Commencing implementation of a genetic evaluation system for livestock working dogs by C. M. Wade, D. van Rooy, E. R. Arnott, J. B. Early and P. D. McGreevy June 2021

Commencing implementation of a genetic evaluation system for livestock working dogs

by C. M. Wade, D. van Rooy, E. R. Arnott, J. B. Early and P. D. McGreevy

June 2021

i © 2021 AgriFutures Australia All rights reserved.

ISBN 978-1-76053-135-5 ISSN 1440-6845

Commencing implementation of a genetic evaluation for livestock working dogs Publication No. 20-117 Project No: PRJ-010413

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Researcher contact details

Professor Claire Wade RMC Gunn B19-301 University of Sydney Camperdown NSW 2006

02 9351 8097 [email protected]

In submitting this report, the researcher has agreed to AgriFutures Australia publishing this material in its edited form.

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Electronically published by AgriFutures Australia at www.agrifutures.com.au in June 2021

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Photo credit: Page i – Al Dodge Photography

ii Foreword

The contribution of working dogs to Australia’s livestock industries is well-recognised, but the complex array of factors that influence breeding, selection and performance are yet to be fully understood.

From previous research funded by AgriFutures Australia, we know that on average Australian livestock producers own three to four dogs, and 90% of these are Kelpies, Collies or a cross of these two breeds. Most working dogs are purchased from a dedicated breeder and used as ‘all-rounders’ in terms of their daily activities, however up to 20% of dogs are culled due to a lack of ‘natural ability’, poor temperament or training issues.

With the potential to deliver in excess of a five-fold return on investment in terms of their contribution to the livestock enterprises in which they work, further investment in exploring the factors that lead to working dog success is warranted. This project builds on the knowledge gained through the previous project and delivers insights that will enable working dog breeders and buyers to select animals with a higher potential for success across a variety of working contexts. The project also delivers confidence in the genetic depth of the Australian Working Kelpie as a purebred breed.

The resources provided from this project have characterised the livestock herding dog industry’s production of a cluster of specialist dog types. If the level of canine specialisation across the sector is better appreciated, then working dog buyers are more likely to seek animals from appropriate breeders, improving the perceived value of dogs purchased and reducing the likelihood of dog failure.

This project was funded by AgriFutures Australia, The University of Sydney and the Working Kelpie Council of Australia (WKC).

This report is an addition to AgriFutures Australia’s diverse range of more than 2,000 research publications. It forms part of our Emerging Industries Program, which aims to support new and emerging rural industries.

Most of AgriFutures Australia’s publications are available for viewing, free downloading or purchase online at www.agrifutures.com.au.

Michael Beer General Manager, Business Development AgriFutures Australia

iii About the authors

Professor Claire Wade is Chair of Animal Genetics and Computational Biology at The University of Sydney. Prof Wade leads a programme in medical and behavioural genetics with particular focus on the dog. In recent years, her focus has included key roles in the analysis of the canine reference genome, the development of three canine gene-mapping arrays, and the mapping of several genes for canine diseases leading thus far to three commercially available tests for genetic diseases. She has current projects exploring the genetics of separation-related distress disorder, aggression, deafness, congenital birth defects, and pigmentation in the dog. Prof Wade has published more than 100 journal articles and has more than 16,000 citations from works in elite journals including Nature, Nature Genetics, and Science. Claire was a researcher and co-author of the previous AgriFutures Australia working dog project Valuable behavioural phenotypes in Australian farm dogs (PRJ-007806).

Dr Diane van Rooy graduated in Veterinary Science from The University of Melbourne in 1990. Her interest in animal behaviour grew over the next 17 years in veterinary practice in outer Melbourne. In 2004, Dr van Rooy attained membership of the Veterinary Behaviour chapter of the Australian and New Zealand College of Veterinary Scientists (the College). In 2019, she completed her PhD at The University of Sydney, researching the genetic basis of separation-related distress in dogs. She continues to combine research with private behaviour consultations and veterinary practice.

Dr Liz Arnott graduated from The University of Sydney in 2003 and worked in small animal practice in regional NSW, Sydney and the United Kingdom. Dr Arnott was awarded a Masters in Small Animal Practice from Murdoch University and achieved membership to the Australian and New Zealand College of Veterinary Scientists (the College) in small animal medicine in 2011. In 2014, she became a member of the Animal Welfare chapter of the College and completed a PhD in livestock working dog behaviour in 2018. Dr Arnott now works in the field of animal welfare policy and legislation.

Jonathan Early graduated in Veterinary Science from The University of Sydney in 2005. During his undergraduate training, he developed a particular interest in animal behaviour and welfare. Since graduating, he has worked in mixed practice in Victoria, small animal and exotics practice in Hobart and locumed across the United Kingdom. Prior to beginning his PhD in livestock working dogs, Mr Early worked in the Animal Health Policy Branch within the Commonwealth Department of Agriculture, Fisheries and Forestry in Canberra. He recently attained membership of the Australian and New Zealand College of Veterinary Scientists in Veterinary Behaviour.

Professor Paul McGreevy is Chair of Animal Behaviour and Animal Welfare Science at the Sydney School of Veterinary Science. He is one of only five veterinarians recognised worldwide by the Royal College of Veterinary Surgeons as Specialists in Veterinary Behavioural Medicine and has written nine books, 30 chapters and more than 250 articles in peer-reviewed journals. His team achieved significant success in revealing the nature of canine cognition and identifying early behavioural (and morphological) traits associated with success in puppies undergoing training for guide work. He is on the expert panel of the UK’s Dog Breeding Advisory Council. Prof McGreevy was the primary co- author of the previous working dog project Valuable behavioural phenotypes in Australian farm dogs (PRJ-007806), a project that delivered more than 10 peer-reviewed articles relevant to the current topic.

iv Acknowledgments

We wish to acknowledge the assistance of the Working Kelpie Council of Australia, which provided guidance and input into the design of this work. In particular, Mrs Barbara Cooper and Dr Don Robertson provided invaluable advice and assistance. We further wish to thank the dog owners and handlers who contributed data for this work. We particularly wish to thank Peri Chappell from Herds2Homes working dog rescue, who provided valuable assistance in the questionnaire validation, and Glenda Forster, who worked to liaise with working dog breeders and owners across Australia. Thanks also go to AgriFutures Australia for providing valuable funding support.

Abbreviations

ANKC Australian National Kennel Council

AWK Australian working kelpie

EBV Estimated breeding value

GBV Genomic breeding value

LHDEF Livestock Herding Dog Evaluation Form

WDP Working Dog Project

WKC Working Kelpie Council of Australia

v Contents

Foreword ...... iii About the authors ...... iv Acknowledgments ...... v Abbreviations ...... v Executive summary ...... viii Introduction ...... 1 Objectives...... 3 Methodology ...... 4 Stakeholder collaboration ...... 4 Data collection ...... 4 Data analysis ...... 6 DNA-based analyses ...... 9 Results ...... 12 Stakeholder collaboration ...... 12 Phenotypic data ...... 12 DNA-based analyses ...... 20 Implications ...... 27 Recommendations ...... 28 References ...... 29 Appendix 1 ...... 30 Appendix 2 ...... 32 Appendix 3 ...... 35

vi Tables

Table 1 Working and behavioural traits assessed by the LHDEF*...... 5

Table 2 Pearson correlations among normalised questionnaire scores for 588 dogs and 36 traits sorted according to association with ‘natural ability’...... 14

Table 3 Pooling of individual traits into three super-traits (criterion for pooling is r>=0.5 or r<=-0.5 among the pooled traits where r is the Pearson correlation)...... 16

Table 4 Normalised breed scores for some working traits differ significantly among Kelpies, Border Collies and their mixes...... 17

Table 5 Mean trait scores for 10 traits that exhibit differentiation between work contexts...... 18

Table 6 Correlations among survey scores for different work contexts...... 18

Table 7 Behaviour test correlations with anticipation, initiative-taking and the pooled trait instinct. . 19

Table 8 Behaviour test correlations with fearfulness...... 20

Table 9 Australian Working Kelpie genealogical analysis compared with breeds of similar registry size from Shariflou et al (2012) [2]...... 24

Table 10 Questionnaires per kennel of origin (AWK only)...... 26

Figures

Figure 1 Sheepmeat and wool zones of Australia ...... viii

Figure 2 The Livestock Herding Dog Evaluation Form ...... 4

Figure 3 Genome-wide association of working traits in the Australian Working Kelpie (n=94), a. Working skill, b. Instinct, c. Fearfulness. Genome-wide association of working traits in the Australian Working Kelpie (n=94), a. Working skill, b. Instinct, c. Fearful ...... 22

Figure 4 Pups registered with the Working Kelpie Council of Australia by year ...... 24

Figure 5 Mean inbreeding coefficient by year of birth for Australian Working Kelpies registered with the Working Kelpie Council of Australia ...... 25

vii Executive summary

Who is the report targeted at?

This report is primarily targeted at livestock herding dog breeders and owners looking to select dogs with the genetic potential to work with a given context (e.g. yards vs paddock). Beyond breeding, more accurate prediction of the best working context for individual dogs is expected to increase success rates among working dogs, reduce failure rates and improve overall animal welfare outcomes in the working dog sector.

Others who may be interested in the report are those who have an interest in the long-term sustainability and associated health of the Australian Working Kelpie breed.

Where are the relevant industries located in Australia?

Although the term ‘working dog’ can be applied to dogs across a variety of sectors, such as law enforcement, military or service, this project focused on dogs used for herding livestock, and more specifically the Australian Working Kelpie (Kelpie). In the context of the Australian extensive livestock industry, particularly the wool and sheepmeat industries, the working dog is a key member of the workforce, contributing in excess of a five-fold return on investment. Given the nature of the work carried out by these dogs, the working dog industry is inextricably linked to the sheepmeat and wool zones of rural Australia (Figure 1). In most cases, working dog breeders are located in regional areas close to their target market.

Figure 1 Sheepmeat and wool zones of Australia. Source: Australian Wool Innovation 2019

viii Background

From previous research funded by AgriFutures Australia, it is known that on average Australian livestock producers own three to four dogs and 90% of these are Kelpies, Collies or a cross of these two breeds. Most livestock herding dogs are purchased from a dedicated breeder and used as ‘all- rounders’ in terms of their daily activities, however up to 20% of dogs are culled due to a lack of ‘natural ability’, poor temperament or training issues.

Aims/objectives

The aim of the current project was to build on previous knowledge and establish a mechanism for performance recording of the phenotypic traits of livestock herding (working) dogs of various breeds.

By effectively connecting performance records with pedigree records, this performance recording scheme aimed to enable the calculation of estimated breeding values (EBVs), which could be used to evaluate the breeding potential of elite working dogs across a variety of important performance criteria.

The project also aimed to explore the genetic diversity of the Australian Working Kelpie population to ascertain the long-term sustainability of the breed through a genealogical (pedigree) analysis.

Methods used

A web-based questionnaire, developed during the previous project and refined during this project, was used to collect feedback from working dog owners about the working ability of individual dogs. To prevent bias, the data provided was supplied by owners and handlers of the dogs and not from the dog breeders. To reduce sampling error, assessment of breeding stock was carried out only when multiple data points were obtained for each parent animal. Individual dog information was visible only to the respondent who supplied the information, although breed average results could be viewed by all survey respondents.

