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\begin{document}

\title{Heterozygosity is low where rare colour variants in wild carnivores
prevail.}



\author[1]{Laura Tensen}%
\author[1]{Klaus Fischer}%
\affil[1]{Universität Koblenz}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Coat colour and pattern are a distinguished feature in mammalian
carnivores, shaped by climatic cycles and habitat type. It can be
expressed in various ways, such as gradients, polymorphisms, and rare
colour variants. Although natural selection explains much of the
phenotypic variation found in the wild, genetic drift and heterozygote
deficiency, as prominent in small and fragmented populations, may also
affect phenotypic variability through the fixation of recessive alleles.
The aim of this study was to test whether rare colour variants in the
wild could relate to a deficiency of heterozygotes, resulting from
habitat fragmentation and small population size. We present an overview
of all rare colour variants in the order Carnivora, and compiled
demographic and genetic data of the populations where they did and did
not occur, to test for significant correlations. We also tested how
phylogeny and body weight influenced the presence of colour variants
with phylogenetic generalized linear mixed models (PGLMMs). We found 40
colour-variable species and 59 rare colour variants. In 17 variable
phenotypic populations for which genetic diversity was available, the
average AR was 4.18, HO = 0.59, and HE = 0.66, and FIS = 0.086. We found
that variable populations displayed a significant reduction in
heterozygosity and allelic richness compared to non-variable populations
across species. We also found a significant negative correlation between
population size and inbreeding coefficients. Therefore, it is possible
that small effective size had phenotypic consequences on the extant
populations. The high frequency of the rare colour variants (averaging
20\%) also implies that genetic drift is locally overruling natural
selection in small effective populations. As such, rare colour variants
could be added to the list of phenotypic consequences of inbreeding in
the wild.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Phenotypic variation occurs throughout the animal kingdom, and has been
widely used to investigate evolutionary processes such as balancing
selection and sympatric speciation (McLean \& Stuart-Fox, 2014). Coat
colour and pattern, a type of phenotypic variation, have been of
particular interest to evolutionary biologists due to their traceability
in the wild (McKinnon \& Pierotti, 2010). They are a prominent feature
in mammalian carnivores, shaped by environmental factors such as past
climatic cycles and habitat type (Eizirik et al., 2010). Coat colour
variation can be expressed in various ways, such as gradients,
polymorphisms, and rare colour variants (Figure 1). Colour gradients
occur in many species, in which intermediate phenotypes or clinal
variation occur along latitudal gradients in response to changing
vegetation types (Taylor et al., 1990; McLean \& Stuart-Fox, 2014). A
famous example of ecogeographical habitat matching is the Gloger's rule,
which describes how variation in coloration relates to climatic
gradients, in which animals are more pigmented towards the equator
(Caro, 2005; Delhey, 2017). It has been illustrated in, among many other
taxa, hog-nosed skunks \emph{Conepatus leuconotus} (Ferguson et al.,
2022) and striped skunks \emph{Mephitis mephitis} (Walker et al., 2023),
which have reduced whiteness towards Central America, related to canopy
cover. Furthermore, reddish variants are more common in open dry
habitats, whereas grey and black variants are more often associated with
wet areas and tropical forests respectively (Da Silva et al., 2016;
2017). Complex coat patterns, such as spots or rosettes, are more
frequently found in arboreal carnivores living in densely forested
areas, whereas those with uniform coats more often occur in open
habitats (Allen et al., 2011).

When clearly distinguished coat variations are established within a
species range, albeit at different frequencies, it is often referred to
as colour polymorphism (Roulin, 2004). A species is considered
polymorphic when individuals in a population display one or several
colour variants that are genetically inherited and which expression is
unrelated to the environment, age, sex or body condition (Buckley, 1987;
Roulin, 2004). Occasional rare colour variants, that tend to be
restricted to one population, are commonly not yet considered
polymorphism at the species level. In the wild, rare colour variants are
normally lost through fixation unless they are maintained through a
rare-morph advantage or heterozygote advantage in stable populations
(Gray \& McKinnon, 2007). They may become abundant in populations if
they have a selective benefit, such as for thermoregulation, sexual
signalling, warning, or camouflage (Caro \& Mallarino, 2020). Colour
polymorphism may also face disruptive selection in different
microhabitats, due to which spatial or temporal niche segregation can be
observed in the same area (Nijhawan et al., 2019; Graipel et al., 2014;
Da Silva et al., 2017). As such, new traits may allow populations to
occupy new niches and facilitate range expansion (Forsman et al., 2008).
Colour morphs could also be subjected to strong sexual selection, which
can lead to assortative mating (Pryke \& Griffith, 2007; Wellenreuther
et al., 2014). Over time, this may lead to a speciation event, which
implies that colour polymorphic species may represent incomplete
speciation (Ford, 1945). In bird species, it was illustrated that colour
polymorphic species speciate more rapidly and give rise to monomorphic
daughter species (Hugall \& Stuart-Fox, 2012).

