and forelimb muscle anatomy

Indices

Four of the bone shape indices – BI, HTRI (the humerus transverse robustness index),

HCRI and IFA – showed significant covariation associated with muscle mass; three of these –

BI, HCRI, IFA – were those indices also correlated with body mass. Two indices covaried with muscle PCSA: HCRI, SMI (shoulder moment index) (Table 4.4). When allometric relationships were removed, only two indices retained significant covariation: URI (ulna robustness index) covaried with muscle mass (largely the FCR [flexor carpi radialis], and FDS [flexor digitorum superficialis]), while SMI covaried with muscle PCSA (largely driven by the Inf [infraspinatus], and LtD [latissimus dorsi]).

Table 4.4: Results of the two-block partial least square (2b-PLS) analyses using (a) indices and (b) landmark coordinates. Raw data Residual values

N LOG mm LOG PCSA mm PCSA

r-PLS P r-PLS P r-PLS P r-PLS P

a. Indices

Brachial index 32 0.501 0.011 0.304 0.176 0.388 0.161 0.189 0.645

Shoulder moment index 32 0.281 0.122 0.469 0.016 0.397 0.055 0.450 0.035

Humerus transverse robustness index 32 0.625 0.001 0.219 0.435 0.295 0.239 0.136 0.837

Humerus cranial-caudal robustness index 32 0.702 0.001 0.507 0.011 0.337 0.123 0.367 0.089

Epicondyle index 32 0.258 0.216 0.249 0.362 0.194 0.589 0.287 0.275

Ulna robustness index 32 0.217 0.308 0.254 0.329 0.518 0.024 0.222 0.497

Index of fossorial ability 32 0.472 0.006 0.305 0.142 0.234 0.401 0.160 0.606

b. Landmark coordinates

Scapula 17 0.875 0.006 0.860 0.014 0.848 0.035 0.763 0.274

Humerus 30 0.880 0.001 0.866 0.001 0.837 0.001 0.757 0.031

Ulna 30 0.568 0.156 0.567 0.172 0.518 0.421 0.492 0.723

Third metacarpal 32 0.901 0.001 0.894 0.001 0.784 0.004 0.732 0.048

Note: Columns show analyses for log muscle mass and then muscle PCSA data (left-hand block; log-transformed values for the indices and associated muscle anatomy) and then residual data (right-hand block). Values shown are the r-PLS (correlation coefficient between the scores of the indices and muscle data) and associated p-value. Bold values indicate a significant relationship.

Landmark coordinates

Covariation between the landmark coordinates and forelimb muscle anatomy (both mass and PCSA) was evident for the scapula, humerus and third metacarpal; no covariation was seen between the ulnar shape and muscle anatomy. When the influence of body mass was removed (analysis of residuals), covariance remained significant in all bones except between scapular shape and muscle PCSA (Table 4.4).

Variation in muscle anatomy was associated with changes in the scapular shape at the cranial and caudal borders, as well as the scapula spine, and therefore change in shape of 85

the infraspinous, supraspinous and subscapular fossae (Figure 4.3b, d). Scapular shape varied with mass of the omotransversarius (OmT), rhomboideus (Rho) and infraspinatus (Inf)

(Figure 4.3c). Muscle PCSA varied additionally with the subscapularis (Sub) (Figure 4.3e) although this association was not significant when using the residual data. Individuals with more massive muscles and greater PCSA values had a scapula with a relatively more rounded and wider supraspinous fossa.

For the humerus, variation in muscle anatomy was associated with changes in the humeral capitulum, lesser tuberosity and the pectoral ridge, and therefore changes in articulation at the elbow joint, and muscle origin and insertion points (Figure 4.4b, d).

Humeral shape varied with mass of the carpal/digital extensors, pectoralis (Pec) and the pronator teres (PrT) (Figure 4.4c). Humeral shape varied with PCSA of the subscapularis

(Sub), infraspinatus (Inf) and extensor carpi radialis (ECR) (Figure 4.4e). Individuals with more massive muscles and greater PCSA values had a larger lesser tuberosity and a rotated/bowed humeral shaft.

There was no significant covariation between muscle anatomy (mass and PCSA) and ulnar shape (Figure 4.5b, d). The shape change evident in the ulna was concentrated at the trochlear notch, coronoid process and olecranon, and therefore changes in the articulation surface and the insertion for triceps brachii.

For the third metacarpal variation in muscle anatomy was associated with changes in the proximal end shape and shaft robustness (Figure 4.6b, d), and therefore changes in the articulation surface with the carpal bones. Shape of the third metacarpal varied with mass of the flexor digitorum superficialis (FDS), extensor carpi ulnaris (ECU), and extensor digitorum lateralis (EDL). Shape of the third metacarpal varied with PCSA (Figure 4.6e) of 86

flexor digitorum superficialis (FDS) and extensor carpi radialis (ECR). Individuals with more massive muscles and greater PCSA values had a more robust metacarpal shaft.

4.6 Discussion

Few studies in the literature have assessed the relationship between muscle anatomy and bones, and rarely have quantified the covariation of the forelimb muscle anatomy with bone shape (Cornette et al., 2013; Fabre et al., 2014; Fabre et al., 2018). We present the first study to demonstrate strong quantitative relationships between muscle anatomy and bone shape in limbs of a digging mammal. Independent of the allometric relationships with body mass, we identified strong correlations between muscle anatomy (mass and PCSA) and forelimb bone shape (quantified using landmark coordinates) in the Quenda. This study is, therefore, an important step to understand how the musculoskeletal system functions, and can be used to formulate questions concerning the functional significance of morphological traits in the forelimb.

4.6.1 Sex differences in bone shape change

Quenda are sexually dimorphic in body mass (Warburton & Travouillon, 2016) and we, therefore, predicted that there may be some sex differences in forelimb musculature or bone shape. Despite these differences, we previously identified that Quenda forelimb muscle anatomy (muscle mass, PCSA, and fibre length) showed no significant difference between the sexes (Martin et al., 2019b). Furthermore, there were only minor sex differences in a single forelimb bone in the Quenda in the present study; the third metacarpal showed shape differences between the sexes, with males having a smaller, less elongated proximal end. These minor differences in bone shape development, which were

likely to be driven by body mass and associated forelimb muscle anatomy, were unlikely to result in major functional differences in forelimb mechanics between males and females.

4.6.2 Indices associated with body mass and muscle anatomy

The indices are ratios of the forelimb bones and represent relative proportions that are commonly used to determine locomotion and digging ability (Lagaria & Youlatos, 2006;

Rose et al., 2014). While typically used to distinguish between species of differing digging, climbing and running abilities (Hopkins & Davis, 2009), these indices are rarely used for single-species ontogenetic studies. Since the ratio of bone proportions within one species likely exhibits less variability (intra-specific variation) compared with the large variability evident between species (inter-specific variation), it was unsurprising that the seven bone indices showed little association with muscle anatomy in this ontogenetic study. However, the brachial index (BI), HCRI, and index of fossorial ability (IFA) all increased with body mass in the Quenda, suggesting increased mechanical adaptation for digging as individuals grow.

The BI reflects the relative distal out-lever length and therefore mechanical leverage able to be applied across the whole limb. Throughout the literature, semi-fossorial species often show small values for the BI (Vizcaíno et al., 1999; Vizcaíno & Milne, 2002). BI increased with body mass in the Quenda, which was unexpected as digging species generally show relatively shorter forearms (Warburton et al., 2013b).

Greater values for the two humeral robustness indices (HTRI or HCRI) is a common finding for digging mammals (Lagaria & Youlatos, 2006; Elissamburu & de Santis, 2011).

Digging mammals that employ a sprawled and abducted limb during humeral rotation digging, such as echidnas (Tachyglosidae) and moles (Talpidae) (Barnosky, 1981; Hopkins &

Davis, 2009), have an increased robustness in the transverse axis. By contrast, the HCRI

(rather than HTRI) increased significantly with body mass in Quenda. These animals scratch- dig in an cranial-caudal plane (parasagittal), and therefore forces are concentrated in this axis (Warburton et al., 2013b). The HCRI, therefore, captures the ability of the humerus to resist the high bending loads in this plane.

Lastly, of all the indices, IFA is reported to be the best predictor of fossorial ability

(Vizcaíno et al., 1999; Vizcaíno & Milne, 2002; Elissamburu & Vizcaíno, 2004; Lagaria &

Youlatos, 2006; Warburton et al., 2013b; Rose et al., 2014). IFA likely increases with body mass to increase the mechanical advantage of the triceps and elbow extensor muscles.

Few indices also showed covariation with both the residual muscle masses (URI) and

PCSA (SMI). For URI, robustness of the ulna is potentially driven by antebrachium muscle mass. SMI covaried with muscle PCSA and showed shape change in the deltoid and pectoral ridge, which is reasonable as SMI represents the mechanical advantage of the pectoral and deltoid muscles across the shoulder. Few of the indices showed significant covariation with muscle anatomy, and indices, therefore, show limited use for predicting muscle anatomy in the forelimb.

4.6.3 Landmark coordinates associated with body mass and muscle anatomy

The shape of all four forelimb bones of the Quenda changed with allometry and showed strong covariation with forelimb muscle mass and PCSA. Although the strongest relationships were exhibited for body mass, even the residual muscle and bone shape data showed significant relationships. The minimal difference between the strength of covariation calculated from log-transformed muscle anatomy and the residual muscle

anatomy suggests that the association of muscle on bone shape is not just associated with allometry, but other factors also contribute.

In response to both increasing body mass and increasing muscularity, the scapula became more rounded, showed an increase in surface area (specifically infraspinous and supraspinous fossae), and widening of the acromion. The infraspinatus and supraspinatus drove this shape change and their large fleshy origins were likely to be a stronger influence on scapular shape compared with the influence of discrete or tendinous insertions shown by other muscles. The broad scapula seen in the Quenda is commonly seen in digging mammals

(Hildebrand, 1985; Moore et al., 2013; Rose et al., 2014) and assists in stabilising the shoulder by increasing surface area for the rotator cuff muscles, increasing force output while digging (Jenkins, 1973; Argot, 2001; Warburton et al., 2013b).

Allometry with body mass and covariation with muscle anatomy showed the humerus became more robust (wider humeral shaft) with an increased pectoral ridge and lesser tuberosity, while the capitulum decreased in relative size. Larger specimens had relatively smaller distal joints, a pattern that has also been observed for extant and fossil armadillos

(order Cingulata; Milne et al., 2009) and has been attributed to allometric principles in that the surface area of bone increases in proportion to the square of the length increase.

Different muscles covaried with humeral shape for the analyses of muscle mass or PCSA measures, suggesting that muscle size and force production influence humeral shape differently. Rotation of the pectoral ridge would be driven by an increase in the pectoralis muscle mass and by the distal insertion of the pectoralis, an observation that has been noted previously (Warburton et al., 2013b). Enlarged deltopectoral ridges are associated

with digging (Milne et al., 2009) as well as a large lesser tuberosity, which allows for increased surface area for the subscapularis to stabilise the shoulder joint (Argot, 2001).

Shape of the ulna showed the weakest association with body mass and muscle anatomy, suggesting that there was substantial variation in ulnar shape that did not strongly reflect ontogeny. The patterns that were observed align with previous studies showing that digging species develop a deeper concave trochlea surface to stabilise the elbow joint (Rose et al., 2014; Sesoko et al., 2015) and limit rotation in the elbow (Jenkins, 1973; Argot, 2001;

Andersson, 2004). Interestingly, the ulna robustness index (URI) significantly covaried with muscle mass; therefore, the URI may be better than landmark coordinates in representing muscles around the ulna.

The minimal shape change in the proximal end of the third metacarpal bone was strongly associated with body mass and muscle anatomy. Digging mammals have relatively short and robust metacarpals with elongated claws to provide great out-forces for digging

(Hildebrand, 1985; Salton & Sargis, 2008; Moore et al., 2013). All the forces generated throughout the limb are concentrated into the metacarpals and claws to cut through compact soil (Rose et al., 2014). Therefore, the metacarpals would be subject to large selective pressures. This may explain the strong patterns we observed, despite little obvious shape change.

4.6.4 Comparison of indices and landmark coordinates reveals the strengths and

limitations of both methods

Indices were designed to be a proxy of fossorial ability across a wide variety of taxa

(Hopkins & Davis, 2009) using large sample sizes. We found that indices were largely unable

to distinguish small changes in the forelimb that occur in an ontogenetic series, probably because many of the changes in the bones are not captured in the linear measurements

(Zelditch et al., 2004). Compared with bone indices, Quenda bone shape captured as three- dimensional landmarks more strongly covaried with muscle anatomy. Landmark and semi- sliding landmarks allow for detailed comparison (Bernal, 2007; Maderbacher et al., 2008;

Breno et al., 2011), and the landmark coordinate method is, therefore, the most suitable to represent/act as a proxy for muscle anatomy in an ontogenetic study. Our data was collected through 3D digital methods (micro-CT); however, landmarking is also possible from photographs (2D) that is an inexpensive and time-efficient method of landmarking.

Arguably, 2D and 3D produce similar results (e.g., Cardini, 2014; Buser et al., 2018).

4.7 Conclusion

We present the first study to show a strong association between muscle anatomy and bone shape irrespective of their inherent allometric correlations with body mass. Allometry with body mass was a large driver of bone shape, but we also identify relationships between muscle anatomy (mass and PCSA) that determined bone shape in the Quenda. Notably, muscles that were drivers on the bone shape were shoulder stabilisers and humeral retractors (main movers in the power stroke of digging), vital for generating large out-forces and pulling the forelimbs horizontally against the resistance of the soil in this digging species. Our results show that bone shape (3D landmarks) can be a good proxy for muscle anatomy, and bone shape analysis could, therefore, be used in the future for reconstructions of extinct species.

4.8 Acknowledgements

The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Centre for Microscopy, Characterisation & Analysis, The

University of Western Australia, a facility funded by the University, State and

Commonwealth Governments. The authors also thank Kanyana Wildlife Rehabilitation

Centre for access to specimens. This study was carried out with the technical support at

Murdoch University, and we thank Diana Nottle, Zsa Zsa Wong and Joe Hong for their assistance. Financial support was provided by Murdoch University.

Chapter 5 The covariation of bone shape and muscle

anatomy; a comparative study of Australian digging

marsupials

5.1 Abstract

There is a large degree of ecological variation within and between marsupial lineages, with species variously adapted to digging, climbing and locomotion. These adaptations should therefore be reflected in musculoskeletal anatomy. Here, we tested the extent to which a species’ digging ability (burrower, forager, non-digger) influences forelimb bone shape and muscle force generation (quantified using physiological cross-sectional area;

PCSA) in 11 marsupial species. Forelimb bone shape was quantified using bone indices and landmark coordinates. Both measures of bone shape as well as muscle PCSA described significantly different forelimb morphology between the digging categories. Muscle PCSA, and the shape of the ulna and third metacarpal remained significantly different after the phylogenetic correction. By quantifying covariation between our measures using two-block partial least square analyses, we also assessed the capacity for bone shape to predict muscle

PCSA. The analyses revealed a relationship between the scapular and third metacarpal shape with muscle PCSA, but after phylogenetic correction, the scapular, humeral, third metacarpal 3D shape and four of the bone indices had strong covariation with muscle PCSA.

This demonstrates that forelimb muscle force and, to a lesser extent, ulnar and third metacarpal shape is altered by digging behaviour and that there is strong covariation with muscle forces acting on the forelimb bones. These data will assist in interpretations of 95

forelimb variation and morphology and for refining biomechanical models in extant and extinct marsupials.

KEYWORDS: Bone index – fossorial adaptations – geometric morphometrics – muscle architecture – PCSA – two-block partial least square.

5.2 Introduction

The vertebrate musculoskeletal system is an integrated system dependent on a combination of bones, cartilage elements, ligaments, muscles and tendons to support the animal’s body mass (Huang, 2017). The link between musculoskeletal form and function is a fundamental principle in evolutionary biology, although different regional modules reflect this in different ways. The forelimb, for example, as the principal support for a large proportion of the body mass during locomotion in quadrupedal mammals (Schmitt &

Lemelin, 2002; Raichlen et al., 2009) must resist the stresses and strains caused by locomotion (Fabre et al., 2015). Behaviour other than locomotion, including digging, climbing, social displays, mating and grooming, also employ the use of the forelimb and impose a suite of functional demands that produce differential selective pressures on forelimb functional morphology and evolution (digging: Moore et al., 2013; climbing:

Leischner et al., 2018; social displays: Martin et al., 2018).