In addition to the web-based questionnaire, DNA samples were collected from 430 dogs via blood or saliva sample. For dogs with both questionnaire and DNA data (n=94), genome-wide genetic analyses were carried out to enable genetic mapping of traits shown to have a strong statistical correlation with owners’ perceptions of working ability. The DNA results also enabled the relationships among individual dogs in the data to be estimated – assisting the generation of genomic breeding values (GBVs).

The validity of the owners’ evaluation of their dogs via the questionnaire was tested by having a series of dogs scored using both the questionnaire results and the application of a behaviour test, carried out by a veterinarian with additional qualifications in animal behaviour (Dr Diane van Rooy).

A pedigree analysis of the Australian Working Kelpie was carried out to determine the status of the population’s genetic diversity.

ix Results/key findings

The project has delivered a secure database resource to capture and retain phenotypic data on the performance of Australian working dogs, which can be accessed via https://doggenetics.net.au/Kelpie/FarmSurvey.html. At the time of writing, performance data for more than 650 dogs had been entered into the database (Table 1).

Analysis of the questionnaire data revealed the key traits affecting owner perceptions of the dogs’ working quality. An analysis of the data highlighted three ‘super’ traits that impact performance across a variety of work contexts – working skill, instinct and fearfulness or timidity. These traits formed the basis of gene mapping of working performance traits relating to ‘natural ability’ in livestock herding dogs.

Purpose-bred purebred dogs were found to outperform mixed-breed dogs for key traits, including natural ability, trainability, cover, balance, break and impulsiveness. Kelpies and Border Collies were considered to have similar natural ability.

Profiling of elite working dog behaviours across working contexts (yard work, paddock work and a combination of both yard and paddock work) generated a set of work context templates by which any dogs phenotyped by questionnaires in the future can be assessed for their optimal work type.

Owner assessments of dog behaviour correlated well with behaviour tests designed to assess the pooled behaviour traits of instinct and strength of character, as indicated by a lack of timidity of fear.

The strong phenotypic correlation between trait scores for working skill and instinct were supported by a common strongest gene mapping locus on canine chromosome six. Timidity appeared to be associated with a locus on canine chromosome 29. The association signals for instinct and timidity satisfy genome-wide significance.

The results of the regional analysis for trait mapping in all cases indicate preliminary association signals that merit further genomic analysis for validation in the same breed or a different breed. At this stage, the interim results are inadequate for use as selection tools.

The genealogical (pedigree) analysis suggests the Australian Working Kelpie population is of a sustainable size to enable effective selection for working ability without compromising genetic diversity.

The structure of the breed population is too dispersed to conduct meaningful phenotypic evaluation of EBVs using traditional methods.

The collection of the DNA samples from 430 Kelpies is the most extensive DNA-based representation of the breed worldwide. This resource has proven to be valuable not only for characterising working behaviours in the breed, but also for enabling the discovery of the genetic basis of important inherited disorders (Cerebellar abiotrophy and Lipid malabsorption) in Australian Kelpies. Although this was not a direct objective of the project, the outcome has positive ramifications for the breed.

x Implications for relevant stakeholders

Industry

The knowledge gained during this project will support the development of tools to assist working dog owners and breeders to identify superior working animals for a variety of working contexts, increasing success rates, reducing failure rates and improving overall animal welfare outcomes.

These results will also help breeders identify potential breeding matches and enable outcrossing without losing valued working attributes.

Community

There is strong public interest in breeding practices within registered dog breeds. In particular, the public is concerned about levels of inbreeding and diversity in pedigreed dogs. The outcomes of this research will reassure concerned community members that the Australian Working Kelpie population is of a sustainable population size with sufficient genetic diversity.

Recommendations

The calculation of EBVs for phenotypic traits in working dogs is possible, but requires better real- time connectivity between pedigree data and phenotypic questionnaire data. As such, voluntary phenotyping is unlikely to generate sufficient replication of either working contexts or bloodlines to support the creation of high-quality EBVs for working dogs.

GBVs offer a way forward for the industry, but require greater connectivity between the phenotypic data collection resource and DNA-based results. This might be enacted by using genotyping array data to support this enterprise and to validate animal parentage within the registry.

Industry-wide, GBVs offer better possibilities for including breeds with low representation of numbers and crossbred animals into the evaluation system. The barrier to this is that the data and analysis would need to be entrusted to a single provider.

xi Introduction

Working dogs play an important role in Australia’s agricultural history and continue to be vital to the viability of our extensive livestock industries. With an estimated 94,500 livestock working dogs in Australia1, these dogs comprise a significant proportion of the agricultural workforce and contribute in excess of a five-fold return on investment.

The previous AgriFutures Australia Working Dog Project (WDP) (Valuable behavioural phenotypes in Australian farm dogs, PRJ-007806) collected data from 800 primary producers on more than 4,000 dogs and identified key management factors that contribute to the success of working dogs across a variety of livestock handling contexts. The previous project also highlighted the variation in terminology used to describe desirable traits in working dogs, and learnings from this project were implemented in the current project.

Careful selection of breeding animals increases the frequency of desirable traits and decreases undesirable traits. By consulting with stakeholders to identify and prioritise both the traits to be conserved and traits for genetic improvement in working dogs, a suitable breeding index could be developed to facilitate a streamlined and successful breeding and selection program.

The length of a dog’s working life, whether for breeding, success at trials or working with stock, can be affected by both behavioural and health attributes. This project measured and recorded both attributes in farm dogs.

The data collected in this project takes two key forms – phenotypic performance data provided by working dog owners through an online questionnaire and genetic data collected through DNA samples of 430 Australian Working Kelpies.

The collection of performance data for individual dogs through the online questionnaire enabled the research team to characterise the daily activities of Australian working dogs. The ratings provided by respondents for these dogs also highlighted traits of value, both from the perspectives of the owners and the rankings of particular behavioural traits in dogs of specific breeds and across specific working contexts.

The collection of the DNA samples from 430 Kelpies is most extensive DNA-based representation of the breed worldwide. This resource has proven to be valuable not only for characterising working behaviours in the breed, but also for enabling the discovery of the genetic basis of important inherited disorders (Cerebellar abiotrophy and Lipid malabsorption) in Australian Kelpies. Although this was not a direct objective of the project, the outcome has positive ramifications for the breed.

Individual performance metrics for animals are affected by several factors that can hide the dogs’ underlying genetic potential. The early environment of the dog, its exposure to training, and access to livestock all affect its ability to reach its true potential. The expertise of the trainer or handler is critical to foster the dog’s skills so it can demonstrate elite performance.

Estimated breeding values (EBVs) better reflect genetic merit than individual performance metrics, because they do not rely solely on the performances of individual dogs. Instead they incorporate data across a larger group of genetically related animals. By assessing dogs from different bloodlines within similar training environments, and by assessing dogs with common bloodlines across different environments, the environmental effects and the effects of handler expertise can be corrected for. This removes the impacts of individuals who may seek to manipulate the results. By assessing the progeny

1 McGreevy, P. D., Wade, C. M., Arnott, E. R., and Early, J. B. (2015). Valuable behavioural phenotypes in Australian farm dogs.

1 of sires and dams across a variety of working environments, we can identify animals that pass on elite working skills to their progeny.

EBVs are commonly used to assist selection of livestock such as cattle and sheep, but to date have been rarely employed by dog breeders. Genetic progress will be maximised if breeders collectively agree on breeding goals (i.e. the desired working traits), and their ability to meet the needs of the market will be enhanced by connecting with other breeders who share the same goals.

The Livestock Herding Dog Evaluation form was developed to assess both behavioural and working traits of working dogs across a variety working contexts. The collective results were designed to enable breeders to assess their dogs and compare them with dogs bred by their peers. This requires users to provide permission to share their results, and at this stage the sharing of such results has not occurred, but the information remains within the database.

Using modern canine genomic technologies, genetic markers that predict working dog trainability and workplace success can be identified. This will increase the aptitude of working dogs and reduce the failure rate (estimated at 20%). Not only does this have positive financial implications for those investing in working dogs, it also offers benefits from a welfare perspective for both the dog and the owner, who is keen to for their dog to succeed in the workplace.

2 Objectives

The major aim of this project (and the previous project) was to contribute to the development of a system and set of tools and resources to genetically evaluate valuable phenotypic traits of working dogs, and to provide working dog breeders and owners with a decision-support tool for selecting dogs with the highest success rate in a given working context.

To do this, working dog owners need to participate in an ongoing, long-term national breeding program to conserve and improve working dog breeds used in Australia’s livestock industries.

The primary goal of the current project was to develop a user interface that could enable the long-term collection of phenotypic and genetic data on working dog performance.

In addition, the ongoing collection of DNA samples from active workings dogs will facilitate the understanding of working dog genomics and the genomics of dog behaviour in future research efforts.

A genealogical analysis (pedigree analysis) of the Australian Working Kelpie breed would establish the value of the open registry in maintaining population quality and genetic diversity. Such a registry would enable a comparison of this breed to other Australian registered dog breeds in terms of population size and management.

3 Methodology

Stakeholder collaboration

A variety of key stakeholders collaborated on this project, including working dog breeders, working dog trainers and working dog owners. Participation was facilitated through media releases, in-person attendance at sheep dog trial events, attendance at focus group meetings and participation in international meetings relating to dog science.

Stakeholders provided information about the traits to be assessed and dog owners assessed and provided feedback on the individual dogs that form the basis of the trait data analysed in this project.

Data collection

Performance data for this project was collected via the online Livestock Herding Dog Evaluation Form (LHDEF) (Figure 2).

Figure 2 The Livestock Herding Dog Evaluation Form.

This questionnaire builds on the Farm Dog Survey employed during the previous project and elicits data from working dog owners on the perceived quality of their dogs’ performance according to 63 working and behavioural metrics (Table 1). Owners were asked to rank their dog from very low to very high (or not applicable) for each trait, according to the definitions provided.

4 The original Farm Dog Survey asked about the dogs’ activities and behaviours when interacting with stock and when not interacting with stock. Analysis of the preliminary data showed participants ranked the two situations similarly. As a result, during this project the research team removed the reference to the behaviour occurring ‘with or without stock’, enabling the number of questions to be reduced.

At the request of stakeholders, two new rankings were added relating to the sensitivity and resilience of the dogs.

Table 1 Working and behavioural traits assessed by the LHDEF*. Working Behavioural Cast – level and appropriateness Confidence – with and without stock Gather Calmness – with and without stock Force – level and appropriateness Intelligence – with and without stock Cover Trainability – with and without stock Head Boldness – with and without stock Hold Patience – with and without stock Balance Timidness – with and without stock Break Persistence – with and without stock Back Hyperactivity – with and without stock Initiative Initiative – with and without stock Anticipation Excitability – with and without stock Trainability – level Obedience – with and without stock Natural ability Nervousness – with and without stock Eye Impulsiveness – with and without stock Confidence – level Stamina Calmness – level Sociability Boldness Friendliness Bite – appropriateness and frequency Sensitivitya Bark – appropriateness and frequency Resilienceb Obedience – recall Obedience – sit Obedience – stay Listening Obedience – latency Tricks Distractibility Obedience – fetch Overall ability Traits assessed in the previous project are in bold and new traits are shown in italics. a Sensitivity: ‘soft’ reacts negatively to censure; b Resilience: bounces back from hardship

The data entry process was simplified using an online interface and the data stored in a secure online repository, housed by the University of Sydney. The new user interface meant participants with multiple dogs had a streamlined process for data entry and comparison between dogs compared with the original questionnaire format.