Although natural selection, which may vary within a species' geographic
range, explains much of the phenotypic variation found in the wild (Da
Silva et al., 2016), genetic drift and heterozygote deficiency, as
prominent in small populations, may also affect phenotypic variability
through the fixation of recessive alleles (Eizirik et al., 2010). It has
been suggested that recent changes in coat colour or pattern may have
resulted from population bottlenecks, caused by habitat fragmentation
and human-induced mortality (Kubala et al., 2020). Habitat fragmentation
and human-induced mortality reduce the effective size of animal
populations and increases spatial isolation, which can lead to the
erosion of genetic variation (Templeton et al., 1990). Specifically,
inbreeding and genetic drift could result in reduced heterozygosity and
allelic richness in isolated populations, which may increase the
expression of rare alleles (Reed \& Frankham, 2003). In other contexts,
phenotypic change in small and isolated populations has already been
confirmed, such as fluctuating asymmetry in brown bears \emph{Ursus
arctos} (Loy et al., 2021) and bone deformities in wolves \emph{Canis
lupus} (R\selectlanguage{ngerman}äikkönen et al., 2009). It is therefore possible that the
occurrence of rare colour variants may also relate to small effective
population sizes. Indeed, there is some evidence that genetic drift has
increased the occurrence of rare colour variants in wild carnivores,
such as black tigers \emph{Panthera tigris}(Sagar et al., 2021) and red
leopards \emph{P. pardus} (Tensen et al., 2022). At least in these
cases, it is possible that small effective size had phenotypic
consequences on the extant populations.

The prerequisite for colour polymorphism is genetic variation through
point mutations, or base pair deletions and duplications, which can
evolve in allopatry or sympatry, and through hybridization (Roulin,
2004). As illustrated in the Figure 1, many different loci and genotypes
can underlie colour variants (Lyons et al., 2005a), and they can be
inherited via dominant or recessive genes (Anderson et al., 2009).
Important pathways involved with colour variation are pigment-type
switching genes such as in the Melanocortin 1 receptor~(MC1R) and Agouti
signalling protein~(ASIP) (Kaelin \& Barsh, 2013). Typically, associated
mutations are recessive and need to occur in homozygosity to affect the
phenotype (Schneider et al., 2012). Due to recessive inheritance,
increased frequencies of colour variants in captive-bred animals are
normally achieved by establishing some level of inbreeding, increasing
the level of homozygosity (Cieslak et al., 2011). For instance, many
domestic cat breeds have originated from selecting missense point
mutations with an autosomal~recessive~mode of inheritance in the
tyrosinase (TYR) and tyrosinase-related protein 1 (TYRP1)
(Schmidt-Küntzel et al., 2005), which is related to the albinism pathway
(Lyons et al., 2005b).~Therefore, rare colour variants in the wild may
also be associated with the expression of recessive alleles in small and
fragmented populations.

The aim of our study was to test whether colour variants in carnivores
species could relate to a deficiency of heterozygotes in the wild.
Although potential selective drivers cannot be ruled out, we consider
that the occurrence and frequency of rare colour variants are likely
associated with the increased expression of recessive alleles in small
and fragmented populations. Specifically, we test the following
hypotheses: (i) populations in which rare colour variants occur have a
lower allelic richness and higher heterozygote deficiency than
populations in which they are absent, (ii) rare colour variants occur
more frequently in areas that suffer from habitat fragmentation, and
(iii) rare colour variants occur more frequently in small populations.
We tested these hypotheses on mammalian carnivores, because they are
particularly rich in coat colour and patterns (Eizirik \& Trindade,
2021). Furthermore, carnivores are vulnerable to anthropogenic habitat
loss, due to their large area and food requirements, and low population
densities and reproductive rates, which particularly holds true for
large carnivores (Ripple et al., 2014). We here present an overview of
all rare colour variants in the order Carnivora that have been reported
in the wild, and compile data on genetic processes underlying these
natural occurrences. By this means, we hope to gain more insights into
the relationship between phenotypic variation in the wild and their
underlying causes in small and fragmented populations.

\textbf{Methods}

\emph{2.1 Literature review}

We included all rare colour variants that have been reported in
scientific literature or online papers over the past five decades (from
1970 to 2023) and tried to retrieve genetic data from the population
where the colour variant occurred. We conducted common Google and
scientific web searches (e.g. Google Scholar and Web of Science) to find
rare colour variants. We define a rare colour variant as ``a distinct
phenotypic form of coat colour or pattern that typically occurs in a
specific region and at low frequencies''. To the contrary, colour
polymorphism is described as phenotypic variation that is well
established throughout a species' range (Wildtype phenotypes), albeit at
different frequencies. Hence, we do not consider a rare colour variant
to be polymorphism at the species level. Although rare colour variants
may become of adaptive significance in the future, counting them as
polymorphism in this study may distort the correlation we make with
genetic drift and homozygote access. We included all land-living
carnivores and only those species for which scientific publications with
photographic evidence were available.