The inference of muscular adaptation from bony morphology has a long history in zoology and palaeontology (Bock & Von Wahlert, 1965), although the exact nature of this relationship remains unclear as few studies have attempted to directly correlate quantitative data from these two integrated systems (e.g., Fabre et al., 2014; Cornette et al.,

2015; Noback & Harvati, 2015; Fabre et al., 2018; Sella-Tunis et al., 2018). For instance,

interpretations of the adaptations for digging have found common patterns within and between groups including armadillos (Vizcaíno et al., 1999; Vizcaíno & Milne, 2002; Olson et al., 2016), rodents (Stein, 2000), marsupials (Warburton et al., 2013b), golden moles (Puttick

& Jarvis, 1977; Gasc et al., 1986), talpid moles (Gambaryan et al., 2002) and monotremes

(Regnault & Pierce, 2018) based on a range of bone measurements, and/or muscular anatomy. Physiological cross-sectional area (PCSA) is the most commonly used measure of the architectural properties of a muscle or group of muscles (muscle mass, fibre length, pennation angle) to estimate the capacity of that muscle to produce contractile force

(Lieber & Fridén, 2000; reviewed in Martin et al., 2020). The PCSA may, therefore, be used as a reflection of the capacity of limb muscles to generate movement. Only recently has there been quantitative postcranial analyses of the covariation in the anatomy of these two systems (Böhmer et al., 2018; Martin et al., 2019a; Sahd et al., 2019).

Digging mammals, often referred to as ecosystem engineers, play an important ecological role in soil turnover, hydrology, dispersal of subterranean fungal fruiting bodies, and seedling recruitment and vegetation growth and composition (Fleming et al., 2014;

Valentine et al., 2017; Dundas et al., 2018; Tay et al., 2018; Valentine et al., 2018). Many of these animals spend substantial amounts of effort digging to produce burrows for cover or to find food (Löffler & Margules, 1980; Mallick et al., 1997; Garkaklis et al., 2004; Newell,

2008; Valentine et al., 2013), and consequently, this behaviour places selective pressures on their anatomy. For example, muscles that are active during the power-stroke of digging are enlarged to increase the magnitude of the in-force against the soil (Hildebrand, 1985), with forelimb muscles responsible for producing large out-forces during the power-stroke (i.e., those involved in scapula stabilisation, humeral retraction, elbow extension and

carpal/digital flexion) becoming enlarged in many digging species (examples; Moore et al.,

2013; Rose et al., 2013; Warburton et al., 2013b; Martin et al., 2019b). Comparative analysis of the force generation of muscles between different species is therefore likely to reflect adaptations to differing behaviour or ecological niches.

Figure 5.1: Quenda, Isoodon fusciventer and an example of a foraging pit. Quenda nose poke searching for subterranean hypogeous fungi, invertebrates, plant roots and tubules. Photographs by Trish Fleming and Simon Cherriman.

Marsupials have a limited range of forelimb ecomorphologies in comparison to placental mammals (Sears & Janis, 2004; Martín-Serra & Benson, 2020). It has been suggested that this reflects a phylogenetic constraint of marsupial reproductive biology in that forelimbs develop relatively early to enable the neonatal crawl to the pouch (Sears &

Janis, 2004; Martín-Serra & Benson, 2020). Adaptations for digging have independently evolved in numerous examples within all four Australian marsupial orders (Diprotodontia,

Dasyuromorphia, Peramelemorphia and Notoryctemorphia), so it would be expected that their forelimb anatomy is driven by functional demands of behaviour in comparison to being driven by phylogeny alone. The most specialised marsupials are the subterranean marsupial

moles (Notoryctes caurinus and N. typhlops) which ‘swim’ through the soil (Warburton,

2006). Other species dig deep burrows up to 4 m in depth for shelter (defined as burrowers), including the bilby, Macrotis lagotis (Moseby & O'Donnell, 2003; Dawson et al.,

2019) and the boodie, Bettongia lesueur (Newell, 2008). Many more species produce shallow diggings as they forage for subterranean foods, including the bandicoots, bettongs and potoroos (Martin, 2003; Garkaklis et al., 2004; Valentine et al., 2013; Warburton &

Travouillon, 2016). The purpose, method and amount of time spent daily digging, therefore, imposes different functional demands on the forelimbs and should separate the forelimb anatomy of burrowing and foraging marsupials from lineages that are not specialised for digging (‘non-diggers’).

Figure 5.2: Phylogeny of Australian marsupials used in the study, based on (Mitchell et al., 2014; Kealy & Beck, 2017; Travouillon & Phillips, 2018). The three symbols indicate the species digging category; circles represent burrowing, triangles foraging and squares are non-digging species, while the colours represent the species phylogeny; dark grey Diprotodontia, light grey Dasyuromorphia and white Peramelemorphia. Number of specimens analysed per species indicated as n.

The present study examined the relationship between the forelimb bone shape and the forelimb muscle physiological cross-sectional area (PCSA) in 11 extant Australian marsupial species, while testing the extent to which forelimb bone shape and muscle PCSA reflects the species’ digging ecology (comparing burrowing, foraging and non-digging).

Australian marsupials provide an ideal sample for a study of this kind as digging has evolved independently across multiple lineages. Furthermore, marsupials are amongst some of the world’s most specialised diggers and their role in the Australian environment is vital for the health of the ecosystem. Understanding the quantitative link between muscles and bones across a range of species of different primary behaviours will also assist in understanding the roles of extinct species for which we know very little about their ecology, and thus aid in the inference of palaeoecologies and thus palaeoenvironments through time.

5.3 Material and methods

5.3.1 Specimens

Our sample consisted of 11 species representing three Australian marsupial orders

(Diprotodontia, Dasyuromorphia and Peramelemorphia) (Figure 5.2). These were: two burrowers that dig deep burrows for shelter (greater bilby Macrotis lagotis and boodie

Bettongia lesueur), six foragers that dig shallow to moderately deep foraging pits in search of subterranean food (Figure 5.1) (B. penicillata, three Isoodon and two Perameles spp.) and three non-diggers (the quokka Setonix brachyurus and two quolls Dasyurus spp.). Specimens

(n=48; male=30, female=18) were collected opportunistically throughout the Perth metropolitan area (Regulation 17 licence SF010344), donated to the Murdoch Anatomy

Department from wildlife rehabilitation centres, veterinary clinics and zoos, or borrowed

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from museum collections (Table S5.1). All specimens were all adults with fully fused epiphyseal plates of the forelimb bones. All methods and analyses are summarised in Figure

5.3.

Figure 5.3: Methods and analyses summary. All data was collected from specimens that were donated to Murdoch University or part of the Western Australian Museum collection.

Specimens were initially stored frozen at -18oC, and defrosted 48h before dissection.

Body mass was recorded, then specimens were skinned, eviscerated and embalmed in a solution of 10% formalin and 4% glycerol for 7 days. Specimens were transferred to 70%

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ethanol solution for storage until later analysed for muscle dissection, linear bone measurements recorded for two-dimensional analysis, and microCT scanned for a detailed three-dimensional geometric morphometric analysis.

5.3.2 Muscle PCSA

Muscle dissections were performed using standard dissection techniques and followed muscle descriptions (names, origins and insertions) from Warburton et al. (2013b) and Warburton and Marchal (2017), with PCSA methods following Martin et al. (2019b).

Briefly, the right forelimb was dissected unless it was severely damaged, in which case the left forelimb was dissected. The 29 forelimb muscles were dissected in their major functional groups, progressing from superficial to deep aspects of the limb. Individual muscles were isolated, removed, and the muscle mass recorded (mm, ±0.001g; Mettler

Toledo BB240 Precision Balance, Toronto Surplus & Scientific Inc., Richmond Hill Canada).

Photographs (Olympus Stylus Tough TG-3 Digital Camera, Olympus Australia, Pty Ltd.,

Notting Hill, Australia) of individual isolated muscle bellies were used to determine pennation angle using the ‘angle’ tool in ImageJ v1.49 (Abramoff et al., 2004) at 10 randomised locations throughout the muscle belly. Individual muscles were then placed in

30% nitric acid for 24–48h to loosen the connective tissue between fibres, such that individual fibres could be dissected out and fibre length (FL) measured for 10 individual fibres using a metal ruler. Superficial fibres were avoided as they tend to be longer (Cheng &

Scott, 2000). PCSA was calculated:

muscle mass (g) × pennation angle (cos θ) PCSA (cm2) = muscle density (g/cm3) × fibre length (cm)

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where an estimate of vertebrate muscle density (Mendez & Keys, 1960) was used as the muscle density constant (1.06g/cm3).

To compare muscle forces between species that varied in body size, PCSA estimates were standardised by expressing each individual muscle PCSA as a proportion of the total forelimb muscle PCSA (sum of all 29 muscle PCSAs). One-way ANOVA was initially used to assess the differences between the three digging categories for muscle PCSA. A principal component analysis (PCA) was then performed on the 29-individual muscle PCSA proportions to investigate the variation in the muscle PCSA.

5.3.3 Bone shape

We used two methods of examining bone shape; traditional morphometrics using linear measurements to calculate bone indices that reflect the proportional dimensions of bones, and landmark coordinate methods utilising geometric morphometrics that retain more information on individual bone shapes.

Bone indices: Once the muscle dissections were complete and bones cleaned manually of any remaining tissue, osteological measurements were made of the humerus and ulna using PEC digital callipers to nearest 0.01mm. Linear measurements were adapted from the literature and combined to calculate seven functional indices that represent attributes of the anatomy of the forelimb to potential mechanical efficiencies for digging:

1) brachial index (BI) is an indication of the relative distal out-lever length

(Vizcaíno & Milne, 2002; Warburton et al., 2013b; Rose et al., 2014; Martin et

al., 2019a);

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2) shoulder moment index (SMI) represents the mechanical advantage of the

pectorals and deltoid acting across the shoulder (Elissamburu & Vizcaíno, 2004;

Lagaria & Youlatos, 2006; Elissamburu & de Santis, 2011; Warburton et al.,

2013b; Rose et al., 2014; Martin et al., 2019a);

3) epicondyle index (EI) represents the width of the forearm for muscle

attachment (Elissamburu & Vizcaíno, 2004; Lagaria & Youlatos, 2006;

Elissamburu & de Santis, 2011; Warburton et al., 2013b; Rose et al., 2014;

Martin et al., 2019a);

4) humerus transverse robustness indices (HTRI) represent the robustness of the

humerus in transverse planes (Elissamburu & Vizcaíno, 2004; Lagaria &

Youlatos, 2006; Elissamburu & de Santis, 2011; Warburton et al., 2013b; Rose

et al., 2014; Martin et al., 2019a);

5) humerus cranial-caudal robustness indices (HCRI) represent the robustness of

the humerus in a cranial-caudal plane (Elissamburu & Vizcaíno, 2004; Lagaria &

Youlatos, 2006; Elissamburu & de Santis, 2011; Warburton et al., 2013b; Rose

et al., 2014; Martin et al., 2019a);

6) ulna robustness index (URI) represent the robustness of the ulna (Elissamburu

& Vizcaíno, 2004; Lagaria & Youlatos, 2006; Elissamburu & de Santis, 2011;

Warburton et al., 2013b; Rose et al., 2014; Martin et al., 2019a); and

7) the index of fossorial ability (IFA) is an indication of the mechanical advantage

of the triceps on elbow extension and is considered one of the best indicators

of fossorial ability (Elissamburu & Vizcaíno, 2004; Lagaria & Youlatos, 2006;

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Elissamburu & de Santis, 2011; Warburton et al., 2013b; Rose et al., 2014;

Martin et al., 2019a).

These seven indices are used extensively throughout the literature to measure and predict the level of fossorial ability in mammals. As the indices are ratios, size variation in the sample is controlled (but will contain allometry). Initially, one-way ANOVA was used to assess the differences between species and between the three digging categories for all seven bone indices. Kruskal-Wallis (non-parametric ANOVA) and Dunn’s post-hoc pairwise comparison was then run to assess the differences between species and between the three digging categories of each individual index. Principal component analysis (PCA) was performed using all seven indices.

Landmark coordinates: The scapula, humerus, ulna and third metacarpal were scanned using a Skyscan 1176 Bruker microCT (Centre of Microscopy, Characterisation and

Analysis, The University of Western Australia). Bones were scanned at a resolution of 35µm, with a 0.2mm aluminium filter, voltage at 45kv and amperage 550µA. The scans were reconstructed using NRecon (Burker microCT) which produces stacks of images that were visualised in CTAn (Burker microCT) as a 3D digital volume. The volumes were processed via a threshold approach to digitally dissect the bones and then surface models were exported as PLY files. The software IDAV Landmark Editor v.3.6 (Wiley et al., 2005) was used to place homologous landmarks and curves of semi-sliding landmarks to the surface models.

Landmarks were from Martin et al. (2019a). Landmark coordinate analyses were carried out using the geomorph v.3.1.2 package (Adams & Otárola-Castillo, 2013) in R (R Development

Core Team, 2018). Landmarks were aligned using Generalised Procrustes Superimposition to remove the effect of size, position and orientation, leaving only shape variation (Rohlf &

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Slice, 1990). Once landmarks were obtained, a Generalised Procrustes Analysis (GPA) was run where the semi-sliding landmarks were permitted to slide along their tangent directions to minimise the Procrustes distance between the specimens.

Initially, we used ANOVA to test bone shape between the three digging categories using function ‘ProcD.lm’ in the package geomorph v.3.1.2 (Adams & Otárola-Castillo, 2013).

A PCA was performed on the landmark coordinates to evaluate the distribution of the species in the morphospace. A mean shape was calculated for each species, and the resulting Procrustes coordinates, which account for size differences between specimens, were used for all subsequent analyses.

5.3.4 Phylogenetic signal

As the species have a closely shared evolutionary history, they cannot be considered as independent, therefore, phylogenetic comparative analyses are performed (Felsenstein,

1985). The phylogenetic tree of Australian marsupials used in our analyses was built from trees reported in the literature (Mitchell et al., 2014; Kealy & Beck, 2017; Travouillon &

Phillips, 2018). Trees were pruned to only include the species included within our analyses

(Figure 5.2) and built-in Mesquite (Maddison & Maddison, 2018). To estimate the phylogenetic signal of the muscle and bone measures (indices and landmark coordinated), we used a randomised test (Blomberg et al., 2003; Adams & Felice, 2014).

A multivariate K-statistic (Adams & Felice, 2014) on the muscle PCSA scores, bone indices and Procrustes coordinates using ‘physignal’ in Geomorph v.3.1.2 (Adams & Otárola-

Castillo, 2013). Secondly, univariate K-statistics were calculated for the first four principal components (PC) for each of the muscle PCSA, bone indices, and bone shape landmark

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coordinates (for each of the four bones: scapula, humerus, ulna and third metacarpal) using the function ‘phylosignal’ function in the picante v1.8 package (Kembel et al., 2010). The K- statistic reflects the difference between the observed data values and the expected values under the Brownian motion model for the given phylogeny (Blomberg et al., 2003). Values of K close to 1 suggests a Brownian pattern, values >1 indicate more resemblance among related species than expected under Brownian motion, and values <1 indicate less resemblance (Kembel et al., 2010). The raw values of the K-statistic assess the fit of the

Brownian motion model, while a significant p-value (p<0.05) indicates there is a significant phylogenetic signal in the data.

5.3.5 Methods for assessing the effect of phylogeny and digging ability on forelimb shape

A traditional ANCOVA was performed on the first two PCs of the muscle PCSA, bone indices, and landmark coordinates testing for the effects of digging category (predictor variables). A phylogenetic ANCOVA was then run on each of the first two PCs of the muscle

PCSA, bone indices, and landmark data (scapula, humerus, ulna and third metacarpal) to test for significant differences between digging categories while taking into account total body mass as a covariate. The ANCOVA was performed using ‘procD.lm’ and phylogenetic

ANCOVA using ‘procD.pgls’ functions in geomorph v.3.1.2 (Adams & Otárola-Castillo, 2013).

5.3.6 Methods for assessing covariation between forelimb bone shape and muscle PCSA

To quantify the covariation between muscle PCSA (proportional PCSA data) and forelimb bone shape (both seven indices and the four landmark coordinate bones), a series two-block partial least square (2b-PLS) analyses (Rohlf & Corti, 2000) was implemented using ‘two.b.pls’ function from the library geomorph. First, 2b-PLS was performed

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individually for muscle PCSA proportions against the seven indices, including only muscles associated with the bones used to calculate the index. Similarly, muscle PCSA proportions were compared against the bone shape landmark coordinates for each of the four forelimb bones analysed, including only muscles associated with the bone included in the analysis.

