Commencing implementation - of a genetic evaluation system for livestock working dogs - AgriFutures Australia
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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
claire.wade@sydney.edu.au
In submitting this report, the researcher has agreed to AgriFutures Australia publishing this material in its edited
form.
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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
iiiAbout 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.
ivAcknowledgments
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
vContents
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
viTables 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
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
viiiBackground
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.
ixResults/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.
xImplications 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.
xiIntroduction
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
Australia 1, 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.
1of 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.
2Objectives
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.
3Methodology
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.
4The 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.
5At 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’.
6The 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.
7Interaction 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
81 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
103.1), 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.
11Results
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.
12The 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).
13-0.2 -0.2 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 1.0 Natural ability
-0.2 -0.2 -0.2 0.0 -0.2 0.0 0.1 0.1 -0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.2 0.3 0.2 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.5 1.0 0.6 Overall ability
-0.2 -0.2 -0.1 0.0 -0.1 0.1 0.0 0.1 0.0 0.1 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.7 1.0 0.5 0.6 Anticipation
-0.2 -0.2 -0.1 0.1 -0.1 0.1 0.0 0.1 0.0 0.2 0.3 0.2 0.3 0.4 0.3 0.3 0.3 0.2 0.2 0.4 0.4 0.3 0.6 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.5 1.0 0.7 0.5 0.6 Intitiative
-0.2 -0.1 -0.2 0.0 -0.1 0.1 -0.1 0.0 0.0 0.1 0.1 0.3 0.2 0.2 0.4 0.4 0.2 0.3 0.4 0.3 0.4 0.5 0.5 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.4 1.0 0.5 0.4 0.4 0.5 Intelligence
-0.1 -0.1 -0.2 -0.1 -0.1 0.0 -0.1 0.0 0.0 0.0 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.1 0.6 0.2 0.3 0.7 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.4 1.0 0.4 0.4 0.4 0.5 0.5 Trainability
-0.1 -0.2 -0.2 -0.1 -0.2 0.0 0.0 0.0 -0.1 0.0 0.1 0.1 0.3 0.1 0.3 0.3 0.1 0.2 0.2 0.3 0.3 0.3 0.3 0.5 0.6 0.6 0.6 0.6 0.6 1.0 0.4 0.3 0.5 0.5 0.4 0.5 Balance
-0.1 -0.2 -0.2 -0.1 -0.2 0.0 0.0 0.1 -0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.3 0.2 0.3 0.2 0.3 0.3 0.2 0.4 0.5 0.6 0.6 0.6 0.6 1.0 0.6 0.3 0.3 0.4 0.5 0.5 0.5 Cover
-0.2 -0.2 -0.2 -0.1 -0.2 0.0 0.0 0.0 -0.2 0.0 0.1 0.1 0.5 0.2 0.4 0.3 0.2 0.3 0.2 0.3 0.3 0.3 0.4 0.7 0.5 0.6 0.5 1.0 0.6 0.6 0.4 0.4 0.5 0.5 0.5 0.5 Gather