We used some of the following search strings: carnivores OR Carnivora OR
family name (of all 12 Carnivora families) AND colour morph OR anomalous
coloration OR phenotypic variation OR leucistic OR melanistic.
Scientific papers were also drawn from the reference list of the
articles that were found. We did not list a colour variant twice (i.e.,
from different locations), but instead included the best scientifically
documented case for each species. We acknowledge that our approach is
not holistic and prone to errors. For instance, camera traps can cause
biases in human perception and are not always able to tell the
difference between colour variants such as pseudomelanism versus
intraspecific coat variation or seasonal moulting, or between leucism or
albinism (Olson \& Allen, 2019). Albinism is a recessively inherited
disorder that occurs throughout all taxa, whereas leucism can have
adaptive significance (Olson \& Allen, 2019). Because albinism in a
species does not mean the presence of colour variation in a population,
we have excluded this from our dataset. Overlooking or mislabelling
colour variants will most likely not affect our overall results, which
aims to find a link between rare phenotypes and genetic diversity.

\emph{2.2 Parameters}

We also searched the scientific literature to find a possible link
between the colour variation in populations and a possible cause of
their frequency, such as a selective advantage, population bottleneck,
or heterozygote deficiency. Because the latter is merely speculative, we
also tried to retrieve genetic data from the same population. In total,
we noted for each colour variant: the type (e.g. dilution, melanism,
leucism), geographic location, speculated cause, gene mutation,
frequency, allelic richness (A\textsubscript{R}), observed
(H\textsubscript{O}) and expected (H\textsubscript{E}) heterozygosity,
and inbreeding coefficient (F\textsubscript{IS}), when such data was
available. All genetic papers were based on microsatellites, with a
minimum of 5 loci. When F\textsubscript{IS} values could be retrieved,
we compared these to the body size, population size, and level of
habitat fragmentation in the area where the colour variable population
occurs, to draw potential correlations. Because exact population sizes
are often difficult to retrieve, they were divided into categories
(\textless{} 50, \textless{} 250, \textless{} 1000, \textgreater{} 1000
adult individuals), derived from literature. The level of habitat
fragmentation was divided into categories (high, medium, low) based on
metrics provided by Crooks et al. (2017).

\emph{2.3 Statistical analysis}

To calculate statistical correlations and differences between
populations at the 5\% level of significance, we performed
Kruskal-Wallis H and Wilcoxon rank tests for multiple groups, and
Mann-Whitney U-tests for two groups, using R (R Core Team). We also
compared numerical data of colour variable populations to non-variable
conspecifics that are less constrained by population and / or habitat
size. In our analyses, the dependent variable (genetic diversity) and
independent variables (body size, population size, and level of habitat
fragmentation) were compared. However, because the species included in
our study are mainly found in three different taxonomic families, the
data lacks independence, due to which we calculated the difference
between variable and non-variable populations for each metric for each
species, assuming that what happens within each species in terms of
population divergence is independent of what is happening in other
species. From this difference data for each species we calculated 95\%
Confidence Intervals (CI) with an independent T-test to determine
whether the mean difference was different from zero. This was done for
allelic richness, expected heterozygosity, and inbreeding coefficients.
All significant correlations were visualized with ggplot2 in R (Wickham
et al., 2016).

We also tested how body weight influenced colour variants in species
with a phylogenetic generalized linear mixed models (PGLMMs), which
estimates regression coefficients for binary and continuous data while
incorporating interspecific relatedness (Ives \& Halmus, 2011). A
phylogeny and data frame of 70 carnivore species (Datafile S1) were
adopted from Diniz-Filho \& Tôrres (2002), to which colour variant
presence / absence data were added (Datafile S2). We evaluated PGLMMs in
`phyr' 1.0.3 R package (Li et al., 2020), to test whether body weight
(log-transformed) was associated with the presence of colour variants.
Log transformation~normalizes the strong right skew in metrics, which is
recommended for comparative approaches (Ives \& Garland, 2014). Because
species only occur once as tips of the phylogeny, we used the
pglmm\_compare() function. Range size (log-transformed), and
phylogenetic covariance were set as a random factor in our models, and
results were presented with standard error (SE). We calculated
R\emph{\textsuperscript{2}} for PGLMM models using the package `rr2'
1.0.2 (Ives, 2019).

\textbf{Results}

We found a total of 40 colour-variable species and 59 rare colour
variants (Figure 2). Rare colour variants were primarily found in the
Felidae (N = 20), Mustelidae (N = 15) and Canidae (N = 11) families.
Hyaenidae was the only carnivore family for which no colour variants was
detected. Twenty of the colour variants were described as leucistic
(34\%), and seventeen as melanistic (29\%). The average frequency of
colour variants was 20\% in the population where it occured, based on
data from 16 publications.

