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Journal of Feline Medicine and Surgery
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Introduction
Domestic cats are a popular pet in the USA and the
interest in their welfare is growing. In 2017–2018, the
American Pet Products Association estimated that 38%
of US households owned at least one cat, with an aver-
age of two cats owned per household.1 Multiple studies
have correlated stress, living in multi-cat households,
intercat conflict and an increased risk of feline idiopathic
cystitis and periuria (house-soiling).2–8
Although several studies exist documenting the
occurrence of affiliative and conflict behaviors in a small
group of cats or groups in shelters or laboratory settings,
few studies have documented the frequency of these
behaviors in a larger sample size of typical households
Conict and affiliative behavior
frequency between cats in multi-cat
households: a survey-based
study
Ashley L Elzerman1, Theresa L DePorter1, Alexandra Beck2
and Jean-François Collin2
Abstract
Objectives The objective of this study was to collect information from cat owners about the frequency of conflict
and affiliative signs in their households in order to: (1) assess correlations with the owners’ ratings of household
cat–cat harmony; and (2) determine if relationships exist between household variables, cat population variables
and behavior frequencies.
Methods Responses to an online survey of adult residents of the USA who were the primary caregiver of 2–4 indoor
or indoor–outdoor cats were included in the analysis. Spearman’s correlations and χ2 tests were used to compare
behavior frequencies with household and cat population variables.
Results Of 2492 owners of multiple cats, 73.3% noted conflict signs from the very beginning when introducing the
cats. The more cats in the house, the more frequent the conflict signs. Staring was the most frequently observed
conflict sign, occurring at least daily in 44.9% of households, followed, in order of decreasing frequency, by chasing,
stalking, fleeing, tail twitching, hissing and wailing/screaming. Hissing occurred at least daily in 18% of households.
Affiliative signs were observed more frequently than conflict signs. Physical contact between cats was observed
at least daily in around half of the multi-cat households. Higher harmony scores were correlated with less frequent
conflict signs and more frequent affiliative signs. No household or cat population variable, including home size or
numerically adequate resources provision, was strongly predictive of the frequency of conflict or affiliative signs.
Conclusions and relevance This is the first large-scale online survey to obtain frequencies of conflict and affiliative
behaviors and compare them with factors linked to the cats or the home settings. The study confirmed that feline
relationships are correlated with the owner’s perceived impression of the initial introduction, but other household
factors and cat population variables included in the study were not strongly predictive of the frequency of conflict
or affiliative signs.
Keywords: Conflict; affiliative behavior; resource; survey; intercat aggression
Accepted: 30 August 2019
1Oakland Veterinary Referral Services, Bloomfield Hills, MI, USA
2Ceva Santé Animale, Libourne, France
Corresponding author:
Ashley L Elzerman DVM, Oakland Veterinary Referral Services,
1400 South Telegraph Road, Bloomfield Hills, MI 48302, USA
Email: dr.elzerman@gmail.com
877988JFM Journal of Feline Medicine and SurgeryElzerman et al
Original Article
2 Journal of Feline Medicine and Surgery
or correlated these frequencies with the owner’s general
overall impression of relationships within the house-
hold.9–14 A survey by Voith and Borchelt in 1986 included
887 questionnaires of cat owners waiting for care at one
of four veterinary clinics on the East Coast, USA. The
survey reported the frequency of affiliative and conflict
behaviors, but included a relatively small sample size
and did not report household factors that may affect
these frequencies.15,16 A retrospective study of cats pre-
sented to a behavior service for treatment of intercat
aggression found that male cats were more likely to be
the initiators of aggression than female cats, but the
aggression was equally likely to be directed at a same sex
as an opposite-sex housemate.17 Other factors docu-
mented or hypothesized to affect the intercat relation-
ships in a household are relatedness, available resources
and the personalities of the cats in the household.18,19
Analysis of relationships of cats within a household
presents a complex problem, with potential interactions
between multiple variables. Bayesian networks have
successfully been used in human health and epidemiol-
ogy research as a graphical model to analyze multiple
variables and their interdependence, while decreasing
the risk of false positives owing to the large number of
variables.20,21 In the context of this research, a Bayesian
network was used to build a predictive model regarding
the affiliative and conflict behavior frequencies and to
select the key factors with the most important probabil-
istic relationships with those two responses.
In order to assess the impact of intercat relationships
on the stress and welfare of cats, a first step is to identify
the frequency of affiliative and conflict behaviors in ‘typi-
cal’ multi-cat households. The goal of the present study
was to collect information from owners about how often
they observed specific conflict and affiliative behaviors in
their households in order to: (1) see if the frequency of
specific behaviors correlated with the owners’ overall
general assessment of household cat–cat harmony; and
(2) determine if relationships exist between household
factors and frequency of behaviors. We hypothesized that
as the number of cats in a household increased, the fre-
quency of conflict behaviors would increase, and an
increase in the frequency of conflict behaviors would be
correlated with a decrease in the frequency of affiliative
behaviors.
Materials and methods
A survey was developed by the authors to collect infor-
mation from owners regarding the frequency of affilia-
tive and conflict behaviors noted between their cats on a
household level. Additional information was gathered
to assess the impact of demographics, resources, person-
ality and introductions on these frequencies. Households
in the USA with 1–4 indoor or indoor–outdoor cats cur-
rently living in the home were targeted through postings
on social media and flyers distributed at veterinary clin-
ics and veterinary conferences. Participating cat owners
who were the primary caregiver of at least one cat in the
house are subsequently referred to as respondents. The
survey was anonymous; no personal information was
collected from the respondents and completion of the
survey implied consent.