Second, the degree of covariation between muscle PCSA proportions and bone shape while accounting for phylogeny was quantified using ‘phylo.integration’ function in geomorph, using partial generalised least squares algorithm under a Brownian motion model (Adams &

Felice, 2014). The two.b.PLS and phylo.integration analyses identify axes in two multivariate datasets (‘blocks’) that explain their covariance and quantifies the degree of association between the blocks by constructing pairs of variables that are linear combinations of the variables within each of the two blocks. The new variables are constructed from the linear combinations and account for as much of the covariation as possible between the two blocks. The results of the 2b-PLS analysis were r-PLS values (the correlation coefficient between the scores of projected values from the two blocks) and associated p-value that were empirically calculated from the resampling procedure (Rohlf & Corti, 2000; Adams et al., 2018).

5.4 Results

5.4.1 Phylogenetic signal

Muscle PCSA, bone indices and landmark coordinates (Procrustes coordinates) all showed a significant phylogenetic signal (Table 5.1). The results of the univariate K-statistic and associated P-values show no significant phylogenetic signal for the first two PCs of muscle PCSA (Table 5.2a); however, the first PC of the bone indices (79.8% of the variance

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for these indices, largely determined by BI and IFA; Table 5.2b), PC1 scapula coordinates

(35.6% of the variance in scapular shape), and PC1 ulna coordinates (66.0% of the variance in ulnar shape), as well as PC2 of the third metacarpal coordinates (26.6% of the variance in third metacarpal shape) (Table 5.2c) all showed a significant phylogenetic signal. The higher the K-statistic, the stronger the phylogenetic signal, with values above 1 indicating the traits are conserved within the phylogeny and closer resemblance amongst related species than expected under the Brownian motion. Within our study, PC1 of the bone indices showed the strongest signal with a K-statistic of 1.11 being the only value above 1. All other K-statistics were below 1 indicating a weaker phylogenetic signal and a stronger morphological convergence.

Table 5.1: Results of the initial statistical analysis on the “raw data”: 29 muscle PCSA (proportions of total forelimb PCSA), seven bone indices, and the Procrustes landmark coordinates for the scapula, humerus, ulna and third metacarpal. Univariate phylogenetic signal results of muscle PCSA and bone indices, and multivariate phylogenetic signal* of the landmark coordinates. Also, results of the one- way ANOVA to test if muscle PCSA, bone indices and shape measured using landmark coordinates are significantly different between the three digging categories. Phylogenetic signal ANOVA K P-value F2 P Muscle PCSA 0.198 0.028 3.24 0.004 Bone indices 0.524 0.002 53.8 <0.001 Scapula 0.440* 0.001 10.6 0.001 Humerus 0.512* 0.001 27.1 0.001 Ulna 0.421* 0.005 36.3 0.001 Third metacarpal 0.372* 0.003 24.0 0.001

5.4.2 Muscle PCSA

Initially, we tested whether digging behaviour influenced overall muscle PCSA distribution; the one-way ANOVA indicated a significant difference in the distribution of muscle PCSA proportions between the three digging categories (F2=3.24, P=0.004). From the

PCA of all 29 muscle PCSAs, the first two principal components (PC) account for 53% of the

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total variation (Table 5.2a). PC1 was driven by the pectoral group (Pec) and triceps brachii

(TrB), while flexor digitorum profundus (FDP) and serratus ventralis (SeV) drove PC2 (Figure

5.4). PC1 of muscle PCSA showed a significant difference between the three digging categories (Table 5.2a), both before (P=0.001) and after (P=0.004) phylogenetic correction, with the three non-digging species on the negative end of the axis, the two burrowing species somewhat in the middle, and the six foraging species at the positive end of the axis

(Figure 5.4). PC2 was entirely overlapping with the three digging categories and showed no statistical difference before (P=0.063) or after (P=0.935) phylogenetic correction.

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Figure 5.4: Scatterplot (B) of the first two principal components (phylogenetically corrected- purple; non-digging, blue- foraging, red- burrowing), and the loadings of the muscle PCSA proportion on PC1 (C) and PC2 (A). Muscle colours represent muscle functional groups (plots A & C); orange- scapular stabilisers, dark red- humeral retractors, medium red- elbow extensors, pink- carpal/digital flexors, dark blue- humeral protractors, medium blue- elbow flexors, light blue- carpal/digital extensors, light green- pronators. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, Rho; rhomboideus, SeV; serratus ventralis, LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, TrB: triceps brachii, Anc: anconeus, TFA; tensor fascia antebrachii, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, Sup: supraspinatus, BiB: biceps brachii, Bra: brachialis, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum longus, ECU: extensor carpi ulnaris, AbdL: abductor digiti longus, PrT: pronator teres, PrQ; pronator quadratus, Spr: supinator.

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5.4.3 Bone indices

Initially, to test whether digging behaviour influenced the seven bone indices overall, we used a one-way ANOVA that indicated a significant difference between the three digging categories (F2=53.8, P<0.001, Table 5.1). Kruskal-Wallis (non-parametric ANOVA) and Dunn’s pairwise comparison was next used to assess the difference between the species, and the digging categories for each bone index (Figure 5.5). All seven indices showed a significant difference between the species (P<0.001) and between the three digging categories

(P<0.020).

The first two principal components (PC) accounted for 93% of the total variation in bone indices (Table 5.2b). The PCA of bone indices was driven by brachial index (BI) and the index of fossorial ability (IFA) on PC1 and by shoulder moment index (SMI) and IFA on PC2.

There was a significant difference between the digging categories in the raw PC factor scores of the bone indices (Table 5.2b; PC1 and PC2 both P=0.001); however, there were no significant differences attributable to digging category after phylogeny was accounted for

(PC1 P=0.938, PC2 P=0.334).

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Table 5.2: Results of the principal component, phylogenetic signal and ANCOVA/phylogenetic ANCOVA to assess the effect of digging category on muscle PCSA (a), bone indices (b) and landmark coordinates (c). The first column explained variance for the first two principal components (PCs) from the PCA. Second column, results of the K-statistic and associated P-value of univariate on the first two PCs. Third and fourth columns, results of ANCOVAs (F and P-values) and phylogenetic ANCOVAs (F and Pphy-value) for the first two PCs to test the effect of digging category on muscle PCSA and bone shape. PCA Phylogenetic signal ANCOVA Phylogenetic ANCOVA % variance K P-value F P F P a. Muscle PCSA PC1 31.63 0.258 0.117 15.7 0.001 15.5 0.004 PC2 21.11 0.209 0.400 3.10 0.063 0.070 0.935 b. Bone indices PC1 79.82 1.11 0.001 49.8 0.001 0.061 0.938 PC2 14.00 0.129 0.599 117 0.001 1.32 0.334 c. Bone landmark coordinates Scapula PC1 35.63 0.558 0.026 5.69 0.008 1.67 0.277 PC2 18.79 0.230 0.213 0.664 0.534 0.387 0.697 Humerus PC1 56.13 0.113 0.758 134.0 0.001 1.59 0.279 PC2 8.32 0.273 0.104 77.4 0.001 3.68 0.083 Ulna PC1 66.04 0.675 0.022 78.2 0.001 0.613 0.554 PC2 8.23 0.379 0.060 21.0 0.001 9.42 0.027 Metacarpal PC1 34.57 0.365 0.061 191 0.001 7.43 0.040 PC2 26.64 0.501 0.013 69.5 0.001 1.09 0.397

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Figure 5.5: The average, minimum and maximum values for each bone indices separated by species and separated by digging category (not phylogenetically corrected data). Using Kruskal-Wallis and Dunn’s pairwise comparison, each index was tested for a significant difference between the 11 species, and between the three digging categories. Letters and numbers represent the pairwise comparison results. Results show significant differences between species for all seven indices (P<0.001) and significant differences between the three digging categories (P<0.02).

5.4.4 Landmark coordinates

The shape of each of the four forelimb bones (determined using landmark coordinates) were significantly different between the three digging categories (ANOVA p=0.001 for each; Table 5.1, Figure 5.6). The landmark PC factors for all four forelimb bones also showed significant differences by digging category (humerus, ulna and metacarpal both

PC factors P=0.001, scapula PC1 P=0.008). After accounting for phylogeny, however, significant differences were retained for the shape of the ulna (PC2 P=0.027) and metacarpal (PC1 P=0.040), but not for either PC factor describing the shape of the scapula or humerus (Table 5.2c).

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Figure 5.6: Mean shapes of scapula, humerus, ulna and the third metacarpal for the three digging categories; burrowing, foraging and non-digging.

5.4.5 Covariation between bone shape and muscle PCSA

Bone indices:

Before phylogenetic correction, none of the seven indices showed significant covariation with muscle PCSA; however, when phylogeny was accounted for, four indices

(SMI, HTRI, HCRI and EI) showed significant covariation with muscle PCSA (Table 5.3). The scatterplots of the phylogenetic 2b-PLS for these four indices (Figure 5.7) place burrowing and non-digging species in the middle of the axis, while foraging species had values from positive to negative ends of the axis. When their close phylogeny is taken into account, the differences in the anatomy of the two closest related species, I. fusciventer and I. auratus, meant that these two species were most separated on the plots. The three indices SMI, HTRI

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and HCRI represent aspects of the proximal humerus, and their covariation with muscle

PCSA was driven by a large proportion of the pectorals (Pec) and relatively small proportions of subscapularis (Sub) and triceps brachii (TrB). Epicondyle index (EI) represents the ratio of the distal end of the humerus to the total humeral length, and the covariation with muscle

PCSA was driven by the proportions of triceps brachii (TrB), pronator teres (PrT) and flexor digitorum profundus (FDP).

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Table 5.3: Results of the covariation between forelimb bone shape (measured using of each bone measured using both indices and Procrustes landmark coordinates associated muscle PCSA proportions. The analysis was repeated with phylogenetic correction. PCSA Phylogenetic PCSA r-PLS P-value r-PLS P-value a) Bone indices BI 0.859 0.092 0.734 0.069 SMI 0.738 0.147 0.853 0.009 HTRI 0.543 0.337 0.751 0.027 HCRI 0.556 0.328 0.762 0.022 EI 0.632 0.258 0.807 0.006 URI 0.440 0.655 0.337 0.724 IFA 0.514 0.217 0.513 0.137 b) Landmark coordinates Scapula 0.909 0.048 0.968 0.002 Humerus 0.821 0.245 0.973 0.001 Ulna 0.690 0.356 0.766 0.117 Metacarpal 0.826 0.042 0.898 0.027

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Figure 5.7: Results of phylogenetic 2b-PLS analysis of four of the bone indices that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction: shoulder moment index (A-B), humerus transverse robustness index (C-D), humerus cranial-caudal robustness index (E-F) and epicondyle index (G-H). The scatterplot of the first PLS axis describing the shape (y- axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loadings associated with the PCSA axis values. Muscle abbreviations: LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps brachii, Anc: anconeus, BiB: biceps brachii, Bra: brachialis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum lateralis, ECU: extensor carpi ulnaris, PrT: pronator teres.

Landmark coordinates:

Before phylogenetic correction, covariation with muscle PCSA was evident for landmark coordinates of the scapula and third metacarpal; no covariation was seen with the humerus or ulna. When phylogeny was accounted for, covariation with muscle PCSA was significant for the scapula, humerus and third metacarpal (Table 5.3, Figure 5.8 - Figure

5.10).

For the scapula, shape change was evident at the cranial border and scapula spine, reflecting the change in shape of the supraspinous and subscapular fossae (Figure 5.8).

Burrowing and non-digging species clustered together in the middle of the axes, while two foraging species occupied both extremes of this axis. Isoodon fusciventer had a greater relative investment in their Pec/SeV/Tra muscles (Figure 5.8) with a relatively rectangular scapula and large supraspinous and subscapular fossae and relatively large and robust coracoid process. In comparison, I. auratus had a greater relative investment in the

TrB/Sup/Sub muscles associated with a relatively rounder supraspinous and subscapular fossae with an elongated acromion.

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For the humerus, there was no significant covariation with uncorrected 2b-PLS, but significant covariation with PCSA for the phylogenetically-corrected analysis (Table 5.3,

Figure 5.9). Again, I. fusciventer and I. auratus were positioned at the extremes of the muscle force dimension (x-axis in Figure 5.9). The greater differential investment in the pectoral (Pec) and trapezius (Tra) muscles for I. fusciventer was associated with wide and robust deltoid tuberosity and a wide and robust distal end for the humerus, specifically medial epicondyle and the capitulum. By contrast, I. auratus had greater differential investment in FDP and TrB with a more elongate gracile humerus and relatively smaller medial epicondyle and medially rotated trochlea.

The third metacarpal showed covariation between muscle PCSA and landmarks analysis both before and after phylogenetic correction. The variation in muscle PCSA proportions was associated with a small change in the proximal end of the metacarpal

(Figure 5.10). Isoodon fusciventer had the most robust proximal end (i.e., articulation surface with the carpal bones), which was associated with a slightly greater differential investment in FCR, while I. auratus typified the other extreme of this axis, with a more gracile third metacarpal associated with greater relative investment in the FDP.

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Figure 5.8: Results of the phylogenetic 2b-PLS analysis of the scapula landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, Rho: rhomboideus, SeV: serratus ventralis, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps brachii, BiB: biceps brachii.

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Figure 5.9: Results of the phylogenetic 2b-PLS analysis of the humerus landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: Tra: trapezius, OmT: omotransverse, LtD: latissimus dorsi, Pec: pectoralis group, Del: deltoideus, Inf: infraspinatus, TMj: teres major, Sub: subscapularis, Sup: supraspinatus, TrB: triceps brachii, Anc: anconeus, BiB: biceps brachii, Bra: brachialis, FCR: flexor carpi radialis, FCU: flexor carpi ulnaris, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis, EDL: extensor digitorum longus, ECU: extensor carpi ulnaris, AbdL: abductor digiti longus, PrT: pronator teres, Spr: supinator.

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Figure 5.10: Results of the phylogenetic 2b-PLS analysis of the third metacarpal landmark coordinates that showed a significant covariation with muscle force generation (PCSA) after phylogenetic correction. The scatterplot of the first PLS axis describing the shape (y-axis) against muscle PCSA (x-axis) and the paired histograms represent the muscles loading associated with the PCSA axis values. Muscle abbreviations: FCR: flexor carpi radialis, PaL: palmaris longus, FDS: flexor digitorum superficialis, FDP: flexor digitorum profundus, ECR: extensor carpi radialis, EDC: extensor digitorum communis.

5.5 Discussion

5.5.1 Muscle PCSA is significantly different between the three digging categories

Burrowing, foraging and non-digging species showed significant differences in muscle force generation (PCSA) before and after phylogenetic correction. The principal component

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analysis of muscle PCSA revealed foraging species to have a relatively greater investment in the pectorals (Pec) and flexor digitorum profundus (FDP), which contribute to humeral retraction (HR) and carpal/digital flexion (CDF) of the wrist during the power-stroke of digging. In contrast, non-digging species showed the greatest investment in triceps brachii

(TrB), subscapularis (Sub) and supraspinatus (Sup). The Sub and Sup muscles are classified as rotator cuff muscles to support and protect the shoulder joint, while TrB is the largest elbow extensor (EE).

Based on previous studies, we would expect digging species to show enlarged humeral retractor (HR) and elbow extensor (EE) muscles to increase the out-force on the substrate during the power-stroke of digging (Moore et al., 2013; Warburton et al., 2013b; Rupert et al., 2015; Olson et al., 2016) compared with non-digging species; however, we saw non- digging species with the greatest investment in the larger TrB for powerful elbow extension.

The TrB is active during the propulsive phase of locomotion and/or climbing and provides the animal with a powerful axial thrust of the limb by extending the elbow (Argot, 2001;

Carrizo et al., 2014), therefore is not only associated with digging. Significant differences in muscle PCSA corresponding to differences in lifestyles and behaviour have been reported in lizards, great apes and carnivorans (Oishi et al., 2009; Lowie et al., 2018; Taverne et al.,

2018); while other studies have seen no significant differences in PCSA between species according to lifestyle. Our study revealed that muscle PCSA does show adaptations to behaviours such as digging, and that there are differences in investment of muscle PCSA between the three categories.