-0.2 -0.2 -0.2 -0.1 -0.2 0.1 0.0 0.1 -0.1 0.1 0.2 0.1 0.3 0.2 0.3 0.3 0.2 0.2 0.2 0.3 0.3 0.2 0.4 0.4 0.6 0.6 1.0 0.5 0.6 0.6 0.3 0.3 0.5 0.5 0.5 0.5 Hold
-0.2 -0.2 -0.1 -0.2 -0.2 0.0 0.0 0.1 -0.1 0.1 0.1 0.1 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.2 0.4 0.5 0.5 1.0 0.6 0.6 0.6 0.6 0.3 0.3 0.5 0.5 0.4 0.5 Heading
-0.2 -0.2 -0.2 0.0 -0.1 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.3 0.3 0.2 0.2 0.2 0.3 0.2 0.3 0.3 0.2 0.4 0.4 1.0 0.5 0.6 0.5 0.6 0.6 0.3 0.3 0.5 0.5 0.4 0.5 Break
-0.1 -0.1 -0.2 -0.1 -0.2 -0.1 -0.1 0.0 -0.2 -0.1 0.1 0.1 0.6 0.1 0.4 0.3 0.1 0.2 0.3 0.2 0.2 0.3 0.3 1.0 0.4 0.5 0.4 0.7 0.5 0.5 0.3 0.3 0.4 0.4 0.4 0.4 Cast
-0.2 -0.2 -0.1 0.0 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.2 0.2 0.3 0.2 0.2 0.3 0.2 0.2 0.4 0.4 0.3 1.0 0.3 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.5 0.6 0.5 0.4 0.4 Initiative taking
-0.1 0.0 -0.2 0.0 -0.1 0.0 -0.1 0.0 0.0 0.0 0.1 0.2 0.2 0.1 0.3 0.3 0.2 0.1 0.6 0.2 0.2 1.0 0.3 0.3 0.2 0.2 0.2 0.3 0.2 0.3 0.7 0.5 0.3 0.3 0.4 0.4 Trainability (ease of training)
-0.2 -0.2 0.0 0.0 0.1 0.1 0.1 0.2 0.1 0.3 0.1 0.4 0.1 0.3 0.1 0.2 0.4 0.2 0.2 0.4 1.0 0.2 0.4 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 Persistence
-0.4 -0.4 0.0 0.1 0.0 0.1 0.1 0.1 0.1 0.3 0.2 0.3 0.1 0.5 0.2 0.1 0.6 0.1 0.1 1.0 0.4 0.2 0.4 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.3 0.4 0.3 0.4 0.3 Confidence
-0.1 0.0 -0.2 0.0 -0.2 0.0 -0.1 0.0 -0.1 0.0 0.1 0.2 0.1 0.1 0.4 0.4 0.1 0.1 1.0 0.1 0.2 0.6 0.2 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.6 0.4 0.2 0.2 0.3 0.3 Obedience
-0.1 0.0 -0.1 0.0 0.0 -0.1 0.1 0.0 0.1 0.0 0.0 0.1 0.2 0.0 0.1 0.1 0.2 1.0 0.1 0.1 0.2 0.1 0.2 0.2 0.3 0.2 0.2 0.3 0.3 0.2 0.1 0.3 0.2 0.3 0.2 0.3 Eye-strength
-0.3 -0.4 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.0 0.5 0.0 0.0 1.0 0.2 0.1 0.6 0.4 0.2 0.3 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.3 0.3 0.3 0.3 Boldness
-0.1 0.0 -0.4 -0.1 -0.4 -0.1 -0.2 -0.1 -0.3 -0.2 0.0 0.0 0.2 -0.1 0.7 1.0 0.0 0.1 0.4 0.1 0.2 0.3 0.2 0.3 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.3 0.3 0.2 0.3 Patience
-0.2 0.0 -0.4 -0.1 -0.5 0.0 -0.1 -0.1 -0.4 -0.1 0.0 0.1 0.2 0.0 1.0 0.7 0.0 0.1 0.4 0.2 0.1 0.3 0.2 0.4 0.2 0.3 0.3 0.4 0.3 0.3 0.3 0.4 0.3 0.3 0.3 0.3 Calmness
Phenotypic predictors of natural ability
-0.2 -0.3 0.1 0.3 0.1 0.3 0.2 0.2 0.1 0.5 0.4 0.2 0.0 1.0 0.0 -0.1 0.5 0.0 0.1 0.5 0.3 0.1 0.3 0.1 0.3 0.3 0.2 0.2 0.3 0.1 0.2 0.2 0.4 0.3 0.3 0.3 Force
14
0.0 0.0 -0.1 -0.1 -0.1 0.0 0.0 0.0 -0.1 0.0 0.0 0.0 1.0 0.0 0.2 0.2 0.0 0.2 0.1 0.1 0.1 0.2 0.2 0.6 0.3 0.3 0.3 0.5 0.3 0.3 0.2 0.2 0.3 0.2 0.3 0.3 Cast-adequacy
-0.2 -0.2 0.0 0.1 0.1 0.2 0.0 0.0 0.2 0.2 0.1 1.0 0.0 0.2 0.1 0.0 0.3 0.1 0.2 0.3 0.4 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.3 0.2 0.2 0.2 0.2 Stamina
-0.1 -0.1 0.0 0.3 0.1 0.1 0.1 0.1 0.1 0.2 1.0 0.1 0.0 0.4 0.0 0.0 0.3 0.0 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.3 0.2 0.2 0.1 Back
-0.1 -0.2 0.2 0.2 0.2 0.3 0.2 0.2 0.2 1.0 0.2 0.2 0.0 0.5 -0.1 -0.2 0.3 0.0 0.0 0.3 0.3 0.0 0.1 -0.1 0.1 0.1 0.1 0.0 0.1 0.0 0.0 0.1 0.2 0.1 0.2 0.1 Force-adequacy
0.1 -0.1 0.4 0.2 0.7 0.1 0.1 0.1 1.0 0.2 0.1 0.2 -0.1 0.1 -0.4 -0.3 0.2 0.1 -0.1 0.1 0.1 0.0 0.1 -0.2 0.0 -0.1 -0.1 -0.2 -0.1 -0.1 0.0 0.0 0.0 0.0 -0.1 0.0 Excitability
traits sorted according to association with ‘natural ability’.