The survey was hosted online on the Survey Monkey
platform. The survey included multiple choice or Likert-
scale questions with options to enter additional informa-
tion on some questions. Household-level questions on
conflict and affiliative behaviors, and overall household
harmony were adapted from the Oakland Feline Social
Interaction Scale (OFSIS).22 The OFSIS is a questionnaire
developed to measure the incidence, frequency and
intensity of 12 cat–cat interactions reflecting conflict
between cats. Before opening the survey to respondents,
the survey was pre-tested with a small group of cat own-
ers to test the usability of the platform, question flow
and content. These pre-test data were not included in the
analysis as minor changes were made to the survey fol-
lowing testing. Demographic information, resources and
frequency of cat–cat conflict and affiliative behaviors
were collected at the household level. All other questions
were asked about each cat in the household, so the length
of the survey depended on the number of cats in
the household (range 28–73 questions). The amount of
time to complete the survey ranged from 5 to 30 mins.
The survey was available from 30 November 2016 to
14 March 2017. Survey questions can be found in
Appendix 1 in the supplementary material.
Respondents were exited from the survey if they were
younger than 18 years of age, did not own at least one
indoor cat, did not live in the USA, or lived in house-
holds with more than four indoor cats or more than four
dogs. All questions in the survey required an answer.
Surveys that were started but not completed, or included
cats living only outdoors, were excluded from analysis.
Owners with only outdoor cats were excluded from the
survey owing to concerns that they were less likely to be
present during their cat’s interactions. Households with
up to four cats comprise 95% of feline households in the
USA and even more in several European countries (Ceva
unpublished market research, 2014). Based on expected
survey completion rates, the ability to collect sufficient
sample sizes to allow for analysis of households with
more than four cats was unlikely and the results of these
larger households would not be broadly generalizable to
the average feline household in the USA; therefore, the
survey was restricted to households of four cats or fewer.
Similarly, households with more than four dogs were
excluded as they represent <1% of dog owning house-
holds in the USA and other countries, and also owing to
concerns that the presence of more dogs would affect the
interactions of the cats in the house.23
Elzerman et al 3
The survey included nine sections: owner demo-
graphic information; resources; cat demographic and
owner bond to cat; harmony questions; reactions and
interventions when cat added to the home; personality
and biting history; current behavior problems; affiliative
and conflict behavior frequencies; and conflict interven-
tions. The data collected in the large survey were
too extensive to be discussed fully in one paper, so the
decision was made to limit the scope of this paper to
behaviors of conflict and aggression and factors that
may affect these behaviors. The sections of the survey
used in this study are described in more detail below.
Household descriptive data collected included the
size and type of house, and the quantity of litter boxes,
feeding stations and scratching posts in the house.
Respondents were asked to list the names of their cats,
starting with the cat that had lived in the house for the
longest period followed by the most recent additions. If
cats were added at the same time, the respondent chose
which cat to list first. This allowed us to look at any effect
of the order of addition into the house on the cats’
relationships.
Individual cat information collected included sex,
age category, breed, neuter status, declaw status, life-
style, current chronic or debilitating health problems,
acquisition information and current behavioral prob-
lems. Respondents were asked to rate on a scale of 1–5
how well the two descriptions, ‘active and curious’,
and ‘sedentary and shy’, described each of their cats.
Respondents were asked to choose the observed fre-
quency of specified affiliative and conflict behaviors in
five categories: several times a day; daily; weekly; once
a month or less; and never. The frequencies of these
behaviors may vary from cat to cat in the house, but the
questions were asked about the frequency of the behav-
iors in the household, so the frequency of each cat’s
behavior was added together and reported by the
respondent as a household summation. Affiliative cat–
cat behaviors included nose-touching, sleeping in the
same room, sleeping while touching and allogrooming.
The cat–cat conflict behaviors assessed were staring,
stalking, chasing, fleeing, hissing, wailing or screaming
and twitching of the tail. Respondents were asked to
rate the cat–cat harmony in their house on a 5-point
Likert scale. This rating is subsequently referred to as
the harmony score. For the introduction of each cat to
the house, the respondent was asked to choose which
description was the best fit: ‘the introduction went well’
or ‘the introduction did not go well’.
Prior to data analysis, the individual cat data were
used to calculate household-level variables (expressed as
percentages) for each household. For example, the per-
centage of females was calculated by dividing the number
of female cats by the total number of cats in the house-
hold. An ‘active and curious’ score and a ‘sedentary and
shy’ score for the household were calculated by averaging
the scores for all cats in the household. Surveys where the
respondents answered that they preferred not to answer
the home size question (n = 45) were not included in
home-size analyses.
Resource allocation was divided into two categories,
households providing a recommended quantity of the
resource vs households not providing an adequate quan-
tity of resources. Adequate was defined as a quantity of
litter boxes equaling the number of cats plus one and a
quantity of food stations or bowls and scratching posts
equal to the number of cats in the house.23,24
To visualize the inter-relationships in the data, an
unsupervised Bayesian network using a maximum
weight spanning tree algorithm was performed with all
variables. Using this network, a clustering analysis was
carried out to identify latent variables (called factors).
Finally, an unsupervised Bayesian network using a
Taboo algorithm was built on top of those latent varia-
bles to have a final probabilistic structural equation
model. The algorithm cut the numerical values into
classes to find the probabilistic relationships among the
variables. If two nodes had no path between them it
meant that knowledge of the state of one variable pro-
vided no information on the state of the other variable,
and so they were considered independent. Variables
contributing <20% of the conditional dependence were
also removed from the model in order to keep only the
most important effects in the analysis. The Pearson’s
coefficient was calculated for remaining nodes.