5 At the conclusion of the refinement, there were 38 questions in the questionnaire, which could be answered by using a mouse click on a radio button (Appendix 3). Complete data are available for 36 traits present in datasets from both projects.

A stand-alone virtual machine server was requisitioned from the University of Sydney. A domain name “doggenetics.net.au” was purchased via a commercial domain-name provider (GoDaddy™) and connected with the University of Sydney virtual machine.

A user interface was developed using a combination of a MySQL database, the user interface language HTTP and the programming interface PHP to connect the user input with the MySQL database in the back end. To prevent typographical errors, typed responses were replaced with radio- button or pull-down menu items where possible. The user interface was designed to recognise returning users based on their username and postcode.

Ethics approval was sought through the University of Sydney ethics committee and this was awarded in 2018.

University of Sydney human ethics clearances (2012/658 and 2018/182) were obtained for the updated questionnaire tool and behavioural tests to validate the questionnaire outcomes.

Data analysis

Preliminary questionnaire data for 298 dogs were analysed to detect correlations among responses for individual dogs in order to reduce the number of questions in the questionnaire. Of the responses recorded as of 19 May 2017, 298 dogs were described as ‘working Kelpies’. Among these, 35 were described as predominantly ‘yard dogs’, 115 as ‘paddock dogs’ and 145 as ‘utility dogs’.

For each trait (such as eye) and desirable manoeuvre (such as cast), the descriptive metrics from the questionnaire were converted to numerical scores. For these scores, means and variances were estimated within each of the three dog working classifications. Dog ability scores for each trait and manoeuvre were compared across work classifications (paddock versus yard, paddock versus utility, yard versus utility). Significant t-test scores were used to define group characteristic traits and behaviours.

Traits were regarded as unique to a work type if the work type obtained a trait score distribution statistically significantly different, at the 0.05 level, from the trait score distributions of the other two work types.

The DNA-based genetic similarity between working classifications was assessed through the observation of genetic similarity as assessed by DNA-based genotypes for dogs classified as paddock dogs (n=19), yard dogs (n=11) and utility dogs (n=34).

Predictors of natural ability

While ‘overall ability’ may be an important value metric, it is impacted strongly by the age of the dog and its exposure to experienced trainers. Many breeders believe a more useful breeding objective is ‘natural ability’, which assesses the aptitude of the dog or the pup regardless of training.

Questionnaire measures that best predicted ‘natural ability’ were ascertained from a set of dogs filtered to remove duplicates and dogs with incomplete data. The responses to 36 questions that appeared in both versions of the questionnaire were normalised (mean of zero and variance of one) across the individual dogs. Pearson correlations between metrics were calculated in Microsoft Excel. Traits with pair-wise correlations of greater than 0.5 with ‘natural ability’ were regarded as reliable predictors of ‘natural ability’.

6 The resulting pair-wise trait correlations were sorted according to their correlation with ‘natural ability’. The architecture of relationships among the traits was further explored to identify questionnaire traits that could be pooled. Pooled traits demonstrated the same substantive positive and negative correlation patterns with other traits in the analysis. Pooling was deemed to be feasible where the correlations among pooled traits were all greater than 0.5 or less than -0.5.

This analysis was used to identify phenotypes suited to genetic mapping to detect potential genomic association with work performance.

The importance of work context on dog value

Working dog owners were asked to nominate characteristics that were particularly important in their specific working context (paddock, utility, yard or trial). The owners were asked to rate the importance of each of the working dog traits in their enterprise. The questionnaire instrument used in this analysis was the ‘Australian Farm Dog Survey’, which emerged from our previous project Valuable behavioural phenotypes in Australian farm dogs (PRJ-007806).

Working differences between breeds

Owners were asked to rate the performances of their dogs. Responses were invited from all breeds and animals without pedigree. This enabled us to compare the ratings over the traits in the revised questionnaire across the breeds with adequate representation in the data. For this study, the normalised questionnaire results for dogs were compiled and categorised according to breed. Results that differed significantly between breeds, by one-way analysis of variance, were reported.

Elite herding dog phenotypic profiles

Kelpies rated as having the highest-possible scores for ‘natural ability’ and ‘overall ability’ were assessed for their working context. The behavioural profiles of these dogs were used to identify mean- trait values that were hallmarks of working context. These mean values could then be used as templates to score the ‘optimal’ work context for other dogs in the data.

One-way analysis of variance was used to identify traits that differed significantly among work contexts and represented important working values for that work type.

Behavioural validation of survey responses

To validate the questionnaire responses, behavioural tests were performed with 11 Kelpies for whom questionnaire data had been collected. The survey responses were compared to the test results.

The Interaction with stranger test and Toy interaction test were included to identify any confounding effects from the tester’s presence and other distractions.

Intelligence was tested using the Detour test, Memory test and Towel test. Intelligence is poorly defined in humans and dogs. To some degree, the definition may vary depending on the tasks required of the dog. When dog handlers were questioned as to what they consider makes a working dog intelligent, replies included: “ability to learn and retain new information”, “problem solve independently”, and “have a healthy dose of self-preservation”. That said, it is generally acknowledged there are multiple aspects to intelligence. Testing for intelligence is complicated. Human intelligence is quantified by a battery of tests, but there is still some question as to the best measure. These tests are continually being modified. To incorporate the other aspects of intelligence, a variety of tests based on those discussed in the canine literature were used in a pilot study, aimed to identify limitations.

Fearfulness was tested using the Startle test, Interaction with stranger test, Towel test and Handling test. All test results were recorded, and measurements made from the recordings.

7 Interaction with stranger

This test was applied to assess whether the presence of the tester was likely to affect the results in subsequent tests. The dogs were brought into the testing shed by their handler and taken off the lead approximately 10 m from the tester. Their response to the presence of a stranger (the tester), time taken to approach the stranger (if at all) and the presence of any signs of stress exhibited by the dog were noted. Signs of stress included head turning away from tester, lip-licking, yawning, holding up paw, trembling, ears flattening, and tail being tucked under body.

The tester initially stood passively, not interacting with the dog at all. If there had been no attempt by the dog to interact with the tester, and the dog was exhibiting no signs of stress, after 15 seconds, the tester encouraged the dog to approach by calling, and response was noted.

Toy interaction test

The aim of this test was to identify dogs motivated by food and dogs that may be distressed when alone. The dog was placed inside a pen containing 11 dog toys: Kong® (large Classic), two balls (large Gorilla balls, Petstock), squeaky toy (large Kong Squeezz® Bitz Stick), maze (large Kruuse Buster Dog Maze), treat ball (large Aussie Dog Tucker Ball), wobbler (large Kong Wobbler™), treat maze (Nina Ottosson® Outward Hound® Dog Treat Maze™), and three slotted plastic cones with food placed beneath them.

The toys were spread throughout the pen, with at least 30 cm gap between each toy. The handler and tester moved away from the pen, observing from at least 5 m away. The dog was left in the pen for five minutes. Time spent interacting with the toys, eating the food, and the number of toys interacted with was recorded.

Detour test

The detour test is often used as a test of so-called intelligence in dogs, testing for spatial problem- solving abilities. The previous project, Valuable behavioural phenotypes in Australian farm dogs (PRJ-007806), found little correlation between the results of the detour test and the owner/handler rated intelligence item within LHDEF. That said, it did identify that food is often not the best motivation for working Kelpies.

Keeping this in mind, the test was modified slightly. The set-up was the same as in PRJ-007806, namely a V-shaped see-through fence (made from wooden dowels). Rather than placing food on the other side of the fence to the dog, the handler stood there. The dog was not permitted to watch the movement of the handler. The dog was walked to a starting point 2 m from the point of the V. The handler called the dog, who was released. Time taken for the dog to reach the side of the handler was measured. The direction taken by the dog (left or right) was also recorded. Each dog undertook the test three times.

Memory test

This was a test of short-term memory. The dog watched as its familiar handler placed food (a raw chicken wing) beneath one of three identical inverted plastic cups in a row. The dog was then led away and distracted for 60 seconds before being brought back to the cups. The dog was released, and time taken to approach the cup containing the food and the time taken to obtain the food were recorded.

Startle test

Startle test was carried out to validate the pooled trait of fearfulness, testing the dog’s response to an unexpected noise. The dog was led towards a sheet of corrugated iron. When the dog was standing within 2 m of the iron, a small rock (50 g) was dropped onto the surface of the iron from a height of

8 1 m. This level of noise was selected so as to startle the dog but not be so loud as to produce a generalised fear response. The intensity of the startle response and the time taken for the dog to approach the rock (within 1 m) were recorded. Intensity was scored from 0 (Slight jump. No avoidance reaction) to 3 (Severe avoidance, cowering, fleeing). This scoring was based on that used in the Dog Mentality Assessment protocol.

Towel test

This test was designed to assess both the problem-solving ability of the dog and its tendency to show any putative fearful response. The dog was shown a small towel and allowed to sniff it before the towel was slowly draped over the dog’s head, covering their eyes. Time taken for the dog to remove the towel was recorded. The test was repeated twice (three times in all). Stress levels were assessed in the 30 seconds between trials. Recovery was scored from 0 (no avoidance reaction) to 3 (actively avoids towel).

Handling test

This test was designed to identify those dogs with a fear of close contact or a sensitivity to touch. The handler ran their hands over the dog’s body for 10 seconds, starting at the head and working towards the tail. The number of signs of stress, as detailed in the Stranger interaction test (see above), were recorded.

Activity collar

Each dog was fitted with a flat dog collar with a Fitbark™ activity monitor attached by their handler before being brought into the testing shed. The collar was removed at the end of the final test. The steps taken by each dog during the test was recorded and the steps per minute calculated.

DNA-based analyses

DNA samples

Blood samples (n=52) were obtained by venipuncture and the samples transferred to Whatman FTA (Flinders Technology Associates) cards for submission to the genotyping supplier. Alternatively, dogs were sampled using Performagene saliva collection kits (DNA Genotek, Ontario Canada) (n=378) and DNA was extracted following standard kit-issued protocol.

Samples were collected with the University of Sydney animal ethics committee’s approvals (N00/10- 2012/3/5837, N00/10-2012/3/5928, 2015/902 and 2018/1449).

Genotyping was conducted on the Illumina Canine High Density Genotyping array by Neogen/Geneseek (Nebraska, USA). Individual dog samples were either provided by owners who mailed the samples or were directly collected at working dog trials and farms.

Population history, coat colour and ear phenotype

DNA-based genetic analysis was conducted over the major identified coat colour and ear type loci for domestic dogs that affect external similarity between the Kelpie and the Dingo. The loci analysed were for the genes: Agouti Signalling Protein (ASIP), RNA-Binding Protein (Autoantigenic, HnRNP- Associated With Lethal Yellow) (RALY), and canine β-defensin 103 (CBD103) for tan points; 5,6- dihydroxyindole-2-carboxylic acid oxidase precursor (TYRP1) or melanocortin-1 receptor (MC1R) result in cream, ginger, red, brown and chocolate. Blue, fawn, and cream all result from dilution at Melanophilin (MLPH) of black, brown and ginger, respectively. White markings are controlled by the gene Micropthalmia Transcription Factor (MITF). The gene methionine sulfoxide reductase 3

9 (MSRB3) has been proposed as a strong regional candidate gene for prick-ear versus drop-ear phenotype.