\emph{3.1 Genetic parameters}

Twenty publications speculated about the underlying cause of the colour
variant frequency, of which 10 papers hinted towards heterozygote
deficiency (e.g. due to bottleneck, inbreeding, genetic drift), while 8
papers presumed a selective benefit. Three other papers speculated
hybridization to be the underlying cause. In 17 variable phenotypic
populations there was data on genetic diversity available from the
population or region containing the colour variant, regardless of their
frequency. In these populations, the average A\textsubscript{R} was
4.18, H\textsubscript{O} = 0.59, and H\textsubscript{E} = 0.66 (Table
1). This means that we found an overall heterozygote deficiency, with an
average F\textsubscript{IS} of 0.086 (-0.05--0.19) across all colour
variable carnivores (the full dataset is available in Table S1). In
non-variable phenotypic populations, the average A\textsubscript{R} was
5.09, H\textsubscript{O} = 0.63, H\textsubscript{E} = 0.66, and
F\textsubscript{IS} = 0.046 in non-variable populations (Table S2). In
large and stable leopard populations, the number of alleles normally
ranges between 5 to 20 per locus, and heterozygosity levels are usually
high (0.70-0.75) with no or little heterozygote deficiency (0.0-0.05)
(Spong et al., 2000), which can serve as a proxy for other carnivore
species. When comparing the pairwise increase of heterozygote deficiency
from non-variable populations to variable populations within species, we
found a significant directional deviation from zero (T-test, t value =
1.88; P = 0.034). This implies an increase of heterozygote deficiency in
populations where rare colour variants occurred (Figure 3a). We also
found a negative deviation from zero for allelic richness (T-test, t
value = -2.23; P = 0.016), implying that variable populations have
reduced genetic diversity Figure 3b).

\emph{3.2 Demographic parameters}

In an attempt to correlate heterozygote deficiency to demographic data,
we have retrieved the population size and level of habitat fragmentation
of colour variable populations (Table 1). The values of 19 variable
populations were statistically compared to similar data of non-variable
populations that were less constrained by population or habitat size (an
overview of all tests and results are available in Table S3). Low
population size generally yielded higher F\textsubscript{IS} values, but
this relation was non-significant (Kruskal--Wallis;
X\textsuperscript{2}= 7.25; P = 0.06). The correlation was tested
significant when comparing threatened populations (\textless{}250
individuals) to non-threatened populations (\textgreater{}250
individuals), in which threatened populations had higher inbreeding
coefficients (Mann-Whitney; W = 194; P = 0.01; Figure 3c). Although we
found a weak positive correlation, a higher level of habitat
fragmentation did not yield significantly higher inbreeding coefficients
(Kruskal--Wallis; X\textsuperscript{2} = 4.96; P = 0.08). We did find
that body weight negatively correlates with allelic richness (Spearman;
R = 0.36; P = 0.04; Figure 3d) and expected heterozygosity (Spearman; R
= 0.40; P = 0.02). To account for phylogenetic nonindependence of our
species data due to shared ancestry, we used a PGLMM to test the effect
of body weight on the presence of rare colour variants. The results
suggests that variants do not vary in their occurrence in relation to
body weight (PGLMM coefficient: 0.069\selectlanguage{english} +-\selectlanguage{english} 0.088 SE, Z = 0.579, P = 0.42).
The intercepts for species were tested significant, meaning that close
relatives had similar values (PGLMM coefficient: 0.93\selectlanguage{english} +-\selectlanguage{english} 0.45 SE, Z =
2.04, P = 0.04). Thus, the results imply an overall phylogenetic effect,
and no (or little) body weight effect. The R\textsuperscript{2} value
suggests that body weight alone explains approximately 15.9\% of colour
variation in populations, whereas this is reduced to 11.5\% if phylogeny
is incorporated.

\textbf{Discussion}

The aim of our study was to test whether rare colour variants in the
wild may relate to a deficiency of heterozygotes, possibly resulting
from habitat fragmentation and reduced effective population size. We
found that variable populations displayed a significant reduction in
heterozygosity and allelic richness compared to non-variable populations
across species. We also found a significant correlation between
population size and inbreeding coefficients, in which small populations
suffer from larger heterozygote deficiencies. This supports the
hypothesis that heterozygote deficiency in small populations might have
an influence on the prevalence of rare colour variants due to the
expression of rare recessive alleles. The high frequency of the rare
colour variants in some populations also implies that genetic drift is
occurring in small populations.