Although the Bayesian network model did not indi-
cate a >20% contribution of any of the cat or household
variables, more traditional statistics were pursued to
elucidate if any variables had a small, yet statistically
significant, relationship with conflict or affiliative signs
and to confirm the findings of the Bayesian network
analysis. Variables included in the further analysis were
chosen to minimize replication of key concepts but
included variables that intuitively may be related to
intercat conflict based on our clinical experience and the
current literature. For example, in the entire survey,
questions were asked about the number of litter boxes
in the house, how many different rooms contained a lit-
ter box and how many different floors contained a litter
box. This question was asked to help identify trends in
resource allocation, as many owners report multiple lit-
ter boxes but fail to understand that the relative place-
ment of these is an important factor in their use. In the
statistical analysis below, the number of litter boxes in
the house was included, but the number of rooms and
floors with litter boxes was excluded to avoid inclusion
of data that replicated key concepts and that are not
independent of each other.
Shapiro–Wilk tests were conducted for all scale vari-
ables and descriptive statistics calculated for all other
4 Journal of Feline Medicine and Surgery
variables. This analysis showed that the distributions of
the data were significantly different from a normal dis-
tribution, so tests that do not require normality were
used for analysis (Spearman’s correlation and χ2 test of
independence).
A Spearman’s correlation analysis was conducted to
assess if a relationship existed between the frequencies
of conflict and affiliative signs vs harmony scale, as well
as relationships between conflict and affiliative signs.
The assumption of monotonicity was assessed graphi-
cally with a scatterplot prior to analysis. Significance
was set a priori at P = 0.05, using a two-tailed test.
Relationships between each household factor and the
frequencies of conflict and affiliative signs were assessed
with Spearman’s correlation for ordinal or scale variables
and a χ2 test of independence for nominal variables. All
tests were two-tailed. As multiple analyses were con-
ducted on the same dependent variables, a Bonferroni
correction was conducted on the original P <0.05 with 20
analyses per dependent variable to decrease the risk of a
type I error. The corrected P <0.0025 with a critical value
for Spearman’s correlations of 0.061 was set as the level of
significance. Cohen’s standard was used to evaluate the
strength of the relationships, where coefficients between
0.10 and 0.29 represent a small effect size, coefficients
between 0.30 and 0.49 represent a moderate effect size
and coefficients >0.50 indicate a large effect size.25
Bayesian network analysis was performed using
Bayesialab 8.0. All other analyses were completed with
Intellectus Statisticus.26
Results
Of the 5978 surveys started, 1127 respondents were
exited from the survey for not meeting the inclusion cri-
teria (four were <18 years old, 51 did not have an indoor
cat, 356 lived outside the USA, 623 had ⩾5 cats, 98 had
>4 dogs). Of the surveys completed, 928 were excluded
because the respondent started but did not finish the
survey, and a further three were excluded because they
contained outdoor-only cats.
For the 3920 responses that met the inclusion criteria,
1428 (36.4%) respondents owned one cat, 1424 (36.3%)
owned two cats, 689 (17.6%) owned three cats and 379
(9.7%) owned four cats. Female respondents comprised
94.2% (n = 3693) of the completed surveys, 4.3% (n = 169)
of the respondents were male and 1.5% (n = 58) preferred
not to answer. The data reported below are restricted to
the 2492 multi-cat households (6431 cats).
Affiliative and conict behaviors
The signs of conflict behavior between housemate cats,
from the most to the least frequently displayed, are pre-
sented in Table 1 and were stare, chase, stalk, flee, twitch
tail, hiss and wail/scream. The most abundant frequency
category for each behavior was: daily for stare, chase
and stalk; weekly for flee and twitch tail; and never for
hiss and wail/scream.
Of 2492 households with multiple cats, 12.3% (n = 307)
reported that these signs of conflict never occured
between their cats. Of the 2185 households that reported
some of the signs, 73.3% (n = 1602) of owners noted
them from the very beginning when introducing the
cats, 23.6% (n = 515) noted that their cats’ relationships
changed gradually and 3.1% (n = 68) noted that their
cats’ behavior changed abruptly (Figure 1).
For households reporting conflict, the evolution of
the conflict signs over time was described as maintain-
ing the same frequency for 50.6% of the cases (n = 1115),
becoming less frequent in 46.2% (n = 1019) and only
3.2% being more frequent over time (70 households).
These tendencies were similar, regardless of the num-
ber of cats in the home: conflict maintained the same
frequency (53%, 51%, 44% for the two-cat, three-cat
and four-cat households, respectively), became less
frequent (44%, 46% and 52%, respectively) and
was more frequent over time (3%, 3% and 4%,
respectively).
The signs of affiliative behavior between housemate
cats are presented in Table 2, from the most to the least
frequently displayed. Physical contact between cats
Table 1 Frequency of cat–cat conflict behaviors noted in any of the cats in the multi-cat household in response to
housemate cats (n = 2492)
Conflict behaviors Several times a day Daily Weekly Monthly or less Never
Stare 329 (13.2) 789 (31.7) 546 (21.9) 354 (14.2) 474 (19.0)
Chase 307 (12.3) 789 (31.7) 700 (28.1) 341 (13.7) 355 (14.2)
Stalk 234 (9.4) 638 (25.6) 627 (25.2) 390 (15.7) 603 (24.2)
Flee 190 (7.6) 560 (22.5) 615 (24.7) 455 (18.3) 672 (27.0)
Twitch tail 152 (6.1) 476 (19.1) 686 (27.5) 563 (22.6) 615 (24.7)
Hiss 109 (4.4) 340 (13.6) 565 (22.7) 733 (29.4) 745 (29.9)
Wail/scream 41 (1.6) 91 (3.6) 206 (8.3) 425 (17.1) 1729 (69.4)
Data are n (%)
Elzerman et al 5
was observed at least daily in around half of the multi-
cat homes. The affiliative behaviors from the most to
the least frequently displayed were sleeping in the
same room as another cat, grooming another cat by
licking around the head or ears, sleep-touching with a
housemate cat and nose-touching with a housemate
cat. The most abundant frequency category was several
times a day for all affiliative behaviors except nose-
touching, which was daily.