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5.5.2 Bone indices

Distinct separation of burrowing, foraging and non-digging species was clear using the seven bone indices, although the separation was lost after phylogenetic correction, which suggests there are potentially adaptations on the bone for the orders of marsupials, that are not only driven by digging behaviour. In the literature, the index of fossorial ability (IFA) is commonly the most successful in differentiating the digging abilities, as it represents the mechanical advantage of the triceps in elbow extension (Vizcaíno et al., 1999; Vizcaíno &

Milne, 2002; Elissamburu & de Santis, 2011). In our sample, IFA was able to separate the digging species (foraging and burrowing) from the non-digging species, and HCRI and EI were successful in separating all three categories. These results are comparable with Rose et al. (2014) in which HRI, EI and IFA characterised the forelimb specialisations in badgers, with increasing index scores moving from the most terrestrial to most fossorial species.

As our sample of species was limited, and the digging categories were primarily grouped by phylogenetic order (with the exception of Diprotodontia), therefore, it is unsurprising that there was no signal for digging ability after phylogenetic correction. The closely related species have similar anatomy due to phylogeny, but also because they have similar forelimb adaptations for digging. With the addition of species with a variety of phylogenetic backgrounds and level of digging abilities to widen the sample, the bone indices may show a signal for digging ability after phylogenetic correction.

5.5.3 Landmark coordinates

Distinct shapes reflecting digging category were seen for all four forelimb bones, but after phylogenetic correction, digging category signal was present for the third metacarpal

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and some extent the ulna, but not the scapula or humerus. This suggests there may not be a strong signal for digging ability since the shape was heavily linked with phylogeny (i.e.,

Peramelemorphia 5/6 foraging species and Dasyuromorphia 2/3 non-digging species). Since the sample size was so small with little variety in digging ability within the orders, it was difficult to conclude if the forelimb bone shape is partly adaptive and evolving in response to the constraints imposed by digging, or if the bone shape is evolving within the lineages.

The digging species displayed shortened and robust third metacarpals to provide a mechanical advantage by shortening the out-lever arm of the hand that allows for the exertion of a greater out-force (Hildebrand, 1985; Morgan & Verzi, 2011). All species in our study showed digit III being the longest on the hand, which is commonly seen in other fossorial species i.e., pangolins (Steyn et al., 2018), blind mole-rats (Ozkan, 2002), golden moles (Gasc et al., 1986), and suggests that the out-forces generated in the forelimb during digging are applied mainly through the third digit. This concentrates the muscle force into a single point which is advantageous for digging, and this may be why we see the strongest signal of digging in the third metacarpal. Some other digging mammals, such as Talpid moles, increase the surface area of their hand (by adding a sixth pseudodigit), which instead widens the manus in comparison to lengthen the hand, as was seen in our sample

(Mitgutsch et al., 2012). As all the force that is, generated in the limb is transferred to the soil during digging through the manus, it is intuitive that the metacarpals, specifically the third metacarpals are heavily influenced by the functional demands of digging. In comparison, non-digging species showed an elongated and gracile third metacarpal to facilitate grasping for climbing or food manipulation (Samuels & Van Valkenburgh, 2008).

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In general, foraging and burrowing species showed similar shortened and robust humeral and ulnar shapes compared with the more gracile and elongated forelimb bones of non-digging species. The humerus of the digging species displayed wide epicondyles for increased surface area for the carpal/digital flexor attachment (Milne et al., 2009; Moore et al., 2013; Fabre et al., 2015), a wide and robust pectoral ridge for increased surface area for pectoral muscles, and a rounded capitulum to pair with a deep concave trochlear on the ulna that helps stabilise and prevent rotation of the elbow during scratch-digging (Jenkins,

1973; Argot, 2001; Andersson, 2004). The olecranon process is also elongated and robust to increase the surface area for the TrB and to increase the in-lever of the elbow joint

(Hildebrand, 1985; Hopkins & Davis, 2009; Moore et al., 2013; Samuels et al., 2013; Rose et al., 2014; Fabre et al., 2015). In comparison, the humerus of non-digging species had relatively narrow epicondyles paired with a short olecranon and narrower trochlear notch on the ulna, adaptations for a mobile elbow joint that can fully extend with greater flexibility

(Samuels & Van Valkenburgh, 2008; Fabre et al., 2015). Compared with digging species that showed specialised adaptations to increase the out-force for scratch-digging in a parasagittal plane, non-digging species showed a more generalist morphology for climbing, manipulating food and for locomotion.

5.5.4 Covariation between muscle PCSA and bone shape significant after phylogenetic

correction

When looking at the covariation between muscle PCSA and bone shape, there was a common pattern of the distribution of the species. The burrowing and non-digging species

(especially Dasyuromorphia and Diprotodontia species) contrasts with the wide distribution

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of Peramelemorphia species. This suggests that there was significantly more variation within the Peramelemorphia order of marsupials in comparison to the other two orders.

For all four indices and the three bones, I. auratus and I. fusciventer (closest related species in our sample) were at opposite ends of the x and y axes. Before phylogenetic correction, the species clustered together on the scatterplots, but they were separated to opposite extremes after phylogenetic correction. Both these species are classed as foraging diggers; however, I. fusciventer (consuming a largely subterranean diet of hypogeous fungi, invertebrates, plant roots and tubules) is more reliant on digging than I. auratus, which primarily eats termites and invertebrates (Dickman & Woodford Ganf, 2015). Analyses showed I. fusciventer had a relatively larger investment in muscles to protract the scapula and retract the limb during the power-stroke of digging (Tra, LtD, SeV and Rec) in comparison to I. auratus, which showed greater relative investment in force generation of the rotator cuff muscles for stability of the shoulder joint and to protect the limb from anterior dislocation of the shoulder (Warburton et al., 2013b) (Sup, Inf, Sub and TrB). The

Shark Bay bandicoot (P. bougainville) also appeared to be less adapted to digging (negative end of the axes on scatterplots), corresponding to its diet primarily of insects, along with seeds, berries and skinks, items that are not highly reliant of digging (Short, 2016).

5.5.5 Ulnar shape and ulnar bone indices show minimal covariation with muscle PCSA

Bone indices and landmark coordinates showed a strong covariation with muscle PCSA after phylogenetic correction, in comparison to our previous study of covariation over an ontogenetic series in I. fusciventer (Martin et al., 2019a). In this previous study, muscle PCSA covaried with SMI and HCRI. These two indices similarly showed a significant relationship

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with muscle PCSA in the current study, as did HTRI and EI. All four of these indices are primarily based on the humerus, and none of the indices relating to the ulna showed significant covariation. The ulnar shape measured by landmark coordinates also showed no covariation with muscle PCSA in either study, suggesting that ulnar shape is not driven by forelimb musculature but rather by digging ability and functional demands imposed by behaviour.

5.5.6 Conservation management

Adaptation for digging in species needs to be taken into account when looking at relocation efforts for Australian marsupials. Until recently, P. bougainville was considered to have multiple subspecies, with distribution expanding from Western Australia to New South

Wales. A recent revision by Travouillon and Phillips (2018) suggested splitting P. bougainville into six species, with the Shark Bay bandicoot being the only surviving taxon with a past and current distribution only in Western Australia. Conservation efforts for the Shark Bay bandicoot need to carefully assess the relocation habitats and environments due to the reassessment of past distribution of this species, and ensure environments are suitable for a species that may be less adapted to digging than the other bandicoot species.

5.5.7 Limitations of this study

Low sample sizes mean that it was not possible to robustly account for intraspecific variation in anatomy in a comparative study such as this one. For example, sex differences or ontogenetic variation may have influenced the sample means calculated for a species.

While we had sufficient material to test for sex differences in I. fusciventer (Martin et al.,

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2019a; Martin et al., 2019b), we did not have similar sample sizes to adequately test for sex differences in other species.

The number of species was also limited by specimen availability, and therefore, the conclusions drawn regarding phylogeny are limited, and must be interpreted with caution.

Diprotodontia was the only lineage in our dataset that had a species of each digging category, and a lack of range of behaviour for the other lineages limits conclusions that we can draw regarding phylogeny or functional demands due to behaviour driving shape of the forelimb bones. This distribution in the covariation analysis was also driven by the phylogenetic correction, so the addition of more species with varying levels of digging abilities and more distantly related species would assist in interpreting the overall relationship between muscle PCSA and bone shape.

5.6 Conclusion

From the current study, we revealed that marsupials with different behaviour showed morphological differences, however, within our small sample, phylogeny had a large influence on the forelimb bone shape. We also showed a strong covariation between muscle

PCSA and both bone indices and landmark coordinates as representations of bone shape.

The shape of the ulna, however, showed no covariation with muscle PCSA, which suggests that other factors influence the development of bone shape. Additional species and quantitative data on behaviour with the complimentary muscle PCSA and bone shape analysis are needed to provide a better understanding and interpretation of the evolution of forelimb morphology in the context of digging ability.

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5.7 Acknowledgments

Specimens provided by Alice Springs Desert Park, Australian Museum, Australian

Wildlife Conservancy, Kanyana Rehabilitation Centre, Perth Zoo, South Australian Museum, the Department of Biodiversity, Conservation and Attractions, and Western Australian

Museum. The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Centre for Microscopy, Characterisation & Analysis,

The University of Western Australia, a facility funded by the University, State and

Commonwealth Governments. Technical support from Murdoch University: Diana Nottle and Zsa Zsa Wong, and Natasha Tay for assisting in the preparation and sourcing of the animals. Financial support was provided by Murdoch University and Graduate Women WA via the Mary Walters Award.

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Chapter 6 Phylogeny and ecology drive the shape of the

forelimb in monotremes and marsupials

6.1 Abstract

The shape and construction of bones within the vertebrate skeleton is influenced by phylogenetic background as well as by the forces imposed by an animal’s behaviour such as digging, climbing or locomotion. Here, we quantify and compare the shape of four forelimb bones in 56 monotreme and marsupial species with different primary behaviours: seven burrowing species, 22 species that dig for food (foraging), five scansorial species, 11 arboreal species, and those that do not dig but use their forelimbs for locomotion only (10 terrestrial species and one aquatic species). Bone shape was quantified using bone indices

(ratios of linear bone measurements) and landmark coordinates (three-dimensional geometric morphometrics). Phylogeny primarily drove the shape of the scapula, humerus and third metacarpal, only showing significant differences between the digging categories before phylogenetic correction. There were similarities between burrowing and foraging species, both groups displaying robust and shortened forelimbs that would help to increase mechanical advantage in response to load during scratch-digging. Terrestrial species showed intermediate forelimb shape, while scansorial species display elongated and gracile bones with increased joint mobility. The aquatic species showed similar forelimb adaptations to the burrowing and foraging species. These analyses reveal that monotremes and marsupials that have different ecologies show significantly different forelimb bone shapes.

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Keywords: semi-fossorial, arboreal, scansorial, terrestrial, phylogeny, geometric morphometric, bone index

6.2 Introduction

Vertebrate limbs are integrated evolutionary units that are shaped by forces imposed by an animal’s locomotion and behaviour. At a minimum, the limbs of terrestrial animals are required to support the body mass without breaking and collapsing (Schmitt & Lemelin,

2002; Raichlen et al., 2009); however, they also need to resist the stresses and strains of an animals day to day lifestyle. Consequently, the forces acting on limb bones are the product of gravity as well the mechanical advantage created by the muscles acting on the bones, which will reflect how the limbs are used.

In addition to weight-bearing and locomotion, many terrestrial mammal species also use their forelimbs for specialised behaviour; each of these specialised behavioural modes imposes different functional demands on the forelimbs. For example, many mammal species use their forelimbs to dig burrows for shelter, or to forage for subterranean food

(Shimer, 1903). Digging mammals often have shortened and robust bones, enlarged distal epicondyles on the humerus and increased surface areas for enlarged muscle attachment on the humerus and scapula to increase the out-force applied to the soil (as seen in Salton &

Sargis, 2008; Milne et al., 2009; Rose et al., 2014; Sesoko et al., 2015). In contrast, species that climb have elongated and gracile humeri to facilitate greater reach, with a broad capitulum to increase the joint mobility and range of motion in the elbow (Argot, 2001;

Flores & Diaz, 2009; Ercoli et al., 2015; Fabre et al., 2015). Swimming species have shorter and stronger lateral epicondylar crests on the humerus, which is suggested to be used to

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improve the antebrachial supination and elbow extension, and strong development of the trochlea and capitulum of the humerus to stabilise the elbow during the flexion and extension (Ercoli et al., 2012). As the forelimbs play such vital roles in these behaviours, it is expected that the bony elements will show strong phylogenetic signal (Flores & Diaz, 2009;

Martín-Serra et al., 2014; Tulli et al., 2015; Leischner et al., 2018).

There is a wide diversity of anatomical specialisations in Australian monotremes and marsupials that reflects a history of extreme adaptative radiation (Archer et al., 2006; Black et al., 2012). Monotremes and marsupials utilise a wide range of food and habitat resources and have distinctive anatomical adaptations that allow them to survive and thrive in some of the most extreme habitats anywhere on earth (Tyndale-Biscoe, 2002). From aquatic through to arboreal habitats (Dickman & Woodford Ganf, 2015; Jackson & Groves, 2015), and occupying biomes from dense tropical rainforests through to arid deserts, monotremes and marsupials display some of the most unique anatomy and physiology among vertebrates.

Marsupial neonates are born highly altricial and climb from the urogenital sinus to the pouch using their well-developed forelimbs (Gemmell et al., 2002). This early development is suggested to constrain the forelimb anatomy (Sears & Janis, 2004; Cooper & Steppan,

2010; Kelly & Sears, 2011; Garland et al., 2017). Here, we use linear bone measurements and 3D geometric morphometric methods to investigate the morphology of the forelimb long bones in Australian and New Guinea monotremes and marsupials. This allowed us to test the extent to which behaviour influenced the shape of the forelimb in monotremes and marsupials within the constraints of phylogeny. We predicted that the behaviour of the 56 species would reflect their lifestyle as the functional demands of digging, climbing and

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locomotion create different selective pressures acting on the forelimb, and therefore forelimb bone shapes.

6.3 Materials and methods

6.3.1 Specimens

Bone data for this study were collected from 815 specimens representing 56 species across all five orders of monotremes and marsupials (Table 6.1). Specimens were collected

(Regulation 17 licence SF010344) and obtained from various Australian and international collections. All specimens were adults and predominantly from wild-caught origin. The phylogenetic tree used in our analysis (Table 6.1) was built from three trees in the literature that used both DNA and morphological traits (Meredith et al., 2008; Mitchell et al., 2014;

Travouillon & Phillips, 2018). We defined five categories of primary behaviour: burrowing, foraging, scansorial, arboreal and terrestrial. Descriptions of behavioural categories were adapted from work by Gálvez-López (2020) and species classified according to behaviour descriptions in the literature (Table 6.1). Species that would commonly be categorised into semi-fossorial in ecomorphological studies, were classified into either foraging or burrowing to differentiate between digging adaptations relating to the depth of digging (i.e., digging on the surface compared to digging a deep burrow).