In 19 variable populations for which we found genetic data, there was
some indication of a heterozygosity deficiency. This was particularly
evident in charismatic species such as black tigers (Kolipakam et al.,
2019), red leopards (Tensen et al., 2022), and white cougars \emph{Puma
concolor} (Saranholi et al., 2017). Non-variable populations of many
species also suffered from heterozygote deficiencies due to demographic
losses, but the difference with non-variable populations was nonetheless
profound in some cases and significant across species. The direct
comparison between variable and non-variable populations remains
challenging, because stochastic events or loss of rare variants will
interact with selective pressures (Caro \& Mallarino, 2020).
Furthermore, as our data is based on literature, we were not always able
to include the populations of choice or retrieve historic frequency and
presence / absence data. Studies with different sample sizes were also
compared, which introduces the risk of bias, and the PGLMM showed an
influence of phylogeny on colour variant presence. Despite these
limitations, the loss of genetic diversity in populations where rare
colour variants prevail is remarkable, and adds to the body of knowledge
on the phenotypic effects of inbreeding in wild populations. The level
of habitat fragmentation did not appear to influence the presence of
rare colour variants, possibly because our dataset did not allow for
enough fine-scale detail. The relationship between habitat fragmentation
and genetic diversity remains indisputable (Templeton et al., 1990;
Keyghobadi, 2007; Lino et al., 2019), and has been linked to rare colour
variants before, such as for Glacier and Spirit bears \emph{Ursus
americanus} in Alaska (Lewis et al., 2020), where fjords act as barriers
to gene flow. Likewise, spatial variation appears to be the primary
driver of colour morph variation in dragon lizard \emph{Ctenophorus
decressi} in Australia (McLean et al., 2015).

Another effect of geographic dispersal barriers is that local effective
population sizes are smaller, increasing the effect of genetic drift in
relation to migration and selection as an evolutionary force (Runemark
et al., 2010). A striking example of genetic drift in small populations
is the tiger population in the isolated Similipal tiger reserve, India,
where pseudomelanism reached a frequency of 78\%, and in parallel an
exceptionally low heterozygosity was observed (H\textsubscript{O} =
0.28) (Sagar et al., 2021). It has been revealed that nearly 14\% of
homologous alleles, both neutral and damaging, were fixed in small and
isolated tiger populations (Khan et al., 2021). India has a particularly
high degree of habitat fragmentation, and 56 mammalian colour variants
have been recorded between 1886 and 2017 (Mahabal et al., 2019). A
similar situation appears to occur in the highly fragmented Atlantic
rainforest, where melanism in jaguars \emph{Panthera onca} is
increasing, reaching frequencies of up to 38\% (Haag et al., 2010a;b). A
leucistic cougar has been spotted in the same area, which was speculated
to be a sign of inbreeding (Cronemberger et al., 2018). Likewise, in
Eurasian lynx \emph{Lynx lynx} a wide range of natural coat patterns
exists, associated with geographic location (Darul et al., 2022), which
frequencies have recently altered due to population bottlenecks
resulting from habitat fragmentation (Kubala et al., 2020). In South
Africa, red leopards are spreading in northern provinces, where
effective population sizes are low due to high local population offtake
(Tensen et al., 2022). Even though this population is not isolated, high
population turnover has led to natal philopatry, increasing the overall
relatedness in the population and chances of recessive alleles being
expressed.

We expect this trend to hold true for many other wild carnivores, in
which genetic drift is locally overruling natural selection in small
effective populations. For instance, phenotypic differences in Arctic
foxes \emph{Vulpes lagopus}reflect distinct selective advantages across
their range, however, a recent increase of blue~homozygotes~in Arctic
foxes \emph{Vulpes lagopus} Scandinavia is considered the result of
genetic drift (Tietgen et al., 2021). Furthermore, it appears that
melanistic and leucistic genets \emph{Genetta genetta} and mongoose
\emph{Herpestes ichneumon}are much more common in Europe, where they
have been introduced, compared to their natural range in Africa (Delibes
et al., 2013; Descalzo et al., 2013). Although it has been suggested
that the phenotypic variation in Spain may relate to a relaxation of
selective pressures (Descalzo et al., 2013), the founder effect has been
overlooked. Likewise, melanistic wolves are common in Yellowstone
National Park, United States, where they were introduced from in and
around Jasper National Park, Canada, where melanism is also common
(Hedrick et al., 2014). Melanism has reached frequencies of
approximately 50\% in these areas, which is unlikely to relate to a
concealment advantage (Dekker, 2009) or reproductive fitness (Stahler et
al., 2013), and may instead be the result of genetic drift. The binary
appearance of melanistic wolves, as well as coyote, across their range
(Hinton et al., 2022) supports the notion that neutral, rather than
selective, processes are at play. In European badgers \emph{Meles meles}
, a population has recently established in central Norway where leucism
now seems abundant, likely as a result of a founder effect (Hofmeester
et al., 2021). The same could be the case for a leucistic coyote
\emph{Canis latrans} in Costa Rica (Arroyo-Arce et al., 2019), and
melanistic bobcats at the periphery of their range (McAlpine, 2021),
although this is speculative.