Spearman’s correlation analysis was conducted to
determine if relationships existed between the frequency
of conflict and affiliative signs. Table 3 is a Spearman’s
correlation matrix reporting the Spearman’s correlation
(rs) for these relationships. A Spearman’s correlation
value can range from –1 (indicating a perfect negative
correlation) to +1 (indicating a perfect positive correla-
tion). A correlation of zero indicates no relationship
between the variables. The statistically significant corre-
lation values are shown in bold in Table 3. Significant
positive correlations were observed between each con-
flict sign, indicating that as the frequency of one conflict
sign increases, so does the frequency of the other con-
flict sign. A similar positive correlation was observed
for affiliative signs. Weak correlations were observed
between some pairs of affiliative and conflict signs, most
notably a negative correlation between the conflict signs
flee and hiss, and all affiliative signs, and a positive
correlation between chase and all affiliative signs except
sleep-touching.
Reported conict and affiliative signs and
reported intercat aggression
In 421 households (16.9% of surveyed households), own-
ers reported intercat aggression as a problem they were
experiencing with their cats. The mean ± SD number of
cats was slightly higher for households with reported
intercat aggression (2.90 ± 0.80; SEM = 0.04) vs house-
holds without reported intercat aggression (2.51 ± 0.71;
SEM = 0.02).
To validate that the frequency of conflict signs would
be higher in households reporting intercat aggression,
the percentage of cats in the house reported to have
intercat aggression was compared with each of the con-
flict signs. Positive correlations were observed between
the percentage of cats with intercat aggression in a
household and conflict signs: stare (rs = 0.28), chase
(rs = 0.24), stalk (rs = 0.30), flee (rs = 0.35), twitch tail
(rs = 0.28), hiss (rs = 0.40) and wail/scream (rs = 0.35).
Negative correlations were observed between the per-
centage of cats with intercat aggression and affiliative
signs, but although the correlations were statistically sig-
nificant, they were small in absolute value indicating a
weak relationship: nose-touching (rs = –0.11), allogroom-
ing (rs = –0.11), sleeping in the same room (rs = –0.10)
and sleep-touching (rs = –0.11). P values for all correla-
tions with intercat aggression were P <0.001. These cor-
relations indicate that as the percentage of cats in a
household with intercat aggression increases, the fre-
quency of conflict signs increases and the frequency of
affiliative signs decreases slightly.
Reported harmony score and conict and
affiliative signs
The harmony score for each household was compared
with each of the conflict signs. Negative correlations were
observed between the harmony score and frequency of
conflict signs: stare (rs = –0.28), chase (rs = –0.19), stalk
(rs = –0.27), flee (rs = –0.40), twitch tail (rs = –0.35), hiss
Figure 1 Initiation of conflict between cats
Table 2 Frequency of cat–cat affiliative behaviors noted in any of the cats in the multi-cat household in response to
housemate cats (n = 2492)
Affiliative behaviors Several times
a day
Daily Weekly Monthly
or less
Never
Sleep in the same room
as another cat
1440 (57.7) 749 (30.1) 182 (7.3) 64 (2.6) 57 (2.3)
Groom another cat by licking
around the head or ears
700 (28.1) 575 (23.1) 447 (17.9) 263 (10.6) 507 (20.4)
Sleep-touching with housemate cat 657 (26.4) 455 (18.3)) 418 (16.8) 380 (15.2) 582 (23.4)
Nose-touching with housemate cat 574 (23.0) 792 (31.8) 556 (22.3) 274 (11.0) 296 (11.9)
Data are n (%)
6 Journal of Feline Medicine and Surgery
(rs = –0.48) and wail/scream (rs = –0.31). The conflict signs
with a moderate effect size were flee, twitch tail, hiss
and wail/scream. Positive correlations were observed
between the harmony score and frequency of affiliative
signs: nose-touching (rs = 0.33), allogrooming (rs = 0.38),
sleeping in the same room (rs = 0.31) and sleep-touching
(rs = 0.39). P values for all correlations with intercat
aggression were P <0.001. These correlations indicate that
higher harmony scores (as perceived by the respondents)
are correlated with a decreased frequency of conflict signs
and an increased frequency of affiliative signs.
Household and cat population variables
Descriptive statistics for the household and cat popula-
tion variables that were analyzed are listed in Tables 4
and 5. Resources are listed as households providing a rec-
ommended quantity of the resource (yes) vs households
not providing an adequate quantity of resources (no)
based on the number of cats in the household. An ade-
quate quantity was defined as a quantity of litter boxes
equaling the number of cats plus one and a quantity of
food stations or bowls and scratching posts equal to the
number of cats in the house.23,24 Individual cat demo-
graphic data (vs pooled by household) and resources pro-
vided listed by number of cats in the house can be found
in Appendices 2 and 3 in the supplementary material.
Household variables (as defined in Table 4) and the
frequency of each conflict sign were compared with a χ2
test of independence. The results of the χ2 tests between
conflict signs and home size were not significant, nor
were the results comparing conflict signs and an ade-
quate quantity of resources. The observed frequencies
were not significantly different than the expected fre-
quencies, indicating that conflict signs are independent
of providing adequate quantities of resources. Adding a
new cat to the house within the past 6 months was
related to the frequency of all conflict signs except star-
ing. The observed frequencies of the conflict signs were
higher than expected frequencies for ‘daily’ and ‘several
times a day’ in households that added a cat within the
past 6 months. Table 6 presents the results of the χ2 tests.