During the previous project, a DNA-based selective sweep analysis was applied to test for genomic regions strongly divergent between Australian Working Kelpies and other Kelpie types [3]. The Australian Working Kelpie exhibits a variety of colours, including ginger and cream, that are unacceptable according to the breed standard of the Australian Kelpie (registered with the Australian National Kennel Council; ANKC). Additionally, the ANKC-registered Australian Kelpie tends to lack tan points that are common in the Australian Working Kelpie. For many years, it has been presumed the inheritance of tan points (i.e. tan legs, muzzle, under tail and eyebrows) was coded by what was traditionally termed the A-locus (the region of the genes ASIP and RALY described above). The selective sweep analysis showed a strong divergence of the two Kelpie varieties in the vicinity of the K-locus (CBD103), which might also code for tan points. This project aimed to establish whether the selective sweep signal observed is caused by a genetic mutation that might cause the major observable coat colour distinction between the varieties.

There is a large amount of anecdotal data to suggest that resilience in the Kelpie results from an infusion of Dingo in the early days of the breed. Media reports discussing the history and origin of the breed both support and reject this assertion, but the hypothesis of Dingo infusion has gained attention during recent times. The project team compared the loci known to impact the external appearance of the dog, in particular key Kelpie traits such as pricked ears, with the DNA of the Dingo. Determining the influence of Dingo in the genetic make-up of the modern Kelpie might provide clues as to regions affecting important resilience traits.

Genetic markers for key performance traits

The selective sweep analysis carried out during the previous project (Valuable behavioural phenotypes in Australian farm dogs, PRJ-007806) identified a region on canine chromosome three that appeared to relate to working ability, as it was swept to near fixation in the Australian Working Kelpie but variable in other Kelpie types. The selective sweep analysis did not allow for inter- individual differences among working dogs or their individual working contexts. The current study sought to better understand the variation for working performance that exists within the Australian Working Kelpie. It is expected that by exploring the individual traits, new genetic loci that influence dog (and perhaps human) behaviours will be revealed.

Australian Working Kelpies with both LWHDAF and DNA data were genotyped using the Illumina Canine Genotyping array (Neogen Inc, Nebraska USA). Genotyping data were combined with LHDEF scores for individual traits and pooled traits identified via the inter-trait correlations. The data were analysed using the program Plink [4]. All marker coordinates were analysed relative to the Canfam 3.1 canine reference genome [5].

It is worth noting the version of use is important, because the genomic positions of markers used in the analysis change between different reference assembly versions.

The run-line for the quantitative association included filtering for minor allele frequency (0.05), genotyping rate (0.2) and Hardy-Weinberg equilibrium (0.00005). Analysis was conducted on three pooled traits defined as phenotypic predictors of natural ability. The negative logarithm of the association probability for DNA-based genetic markers relative to each pooled trait was plotted using Haploview [6]. The threshold for genome-wide significance was set at 1e-6.

Regional candidate genes for pooled traits

Genes within the identified regions of genome-wide significant association were assessed for potential function in relation to the mapped phenotype through literature search, the use of the University of California Santa Cruz genome browser (genome.ucsc.edu) for the canine reference genome (Canfam

10 3.1), and the Jackson Laboratories mouse phenome browser (http://jbrowse.informatics.jax.org/?data=data/mouse).

Genealogical analysis

To date, most canine populations assessed for levels of inbreeding and for breeding practices have been from closed-registry breeds. The WKC of Australia maintains the breed pedigrees and dogs have been registered with this organisation since 1933, but with significant registration numbers since 1967. The analysis of this breed is of interest because the registry is maintained as an open registry, meaning dogs may be registered with the organisation from those of unknown parentage for breeding purposes. The primary criterion of selection is ‘working ability across a number of work contexts’.

The records were analysed using the program CFC [7] to ascertain the population history of the breed and genetic trends with respect to the accumulation of inbreeding and the effective population size.

The effective population size (Ne) was estimated from the rate of inbreeding per generation, using the formula Ne = 1/2ΔF. ΔF was calculated as ΔF = (b×L)/(1-(Fly-b×L)) where ‘b’ is the regression coefficient of the average inbreeding coefficient on year of birth (post-1967, when the registry became active), ‘L’ is the average generation interval and ‘Fly’ is the average inbreeding coefficient in the last year of birth available for the study (2015) [2].

Estimated breeding values

Pedigree records and phenotypic scores from the LHDEF were collected to assess EBVs for dogs with registration numbers within the WKC. The proposed model would adjust data for year of birth, sire, dam and handler.

11 Results

Stakeholder collaboration

The findings of the previous WDP are not purely theoretical. The findings must be communicated effectively from academic research to producers to be useful for those who breed, use or trial working dogs. This was achieved by publications, not only in open access peer-reviewed journals, but production of a brochure to be distributed to farmers and other stakeholders, and reports presented to the WKC.

During April 2017, Professor Claire Wade included the work of this project in her keynote presentation at the International Working Dog Breeding Association meeting in Banff, Alberta, Canada. The findings were also presented as part of a keynote address at the SPARCS18 Canine Science Symposium in Amenia NY, USA, held in June 2018. The work is freely viewable on the SPARCS website. This initiative increases the accessibility of high-quality canine research to a global audience. The estimated reach is 50,000 participants across the globe. http://www.sparcsinitiative.org/events/sparcs-2018/

Phenotypic data

The new questionnaire interface was completed by the start of 2018. The creation of the new interface necessitated the generation of new human ethics committee permission for the questionnaire. There was a short delay in the availability of the human ethics approval, which meant the new questionnaire interface could not be applied until mid-2018.

At the time of writing, data for 653 dogs from nine breeds and their mixes had been collected. The most commonly evaluated dogs were: Australian Working Kelpie (n=510), Border Collie (n=62) and Kelpie x Collie mixes (n=26).

Other breeds represented include Australian Cattle Dog, Koolie, Black Mouth Cur, Corgi, Bearded Collie, English shepherd and New Zealand Huntaway.

Complete data are available for 36 traits present in datasets from both this project and the previous project.

The user interface remains open and continues to collect data.

Outcome: A secure database resource was created for the capture and retention of phenotypic data on the performance of livestock herding dogs in the long-term. The resource can be accessed via: https://doggenetics.net.au/Kelpie/FarmSurvey.html.

Predictors of overall ability

Through discussions with stakeholders it became clear working dogs are required to perform a large number of work types (working contexts). Each working context (e.g. paddock vs yards) requires dogs with a different skillset. This implies the value of a particular breed of dog for one working context is likely to differ from its value elsewhere.

12 The skills required of a paddock dog include excellent livestock interaction skills, the ability to work quietly, and a large degree of independence of thought (anticipation), as the dogs typically work at long distances from the handler, or out of sight of the handler.

By contrast, dogs used in yards must be much bolder than paddock dogs. If they work cattle, then they may need to bark or nip to move resistant animals. These dogs work much more closely with the stock and may be required to ‘back’ the stock to move freely through the confined spaces. Such dogs typically exhibit a higher energy level than paddock dogs.

The behaviour of working dogs is assessed competitively in yard trials, three-sheep trials, utility trials and cattle dog trials. In such competitions, dogs work in close communication with their handler. They need to be highly responsive to handler commands.

While the concept of working dogs having defined working contexts is well appreciated by people actively involved with the handling of the dogs, it is not well known to many outside the industry. The analysis in this published study [9] highlighted how the value of skills differed between working contexts. If we wish to perform genetic evaluations of dogs, then it is critical the dog is evaluated in its most relevant context. If the metric of success is ‘overall ability’, then this finding confirms the metric must be applied within a working context.

A summary of the open-access paper is reproduced below.

The importance of work context on trait value

The importance of working context on the relative importance of the assessed traits was explored in two open access publications [7, 8] (Appendix 2).

13 Phenotypic predictors of natural ability

After quality filtering, data from 600 dogs and 38 questionnaire metrics, more than 555 responses were available. Correlations among normalised LHDEF questionnaire metrics are shown in Table 2.

Table 2 Pearson correlations among normalised questionnaire scores for 588 dogs and 36 traits sorted according to association with ‘natural ability’. Natural ability Natural ability Overall Anticipation Intitiative Intelligence Trainability Balance Cover Gather Hold Heading Break Cast Initiative taking training) of (ease Trainability Persistence Confidence Obedience Eye-strength Boldness Patience Calmness Force Cast-adequacy Stamina Back Force-adequacy Excitability Bite-adequacy Bite-frequency Bark-adequacy Hyperactivity Bark-frequency Impulsiveness (has sudden, urges act) to strong Timidness Nervousness

1.0 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.2 -0.2 Natural ability

0.6 1.0 0.5 0.5 0.4 0.5 0.4 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.2 0.3 0.2 0.3 0.3 0.3 0.2 0.2 0.2 -0.1 0.1 0.1 0.0 -0.2 0.0 -0.2 -0.2 -0.2 Overall ability

0.6 0.5 1.0 0.7 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.3 0.4 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.0 0.1 0.0 0.1 -0.1 0.0 -0.1 -0.2 -0.2 Anticipation

0.6 0.5 0.7 1.0 0.5 0.4 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.6 0.3 0.4 0.4 0.2 0.2 0.3 0.3 0.3 0.4 0.3 0.2 0.3 0.2 0.0 0.1 0.0 0.1 -0.1 0.1 -0.1 -0.2 -0.2 Intitiative

0.5 0.4 0.4 0.5 1.0 0.4 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.5 0.5 0.4 0.3 0.4 0.3 0.2 0.4 0.4 0.2 0.2 0.3 0.1 0.1 0.0 0.0 -0.1 0.1 -0.1 0.0 -0.2 -0.1 -0.2 Intelligence

0.5 0.5 0.4 0.4 0.4 1.0 0.4 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.7 0.3 0.2 0.6 0.1 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.0 0.0 0.0 -0.1 0.0 -0.1 -0.1 -0.2 -0.1 -0.1 Trainability

0.5 0.4 0.5 0.5 0.3 0.4 1.0 0.6 0.6 0.6 0.6 0.6 0.5 0.3 0.3 0.3 0.3 0.2 0.2 0.1 0.3 0.3 0.1 0.3 0.1 0.1 0.0 -0.1 0.0 0.0 0.0 -0.2 -0.1 -0.2 -0.2 -0.1 Balance

0.5 0.5 0.5 0.4 0.3 0.3 0.6 1.0 0.6 0.6 0.6 0.6 0.5 0.4 0.2 0.3 0.3 0.2 0.3 0.2 0.3 0.3 0.3 0.3 0.1 0.1 0.1 -0.1 0.1 0.0 0.0 -0.2 -0.1 -0.2 -0.2 -0.1 Cover

0.5 0.5 0.5 0.5 0.4 0.4 0.6 0.6 1.0 0.5 0.6 0.5 0.7 0.4 0.3 0.3 0.3 0.2 0.3 0.2 0.3 0.4 0.2 0.5 0.1 0.1 0.0 -0.2 0.0 0.0 0.0 -0.2 -0.1 -0.2 -0.2 -0.2 Gather

0.5 0.5 0.5 0.5 0.3 0.3 0.6 0.6 0.5 1.0 0.6 0.6 0.4 0.4 0.2 0.3 0.3 0.2 0.2 0.2 0.3 0.3 0.2 0.3 0.1 0.2 0.1 -0.1 0.1 0.0 0.1 -0.2 -0.1 -0.2 -0.2 -0.2 Hold