A general trend of an increase in melanistic morphs towards the tropics
and leucistic morphs towards the poles, as predicted by the Gloger's
rule (Caro \& Mallarino, 2020), could not be detected in this study. For
instance, melanistic bobcats \emph{Lynx rufus} are readily found in the
northern tip of the United States (McAlpine, 2021) and Florida (Regan \&
Maehr, 1990), and a melanistic Canada lynx was found in the northern tip
of Alaska (Jung, 2023). Although the Canada lynx appears to be a single
sighting and is likely to suffer a selective disadvantage due to
increased visibility, melanistic bobcats have become fairly common
(McAlpine, 2021). When looking at the map of colour morphs, we can also
see the presence of many leucistic animals in the tropical forests of
Mid-America, such as leucistic coyote \emph{Canis latrans} ,
coati\emph{Nasua narica} , and neotropical otters (\emph{Lontra
longicaudis} ). Furthermore, leucistic tayra \emph{Eira barbara} have
become abundant in the Guyana shield in Brazil (Mendes Pontes et al.,
2020). This may further strengthen the theory that rare colour variants
are a random, sometimes maladaptive, occurrence, instead of a selective
driver in response to spatially or temporally variable environments
(Svensson, 2017). When not influenced by genetic drift, rare colour
variants can only be maintained through balancing or frequency-dependent
selection (Gray \& McKinnon, 2007), which seems unlikely for the
before-mentioned examples. Furthermore, colour variants will only be
fixed throughout the range of a species when they have a selective
benefit, at which stage they are normally referred to as colour
polymorphism (Svensson, 2017). Ecogeographical rules, as such, are more
likely to imply to the latter category, and not to rare colour variants
that seems more selectively neutral or disadvantageous.

It has previously been suggested that leucism mainly prevails in the
Mustelidae, based on the number of published studies (Olson \& Allen,
2019), for which we found no evidence during this study. In general, it
appeared that colour variants are most abundant in the Felidae family
(37.5\% of cat species), representing 20 out of 59 colour variants
recorded in this study. To compare, the proportion of species that
contain rare colour variants is 23\% in Canidae, 15\% in Mustelidae,
12.5\% in Ursidae, and 3\% in Herpestidae and Viverridae (Table S1).
Therefore, the high frequency of variable cats can be considered
somewhat unusual. Furthermore, the PGLMM has illustrated a strong effect
of phylogeny on the presence of colour variants, implying shared
inherited traits among closely related taxa. Because phenotypic
variation is a driver of genetic variation, it is possible that Felidae
species contain more protein polymorphisms associated with pigment-type
switching genes (Andersson \& Purugganan, 2022). A similar range of coat
patterns is observed in domestic cats, suggesting it is a conserved
mechanism that can be altered by selection (Kaelin et al., 2012). Wild
cat species exhibit the greatest diversity in colours and spots of all
terrestrial carnivores, likely because they are more reliant on
camouflage compared to for instance canids, which rely more on the chase
(Schneider et al., 2012; Darul et al., 2022). However, it is worth
noting that even though polymorphisms in wild canids are rare, domestic
dogs show more phenotypic variation than any other domesticated animal
(Kaelin \& Barsh, 2013). This is due to genome-wide slippage mutations
and pure repetitive sequences in domestic dogs, which occur at a much
greater rate than in other carnivores, likely resulting from strong
population bottlenecks associated with the origin of dog-breeds (Laidlaw
et al., 2007).

Although we found that body size is generally a poor predictor of colour
variation in populations, we did find a correlation between allelic
richness and body weight. Furthermore, a high proportion (33\%) of the
colour variants occurred in large carnivore species (\textgreater{}10
kg), even though they have a low species diversity (representing 18\% of
all carnivore species). For instance, six of the listed colour variants
were found in large cats (Panthera \emph{spp} .), of which only five
species exist. The potential bias towards large carnivores could also
partially result from the fact that they are more extensively studied
and more easily observed (Tensen, 2018). They also suffer more from
demographic and genetic deterioration compared to medium-sized and small
predators, based on relatively low effective population sizes (Lino et
al., 2019). Even though we found no significant trend that apex
predators contain more colour variants, this was previously found in
birds. While polymorphisms occur in approximately 3.5\% of all bird
species, an estimated 30\% of raptorial species are known to be
polymorphic (Hugall \& Stuart-Fox, 2012). In birds, colour polymorphism
was associated with accelerated speciation in avian apex predators, due
to a larger niche breath, consistent with theoretical models where
speciation is driven by the fixation of one or more morphs (Hugall \&
Stuart-Fox, 2012). Polymorphic bird species also tended to be younger
than monomorphic species, with shorter phylogenetic branches, which can
further increase speciation rates (Gray \& McKinnon, 2007; Forsman et
al., 2008). For future research, it would be interesting to test whether
this holds true for polymorphic carnivores as well.

In conclusion, we have collected some compelling evidence that
heterozygote deficiency might contribute to the prevalence of rare
colour variants in the wild, due to the expression of recessive alleles
following reduction in effective population size. As such, it can be
added to the list of phenotypic consequences of population bottlenecks
in the wild, alongside fluctuating asymmetry and bone deformities.
Further investigation of phenotypically variable populations is needed
to determine the genetic basis and the adaptive and evolutionary
significance of rare colour variants to help preserve genetic diversity.
Ideally, a more comprehensive future study should directly sample,
genotype, and compare variable and non-variable populations of species.
With an increase in genomic studies, we may also gain more insights into
random processes versus directional selection in small and fragmented
populations, and their relationship to colour variation in the wild.