Tables for observed vs expected frequencies of χ2 tests
can be found in Appendix 4 in the supplementary
material.
Cat population variables and the frequency of conflict
signs were compared using a Spearman’s correlation. The
strongest relationships found between cat population var-
iables and conflict signs were with the number of cats, age
of the cats (young or senior), personality (‘active and curi-
ous’ and ‘shy and sedentary’ scores) (Table 7). Although
the values in bold in Table 7 are statistically significant,
these correlations were relatively low in absolute value.
Household variables and the frequency of each affili-
ative sign were compared with a χ2 test of independence.
The results of the χ2 test between affiliative signs and
home size were not significant. Providing an adequate
quantity of litter boxes and food stations was related to
a decrease in the frequency of allogrooming, sleeping
in the same room and sleep-touching, but not nose-
touching. A relationship was also observed between
allogrooming and sleep-touching and adding a cat to
the house in the past 6 months. The observed frequencies
of allogrooming and sleep-touching were higher than
expected frequencies for ‘daily’ and ‘several times a
Table 3 Spearman’s correlation matrix among conflict and affiliative sign frequencies
Variable Conflict signs Affiliative signs
Stare Chase Stalk Flee Twitch
tail
Hiss Wail/
scream
Sleep
same
room
Nose-
touching
Allogroom Sleep-
touching
Conflict signs
Stare −
Chase 0.55 −
Stalk 0.65 0.74 −
Flee 0.51 0.66 0.65 −
Twitch tail 0.49 0.48 0.51 0.55 −
Hiss 0.45 0.42 0.45 0.55 0.55 −
Wail/scream 0.29 0.31 0.35 0.42 0.39 0.44 −
Affiliative signs
Sleep same room 0.00 0.08 0.00 −0.08 −0.07 −0.12 −0.05 −
Nose-touching 0.02 0.07 0.02 −0.06 −0.03 −0.11 −0.03 0.39 −
Allogroom −0.07 0.06 −0.04 −0.12 −0.11 −0.19 −0.05 0.51 0.58 −
Sleep-touching −0.07 0.01 −0.05 −0.16 −0.13 −0.20 −0.06 0.54 0.52 0.77 −
The critical value is 0.061 for a significance level of P <0.0025 used on a Bonferroni correction with 20 analyses per dependent variable. The
values in bold meet this significance level
Elzerman et al 7
day’ in households that added a cat within the past
6 months. Table 8 presents the results of the χ2 tests. Tables
for observed vs expected frequencies of χ2 tests can be
found in Appendix 5 in the supplementary material.
Cat population variables and the frequency of conflict
signs were compared using a Spearman’s correlation. The
strongest relationships found between cat population var-
iables and affiliative signs were with number of cats, life-
style (indoor–outdoor), sex and age of the cats, length of
time in house and personality (‘active and curious’ and
‘shy and sedentary’ scores) (Table 9). Although the values
in bold in Table 9 are statistically significant, these correla-
tions are relatively low in absolute value.
The introduction of the second cat into a two-cat
household was examined to see if there was a difference
in the frequency of conflict and affiliative behaviors in
households where ‘the introduction went well’ vs ‘the
introduction did not go well’. There was a statistically
significant relationship between the introduction de-
scription and all current conflict and affiliative signs
except chase. Table 10 presents the results of the χ2 tests.
Conflict signs occurred more frequently and affiliative
signs less frequently in households where the introduc-
tion did not go well than in households where the intro-
duction was described as going well. Tables for observed
vs expected frequencies of the χ2 tests can be found in
Appendix 6 in the supplementary material. As an exam-
ple of this relationship, Figure 2 compares the frequency
of nose-touching in households where (a) ‘the introduc-
tion went well’ vs households where (b) ‘the introduc-
tion did not go well’.
Bayesian network analysis
A Bayesian network model (Figure 3) was used to iden-
tify clusters of variables, take into account the inter-
dependence between the parameters, identify key
parameters and characterize the relationship between all
variables collected during the study. Four clusters were
identified by the Taboo algorithm: one affiliative factor;
two conflict factors; and one personality factor.
The key variables that characterized the affiliative fac-
tor were sleeping in the same room (22.1% contribution),
allogrooming (48.7% contribution) and sleep-touching
(29.2% contribution), with allogrooming being the most
important. The conflict variables can be divided into two
main clusters of signs: one characterized by fleeing
(34.7% contribution) and tail twitch (65.4% contribu-
tion); and the second characterized by stalking (40.2%
contribution) and chasing (59.7% contribution).
As indicated by the descriptive analysis, there is a
relationship between conflict and affiliative behaviors,
but the relationship is weak. The flee/tail twitch conflict
factor and the affiliative factor are directly connected to
each other indicating that frequency of fleeing and tail
twitching are predictive of the value of the affiliative fac-
tor, but the connection is not strong (overall contribution
in the network of 0.2%). No household or cat population
variable was strongly predictive of the frequency of
conflict or affiliative signs. A higher active score was
predictive of a higher affiliative factor, confirming
the relationship noted by Spearman’s correlation.