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Table 6.1: Phylogenetic relationship between the 56 species used in the study and the classification of their primary behaviour. All species were measured to calculate the bone indices, and a subset of the bones were micro-CT scanned and analysed using landmark coordinates. Numbers in columns for Indices and Landmarks reflect sample sizes. Order Species Behaviour* Indices Landmarks Source ƚ Macrotis lagotis Burrowing1 44 9 A-F Echymipera ruffescens Foraging1 2 F-G Echymipera kalubu Foraging2 7 A,F-G Microperoryctes longicauda Foraging3 5 A,F Peroryctes raffrayana Foraging2 4 A,F Perameles bougainville Foraging1 9 6 C,E Perameles gunnii Foraging1 40 1 A-B,E Perameles nasuta Foraging1 79 A-C,F-G 1 Peramelemorphia Isoodon macrourus Foraging 31 2 A-B,D-G Isoodon obesulus Foraging1 27 A-B,D Isoodon fusciventer Foraging1 58 41 C,E Isoodon auratus Foraging1 9 6 C,E-F Myrmecobius fasciatus Foraging1 15 5 A,E,G Phascogale tapotafa Arboreal4 2 E Dasyuroides byrnei Burrowing4 2 D Dasycercus blythi Burrowing4 8 A Sarcophilus harrisii Terrestrial4 15 B,D-G Dasyurus hallucatus Scansorial4 24 11 C-F Dasyurus maculatus Scansorial4 32 A,F-G Dasyuromorphia Dasyurus viverrinus Scansorial4 31 1 A-B,E-F Dasyurus albopunctatus Scansorial4 2 A Dasyurus geoffroii Scansorial4 25 12 B-C,E-F Notoryctemorphia Notoryctes typhlops Burrowing1 4 D,F Phascolarctos cinereus Arboreal4 30 D-E Lasiorhinus latifrons Burrowing1 30 A-B,D-E,G Vombatus ursinus Burrowing1 34 A-B,D-G Trichosurus vulpecula Arboreal4 6 E Wyulda squamicaudata Arboreal4 2 E Phalanger maculatus Arboreal5 5 E Phalanger orientalis Arboreal6 2 E Petropseudes dahli Arboreal4 1 E Pseudocheirus peregrinus Arboreal4 1 E Pseudocheirus occidentalis Arboreal4 10 E Hypsiprymnodon moschatus Foraging4 1 G Lagostrophus fasciatus Terrestrial4 6 1 E

Setonix brachyurus Terrestrial4 19 8 C,E

Lagorchestes conspicillatus Terrestrial4 4 E Osphranter robustus Terrestrial4 4 E Notamacropus parma Terrestrial4 2 E

Notamacropus Irma Terrestrial4 4 E Diprotodontia Dendrolagus goodfellowi Arboreal7 3 G Dendrolagus lumbolzi Arboreal4 3 E Petrogale rothschildi Terrestrial4 2 E Petrogale lateralis Terrestrial4 2 E Petrogale brachyotis Terrestrial4 5 E Potorous longipes Foraging1 16 A-B Potorous tridactylus Foraging1 50 A-B,D-G Potorous gilbertii Foraging1 2 1 E Aepyprymnus rufescens Foraging1 26 1 A-B,D-G Bettongia tropica Foraging1 3 A Bettongia lesueur Burrowing1 16 3 A,C-F Bettongia penicillata Foraging1 33 10 A-G Bettongia gaimardi Foraging1 6 1 B,E-G Monotremata Tachyglossus aculeatus Foraging1 5 F-G Zaglossus bartoni Foraging1 1 F Ornithorhynchus anatinus Aquatic8 6 F-G

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* Behavioural categories- Burrowing: species that dig deep burrows for shelter; Foraging: Species that scratch the surface soil searching for food or make nests; Scansorial: species that are mostly terrestrial, however, can climb well and readily do so; Arboreal: species that spend a significant amount of time in trees (over 75%); Terrestrial: species that spend the majority of their time on the ground, but occasionally dig, climb or swim; Aquatic: Species that spend the majority of their time in the water. References for behavioural categories- 1(Fleming et al., 2014), 2(Cuthbert & Denny, 2014), 3(Cornelio, 2010), 4(Dickman & Woodford Ganf, 2015), 5(Henderson et al., 2017), 6 (Heinsohn, 2005), 7(Bowyer et al., 2003), 8(Fish et al., 1997). ƚ Source of specimens: A: Australian Museum (AM), Sydney, Australia; B: Melbourne Museum (MV), Melbourne, Australia; C: Murdoch collection; D: South Australian Museum (SAM), Adelaide, Australia; E: Western Australian Museum (WAM), Welshpool, Australia; F: American Museum of Natural History (AMNH), New York, USA; G: National Museum of Natural History (NMNH), Washington, District of Columbia, USA.

6.3.2 Quantifying bone shape

Indices: The morphological indices for fossoriality (Table 6.2) were developed based

on descriptions of digging species by Hildebrand (1985) and Shimer (1903) and previously

used in the literature (Vizcaíno et al., 1999). Linear measurements (to the nearest 0.01mm)

were taken with digital callipers (PES digital callipers), on the humerus and ulna and used to

calculate seven indices from the literature. To test if there was a significant difference

between the five behavioural categories, each index was assessed using a one-way ANOVA

with a pairwise analysis using the ‘ProcD.lm’ and ‘pairwise’ functions. Each index was also

assessed using a phylogenetic ANOVA with the function ‘ProcD.gpls’ from the package

geomorph v.3.1.2 (Adams & Otárola-Castillo, 2013) and package RRPP v.0.6.0 (Collyer &

Adams, 2018). The only aquatic species, Ornithorhynchus anatinus, was not included in the

analyses as a single value per category is not a valid statistical analysis, however, they were

still displayed on graphs as a visual comparison. A phylogenetic principal component

analysis was also performed using ‘phyl.pca’ in the package phytools v0.6.99 (Revell, 2012)

to investigate the variation between the seven indices and the 56 species. To assess if the

two main sources of variance (PC1 and PC2) showed statistical differences between the five

behaviour categories, we ran a one-way ANCOVA.

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Table 6.2: Lists of the abbreviations, calculations and descriptions of the seven indices and how they relate to the functional morphology of the forelimb. Calculation Description Brachial index (BI) BI= 푈퐿−푂퐿 x 100 Indication of relative distal out-lever length. 퐻퐿

Shoulder moment index (SMI) SMI= 퐷퐿퐻 x 100 Indication of the mechanical advantage of the 퐻퐿 pectoral muscles and deltoid to act across the shoulder joint. Humerus transverse HTRI= 푇퐷퐻 x 100 Indication of the robustness of the humerus. robustness index (HTRI) 퐻퐿

Humerus cranial-caudal HCRI= 퐶퐶퐷퐻 x 100 Indication of the robustness of the humerus. robustness index (HCRI) 퐻퐿

Epicondyle index (EI) EI= 퐷퐸퐻 x 100 Indication of width available for the flexor, pronator 퐻퐿 and supinator muscles of the forearm to attach.

Ulna robustness index (URI) URI= 푇퐷푈 x 100 Indication of the robustness of the forearm. 푈퐿−푂퐿

Index of fossorial ability (IFA) IFA= 푂퐿 x 100 Indication of the mechanical advantage of the 푈퐿−푂퐿 triceps in the elbow extension and is considered a good indicator of fossoriality. Abbreviations for calculations: CCDH: cranial-caudal diameter of humerus, DEH: diameter of the epicondyles, DLH: deltoid length of humerus, HL: humeral length, OL: olecranon length, TDH: transverse diameter of humerus, TDU: transverse diameter of the ulna, UL: ulna length.

Landmark coordinates: A subset of these specimens (135 individuals; Table 6.1) were used for three-dimensional analysis to investigate a more detailed aspect of bone shape.

Four of the forelimb bones (103 scapula, 120 humeri, 126 ulna and 109 third metacarpals) were scanned using a Skyscan 1176 Burker microCT (Centre of Microscopy, Characterisation and Analysis, The University of Western Australia). Bones were scanned at a resolution of

35µm, with a 0.2-mm aluminium filter, voltage at 45kv and amperage 550µA. The scans were reconstructed using NRecon (Burker microCT), which produces stacks of images that were visualised in CTAn (Burker microCT) as a 3D digital volume. The volumes were processed via a threshold approach to digitally dissect the bones and then surface models were exported as PLY files. The program IDAV Landmark Editor v.3.6 (Wiley et al., 2005) was

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used to place homologous landmarks and curves of semi-sliding landmarks on the surface models (Figures and detailed descriptions of landmarks presented in Martin et al. (2019a), and the supplementary information). Landmark coordinate analyses were performed in R (R

Development Core Team, 2018), specifically using the package geomorph v.3.1.2 (Adams &

Otárola-Castillo, 2013). Landmarks were aligned using Generalised Procrustes

Superimposition to remove the effect of scale, position and orientation, leaving only shape variation (Rohlf & Slice, 1990). Once landmarks were obtained, a Generalised Procrustes

Analysis (GPA) was run, and the semi-sliding landmarks were permitted to slide along their tangent directions to minimise the Procrustes distance between the specimens.

To test if there was an overall difference in shape between the four behaviour categories, each bone was assessed using a one-way ANOVA with a pairwise analysis using the ‘ProcD.lm’ and ‘pairwise’ functions. Each bone was also assessed using a phylogenetic

ANOVA with the function ‘ProcD.gpls’ from the package geomorph v.3.1.2 (Adams &

Otárola-Castillo, 2013) and package RRPP v.0.6.0 (Collyer & Adams, 2018). Finally, a principal component analysis (PCA) for each of the four bones was performed on the landmark coordinates to assess the distribution and variation in shape between the behavioural categories in the morphospace. The phylogeny was mapped onto the morphospace using the ‘phylomorphspace’ function from Phytool v0.6.99 (Revell, 2012). To test if behaviour influences forelimb bone shape variation, we performed ANCOVAs and phylogenetic

ANCOVAs (Garland et al., 1993) on the first two principal components of the four individual bones.

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6.3.3 Phylogenetic signal

To estimate the phylogenetic signal in the indices and the landmark coordinates, we used multivariate and univariate k-statistics. Landmark coordinates for each of the four bones were tested using the multivariate K-statistic that follows a randomised test of

Blomberg et al. (2003) using the function ’physignal’ and the package geomorph v3.1.3

(Adams & Otárola-Castillo, 2013). Next, the univariate K-statistic was calculated for the first two PCs from the four landmark coordinates PCAs using the function ‘phylosignal’ from the package picante v1.8 (Kembel et al., 2010) in R. The K-statistic reflects the difference between the observed data values and the expected values under the Brownian motion model for the given phylogeny (Blomberg et al., 2003). Values of K close to 1 suggests a

Brownian pattern, values >1 indicate more resemblance among related species than expected under Brownian motion, and values <1 indicate less resemblance (Kembel et al.,

2010). The raw values of the K-statistic assess the fit of the Brownian motion model, while a significant p-value (p<0.05) indicates there is a significant phylogenetic signal in the data.

6.4 Results

6.4.1 Indices variation

All seven indices showed a significant phylogenetic signal (p<0.001), with four of the indices showing a K-statistic less than one indicating less resemblance between species than expected under Brownian motion (Table 6.3). The index of fossorial ability (IFA) had a K- statistic of 1.06, which indicates that the index shows the expected resemblance between species under Brownian motion. The humerus transverse robustness index (HTRI) and

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epicondyle index (EI) show K-statistics greater than 1, which suggests that these indices show much more resemblance between species than expected under Brownian motion.

Table 6.3: Summary of the statistical analyses on the seven bone indices. Phylogenetic signal assessment on individual indices, and results of the one-way fixed-factor ANOVA and phylogenetic ANOVA carried out on each variable to determine whether significant differences existed between the mean values of the different locomotor types. Phylogenetic PCA with PC1 accounting for 65.3% of the total variance, and PC2 accounting for 19.6% total variance. Phylogenetic signal One-way ANOVA Phylo ANOVA Phylo PCA K-statistic P-value F4 P-value F4 P-value PC1 loading PC2 loading BI 0.333 0.001 5.96 0.001 2.33 0.059 0.982 -0.177 SMI 0.137 0.005 5.56 0.003 1.52 0.212 -0.184 -0.771 HTRI 3.266 0.001 1.45 0.193 2.15 0.102 -0.398 -0.454 HCRI 0.995 0.001 4.77 0.006 5.51 0.002 -0.409 -0.704 EI 3.376 0.001 0.83 0.495 2.02 0.123 -0.329 -0.629 URI 0.460 0.001 5.49 0.005 2.07 0.094 -0.702 -0.226 IFA 1.057 0.001 4.68 0.004 3.18 0.055 -0.663 -0.573 F4=5.38, p=0.002 F4=4.94, p=0.009

Five of the bone indices showed a significant difference between the behavioural categories (Table 6.3, Figure 6.1), with no significant difference in the HTRI and EI between the behavioural categories. In general, the indices showed a trend of decreasing indices scores from the most digging adapted (burrowing), to least digging adapted (terrestrial) categories (e.g., ulna robustness index (URI) and IFA). The exception to this trend was the brachial index (BI), in which, the somewhat reverse occurred, with the burrowing, foraging and scansorial species showing the lowest scores of BI in comparison with arboreal and terrestrial species. The aquatic platypus (Ornithorhynchus anatinus) often sat within the range of the burrowing species or showed a more extreme index score (HTRI, HCRI, EI). The burrowing category included the marsupial mole. The foraging category included the two species of echidna (Tachyglossus spp.), which often showed as outliers, or increased the range of the index significantly. Once phylogeny was accounted for in the bone indices, only humerus cranial-caudal robustness index (HCRI) showed significant differences between the

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five behavioural categories. From the pairwise comparison of the phylogenetic ANOVA, the arboreal category was significantly different from the burrowing, foraging and scansorial categories. Burrowing species were also significantly different from the terrestrial category.

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Figure 6.1: Box and whisker plots of each of the seven indices grouped by behavioural category. Results of one-way ANOVA and pair-wise comparison. Aquatic species were not included in the analysis due to only one species in the behavioural category. Behaviour groups with the same letter indicate they are significantly different from each other by the pair-wise comparison. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial, and blue: aquatic. 144

The first two principal components from the phylogenetic PCA of bone indices explained 85% of the total variance. The first PC was driven BI, URI and IFA, while PC2 was loaded heavily by SMI, HCRI and EI (Table 6.3). Both PC1 and PC2 showed a significant difference between the five behavioural categories (aquatic category not included).

6.4.2 Landmark coordinates variation

The results of the multivariate K-statistic calculated on the forelimb shape data using landmark coordinates showed a significant phylogenetic signal, but the K-statistics were below one indicating less resemblance between species than expected under Brownian motion (Table 6.4). The K-statistic calculated for the first two PC axes of the landmark coordinate shapes were also lower than one, but the randomised tests showed a significant phylogenetic signal for the first PC for the humerus and the ulna, as well as the second PC for the third metacarpal.

Table 6.4: Details of the results of the principal components analysis, phylogenetic signal and ANCOVA/phylogenetic ANCOVA to assess the effect of primary behaviour on landmark coordinates. The first column explained variance for the first two principal components (PCs) from the PCA. Second column, results of the K-statistic and associated P-value of multivariate (denoted by * on Procrustes coordinates) and univariate on the first four PCs. Third and fourth columns, results of ANCOVAs (F and P-values) and phylogenetic ANCOVAs (F and Pphy-value) for the first four PCs to test the effect of primary behavioural category on bone shape. All significant values indicated in bold. PCA Phylogenetic signal ANCOVA Phylogenetic ANCOVA % variance K P-value F P F Pphyl Scapula 0.492 0.001* PC1 30.24 0.235 0.148 4.93 0.005 2.02 0.168 PC2 21.95 0.329 0.058 93.3 0.001 10.2 0.003 Humerus 0.379 0.001* PC1 51.61 0.532 0.001 15.7 0.001 0.184 0.890 PC2 8.61 0.315 0.059 32.2 0.001 3.21 0.068 Ulna 0.403 0.001* PC1 62.10 0.375 0.010 32.5 0.001 4.78 0.015 PC2 9.31 0.227 0.154 8.99 0.001 1.07 0.397 Third metacarpal 0.320 0.001* PC1 32.72 0.153 0.541 35.7 0.001 2.61 0.112 PC2 25.42 0.432 0.011 14.3 0.001 1.67 0.228

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All four forelimb bones showed significant shape differences between the four behavioural categories using the Procrustes coordinates, however, only the ulna showed a significant difference once phylogeny was accounted for (scapula F3=10.0, P=0.001; F3=1.58,

Pphy=0.116; humerus F3=11.5, P=0.001; F3=1.27, Pphy=0.204; ulna F3=18.6, P=0.001; F3=2.21,

Pphy=0.042; and third metacarpal F3=8.87, P=0.001; F3=1.96, Pphy=0.059). Mean shapes of the bones separated by behavioural category are presented in Figure 6.2-Figure 6.5.

Scapula landmark coordinates: The first two PCs of the scapula accounted for 52% of the variance (Figure 6.2). Traditional ANCOVA showed significant differences between the behavioural categories; however, only PC2 was significant once phylogeny was accounted for. PC2 separated the Dasyuromorphia from the remaining species. Along PC2, Dasyurus spp. and M. fasciatus had an extremely enlarged and prominent supraspinous fossa and elongated acromion process, while the scapula for remaining species was triangular shaped with a relatively larger infraspinous fossa and wide glenoid cavity with a shortened acromion process at the positive end of PC2.

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Figure 6.2: Results of the PCAs performed on the morphometric data of the scapula and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), two terrestrial species (both diprotodontian) and no data for arboreal and aquatic categories.

Humeral landmark coordinates: The first two PCs of the humerus accounted for 60% of the variance (Figure 6.3). Traditional ANCOVA showed significant differences between the behavioural categories; however, these were largely explained by phylogeny, with the phylogenetic ANCOVA showing no statistical differences between the behavioural categories.