0.5 0.4 0.5 0.5 0.3 0.3 0.6 0.6 0.6 0.6 1.0 0.5 0.5 0.4 0.2 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.1 0.1 0.1 -0.1 0.1 0.0 0.0 -0.2 -0.2 -0.1 -0.2 -0.2 Heading

0.5 0.4 0.5 0.5 0.3 0.3 0.6 0.6 0.5 0.6 0.5 1.0 0.4 0.4 0.2 0.3 0.3 0.2 0.3 0.2 0.2 0.2 0.3 0.3 0.1 0.1 0.1 0.0 0.0 0.0 0.1 -0.1 0.0 -0.2 -0.2 -0.2 Break

0.4 0.4 0.4 0.4 0.3 0.3 0.5 0.5 0.7 0.4 0.5 0.4 1.0 0.3 0.3 0.2 0.2 0.3 0.2 0.1 0.3 0.4 0.1 0.6 0.1 0.1 -0.1 -0.2 0.0 -0.1 -0.1 -0.2 -0.1 -0.2 -0.1 -0.1 Cast

0.4 0.4 0.5 0.6 0.5 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.3 1.0 0.3 0.4 0.4 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.1 0.1 0.1 0.1 0.0 0.1 0.0 0.0 -0.1 -0.2 -0.2 Initiative taking

0.4 0.4 0.3 0.3 0.5 0.7 0.3 0.2 0.3 0.2 0.2 0.2 0.3 0.3 1.0 0.2 0.2 0.6 0.1 0.2 0.3 0.3 0.1 0.2 0.2 0.1 0.0 0.0 0.0 -0.1 0.0 -0.1 0.0 -0.2 0.0 -0.1 Trainability (ease of training)

0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.4 0.2 1.0 0.4 0.2 0.2 0.4 0.2 0.1 0.3 0.1 0.4 0.1 0.3 0.1 0.2 0.1 0.1 0.1 0.0 0.0 -0.2 -0.2 Persistence

0.3 0.4 0.3 0.4 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.4 0.2 0.4 1.0 0.1 0.1 0.6 0.1 0.2 0.5 0.1 0.3 0.2 0.3 0.1 0.1 0.1 0.1 0.0 0.1 0.0 -0.4 -0.4 Confidence

0.3 0.3 0.2 0.2 0.4 0.6 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.2 0.6 0.2 0.1 1.0 0.1 0.1 0.4 0.4 0.1 0.1 0.2 0.1 0.0 -0.1 0.0 -0.1 0.0 -0.2 0.0 -0.2 0.0 -0.1 Obedience

0.3 0.2 0.3 0.2 0.3 0.1 0.2 0.3 0.3 0.2 0.2 0.3 0.2 0.2 0.1 0.2 0.1 0.1 1.0 0.2 0.1 0.1 0.0 0.2 0.1 0.0 0.0 0.1 0.0 0.1 -0.1 0.0 0.0 -0.1 0.0 -0.1 Eye-strength

0.3 0.3 0.3 0.3 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.3 0.2 0.4 0.6 0.1 0.2 1.0 0.0 0.0 0.5 0.0 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.1 0.1 -0.4 -0.3 Boldness

0.3 0.2 0.3 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.3 0.2 0.3 0.2 0.1 0.4 0.1 0.0 1.0 0.7 -0.1 0.2 0.0 0.0 -0.2 -0.3 -0.1 -0.2 -0.1 -0.4 -0.1 -0.4 0.0 -0.1 Patience

0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.2 0.4 0.2 0.3 0.1 0.2 0.4 0.1 0.0 0.7 1.0 0.0 0.2 0.1 0.0 -0.1 -0.4 -0.1 -0.1 0.0 -0.5 -0.1 -0.4 0.0 -0.2 Calmness

0.3 0.3 0.3 0.4 0.2 0.2 0.1 0.3 0.2 0.2 0.3 0.3 0.1 0.3 0.1 0.3 0.5 0.1 0.0 0.5 -0.1 0.0 1.0 0.0 0.2 0.4 0.5 0.1 0.2 0.2 0.3 0.1 0.3 0.1 -0.3 -0.2 Force

0.3 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.5 0.3 0.3 0.3 0.6 0.2 0.2 0.1 0.1 0.1 0.2 0.0 0.2 0.2 0.0 1.0 0.0 0.0 0.0 -0.1 0.0 0.0 0.0 -0.1 -0.1 -0.1 0.0 0.0 Cast-adequacy

0.2 0.2 0.2 0.2 0.3 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.4 0.3 0.2 0.1 0.3 0.0 0.1 0.2 0.0 1.0 0.1 0.2 0.2 0.0 0.0 0.2 0.1 0.1 0.0 -0.2 -0.2 Stamina

0.1 0.2 0.2 0.3 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.0 0.3 0.0 0.0 0.4 0.0 0.1 1.0 0.2 0.1 0.1 0.1 0.1 0.1 0.3 0.0 -0.1 -0.1 Back

0.1 0.2 0.1 0.2 0.1 0.0 0.0 0.1 0.0 0.1 0.1 0.1 -0.1 0.1 0.0 0.3 0.3 0.0 0.0 0.3 -0.2 -0.1 0.5 0.0 0.2 0.2 1.0 0.2 0.2 0.2 0.3 0.2 0.2 0.2 -0.2 -0.1 Force-adequacy

0.0 -0.1 0.0 0.0 0.0 0.0 -0.1 -0.1 -0.2 -0.1 -0.1 0.0 -0.2 0.1 0.0 0.1 0.1 -0.1 0.1 0.2 -0.3 -0.4 0.1 -0.1 0.2 0.1 0.2 1.0 0.1 0.1 0.1 0.7 0.2 0.4 -0.1 0.1 Excitability

0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.1 0.0 0.1 0.1 0.0 0.0 0.1 0.0 0.2 0.1 0.0 0.0 0.2 -0.1 -0.1 0.2 0.0 0.0 0.1 0.2 0.1 1.0 0.5 0.2 0.0 0.1 0.2 -0.1 0.0 Bite-adequacy

0.0 0.1 0.0 0.0 -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 0.0 -0.1 0.1 0.1 -0.1 0.1 0.2 -0.2 -0.1 0.2 0.0 0.0 0.1 0.2 0.1 0.5 1.0 0.1 0.1 0.2 0.1 0.0 0.0 Bite-frequency

0.0 0.0 0.1 0.1 0.1 0.0 0.0 0.0 0.0 0.1 0.0 0.1 -0.1 0.1 0.0 0.1 0.1 0.0 -0.1 0.2 -0.1 0.0 0.3 0.0 0.2 0.1 0.3 0.1 0.2 0.1 1.0 0.2 0.5 0.1 -0.2 -0.1 Bark-adequacy

0.0 -0.2 -0.1 -0.1 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.2 0.0 -0.1 0.1 0.0 -0.2 0.0 0.2 -0.4 -0.5 0.1 -0.1 0.1 0.1 0.2 0.7 0.0 0.1 0.2 1.0 0.2 0.4 0.0 0.1 Hyperactivity

0.0 0.0 0.0 0.1 0.0 -0.1 -0.1 -0.1 -0.1 -0.1 -0.2 0.0 -0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 -0.1 -0.1 0.3 -0.1 0.1 0.3 0.2 0.2 0.1 0.2 0.5 0.2 1.0 0.1 -0.1 0.0 Bark-frequency

-0.1 -0.2 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.2 -0.2 -0.1 -0.2 0.0 0.0 -0.2 -0.1 0.1 -0.4 -0.4 0.1 -0.1 0.0 0.0 0.2 0.4 0.2 0.1 0.1 0.4 0.1 1.0 0.0 0.2 Impulsiveness (has sudden, strong urges to act)

-0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.2 0.0 -0.2 -0.4 0.0 0.0 -0.4 0.0 0.0 -0.3 0.0 -0.2 -0.1 -0.2 -0.1 -0.1 0.0 -0.2 0.0 -0.1 0.0 1.0 0.6 Timidness

-0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.1 -0.2 -0.1 -0.2 -0.4 -0.1 -0.1 -0.3 -0.1 -0.2 -0.2 0.0 -0.2 -0.1 -0.1 0.1 0.0 0.0 -0.1 0.1 0.0 0.2 0.6 1.0 Nervousness

14 During discussions with stakeholders, it was apparent the main breeding objective trait for working dogs was ‘natural ability’. While ‘overall ability’ is the general indicator of ultimate success, it was thought this trait is strongly affected by the opportunities afforded to the pup during its lifetime in terms of the expertise of its trainer and/or owner, and its lifetime exposure to effective training environments.

From the data provided, the traits most strongly correlated with ‘natural ability’ were overall ability, anticipation, initiative, intelligence, trainability and balance (r>0.5).

A number of working skills also correlated at r>0.5 with ‘natural ability’, including cover, gather and hold.

The traits with the strongest negative correlations with ‘natural ability’ were impulsiveness (r=-0.13), timidness (r=-0.21) and nervousness (r=-0.24).

Natural ability and cover were correlated at r>0.5 with ‘overall ability’.

The traits with the strongest negative correlations with overall ability were hyperactivity (r=-0.16), impulsiveness (r=-0.19), timidness (r=-0.22) and nervousness (r=-0.24).

Owners and handlers value dogs with ‘sheep sense’, which helps dogs predict the responses of sheep to their actions. In general, most users do not value dogs that display nervousness or timidness.

The analysis of correlations revealed strong pair-wise trait correlations among the trait groupings of:

1. Working skill: balance, cover, hold, heading and break

2. Instinct: ‘natural ability’, anticipation and initiative taking.

3. Fearfulness: nervousness and timidness

The correlations analysis was used to identify similarity in the behaviour of trait scores across the entire data resource. For example, the individual traits of ‘natural ability’ and ‘anticipation’ share strong positive correlations with traits in points 1 and 2 above and modest negative correlations with traits in point 3 above (Table 3).

15 Table 3 Pooling of individual traits into three super-traits (criterion for pooling is r>=0.5 or r<=- 0.5 among the pooled traits where r is the Pearson correlation).

Pooled trait Individual traits contributing to pooled trait

Balance Cover Gather Hold Heading Break Balance 1.00 Cover 0.62 1.00 Working skill Gather 0.63 0.62 1.00 Hold 0.61 0.63 0.54 1.00 Heading 0.61 0.62 0.60 0.60 1.00 Break 0.59 0.62 0.51 0.57 0.52 1.00

Instinct Natural ability Anticipation Initiative Natural ability 1.00 Anticipation 0.57 1.00 Initiative 0.55 0.67 1.00

Fearfulness Timidness Nervousness Timidness 1.00 Nervousness 0.56 1.00

Outcome: From this analysis we identified three trait groupings suitable for gene mapping of working performance traits relating to ‘natural ability’ in working dogs — working skill, instinct and fearfulness.

16 Working differences between breeds

Normalised breed scores for working traits with scores that differed significantly across the tested breed groups are shown in Table 4.

Table 4 Normalised breed scores for some working traits differ significantly among Kelpies, Border Collies and their mixes.