Discussion
These findings confirm that an increase in conflict
behaviors does correlate with a decrease in affiliative
behaviors for most of the included behaviors except
chase. More frequent chase might not be correlated
with a decrease in affiliative behaviors because it may
be difficult to distinguish pursuit from play-related
chase. In the sections of the survey where owners could
Table 4 Household variables (n = 2492 multi-cat
households)
Household variables n (%)
Size of house (square feet)
<1499 1031 (41.4)
>1500–3499 1307 (52.4)
3500 109 (4.4)
Prefer not to answer 45 (1.8)
Newly added cat
A new cat added to the house
in the past 6 months
Yes: 294 (11.8)
No: 2198 (88.2)
Resources
Litter boxes ⩾ number of cats plus
one
Yes: 1839 (73.8)
No: 653 (26.2)
Food stations or bowls ⩾ number of
cats
Yes: 1560 (62.6)
No: 932 (37.4)
Scratching posts ⩾ number of cats Yes: 1541 (61.8)
No: 951 (38.2)
Table 5 Cat population variables reported as a
percentage of cats in the household meeting the
description
Variable Mean (%) SD
Indoor–outdoor (vs indoor-only) 27.14 0.74
Declawed 17.58 33.24
Female 48.75 33.31
Young (<1 year) 6.09 17.94
Adult (1–7 years) 45.47 37.76
Mature (7–12 years) 32.23 34.34
Senior (>12 years) 16.21 26.79
Chronic or debilitating health issue 15.45 25.75
Living in house <6 months 5.74 17.41
Living in house 6 months to 2 years 16.75 28.64
Living in house >2 years 77.51 32.3
8 Journal of Feline Medicine and Surgery
add comments, some owners mentioned that they con-
sidered their cats chasing each other to be a play behavior
rather than a conflict-related one. Owners may struggle
to distinguish low-level conflict from light play.
Compared with the extensive numbers of behaviors
encompassed by comprehensive ethograms, such as
Stanton et al27 and Cameron-Beaumont,28 this survey
focused on a narrow subset of conflict and affiliative
behaviors. Despite the limited number of behaviors in-
cluded, there was a correlation between owner-reported
intercat aggression and the conflict and affiliative behav-
iors selected, indicating that a subset of some key behav-
iors may be able to be examined to assess the presence of
intercat conflict.
Higher harmony scores were associated with a lower
frequency of conflict signs and a higher frequency of
affiliative signs. This confirms that owners were able to
rate the overall harmony of the cat–cat relationships in
their house but may be more aware of overt conflict
signs vs subtle signs, as the highest negative correlation
Table 6 χ2 test of independence among conflict sign frequency and household variables
Variable Flee Chase Stalk Stare Hiss Twitch tail Wail/
scream
Home Size (df = 8) 6.3 3.31 4.44 5.64 4.41 8.06 8.03
P = 0.614 P = 0.914 P = 0.816 P = 0.688 P = 0.818 P = 0.428 P = 0.431
Litter boxes ⩾number of
cats plus one (df = 4)
7.59 15.42 2.71 10.6 16.12 0.82 5.79
P = 0.108 P = 0.004 P = 0.608 P = 0.031 P = 0.003 P = 0.936 P = 0.216
Food stations or bowls
⩾number of cats (df = 4)
2.66 3.85 3.74 6.58 0.98 9.15 5.08
P = 0.617 P = 0.427 P = 0.443 P = 0.160 P = 0.913 P = 0.057 P = 0.279
Scratching posts ⩾number
of cats (df = 4)
10.33 4.73 2.57 2.82 1.06 2.99 2.82
P = 0.035 P = 0.316 P = 0.632 P = 0.589 P = 0.901 P = 0.560 P = 0.588
A new cat added to the
house in the past 6 months
(df = 4)
20.43 60.77 35.03 7.33 17.44 27.81 18.81
P <0.001 P <0.001 P <0.001 P = 0.119 P = 0.002 P <0.001 P <0.001
Significance level of P <0.0025 based on a Bonferroni correction with 20 analyses per dependent variable. The values in bold meet this
significance level
df = degrees of freedom
Table 7 Spearman’s correlation among conflict sign frequency and cat population variables
Variable Flee Chase Stalk Stare Hiss Twitch tail Wail/
scream
Number of cats 0.17 0.13 0.12 0.11 0.22 0.13 0.15
Indoor–outdoor −0.01 −0.03 0.00 0.03 0.03 0.03 0.01
Declawed −0.04 −0.05 −0.04 −0.02 0.01 −0.02 0.03
Female 0.05 0.00 −0.01 0.06 0.14 0.02 −0.06
Young (<1 year) 0.02 0.14 0.07 0.03 0.00 0.00 −0.01
Adult (1−7 years) 0.05 0.16 0.10 0.05 −0.04 −0.01 0.00
Mature (7−12 years) 0.01 −0.09 −0.04 −0.03 0.09 0.06 0.03
Senior (>12 years) −0.03 −0.16 −0.09 −0.02 0.01 0.00 0.01
Chronic or debilitating health issue 0.03 −0.04 0.01 0.01 0.06 0.05 0.03
Living in house <6 months −0.07 −0.07 −0.10 −0.05 −0.01 −0.02 0.02
Living in house 6 months to 2 years 0.06 0.12 0.08 0.04 −0.01 0.01 −0.01
Living in house >2 years 0.05 0.13 0.07 0.03 0.06 0.04 0.01
‘Active and curious’ score −0.08 0.05 0.01 0.00 −0.11 −0.08 −0.06
‘Sedentary and shy’ score 0.09 0.00 0.02 0.06 0.10 0.10 0.06
The critical value is 0.061 for significance level of P <0.0025 used on a Bonferroni correction with 20 analyses per dependent variable. The
values in bold meet this significance level
Elzerman et al 9
between harmony score and a conflict sign was found
for hissing instead of the more frequent signs of stare,
chase, stalk, flee and twitch tail.
Cat population variables and conflict or affiliative
behaviors that were significantly correlated exhibited
only a weak relationship. Subtle differences emerged,
indicating that in young or adult households, conflict
may be more active and include chasing and stalking,
whereas these behaviors are less common in mature or
senior cat households.