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Ulnar landmark coordinates: The two PCs of the ulna accounted for 70% of the variance (Figure 6.4). Traditional ANCOVA showed significant differences between the behavioural categories, and PC1 remained significantly different once phylogeny was accounted for. The scansorial and the terrestrial species clustered at the negative end of

PC1 axes, with extremely elongated and gracile ulnar shape and relatively small olecranon and trochlear notch. Foraging species showed a large amount of ulnar shape variation, while

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burrowing species clustered closer together with relatively long olecranon and robust wide ulnar shafts.

Figure 6.4: Results of the PCAs performed on the morphometric data of the ulna and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), two terrestrial species (both diprotodontian) and no data for arboreal and aquatic categories.

Third metacarpal landmark coordinates: The first two PCs describing the shape of the third metacarpal accounted for 58% of the total variance. The third metacarpal shape visually clustered by phylogeny, rather than behavioural category (Figure 6.5). Traditional

ANCOVA showed significant differences between the behavioural categories; however, the phylogenetic ANCOVA showed no statistical differences between the behavioural

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categories. Diprotodontians were intermediate on PC1 and high on PC2 with shortened and relatively wide shaft, the Isoodon spp. clustered (with some scatter) at the negative end of

PC1 and the PC2 axis showing intermediate robustness and length of the metacarpal, while dasyurids are clustered at the positive end of PC1 with extremely gracile distal end of the metacarpal.

Figure 6.5: Results of the PCAs performed on the morphometric data of the third metacarpal and the associated mean shape for each of the behaviours. The phylogeny is plotted in the shape space. Symbols are as follows: circles for Diprotodontia; squares for Dasyuromorphia; and triangles for Peramelemorphia. Brown polygons indicate burrowing species; yellow polygons indicate foraging species; purple polygons indicate scansorial species; and green polygons indicate terrestrial species. There was landmark data for only two burrowing species (for one diprotodontian and one peramelemorphian), one terrestrial species (diprotodontian) and no data for arboreal and aquatic categories.

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6.5 Discussion

6.5.1 Bone indices are great predictors of primary behaviour

The humerus cranial-caudal robustness index (HCRI) and the index of fossorial ability

(IFA) were best at predicting forelimb specialisation and behaviour in monotremes and marsupials. From terrestrial/arboreal to the extremes of burrowing behaviour, there is an increase in humeral and ulnar robustness (measured by HTRI, HCRI, URI), relative length of the olecranon process (IFA) and relative size of the epicondyles of the humerus (EI). Similar trends have previously been reported in badgers (Rose et al., 2014), rodents (Elissamburu & de Santis, 2011) and armadillos (Vizcaíno & Milne, 2002). Terrestrial species had a high relative length of the deltoid crest (SMI) in comparison to scansorial and arboreal species, so an increase from arboreal – scansorial – terrestrial – foraging – burrowing was seen. Digging species (burrowing and foraging) have shortened and robust forelimbs, with high development of the flexor, pronators and supinators of the forearm, and longer moment arms for flexing the humerus, and extending the ulna. The non-digging species (scansorial, arboreal, terrestrial) have much more gracile (less robust) humeri and ulna with a reduced olecranon process and therefore smaller mechanical advantage of the triceps in elbow extension. The arrangement of the forelimb is consistent with digging species being adapted for high force production for digging in comparison to non-digging species that have a lower force production.

The phylogenetic corrected PCA also highlighted the ability for indices to separate the behavioural categories, with the first two PCs successfully differentiating between species in these behavioural categories. The brachial index (BI), ulna robustness index (URI) and IFA

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loaded heavily on PC1, driving over 65% of the variance seen between the 56 species.

Shoulder moment index (SMI) and HCRI drove PC2 with 19.6% total variance. Brachial index was seen as a good predictor of digging ability in armadillos (Vizcaíno & Milne, 2002), and marsupials (Warburton et al., 2013b), but was unable to separate different behaviours in badgers (Rose et al., 2014). Digging species have smaller values of BI as digging species have relatively short distal out-levers (Vizcaíno et al., 1999; Vizcaíno & Milne, 2002) in comparison to generalised or arboreal species (Rose et al., 2014) that was partly seen in our study. The foraging and burrowing species had a large variation in BI, with peramelemorphians having relatively lower BI scores in comparison to the diprotodontian and monotreme foraging and burrowing species.

Our samples of burrowing and foraging species have much greater variation than would typically be expected. This is due to the monotremes (two foraging species of echidna), and the marsupial mole (burrower) in the sample. These three species are highly specialised for digging and have extreme adaptations to their forelimbs (Warburton, 2006;

Regnault & Pierce, 2018; Regnault et al., 2020). Echidnas have adapted to a different mode of foraging and digging, in which they have a sprawled abducted forelimb posture and rotate the humerus while the forelimb extends and sweeps the substrate behind them

(Barnosky, 1981; Hopkins & Davis, 2009). This mode of digging requires extremely robust humeri with greatly expanded epicondyles on the humerus in comparison to species adapted to the scratch-digging (Regnault & Pierce, 2018). The southern marsupial mole in comparison is the only almost entirely subterranean species, which has a highly modified skeleton for their fossorial lifestyle. They also have extremely robust humeri and ulna with an extremely elongated and robust olecranon process (Warburton, 2006). These three

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species have significantly more specialisations to digging than any of the other species in the study, and therefore may be treated as outliers.

6.5.2 Landmark coordinates

The shape of the four forelimb bones measured using landmark coordinates was significantly different between the four behavioural categories, although only the ulnar shape remained significant after phylogenetic correction. The scapular, humeral and third metacarpal shapes were not different between the behavioural categories after phylogenetic correction, which suggests their shape is primarily driven by phylogeny. As the ulnar shape remained significant after phylogenetic correction this suggests that the shape is more adaptive and has evolved in response to the constraints that are imposed by the primary behaviour.

Mean shape of the ulna

From previous studies of marsupial forelimbs, the ulna has shown significantly different shapes before and somewhat after phylogenetic correction (PC2 accounting for

8.2% total variance) between marsupial species. With an increase of species, and separating the non-digging species from the Chapter 5 study into scansorial and terrestrial species, we are able to see a clearer separation of ulnar shape between the different behavioural categories. The ulna was also the only bone in the forelimb that showed no significant covariation with forelimb musculature, both within a species (Chapter 4- Martin et al.,

2019a) and between species (Chapter 5). The significant shape differences between behavioural categories and the lack of covariation with musculature suggests that the ulnar

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shape is not solely influenced by the forces imposed by muscles, but is driven by the overall functional demands of the primary behaviour.

The mean shape of the ulna was distinctly different between the digging species

(burrowing and foraging) and non-digging species (scansorial and terrestrial). The ulna of burrowing species was robust with a long and well-developed olecranon, with foraging species olecranon process slightly longer and a wider diaphysis. The increased length of the olecranon provides a mechanical advantage for the triceps by increasing the in-lever and increasing the moment-arm at the elbow joint, therefore increasing the out-force during digging (Hildebrand, 1985; Samuels & Van Valkenburgh, 2008; Hopkins & Davis, 2009;

Samuels et al., 2013; Woodman & Wilken, 2019). Foraging species have a medially orientated olecranon process, which has previously been seen (Moore et al., 2013; Fabre et al., 2015) where it relates to the triceps applying high torque at the elbow to increase the out-force on the soil while digging. A deep trochlear notch was also found, reflecting enhanced stability of the elbow joint to prevent dislocation and mobility (Rose et al., 2014;

Sesoko et al., 2015). Our results are consistent with previous studies of semi-fossorial species, with well-developed robust ulna with elongated olecranon processes for increased mechanical advantages during digging.

The ulnar shape of scansorial and terrestrial species was elongated and gracile with a shortened medially orientated olecranon process, with a relatively wide trochlear and radial notch. The articulation surface of the trochlear notch is relatively small that suggests stability of the elbow but with more mobile joint than the digging species. It is well reported that scansorial/arboreal species have gracile bones (Sargis, 2002; Fabre et al., 2013), along with a straight diaphysis which is thought to help transmit the loading during locomotion

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and to stabilise the forelimb (Flores & Diaz, 2009; Fabre et al., 2013; Kilbourne &

Hutchinson, 2019). A relatively shorten olecranon process reflects decreased force output but often an increase in the speed of movement (i.e., rapid movement throughout the environment) (Argot, 2001; Fabre et al., 2013). The short olecranon process also allows for full extension of the elbow to increase mobility (Samuels & Van Valkenburgh, 2008). Our results are consistent with previous studies of arboreal/scansorial and terrestrial species having elongated and gracile limb bones to increase mobility in the limb.

Mean shape of scapula

The mean shape of the scapula of the burrowing and foraging species is characterised by a rectangular supraspinous and infraspinous fossae with a wide and robust glenoid cavity, coracoid process and relatively short acronium. These features increase the surface area for the rotator cuff muscles to stabilise the shoulder joint to withstand high load during digging (Warburton et al., 2013b). Both foraging and burrowing species have prominent teres process for an increased surface area of the teres major to increase the moment arm for humeral retraction, and a prominent caudal angle (especially the burrowing species) that is the origin of the teres major muscle (Argot, 2001). These scapular adaptations for foraging and burrowing species are similar to what has been described previously in badgers

(Mustelidae; Rose et al., 2014), bandicoots and bilbies (Peramelemorphia; Warburton et al.,

2013b; Martin et al., 2019a) and have strong adaptations for withstanding high loads during scratch-digging.

The scansorial mean scapular shape was elongated with a cranially developed acromion, which suggests a well-developed deltoideus muscle. Scansorial animals also have a large supraspinous fossa for the supraspinous muscle. These two muscles together assist 155

in abduction of the humerus and potentially allow for more rotation of the limb. A large supraspinatus muscle is also associated with terrestrial species, as it may absorb some of the energy as the forelimbs touch the ground (Jenkins, 1974; Argot, 2001; Astúa, 2009).

Both terrestrial and arboreal Didelphidae have an acromion that overhung the glenoid cavity to assist in abduction and rotation of the forelimb (Argot, 2001; Astúa, 2009).

Mean shape of humerus

The mean humeral shape of the burrowing species was extremely robust and broad with a wide deltopectoral ridge and the distal end, with the foraging species similar, with a less robust and wide pectoral ridge, and a larger lateral epicondyle. The shape of the terrestrial species was an intermediate between the burrowing and foraging shape. The robust and shortened humerus of the digging species allows for a reduction of the out-lever and therefore increase in the in-lever of the humeral retractor muscles (Samuels & Van

Valkenburgh, 2008; Moore et al., 2013; Samuels et al., 2013; Fabre et al., 2015), and to better withstand the high bending loads associated with digging (Rose et al., 2014;

Kilbourne & Hutchinson, 2019; Woodman & Wilken, 2019). The distally extending deltopectoral crest in the area of insertion of the pectoral group of muscles on the shaft and this assists in both stabilising the shoulder, and a powerful humeral retraction for digging

(Moore et al., 2013; Fabre et al., 2015). The wide distal articulation of the humerus is associated with increased surface area for the carpal/digital flexor muscles (Warburton et al., 2013b; Martin et al., 2019b). The enlargement of the medial epicondyle in comparison to the lateral epicondyle suggests that there is proportionally more muscle mass and force production located in the flexors in comparison to the extensors (on the lateral epicondyle) for the forearm of the limb.

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In contrast to digging species, the scansorial mean humeral shape is extremely elongated, gracile and has a large prominent greater and lesser tubercle, with a narrow distal end (condyles and medial and lateral epicondyle). Long and gracile humeri are often associated with arboreal and semi-arboreal species to increase the joint mobility and increase the range of motion in the forelimb (Flores & Diaz, 2009; Ercoli et al., 2012; Fabre et al., 2013). The medially directed lesser tuberosity has been seen in some tree shrews

(Sargis, 2002), didelphids (Argot, 2001) and carnivorans (Gebo & Rose, 1993) for active climbing, in comparison to suspensory climbing which has a reduction in the lesser tuberosity to allow for greater rotation of the shoulder (Arias-Martorell, 2018). The medial orientation of the lesser tuberosity provides a greater moment arm for the subscapularis muscles that functions for rotation of the humerus medially (Janis et al., 2020). The scansorial and terrestrial species humeral shape have increased joint mobility, in comparison to shortened and robust humeri of the digging species.

Mean shape of the third metacarpal

Burrowing and foraging species have robust and shortened third metacarpals with wide proximal ends. Digging species have short stout carpals and metacarpals to increase the power and stability of the manus with elongated phalanges and claws to lengthen the paddle-like manus that is in contact with the soil while digging (Salton & Sargis, 2008;

Hopkins & Davis, 2009; Woodman & Wilken, 2019). All forces that are generated in the limb are concentrated through the third metacarpal and claws to cut through the soil while digging, which imposes a large selective pressure on this bone (Martin et al., 2019a). This may be why we see such large differences between digging and non-digging species third metacarpal shapes. By comparison, scansorial species that have elongated gracile bones

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with a relatively small proximal end. Terrestrial and scansorial species metacarpals and phalanges are elongated to increase the length of the manus. This assists in locomotion and increased stride length, or to increase grip strength in scansorial species.

6.5.3 Limitations of this study

This chapter reported bone indices for a comprehensive sample of monotremes and marsupials (815 specimens from 56 species), which covered 15 of the 19 extant families.

However, there was a low number of species used for the landmark coordinate analyses that significantly affects any results and conclusions that can be drawn from the three- dimensional section of the study. More species are required, especially species from all the behavioural categories excluding foraging to balance out the analyses, i.e., terrestrial kangaroos/wallabies, arboreal possums. Ideally, all specimens would be from wild-caught origin not captive to ensure the anatomy was a true representation of the wild population.

Within each of the behavioural categories assigned to the species, there is a large variation in lifestyle, i.e., varying substrates species primarily dig and forage in, or varying levels of arboreal ability. In future studies, more in-depth quantitative behavioural data could be utilised to assess the influence of behaviour on bone shape. Scoring behaviour for forelimb use throughout a day would provide the most accurate measure of forelimb usage and therefore adaptations.

6.6 Conclusion

Australian monotreme and marsupial forelimb bone morphology reflects their phylogeny as well as their digging behaviour. After phylogenetic correction, bone indices and ulnar shape were the best predictors of behaviour. Ulnar shape was primarily driven by 158

the functional demands imposed by behaviour and the environment in comparison to the other forelimb long bones. Our future work will use geometric morphometric methods to replicate the size of the 2D analysis to further understand the locomotor adaptations in monotremes and marsupials and the roles of extinct species played in past ecosystems.

6.7 Acknowledgements

The authors acknowledge the facilities, and the scientific and technical assistance of the National Imaging Facility at the Centre for Microscopy, Characterisation & Analysis, The

University of Western Australia, a facility funded by the University, State and

Commonwealth Governments. The authors would also like to thank the following organisations for providing specimens for this research; Alice Springs Desert Park, Australian

Wildlife Conservancy, Kanyana Rehabilitation Centre, Perth Zoo, The Department of

Biodiversity, Conservation and Attraction and Western Australian Museum. Financial support was provided by Murdoch University and Graduate Women WA via the Mary

Walters Award.

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Chapter 7 General discussion

7.1 Summary

This thesis provides an in-depth quantitative examination of forelimb morphology across Australian marsupials and monotremes. Muscle architectural data was collected for

11 species, and bone shape data was quantified for 56 species including species from both monotreme families and all four orders of Australian marsupials. As a novel contribution to the field of comparative functional anatomy, I have linked forelimb muscle architecture and bone shape in the context of ecology and phylogeny to enable a deeper understanding of these complex interactions than has previously been attempted. To achieve this, I have explored intra- and inter-specific variation in monotremes and marsupials to address questions regarding the intrinsic associations between muscles and bone, and how these change within and between species.