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\textbf{Author contribution}

L.T. generated data, designed the methods, and wrote the paper. K.F.
supervised the research and reviewed the paper.

\textbf{Competing interests}

The authors declare no competing interests.

\textbf{Data Availability Statement}

All data generated or analyzed during this study are included in this
published article and its supplementary information files.

Table 1. Demographic and genetic parameters of colour variable (VAR) and
non-variable (NON) carnivore populations. Population sizes (PS) were
divided over four categories: 1 = \textless{} 50 adult individuals, 2 =
\textless{} 250 individuals, 3 = \textless{} 1000 individuals, and 4 =
\textgreater{} 1000 individuals. The level of habitat fragmentation (HF)
was divided in three categories: 1 = high, 2 = medium, 3 = low.
Demographic and genetic parameters are retrieved from various sources,
which are listed in Supplementary Table S2.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& \emph{Demographic parameters} & \emph{Demographic parameters} &
\emph{Demographic parameters} & \emph{Genetic parameters} &
\emph{Genetic parameters} & \emph{Genetic parameters} & \emph{Genetic
parameters}\tabularnewline
\midrule
\endhead
\textbf{Species\textsuperscript{*1}} & \textbf{PS} & \textbf{Region} &
\textbf{HF} & \textbf{A\textsubscript{R}\textsuperscript{*2}} &
\textbf{H\textsubscript{O}} & \textbf{H\textsubscript{E}} &
\textbf{F\textsubscript{IS}}\tabularnewline
Leopard \textsuperscript{VAR} & 2 & North West, South Africa & 2 & 5.7 &
0.54 & 0.66 & 0.18\tabularnewline
Leopard \textsuperscript{NON} & 4 & Selous, Tanzania & 3 & - & 0.77 &
0.84 & 0.06\tabularnewline
Tiger \textsuperscript{VAR} & 2 & Assam, India & 1 & 4.05 & 0.71 & 0.82
& 0.14\tabularnewline
Tiger \textsuperscript{VAR} & 3 & Western Ghats, India & 1 & 4.09 & 0.70
& 0.83 & 0.16\tabularnewline
Tiger \textsuperscript{NON} & 2 & Terai Arc, Nepal & 1 & 3.51 & 0.54 &
0.61 & 0.11\tabularnewline
Jaguar \textsuperscript{VAR} & 1 & S\selectlanguage{ngerman}ão Paulo, Brazil & 1 & 7.19 & 0.68 &
0.73 & 0.07\tabularnewline
Jaguar \textsuperscript{NON} & 4 & Amazon, Brazil & 3 & 9.38 & 0.67 &
0.76 & 0.11\tabularnewline
Lion \textsuperscript{VAR} & 2 & Reserves South Africa & 2 & 4.79 & 0.55
& 0.71 & 0.18\tabularnewline
Lion \textsuperscript{NON} & 4 & Okavango, Botswana & 3 & 5.88 & 0.65 &
0.69 & 0.06\tabularnewline
Bobcat \textsuperscript{VAR} & 4 & New Brunswick, Canada & 3 & 4.04 &
0.66 & 0.68 & 0.03\tabularnewline
Bobcat \textsuperscript{NON} & 4 & Central United States & 2 & 6.14 &
0.75 & 0.78 & 0.04\tabularnewline
Cougar \textsuperscript{VAR} & 1 & Rio de Janeiro, Brazil & 1 & 5.71 &
0.56 & 0.78 & 0.17\tabularnewline
Cougar \textsuperscript{NON} & 4 & Wyoming, United States & 3 & - & 0.54
& 0.54 & 0\tabularnewline
Fishing cat \textsuperscript{VAR} & 3 & Andhra Pradesh, India & 1 & 2.80
& 0.46 & 0.45 & -0.01\tabularnewline
Fishing cat \textsuperscript{NON} & 2 & Howrah, India & 1 & 2.50 & 0.46
& 0.51 & 0.10\tabularnewline
Coyote \textsuperscript{VAR} & 4 & Florida, United States & 1 & 5.09 &
0.86 & 0.85 & -0.01\tabularnewline
Coyote \textsuperscript{NON} & 4 & Ohio, United States & 3 & 4.29 & 0.86
& 0.85 & -0.01\tabularnewline
Golden jackal \textsuperscript{VAR} & 4 & Artvin, Turkey & 2 & 3.57 &
0.52 & 0.61 & 0.15\tabularnewline
Golden jackal \textsuperscript{NON} & 4 & Central Europe & 2 & 5.07 &
0.47 & 0.51 & 0.08\tabularnewline
Maned wolf \textsuperscript{VAR} & 2 & Minais Gerais, Brazil & 1 & 3.65
& 0.75 & 0.72 & -0.05\tabularnewline
Maned wolf \textsuperscript{NON} & 3 & Central West Brazil & 2 & 5.95 &
0.71 & 0.65 & -0.07\tabularnewline
Pine marten \textsuperscript{VAR} & 3 & Elba Island, Italy & 1 & 3.29 &
0.42 & 0.52 & 0.04\tabularnewline
Pine marten \textsuperscript{NON} & 4 & Ardennes, France & 2 & 3.42 &
0.59 & 0.61 & 0.04\tabularnewline
Small-clawed otter \textsuperscript{VAR} & 2 & Sumatra, Indonesia & 1 &
4.62 & 0.59 & 0.73 & 0.19\tabularnewline
Small-clawed otter \textsuperscript{NON} & 3 & Singapore & 1 & 3.29 &
0.51 & 0.47 & -0.09\tabularnewline
European badger \textsuperscript{VAR} & 3 & Orkdal, Norway & 3 & 2.73 &
0.46 & 0.48 & 0.04\tabularnewline
European badger \textsuperscript{NON} & 4 & Scarborough, UK & 1 & 4.30 &
0.52 & 0.61 & 0.14\tabularnewline
Fisher \textsuperscript{VAR} & 4 & Wisconsin, USA & 2 & 3.79 & 0.48 &
0.49 & 0.02\tabularnewline
Fisher \textsuperscript{NON} & 4 & Maine, USA & 3 & 4.55 & 0.60 & 0.61 &
-0.01\tabularnewline
Black bear \textsuperscript{VAR} & 3 & Gribbell, Canada & 3 & - & 0.54 &
0.55 & 0.04\tabularnewline
Black bear \textsuperscript{VAR} & 4 & Yakutat, Alaska & 3 & 3.71 & 0.59
& 0.58 & 0.02\tabularnewline
Black bear \textsuperscript{NON} & 4 & Minnesota, USA & 3 & 8.40 & 0.79
& 0.79 & 0\tabularnewline
Common genet \textsuperscript{VAR} & 4 & Catalonia, Spain & 2 & 3.00 &
0.47 & 0.52 & 0.13\tabularnewline
Common genet \textsuperscript{NON} & 4 & Algeria and Tunisia & 2 & 4.30
& 0.62 & 0.63 & 0.09\tabularnewline
White-nosed coati \textsuperscript{VAR} & 3 & Quintana Roo, Mexico & 2 &
5.03 & 0.67 & 0.77 & 0.13\tabularnewline
White-nosed coati \textsuperscript{NON} & 4 & Central Mexico & 2 & 4.80
& 0.65 & 0.75 & 0.13\tabularnewline
\bottomrule
\end{longtable}