Personality measures have been suggested as a
method to improve cat welfare by grouping compatible
cats in multi-cat households.29 In this study, ‘sedentary
and shy’ cats were more likely to flee, stare and hiss than
‘active and curious’ cats. Evaluation of feline personality
utilizing recently published models of feline personality,
either the ‘Feline Five’ described by Litchfield etal,29 or
the six dimensions described by Bennett etal,30 and the
effects of personality of conflict signs would be an inter-
esting area for further investigation.
It was surprising this survey found that providing
an adequate quantity of food, litter box and scratching
resources was not related to the frequency of conflict
signs, as providing an adequate quantity of resources
is a common recommendation for households with
intercat conflict. Other studies have found that cats have
preferences for litter box size and type, if the box was
previously used and litter type, so these factors may be
more important than the quantity of boxes provided.31–35
Other resources not examined by this survey, such as the
provision of resting locations and hiding locations, may
also be important and affect the frequency of conflict
Table 8 χ2 test of independence among affiliative sign frequency and household variables
Variable Nose-touching Allogrooming Sleep in same room Sleep-touching
Home size (df = 12) 4.35 12.77 13.55 11.1
P = 0.824 P = 0.120 P = 0.094 P = 0.196
Litter boxes ⩾number of cats plus
one (df = 4)
11.23 23.07 27.76 21.04
P = 0.024 P <0.001 P <0.001 P <0.001
Food stations or bowls ⩾number
of cats (df = 4)
12.63 23.62 22.03 34.72
P = 0.013 P <0.001 P <0.001 P <0.001
Scratching posts ⩾number of
cats (df = 4)
2.4 2.55 7.02 7.2
P = 0.663 P = 0.635 P = 0.135 P = 0.126
A new cat added to the house in
the past 6 months (df = 4)
6.39 25.11 9.43 23.97
P = 0.172 P <0.001 P = 0.051 P <0.001
Significance level of P <0.0025 based on a Bonferroni correction with 20 analyses per dependent variable. The values in bold meet this
significance level
df = degrees of freedom
Table 9 Spearman correlation among affiliative sign frequency and cat population variables
Variable Nose-touching Allogrooming Sleep in same room Sleep-touching
Number of cats 0.22 0.24 0.19 0.26
Indoor–outdoor −0.06 −0.12 −0.10 −0.11
Declawed −0.01 0.01 −0.01 0.03
Female −0.13 −0.20 −0.11 −0.19
Young (<1 year) 0.08 0.12 0.09 0.12
Adult (1−7 years) 0.11 0.09 0.05 0.08
Mature (7−12 years) −0.07 −0.10 −0.07 −0.09
Senior (>12 years) −0.09 −0.09 −0.05 −0.09
Chronic or debilitating health issue 0.00 0.00 0.01 −0.01
Living in house <6 months 0.05 0.06 0.04 0.08
Living in house 6 months to 2 years 0.05 0.03 0.01 0.04
Living in house >2 years −0.06 0.06 0.04 −0.09
‘Active and curious’ score 0.21 0.16 0.15 0.16
‘Sedentary and shy’ score −0.15 −0.15 −0.15 −0.15
The critical value is 0.061 for a significance level of P <0.0025 based on a Bonferroni correction with 20 analyses per dependent variable. The
values in bold meet this significance level
10 Journal of Feline Medicine and Surgery
behaviors, and their omission is a limitation of this
study. In contrast to the conflict behaviors, allogroom-
ing, sleeping in the same room and sleep-touching were
decreased in frequency in households providing an
adequate quantity of litter boxes and food stations.
Although house size was not related to the frequency of
these affiliative signs, providing a greater quantity of
resources may indicate more space for the cats to utilize
and therefore fewer interactions. A future study could
analyze resource allocation, space usage, and the corre-
lations with frequency and character of interactions.
Signs of conflict were most likely to occur from the
very beginning when introducing a new cat. The charac-
terization of the introduction as ‘went well’ or ‘did not
go well’ correlated with the current frequency of conflict
and affiliative signs. If the introduction ‘went well’, more
frequent affiliative signs and less frequent conflict signs
were noted than in households where the introduction
‘did not go well’. Adding a new cat to the house in the
past 6 months was related to the frequency of all conflict
signs except stare. This is consistent with the findings of
Levine et al36 that the owner’s perception of how the
Figure 2 Frequency of nose-touching behavior in households where (a) ‘the introduction went well’ vs households where (b)
‘the introduction did not go well’
Table 10 χ2 test of independence among conflict and affiliative sign frequency and introduction of the second cat into a
two-cat household (n = 1424)
Conflict behaviors Flee Chase Stalk Stare Hiss Twitch tail Wail/
scream
Introduction
characterized as ‘went
well’ or ‘did not go well’
(df = 4)
43.52 12.17 21.28 24.65 71.81 24.43 15.9
P <0.001 P = 0.016 P <0.001 P <0.001 P <0.001 P <0.001 P <0.001
Affiliative behaviors Nose-
touching
Allogrooming Sleep same
room
Sleep-
touching
Introduction
characterized as ‘went
well’ or ‘did not go well’
(df = 4)
47.93 83.36 71.97 71.19
P <0.001 P <0.001 P <0.001 P <0.001
Significance level of P <0.0025 based on a Bonferroni correction with 20 analyses per dependent variable. The values in bold meet this
significance level
df = degrees of freedom
Elzerman et al 11
initial introduction went may be predictive of the quality
of the cat–cat relationships in the house in the first year.