Working trait Breed mean score (normalised) F Significance Kelpie Border Collie Kelpie x Collie Impulsiveness -0.07 0.19 0.30 3.54 * Cover 0.06 -0.05 -0.42 3.16 * Balance 0.05 0.11 -0.53 4.85 * Break 0.09 -0.15 -0.29 3.11 * Back 0.04 -0.58 0.26 10.74 *** Trainability 0.05 -0.09 -0.50 4.46 * Natural ability 0.03 -0.01 -0.51 4.04 * Eye-strength 0.09 -0.41 -0.26 7.58 ** Bark-frequency 0.04 -0.73 0.08 14.98 *** *p<0.05, **p<0.01, ***p<0.001

Sufficient samples were available for Kelpie (n=462), Border Collie (n=50) and Kelpie x Collie (n=27) to meaningfully compare the working styles of these three groups.

Highly significant differences were observed for the traits of back and bark frequency. For both traits, Border Collies scored significantly lower than Kelpies and mixed-breed dogs.

Other observations revealed that mixed-breed dogs performed more poorly than Kelpies and Border Collies for cover, impulsiveness, break and trainability. Border Collies showed less eye than either purebred or mixed-breed Kelpies.

The results for differentiation of bark frequency and back were expected given known behavioural differences between Kelpies and Border Collies. The current findings suggest, according to owners and handlers responding to this survey, that purpose-bred purebred dogs performed more reliably across a variety of working contexts. It is unclear at this time whether this is a general result or one which reflects the dedication of the participants to maintaining working ability in their purebred lines.

Outcome: Purpose-bred purebred dogs outperformed mixed-breed dogs for ‘natural ability’, trainability, cover, balance, break and impulsiveness. Border Collie and Kelpie dogs were considered to have similar ‘natural ability’.

Elite herding dog phenotypic profiles

Thirty-one dogs were rated the highest-possible score for ‘natural ability’ and ‘overall ability’. All of these dogs were Kelpies, and this is likely because this breed was the most commonly reported in the current dataset. Of these 31 dogs, two were classified as working in a droving context, one in a trial context, and one in a yard context. Five were paddock dogs and 16 were classified as utility dogs. The remainder worked in blended contexts that were difficult to define.

Elite dog work context means and inter-trait correlations for the 10 differentiated traits are shown in Table 5 (means) and Table 6 (correlations). They show that the most differentiated work context was

17 the yard context. The yard context correlated negatively with the other work types across the 10 trait means. The strongest differences from the other groups were in calmness, eye strength and gather. The second-most differentiated was the trial context. The elite trialling dogs demonstrated outlying values for cast adequacy (strongly positive), higher timidness, and lower hyperactivity and excitability than elite dogs in other work contexts. Quality dogs in a droving context had reduced impulsiveness and nervousness compared with other work types.

Utility and paddock dogs were not well differentiated from one another with their major differences being observed in bark-frequency and excitability. Utility dog handlers tolerated higher levels of barking and excitability than paddock dog handlers.

Table 5 Mean trait scores for 10 traits that exhibit differentiation between work contexts.

Work-context N Trait

strength adequacy frequency

Gather Calmness Timidness Excitability Eye Nervousness Hyperactivity Impulsiveness Cast Bark Utility 16 0.58 -0.18 -0.34 0.02 -0.08 -0.22 0.61 0.18 0.21 0.04 Paddock 5 0.92 -0.67 -0.89 -0.79 -0.42 -0.60 1.00 0.70 0.21 -0.80 Droving 2 1.31 -0.57 0.07 0.63 -1.01 -1.02 1.22 0.60 0.21 -0.09 Trials 1 1.31 0.92 -1.54 -1.14 0.98 1.96 1.22 1.09 2.62 1.32 Yard 1 -1.61 -1.07 1.68 1.52 1.98 -0.60 -0.93 2.06 0.21 2.03

Table 6 Correlations among survey scores for different work contexts.

Utility Paddock Droving Trials Yard Utility 1.00 Paddock 0.89 1.00 Droving 0.80 0.70 1.00 Trials 0.39 0.45 -0.13 1.00 Yard -0.42 -0.42 -0.23 -0.41 1.00

The lack of replication for elite trial and yard dogs may mean the values for these dogs are inaccurate. However, the results are supported by earlier studies of the value of the different working traits across work contexts, as determined from both the owner/handler perspective8 and the actual behaviours of animals in those contexts9.

Outcome: Profiling of elite working dog behaviours across working contexts provides a set of work context templates against which any questionnaire phenotyped dog can be assessed for its optimal work type.

18 Behavioural validation of survey responses

Results for the behavioural tests carried out to validate the questionnaire responses are presented below.

Motivation

Many of the tests commonly employed to assess intelligence in dogs use food or toys to motivate performance. In many working dogs, food is often less motivating than exploring or working stock. Of the 11 dogs subjected to the behavioural testing, two did not eat any treat or interact with any toy. None of the dogs showed any interest in the non-food toys. Two dogs ate loose treats, sniffed at toys but did not interact with them in any way. The remaining dogs showed varying degrees of interest in the toys, with two dogs persevering to get all treats from the Buster maze. One dog ignored all toys and remained focussed on the handler until the handler moved closer, then started exploring the toys, indicating some stress when left alone. This dog had participated in the presence of stranger test, toy interaction test and detour test when it began to rain. The dog then exhibited stress-related behaviours, possibly due to the sound of the rain on the shed roof. This dog did not take part in any further testing and we delayed testing other dogs until the rain had stopped. No dog was excessively fearful in the presence of a stranger, although there were some signs of low-grade stress in a couple of dogs.

Intelligence testing

The results for the tests for intelligence were compared with LHDEF responses for both the single traits of anticipation and initiative-taking and the pooled response of instinct, using the average of natural ability, anticipation and initiative-taking (Table 5).

Table 7 Behaviour test correlations with anticipation, initiative-taking and the pooled trait instinct. Test Correlation with Correlation with Correlation with Instinct (p) anticipation (p) initiative-taking (p) Detour -0.84 * (0.0012) -0.31 (0.36) -0.68 * (0.022) Memory -0.48 (0.159) 0.35 (0.32) -0.71 * (0.022) Towel -0.37 (0.287) -0.51 (0.136) -0.13 (0.714) Pearson correlations, * p<0.05

The detour test relied on recognising and obeying the recall command, rather than food. A total of 10 dogs responded well to recall, while one dog (who was younger with poor recall) didn’t complete the test. No dog showed a significant difference between the time taken for the first trial and subsequent two trials, and the average time for each dog was negatively correlated with the LHDEF scores for anticipation and initiative-taking (i.e. dogs with a higher score completed the detour test more quickly). The relationship was much stronger with the pooled result for instinct, suggesting this test relies on a variety of traits.

The same was true of the results for the memory test. There was a strong negative correlation with the pooled score, i.e. dogs with a higher score in instinct and initiative-taking located the food more quickly in the memory test.

However the score for the memory test also correlated with the time spent interacting with food- dispensing toys (r = -0.62), indicating that the dogs that were more motivated by food were quicker to locate the food in the memory test.

The average latency to remove the towel was only weakly correlated with the pooled result and moderately correlated with the lone intelligence item.

19 In general, the LHDEF responses from the handler predicted the results during the tests. These results support the view that no single test can fully assess the intelligence of an individual dog, and that tests can be influenced by motivation, the dog’s temperament, level of training, previous experience and the immediate environment.

Fearfulness testing

The results for the tests for fearfulness were compared with scores for the pooled trait of fearfulness (an average of scores for timidness and nervousness).

Table 8 Behaviour test correlations with fearfulness. Test Correlation Interaction with stranger 0.51 Startle intensity 0.65 * (p=0.044) Startle recovery 0.61 Handling stress signs 0.50 Pearson correlation, *p<0.05

While no dog was excessively fearful in the presence of a stranger or with handling, there was a moderate correlation between the number of stress-related behaviours a dog exhibited and its score for the pooled trait of fearfulness. No dogs showed any signs of stress as the towel was placed on their head or between trials.

The intensity of startle response was significantly correlated with fearfulness. There was a strong correlation between the intensity of the startle response and the time taken to approach the rock (r=0.9). Some dogs barely reacted to the sound and acted as though the rock was a treat or toy, attempting to mouth it. Other dogs jumped back then slowly approached. The correlation between startle recovery time and the fearfulness score just failed to reach statistical significance (p=0.059).

Overall, there was agreement between the responses in the tests for fearfulness and the handler’s assessment of their dog’s fearfulness.

Outcome: Owner/handler assessments of dog behaviour correlated well with behaviour tests designed to assess the pooled behaviour traits of instinct and fearfulness.

DNA-based analyses

DNA samples

Matching DNA and questionnaire data were available for 94 Kelpies. Although matching DNA and questionnaire data were available for other animals, their breeds were insufficiently represented to perform DNA analysis. Some samples that might have had matching DNA and questionnaire data were lost from the analysis because participants used a different form of the questionnaire, which did not have ethics approval through our institution and therefore produced data that could not be used.

In all, the samples connected with this project include 601 unique dog phenotype questionnaires, 12 dogs with whole genome sequences (WGS) and 320 dogs with whole genome genotyping arrays (array). The approximate replacement value of the DNA resource is: WGS: $24,000, Array: $41,600. Upon publication, all computational data are moved to the public domain where they can be accessed via the European Variant Archive or the National Center for Biotechnology Information (Genbank).

20 Population history, coat colour and ear phenotype

The full analysis of the genetics of external appearance of the Australian Working Kelpie is described within the open access published paper [10]. This paper generated some controversy as the media erroneously reported that we had stated definitively there was no Dingo in the Kelpie. This analysis shows that if there is Dingo DNA in the Australian Working Kelpie, it does not affect the external body traits most lay people associate with the Dingo origin (i.e. the pricked ears and that some Kelpies have yellow fur). The yellow fur sometimes observed in the Kelpie was found to have a different genetic origin from Dingo yellow fur.

21 Genetic markers for key performance traits

Manhattan plots for the three pooled traits (working skill, instinct and fearfulness) in 94 dogs with both phenotypic information from the LHDEF and genotyping array data are shown in Figure 3. The phenotypic correlation analysis (Table 3) showed that working skill and instinct are strongly correlated. The genetic association analysis validates this finding with a common strong signal of association on chromosome six for both of the pooled traits of working skill and instinct.

a.

b.

c.

Figure 3 Genome-wide association of working traits in the Australian Working Kelpie (n=94), a. Working skill, b. Instinct, c. Fearfulness. Genome-wide association of working traits in the Australian Working Kelpie (n=94), a. Working skill, b. Instinct, c. Fearful.

22 The pooled trait of fearfulness was strongly associated with a region on chromosome 29.

Of the individual constituent traits of instinct, anticipation and initiative were both associated with the chromosome six locus. The association of this locus with anticipation was genome-wide significant. Natural ability was most associated with a locus on chromosome 29.

The genomic association with pooled trait of fearfulness shared the same association with the individual trait of nervousness.

Outcome: The strong phenotypic correlation between scores for working skill and instinct were supported by a common strongest locus on chromosome six. Fearfulness appears to be associated with a locus on chromosome 29. The association signals for instinct and fearfulness satisfy genome-wide significance.