7.2 Ontogenetic differences in Quenda

Scaling muscle architectural properties against body size can provide insight into locomotor or behavioural adaptations, helping us understand how animals respond to changes from juveniles to adults. Few studies have explored or documented muscle ontogeny (Lamas et al., 2014; Picasso, 2015; Butcher et al., 2019), with very few studies focussing on the forelimbs (Allen et al., 2010; Allen et al., 2014). In this thesis (Chapter 3), I have assessed the scaling of forelimb muscle architecture with the body mass of furred young to adult Quenda (Isoodon fusciventer) and evaluated the differences between muscles that are main movers of the power stroke in comparison to recovery-stroke of

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scratch-digging. I demonstrated that Quenda experience a relative increase in muscle force production (physiological cross-sectional area, PCSA) as they grow, with larger Quenda having relatively stronger forelimb muscles in comparison to smaller Quenda. Fibre length, in contrast, developed isometrically with body mass; therefore, speed and flexibility of the muscles did not change through ontogeny. However, overall, positive allometry was evident for only one-third of the muscles’ maximum isometric force (Fmax) (ten power stroke and one recovery stroke muscle); thus, the majority of the muscles’ force production remains the same through ontogeny. As the majority of the forelimb is mechanically similar across the body mass range, this suggests there is no disadvantage for smaller Quenda, or advantage of being a larger Quenda. Currently, there is no information available on the substrate selection, scratch-digging frequencies or the depths of the digging between animals of varying sizes for Quenda, or similar species. This data would be informative and provide a behavioural link with the musculature morphology through the ontogenetic series, and allow us to further investigate the development from furred juvenile to adult.

This study highlights the importance (and also the rigour) of thoroughly understanding the allometric patterns of individual species in order to predict the anatomy of a species through ontogeny. The changes I observed in muscles could be used to enable predictions of ontogenetic development in other digging mammals. My data can also provide valuable information for future biomechanical models looking at scratch-digging, for extant and extinct mammals. Digging biomechanical models or in vivo behavioural studies are currently limited to invertebrates (Ansell & Trueman, 1967, 1968; Brown & Trueman, 1996), caecillians (O'Reilly et al., 1997; Navas et al., 2004), subterranean moles (Gasc et al., 1986;

Gambaryan et al., 2002; Lin et al., 2019), and rodents (Moore Crisp et al., 2019) in which

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majority of species are burrowing below the surface, and with no assessments of changes in digging behaviour through an ontogenetic series. Linking my morphological data (muscle and bone morphology) to in vivo data of scratch-digging (foraging) and measuring digging speed, digging depth, economy and peak bone strain would produce a larger picture of the adaptation to digging in scratch-digging mammals.

While I have examined the muscle shape, and force generating capacity for the forelimb muscles, tendon morphology was not explored. Tendon variables have previously been presented as better descriptors of locomotory types in comparison to the muscle variables in sigmodontine rodents (Sigmodontinae: Carrizo et al., 2014), however, tendon morphology commonly focusses on the hindlimb of marsupials as they are vital for locomotion. Fossorial species have shown to have long and robust insertion tendons for the carpal and digital flexor and extensor tendons for the wrist and hand mobility during excavation of soil. Investigating the relatively change in length of tendons, especially over the wrist through an ontogenetic series, as well as comparatively between species of different primary behaviour would assist in further understanding adaptations to digging in scratch-digging mammals.

Fossil material uncovered from Australian deposits has the potential to assist in making inferences of the behaviour and ecology of extinct species, and the roles they may have played within the Australian ecosystem through time and space. While attributes such as size may be inferred from linear measurements of fossil femurs or jawbones, being able to couple this with our ability to predict muscle architecture more precisely from forelimb morphology will allow deeper understanding the growth and ecology of extinct marsupials.

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7.3 Quantifying the link between muscle architecture and bone shape

It is broadly intuitive that there is a close association between muscular and skeletal morphology, because limb bone shapes adapt and grow to resist and support the mechanical loading during locomotion (Currey, 2013). However, few studies have quantified this relationship. The interplay between jaw muscles, the cranium and mandible has been described in mammals using a multitude of different statistical analyses (e.g., Hylander et al., 1992; Cornette et al., 2015; Penrose et al., 2016; Ravosa et al., 2016). This thesis expands on works by Fabre et al. (2014), Noback and Harvati (2015), Fabre et al. (2018), Sella-Tunis et al. (2018) and recently Brassard et al. (2020) who have documented covariation in cranial shape with masticatory muscles (PCSA) using two-block partial least square analyses (2b-

PLS).

The link between forelimb musculature and bone shape has been inferred in

Australian marsupials (Warburton, 2006; Harvey & Warburton, 2010; Warburton et al.,

2011; Warburton et al., 2013b; Warburton & Marchal, 2017) and some recent studies on monotremes (Regnault & Pierce, 2018; Regnault et al., 2020). However, to my knowledge, there has been very little information produced to quantify the relationship between the two morphological measures. My studies have built a framework of statistical methods to assess covariation between muscle and bone shape and applied 2b-PLS to analysis of the post-cranial forelimb assessing covariation between forelimb muscle force (PCSA) and forelimb bone shape through ontogeny (Chapter 4), and to assess inter-specific variation

(Chapter 5). In both studies, I detected a significant association between forelimb musculature and forelimb bone shape (measured using landmark coordinates).

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Supporting the observation that bone indices within one species exhibit less variation compared to between species (Maderbacher et al., 2008), bone indices showed weak covariation with muscle architecture in the Quenda (Chapter 4) but captured substantial inter-specific variation (Chapter 5) between the species. The indices lacked the ability to distinguish small changes in the forelimb through ontogeny compared to landmark coordinates, as the 3D landmark approach captures the entire bone shape and shows specific changes in the bone shape that occur around muscle attachments that can be directly correlated with changes in musculature. For future studies, landmark coordinates are the most suitable measure of bone shape when assessing variation within a species, while both landmark coordinates and bone indices are both suitable for assessing inter- specific variation (Bernal, 2007; Breno et al., 2011).

My 3D data was collected through 3D digital methods (micro-CT); however, landmarking is also possible from photographs (2D) which is an inexpensive method of landmarking. Arguably, 2D and 3D approaches to landmarking produce similar results (e.g.,

Cardini, 2014; Buser et al., 2018) and assessing the ability for 2D landmarks to predict musculature is an avenue that could be further explored.

Exploration of the remodelling process of bone throughout postnatal life would assist in understanding how specialisations to digging effect bone growth and development. The physiological of bone remodelling provides a mechanism for the skeleton to adapt to repetitive mechanical loading (Goldring, 2015). This adaptive process is reliant on the coordinated activity of the osteoblasts and osteoclasts to form a bone multicellular unit.

This unit remodels the trabecular and cortical bone through osteoclast-mediated bone resorption, followed by bone formation mediated by the osteoblasts (Eriksen, 2010) in areas

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of low and high loading respectively (Christen et al., 2014). Changes in mechanical demands can be interpreted at the microstructural level where bone remodelling or modelling has occurred (Schulte et al., 2013; Montoya-Sanhueza & Chinsamy, 2017). Bone strength is also modulated through activity, and is depended on both the matrix composition and the bone shape. Bone strength could be tested via loading tests to further understand the change in bone strength over ontogeny. A study of bone microstructure integrated with muscle architecture and bone strength, would provide a great assessment to further the understanding of resorption/ remodelling and bone strength changes associated to digging through ontogeny.

The benefits of these studies and methods are to assist in the interpretations of the fossil record; by understanding and quantifying the relationship between musculature and skeletal elements of extant species across multiple lineages, inferences can be made for fossil skeletons regarding the musculature, and therefore the behaviour and locomotion of the species. Comparative studies that include a diverse phylogenetic sample for each primary behaviour would allow us to quantify bone shape and musculature in the context of phylogeny and behaviour. Using this knowledge of the bone shapes and musculature for the different behaviour categories, we could use phylogenetic bracketing and discriminate analyses to predict the musculature and behavioural traits of fossil taxa. The more we understand about the relationship between musculature and bone shape in extant species in a phylogenetic diverse dataset, the more we will be able to apply the knowledge and build models of extinct species, concerning locomotion and behaviour.

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7.4 Association between ecology and bone shape

As form follows function, forelimb musculoskeletal morphology is expected to be driven by the mechanical demands of day-to-day activities, within the constraints imposed by their phylogenetic background (Biewener, 2005). Many researchers before me have documented this close relationship between ecology and the species phenotype, and I have built on these findings to produce a synthesis of data for marsupials. My findings align with recent studies using multivariate and phylogenetic method to find an association between shape and behaviour using bone indices (Vizcaíno et al., 1999; Vizcaíno & Milne, 2002;

Hopkins & Davis, 2009; Elissamburu & de Santis, 2011; Rose et al., 2014) and landmark coordinates (Milne et al., 2009; Fabre et al., 2013; Samuels et al., 2013; Martín-Serra et al.,

2014; Fabre et al., 2015; Fabre et al., 2019). Through the chapters of this thesis, I have directly compared the predictive strength of both bone indices and landmark coordinates as methods of quantifying bone shape to assess ecology, and provided an abundance of information, three-dimensional models and index data for a morphological record.

Within Australian marsupials, distinct shapes of the forelimb bones reflect burrowing, foraging and non-digging (scansorial and terrestrial) species using landmark coordinates

(Figure 7.1). The third metacarpal (Chapter 5) and ulnar (Chapter 6) shapes were significantly different between animals categorised by their digging behaviour, suggesting the shape was driven by the functional demands imposed by such behaviour. Phylogeny primarily drove the shape of the scapula and humerus, since the statistical difference between the behaviours was removed after phylogenetic correction. The difference in signal of behaviour between the four bones of the forelimb highlights the need to assess bones individually in comparison to the forelimb as one system. 166

Figure 7.1: Mean shapes of scapula, humerus, ulna and third metacarpal summarised for the four behaviour categories assessed in Chapter 6.

Of the four forelimb bones examined, the ulna consistently showed no association with muscle mass or PCSA. Except for the triceps brachii, there are relatively few muscles originating or inserting onto the ulna with fleshy attachments and, therefore, it is intuitive that there is less influence in shape by muscles. This suggests that mechanical forces resulting from behaviour (i.e., transmission of body mass during locomotion, or digging) are, therefore, more likely to generate the forces that drive changes in the shape of this bone.

Consistent with this interpretation, the ulnar shape was distinctively different between digging behaviour categories in my dataset. The ulna, especially the relative length of the olecranon process, is the characteristic most commonly used and is the most successful trait for distinguishing between digging and non-digging species. Elongation of the olecranon process provides the triceps brachii a mechanical advantage by increasing the in-lever and increasing the moment-arm at the elbow joint. This, in turn, increases the generation of out-

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force through the forelimb during scratch-digging (Hildebrand, 1985; Hopkins & Davis, 2009;

Samuels et al., 2013; Woodman & Wilken, 2019).

The ulna of the burrowing species measured was robust with a well-developed, elongated and medially-orientated olecranon. The ulna of the foraging species was similar, with a slightly longer and wider diaphysis and a more pronounced medial orientation of the olecranon process due to the insertion of the triceps brachii applying high-torque at the elbow during digging (Moore et al., 2013; Fabre et al., 2015). In contrast, the non-digging

(scansorial and terrestrial) species have elongated and gracile ulnar shapes, with shorter olecranon processes to allow for full extension of the elbow, but relatively wider trochlear and radial notches for increased mobility at the elbow joint. Future studies integrating maximal joint range of motion (ROM) would be informative to further understand the elbow mobility with the prediction that digging species would show a smaller ROM in comparison to the scansorial and terrestrial species. The different ulnar shapes between digging and non-digging species is a clear representation of behaviour and ecology, which can be used as a predictive tool for future studies of the fossil record and to further investigate other lineages of digging mammals.

In contrast to the ulna, the scapula, humerus and third metacarpal shape measured with landmark coordinates was driven both by both body mass and forelimb musculature, especially the muscles with large fleshy origins and insertions on the bones (in contrast to smaller tendinous attachments). Results of the phylogenetic correction on the covariation between bone shape and musculature (Chapter 6), showed less clustering between species of similar digging behaviours than expected, and less than has previously been observed in studies associated with mastication (Fabre et al., 2018; Brassard et al., 2020). Unlike muscles

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of mastication, the ecology and behaviours associated with the forelimb are more complex and varied for most animals, and thus the forelimb muscles influence skeletal anatomy will reflect the entire suite of behaviours that rely, at least in part, on the forelimb structure.

This is the inherent tension between form and function necessarily reflecting a compromise between all of the various selective pressures on a limb, and is an important tension to remember when considering the interpretation of limb anatomy. Analysis of additional species or a more detailed ecological analysis (e.g., quantifying the relative amount of time spent digging) may further our understanding of the functional implications of digging, and the development of the forelimb anatomy.

During the scratch-digging motion, out-force is generated throughout the forelimb and applied to the substrate through the manus and the longest (third) digit. Intuitively, the metacarpal is heavily influenced by the functional demands of digging in comparison to other behaviours such as locomotion and climbing. Burrowing and foraging species display shortened and robust third metacarpals to shorten the out-lever of the manus (Morgan &

Verzi, 2011), which assists in generating a greater out-force on the substrate. In contrast, non-digging species evolved elongated and narrow/gracile third metacarpals to facilitate grasping for climbing or food manipulation (Samuels & Van Valkenburgh, 2008). Comparison of the shape of the third metacarpal can, therefore, act as one method of differentiating ecology, and predicting ecology in the fossil record.

Although the humeral shape in our sample was primarily driven by phylogeny, there are clear morphological differences reflecting differential selective pressure with different behaviours. The humerus is under high bending loading, torsional loading (twisting), and is the origin or insertion of many of the main movers of the power stroke of scratch-digging

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(Rose et al., 2014; Woodman & Wilken, 2019). In burrowing species, the humeral shape was extremely robust and broad, with a pronounced deltopectoral ridge, and expansive medial and lateral epicondyles. The humeral shape of the foraging species was less robust but with wider lateral epicondyle. In contrast, the mean humeral shape in scansorial taxa was extremely elongated and gracile, with large or prominent greater and lesser tubercles, but relatively narrow medial and lateral epicondyles. Long and gracile humeri together with increased joint mobility enhance the range of motion in the forelimb for climbing (Flores &

Diaz, 2009; Ercoli et al., 2012; Fabre et al., 2013). The less obvious separation of humeral shapes between the digging and non-digging, suggests that humeral shape is less useful in predicting digging behaviour in comparison to the shape of either the ulna or the third metacarpal.

The seven bone indices do successfully distinguish the five behaviour groups, with the humerus cranial-caudal robustness index (HCRI) and the index of fossorial ability (IFA) best at predicting forelimb specialisations for digging. Along a gradient from terrestrial/arboreal species to the extremes of burrowing behaviour, there is an increase in the humeral and ulnar robustness (HTRI, HCRI, URI), the relative width of the epicondyles of the humerus (EI) and relative length of the olecranon process (IFA) (Vizcaíno & Milne, 2002; also seen in,

Elissamburu & de Santis, 2011; Rose et al., 2014). The burrowing and foraging species have shortened and robust humeri and ulna, with greater development of the flexor, pronators and supinators of the forearm, as well as longer moment arms for flexing the humerus and extending the ulna. In contrast, the non-digging species (scansorial, arboreal, terrestrial) have less robust humeri and ulna with a reduced olecranon process and therefore smaller mechanical advantage of the triceps in elbow extension. The results and arrangement of the

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forelimb ratios are consistent with the landmark bone shape; however, since the data set of bone indices had significantly more species for the phylogenetic analysis, they were more successful at distinguishing between the behaviour categories after phylogenetic correction using a PCA.

Direct comparisons of traditional morphometrics (i.e., linear bone measurements and ratios/indices) and geometric morphometrics (i.e., using landmark coordinates to capture

3D geometry; (Rohlf & Marcus, 1993; Zelditch et al., 2004; Breno et al., 2011) are lacking in the literature (Bernal, 2007; Bonnan et al., 2008; Breno et al., 2011). In this thesis, I have demonstrated that both traditional and geometric morphometrics are successful at quantifying morphological variation in intra- and inter-specific contexts (Chapters 4-6). Bone indices are a time effective and inexpensive measure of bone shape and are efficiently utilised to answer simple ecological questions regarding shape in inter-specific context.

They are, however, less useful when trying to make predictions about muscle adaptations.

From my analysis, it seems that both bone indices and geometric morphometric methods could be used for identification of the behaviour of species represented in the

Australian fossil record. The addition of fossil specimens to the current dataset could be analysed using a PCA (for either bone indices or landmark coordinates), or a discriminate analysis using the seven bone indices as predictors of ecology (Hopkins & Davis, 2009). Using extant species forelimb morphology with known ecology will allow us to better assess fossil morphology and predict ecology.

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7.5 The place of literature reviews

The amount of literature that has been published in the last 20 years utilising physiological cross-sectional area (PCSA) as a proxy measure of maximum isometry force is overwhelming, and presents many different methods of collecting and measuring data. In

Chapter 2, I review all the recent literature that had used PCSA to answer ecological questions, and produce a general set of “guidelines” to quantify muscle architectural properties. This review summarised the literature and compiled a list of the advantages and limitations for methods of measuring muscle mass, fascicle/fibre length, and pennation angle, as well as offering suggestions on how to best perform the measurements to investigate muscle architecture, and calculate PCSA.