A future direction in the study of intercat conflict could
be to look at the evolution of conflict signs over time to
see if early conflict includes more chasing, fleeing and
hissing, and then if the conflict evolves into staring.
It is important to acknowledge the limitations of this
study. First, the survey was composed of a convenience
sample, not a random sample of cat owners. The link to
the survey was predominantly promoted by animal
experts, which likely skewed it toward owners more
knowledgeable about cat behavior and potentially more
knowledgeable about the prevention of intercat conflict.
Future studies could implement a more random sam-
pling method, or collect data to describe the demo-
graphics of the general US cat-owning households and
then restrict the data collection to certain quotas of dif-
ferent categories or weight the results to accurately
reflect the demographics of the general population. An
additional option would be to ask questions in the sur-
vey to try and assess the owner’s knowledge about cats
to determine if knowledge correlated with conflict or
measures used to prevent and address conflict.
Second, the survey relied on the subjective reporting
of behaviors by owners and did not include observations
of the behaviors by an expert. However, online surveys
have been shown to be a reliable method for behavioral
data collection in both dogs and cats.37,38 This provides
access to a larger sample population than an observation-
based study and allows for screening of factors for fur-
ther smaller, observation-based studies.
The third limitation was the limited range of behav-
iors and resources included in the survey. This was done
intentionally to simplify the survey and reach a wider
sample of cat owners than would be possible with an in-
depth ethological study. Both the second and third limi-
tations could be addressed in future studies by directly
observing a subsample of the surveyed cats to assess the
reliability of owner observations and reporting, as well
as compare results of a more complete ethological obser-
vation with the limited range of behaviors.
The fourth limitation was that questions about cats’
relatedness or identification of dyads within the house-
hold were omitted owing to concerns about survey
length and complexity. As many owners acquire their
cats from rescue shelters, or as strays, the true related-
ness of these cats may not be known, even for cats
acquired at the same time. Identification of dyads within
the household requires that the owner be knowledgeable
about feline behavior, as well as accurately characterize
the relationships within the household, so these factors
seemed difficult to determine by online survey with cer-
tainty. However, relatedness and dyad groupings have
been well documented to affect the frequency of affilia-
tive signs and relationships between cats.14,39
The final limitation of the study was the decision to
collect and analyze data on a household level instead of
an individual cat basis. The number of cats in the house
was found to correlate with the frequency of conflict and
affiliative signs, but it is difficult to determine if this
reflects a true increase in conflict as the number of cats in
the house increases or if it is an artifact of the owner
Figure 3 Bayesian network model for the relationship between household and cat population variables. Variables with less
than a 20% contribution were removed
12 Journal of Feline Medicine and Surgery
summing together the behaviors of all of the cats in the
house. The average number of cats in households with
reported intercat aggression was slightly higher at 2.90 vs
2.51 for households with no reported intercat aggression,
so although it is difficult to determine the extent, a rela-
tionship likely exists between the number of cats in the
house and the frequency of conflict behaviors.
Conclusions
This study is the first large-scale online survey used
to obtain frequencies of conflict and affiliative behaviors
in US households, and compare them with factors linked
to the cats or the home settings. Affiliative signs were
observed more frequently than conflict signs. Higher
harmony scores (as perceived by the respondents) were
correlated with less frequent conflict signs and more fre-
quent affiliative signs. Hissing occured on a daily or
more frequent basis in 18% of households. Physical con-
tact between cats was observed at least daily in around
half of the multi-cat households. The study confirmed
that feline relationships are influenced by the behaviors
displayed at the initial introduction, but other household
factors and cat population variables were not strongly
predictive of the frequency of affiliative and/or conflict
signs. In multi-cat households, 73.3% of owners noted
conflict signs from the very beginning when introducing
the cats. The recent addition of a new cat to the home
was correlated with the frequency of conflict signs.
Several trends emerged for interesting areas of future
research, including the evolution of conflict and affilia-
tive behaviors over time, the effects of personality and
age on these behaviors, and the impact of conflict or
affiliative relationships on the health and welfare of cats.
Acknowledgments The authors are grateful to all the cat
owners who took the time to give us insight into their cats’
lives, and the friends and colleagues who shared the survey
through Facebook and other channels to help us reach so many
respondents.
Author note Some of the results were presented as an
abstract (<250 words) and a poster presentation at the Euro-
pean Veterinary Congress of Behavioural Medicine and Animal
Welfare in Berlin, Germany, 2018.
Supplementary material The following files are available
online:
Appendix 1: Feline survey questions, Elzerman etal
Appendix 2: Cat demographic information per cat in multi-cat
households
Appendix 3: Resources provided listed by number of cats
Appendix 4: χ2 results (Table 6 Conflict signs)
Appendix 5: χ2 results (Table 8 Affiliative signs)
Appendix 6: χ2 results (Table 10 Introduction of cats)
Conict of interest This study was funded by Ceva Santé
Animale, which employs Alexandra Beck and Jean-François
Collin. Theresa L DePorter provides regular consulting ser-
vices for Ceva Santé Animale, and Ashley L Elzerman is
currently undertaking a residency in veterinary behavior
sponsored by Ceva Santé Animale.
Funding Funding for this study was provided by Ceva Santé
Animale.
Ethical approval This work did not involve research on ani-
mals and the survey did not collect any personally identifiable
data or information on sensitive subjects so is exempt from the
requirement of institutional review board or ethical committee
review.
Informed consent The survey was anonymous; no personal
information was collected from the respondents and comple-
tion of the survey implied consent. No animals or humans are
identifiable within this publication, and therefore additional
informed consent for publication was not required.
ORCID iD Ashley Elzerman https://orcid.org/0000-
0001-6884-9244
Theresa DePorter https://orcid.org/0000-0003-3710-8915
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