*\textsuperscript{1} The following species are included:
leopard\emph{Panthera pardus}, tiger \emph{P. tigris}, jaguar, \emph{P.
onca}, lion \emph{P. leo}, bobcat \emph{Lynx rufus}, cougar \emph{Puma
concolor}, fishing cat \emph{Prionailurus viverrinus}, coyote\emph{Canis
latrans}, golden jackal \emph{C. aureus}, maned wolf\emph{Chrysocyon
brachyurus}, pine marten \emph{Martes martes}, small-clawed otter
\emph{Aonyx cinereus}, European badger \emph{Meles meles}, fisher
\emph{Pekania pennanti}, black bear \emph{Ursus americanus}, common
genet \emph{Genetta genetta}, and white-nosed coati\emph{Nasua narica}.
*\textsuperscript{2} A\textsubscript{R} = allelic richness;
H\textsubscript{O} = observed heterozygosity, H\textsubscript{E} =
expected heterozygosity; F\textsubscript{IS} = inbreeding coefficient.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. The difference between coat colour gradients, colour
polymorphism, and rare colour variants. As an illustrative example,
genotypes consist of the B locus = Brown (these variants have reduced
eumelanin and appear brownish in colour); D locus = Dilution (causes
uneven distribution of the pigment in the fur shaft); A locus = Bengal
(the agouti signalling protein switches between black and red pigments,
creating a banding pattern in individual hairs); C locus = Colour
restriction (through reduced or increased production of melanin). Rare
colour variants (in this example the C locus) can become fixed in
populations (such as the D locus), after which it may be considered a
new Wildtype colour polymorphism.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2\textbf{.} A map with all colour morphs found in carnivores,
locations being marked with green dots (N = 59). For some species, we
provide photographic evidence and mention the suspected cause of colour
morph frequency in the wild. The entire list of colour morphs and
scientific names can be found in Supplementary Table S1 online.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\par\null

Figure 3. Correlations of genetic diversity and the presence of rare
colour variants. A. The relationship between inbreeding coefficient and
variable phenotypic populations (T-test, t value = 1.88; P = 0.034); B.
The relationship between allelic richness and variable phenotypic
populations (T-test, t value = -2.23; P = 0.016); C. The relationship
between inbreeding coefficients and populations with threatened
(\textless{} 250 individuals) and non-threatened (\textgreater{} 250
individuals) conservation status (Mann-Whitney; \emph{W} = 194;
\emph{P}= 0.01); D. The relationship between allelic richness and body
weight in kilograms (kg) (Spearman; \emph{R} = 0.36; \emph{P} = 0.04).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