Regional candidate genes for pooled trait associations

The locus identified on chromosome six was associated with working skill and working instinct in the Australian Working Kelpie. In humans, this region has been implicated in speech development, intellectual development and autism. The phenotypes of human individuals affected by karyotypic disruption of the equivalent gene region suggests the critical interval relating to intellectual development includes three genes (F-box and leucine rich repeat protein 18 (FBXL18), actin beta (ACTB) and fascin actin-bundling protein 1(FSCN1))[11]. Comparative genomics suggests that the association may have credence. These genes inhabit a relatively small portion of the associated region which spans 10-20 megabases on chromosome six and houses many genes that may have functional significance for these phenotypes.

Canine chromosome 29 was associated with fearfulness in the Australian Working Kelpie. The most associated markers occurred over two genes PREX2 and C8ORF34. Neither of these genes has any connection with behaviour in the literature.

Outcome: In all cases, the results of the regional analysis indicate preliminary association signals that require further genomic analysis to understand and for validation in the same or a different breed. At this stage, the results are inadequate for use as tools for selection.

Genealogical analysis

Genealogical (pedigree) analysis shows the breeding practices and population structure of this open- registry breed are not markedly different from those of other breeds with similar registry sizes in closed-registry organisations [2].

The Australian Working Kelpie breed, founded during the 1870s, records individuals with mean generation lengths up to 11 generations, and some dogs with ancestors of more than 20 generations are recorded.

Pedigree records of 86,671 dogs (6,161 sires, 9,216 dams and 71,294 individuals leaving no progeny) were examined, with pedigrees registered by the Working Kelpie Council up until 2015. There were about 2700 registrations annually in the decade prior to the analysis, making the Australian Working Kelpie among the 10 most popular Australian registered dog breeds registered with the major pedigree registry in Australia (the Australian National Kennel Council).

The full breed analysis in comparison with dog breeds with similar registry sizes registered by the Australian National Kennel Council is shown in Table 9. The mean inbreeding coefficient in

23 Australian Working Kelpies is 0.049. When we consider only animals with six generations or more in the pedigree (and so exclude the influence of recent registrants from the opening of the registry) the mean inbreeding coefficient increases to 0.076, demonstrating that opening the registry does have a beneficial impact on the level of inbreeding in the population in the longer term. As the Australian Working Kelpie is a locally derived breed, unlike other registered dog breeds, the importation of new individuals of the same breed from other international locations is unlikely to improve breed diversity, since those individuals were likely derived from the same breed founders. The maintenance of the open registry for this breed is to be encouraged.

The mean numbers of dogs registered per year and average coefficient of inbreeding per year demonstrate patterns consistent with other dog populations of similar size.

Table 9 Australian Working Kelpie genealogical analysis compared with breeds of similar registry size from Shariflou et al (2012) [2].

Effective Mean Registry Australian Generation Population Inbreeding Cohort size Origin Equivalents size Coefficient Australian Working Kelpie 86,671 Yes 5.5 84 0.049 Australian Cattle Dog (ANKC) 74,290 Yes 9.7 57 0.057 Golden Retriever 98,542 No 6.7 1090 0.051 English Cocker Spaniel 78,902 No 7.3 89 0.044 Rottweiler 98,727 No 4.9 191 0.025

4000

3500

3000

2500

2000

1500 Number of pups registered 1000

500

0 1933 1935 1937 1939 1941 1943 1945 1947 1949 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 Year of birth

Figure 4 Pups registered with the Working Kelpie Council of Australia by year.

24 0.08

0.07

0.06

0.05

0.04

0.03

0.02 Mean inbreeding coefficient 0.01

0

-0.01 1932 1934 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Year of birth

Figure 5 Mean inbreeding coefficient by year of birth for Australian Working Kelpies registered with the Working Kelpie Council of Australia.

Outcome: The Australian Working Kelpie population is of a size that will enable effective selection for working ability without compromising genetic diversity.

Estimated breeding values

Pedigree depth was insufficient to enable effective calculation of estimated breeding values at this time. A large number of participant dogs were not pedigreed. Unlike the livestock industries, in which a producer can submit hundreds of data records for stock born on their property, dog breeding is conducted in a dispersed industry, with relatively few dogs bred per property. These dogs are then dispersed to new owners who then monitor the effectiveness of the dog for its chosen task. This scenario makes information flow throughout the industry challenging.

We gained access to comprehensive pedigrees for one working breed up to 2015 (Australian Working Kelpie). Many participant dogs were born after 2015. The LHDEF asked about registration status, registration number, sire and dam independently of the pedigrees provided by the WKC. The most numerous breeds in the data (AWK) were represented by 250 questionnaires with pedigree information. Of these, 10 were duplicate entries. These data represented 99 separate breeding kennels. Only 15 kennels were represented by more than two individuals with phenotypes (Table 9). The most represented kennel had nine participating dogs.

25 Table 10 Questionnaires per kennel of origin (AWK only).

LHDEF per kennel Frequency 1 74 2 10 3 4 4 5 5 3 6 1 7 1 More 1

Excluding duplicates, too few data points were attained for individual sires and dams to afford the opportunity to create an effective resource based on traditional phenotypic selection. It was envisaged that sire referencing might be possible, but confounding between property of use, trainer, and genetics made this task statistically unachievable at the quality sought at this time.

The availability of DNA-based technologies opens the door for assessment using genomic tools. Our efforts to develop a knowledge base for genomic selection of livestock herding dogs are covered in earlier sections of this report.

Outcome: The structure of the breed population is too dispersed to conduct meaningful phenotypic evaluation of breeding values using traditional methods.

26 Implications

We have created a new database resource that will enable the collection, curation and analysis of livestock herding dog performance data. The resource web URL is: doggenetics.net.au/Kelpie/FarmSurvey.html

Our work over the course of this project and our previous project has created an unmatched DNA- based genomic resource for Australian livestock herding dogs. This resource includes 12 whole genome sequences from Australian Kelpies and electronic genotyping array data from 320 Kelpies, as well as smaller numbers of samples of working Border Collies, Australian Cattle Dogs and Australian Stumpy-tailed Cattle Dogs. All data are released to the public domain upon their appearance in relevant publications, enabling future use of the resources.

Using the phenotypic resource, our team has generated a substantive body of knowledge about the value of livestock herding dogs in Australia. We have identified the key traits that are the hallmarks of elite dogs within each working context (yard, paddock and utility), traits that might predict the best work context for any dog evaluated in the phenotypic survey. We have profiled the traits of two major working breeds (the Kelpie and the Border Collie) as well as the breed cross (Kelpie x Collie).

The objective of generating high-quality estimated breeding values (EBVs) for working dog performance was unable to be achieved within this project. The phenotypic data must be connected with the dog pedigrees in real time. To date, we have been offered the recorded pedigrees for one breed (the Australian Working Kelpie). Incompatibility between the databases used for recording the pedigrees and recording of the phenotypes means at this time it is not possible to merge the two resources in real time. The full value of EBV prediction will not be possible unless the two resources are co-located. This is currently hampered by ethical considerations surrounding university research and contributor data privacy, meaning that if devised it would be best if this resource was in the hands of the working dog industry.

A second consideration in the creation of quality EBVs is that meaningful estimation of environmental effects (e.g. time in work, property of use, age, trainer and handler) require sound replication. In the current industry structure, few properties have sufficient replication of genetic lineage to enable the estimation of genetic effects with any confidence. Thus, due to the structure of the livestock herding dog breeding industry, we suggest a traditional performance recording scheme is not in the interests of the industry due to potential biases introduced by lack of replication of bloodlines across properties and handlers at this time.

A way forward for the industry may be to use DNA-based data rather than traditional pedigrees to establish the relationships among animals. The two strategies (pedigree-based and DNA-based relationship estimation) can differ substantially in individual animals. Using this approach would require only that phenotyped dogs are accurately identified by their DNA sample. This approach, called genomic selection, has become popular in livestock industries that use multiple sire mating (e.g. in sheep) or where the time taken to obtain phenotypic data is extended into adulthood (dairy cattle). The attractive feature of a genomic selection approach is that because the DNA-based relationship is used, the approach is not limited to pedigreed dogs, and so mixed-breed dogs can offer useful information.

27 Recommendations

Owner-handler questionnaire responses accurately assess dog working quality and form a convenient method for collecting phenotypic data from livestock herding dogs in dispersed populations.

Correlations between owner-handler questionnaire ratings and behaviour-tested traits are strongest when appropriate motivators are used in the behavioural testing. Traditional testing strategies that use food-based motivators are generally inappropriate in the context of livestock herding dogs.

Natural ability is well-correlated with overall ability, anticipation, initiative-taking and dog working skills. Pooled traits of working skill and instinct have a strong, common signal of genomic association in the current data. Fearfulness has a distinct mapping signal from the positive working traits. Further work is required to understand how the genetic basis of the mapping signals affects important working behaviours.

The dispersal of bloodlines in the livestock herding dog population means it is difficult to achieve the replication of bloodline or environment required to achieve accurate phenotypic-based estimated breeding values. If phenotypic EBVs are to be generated in the population, then the phenotype and pedigree resources should be better connected to enable real-time processing.

Collection of phenotype and genotype information can overcome inadequacies of phenotype-pedigree connectivity by accurately representing genomic relationships among animals in the population. Over time, sufficient data (on at least 2000 dogs) will accumulate to enable training data for calculating genomic estimated breeding values. The creation of a genomic breeding value resource relies upon databasing of electronic representations of dog DNA information.

28 References

1. Australian Companion Animal Council. (2010). Contribution of the Pet Care Industry to the Australian Economy, 7th edition.

2. Shariflou, M.R., et al. (2011). A genealogical survey of Australian registered dog breeds. Vet J, 189(2): p. 203-10.

3. Arnott, E.R., et al. (2015). Strong selection for behavioural resilience in Australian stock working dogs identified by selective sweep analysis. Canine Genet Epidemiol, 2: p. 6.

4. Chang, C.C., et al. (2015). Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience, 4: p. 7.

5. Hoeppner, M.P., et al. (2014). An improved canine genome and a comprehensive catalogue of coding genes and non-coding transcripts. PLoS One, 9(3): p. e91172.

6. Barrett, J.C. (2009). Haploview: Visualization and analysis of SNP genotype data. Cold Spring Harb Protoc, 2009(10): p. pdb ip71.

7. Sargolzaie, M., Iwaisaki, H., and Colleau, J. (2006). CFC: A tool for monitoring genetic diversity. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, p. 27-28.

8. Early, J.B., et al. (2019). The Perceived Value of Behavioural Traits in Australian Livestock Herding Dogs Varies with the Operational Context. Animals (Basel), 9(7).

9. Early, J.B., et al. (2018). Work-type influences perceived livestock herding success in Australian Working Kelpies. Canine Genet Epidemiol, 5: p. 5.

10. Chew, T., et al. (2019). Genomic Characterization of External Morphology Traits in Kelpies Does Not Support Common Ancestry with the Australian Dingo. Genes (Basel), 10(5).

11. Goitia, V., Oquendo, M., and Stratton, R. (2015). Case of 7p22.1 Microduplication Detected by Whole Genome Microarray (REVEAL) in Workup of Child Diagnosed with Autism. Case Rep Genet, 2015: p. 212436.

29 Appendix 1

Glossary

Back/Backing: Action of a dog jumping up onto sheep’s backs in order to assist in moving them in tight spaces such as in yards, sheds or trucks.

Balance: Position a dog assumes in relation to the livestock and the handler that is best suited to move the livestock to the desired location efficiently.

Break: Type of movement a dog performs to move around and redirect livestock, usually when some animals separate from the main group.