0.0 -0.1 0.2 0.1 0.0 0.2 0.5 1.0 0.1 0.2 0.1 0.0 0.0 0.2 -0.1 -0.1 0.2 0.0 0.0 0.1 0.2 0.0 0.1 0.0 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.1 0.1 0.1 0.0 Bite-adequacy
0.0 0.0 0.1 0.2 0.1 0.1 1.0 0.5 0.1 0.2 0.1 0.0 0.0 0.2 -0.1 -0.2 0.2 0.1 -0.1 0.1 0.1 -0.1 0.0 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 -0.1 -0.1 0.0 0.0 0.1 0.0 Bite-frequency
-0.1 -0.2 0.1 0.5 0.2 1.0 0.1 0.2 0.1 0.3 0.1 0.2 0.0 0.3 0.0 -0.1 0.2 -0.1 0.0 0.1 0.1 0.0 0.1 -0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.0 0.1 0.1 0.1 0.0 0.0 Bark-adequacy
0.1 0.0 0.4 0.2 1.0 0.2 0.1 0.0 0.7 0.2 0.1 0.1 -0.1 0.1 -0.5 -0.4 0.2 0.0 -0.2 0.0 0.1 -0.1 0.0 -0.2 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1 -0.1 -0.2 0.0 Hyperactivity
0.0 -0.1 0.1 1.0 0.2 0.5 0.2 0.1 0.2 0.2 0.3 0.1 -0.1 0.3 -0.1 -0.1 0.1 0.0 0.0 0.1 0.0 0.0 0.0 -0.1 0.0 -0.2 -0.1 -0.1 -0.1 -0.1 -0.1 0.0 0.1 0.0 0.0 0.0 Bark-frequency
0.2 0.0 1.0 0.1 0.4 0.1 0.1 0.2 0.4 0.2 0.0 0.0 -0.1 0.1 -0.4 -0.4 0.1 -0.1 -0.2 0.0 0.0 -0.2 -0.1 -0.2 -0.2 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.2 -0.1 Impulsiveness (has sudden, strong urges to act)
0.6 1.0 0.0 -0.1 0.0 -0.2 0.0 -0.1 -0.1 -0.2 -0.1 -0.2 0.0 -0.3 0.0 0.0 -0.4 0.0 0.0 -0.4 -0.2 0.0 -0.2 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 Timidness
1.0 0.6 0.2 0.0 0.1 -0.1 0.0 0.0 0.1 -0.1 -0.1 -0.2 0.0 -0.2 -0.2 -0.1 -0.3 -0.1 -0.1 -0.4 -0.2 -0.1 -0.2 -0.1 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 Nervousness
Cast
Back
Hold
Force
Break
Cover
Gather
Balance
Stamina
Heading
Patience
Boldness
Calmness
Intitiative
Timidness
Obedience
Excitability
Trainability
Confidence
Persistence
Intelligence
Anticipation
Eye-strength
Nervousness
Hyperactivity
Overall ability
Natural ability
Bite-adequacy
Cast-adequacy
Bark-adequacy
Bite-frequency
Bark-frequency
Initiative taking
Force-adequacy
Trainability (ease of training)
Table 2 Pearson correlations among normalised questionnaire scores for 588 dogs and 36
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.
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