Methodology reviews are helpful resources for researchers starting in a new field of study, new students, and experienced academics. Such reviews synthesise common findings into a single document and highlight gaps in the literature as well as highlighting flaws in current methods. While conducting this review, a general theme became apparent; many peer-reviewed articles do not sufficiently report their methods to allow others to repeat their study. Sufficient methodology is vital to ensure methods are repeatable, and valid comparison between studies of comparable methods can be made.

The technologies that was currently being used to study anatomy are developing quickly and becoming more accessible, and therefore, methodological reviews are required to be updated and published frequently. The way we study muscle architecture may change in the near future, with the development of methods using diceCT (diffusible iodine-based contrast-enhanced computed tomography; e.g., Dickinson et al., 2018; Santana, 2018;

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Dickinson et al., 2020), which minimises the need for destructive dissections of specimens to visualise the internal organisation of muscle architecture. Such methods will allow us to further investigate valuable/rare/extinct specimens that are stored in national museums and collections. As new methods are developing and changing, I believe reviews and well- presented methods sections in peer-reviewed journals are vital to ensure the latest methodology is understood and to guarantee comparable results are published. The review presented in Chapter 2 provides a theoretical and practical guide of methods for muscle architecture, which will assist new researchers by providing them with the necessary information to guide them with their own research and methodology, and will contribute to comparable datasets for future studies.

7.6 Concluding remarks

The research detailed in this thesis provides a solid foundation for future studies that are required to understand the anatomy and morphology of marsupials. The methods described can be applied to the wider understanding of digging adaptations in mammals.

Overall, the results point to a promising future in which PCSA and bone shape analysis is a valuable tool for assessing ecology of species and ultimately assist in interpreting the fossil record and the role these species played in the Australian ecosystem.

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Appendix Supplementary Materials

A.1 Supplements to Chapter 3

Table S3.1: Detailed summary of information on the specimens used in the study. Id Total body mass (g) Sex Locality of collection Season collected IF04 1204 Female Kanyana Autumn IF05 1114 Female Kanyana Autumn IF08 748 Female Kanyana Winter IF10 1002 Female Kanyana Winter IF11 1952 Male Millendon Spring IF12 1093 Male Whiteman Park Spring IF16 958 Female Murdoch Unknown IF17 1624 Male South-west WA Unknown IF18 1386 Male South-west WA Unknown IF19 1220 Male Kanyana Winter IF20 840 Female Kanyana Winter IF21 1320 Female East Perth Unknown IF22 713 Male Beaconsfield Autumn IF27 124 Male South-west WA Unknown IF28 205 Male Kenwick Summer IF29 367 Female South-west WA Unknown IF30 208 Female South-west WA Unknown IF35 228 Male Kanyana Spring IF36 1048 Female Mundaring Winter IF37 899 Female Mundaring Winter IF39 691 Male Leeming Autumn IF40 669 Male Lesmurdie Summer IF41 462 Female Kalamunda Unknown IF42 734 Male Carmel Autumn IF43 774 Male Maddington Autumn IF44 633 Male Lesmurdie Winter IF45 2390 Male Lesmurdie Spring IF46 420 Male Kanyana Summer IF47 359 Female Kanyana Winter IF50 1840 Male Murdoch Unknown IF51 1385 Male Kalamunda Winter IF52 260 Male Kanyana Unknown IF53 1610 Male Lesmurdie Winter IF54 1917 Male Beeliar Autumn

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Table S4.1: Definition of the homologous landmarks on the scapula used for the GM analyses. Landmark Definition 1 Intersection of the distal end of the spine and the scapula neck 2 Most cranial tip of acromium 3 Most caudal tip of acromium 4 Cranial tip of coracoid process 5 End of the cranial border at the cranial notch 6 Middle of infraglenoid tubercle 7 Tip of supraglenoid tubercle 8 Most caudal extent of glenoid fossa border 9 Most lateral extent of glenoid fossa border 10 Most medial extend of glenoid fossa border 11 Intersection point of lines passing through 7-8 and 9-10 on glenoid fossa surface 12 Most proximal dip of the scapula spine 13 Intersection of spine and vertebral border, between infraspinous fossa and supraspinous fossa 14 Intersection of acillary ridge and vertebral border between teres fossa and infraspinatus (caudal angle) 15 Most concave point of the acronium between landmark 1 and landmark 2 c1 Curvature of the intersection of the infraspinatus fossa and scapula spine (from landmark 1 to 12) c2 Curvature of the scapula spine (from landmark 3 to landmark 12) c3 Curvature of vertebral border from the spine to the caudal angle (landmark 13 to 14) c4 Curvature of caudal border of the scapula c5 Curvature of the post-scapular fossa along the caudal border of the scapula c6 Curvature of cranial border of the scapula

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Table S4.2: Definition of the homologous landmarks on the humerus used for the GM analyses. Landmark Definition 1 Most medial-distal point of the caudal part of the trochlea 2 Most medial and proximal point of the caudal side of the trochlea 3 Point of maximum of curvature of the olecranon fossa 4 Most lateral and proximal point of the caudal side of the trochlea 5 Most proximal tip of the medial epicondyle 6 Most distal tip of the medial epicondyle 7 Most medial and proximal point of the cranial side of the trochlea 8 Most medial point of the cranial side of the trochlea 9 Most proximal point of contact between the trochlea and the capitulum 10 Most lateral point of the cranial side of the capitulum 11 Most lateral and proximal point of the cranial side of the capitulum 12 Most lateral and distal point of the cranial side of the capitulum 13 Most distal point of contact between the trochlea and the capitulum 14 Most distal point of the deltopectoral crest 15 Most disto-dorsal extent of the humeral head border 16 Intertubercular groove (bottom lesser tuberosity) 17 Tip of lesser tuberosity 18 Most lateral point of the lateral epicondyle 19 Point of maximum of concavity of the caudo-medio-distal part of the capitulum 20 Concavity of trochlea distally 21 Distal part of the medial aspect of the humeral shaft in most lateral divit 22 Distal part of the medial aspect of the humeral shaft in most medial divit 23 Distal point of the medial epicondylar foramen 24 Proximal point of the medial epicondylar foramen 25 Proximal point of the lateral epicondylar foramen 26 Distal point of the lateral epicondylar foramen 27 Maximum point of curvature of the greater tuberosity 28 Most proximal point of greater tuberosity 29 Most distal junction of the lesser tuberosity and anatomical neck of humeral head 30 Distal point of projection on lesser tuberosity 31 Intertubercular groove (top lesser tuberosity) 32 Intertubercular groove (top greater tuberosity) 33 Most distal junction of the greater tuberosity and anatomical neck of humeral head 34 Proximal point of depression of infraspinatus origin on greater tuberosity 35 Midpoint between most disto-dorsal extent of the humeral head border and most proximal point on the humeral head 36 Middle point between landmark 28 and 32. Most proximal point 37 Most medial point of interubercular groove between the lesser and greater tuberosity 38 Most distal point of the deltopectoral crest 39 Intertubercular groove (bottom greater and lesser tuberosity) 40 Intersection of deltoid and pectoral ridge 41 Most proximal point on the humeral head c1 Curvature of the junction between greater tuberosity and the humeral shaft c2 Curvature of deltoid tuberosity c3 Curvature of anatomical neck of humeral head c4 Curvature of the humeral shaft between the lesser tuberosity and medial epicondyle c5 Curvature of the pectoral ridge on the shaft

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Table S4.3: Definition of the homologous landmarks on the ulna used for the GM analyses. Landmark Definition 1 Most lateral point of contact between the trochlear notch and the radial notch 2 Most proximo-lateral point of the incisure of the trochlear notch 3 Point of maximum of concavity of the proximal part of the trochlear notch 4 Most proximo-medial point of the incisure of the trochlear notch 5 Most palmar-lateral point of olecranon process 6 Most palmar-medial point of olecranon process 7 Most dorsal-medial point of olecranon process 8 Most dorsal-lateral point of olecranon process 9 Point where the most medial part of the coronoid process meets the most medial distal part of the trochlear notch 10 Point of maximum concavity between the radial notch and the troclear notch 11 Most anterior point of contact between the trochlear notch and the radial notch 12 Most latero-distal point of insertion of the radial notch 13 Tip of the styloid process 14 Most distal point of the articular facet that articulated with the radius 15 Most proximal point of the articular facet that articulates with the radius 16 Point where the proximo-lateral part of the coronoid process meets the lateral part of the trochlear notch 17 Most proxo-lateral point of the olecranon process 18 Most proximal point of the ulnar tuberosity 19 Most distal end of the ulnar tuberosity ridge c1 Curvature from the olecranon to the radialarticular facet that articulates with the radius c2 Curvature from the ulnar tuberosity to the distal point of the ulna c3 Curvature of the radial notch c4 Curvature of the lateral side of the trochlear notch and the olecranon c5 Curvature of the coronoid process

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Table S4.4: Definition of the homologous landmarks on the third metacarpal used for the GM analyses. Landmark Definition 1 Maximum point of curvature of distal extent of anterior portion of articular facet of MCII 2 Anteromedial corner of articular facet for magnum 3 Anterolateral corner of articular facet for magnum 4 Lateral depression for collateral ligament of the metacarpal head 5 Medial depression for collateral ligament of the metacarpal head 6 Maximum point of curvature of distal extent of anterior portion of articular facet for MCIV 7 Most lateral tip of tubercle on lateral side of MCIII head 8 Intersection of posterior surface of MCIII shaft, articular facet for proximal phalanx, and medial side of MCIII 9 Most lateral point of articular facet for proximal phallanx 10 Most distal tip of articular facet for proximal phallanx 11 Most medial point of articular facet for proximal phallanx 12 Intersection of posterior surface of MCIII shaft, articular facet for proximal phalanx, and lateral side of MCIII 13 Most medial tip of tubercle on medial side of MCIII head 14 Medial side of the proximal MCIV articulated with MCIII c1 Curvature of proximal end of MCIII and capitate bone c2 Curvature of dorsal side c3 Curvature of lateral side c4 Curvature of medial side c5 Curvature of ventral side

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Table S4.5: Examining allometry: MANCOVAs of the seven indices shape by size (total body mass) and sex (Y ~ size*sex). Degrees of freedom (Df), sums of squares (SS), coefficient of determination (R2), and the F ratio. Df SS MS R2 F P Shoulder moment index shape Log(size) 1 36.11 36.11 0.10 3.23 0.077 Sex 1 7.30 7.30 0.02 0.65 0.386 Log(size): Sex 1 <0.01 <0.01 <0.01 <0.01 0.996 Residuals 28 312.98 11.18 Total 31 356.4 Humerus transverse robustness index shape Log(size) 1 0.06 0.06 0.01 0.12 0.733 Sex 1 0.71 0.71 0.05 1.50 0.238 Log(size): Sex 1 0.69 0.69 0.05 0.81 0.224 Residuals 28 13.19 0.47 Total 31 14.65 Epicondyle index shape Log(size) 1 0.32 0.32 0.01 0.22 0.639 Sex 1 1.45 1.45 0.03 0.98 0.313 Log(size): Sex 1 0.51 0.51 0.01 0.35 0.552 Residuals 28 41.30 1.48 Total 31 43.58 Ulna robustness index shape Log(size) 1 1.49 1.49 0.02 0.78 0.396 Sex 1 3.64 3.64 0.06 1.90 0.174 Log(size): Sex 1 2.46 2.46 0.04 1.29 0.249 Residuals 28 53.52 1.91 Total 31 61.11

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Table S5.1: List of specimens used in the study. ID Species TBM (g) Sex Collection site Sourced BP01 Bettongia penicillata 1108 Male Karakamia, WA AWC BP02 Bettongia penicillata 1090 Male Karakamia, WA AWC BP03 Bettongia penicillata 940 Male Karakamia, WA AWC BP05 Bettongia penicillata 880 Female Kanyana, WA Kanyana WRC BP06 Bettongia penicillata 1186 Female Karakamia, WA AWC BL01 Bettongia lesueur 672 Male Lorna Glen, WA DBCA BL03 Bettongia lesueur 569 Female Lorna Glen, WA DBCA BL04 Bettongia lesueur 1152 Male Lorna Glen, WA DBCA DG01 Dasyurus geoffroii 1273 Male Julman Rd Westside, WA Murdoch University *DG04 Dasyurus geoffroii 1200 Male Desert park, NT AS Desert park *DG05 Dasyurus geoffroii 1500 Male Desert park, NT AS Desert park *DG06 Dasyurus geoffroii 1770 Male Desert park, NT AS Desert park *DG07 Dasyurus geoffroii 1278 Male Perth Zoo, WA Perth Zoo *DG08 Dasyurus geoffroii 826 Female Desert park, NT AS Desert park DH01 Dasyurus hallucatus 250 Female Pilbra, WA WA Museum DH02 Dasyurus hallucatus 450 Female Pilbra, WA WA Museum DH03 Dasyurus hallucatus 620 Female Pilbra, WA WA Museum DH04 Dasyurus hallucatus 980 Male Pilbra, WA WA Museum DH05 Dasyurus hallucatus 558 Male Pilbra, WA WA Museum *IA01 Isoodon auratus 350 Female Desert park, NT AS Dessert park *IA02 Isoodon auratus 446 Male Desert park, NT AS Dessert park IA04 Isoodon auratus 150 Male Barrow Island, WA WA Museum IA05 Isoodon auratus 200 Female Barrow Island, WA WA Museum IA06 Isoodon auratus 220 Female Barrow Island, WA WA Museum IM01 Isoodon macrourus 786 Female Sommersby, NSW Australian Museum IM02 Isoodon macrourus 1366 Male Teprigal, NSW Australian Museum IM03 Isoodon macrourus 1477 Male NSW Australian Museum IM04 Isoodon macrourus 1127 Female NSW Australian Museum IO22 Isoodon fusciventer 713 Male Kanyana, WA Kanyana WRC IO37 Isoodon fusciventer 899 Female Kanyana, WA Kanyana WRC IO18 Isoodon fusciventer 1386 Male Kanyana, WA Kanyana WRC IO19 Isoodon fusciventer 1220 Male Kanyana, WA Kanyana WRC ML01 Macrotis lagotis 861 Female Lorna Glen, WA DBCA ML02 Macrotis lagotis 1705 Male Lorna Glen, WA DBCA ML03 Macrotis lagotis 1050 Female SA SA Museum ML06 Macrotis lagotis 700 Male Lorna Glen, WA DBCA *ML07 Macrotis lagotis 1997 Male Desert park, NT AS Dessert Park PB01 Perameles bougainville 190 Male Faure Island, WA WA Museum PB02 Perameles bougainville 230 Female Faure Island, WA WA Museum PB03 Perameles bougainville 145 Female Faure Island, WA WA Museum PB04 Perameles bougainville 90 Male Faure Island, WA WA Museum PN01 Perameles nasuta 581 Female Narrareen Lake, NSW Australian Museum PN02 Perameles nasuta 1639 Male Niagara Park, NSW Australian Museum PN03 Perameles nasuta 1364 Male NSW Australian Museum PN04 Perameles nasuta 1194 Male NSW Australian Museum SB01 Setonix brachyurus 4780 Male Jarrahdale Road, WA Murdoch University SB02 Setonix brachyurus 2755 Male Nornalup dirt road, WA Murdoch University SB03 Setonix brachyurus 4652 Male Northern jarrah forest, WA Murdoch University AS Desert park: Alice Springs Desert Park; AWC: Australian Wildlife Conservancy; DBCA: Department of Biodiversity, Conservation & Attractions; Kanyana WRC: Kanyana Wildlife Rehabilitation Centre; SA Museum: South Australian Museum; WA Museum: Western Australian Museum. * Specimens that were not sourced from wild origin.

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Figure S6.1: Distribution of the brachial index (BI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.2: Distribution of the shoulder moment index (SMI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.3: Distribution of the humeral transverse robustness index (HTRI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.4: Distribution of the humerus cranial-caudal robustness index (HCRI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.5: Distribution of the epicondyle index (EI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.6: Distribution of the ulna robustness index (URI) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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Figure S6.7: Distribution of the index of fossorial ability (IFA) of all species in the study. Coloured hulls surrounding the six behaviour categories. Brown: burrowing, yellow: foraging, purple: scansorial, red: arboreal, green: terrestrial and blue: aquatic.

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