Cast: Initial movement of a dog around to the far side, in relation to the handler, of the livestock in order to gather and deliver them back towards the handler.

Classical conditioning: A training procedure in which some initially neutral stimulus (conditioned stimulus, e.g. a sound of low to moderate intensity) is paired with a response-eliciting event (unconditioned stimulus, e.g. food) with the frequent result that the conditioned stimulus comes to elicit the same or a related response.

Conformation: Features of the external morphology (viz relative musculoskeletal dimensions) of a dog that interest breeders and exhibitors, not least because they can affect its performance.

Conscientiousness: The personality trait of being, thorough, careful or vigilant. Conscientiousness implies the intention to do a job well.

Cover: Type of movement a dog uses around livestock while keeping them together.

Cue: Stimulus (including command or context) that elicits an instrumental response (see Discriminative stimulus) or signals the arrival of a positive reinforcer (see Conditioned stimulus).

Epigenetics: The study of changes in organisms caused by modification of gene expression rather than alterations in the genetic code itself.

Ethology: Systematic observation and description of behaviour intended to improve understanding of its mechanism, function, development and evolution.

Eye: Postural behaviour that involves staring at livestock from a stationary position or involves stalking-like movement. Considered to be a remnant of stalking behaviour that forms part of the predatory sequence in wild dogs and wolves.

Exploration: Any activity that offers the individual the potential to acquire new information about itself or its environment.

Force: Pressure applied by the dog in order to move livestock.

Genome: A genome is an organism’s complete set of DNA, including all its genes.

Heading: Movement of a dog to the front of a group of livestock to stop or redirect its movement.

Hold: The action of a dog to keep livestock together.

30 Heritability: The proportion of the phenotypic variance attributable to differences in genetic merit. As it represents the extent to which relatives will resemble each other, it also expresses the expected effectiveness of a selective breeding program.

Latency: The time interval between stimulation and response.

Learning: The process underlying relatively permanent changes in behaviour or acquisition of knowledge.

Negative punishment: A procedure whereby a reinforcer is removed or made unavailable if an unwanted response is made. See also omission training (qv).

Neophobia: Fear of novel stimuli.

Neuroticism: Neuroticism is also sometimes called emotional stability. This dimension relates to one’s emotional stability and degree of negative emotions. People that score high on neuroticism often experience emotional instability and negative emotions. Traits include being moody and tense.

Obedience trials: Competitions to compare the compliance of dogs to handlers’ commands in a number of traditional exercises on and off the lead.

Punishment: A decrease in the likelihood of a response due to the presentation of an aversive stimulus or, in the case of negative punishment, the removal of a reinforcing stimulus.

Reinforcement: In instrumental conditioning (qv), this refers to the process whereby some event, usually one of some significance to the animal, makes the preceding response more likely to occur in future.

Send away: An obedience exercise that involves a dog travelling away from its handler in a given direction governed by the handler.

Sit Stay: An obedience exercise that involves a dog remaining in a sitting position for a defined period with or, in the case of advanced dogs, without the owner present.

Standard error: A measure of the accuracy with which a sample represents a population. The smaller the standard error, the more representative the sample will be of the overall population.

Stress: Refers either to a set of events, usually aversive ones, that put pressure on an individual, or to the state induced by such pressure.

Trait: Characteristics or attributes of an organism that are expressed by genes and/or influenced by the environment. Traits include physical attributes, such as coat colour in horses, and behavioural characteristics, such as nesting in birds.

Working dog trials: Competitions designed to show the absolute and relative ability of dogs as they perform specific trained responses in challenges, categorised according to their complexity, which include companion dog (CD), trials dog (TD), working dog (WD) and police dog (PD) classes.

31 Appendix 2

Publications resulting from this project

Early, J.B, Arnott, E.A., Mascord, L., van Rooy, D., McGreevy, P.D. and Wade, C.M. (2018). Work- type influences perceived livestock herding success in Australian Working Kelpies. Canine Genetics and Epidemiology, 5:5 [9]

Background

Working dog handlers and breeders have very different behavioural requirements in the animals that they employ for managing livestock. The Australian Working Kelpie breed may be used in several working contexts, notably yards, paddocks and a combination of both. The working context influences the skillsets required and gives rise to three corresponding work- types: Yard, Paddock and Utility Kelpies. In particular, dogs used for working stock in the confines of yards and trucks interact with stock more forcefully than those mustering in larger areas (paddocks) where they can herd stock effectively from a greater distance. This article explores owner assessments of dog working quality and assessment of genomic similarity by multidimensional scaling, to ask whether it is sufficient for breeders to aim for a multipurpose breeding objective, or whether breeding only specialist lines maximises user satisfaction for yard and paddock work.

Results

Reported owner perceptions of 298 dogs assessed with the Livestock Herding Dog assessment tool showed that dog handlers across all working types were very happy with their dogs’ level of general skills.

Compared with both Yard and Utility Kelpies, Paddock Kelpies had significantly lower trait scores for force (pressure applied by the dog to move livestock), willingness to back the stock (run along a sheep’s dorsum) and bite (frequency of using the mouth to grab or bite the livestock). Meanwhile, compared with both Paddock and Utility Kelpies, the Yard Kelpies had significantly higher scores for hyperactivity and excitability (both with and without stock) and impulsiveness without stock. As one would predict for all-rounders, Utility Kelpies had intermediate scores for all behaviours and working traits.

Conclusions

Specialist characteristics were displayed by dogs in the Yard Kelpie and Paddock Kelpie groups. In particular, Yard Kelpies demonstrate higher excitability, willingness to back the stock, and a higher tendency to bark and bite the stock. Conversely, Paddock Kelpies rarely display these characteristics. Utility Kelpies, as the name suggests, are intermediate between the other two groups and display the characteristics of both. Genetic analysis suggests that the Yard, Utility and Paddock Kelpies are not distinguishable at a DNA level. In conclusion, at this time there is no suggestion of a breed split in the Australian Working Kelpie generated by selection for work type. A common breeding objective should enable dogs to be produced that fulfil all potential working requirements. This reinforces the importance of breeder skill in recognising the phenotypic potential of pups in order to place them in appropriate working contexts.

32 Early, J.B, Arnott, E.A., Wilson, B., Wade, C.M. and McGreevy, P.D. (2019). The Perceived Value of Behavioural Traits in Australian Livestock Herding Dogs Varies with the Operational Context. Animals, 9(7) 10.3390/ani9070448 [8]

Summary

Information on Australian livestock herding dogs and their handlers and breeders is limited. This study aimed to collate baseline information on how handlers and breeders value various behavioural traits relevant to the work of these dogs. A survey was presented to explore herding dog behaviour in four contexts including work and competition. The behavioural traits were divided into three groups: working manoeuvres, working attributes and general attributes. Data from 811 respondents revealed that several behavioural traits were of high and low value to handlers and breeders across all contexts, while others were unique to only one or two contexts. For example, cast, force, gather, trainable, confident and friendly were of most value, whereas bite, bark and back were of less value. Further analysis revealed that respondents can be considered as coming from two main groups: firstly, handlers with a preference for specialised dogs in the utility context and, secondly, handlers focussed on the yard context, who need dogs that have a broad range of skills and that are easy to work with. This information may assist in matching handlers with suitable dogs. Future research should clarify handlers’ understanding of innate and learnt behaviours.

Chew, T., Willet, C.E., Haase, B. and Wade, C.M. (2019). Genomic Characterization of External Morphology Traits in Kelpies Does Not Support Common Ancestry with the Australian Dingo. Genes (Basel), 10(5) [10].

Abstract

The Kelpie is a breed developed in Australia for use as a livestock herding dog. It has been proposed that the development of the breed included gene flow from the Australian Dingo (Canis dingo), a canid species present on the Australian continent for around 4000 years. The Kelpie breed is split between working and conformation types that have readily recognizable differences in external morphology. We characterize known gene variants relating to external morphology in sequenced representatives of both Kelpie types (Australian Kelpie— conformation; Australian Working Kelpie—herding) and compare the variants present with those in sequenced Australian Dingoes, including 25 canids with locus-constrained data and one with a whole genome sequence. Variants assessed include identified coat colour and ear morphology variants. We describe a new variant site in the transcribed region of methionine sulfoxide reductase 3 that may relate to ear phenotype. None of the morphology variants analysed offer support for co-ancestry of the Kelpie breed with the Australian Dingo.

Keywords

coat-colour; ear-type; Australian working Kelpie; Australian Kelpie; Dingo

33 Early, J.B., Aalders, J., Arnott, E.A., Wade, C.M. and McGreevy, P.D. (2020) Sequential Analysis of Livestock Herding Dog and Sheep Interactions. Animals, 10 (2), 352

Summary

Although livestock herding dogs have contributed significantly to Australian agriculture, there are virtually no studies examining the interactions between dog and livestock during herding. One statistical approach that may assist our understanding of such interactions during herding is lag sequential analysis that reveals links between one event and the next. Using high-definition video recordings of herding in a yard-based competition trial and software to code the main dog and sheep behaviours, we identified several significant behavioural interactions. These included the dog ceasing all movement followed by the sheep also ceasing movement; the dog chasing the sheep and a group of sheep escaping the main flock; a single sheep escaping the flock and the dog chasing; sheep initiating movement followed by the dog following; foot-stamping followed by the dog ceasing all movement; and foot-stamping by the sheep and the dog lip-licking in response. Further statistical analysis found no significant sex differences in the herding styles of the dogs included in the study. Of benefit to livestock herding dog handlers and breeders was the identification of trial score as a predictor of efficient performance.

Abstract

Livestock herding dogs are crucial contributors to Australian agriculture. However, there is a dearth of empirical studies of the behavioural interactions between dog and livestock during herding. A statistical approach that may reveal cause and effect in such interactions is lag sequential analysis. Using 48 video recordings of livestock herding dogs and sheep in a yard trial competition, event-based (time between behaviours is irrelevant) and time-based (time between behaviours is defined) lag sequential analyses identified several significant behavioural interactions (adjusted residuals greater than 2.58; the maximum likelihood-ratio chi-squared statistic for all eight contingency tables identified all sequences as highly significant (p < 0.001)). These sequences were: The dog ceasing all movement followed by the sheep also ceasing movement; the dog chasing the sheep and a group of sheep escaping the main flock; a single sheep escaping the flock and the dog chasing; sheep initiating movement followed by the dog following; foot-stamping followed by the dog ceasing all movement; and foot-stamping by the sheep and the dog lip-licking in response. Log linear regression identified significant relationships among undesirable behaviours in sheep and both observed trial duration (p = 0.001) and trial score (p = 0.009). No differences in the herding styles of dogs were identified between sex of dog and frequency of sheep escape behaviours (p = 0.355) nor the sex of dog and competition level (p = 0.116). The identification of trial score as a predictor of efficient performance confirms the benefits of incorporating extant objective measures to assess livestock herding dogs.

Keywords

livestock; behaviour; herding; sheep; welfare; working dog; livestock herding dog; lag sequential analysis

34 Appendix 3

Sample of questionnaire

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38 Commencing implementation of a genetic evaluation system for livestock working dogs by C. M. Wade, D. van Rooy, E. R. Arnott, J. B. Early and P. D. McGreevy June 2021

AgriFutures Australia Publication No. 20-117 AgriFutures Australia Project No. PRJ-010413 ISBN: 978-1-76053-135-5

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